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Ravens: LB Terrell Suggs pleaded not guilty Friday to misdemeanor charges from his March 5 one-car accident in Arizona (ESPN)
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MikeF_Trading
#Stock Market Prop Trader, Scalper, #Daytrader, #Swingtrader for more than 10 years. https://t.co/ddzr3pP2vi Do your own due diligence on every ideas!
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Europa: Discover Life Under the Ice
Friday, April 1, 2016
Oboe Concerto in G minor, Schrank II/33/52 (Anonymous)
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Orioles Video: Scout Ryan Powell returns to diamond in intrasquad game to fulfill wish of cancer-stricken mother (ESPN)
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ISS Daily Summary Report – 03/31/16
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Hacker Hijacks a Police Drone from 2 Km Away with $40 Kit
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Just One? No, FBI to Unlock More iPhones with its Secret Technique
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Big Dipper to Southern Cross
Arctic Sea Ice Maximum - 2016
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Thursday, March 31, 2016
Instrumental Piece in G minor, Schrank II/38/39 (Anonymous)
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Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding. (arXiv:1603.09405v1 [cs.CL])
Neural network based approaches for sentence relation modeling automatically generate hidden matching features from raw sentence pairs. However, the quality of matching feature representation may not be satisfied due to complex semantic relations such as entailment or contradiction. To address this challenge, we propose a new deep neural network architecture that jointly leverage pre-trained word embedding and auxiliary character embedding to learn sentence meanings. The two kinds of word sequence representations as inputs into multi-layer bidirectional LSTM to learn enhanced sentence representation. After that, we construct matching features followed by another temporal CNN to learn high-level hidden matching feature representations. Experimental results demonstrate that our approach consistently outperforms the existing methods on standard evaluation datasets.
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Ordinal Conditional Functions for Nearly Counterfactual Revision. (arXiv:1603.09429v1 [cs.AI])
We are interested in belief revision involving conditional statements where the antecedent is almost certainly false. To represent such problems, we use Ordinal Conditional Functions that may take infinite values. We model belief change in this context through simple arithmetical operations that allow us to capture the intuition that certain antecedents can not be validated by any number of observations. We frame our approach as a form of finite belief improvement, and we propose a model of conditional belief revision in which only the "right" hypothetical levels of implausibility are revised.
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A New Approach for Revising Logic Programs. (arXiv:1603.09465v1 [cs.AI])
Belief revision has been studied mainly with respect to background logics that are monotonic in character. In this paper we study belief revision when the underlying logic is non-monotonic instead--an inherently interesting problem that is under explored. In particular, we will focus on the revision of a body of beliefs that is represented as a logic program under the answer set semantics, while the new information is also similarly represented as a logic program. Our approach is driven by the observation that unlike in a monotonic setting where, when necessary, consistency in a revised body of beliefs is maintained by jettisoning some old beliefs, in a non-monotonic setting consistency can be restored by adding new beliefs as well. We will define a syntactic revision function and subsequently provide representation theorem for characterising it.
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Building the Signature of Set Theory Using the MathSem Program. (arXiv:1603.09488v1 [cs.LO])
Knowledge representation is a popular research field in IT. As mathematical knowledge is most formalized, its representation is important and interesting. Mathematical knowledge consists of various mathematical theories. In this paper we consider a deductive system that derives mathematical notions, axioms and theorems. All these notions, axioms and theorems can be considered as the part of elementary set theory. This theory will be represented as a semantic net.
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Reactive Policies with Planning for Action Languages. (arXiv:1603.09495v1 [cs.AI])
We describe a representation in a high-level transition system for policies that express a reactive behavior for the agent. We consider a target decision component that figures out what to do next and an (online) planning capability to compute the plans needed to reach these targets. Our representation allows one to analyze the flow of executing the given reactive policy, and to determine whether it works as expected. Additionally, the flexibility of the representation opens a range of possibilities for designing behaviors.
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Verifiability of Argumentation Semantics. (arXiv:1603.09502v1 [cs.AI])
Dung's abstract argumentation theory is a widely used formalism to model conflicting information and to draw conclusions in such situations. Hereby, the knowledge is represented by so-called argumentation frameworks (AFs) and the reasoning is done via semantics extracting acceptable sets. All reasonable semantics are based on the notion of conflict-freeness which means that arguments are only jointly acceptable when they are not linked within the AF. In this paper, we study the question which information on top of conflict-free sets is needed to compute extensions of a semantics at hand. We introduce a hierarchy of so-called verification classes specifying the required amount of information. We show that well-known standard semantics are exactly verifiable through a certain such class. Our framework also gives a means to study semantics lying inbetween known semantics, thus contributing to a more abstract understanding of the different features argumentation semantics offer.
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Distributing Knowledge into Simple Bases. (arXiv:1603.09511v1 [cs.AI])
Understanding the behavior of belief change operators for fragments of classical logic has received increasing interest over the last years. Results in this direction are mainly concerned with adapting representation theorems. However, fragment-driven belief change also leads to novel research questions. In this paper we propose the concept of belief distribution, which can be understood as the reverse task of merging. More specifically, we are interested in the following question: given an arbitrary knowledge base $K$ and some merging operator $\Delta$, can we find a profile $E$ and a constraint $\mu$, both from a given fragment of classical logic, such that $\Delta_\mu(E)$ yields a result equivalent to $K$? In other words, we are interested in seeing if $K$ can be distributed into knowledge bases of simpler structure, such that the task of merging allows for a reconstruction of the original knowledge. Our initial results show that merging based on drastic distance allows for an easy distribution of knowledge, while the power of distribution for operators based on Hamming distance relies heavily on the fragment of choice.
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Characterizing Realizability in Abstract Argumentation. (arXiv:1603.09545v1 [cs.AI])
Realizability for knowledge representation formalisms studies the following question: given a semantics and a set of interpretations, is there a knowledge base whose semantics coincides exactly with the given interpretation set? We introduce a general framework for analyzing realizability in abstract dialectical frameworks (ADFs) and various of its subclasses. In particular, the framework applies to Dung argumentation frameworks, SETAFs by Nielsen and Parsons, and bipolar ADFs. We present a uniform characterization method for the admissible, complete, preferred and model/stable semantics. We employ this method to devise an algorithm that decides realizability for the mentioned formalisms and semantics; moreover the algorithm allows for constructing a desired knowledge base whenever one exists. The algorithm is built in a modular way and thus easily extensible to new formalisms and semantics. We have also implemented our approach in answer set programming, and used the implementation to obtain several novel results on the relative expressiveness of the abovementioned formalisms.
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Neural Language Correction with Character-Based Attention. (arXiv:1603.09727v1 [cs.CL])
Natural language correction has the potential to help language learners improve their writing skills. While approaches with separate classifiers for different error types have high precision, they do not flexibly handle errors such as redundancy or non-idiomatic phrasing. On the other hand, word and phrase-based machine translation methods are not designed to cope with orthographic errors, and have recently been outpaced by neural models. Motivated by these issues, we present a neural network-based approach to language correction. The core component of our method is an encoder-decoder recurrent neural network with an attention mechanism. By operating at the character level, the network avoids the problem of out-of-vocabulary words. We illustrate the flexibility of our approach on dataset of noisy, user-generated text collected from an English learner forum. When combined with a language model, our method achieves a state-of-the-art $F_{0.5}$-score on the CoNLL 2014 Shared Task. We further demonstrate that training the network on additional data with synthesized errors can improve performance.
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A Survey of League Championship Algorithm: Prospects and Challenges. (arXiv:1603.09728v1 [cs.AI])
The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.
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r-Extreme Signalling for Congestion Control. (arXiv:1404.2458v3 [math.OC] UPDATED)
In many "smart city" applications, congestion arises in part due to the nature of signals received by individuals from a central authority. In the model of Marecek et al. [arXiv:1406.7639, Int. J. Control 88(10), 2015], each agent uses one out of multiple resources at each time instant. The per-use cost of a resource depends on the number of concurrent users. A central authority has up-to-date knowledge of the congestion across all resources and uses randomisation to provide a scalar or an interval for each resource at each time. In this paper, the interval to broadcast per resource is obtained by taking the minima and maxima of costs observed within a time window of length r, rather than by randomisation. We show that the resulting distribution of agents across resources also converges in distribution, under plausible assumptions about the evolution of the population over time.
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Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer. (arXiv:1508.05328v2 [cs.MA] UPDATED)
Reinforcement learning has significant applications for multi-agent systems, especially in unknown dynamic environments. However, most multi-agent reinforcement learning (MARL) algorithms suffer from such problems as exponential computation complexity in the joint state-action space, which makes it difficult to scale up to realistic multi-agent problems. In this paper, a novel algorithm named negotiation-based MARL with sparse interactions (NegoSI) is presented. In contrast to traditional sparse-interaction based MARL algorithms, NegoSI adopts the equilibrium concept and makes it possible for agents to select the non-strict Equilibrium Dominating Strategy Profile (non-strict EDSP) or Meta equilibrium for their joint actions. The presented NegoSI algorithm consists of four parts: the equilibrium-based framework for sparse interactions, the negotiation for the equilibrium set, the minimum variance method for selecting one joint action and the knowledge transfer of local Q-values. In this integrated algorithm, three techniques, i.e., unshared value functions, equilibrium solutions and sparse interactions are adopted to achieve privacy protection, better coordination and lower computational complexity, respectively. To evaluate the performance of the presented NegoSI algorithm, two groups of experiments are carried out regarding three criteria: steps of each episode (SEE), rewards of each episode (REE) and average runtime (AR). The first group of experiments is conducted using six grid world games and shows fast convergence and high scalability of the presented algorithm. Then in the second group of experiments NegoSI is applied to an intelligent warehouse problem and simulated results demonstrate the effectiveness of the presented NegoSI algorithm compared with other state-of-the-art MARL algorithms.
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Investigating practical linear temporal difference learning. (arXiv:1602.08771v2 [cs.LG] UPDATED)
Off-policy reinforcement learning has many applications including: learning from demonstration, learning multiple goal seeking policies in parallel, and representing predictive knowledge. Recently there has been an proliferation of new policy-evaluation algorithms that fill a longstanding algorithmic void in reinforcement learning: combining robustness to off-policy sampling, function approximation, linear complexity, and temporal difference (TD) updates. This paper contains two main contributions. First, we derive two new hybrid TD policy-evaluation algorithms, which fill a gap in this collection of algorithms. Second, we perform an empirical comparison to elicit which of these new linear TD methods should be preferred in different situations, and make concrete suggestions about practical use.
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The darke is my delight (Anonymous)
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Serve4Sure
Serve4Sure is leading the Attorney Service Industry into the future. Our software utilizes the most advanced technology to provide an efficient workflow.
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If not, try an anonymous function
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Novartis AG (NVS) Investigates $85 Million Bribery Allegations Made By an Anonymous ...
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Here's the Exploit to Bypass Apple Security Feature that Fits in a Tweet
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ISS Daily Summary Report – 03/30/16
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[FD] Python v2.7 v1.5.4 iOS - Filter Bypass & Persistent Vulnerability
[FD] Trend Micro (SSO) - (Backend) SSO Redirect & Session Vulnerability
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[FD] Docker UI v0.10.0 - Multiple Persistent Vulnerabilities
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[FD] Docker UI v0.10.0 - Multiple Client Side Cross Site Request Forgery Web Vulnerabilities
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[FD] WP External Links v1.80 - Cross Site Scripting Web Vulnerabilities
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TaeTweets
The parody account of Tay, Microsoft's A.I. fam from the internet that's got zero chill! The more you talk the smarter Tay gets
teh interwebs
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Advanced Malware targeting Internet of the Things and Routers
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Microsoft adds Linux Bash Shell and Ubuntu Binaries to Windows 10
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Crime watch app filters racial bias and enables anonymous text chat
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NGC 6188 and NGC 6164
Wednesday, March 30, 2016
Local Search Yields a PTAS for k-Means in Doubling Metrics. (arXiv:1603.08976v1 [cs.DS])
The most well known and ubiquitous clustering problem encountered in nearly every branch of science is undoubtedly $k$-means: given a set of data points and a parameter $k$, select $k$ centres and partition the data points into $k$ clusters around these centres so that the sum of squares of distances of the points to their cluster centre % (called the cost of the solution) is minimized. Typically these data points lie in Euclidean space $\mathbb{R}^d$ for some $d\geq 2$.
The most commonly used algorithm in practice is known as Lloyd-Forgy, which is also referred to as "the" $k$-means algorithm, and various extensions of it often work very well in practice. However, they may produce solutions whose cost is arbitrarily large compared to the optimum solution. Kanungo et al. [2004] analyzed a very simple local search heuristic to get a polynomial-time algorithm with approximation ratio $9+\epsilon$ for any fixed $\epsilon>0$ for $k$-means in Euclidean space.
Finding an algorithm with a better worst-case approximation guarantee has remained one of the biggest open questions in this area, in particular whether one can get a true PTAS for fixed dimension Euclidean space. We settle this problem by showing that a simple local search algorithm provides a PTAS for $k$-means for $\mathbb{R}^d$ for any fixed $d$.
More precisely, for any error parameter $\epsilon>0$, the local search algorithm that considers swaps of up to $\rho=d^{O(d)}\cdot{\epsilon}^{-O(d/\epsilon)}$ centres will produce a solution whose cost is at most $1+\epsilon$ times greater than the optimum cost. Our analysis extends very easily to the more general setting where the metric has fixed doubling dimension and to where we are interested in minimizing the sum of the $q$-th powers of the distances for fixed $q$.
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Towards Practical Bayesian Parameter and State Estimation. (arXiv:1603.08988v1 [cs.AI])
Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and practically relevant probabilistic models, the silver bullet---an efficient and general online inference algorithm for such problems---remains elusive, forcing users to write special-purpose code for each application. We propose a novel blackbox algorithm -- a hybrid of particle filtering for state variables and assumed density filtering for parameter variables. It has following advantages: (a) it is efficient due to its online nature, and (b) it is applicable to both discrete and continuous parameter spaces . On a variety of toy and real models, our system is able to generate more accurate results within a fixed computation budget. This preliminary evidence indicates that the proposed approach is likely to be of practical use.
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Maximize Pointwise Cost-sensitively Submodular Functions With Budget Constraint. (arXiv:1603.09029v1 [cs.AI])
We study the worst-case adaptive optimization problem with budget constraint. Unlike previous works, we consider the general setting where the cost is a set function on sets of decisions. For this setting, we investigate the near-optimality of greedy policies when the utility function satisfies a novel property called pointwise cost-sensitive submodularity. This property is an extension of cost-sensitive submodularity, which in turn is a generalization of submodularity to general cost functions. We prove that two simple greedy policies for the problem are not near-optimal but the best between them is near-optimal. With this result, we propose a combined policy that is near-optimal with respect to the optimal worst-case policy that uses half of the budget. We discuss applications of our theoretical results and also report experimental results comparing the greedy policies on the active learning problem.
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Phoenix: A Self-Optimizing Chess Engine. (arXiv:1603.09051v1 [cs.AI])
Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. However there are still many areas in which humans excel in comparison with the machines. One such area is chess. Even with great advances in the speed and computational power of modern machines, Grandmasters often beat the best chess programs in the world with relative ease. This may be due to the fact that a game of chess cannot be won by pure calculation. There is more to the goodness of a chess position than some numerical value which apparently can be seen only by the human brain. Here an effort has been made to improve current chess engines by letting themselves evolve over a period of time. Firstly, the problem of learning is reduced into an optimization problem by defining Position Evaluation in terms of Positional Value Tables (PVTs). Next, the PVTs are optimized using Multi-Niche Crowding which successfully identifies the optima in a multimodal function, thereby arriving at distinctly different solutions which are close to the global optimum.
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Iterated Ontology Revision by Reinterpretation. (arXiv:1603.09194v1 [cs.AI])
Iterated applications of belief change operators are essential for different scenarios such as that of ontology evolution where new information is not presented at once but only in piecemeal fashion within a sequence. I discuss iterated applications of so called reinterpretation operators that trace conflicts between ontologies back to the ambiguous of symbols and that provide conflict resolution strategies with bridging axioms. The discussion centers on adaptations of the classical iteration postulates according to Darwiche and Pearl. The main result of the paper is that reinterpretation operators fulfill the postulates for sequences containing only atomic triggers. For complex triggers, a fulfillment is not guaranteed and indeed there are different reasons for the different postulates why they should not be fulfilled in the particular scenario of ontology revision with well developed ontologies.
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Collaborative Filtering Bandits. (arXiv:1502.03473v5 [cs.LG] UPDATED)
Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.
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Network of Bandits. (arXiv:1602.03779v5 [cs.AI] UPDATED)
The distribution of the best arm identification task on the user's devices offers several advantages for application purposes: scalability, reduction of deployment costs and privacy. We propose a distributed version of the algorithm Successive Elimination using a simple architecture based on a single server which synchronizes each task executed on the user's devices. We show that this algorithm is near optimal both in terms of transmitted number of bits and in terms of number of pulls per player. Finally, we propose an extension of this approach to distribute the contextual bandit algorithm Bandit Forest, which is able to finely exploit the user's data while guaranteeing the privacy.
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Analyzing Games with Ambiguous Player Types using the ${\rm MINthenMAX}$ Decision Model. (arXiv:1603.01524v2 [cs.GT] UPDATED)
In many common interactive scenarios, participants lack information about other participants, and specifically about the preferences of other participants. In this work, we model an extreme case of incomplete information, which we term games with type ambiguity, where a participant lacks even information enabling him to form a belief on the preferences of others. Under type ambiguity, one cannot analyze the scenario using the commonly used Bayesian framework, and therefore he needs to model the participants using a different decision model.
In this work, we present the ${\rm MINthenMAX}$ decision model under ambiguity. This model is a refinement of Wald's MiniMax principle, which we show to be too coarse for games with type ambiguity. We characterize ${\rm MINthenMAX}$ as the finest refinement of the MiniMax principle that satisfies three properties we claim are necessary for games with type ambiguity. This prior-less approach we present her also follows the common practice in computer science of worst-case analysis.
Finally, we define and analyze the corresponding equilibrium concept assuming all players follow ${\rm MINthenMAX}$. We demonstrate this equilibrium by applying it to two common economic scenarios: coordination games and bilateral trade. We show that in both scenarios, an equilibrium in pure strategies always exists and we analyze the equilibria.
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Sinfonia in G major, Schrank II/38/21 (Anonymous)
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Sinfonia in B-flat major, Schrank II/38/24 (Anonymous)
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Orioles release P Miguel Gonzalez after four seasons in Baltimore; 9.78 ERA in six spring training starts (ESPN)
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Orioles: RP Mychal Givens among David Schoenfield's 10 sleeper pitchers for 2016; 1.80 ERA in 22 games last season (ESPN)
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Anguilla Anonymous?
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Ravens: Biggest remaining roster hole is at inside linebacker, writes Jamison Hensley; cut Daryl Smith in the offseason (ESPN)
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Anonymous open fire at shop in Riaq
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Adam Thomas
#freelancer web designer. #SEO, #blogging, #html, #webdesign, #socialmedia #marketing, #bootstrap, #wordpress and anything #tech!
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Google has also been Ordered to Unlock 9 Android Phones
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Nonna Box
Delicious Italian products delivered monthly in a box. Every box features a different Italian region and a nonna from Italy that will share her favorite recipes
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Ravens: Justin Forsett hosted football camp in Italy; trip included bad turbulence, narrow streets, Italian Ryan Gosling (ESPN)
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ISS Daily Summary Report – 03/29/16
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