OpenRaiser/Intern-Atlas huggingface dataset:
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization
Venue: arXiv cs.CR 2017
Year: 2017

Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed from data collected "just in case" would help these organizations to limit the latter's exposure to attack. A natural approach might be to monitor data use and retain only the working-set of in-use data in accessible storage; unused data can be evicted to a highly protected store. However, many of today's big data applications rely on machine learning (ML) workloads that are periodically retrained by accessing, and thus exposing to attack, the entire data store. Training set minimization methods, such as count featurization, are often used to limit the data needed to train ML workloads to improve performance or scalability. We present Pyramid, a limited-exposure data management system that builds upon count featurization to enhance data protection. As such, Pyramid uniquely introduces both the idea and proof-of-concept for leveraging training set minimization methods to instill rigor and selectivity into big data management. We integrated Pyramid into Spark Velox, a framework for ML-based targeting and personalization. We evaluate it on three applications and show that Pyramid approaches state-of-the-art models while training on less than 1% of the raw data.
Corrupted Sensing with Sub-Gaussian Measurements
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies the problem of accurately recovering a structured signal from a small number of corrupted sub-Gaussian measurements. We consider three different procedures to reconstruct signal and corruption when different kinds of prior knowledge are available. In each case, we provide conditions for stable signal recovery from structured corruption with added unstructured noise. The key ingredient in our analysis is an extended matrix deviation inequality for isotropic sub-Gaussian matrices.
A Note on the Information-Theoretic-(in)Security of Fading Generated Secret Keys
Venue: arXiv cs.IT 2017
Year: 2017

In this work we explore the security of secret keys generated via the electromagnetic reciprocity of the wireless fading channel. Identifying a new sophisticated colluding attack, we explore the information-theoretic-security for such keys in the presence of an all-powerful adversary constrained only by the laws of quantum mechanics. Specifically, we calculate the reduction in the conditional mutual information between transmitter and receiver that can occur when an adversary with unlimited computational and communication resources places directional antenna interceptors at chosen locations. Such locations, in principal, can be arbitrarily far from the intended receiver yet still influence the secret key rate.
An Overview of Massive MIMO Research at the University of Bristol
Venue: arXiv cs.IT 2017
Year: 2017

Massive MIMO has rapidly gained popularity as a technology crucial to the capacity advances required for 5G wireless systems. Since its theoretical conception six years ago, research activity has grown exponentially, and there is now a developing industrial interest to commercialise the technology. For this to happen effectively, we believe it is crucial that further pragmatic research is conducted with a view to establish how reality differs from theoretical ideals. This paper presents an overview of the massive MIMO research activities occurring within the Communication Systems & Networks Group at the University of Bristol centred around our 128-antenna real-time testbed, which has been developed through the BIO programmable city initiative in collaboration with NI and Lund University. Through recent preliminary trials, we achieved a world first spectral efficiency of 79.4 bits/s/Hz, and subsequently demonstrated that this could be increased to 145.6 bits/s/Hz. We provide a summary of this work here along with some of our ongoing research directions such as large-scale array wave-front analysis, optimised power control and localisation techniques.
Mira: A Framework for Static Performance Analysis
Venue: arXiv cs.PF 2017
Year: 2017

The performance model of an application can pro- vide understanding about its runtime behavior on particular hardware. Such information can be analyzed by developers for performance tuning. However, model building and analyzing is frequently ignored during software development until perfor- mance problems arise because they require significant expertise and can involve many time-consuming application runs. In this paper, we propose a fast, accurate, flexible and user-friendly tool, Mira, for generating performance models by applying static program analysis, targeting scientific applications running on supercomputers. We parse both the source code and binary to estimate performance attributes with better accuracy than considering just source or just binary code. Because our analysis is static, the target program does not need to be executed on the target architecture, which enables users to perform analysis on available machines instead of conducting expensive exper- iments on potentially expensive resources. Moreover, statically generated models enable performance prediction on non-existent or unavailable architectures. In addition to flexibility, because model generation time is significantly reduced compared to dynamic analysis approaches, our method is suitable for rapid application performance analysis and improvement. We present several scientific application validation results to demonstrate the current capabilities of our approach on small benchmarks and a mini application.
Application of the Computer Capacity to the Analysis of Processors Evolution
Venue: arXiv cs.PF 2017
Year: 2017

The notion of computer capacity was proposed in 2012, and this quantity has been estimated for computers of different kinds. In this paper we show that, when designing new processors, the manufacturers change the parameters that affect the computer capacity. This allows us to predict the values of parameters of future processors. As the main example we use Intel processors, due to the accessibility of detailed description of all their technical characteristics.
Simulation Experiment of BCI Based on Imagined Speech EEG Decoding
Venue: arXiv cs.HC 2017
Year: 2017

Brain Computer Interface (BCI) can help patients of neuromuscular diseases restore parts of the movement and communication abilities that they have lost. Most of BCIs rely on mapping brain activities to device instructions, but limited number of brain activities decides the limited abilities of BCIs. To deal with the problem of limited ablility of BCI, this paper verified the feasibility of constructing BCI based on decoding imagined speech electroencephalography (EEG). As sentences decoded from EEG can have rich meanings, BCIs based on EEG decoding can achieve numerous control instructions. By combining a modified EEG feature extraction mehtod with connectionist temporal classification (CTC), this paper simulated decoding imagined speech EEG using synthetic EEG data without help of speech signal. The performance of decoding model over synthetic data to a certain extent demonstrated the feasibility of constructing BCI based on imagined speech brain signal.
Robust Localized Multi-view Subspace Clustering
Venue: arXiv cs.CV 2017
Year: 2017

In multi-view clustering, different views may have different confidence levels when learning a consensus representation. Existing methods usually address this by assigning distinctive weights to different views. However, due to noisy nature of real-world applications, the confidence levels of samples in the same view may also vary. Thus considering a unified weight for a view may lead to suboptimal solutions. In this paper, we propose a novel localized multi-view subspace clustering model that considers the confidence levels of both views and samples. By assigning weight to each sample under each view properly, we can obtain a robust consensus representation via fusing the noiseless structures among views and samples. We further develop a regularizer on weight parameters based on the convex conjugacy theory, and samples weights are determined in an adaptive manner. An efficient iterative algorithm is developed with a convergence guarantee. Experimental results on four benchmarks demonstrate the correctness and effectiveness of the proposed model.
Cost-Performance Tradeoffs in Fusing Unreliable Computational Units
Venue: arXiv cs.PF 2017
Year: 2017

We investigate fusing several unreliable computational units that perform the same task. We model an unreliable computational outcome as an additive perturbation to its error-free result in terms of its fidelity and cost. We analyze performance of repetition-based strategies that distribute cost across several unreliable units and fuse their outcomes. When the cost is a convex function of fidelity, the optimal repetition-based strategy in terms of incurred cost while achieving a target mean-square error (MSE) performance may fuse several computational units. For concave and linear costs, a single more reliable unit incurs lower cost compared to fusion of several lower cost and less reliable units while achieving the same MSE performance. We show how our results give insight into problems from theoretical neuroscience, circuits, and crowdsourcing.
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs
Venue: arXiv cs.CR 2017
Year: 2017

This paper presents an approach to formalizing and enforcing a class of use privacy properties in data-driven systems. In contrast to prior work, we focus on use restrictions on proxies (i.e. strong predictors) of protected information types. Our definition relates proxy use to intermediate computations that occur in a program, and identify two essential properties that characterize this behavior: 1) its result is strongly associated with the protected information type in question, and 2) it is likely to causally affect the final output of the program. For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior. Recognizing that not all instances of proxy use of a protected information type are inappropriate, we make use of a normative judgment oracle that makes this inappropriateness determination for a given witness. Our repair algorithm uses the witness of an inappropriate proxy use to transform the model into one that provably does not exhibit proxy use, while avoiding changes that unduly affect classification accuracy. Using a corpus of social datasets, our evaluation shows that these algorithms are able to detect proxy use instances that would be difficult to find using existing techniques, and subsequently remove them while maintaining acceptable classification performance.
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels
Venue: arXiv cs.CV 2017
Year: 2017

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DepthCut, a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 15 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings.
Symmetry Breaking in the Congest Model: Time- and Message-Efficient Algorithms for Ruling Sets
Venue: arXiv cs.DC 2017
Year: 2017

We study local symmetry breaking problems in the CONGEST model, focusing on ruling set problems, which generalize the fundamental Maximal Independent Set (MIS) problem. A $β$-ruling set is an independent set such that every node in the graph is at most $β$ hops from a node in the independent set. Our work is motivated by the following central question: can we break the $Θ(\log n)$ time complexity barrier and the $Θ(m)$ message complexity barrier in the CONGEST model for MIS or closely-related symmetry breaking problems? We present the following results: - Time Complexity: We show that we can break the $O(\log n)$ "barrier" for 2- and 3-ruling sets. We compute 3-ruling sets in $O\left(\frac{\log n}{\log \log n}\right)$ rounds with high probability (whp). More generally we show that 2-ruling sets can be computed in $O\left(\log Δ\cdot (\log n)^{1/2 + \varepsilon} + \frac{\log n}{\log\log n}\right)$ rounds for any $\varepsilon > 0$, which is $o(\log n)$ for a wide range of $Δ$ values (e.g., $Δ= 2^{(\log n)^{1/2-\varepsilon}}$). These are the first 2- and 3-ruling set algorithms to improve over the $O(\log n)$-round complexity of Luby's algorithm in the CONGEST model. - Message Complexity: We show an $Ω(n^2)$ lower bound on the message complexity of computing an MIS (i.e., 1-ruling set) which holds also for randomized algorithms and present a contrast to this by showing a randomized algorithm for 2-ruling sets that, whp, uses only $O(n \log^2 n)$ messages and runs in $O(Δ\log n)$ rounds. This is the first message-efficient algorithm known for ruling sets, which has message complexity nearly linear in $n$ (which is optimal up to a polylogarithmic factor).
Liquid Cloud Storage
Venue: arXiv cs.DC 2017
Year: 2017

A liquid system provides durable object storage based on spreading redundantly generated data across a network of hundreds to thousands of potentially unreliable storage nodes. A liquid system uses a combination of a large code, lazy repair, and a flow storage organization. We show that a liquid system can be operated to enable flexible and essentially optimal combinations of storage durability, storage overhead, repair bandwidth usage, and access performance.
A Low-Power Accelerator for Deep Neural Networks with Enlarged Near-Zero Sparsity
Venue: arXiv cs.AR 2017
Year: 2017

It remains a challenge to run Deep Learning in devices with stringent power budget in the Internet-of-Things. This paper presents a low-power accelerator for processing Deep Neural Networks in the embedded devices. The power reduction is realized by avoiding multiplications of near-zero valued data. The near-zero approximation and a dedicated Near-Zero Approximation Unit (NZAU) are proposed to predict and skip the near-zero multiplications under certain thresholds. Compared with skipping zero-valued computations, our design achieves 1.92X and 1.51X further reduction of the total multiplications in LeNet-5 and Alexnet respectively, with negligible lose of accuracy. In the proposed accelerator, 256 multipliers are grouped into 16 independent Processing Lanes (PL) to support up to 16 neuron activations simultaneously. With the help of data pre-processing and buffering in each PL, multipliers can be clock-gated in most of the time even the data is excessively streaming in. Designed and simulated in UMC 65 nm process, the accelerator operating at 500 MHz is $>$ 4X faster than the mobile GPU Tegra K1 in processing the fully-connected layer FC8 of Alexnet, while consuming 717X less energy.
Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV
Venue: arXiv cs.RO 2017
Year: 2017

We present an efficient path planning algorithm for an Unmanned Aerial Vehicle surveying a cluttered urban landscape. A special emphasis is on maximizing area surveyed while adhering to constraints of the UAV and partially known and updating environment. A Voronoi bias is introduced in the probabilistic roadmap building phase to identify certain critical milestones for maximal surveillance of the search space. A kinematically feasible but coarse tour connecting these milestones is generated by the global path planner. A local path planner then generates smooth motion primitives between consecutive nodes of the global path based on UAV as a Dubins vehicle and taking into account any impending obstacles. A Markov Decision Process (MDP) models the control policy for the UAV and determines the optimal action to be undertaken for evading the obstacles in the vicinity with minimal deviation from current path. The efficacy of the proposed algorithm is evaluated in an updating simulation environment with dynamic and static obstacles.
Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices
Venue: arXiv cs.LG 2017
Year: 2017

In a previous work we have detailed the requirements to obtain a maximal performance benefit by implementing fully connected deep neural networks (DNN) in form of arrays of resistive devices for deep learning. This concept of Resistive Processing Unit (RPU) devices we extend here towards convolutional neural networks (CNNs). We show how to map the convolutional layers to RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed due to analog nature of the computations performed on the arrays effect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of RPU approach for large class of neural network architectures.
Detection Algorithms for Communication Systems Using Deep Learning
Venue: arXiv cs.LG 2017
Year: 2017

The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel, which dictates the relationship between the transmitted and the received signals. However, in some systems, such as molecular communication systems where chemical signals are used for transfer of information, it is not possible to accurately model this relationship. In these scenarios, because of the lack of mathematical channel models, a completely new approach to design and analysis is required. In this work, we focus on one important aspect of communication systems, the detection algorithms, and demonstrate that by borrowing tools from deep learning, it is possible to train detectors that perform well, without any knowledge of the underlying channel models. We evaluate these algorithms using experimental data that is collected by a chemical communication platform, where the channel model is unknown and difficult to model analytically. We show that deep learning algorithms perform significantly better than a simple detector that was used in previous works, which also did not assume any knowledge of the channel.
An Improved Secretive Coded Caching Scheme exploiting Common Demands
Venue: arXiv cs.IT 2017
Year: 2017

Coded caching schemes on broadcast networks with user caches help to offload traffic from peak times to off-peak times by prefetching information from the server to the users during off-peak times and thus serving the users more efficiently during peak times using coded transmissions. We consider the problem of secretive coded caching which was proposed recently, in which a user should not be able to decode any information about any file that the user has not demanded. We propose a new secretive coded caching scheme which has a lower average rate compared to the existing state-of-the-art scheme, for the same memory available at the users. The proposed scheme is based on exploiting the presence of common demands between multiple users.
Transformation of Python Applications into Function-as-a-Service Deployments
Venue: arXiv cs.DC 2017
Year: 2017

New cloud programming and deployment models pose challenges to software application engineers who are looking, often in vain, for tools to automate any necessary code adaptation and transformation. Function-as-a-Service interfaces are particular non-trivial targets when considering that most cloud applications are implemented in non-functional languages. Among the most widely used of these languages is Python. This starting position calls for an automated approach to transform monolithic Python code into modular FaaS units by partially automated decomposition. Hence, this paper introduces and evaluates Lambada, a Python module to dynamically decompose, convert and deploy unmodified Python code into AWS Lambda functions. Beyond the tooling in the form of a measured open source prototype implementation, the paper contributes a description of the algorithms and code rewriting rules as blueprints for transformations of other scripting languages.
Multilevel Monte Carlo Simulation of the Eddy Current Problem With Random Parameters
Venue: arXiv cs.CE 2017
Year: 2017

The multilevel Monte Carlo method is applied to an academic example in the field of electromagnetism. The method exhibits a reduced variance by assigning the samples to multiple models with a varying spatial resolution. For the given example it is found that the main costs of the method are spent on the coarsest level.
Correlation Alignment by Riemannian Metric for Domain Adaptation
Venue: arXiv cs.CV 2017
Year: 2017

Domain adaptation techniques address the problem of reducing the sensitivity of machine learning methods to the so-called domain shift, namely the difference between source (training) and target (test) data distributions. In particular, unsupervised domain adaptation assumes no labels are available in the target domain. To this end, aligning second order statistics (covariances) of target and source domains have proven to be an effective approach ti fill the gap between the domains. However, covariance matrices do not form a subspace of the Euclidean space, but live in a Riemannian manifold with non-positive curvature, making the usual Euclidean metric suboptimal to measure distances. In this paper, we extend the idea of training a neural network with a constraint on the covariances of the hidden layer features, by rigorously accounting for the curved structure of the manifold of symmetric positive definite matrices. The resulting loss function exploits a theoretically sound geodesic distance on such manifold. Results show indeed the suboptimal nature of the Euclidean distance. This makes us able to perform better than previous approaches on the standard Office dataset, a benchmark for domain adaptation techniques.