Reviewerly/exHarmony huggingface dataset:
Three-step coreference-based summarizer for Polish news texts

Abstract This article addresses the problem of automatic summarization of press articles in Polish. The main novelty of this research lays in the proposal of a three-step summarization algorithm which benefits from using coreference information. In related work section, all coreference-based approaches to summarization are presented. Then we describe in detail all publicly available summarization tools developed for Polish language. We state the problem of single-document press article summarization for Polish, describing the training and evaluation dataset: the P OLISH S UMMARIES CORPUS. Next, a new coreference-based extractive summarization system NICOLAS is introduced. Its algorithm utilises advanced third-party preprocessing tools to extract the coreference information from the text to be summarized. This information is transformed into a complex set of features related to coreference concepts (mentions and coreference clusters) that are used for training the summarization system (on the basis of a manually prepared gold summaries corpus). The proposed solution is compared to the best publicly available summarization systems for Polish language and two state-of-the-art tools, developed for English language, but adapted to Polish for this article. NICOLAS summarization system obtains best scores, for selected metrics outperforming other systems in a statistically significant way. The evaluation also contains calculation of interesting upper-bounds: human performance and theoretical upper-bound.
Domain Dependent Wordnet for Real Estate

WordNet is a thesaurus which represents word senses, meanings of lemmas, relations between senses, and definitions of these senses. In this paper, we propose a method to simplify the morphological analysis and disambiguation tasks performed in a specific domain by creating a subset of original Turkish dictionary and WordNet. As a preliminary study, we create a dictionary for the estate domain with 7,000 words and a WordNet with 11,000 synsets, then validate them on simple tasks.
Direct Sequence Spread-Spectrum Based Covert Communication Using Random Pulse Width Modulation

In this study, a covert communication method called as random pulse width modulation (RPWM) is proposed for secure communication where the signals are obtained from the direct noise sequence instead of the pseudo noise (PN) sequence composed by pulse durations. The aim is to develop a spread-spectrum covert communication mimicking statistical properties of white noise by preventing the repetitive structure of auto correlation function observed in the pseudo noise (PN) sequences. For this purpose, random variables are generated having uniform distribution and rectangular pulses are generated whose durations are determined by these random variables. The binary information is determined by using the difference of the mean values as the statistical parameters obtained from the residence times of positive and negative states in the rectangular waveform.
How to Use Quantum Computing to Check Which Inputs Are Relevant: A Proof That Deutsch-Jozsa Algorithm Is, In Effect, the Only Possibility

One of the main reasons why computations - in particular, engineering computations - take long is that, to be on the safe side, models take into account all possible affecting features, most of which turn out to be not really relevant for the corresponding physical problem. From this viewpoint, it is desirable to find out which inputs are relevant. In general, the problem of checking the input's relevancy is itself NP-hard, which means, crudely speaking, that no feasible algorithm can always solve it. Thus, it is desirable to speed up this checking as much as possible. One possible way to speed up such a checking is to use quantum computing, namely, to use the Deutsch-Josza algorithm. However, it is not clear whether a more efficient quantum algorithm is possible for solving this problem. In this paper, we show that the Deutsch-Josza algorithm is, in effect, the only possible way to use quantum computing for checking which inputs are relevant - so no alternative quantum algorithms are possible.
On-line detection method based on optimal modified lateral shearing interferometry for evaluating the damage growth characteristics

The damage growth characteristic determines the lifetime of optical components and the operating costs of high power laser systems. Knowledge of the lateral size and the depth of damage sites are crucial to evaluate the subsequent growth. An on-line detection method based on optimal modified lateral shearing interferometry is proposed to simultaneously measure the lateral size and the depth of damage sites. Thus, the damage growth characteristics can be estimated more comprehensively and efficiently. In the presented method, critical parameters of the common-path configuration are analyzed and optimized to meet the measurement requirements. Experimental results are presented to confirm the feasibility of the proposed method for on-line measurement. The damage growth characteristics of an optical film under different fluences are also analyzed and discussed simply.
Sentence Boundary Detection in Turkish News with Regular Expressions

In this study, a rule-based sentence boundary detection method for Turkish news is developed. Incorporating direct quotations as well, which have not been previously addressed within the scope of the problem, disambiguation of punctuation marks with respect to sentence boundaries is carried out by means of a single regular expression. The performance of the proposed method is compared with the statistics-based Punkt algorithm in terms of prediction speed and accuracy, applied on news articles collected from the Web. Also, the generated regular expression and annotated test set are made publicly available as a baseline for further research.
Alleviating Label Switching with Optimal Transport

Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures. This issue arises due to invariance of the posterior under actions of a group; for example, permuting the ordering of mixture components has no effect on the likelihood. We propose a resolution to label switching that leverages machinery from optimal transport. Our algorithm efficiently computes posterior statistics in the quotient space of the symmetry group. We give conditions under which there is a meaningful solution to label switching and demonstrate advantages over alternative approaches on simulated and real data.
Memory-Based Parameterized Skills Learning for Mapless Visual Navigation

The recently-proposed reinforcement learning for mapless visual navigation can generate an optimal policy for searching different targets. However, most state-of-the-art deep reinforcement learning (DRL) models depend on hard rewards to learn the optimal policy, which can lead to the lack of previous diverse experiences. Moreover, these pre-trained DRL models cannot generalize well to un-trained tasks. To overcome these problems above, in this paper, we propose a Memory-based Parameterized Skills Learning (MPSL) model for mapless visual navigation. The parameterized skills in our MPSL are learned to predict critic parameters for un-trained tasks in actor-critic reinforcement learning, which can be achieved by transferring memory sequence knowledge from long short term memory network. In order to generalize into un-trained tasks, MPSL aims to capture more discriminative features by using a scene-specific layer. Finally, experiment results on an indoor photographic simulation framework AI2THOR demonstrate the effectiveness of our proposed MPSL model, and the generalization ability to un-trained tasks.
Learning Geographically Distributed Data for Multiple Tasks Using Generative Adversarial Networks

We present a novel method that supports the learning of multiple classification tasks from geographically distributed data. By combining locally trained generative adversarial networks (GANs) with a small fraction of original data samples, our proposed scheme can train multiple discriminative models at a central location with low communication overhead. Experiments using common image datasets (MNIST, CIFAR10, LSUN-20, Celeb-A) show that our proposed scheme can achieve comparable classification accuracy as the ideal classifier trained using all data from all sites. We further demonstrate that our method can scale to 10 sites without sacrificing classification accuracy for large datasets such as LSUN-20.
Identification of Conditional Causal Effects under Markov Equivalence

Causal identification is the problem of deciding whether a post-interventional distribution is computable from a combination of qualitative knowledge about the data-generating process, which is encoded in a causal diagram, and an observational distribution. A generalization of this problem restricts the qualitative knowledge to a class of Markov equivalent causal diagrams, which, unlike a single, fully-specified causal diagram, can be inferred from the observational distribution. Recent work by (Jaber et al., 2019a) devised a complete algorithm for the identification of unconditional causal effects given a Markov equivalence class of causal diagrams. However, there are identifiable conditional causal effects that cannot be handled by that algorithm. In this work, we derive an algorithm to identify conditional effects, which are particularly useful for evaluating conditional plans or policies.
Tunable CNN Compression Through Dimensionality Reduction

Computational complexity of Convolutional Neural Networks (CNNs) makes its integration in embedded systems a challenging task. Methods allowing to simplify these algorithms are therefore of great interest. In this paper, we propose a new CNN compression method based on the application of Principal Component Analysis (PCA) in a layer-wise fashion, and show the benefits of an additional fine-tuning step. Through this method, it is possible to reach very flexible trade-offs between network size and accuracy, such as a x2 reduction in the number of parameters for an accuracy drop inferior to 2%. We also discuss its compatibility with other well-known CNN compression methods.
Research on Stability of Dual-Arm Cooperative Execution Task

To verify the adaptability and stability of the operation task in the complex environment of the dual-arm cooperative robot, a numerical analysis method combining MATLAB and ADAMS is presented, the spatial operation mode of dual-arm multi-DOF robot is analyzed and calculated. Firstly, building 3D simulation model in Solidworks and importing it to ADAMS, dual-arm seven rotational joints are located in the shoulder, big arm, elbow, small arms, wrists, and the end of paw. Secondly, the control method of inverse kinematics solution based on virtual dynamic model is used, the angle values of each joint are calculated according to the position of the target. Finally, experimental simulation of dual-arm cooperative clamping and moving target in ADAMS according to the calculated inverse solution. The results of simulation and analysis show that the average error of the dual-arm reaching the target position is 0.5 mm, and the success rate of dual-arm cooperative clamping and moving target is 99.6%. Compared with the traditional 6-DOF robot, the adaptability and stability of the dual-arm cooperative robot in the complex environment are verified, and the working efficiency and coordination performance of dual-arm robot are improved.
Globally Optimal Learning for Structured Elliptical Losses

Heavy tailed and contaminated data are common in various applications of machine learning. A standard technique to handle regression tasks that involve such data, is to use robust losses, e.g., the popular Huber’s loss. In structured problems, however, where there are multiple labels and structural constraints on the labels are imposed (or learned), robust optimization is challenging, and more often than not the loss used is simply the negative log-likelihood of a Gaussian Markov random field. Heavy tailed and contaminated data are common in various applications of machine learning. A standard technique to handle regression tasks that involve such data, is to use robust losses, e.g., the popular Huber’s loss. In structured problems, however, where there are multiple labels and structural constraints on the labels are imposed (or learned), robust optimization is challenging, and more often than not the loss used is simply the negative log-likelihood of a Gaussian Markov random field. In this work, we analyze robust alternatives. Theoretical understanding of such problems is quite limited, with guarantees on optimization given only for special cases and non-structured settings. The core of the difficulty is the non-convexity of the objective function, implying that standard optimization algorithms may converge to sub-optimal critical points. Our analysis focuses on loss functions that arise from elliptical distributions, which appealingly include most loss functions proposed in the literature as special cases. We show that, even though these problems are non-convex, they can be optimized efficiently. Concretely, we prove that at the limit of infinite training data, due to algebraic properties of the problem, all stationary points are globally optimal. Finally, we demonstrate the empirical appeal of using these losses for regression on synthetic and real-life data.
DAC: The Double Actor-Critic Architecture for Learning Options

We reformulate the option framework as two parallel augmented MDPs. Under this novel formulation, all policy optimization algorithms can be used off the shelf to learn intra-option policies, option termination conditions, and a master policy over options. We apply an actor-critic algorithm on each augmented MDP, yielding the Double Actor-Critic (DAC) architecture. Furthermore, we show that, when state-value functions are used as critics, one critic can be expressed in terms of the other, and hence only one critic is necessary. We conduct an empirical study on challenging robot simulation tasks. In a transfer learning setting, DAC outperforms both its hierarchy-free counterpart and previous gradient-based option learning algorithms.
SySCD: A System-Aware Parallel Coordinate Descent Algorithm

In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm with convergence guarantees that exhibits strong scalability. We start by studying a state-of-the-art parallel implementation of SCD and identify scalability as well as system-level performance bottlenecks of the respective implementation. We then take a principled approach to develop a new SCD variant which is designed to avoid the identified system bottlenecks, such as limited scaling due to coherence traffic of model sharing across threads, and inefficient CPU cache accesses. Our proposed system-aware parallel coordinate descent algorithm (SySCD) scales to many cores and across numa nodes, and offers a consistent bottom line speedup in training time of up to x12 compared to an optimized asynchronous parallel SCD algorithm and up to x42, compared to state-of-the-art GLM solvers (scikit-learn, Vowpal Wabbit, and H2O) on a range of datasets and multi-core CPU architectures.
Piecewise Strong Convexity of Neural Networks

We study the loss surface of a feed-forward neural network with ReLU non-linearities, regularized with weight decay. We show that the regularized loss function is piecewise strongly convex on an important open set which contains, under some conditions, all of its global minimizers. This is used to prove that local minima of the regularized loss function in this set are isolated, and that every differentiable critical point in this set is a local minimum, partially addressing an open problem given at the Conference on Learning Theory (COLT) 2015; our result is also applied to linear neural networks to show that with weight decay regularization, there are no non-zero critical points in a norm ball obtaining training error below a given threshold. We also include an experimental section where we validate our theoretical work and show that the regularized loss function is almost always piecewise strongly convex when restricted to stochastic gradient descent trajectories for three standard image classification problems.
Fast and Robust Homography Estimation by Adaptive Graduated non-Convexity

This paper proposes a novel fast and robust homography estimation by adaptively controlling the threshold of graduated non-convexity (GNC). Based on the fact that GNC is a variant of deterministic annealing, we provide a new method for updating the inlier threshold at each GNC iteration by utilizing the statistical properties of residuals of potential inliers. Contrary to RANSAC, our approach gives the same unique parameter for a single input due to without random sampling. Moreover, computational time increases linearly against outlier ratio changes, whereas RANSAC increases exponentially. Synthetic data evaluation shows that the proposed method is more robust and faster than RANSAC for highly contaminated data containing more than 80% outliers. Additionally, we demonstrate that our method works on severe real images that the state-of-the-art RANSAC method fails.
A Latent Variational Framework for Stochastic Optimization

This paper provides a unifying theoretical framework for stochastic optimization algorithms by means of a latent stochastic variational problem. Using techniques from stochastic control, the solution to the variational problem is shown to be equivalent to that of a Forward Backward Stochastic Differential Equation (FBSDE). By solving these equations, we recover a variety of existing adaptive stochastic gradient descent methods. This framework establishes a direct connection between stochastic optimization algorithms and a secondary latent inference problem on gradients, where a prior measure on gradient observations determines the resulting algorithm.
Understanding Sparse JL for Feature Hashing

Feature hashing and other random projection schemes are commonly used to reduce the dimensionality of feature vectors. The goal is to efficiently project a high-dimensional feature vector living in R^n into a much lower-dimensional space R^m, while approximately preserving Euclidean norm. These schemes can be constructed using sparse random projections, for example using a sparse Johnson-Lindenstrauss (JL) transform. A line of work introduced by Weinberger et. al (ICML '09) analyzes the accuracy of sparse JL with sparsity 1 on feature vectors with small l_infinity-to-l_2 norm ratio. Recently, Freksen, Kamma, and Larsen (NeurIPS '18) closed this line of work by proving a tight tradeoff between l_infinity-to-l_2 norm ratio and accuracy for sparse JL with sparsity 1. In this paper, we demonstrate the benefits of using sparsity s greater than 1 in sparse JL on feature vectors. Our main result is a tight tradeoff between l_infinity-to-l_2 norm ratio and accuracy for a general sparsity s, that significantly generalizes the result of Freksen et. al. Our result theoretically demonstrates that sparse JL with s > 1 can have significantly better norm-preservation properties on feature vectors than sparse JL with s = 1; we also empirically demonstrate this finding.
Learning Neural Networks with Adaptive Regularization

Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis. While most previous works aim to diversify the representations, we explore the complementary direction by performing an adaptive and data-dependent regularization motivated by the empirical Bayes method. Specifically, we propose to construct a matrix-variate normal prior (on weights) whose covariance matrix has a Kronecker product structure. This structure is designed to capture the correlations in neurons through backpropagation. Under the assumption of this Kronecker factorization, the prior encourages neurons to borrow statistical strength from one another. Hence, it leads to an adaptive and data-dependent regularization when training networks on small datasets. To optimize the model, we present an efficient block coordinate descent algorithm with analytical solutions. Empirically, we demonstrate that the proposed method helps networks converge to local optima with smaller stable ranks and spectral norms. These properties suggest better generalizations and we present empirical results to support this expectation. We also verify the effectiveness of the approach on multiclass classification and multitask regression problems with various network structures. Our code is publicly available at:~\url{https://github.com/yaohungt/Adaptive-Regularization-Neural-Network}.
Model-Based Run-Time Synthesis of Architectural Configurations for Adaptive MILS Systems

In order to be resilient, a system must be adaptable. Trustworthy adaptation requires that a system can be dynamically reconfigured at run-time without compromising the robustness and integrity of the system. Adaptive MILS extends MILS, a successful paradigm for rigorously developed and assured composable static systems, with reconfiguration mechanisms and a framework within which those mechanisms may be safely and securely employed for adaptation. In this paper, we address the problem of synthesizing at run-time reconfigurations that are trustworthy taking into account the entwining of information flows and reconfigurations. The approach is based on a new extension of the Architecture Analysis & Design Language (AADL), already used for specifying MILS policy architectures, which is now enhanced to specify the configuration state space in terms of parameters, the possible reconfigurations, monitoring properties and the related alarms. Supporting tools have been developed for the run-time synthesis of new architectural configurations that preserve safety and security properties formalized in terms of invariants and information flow.
Adaptive Deployment of Safety Monitors for Autonomous Systems

This article discusses the problem of deploying safety-critical software for an autonomous system, namely a collaborative robot operating in domestic environments. We present a deployment infrastructure to enhance both humans and robots in carrying out their deployment activities. We develop means to enable humans to explicitly specify the requirements of the software to be deployed, along with the resources of the robot platform on which the software will be executed. In addition, we propose an architecture which enables robots to autonomously re-deploy their software at run-time in order to account for changing requirements imposed by their task, platform and environment. We show how the architecture enables a collaborative robot to autonomously re-deploy safety monitors for detecting in-hand slippage often occuring in human-robot handover tasks. By doing so, the robot autonomously maintains a certain safety level as the functioning of the monitor depends on both selecting and deploying the correct monitoring strategy for the situation at hand.
Discriminant Analysis Regularization in Lightweight Deep CNN Models

In this paper, we first propose lightweight deep CNN models, capable of effectively operating on-drone, in order to address various classification problems, i.e. crowd, football player, and bicycle detection, in the context of media coverage of specific sport events by drones with increased decisional autonomy. Subsequently, we propose a regularization technique, namely Discriminant Analysis regularization, aiming to enhance the generalization ability of the proposed models. The experimental evaluation validates the enhanced performance of the proposed regularizer.