OpenRaiser/Intern-Atlas huggingface dataset:
Medusa: A Scalable Interconnect for Many-Port DNN Accelerators and Wide DRAM Controller Interfaces
Venue: arXiv cs.AR 2018
Year: 2018

To cope with the increasing demand and computational intensity of deep neural networks (DNNs), industry and academia have turned to accelerator technologies. In particular, FPGAs have been shown to provide a good balance between performance and energy efficiency for accelerating DNNs. While significant research has focused on how to build efficient layer processors, the computational building blocks of DNN accelerators, relatively little attention has been paid to the on-chip interconnects that sit between the layer processors and the FPGA's DRAM controller. We observe a disparity between DNN accelerator interfaces, which tend to comprise many narrow ports, and FPGA DRAM controller interfaces, which tend to be wide buses. This mismatch causes traditional interconnects to consume significant FPGA resources. To address this problem, we designed Medusa: an optimized FPGA memory interconnect which transposes data in the interconnect fabric, tailoring the interconnect to the needs of DNN layer processors. Compared to a traditional FPGA interconnect, our design can reduce LUT and FF use by 4.7x and 6.0x, and improves frequency by 1.8x.
DeSTNet: Densely Fused Spatial Transformer Networks
Venue: arXiv cs.CV 2018
Year: 2018

Modern Convolutional Neural Networks (CNN) are extremely powerful on a range of computer vision tasks. However, their performance may degrade when the data is characterised by large intra-class variability caused by spatial transformations. The Spatial Transformer Network (STN) is currently the method of choice for providing CNNs the ability to remove those transformations and improve performance in an end-to-end learning framework. In this paper, we propose Densely Fused Spatial Transformer Network (DeSTNet), which, to our best knowledge, is the first dense fusion pattern for combining multiple STNs. Specifically, we show how changing the connectivity pattern of multiple STNs from sequential to dense leads to more powerful alignment modules. Extensive experiments on three benchmarks namely, MNIST, GTSRB, and IDocDB show that the proposed technique outperforms related state-of-the-art methods (i.e., STNs and CSTNs) both in terms of accuracy and robustness.
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
Venue: arXiv cs.CV 2018
Year: 2018

The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional layer for the extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for enforcing the temporal coherence across video frames and exploiting the temporal redundancy of a signal, and a regularized loss function, called \textit{CyclicLoss}, to impose our prior knowledge about the periodicity of the observed signal. We present experimental evidence suggesting that the proposed architecture can reach an accuracy substantially superior to previously proposed methods, providing an average reduction of the mean squared error from $0.31 mm^2$ (state-of-art) to $0.09 mm^2$, and a relative error reduction from $8.1\%$ to $5.3\%$. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use.
Formalizing Constructive Quantifier Elimination in Agda
Venue: arXiv cs.LO 2018
Year: 2018

In this paper a constructive formalization of quantifier elimination is presented, based on a classical formalization by Tobias Nipkow. The formalization is implemented and verified in the programming language/proof assistant Agda. It is shown that, as in the classical case, the ability to eliminate a single existential quantifier may be generalized to full quantifier elimination and consequently a decision procedure. The latter is shown to have strong properties under a constructive metatheory, such as the generation of witnesses and counterexamples. Finally, this is demonstrated on a minimal theory on the natural numbers.
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
Venue: arXiv cs.LG 2018
Year: 2018

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.
Variational Capsules for Image Analysis and Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

A capsule is a group of neurons whose activity vector models different properties of the same entity. This paper extends the capsule to a generative version, named variational capsules (VCs). Each VC produces a latent variable for a specific entity, making it possible to integrate image analysis and image synthesis into a unified framework. Variational capsules model an image as a composition of entities in a probabilistic model. Different capsules' divergence with a specific prior distribution represents the presence of different entities, which can be applied in image analysis tasks such as classification. In addition, variational capsules encode multiple entities in a semantically-disentangling way. Diverse instantiations of capsules are related to various properties of the same entity, making it easy to generate diverse samples with fine-grained semantic attributes. Extensive experiments demonstrate that deep networks designed with variational capsules can not only achieve promising performance on image analysis tasks (including image classification and attribute prediction) but can also improve the diversity and controllability of image synthesis.
VFunc: a Deep Generative Model for Functions
Venue: arXiv cs.LG 2018
Year: 2018

We introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variational lower bound on the entropy H(f). We can thus maximize objectives of the form E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term or an expected reward. Such objectives encompass Bayesian deep learning in function space, rather than parameter space, and Bayesian deep RL with representations of uncertainty that offer benefits over bootstrapping and parameter noise. In this short paper we describe our model, situate it in the context of prior work, and present proof-of-concept experiments for regression and RL.
ThingPot: an interactive Internet-of-Things honeypot
Venue: arXiv cs.NI 2018
Year: 2018

The Mirai Distributed Denial-of-Service (DDoS) attack exploited security vulnerabilities of Internet-of-Things (IoT) devices and thereby clearly signalled that attackers have IoT on their radar. Securing IoT is therefore imperative, but in order to do so it is crucial to understand the strategies of such attackers. For that purpose, in this paper, a novel IoT honeypot called ThingPot is proposed and deployed. Honeypot technology mimics devices that might be exploited by attackers and logs their behavior to detect and analyze the used attack vectors. ThingPot is the first of its kind, since it focuses not only on the IoT application protocols themselves, but on the whole IoT platform. A Proof-of-Concept is implemented with XMPP and a REST API, to mimic a Philips Hue smart lighting system. ThingPot has been deployed for 1.5 months and through the captured data we have found five types of attacks and attack vectors against smart devices. The ThingPot source code is made available as open source.
TherML: Thermodynamics of Machine Learning
Venue: arXiv cs.LG 2018
Year: 2018

In this work we offer a framework for reasoning about a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynamics and discuss its implications.
A Fixed-Parameter Linear-Time Algorithm for Maximum Flow in Planar Flow Networks
Venue: arXiv cs.DS 2018
Year: 2018

We pull together previously established graph-theoretical results to produce the algorithm in the paper's title. The glue are three easy elementary lemmas.
Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Postoperative wound complications are a significant cause of expense for hospitals, doctors, and patients. Hence, an effective method to diagnose the onset of wound complications is strongly desired. Algorithmically classifying wound images is a difficult task due to the variability in the appearance of wound sites. Convolutional neural networks (CNNs), a subgroup of artificial neural networks that have shown great promise in analyzing visual imagery, can be leveraged to categorize surgical wounds. We present a multi-label CNN ensemble, Deepwound, trained to classify wound images using only image pixels and corresponding labels as inputs. Our final computational model can accurately identify the presence of nine labels: drainage, fibrinous exudate, granulation tissue, surgical site infection, open wound, staples, steri strips, and sutures. Our model achieves receiver operating curve (ROC) area under curve (AUC) scores, sensitivity, specificity, and F1 scores superior to prior work in this area. Smartphones provide a means to deliver accessible wound care due to their increasing ubiquity. Paired with deep neural networks, they offer the capability to provide clinical insight to assist surgeons during postoperative care. We also present a mobile application frontend to Deepwound that assists patients in tracking their wound and surgical recovery from the comfort of their home.
Subsampled Turbulence Removal Network
Venue: arXiv cs.CV 2018
Year: 2018

We present a deep-learning approach to restore a sequence of turbulence-distorted video frames from turbulent deformations and space-time varying blurs. Instead of requiring a massive training sample size in deep networks, we purpose a training strategy that is based on a new data augmentation method to model turbulence from a relatively small dataset. Then we introduce a subsampled method to enhance the restoration performance of the presented GAN model. The contributions of the paper is threefold: first, we introduce a simple but effective data augmentation algorithm to model the turbulence in real life for training in the deep network; Second, we firstly purpose the Wasserstein GAN combined with $\ell_1$ cost for successful restoration of turbulence-corrupted video sequence; Third, we combine the subsampling algorithm to filter out strongly corrupted frames to generate a video sequence with better quality.
On the Approximation Resistance of Balanced Linear Threshold Functions
Venue: arXiv cs.CC 2018
Year: 2018

In this paper, we show that there exists a balanced linear threshold function (LTF) which is unique games hard to approximate, refuting a conjecture of Austrin, Benabbas, and Magen. We also show that the almost monarchy predicate on k variables is approximable for sufficiently large k.
Engineering Collaborative Social Science Toolkits. STS Methods and Concepts as Devices for Interdisciplinary Diplomacy
Venue: arXiv cs.HC 2018
Year: 2018

The smartification of industries is marked by the development of cyber-physical systems, interfaces, and intelligent software featuring knowledge models, empirical real-time data, and feedback-loops. This brings up new requirements and challenges for HMI design and industrial labor. Social sciences can contribute to such engineering projects with their perspectives, concepts and knowledge. Hence, we claim that, in addition to following their own intellectual curiosities, the social sciences can and should contribute to such projects in terms of an 'applied' science, helping to foster interdisciplinary collaboration and providing toolkits and devices for what we call 'interdisciplinary diplomacy'. We illustrate the benefits of such an approach, support them with selected examples of our involvement in such an engineering project and propose using methods as diplomatic devices and concepts as social theory plug-ins. The article ends with an outlook and reflection on the remaining issue of whether and in how far such 'applied' and critical social science can or should be integrated.
Uplink Massive MIMO for Channels with Spatial Correlation
Venue: arXiv cs.IT 2018
Year: 2018

A massive MIMO system entails a large number of base station antennas M serving a much smaller number of users. This leads to large gains in spectral and energy efficiency compared with other technologies. As the number of antennas M grows, the performance of such systems gets limited by pilot contamination interference. Large Scale Fading Precoding/Postcoding (LSFP) was proposed in literature for mitigation of pilot contamination. It was shown recently that in channels without spatial correlation (uncorrelated base station antennas) LSFP leads to large spectral-efficiency gains. Also, recently, it was observed that if a channel has spatial correlation, then one can use this correlation to drastically reduce the pilot contamination interference in the asymptotic regime as M tends to infinity. In this work, we analyze the performance of Uplink (UL) transmission of massive MIMO systems with finitely many antennas M for channels with spatial correlation. We extend the idea of LSFP to correlated channel models and derive SINR expressions that depend only on slow fading channel components for such systems with and without LSFP. These simple expressions lead us to simple algorithms for transmit power optimization. As a result, we obtain a multi-fold increase in data transmission rates.
Predicting Usefulness of Code Review Comments using Textual Features and Developer Experience
Venue: arXiv cs.SE 2018
Year: 2018

Although peer code review is widely adopted in both commercial and open source development, existing studies suggest that such code reviews often contain a significant amount of non-useful review comments. Unfortunately, to date, no tools or techniques exist that can provide automatic support in improving those non-useful comments. In this paper, we first report a comparative study between useful and non-useful review comments where we contrast between them using their textual characteristics, and reviewers' experience. Then, based on the findings from the study, we develop RevHelper, a prediction model that can help the developers improve their code review comments through automatic prediction of their usefulness during review submission. Comparative study using 1,116 review comments suggested that useful comments share more vocabulary with the changed code, contain salient items like relevant code elements, and their reviewers are generally more experienced. Experiments using 1,482 review comments report that our model can predict comment usefulness with 66\% prediction accuracy which is promising. Comparison with three variants of a baseline model using a case study validates our empirical findings and demonstrates the potential of our model.
Eurolab-4-HPC Long-Term Vision on High-Performance Computing
Venue: arXiv cs.CE 2018
Year: 2018

Radical changes in computing are foreseen for the next decade. The US IEEE society wants to "reboot computing" and the HiPEAC Vision 2017 sees the time to "re-invent computing", both by challenging its basic assumptions. This document presents the "EuroLab-4-HPC Long-Term Vision on High-Performance Computing" of August 2017, a road mapping effort within the EC CSA1 Eurolab-4-HPC that targets potential changes in hardware, software, and applications in High-Performance Computing (HPC). The objective of the Eurolab-4-HPC vision is to provide a long-term roadmap from 2023 to 2030 for High-Performance Computing (HPC). Because of the long-term perspective and its speculative nature, the authors started with an assessment of future computing technologies that could influence HPC hardware and software. The proposal on research topics is derived from the report and discussions within the road mapping expert group. We prefer the term "vision" over "roadmap", firstly because timings are hard to predict given the long-term perspective, and secondly because EuroLab-4-HPC will have no direct control over the realization of its vision.
Disentangling Gold Open Access
Venue: arXiv cs.DL 2018
Year: 2018

This chapter focuses on the analysis of current publication trends in gold Open Access (OA). The purpose of the chapter is to develop a full understanding on country patterns, OA journals characteristics and citation differences between gold OA and non-gold OA publications. For this, we will first review current literature regarding Open Access and its relation with its so-called citation advantage. Starting with a chronological perspective we will describe its development, how different countries are promoting OA publishing, and its effects on the journal publishing industry. We will deepen the analysis by investigating the research output produced by different units of analysis. First, we will focus on the production of countries with a special emphasis on citation and disciplinary differences. A point of interest will be identification of national idiosyncrasies and the relation between OA publication and research of local interest. This will lead to our second unit of analysis, OA journals indexed in Web of Science. Here we will deepen on journals characteristics and publisher types to clearly identify factors which may affect citation differences between OA and traditional journals which may not necessarily be derived from the OA factor. Gold OA publishing is being encouraged in many countries as opposed to Green OA. This chapter aims at fully understanding how it affects researchers' publication patterns and whether it ensures an alleged citation advantage as opposed to non-gold OA publications.
Robustness Analysis of Pedestrian Detectors for Surveillance
Venue: arXiv cs.CV 2018
Year: 2018

To obtain effective pedestrian detection results in surveillance video, there have been many methods proposed to handle the problems from severe occlusion, pose variation, clutter background, \emph{etc}. Besides detection accuracy, a robust surveillance video system should be stable to video quality degradation by network transmission, environment variation, etc. In this study, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation. The main contribution of this work includes the following three aspects. First, a large-scale Distorted Surveillance Video Data Set (DSurVD) is constructed from high-quality video sequences and their corresponding distorted versions. Second, we design a method to evaluate detection stability and a robustness measure called Robustness Quadrangle, which can be adopted to visualize detection accuracy of pedestrian detection algorithms on high-quality video sequences and stability with video quality degradation. Third, the robustness of seven existing pedestrian detection algorithms is evaluated by the built DSurVD. Experimental results show that the robustness can be further improved for existing pedestrian detection algorithms. Additionally, we provide much in-depth discussion on how different distortion types influence the performance of pedestrian detection algorithms, which is important to design effective pedestrian detection algorithms for surveillance. The DSurVD data set can be download from BaiduYunDisk, https://pan.baidu.com/s/1I9Kqj8rmubOYu7bkBfkUpA, Password: lqmc
When deep learning meets security
Venue: arXiv cs.CR 2018
Year: 2018

Deep learning is an emerging research field that has proven its effectiveness towards deploying more efficient intelligent systems. Security, on the other hand, is one of the most essential issues in modern communication systems. Recently many papers have shown that using deep learning models can achieve promising results when applied to the security domain. In this work, we provide an overview for the recent studies that apply deep learning techniques to the field of security.
Transfer Operator Theoretic Framework for Monitoring Building Indoor Environment in Uncertain Operating Conditions
Venue: arXiv cs.SY 2018
Year: 2018

Dynamical system-based linear transfer Perron- Frobenius (P-F) operator framework is developed to address analysis and design problems in the building system. In particular, the problems of fast contaminant propagation and optimal placement of sensors in uncertain operating conditions of indoor building environment are addressed. The linear nature of transfer P-F operator is exploited to develop a computationally efficient numerical scheme based on the finite dimensional approximation of P-F operator for fast propagation of contaminants. The proposed scheme is an order of magnitude faster than existing methods that rely on simulation of an advection-diffusion partial differential equation for contami- nant transport. Furthermore, the system-theoretic notion of observability gramian is generalized to nonlinear flow fields using the transfer P-F operator. This developed notion of observability gramian for nonlinear flow field combined with the finite dimensional approximation of P-F operator is used to provide a systematic procedure for optimal placement of sensors under uncertain operating conditions. Simulation results are presented to demonstrate the applicability of the developed framework on the IEA-annex 2D benchmark problem.
Integrating Risk in Humanoid Robot Control for Applications in the Nuclear Industry
Venue: arXiv cs.RO 2018
Year: 2018

This paper discuss the integration of risk into a robot control framework for decommissioning applications in the nuclear industry. Our overall objective is to allow the robot to evaluate a risk associated with several methods of completing the same task by combining a set of action sequences. If the environment is known and in the absence of sensing errors each set of actions would successfully complete the task. In this paper, instead of attempting to model the errors associated with each sensing system in order to compute an exact solution, a set of solutions are obtained along with a prescribed risk index. The risk associated with each set of actions can then be compared to possible payoffs or rewards, for instance task completion time or power consumption. This information is then sent to a high level decision planner, for instance a human teleoperator, who can then make a more informed decision regarding the robots actions. In order to illustrate the concept, we introduce three specific risk measures, namely, the collision risk and the risk of toppling and failure risk associated with grasping an object. We demonstrate the results from this foundational study of risk-aware compositional robot autonomy in simulation using NASA's Valkyrie humanoid robot, and the grasping simulator HAPTIX.