OpenRaiser/Intern-Atlas huggingface dataset:
The Renyi Capacity and Center
Venue: arXiv cs.IT 2019
Year: 2019

Renyi's information measures ---the Renyi information, mean, capacity, radius, and center--- are analyzed relying on the elementary properties of the Renyi divergence and the power means. The van Erven-Harremoes conjecture is proved for any positive order and for any set of probability measures on a given measurable space and a generalization of it is established for the constrained variant of the problem. The finiteness of the order $α$ Renyi capacity is shown to imply the continuity of the Renyi capacity on $(0,α]$ and the uniform equicontinuity of the Renyi information, both as a family of functions of the order indexed by the priors and as a family of functions of the prior indexed by the orders. The Renyi capacities and centers of various families of Poisson processes are derived as examples.
An Analysis of Lemmatization on Topic Models of Morphologically Rich Language
Venue: arXiv cs.CL 2019
Year: 2019

Topic models are typically represented by top-$m$ word lists for human interpretation. The corpus is often pre-processed with lemmatization (or stemming) so that those representations are not undermined by a proliferation of words with similar meanings, but there is little public work on the effects of that pre-processing. Recent work studied the effect of stemming on topic models of English texts and found no supporting evidence for the practice. We study the effect of lemmatization on topic models of Russian Wikipedia articles, finding in one configuration that it significantly improves interpretability according to a word intrusion metric. We conclude that lemmatization may benefit topic models on morphologically rich languages, but that further investigation is needed.
A note on the definition of Bayesian Nash equilibrium of a mechanism when strategies of agents are costly actions
Venue: arXiv cs.GT 2019
Year: 2019

In mechanism design theory, a designer would like to implement a desired social choice function which specifies her favorite outcome for each possible profile of agents' types. To do so, the designer constructs a mechanism which describes each agent's feasible strategy set and the outcome function. Generally speaking, each agent's strategy in a mechanism has two possible formats: an action, or a message. In this paper, we focus on the former format and claim that the notion of Bayesian Nash equilibrium of a mechanism should be based on a profit function instead of the conventional utility function when strategies of agents are costly actions. Next, we derive the main result: Given a social choice function which can be implemented by an indirect mechanism in Bayesian Nash equilibrium, if all strategies of agents are costly actions, then it cannot be inferred that there exists a direct mechanism that can truthfully implement the social choice function in Bayesian Nash equilibrium.
Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation
Venue: arXiv cs.RO 2019
Year: 2019

In the absence of reliable and accurate GPS, visual odometry (VO) has emerged as an effective means of estimating the egomotion of robotic vehicles. Like any dead-reckoning technique, VO suffers from unbounded accumulation of drift error over time, but this accumulation can be limited by incorporating absolute orientation information from, for example, a sun sensor. In this paper, we leverage recent work on visual outdoor illumination estimation to show that estimation error in a stereo VO pipeline can be reduced by inferring the sun position from the same image stream used to compute VO, thereby gaining the benefits of sun sensing without requiring a dedicated sun sensor or the sun to be visible to the camera. We compare sun estimation methods based on hand-crafted visual cues and Convolutional Neural Networks (CNNs) and demonstrate our approach on a combined 7.8 km of urban driving from the popular KITTI dataset, achieving up to a 43% reduction in translational average root mean squared error (ARMSE) and a 59% reduction in final translational drift error compared to pure VO alone.
Parameterized and Approximation Complexity of Partial VC Dimension
Venue: arXiv cs.DS 2019
Year: 2019

We introduce the problem Partial VC Dimension that asks, given a hypergraph $H=(X,E)$ and integers $k$ and $\ell$, whether one can select a set $C\subseteq X$ of $k$ vertices of $H$ such that the set $\{e\cap C, e\in E\}$ of distinct hyperedge-intersections with $C$ has size at least $\ell$. The sets $e\cap C$ define equivalence classes over $E$. Partial VC Dimension is a generalization of VC Dimension, which corresponds to the case $\ell=2^k$, and of Distinguishing Transversal, which corresponds to the case $\ell=|E|$ (the latter is also known as Test Cover in the dual hypergraph). We also introduce the associated fixed-cardinality maximization problem Max Partial VC Dimension that aims at maximizing the number of equivalence classes induced by a solution set of $k$ vertices. We study the algorithmic complexity of Partial VC Dimension and Max Partial VC Dimension both on general hypergraphs and on more restricted instances, in particular, neighborhood hypergraphs of graphs.
Temporal Data Exchange
Venue: arXiv cs.DB 2019
Year: 2019

Data exchange is the problem of transforming data that is structured under a source schema into data structured under another schema, called the target schema, so that both the source and target data satisfy the relationship between the schemas. Even though the formal framework of data exchange for relational database systems is well-established, it does not immediately carry over to the settings of temporal data, which necessitates reasoning over unbounded periods of time. In this work, we study data exchange for temporal data. We first motivate the need for two views of temporal data: the concrete view, which depicts how temporal data is compactly represented and on which the implementations are based, and the abstract view, which defines the semantics of temporal data as a sequence of snapshots. We first extend the chase procedure for the abstract view to have a conceptual basis for the data exchange for temporal databases. Considering non-temporal source-to-target tuple generating dependencies and equality generating dependencies, the chase algorithm can be applied on each snapshot independently. Then we define a chase procedure (called c-chase) on concrete instances and show the result of c-chase on a concrete instance is semantically aligned with the result of chase on the corresponding abstract instance. In order to interpret intervals as constants while checking if a dependency or a query is satisfied by a concrete database, we will normalize the instance with respect to the dependency or the query. To obtain the semantic alignment, the nulls in the concrete view are annotated with temporal information. Furthermore, we show that the result of the concrete chase provides a foundation for query answering. We define naive evaluation on the result of the c-chase and show it produces certain answers.
Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems
Venue: arXiv cs.AI 2019
Year: 2019

Bayesian optimization has become a fundamental global optimization algorithm in many problems where sample efficiency is of paramount importance. Recently, there has been proposed a large number of new applications in fields such as robotics, machine learning, experimental design, simulation, etc. In this paper, we focus on several problems that appear in robotics and autonomous systems: algorithm tuning, automatic control and intelligent design. All those problems can be mapped to global optimization problems. However, they become hard optimization problems. Bayesian optimization internally uses a probabilistic surrogate model (e.g.: Gaussian process) to learn from the process and reduce the number of samples required. In order to generalize to unknown functions in a black-box fashion, the common assumption is that the underlying function can be modeled with a stationary process. Nonstationary Gaussian process regression cannot generalize easily and it typically requires prior knowledge of the function. Some works have designed techniques to generalize Bayesian optimization to nonstationary functions in an indirect way, but using techniques originally designed for regression, where the objective is to improve the quality of the surrogate model everywhere. Instead optimization should focus on improving the surrogate model near the optimum. In this paper, we present a novel kernel function specially designed for Bayesian optimization, that allows nonstationary behavior of the surrogate model in an adaptive local region. In our experiments, we found that this new kernel results in an improved local search (exploitation), without penalizing the global search (exploration). We provide results in well-known benchmarks and real applications. The new method outperforms the state of the art in Bayesian optimization both in stationary and nonstationary problems.
Data-centric Dynamic Partial Order Reduction
Venue: arXiv cs.PL 2019
Year: 2019

We present a new dynamic partial-order reduction method for stateless model checking of concurrent programs. A common approach for exploring program behaviors relies on enumerating the traces of the program, without storing the visited states (aka stateless exploration). As the number of distinct traces grows exponentially, dynamic partial-order reduction (DPOR) techniques have been successfully used to partition the space of traces into equivalence classes (Mazurkiewicz partitioning), with the goal of exploring only few representative traces from each class. We introduce a new equivalence on traces under sequential consistency semantics, which we call the observation equivalence. Two traces are observationally equivalent if every read event observes the same write event in both traces. While the traditional Mazurkiewicz equivalence is control-centric, our new definition is data-centric. We show that our observation equivalence is coarser than the Mazurkiewicz equivalence, and in many cases even exponentially coarser. We devise a DPOR exploration of the trace space, called data-centric DPOR, based on the observation equivalence. For acyclic architectures, our algorithm is guaranteed to explore exactly one representative trace from each observation class, while spending polynomial time per class. Hence, our algorithm is optimal wrt the observation equivalence, and in several cases explores exponentially fewer traces than any enumerative method based on the Mazurkiewicz equivalence. For cyclic architectures, we consider an equivalence between traces which is finer than the observation equivalence; but coarser than the Mazurkiewicz equivalence, and in some cases is exponentially coarser. Our data-centric DPOR algorithm remains optimal under this trace equivalence.
A Pragmatic Non-Blocking Concurrent Directed Acyclic Graph
Venue: arXiv cs.DC 2019
Year: 2019

In this paper, we have developed two algorithms for maintaining acyclicity in a concurrent directed graph. The first algorithm is based on a wait-free reachability query and the second one is based on partial snapshot-based obstruction-free reachability query. Interestingly, we are able to achieve the acyclic property in the dynamic setting without the need of helping using descriptors by other threads or clean double collect mechanism. We present a proof to show that the graph remains acyclic at all times in the concurrent setting. We also prove that the acyclic graph data-structure operations are linearizable. We implement both the algorithms in C++ and test through a number of micro-benchmarks. Our experimental results show an average of 7x improvement over the sequential and global lock implementation.
Efficient Parallel Verification of Galois Field Multipliers
Venue: arXiv cs.SC 2019
Year: 2019

Galois field (GF) arithmetic is used to implement critical arithmetic components in communication and security-related hardware, and verification of such components is of prime importance. Current techniques for formally verifying such components are based on computer algebra methods that proved successful in verification of integer arithmetic circuits. However, these methods are sequential in nature and do not offer any parallelism. This paper presents an algebraic functional verification technique of gate-level GF (2m ) multipliers, in which verification is performed in bit-parallel fashion. The method is based on extracting a unique polynomial in Galois field of each output bit independently. We demonstrate that this method is able to verify an n-bit GF multiplier in n threads. Experiments performed on pre- and post-synthesized Mastrovito and Montgomery multipliers show high efficiency up to 571 bits.
Explicablility as Minimizing Distance from Expected Behavior
Venue: arXiv cs.AI 2019
Year: 2019

In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop. When the agent's task plans are generated without such considerations, they may often demonstrate inexplicable behavior from the human's point of view. This problem may arise due to the human's partial or inaccurate understanding of the agent's planning model. This may have serious implications from increased cognitive load to more serious concerns of safety around a physical agent. In this paper, we address this issue by modeling plan explicability as a function of the distance between a plan that agent makes and the plan that human expects it to make. We learn a regression model for mapping the plan distances to explicability scores of plans and develop an anytime search algorithm that can use this model as a heuristic to come up with progressively explicable plans. We evaluate the effectiveness of our approach in a simulated autonomous car domain and a physical robot domain.
The Sphere Packing Bound via Augustin's Method
Venue: arXiv cs.IT 2019
Year: 2019

A sphere packing bound (SPB) with a prefactor that is polynomial in the block length $n$ is established for codes on a length $n$ product channel $W_{[1,n]}$ assuming that the maximum order $1/2$ Renyi capacity among the component channels, i.e. $\max_{t\in[1,n]} C_{1/2,W_{t}}$, is $\mathit{O}(\ln n)$. The reliability function of the discrete stationary product channels with feedback is bounded from above by the sphere packing exponent. Both results are proved by first establishing a non-asymptotic SPB. The latter result continues to hold under a milder stationarity hypothesis.
VM Scaling and Load Balancing via Cost Optimal MDP Solution
Venue: arXiv cs.NI 2019
Year: 2019

We address a cost optimization problem faced by a user who runs instances of applications in a remote cloud configuration constructed of multiple virtual machines (VMs). Each VM runs a single application instance which can execute tasks specific to that application. Managing the VMs involves a sophisticated trade-off between cloud-related demands, which are expressed by the provisional costs of leased cloud resources, and exogenous cost demands expressed by service revenues that are typically bound to SLAs. The internal costs may include VM deployment/termination cost, and VM lease cost. The exogenous costs refer to rewards accumulated due to the successfully accomplished tasks being run by each application instance. In the case where the SLA restricts performance to a certain load level at each VM, tasks incoming at VMs that reached that level are rejected. Rejections cause fines deducted against the rewards. The performance level is also quantified, namely, by means of a delay cost, according to the average delay experienced by tasks. Typical examples for specific applications which fall within this class of problems include handling of scientific worklflows and network functioning virtualization (NFV). We model this problem by cost-optimal load balancing to a queuing system with a flexible number of queues, where a queue (VM) can be deployed, can have a task directed to it and can be terminated. We analyze the system by Markov decision process (MDP) and numerically solve it to find the optimal policy, which captures the aforementioned costs and performance constraints. Within this constrained framework, we also investigate the impact of average VM deployment time. We show that the optimal policy possesses decision thresholds which depend on several parameters. We validate policies found by MDP, through directing an exogenous computational tasks flow to a set-up implemented on AWS.
A modelling and computational study of the frustration index in signed networks
Venue: arXiv cs.SI 2019
Year: 2019

Computing the frustration index of a signed graph is a key step toward solving problems in many fields including social networks, political science, physics, chemistry, and biology. The frustration index determines the distance of a network from a state of total structural balance. Although the definition of the frustration index goes back to the 1950's, its exact algorithmic computation, which is closely related to classic NP-hard graph problems, has only become a focus in recent years. We develop three new binary linear programming models to compute the frustration index exactly and efficiently as the solution to a global optimisation problem. Solving the models with prioritised branching and valid inequalities in Gurobi, we can compute the frustration index of real signed networks with over 15000 edges in less than a minute on inexpensive hardware. We provide extensive performance analysis for both random and real signed networks and show that our models outperform all existing approaches by large factors. Based on solve time, algorithm output, and effective branching factor we highlight the superiority of our models to both exact and heuristic methods in the literature.
Search-and-Fetch with 2 Robots on a Disk: Wireless and Face-to-Face Communication Models
Venue: arXiv cs.DS 2019
Year: 2019

We initiate the study of a new problem on searching and fetching in a distributed environment concerning treasure-evacuation from a unit disk. A treasure and an exit are located at unknown positions on the perimeter of a disk and at known arc distance. A team of two robots start from the center of the disk, and their goal is to fetch the treasure to the exit. At any time the robots can move anywhere they choose on the disk, independently of each other, with the same speed. A robot detects an interesting point (treasure or exit) only if it passes over the exact location of that point. We are interested in designing distributed algorithms that minimize the worst-case treasure-evacuation time, i.e. the time it takes for the treasure to be discovered and brought (fetched) to the exit by any of the robots. The communication protocol between the robots is either wireless, where information is shared at any time, or face-to-face (i.e. non-wireless), where information can be shared only if the robots meet. For both models we obtain upper bounds for fetching the treasure to the exit. Our main technical contribution pertains to the face-to-face model. More specifically, we demonstrate how robots can exchange information without meeting, effectively achieving a highly efficient treasure-evacuation protocol which is minimally affected by the lack of distant communication. Finally, we complement our positive results above by providing a lower bound in the face-to-face model.
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
Venue: arXiv cs.CL 2019
Year: 2019

Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user's curiosity gap and lure them to click on links that often disappoint them. Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge. Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits. Our model relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks. Experimental results on a dataset of news headlines show that our model outperforms existing techniques for clickbait detection with an accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.
Randomized Learning of the Second-Moment Matrix of a Smooth Function
Venue: arXiv cs.IT 2019
Year: 2019

Consider an open set $\mathbb{D}\subseteq\mathbb{R}^n$, equipped with a probability measure $μ$. An important characteristic of a smooth function $f:\mathbb{D}\rightarrow\mathbb{R}$ is its \emph{second-moment matrix} $Σ_μ:=\int \nabla f(x) \nabla f(x)^* μ(dx) \in\mathbb{R}^{n\times n}$, where $\nabla f(x)\in\mathbb{R}^n$ is the gradient of $f(\cdot)$ at $x\in\mathbb{D}$ and $*$ stands for transpose. For instance, the span of the leading $r$ eigenvectors of $Σ_μ$ forms an \emph{active subspace} of $f(\cdot)$, which contains the directions along which $f(\cdot)$ changes the most and is of particular interest in \emph{ridge approximation}. In this work, we propose a simple algorithm for estimating $Σ_μ$ from random point evaluations of $f(\cdot)$ \emph{without} imposing any structural assumptions on $Σ_μ$. Theoretical guarantees for this algorithm are established with the aid of the same technical tools that have proved valuable in the context of covariance matrix estimation from partial measurements.
Natural Transformations as Rewrite Rules and Monad Composition
Venue: arXiv cs.LO 2019
Year: 2019

Eklund et al. (2002) present a graphical technique aimed at simplifying the verification of various category-theoretic constructions, notably the composition of monads. In this note we take a different approach involving string rewriting. We show that a given tuple $(T,μ,η)$ is a monad if and only if $T$ is a terminal object in a certain category of strings and rewrite rules, and that this fact can be established by proving confluence of the rewrite system. We illustrate the technique on the monad composition problem. We also give a characterization of adjunctions in terms of rewrite categories.
Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA
Venue: arXiv cs.LG 2019
Year: 2019

Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task. Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22,000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.