m4m

Want to give an anonymous guy anonymous head in a darkroom setting eg. rooms with no light or windows, bathrooms, hotel rooms, garages, sheds ...

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Needed to give permissions for anonymous users to view published sitemaps

I chekced my site as anonymous user and wen't to sitemap link then got "You are not authorized to access this page." ... Only when I went to ...

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[FD] CVE-2013-0019: MSIE 9 CDoc::ExecuteScriptUri use-after-free

Since November I have been releasing details on all vulnerabilities I found in web-browsers that I had not released before. I will try to continue to publish all my old vulnerabilities, including those not in web-browser, as long as I can find some time to do so. If you find this information useful, you can help me make some time available by donating bitcoin to 183yyxa9s1s1f7JBp­PHPmz­Q346y91Rx5DX. This is the twenty-fourth entry in the series. This information is available in more detail on my blog at http://ift.tt/2gZr7Ix. There you can find a repro that triggered this issue in addition to the information below. Today's release is interesting to me personally, as this was the first bug I sold as a bug bounty hunter after I quit my job at Google to live off security bug bounties. Unfortunately, this was quite some time ago, before I had proper tools or incentive to analyze the issues I was finding, so there aren't many details in this release. Follow me on http://twitter.com/berendjanwever for daily browser bugs. MSIE 9 CDoc::ExecuteScriptUri use-after-free ========================================== (MS13-009, CVE-2013-0019) Synopsis

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PTSD Anonymous offers peer support for Wisconsin vets

PTSD Anonymous, launched a month ago, isn't meant to replace professional counseling, but to offer a sympathetic ear from someone who ...

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Reader letter: Anonymous helpers thanked for good deed

I was helped out today, Wednesday, Nov. 30, by two young men in a white work van and I want to thank.

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Anonymous Bttm looking

Waiting naked lubed ass for discreet hung masc top. I'll send the address. I'm hosting in NE I'm medium skin tone with athletic build. Hiv Neg DDF.

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Superheroes Anonymous

Showing 1-1 of 1 results for “Superheroes Anonymous”. Sorted by date added, Popularity, Relevance, Release date, Title, Author. Filters. Filter search ...

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A Triple Star is Born

A triple star system is forming, enshrouded within this dusty natal disk some 750 light-years away in the Perseus molecular cloud. Imaged at millimeter wavelengths by the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile, the extreme close-up shows two protostars separated by a mere 61 AU (1 AU is the Earth-Sun distance) with a third 183 AU from the central protostar. The ALMA image also reveals a clear spiral structure indicating instability and fragmentation led to the multiple protostellar objects within the disk. Astronomers estimate that the system, cataloged as L1448 IRS3B, is less than 150,000 years old. Captured at an early phase, the starforming scenario is likely not at all uncommon, since almost half of all sun-like stars have at least one companion. via NASA http://ift.tt/2fOoNI1

Anonymous' public face & journalist Barrett Brown speaks with Sputnik after release from US prison

Brown, 35, who was once the most public face of Anonymous, was indicted in December 2012 on multiple charges including sharing a link to hacked ...

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PubPeer dealt blow in lawsuit against anonymous commenters

PubPeer has suffered a setback in an ongoing lawsuit filed by a scientist who alleges the site's anonymous commenters cost him a job. This week ...

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Add "view media" permission to anonymous users by default

Any reason anonymous users don't have access to view media by default? I expect it to be like for the "view published content" permission.

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Ocean City, MD's surf is at least 5.26ft high

Maryland-Delaware, December 07, 2016 at 02:00AM

Ocean City, MD Summary
At 2:00 AM, surf min of 5.26ft. At 8:00 AM, surf min of 3.35ft. At 2:00 PM, surf min of 1.2ft. At 8:00 PM, surf min of 0.0ft.

Surf maximum: 6.23ft (1.9m)
Surf minimum: 5.26ft (1.6m)
Tide height: 2.77ft (0.84m)
Wind direction: W
Wind speed: 15.3 KTS


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ISS Daily Summary Report – 12/01/2016

65 Progress (65P) Launch:  65P launched from the Baikonur, Kazakhstan Cosmodrome today at 8:51 am CST.  Telemetry indicated that the 3rd stage separation occurred a little more than 2 minutes early.  Telemetry over Russian Ground Sites (RGS) was lost earlier than expected.  Communication with the vehicle was never reacquired with the vehicle.  The 65P vehicle and all cargo is considered lost.  A Russian State Commission will convene to investigate the anomaly.  Robotic External Leak Locator (RELL) Operations: Today, RELL checkout and demonstration continues with scans of the Port 1 Truss (P1) Truss Radiator Beam Valve Modules (RBVMs), and the P1 Ammonia (NH3). RELL is an instrumentation package that is maneuvered externally by the SSRMS/SPDM to detect local pressure variations to help in locating a leak. For this demonstration, a predetermined scan/survey procedure will be executed that characterized the ISS environment and scans various ISS elements containing ammonia lines and systems. Planned operations later this week will also attempt to identify the source of the “white flakes” that have been noticed on downlink video. Synchronized Position Hold, Engage, Reorient, Experimental Satellites Tether Demonstration (SPHERES Tether Demo): The crew conducted a conference with the ground team then setup and checkout the SPHERES, the work area in the Japanese Experiment Module (JEM), and the EXpedite PRocessing of Experiments to Space Station (EXPRESS) Laptop Computer (ELC) prior to testing. The crew loaded the test specific software to the satellites and executed the first of two test sessions with ground support teams. The goal of the SPHERES Tether Demo is to study the dynamics of a tethered capture object and a “space tug” chase vehicle, improving computer programs and modeling needed for removing space debris as well as capturing scientific samples from other planets. Sarcolab-3 Deconfigure and Stow: After the successful completion of the Flight Day (FD) 5-10 sessions on Tuesday and Wednesday, the crew deconfigured and stowed the Muscle Atrophy Research & Exercise System (MARES). Today, the crew verified and downlinked the data and stowed the remaining ancillary Sarcolab-3 hardware. The next session will be in the FD 30-60 window. Myotendinous and Neuromuscular Adaptation to Long-term Spaceflight (Sarcolab) investigates the adaptation and deterioration of the soleus, or calf muscle, where it joins the Achilles tendon, which links it to the heel and carries loads from the entire body. Muscle fiber samples are taken from crew members before and after flight, and analyzed for changes in structural and chemical properties. MRI and ultrasound tests and electrode stimulation are conducted to help assess muscle and tendon changes caused by microgravity exposure.  Aerosol Sampler: The crew closed the drawers on the passive samplers located in the various USOS modules. The Aerosol Sampling Experiment (Aerosol Sampler) collects airborne particles in the ISS cabin air, and is returned to Earth so scientists can study the particles with powerful microscopes. For this experiment, particles collected on the cabin air samples are analyzed using a variety of microscopic techniques including: light microscopy, Raman spectroscopy, scanning electron microscopy, computer controlled scanning electron microscopy; and scanning transmission electron microscopy. Packed Bed Reactor Experiment (PBRE) Setup: The crew configured the PBRE hardware components and connected the hoses and cables within the Microgravity Science Glovebox (MSG). This module is scheduled to run for approximately three weeks in the MSG. PBRE studies the behavior of gases and liquids when they flow simultaneously through a column filled with fixed porous media. The porous media or “packing” can be made of different shapes and materials and are used widely in chemical engineering as a means to enhance the contact between two immiscible fluid phases (e.g., liquid-gas, water-oil, etc.). Packed columns can serve as reactors, scrubbers, strippers, etc. in systems where efficient interphase contact is desired, both on Earth and in space. Today’s Planned Activities All activities were completed unless otherwise noted. ISS Crew and ГОГУ (RSA Flight Control Team) weekly conference HTV HARDWARE COMMAND PANEL (HCP) and HCP Power/Data Cables Setup Inventory Management System (IMS) conference Regenerative Environmental Control and Life Support System (ECLSS) Recycle Tank Drain MARES payload and laptop start up Download of BRI log from RSS1 ALGOMETRIYA. Pressure and Thermal Algometry preparation for and measurement session MELFI Overview OBT Dose Tracker Data Entry Subject HTV PROX Switch ON Regenerative Environmental Control and Life Support System (ECLSS) Recycle Tank Drain Part 2 MARES Guided Data Export SPLANKH. Terminate EGEG Recording. Closeout Ops. Tagup with specialists Removal and Relocation of Payload Complement (КЦН) hardware to MRM2 (ТБУ-В No.02 and No.04). Tagup with specialists as necessary SPLANKH. Photography of the Experiment Session Regenerative Environmental Control and Life Support System (RGN) WSTA Fill Aerosol Passive Samplers Drawer 1 Closure Flushing Progress 433 (DC1) Rodnik H2O Tank 1 and Tank 2 H2O connectors В1, B2 MRM2 comm config to support the P/L Ops KULONOVSKIY KRISTALL. Hardware Setup and Configuration. Tagup with specialists SPHERES Payload OBT Removal and Relocation of Payload Complement (КЦН) hardware from MRM1 to FGB (3 rack modules with fasteners and narrow bridge-frame with fasteners). Tagup with specialists as necessary USOS IVA Hatch Window Cover Audit Regenerative Environmental Control and Life Support System (RGN) WSTA Fill SPHERES Crew Conference MARES knee configuration deinstallation KULONOVSKIY KRISTALL. Experiment Ops. Tagup with specialists SPHERES Test Session Setup Active camera temperature monitoring and ТБУ-В No.4 deactivation MRM2 Comm Reconfig for Nominal Ops Crew time for ISS adaptation and orientation KULONOVSKIY KRISTALL. Copy and Downlink Data via RSPI Regenerative Environmental Control and Life Support System (RGN) WSTA Fill HTV HARDWARE COMMAND PANEL(HCP) SELFCHECK (Side B) ESA Weekly crew conference ALGOMETRIYA. Pressure Algometry Measurement Ops MARES files transfer verification and shutdown Regenerative Environmental Control and Life Support System (ECLSS) Recycle Tank Fill Part 3 Removal and Relocation of Payload Complement (КЦН) hardware from MRM1 to FGB (2 rack modules with fasteners and narrow bridge-frame with fasteners). Tagup with specialists as necessary HTV HARDWARE COMMAND PANEL(HCP) SELFCHECK (Side A) Packed Bed Reactor Experiment Hardware Setup SPHERES Payload OBT SPHERES Tether Demo Test Run EPM Laptop relocation for files downlink preparation Crew time for ISS […]

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Ocean City, MD's surf is at least 5.88ft high

Maryland-Delaware, December 06, 2016 at 02:00PM

Ocean City, MD Summary
At 2:00 AM, surf min of 1.77ft. At 8:00 AM, surf min of 4.06ft. At 2:00 PM, surf min of 5.88ft. At 8:00 PM, surf min of 4.42ft.

Surf maximum: 6.46ft (1.97m)
Surf minimum: 5.88ft (1.79m)
Tide height: 2.9ft (0.88m)
Wind direction: ESE
Wind speed: 11.53 KTS


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Computer Assisted Composition with Recurrent Neural Networks. (arXiv:1612.00092v1 [cs.AI])

Sequence modeling with neural networks has lead to powerful models of symbolic music data. We address the problem of exploiting these models to reach creative musical goals. To this end we generalise previous work, which sampled Markovian sequence models under the constraint that the sequence belong to the language of a given finite state machine. We consider more expressive non-Markov models, thereby requiring approximate sampling which we provide in the form of an efficient sequential Monte Carlo method. In addition we provide and compare with a beam search strategy for conditional probability maximisation. Our algorithms are capable of convincingly re-harmonising famous musical works. To demonstrate this we provide visualisations, quantitative experiments, a human listening test and illustrative audio examples. We find both the sampling and optimisation procedures to be effective, yet complementary in character. For the case of highly permissive constraint sets, we find that sampling is to be preferred due to the overly regular nature of the optimisation based results.

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Optimizing Quantiles in Preference-based Markov Decision Processes. (arXiv:1612.00094v1 [cs.AI])

In the Markov decision process model, policies are usually evaluated by expected cumulative rewards. As this decision criterion is not always suitable, we propose in this paper an algorithm for computing a policy optimal for the quantile criterion. Both finite and infinite horizons are considered. Finally we experimentally evaluate our approach on random MDPs and on a data center control problem.

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Robust Optimization for Tree-Structured Stochastic Network Design. (arXiv:1612.00104v1 [cs.AI])

Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation planning, pre-disaster network preparation, and river network optimization. A common assumption in previous work has been made that network parameters (e.g., probability of species colonization) are precisely known, which is unrealistic in real- world settings. We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers. We assume that fish passability probabilities are known only imprecisely, but are within some interval bounds. We then develop a planning approach that computes the policies with either high robust ratio or low regret. Empirically, our approach scales well to large river networks. We also provide insights into the solutions generated by our robust approach, which has significantly higher robust ratio than the baseline solution with mean parameter estimates.

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When to Reset Your Keys: Optimal Timing of Security Updates via Learning. (arXiv:1612.00108v1 [cs.LG])

Cybersecurity is increasingly threatened by advanced and persistent attacks. As these attacks are often designed to disable a system (or a critical resource, e.g., a user account) repeatedly, it is crucial for the defender to keep updating its security measures to strike a balance between the risk of being compromised and the cost of security updates. Moreover, these decisions often need to be made with limited and delayed feedback due to the stealthy nature of advanced attacks. In addition to targeted attacks, such an optimal timing policy under incomplete information has broad applications in cybersecurity. Examples include key rotation, password change, application of patches, and virtual machine refreshing. However, rigorous studies of optimal timing are rare. Further, existing solutions typically rely on a pre-defined attack model that is known to the defender, which is often not the case in practice. In this work, we make an initial effort towards achieving optimal timing of security updates in the face of unknown stealthy attacks. We consider a variant of the influential FlipIt game model with asymmetric feedback and unknown attack time distribution, which provides a general model to consecutive security updates. The defender's problem is then modeled as a time associative bandit problem with dependent arms. We derive upper confidence bound based learning policies that achieve low regret compared with optimal periodic defense strategies that can only be derived when attack time distributions are known.

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CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation. (arXiv:1612.00132v1 [cs.CV])

Problems such as predicting an optical flow field (Y) for an image (X) are ambiguous: many very distinct solutions are good. Representing this ambiguity requires building a conditional model P(Y|X) of the prediction, conditioned on the image. It is hard because training data usually does not contain many different flow fields for the same image. As a result, we need different images to share data to produce good models. We demonstrate an improved method for building conditional models, the Co-Embedding Deep Variational Auto Encoder. Our CDVAE exploits multiple encoding and decoding layers for both X and Y. These are tied during training to produce a model of the joint distribution P(X, Y), which provides the necessary smoothing. Our tying procedure is designed to yield a conditional model easy at test time. We demonstrate our model on three example tasks using real data: image saturation adjustment, image relighting, and motion prediction. We describe quantitative evaluation metrics to evaluate ambiguous generation results. Our results quantitatively and qualitatively advance the state of the art.

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Analysis of the Human-Computer Interaction on the Example of Image-based CAPTCHA by Association Rule Mining. (arXiv:1612.00203v1 [cs.HC])

The paper analyzes the interaction between humans and computers in terms of response time in solving the image-based CAPTCHA. In particular, the analysis focuses on the attitude of the different Internet users in easily solving four different types of image-based CAPTCHAs which include facial expressions like: animated character, old woman, surprised face, worried face. To pursue this goal, an experiment is realized involving 100 Internet users in solving the four types of CAPTCHAs, differentiated by age, Internet experience, and education level. The response times are collected for each user. Then, association rules are extracted from user data, for evaluating the dependence of the response time in solving the CAPTCHA from age, education level and experience in internet usage by statistical analysis. The results implicitly capture the users' psychological states showing in what states the users are more sensible. It reveals to be a novelty and a meaningful analysis in the state-of-the-art.

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Interaction Networks for Learning about Objects, Relations and Physics. (arXiv:1612.00222v1 [cs.AI])

Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. Here we introduce the interaction network, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system. Our model takes graphs as input, performs object- and relation-centric reasoning in a way that is analogous to a simulation, and is implemented using deep neural networks. We evaluate its ability to reason about several challenging physical domains: n-body problems, rigid-body collision, and non-rigid dynamics. Our results show it can be trained to accurately simulate the physical trajectories of dozens of objects over thousands of time steps, estimate abstract quantities such as energy, and generalize automatically to systems with different numbers and configurations of objects and relations. Our interaction network implementation is the first general-purpose, learnable physics engine, and a powerful general framework for reasoning about object and relations in a wide variety of complex real-world domains.

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On Coreferring Text-extracted Event Descriptions with the aid of Ontological Reasoning. (arXiv:1612.00227v1 [cs.AI])

Systems for automatic extraction of semantic information about events from large textual resources are now available: these tools are capable to generate RDF datasets about text extracted events and this knowledge can be used to reason over the recognized events. On the other hand, text based tasks for event recognition, as for example event coreference (i.e. recognizing whether two textual descriptions refer to the same event), do not take into account ontological information of the extracted events in their process. In this paper, we propose a method to derive event coreference on text extracted event data using semantic based rule reasoning. We demonstrate our method considering a limited (yet representative) set of event types: we introduce a formal analysis on their ontological properties and, on the base of this, we define a set of coreference criteria. We then implement these criteria as RDF-based reasoning rules to be applied on text extracted event data. We evaluate the effectiveness of our approach over a standard coreference benchmark dataset.

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An Evaluation of Models for Runtime Approximation in Link Discovery. (arXiv:1612.00240v1 [cs.AI])

Time-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime the fastest planners. However, no other category of models has been studied for this purpose so far. In this paper, we study non-linear runtime estimation functions for runtime estimation. In particular, we study exponential and mixed models for the estimation of the runtimes of planners. To this end, we evaluate three different models for runtime on six datasets using 400 link specifications. We show that exponential and mixed models achieve better fits when trained but are only to be preferred in some cases. Our evaluation also shows that the use of better runtime approximation models has a positive impact on the overall execution of link specifications.

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A Compositional Object-Based Approach to Learning Physical Dynamics. (arXiv:1612.00341v1 [cs.AI])

We present the Neural Physics Engine (NPE), an object-based neural network architecture for learning predictive models of intuitive physics. We propose a factorization of a physical scene into composable object-based representations and also the NPE architecture whose compositional structure factorizes object dynamics into pairwise interactions. Our approach draws on the strengths of both symbolic and neural approaches: like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions, but as a neural network it can also be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that our model's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize to different numbers of objects, and infer latent properties of objects such as mass.

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Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data. (arXiv:1612.00347v1 [cs.CL])

We present a method for inducing new dialogue systems from very small amounts of unannotated dialogue data, showing how word-level exploration using Reinforcement Learning (RL), combined with an incremental and semantic grammar - Dynamic Syntax (DS) - allows systems to discover, generate, and understand many new dialogue variants. The method avoids the use of expensive and time-consuming dialogue act annotations, and supports more natural (incremental) dialogues than turn-based systems. Here, language generation and dialogue management are treated as a joint decision/optimisation problem, and the MDP model for RL is constructed automatically. With an implemented system, we show that this method enables a wide range of dialogue variations to be automatically captured, even when the system is trained from only a single dialogue. The variants include question-answer pairs, over- and under-answering, self- and other-corrections, clarification interaction, split-utterances, and ellipsis. This generalisation property results from the structural knowledge and constraints present within the DS grammar, and highlights some limitations of recent systems built using machine learning techniques only.

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Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. (arXiv:1612.00367v1 [cs.LG])

The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising. With this paper, we provide real-world data and a standardized test-bed to systematically investigate these algorithms using data from display advertising. In particular, we consider the problem of filling a banner ad with an aggregate of multiple products the user may want to purchase. This paper presents our test-bed, the sanity checks we ran to ensure its validity, and shows results comparing state-of-the-art off-policy learning methods like doubly robust optimization, POEM, and reductions to supervised learning using regression baselines. Our results show experimental evidence that recent off-policy learning methods can improve upon state-of-the-art supervised learning techniques on a large-scale real-world data set.

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Multi-modal Variational Encoder-Decoders. (arXiv:1612.00377v1 [cs.CL])

Recent advances in neural variational inference have facilitated efficient training of powerful directed graphical models with continuous latent variables, such as variational autoencoders. However, these models usually assume simple, uni-modal priors - such as the multivariate Gaussian distribution - yet many real-world data distributions are highly complex and multi-modal. Examples of complex and multi-modal distributions range from topics in newswire text to conversational dialogue responses. When such latent variable models are applied to these domains, the restriction of the simple, uni-modal prior hinders the overall expressivity of the learned model as it cannot possibly capture more complex aspects of the data distribution. To overcome this critical restriction, we propose a flexible, simple prior distribution which can be learned efficiently and potentially capture an exponential number of modes of a target distribution. We develop the multi-modal variational encoder-decoder framework and investigate the effectiveness of the proposed prior in several natural language processing modeling tasks, including document modeling and dialogue modeling.

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Playing Doom with SLAM-Augmented Deep Reinforcement Learning. (arXiv:1612.00380v1 [cs.AI])

A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios. Inspired from prior work in human cognition that indicates how humans employ a variety of semantic concepts and abstractions (object categories, localisation, etc.) to reason about the world, we build an agent-model that incorporates such abstractions into its policy-learning framework. We augment the raw image input to a Deep Q-Learning Network (DQN), by adding details of objects and structural elements encountered, along with the agent's localisation. The different components are automatically extracted and composed into a topological representation using on-the-fly object detection and 3D-scene reconstruction.We evaluate the efficacy of our approach in Doom, a 3D first-person combat game that exhibits a number of challenges discussed, and show that our augmented framework consistently learns better, more effective policies.

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Generalizing Skills with Semi-Supervised Reinforcement Learning. (arXiv:1612.00429v1 [cs.LG])

Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images. However, real-world applications of such methods require generalizing to the vast variability of the real world. Deep networks are known to achieve remarkable generalization when provided with massive amounts of labeled data, but can we provide this breadth of experience to an RL agent, such as a robot? The robot might continuously learn as it explores the world around it, even while deployed. However, this learning requires access to a reward function, which is often hard to measure in real-world domains, where the reward could depend on, for example, unknown positions of objects or the emotional state of the user. Conversely, it is often quite practical to provide the agent with reward functions in a limited set of situations, such as when a human supervisor is present or in a controlled setting. Can we make use of this limited supervision, and still benefit from the breadth of experience an agent might collect on its own? In this paper, we formalize this problem as semisupervised reinforcement learning, where the reward function can only be evaluated in a set of "labeled" MDPs, and the agent must generalize its behavior to the wide range of states it might encounter in a set of "unlabeled" MDPs, by using experience from both settings. Our proposed method infers the task objective in the unlabeled MDPs through an algorithm that resembles inverse RL, using the agent's own prior experience in the labeled MDPs as a kind of demonstration of optimal behavior. We evaluate our method on challenging tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available. We also show that our method outperforms direct supervised learning of the reward.

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KGEval: Estimating Accuracy of Automatically Constructed Knowledge Graphs. (arXiv:1610.06912v2 [cs.AI] UPDATED)

Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently. Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity. This important problem has largely been ignored in prior research we fill this gap and propose KGEval. KGEval binds facts of a KG using coupling constraints and crowdsources the facts that infer correctness of large parts of the KG. We demonstrate that the objective optimized by KGEval is submodular and NP-hard, allowing guarantees for our approximation algorithm. Through extensive experiments on real-world datasets, we demonstrate that KGEval is able to estimate KG accuracy more accurately compared to other competitive baselines, while requiring significantly lesser number of human evaluations.

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Rumor Central: Orioles more interested in OF Curtis Granderson than Jay Bruce - FanRag Sports; .237, 30 HR in 2016 (ESPN)

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Rumor Central: Orioles' acquisition of P Logan Verrett could signal P Vance Worley non-tender - MASNsports.com (ESPN)

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Orioles sign OF Logan Schafer, 30, to minor league deal Thursday; .214, 5 HR, 53 RBI in 318 games in 6-year career (ESPN)

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[FD] New CSRF vulnerabilities in D-Link DAP-1360

Hello list! After previous Cross-Site Request Forgery and Cross-Site Scripting vulnerabilities, here are new ones. There are Cross-Site Request Forgery vulnerabilities in D-Link DAP-1360 (Wi-Fi Access Point and Router).

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[FD] WinPower V4.9.0.4 Privilege Escalation

# Exploit Title: WinPower V4.9.0.4 Privilege Escalation # Date: 29-11-2016 # Software Link: http://ift.tt/2fXhXfD # Exploit Author: Kacper Szurek # Contact: http://twitter.com/KacperSzurek # Website: http://ift.tt/1AaOgjR # Category: local 1. Description UPSmonitor runs as SYSTEM process. We can communicate with monitor using RMI interface. In manager app there’s an “Administrator” password check, but the password isn’t verified inside monitor process. So we can modify any application settings without knowing administrator password. What is more interesting we can set command which will be executed when monitor get “remote shutdown command”. Because monitor runs as SYSTEM process, this command is also executed with SYSTEM privileges. So using this we can create new administrator account. http://ift.tt/2gBP8sJ 2. Proof of Concept http://ift.tt/2gZFkZS http://ift.tt/2gN340m

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[FD] XSS in tooltip plugin of Zurb Foundation 5

XSS vulnerabilty in the tooltip plugin of Zurb Foundation 5.x ============================================================= URL to this advisory: http://ift.tt/2g14tPP Vendor ====== http://zurb.com/ Product ======= (Taken from http://ift.tt/1NJf52K) Foundation is the most advanced, responsive front-end framework in the world. The framework is mobile friendly and ready for you to customize it any way you want to use it. Vulnerability Type ================== Cross-Site-Scripting Vulnerability CVE Reference ============= N/A Vulnerability Details ===================== The Foundation framework provides an easy way to insert tooltips into your code. The corresponding plugin for this is *foundation.tooltip.js*. Unfortunately the plugin takes HTML-encoded code from the title parameter and returns it as actual HTML, allowing an attacker to inject dynamic HTML/JS into an application, if the application allows to inject user input into title fields of a tooltip'ed SPAN entity. Even if the user input is correctly encoded, before adding it to the tooltip'ed SPAN, the Foundation Tooltip JavaScript will re-decode it and inject the actul HTML code. Example code ============ This code snippet shows a simple HTML page with encoded HTML in a tooltip, utilizing the Foundation JS plugin. This is a tooltip'ed SPAN When opened in a browser, the encoded HTML from the title-parameter in line 12 will be grabbed by the tooltip plugin and re-decoded to plain HTML in the output, causing the JavaScript to be injected into the page. A JavaScript alert window will pop up. Quick fix/Workaround ==================== The problem lies in line 197 of *foundation.tooltip.js*: var $tip = $(tip_template(this.selector($target), $('').html($target.attr('title')).html())), classes = this.inheritable_classes($target); The title attribute is read and provided as HTML without any sanitization. As a quick fix, one could use the .text() function of jQuery to sanitize the read value from the title attribute. This might not be a full fix for the issue, but at least worked in my examples. Here is a diff for quick patching:

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[FD] Eagle Speed USB MODEM SOFTWARE Privilege Escalation

# Vulnerability Description: # When the Eagle Speed software is installed a service with name ZDServ is installed. # The service itself has the right permissions which do not allow to reconfigure the binary # but the path the binary is writable by any authenticated user. # # C:\Users\lowpriv>sc qc zdserv # [SC] QueryServiceConfig SUCCESS # # SERVICE_NAME: zdserv # TYPE : 110 WIN32_OWN_PROCESS (interactive) # START_TYPE : 2 AUTO_START # ERROR_CONTROL : 1 NORMAL # BINARY_PATH_NAME : "C:\ProgramData\ZDSupport\ZDServ\ZDServ.exe" # LOAD_ORDER_GROUP : # TAG : 0 # DISPLAY_NAME : ZDServ # DEPENDENCIES : # SERVICE_START_NAME : LocalSystem # # # # C:\Users\lowpriv>icacls "C:\ProgramData\ZDSupport\ZDServ\ZDServ.exe" # C:\ProgramData\ZDSupport\ZDServ\ZDServ.exe Everyone:(I)(F)

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[FD] Announcing NorthSec 2017 CFP + Reg - Montreal, May 16-21

www.nsec.io - northsec.eventbrite.ca NorthSec 2017, one of the biggest applied security event in Canada, coming up in Montreal in May 2017: May 16-17 - Professional Training Sessions - Syllabus Announced Soon May 18-19 - Security Conference & Workshops May 19-21 - The biggest 48H on-site CTF in North America, with 350+ attendees * We are looking for great speakers to submit to our 2017 CFP at http://www.nsec.io/cfp Subjects covered range from Application & Infrastructure security : pentesting, network security, software and/or hardware exploitation, web hacking, reverse engineering, malware/virii/rootkits or anything low-level that makes your personal clock tick! We also will talk about cryptography & obfuscation, from theoretical cryptosystems to applied cryptography exploitation, cryptocurrencies, steganography and covert communication systems, anything goes! Society & Ethics issues will be part of the show, since technical subjects are at the core of the security field, but they also exist in a world of humans, affected by their social environment & political context. If you want to exchange great ideas about the digital society and it's security implications, NorthSec's the place! * Registration for the event is up at http://ift.tt/1EVrqLo (and 50% OFF for a limited time) -

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[FD] CVE-2015-6168: MS Edge CMarkup::EnsureDeleteCFState use-after-free details

As I am sure you are by now well aware, in November I decided to start releasing details on all vulnerabilities I found in web-browsers that I had not released before. As I was unable to publish all of them within a single month, I will try to continue to publish all my old vulnerabilities, including those not in web-browser, as long as I can find some time to do so. If you find this information useful, you can help me make some time available by donating bitcoin to 183yyxa9s1s1f7JBp­PHPmz­Q346y91Rx5DX. This is the twenty-third entry in the series. This information is available in more detail on my blog at http://ift.tt/2gMZJhP. There you can find a repro that triggered this issue in addition to the information below. Follow me on http://twitter.com/berendjanwever for daily browser bugs. MS Edge CMarkup::EnsureDeleteCFState use-after-free =================================================== (MS15-125, CVE-2015-6168) Synopsis

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[FD] [FOXMOLE SA 2016-05-02] e107 Content Management System (CMS) - Multiple Issues

-----BEGIN PGP SIGNED MESSAGE-

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[FD] Opera foreignObject textNode::removeChild use-after-free details

Throughout November, I plan to release details on vulnerabilities I found in web-browsers which I've not released before. This is the twenty-second entry in that series. Unfortunately I won't be able to publish everything within one month at the current rate, so I may continue to publish these through December and January. Due to the recent Firefox 0-day, I've selected a very old and not so interesting bug for today, so you can get back to looking at the former as soon as possible. The below information is available in more detail on my blog at http://ift.tt/2gqKOMc. There you can find a repro that triggered this issue and an exploit that may or may not work in addition to the information below. Follow me on http://twitter.com/berendjanwever for daily browser bugs. Opera foreignObject textNode::removeChild use-after-free ======================================================== (The fix and CVE number for this issue are unknown) Synopsis

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[FD] Google Chrome Accessibility blink::Node corruption details

Throughout November, I plan to release details on vulnerabilities I found in web-browsers which I've not released before. This is the twenty-first entry in that series. Unfortunately I won't be able to publish everything within one month at the current rate, so I may continue to publish these through December and January. The below information is available in more detail on my blog at http://ift.tt/2gDWJ69. There you can find a repro that triggered this issue and relevant code snippets in addition to the information below. Follow me on http://twitter.com/berendjanwever for daily browser bugs. Google Chrome Accessibility blink::Node corruption ================================================== (The fix and CVE number for this issue are unknown) Synopsis

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use session id when an anonymous users flags/unflags

You get a session ID from something, you store it in the flagging. Then you use that when retrieving flaggings for an anonymous user.

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new support group - clutterers anonymous

What is clutter? Possessions not needed, wanted, or used that takes time, energy, or space and destroys serenity. Also known as hoarding. Meeting ...

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ISS Daily Summary Report – 11/30/2016

Robotic External Leak Locator (RELL) Operations: Yesterday, Ground Controllers used the SPDM to remove the RELL from the JEMAL Slide Table. A checkout was performed by pointing RELL towards deep space in order to scan a clean environment as a baseline. Today, RELL performed background scans along the Port 1 (P1) truss segment in order to establish a baseline for the ISS environment. Overnight, RELL will be pointed near truss radiators to characterize the ISS environment near structure. RELL is an instrumentation package that is maneuvered externally by the SSRMS/SPDM to detect local pressure variations to help in locating a leak. For this demonstration, a predetermined scan/survey procedure will be executed that characterized the ISS environment and scans various ISS elements containing ammonia lines and systems. Planned operations later this week will also attempt to identify the source of the “white flakes” that have been noticed on downlink video.  Marangoni Setup: The crew performed the third day of Marangoni setup for the Marangoni Ultrasonic Velocity Profilers (UVP2) experiment.  Today’s operations included a Strobolamp exchange within the Ryutai Rack’s Fluid Physics Experiment Facility (FPEF) followed by configuration of the 2-Dimensional (2-D) and 3-D cameras within the FPEF.  Spatio-temporal Flow Structure in Marangoni Convection (Marangoni-UVP) investigates the fundamental physics of surface tension where liquid and gas meet. Specifically, it investigates a phenomenon known as Marangoni convection, a type of flow that is driven by temperature differences at the liquid and gas interface. The FPEF enables observations of liquid and gas flow in three dimensions, and the microgravity environment on the ISS provides an ideal setting to study convection. Improved understanding of liquid flow physics could lead to more efficient industrial processes, semiconductors, optical materials and biological materials for use in space and on Earth. Selectable Optical Diagnostics Instrument Experiment Diffusion Coefficient Mixture (SODI DCMix)-3 Stow: Following the successful completion of the SODI DCMix-3 experiment the crew removed the experiment from the Microgravity Science Glovebox (MSG) and returned the MSG to its nominal configuration. Tomorrow the crew will install the Packed Bed Reactor Experiment (PBRE) in the MSG. The SODI DCMix-3 investigation measures the diffusion coefficients of selected ternary mixtures taking advantage of the reduced gravity environment available on board the ISS. A combination of different and complementary techniques are used to characterize flight candidate samples among water-based and hydrocarbon mixtures. Experimental results from space experiments, performed in the Selectable Optical Diagnostic Instrument, are used to test thermodiffusion theories and develop physical and mathematical models for the estimation of thermo-diffusion coefficients. Sarcolab-3: The second operational day for the Sarcolab-3 experiment began with converting the Muscle Atrophy Research & Exercise System (MARES) from the ankle measurement configuration to the knee measurement configuration.  Once the setup was completed the subject donned the Percutaneus Electrical Muscle Stimulator (PEMS) and Electromyography (EMG) electrodes, to stimulate and measure calf muscle and tendon response at the back of the knee (calf muscle origin). During the knee joint evaluation, the subject sat on the MARES dynamometer with the chair and pantograph set to obtain knee flexion and extension from 90 degree knee angle to full extension with the knee torque adapter securely fixed to the shin. The inflight data will be compared to preflight and postflight measurements to measure the impact of a hypothesized microgravity induced muscle loss.  Myotendinous and Neuromuscular Adaptation to Long-term Spaceflight (Sarcolab) investigates the adaptation and deterioration of the soleus, or calf muscle, where it joins the Achilles tendon, which links it to the heel and carries loads from the entire body. Muscle fiber samples are taken from crew members before and after flight, and analyzed for changes in structural and chemical properties. MRI and ultrasound tests and electrode stimulation are conducted to help assess muscle and tendon changes caused by microgravity exposure. Crew Health Care System (CHeCS) Emergency Health Maintenance System Contingency Drill Training:  During this onboard training session the crew reviewed procedures, hardware and communication methods necessary to manage a medical emergency onboard ISS. Emergency medical equipment was deployed during the training session for the crew to practice Cardiopulmonary Resuscitation (CPR) positioning to ensure familiarity with procedure execution should a medical emergency occur. Today’s Planned Activities All activities were completed unless otherwise noted. SPLANKH. SPLANKHOGRAPH Setup. EGEG Recording (start). Body Measures Experiment Operations – Subject ESA ACTIVE DOSIMETER MOBILE UNIT SWAP XF305 Camcorder Setup Fluid Physics Experiment Facility (FPEF) Lamp Exchange MARES Knee Shaving Preparation Body Measures Experiment Operations – Operator Samsung Tablets Charging in Soyuz 732 (start) SARCOLAB (MARES). MARES Hardware Installation (Knee) Marangoni Inside (MI) Fluid Physics Experiment Facility (FPEF) Setup СОЖ maintenance SPLANKH. EGEG Recording (end). Closeout Operations. SODI Big Picture Word Review SODI Hardware Stow Alternate EMG electrodes installation and Sarcolab knee protocol execution SARCOLAB (MARES). Assistance during Electrodes Installation and Signal Test (Knee) Photo/TV. Camcorder Settings Verification Samsung Tablets Charging in Soyuz 732 (end) Monitoring of ИП-1 Sensors Installation Photo/TV Camcorder Setup Verification FGB СУБК 11М156М Devices Diagnostics. Marangoni Inside (MI) Fluid Physics Experiment Facility (FPEF) Cable Connection SSC18 (FGB). Wireless Connection Switch Off SARCOLAB (MARES). MARES Hardware and Laptop Deactivation Diagnostics of Filter Unit БФ-2 Device and Main Bus Assembly БСШ-2 of the FGB Power Supply System. OTKLIK. Hardware Monitoring Preparation of АФОТ А-2 for Removal Dose Tracker Data Entry Subject USOS IVA Hatch Window Cover Audit VZAIMODEISTVIYE-2. Experiment Operations Fluid Physics Experiment Facility (FPEF) Load Connector Removal Public Affairs Office (PAO) High Definition (HD) Config Columbus Setup PAO Preparation Photo T/V (P/TV) Advanced Resistive Exercise Device (ARED) Exercise Video Setup CHeCS Emergency Health Maintenance System Contingency Drill Training Public Affairs Office (PAO) Event in High Definition (HD) in Columbus Photo/TV Camcorder Setup Verification ISS Crew Orientation SSC18 (FBG). Connect to WiFi ECG Recording (end) 24-Hour ECG Recording (start) Crew Medical Officer (CMO) On Board Training (OBT) Photo T/V (P/TV) Advanced Resistive Exercise Device (ARED) Exercise Video Stow БД-2 Tension Measurement System Test. Onboard Training (OBT) Robotics On-board Trainer (ROBoT) Setup SPLANKH. Experiment Prep Aerosol Samplers Cartridge Removal  Completed Task List Items Galley UOP Gather VEG03 Pillow […]

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[FD] Apple iOS v10.1 & 10.1.1 - iCloud & Device Lock Bypass on Activate via local Buffer Overflow Vulnerability (Wifi Network)

Title: Apple iOS v10.1 & 10.1.1 - iCloud & Device Lock Bypass on Activate via local Buffer Overflow Vulnerability (Wifi Network) URL: https://www.youtube.com/watch?v=yygvBJBFy4s Ref: http://ift.tt/2fJ5iAH Note: Using the rotate function and night shift to merge the mask, allows as well to bypass the protection for iOS v10.1.1 like in the video demonstrated.

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Anonymous user ac2d94

Name, Anonymous user ac2d94. User since, November 30, 2016. Number of add-ons developed, 1 theme. Average rating of developer's add-ons, Not ...

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Rule 41 — FBI Gets Expanded Power to Hack any Computer in the World

Hacking multiple computers across the world just got easier for the United States intelligence and law enforcement agencies from today onwards. The changes introduced to the Rule 41 of the Federal Rules of Criminal Procedure by the United States Department of Justice came into effect on Thursday, after an effort to block the changes failed on Wednesday. The change grants the FBI much greater

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UPDATE Firefox and Tor to Patch Critical Zero-day Vulnerability

The critical Firefox vulnerability being actively exploited in the wild to unmask Tor users has been patched with the release of new browser updates. Both Mozilla and Tor Project has patched the vulnerability that allows attackers to remotely execute malicious code on Windows operating system via memory corruption vulnerability in Firefox web browser. Tor Browser Bundle is a repackaged

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Password Manager Pro — Easiest Way to Keep Enterprises Secure

Recent corporate breaches have taught us something important — the average enterprise user is spectacularly bad at choosing good passwords. As modern enterprise is becoming a hybrid organization with infrastructure spread across on-premises data centers as well as in the cloud, security of information, applications, and assets has become a paramount concern. Cyber security is no longer an

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Walk-In Counseling offers free, anonymous, immediate help

So she turned to the Walk-In Counseling Center in south Minneapolis for a free, immediate and anonymous counseling session with a professional.

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Milky Way over Shipwreck

What happened to this ship? It was carried aground by a giant storm that struck the coast of Argentina in 2002. The pictured abandoned boat, dubbed Naufragio del Chubasco, wrecked near the nearly abandoned town of Cabo Raso (population: 1). The rusting ship provides a picturesque but perhaps creepy foreground for the beautiful sky above. This sky is crowned by the grand arch of our Milky Way and features galaxies including the Large and Small Magellanic Clouds, stars including Canopus and Altair, planets including Mars and Neptune, and nebulas including the Lagoon, Carina, and the Coal Sack. The mosaic was composed from over 80 images taken in early September. A 360-degree interactive panoramic version of this image is also available. The adventurous astrophotographer reports that the creepiest part of taking this picture was not the abandoned ship, but the unusual prevalence of black and hairy caterpillars. via NASA http://ift.tt/2gJKk0t

The Rivers of Central California

The central California Watershed is the largest drainage basin in California.. The USGS has created a database of this area which indicates the direction of waterflow at each point. By assembling these directions into streamflows, it is possible to trace the path of water from every point of the area to the San Pablo Bay. This animation starts with the points furthest from the bay and reveals the streams and rivers as a steady progression towards the bay until all the major rivers are revealed. The speed of the reveal of the rivers is not dependent on the actual speed of the water flow. The reveal proceeds at a constant velocity along each river path, timed so that all reveals reach the bay at the same time.

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I have a new follower on Twitter

David Fish
I specialize in working with multinational organisations to design & deploy right sized business critical Telecommunication services.
United Kingdom
https://t.co/vpMZLC1RmW
Following: 5907 - Followers: 6451

November 30, 2016 at 10:45PM via Twitter http://twitter.com/david_a_fish

I have a new follower on Twitter

Merav Yuravlivker
Co-founder of @datasocietyco; co-organizer of @WomenDataSci, word nerd, science lover, data junkie, cheese aficionado
Washington, DC
http://t.co/PvmdZbTUpd
Following: 8048 - Followers: 8782

November 30, 2016 at 10:35PM via Twitter http://twitter.com/Merav_Yurav

How can I track respondents on an anonymous survey?

I would like to anonymously track a user across surveys. To help explain: I am evaluating the effectiveness of social outreach programs. I would like ...

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I have a new follower on Twitter

StampedeCon
StampedeCon Big Data Conference - July 2017 in St. Louis, MO! Use #StampedeCon to join the convo and join the list to be in the loop: https://t.co/ITG3eT0A3b
St. Louis, MO, USA
https://t.co/ywafoyQ49c
Following: 4306 - Followers: 5094

November 30, 2016 at 10:25PM via Twitter http://twitter.com/StampedeCon

Rumor Central: Orioles have free-agent OF Angel Pagan on their radar - BaltimoreBaseball.com; .277, 55 RBI, 15 SB in '16 (ESPN)

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I have a new follower on Twitter

Pyramid Analytics
Bridging the gap between business and IT user needs with a self-service Governed #Data Discovery platform available on any device. #BIOffice #BI #Analytics
USA, UK, Netherlands & Israel
http://t.co/eIl6u29VSn
Following: 3833 - Followers: 4765

November 30, 2016 at 10:10PM via Twitter http://twitter.com/PyramidAnalytic

I have a new follower on Twitter

Benjamin 🌴 🐶
Dog lover. Programmer. Distracted like an ADHD Squirrel. #marketing, #growthhacking, #marketingtech Need a custom marketing stack? Just let me know 😁
San Diego, CA
https://t.co/nymYQiIvM5
Following: 7149 - Followers: 6844

November 30, 2016 at 10:05PM via Twitter http://twitter.com/simplemarketer1

I have a new follower on Twitter

Certis Info Svcs Inc
Oil & Gas data and process management services. Aligning e&p workflows and data to corporate goals.

https://t.co/SRiHKdHFpt
Following: 3150 - Followers: 5133

November 30, 2016 at 09:50PM via Twitter http://twitter.com/certisinc

I have a new follower on Twitter

Charles Wheeler
Regional Sales Manager @infobldrs - #businessintelligence #bigdata #analytics #data #predictiveanalytics #IoT #MDM #wine
Dallas, TX
https://t.co/1s19IjkKDj
Following: 5136 - Followers: 5661

November 30, 2016 at 09:45PM via Twitter http://twitter.com/CFWheelerIII

Roman Aloy (@romanaloy) liked one of your Tweets!

@mistermcguire: Re: [FD] Stored Cross-Site Scripting in WP Canvas - Shortcodes WordPress Plugin   Roman Aloy liked your Tweet. View   Patrick McGuire @mistermcguire =   Re: [FD] Stored Cross-Site Scripting in WP Canvas - Shortcodes WordPress Plugin ift.tt/2fHJvrI   Settings | Help | Opt-out | Download app Twitter, Inc. 1355 Market Street, Suite 900 San Francisco, CA 94103

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Exploration for Multi-task Reinforcement Learning with Deep Generative Models. (arXiv:1611.09894v1 [cs.AI])

Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models. We supplement our method with a low dimensional energy model to learn the underlying MDP distribution and provide a resilient and adaptive exploration signal to the agent. We evaluate our method on a new set of environments and provide intuitive interpretation of our results.

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C-RNN-GAN: Continuous recurrent neural networks with adversarial training. (arXiv:1611.09904v1 [cs.AI])

Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.

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Capacity and Trainability in Recurrent Neural Networks. (arXiv:1611.09913v1 [stat.ML])

Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and per-unit capacity bounds with careful training, for a variety of tasks and stacking depths. They can store an amount of task information which is linear in the number of parameters, and is approximately 5 bits per parameter. They can additionally store approximately one real number from their input history per hidden unit. We further find that for several tasks it is the per-task parameter capacity bound that determines performance. These results suggest that many previous results comparing RNN architectures are driven primarily by differences in training effectiveness, rather than differences in capacity. Supporting this observation, we compare training difficulty for several architectures, and show that vanilla RNNs are far more difficult to train, yet have higher capacity. Finally, we propose two novel RNN architectures, one of which is easier to train than the LSTM or GRU.

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Choquet integral in decision analysis - lessons from the axiomatization. (arXiv:1611.09926v1 [q-fin.EC])

The Choquet integral is a powerful aggregation operator which lists many well-known models as its special cases. We look at these special cases and provide their axiomatic analysis. In cases where an axiomatization has been previously given in the literature, we connect the existing results with the framework that we have developed. Next we turn to the question of learning, which is especially important for the practical applications of the model. So far, learning of the Choquet integral has been mostly confined to the learning of the capacity. Such an approach requires making a powerful assumption that all dimensions (e.g. criteria) are evaluated on the same scale, which is rarely justified in practice. Too often categorical data is given arbitrary numerical labels (e.g. AHP), and numerical data is considered cardinally and ordinally commensurate, sometimes after a simple normalization. Such approaches clearly lack scientific rigour, and yet they are commonly seen in all kinds of applications. We discuss the pros and cons of making such an assumption and look at the consequences which axiomatization uniqueness results have for the learning problems. Finally, we review some of the applications of the Choquet integral in decision analysis. Apart from MCDA, which is the main area of interest for our results, we also discuss how the model can be interpreted in the social choice context. We look in detail at the state-dependent utility, and show how comonotonicity, central to the previous axiomatizations, actually implies state-independency in the Choquet integral model. We also discuss the conditions required to have a meaningful state-dependent utility representation and show the novelty of our results compared to the previous methods of building state-dependent models.

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Neural Combinatorial Optimization with Reinforcement Learning. (arXiv:1611.09940v1 [cs.AI])

This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent network using a policy gradient method. We compare learning the network parameters on a set of training graphs against learning them on individual test graphs. The best results are obtained when the network is first optimized on a training set and then refined on individual test graphs. Without any supervision and with minimal engineering, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.

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Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics. (arXiv:1611.09948v1 [cs.AI])

An objective of this work is to contextualize the analysis of large and multi-faceted data sources. Consider for example, health research in the context of social characteristics. Also there may be social research in the context of health characteristics. Related to this can be requirements for contextualizing Big Data analytics. A major challenge in Big Data analytics is the bias due to self selection. In general, and in practical settings, the aim is to determine the most revealing coupling of mainstream data and context. This is technically processed in Correspondence Analysis through use of the main and the supplementary data elements, i.e., individuals or objects, attributes and modalities.

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t-Exponential Triplet Embedding. (arXiv:1611.09957v1 [cs.AI])

Given a set of relative similarities between objects in the form of triplets "object i is more similar to object j than to object k", we consider the problem of finding an embedding of these objects in a metric space. This problem is generally referred to as triplet embedding. Our main focus in this paper is the case where a subset of triplets are corrupted by noise, such that the order of objects in a triple is reversed. In a crowdsourcing application, for instance, this noise may arise due to varying skill levels or different opinions of the human evaluators. As we show, all existing triplet embedding methods fail to handle even low levels of noise. Inspired by recent advances in robust binary classification and ranking, we introduce a new technique, called t-Exponential Triplet Embedding (t-ETE), that produces high-quality embeddings even in the presence of significant amount of noise in the triplets. By an extensive set of experiments on both synthetic and real-world datasets, we show that our method outperforms all the other methods, giving rise to new insights on real-world data, which have been impossible to observe using the previous techniques.

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System-Generated Requests for Rewriting Proposals. (arXiv:1611.10095v1 [cs.AI])

We present an online deliberation system using mutual evaluation in order to collaboratively develop solutions. Participants submit their proposals and evaluate each other's proposals; some of them may then be invited by the system to rewrite 'problematic' proposals. Two cases are discussed: a proposal supported by many, but not by a given person, who is then invited to rewrite it for making yet more acceptable; and a poorly presented but presumably interesting proposal. The first of these cases has been successfully implemented. Proposals are evaluated along two axes-understandability (or clarity, or, more generally, quality), and agreement. The latter is used by the system to cluster proposals according to their ideas, while the former is used both to present the best proposals on top of their clusters, and to find poorly written proposals candidates for rewriting. These functionalities may be considered as important components of a large scale online deliberation system.

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Fusion of EEG and Musical Features in Continuous Music-emotion Recognition. (arXiv:1611.10120v1 [cs.AI])

Emotion estimation in music listening is confronting challenges to capture the emotion variation of listeners. Recent years have witnessed attempts to exploit multimodality fusing information from musical contents and physiological signals captured from listeners to improve the performance of emotion recognition. In this paper, we present a study of fusion of signals of electroencephalogram (EEG), a tool to capture brainwaves at a high-temporal resolution, and musical features at decision level in recognizing the time-varying binary classes of arousal and valence. Our empirical results showed that the fusion could outperform the performance of emotion recognition using only EEG modality that was suffered from inter-subject variability, and this suggested the promise of multimodal fusion in improving the accuracy of music-emotion recognition.

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Unit Commitment using Nearest Neighbor as a Short-Term Proxy. (arXiv:1611.10215v1 [cs.LG])

We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems. Experimental results on an updated version of IEEE-RTS96 show high accuracy measured on operational cost, achieved in run-times that are lower in several orders of magnitude than the traditional approach.

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SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data. (arXiv:1611.10252v1 [q-bio.NC])

Understanding how brain functions has been an intriguing topic for years. With the recent progress on collecting massive data and developing advanced technology, people have become interested in addressing the challenge of decoding brain wave data into meaningful mind states, with many machine learning models and algorithms being revisited and developed, especially the ones that handle time series data because of the nature of brain waves. However, many of these time series models, like HMM with hidden state in discrete space or State Space Model with hidden state in continuous space, only work with one source of data and cannot handle different sources of information simultaneously. In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms. We apply this model to decode the mind state of students during lectures based on their brain waves and reach a significant better results compared to traditional methods.

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The observer-assisted method for adjusting hyper-parameters in deep learning algorithms. (arXiv:1611.10328v1 [cs.LG])

This paper presents a concept of a novel method for adjusting hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer monitors a performance of a selected Deep Learning algorithm. The observer learns to model the DL algorithm using a series of random experiments. Consequently, it may be used for predicting a response of the DL algorithm in terms of a selected quality measurement to a set of hyper-parameters. This allows to construct an ensemble composed of a series of evaluators which constitute an observer-assisted architecture. The architecture may be used to gradually iterate towards to the best achievable quality score in tiny steps governed by a unit of progress. The algorithm is stopped when the maximum number of steps is reached or no further progress is made.

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Joint Causal Inference on Observational and Experimental Datasets. (arXiv:1611.10351v1 [cs.LG])

We introduce Joint Causal Inference (JCI), a powerful formulation of causal discovery over multiple datasets that allows to jointly learn both the causal structure and targets of interventions from statistical independences in pooled data. Compared with existing constraint-based approaches for causal discovery from multiple data sets, JCI offers several advantages: it allows for several different types of interventions, it can learn intervention targets, it systematically pools data across different datasets which improves the statistical power of independence tests, and it improves on the accuracy and identifiability of the predicted causal relations. A technical complication that arises in JCI are the occurrence of faithfulness violations due to deterministic relations. We propose a simple but effective strategy for dealing with this type of faithfulness violations. We implement it in ACID, a determinism-tolerant extension of Ancestral Causal Inference (ACI) (Magliacane et al., 2016), a recently proposed logic-based causal discovery method that improves reliability of the output by exploiting redundant information in the data. We illustrate the benefits of JCI with ACID with an evaluation on a simulated dataset.

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On Learning High Dimensional Structured Single Index Models. (arXiv:1603.03980v2 [stat.ML] UPDATED)

Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combination of features. Estimation in this context requires learning both the feature weights and the nonlinear function that relates features to observations. While methods have been described to learn SIMs in the low dimensional regime, a method that can efficiently learn SIMs in high dimensions, and under general structural assumptions, has not been forthcoming. In this paper, we propose computationally efficient algorithms for SIM inference in high dimensions with structural constraints. Our general approach specializes to sparsity, group sparsity, and low-rank assumptions among others. Experiments show that the proposed method enjoys superior predictive performance when compared to generalized linear models, and achieves results comparable to or better than single layer feedforward neural networks with significantly less computational cost.

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A Deep Hierarchical Approach to Lifelong Learning in Minecraft. (arXiv:1604.07255v3 [cs.AI] UPDATED)

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft.

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Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. (arXiv:1609.05796v2 [astro-ph.IM] UPDATED)

Understanding the nature of dark energy, the mysterious force driving the accelerated expansion of the Universe, is a major challenge of modern cosmology. The next generation of cosmological surveys, specifically designed to address this issue, rely on accurate measurements of the apparent shapes of distant galaxies. However, shape measurement methods suffer from various unavoidable biases and therefore will rely on a precise calibration to meet the accuracy requirements of the science analysis. This calibration process remains an open challenge as it requires large sets of high quality galaxy images. To this end, we study the application of deep conditional generative models in generating realistic galaxy images. In particular we consider variations on conditional variational autoencoder and introduce a new adversarial objective for training of conditional generative networks. Our results suggest a reliable alternative to the acquisition of expensive high quality observations for generating the calibration data needed by the next generation of cosmological surveys.

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Solving Marginal MAP Problems with NP Oracles and Parity Constraints. (arXiv:1610.02591v2 [cs.AI] UPDATED)

Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them. We propose XOR_MMAP, a novel approach to solve the Marginal MAP Problem, which represents the intractable counting subproblem with queries to NP oracles, subject to additional parity constraints. XOR_MMAP provides a constant factor approximation to the Marginal MAP Problem, by encoding it as a single optimization in polynomial size of the original problem. We evaluate our approach in several machine learning and decision making applications, and show that our approach outperforms several state-of-the-art Marginal MAP solvers.

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Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. (arXiv:1611.02266v2 [stat.ML] UPDATED)

We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The proposed attention model can handle both the propagation of uncertainty when following a series of relations and also the conjunction of conditions in a natural way. On a dataset of soccer players who participated in the FIFA World Cup 2014, we demonstrate that our model can handle both path queries and conjunctive queries well.

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Learning to Navigate in Complex Environments. (arXiv:1611.03673v2 [cs.AI] UPDATED)

Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task performance can be dramatically improved by relying on additional auxiliary tasks. In particular we consider jointly learning the goal-driven reinforcement learning problem with a self-supervised depth prediction task and a self-supervised loop closure classification task. This approach can learn to navigate from raw sensory input in complicated 3D mazes, approaching human-level performance even under conditions where the goal location changes frequently. We provide detailed analysis of the agent behaviour, its ability to localise, and its network activity dynamics, showing that the agent implicitly learns key navigation abilities.

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Rumor Central: Orioles would like to re-sign free-agent C Matt Wieters - Washington Post; .711 OPS, 17 HR in '16 (ESPN)

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How can I track respondants on an anonymous survey?

I am having students fill out a survey once at the start of the year and again at the end of the year. I would like to keep the responses anonymous but I ...

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Santas Anonymous is set for a record year in our community

Santas Anonymous would like to thank everyone who referred families in need in our community and that the referral deadline has come to an end, ...

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Ravens: CB Jimmy Smith (back) returns to practice Wednesday after missing 2 games; participated on a limited basis (ESPN)

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Over 1 Million Google Accounts Hacked by 'Gooligan' Android Malware

If you own an Android smartphone, Beware! A new Android malware that has already breached more than 1 Million Google accounts is infecting around 13,000 devices every day. Dubbed Gooligan, the malware roots vulnerable Android devices to steal email addresses and authentication tokens stored on them. With this information in hands, the attackers are able to hijack your Google account and

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Publications on Russian propaganda list consider suing anonymous

Several American news outlets are considering legal action against the anonymous person or group that last week published a widely distributed list ...

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Anonymous Lost Property Volume 2 12

Under the banner elusive of PBR Streetgangs 'Lost Property' These 'found items' are rumored to be the work of Bonar Bradberry from his secret vault ...

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ISS Daily Summary Report – 11/29/2016

Robotic External Leak Locator (RELL) Operations: On GMT 333, upon powering up the Space Station Remote Manipulator System (SSRMS) Special Purpose Dexterous Manipulator (SPDM) for RELL ops, the Dexterous Manipulator Control Software (DMCS) Payload Status showed “Unloaded” for ORU Tool Changeout Mechanism (OTCM) 2 instead of “Secure” which was unexpected since it was captured to Robotic Micro Conical Tool (RMCT) 2. Root cause was traced to software changes made with the “Mount Logan” software patch. Ground teams agreed to completely rolling back the original software MSS 8.3 software version (pre-Mt Logan). Overnight the team successfully re-installed a pre-Mt. Logan version of MSS software, and reinitialized the SPDM. OTCM2 is now showing “secure” (versus the earlier improper “unloaded” signature). This allowed ground controllers to perform the RELL operations that were originally planned yesterday. The Japanese Experiment Module (JEM) Airlock (JEMAL) slide table with the RELL package was extended. Ground Controllers then grasped the RELL with the SPDM and began the checkout by pointing RELL towards deep space in order to scan a clean environment as a baseline, and data is being collected. Tomorrow will begin the planned surveys. RELL is an instrumentation package that is maneuvered externally by the SSRMS/SPDM to detect local pressure variations to help in locating a leak. For this demonstration, a predetermined scan/survey procedure will be executed that characterized the ISS environment and scans various ISS elements containing ammonia lines and systems. Planned operations later this week will also attempt to identify the source of the “white flakes” that have been noticed on downlink video.  Marangoni Ultrasonic Velocity Profiler-2 (UVP2) Installation: The crew began the installation activities for the next Marangoni experiment in the Fluid Physics Experiment Facility (FPEF) within the Japanese Experiment Module’s (JEM’s) Ryutai Rack. The crew cleaned the FPEF Infrared (IR) Imager forward and downward mirrors, then installed the Marangoni-UVP-2 cassette. The crew then connected cables and performed leak checks. Spatio-temporal Flow Structure in Marangoni Convection (Marangoni-UVP) investigates the fundamental physics of surface tension where liquid and gas meet. Specifically, it investigates a phenomenon known as Marangoni convection, a type of flow that is driven by temperature differences at the liquid and gas interface. The FPEF enables observations of liquid and gas flow in three dimensions, and the microgravity environment on the ISS provides an ideal setting to study convection. Improved understanding of liquid flow physics could lead to more efficient industrial processes, semiconductors, optical materials and biological materials for use in space and on Earth. Sarcolab-3: After successfully configuring the Muscle Atrophy Research & Exercise System (MARES) in the Columbus module yesterday, today the crew will begin the three day operations phase of joint-NASA-ESA-Russia Sarcolab experiment. Today the subject ingressed MARES, followed by the operator performing ultrasound measurements of the right calf muscle, with remote guidance and direction assistance from ground specialists.  The subject then donned Percutaneus Electrical Muscle Stimulator (PEMS), for electrical stimulation at rest and during voluntary muscle contraction, and Electromyography (EMG) electrodes to measure calf muscle and tendon response at the ankle (calf muscle insertion). The MARES equipment and software is used to measure calf strength during spaceflight; the inflight data will then be compared to preflight and postflight measurements to measure the impact of a hypothesized microgravity induced muscle loss. The goal of the Myotendinous and Neuromuscular Adaptation to Long-term Spaceflight (Sarcolab) investigation is to advance the understanding of muscle function and atrophy in space. Results will contribute to further improvement of countermeasures for long duration spaceflight. Aerosol Sampler Deployment: The crew will deploy seven passive and two active Aerosol Samplers in various locations around the ISS. Aerosols are small particles suspended in the air, and in Earth’s atmosphere, aerosols include soot, dust, pollen and a wide range of other natural and human-made materials. But smoke does not rise and dust does not settle in microgravity the way they do on Earth, causing aerosols to behave differently and pose hazards for crew members breathing the air. The Aerosol Sampling Experiment (Aerosol Sampler) collects airborne particles in the International Space Station’s (ISS) cabin air, and returns them to Earth so scientists can study the particles with powerful microscopes. For this experiment, particles collected on the cabin air samples are analyzed using a variety of microscopic techniques including: light microscopy, Raman spectroscopy, scanning electron microscopy, computer controlled scanning electron microscopy, and scanning transmission electron microscopy. Today’s Planned Activities All activities are on schedule unless otherwise noted. МО-11. Biochemical blood test. Fluid Shifts DPOAE Data Transfer Fine Motor Skills Experiment Test – Subject Photo T/V (P/TV) Advanced Resistive Exercise Device (ARED) Exercise Video Setup JEM Mesh Cover-Return Grille Cleaning Fine Motor Skills Experiment Test – Subject Aerosol Passive Samplers Hardware Installation Human Research Facility Ultrasound 2 set up and power on MARES Ankle Shaving Preparation CONTURE-2. Experiment Ops (Session 1). Data Archiving and Hardware Power down. MARES Ultrasound session XF305 Camcorder Setup Marangoni Inside (MI) Preparation Aerosol Samplers Battery Charge MARES subject electrodes equipment and start of Crew Guided Procedure Ultrasound 2 Guided Data Export MARES subject electrodes equipment assistance Checkout of the installed БРП-М Cable-Insert. Marangoni Inside (MI) Cassette Installation Photo/TV Camcorder Setup Verification Marangoni Inside (MI) Core Installation Private Medical Conference (PMC) Human Research Facility Ultrasound 2 power off and stowage MARES and Laptop Power OFF Aerosol Active Samplers Hardware Installation CONTURE-2. Hardware activation, Experiment Session 2. Hardware Removal. Water Recovery System Waste Water Tank Drain Init Aerosol Active Samplers Hardware Photography ISS Crew Orientation Preparing for Troubleshooting FGB Power Supply System and СУБК devices. Water Recovery System Waste Water Tank Drain Termination and Sample SPHERES Tether Demo Test Session Review DAN. Experiment Ops. DAN. Experiment Operator Assistance Crew Medical Officer (CMO) On Board Training (OBT) Public Affairs Office (PAO) High Definition (HD) Config LAB Setup PAO Preparation Public Affairs Office (PAO) Event in High Definition (HD) – Lab 24-hour ECG Recording (start) СОЖ maintenance Body Measures Equipment Gather Health Maintenance System (HMS) Spaceflight Cognitive Assessment Tool for Windows (WinSCAT) Test Packed Bed Reactor Experiment Operations Overview SPLANKH. Preparation for […]

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Anonymous Hacktivist 'Barrett Brown' Released From Prison

Barrett Brown, a journalist, formerly served as an unofficial spokesman for the hacktivist collective Anonymous, finally walked free from prison on Tuesday morning after serving more than four years behind bars. The Dallas-born investigative journalist was arrested in 2012 from his home while he was in the middle of an online chat after posting tweets and YouTube video threatening revenge

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Anonymous Disciples Read Download PDF/Audiobook. File Name: Anonymous Disciples Total Downloads: 1628. Formats: djvu | pdf | epub | mp3 | ...

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Press Shift + F10 during Windows 10 Upgrade to Launch Root CLI & bypass BitLocker

If your computer's security relies on Windows BitLocker Hard Drive Encryption software, then Beware! Because anyone with physical access to your PC can still access your files within few seconds. All an attacker need to do is hold SHIFT+F10 during Windows 10 update procedure. Security researcher Sami Laiho discovered this simple method of bypassing BitLocker, wherein an attacker can open a

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Firefox Zero-Day Exploit to Unmask Tor Users Released Online

Hackers are actively exploiting a zero-day vulnerability in Firefox to unmask Tor Browser users, similar to what the FBI exploited during an investigation of a child pornography site. Tor (The Onion Router) is an anonymity software that not only provides a safe heaven to human rights activists, journalists, government officials, but also is a place where drugs, assassins for hire, child

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27NOV16 CMA Announcements

27NOV16 CMA Announcements The next CMA Intergroup meeting is December 9th, 8PM at the Center on Halsted, meeting room 204. Zoolights at ...

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Eating Disorders Anonymous

Eating Disorders Anonymous: The Story of How We Recovered from Our Eating Disorders presents the accumulated experience, strength, and hope ...

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I have a new follower on Twitter

NECSI
The leading research institute in complex systems science theory and applications.
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Following: 1432 - Followers: 4102

November 29, 2016 at 10:06PM via Twitter http://twitter.com/NECSI

Adams Conditioning and Likelihood Ratio Transfer Mediated Inference. (arXiv:1611.09351v1 [cs.AI])

Forensic science advocates the use of inference mechanisms which may be viewed as simple multi-agent protocols. An important protocol of this kind involves an agent FE (forensic expert) who communicates to a second agent TOF (trier of fact) first its value of a certain likelihood ratio with respect to its own belief state which is supposed to be captured by a probability function on FE's proposition space. Subsequently FE communicates its recently acquired confirmation that a certain evidence proposition is true. The inference part of this sort of reasoning, here referred to as likelihood ratio transfer mediated reasoning, involves TOF's revision of its own belief state, and in particular an evaluation of the resulting belief in the hypothesis proposition.

Different realizations of likelihood ratio transfer mediated reasoning are distinguished: if the evidence hypothesis is included in the prior proposition space of TOF then a comparison is made between understanding the TOF side of a belief revision step as a composition of two successive steps of single likelihood Adams conditioning followed by a Bayes conditioning step, and as a single step of double likelihood Adams conditioning followed by Bayes conditioning; if, however the evidence hypothesis is initially outside the proposition space of TOF an application of proposition kinetics for the introduction of the evidence proposition precedes Bayesian conditioning, which is followed by Jeffrey conditioning on the hypothesis proposition.

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Split-door criterion for causal identification: Automatic search for natural experiments. (arXiv:1611.09414v1 [stat.ME])

Unobserved or unknown confounders complicate even the simplest attempts to estimate the effect of one variable on another using observational data. When cause and effect are both affected by unobserved confounders, methods based on identifying natural experiments have been proposed to eliminate confounds. However, their validity is hard to verify because they depend on assumptions about the independence of variables, that by definition, cannot be measured. In this paper we investigate a particular scenario in time series data that permits causal identification in the presence of unobserved confounders and present an algorithm to automatically find such scenarios. Specifically, we examine what we call the split-door setting, when the effect variable can be split up into two parts: one that is potentially affected by the cause, and another that is independent of it. We show that when both of these variables are caused by the same (unobserved) confounders, the problem of identification reduces to that of testing for independence among observed variables. We discuss various situations in which split-door variables are commonly recorded in both online and offline settings, and demonstrate the method by estimating the causal impact of Amazon's recommender system, obtaining more than 23,000 natural experiments that provide similar---but more precise---estimates than past studies.

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Emergence of foveal image sampling from learning to attend in visual scenes. (arXiv:1611.09430v1 [cs.NE])

We describe a neural attention model with a learnable retinal sampling lattice. The model is trained on a visual search task requiring the classification of an object embedded in a visual scene amidst background distractors using the smallest number of fixations. We explore the tiling properties that emerge in the model's retinal sampling lattice after training. Specifically, we show that this lattice resembles the eccentricity dependent sampling lattice of the primate retina, with a high resolution region in the fovea surrounded by a low resolution periphery. Furthermore, we find conditions where these emergent properties are amplified or eliminated providing clues to their function.

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Intelligible Language Modeling with Input Switched Affine Networks. (arXiv:1611.09434v1 [cs.AI])

The computational mechanisms by which nonlinear recurrent neural networks (RNNs) achieve their goals remains an open question. There exist many problem domains where intelligibility of the network model is crucial for deployment. Here we introduce a recurrent architecture composed of input-switched affine transformations, in other words an RNN without any nonlinearity and with one set of weights per input. We show that this architecture achieves near identical performance to traditional architectures on language modeling of Wikipedia text, for the same number of model parameters. It can obtain this performance with the potential for computational speedup compared to existing methods, by precomputing the composed affine transformations corresponding to longer input sequences. As our architecture is affine, we are able to understand the mechanisms by which it functions using linear methods. For example, we show how the network linearly combines contributions from the past to make predictions at the current time step. We show how representations for words can be combined in order to understand how context is transferred across word boundaries. Finally, we demonstrate how the system can be executed and analyzed in arbitrary bases to aid understanding.

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Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints. (arXiv:1611.09474v1 [cs.DS])

We consider the problem of maximizing a non-monotone DR-submodular function subject to a cardinality constraint. Diminishing returns (DR) submodularity is a generalization of the diminishing returns property for functions defined over the integer lattice. This generalization can be used to solve many machine learning or combinatorial optimization problems such as optimal budget allocation, revenue maximization, etc. In this work we propose the first polynomial-time approximation algorithms for non-monotone constrained maximization. We implement our algorithms for a revenue maximization problem with a real-world dataset to check their efficiency and performance.

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Learning Filter Banks Using Deep Learning For Acoustic Signals. (arXiv:1611.09526v1 [cs.SD])

Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urban- sound8K dataset, the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.

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Learning Concept Hierarchies through Probabilistic Topic Modeling. (arXiv:1611.09573v1 [cs.AI])

With the advent of semantic web, various tools and techniques have been introduced for presenting and organizing knowledge. Concept hierarchies are one such technique which gained significant attention due to its usefulness in creating domain ontologies that are considered as an integral part of semantic web. Automated concept hierarchy learning algorithms focus on extracting relevant concepts from unstructured text corpus and connect them together by identifying some potential relations exist between them. In this paper, we propose a novel approach for identifying relevant concepts from plain text and then learns hierarchy of concepts by exploiting subsumption relation between them. To start with, we model topics using a probabilistic topic model and then make use of some lightweight linguistic process to extract semantically rich concepts. Then we connect concepts by identifying an "is-a" relationship between pair of concepts. The proposed method is completely unsupervised and there is no need for a domain specific training corpus for concept extraction and learning. Experiments on large and real-world text corpora such as BBC News dataset and Reuters News corpus shows that the proposed method outperforms some of the existing methods for concept extraction and efficient concept hierarchy learning is possible if the overall task is guided by a probabilistic topic modeling algorithm.

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