pwc-archive/papers-with-abstracts huggingface dataset:
The Role of Emotion in Problem Solving: First Results from Observing Chess
Authors: Guntz Thomas  PERVASIVE, Crowley James  PERVASIVE, Vaufreydaz Dominique  PERVASIVE, Balzarini Raffaella  PERVASIVE, Dessus Philippe  LSE, PERVASIVE
Date: 2018-10-17 00:00:00

In this paper we present results from recent experiments that suggest that
chess players associate emotions to game situations and reactively use these
associations to guide search for planning and problem solving. We describe the
design of an instrument for capturing and interpreting multimodal signals of
humans engaged in solving challenging problems. We review results from a pilot
experiment with human experts engaged in solving challenging problems in Chess
that revealed an unexpected observation of rapid changes in emotion as players
attempt to solve challenging problems. We propose a cognitive model that
describes the process by which subjects select chess chunks for use in
interpretation of the game situation and describe initial results from a second
experiment designed to test this model.
Factor-Driven Two-Regime Regression
Date: 2020-09-10 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Vocal Bursts Valence Prediction

We propose a novel two-regime regression model where regime switching is
driven by a vector of possibly unobservable factors. When the factors are
latent, we estimate them by the principal component analysis of a panel data
set. We show that the optimization problem can be reformulated as mixed integer
optimization, and we present two alternative computational algorithms. We
derive the asymptotic distribution of the resulting estimator under the scheme
that the threshold effect shrinks to zero. In particular, we establish a phase
transition that describes the effect of first-stage factor estimation as the
cross-sectional dimension of panel data increases relative to the time-series
dimension. Moreover, we develop bootstrap inference and illustrate our methods
via numerical studies.
Dead Time Compensation for High-Flux Ranging
Date: 2019-02-22 00:00:00
Tasks: Depth Estimation, Vocal Bursts Intensity Prediction

Dead time effects have been considered a major limitation for fast data
acquisition in various time-correlated single photon counting applications,
since a commonly adopted approach for dead time mitigation is to operate in the
low-flux regime where dead time effects can be ignored. Through the application
of lidar ranging, this work explores the empirical distribution of detection
times in the presence of dead time and demonstrates that an accurate
statistical model can result in reduced ranging error with shorter data
acquisition time when operating in the high-flux regime. Specifically, we show
that the empirical distribution of detection times converges to the stationary
distribution of a Markov chain. Depth estimation can then be performed by
passing the empirical distribution through a filter matched to the stationary
distribution. Moreover, based on the Markov chain model, we formulate the
recovery of arrival distribution from detection distribution as a nonlinear
inverse problem and solve it via provably convergent mathematical optimization.
By comparing per-detection Fisher information for depth estimation from high-
and low-flux detection time distributions, we provide an analytical basis for
possible improvement of ranging performance resulting from the presence of dead
time. Finally, we demonstrate the effectiveness of our formulation and
algorithm via simulations of lidar ranging.
Prey selection of Amur tigers in relation to the spatiotemporal overlap with prey across the Sino-Russian border
Date: 2019-03-28 00:00:00

The endangered Amur tiger is confined primarily to a narrow area along the
border with Russia in Northeast China. Little is known about the foraging
strategies of this small subpopulation in Hunchun Nature Reserve on the Chinese
side of the border; at this location, the prey base and land use patterns are
distinctly different from those in the larger population of the Sikhote-Alin
Mountains of Russia. Using dietary analysis of scats and camera-trapping data
from Hunchun Nature Reserve, we assessed spatiotemporal overlap of tigers and
their prey and identified prey selection patterns to enhance understanding of
the ecological requirements of tigers in Northeast China. Results indicated
that wild prey constituted 94.9% of the total biomass consumed by tigers;
domestic livestock represented 5.1% of the diet. Two species, wild boar and
sika deer , collectively represented 83% of the biomass consumed by tigers.
Despite lower spatial overlap of tigers and wild boar compared to tigers and
sika deer, tigers preferentially preyed on boar, likely facilitated by high
temporal overlap in activity patterns. Tigers exhibit significant spatial
overlap with sika deer, likely favoring a high level of tiger predation on this
large-sized ungulate. However, tigers did not preferred roe deer (Capreolus
pygargus) and showed a low spatial overlap with roe deer. Overall, our results
suggest that tiger prey selection is determined by prey body size and also
overlap in tiger and prey use of time or space. Also, we suggest that
strategies designed to minimize livestock forays into forested lands may be
important for decreasing the livestock depredation by tigers. This study offers
a framework to simultaneously integrate food habit analysis with the
distribution of predators and prey through time and space to provide a
comprehensive understanding of foraging strategies of large carnivores.
Sequence-dependent sensitivity explains the accuracy of decisions when cues are separated with a gap
Date: 2018-10-26 00:00:00
Tasks: Decision Making, Descriptive, Sensitivity

Most decisions require information gathering from a stimulus presented with
different gaps. Indeed, the brain process of this integration is rarely
ambiguous. Recently, it has been claimed that humans can optimally integrate
the information of two discrete pulses independent of the temporal gap between
them. Interestingly, subjects' performance on such a task, with two discrete
pulses, is superior to what a perfect accumulator can predict. Although
numerous neuronal and descriptive models have been proposed to explain the
mechanism of perceptual decision-making, none can explain human behavior in
this two-pulse task. In order to investigate the mechanism of decision-making
on the noted tasks, a set of modified drift-diffusion models based on different
hypotheses were used. Model comparisons clarified that, in a sequence of
information arriving at different times, the accumulated information of earlier
evidence affects the process of information accumulation of later evidence. It
was shown that the rate of information extraction depends on whether the pulse
is the first or the second one. The proposed model can also explain the
stronger effect of the second pulse as shown by Kiani et al. 2013.
Investigating non-classical correlations between decision fused multi-modal documents
Authors: Gkoumas Dimitris, Uprety Sagar, Song Dawei
Date: 2018-10-26 00:00:00
Tasks: Information Retrieval, Retrieval

Correlation has been widely used to facilitate various information retrieval
methods such as query expansion, relevance feedback, document clustering, and
multi-modal fusion. Especially, correlation and independence are important
issues when fusing different modalities that influence a multi-modal
information retrieval process. The basic idea of correlation is that an
observable can help predict or enhance another observable. In quantum
mechanics, quantum correlation, called entanglement, is a sort of correlation
between the observables measured in atomic-size particles when these particles
are not necessarily collected in ensembles. In this paper, we examine a
multimodal fusion scenario that might be similar to that encountered in physics
by firstly measuring two observables (i.e., text-based relevance and
image-based relevance) of a multi-modal document without counting on an
ensemble of multi-modal documents already labeled in terms of these two
variables. Then, we investigate the existence of non-classical correlations
between pairs of multi-modal documents. Despite there are some basic
differences between entanglement and classical correlation encountered in the
macroscopic world, we investigate the existence of this kind of non-classical
correlation through the Bell inequality violation. Here, we experimentally test
several novel association methods in a small-scale experiment. However, in the
current experiment we did not find any violation of the Bell inequality.
Finally, we present a series of interesting discussions, which may provide
theoretical and empirical insights and inspirations for future development of
this direction.
Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network
Authors: Huang Chaoran, Yao Lina, Wang Xianzhi, Benatallah Boualem, Zhang Xiang
Date: 2018-10-26 00:00:00
Tasks: Community Question Answering, Management, Question Answering

Community Question Answering (CQA) websites can be claimed as the most major
venues for knowledge sharing, and the most effective way of exchanging
knowledge at present. Considering that massive amount of users are
participating online and generating huge amount data, management of knowledge
here systematically can be challenging. Expert recommendation is one of the
major challenges, as it highlights users in CQA with potential expertise, which
may help match unresolved questions with existing high quality answers while at
the same time may help external services like human resource systems as another
reference to evaluate their candidates. In this paper, we in this work we
propose to exploring experts in CQA websites. We take advantage of recent
distributed word representation technology to help summarize text chunks, and
in a semantic view exploiting the relationships between natural language
phrases to extract latent knowledge domains. By domains, the users' expertise
is determined on their historical performance, and a rank can be compute to
given recommendation accordingly. In particular, Stack Overflow is chosen as
our dataset to test and evaluate our work, where inclusive experiment shows our
competence.
Determination of protein structural ensembles using cryo-electron microscopy
Date: 2018-10-26 00:00:00

Achieving a comprehensive understanding of the behaviour of proteins is
greatly facilitated by the knowledge of their structures, thermodynamics and
dynamics. All this information can be provided in an effective manner in terms
of structural ensembles. A structural ensemble can be obtained by determining
the structures, populations and interconversion rates for all the main states
that a protein can occupy. To achieve this goal, integrative methods that
combine experimental and computational approaches provide powerful tools. Here
we focus on cryo-electron microscopy, which has become over recent years an
invaluable resource to bridge the gap from order to disorder in structural
biology. In this review, we provide a perspective of the current challenges and
opportunities in determining protein structural ensembles using integrative
approaches that can combine cryo-electron microscopy data with other available
sources of information, along with an overview of the tools available to the
community.
Fusion of Urban TanDEM-X raw DEMs using variational models
Date: 2018-10-26 00:00:00

Recently, a new global Digital Elevation Model (DEM) with pixel spacing of
0.4 arcseconds and relative height accuracy finer than 2m for flat areas
(slopes < 20%) and better than 4m for rugged terrain (slopes > 20%) was created
through the TanDEM-X mission. One important step of the chain of global DEM
generation is to mosaic and fuse multiple raw DEM tiles to reach the target
height accuracy. Currently, Weighted Averaging (WA) is applied as a fast and
simple method for TanDEM-X raw DEM fusion in which the weights are computed
from height error maps delivered from the Interferometric TanDEM-X Processor
(ITP). However, evaluations show that WA is not the perfect DEM fusion method
for urban areas especially in confrontation with edges such as building
outlines. The main focus of this paper is to investigate more advanced
variational approaches such as TV-L1 and Huber models. Furthermore, we also
assess the performance of variational models for fusing raw DEMs produced from
data takes with different baseline configurations and height of ambiguities.
The results illustrate the high efficiency of variational models for TanDEM-X
raw DEM fusion in comparison to WA. Using variational models could improve the
DEM quality by up to 2m particularly in inner-city subsets.
New Designs on MVDR Robust Adaptive Beamforming Based on Optimal Steering Vector Estimation
Date: 2018-10-26 00:00:00
Tasks: Robust Design

The robust adaptive beamforming design problem based on estimation of the
signal of interest steering vector is considered in the paper. In this case,
the optimal beamformer is obtained by computing the sample matrix inverse and
an optimal estimate of the signal of interest steering vector. The common
criteria to find the best estimate of the steering vector are the beamformer
output SINR and output power, while the constraints assume as little as
possible prior inaccurate knowledge about the signal of interest, the
propagation media, and the antenna array. Herein, a new beamformer output power
maximization problem is formulated and solved subject to a double-sided norm
perturbation constraint, a similarity constraint, and a quadratic constraint
that guarantees that the direction-of-arrival (DOA) of the signal of interest
is away from the DOA region of all linear combinations of the interference
steering vectors. In the new robust design, the prior information required
consists of some allowable error norm bounds, the approximate knowledge of the
antenna array geometry, and the angular sector of the signal of interest. It
turns out that the array output power maximization problem is a non-convex QCQP
problem with inhomogeneous constraints. However, we show that the problem is
still solvable, and develop efficient algorithms for finding globally optimal
estimate of the signal of interest steering vector. The results are generalized
to the case where an ellipsoidal constraint is considered, and sufficient
conditions for the global optimality are derived. In addition, a new quadratic
constraint on the actual signal steering vector is proposed in order to improve
the array performance. To validate our results, simulation examples are
presented, and they demonstrate the improved performance of the new robust
beamformers in terms of the output SINR as well as the output power.
On The Ignition, Propagation and Termination Of The Neuronal Bursting Activity During Ictogenesis In Epileptic Patients
Date: 2018-10-18 00:00:00
Tasks: Rhythm

Epilepsy creates a persistent increase in the probability of spontaneous
seizures. An ictal episode evolves due to acute disturbance of the fine-tuned
balance between excitatory vs. inhibitory inputs within a neural network in
favor of excitation. The current literature that proposes the
activity-dependent disinhibition as a valid mechanism of chronic epilepsy, does
not provide clues on why this mechanism emerges only in epileptic patients and
how the vicious circle resulting of an activity-dependent disinhibition in
over-active ictogenic network would end. A new model, which presents chronic
epilepsy as a disease of faulty architecture of the neural circuit, is
discussed. Wherein; variable genetic or acquired predisposing factors drive
abnormalities in the construction of multiple neural circuits resulting in an
activity-dependent positive feedback excitatory loops which transform normal
neural circuits into ictal foci. Such new mechanism, for igniting an
activity-dependent unstable excitation with subsequent relatively stable
disinhibition, leads to an ictal escape rhythm. The propagation of such
bursting activity occurs either electrochemically via synaptic communication to
remote susceptible circuits, or chemically via a trigger wave which recruits
the non-connected proximal neurons. Termination occurs abruptly when the
inhibitory interneurons functionally recover and reimpose their inhibitory
effect on the ictogenic circuit to transform the escape rhythm into a normal,
under-control output. The proposed model elucidates various enigmatic features
of the disease; and illustrates both the end-result ictogenic mechanism arising
from the wide variety of etiologies of human spontaneous and acquired epilepsy,
and the timing of episodic transitions from normal activity to seizures.
LAMVI-2: A Visual Tool for Comparing and Tuning Word Embedding Models
Authors: Rong Xin, Luckson Joshua, Adar Eytan
Date: 2018-10-22 00:00:00
Tasks: Deep Learning

Tuning machine learning models, particularly deep learning architectures, is
a complex process. Automated hyperparameter tuning algorithms often depend on
specific optimization metrics. However, in many situations, a developer trades
one metric against another: accuracy versus overfitting, precision versus
recall, smaller models and accuracy, etc. With deep learning, not only are the
model's representations opaque, the model's behavior when parameters "knobs"
are changed may also be unpredictable. Thus, picking the "best" model often
requires time-consuming model comparison. In this work, we introduce LAMVI-2, a
visual analytics system to support a developer in comparing hyperparameter
settings and outcomes. By focusing on word-embedding models ("deep learning for
text") we integrate views to compare both high-level statistics as well as
internal model behaviors (e.g., comparing word 'distances'). We demonstrate how
developers can work with LAMVI-2 to more quickly and accurately narrow down an
appropriate and effective application-specific model.
The Information Flow Foundation for Conceptual Knowledge Organization
Authors: Kent Robert E.
Date: 2018-10-19 00:00:00

The sharing of ontologies between diverse communities of discourse allows
them to compare their own information structures with that of other communities
that share a common terminology and semantics - ontology sharing facilitates
interoperability between online knowledge organizations. This paper
demonstrates how ontology sharing is formalizable within the conceptual
knowledge model of Information Flow (IF). Information Flow indirectly
represents sharing through a specifiable, ontology extension hierarchy
augmented with synonymic type equivalencing - two ontologies share terminology
and meaning through a common generic ontology that each extends. Using the
paradigm of participant community ontologies formalized as IF logics, a common
shared extensible ontology formalized as an IF theory, participant community
specification links from the common ontology to the participating community
ontology formalizable as IF theory interpretations, this paper argues that
ontology sharing is concentrated in a virtual ontology of community
connections, and demonstrates how this virtual ontology is computable as the
fusion of the participant ontologies - the quotient of the sum of the
participant ontologies modulo the ontological sharing structure.
A simulated annealing approach to the student-project allocation problem
Authors: Chown Abigail H., Cook Christopher J., Wilding Nigel B.
Date: 2018-10-22 00:00:00

We describe a solution to the student-project allocation problem using
simulated annealing. The problem involves assigning students to projects, where
each student has ranked a fixed number of projects in order of preference. Each
project is offered by a specific supervisor (or supervisors), and the goal is
to find an optimal matching of students to projects taking into account the
students' preferences, the constraint that only one student can be assigned to
a given project, and the constraint that supervisors have a maximum workload.
We show that when applied to a real dataset from a university physics
department, simulated annealing allows the rapid determination of high quality
solutions to this allocation problem. The quality of the solution is quantified
by a satisfaction metric derived from empirical student survey data. Our
approach provides high quality allocations in a matter of minutes that are as
good as those found previously by the course organizer using a laborious
trial-and-error approach. We investigate how the quality of the allocation is
affected by the ratio of the number of projects offered to the number of
students and the number of projects ranked by each student. We briefly discuss
how our approach can be generalized to include other types of constraints and
discuss its potential applicability to wider allocation problems.
Joint Estimation of DOA and Frequency with Sub-Nyquist Sampling in a Binary Array Radar System
Date: 2018-10-26 00:00:00

Recently, several array radar structures combined with sub-Nyquist techniques
and corresponding algorithms have been extensively studied. Carrier frequency
and direction-of-arrival (DOA) estimations of multiple narrow-band signals
received by array radars at the sub-Nyquist rates are considered in this paper.
We propose a new sub-Nyquist array radar architecture (a binary array radar
separately connected to a multi-coset structure with M branches) and an
efficient joint estimation algorithm which can match frequencies up with
corresponding DOAs. We further come up with a delay pattern augmenting method,
by which the capability of the number of identifiable signals can increase from
M-1 to Q-1 (Q is extended degrees of freedom). We further conclude that the
minimum total sampling rate 2MB is sufficient to identify $ {K \leq Q-1}$
narrow-band signals of maximum bandwidth $B$ inside. The effectiveness and
performance of the estimation algorithm together with the augmenting method
have been verified by simulations.
A stochastic modeling framework for single cell migration: coupling contractility and focal adhesions
Date: 2019-03-20 00:00:00

The interaction of the actin cytoskeleton with cell-substrate adhesions is
necessary for cell migration. While the trajectories of motile cells have a
stochastic character, investigations of cell motility mechanisms rarely
elaborate on the origins of the observed randomness. Here, guided by a few
fundamental attributes of cell motility, we construct a minimal stochastic cell
migration model from ground-up. The resulting model couples a deterministic
actomyosin contractility mechanism with stochastic cell-substrate adhesion
kinetics, and yields a well-defined piecewise deterministic process. Numerical
simulations reproduce several experimentally observed results, including
anomalous diffusion, tactic migration, and contact guidance. This work provides
a basis for the development of cell-cell collision and population migration
models.
The Politics of Attention
Date: 2019-01-20 00:00:00

We develop an equilibrium theory of attention and politics. In a spatial
model of electoral competition where candidates have varying policy
preferences, we examine what kinds of political behaviors capture voters'
limited attention and how this concern affects the overall political outcomes.
Following the seminal works of Downs (1957) and Sims (1998), we assume that
voters are rationally inattentive and can process information about the
policies at a cost proportional to entropy reduction. The main finding is an
equilibrium phenomenon called attention- and media-driven extremism, namely as
we increase the attention cost or garble the news technology, a truncated set
of the equilibria captures voters' attention through enlarging the policy
differentials between the varying types of the candidates. We supplement our
analysis with historical accounts, and discuss its relevance in the new era
featured with greater media choices and distractions, as well as the rise of
partisan media and fake news.