OpenRaiser/Intern-Atlas huggingface dataset:
In-Order Delivery Delay of Transport Layer Coding
Venue: arXiv cs.IT 2014
Year: 2014

A large number of streaming applications use reliable transport protocols such as TCP to deliver content over the Internet. However, head-of-line blocking due to packet loss recovery can often result in unwanted behavior and poor application layer performance. Transport layer coding can help mitigate this issue by helping to recover from lost packets without waiting for retransmissions. We consider the use of an on-line network code that inserts coded packets at strategic locations within the underlying packet stream. If retransmissions are necessary, additional coding packets are transmitted to ensure the receiver's ability to decode. An analysis of this scheme is provided that helps determine both the expected in-order packet delivery delay and its variance. Numerical results are then used to determine when and how many coded packets should be inserted into the packet stream, in addition to determining the trade-offs between reducing the in-order delay and the achievable rate. The analytical results are finally compared with experimental results to provide insight into how to minimize the delay of existing transport layer protocols.
Priorities Without Priorities: Representing Preemption in Psi-Calculi
Venue: arXiv cs.LO 2014
Year: 2014

Psi-calculi is a parametric framework for extensions of the pi-calculus with data terms and arbitrary logics. In this framework there is no direct way to represent action priorities, where an action can execute only if all other enabled actions have lower priority. We here demonstrate that the psi-calculi parameters can be chosen such that the effect of action priorities can be encoded. To accomplish this we define an extension of psi-calculi with action priorities, and show that for every calculus in the extended framework there is a corresponding ordinary psi-calculus, without priorities, and a translation between them that satisfies strong operational correspondence. This is a significantly stronger result than for most encodings between process calculi in the literature. We also formally prove in Nominal Isabelle that the standard congruence and structural laws about strong bisimulation hold in psi-calculi extended with priorities.
Matching in the Pi-Calculus
Venue: arXiv cs.LO 2014
Year: 2014

We study whether, in the pi-calculus, the match prefix-a conditional operator testing two names for (syntactic) equality-is expressible via the other operators. Previously, Carbone and Maffeis proved that matching is not expressible this way under rather strong requirements (preservation and reflection of observables). Later on, Gorla developed a by now widely-tested set of criteria for encodings that allows much more freedom (e.g. instead of direct translations of observables it allows comparison of calculi with respect to reachability of successful states). In this paper, we offer a considerably stronger separation result on the non-expressibility of matching using only Gorla's relaxed requirements.
Distributive Laws and Decidable Properties of SOS Specifications
Venue: arXiv cs.PL 2014
Year: 2014

Some formats of well-behaved operational specifications, correspond to natural transformations of certain types (for example, GSOS and coGSOS laws). These transformations have a common generalization: distributive laws of monads over comonads. We prove that this elegant theoretical generalization has limited practical benefits: it does not translate to any concrete rule format that would be complete for specifications that contain both GSOS and coGSOS rules. This is shown for the case of labeled transition systems and deterministic stream systems.
Session Types as Generic Process Types
Venue: arXiv cs.PL 2014
Year: 2014

Behavioural type systems ensure more than the usual safety guarantees of static analysis. They are based on the idea of "types-as-processes", providing dedicated type algebras for particular properties, ranging from protocol compatibility to race-freedom, lock-freedom, or even responsiveness. Two successful, although rather different, approaches, are session types and process types. The former allows to specify and verify (distributed) communication protocols using specific type (proof) systems; the latter allows to infer from a system specification a process abstraction on which it is simpler to verify properties, using a generic type (proof) system. What is the relationship between these approaches? Can the generic one subsume the specific one? At what price? And can the former be used as a compiler for the latter? The work presented herein is a step towards answers to such questions. Concretely, we define a stepwise encoding of a pi-calculus with sessions and session types (the system of Gay and Hole) into a pi-calculus with process types (the Generic Type System of Igarashi and Kobayashi). We encode session type environments, polarities (which distinguish session channels end-points), and labelled sums. We show forward and reverse operational correspondences for the encodings, as well as typing correspondences. To faithfully encode session subtyping in process types subtyping, one needs to add to the target language record constructors and new subtyping rules. In conclusion, the programming convenience of session types as protocol abstractions can be combined with the simplicity and power of the pi-calculus, taking advantage in particular of the framework provided by the Generic Type System.
Ordering without forbidden patterns
Venue: arXiv cs.DM 2014
Year: 2014

Let F be a set of ordered patterns, i.e., graphs whose vertices are linearly ordered. An F-free ordering of the vertices of a graph H is a linear ordering of V(H) such that none of patterns in F occurs as an induced ordered subgraph. We denote by ORD(F) the decision problem asking whether an input graph admits an F-free ordering; we also use ORD(F) to denote the class of graphs that do admit an F-free ordering. It was observed by Damaschke (and others) that many natural graph classes can be described as ORD(F) for sets F of small patterns (with three or four vertices). Damaschke also noted that for many sets F consisting of patterns with three vertices, ORD(F) is polynomial-time solvable by known algorithms or their simple modifications. We complete the picture by proving that all these problems can be solved in polynomial time. In fact, we provide a single master algorithm, i.e., we solve in polynomial time the problem $ORD_3$ in which the input is a set F of patterns with at most three vertices and a graph H, and the problem is to decide whether or not H admits an F-free ordering of the vertices. Our algorithm certifies non-membership by a forbidden substructure, and thus provides a single forbidden structure characterization for all the graph classes described by some ORD(F) with F consisting of patterns with at most three vertices. Many of the problems ORD(F) with F consisting of larger patterns have been shown to be NP-complete by Duffus, Ginn, and Rodl, and we add two simple examples. We also discuss a bipartite version of the problem, BORD(F), in which the input is a bipartite graph H with a fixed bipartition of the vertices, and we are given a set F of bipartite patterns. We also describe some examples of digraph ordering problems and algorithms. We conjecture that for every set F of forbidden patterns, ORD(F) is either polynomial or NP-complete.
Query DAGs: A Practical Paradigm for Implementing Belief Network Inference
Venue: arXiv cs.AI 2014
Year: 2014

We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG). Each non-leaf node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the algorithms for exact inference in belief networks --- we show how they can be generated using clustering and conditioning algorithms. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based; that of a Q-DAG on-line evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms, given the simplicity of the Q-DAG evaluation algorithm. This paper describes this new paradigm for probabilistic inference, explaining how it works, its uses, and outlines some of the research directions that it leads to.
Learning to Cooperate via Policy Search
Venue: arXiv cs.AI 2014
Year: 2014

Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to value-based methods for partially observable environments. In this paper, we provide a gradient-based distributed policy-search method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain.
Truthful Prioritization Schemes for Spectrum Sharing
Venue: arXiv cs.GT 2014
Year: 2014

We design a protocol for dynamic prioritization of data on shared routers such as untethered 3G/4G devices. The mechanism prioritizes bandwidth in favor of users with the highest value, and is incentive compatible, so that users can simply report their true values for network access. A revenue pooling mechanism also aligns incentives for sellers, so that they will choose to use prioritization methods that retain the incentive properties on the buy-side. In this way, the design allows for an open architecture. In addition to revenue pooling, the technical contribution is to identify a class of stochastic demand models and a prioritization scheme that provides allocation monotonicity. Simulation results confirm efficiency gains from dynamic prioritization relative to prior methods, as well as the effectiveness of revenue pooling.
Change Impact Analysis Based Regression Testing of Web Services
Venue: arXiv cs.SE 2014
Year: 2014

Reducing the effort required to make changes in web services is one of the primary goals in web service projects maintenance and evolution. Normally, functional and non-functional testing of a web service is performed by testing the operations specified in its WSDL. The regression testing is performed by identifying the changes made thereafter to the web service code and the WSDL. In this thesis, we present a tool-supported approach to perform efficient regression testing of web services. By representing a web service as a directed graph of WSDL elements, we identify and gathers the changed portions of the graph and use this information to reduce regression testing efforts. Specifically, we identify, categorize, and capture the web service testing needs in two different ways, namely, Operationalized Regression Testing of Web Service (ORTWS) and Parameterized Regression Testing of Web Service (PRTWS). Both of the approach can be combined to reduce the regression testing efforts in the web service project. The proposed approach is prototyped as a tool, named as Automatic Web Service Change Management (AWSCM), which helps in selecting the relevant test cases to construct reduced test suite from the old test suite. We present few case studies on different web service projects to demonstrate the applicability of the proposed tool. The reduction in the effort for regression testing of web service is also estimated.
Preventing False Discovery in Interactive Data Analysis is Hard
Venue: arXiv cs.LG 2014
Year: 2014

We show that, under a standard hardness assumption, there is no computationally efficient algorithm that given $n$ samples from an unknown distribution can give valid answers to $n^{3+o(1)}$ adaptively chosen statistical queries. A statistical query asks for the expectation of a predicate over the underlying distribution, and an answer to a statistical query is valid if it is "close" to the correct expectation over the distribution. Our result stands in stark contrast to the well known fact that exponentially many statistical queries can be answered validly and efficiently if the queries are chosen non-adaptively (no query may depend on the answers to previous queries). Moreover, a recent work by Dwork et al. shows how to accurately answer exponentially many adaptively chosen statistical queries via a computationally inefficient algorithm; and how to answer a quadratic number of adaptive queries via a computationally efficient algorithm. The latter result implies that our result is tight up to a linear factor in $n.$ Conceptually, our result demonstrates that achieving statistical validity alone can be a source of computational intractability in adaptive settings. For example, in the modern large collaborative research environment, data analysts typically choose a particular approach based on previous findings. False discovery occurs if a research finding is supported by the data but not by the underlying distribution. While the study of preventing false discovery in Statistics is decades old, to the best of our knowledge our result is the first to demonstrate a computational barrier. In particular, our result suggests that the perceived difficulty of preventing false discovery in today's collaborative research environment may be inherent.
Time-Asynchronous Gaussian Multiple Access Relay Channel with Correlated Sources
Venue: arXiv cs.IT 2014
Year: 2014

We study the transmission of a set of correlated sources $(U_1,\cdots,U_K)$ over a Gaussian multiple access relay channel with time asynchronism between the encoders. We assume that the maximum possible offset ${\mathsf{d_{max}}}(n)$ between the transmitters grows without bound as the block length $n \rightarrow \infty$ while the relative ratio ${\mathsf{d_{max}}(n) / n}$ of the maximum possible offset to the block length asymptotically vanishes. For such a joint source-channel coding problem, and under specific gain conditions, we derive necessary and sufficient conditions for reliable communications and show that separate source and channel coding achieves optimal performance. In particular, we first derive a general outer bound on the source entropy content for all channel gains as our main result. Then, using Slepian-Wolf source coding combined with the channel coding scheme introduced in \cite{Cover_McEliece:81} on top of block Markov coding, we show that the thus achieved inner bound matches the outer bound. Consequently, as a corollary, we also address the problem of sending a pair of correlated sources over a two user interference channel in the same context.
Distributed power control over interference channels using ACK/NACK feedback
Venue: arXiv cs.IT 2014
Year: 2014

In this work, we consider a network composed of several single-antenna transmitter-receiver pairs in which each pair aims at selfishly minimizing the power required to achieve a given signal-to-interference-plus-noise ratio. This is obtained modeling the transmitter-receiver pairs as rational agents that engage in a non-cooperative game. Capitalizing on the well-known results on the existence and structure of the generalized Nash equilibrium (GNE) point of the underlying game, a low complexity, iterative and distributed algorithm is derived to let each terminal reach the GNE using only a limited feedback in the form of link-layer acknowledgement (ACK) or negative acknowledgement (NACK). Numerical results are used to prove that the proposed solution is able to achieve convergence in a scalable and adaptive manner under different operating conditions.
Microtask crowdsourcing for disease mention annotation in PubMed abstracts
Venue: arXiv cs.CL 2014
Year: 2014

Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses. Many biological natural language process (BioNLP) projects attempt to address this challenge, but the state of the art in BioNLP still leaves much room for improvement. Progress in BioNLP research depends on large, annotated corpora for evaluating information extraction systems and training machine learning models. Traditionally, such corpora are created by small numbers of expert annotators often working over extended periods of time. Recent studies have shown that workers on microtask crowdsourcing platforms such as Amazon's Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text. Here, we investigated the use of the AMT in capturing disease mentions in PubMed abstracts. We used the NCBI Disease corpus as a gold standard for refining and benchmarking our crowdsourcing protocol. After several iterations, we arrived at a protocol that reproduced the annotations of the 593 documents in the training set of this gold standard with an overall F measure of 0.872 (precision 0.862, recall 0.883). The output can also be tuned to optimize for precision (max = 0.984 when recall = 0.269) or recall (max = 0.980 when precision = 0.436). Each document was examined by 15 workers, and their annotations were merged based on a simple voting method. In total 145 workers combined to complete all 593 documents in the span of 1 week at a cost of $.06 per abstract per worker. The quality of the annotations, as judged with the F measure, increases with the number of workers assigned to each task such that the system can be tuned to balance cost against quality. These results demonstrate that microtask crowdsourcing can be a valuable tool for generating well-annotated corpora in BioNLP.
Gabor-like Image Filtering using a Neural Microcircuit
Venue: arXiv cs.CV 2014
Year: 2014

In this letter, we present an implementation of a neural microcircuit for image processing employing Hebbian-adaptive learning. The neuronal circuit utilizes only excitatory synapses to correlate action potentials, extracting the uncorrelated ones, which contain significant image information. This circuit is capable of approximating Gabor-like image filtering and other image processing functions
Bandit Algorithms for Tree Search
Venue: arXiv cs.AI 2014
Year: 2014

Bandit based methods for tree search have recently gained popularity when applied to huge trees, e.g. in the game of go [6]. Their efficient exploration of the tree enables to re- turn rapidly a good value, and improve preci- sion if more time is provided. The UCT algo- rithm [8], a tree search method based on Up- per Confidence Bounds (UCB) [2], is believed to adapt locally to the effective smoothness of the tree. However, we show that UCT is "over-optimistic" in some sense, leading to a worst-case regret that may be very poor. We propose alternative bandit algorithms for tree search. First, a modification of UCT us- ing a confidence sequence that scales expo- nentially in the horizon depth is analyzed. We then consider Flat-UCB performed on the leaves and provide a finite regret bound with high probability. Then, we introduce and analyze a Bandit Algorithm for Smooth Trees (BAST) which takes into account ac- tual smoothness of the rewards for perform- ing efficient "cuts" of sub-optimal branches with high confidence. Finally, we present an incremental tree expansion which applies when the full tree is too big (possibly in- finite) to be entirely represented and show that with high probability, only the optimal branches are indefinitely developed. We illus- trate these methods on a global optimization problem of a continuous function, given noisy values.
Bayesian Multitask Learning with Latent Hierarchies
Venue: arXiv cs.LG 2014
Year: 2014

We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share covariance structure. Our hierarchical model is seen to subsume several previously proposed multitask learning models and performs well on three distinct real-world data sets.
Efficient Clustering with Limited Distance Information
Venue: arXiv cs.LG 2014
Year: 2014

Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points. In our model we assume that we have access to one versus all queries that given a point s 2 S return the distances between s and all other points. We show that given a natural assumption about the structure of the instance, we can efficiently find an accurate clustering using only O(k) distance queries. We use our algorithm to cluster proteins by sequence similarity. This setting nicely fits our model because we can use a fast sequence database search program to query a sequence against an entire dataset. We conduct an empirical study that shows that even though we query a small fraction of the distances between the points, we produce clusterings that are close to a desired clustering given by manual classification.
Selecting Computations: Theory and Applications
Venue: arXiv cs.AI 2014
Year: 2014

Sequential decision problems are often approximately solvable by simulating possible future action sequences. Metalevel decision procedures have been developed for selecting which action sequences to simulate, based on estimating the expected improvement in decision quality that would result from any particular simulation; an example is the recent work on using bandit algorithms to control Monte Carlo tree search in the game of Go. In this paper we develop a theoretical basis for metalevel decisions in the statistical framework of Bayesian selection problems, arguing (as others have done) that this is more appropriate than the bandit framework. We derive a number of basic results applicable to Monte Carlo selection problems, including the first finite sampling bounds for optimal policies in certain cases; we also provide a simple counterexample to the intuitive conjecture that an optimal policy will necessarily reach a decision in all cases. We then derive heuristic approximations in both Bayesian and distribution-free settings and demonstrate their superiority to bandit-based heuristics in one-shot decision problems and in Go.
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Venue: arXiv cs.AI 2014
Year: 2014

A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths in the true network. Such beliefs correspond to knowledge about the possible causal and associative relations between pairs of variables. This type of knowledge naturally arises from prior experimental and observational data, among others. In addition, a novel search-operator is proposed to take advantage of such prior knowledge. Experiments show that, using path beliefs improves the learning of the skeleton, as well as the edge directions in the network.