OpenRaiser/Intern-Atlas huggingface dataset:
The case for shifting the Renyi Entropy
Venue: arXiv cs.IT 2018
Year: 2018

We introduce a variant of the Rényi entropy definition that aligns it with the well-known Hölder mean: in the new formulation, the r-th order Rényi Entropy is the logarithm of the inverse of the r-th order Hölder mean. This brings about new insights into the relationship of the Rényi entropy to quantities close to it, like the information potential and the partition function of statistical mechanics. We also provide expressions that allow us to calculate the Rényi entropies from the Shannon cross-entropy and the escort probabilities. Finally, we discuss why shifting the Rényi entropy is fruitful in some applications.
Effect Handling for Composable Program Transformations in Edward2
Venue: arXiv cs.PL 2018
Year: 2018

Algebraic effects and handlers have emerged in the programming languages community as a convenient, modular abstraction for controlling computational effects. They have found several applications including concurrent programming, meta programming, and more recently, probabilistic programming, as part of Pyro's Poutines library. We investigate the use of effect handlers as a lightweight abstraction for implementing probabilistic programming languages (PPLs). We interpret the existing design of Edward2 as an accidental implementation of an effect-handling mechanism, and extend that design to support nested, composable transformations. We demonstrate that this enables straightforward implementation of sophisticated model transformations and inference algorithms.
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Venue: arXiv cs.CV 2018
Year: 2018

Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from outdoors to indoor scenes. The main idea is to introduce geometric structure in the learning process, by modeling the scene and the individual objects; camera ego-motion and object motions are learned from monocular videos as input. Furthermore an online refinement method is introduced to adapt learning on the fly to unknown domains. The proposed approach outperforms all state-of-the-art approaches, including those that handle motion e.g. through learned flow. Our results are comparable in quality to the ones which used stereo as supervision and significantly improve depth prediction on scenes and datasets which contain a lot of object motion. The approach is of practical relevance, as it allows transfer across environments, by transferring models trained on data collected for robot navigation in urban scenes to indoor navigation settings. The code associated with this paper can be found at https://sites.google.com/view/struct2depth.
Plan Interdiction Games
Venue: arXiv cs.CR 2018
Year: 2018

We propose a framework for cyber risk assessment and mitigation which models attackers as formal planners and defenders as interdicting such plans. We illustrate the value of plan interdiction problems by first modeling network cyber risk through the use of formal planning, and subsequently formalizing an important question of prioritizing vulnerabilities for patching in the plan interdiction framework. In particular, we show that selectively patching relatively few vulnerabilities allows a network administrator to significantly reduce exposure to cyber risk. More broadly, we have developed a number of scalable approaches for plan interdiction problems, making especially significant advances when attack plans involve uncertainty about system dynamics. However, important open problems remain, including how to effectively capture information asymmetry between the attacker and defender, how to best model dynamics in the attacker-defender interaction, and how to develop scalable algorithms for solving associated plan interdiction games.
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
Venue: arXiv cs.CL 2018
Year: 2018

This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.
Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments
Venue: arXiv cs.MA 2018
Year: 2018

This paper tackles the distributed leader-follower (L-F) control problem for heterogeneous mobile robots in unknown environments requiring obstacle avoidance, inter-robot collision avoidance, and reliable robot communications. To prevent an inter-robot collision, we employ a virtual propulsive force between robots. For obstacle avoidance, we present a novel distributed Negative-Imaginary (NI) variant formation tracking control approach and a dynamic network topology methodology which allows the formation to change its shape and the robot to switch their roles. In the case of communication or sensor loss, a UAV, controlled by a Strictly-Negative-Imaginary (SNI) controller with good wind resistance characteristics, is utilized to track the position of the UGV formation using its camera. Simulations and indoor experiments have been conducted to validate the proposed methods.
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
Venue: arXiv cs.CL 2018
Year: 2018

In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.
Effect of correlations on routing and modeling of Time Varying Communication Networks
Venue: arXiv cs.NI 2018
Year: 2018

Most of the real world networks are complex as well as evolving. Therefore, it is important to study the effect of network topology on the dynamics of traffic and congestion in the network. To account this problem, we have designed a time-varying network model where a new node will join a node in the existing network with probability proportional to its degree and disassortativity with its neighbors. Betweenness centrality (BC) plays an important role to find the influential node and user's shortest route in the network. As shortest route comprised of hub nodes and chances of congestion is more on these nodes. Hence, BC-BC correlation is used to find user's route. A connection between two hub nodes reduces the data forwarding capacity of connecting link with higher probability. If a node shows disassortativity with its neighbors then it may forward more packets and may be chosen for routing. Furthermore, user's optimal data sending rate as well as critical packet generation rate of the proposed model is calculated and shown improved results in comparison than the classical scale-free network model.
Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model
Venue: arXiv cs.CY 2018
Year: 2018

This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the effectiveness of ICWT is investigated in a sewer system in Drammen, Norway. Concerning the whole system performance, we found that the Søren Lemmich pump station plays a vital role in the ICWT framework. To enhance the operation of this pump station, it is imperative to construct a multi-step ahead water level prediction model. Hence, one of the most promising artificial intelligence techniques, Long Short Term Memory (LSTM), is employed to undertake this task. Experiments demonstrated that LSTM is superior to Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural Network (FFNN) and Support Vector Regression (SVR).
Tropical cryptography II: extensions by homomorphisms
Venue: arXiv cs.CR 2018
Year: 2018

We use extensions of tropical algebras as platforms for very efficient public key exchange protocols.
Effects of Beamforming and Antenna Configurations on Mobility in 5G NR
Venue: arXiv cs.IT 2018
Year: 2018

The future 5G systems are getting closer to be a reality. It is envisioned, indeed, that the roll-out of first 5G network will happen around end of 2018 and beginning of 2019. However, there are still a number of issues and problems that have to be faces and new solutions and methods are needed to solve them. Along these lines, the effects that beamforming and antenna configurations may have on the mobility in 5G New Radio (NR) is still unclear. In fact, with the use of directive antennas and high frequencies (e.g., above 10 GHz), in order to meet the stringent requirements of 5G (e.g., support of 500km/h) it is crucial to understand how the envisioned 5G NR antenna configurations may impact mobility (and thus handovers). In this article, first we will briefly survey mobility enhancements and solution currently under discussion in 3GPP Release 15. In particular, we focus our analysis on the physical layer signals involved in the measurement reporting and the new radio measurement model used in 5G NR to filter the multiple beams typical of directive antenna with a large number of antenna elements. Finally, the critical aspect of mobility identified in the previous sections will be analyzed in more details through the obtained results of an extensive system-level evaluation analysis.
Adversarial Examples from Cryptographic Pseudo-Random Generators
Venue: arXiv cs.LG 2018
Year: 2018

In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption.
Contextual Care Protocol using Neural Networks and Decision Trees
Venue: arXiv cs.LG 2018
Year: 2018

A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid model leverages neural networks and decision trees. The neural network estimates the chances of each disease and each tree in the decision trees represents care protocol for a disease. These trees are subject to change in case of aberrations found by the diagnosticians. These corrections or prediction errors are clustered into similar groups for scalability and review by the experts. The corrections as suggested by the experts are incorporated into the model.
Secretary Ranking with Minimal Inversions
Venue: arXiv cs.DS 2018
Year: 2018

We study a twist on the classic secretary problem, which we term the secretary ranking problem: elements from an ordered set arrive in random order and instead of picking the maximum element, the algorithm is asked to assign a rank, or position, to each of the elements. The rank assigned is irrevocable and is given knowing only the pairwise comparisons with elements previously arrived. The goal is to minimize the distance of the rank produced to the true rank of the elements measured by the Kendall-Tau distance, which corresponds to the number of pairs that are inverted with respect to the true order. Our main result is a matching upper and lower bound for the secretary ranking problem. We present an algorithm that ranks $n$ elements with only $O(n^{3/2})$ inversions in expectation, and show that any algorithm necessarily suffers $Ω(n^{3/2})$ inversions when there are $n$ available positions. In terms of techniques, the analysis of our algorithm draws connections to linear probing in the hashing literature, while our lower bound result relies on a general anti-concentration bound for a generic balls and bins sampling process. We also consider the case where the number of positions $m$ can be larger than the number of secretaries $n$ and provide an improved bound by showing a connection of this problem with random binary trees.
Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems
Venue: arXiv cs.AI 2018
Year: 2018

This paper introduces Adversarial Resilience Learning (ARL), a concept to model, train, and analyze artificial neural networks as representations of competitive agents in highly complex systems. In our examples, the agents normally take the roles of attackers or defenders that aim at worsening or improving-or keeping, respectively-defined performance indicators of the system. Our concept provides adaptive, repeatable, actor-based testing with a chance of detecting previously unknown attack vectors. We provide the constitutive nomenclature of ARL and, based on it, the description of experimental setups and results of a preliminary implementation of ARL in simulated power systems.
Towards Explainable Deep Learning for Credit Lending: A Case Study
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predictions made by a neural network. We provide an initial investigation into these techniques for the assessment of credit risk with neural networks.
Large-Scale Distributed Algorithms for Facility Location with Outliers
Venue: arXiv cs.DC 2018
Year: 2018

This paper presents fast, distributed, $O(1)$-approximation algorithms for metric facility location problems with outliers in the Congested Clique model, Massively Parallel Computation (MPC) model, and in the $k$-machine model. The paper considers Robust Facility Location and Facility Location with Penalties, two versions of the facility location problem with outliers proposed by Charikar et al. (SODA 2001). The paper also considers two alternatives for specifying the input: the input metric can be provided explicitly (as an $n \times n$ matrix distributed among the machines) or implicitly as the shortest path metric of a given edge-weighted graph. The results in the paper are: - Implicit metric: For both problems, $O(1)$-approximation algorithms running in $O(\mbox{poly}(\log n))$ rounds in the Congested Clique and the MPC model and $O(1)$-approximation algorithms running in $\tilde{O}(n/k)$ rounds in the $k$-machine model. - Explicit metric: For both problems, $O(1)$-approximation algorithms running in $O(\log\log\log n)$ rounds in the Congested Clique and the MPC model and $O(1)$-approximation algorithms running in $\tilde{O}(n/k)$ rounds in the $k$-machine model. Our main contribution is to show the existence of Mettu-Plaxton-style $O(1)$-approximation algorithms for both Facility Location with outlier problems. As shown in our previous work (Berns et al., ICALP 2012, Bandyapadhyay et al., ICDCN 2018) Mettu-Plaxton style algorithms are more easily amenable to being implemented efficiently in distributed and large-scale models of computation.
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
Venue: arXiv cs.CV 2018
Year: 2018

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1,226 slides, and evaluated on an independent validation dataset of 331 slides, where the reference standard was established by genitourinary specialist pathologists. On the validation dataset, the mean accuracy among 29 general pathologists was 0.61. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.
Predicting enterprise cyber incidents using social network analysis on the darkweb hacker forums
Venue: arXiv cs.SI 2018
Year: 2018

With rise in security breaches over the past few years, there has been an increasing need to mine insights from social media platforms to raise alerts of possible attacks in an attempt to defend conflict during competition. We use information from the darkweb forums by leveraging the reply network structure of user interactions with the goal of predicting enterprise cyber attacks. We use a suite of social network features on top of supervised learning models and validate them on a binary classification problem that attempts to predict whether there would be an attack on any given day for an organization. We conclude from our experiments using information from 53 forums in the darkweb over a span of 12 months to predict real world organization cyber attacks of 2 different security events that analyzing the path structure between groups of users is better than just studying network centralities like Pagerank or relying on the user posting statistics in the forums.
Automatic Text Document Summarization using Semantic-based Analysis
Venue: arXiv cs.IR 2018
Year: 2018

Since the advent of the web, the amount of data on wen has been increased several million folds. In recent years web data generated is more than data stored for years. One important data format is text. To answer user queries over the internet, and to overcome the problem of information overload one possible solution is text document summarization. This not only reduces query access time, but also optimize the document results according to specific users requirements. Summarization of text document can be categorized as abstractive and extractive. Most of the work has been done in the direction of Extractive summarization. Extractive summarized result is a subset of original documents with the objective of more content coverage and lea redundancy. Our work is based on Extractive approaches. In the first approach, we are using some statistical features and semantic-based features. To include sentiment as a feature is an idea cached from a view that emotion plays an important role. It effectively conveys a message. So, it may play a vital role in text document summarization.
Study on the numerical value and the distance of symptoms on the Medical Decision Support System
Venue: arXiv cs.CY 2018
Year: 2018

It is an important subject how deal with the symptom's data, input data, to improve the accuracy and efficiency of the diagnostic algorithm in the medical decision support systems. In this paper, we described a method for the numerical value of symptoms to develop the medical decision support system that diagnose a large number of diseases, not one or two diseases. The numerical value of symptoms is realized by dividing the symptom into several parts in consideration of the following contents and setting up the specific value in each part. That is, the symptom is appeared in where, when the symptom is appeared, what about the symptom, How long the symptom is, etc. Then we decided the distance(similarity) between symptoms on the basis of the numerical value of symptoms. Determination of the distance between symptoms by numerical value enables estimating the symptoms of which disease is nearer from the symptoms of patient and obtaining the suitable diagnostic result. So, we established useful method of high accuracy to diagnose various diseases revealed by different symptoms.
Detecting The Objects on The Road Using Modular Lightweight Network
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a modular lightweight network model for road objects detection, such as car, pedestrian and cyclist, especially when they are far away from the camera and their sizes are small. Great advances have been made for the deep networks, but small objects detection is still a challenging task. In order to solve this problem, majority of existing methods utilize complicated network or bigger image size, which generally leads to higher computation cost. The proposed network model is referred to as modular feature fusion detector (MFFD), using a fast and efficient network architecture for detecting small objects. The contribution lies in the following aspects: 1) Two base modules have been designed for efficient computation: Front module reduce the information loss from raw input images; Tinier module decrease model size and computation cost, while ensuring the detection accuracy. 2) By stacking the base modules, we design a context features fusion framework for multi-scale object detection. 3) The propose method is efficient in terms of model size and computation cost, which is applicable for resource limited devices, such as embedded systems for advanced driver assistance systems (ADAS). Comparisons with the state-of-the-arts on the challenging KITTI dataset reveal the superiority of the proposed method. Especially, 100 fps can be achieved on the embedded GPUs such as Jetson TX2.