pwc-archive/papers-with-abstracts huggingface dataset:
Time-consistent conditional expectation under probability distortion
Date: 2020-06-25 00:00:00
Tasks: Stochastic Optimization, valid

We introduce a new notion of conditional nonlinear expectation under
probability distortion. Such a distorted nonlinear expectation is not
sub-additive in general, so it is beyond the scope of Peng's framework of
nonlinear expectations. A more fundamental problem when extending the distorted
expectation to a dynamic setting is time-inconsistency, that is, the usual
"tower property" fails. By localizing the probability distortion and
restricting to a smaller class of random variables, we introduce a so-called
distorted probability and construct a conditional expectation in such a way
that it coincides with the original nonlinear expectation at time zero, but has
a time-consistent dynamics in the sense that the tower property remains valid.
Furthermore, we show that in the continuous time model this conditional
expectation corresponds to a parabolic differential equation whose coefficient
involves the law of the underlying diffusion. This work is the first step
towards a new understanding of nonlinear expectations under probability
distortion, and will potentially be a helpful tool for solving
time-inconsistent stochastic optimization problems.
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism
Date: 2018-09-21 00:00:00

Motivation: Despite being often perceived as the main contributors to cell
fate and physiology, genes alone cannot predict cellular phenotype. During the
process of gene expression, 95% of human genes can code for multiple proteins
due to alternative splicing. While most splice variants of a gene carry the
same function, variants within some key genes can have remarkably different
roles. To bridge the gap between genotype and phenotype, condition- and
tissue-specific models of metabolism have been constructed. However, current
metabolic models only include information at the gene level. Consequently, as
recently acknowledged by the scientific community, common situations where
changes in splice-isoform expression levels alter the metabolic outcome cannot
be modeled. Results: We here propose GEMsplice, the first method for the
incorporation of splice-isoform expression data into genome-scale metabolic
models. Using GEMsplice, we make full use of RNA-Seq quantitative expression
profiles to predict, for the first time, the effects of splice isoform-level
changes in the metabolism of 1455 patients with 31 different breast cancer
types. We validate GEMsplice by generating cancer-versus-normal predictions on
metabolic pathways, and by comparing with gene-level approaches and available
literature on pathways affected by breast cancer. GEMsplice is freely available
for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to
state-of-the-art methods, we anticipate that GEMsplice will enable for the
first time computational analyses at transcript level with splice-isoform
resolution.
A Step-by-Step Guide to Using BioNetFit
Date: 2018-09-21 00:00:00
Tasks: global-optimization

BioNetFit is a software tool designed for solving parameter identification
problems that arise in the development of rule-based models. It solves these
problems through curve fitting (i.e., nonlinear regression). BioNetFit is
compatible with deterministic and stochastic simulators that accept BioNetGen
language (BNGL)-formatted files as inputs, such those available within the
BioNetGen framework. BioNetFit can be used on a laptop or standalone multicore
workstation as well as on many Linux clusters, such as those that use the Slurm
Workload Manager to schedule jobs. BioNetFit implements a metaheuristic
population-based global optimization procedure, an evolutionary algorithm (EA),
to minimize a user-defined objective function, such as a residual sum of
squares (RSS) function. BioNetFit also implements a bootstrapping procedure for
determining confidence intervals for parameter estimates. Here, we provide
step-by-step instructions for using BioNetFit to estimate the values of
parameters of a BNGL-encoded model and to define bootstrap confidence
intervals. The process entails the use of several plain-text files, which are
processed by BioNetFit and BioNetGen. In general, these files include 1) one or
more EXP files, which each contains (experimental) data to be used in parameter
identification/bootstrapping; 2) a BNGL file containing a model section, which
defines a (rule-based) model, and an actions section, which defines simulation
protocols that generate GDAT and/or SCAN files with model predictions
corresponding to the data in the EXP file(s); and 3) a CONF file that
configures the fitting/bootstrapping job and that defines algorithmic parameter
settings.
Tail probabilities for short-term returns on stocks
Date: 2019-03-20 00:00:00

We consider the tail probabilities of stock returns for a general class of
stochastic volatility models. In these models, the stochastic differential
equation for volatility is autonomous, time-homogeneous and dependent on only a
finite number of dimensional parameters. Three bounds on the high-volatility
limits of the drift and diffusion coefficients of volatility ensure that
volatility is mean-reverting, has long memory and is as volatile as the stock
price. Dimensional analysis then provides leading-order approximations to the
drift and diffusion coefficients of volatility for the high-volatility limit.
Thereby, using the Kolmogorov forward equation for the transition probability
of volatility, we find that the tail probability for short-term returns falls
off like an inverse cubic. Our analysis then provides a possible explanation
for the inverse cubic fall off that Gopikrishnan et al. (1998) report for
returns over 5-120 minutes intervals. We find, moreover, that the tail
probability scales like the length of the interval, over which the return is
measured, to the power 3/2. There do not seem to be any empirical results in
the literature with which to compare this last prediction.
Coarse--graining, fixed points, and scaling in a large population of neurons
Date: 2018-09-22 00:00:00
Tasks: Hippocampus

We develop a phenomenological coarse--graining procedure for activity in a
large network of neurons, and apply this to recordings from a population of
1000+ cells in the hippocampus. Distributions of coarse--grained variables seem
to approach a fixed non--Gaussian form, and we see evidence of scaling in both
static and dynamic quantities. These results suggest that the collective
behavior of the network is described by a non--trivial fixed point.
Query Understanding via Entity Attribute Identification
Authors: Nobari Arash Dargahi, Askari Arian, Hasibi Faegheh, Neshati Mahmood
Date: 2018-09-23 00:00:00
Tasks: Attribute, Learning-To-Rank

Understanding searchers' queries is an essential component of semantic search
systems. In many cases, search queries involve specific attributes of an entity
in a knowledge base (KB), which can be further used to find query answers. In
this study, we aim to move forward the understanding of queries by identifying
their related entity attributes from a knowledge base. To this end, we
introduce the task of entity attribute identification and propose two methods
to address it: (i) a model based on Markov Random Field, and (ii) a learning to
rank model. We develop a human annotated test collection and show that our
proposed methods can bring significant improvements over the baseline methods.
EC-GSM-IoT Network Synchronization with Support for Large Frequency Offsets
Authors: Stefan Lippuner, Benjamin Weber, Mauro Salomon, Matthias Korb, Qiuting Huang
Date: 2018-09-23 00:00:00

EDGE-based EC-GSM-IoT is a promising candidate for the billion-device cellular IoT (cIoT), providing similar coverage and battery life as NB-IoT. The goal of 20 dB coverage extension compared to EDGE poses significant challenges for the initial network synchronization, which has to be performed well below the thermal noise floor, down to an SNR of -8.5 dB. We present a low-complexity synchronization algorithm supporting up to 50 kHz initial frequency offset, thus enabling the use of a low-cost +/-25 ppm oscillator. The proposed algorithm does not only fulfill the 3GPP requirements, but surpasses them by 3 dB, enabling communication with an SNR of -11.5 dB or a maximum coupling loss of up to 170.5 dB.
Understanding the Gist of Images - Ranking of Concepts for Multimedia Indexing
Authors: Weiland Lydia, Ponzetto Simone Paolo, Effelsberg Wolfgang, Dietz Laura
Date: 2018-09-23 00:00:00
Tasks: Learning-To-Rank

Nowadays, where multimedia data is continuously generated, stored, and
distributed, multimedia indexing, with its purpose of group- ing similar data,
becomes more important than ever. Understanding the gist (=message) of
multimedia instances is framed in related work as a ranking of concepts from a
knowledge base, i.e., Wikipedia. We cast the task of multimedia indexing as a
gist understanding problem. Our pipeline benefits from external knowledge and
two subsequent learning- to-rank (l2r) settings. The first l2r produces a
ranking of concepts rep- resenting the respective multimedia instance. The
second l2r produces a mapping between the concept representation of an instance
and the targeted class topic(s) for the multimedia indexing task. The
evaluation on an established big size corpus (MIRFlickr25k, with 25,000
images), shows that multimedia indexing benefits from understanding the gist.
Finally, with a MAP of 61.42, it can be shown that the multimedia in- dexing
task benefits from understanding the gist. Thus, the presented end-to-end
setting outperforms DBM and competes with Hashing-based methods.
Exact Solutions for a GBM-type Stochastic Volatility Model having a Stationary Distribution
Date: 2019-05-25 00:00:00

We find various exact solutions for a new stochastic volatility (SV) model:
the transition probability density, European-style option values, and (when it
exists) the martingale defect. This may represent the first example of an SV
model combining exact solutions, GBM-type volatility noise, and a stationary
volatility density.
On the Information in Extreme Measurements for Parameter Estimation
Date: 2018-09-23 00:00:00
Tasks: Data Compression, parameter estimation

This paper deals with parameter estimation from extreme measurements. While
being a special case of parameter estimation from partial data, in scenarios
where only one sample from a given set of K measurements can be extracted,
choosing only the minimum or the maximum (i.e., extreme) value from that set is
of special interest because of the ultra-low energy, storage, and processing
power required to extract extreme values from a given data set. We present a
new methodology to analyze the performance of parameter estimation from extreme
measurements. In particular, we present a general close-form approximation for
the Cramer-Rao Lower Bound on the parameter estimation error, based on extreme
values. We demonstrate our methodology on the case where the original
measurements are exponential distributed, which is related to many practical
applications. The analysis shows that the maximum values carry most of the
information about the parameter of interest and that the additional information
in the minimum is negligible. Moreover, it shows that for small sets of iid
measurements (e.g. K=15) the use of the maximum can provide data compression
with factor 15 while keeping about 50% of the information stored in the
complete set.
Financial accumulation implies ever-increasing wealth inequality
Authors: Yuri Biondi, Stefano Olla
Date: 2018-09-23 00:00:00

Wealth inequality is an important matter for economic theory and policy. Ongoing debates have been discussing recent rise in wealth inequality in connection with recent development of active financial markets around the world. Existing literature on wealth distribution connects the origins of wealth inequality with a variety of drivers. Our approach develops a minimalist modelling strategy that combines three featuring mechanisms: active financial markets; individual wealth accumulation; and compound interest structure. We provide mathematical proof that accumulated financial investment returns involve ever-increasing wealth concentration and inequality across individual investors through time. This cumulative effect through space and time depends on the financial accumulation process and holds also under efficient financial markets, which generate some fair investment game that individual investors do repeatedly play through time.
Contribution of social network analysis and collective phenomena to understanding social complexity and cognition
Date: 2018-09-24 00:00:00

The social brain hypothesis postulates the increasing complexity of social
interactions as a driving force for the evolution of cognitive abilities.
Whereas dyadic and triadic relations play a basic role in defining social
behaviours and pose many challenges for the social brain, individuals in animal
societies typically belong to relatively large networks. How the structure and
dynamics of these networks also contribute to the evolution of cognition, and
vice versa, is less understood. Here we review how collective phenomena can
occur in systems where social agents do not require sophisticated cognitive
skills, and how complex networks can grow from simple probabilistic rules, or
even emerge from the interaction between agents and their environment, without
explicit social factors. We further show that the analysis of social networks
can be used to develop good indicators of social complexity beyond the
individual or dyadic level. We also discuss the types of challenges that the
social brain must cope with in structured groups, such as higher information
fluxes, originating from individuals playing different roles in the network, or
dyadic contacts of widely varying durations and frequencies. We discuss the
relevance of these ideas for primates and other animals societies.
Graphical Requirements for Multistationarity in Reaction Networks and their Verification in BioModels
Date: 2018-09-24 00:00:00
Tasks: ARC

Thomas's necessary conditions for the existence of multiple steady states in
gene networks have been proved by Soul\'e with high generality for dynamical
systems defined by differential equations. When applied to (protein) reaction
networks however, those conditions do not provide information since they are
trivially satisfied as soon as there is a bimolecular or a reversible reaction.
Refined graphical requirements have been proposed to deal with such cases. In
this paper, we present for the first time a graph rewriting algorithm for
checking the refined conditions given by Soliman, and evaluate its practical
performance by applying it systematically to the curated branch of the
BioModels repository. This algorithm analyzes all reaction networks (of size up
to 430 species) in less than 0.05 second per network, and permits to conclude
to the absence of multistationarity in 160 networks over 506. The short
computation times obtained in this graphical approach are in sharp contrast to
the Jacobian-based symbolic computation approach. We also discuss the case of
one extra graphical condition by arc rewiring that allows us to conclude on 20
more networks of this benchmark but with a high computational cost. Finally, we
study with some details the case of phosphorylation cycles and MAPK signalling
models which show the importance of modelling the intermediate complexations
with the enzymes in order to correctly analyze the multistationarity
capabilities of such biochemical reaction networks.
Functional Intrusive Load Monitor (FILM): A Model-based Platform for Non-Intrusive Load Monitoring System Development
Date: 2018-09-19 00:00:00
Tasks: Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is an important application to monitor
household appliance activities and provide related information to house owner
or/and utility company via a single sensor installed at the electrical entry of
the house. It can be used for different purposes in residential and industrial
sectors. Thus, an increasing number of new algorithms have been developed in
recent years. In these algorithms, researchers either use existing public
datasets or collect their own data which causes such problems as insufficiency
of electrical parameters, missing of ground-truth data, absence of many
appliances, and lack of appliance information. To solve these problems, this
paper presents a model-based platform for NILM system development, namely
Functional Intrusive Load Monitor (FILM). By using this platform, the state
transitions and activities of all the involved appliances can be preset by
researchers, and multiple electrical parameters such as harmonics and power
factor can be monitored or calculated. This platform will help researchers save
the time of collecting experimental data, utilize precise control of individual
appliance activities, and develop load signatures of devices. This paper
describes the steps, structure, and requirements of building this platform.
Case study is presented to help understand this platform.
Millimeter-Wave Over-the-Air Signal-to-Interference-plus-Noise-Ratio Measurements Using a MIMO Testbed
Date: 2018-09-19 00:00:00

In this paper, over-the-air experiments with external and internal
interferences were performed using Chalmers millimeter-wave
multiple-input-multiple-output testbed MATE. The resulting SINR for both
interference experiments are compared and discussed.
How trait distributions evolve in heterogeneous populations
Date: 2018-09-21 00:00:00

We consider the problem of determining the time evolution of a trait
distribution in a mathematical model of non-uniform populations with parametric
heterogeneity. This means that we consider only heterogeneous populations in
which heterogeneity is described by an individual specific parameter that
differs in general from individual to individual, but does not change with time
for the whole lifespan of this individual. Such a restriction allows obtaining
a number of simple and yet important analytical results. In particular we show
that initial assumptions on time-dependent behavior of various characteristics,
such as the mean, variance, of coefficient of variation, restrict severely
possible choices for the exact form of the trait distribution. We illustrate
our findings by in-depth analysis of the variance evolution. We also reanalyze
a well known mathematical model for gypsy moth population showing that the
knowledge of how distributions evolve allows producing oscillatory behaviors
for highly heterogeneous populations.
Optical and RF Metrology for 5G
Date: 2018-09-21 00:00:00

Specification standards will soon be available for 5G mobile RF
communications. What optical and electrical metrology is needed or available to
support the development of the supporting optical communication systems? Device
measurement, digital oscilloscope impairments and improving system resolution
are discussed.
Uncovering Multi-Site Identifiability Based on Resting-State Functional Connectomes
Date: 2019-04-23 00:00:00
Tasks: Functional Connectivity

Multi-site studies are becoming important to increase statistical power,
enhance generalizability, and to improve the likelihood of pooling relevant
subgroups together activities. Even with harmonized imaging sequences,
site-dependent variability can mask the advantages of these multi-site studies.
The aim of this study was to assess multi-site reproducibility in resting-state
functional connectivity fingerprints, and to improve identifiability of
functional connectomes. The individual fingerprinting of functional
connectivity profiles is promising due to its potential as a robust
neuroimaging biomarker. We evaluated, on two independent multi-site datasets,
individual fingerprints in test-retest visit pairs within and across two sites
and present a generalized framework based on principal component analysis to
improve identifiability. Those components that maximized differential
identifiability of a training dataset were used as an orthogonal connectivity
basis to reconstruct the functional connectomes of training and validation
sets. The optimally reconstructed functional connectomes showed a substantial
improvement in individual fingerprinting within and across the two sites
relative to the original data. A notable increase in ICC values for functional
edges and resting-state networks was also observed. Improvements in
identifiability were not found to be affected by global signal regression.
Post-hoc analyses assessed the effect of the number of fMRI volumes on
identifiability and showed that multi-site differential identifiability was for
all cases maximized after optimal reconstruction. The generalizability of the
optimal set of orthogonal basis of each dataset was evaluated through a
leave-one-out procedure. Overall, results demonstrate that the framework
presented in this study systematically improves identifiability in
resting-state functional connectomes in multi-site studies.
On a gap between rational annuitization price for producer and price for customer
Date: 2018-09-24 00:00:00
Tasks: Decision Making

The paper studies pricing of insurance products focusing on the pricing of
annuities under uncertainty. This pricing problem is crucial for financial
decision making and was studied intensively, however, many open questions still
remain. In particular, there is a so-called "annuity puzzle" related to certain
inconsistency of existing financial theory with the empirical observations for
the annuities market. The paper suggests a pricing method based on the risk
minimization such that both producer and customer seek to minimize the mean
square hedging error accepted as a measure of risk. This leads to two different
versions of the pricing problem: the selection of the annuity price given the
rate of regular payments, and the selection of the rate of payments given the
annuity price. It appears that solutions of these two problems are different.
This can contribute to explanation for the "annuity puzzle".
A Fairness-aware Hybrid Recommender System
Authors: Farnadi Golnoosh, Kouki Pigi, Thompson Spencer K., Srinivasan Sriram, Getoor Lise
Date: 2018-09-13 00:00:00
Tasks: Fairness, Movie Recommendation, Probabilistic Programming, Recommendation Systems

Recommender systems are used in variety of domains affecting people's lives.
This has raised concerns about possible biases and discrimination that such
systems might exacerbate. There are two primary kinds of biases inherent in
recommender systems: observation bias and bias stemming from imbalanced data.
Observation bias exists due to a feedback loop which causes the model to learn
to only predict recommendations similar to previous ones. Imbalance in data
occurs when systematic societal, historical, or other ambient bias is present
in the data. In this paper, we address both biases by proposing a hybrid
fairness-aware recommender system. Our model provides efficient and accurate
recommendations by incorporating multiple user-user and item-item similarity
measures, content, and demographic information, while addressing recommendation
biases. We implement our model using a powerful and expressive probabilistic
programming language called probabilistic soft logic. We experimentally
evaluate our approach on a popular movie recommendation dataset, showing that
our proposed model can provide more accurate and fairer recommendations,
compared to a state-of-the art fair recommender system.
Decision Level Fusion: An Event Driven Approach
Date: 2018-09-29 00:00:00

This paper presents a technique that combines the occurrence of certain
events, as observed by different sensors, in order to detect and classify
objects. This technique explores the extent of dependence between features
being observed by the sensors, and generates more informed probability
distributions over the events. Provided some additional information about the
features of the object, this fusion technique can outperform other existing
decision level fusion approaches that may not take into account the
relationship between different features.