pwc-archive/papers-with-abstracts huggingface dataset:
5Gperf: signal processing performance for 5G
Date: 2018-10-25 00:00:00
Tasks: CPU

The 5Gperf project was conducted by Huawei research teams in 2016-17. It was
concerned with the acceleration of signal-processing algorithms for a 5G
base-station prototype. It improved on already optimized SIMD-parallel CPU
algorithms and designed a new software tool for higher programmer productivity
when converting MATLAB code to optimized C
Analysis of the Smart Grid as a System of Systems
Date: 2018-10-25 00:00:00

The energy grid is currently undergoing a historic change of state from the
traditional structure where a utility owns the generation, transmission and
distribution services into an integrated smart grid in a monopolistic market
which introduce consumers as active players in managing and controlling the
power. This evolution adds more complexity to the energy scene as it requires
an unprecedented partnership of different fields and technologies to establish
successful integration of components with a bidirectional transfer of
information and energy. The Smart Grid Interoperability Panel (SGIP) was
incorporated in late 2009 to oversee the efforts in defining a set of standards
and interoperability layers to ensure a secure integration and operation of
various elements of the system. This paper discusses the smart grid concept as
a system-of-systems (SoS); analyzes the interoperability framework models
developed by SGIP and the European Union Smart Grid Cooperation Group
respectively; presents the stakeholders as domains and zones associated with
the interoperability layers; and illustrates other architectural concepts such
as fault-tolerance, software integration platform and design characteristics of
smart grid as an elegant system.
Optimal Trade Execution with Uncertain Volume Target
Authors: Julien Vaes, Raphael Hauser
Date: 2018-10-28 00:00:00

In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and becomes more accurate when approaching a specified delivery time. During the course of execution, a trader should then constantly adapt their trading strategy to meet their fluctuating volume target. In this paper, we develop a model that accounts for volume uncertainty and we show that a risk-averse trader has benefit in delaying their trades. More precisely, we argue that the optimal strategy is a trade-off between early and late trades in order to balance risk associated with both price and volume. By incorporating a risk term related to the volume to trade, the static optimal strategies suggested by our model avoid the explosion in the algorithmic complexity usually associated with dynamic programming solutions, all the while yielding competitive performance.
Economic Impact of Wind Generation Penetration in the Colombian Electricity Market
Date: 2018-10-28 00:00:00

The creation of the Renewable Energy Law (Law 1715 of 2014) promotes the
introduction of large-scale renewable energy generation in the Colombian
electricity market. The new legislation aims to diversify the country's
generation matrix, mainly composed of hydro and fuel-based generation, with a
share of 66% and 34% respectively. Currently, three wind generation projects,
with an aggregated capacity of 500 MW, have been commissioned in the North of
the country. This study analyses the economic impact of the large-scale
introduction of wind generation on both, the market spot price and conventional
generation plants operation. For this purpose, the study builds a unit
commitment model to mimic the current market legislation and the system's
generation data. We show that the introduction of wind energy into the
Colombian electricity market would impact the generation share of large hydro
and gas-fired power plants. The hydro generation has an important role in
balancing the generation for fluctuations on the wind resource. Meanwhile, the
gas-fired plants would decrease their participation in the market,
proportionally to the introduction of wind generation in the system, by as low
as 20% of its current operation.
Optimal Incentive Contract with Endogenous Monitoring Technology
Date: 2019-11-20 00:00:00
Tasks: Management

Recent technology advances have enabled firms to flexibly process and analyze
sophisticated employee performance data at a reduced and yet significant cost.
We develop a theory of optimal incentive contracting where the monitoring
technology that governs the above procedure is part of the designer's strategic
planning. In otherwise standard principal-agent models with moral hazard, we
allow the principal to partition agents' performance data into any finite
categories and to pay for the amount of information the output signal carries.
Through analysis of the trade-off between giving incentives to agents and
saving the monitoring cost, we obtain characterizations of optimal monitoring
technologies such as information aggregation, strict MLRP, likelihood
ratio-convex performance classification, group evaluation in response to rising
monitoring costs, and assessing multiple task performances according to agents'
endogenous tendencies to shirk. We examine the implications of these results
for workforce management and firms' internal organizations.
Intermediated Implementation
Date: 2020-01-20 00:00:00

We examine problems of ``intermediated implementation,'' in which a single
principal can only regulate limited aspects of the consumption bundles traded
between intermediaries and agents with hidden characteristics. An example is
sales, in which retailers offer menus of consumption bundles to customers with
hidden tastes, whereas a manufacturer with a potentially different goal from
retailers' is limited to regulating sold consumption goods but not retail
prices by legal barriers. We study how the principal can implement through
intermediaries any social choice rule that is incentive compatible and
individually rational for agents. We demonstrate the effectiveness of per-unit
fee schedules and distribution regulations, which hinges on whether
intermediaries have private or interdependent values. We give further
applications to healthcare regulation and income redistribution.
Automatic Identification and Ranking of Emergency Aids in Social Media Macro Community
Authors: Gautam Bhaskar, Basava Annappa
Date: 2018-10-26 00:00:00
Tasks: Information Retrieval, Retrieval

Online social microblogging platforms including Twitter are increasingly used
for aiding relief operations during disaster events. During most of the
calamities that can be natural disasters or even armed attacks,
non-governmental organizations look for critical information about resources to
support effected people. Despite the recent advancement of natural language
processing with deep neural networks, retrieval and ranking of short text
becomes a challenging task because a lot of conversational and sympathy content
merged with the critical information. In this paper, we address the problem of
categorical information retrieval and ranking of most relevance information
while considering the presence of short-text and multilingual languages that
arise during such events. Our proposed model is based on the formation of
embedding vector with the help of textual and statistical preprocessing, and
finally, entire training 2,100,000 vectors were normalized using feed-forward
neural network for need and availability tweets. Another important contribution
of this paper lies in novel weighted Ranking Key algorithm based on top five
general terms to rank the classified tweets in most relevance with
classification. Lastly, we test our model on Nepal Earthquake dataset (contains
short text and multilingual language tweets) and achieved 6.81% of mean average
precision on 5,250,000 unlabeled embedding vectors of disaster relief tweets.
Common lines modeling for reference free ab-initio reconstruction in cryo-EM
Date: 2018-10-26 00:00:00

We consider the problem of estimating an unbiased and reference-free ab-inito
model for non-symmetric molecules from images generated by single-particle
cryo-electron microscopy. The proposed algorithm finds the globally optimal
assignment of orientations that simultaneously respects all common lines
between all images. The contribution of each common line to the estimated
orientations is weighted according to a statistical model for common lines'
detection errors. The key property of the proposed algorithm is that it finds
the global optimum for the orientations given the common lines. In particular,
any local optima in the common lines energy landscape do not affect the
proposed algorithm. As a result, it is applicable to thousands of images at
once, very robust to noise, completely reference free, and not biased towards
any initial model. A byproduct of the algorithm is a set of measures that allow
to asses the reliability of the obtained ab-initio model. We demonstrate the
algorithm using class averages from two experimental data sets, resulting in
ab-initio models with resolutions of 20A or better, even from class averages
consisting of as few as three raw images per class.
Learn to Fly: A Distributed Mechanism for Joint 3D Placement and Users Association in UAVs-assisted Networks
Date: 2018-10-15 00:00:00

In this paper, we study the joint 3D placement of unmanned aerial vehicles
(UAVs) and UAVs-users association under bandwidth limitation and quality of
service constraint. In particular, in order to allow to UAVs to dynamically
improve their 3D locations in a distributed fashion while maximizing the
network's sum-rate, we break the underlying optimization into 3 subproblems
where we separately solve the 2D UAVs positioning, the altitude optimization,
and the UAVs-users association. First, given fixed 3D positions of UAVs, we
propose a fully distributed matching based association that alleviates the
bottlenecks of bandwidth allocation and guarantees the required quality of
service. Next, to address the 2D positions of UAVs, we adopt a modified version
of K-means algorithm, with a distributed implementation, where UAVs dynamically
change their 2D positions in order to reach the barycenter of the cluster that
is composed of the served ground users. In order to optimize the UAVs
altitudes, we study a naturally defined game-theoretic version of the problem
and show that under fixed UAVs 2D coordinates, a predefined association scheme,
and limited-interference, the UAVs altitudes game is a non-cooperative
potential game where the players (UAVs) can maximize the limited-interference
sum-rate by only optimizing a local utility function. Therefore, we adopt the
best response dynamics to reach a Nash equilibrium of the game which is also a
local optimum of the social welfare function. Our simulation results show that,
using the proposed approach, the network's sum rate of the studied scenario is
improved as compared with the trivial case where the classical version of
K-means is adopted and users are assigned, at each iteration, to the closest
UAV.
Sampling of Planar Curves: Theory and Fast Algorithms
Date: 2019-11-12 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

We introduce a continuous domain framework for the recovery of a planar curve
from a few samples. We model the curve as the zero level set of a trigonometric
polynomial. We show that the exponential feature maps of the points on the
curve lie on a low-dimensional subspace. We show that the null-space vector of
the feature matrix can be used to uniquely identify the curve, given a
sufficient number of samples. The worst-case theoretical guarantees show that
the number of samples required for unique recovery depends on the bandwidth of
the underlying trigonometric polynomial, which is a measure of the complexity
of the curve. We introduce an iterative algorithm that relies on the low-rank
property of the feature maps to recover the curves when the samples are noisy
or when the true bandwidth of the curve is unknown. We also demonstrate the
preliminary utility of the proposed curve representation in the context of
image segmentation.
Expected Utility Maximization and Conditional Value-at-Risk Deviation-based Sharpe Ratio in Dynamic Stochastic Portfolio Optimization
Date: 2018-10-27 00:00:00
Tasks: Portfolio Optimization

In this paper we investigate the expected terminal utility maximization
approach for a dynamic stochastic portfolio optimization problem. We solve it
numerically by solving an evolutionary Hamilton-Jacobi-Bellman equation which
is transformed by means of the Riccati transformation. We examine the
dependence of the results on the shape of a chosen utility function in regard
to the associated risk aversion level. We define the
  Conditional value-at-risk deviation ($CVaRD$) based Sharpe ratio for
measuring risk-adjusted performance of a dynamic portfolio. We compute optimal
strategies for a portfolio investment problem motivated by the German DAX 30
Index and we evaluate and analyze the dependence of the $CVaRD$-based Sharpe
ratio on the utility function and the associated risk aversion level.
Fast determination of coarse grained cell anisotropy and size in epithelial tissue images using Fourier transform
Date: 2019-03-17 00:00:00
Tasks: Segmentation

Mechanical strain and stress play a major role in biological processes such
as wound healing or morphogenesis. To assess this role quantitatively, fixed or
live images of tissues are acquired at a cellular precision in large fields of
views. To exploit these data, large numbers of cells have to be analyzed to
extract cell shape anisotropy and cell size. Most frequently, this is performed
through detailed individual cell contour determination, using so-called
segmentation computer programs, complemented if necessary by manual detection
and error corrections. However, a coarse grained and faster technique can be
recommended in at least three situations. First, when detailed information on
individual cell contours is not required, for instance in studies which require
only coarse-grained average information on cell anisotropy. Second, as an
exploratory step to determine whether full segmentation can be potentially
useful. Third, when segmentation is too difficult, for instance due to poor
image quality or too large a cell number. We developed a user-friendly, Fourier
transform-based image analysis pipeline. It is fast (typically $10^4$ cells per
minute with a current laptop computer) and suitable for time, space or ensemble
averages. We validate it on one set of artificial images and on two sets of
fully segmented images, one from a Drosophila pupa and the other from a chicken
embryo; the pipeline results are robust. Perspectives include \textit{in vitro}
tissues, non-biological cellular patterns such as foams, and $xyz$ stacks.
Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals
Date: 2018-10-27 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series, Time Series Analysis

Recent research has shown that human motions and positions can be recognized
through WiFi signals. The key intuition is that different motions and positions
introduce different multi-path distortions in WiFi signals and generate
different patterns in the time-series of channel state information (CSI). In
this paper, we propose Wi-Motion, a WiFi-based human activities recognition
system. Unlike existing systems, Wi-Motion adopts the amplitude and phase
information extracted from the CSI sequence to construct the classifiers
respectively, and combines the results using a combination strategy based on
posterior probability. As the simulation results shows, Wi-Motion can recognize
six human activities with the mean accuracy of 98:4%.
From Communication to Sensing : Recognizing and Counting Repetitive Motions with Wireless Backscattering
Date: 2018-10-27 00:00:00

Recently several ground-breaking RF-based motion recognition systems were
proposed to detect and/or recognize macro/micro human movements. These systems
often suffer from various interferences caused by multiple-users moving
simultaneously, resulting in extremely low recognition accuracy. To tackle this
challenge, we propose a novel system, called Motion-Fi, which marries battery
free wireless backscattering and device-free sensing. Motion-Fi is an accurate,
interference tolerable motion-recognition system, which counts repetitive
motions without using scenario-dependent templates or profiles and enables
multi-users performing certain motions simultaneously because of the relatively
short transmission range of backscattered signals. Although the repetitive
motions are fairly well detectable through the backscattering signals in
theory, in reality they get blended into various other system noises during the
motion. Moreover, irregular motion patterns among users will lead to expensive
computation cost for motion recognition. We build a backscattering wireless
platform to validate our design in various scenarios for over 6 months when
different persons, distances and orientations are incorporated. In our
experiments, the periodicity in motions could be recognized without any
learning or training process, and the accuracy of counting such motions can be
achieved within 5% count error. With little efforts in learning the patterns,
our method could achieve 93.1% motion-recognition accuracy for a variety of
motions. Moreover, by leveraging the periodicity of motions, the recognition
accuracy could be further improved to nearly 100% with only 3 repetitions. Our
experiments also show that the motions of multiple persons separating by around
2 meters cause little accuracy reduction in the counting process.
FBMC Prototype Filter Design via Convex Optimization
Date: 2018-10-27 00:00:00
Tasks: Vocal Bursts Type Prediction

In this work, we propose a prototype filter design for Filter Bank
MultiCarrier (FBMC) systems based on convex optimization, aiming superior
spectrum features while maintaining a high symbol reconstruction quality.
Initially, the proposed design is written as a non-convex Quadratically
Constrained Quadratic Programming (QCQP), which is relaxed into a convex QCQP
guided by a line search. Through the resulting problem, we design three
prototype filters: Type-I, II and III. In particular, the Type-II filter shows
a slightly better performance than the classical Mirabasi-Martin design, while
Type-I and III filters offer a much more contained spectrum than most of the
prototype filters suitable for FBMC applications. Furthermore, numerical
results corroborate the effectiveness of the designed filters as the proposed
filters offer fast decay and contained spectrum while not jeopardizing symbol
reconstruction in practice.
Total Power Minimization: Joint Antenna Selection and Beamforming Design
Date: 2019-08-28 00:00:00
Tasks: Form

In this paper, we consider the total power minimization problem when we have
signal-to-interference-plus-noise ratio (SINR) constraints. The consumed power
in the circuits depends on the number of active antennas, which can be modeled
using zero-norm. Due to the difficulty of dealing with the non-convex
zero-norm, we used the standard alternate weighted one-norm approach. We
addressed the total power minimization for a narrowband system with and without
per-antenna power constraints (PAPCs). We derived iterative closed-form
expressions in both cases. Then we analysed the case when we have multiple
bands operating at the same time. Analogous closed-form expressions are
provided. Our simulation results show that significant gains can be obtained in
terms of the total power required compared to standard methods that do not take
into account the circuit power.
Life at the edge: complexity and criticality in biological function
Authors: Dante R. Chialvo
Date: 2018-10-28 00:00:00

Why life is complex and --most importantly-- what is the origin of the over abundance of complexity in nature? This is a fundamental scientific question which, paraphrasing the late Per Bak, "is screaming to be answered but seldom is even being asked". In these lectures we review recent attempts across several scales to understand the origins of complex biological problems from the perspective of critical phenomena. To illustrate the approach three cases are discussed, namely the large scale brain dynamics, the characterisation of spontaneous fluctuations of proteins and the physiological complexity of the cell mitochondria network.
Through synapses to spatial memory maps: a topological model
Date: 2018-10-28 00:00:00

Various neurophysiological and cognitive functions are based on transferring
information between spiking neurons via a complex system of synaptic
connections. In particular, the capacity of presynaptic inputs to influence the
postsynaptic outputs---the efficacy of the synapses---plays a principal role in
all aspects of hippocampal neurophysiology. However, a direct link between the
information processed at the level of individual synapses and the animal's
ability to form memories at the organismal level has not yet been fully
understood. Here, we investigate the effect of synaptic transmission
probabilities on the ability of the hippocampal place cell ensembles to produce
a cognitive map of the environment. Using methods from algebraic topology, we
find that weakening synaptic connections increase spatial learning times,
produce topological defects in the large-scale representation of the ambient
space and restrict the range of parameters for which place cell ensembles are
capable of producing a map with correct topological structure. On the other
hand, the results indicate a possibility of compensatory phenomena, namely that
spatial learning deficiencies may be mitigated through enhancement of neuronal
activity.
Active Anomaly Detection with Switching Cost
Date: 2018-10-28 00:00:00
Tasks: Anomaly Detection

The problem of detecting a single anomalous process among multiple independent processes is considered. Under a constraint on the number of processes that can be probed simultaneously, the decision maker should decide which processes to probe at each time and when to terminate the probing. Compared with previous work considering only the observation costs, the switching costs of switchings across processes also need to be taken into account in many practical scenarios. The objective is an active inference strategy that minimizes the Bayesian risk taking into account of the sample complexity, switching cost, as well as detection errors. Based on the framework of sequential design of experiments, we propose a low-complexity, low-switching deterministic policy for two scenarios where the total switching cost is negligible and the total switching cost is comparable to the total observation cost. We show that the proposed algorithm is asymptotically optimal in the former scenario and is order optimal in the latter scenario. Simulation results demonstrate strong performance in the finite regime for both scenarios.
Bifurcations and multi-stability in a model of cytokine-mediated autoimmunity
Date: 2018-10-28 00:00:00

This paper investigates the dynamics of immune response and autoimmunity with
particular emphasis on the role of regulatory T cells (Tregs), T cells with
different activation thresholds, and cytokines in mediating T cell activity.
Analysis of the steady states yields parameter regions corresponding to regimes
of normal clearance of viral infection, chronic infection, or autoimmune
behaviour, and the boundaries of stability and bifurcations of relevant steady
states are found in terms of system parameters. Numerical simulations are
performed to illustrate different dynamical scenarios, and to identify basins
of attraction of different steady states and periodic solutions, highlighting
the important role played by the initial conditions in determining the outcome
of immune interactions.