OpenRaiser/Intern-Atlas huggingface dataset:
Qubits through Queues: The Capacity of Channels with Waiting Time Dependent Errors
Venue: arXiv cs.IT 2019
Year: 2019

We consider a setting where qubits are processed sequentially, and derive fundamental limits on the rate at which classical information can be transmitted using quantum states that decohere in time. Specifically, we model the sequential processing of qubits using a single server queue, and derive explicit expressions for the capacity of such a `queue-channel.' We also demonstrate a sweet-spot phenomenon with respect to the arrival rate to the queue, i.e., we show that there exists a value of the arrival rate of the qubits at which the rate of information transmission (in bits/sec) through the queue-channel is maximized. Next, we consider a setting where the average rate of processing qubits is fixed, and show that the capacity of the queue-channel is maximized when the processing time is deterministic. We also discuss design implications of these results on quantum information processing systems.
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, the performance of face verification and recognition systems based on deep convolutional neural networks (DCNNs) has significantly improved. A typical pipeline for face verification includes training a deep network for subject classification with softmax loss, using the penultimate layer output as the feature descriptor, and generating a cosine similarity score given a pair of face images or videos. The softmax loss function does not optimize the features to have higher similarity score for positive pairs and lower similarity score for negative pairs, which leads to a performance gap. In this paper, we propose a new loss function, called Crystal Loss, that restricts the features to lie on a hypersphere of a fixed radius. The loss can be easily implemented using existing deep learning frameworks. We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems. We achieve state-of-the-art performance for face verification and recognition on challenging LFW, IJB-A, IJB-B and IJB-C datasets over a large range of false alarm rates (10-1 to 10-7).
SBFT: a Scalable and Decentralized Trust Infrastructure
Venue: arXiv cs.DC 2019
Year: 2019

SBFT is a state of the art Byzantine fault tolerant permissioned blockchain system that addresses the challenges of scalability, decentralization and world-scale geo-replication. SBFTis optimized for decentralization and can easily handle more than 200 active replicas in a real world-scale deployment. We evaluate \sysname in a world-scale geo-replicated deployment with 209 replicas withstanding f=64 Byzantine failures. We provide experiments that show how the different algorithmic ingredients of \sysname increase its performance and scalability. The results show that SBFT simultaneously provides almost 2x better throughput and about 1.5x better latency relative to a highly optimized system that implements the PBFT protocol. To achieve this performance improvement, SBFT uses a combination of four ingredients: using collectors and threshold signatures to reduce communication to linear, using an optimistic fast path, reducing client communication and utilizing redundant servers for the fast path.
Learning Strict Identity Mappings in Deep Residual Networks
Venue: arXiv cs.CV 2019
Year: 2019

A family of super deep networks, referred to as residual networks or ResNet, achieved record-beating performance in various visual tasks such as image recognition, object detection, and semantic segmentation. The ability to train very deep networks naturally pushed the researchers to use enormous resources to achieve the best performance. Consequently, in many applications super deep residual networks were employed for just a marginal improvement in performance. In this paper, we propose epsilon-ResNet that allows us to automatically discard redundant layers, which produces responses that are smaller than a threshold epsilon, with a marginal or no loss in performance. The epsilon-ResNet architecture can be achieved using a few additional rectified linear units in the original ResNet. Our method does not use any additional variables nor numerous trials like other hyper-parameter optimization techniques. The layer selection is achieved using a single training process and the evaluation is performed on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. In some instances, we achieve about 80% reduction in the number of parameters.
Iterative Detection and Phase-Noise Compensation for Coded Multichannel Optical Transmission
Venue: arXiv cs.IT 2019
Year: 2019

The problem of phase-noise compensation for correlated phase noise in coded multichannel optical transmission is investigated. To that end, a simple multichannel phase-noise model is considered and the maximum a posteriori detector for this model is approximated using two frameworks, namely factor graphs (FGs) combined with the sum-product algorithm (SPA), and a variational Bayesian (VB) inference method. The resulting pilot-aided algorithms perform iterative phase-noise compensation in cooperation with a decoder, using extended Kalman smoothing to estimate the a posteriori phase-noise distribution jointly for all channels. The system model and the proposed algorithms are verified using experimental data obtained from space-division multiplexed multicore-fiber transmission. Through Monte Carlo simulations, the algorithms are further evaluated in terms of phase-noise tolerance for coded transmission. It is observed that they significantly outperform the conventional approach to phase-noise compensation in the optical literature. Moreover, the FG/SPA framework performs similarly or better than the VB framework in terms of phase-noise tolerance of the resulting algorithms, for a slightly higher computational complexity.
Combinatorial Register Allocation and Instruction Scheduling
Venue: arXiv cs.PL 2019
Year: 2019

This paper introduces a combinatorial optimization approach to register allocation and instruction scheduling, two central compiler problems. Combinatorial optimization has the potential to solve these problems optimally and to exploit processor-specific features readily. Our approach is the first to leverage this potential in practice: it captures the complete set of program transformations used in state-of-the-art compilers, scales to medium-sized functions of up to 1000 instructions, and generates executable code. This level of practicality is reached by using constraint programming, a particularly suitable combinatorial optimization technique. Unison, the implementation of our approach, is open source, used in industry, and integrated with the LLVM toolchain. An extensive evaluation confirms that Unison generates better code than LLVM while scaling to medium-sized functions. The evaluation uses systematically selected benchmarks from MediaBench and SPEC CPU2006 and different processor architectures (Hexagon, ARM, MIPS). Mean estimated speedup ranges from 1.1% to 10% and mean code size reduction ranges from 1.3% to 3.8% for the different architectures. A significant part of this improvement is due to the integrated nature of the approach. Executing the generated code on Hexagon confirms that the estimated speedup results in actual speedup. Given a fixed time limit, Unison solves optimally functions of up to 946 instructions, nearly an order of magnitude larger than previous approaches. The results show that our combinatorial approach can be applied in practice to trade compilation time for code quality beyond the usual compiler optimization levels, identify improvement opportunities in heuristic algorithms, and fully exploit processor-specific features.
Programmatically Interpretable Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural "oracle". We evaluate NDPS on the task of learning to drive a simulated car in the TORCS car-racing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.
Environmental Sound Recognition using Masked Conditional Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties. The ConditionaL Neural Network (CLNN) is designed to consider the relational properties across frames in a temporal signal, and its extension the Masked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within the network, which enforces the network to learn in frequency bands rather than bins. Additionally, it automates the exploration of different feature combinations analogous to handcrafting the optimum combination of features for a recognition task. We applied the MCLNN to the environmental sounds of the ESC-10 dataset. The MCLNN achieved competitive accuracies compared to state-of-the-art convolutional neural networks and hand-crafted attempts.
On the Access Complexity of PIR Schemes
Venue: arXiv cs.IT 2019
Year: 2019

Private information retrieval has been reformulated in an information-theoretic perspective in recent years. The two most important parameters considered for a PIR scheme in a distributed storage system are the storage overhead and PIR rate. The complexity of the computations done by the servers for the various tasks of the distributed storage system is an important parameter in such systems which didn't get enough attention in PIR schemes. As a consequence, we take into consideration a third parameter, the access complexity of a PIR scheme, which characterizes the total amount of data to be accessed by the servers for responding to the queries throughout a PIR scheme. We use a general covering codes approach as the main tool for improving the access complexity. With a given amount of storage overhead, the ultimate objective is to characterize the tradeoff between the rate and access complexity of a PIR scheme. This covering codes approach raises a new interesting coding problem of generalized coverings similarly to the well-known generalized Hamming weights.
Do Android Taint Analysis Tools Keep Their Promises?
Venue: arXiv cs.SE 2019
Year: 2019

In recent years, researchers have developed a number of tools to conduct taint analysis of Android applications. While all the respective papers aim at providing a thorough empirical evaluation, comparability is hindered by varying or unclear evaluation targets. Sometimes, the apps used for evaluation are not precisely described. In other cases, authors use an established benchmark but cover it only partially. In yet other cases, the evaluations differ in terms of the data leaks searched for, or lack a ground truth to compare against. All those limitations make it impossible to truly compare the tools based on those published evaluations. We thus present ReproDroid, a framework allowing the accurate comparison of Android taint analysis tools. ReproDroid supports researchers in inferring the ground truth for data leaks in apps, in automatically applying tools to benchmarks, and in evaluating the obtained results. We use ReproDroid to comparatively evaluate on equal grounds the six prominent taint analysis tools Amandroid, DIALDroid, DidFail, DroidSafe, FlowDroid and IccTA. The results are largely positive although four tools violate some promises concerning features and accuracy. Finally, we contribute to the area of unbiased benchmarking with a new and improved version of the open test suite DroidBench.
Polyphonic Pitch Tracking with Deep Layered Learning
Venue: arXiv cs.SD 2019
Year: 2019

This paper presents a polyphonic pitch tracking system able to extract both framewise and note-based estimates from audio. The system uses several artificial neural networks in a deep layered learning setup. First, cascading networks are applied to a spectrogram for framewise fundamental frequency (f0) estimation. A sparse receptive field is learned by the first network and then used as a filter kernel for parameter sharing throughout the system. The f0 activations are connected across time to extract pitch contours. These contours define a framework within which subsequent networks perform onset and offset detection, operating across both time and smaller pitch fluctuations at the same time. As input, the networks use, e.g., variations of latent representations from the f0 estimation network. Finally, incorrect tentative notes are removed one by one in an iterative procedure that allows a network to classify notes within an accurate context. The system was evaluated on four public test sets: MAPS, Bach10, TRIOS, and the MIREX Woodwind quintet, and performed state-of-the-art results for all four datasets. It performs well across all subtasks: f0, pitched onset, and pitched offset tracking.
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
Venue: arXiv cs.SI 2019
Year: 2019

A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false information campaigns with targeted manipulation of public opinion on specific topics. These false information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one, and needs increased public awareness, as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community. In this paper, we make a step in this direction by providing a typology of the Web's false information ecosystem, comprising various types of false information, actors, and their motives. We report a comprehensive overview of existing research on the false information ecosystem by identifying several lines of work: 1) how the public perceives false information; 2) understanding the propagation of false information; 3) detecting and containing false information on the Web; and 4) false information on the political stage. In this work, we pay particular attention to political false information as: 1) it can have dire consequences to the community (e.g., when election results are mutated) and 2) previous work show that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false information dissemination on the Web.
Don't cry to be the first!Symmetric fair division exist
Venue: arXiv cs.GT 2019
Year: 2019

In this article we study a cake cutting problem. More precisely, we study symmetric fair division algorithms, that is to say we study algorithms where the order of the players do not influence the value obtained by each player. In the first part of the article, we give a symmetric and envy-free fair division algorithm. More precisely, we show how to get a symmetric and envy-free fair division algorithm from an envy-free division algorithm. In the second part, we give a proportional and symmetric fair division algorithm with a complexity in O(n 3) in the Robertson-Webb model of complexity. This algorithm is based on Kuhn's algorithm. Furthermore, our study has led us to introduce a new notion: aristotelian fair division. This notion is an interpretation of Aristotle's principle: give equal shares to equal people. We conclude this article with a discussion and some questions about the Robertson-Webb model of computation.
Real-world plant species identification based on deep convolutional neural networks and visual attention
Venue: arXiv cs.CV 2019
Year: 2019

This paper investigates the issue of real-world identification to fulfill better species protection. We focus on plant species identification as it is a classic and hot issue. In tradition plant species identification the samples are scanned specimen and the background is simple. However, real-world species recognition is more challenging. We first systematically investigate what is realistic species recognition and the difference from tradition plant species recognition. To deal with the challenging task, an interdisciplinary collaboration is presented based on the latest advances in computer science and technology. We propose a novel framework and an effective data augmentation method for deep learning in this paper. We first crop the image in terms of visual attention before general recognition. Besides, we apply it as a data augmentation method. We call the novel data augmentation approach attention cropping (AC). Deep convolutional neural networks are trained to predict species from a large amount of data. Extensive experiments on traditional dataset and specific dataset for real-world recognition are conducted to evaluate the performance of our approach. Experiments first demonstrate that our approach achieves state-of-the-art results on different types of datasets. Besides, we also evaluate the performance of data augmentation method AC. Results show that AC provides superior performance. Compared with the precision of methods without AC, the results with AC achieve substantial improvement.
Clustering Strategies for Multicast Precoding in Multi-Beam Satellite Systems
Venue: arXiv cs.IT 2019
Year: 2019

Next generation multi-beam SatCom architectures will heavily exploit full frequency reuse schemes along with interference management techniques, e.g., precoding or multiuser detection, to drastically increase the system throughput. In this framework, we address the problem of the user selection for multicast precoding by formulating it as a clustering problem. By introducing a novel mathematical framework, we design fixed-/variable-size clustering algorithms that group users into simultaneously precoded and served clusters while maximising the system throughput. Numerical simulations are used to validate the proposed algorithms and to identify the main system-level trade-offs.
Projection image-to-image translation in hybrid X-ray/MR imaging
Venue: arXiv cs.CV 2019
Year: 2019

The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding X-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating X-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
Venue: arXiv cs.CV 2019
Year: 2019

Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Towards such goal, Convolutional Networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patches. The definition of the input patch size is usually performed empirically (evaluating several sizes) or imposed (by network constraint). Both strategies suffer from drawbacks and could not lead to the best patch size. To alleviate this problem, several works exploited multi-context information by combining networks or layers. This process increases the number of parameters resulting in a more difficult model to train. In this work, we propose a novel technique to perform semantic segmentation of remote sensing images that exploits a multi-context paradigm without increasing the number of parameters while defining, in training time, the best patch size. The main idea is to train a dilated network with distinct patch sizes, allowing it to capture multi-context characteristics from heterogeneous contexts. While processing these varying patches, the network provides a score for each patch size, helping in the definition of the best size for the current scenario. A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties. Our results show that the proposed algorithm provides improvements in pixelwise classification accuracy when compared to state-of-the-art methods.
English Out-of-Vocabulary Lexical Evaluation Task
Venue: arXiv cs.CL 2019
Year: 2019

Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to provide solutions for OOV lexical classification and prediction. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we utilize unsupervised word embedding methods such as Word2Vec and Word2GM to perform the baseline experiments on the categorical classification task and OOV words attribute prediction tasks.
Mobility Support for Cellular Connected Unmanned Aerial Vehicles: Performance and Analysis
Venue: arXiv cs.NI 2019
Year: 2019

Beyond visual line-of-sight connectivity is key for use cases of unmanned aerial vehicles (UAVs) such as package delivery, infrastructure inspection, and rescue missions. Cellular networks stand ready to support flying UAVs by providing wide-area, quality, and secure connectivity for UAV operations. Ensuring reliable connections in the presence of UAV movements is important for safety control and operations of UAVs. With increasing height above the ground, the radio environment changes. Using terrestrial cellular networks to provide connectivity to the UAVs moving in the sky may face new challenges. In this article, we share some of our findings in mobility support for cellular connected UAVs. We first identify how the radio environment changes with altitude and analyze the corresponding implications on mobility performance. We then present evaluation results to shed light on the mobility performance of cellular connected UAVs. We also discuss potential enhancements for improving mobility performance in the sky.