OpenRaiser/Intern-Atlas huggingface dataset:
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data
Venue: arXiv cs.LG 2016
Year: 2016

Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU). To allow for risk scoring and patient monitoring in such a setting, we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the inpatients' physiol- ogy. The model captures the patients' latent clinical states and their corresponding observable lab tests and vital signs. We present an efficient unsupervised learn- ing algorithm that capitalizes on the informatively censored data in the electronic health records (EHR) to learn the parameters of the SSLGM; the learned model is then used to assess the new inpatients' risk for clinical deterioration in an online fashion, allowing for timely ICU admission. Experiments conducted on a het- erogeneous cohort of 6,094 patients admitted to a large academic medical center show that the proposed model significantly outperforms the currently deployed risk scores such as Rothman index, MEWS, SOFA and APACHE.
Single Chip Self-Tunable N-Input N-Output PID Control System with Integrated Analog Front-end for Miniature Robotics
Venue: arXiv cs.SY 2016
Year: 2016

In this work, we explore the design of an integrated, low power single chip multi-channel Proportional-Integral-Derivative (PID) controller for emerging miniature robotics, that includes N inputs and N corresponding outputs thereby resulting in N parallel channels in the control system. It includes analog front-end (AFE) and analog PID controllers for PID parameter tuning based on PSO algorithm. The AFE incorporates adaptive biasing to ensure low power. The PSO is optimized with respect to tuning precision, power and area. This makes it attractive for real-time tuning of multiple miniaturized robotic devices with a single PSO tuning algorithm block assigned for the task. For simulation and testing purposes, we take N as 3 with the channels being defined by their application-ends or plants, namely: dc motor, temperature sensor and gyroscope.
Joint Network based Attention for Action Recognition
Venue: arXiv cs.CV 2016
Year: 2016

By extracting spatial and temporal characteristics in one network, the two-stream ConvNets can achieve the state-of-the-art performance in action recognition. However, such a framework typically suffers from the separately processing of spatial and temporal information between the two standalone streams and is hard to capture long-term temporal dependence of an action. More importantly, it is incapable of finding the salient portions of an action, say, the frames that are the most discriminative to identify the action. To address these problems, a \textbf{j}oint \textbf{n}etwork based \textbf{a}ttention (JNA) is proposed in this study. We find that the fully-connected fusion, branch selection and spatial attention mechanism are totally infeasible for action recognition. Thus in our joint network, the spatial and temporal branches share some information during the training stage. We also introduce an attention mechanism on the temporal domain to capture the long-term dependence meanwhile finding the salient portions. Extensive experiments are conducted on two benchmark datasets, UCF101 and HMDB51. Experimental results show that our method can improve the action recognition performance significantly and achieves the state-of-the-art results on both datasets.
Temporal Convolutional Networks for Action Segmentation and Detection
Venue: arXiv cs.CV 2016
Year: 2016

The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond. Typical approaches decouple this problem by first extracting local spatiotemporal features from video frames and then feeding them into a temporal classifier that captures high-level temporal patterns. We introduce a new class of temporal models, which we call Temporal Convolutional Networks (TCNs), that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection. Our Encoder-Decoder TCN uses pooling and upsampling to efficiently capture long-range temporal patterns whereas our Dilated TCN uses dilated convolutions. We show that TCNs are capable of capturing action compositions, segment durations, and long-range dependencies, and are over a magnitude faster to train than competing LSTM-based Recurrent Neural Networks. We apply these models to three challenging fine-grained datasets and show large improvements over the state of the art.
CareerMapper: An Automated Resume Evaluation Tool
Venue: arXiv cs.CY 2016
Year: 2016

The advent of the Web brought about major changes in the way people search for jobs and companies look for suitable candidates. As more employers and recruitment firms turn to the Web for job candidate search, an increasing number of people turn to the Web for uploading and creating their online resumes. Resumes are often the first source of information about candidates and also the first item of evaluation in candidate selection. Thus, it is imperative that resumes are complete, free of errors and well-organized. We present an automated resume evaluation tool called "CareerMapper". Our tool is designed to conduct a thorough review of a user's LinkedIn profile and provide best recommendations for improved online resumes by analyzing a large number of online user profiles.
File Synchronization Systems Survey
Venue: arXiv cs.DC 2016
Year: 2016

Several solutions exist for file storage, sharing, and synchronization. Many of them involve a central server, or a collection of servers, that either store the files, or act as a gateway for them to be shared. Some systems take a decentralized approach, wherein interconnected users form a peer-to-peer (P2P) network, and partake in the sharing process: they share the files they possess with others, and can obtain the files owned by other peers. In this paper, we survey various technologies, both cloud-based and P2P-based, that users use to synchronize their files across the network, and discuss their strengths and weaknesses.
The Life of Lazarillo de Tormes and of His Machine Learning Adversities
Venue: arXiv cs.CL 2016
Year: 2016

Summit work of the Spanish Golden Age and forefather of the so-called picaresque novel, The Life of Lazarillo de Tormes and of His Fortunes and Adversities still remains an anonymous text. Although distinguished scholars have tried to attribute it to different authors based on a variety of criteria, a consensus has yet to be reached. The list of candidates is long and not all of them enjoy the same support within the scholarly community. Analyzing their works from a data-driven perspective and applying machine learning techniques for style and text fingerprinting, we shed light on the authorship of the Lazarillo. As in a state-of-the-art survey, we discuss the methods used and how they perform in our specific case. According to our methodology, the most likely author seems to be Juan Arce de Otálora, closely followed by Alfonso de Valdés. The method states that not certain attribution can be made with the given corpus.
Sensitivity Analysis for Convex Separable Optimization over Integral Polymatroids
Venue: arXiv cs.DM 2016
Year: 2016

We study the sensitivity of optimal solutions of convex separable optimization problems over an integral polymatroid base polytope with respect to parameters determining both the cost of each element and the polytope. Under convexity and a regularity assumption on the functional dependency of the cost function with respect to the parameters, we show that reoptimization after a change in parameters can be done by elementary local operations. Applying this result, we derive that starting from any optimal solution there is a new optimal solution to new parameters such that the L1-norm of the difference of the two solutions is at most two times the L1 norm of the difference of the parameters. We apply these sensitivity results to a class of non-cooperative polymatroid games and derive the existence of pure Nash equilibria. We complement our results by showing that polymatroids are the maximal combinatorial structure enabling these results. For any non-polymatroid region, there is a corresponding optimization problem for which the sensitivity results do not hold. In addition, there is a game where the players strategies are isomorphic to the non-polymatroid region and that does not admit a pure Nash equilibrium.
DeepCas: an End-to-end Predictor of Information Cascades
Venue: arXiv cs.SI 2016
Year: 2016

Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structure. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features and heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines that include feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.
hMAC: Enabling Hybrid TDMA/CSMA on IEEE 802.11 Hardware
Venue: arXiv cs.NI 2016
Year: 2016

We present our current work-in-progress on the design and implementation of a hybrid TDMA/CSMA medium access architecture, hereafter referred to as hMAC, which can be used on top of commercial IEEE 802.11 off-the-shelf hardware. The software only solution is based on the popular Linux ATH9K softMAC driver and hence can be used with standard Linux systems using Atheros based wireless network devices. The proposed hMAC exploits the standard 802.11 power saving functionality present in the ATH9K device driver to enable control of the software packet queues. This allows the assignment of TDMA time slots on wireless link and traffic class basis. While the solution is placed only in the device driver, the CSMA/CA functionality on hardware level is still active. This enables inter-working with standard unmodified 802.11 devices. We tested our prototypical hMAC implementation in a small test-bed. Therefore, we implemented a centralized interference management scheme in which pairs of links suffering from a hidden node problem are assigned to TDMA time slots on a per-link basis. To show the benefits of the proposed hMAC approach we compared the results with standard 802.11 DCF and classical, i.e. per-node, TDMA. Finally, to enable collaboration with the research community, the hMAC source code is provided as open-source.
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
Venue: arXiv cs.CV 2016
Year: 2016

Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. In the context of deep neural networks, this idea is often realized by hand-designed network architectures with layers that are shared across tasks and branches that encode task-specific features. However, the space of possible multi-task deep architectures is combinatorially large and often the final architecture is arrived at by manual exploration of this space subject to designer's bias, which can be both error-prone and tedious. In this work, we propose a principled approach for designing compact multi-task deep learning architectures. Our approach starts with a thin network and dynamically widens it in a greedy manner during training using a novel criterion that promotes grouping of similar tasks together. Our Extensive evaluation on person attributes classification tasks involving facial and clothing attributes suggests that the models produced by the proposed method are fast, compact and can closely match or exceed the state-of-the-art accuracy from strong baselines by much more expensive models.
Multipliers: comparison of Fourier transformation based method and Synopsys design technique for up to 32 bits inputs in regular and saturation arithmetics
Venue: arXiv cs.AR 2016
Year: 2016

The technique for hardware multiplication based upon Fourier transformation has been introduced. The technique has the highest efficiency on multiplication units with up to 8 bit range. Each multiplication unit is realized on base of the minimized Boolean functions. Experimental data showed that this technique the multiplication process speed up to 20% higher for 2-8 bit range of input operands and up to 3% higher for 8-32 bit range of input operands than analogues designed by Synopsys technique.
Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach
Venue: arXiv cs.IR 2016
Year: 2016

Collaborative Filtering (CF) is widely used in large-scale recommendation engines because of its efficiency, accuracy and scalability. However, in practice, the fact that recommendation engines based on CF require interactions between users and items before making recommendations, make it inappropriate for new items which haven't been exposed to the end users to interact with. This is known as the cold-start problem. In this paper we introduce a novel approach which employs deep learning to tackle this problem in any CF based recommendation engine. One of the most important features of the proposed technique is the fact that it can be applied on top of any existing CF based recommendation engine without changing the CF core. We successfully applied this technique to overcome the item cold-start problem in Careerbuilder's CF based recommendation engine. Our experiments show that the proposed technique is very efficient to resolve the cold-start problem while maintaining high accuracy of the CF recommendations.
Semantic Regularisation for Recurrent Image Annotation
Venue: arXiv cs.CV 2016
Year: 2016

The "CNN-RNN" design pattern is increasingly widely applied in a variety of image annotation tasks including multi-label classification and captioning. Existing models use the weakly semantic CNN hidden layer or its transform as the image embedding that provides the interface between the CNN and RNN. This leaves the RNN overstretched with two jobs: predicting the visual concepts and modelling their correlations for generating structured annotation output. Importantly this makes the end-to-end training of the CNN and RNN slow and ineffective due to the difficulty of back propagating gradients through the RNN to train the CNN. We propose a simple modification to the design pattern that makes learning more effective and efficient. Specifically, we propose to use a semantically regularised embedding layer as the interface between the CNN and RNN. Regularising the interface can partially or completely decouple the learning problems, allowing each to be more effectively trained and jointly training much more efficient. Extensive experiments show that state-of-the art performance is achieved on multi-label classification as well as image captioning.
Self-Stabilizing Maximal Matching and Anonymous Networks
Venue: arXiv cs.DC 2016
Year: 2016

We propose a self-stabilizing algorithm for computing a maximal matching in an anonymous network. The complexity is $O(n^3)$ moves with high probability, under the adversarial distributed daemon. In this algorithm, each node can determine whether one of its neighbors points to it or to another node, leading to a contradiction with the anonymous assumption. To solve this problem, we provide under the classical link-register model, a self-stabilizing algorithm that gives a unique name to a link such that this name is shared by both extremities of the link.
Backpressure on the Backbone: A Lightweight, Non-intrusive Traffic Engineering Approach
Venue: arXiv cs.NI 2016
Year: 2016

The present study proposes a novel collaborative traffic engineering scheme for networks of autonomous systems. Backpressure routing principles are used for deriving priority routing rules that optimally stabilize a network, while maximizing its throughput under latency considerations. The routing rules are deployed to the network following simple SDN principles. The proposed scheme requires minimal, infrequent interaction with a central controller, limiting its imposed workload. Furthermore, it respects the internal structure of the autonomous systems and their existing peering relations. In addition, it co-exists smoothly with underlying distance vector-based routing schemes. The proposed scheme combines simplicity with substantial gains in served transit traffic volume, as shown by simulations in realistic setups and proven via mathematical analysis.
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
Venue: arXiv cs.CV 2016
Year: 2016

We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employing very fast classical matching scores instead. At the same time, our approach uses a deep convolutional network to predict the local parameters of cost volume aggregation process, which in this paper we implement using differentiable domain transform. By treating such transform as a recurrent neural network, we are able to train our whole system that includes cost volume computation, cost-volume aggregation (smoothing), and winner-takes-all disparity selection end-to-end. The resulting method is highly efficient at test time, while achieving good matching accuracy. On the KITTI 2015 benchmark, it achieves a result of 6.34\% error rate while running at 29 frames per second rate on a modern GPU.
Unimodal Thompson Sampling for Graph-Structured Arms
Venue: arXiv cs.LG 2016
Year: 2016

We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this setting, each arm corresponds to a node of a graph and each edge provides a relationship, unknown to the learner, between two nodes in terms of expected reward. Furthermore, for any node of the graph there is a path leading to the unique node providing the maximum expected reward, along which the expected reward is monotonically increasing. Previous results on this setting describe the behavior of frequentist MAB algorithms. In our paper, we design a Thompson Sampling-based algorithm whose asymptotic pseudo-regret matches the lower bound for the considered setting. We show that -as it happens in a wide number of scenarios- Bayesian MAB algorithms dramatically outperform frequentist ones. In particular, we provide a thorough experimental evaluation of the performance of our and state-of-the-art algorithms as the properties of the graph vary.
Generalized BackPropagation, Étude De Cas: Orthogonality
Venue: arXiv cs.CV 2016
Year: 2016

This paper introduces an extension of the backpropagation algorithm that enables us to have layers with constrained weights in a deep network. In particular, we make use of the Riemannian geometry and optimization techniques on matrix manifolds to step outside of normal practice in training deep networks, equipping the network with structures such as orthogonality or positive definiteness. Based on our development, we make another contribution by introducing the Stiefel layer, a layer with orthogonal weights. Among various applications, Stiefel layers can be used to design orthogonal filter banks, perform dimensionality reduction and feature extraction. We demonstrate the benefits of having orthogonality in deep networks through a broad set of experiments, ranging from unsupervised feature learning to fine-grained image classification.
Analysis of a Design Pattern for Teaching with Features and Labels
Venue: arXiv cs.AI 2016
Year: 2016

We study the task of teaching a machine to classify objects using features and labels. We introduce the Error-Driven-Featuring design pattern for teaching using features and labels in which a teacher prefers to introduce features only if they are needed. We analyze the potential risks and benefits of this teaching pattern through the use of teaching protocols, illustrative examples, and by providing bounds on the effort required for an optimal machine teacher using a linear learning algorithm, the most commonly used type of learners in interactive machine learning systems. Our analysis provides a deeper understanding of potential trade-offs of using different learning algorithms and between the effort required for featuring (creating new features) and labeling (providing labels for objects).
Stochastic Recursive Inclusions in two timescales with non-additive iterate dependent Markov noise
Venue: arXiv cs.SY 2016
Year: 2016

In this paper we study the asymptotic behavior of a stochastic approximation scheme on two timescales with set-valued drift functions and in the presence of non-additive iterate-dependent Markov noise. It is shown that the recursion on each timescale tracks the flow of a differential inclusion obtained by averaging the set-valued drift function in the recursion with respect to a set of measures which take into account both the averaging with respect to the stationary distributions of the Markov noise terms and the interdependence between the two recursions on different timescales. The framework studied in this paper builds on the works of \it{A. Ramaswamy et al. }\rm by allowing for the presence of non-additive iterate-dependent Markov noise. As an application, we consider the problem of computing the optimum in a constrained convex optimization problem where the objective function and the constraints are averaged with respect to the stationary distribution of an underlying Markov chain. Further the proposed scheme neither requires the differentiability of the objective function nor the knowledge of the averaging measure.