pwc-archive/papers-with-abstracts huggingface dataset:
Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes
Authors: Nima Anari, Shayan Oveis Gharan, Alireza Rezaei
Date: 2016-02-16 00:00:00
Tasks: Open-Ended Question Answering, Point Processes

Strongly Rayleigh distributions are natural generalizations of product and
determinantal probability distributions and satisfy strongest form of negative
dependence properties. We show that the "natural" Monte Carlo Markov Chain
(MCMC) is rapidly mixing in the support of a {\em homogeneous} strongly
Rayleigh distribution. As a byproduct, our proof implies Markov chains can be
used to efficiently generate approximate samples of a $k$-determinantal point
process. This answers an open question raised by Deshpande and Rademacher.
Part-of-Speech Relevance Weights for Learning Word Embeddings
Authors: Quan Liu, Zhen-Hua Ling, Hui Jiang, Yu Hu
Date: 2016-03-24 00:00:00
Tasks: Learning Word Embeddings, POS, Position, Word Embeddings, Word Similarity

This paper proposes a model to learn word embeddings with weighted contexts
based on part-of-speech (POS) relevance weights. POS is a fundamental element
in natural language. However, state-of-the-art word embedding models fail to
consider it. This paper proposes to use position-dependent POS relevance
weighting matrices to model the inherent syntactic relationship among words
within a context window. We utilize the POS relevance weights to model each
word-context pairs during the word embedding training process. The model
proposed in this paper paper jointly optimizes word vectors and the POS
relevance matrices. Experiments conducted on popular word analogy and word
similarity tasks all demonstrated the effectiveness of the proposed method.
Predictive Analytics Using Smartphone Sensors for Depressive Episodes
Authors: Taeheon Jeong, Diego Klabjan, Justin Starren
Date: 2016-03-24 00:00:00
Tasks: Anomaly Detection

The behaviors of patients with depression are usually difficult to predict
because the patients demonstrate the symptoms of a depressive episode without a
warning at unexpected times. The goal of this research is to build algorithms
that detect signals of such unusual moments so that doctors can be proactive in
approaching already diagnosed patients before they fall in depression. Each
patient is equipped with a smartphone with the capability to track its sensors.
We first find the home location of a patient, which is then augmented with
other sensor data to identify sleep patterns and select communication patterns.
The algorithms require two to three weeks of training data to build standard
patterns, which are considered normal behaviors; and then, the methods identify
any anomalies in day-to-day data readings of sensors. Four smartphone sensors,
including the accelerometer, the gyroscope, the location probe and the
communication log probe are used for anomaly detection in sleeping and
communication patterns.
Recursive Neural Language Architecture for Tag Prediction
Authors: Saurabh Kataria
Date: 2016-03-24 00:00:00
Tasks: Prediction, Representation Learning, TAG

We consider the problem of learning distributed representations for tags from
their associated content for the task of tag recommendation. Considering
tagging information is usually very sparse, effective learning from content and
tag association is very crucial and challenging task. Recently, various neural
representation learning models such as WSABIE and its variants show promising
performance, mainly due to compact feature representations learned in a
semantic space. However, their capacity is limited by a linear compositional
approach for representing tags as sum of equal parts and hurt their
performance. In this work, we propose a neural feedback relevance model for
learning tag representations with weighted feature representations. Our
experiments on two widely used datasets show significant improvement for
quality of recommendations over various baselines.
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
Authors: Qinqing Zheng, John Lafferty
Date: 2015-06-19 00:00:00
Proceeding: NeurIPS 2015 12

We propose a simple, scalable, and fast gradient descent algorithm to
optimize a nonconvex objective for the rank minimization problem and a closely
related family of semidefinite programs. With $O(r^3 \kappa^2 n \log n)$ random
measurements of a positive semidefinite $n \times n$ matrix of rank $r$ and
condition number $\kappa$, our method is guaranteed to converge linearly to the
global optimum.
Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow
Authors: Stephen Yu, Mike Wu
Date: 2016-03-24 00:00:00
Tasks: CPU, Optical Flow Estimation, Position

The proposed method uses live image footage which, based on calculations of
pixel motion, decides whether or not an object is in the blind-spot. If found,
the driver is notified by a sensory light or noise built into the vehicle's
CPU. The new technology incorporates optical vectors and flow fields rather
than expensive radar-waves, creating cheaper detection systems that retain the
needed accuracy while adapting to the current processor speeds.
Semantic Properties of Customer Sentiment in Tweets
Authors: Eun Hee Ko, Diego Klabjan
Date: 2016-03-24 00:00:00
Tasks: Clustering, Sentiment Analysis

An increasing number of people are using online social networking services
(SNSs), and a significant amount of information related to experiences in
consumption is shared in this new media form. Text mining is an emerging
technique for mining useful information from the web. We aim at discovering in
particular tweets semantic patterns in consumers' discussions on social media.
Specifically, the purposes of this study are twofold: 1) finding similarity and
dissimilarity between two sets of textual documents that include consumers'
sentiment polarities, two forms of positive vs. negative opinions and 2)
driving actual content from the textual data that has a semantic trend. The
considered tweets include consumers opinions on US retail companies (e.g.,
Amazon, Walmart). Cosine similarity and K-means clustering methods are used to
achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic
modeling algorithm, is used for the latter purpose. This is the first study
which discover semantic properties of textual data in consumption context
beyond sentiment analysis. In addition to major findings, we apply LDA (Latent
Dirichlet Allocations) to the same data and drew latent topics that represent
consumers' positive opinions and negative opinions on social media.
Going Out of Business: Auction House Behavior in the Massively Multi-Player Online Game
Authors: Anders Drachen, Joseph Riley, Shawna Baskin, Diego Klabjan
Date: 2016-03-24 00:00:00

The in-game economies of massively multi-player online games (MMOGs) are
complex systems that have to be carefully designed and managed. This paper
presents the results of an analysis of auction house data from the MMOG Glitch,
across a 14 month time period, the entire lifetime of the game. The data
comprise almost 3 million data points, over 20,000 unique players and more than
650 products. Furthermore, an interactive visualization, based on Sankey flow
diagrams, is presented which shows the proportion of the different clusters
across each time bin, as well as the flow of players between clusters. The
diagram allows evaluation of migration of players between clusters as a
function of time, as well as churn analysis. The presented work provides a
template analysis and visualization model for progression-based or
temporal-based analysis of player behavior broadly applicable to games.
Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL
Authors: Yevgeni Berzak, Roi Reichart, Boris Katz
Date: 2016-03-24 00:00:00
Proceeding: CONLL 2015 7
Tasks: Language Acquisition

This work examines the impact of cross-linguistic transfer on grammatical
errors in English as Second Language (ESL) texts. Using a computational
framework that formalizes the theory of Contrastive Analysis (CA), we
demonstrate that language specific error distributions in ESL writing can be
predicted from the typological properties of the native language and their
relation to the typology of English. Our typology driven model enables to
obtain accurate estimates of such distributions without access to any ESL data
for the target languages. Furthermore, we present a strategy for adjusting our
method to low-resource languages that lack typological documentation using a
bootstrapping approach which approximates native language typology from ESL
texts. Finally, we show that our framework is instrumental for linguistic
inquiry seeking to identify first language factors that contribute to a wide
range of difficulties in second language acquisition.
Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition
Authors: Yan Yan, Hanzi Wang, David Suter
Date: 2016-03-24 00:00:00
Tasks: Face Recognition, Robust Face Recognition

In this paper, we propose an effective feature extraction algorithm, called
Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face
recognition. MS-CFB combines the benefits of global-based and local-based
feature extraction algorithms, where multiple correlation filters correspond-
ing to different face subregions are jointly designed to optimize the overall
correlation outputs. Furthermore, we reduce the computational complexi- ty of
MS-CFB by designing the correlation filter bank in the spatial domain and
improve its generalization capability by capitalizing on the unconstrained form
during the filter bank design process. MS-CFB not only takes the d- ifferences
among face subregions into account, but also effectively exploits the
discriminative information in face subregions. Experimental results on various
public face databases demonstrate that the proposed algorithm pro- vides a
better feature representation for classification and achieves higher
recognition rates compared with several state-of-the-art algorithms.
Semantic Regularities in Document Representations
Authors: Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, Xue-Qi Cheng
Date: 2016-03-24 00:00:00

Recent work exhibited that distributed word representations are good at
capturing linguistic regularities in language. This allows vector-oriented
reasoning based on simple linear algebra between words. Since many different
methods have been proposed for learning document representations, it is natural
to ask whether there is also linear structure in these learned representations
to allow similar reasoning at document level. To answer this question, we
design a new document analogy task for testing the semantic regularities in
document representations, and conduct empirical evaluations over several
state-of-the-art document representation models. The results reveal that neural
embedding based document representations work better on this analogy task than
conventional methods, and we provide some preliminary explanations over these
observations.
Clustering Time-Series Energy Data from Smart Meters
Authors: Alexander Lavin, Diego Klabjan
Date: 2016-03-24 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Investigations have been performed into using clustering methods in data
mining time-series data from smart meters. The problem is to identify patterns
and trends in energy usage profiles of commercial and industrial customers over
24-hour periods, and group similar profiles. We tested our method on energy
usage data provided by several U.S. power utilities. The results show accurate
grouping of accounts similar in their energy usage patterns, and potential for
the method to be utilized in energy efficiency programs.
Borrowing strengh in hierarchical Bayes: Posterior concentration of the Dirichlet base measure
Authors: XuanLong Nguyen
Date: 2013-01-04 00:00:00

This paper studies posterior concentration behavior of the base probability
measure of a Dirichlet measure, given observations associated with the sampled
Dirichlet processes, as the number of observations tends to infinity. The base
measure itself is endowed with another Dirichlet prior, a construction known as
the hierarchical Dirichlet processes (Teh et al. [J. Amer. Statist. Assoc. 101
(2006) 1566-1581]). Convergence rates are established in transportation
distances (i.e., Wasserstein metrics) under various conditions on the geometry
of the support of the true base measure. As a consequence of the theory, we
demonstrate the benefit of "borrowing strength" in the inference of multiple
groups of data - a powerful insight often invoked to motivate hierarchical
modeling. In certain settings, the gain in efficiency due to the latent
hierarchy can be dramatic, improving from a standard nonparametric rate to a
parametric rate of convergence. Tools developed include transportation
distances for nonparametric Bayesian hierarchies of random measures, the
existence of tests for Dirichlet measures, and geometric properties of the
support of Dirichlet measures.
Mapping Out Narrative Structures and Dynamics Using Networks and Textual Information
Authors: Semi Min, Juyong Park
Date: 2016-03-24 00:00:00
Tasks: Sentiment Analysis

Human communication is often executed in the form of a narrative, an account
of connected events composed of characters, actions, and settings. A coherent
narrative structure is therefore a requisite for a well-formulated narrative --
be it fictional or nonfictional -- for informative and effective communication,
opening up the possibility of a deeper understanding of a narrative by studying
its structural properties. In this paper we present a network-based framework
for modeling and analyzing the structure of a narrative, which is further
expanded by incorporating methods from computational linguistics to utilize the
narrative text. Modeling a narrative as a dynamically unfolding system, we
characterize its progression via the growth patterns of the character network,
and use sentiment analysis and topic modeling to represent the actual content
of the narrative in the form of interaction maps between characters with
associated sentiment values and keywords. This is a network framework advanced
beyond the simple occurrence-based one most often used until now, allowing one
to utilize the unique characteristics of a given narrative to a high degree.
Given the ubiquity and importance of narratives, such advanced network-based
representation and analysis framework may lead to a more systematic modeling
and understanding of narratives for social interactions, expression of human
sentiments, and communication.
Fine-scale Surface Normal Estimation using a Single NIR Image
Authors: Youngjin Yoon, Gyeongmin Choe, Namil Kim, Joon-Young Lee, In So Kweon
Date: 2016-03-24 00:00:00
Tasks: Generative Adversarial Network, Surface Normal Estimation

We present surface normal estimation using a single near infrared (NIR)
image. We are focusing on fine-scale surface geometry captured with an
uncalibrated light source. To tackle this ill-posed problem, we adopt a
generative adversarial network which is effective in recovering a sharp output,
which is also essential for fine-scale surface normal estimation. We
incorporate angular error and integrability constraint into the objective
function of the network to make estimated normals physically meaningful. We
train and validate our network on a recent NIR dataset, and also evaluate the
generality of our trained model by using new external datasets which are
captured with a different camera under different environment.
Data fluidity in DARIAH -- pushing the agenda forward
Authors: Laurent Romary, Mike Mertens, Anne Baillot
Date: 2016-03-10 00:00:00
Tasks: Management

This paper provides both an update concerning the setting up of the European
DARIAH infrastructure and a series of strong action lines related to the
development of a data centred strategy for the humanities in the coming years.
In particular we tackle various aspect of data management: data hosting, the
setting up of a DARIAH seal of approval, the establishment of a charter between
cultural heritage institutions and scholars and finally a specific view on
certification mechanisms for data.
Deep Extreme Feature Extraction: New MVA Method for Searching Particles in High Energy Physics
Authors: Chao Ma, Tianchenghou, Bin Lan, Jinhui Xu, Zhenhua Zhang
Date: 2016-03-24 00:00:00
Tasks: Ensemble Learning

In this paper, we present Deep Extreme Feature Extraction (DEFE), a new
ensemble MVA method for searching $\tau^{+}\tau^{-}$ channel of Higgs bosons in
high energy physics. DEFE can be viewed as a deep ensemble learning scheme that
trains a strongly diverse set of neural feature learners without explicitly
encouraging diversity and penalizing correlations. This is achieved by adopting
an implicit neural controller (not involved in feedforward compuation) that
directly controls and distributes gradient flows from higher level deep
prediction network. Such model-independent controller results in that every
single local feature learned are used in the feature-to-output mapping stage,
avoiding the blind averaging of features. DEFE makes the ensembles 'deep' in
the sense that it allows deep post-process of these features that tries to
learn to select and abstract the ensemble of neural feature learners. With the
application of this model, a selection regions full of signal process can be
obtained through the training of a miniature collision events set. In
comparison of the Classic Deep Neural Network, DEFE shows a state-of-the-art
performance: the error rate has decreased by about 37\%, the accuracy has
broken through 90\% for the first time, along with the discovery significance
has reached a standard deviation of 6.0 $\sigma$. Experimental data shows that,
DEFE is able to train an ensemble of discriminative feature learners that
boosts the overperformance of final prediction.
Revealing the Hidden Patterns of News Photos: Analysis of Millions of News Photos Using GDELT and Deep Learning-based Vision APIs
Authors: Haewoon Kwak, Jisun An
Date: 2016-03-15 00:00:00

In this work, we analyze more than two million news photos published in
January 2016. We demonstrate i) which objects appear the most in news photos;
ii) what the sentiments of news photos are; iii) whether the sentiment of news
photos is aligned with the tone of the text; iv) how gender is treated; and v)
how differently political candidates are portrayed. To our best knowledge, this
is the first large-scale study of news photo contents using deep learning-based
vision APIs.
Attentive Contexts for Object Detection
Authors: Jianan Li, Yunchao Wei, Xiaodan Liang, Jian Dong, Tingfa Xu, Jiashi Feng, Shuicheng Yan
Date: 2016-03-24 00:00:00
Tasks: Object, object-detection, Object Detection

Modern deep neural network based object detection methods typically classify
candidate proposals using their interior features. However, global and local
surrounding contexts that are believed to be valuable for object detection are
not fully exploited by existing methods yet. In this work, we take a step
towards understanding what is a robust practice to extract and utilize
contextual information to facilitate object detection in practice.
Specifically, we consider the following two questions: "how to identify useful
global contextual information for detecting a certain object?" and "how to
exploit local context surrounding a proposal for better inferring its
contents?". We provide preliminary answers to these questions through
developing a novel Attention to Context Convolution Neural Network (AC-CNN)
based object detection model. AC-CNN effectively incorporates global and local
contextual information into the region-based CNN (e.g. Fast RCNN) detection
model and provides better object detection performance. It consists of one
attention-based global contextualized (AGC) sub-network and one multi-scale
local contextualized (MLC) sub-network. To capture global context, the AGC
sub-network recurrently generates an attention map for an input image to
highlight useful global contextual locations, through multiple stacked Long
Short-Term Memory (LSTM) layers. For capturing surrounding local context, the
MLC sub-network exploits both the inside and outside contextual information of
each specific proposal at multiple scales. The global and local context are
then fused together for making the final decision for detection. Extensive
experiments on PASCAL VOC 2007 and VOC 2012 well demonstrate the superiority of
the proposed AC-CNN over well-established baselines. In particular, AC-CNN
outperforms the popular Fast-RCNN by 2.0% and 2.2% on VOC 2007 and VOC 2012 in
terms of mAP, respectively.
A Diagram Is Worth A Dozen Images
Authors: Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi
Date: 2016-03-24 00:00:00
Tasks: Visual Question Answering (VQA)

Diagrams are common tools for representing complex concepts, relationships
and events, often when it would be difficult to portray the same information
with natural images. Understanding natural images has been extensively studied
in computer vision, while diagram understanding has received little attention.
In this paper, we study the problem of diagram interpretation and reasoning,
the challenging task of identifying the structure of a diagram and the
semantics of its constituents and their relationships. We introduce Diagram
Parse Graphs (DPG) as our representation to model the structure of diagrams. We
define syntactic parsing of diagrams as learning to infer DPGs for diagrams and
study semantic interpretation and reasoning of diagrams in the context of
diagram question answering. We devise an LSTM-based method for syntactic
parsing of diagrams and introduce a DPG-based attention model for diagram
question answering. We compile a new dataset of diagrams with exhaustive
annotations of constituents and relationships for over 5,000 diagrams and
15,000 questions and answers. Our results show the significance of our models
for syntactic parsing and question answering in diagrams using DPGs.
Predicting litigation likelihood and time to litigation for patents
Authors: Papis Wongchaisuwat, Diego Klabjan, John O. McGinnis
Date: 2016-03-23 00:00:00

Patent lawsuits are costly and time-consuming. An ability to forecast a
patent litigation and time to litigation allows companies to better allocate
budget and time in managing their patent portfolios. We develop predictive
models for estimating the likelihood of litigation for patents and the expected
time to litigation based on both textual and non-textual features. Our work
focuses on improving the state-of-the-art by relying on a different set of
features and employing more sophisticated algorithms with more realistic data.
The rate of patent litigations is very low, which consequently makes the
problem difficult. The initial model for predicting the likelihood is further
modified to capture a time-to-litigation perspective.
Face Recognition Using Deep Multi-Pose Representations
Authors: Wael Abd-Almageed, Yue Wua, Stephen Rawlsa, Shai Harel, Tal Hassner, Iacopo Masi, Jongmoo Choi, Jatuporn Toy Leksut, Jungyeon Kim, Prem Natarajan, Ram Nevatia, Gerard Medioni
Date: 2016-03-23 00:00:00
Tasks: Face Recognition, Face Verification, Model Selection

We introduce our method and system for face recognition using multiple
pose-aware deep learning models. In our representation, a face image is
processed by several pose-specific deep convolutional neural network (CNN)
models to generate multiple pose-specific features. 3D rendering is used to
generate multiple face poses from the input image. Sensitivity of the
recognition system to pose variations is reduced since we use an ensemble of
pose-specific CNN features. The paper presents extensive experimental results
on the effect of landmark detection, CNN layer selection and pose model
selection on the performance of the recognition pipeline. Our novel
representation achieves better results than the state-of-the-art on IARPA's CS2
and NIST's IJB-A in both verification and identification (i.e. search) tasks.