OpenRaiser/Intern-Atlas huggingface dataset:
VPE: Variational Policy Embedding for Transfer Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

Reinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment conditions often vary and data collection is expensive, making retraining undesirable. Simulation training allows for feasible training times, but on the other hand suffers from a reality-gap when applied in real-world settings. This raises the need of efficient adaptation of policies acting in new environments. We consider this as a problem of transferring knowledge within a family of similar Markov decision processes. For this purpose we assume that Q-functions are generated by some low-dimensional latent variable. Given such a Q-function, we can find a master policy that can adapt given different values of this latent variable. Our method learns both the generative mapping and an approximate posterior of the latent variables, enabling identification of policies for new tasks by searching only in the latent space, rather than the space of all policies. The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments. We demonstrate the method on both a pendulum swing-up task in simulation, and for simulation-to-real transfer on a pushing task.
On the Capacity Region for Secure Index Coding
Venue: arXiv cs.IT 2018
Year: 2018

We study the index coding problem in the presence of an eavesdropper, where the aim is to communicate without allowing the eavesdropper to learn any single message aside from the messages it may already know as side information. We establish an outer bound on the underlying secure capacity region of the index coding problem, which includes polymatroidal and security constraints, as well as the set of additional decoding constraints for legitimate receivers. We then propose a secure variant of the composite coding scheme, which yields an inner bound on the secure capacity region of the index coding problem. For the achievability of secure composite coding, a secret key with vanishingly small rate may be needed to ensure that each legitimate receiver who wants the same message as the eavesdropper, knows at least two more messages than the eavesdropper. For all securely feasible index coding problems with four or fewer messages, our numerical results establish the secure index coding capacity region.
Robust Resource Allocation for UAV Systems with UAV Jittering and User Location Uncertainty
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate resource allocation algorithm design for multiuser unmanned aerial vehicle (UAV) communication systems in the presence of UAV jittering and user location uncertainty. In particular, we jointly optimize the two-dimensional position and the downlink beamformer of a fixed-altitude UAV for minimization of the total UAV transmit power. The problem formulation takes into account the quality-of-service requirements of the users, the imperfect knowledge of the antenna array response (AAR) caused by UAV jittering, and the user location uncertainty. Despite the non-convexity of the resulting problem, we solve the problem optimally employing a series of transformations and semidefinite programming relaxation. Our simulation results reveal the dramatic power savings enabled by the proposed robust scheme compared to two baseline schemes. Besides, the robustness of the proposed scheme with respect to imperfect AAR knowledge and user location uncertainty at the UAV is also confirmed.
How much should you ask? On the question structure in QA systems
Venue: arXiv cs.CL 2018
Year: 2018

Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner. However, users are still used to query-like systems where they type in keywords to search for answer. In this study we validate which parts of questions are essential for obtaining valid answer. In order to conclude that, we take advantage of LIME - a framework that explains prediction by local approximation. We find that grammar and natural language is disregarded by QA. State-of-the-art model can answer properly even if 'asked' only with a few words with high coefficients calculated with LIME. According to our knowledge, it is the first time that QA model is being explained by LIME.
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we present the results of an investigation of the importance of verbs in a deep learning QA system trained on SQuAD dataset. We show that main verbs in questions carry little influence on the decisions made by the system - in over 90% of researched cases swapping verbs for their antonyms did not change system decision. We track this phenomenon down to the insides of the net, analyzing the mechanism of self-attention and values contained in hidden layers of RNN. Finally, we recognize the characteristics of the SQuAD dataset as the source of the problem. Our work refers to the recently popular topic of adversarial examples in NLP, combined with investigating deep net structure.
Joint Spatial Division and Diversity for Massive MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

We propose a downlink beamforming scheme that combines spatial division and orthogonal space-time block coding (OSTBC) in multi-user massive MIMO systems. The beamformer is divided into two parts: a pre-beamforming matrix to separate the users into different beams with no interference between each other, which is designed based on the low rank covariance matrix of the downlink channel, and a linear precoding matrix using partial or even no channel state information (CSI) concatenated by an OSTBC. To construct the pre-beamforming matrix, a simple method that selects columns from DFT matrix is presented. To design the linear precoding matrix with partial CSI of the effective channel after the pre-beamforming, we solve an optimization problem to minimize the pairwise error probability (PEP) of the users under an individual power or sum power constraint, respectively. For the individual power constraint, a semi-definite relaxing (SDR) method with a sufficient condition achieving the globally optimal solution is proposed to provide a performance benchmark. In addition, an efficient iterative successive convex approximation (SCA) method is provided to achieve a suboptimal solution. Furthermore, closed form solutions are derived under some special cases. For the sum power constraint, we consider two different designs, i.e., minimizing the average PEP and minimizing the maximum PEP of all users. We find that both non-convex problems have a similar structure, and proposed a unified SCA-Alternating Direction Method of Multipliers (ADMM) algorithm to handle them. The SCA-ADMM method can be implemented in a parallel manner, and thus is with great efficiency. Simulation results show the efficiency of our proposed JSDD scheme and the optimization method.
3D Human Body Reconstruction from a Single Image via Volumetric Regression
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes the use of an end-to-end Convolutional Neural Network for direct reconstruction of the 3D geometry of humans via volumetric regression. The proposed method does not require the fitting of a shape model and can be trained to work from a variety of input types, whether it be landmarks, images or segmentation masks. Additionally, non-visible parts, either self-occluded or otherwise, are still reconstructed, which is not the case with depth map regression. We present results that show that our method can handle both pose variation and detailed reconstruction given appropriate datasets for training.
A new exact algorithm for solving single machine scheduling problems with learning effects and deteriorating jobs
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, the single machine scheduling problem with deteriorating jobs and learning effects are considered, which is shown in the previous research that the SDR method no longer provides an optimal solution for the problem. In order to solve the problem, a new exact algorithm is proposed. Various test problems are solved to evaluate the performance of the proposed heuristic algorithm using different measures. The results indicate that the algorithm can solve various test problems with small, medium and large sizes in a few seconds with an error around 1% where solving the test problems with more than 15 jobs is almost impossible by examining all possible permutations in both complexity and time aspects.
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses. As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution. Finally, we demonstrate generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.
Efficient Multimedia Similarity Measurement Using Similar Elements
Venue: arXiv cs.MM 2018
Year: 2018

Online social networking techniques and large-scale multimedia systems are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data. This trend has put forward higher requirements and greater challenges on massive multimedia data retrieval. In this paper, we investigate the problem of image similarity measurement which is used to lots of applications. At first we propose the definition of similarity measurement of images and the related notions. Based on it we present a novel basic method of similarity measurement named SMIN. To improve the performance of calculation, we propose a novel indexing structure called SMI Temp Index (SMII for short). Besides, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that our solution outperforms state-of-the-art method.
Does network quality matter? A field study of mobile user satisfaction
Venue: arXiv cs.NI 2018
Year: 2018

Mobile quality of experience and user satisfaction are growing research topics. However, the relationship between a user's satisfaction with network quality and the networks real performance in the field remains unexplored. This paper is the first to study both network and non-network predictors of user satisfaction in the wild. We report findings from a large sample (2224 users over 12 months) combining both questionnaires and network measurements. We found that minimum download goodput and device type predict satisfaction with network availability. Whereas for network speed, only download factors predicted satisfaction. We observe that users integrate over many measurements and exhibit a known peak-end effect in their ratings. These results can inform modeling efforts in quality of experience and user satisfaction.
Deep Inferential Spatial-Temporal Network for Forecasting Air Pollution Concentrations
Venue: arXiv cs.LG 2018
Year: 2018

Air pollution poses a serious threat to human health as well as economic development around the world. To meet the increasing demand for accurate predictions for air pollutions, we proposed a Deep Inferential Spatial-Temporal Network to deal with the complicated non-linear spatial and temporal correlations. We forecast three air pollutants (i.e., PM2.5, PM10 and O3) of monitoring stations over the next 48 hours, using a hybrid deep learning model consists of inferential predictor (inference for regions without air pollution readings), spatial predictor (capturing spatial correlations using CNN) and temporal predictor (capturing temporal relationship using sequence-to-sequence model with simplified attention mechanism). Our proposed model considers historical air pollution records and historical meteorological data. We evaluate our model on a large-scale dataset containing air pollution records of 35 monitoring stations and grid meteorological data in Beijing, China. Our model outperforms other state-of-art methods in terms of SMAPE and RMSE.
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Venue: arXiv cs.PL 2018
Year: 2018

The rise of modern blockchains has facilitated the emergence of smart contracts: autonomous programs that live and run on the blockchain. Smart contracts have seen a rapid climb to prominence, with applications predicted in law, business, commerce, and governance. Smart contracts are commonly written in a high-level language such as Ethereum's Solidity, and translated to compact low-level bytecode for deployment on the blockchain. Once deployed, the bytecode is autonomously executed, usually by a %Turing-complete virtual machine. As with all programs, smart contracts can be highly vulnerable to malicious attacks due to deficient programming methodologies, languages, and toolchains, including buggy compilers. At the same time, smart contracts are also high-value targets, often commanding large amounts of cryptocurrency. Hence, developers and auditors need security frameworks capable of analysing low-level bytecode to detect potential security vulnerabilities. In this paper, we present Vandal: a security analysis framework for Ethereum smart contracts. Vandal consists of an analysis pipeline that converts low-level Ethereum Virtual Machine (EVM) bytecode to semantic logic relations. Users of the framework can express security analyses in a declarative fashion: a security analysis is expressed in a logic specification written in the \souffle language. We conduct a large-scale empirical study for a set of common smart contract security vulnerabilities, and show the effectiveness and efficiency of Vandal. Vandal is both fast and robust, successfully analysing over 95\% of all 141k unique contracts with an average runtime of 4.15 seconds; outperforming the current state of the art tools---Oyente, EthIR, Mythril, and Rattle---under equivalent conditions.
Can LSTM Learn to Capture Agreement? The Case of Basque
Venue: arXiv cs.CL 2018
Year: 2018

Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical phenomena can they acquire? We focus on the task of agreement prediction in Basque, as a case study for a task that requires implicit understanding of sentence structure and the acquisition of a complex but consistent morphological system. Analyzing experimental results from two syntactic prediction tasks -- verb number prediction and suffix recovery -- we find that sequential models perform worse on agreement prediction in Basque than one might expect on the basis of a previous agreement prediction work in English. Tentative findings based on diagnostic classifiers suggest the network makes use of local heuristics as a proxy for the hierarchical structure of the sentence. We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.
Resilient Active Target Tracking with Multiple Robots
Venue: arXiv cs.RO 2018
Year: 2018

The problem of target tracking with multiple robots consists of actively planning the motion of the robots to track the targets. A major challenge for practical deployments is to make the robots resilient to failures. In particular, robots may be attacked in adversarial scenarios, or their sensors may fail or get occluded. In this paper, we introduce planning algorithms for multi-target tracking that are resilient to such failures. In general, resilient target tracking is computationally hard. Contrary to the case where there are no failures, no scalable approximation algorithms are known for resilient target tracking when the targets are indistinguishable, or unknown in number, or with unknown motion model. In this paper we provide the first such algorithm, that also has the following properties: First, it achieves maximal resiliency, since the algorithm is valid for any number of failures. Second, it is scalable, as our algorithm terminates with the same running time as state-of-the-art algorithms for (non-resilient) target tracking. Third, it provides provable approximation bounds on the tracking performance, since our algorithm guarantees a solution that is guaranteed to be close to the optimal. We quantify our algorithm's approximation performance using a novel notion of curvature for monotone set functions subject to matroid constraints. Finally, we demonstrate the efficacy of our algorithm through MATLAB and Gazebo simulations, and a sensitivity analysis; we focus on scenarios that involve a known number of distinguishable targets.
Zoom: SSD-based Vector Search for Optimizing Accuracy, Latency and Memory
Venue: arXiv cs.CV 2018
Year: 2018

With the advancement of machine learning and deep learning, vector search becomes instrumental to many information retrieval systems, to search and find best matches to user queries based on their semantic similarities.These online services require the search architecture to be both effective with high accuracy and efficient with low latency and memory footprint, which existing work fails to offer. We develop, Zoom, a new vector search solution that collaboratively optimizes accuracy, latency and memory based on a multiview approach. (1) A "preview" step generates a small set of good candidates, leveraging compressed vectors in memory for reduced footprint and fast lookup. (2) A "fullview" step on SSDs reranks those candidates with their full-length vector, striking high accuracy. Our evaluation shows that, Zoom achieves an order of magnitude improvements on efficiency while attaining equal or higher accuracy, comparing with the state-of-the-art.
Usable Differential Privacy: A Case Study with PSI
Venue: arXiv cs.HC 2018
Year: 2018

Differential privacy is a promising framework for addressing the privacy concerns in sharing sensitive datasets for others to analyze. However differential privacy is a highly technical area and current deployments often require experts to write code, tune parameters, and optimize the trade-off between the privacy and accuracy of statistical releases. For differential privacy to achieve its potential for wide impact, it is important to design usable systems that enable differential privacy to be used by ordinary data owners and analysts. PSI is a tool that was designed for this purpose, allowing researchers to release useful differentially private statistical information about their datasets without being experts in computer science, statistics, or privacy. We conducted a thorough usability study of PSI to test whether it accomplishes its goal of usability by non-experts. The usability test illuminated which features of PSI are most user-friendly and prompted us to improve aspects of the tool that caused confusion. The test also highlighted some general principles and lessons for designing usable systems for differential privacy, which we discuss in depth.
On the Beneficial Roles of Fading and Transmit Diversity in Wireless Power Transfer with Nonlinear Energy Harvesting
Venue: arXiv cs.IT 2018
Year: 2018

We study the effect of channel fading in Wireless Power Transfer (WPT) and show that fading enhances the RFto- DC conversion efficiency of nonlinear RF energy harvesters. We then develop a new form of signal design for WPT, denoted as Transmit Diversity, that relies on multiple dumb antennas at the transmitter to induce fast fluctuations of the wireless channel. Those fluctuations boost the RF-to-DC conversion efficiency thanks to the energy harvester nonlinearity. In contrast with (energy) beamforming, Transmit Diversity does not rely on Channel State Information at the Transmitter (CSIT) and does not increase the average power at the energy harvester input, though it still enhances the overall end-to-end power transfer efficiency. Transmit Diversity is also combined with recently developed (energy) waveform and modulation to provide further enhancements. The efficacy of the scheme is analyzed using physics-based and curve fitting-based nonlinear models of the energy harvester and demonstrated using circuit simulations, prototyping and experimentation. Measurements with two transmit antennas reveal gains of 50%in harvested DC power over a single transmit antenna setup. The work (again) highlights the crucial role played by the harvester nonlinearity and demonstrates that multiple transmit antennas can be beneficial to WPT even in the absence of CSIT.
Detecting egregious responses in neural sequence-to-sequence models
Venue: arXiv cs.AI 2018
Year: 2018

In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficiently. We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them. Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users. In our experiments, we apply this approach to dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses. We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training.
Simultaneous Localization and Layout Model Selection in Manhattan Worlds
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we will demonstrate how Manhattan structure can be exploited to transform the Simultaneous Localization and Mapping (SLAM) problem, which is typically solved by a nonlinear optimization over feature positions, into a model selection problem solved by a convex optimization over higher order layout structures, namely walls, floors, and ceilings. Furthermore, we show how our novel formulation leads to an optimization procedure that automatically performs data association and loop closure and which ultimately produces the simplest model of the environment that is consistent with the available measurements. We verify our method on real world data sets collected with various sensing modalities.