pwc-archive/papers-with-abstracts huggingface dataset:
Bifurcation analysis of the dynamics of interacting populations of spiking networks
Date: 2018-09-13 00:00:00

We analyze the collective dynamics of hierarchically structured networks of
densely connected spiking neurons. These networks of sub-networks may represent
interactions between cell assemblies or different nuclei in the brain. The
dynamical activity pattern that results from these interactions depends on the
strength of synaptic coupling between them. Importantly, the overall dynamics
of a brain region in the absence of external input, so called ongoing brain
activity, has been attributed to the dynamics of such interactions. In our
study, two different network scenarios are considered: a system with one
inhibitory and two excitatory sub-networks, and a network representation with
three inhibitory sub-networks. To study the effect of synaptic strength on the
global dynamics of the network, two parameters for relative couplings between
these sub-networks are considered. For each case, a co-dimension two
bifurcation analysis is performed and the results have been compared to
large-scale network simulations. Our analysis shows that Generalized
Lotka-Volterra (GLV) equations, well-known in predator-prey studies, yield a
meaningful population-level description for the collective behavior of spiking
neuronal interaction, which have a hierarchical structure. In particular, we
observed a striking equivalence between the bifurcation diagrams of spiking
neuronal networks and their corresponding GLV equations. This study gives new
insight on the behavior of neuronal assemblies, and can potentially suggest new
mechanisms for altering the dynamical patterns of spiking networks based on
changing the synaptic strength between some groups of neurons.
Measuring Systematic Risk with Neural Network Factor Model
Date: 2018-09-13 00:00:00
Tasks: Feature Engineering, model, Variational Inference

In this paper, we measure systematic risk with a new nonparametric factor
model, the neural network factor model. The suitable factors for systematic
risk can be naturally found by inserting daily returns on a wide range of
assets into the bottleneck network. The network-based model does not stick to a
probabilistic structure unlike parametric factor models, and it does not need
feature engineering because it selects notable features by itself. In addition,
we compare performance between our model and the existing models using 20-year
data of S&P 100 components. Although the new model can not outperform the best
ones among the parametric factor models due to limitations of the variational
inference, the estimation method used for this study, it is still noteworthy in
that it achieves the performance as best the comparable models could without
any prior knowledge.
Multimodal Local-Global Ranking Fusion for Emotion Recognition
Authors: Liang Paul Pu, Zadeh Amir, Morency Louis-Philippe
Date: 2018-08-12 00:00:00
Tasks: Emotion Recognition

Emotion recognition is a core research area at the intersection of artificial
intelligence and human communication analysis. It is a significant technical
challenge since humans display their emotions through complex idiosyncratic
combinations of the language, visual and acoustic modalities. In contrast to
traditional multimodal fusion techniques, we approach emotion recognition from
both direct person-independent and relative person-dependent perspectives. The
direct person-independent perspective follows the conventional emotion
recognition approach which directly infers absolute emotion labels from
observed multimodal features. The relative person-dependent perspective
approaches emotion recognition in a relative manner by comparing partial video
segments to determine if there was an increase or decrease in emotional
intensity. Our proposed model integrates these direct and relative prediction
perspectives by dividing the emotion recognition task into three easier
subtasks. The first subtask involves a multimodal local ranking of relative
emotion intensities between two short segments of a video. The second subtask
uses local rankings to infer global relative emotion ranks with a Bayesian
ranking algorithm. The third subtask incorporates both direct predictions from
observed multimodal behaviors and relative emotion ranks from local-global
rankings for final emotion prediction. Our approach displays excellent
performance on an audio-visual emotion recognition benchmark and improves over
other algorithms for multimodal fusion.
Foraging as an evidence accumulation process
Date: 2018-09-13 00:00:00
Tasks: Decision Making

A canonical foraging task is the patch-leaving problem, in which a forager
must decide to leave a current resource in search for another. Theoretical work
has derived optimal strategies for when to leave a patch, and experiments have
tested for conditions where animals do or do not follow an optimal strategy.
Nevertheless, models of patch-leaving decisions do not consider the imperfect
and noisy sampling process through which an animal gathers information, and how
this process is constrained by neurobiological mechanisms. In this theoretical
study, we formulate an evidence accumulation model of patch-leaving decisions
where the animal averages over noisy measurements to estimate the state of the
current patch and the overall environment. Evidence accumulation models belong
to the class of drift diffusion processes and have been used to model decision
making in different contexts. We solve the model for conditions where foraging
decisions are optimal and equivalent to the marginal value theorem, and perform
simulations to analyze deviations from optimal when these conditions are not
met. By adjusting the drift rate and decision threshold, the model can
represent different strategies, for example an increment-decrement or counting
strategy. These strategies yield identical decisions in the limiting case but
differ in how patch residence times adapt when the foraging environment is
uncertain. To account for sub-optimal decisions, we introduce an
energy-dependent utility function that predicts longer than optimal patch
residence times when food is plentiful. Our model provides a quantitative
connection between ecological models of foraging behavior and evidence
accumulation models of decision making. Moreover, it provides a theoretical
framework for potential experiments which seek to identify neural circuits
underlying patch leaving decisions.
Information Acquisition and Time-Risk Preference
Authors: Daniel Chen, Weijie Zhong
Date: 2018-09-13 00:00:00

An agent acquires information dynamically until her belief about a binary state reaches an upper or lower threshold. She can choose any signal process subject to a constraint on the rate of entropy reduction. Strategies are ordered by "time risk"-the dispersion of the distribution of threshold-hitting times. We construct a strategy maximizing time risk (Greedy Exploitation) and one minimizing it (Pure Accumulation). Under either strategy, beliefs follow a compensated Poisson process. In the former, beliefs jump to the threshold that is closer in Bregman divergence. In the latter, beliefs jump to the unique point with the same entropy as the current belief.
Interpreting search result rankings through intent modeling
Authors: Singh Jaspreet, Anand Avishek
Date: 2018-09-13 00:00:00
Tasks: Document Ranking, Retrieval

Given the recent interest in arguably accurate yet non-interpretable neural
models, even with textual features, for document ranking we try to answer
questions relating to how to interpret rankings. In this paper we take first
steps towards a framework for the interpretability of retrieval models with the
aim of answering 3 main questions "What is the intent of the query according to
the ranker?", "Why is a document ranked higher than another for the query?" and
"Why is a document relevant to the query?"
  Our framework is predicated on the assumption that text based retrieval model
behavior can be estimated using query expansions in conjunction with a simpler
retrieval model irrespective of the underlying ranker. We conducted experiments
with the Clueweb test collection. We show how our approach performs for both
simpler models with a closed form notation (which allows us to measure the
accuracy of the interpretation) and neural ranking models. Our results indicate
that we can indeed interpret more complex models with reasonable accuracy under
certain simplifying assumptions. In a case study we also show our framework can
be employed to interpret the results of the DRMM neural retrieval model in
various scenarios.
Statistical Data Assimilation: Formulation and Examples from Neurobiology
Date: 2018-09-13 00:00:00
Tasks: Functional Connectivity

For the Research Topic Data Assimilation and Control: Theory and Applications
in Life Sciences we first review the formulation of statistical data
assimilation (SDA) and discuss algorithms for exploring variational
approximations to the conditional expected values of biophysical aspects of
functional neural circuits. Then we report on the application of SDA to (1) the
exploration of properties of individual neurons in the HVC nucleus of the avian
song system, and (2) characterizing individual neurons formulated as very large
scale integration (VLSI) analog circuits with a goal of building functional,
biophysically realistic, VLSI representations of functional nervous systems.
Networks of neurons pose a substantially greater challenge, and we comment on
formulating experiments to probe the properties, especially the functional
connectivity, in song command circuits within HVC.
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection
Authors: Gupta Shashank, Thirukovalluru Raghuveer, Sinha Manjira, Mannarswamy Sandya
Date: 2018-09-14 00:00:00
Tasks: Articles, Fake News Detection, News Recommendation

Detecting whether a news article is fake or genuine is a crucial task in
today's digital world where it's easy to create and spread a misleading news
article. This is especially true of news stories shared on social media since
they don't undergo any stringent journalistic checking associated with main
stream media. Given the inherent human tendency to share information with their
social connections at a mouse-click, fake news articles masquerading as real
ones, tend to spread widely and virally. The presence of echo chambers (people
sharing same beliefs) in social networks, only adds to this problem of
wide-spread existence of fake news on social media. In this paper, we tackle
the problem of fake news detection from social media by exploiting the very
presence of echo chambers that exist within the social network of users to
obtain an efficient and informative latent representation of the news article.
By modeling the echo-chambers as closely-connected communities within the
social network, we represent a news article as a 3-mode tensor of the structure
- <News, User, Community> and propose a tensor factorization based method to
encode the news article in a latent embedding space preserving the community
structure. We also propose an extension of the above method, which jointly
models the community and content information of the news article through a
coupled matrix-tensor factorization framework. We empirically demonstrate the
efficacy of our method for the task of Fake News Detection over two real-world
datasets. Further, we validate the generalization of the resulting embeddings
over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and
\textbf{2)} Collaborative News Recommendation. Our proposed method outperforms
appropriate baselines for both the tasks, establishing its generalization.
Extended temporal association memory by inhibitory Hebbian learning
Date: 2018-09-14 00:00:00

Hebbian learning of excitatory synapses plays a central role in storing
activity patterns in associative memory models. Furthermore, interstimulus
Hebbian learning associates multiple items in the brain by converting temporal
correlation to spatial correlation between attractors. However, growing
experimental evidence suggests that learning of inhibitory synapses creates
"inhibitory engrams", which presumably balance with the patterns encoded in the
excitatory network. Controlling inhibitory engrams may modify the behavior of
associative memory in neural networks, but the consequence of such control has
not been theoretically understood. Noting that Hebbian learning of inhibitory
synapses yields an anti-Hebbian effect, we show that the combination of Hebbian
and anti-Hebbian learning can increase the span of temporal association between
the correlated attractors. The balance of targetted and global inhibition
regulates this span of association in the network. Our results suggest a
nontrivial role of anti-Hebbian learning and inhibitory engrams in associative
memory.
A linear algorithm for reliable predictive network control
Date: 2018-09-14 00:00:00
Tasks: Scheduling

This paper introduces a novel control approach for network scheduling and
routing that is predictive and reliable in its nature, yet builds upon a linear
program, making it fast in execution. First, we describe the canonical system
model and how we expand it to be able to predict the success of transmissions.
Furthermore, we define a notion of reliability and then explain the algorithm.
With extended simulations, we demonstrate the gains in performance over the
well known MaxWeight policy.
Computing Credit Valuation Adjustment solving coupled PIDEs in the Bates model
Date: 2018-09-14 00:00:00

Credit value adjustment (CVA) is the charge applied by financial institutions
to the counterparty to cover the risk of losses on a counterpart default event.
In this paper we estimate such a premium under the Bates stochastic model
(Bates [4]), which considers an underlying affected by both stochastic
volatility and random jumps. We propose an efficient method which improves the
finite-difference Monte Carlo (FDMC) approach introduced by de Graaf et al.
[11]. In particular, the method we propose consists in replacing the Monte
Carlo step of the FDMC approach with a finite difference step and the whole
method relies on the efficient solution of two coupled partial
integro-differential equations (PIDE) which is done by employing the Hybrid
Tree-Finite Difference method developed by Briani et al. [6, 7, 8]. Moreover,
the direct application of the hybrid techniques in the original FDMC approach
is also considered for comparison purposes. Several numerical tests prove the
effectiveness and the reliability of the proposed approach when both European
and American options are considered.
Characterizing functional brain networks and emotional centers based on Rasa theory of Indian aesthetics
Date: 2018-09-14 00:00:00
Tasks: Community Detection, EEG, Electroencephalogram (EEG), Functional Connectivity

In Indian history of arts, Rasas are the aesthetics associated with any
auditory, visual, literary or musical piece of art that evokes highly
orchestrated emotional states. In this work, we study the functional response
of the brain to movie clippings meant to evoke the Rasas through network
analysis. We extract functional brain networks using coherence measures on EEG
recordings of film clips from popular Indian Bollywood movies representing nine
Rasas in the Indian Natyasastra. Structural and functional network measures
were computed for these brain networks, averaging over a range of significant
edge weights, in different brainwave frequency bands. We identify segregation
of neuronal wiring in the brain into modules using a community detection
algorithm. Further, using mutual information measure, we compare and contrast
the modular organizations of brain network corresponding to different Rasas.
Hubs identified using centrality measure reveal the network nodes that are
central to information propagation across all Rasas. We also observe that the
functional connectivity is suppressed when high-frequency waves such as beta
and gamma are dominant in the brain. The significant links causing differences
between the Rasa pairs are extracted statistically.
On the Choice of Instruments in Mixed Frequency Specification Tests
Date: 2018-09-14 00:00:00
Tasks: regression

Time averaging has been the traditional approach to handle mixed sampling
frequencies. However, it ignores information possibly embedded in high
frequency. Mixed data sampling (MIDAS) regression models provide a concise way
to utilize the additional information in high-frequency variables. In this
paper, we propose a specification test to choose between time averaging and
MIDAS models, based on a Durbin-Wu-Hausman test. In particular, a set of
instrumental variables is proposed and theoretically validated when the
frequency ratio is large. As a result, our method tends to be more powerful
than existing methods, as reconfirmed through the simulations.
Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells
Authors: Kevin Y. Chen, Daniel M. Zuckerman, Philip C. Nelson
Date: 2018-09-15 00:00:00

Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential-equation approach by generating typical system histories instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch, that is, without recourse to black-box packages. In some cases, their results can also be compared directly to single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies, involving gene expression and error correction, respectively. Code samples and resulting animations showing results are given in the online supplements.
Optimal spatial-dynamic management to minimize the damages caused by aquatic invasive species
Date: 2018-09-15 00:00:00
Tasks: Management

Invasive species have been recognized as a leading threat to biodiversity. In
particular, lakes are especially affected by species invasions because they are
closed systems sensitive to disruption. Accurately controlling the spread of
invasive species requires solving a complex spatial-dynamic optimization
problem. In this work we propose a novel framework for determining the optimal
management strategy to maximize the value of a lake system net of damages from
invasive species, including an endogenous diffusion mechanism for the spread of
invasive species through boaters' trips between lakes. The proposed method
includes a combined global iterative process which determines the optimal
number of trips to each lake in each season and the spatial-dynamic optimal
boat ramp fee.
Commentary on Quantum-Inspired Information Retrieval
Authors: Ashoori Elham, Rudolph Terry
Date: 2018-09-15 00:00:00
Tasks: Information Retrieval, Retrieval

There have been suggestions within the Information Retrieval (IR) community
that quantum mechanics (QM) can be used to help formalise the foundations of
IR. The invoked connection to QM is mathematical rather than physical. The
proposed ideas are concerned with information which is encoded, processed and
accessed in classical computers. However, some of the suggestions have been
thoroughly muddled with questions about applying techniques of quantum
information theory in IR, and it is often unclear whether or not the suggestion
is to perform actual quantum information processing on the information. This
paper is an attempt to provide some conceptual clarity on the emerging issues.
Inhibition in Random Neuronal Networks Enhances Response Variability and Disrupts Stimulus Discrimination
Date: 2018-09-15 00:00:00
Tasks: Anatomy, Blocking

Inhibition is considered to shape neural activity, and broaden its pattern
repertoire. In the sensory organs, where the anatomy of neural circuits is
highly structured, lateral inhibition sharpens contrast among stimulus
properties. The impact of inhibition on stimulus processing and the involvement
of lateral inhibition is less clear when activity propagates to the
less-structured relay stations. Here we take a synthetic approach to
disentangle the impacts of inhibition from that of specialized anatomy on the
repertoire of evoked activity patterns, and as a result, the network capacity
to uniquely represent different stimuli. To this aim, we blocked inhibition in
randomly rewired networks of cortical neurons in-vitro, and quantified response
variability and stimulus discrimination among stimuli provided at different
spatial loci, before and after the blockade. We show that blocking inhibition
quenches variability of responses evoked by repeated stimuli through any
spatial source; for all tested response features. Despite the sharpening role
of inhibition in the highly structured sensory organs, in these random networks
we find that blocking inhibition enhances stimulus discrimination between
spatial sources of stimulation, when based on response features that emphasize
the relation among spike times recorded through different electrodes. We
further show that under intact inhibition, responses to a given stimulus are a
noisy version of those revealed by blocking inhibition; such that intact
inhibition disrupts an otherwise coherent, wave propagation of activity.
Structural subnetwork evolution across the life-span: rich-club, feeder, seeder
Date: 2018-09-16 00:00:00

The impact of developmental and aging processes on brain connectivity and the
connectome has been widely studied. Network theoretical measures and certain
topological principles are computed from the entire brain, however there is a
need to separate and understand the underlying subnetworks which contribute
towards these observed holistic connectomic alterations. One organizational
principle is the rich-club - a core subnetwork of brain regions that are
strongly connected, forming a high-cost, high-capacity backbone that is
critical for effective communication in the network. Investigations primarily
focus on its alterations with disease and age. Here, we present a systematic
analysis of not only the rich-club, but also other subnetworks derived from
this backbone - namely feeder and seeder subnetworks. Our analysis is applied
to structural connectomes in a normal cohort from a large, publicly available
lifespan study. We demonstrate changes in rich-club membership with age
alongside a shift in importance from 'peripheral' seeder to feeder subnetworks.
Our results show a refinement within the rich-club structure (increase in
transitivity and betweenness centrality), as well as increased efficiency in
the feeder subnetwork and decreased measures of network integration and
segregation in the seeder subnetwork. These results demonstrate the different
developmental patterns when analyzing the connectome stratified according to
its rich-club and the potential of utilizing this subnetwork analysis to reveal
the evolution of brain architectural alterations across the life-span.
An incomplete equilibrium with a stochastic annuity
Date: 2018-09-16 00:00:00

We prove the global existence of an incomplete, continuous-time finite-agent
Radner equilibrium in which exponential agents optimize their expected utility
over both running consumption and terminal wealth. The market consists of a
traded annuity, and, along with unspanned income, the market is incomplete. Set
in a Brownian framework, the income is driven by a multidimensional diffusion,
and, in particular, includes mean-reverting dynamics.
  The equilibrium is characterized by a system of fully coupled quadratic
backward stochastic differential equations, a solution to which is proved to
exist under Markovian assumptions.