Reviewerly/exHarmony huggingface dataset:
Auxiliary Guided Autoregressive Variational Autoencoders

Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models that encode global image structure into latent variables while autoregressively modeling low level detail. Previous approaches to such hybrid models restrict the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables and only rely on autoregressive modeling. Our contribution is a training procedure relying on an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder. Our approach can leverage arbitrarily powerful autoregressive decoders, achieves state-of-the art quantitative performance among models with latent variables, and generates qualitatively convincing samples.
Implicit Argument Prediction as Reading Comprehension

Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples. We present a new model for implicit argument prediction that draws on reading comprehension, casting the predicate-argument tuple with the missing argument as a query. We also draw on pointer networks and multi-hop computation. Our model shows good performance on an argument cloze task as well as on a nominal implicit argument prediction task.
Automatic feature selection in EUV scatterometry

Scatterometry is an important nonimaging and noncontact method for optical metrology.In scatterometry certain parameters of interest are determined by solving an inverse problem.This is done by minimizing a cost functional that quantifies the discrepancy among measured data and model evaluation.Solving the inverse problem is mathematically challenging owing to the instability of the inversion and to the presence of several local minima that are caused by correlation among parameters.This is a relevant issue, particularly when the inverse problem to be solved requires the retrieval of a high number of parameters.In such cases, methods to reduce the complexity of the problem are to be sought.In this work, we propose an algorithm suitable to automatically determine which subset of the parameters is mostly relevant in the model, and we apply it to the reconstruction of 2D and 3D scatterers.We compare the results with local sensitivity analysis and with the screening method proposed by Morris.
NGraph: Parallel Graph Processing in Hybrid Memory Systems

Big data applications like graph processing are highly imposed on memory capacity. Byte-addressable non-volatile memory (NVM) technologies can offer much larger memory capacity, lower cost per bit relative to traditional DRAM. They are expected to play a crucial role in mitigating I/O operations for big data processing. However, since the NVMs show higher access latency and lower bandwidth compared with DRAM, it is still challenging to fully exploit the advantages of both the DRAM and NVM for graph processing. In this paper, we propose NGraph, a new parallel graph processing framework specially designed for hybrid memory systems. According to different access patterns of graph data, NGraph exploits memory heterogeneity-aware data placement strategies to avoid random accesses and frequent updates to NVM. NGraph partitions graph by destination vertices and exploits a task decomposition scheme to avoid data contention between multicores. Meanwhile, the NGraph balances the execution time of parallel graph data processing on multicores through a work-stealing strategy. Moreover, the NGraph also proposes software-based data pre-fetching to improve cache hit rate, and supports huge page to reduce address translation overhead. We evaluate NGraph using a hybrid memory emulator. The experimental results show that NGraph can achieve up to 48.28% performance improvement for several typical benchmarks compared with the state-of-the-art systems Ligra and Polymer.
Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection

This paper describes our submission 1 to the SemEval 2019 Hyperpartisan News Detection task.Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining good performance.To this goal, we follow a feature-based approach and perform several experiments with different machine learning classifiers.On the main task, our model achieved an accuracy of 71.7%, which was improved after the task's end to 72.9%.We also participate in the meta-learning sub-task, for classifying documents with the binary classifications of all submitted systems as input, achieving an accuracy of 89.9%.
On Scalable and Efficient Computation of Large Scale Optimal Transport

Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport). Specifically, we approximate the optimal transport plan by a pushforward of a reference distribution, and cast the optimal transport problem into a minimax problem. We then can solve OT problems efficiently using primal dual stochastic gradient-type algorithms. We also show that we can recover the density of the optimal transport plan using neural ordinary differential equations. Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior. SPOT also allows us to efficiently sample from the optimal transport plan, which benefits downstream applications such as domain adaptation.
The streaming k-mismatch problem

We consider the streaming complexity of a fundamental task in approximate pattern matching: the k-mismatch problem. In this problem, we must compute Hamming distances between a pattern of length n and all length-n substrings of a text for which the Hamming distance does not exceed a given threshold k. In our problem formulation, we report not only the Hamming distance but also, on demand, the full mismatch information, that is the list of mismatched pairs of symbols and their indices. The twin challenges of streaming pattern matching derive from the need both to achieve small working space and also to guarantee that every arriving input symbol is processed quickly.We present a streaming algorithm for the k-mismatch problem which uses O(k lognlog n/k) bits of space and spends [MATH HERE] time on each symbol of the input stream. In our formulation, the pattern is also in the stream, arriving directly before the text. The running time almost matches the classic offline solution [5] and the space usage is within a logarithmic factor of optimal. Our new algorithm therefore effectively resolves and also extends a problem first introduced in FOCS'09 [38]. En route to this solution, we also give a deterministic [MATH HERE]-bit encoding of all the alignments with Hamming distance at most k of a length-n pattern within a text of length O(n). This secondary result provides an optimal solution to a natural encoding problem which may be of independent interest.
Lower bound of local quantum uncertainty for high-dimensional bipartite quantum systems

Quantum correlations are of fundamental importance in quantum phenomena and studies related to quantum information processing. The measurement of quantum correlations is a central challenge. A recently proposed measure of quantum correlations, local quantum uncertainty (LQU), satisfies all the physical requirements as a measure of quantum correlations. This study derives a closed-form lower bound of the LQU for arbitrary-dimensional bipartite quantum states using operator relaxation. We also compared the lower bound with the optimized LQU for several typical sets of quantum states. The results show that the lower bound is near to the optimized LQU for three-dimensional bipartite quantum systems.
Adaptive Robust Visual Servoing/Force Control for Robot Manipulator With Dead-Zone Input

In this paper, we investigate the adaptive robust visual servoing/force control problem for the uncertain robot manipulator. The robot is considered to work with an unknown constraint surface and the unknown depth of feature point in image plane is assumed to be time-varying. The unknown constraint surface is linearly parameterized, and new adaptive laws are designed to estimate the unknown parameters online. In image based visual servoing control, the unknown time-varying depth plays a special role as it appears nonlinearly in the overall Jacobian matrix and cannot be adapted together with other uncertain kinematic parameters. To handle this problem, a compensatory depth-independent interaction matrix framework and the corresponding adaptive laws are proposed to compensate for the depth in the closed-loop dynamics. Moreover, the dead-zone input constraint is considered and handled by designing the robust compensatory terms and adaptive laws. With the proposed control scheme, it is proved that the image position and force tracking errors converge to zero asymptotically. Finally, numerical simulation and experimental results illustrate the effectiveness of the proposed approach.
Web Table Extraction, Retrieval and Augmentation

This tutorial synthesizes and presents research on web tables over the past two decades. We group the tasks into six main categories of information access tasks: (i) table extraction, (ii) table interpretation, (iii) table search, (iv) question answering on tables, (v) knowledge base augmentation, and (vi) table completion. For each category, we identify and introduce seminal approaches, present relevant resources, and point out interdependencies among the different tasks.
Modeling Local Dependence in Natural Language with Multi-Channel Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been widely used in processing natural language tasks and achieve huge success. Traditional RNNs usually treat each token in a sentence uniformly and equally. However, this may miss the rich semantic structure information of a sentence, which is useful for understanding natural languages. Since semantic structures such as word dependence patterns are not parameterized, it is a challenge to capture and leverage structure information. In this paper, we propose an improved variant of RNN, Multi-Channel RNN (MC-RNN), to dynamically capture and leverage local semantic structure information. Concretely, MC-RNN contains multiple channels, each of which represents a local dependence pattern at a time. An attention mechanism is introduced to combine these patterns at each step, according to the semantic information. Then we parameterize structure information by adaptively selecting the most appropriate connection structures among channels. In this way, diverse local structures and dependence patterns in sentences can be well captured by MC-RNN. To verify the effectiveness of MC-RNN, we conduct extensive experiments on typical natural language processing tasks, including neural machine translation, abstractive summarization, and language modeling. Experimental results on these tasks all show significant improvements of MC-RNN over current top systems.
A study on smart online frame forging attacks against Video Surveillance System

Video Surveillance Systems (VSS) have become an essential infrastructural element of smart cities by increasing public safety and countering criminal activities. A VSS is normally deployed in a secure network to prevent the access from unauthorized personnel. Compared to traditional systems that continuously record video regardless of the actions in the frame, a smart VSS has the capability of capturing video data upon motion detection or object detection, and then extracts essential information and send to users. This increasing design complexity of the surveillance system, however, also introduces new security vulnerabilities. In this work, a smart, real-time frame duplication attack is investigated. We show the feasibility of forging the video streams in real-time as the camera's surroundings change. The generated frames are compared constantly and instantly to identify changes in the pixel values that could represent motion detection or changes in light intensities outdoors. An attacker (intruder) can remotely trigger the replay of some previously duplicated video streams manually or automatically, via a special quick response (QR) code or when the face of an intruder appear in the camera field of view. A detection technique is proposed by leveraging the real-time electrical network frequency (ENF) reference database to match with the power grid frequency.
Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement

Despite great advances witnessed on facial image alignment in recent years, high accuracy high speed face alignment algorithms still have rooms to improve especially for applications where computation resources are limited. Addressing this issue, we propose a new face landmark localization algorithm by combining global regression and local refinement. In particular, for a given image, our algorithm first estimates its global facial shape through a global regression network (GRegNet) and then using cascaded local refinement networks (LRefNet) to sequentially improve the alignment result. Compared with previous face alignment algorithms, our key innovation is the sharing of low level features in GRegNet with LRefNet. Such feature sharing not only significantly improves the algorithm efficiency, but also allows full exploration of rich locality-sensitive details carried with shallow network layers and consequently boosts the localization accuracy. The advantages of our algorithm is clearly validated in our thorough experiments on four popular face alignment benchmarks, 300-W, AFLW, COFW and WFLW. On all datasets, our algorithm produces state-of-the-art alignment accuracy, while enjoys the smallest computational complexity.
Scalable Semi-Supervised SVM via Triply Stochastic Gradients

Semi-supervised learning (SSL) plays an increasingly important role in the big data era because a large number of unlabeled samples can be used effectively to improve the performance of the classifier. Semi-supervised support vector machine (S3VM) is one of the most appealing methods for SSL, but scaling up S3VM for kernel learning is still an open problem. Recently, a doubly stochastic gradient (DSG) algorithm has been proposed to achieve efficient and scalable training for kernel methods. However, the algorithm and theoretical analysis of DSG are developed based on the convexity assumption which makes them incompetent for non-convex problems such as S3VM. To address this problem, in this paper, we propose a triply stochastic gradient algorithm for S3VM, called TSGS3VM. Specifically, to handle two types of data instances involved in S3VM, TSGS3VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient. We use the approximated gradient to update the solution. More importantly, we establish new theoretic analysis for TSGS3VM which guarantees that TSGS3VM can converge to a stationary point. Extensive experimental results on a variety of datasets demonstrate that TSGS3VM is much more efficient and scalable than existing S3VM algorithms.
Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations

We investigate the task of distantly supervised joint entity relation extraction. It’s known that training with distant supervision will suffer from noisy samples. To tackle the problem, we propose to adapt a small manually labelled dataset to the large automatically generated dataset. By developing a novel adaptation algorithm, we are able to transfer the high quality but heterogeneous entity relation annotations in a robust and consistent way. Experiments on the benchmark NYT dataset show that our approach significantly outperforms state-ofthe-art methods.
Weakly Supervised Multi-Label Learning via Label Enhancement

Weakly supervised multi-label learning (WSML) concentrates on a more challenging multi-label classification problem, where some labels in the training set are missing. Existing approaches make multi-label prediction by exploiting the incomplete logical labels directly without considering the relative importance of each label to an instance. In this paper, a novel two-stage strategy named Weakly Supervised Multi-label Learning via Label Enhancement (WSMLLE) is proposed to learn from weakly supervised data via label enhancement. Firstly, the relative importance of each label, i.e., the description degrees are recovered by leveraging the structural information in the feature space and local correlations learned from the label space. Then, a tailored multi-label predictive model is induced by learning from the training instances with the recovered description degrees. To our best knowledge, it is the first attempt to unify the complement of the missing labels and the recovery of the description degrees into the same framework. Extensive experiments across a wide range of real-world datasets clearly validate the superiority of the proposed approach.
KCNN: Kernel-wise Quantization to Remarkably Decrease Multiplications in Convolutional Neural Network

Convolutional neural networks (CNNs) have demonstrated state-of-the-art performance in computer vision tasks. However, the high computational power demand of running devices of recent CNNs has hampered many of their applications. Recently, many methods have quantized the floating-point weights and activations to fixed-points or binary values to convert fractional arithmetic to integer or bit-wise arithmetic. However, since the distributions of values in CNNs are extremely complex, fixed-points or binary values lead to numerical information loss and cause performance degradation. On the other hand, convolution is composed of multiplications and accumulation, but the implementation of multiplications in hardware is more costly comparing with accumulation. We can preserve the rich information of floating-point values on dedicated low power devices by considerably decreasing the multiplications. In this paper, we quantize the floating-point weights in each kernel separately to multiple bit planes to remarkably decrease multiplications. We obtain a closed-form solution via an aggressive Lloyd algorithm and the fine-tuning is adopted to optimize the bit planes. Furthermore, we propose dual normalization to solve the pathological curvature problem during fine-tuning. Our quantized networks show negligible performance loss compared to their floating-point counterparts.
Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm

When designing swarm-robotic systems, system- atic comparison of algorithms from different do- mains is necessary to determine which is capa- ble of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed met- rics as a design tool by solving a large object gath- ering problem in temporally varying operating con- ditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.
Accelerating Distributed Machine Learning by Smart Parameter Server

Parameter Server (PS)-based architecture is widely applied in distributed machine learning (DML), but it is still an open issue how to improve the DML performance in this frame-work. Existing works mainly focus on the view of workers. In this paper, we tackle this problem from another perspective, by leveraging the central control on the PS. Specifically, we propose SmartPS, which transforms the passive role of PS in traditional DML and fully exploits the intelligence of PS. Firstly, the PS holds the global view of parameter dependency, facilitating it to update workers' parameters selectively and proactively. Secondly, the PS records the workers' speeds, and prioritizes parameter transmission to narrow the gap between stragglers and fast workers. Thirdly, the PS considers the parameter dependency in consecutive training iterations, and opportunistically blocks unnecessary pushes from workers. We conduct comparative experiments with two typical benchmarks, Matrix Factorization (MF) and PageRank (PR). The experimental results prove that, compared with all the baseline algorithms (i.e. standard BSP, ASP and SSP), SmartPS can reduce the overall training time by 65.7%~84.9%, with the same training accuracy.
Cooperative Pruning in Cross-Domain Deep Neural Network Compression

The advancement of deep models poses great challenges to real-world deployment because of the limited computational ability and storage space on edge devices. To solve this problem, existing works have made progress to compress deep models by pruning or quantization. However, most existing methods rely on a large amount of training data and a pre-trained model in the same domain. When only limited in-domain training data is available, these methods fail to perform well. This prompts the idea of transferring knowledge from a resource-rich source domain to a target domain with limited data to perform model compression. In this paper, we propose a method to perform cross-domain pruning by cooperatively training in both domains: taking advantage of data and a pre-trained model from the source domain to assist pruning in the target domain. Specifically, source and target pruned models are trained simultaneously and interactively, with source information transferred through the construction of a cooperative pruning mask. Our method significantly improves pruning quality in the target domain, and shed light to model compression in the cross-domain setting.
Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics

We investigate the data complexity of answering queries mediated by metric temporal logic ontologies under the event-based semantics assuming that data instances are finite timed words timestamped with binary fractions. We identify classes of ontology-mediated queries answering which can be done in AC0, NC1, L, NL, P, and coNP for data complexity, provide their rewritings to first-order logic and its extensions with primitive recursion, transitive closure or datalog, and establish lower complexity bounds.
A Restart-based Rank-1 Evolution Strategy for Reinforcement Learning

Evolution strategies have been demonstrated to have the strong ability to roughly train deep neural networks and well accomplish reinforcement learning tasks. However, existing evolution strategies designed specially for deep reinforcement learning only involve the plain variants which can not realize the adaptation of mutation strength or other advanced techniques. The research of applying advanced and effective evolution strategies to reinforcement learning in an efficient way is still a gap. To this end, this paper proposes a restart-based rank-1 evolution strategy for reinforcement learning. When training the neural network, it adapts the mutation strength and updates the principal search direction in a way similar to the momentum method, which is an ameliorated version of stochastic gradient ascent. Besides, two mechanisms, i.e., the adaptation of the number of elitists and the restart procedure, are integrated to deal with the issue of local optima. Experimental results on classic control problems and Atari games show that the proposed algorithm is superior to or competitive with state-of-the-art algorithms for reinforcement learning, demonstrating the effectiveness of the proposed algorithm.