OpenRaiser/Intern-Atlas huggingface dataset:
A Quantum Cellular Automata architecture with nearest neighbor interactions using one quantum gate type
Venue: arXiv cs.ET 2016
Year: 2016

We propose an architecture based on Quantum cellular Automata which allows the use of only one type of quantum gates per computational step in order to perform nearest neighbor interactions. The model is built in partial steps, each one of them analyzed using nearest neighbor interactions, starting with single qubit operations and continuing with two qubit ones. The effectiveness of the model is tested and valuated by developing a quantum circuit implementing the Quantum Fourier Transform. The important outcome of this validation was that the operations are performed in a local and controlled manner thus reducing the error rate of each computational step.
Detecting and Summarizing Emergent Events in Microblogs and Social Media Streams by Dynamic Centralities
Venue: arXiv cs.SI 2016
Year: 2016

Methods for detecting and summarizing emergent keywords have been extensively studied since social media and microblogging activities have started to play an important role in data analysis and decision making. We present a system for monitoring emergent keywords and summarizing a document stream based on the dynamic semantic graphs of streaming documents. We introduce the notion of dynamic eigenvector centrality for ranking emergent keywords, and present an algorithm for summarizing emergent events that is based on the minimum weight set cover. We demonstrate our system with an analysis of streaming Twitter data related to public security events.
Change-point Detection Methods for Body-Worn Video
Venue: arXiv cs.CV 2016
Year: 2016

Body-worn video (BWV) cameras are increasingly utilized by police departments to provide a record of police-public interactions. However, large-scale BWV deployment produces terabytes of data per week, necessitating the development of effective computational methods to identify salient changes in video. In work carried out at the 2016 RIPS program at IPAM, UCLA, we present a novel two-stage framework for video change-point detection. First, we employ state-of-the-art machine learning methods including convolutional neural networks and support vector machines for scene classification. We then develop and compare change-point detection algorithms utilizing mean squared-error minimization, forecasting methods, hidden Markov models, and maximum likelihood estimation to identify noteworthy changes. We test our framework on detection of vehicle exits and entrances in a BWV data set provided by the Los Angeles Police Department and achieve over 90% recall and nearly 70% precision -- demonstrating robustness to rapid scene changes, extreme luminance differences, and frequent camera occlusions.
The Asymptotic Capacity of the Optical Fiber
Venue: arXiv cs.IT 2016
Year: 2016

It is shown that signal energy is the only available degree-of-freedom (DOF) for fiber-optic transmission as the input power tends to infinity. With $n$ signal DOFs at the input, $n-1$ DOFs are asymptotically lost to signal-noise interactions. The main observation is that, nonlinearity introduces a multiplicative noise in the channel, similar to fading in wireless channels. The channel is viewed in the spherical coordinate system, where signal vector $\underline{X}\in\mathbb{C}^n$ is represented in terms of its norm $|\underline{X}|$ and direction $\underline{\hat{X}}$. The multiplicative noise causes signal direction $\underline{\hat{X}}$ to vary randomly on the surface of the unit $(2n-1)$-sphere in $\mathbb{C}^{n}$, in such a way that the effective area of the support of $\underline{\hat{X}}$ does not vanish as $|\underline{X}|\rightarrow\infty$. On the other hand, the surface area of the sphere is finite, so that $\underline{\hat{X}}$ carries finite information. This observation is used to show several results. Firstly, let $\mathcal C(\mathcal P)$ be the capacity of a discrete-time periodic model of the optical fiber with distributed noise and frequency-dependent loss, as a function of the average input power $\mathcal P$. It is shown that asymptotically as $\mathcal P\rightarrow\infty$, $\mathcal C=\frac{1}{n}\log\bigl(\log\mathcal P\bigr)+c$, where $n$ is the dimension of the input signal space and $c$ is a bounded number. In particular, $\lim_{\mathcal P\rightarrow\infty}\mathcal C(\mathcal P)=\infty$ in finite-dimensional periodic models. Secondly, it is shown that capacity saturates to a constant in infinite-dimensional models where $n=\infty$.
Generalized Interval-valued OWA Operators with Interval Weights Derived from Interval-valued Overlap Functions
Venue: arXiv cs.AI 2016
Year: 2016

In this work we extend to the interval-valued setting the notion of an overlap functions and we discuss a method which makes use of interval-valued overlap functions for constructing OWA operators with interval-valued weights. . Some properties of interval-valued overlap functions and the derived interval-valued OWA operators are analysed. We specially focus on the homogeneity and migrativity properties.
Neural Machine Translation with Characters and Hierarchical Encoding
Venue: arXiv cs.CL 2016
Year: 2016

Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output. We propose a model with a hierarchical char2word encoder, that takes individual characters both as input and output. We first argue that this hierarchical representation of the character encoder reduces computational complexity, and show that it improves translation performance. Secondly, by qualitatively studying attention plots from the decoder we find that the model learns to compress common words into a single embedding whereas rare words, such as names and places, are represented character by character.
Novelty Learning via Collaborative Proximity Filtering
Venue: arXiv cs.HC 2016
Year: 2016

The vast majority of recommender systems model preferences as static or slowly changing due to observable user experience. However, spontaneous changes in user preferences are ubiquitous in many domains like media consumption and key factors that drive changes in preferences are not directly observable. These latent sources of preference change pose new challenges. When systems do not track and adapt to users' tastes, users lose confidence and trust, increasing the risk of user churn. We meet these challenges by developing a model of novelty preferences that learns and tracks latent user tastes. We combine three innovations: a new measure of item similarity based on patterns of consumption co-occurrence; model for {\em spontaneous} changes in preferences; and a learning agent that tracks each user's dynamic preferences and learns individualized policies for variety. The resulting framework adaptively provides users with novelty tailored to their preferences for change per se.
Detecting Rainfall Onset Using Sky Images
Venue: arXiv cs.CV 2016
Year: 2016

Ground-based sky cameras (popularly known as Whole Sky Imagers) are increasingly used now-a-days for continuous monitoring of the atmosphere. These imagers have higher temporal and spatial resolutions compared to conventional satellite images. In this paper, we use ground-based sky cameras to detect the onset of rainfall. These images contain additional information about cloud coverage and movement and are therefore useful for accurate rainfall nowcast. We validate our results using rain gauge measurement recordings and achieve an accuracy of 89% for correct detection of rainfall onset.
Forensic Analysis of the ChatSecure Instant Messaging Application on Android Smartphones
Venue: arXiv cs.CR 2016
Year: 2016

We present the forensic analysis of the artifacts generated on Android smartphones by ChatSecure, a secure Instant Messaging application that provides strong encryption for transmitted and locally-stored data to ensure the privacy of its users. We show that ChatSecure stores local copies of both exchanged messages and files into two distinct, AES-256 encrypted databases, and we devise a technique able to decrypt them when the secret passphrase, chosen by the user as the initial step of the encryption process, is known. Furthermore, we show how this passphrase can be identified and extracted from the volatile memory of the device, where it persists for the entire execution of ChatSecure after having been entered by the user, thus allowing one to carry out decryption even if the passphrase is not revealed by the user. Finally, we discuss how to analyze and correlate the data stored in the databases used by ChatSecure to identify the IM accounts used by the user and his/her buddies to communicate, as well as to reconstruct the chronology and contents of the messages and files that have been exchanged among them. For our study we devise and use an experimental methodology, based on the use of emulated devices, that provides a very high degree of reproducibility of the results, and we validate the results it yields against those obtained from real smartphones.
A Localization Approach for Crowdsourced Air Traffic Communication Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this work, we argue that current state-of-the-art methods of aircraft localization such as multilateration are insufficient, in particular for modern crowdsourced air traffic networks with random, unplanned deployment geometry. We propose an alternative, a grid-based localization approach using the k-Nearest Neighbor algorithm, to deal with the identified shortcomings. Our proposal does not require any changes to the existing air traffic protocols and transmitters, and is easily implemented using only low-cost, commercial-off-the-shelf hardware. Using an algebraic multilateration algorithm for comparison, we evaluate our approach using real-world flight data collected with our collaborative sensor network OpenSky. We quantify its effectiveness in terms of aircraft location accuracy, surveillance coverage, and the verification of false position data. Our results show that the grid-based approach can increase the effective air traffic surveillance coverage compared to multilateration by a factor of up to 2.5. As it does not suffer from dilution of precision, it is much more robust in noisy environments and performs better in pre-existing, unplanned receiver deployments. We further find that the mean aircraft location accuracy can be increased by up to 41% in comparison with multilateration while also being able to pinpoint the origin of potential spoofing attacks conducted from the ground.
Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, Kernel PCA is reinterpreted as the solution to a convex optimization problem. Actually, there is a constrained convex problem for each principal component, so that the constraints guarantee that the principal component is indeed a solution, and not a mere saddle point. Although these insights do not imply any algorithmic improvement, they can be used to further understand the method, formulate possible extensions and properly address them. As an example, a new convex optimization problem for semi-supervised classification is proposed, which seems particularly well-suited whenever the number of known labels is small. Our formulation resembles a Least Squares SVM problem with a regularization parameter multiplied by a negative sign, combined with a variational principle for Kernel PCA. Our primal optimization principle for semi-supervised learning is solved in terms of the Lagrange multipliers. Numerical experiments in several classification tasks illustrate the performance of the proposed model in problems with only a few labeled data.
Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

Dense cellular networks (DenseNets) are fast becoming a reality with the rapid deployment of base stations (BSs) aimed at meeting the explosive data traffic demand. In legacy systems however this comes with the penalties of higher network interference and energy consumption. In order to support network densification in a sustainable manner, the system behavior should be made 'load-proportional' thus allowing certain portions of the network to activate on-demand. In this work, we develop an analytical framework using tools from stochastic geometry theory for the performance analysis of DenseNets where load-awareness is explicitly embedded in the design. The model leverages on a flexible cellular network architecture where there is a complete separation of the data and signaling communication functionalities. Using the proposed model, we identify the most energy- efficient deployment solution for meeting certain minimum service criteria and analyze the corresponding power savings through dynamic sleep modes. Based on state-of-the-art system parameters, a homogeneous pico deployment for the data plane with a separate layer of signaling macro-cells is revealed to be the most energy-efficient solution in future dense urban environments.
Review of Action Recognition and Detection Methods
Venue: arXiv cs.CV 2016
Year: 2016

In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter.
Recovering Metabolic Networks using A Novel Hyperlink Prediction Method
Venue: arXiv cs.SI 2016
Year: 2016

Studying metabolic networks is vital for many areas such as novel drugs and bio-fuels. For biologists, a key challenge is that many reactions are impractical or expensive to be found through experiments. Our task is to recover the missing reactions. By exploiting the problem structure, we model reaction recovery as a hyperlink prediction problem, where each reaction is regarded as a hyperlink connecting its participating vertices (metabolites). Different from the traditional link prediction problem where two nodes form a link, a hyperlink can involve an arbitrary number of nodes. Since the cardinality of a hyperlink is variable, existing classifiers based on a fixed number of input features become infeasible. Traditional methods, such as common neighbors and Katz index, are not applicable either, since they are restricted to pairwise similarities. In this paper, we propose a novel hyperlink prediction algorithm, called Matrix Boosting (MATBoost). MATBoost conducts inference jointly in the incidence space and adjacency space by performing an iterative completion-matching optimization. We carry out extensive experiments to show that MATBoost achieves state-of-the-art performance. For a metabolic network with 1805 metabolites and 2583 reactions, our algorithm can successfully recover nearly 200 reactions out of 400 missing reactions.
A Noisy-Influence Regularity Lemma for Boolean Functions
Venue: arXiv cs.CC 2016
Year: 2016

We present a regularity lemma for Boolean functions $f:\{-1,1\}^n \to \{-1,1\}$ based on noisy influence, a measure of how locally correlated $f$ is with each input bit. We provide an application of the regularity lemma to weaken the conditions on the Majority is Stablest Theorem. We also prove a "homogenized" version stating that there is a set of input bits so that most restrictions of $f$ on those bits have small noisy influences. These results were sketched out by [OSTW10], but never published. With their permission, we present the full details here.
Scale-Dependent Pedotransfer Functions Reliability for Estimating Saturated Hydraulic Conductivity
Venue: arXiv cs.CE 2016
Year: 2016

Saturated hydraulic conductivity Ksat is a fundamental characteristic in modeling flow and contaminant transport in soils and sediments. Therefore, many models have been developed to estimate Ksat from easily measureable parameters, such as textural properties, bulk density, etc. However, Ksat is not only affected by textural and structural characteristics, but also by scale e.g., internal diameter and height. Using the UNSODA database and the contrast pattern aided regression (CPXR) method, we recently developed scale-dependent pedotransfer functions to estimate Ksat from textural data, bulk density, and sample dimensions. The main objectives of this study were evaluating the proposed pedotransfer functions using a larger database, and comparing them with seven other models. For this purpose, we selected more than nineteen thousands soil samples from all around the United States. Results showed that the scale-dependent pedotransfer functions estimated Ksat more accurately than seven other models frequently used in the literature.
Learning Cost-Effective Treatment Regimes using Markov Decision Processes
Venue: arXiv cs.AI 2016
Year: 2016

Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions typically involve weighting the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task of learning \emph{cost-effective, interpretable and actionable treatment regimes}. We formulate this as a problem of learning a decision list -- a sequence of if-then-else rules -- which maps characteristics of subjects (eg., diagnostic test results of patients) to treatments. We propose a novel objective to construct a decision list which maximizes outcomes for the population, and minimizes overall costs. We model the problem of learning such a list as a Markov Decision Process (MDP) and employ a variant of the Upper Confidence Bound for Trees (UCT) strategy which leverages customized checks for pruning the search space effectively. Experimental results on real world observational data capturing judicial bail decisions and treatment recommendations for asthma patients demonstrate the effectiveness of our approach.
Efficient Certified Resolution Proof Checking
Venue: arXiv cs.LO 2016
Year: 2016

We present a novel propositional proof tracing format that eliminates complex processing, thus enabling efficient (formal) proof checking. The benefits of this format are demonstrated by implementing a proof checker in C, which outperforms a state-of-the-art checker by two orders of magnitude. We then formalize the theory underlying propositional proof checking in Coq, and extract a correct-by-construction proof checker for our format from the formalization. An empirical evaluation using 280 unsatisfiable instances from the 2015 and 2016 SAT competitions shows that this certified checker usually performs comparably to a state-of-the-art non-certified proof checker. Using this format, we formally verify the recent 200 TB proof of the Boolean Pythagorean Triples conjecture.
Optimization on Submanifolds of Convolution Kernels in CNNs
Venue: arXiv cs.CV 2016
Year: 2016

Kernel normalization methods have been employed to improve robustness of optimization methods to reparametrization of convolution kernels, covariate shift, and to accelerate training of Convolutional Neural Networks (CNNs). However, our understanding of theoretical properties of these methods has lagged behind their success in applications. We develop a geometric framework to elucidate underlying mechanisms of a diverse range of kernel normalization methods. Our framework enables us to expound and identify geometry of space of normalized kernels. We analyze and delineate how state-of-the-art kernel normalization methods affect the geometry of search spaces of the stochastic gradient descent (SGD) algorithms in CNNs. Following our theoretical results, we propose a SGD algorithm with assurance of almost sure convergence of the methods to a solution at single minimum of classification loss of CNNs. Experimental results show that the proposed method achieves state-of-the-art performance for major image classification benchmarks with CNNs.
$C^3$-index: A PageRank based multi-faceted metric for authors' performance measurement
Venue: arXiv cs.DL 2016
Year: 2016

Ranking scientific authors is an important but challenging task, mostly due to the dynamic nature of the evolving scientific publications. The basic indicators of an author's productivity and impact are still the number of publications and the citation count (leading to the popular metrics such as h-index, g-index etc.). H-index and its popular variants are mostly effective in ranking highly-cited authors, thus fail to resolve ties while ranking medium-cited and low-cited authors who are majority in number. Therefore, these metrics are inefficient to predict the ability of promising young researchers at the beginning of their career. In this paper, we propose $C^3$-index that combines the effect of citations and collaborations of an author in a systematic way using a weighted multi-layered network to rank authors. We conduct our experiments on a massive publication dataset of Computer Science and show that - (i) $C^3$-index is consistent over time, which is one of the fundamental characteristics of a ranking metric, (ii) $C^3$-index is as efficient as h-index and its variants to rank highly-cited authors, (iii) $C^3$-index can act as a conflict resolution metric to break ties in the ranking of medium-cited and low-cited authors, (iv) $C^3$-index can also be used to predict future achievers at the early stage of their career.
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles
Venue: arXiv cs.AI 2016
Year: 2016

In this work, we investigate the application of Reinforcement Learning to two well known decision dilemmas, namely Newcomb's Problem and Prisoner's Dilemma. These problems are exemplary for dilemmas that autonomous agents are faced with when interacting with humans. Furthermore, we argue that a Newcomb-like formulation is more adequate in the human-machine interaction case and demonstrate empirically that the unmodified Reinforcement Learning algorithms end up with the well known maximum expected utility solution.