pwc-archive/papers-with-abstracts huggingface dataset:
Unifying the stochastic and the adversarial Bandits with Knapsack
Authors: Rangi Anshuka, Franceschetti Massimo, Tran-Thanh Long
Date: 2018-10-23 00:00:00

This paper investigates the adversarial Bandits with Knapsack (BwK) online
learning problem, where a player repeatedly chooses to perform an action, pays
the corresponding cost, and receives a reward associated with the action. The
player is constrained by the maximum budget $B$ that can be spent to perform
actions, and the rewards and the costs of the actions are assigned by an
adversary. This problem has only been studied in the restricted setting where
the reward of an action is greater than the cost of the action, while we
provide a solution in the general setting. Namely, we propose EXP3.BwK, a novel
algorithm that achieves order optimal regret. We also propose EXP3++.BwK, which
is order optimal in the adversarial BwK setup, and incurs an almost optimal
expected regret with an additional factor of $\log(B)$ in the stochastic BwK
setup. Finally, we investigate the case of having large costs for the actions
(i.e., they are comparable to the budget size $B$), and show that for the
adversarial setting, achievable regret bounds can be significantly worse,
compared to the case of having costs bounded by a constant, which is a common
assumption within the BwK literature.
Reliability Modeling, Analysis and Prediction of Wireless Mobile Communications
Date: 2018-11-29 00:00:00

The future Fifth Generation (5G) mobile cellular networks that are currently
in research phase today enable broad range of services/applications beyond
classical mobile communications. One key enabler for Ultra-Reliable services to
be integrated into mobile networks is the \textit{Reliability} of transmission
success of a given data packet. This is harder mainly owing to the
time-dependent effective link qualities of the communicating devices. However,
successful indication of the availability of the instantaneous link quality
(e.g., by the device) would allow opportunistic access of ultra reliable
services/applications when the link conditions are fair enough. This paper
introduces a framework for modeling, predicting and analyzing the theoretical
reliability of the wireless link based on factors such as fading, mobility,
interference etc. The analysis and prediction is based on the part stress
method\cite{Birolini2010} by assuming time dependent factors as
elements/components and their respective Transmission Times To Failure (TTTF).
The proposed framework also supports other reliability analysis techniques such
as Fault Tree Analysis and Accelerated testing. of wireless systems and to
improve the components
Unimolecular FRET Sensors: Simple Linker Designs and Properties
Date: 2018-11-10 00:00:00

Protein activation and deactivation is central to a variety of biological
mechanisms, including cellular signaling and transport. Unimolecular
fluorescent resonance energy transfer (FRET) probes are a class of fusion
protein sensors that allow biologists to visualize using an optical microscope
whether specific proteins are activated due to the presence nearby of small
drug-like signaling molecules, ligands or analytes. Often such probes comprise
a donor fluorescent protein attached to a ligand binding domain, a sensor or
reporter domain attached to the acceptor fluorescent protein, with these ligand
binding and sensor domains connected by a protein linker. Various choices of
linker type are possible ranging from highly flexible proteins to hinge-like
proteins. It is also possible to select donor and acceptor pairs according to
their corresponding F\"oster radius, or even to mutate binding and sensor
domains so as to change their binding energy in the activated or inactivated
states. The focus of the present work is the exploration through simulation of
the impact of such choices on sensor performance.
Swift Two-sample Test on High-dimensional Neural Spiking Data
Date: 2018-11-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction, Vocal Bursts Valence Prediction

To understand how neural networks process information, it is important to
investigate how neural network dynamics varies with respect to different
stimuli. One challenging task is to design efficient statistical approaches to
analyze multiple spike train data obtained from a short recording time. Based
on the development of high-dimensional statistical methods, it is able to deal
with data whose dimension is much larger than the sample size. However, these
methods often require statistically independent samples to start with, while
neural data are correlated over consecutive sampling time bins. We develop an
approach to pretreat neural data to become independent samples over time by
transferring the correlation of dynamics for each neuron in different sampling
time bins into the correlation of dynamics among different dimensions within
each sampling time bin. We verify the method using simulation data generated
from Integrate-and-fire neuron network models and a large-scale network model
of primary visual cortex within a short time, e.g., a few seconds. Our method
may offer experimenters to use the advantage of the development of statistical
methods to analyze high-dimensional neural data.
Optimal Transmission Using a Self-sustained Relay in a Full-Duplex MIMO System
Date: 2019-01-30 00:00:00

This paper investigates wireless information and power transfer in a
full-duplex MIMO relay channel where the self-sustained relay harvests energy
from both source transmit signal and self-interference signal to decode and
forward source information to a destination. We present a novel technique to
jointly optimize power splitting at the relay and precoding design (power
allocation) for both the source and relay transmissions. We formulate a new
convex optimization problem, establish the dual problem via closed-form optimal
primal solutions, and design an efficient primal-dual algorithm to maximize the
achievable throughput. Numerical results demonstrate the benefits of using
multiple transmit and receive antennas in both information decoding and energy
harvesting. We also extend our analysis to the case when channel state
information is only available at receiving nodes and show how our algorithm can
optimize the power splitting at the relay for it to remain self-sustained.
Through analysis and simulation, we show how an optimal combination of
non-uniform power splitting, variable power allocation, and self-interference
power harvesting effectively exploits a full-duplex MIMO system to achieve
significant performance gains over existing uniform power splitting and
half-duplex transmission techniques.
BioSimulator.jl: Stochastic simulation in Julia
Date: 2018-11-29 00:00:00

Biological systems with intertwined feedback loops pose a challenge to
mathematical modeling efforts. Moreover, rare events, such as mutation and
extinction, complicate system dynamics. Stochastic simulation algorithms are
useful in generating time-evolution trajectories for these systems because they
can adequately capture the influence of random fluctuations and quantify rare
events. We present a simple and flexible package, BioSimulator.jl, for
implementing the Gillespie algorithm, $\tau$-leaping, and related stochastic
simulation algorithms. The objective of this work is to provide scientists
across domains with fast, user-friendly simulation tools. We used the
high-performance programming language Julia because of its emphasis on
scientific computing. Our software package implements a suite of stochastic
simulation algorithms based on Markov chain theory. We provide the ability to
(a) diagram Petri Nets describing interactions, (b) plot average trajectories
and attached standard deviations of each participating species over time, and
(c) generate frequency distributions of each species at a specified time.
BioSimulator.jl's interface allows users to build models programmatically
within Julia. A model is then passed to the simulate routine to generate
simulation data. The built-in tools allow one to visualize results and compute
summary statistics. Our examples highlight the broad applicability of our
software to systems of varying complexity from ecology, systems biology,
chemistry, and genetics. The user-friendly nature of BioSimulator.jl encourages
the use of stochastic simulation, minimizes tedious programming efforts, and
reduces errors during model specification.
Why are prices proportional to embodied energies?
Date: 2018-11-29 00:00:00

The observed proportionality between nominal prices and average embodied
energies cannot be interpreted with conventional economic theory. A model is
presented that places energy transfers as the focal point of scarcity based on
the idea that (1) goods are material rearrangements, and (2) humans can only
rearrange matter with energy transfers. Modified consumer and producer problems
for an autarkic agent show that the opportunity cost of goods are given by
their marginal energy transfers, which depend on subjective and objective
factors (e.g. consumer preferences and direct energy transfers). Allowing for
exchange and under perfect competition, nominal prices arise as social
manifestations of goods' marginal energy transfers. The proportionality between
nominal prices and average embodied energy follows given the relation between
the latter and marginal energy transfers.
Fair Odds for Noisy Probabilities
Date: 2018-11-29 00:00:00

We suggest that one individual holds multiple degrees of belief about an
outcome, given the evidence. We then investigate the implications of such noisy
probabilities for a buyer and a seller of binary options and find the odds
agreed upon to ensure zero-expectation betting, differ from those consistent
with the relative frequency of outcomes. More precisely, the buyer and the
seller agree to odds that are higher (lower) than the reciprocal of their
averaged unbiased probabilities when this average indicates the outcome is more
(less) likely to occur than chance. The favorite-longshot bias thereby emerges
to establish the foundation of an equitable market. As corollaries, our work
suggests the old-established way of revealing someone's degree of belief
through wagers may be more problematic than previously thought, and implies
that betting markets cannot generally promise to support rational decisions.
Share, but unequally: A plausible mechanism for emergence and maintenance of intratumor heterogeneity
Date: 2018-11-29 00:00:00

Intratumor heterogeneity (ITH), referring to coexistence of different cell
subpopulations in a single tumor, has been a major puzzle in cancer research
for almost half a century. The lack of understanding of the underlying
mechanism of ITH hinders progress in developing effective therapies for
cancers. Based on the findings in a recent quantitative experiment on
pancreatic cancer, we developed a general evolutionary model for one type of
cancer, accounting for interactions between different cell populations through
paracrine or juxtacrine factors. We show that the emergence of a stable
heterogeneous state in a tumor requires an unequal allocation of paracrine
growth factors ("public goods") between cells that produce them and those that
merely consume them. Our model provides a quantitative explanation of recent
{\it in vitro} experimental studies in pancreatic cancer in which insulin
growth factor (IGF-II) plays the role of public goods. The calculated phase
diagrams as a function of exogenous resources and fraction of growth factor
producing cells show ITH persists only in a narrow range of concentration of
exogenous IGF-II. Remarkably, maintenance of ITH requires cooperation among
tumor cell subpopulations in harsh conditions, specified by lack of exogenous
IGF-II, whereas surplus exogenous IGF-II elicits competition. Our theory also
quantitatively accounts for measured {\it in vivo} tumor growth in glioblastoma
multiforme (GBM). The predictions for GBM tumor growth as a function of the
fraction of tumor cells are amenable to experimental tests. The mechanism for
ITH also provides hints for devising efficacious therapies.
Blue-Noise Sampling on Graphs
Date: 2019-05-23 00:00:00

In the area of graph signal processing, a graph is a set of nodes arbitrarily
connected by weighted links; a graph signal is a set of scalar values
associated with each node; and sampling is the problem of selecting an optimal
subset of nodes from which a graph signal can be reconstructed. This paper
proposes the use of spatial dithering on the vertex domain of the graph, as a
way to conveniently find statistically good sampling sets. This is done
establishing that there is a family of good sampling sets characterized on the
vertex domain by a maximization of the distance between sampling nodes; in the
Fourier domain, these are characterized by spectrums that are dominated by high
frequencies referred to as blue-noise. The theoretical connection between
blue-noise sampling on graphs and previous results in graph signal processing
is also established, explaining the advantages of the proposed approach.
Restricting our analysis to undirected and connected graphs, numerical tests
are performed in order to compare the effectiveness of blue-noise sampling
against other approaches.
An Energy-Efficient Transaction Model for the Blockchain-enabled Internet of Vehicles (IoV)
Authors: Sharma Vishal
Date: 2018-11-30 00:00:00
Tasks: Clustering

The blockchain is a safe, reliable and innovative mechanism for managing
numerous vehicles seeking connectivity. However, following the principles of
the blockchain, the number of transactions required to update ledgers pose
serious issues for vehicles as these may consume the maximum available energy.
To resolve this, an efficient model is presented in this letter which is
capable of handling the energy demands of the blockchain-enabled Internet of
Vehicles (IoV) by optimally controlling the number of transactions through
distributed clustering. Numerical results suggest that the proposed approach is
40.16% better in terms of energy conservation and 82.06% better in terms of the
number of transactions required to share the entire blockchain-data compared
with the traditional blockchain.
Sampling Schemes for Accurate Reconstruction and Computation of Performance Parameters of Antenna Radiation Pattern
Date: 2018-11-30 00:00:00

In practice, the finite number of samples of the spherical radiation pattern
or antenna gain are taken on the sphere for both the reconstruction of the
antenna radiation pattern and the computation of mobile handset performance
measures such as directivity and mean effective gain (MEG). The acquisition of
samples is time consuming as the measurements are required to be collected over
the range of frequencies and in multiple spatial directions. It is therefore
desired to have a sampling strategy that takes fewer number of samples for the
accurate reconstruction of radiation pattern and incoming signal power
distribution. In this work, we propose to use equiangular sampling,
Gauss-Legendre sampling and optimal dimensionality sampling schemes on the
sphere for the acquisition of measurements of spherical radiation pattern of
the antenna for its reconstruction, analysis and evaluation of performance
parameters of the antenna. By appropriately choosing the spherical harmonic
degree band-limits of the gain and the power distribution model of the incoming
signal, we demonstrate that the proposed sampling strategies require
significantly less number of samples for the accurate evaluation of MEG than
the existing methods that rely on the approximate evaluation of the surface
integral on the sphere.
The observation resolution -- a neglected aspect with critical influence on movement-based foraging indices
Date: 2018-11-30 00:00:00

Movement-based indices such as moves per minute (MPM) and proportion time
moving (PTM) are common methodologies to quantify foraging behaviour. Hundreds
of studies have reported these indices without specifying the temporal
resolution of their original data, despite the likelihood that the minimal stop
and move durations can affect MPM and PTM estimates. Our goal was to
empirically determine the sensitivity of these foraging indices to changes in
the temporal resolution of the observation. We used a high speed camera to
record movement sequences of 20 Acanthodactylus boskianus lizards. We gradually
decreased the data resolution by ignoring short stops and either ignoring,
elongating or leaving short moves unchanged. We then used the manipulated data
to calculate the foraging indices at different temporal resolutions. We found
that movement-based indices are very sensitive to the observation resolution,
so that realistic variation in the minimal duration of stops and moves could
lead to 68 percent and 48 percent difference in MPM and PTM estimates,
respectively. When using the highest resolution, our estimate of MPM was an
order of magnitude higher than all prior reported values for lizards. Also, the
distribution of stop durations was well described by a single heavy tailed
distribution above 0.35 seconds. This suggests that for A. boskianus there is
no reason to ignore short stops above this threshold. Our results raise major
concerns regarding the use of already published movement based indices, and
enable us to recommend how new foraging data should be collected.
Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model
Authors: Grando Felipe, Lamb Luis C.
Date: 2018-10-28 00:00:00
Tasks: regression

Vertex centrality measures are a multi-purpose analysis tool, commonly used
in many application environments to retrieve information and unveil knowledge
from the graphs and network structural properties. However, the algorithms of
such metrics are expensive in terms of computational resources when running
real-time applications or massive real world networks. Thus, approximation
techniques have been developed and used to compute the measures in such
scenarios. In this paper, we demonstrate and analyze the use of neural network
learning algorithms to tackle such task and compare their performance in terms
of solution quality and computation time with other techniques from the
literature. Our work offers several contributions. We highlight both the pros
and cons of approximating centralities though neural learning. By empirical
means and statistics, we then show that the regression model generated with a
feedforward neural networks trained by the Levenberg-Marquardt algorithm is not
only the best option considering computational resources, but also achieves the
best solution quality for relevant applications and large-scale networks.
Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models,
Machine Learning, Regression Model
Origin of Biomolecular Networks
Date: 2018-11-30 00:00:00

Biomolecular networks have already found great utility in characterizing
complex biological systems arising from pair-wise interactions amongst
biomolecules. Here, we review how graph theoretical approaches can be applied
not only for a better understanding of various proximate (mechanistic)
relations, but also, ultimate (evolutionary) structures encoded in such
networks. A central question deals with the evolutionary dynamics by which
different topologies of biomolecular networks might have evolved, as well as
the biological principles that can be hypothesized from a deeper understanding
of the induced network dynamics. We emphasize the role of gene duplication in
terms of signaling game theory, whereby sender and receiver gene players accrue
benefit from gene duplication, leading to a preferential attachment mode of
network growth. Information asymmetry between sender and receiver genes is
hypothesized as a key driver of the resulting network topology. The study of
the resulting dynamics suggests many mathematical/computational problems, the
majority of which are intractable but yield to efficient approximation
algorithms, when studied through an algebraic graph theoretic lens.
Cascade-Net: a New Deep Learning Architecture for OFDM Detection
Date: 2018-11-30 00:00:00
Tasks: Deep Learning

In this paper, we consider using deep neural network for OFDM symbol
detection and demonstrate its performance advantages in combating large Doppler
Shift. In particular, a new architecture named Cascade-Net is proposed for
detection, where deep neural network is cascading with a zero-forcing
preprocessor to prevent the network stucking in a saddle point or a local
minimum point. In addition, we propose a sliding detection approach in order to
detect OFDM symbols with large number of subcarriers. We evaluate this new
architecture, as well as the sliding algorithm, using the Rayleigh channel with
large Doppler spread, which could degrade detection performance in an OFDM
system and is especially severe for high frequency band and mmWave
communications. The numerical results of OFDM detection in SISO scenario show
that cascade-net can achieve better performance than zero-forcing method while
providing robustness against ill conditioned channels. We also show the better
performance of the sliding cascade network (SCN) compared to sliding
zero-forcing detector through numerical simulation.
The Indus Script and Economics. A Role for Indus Seals and Tablets in Rationing and Administration of Labor
Authors: Rao Rajesh P. N.
Date: 2018-11-28 00:00:00

The Indus script remains one of the last major undeciphered scripts of the
ancient world. We focus here on Indus inscriptions on a group of miniature
tablets discovered by Meadow and Kenoyer in Harappa in 1997. By drawing
parallels with proto-Elamite and proto-Cuneiform inscriptions, we explore how
these miniature tablets may have been used to record rations allocated to
porters or laborers. We then show that similar inscriptions are found on stamp
seals, leading to the potentially provocative conclusion that rather than
simply indicating ownership of property, Indus seals may have been used for
generating tokens, tablets and sealings for repetitive economic transactions
such as rations and exchange of canonical amounts of goods, grains, animals,
and labor in a barter-based economy.
Community structures in allelopathic interaction networks: an eco-evolutionary approach
Date: 2018-11-30 00:00:00
Tasks: Diversity

Nowadays, evidence is mounting that the race of living organisms for
adaptation to the chemicals synthesized by their neighbours may drive community
structures. Particularly, some bacterial infections and plant invasions
disruptive of the native community rely on the release of allelochemicals that
inhibit or kill sensitive strains or individuals from their own or other
species. In this report, an eco-evolutionary model for community assembly
through resource competition, allelophatic interactions, and evolutionary
branching is presented and studied by numerical analysis. Our major findings
are that stable communities with increasing biodiversity can emerge at weak
allelopathic suppression, but stronger allelophaty is negatively correlated
with community diversity. In the former regime the allelopathic interaction
networks exhibit Gaussian degree distributions, while in the later one the
network degrees are Weibull distributed.
A Linear Formulation for Power System State Estimation including RTU and PMU Measurements
Date: 2018-11-30 00:00:00
Tasks: State Estimation

In this paper, a novel linear formulation for power system state estimation
that simultaneously treats conventional and synchrophasor measurements is
proposed. A linear circuit model for conventional measurements is introduced to
enable a fully linear equivalent circuit representation of the power system.
The estimated system state is then obtained by formulating the optimization
problem to minimize the measurement errors and solving the resulting linear set
of optimality conditions. To evaluate the accuracy of the proposed method,
simulations are performed on several test cases of various sizes and the
results are presented and discussed.