OpenRaiser/Intern-Atlas huggingface dataset:
CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation
Venue: arXiv cs.CL 2018
Year: 2018

While the general analysis of named entities has received substantial research attention on unstructured as well as structured data, the analysis of relations among named entities has received limited focus. In fact, a review of the literature revealed a deficiency in research on the abstract conceptualization required to organize relations. We believe that such an abstract conceptualization can benefit various communities and applications such as natural language processing, information extraction, machine learning, and ontology engineering. In this paper, we present Comprehensive EVent Ontology (CEVO), built on Levin's conceptual hierarchy of English verbs that categorizes verbs with shared meaning, and syntactic behavior. We present the fundamental concepts and requirements for this ontology. Furthermore, we present three use cases employing the CEVO ontology on annotation tasks: (i) annotating relations in plain text, (ii) annotating ontological properties, and (iii) linking textual relations to ontological properties. These use-cases demonstrate the benefits of using CEVO for annotation: (i) annotating English verbs from an abstract conceptualization, (ii) playing the role of an upper ontology for organizing ontological properties, and (iii) facilitating the annotation of text relations using any underlying vocabulary. This resource is available at https://shekarpour.github.io/cevo.io/ using https://w3id.org/cevo namespace.
Leveraging Quality Prediction Models for Automatic Writing Feedback
Venue: arXiv cs.HC 2018
Year: 2018

User-generated, multi-paragraph writing is pervasive and important in many social media platforms (i.e. Amazon reviews, AirBnB host profiles, etc). Ensuring high-quality content is important. Unfortunately, content submitted by users is often not of high quality. Moreover, the characteristics that constitute high quality may even vary between domains in ways that users are unaware of. Automated writing feedback has the potential to immediately point out and suggest improvements during the writing process. Most approaches, however, focus on syntax/phrasing, which is only one characteristic of high-quality content. Existing research develops accurate quality prediction models. We propose combining these models with model explanation techniques to identify writing features that, if changed, will most improve the text quality. To this end, we develop a perturbation-based explanation method for a popular class of models called tree-ensembles. Furthermore, we use a weak-supervision technique to adapt this method to generate feedback for specific text segments in addition to feedback for the entire document. Our user study finds that the perturbation-based approach, when combined with segment-specific feedback, can help improve writing quality on Amazon (review helpfulness) and Airbnb (host profile trustworthiness) by > 14% (3X improvement over recent automated feedback techniques).
Deep Reinforcement Learning: An Overview
Venue: arXiv cs.LG 2018
Year: 2018

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions. Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant update.
A Load-Buffer Semantics for Total Store Ordering
Venue: arXiv cs.FL 2018
Year: 2018

We address the problem of verifying safety properties of concurrent programs running over the Total Store Order (TSO) memory model. Known decision procedures for this model are based on complex encodings of store buffers as lossy channels. These procedures assume that the number of processes is fixed. However, it is important in general to prove the correctness of a system/algorithm in a parametric way with an arbitrarily large number of processes. In this paper, we introduce an alternative (yet equivalent) semantics to the classical one for the TSO semantics that is more amenable to efficient algorithmic verification and for the extension to parametric verification. For that, we adopt a dual view where load buffers are used instead of store buffers. The flow of information is now from the memory to load buffers. We show that this new semantics allows (1) to simplify drastically the safety analysis under TSO, (2) to obtain a spectacular gain in efficiency and scalability compared to existing procedures, and (3) to extend easily the decision procedure to the parametric case, which allows obtaining a new decidability result, and more importantly, a verification algorithm that is more general and more efficient in practice than the one for bounded instances.
Computing a tree having a small vertex cover
Venue: arXiv cs.DS 2018
Year: 2018

We consider a new Steiner tree problem, called vertex-cover-weighted Steiner tree problem. This problem defines the weight of a Steiner tree as the minimum weight of vertex covers in the tree, and seeks a minimum-weight Steiner tree in a given vertex-weighted undirected graph. Since it is included by the Steiner tree activation problem, the problem admits an O(log n)-approximation algorithm in general graphs with n vertices. This approximation factor is tight up to a constant because it is NP-hard to achieve an o(log n)-approximation for the vertex-cover-weighted Steiner tree problem on general graphs even if the given vertex weights are uniform and a spanning tree is required instead of a Steiner tree. In this paper, we present constant-factor approximation algorithms for the problem with unit disk graphs and with graphs excluding a fixed minor. For the latter graph class, our algorithm can be also applied for the Steiner tree activation problem.
Finite-state Independence and Normal Sequences
Venue: arXiv cs.FL 2018
Year: 2018

We consider the previously defined notion of finite-state independence and we focus specifically on normal words. We characterize finite-state independence of normal words in three different ways, using three different kinds of asynchronous deterministic finite automata with two input tapes containing infinite words. Based on one of the characterizations we give an algorithm to construct a pair of finite-state independent normal words.
Convergence Results for Neural Networks via Electrodynamics
Venue: arXiv cs.DS 2018
Year: 2018

We study whether a depth two neural network can learn another depth two network using gradient descent. Assuming a linear output node, we show that the question of whether gradient descent converges to the target function is equivalent to the following question in electrodynamics: Given $k$ fixed protons in $\mathbb{R}^d,$ and $k$ electrons, each moving due to the attractive force from the protons and repulsive force from the remaining electrons, whether at equilibrium all the electrons will be matched up with the protons, up to a permutation. Under the standard electrical force, this follows from the classic Earnshaw's theorem. In our setting, the force is determined by the activation function and the input distribution. Building on this equivalence, we prove the existence of an activation function such that gradient descent learns at least one of the hidden nodes in the target network. Iterating, we show that gradient descent can be used to learn the entire network one node at a time.
Question Answering through Transfer Learning from Large Fine-grained Supervision Data
Venue: arXiv cs.CL 2018
Year: 2018

We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8%. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task.
A short introduction to secrecy and verifiability for elections
Venue: arXiv cs.CR 2018
Year: 2018

We explore the fundamental properties that are necessary to ensure that election schemes behave as expected. The exploration reveals how our understanding of those expectations has evolved, culminating in the emergence of formal definitions of properties necessary to fulfil expectations. We provide insights into definitions of secrecy and verifiability, allowing us to learn and appreciate the underlying intuition and technical details of these notions. Equipped with definitions, we can build election schemes that can be proven to behave as expected. And, as an illustrative example, we review a variant of the Helios election system that was built and proven secure, in this way. Furthermore, the definitions can be used to analyse existing election schemes, and vulnerabilities have been uncovered. Indeed, we describe a series of vulnerabilities that were discovered during the analysis of the original Helios system, which advanced our understanding of system behaviour and prompted the design of the aforementioned variant. Thus, this article contributes to the science of security by sharing valuable insights into elections, and demonstrating the value that formal definitions and analysis have in building schemes guaranteed to behave as expected.
Automata theory on sliding windows
Venue: arXiv cs.FL 2018
Year: 2018

In a recent paper we analyzed the space complexity of streaming algorithms whose goal is to decide membership of a sliding window to a fixed language. For the class of regular languages we proved a space trichotomy theorem: for every regular language the optimal space bound is either constant, logarithmic or linear. In this paper we continue this line of research: We present natural characterizations for the constant and logarithmic space classes and establish tight relationships to the concept of language growth. We also analyze the space complexity with respect to automata size and prove almost matching lower and upper bounds. Finally, we consider the decision problem whether a language given by a DFA/NFA admits a sliding window algorithm using logarithmic/constant space.
Finding All Useless Arcs in Directed Planar Graphs
Venue: arXiv cs.DS 2018
Year: 2018

We present a linear-time algorithm for simplifying flow networks on directed planar graphs: Given a directed planar graph on $n$ vertices, a source vertex $s$ and a sink vertex $t$, our algorithm removes all the arcs that do not participate in any simple $s,t$-path in linear-time. The output graph produced by our algorithm satisfies the prerequisite needed by the $O(n\log n)$-time algorithm of Weihe [FOCS'94 \& JCSS'97] for computing maximum $s,t$-flow in directed planar graphs. Previously, Weihe's algorithm could not run in $O(n\log n)$-time due to the absence of the preprocessing step; all the preceding algorithms run in $\tildeΩ(n^2)$-time [Misiolek-Chen, COCOON'05 \& IPL'06; Biedl, Brejov{á} and Vinar, MFCS'00]. Consequently, this provides an alternative $O(n\log n)$-time algorithm for computing maximum $s,t$-flow in directed planar graphs in addition to the known $O(n\log n)$-time algorithms [Borradaile-Klein, SODA'06 \& J.ACM'09; Erickson, SODA'10]. Our algorithm can be seen as a (truly) linear-time $s,t$-flow sparsifier for directed planar graphs, which runs faster than any maximum $s,t$-flow algorithm (which can also be seen of as a sparsifier). The simplified structures of the resulting graph might be useful in future developments of maximum $s,t$-flow algorithms in both directed and undirected planar graphs.
Complete Submodularity Characterization in the Comparative Independent Cascade Model
Venue: arXiv cs.SI 2018
Year: 2018

We study the propagation of comparative ideas or items in social networks. A full characterization for submodularity in the comparative independent cascade (Com-IC) model of two-idea cascade is given, for competing ideas and complementary ideas respectively, with or without reconsideration. We further introduce One-Shot model where agents show less patience toward ideas, and show that in One-Shot model, only the strongest idea spreads with submodularity.
Improving the betweenness centrality of a node by adding links
Venue: arXiv cs.DS 2018
Year: 2018

Betweenness is a well-known centrality measure that ranks the nodes according to their participation in the shortest paths of a network. In several scenarios, having a high betweenness can have a positive impact on the node itself. Hence, in this paper we consider the problem of determining how much a vertex can increase its centrality by creating a limited amount of new edges incident to it. In particular, we study the problem of maximizing the betweenness score of a given node -- Maximum Betweenness Improvement (MBI) -- and that of maximizing the ranking of a given node -- Maximum Ranking Improvement (MRI). We show that MBI cannot be approximated in polynomial-time within a factor $(1-\frac{1}{2e})$ and that MRI does not admit any polynomial-time constant factor approximation algorithm, both unless $P=NP$. We then propose a simple greedy approximation algorithm for MBI with an almost tight approximation ratio and we test its performance on several real-world networks. We experimentally show that our algorithm highly increases both the betweenness score and the ranking of a given node ant that it outperforms several competitive baselines. To speed up the computation of our greedy algorithm, we also propose a new dynamic algorithm for updating the betweenness of one node after an edge insertion, which might be of independent interest. Using the dynamic algorithm, we are now able to compute an approximation of MBI on networks with up to $10^5$ edges in most cases in a matter of seconds or a few minutes.
Counting edge-injective homomorphisms and matchings on restricted graph classes
Venue: arXiv cs.CC 2018
Year: 2018

We consider the $\#\mathsf{W}[1]$-hard problem of counting all matchings with exactly $k$ edges in a given input graph $G$; we prove that it remains $\#\mathsf{W}[1]$-hard on graphs $G$ that are line graphs or bipartite graphs with degree $2$ on one side. In our proofs, we use that $k$-matchings in line graphs can be equivalently viewed as edge-injective homomorphisms from the disjoint union of $k$ length-$2$ paths into (arbitrary) host graphs. Here, a homomorphism from $H$ to $G$ is edge-injective if it maps any two distinct edges of $H$ to distinct edges in $G$. We show that edge-injective homomorphisms from a pattern graph $H$ can be counted in polynomial time if $H$ has bounded vertex-cover number after removing isolated edges. For hereditary classes $\mathcal{H}$ of pattern graphs, we complement this result: If the graphs in $\mathcal{H}$ have unbounded vertex-cover number even after deleting isolated edges, then counting edge-injective homomorphisms with patterns from $\mathcal{H}$ is $\#\mathsf{W}[1]$-hard. Our proofs rely on an edge-colored variant of Holant problems and a delicate interpolation argument; both may be of independent interest.
Improving the upper bound on the length of the shortest reset words
Venue: arXiv cs.FL 2018
Year: 2018

We improve the best known upper bound on the length of the shortest reset words of synchronizing automata. The new bound is slightly better than $114 n^3 / 685 + O(n^2)$. The Černý conjecture states that $(n-1)^2$ is an upper bound. So far, the best general upper bound was $(n^3-n)/6-1$ obtained by J.-E.~Pin and P.~Frankl in 1982. Despite a number of efforts, it remained unchanged for about 35 years. To obtain the new upper bound we utilize avoiding words. A word is avoiding for a state $q$ if after reading the word the automaton cannot be in $q$. We obtain upper bounds on the length of the shortest avoiding words, and using the approach of Trahtman from 2011 combined with the well known Frankl theorem from 1982, we improve the general upper bound on the length of the shortest reset words. For all the bounds, there exist polynomial algorithms finding a word of length not exceeding the bound.
Local Redundancy in SAT: Generalizations of Blocked Clauses
Venue: arXiv cs.LO 2018
Year: 2018

Clause-elimination procedures that simplify formulas in conjunctive normal form play an important role in modern SAT solving. Before or during the actual solving process, such procedures identify and remove clauses that are irrelevant to the solving result. These simplifications usually rely on so-called redundancy properties that characterize cases in which the removal of a clause does not affect the satisfiability status of a formula. One particularly successful redundancy property is that of blocked clauses, because it generalizes several other redundancy properties. To find out whether a clause is blocked---and therefore redundant---one only needs to consider its resolution environment, i.e., the clauses with which it can be resolved. For this reason, we say that the redundancy property of blocked clauses is local. In this paper, we show that there exist local redundancy properties that are even more general than blocked clauses. We present a semantic notion of blocking and prove that it constitutes the most general local redundancy property. We furthermore introduce the syntax-based notions of set-blocking and super-blocking, and show that the latter coincides with our semantic blocking notion. In addition, we show how semantic blocking can be alternatively characterized via Davis and Putnam's rule for eliminating atomic formulas. Finally, we perform a detailed complexity analysis and relate our novel redundancy properties to prominent redundancy properties from the literature.
A Novel Method of Discrete-Time Amplification using NEMS Devices
Venue: arXiv cs.ET 2018
Year: 2018

In this paper we propose a novel method of realizing discrete-time (D-T) signal amplification using Nano-Electro-Mechanical (NEMS) devices. The amplifier uses mechanical devices instead of traditional solid-state circuits. The proposed NEMS-based D-T amplifier provides high gain and operate on a wide dynamic range of signals, consuming only a few micro watts of power. The proposed concept is subsequently verified using Verilog-A model of the NEMS device. Modifications in the proposed amplifier to suit different specifications are also presented.
Distributed Gauss-Newton Method for State Estimation Using Belief Propagation
Venue: arXiv cs.IT 2018
Year: 2018

We present a novel distributed Gauss-Newton method for the non-linear state estimation (SE) model based on a probabilistic inference method called belief propagation (BP). The main novelty of our work comes from applying BP sequentially over a sequence of linear approximations of the SE model, akin to what is done by the Gauss-Newton method. The resulting iterative Gauss-Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss-Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework. The paper provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation.
Reasoning with Finite Sets and Cardinality Constraints in SMT
Venue: arXiv cs.LO 2018
Year: 2018

We consider the problem of deciding the satisfiability of quantifier-free formulas in the theory of finite sets with cardinality constraints. Sets are a common high-level data structure used in programming; thus, such a theory is useful for modeling program constructs directly. More importantly, sets are a basic construct of mathematics and thus natural to use when formalizing the properties of computational systems. We develop a calculus describing a modular combination of a procedure for reasoning about membership constraints with a procedure for reasoning about cardinality constraints. Cardinality reasoning involves tracking how different sets overlap. For efficiency, we avoid considering Venn regions directly, as done in previous work. Instead, we develop a novel technique wherein potentially overlapping regions are considered incrementally as needed, using a graph to track the interaction among the different regions. The calculus has been designed to facilitate its implementation within SMT solvers based on the DPLL($T$) architecture. Our experimental results demonstrate that the new techniques are competitive with previous techniques and can scale much better on certain classes of problems.
Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
Venue: arXiv cs.CL 2018
Year: 2018

Advances in natural language processing tasks have gained momentum in recent years due to the increasingly popular neural network methods. In this paper, we explore deep learning techniques for answering multi-step reasoning questions that operate on semi-structured tables. Challenges here arise from the level of logical compositionality expressed by questions, as well as the domain openness. Our approach is weakly supervised, trained on question-answer-table triples without requiring intermediate strong supervision. It performs two phases: first, machine understandable logical forms (programs) are generated from natural language questions following the work of [Pasupat and Liang, 2015]. Second, paraphrases of logical forms and questions are embedded in a jointly learned vector space using word and character convolutional neural networks. A neural scoring function is further used to rank and retrieve the most probable logical form (interpretation) of a question. Our best single model achieves 34.8% accuracy on the WikiTableQuestions dataset, while the best ensemble of our models pushes the state-of-the-art score on this task to 38.7%, thus slightly surpassing both the engineered feature scoring baseline, as well as the Neural Programmer model of [Neelakantan et al., 2016].
Computing the longest common prefix of a context-free language in polynomial time
Venue: arXiv cs.FL 2018
Year: 2018

We present two structural results concerning longest common prefixes of non-empty languages. First, we show that the longest common prefix of the language generated by a context-free grammar of size $N$ equals the longest common prefix of the same grammar where the heights of the derivation trees are bounded by $4N$. Second, we show that each nonempty language $L$ has a representative subset of at most three elements which behaves like $L$ w.r.t. the longest common prefix as well as w.r.t. longest common prefixes of $L$ after unions or concatenations with arbitrary other languages. From that, we conclude that the longest common prefix, and thus the longest common suffix, of a context-free language can be computed in polynomial time.