pwc-archive/papers-with-abstracts huggingface dataset:
A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets
Date: 2019-05-13 00:00:00
Tasks: TAG

Motivation: P values derived from the null hypothesis significance testing
framework are strongly affected by sample size, and are known to be
irreproducible in underpowered studies, yet no suitable replacement has been
proposed. Results: Here we present implementations of non-parametric
standardized median effect size estimates, dNEF, for high-throughput sequencing
datasets. Case studies are shown for transcriptome and tag-sequencing datasets.
The dNEF measure is shown to be more reproducible and robust than P values and
requires sample sizes as small as 3 to reproducibly identify differentially
abundant features. Availability: Source code and binaries freely available at:
https://bioconductor.org/packages/ALDEx2.html , omicplotR, and
https://github.com/ggloor/CoDaSeq .
DreamNLP: Novel NLP System for Clinical Report Metadata Extraction using Count Sketch Data Streaming Algorithm: Preliminary Results
Authors: Choi Sanghyun, Ivkin Nikita, Braverman Vladimir, Jacobs Michael A.
Date: 2018-08-26 00:00:00

Extracting information from electronic health records (EHR) is a challenging
task since it requires prior knowledge of the reports and some natural language
processing algorithm (NLP). With the growing number of EHR implementations,
such knowledge is increasingly challenging to obtain in an efficient manner. We
address this challenge by proposing a novel methodology to analyze large sets
of EHRs using a modified Count Sketch data streaming algorithm termed DreamNLP.
By using DreamNLP, we generate a dictionary of frequently occurring terms or
heavy hitters in the EHRs using low computational memory compared to
conventional counting approach other NLP programs use. We demonstrate the
extraction of the most important breast diagnosis features from the EHRs in a
set of patients that underwent breast imaging. Based on the analysis,
extraction of these terms would be useful for defining important features for
downstream tasks such as machine learning for precision medicine.
The new face of multifractality: Multi-branchedness and the phase transitions in time series of mean inter-event times
Date: 2020-04-25 00:00:00
Tasks: Time Series, Time Series Analysis

Empirical time series of inter-event or waiting times are investigated using
a modified Multifractal Detrended Fluctuation Analysis operating on
fluctuations of mean detrended dynamics. The core of the extended multifractal
analysis is the non-monotonic behavior of the generalized Hurst exponent $h(q)$
-- the fundamental exponent in the study of multifractals. The consequence of
this behavior is the non-monotonic behavior of the coarse H\"older exponent
$\alpha (q)$ leading to multi-branchedness of the spectrum of dimensions. The
Legendre-Fenchel transform is used instead of the routinely used canonical
Legendre (single-branched) contact transform. Thermodynamic consequences of the
multi-branched multifractality are revealed. These are directly expressed in
the language of phase transitions between thermally stable, metastable, and
unstable phases. These phase transitions are of the first and second orders
according to Mandelbrot's modified Ehrenfest classification. The discovery of
multi-branchedness is tantamount in significance to extending multifractal
analysis.
Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands
Date: 2019-09-30 00:00:00
Tasks: Temporal Sequences

A diverse white matter network and finely tuned neuronal membrane properties
allow the brain to transition seamlessly between cognitive states. However, it
remains unclear how static structural connections guide the temporal
progression of large-scale brain activity patterns in different cognitive
states. Here, we analyze the brain's trajectories through a high-dimensional
activity space at the level of single time point activity patterns from
functional magnetic resonance imaging data acquired during passive visual
fixation (rest) and an n-back working memory task. We find that specific state
space trajectories, which represent temporal sequences of brain activity, are
modulated by cognitive load and related to task performance. Using
diffusion-weighted imaging acquired from the same subjects, we use tools from
network control theory to show that linear spread of activity along white
matter connections constrains the brain's state space trajectories at rest.
Additionally, accounting for stimulus-driven visual inputs explains the
different trajectories taken during the n-back task. We also used models of
network rewiring to show that these findings are the result of non-trivial
geometric and topological properties of white matter architecture. Finally, we
examine associations between age and time-resolved brain state dynamics,
revealing new insights into functional changes in the default mode and
executive control networks. Overall, these results elucidate the structural
underpinnings of cognitively and developmentally relevant spatiotemporal brain
dynamics.
A survey of food recommenders
Authors: Anderson Carl
Date: 2018-09-15 00:00:00
Tasks: Survey

Everyone eats. However, people do not always know what to eat. They need a
little help and inspiration. Consequently, a number of apps, services, and
programs have developed recommenders around food. These cover food, meal,
recipe, and restaurant recommendations, which are the most common use cases,
but also other areas such as substitute ingredients, menus, and diets. The
latter is especially important in the area of health and wellness where users
have more specific dietary needs and goals.
  In this survey, we review the food recommender literature. We cover the types
of systems in terms of their goals and what they are recommending, the datasets
and signals that they use to train models, the technical approaches and model
types used, as well as some of the system constraints.
Personalizing Fairness-aware Re-ranking
Authors: Liu Weiwen, Burke Robin
Date: 2018-09-13 00:00:00
Tasks: Diversity, Fairness, Recommendation Systems, Re-Ranking

Personalized recommendation brings about novel challenges in ensuring
fairness, especially in scenarios in which users are not the only stakeholders
involved in the recommender system. For example, the system may want to ensure
that items from different providers have a fair chance of being recommended. To
solve this problem, we propose a Fairness-Aware Re-ranking algorithm (FAR) to
balance the ranking quality and provider-side fairness. We iteratively generate
the ranking list by trading off between accuracy and the coverage of the
providers. Although fair treatment of providers is desirable, users may differ
in their receptivity to the addition of this type of diversity. Therefore,
personalized user tolerance towards provider diversification is incorporated.
Experiments are conducted on both synthetic and real-world data. The results
show that our proposed re-ranking algorithm can significantly promote fairness
with a slight sacrifice in accuracy and can do so while being attentive to
individual user differences.
Cryptocurrencies, Mainstream Asset Classes and Risk Factors - A Study of Connectedness
Date: 2018-09-10 00:00:00

We investigate connectedness within and across two major groups or assets: i)
five popular cryptocurrencies, and ii) six major asset classes plus two
commonly employed risk factors. Granger-causality tests uncover six direct
channels of causality from the elements of the mainstream assets/risk factors
group to digital assets. On the other hand there are two statistically
significant causal links going in the other direction. In order to provide some
perspective on the magnitude of the uncovered linkages we supplement the
analysis by estimating networks from forecast error variance decompositions.
The estimated connectedness within the groups is relatively large, whereas the
linkages across the two groups are small in comparison. Namely, less than 2.2
percent of future uncertainty of any cryptocurrency is sourced from all
non-crypto assets combined, while the joint contribution of all digital assets
to non-crypto uncertainty does not exceed 1.5 percent.
Systematics and symmetry in molecular phylogenetic modelling: perspectives from physics
Date: 2018-09-15 00:00:00

The aim of this review is to present and analyze the probabilistic models of
mathematical phylogenetics which have been intensively used in recent years in
biology as the cornerstone of attempts to infer and reconstruct the ancestral
relationships between species. We outline the development of theoretical
phylogenetics, from the earliest studies based on morphological characters,
through to the use of molecular data in a wide variety of forms. We bring the
lens of mathematical physics to bear on the formulation of theoretical models,
focussing on the applicability of many methods from the toolkit of that
tradition -- techniques of groups and representations to guide model
specification and to exploit the multilinear setting of the models in the
presence of underlying symmetries; extensions to coalgebraic properties of the
generators associated to rate matrices underlying the models, in relation to
the graphical structures (trees and networks) which form the search space for
inferring evolutionary trees. Aspects presented, include relating model classes
to relevant matrix Lie algebras, as well as manipulations with group characters
to enumerate various natural polynomial invariants, for identifying robust,
low-parameter quantities for use in inference. Above all, we wish to emphasize
the many features of multipartite entanglement which are shared between
descriptions of quantum states on the physics side, and the multi-way tensor
probability arrays arising in phylogenetics. In some instances, well-known
objects such as the Cayley hyperdeterminant (the `tangle') can be directly
imported into the formalism -- for models with binary character traits, and
triplets of taxa. In other cases new objects appear, such as the remarkable
quintic `squangle' invariants for quartet tree discrimination and DNA data,
with their own unique interpretation in the phylogenetic modeling context.
Heart Rate Estimation from Ballistocardiography Based on Hilbert Transform and Phase Vocoder
Date: 2018-09-10 00:00:00
Tasks: Heart rate estimation

This paper presents a robust method to monitor heart rate (HR) from BCG
(Ballistocardiography) signal, which is acquired from the sensor embedded in a
chair or a mattress. The proposed algorithm addresses the shortfalls in
traditional Fast Fourier Transform (FFT) based approaches by introducing
Hilbert Transform to extract the pulse envelope that models the repetition of
J-peaks in BCG signal. The frequency resolution is further enhanced by applying
FFT and phase vocoder to the pulse envelope. The performance of the proposed
algorithm is verified by experiment from 7 subjects. For HR estimation, mean
absolute error (MAE) of 0.90 beats per minute (BPM) and standard deviation of
absolute error (STD) of 1.14 BPM are obtained. Pearson correlation coefficient
between estimated HR and ground truth HR of 0.98 is also achieved.
Zero-Crossing Waveform Interferometry: an Alternative to Correlation in Signal Processing
Date: 2018-09-10 00:00:00

It is shown that multiple representations (such as replicas or Hilbert
transforms) of a random waveform can interfere constructively to form a compact
pattern, akin to a wave packet, when the representations are created in
synchrony with zero crossings of the waveform. A function of such 'engineered'
zero-crossing interferograms can exhibit time-delay resolution superior to that
associated with a conventional correlation function, especially for waveforms
with slowly-decaying power spectra. A phenomenon of local slew rate at zero
crossings is exploited to substantially reduce the Cram\'er-Rao bound on
time-delay estimators. A system, based on a concept of elapsed time, is
proposed to determine zero-crossing interferograms in real time.
Studying Confirmation Bias in Hashtag Usage on Twitter
Authors: Kowald Dominik, Lex Elisabeth
Date: 2018-09-10 00:00:00

The micro-blogging platform Twitter allows its nearly 320 million monthly
active users to build a network of follower connections to other Twitter users
(i.e., followees) in order to subscribe to content posted by these users. With
this feature, Twitter has become one of the most popular social networks on the
Web and was also the first platform that offered the concept of hashtags.
Hashtags are freely-chosen keywords, which start with the hash character, to
annotate, categorize and contextualize Twitter posts (i.e., tweets).
  Although hashtags are widely accepted and used by the Twitter community, the
heavy reuse of hashtags that are popular in the personal Twitter networks
(i.e., own hashtags and hashtags used by followees) can lead to filter bubble
effects and thus, to situations, in which only content associated with these
hashtags are presented to the user. These filter bubble effects are also highly
associated with the concept of confirmation bias, which is the tendency to
favor and reuse information that confirms personal preferences. One example
would be a Twitter user who is interested in political tweets of US president
Donald Trump. Depending on the hashtags used, the user could either be stuck in
a pro-Trump (e.g., #MAGA) or contra-Trump (e.g., #fakepresident) filter bubble.
Therefore, the goal of this paper is to study confirmation bias and filter
bubble effects in hashtag usage on Twitter by treating the reuse of hashtags as
a phenomenon that fosters confirmation bias.
Single-molecule kinetics in living cells
Date: 2018-09-10 00:00:00

In the past decades, advances in microscopy have made it possible to study
the dynamics of individual biomolecules in vitro and resolve intramolecular
kinetics that would otherwise be hidden in ensemble averages. More recently,
single-molecule methods have been used to image, localize and track
individually labeled macromolecules in the cytoplasm of living cells, allowing
investigations of intermolecular kinetics under physiologically relevant
conditions. In this review, we illuminate the particular advantages of
single-molecule techniques when studying kinetics in living cells and discuss
solutions to specific challenges associated with these methods.
Classification of grasping tasks based on EEG-EMG coherence
Date: 2018-09-10 00:00:00
Tasks: Activity Recognition, Classification, EEG, Electroencephalogram (EEG)

This work presents an innovative application of the well-known concept of
cortico-muscular coherence for the classification of various motor tasks, i.e.,
grasps of different kinds of objects. Our approach can classify objects with
different weights (motor-related features) and different surface frictions
(haptics-related features) with high accuracy (over 0:8). The outcomes
presented here provide information about the synchronization existing between
the brain and the muscles during specific activities; thus, this may represent
a new effective way to perform activity recognition.
Joint Beamforming and Association Design for MIMO Radar
Date: 2018-09-10 00:00:00

A critical task of a radar receiver is data association, which assigns radar
target detections to target filter tracks. Motivated by its importance, this
paper introduces the problem of jointly designing multiple-input
multiple-output (MIMO) radar transmit beam patterns and the corresponding data
association schemes. We show that the coupling of the beamforming and the
association subproblems can be conveniently parameterized by what we term an
ambiguity graph, which prescribes if two targets are to be disambiguated by the
beamforming design or by the data association scheme. The choice of ambiguity
graph determines which of the two subproblems is more difficult and therefore
allows to trade performance of one versus the other, resulting in a
detection-association trade-off. This paper shows how to design both the beam
pattern and the association scheme for a given ambiguity graph. It then
discusses how to choose an ambiguity graph achieving close to the optimal
detection-association trade-off.
LUDB: a new open-access validation tool for electrocardiogram delineation algorithms
Authors: Alena I. Kalyakulina, Igor I. Yusipov, Victor A. Moskalenko, Alexander V. Nikolskiy, Konstantin A. Kosonogov, Grigory V. Osipov, Nikolai Yu. Zolotykh, Mikhail V. Ivanchenko
Date: 2018-08-30 00:00:00
Tasks: Diversity, ECG Wave Delineation

We report Lobachevsky University Database (LUDB) of ECG signals, an open tool for validating ECG delineation algorithms, that is superior to the existing publicly available data bases in several aspects. LUDB contains 200 recordings of 10-second 12-lead electrocardiograms (ECG) from different subjects, representative of a variety of signal morphologies. The boundaries and peaks of QRS complexes and P and T waves are manually annotated by cardiologists for all recordings and independently for each lead, and all records received an expert classification by abnormalities. We present a case study for the recently proposed wavelet-based algorithm and the broadly used ecg-kit tool, and demonstrate the advantage of multi-lead ECG data analysis. LUDB contributes to the diversity of public databases employed in developing and validating novel ECG analysis algorithms, including the most advanced based on deep learning neural networks.
Systemic Risk and the Dependence Structures
Date: 2018-09-10 00:00:00

We propose a dynamic model of dependence structure between financial
institutions within a financial system and we construct measures for dependence
and financial instability. Employing Markov structures of joint credit
migrations, our model allows for contagious simultaneous jumps in credit
ratings and provides flexibility in modeling dependence structures. Another key
aspect is that the proposed measures consider the interdependence and reflect
the changing economic landscape as financial institutions evolve over time. In
the final part, we give several examples, where we study various dependence
structures and investigate their systemic instability measures. In particular,
we show that subject to the same pool of Markov chains, the simulated Markov
structures with distinct dependence structures generate different sequences of
systemic instability.
The Ladder Theory of Behavioral Decision Making
Date: 2018-09-13 00:00:00
Tasks: Decision Making

We study individual decision-making behavioral on generic view. Using a
formal mathematical model, we investigate the action mechanism of decision
behavioral under subjective perception changing of task attributes. Our model
is built on work in two kinds classical behavioral decision making theory:
"prospect theory (PT)" and "image theory (IT)". We consider subjective
attributes preference of decision maker under the whole decision process.
Strategies collection and selection mechanism are induced according the
description of multi-attributes decision making. A novel behavioral
decision-making framework named "ladder theory (LT)" is proposed. By real four
cases comparing, the results shows that the LT have better explanation and
prediction ability then PT and IT under some decision situations. Furthermore,
we use our model to shed light on that the LT theory can cover PT and IT
ideally. It is the enrichment and development for classical behavioral decision
theory and, it has positive theoretical value and instructive significance for
explaining plenty of real decision-making phenomena. It may facilitate our
understanding of how individual decision-making performed actually.
A class of stochastic games and moving free boundary problems
Authors: Xin Guo, Wenpin Tang, Renyuan Xu
Date: 2018-09-10 00:00:00

In this paper we propose and analyze a class of $N$-player stochastic games that include finite fuel stochastic games as a special case. We first derive sufficient conditions for the Nash equilibrium (NE) in the form of a verification theorem. The associated Quasi-Variational-Inequalities include an essential game component regarding the interactions among players, which may be interpreted as the analytical representation of the conditional optimality for NEs. The derivation of NEs involves solving first a multi-dimensional free boundary problem and then a Skorokhod problem. We call it a "moving free boundary" to highlight the difference between standard control problems and stochastic games. Finally, we present an intriguing connection between these NE strategies and controlled rank-dependent stochastic differential equations.
Comparison of signal detectors for time domain radio SETI
Date: 2018-09-10 00:00:00
Tasks: Sensitivity

The radio Search for Extra Terrestrial Intelligence (SETI) aims at
identifying intelligent and communicative civilizations in the Universe through
the detection of engineered transmissions. In the absence of prior knowledge
concerning the expected signal, SETI detection pipelines necessitate high
sensitivity, versatility, and limited computational complexity to maximize the
search parameter space and minimize the probability of misses. This paper
addresses the SETI detection problem as a binary hypothesis testing problem,
and compares four detection schemes exploiting artificial features of the data
collected by a single receiver radio telescope. After a theoretical comparison,
those detectors are applied to real data collected with the Green Bank
Telescope in West Virginia (USA).
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations
Authors: Karako Chen, Manggala Putra
Date: 2018-09-10 00:00:00
Tasks: Diversity, Fairness, Recommendation Systems, Re-Ranking

The trade-off between relevance and fairness in personalized recommendations
has been explored in recent works, with the goal of minimizing learned
discrimination towards certain demographics while still producing relevant
results.
  We present a fairness-aware variation of the Maximal Marginal Relevance (MMR)
re-ranking method which uses representations of demographic groups computed
using a labeled dataset. This method is intended to incorporate fairness with
respect to these demographic groups.
  We perform an experiment on a stock photo dataset and examine the trade-off
between relevance and fairness against a well known baseline, MMR, by using
human judgment to examine the results of the re-ranking when using different
fractions of a labeled dataset, and by performing a quantitative analysis on
the ranked results of a set of query images. We show that our proposed method
can incorporate fairness in the ranked results while obtaining higher precision
than the baseline, while our case study shows that even a limited amount of
labeled data can be used to compute the representations to obtain fairness.
This method can be used as a post-processing step for recommender systems and
search.
Benefits of Positioning-Aided Communication Technology in High-Frequency Industrial IoT
Date: 2018-09-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The future of industrial applications is shaped by intelligent moving IoT
devices, such as flying drones, advanced factory robots, and connected
vehicles, which may operate (semi-)autonomously. In these challenging
scenarios, dynamic radio connectivity at high frequencies -- augmented with
timely positioning-related information -- becomes instrumental to improve
communication performance and facilitate efficient computation offloading. Our
work reviews the main research challenges and reveals open implementation gaps
in Industrial IoT (IIoT) applications that rely on location awareness and
multi-connectivity in super high and extremely high frequency bands. It further
conducts a rigorous numerical investigation to confirm the potential of precise
device localization in the emerging IIoT systems. We focus on positioning-aided
benefits made available to multi-connectivity IIoT device operation at 28 GHz,
which notably improve data transfer rates, communication latency, and extent of
control overhead.