pwc-archive/papers-with-abstracts huggingface dataset:
Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO)
Authors: Gaurav Gupta, Minghao Yan, Benjamin Coleman, Bryce Kille, R. A. Leo Elworth, Tharun Medini, Todd Treangen, Anshumali Shrivastava
Date: 2019-10-10 00:00:00

DNA sequencing, especially of microbial genomes and metagenomes, has been at the core of recent research advances in large-scale comparative genomics. The data deluge has resulted in exponential growth in genomic datasets over the past years and has shown no sign of slowing down. Several recent attempts have been made to tame the computational burden of sequence search on these terabyte and petabyte-scale datasets, including raw reads and assembled genomes. However, no known implementation provides both fast query and construction time, keeps the low false-positive requirement, and offers cheap storage of the data structure. We propose a data structure for search called RAMBO (Repeated And Merged BloOm Filter) which is significantly faster in query time than state-of-the-art genome indexing methods- COBS (Compact bit-sliced signature index), Sequence Bloom Trees, HowDeSBT, and SSBT. Furthermore, it supports insertion and query process parallelism, cheap updates for streaming inputs, has a zero false-negative rate, a low false-positive rate, and a small index size. RAMBO converts the search problem into set membership testing among $K$ documents. Interestingly, it is a count-min sketch type arrangement of a membership testing utility (Bloom Filter in our case). The simplicity of the algorithm and embarrassingly parallel architecture allows us to stream and index a 170TB whole-genome sequence dataset in a mere 9 hours on a cluster of 100 nodes while competing methods require weeks.
Maneuvering, Multi-Target Tracking using Particle Filters
Date: 2019-10-10 00:00:00

In this work, we develop tracking and estimation techniques relevant to
underwater targets. Particularly, we explore particle filtering techniques for
target tracking. It is a numerical approximation method for implementing a
recursive Bayesian estimation procedure. It does not require the assumptions of
linearity and Guassianity like the traditional Kalman filter based techniques
and is capable of handling non-Gaussian noise distributions and non-linearities
in the measurements as well as target dynamics. The performance of particle
filters is verified using simulations and compared with Extended Kalman Filter.
Particle filters can track multi-targets and highly maneuvering targets.
However, it has higher computational load. The efficient use of particle
filters for multi-target tracking using Independent Partition Particle Filter
(IPPF) and tracking highly maneuvering targets using Multiple Model Particle
Filter(MMPF) are also explored in this work. These techniques require only
smaller number of particles and help in reducing the computational cost. Data
association problem exists in multi-target tracking due to lack of information
at the observer about the proper association between the targets and the
received measurements. The problem becomes more involved when the targets move
much closer and there are clutter and missed target detections at the observer.
Monte Carlo Joint Probabilistic Data Association Filter (MCJPDAF) efficiently
solves data association for the mentioned situation. Due to the inability of
the standard MCJPDAF to track highly maneuvering targets, Monte Carlo Multiple
Model Joint Probabilistic Data Association Filter (MC-MMJPDAF) which combines
the technique of Multiple Model Particle Filter(MMPF) in the framework of
MC-JPDAF has been proposed. The simulation results shows the superiority of the
proposed method.
Increasing the Detectability of Phase-Amplitude Coupling
Date: 2019-10-10 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), Triplet

Background: In electrical brain signals such as Local Field Potential (LFP)
and Electroencephalogram (EEG), oscillations emerge as a result of neural
network activity. The oscillations extend over several frequency bands. Between
their dominant components, various couplings can be observed. Of these,
Phase-Amplitude Coupling (PAC) is intensively studied in relation to brain
function. In the time-frequency domain, however, PAC measurement faces a
dilemma in the choice of filter bandwidth. For a frequency m modulating a
frequency n, filters narrowly tuned around the latter frequency will miss the
modulatory components at frequencies n+m and n-m; wide band tuning will pass
increasing levels of noise. New Method: Our CFC measurement uses three
identical narrow band filters with center frequencies located on n-m, n, and
n+m. The method therefore is free from the bandwidth dilemma. Comparison with
Existing Method(s): The method was tested on diagnostic artificial signals
modeled on local field potentials and compared with four established PAC
detection algorithms. While the proposed method detected the simulated PAC in
high frequency resolution, the other methods detected with poor frequency
resolution, or completely missed the PAC. Conclusion: Using the proposed
triplet-filter banks instead of wideband filtering allows for high resolution
detection of PAC. Moreover, the method successfully detected PAC in wide range
of modulation frequency. Finally, bandwidth is not chosen subjectively in our
new method which makes the comparison of PAC more convenient among different
studies.
Transboundary Pollution Externalities: Think Globally, Act Locally?
Date: 2019-10-10 00:00:00

We analyze the implications of transboundary pollution externalities on
environmental policymaking in a spatial and finite time horizon setting. We
focus on a simple regional optimal pollution control problem in order to
compare the global and local solutions in which, respectively, the
transboundary externality is and is not taken into account in the determination
of the optimal policy by individual local policymakers. We show that the local
solution is suboptimal and as such a global approach to environmental problems
is effectively needed. Our conclusions hold true in different frameworks,
including situations in which the spatial domain is either bounded or
unbounded, and situations in which macroeconomic-environmental feedback effects
are taken into account. We also show that if every local economy implements an
environmental policy stringent enough, then the global average level of
pollution will fall. If this is the case, over the long run the entire global
economy will be able to achieve a completely pollution-free status.
Risk as Challenge: A Dual System Stochastic Model for Binary Choice Behavior
Date: 2019-10-10 00:00:00

Challenge Theory (CT), a new approach to decision under risk departs
significantly from expected utility, and is based on firmly psychological,
rather than economic, assumptions. The paper demonstrates that a purely
cognitive-psychological paradigm for decision under risk can yield excellent
predictions, comparable to those attained by more complex economic or
psychological models that remain attached to conventional economic constructs
and assumptions. The study presents a new model for predicting the popularity
of choices made in binary risk problems. A CT-based regression model is tested
on data gathered from 126 respondents who indicated their preferences with
respect to 44 choice problems. Results support CT's central hypothesis,
strongly associating between the Challenge Index (CI) attributable to every
binary risk problem, and the observed popularity of the bold prospect in that
problem (with r=-0.92 and r=-0.93 for gains and for losses, respectively). The
novelty of the CT perspective as a new paradigm is illuminated by its simple,
single-index (CI) representation of psychological effects proposed by Prospect
Theory for describing choice behavior (certainty effect, reflection effect,
overweighting small probabilities and loss aversion).
Experience Dependent Formation of Global Coherent Representation of Environment by Grid Cells and Head Direction Cells
Date: 2019-10-11 00:00:00
Tasks: Hippocampus, Simultaneous Localization and Mapping

The grid firing patterns are thought to provide an efficient intrinsic metric
capable of supporting universal spatial metric for mammalian spatial navigation
in all environments. However, whether spatial representations of grid cells in
the entorhinal cortex are determined by local environment cues or form globally
coherent patterns remains undetermined. To explore this underlying mechanism,
here we proposed a possible theoretical explanation to describe connection
between the neural space and the physical environment and transition from a
local anchored to a global coherent representation according to relationship
between grid phase distance between physical distance in the physical
environment, and tested our method based on simultaneous localization and
mapping (SLAM) system on a iRat rodent-sized robot platform in a rat-like maze.
Our robotic exploration experiments show that the grid firing firstly is
determined by local environment cues, and after self-correction with
experience-dependence, the regular, continuous grid firing patterns tessellate
the explored space. Head direction (HD) cells also show global patterns in our
experimental results. Our results support that grid firing patterns do provide
a universal spatial metric for mammalian spatial navigation in complex
environments. The results in this study also provide insights into
experience-dependent interactions between path integrative calculation of
location in the entorhinal cortex and learned associations to the external
sensory cues in the hippocampus, which is likely to be critical understanding
spatial memory, even episodic memory.
Decentralized LQ-Consistent Event-triggered Control over a Shared Contention-based Network
Date: 2020-04-29 00:00:00
Tasks: Scheduling

Consider a network of multiple independent stochastic linear systems where,
for each system, a scheduler collocated with the sensors arbitrates data
transmissions to a corresponding remote controller through a shared
contention-based communication network. While the systems are physically
independent, their optimal controller design problems may, in general, become
coupled, due to network contention, if the schedulers trigger transmissions
based on state-dependent events. In this article we propose a class of
probabilistic admissible schedulers for which the optimal controllers, with
respect to local standard LQG costs, have the certainty equivalence property
and can still be determined decentrally. Then, two scheduling policies within
this class are introduced; a non-event-based and an event-based, both with an
easily adjustable triggering probability at every time step. We then prove
that, for each closed-loop system, the event-based scheduler and its optimal
controller outperforms the closed-loop system with the non-event-based
scheduler and its associated optimal controller. Moreover, we show that, for
each closed-loop system, the optimal state estimators for both scheduling
policies follows a linear iteration. Finally, we provide a method to regulate
the triggering probabilities of the schedulers by maximizing a network utility
function.
Joint Cross-layer Optimization in Real-Time Networked Control Systems
Date: 2019-10-10 00:00:00
Tasks: Scheduling

Networked control system (NCS) refer to a set of control loops that are
closed over a communication network. In this article, the joint operation of
control and networking for NCS is investigated wherein the network serves the
sensor-to-controller communication links for multiple stochastic linear
time-invariant (LTI) sub-systems. The sensors sample packets based on the
observed plant state, which they send over a shared multi-hop network. The
network has limited communication resources, which need to be assigned to
competing links to support proper control loop operation. In this set-up, we
formulate an optimization problem to minimize the weighted-sum
linear-quadratic-Gaussian (LQG) cost of all loops, taking into account the
admissible sampling, control, congestion control and scheduling policies. Under
some mild assumptions on the sampling frequencies of the control loops and the
communication network, we find the joint optimal solution to be given by a
certainty equivalence control with threshold-based sampling policy, as well as
a back-pressure type scheduler with a simple pass-through congestion control.
The interface between network and control loops is identified to be the buffer
state of the sensor node, which can be interpreted as network price for
sampling a packet from control perspective. We validate our theoretical claims
by simulating NCSs comprising of multiple LTI stochastic control loops
communicating over a two-hop cellular network.
Fast Spread in Controlled Evolutionary Dynamics
Date: 2020-06-22 00:00:00

We study the spread of a novel state in a network, in the presence of an
exogenous control. The considered controlled evolutionary dynamics is a
non-homogeneous Markov process that describes the evolution of the states of
all nodes in the network. Through a rigorous analysis, we estimate the
performance of the system by establishing upper and lower bounds on the
expected time needed for the novel state to replace the original one. Such
bounds are expressed in terms of the support and intensity of the control
policy (specifically, the set of nodes that can be controlled and its energy)
and of the network topology and establish fundamental limitations on the
system's performance. Leveraging these results, we are able to classify network
structures depending on the possibility to control the system using simple
open-loop control policies. Finally, we propose a feedback control policy that,
using little knowledge of the network topology and of the system's evolution at
a macroscopic level, allows for a substantial speed up of the spreading process
with respect to simple open-loop control policies. All these theoretical
results are presented together with explanatory examples, for which Monte Carlo
simulations corroborate our analytical findings.
Throughput of CDM-based Random Access With SINR Capture
Date: 2019-10-11 00:00:00

Code division multiplexing (CDM)-based random access is used in many
practical wireless systems. With CDM-based random access, a set of sequences is
reserved for random access. A remote station transmits a random access packet
using a randomly selected sequence among the set. If more than one remote
stations transmit random access packets using the same sequence simultaneously,
performance degrades due to sequence collision. In addition, if more than one
remote stations transmit random access packets using different sequences
simultaneously, performance also degrades due to interference. Therefore, the
performance of CDM-based random access is dependent on both sequence collision
and interference. There has been no previous research to analyze the
performance of CDM-based random access considering both sequence collision and
interference. In this paper, throughput of CDM-based random access is
investigated considering both sequence collision and interference based on a
signal to interference plus noise ratio (SINR) capture model. Analysis and
numerical simulation compare the throughputs of several random access schemes
including conventional and channel-adaptive random access. The results show
that channel-adaptive random access can achieve significantly higher throughput
than conventional random access. In addition, based on the results of this
paper, it is possible to analyze the trade-off between the throughput and
implementation complexity with increased number of sequences.
Optimal Trading of a Basket of Futures Contracts
Authors: Bahman Angoshtari, Tim Leung
Date: 2019-10-11 00:00:00

We study the problem of dynamically trading multiple futures contracts with different underlying assets. To capture the joint dynamics of stochastic bases for all traded futures, we propose a new model involving a multi-dimensional scaled Brownian bridge that is stopped before price convergence. This leads to the analysis of the corresponding Hamilton-Jacobi-Bellman (HJB) equations, whose solutions are derived in semi-explicit form. The resulting optimal trading strategy is a long-short policy that accounts for whether the futures are in contango or backwardation. Our model also allows us to quantify and compare the values of trading in the futures markets when the underlying assets are traded or not. Numerical examples are provided to illustrate the optimal strategies and the effects of model parameters.
Valuation of contingent claims with short selling bans under an equal-risk pricing framework
Authors: Guiyuan Ma, Song-Ping Zhu, Ivan Guo
Date: 2019-10-11 00:00:00
Tasks: ERP

This paper studies the valuation of European contingent claims with short selling bans under the equal risk pricing (ERP) framework proposed in Guo and Zhu (2017) where analytical pricing formulae were derived in the case of monotonic payoffs under risk-neutral measures. We establish a unified framework for this new pricing approach so that its range of application can be significantly expanded. The results of Guo and Zhu (2017) are extended to the case of non-monotonic payoffs (such as a butterfly spread option) under risk-neutral measures. We also provide numerical schemes for computing equal-risk prices under other measures such as the original physical measure. Furthermore, we demonstrate how short selling bans can affect the valuation of contingent claims by comparing equal-risk prices with Black-Scholes prices.
Variational collision and obstacle avoidance of multi-agent systems on Riemannian manifolds
Date: 2019-10-11 00:00:00

In this paper we study a path planning problem from a variational approach to
collision and obstacle avoidance for multi-agent systems evolving on a
Riemannian manifold. The problem consists of finding non-intersecting
trajectories between the agent and prescribed obstacles on the workspace, among
a set of admissible curves, to reach a specified configuration, based on
minimizing an energy functional that depends on the velocity, covariant
acceleration and an artificial potential function used to prevent collision
with the obstacles and among the agents. We apply the results to examples of a
planar rigid body, and collision and obstacle avoidance for agents evolving on
a sphere.
Identifying Mood Episodes Using Dialogue Features from Clinical Interviews
Authors: Zakaria Aldeneh, Mimansa Jaiswal, Michael Picheny, Melvin McInnis, Emily Mower Provost
Date: 2019-09-29 00:00:00

Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health. Mental health professionals assess symptom severity through semi-structured clinical interviews. During these interviews, they observe their patients' spoken behaviors, including both what the patients say and how they say it. In this work, we move beyond acoustic and lexical information, investigating how higher-level interactive patterns also change during mood episodes. We then perform a secondary analysis, asking if these interactive patterns, measured through dialogue features, can be used in conjunction with acoustic features to automatically recognize mood episodes. Our results show that it is beneficial to consider dialogue features when analyzing and building automated systems for predicting and monitoring mood.
Psilocybin based therapy for cancer related distress, a systematic review and meta analysis
Date: 2019-10-10 00:00:00

Background : depression and anxiety are common in patients with cancer,
classical antidepressant has no proven efficacy on this type of distress
compared to placebo. A Psilocybin (serotoninergic hallucinogen) based therapy
appear to give promising results among recent studies. Aims : to examine if a
Psilocybin based therapy could be considered for patients with cancer related
depression and anxiety and to assume it's safety. To sum up Heffter institute
work, as the main institute working on this topic. Method : following PRISMA
(Preferred Reporting Items for Systematic reviews and Meta Analyses)
guidelines, a systematic review was conducted, for quantitative and qualitative
studies about psilocybin for treating cancer related depression and anxiety.
Pubmed and the Heffter institute databases have been reached for this purpose,
separating studies in types : qualitative or quantitative. We studied the
effects on cancer related depression and anxiety separately and investigated
the psychological and neurobiological mechanisms. Results : the four studies
included a total of 105 randomized patients, meta analysis on depression and
anxiety with pooled Peto odds ratio showed a significant superiority of
Psilocybin over placebo. The substance appeared to be safe for this type of
patients. Surprising psychological mechanisms hypothesis have been found out.
Conclusion : psilocybin appear to be potentially useful as a treatment for
cancer related depression and anxiety. Future research should verify these
findings on wider population and eventually seek a way to apply therapy to non
hospitalized (ambulatory) patients. Keywords : psilocybin, depression, anxiety,
review, meta-analysis
An Efficient Approach for obtaining Feasible solutions from SOCP formulation of ACOPF
Date: 2019-10-11 00:00:00

Exact Second Order Conic Programming (SOCP) formulation of AC Optimal Power
Flow (ACOPF) consists of non-convex arctangent constraints. Generally, these
constraints have been ignored or approximated (at the expense of increased
computational time) so as to solve the relaxed and convex SOCP formulation of
ACOPF. As a consequence, retrieving unique and feasible bus voltage phasors for
ACOPF of meshed networks is not always possible. In this letter, this issue has
been addressed. The arctangent constraints have been represented by alternate
linear constraints in the relaxed SOCP formulation of ACOPF, by exploiting the
properties of the meshed power network. Numerical tests show that the proposed
formulation gives an unique and feasible ACOPF solution, which is practically
realizable from system operation perspective and with global optimality
feature, as compared to other works reported in the literature. Moreover, the
proposed formulation is extremely efficient as only one execution of
formulation provides a feasible solution.
Rational hyperbolic discounting
Date: 2020-02-28 00:00:00
Tasks: valid

How much should you receive in a week to be indifferent to \$ 100 in six
months? Note that the indifference requires a rule to ensure the similarity
between early and late payments. Assuming that rational individuals have low
accuracy, then the following rule is valid: if the amounts to be paid are much
less than the personal wealth, then the $q$-exponential discounting guarantees
indifference in several periods. Thus, the discounting can be interpolated
between hyperbolic and exponential functions due to the low accuracy to
distinguish time averages when the payments have low impact on personal wealth.
Therefore, there are physical conditions that allow the hyperbolic discounting
regardless psycho-behavioral assumption.