pwc-archive/papers-with-abstracts huggingface dataset:
Cell detection on image-based immunoassays
Date: 2018-10-23 00:00:00
Tasks: Cell Detection

Cell detection and counting in the image-based ELISPOT and Fluorospot
immunoassays is considered a bottleneck. The task has remained hard to
automatize, and biomedical researchers often have to rely on results that are
not accurate. Previously proposed solutions are heuristic, and data-based
solutions are subject to a lack of objective ground truth data. In this paper,
we analyze a partial differential equations model for ELISPOT, Fluorospot, and
assays of similar design. This leads us to a mathematical observation model for
the images generated by these assays. We use this model to motivate a
methodology for cell detection. Finally, we provide a real-data example that
suggests that this cell detection methodology and a human expert perform
comparably.
Estimation of Spatial-Temporal Gait Parameters based on the Fusion of Inertial and Film-Pressure Signals
Date: 2018-10-23 00:00:00

Gait analysis (GA) has been widely used in physical activity monitoring and
clinical contexts, and the estimation of the spatial-temporal gait parameters
is of primary importance for GA. With the quick development of smart tiny
sensors, GA methods based on wearable devices have become more popular
recently. However, most existing wearable GA methods focus on data analysis
from inertial sensors. In this paper, we firstly present a two-foot-worn
in-shoe system (Gaitboter) based on low-cost, wearable and multimodal sensors'
fusion for GA, comprising an inertial sensor and eight film-pressure sensors
with each foot for gait raw data collection while walking. Secondly, a GA
algorithm for estimating the spatial-temporal parameters of gait is proposed.
The algorithm fully uses the fusion of two kinds of sensors' signals: inertial
sensor and film-pressure sensor, in order to estimate the spatial-temporal gait
parameters, such as stance phase, swing phase, double stance phase, cadence,
step time, stride time, stride length, velocity. Finally, to verify the
effectiveness of this system and algorithm of the paper, an experiment is
performed with 27 stoke patients from local hospital that the spatial-temporal
gait parameters obtained with the system and the algorithm are compared with a
GA tool used in medical laboratories. And the experimental results show that it
achieves very good consistency and correlation between the proposed system and
the compared GA tool.
Hybrid Beamforming With Sub-arrayed MIMO Radar: Enabling Joint Sensing and Communication at mmWave Band
Date: 2018-10-24 00:00:00

In this paper, we propose a beamforming design for dual-functional
radar-communication (DFRC) systems at the millimeter wave (mmWave) band, where
hybrid beamforming and sub-arrayed MIMO radar techniques are jointly exploited.
We assume that a base station (BS) is serving a user equipment (UE) located in
a Non-Line-of-Sight (NLoS) channel, which in the meantime actively detects
multiple targets located in a Line-of-Sight (LoS) channel. Given the optimal
communication beamformer and the desired radar beampattern, we propose to
design the analog and digital beamformers under non-convex constant-modulus
(CM) and power constraints, such that the weighted summation of the
communication and radar beamforming errors is minimized. The formulated
optimization problem can be decomposed into three subproblems, and is solved by
the alternating minimization approach. Numerical simulations verify the
feasibility of the proposed beamforming design, and show that our approach
offers a favorable performance tradeoff between sensing and communication.
Asset allocation: new evidence through network approaches
Date: 2018-10-20 00:00:00

The main contribution of the paper is to employ the financial market network
as a useful tool to improve the portfolio selection process, where nodes
indicate securities and edges capture the dependence structure of the system.
Three different methods are proposed in order to extract the dependence
structure between assets in a network context. Starting from this modified
structure, we formulate and then we solve the asset allocation problem. We find
that the portfolios obtained through a network-based approach are composed
mainly of peripheral assets, which are poorly connected with the others. These
portfolios, in the majority of cases, are characterized by an higher trade-off
between performance and risk with respect to the traditional Global Minimum
Variance (GMV) portfolio. Additionally, this methodology benefits of a
graphical visualization of the selected portfolio directly over the graphic
layout of the network, which helps in improving our understanding of the
optimal strategy.
Expropriations, Property Confiscations and New Offshore Entities: Evidence from the Panama Papers
Date: 2018-10-23 00:00:00

Using the Panama Papers, we show that the beginning of media reporting on
expropriations and property confiscations in a country increases the
probability that offshore entities are incorporated by agents from the same
country in the same month. This result is robust to the use of country-year
fixed effects and the exclusion of tax havens. Further analysis shows that the
effect is driven by countries with non-corrupt and effective governments, which
supports the notion that offshore entities are incorporated when reasonably
well-intended and well-functioning governments become more serious about
fighting organized crime by confiscating proceeds of crime.
Term structure modeling for multiple curves with stochastic discontinuities
Date: 2019-12-20 00:00:00

We develop a general term structure framework taking stochastic
discontinuities explicitly into account. Stochastic discontinuities are a key
feature in interest rate markets, as for example the jumps of the term
structures in correspondence to monetary policy meetings of the ECB show. We
provide a general analysis of multiple curve markets under minimal assumptions
in an extended HJM framework and provide a fundamental theorem of asset pricing
based on NAFLVR. The approach with stochastic discontinuities permits to embed
market models directly, unifying seemingly different modeling philosophies. We
also develop a tractable class of models, based on affine semimartingales,
going beyond the requirement of stochastic continuity.
Linear Receivers in Non-stationary Massive MIMO Channels with Visibility Regions
Date: 2018-10-23 00:00:00

In a massive MIMO system with large arrays, the channel becomes spatially
non-stationary. We study the impact of spatial non-stationarity characterized
by visibility regions (VRs) where the channel energy is significant on a
portion of the array. Relying on a channel model based on VRs, we provide
expressions of the signal-to-interference-plus-noise ratio (SINR) of conjugate
beamforming (CB) and zero-forcing (ZF) precoders. We also provide an
approximate deterministic equivalent of the SINR of ZF precoders. We identify
favorable and unfavorable multi-user configurations of the VRs and compare the
performance of both stationary and non-stationary channels through analysis and
numerical simulations.
Most Probable Evolution Trajectories in a Genetic Regulatory System Excited by Stable Lévy Noise
Authors: Xiujun Cheng, Hui Wang, Xiao Wang, Jinqiao Duan, Xiaofan Li
Date: 2018-10-09 00:00:00

We study the most probable trajectories of the concentration evolution for the transcription factor activator in a genetic regulation system, with non-Gaussian stable L\'evy noise in the synthesis reaction rate taking into account. We calculate the most probable trajectory by spatially maximizing the probability density of the system path, i.e., the solution of the associated nonlocal Fokker-Planck equation. We especially examine those most probable trajectories from low concentration state to high concentration state (i.e., the likely transcription regime) for certain parameters, in order to gain insights into the transcription processes and the tipping time for the transcription likely to occur. This enables us: (i) to visualize the progress of concentration evolution (i.e., observe whether the system enters the transcription regime within a given time period); (ii) to predict or avoid certain transcriptions via selecting specific noise parameters in particular regions in the parameter space. Moreover, we have found some peculiar or counter-intuitive phenomena in this gene model system, including (a) a smaller noise intensity may trigger the transcription process, while a larger noise intensity can not, under the same asymmetric L\'evy noise. This phenomenon does not occur in the case of symmetric L\'evy noise; (b) the symmetric L\'evy motion always induces transition to high concentration, but certain asymmetric L\'evy motions do not trigger the switch to transcription. These findings provide insights for further experimental research, in order to achieve or to avoid specific gene transcriptions, with possible relevance for medical advances.
Non-data-aided SNR Estimation for QPSK Modulation in AWGN Channel
Date: 2018-10-20 00:00:00

Signal-to-noise ratio (SNR) estimation is an important parameter that is
required in any receiver or communication systems. It can be computed either by
a pilot signal data-aided approach in which the transmitted signal would be
known to the receiver, or without any knowledge of the transmitted signal,
which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR
estimation algorithm for QPSK signal is proposed. The proposed algorithm
modifies the existing Signal- to-Variation Ratio (SVR) SNR estimation algorithm
in the aim to reduce its bias and mean square error in case of negative SNR
values at low number of samples of it. We first present the existing SVR
algorithm and then show the mathematical derivation of the new NDA algorithm.
In addition, we compare our algorithm to two baselines estimation methods,
namely the M2M4 and SVR algorithms, using different test cases. Those test
cases include low SNR values, extremely high SNR values and low number of
samples. Results showed that our algorithm had a better performance compared to
second and fourth moment estimation (M2M4) and original SVR algorithms in terms
of normalized mean square error (NMSE) and bias estimation while keeping almost
the same complexity as the original algorithms
Health Monitoring of Critical Power System Equipments using Identifying Codes
Date: 2018-10-22 00:00:00

High voltage power transformers are one of the most critical equipments in
the electric power grid. A sudden failure of a power transformer can
significantly disrupt bulk power delivery. Before a transformer reaches its
critical failure state, there are indicators which, if monitored periodically,
can alert an operator that the transformer is heading towards a failure. One of
the indicators is the signal to noise ratio (SNR) of the voltage and current
signals in substations located in the vicinity of the transformer. During
normal operations, the width of the SNR band is small. However, when the
transformer heads towards a failure, the widths of the bands increase, reaching
their maximum just before the failure actually occurs. This change in width of
the SNR can be observed by sensors, such as phasor measurement units (PMUs)
located nearby. Identifying Code is a mathematical tool that enables one to
uniquely identify one or more {\em objects of interest}, by generating a unique
signature corresponding to those objects, which can then be detected by a
sensor. In this paper, we first describe how Identifying Code can be utilized
for detecting failure of power transformers. Then, we apply this technique to
determine the fewest number of sensors needed to uniquely identify failing
transformers in different test systems.
ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler
Authors: Sotoudeh Matthew, Venkat Anand, Anderson Michael, Georganas Evangelos, Heinecke Alexander, Knight Jason
Date: 2018-10-12 00:00:00
Tasks: Code Generation, Deep Learning

Domain specific accelerators present new challenges and opportunities for
code generation onto novel instruction sets, communication fabrics, and memory
architectures.
  In this paper we introduce an intermediate representation (IR) which enables
both deep learning computational kernels and hardware capabilities to be
described in the same IR. We then formulate and apply instruction mapping to
determine the possible ways a computation can be performed on a hardware
system. Next, our scheduler chooses a specific mapping and determines the data
movement and computation order. In order to manage the large search space of
mappings and schedules, we developed a flexible framework that allows
heuristics, cost models, and potentially machine learning to facilitate this
search problem.
  With this system, we demonstrate the automated extraction of matrix
multiplication kernels out of recent deep learning kernels such as
depthwise-separable convolution. In addition, we demonstrate two to five times
better performance on DeepBench sized GEMMs and GRU RNN execution when compared
to state-of-the-art (SOTA) implementations on new hardware and up to 85% of the
performance for SOTA implementations on existing hardware.
A new approach of contextual recommendation based on the method of Hierarchical Analysis of Processes
Authors: Nefzi Halima
Date: 2018-10-23 00:00:00
Tasks: Decision Making, Recommendation Systems

Recommender systems are able to estimate the user's interest for resource
given from some relative information to others similar users and to propriety
of the resource. In this Memory, we introduced a new contextual recommendation
approach based on the AHP Process Hierarchical Analysis method. This work
consisted in making a bibliographic study on the works having proposed systems
of recommendation based on the context of the users in the field of films. The
goal is to design and develop a new approach to recommending movies based on
user context. And we relied on methods of multi-criteria decision making (MCDM)
and more precisely the method of Hierarchical Process Analysis (AHP) for
context integration in the recommendation process.
Recognition of basic hand movements using Electromyography
Date: 2018-10-23 00:00:00
Tasks: Anatomy, Classification, Electromyography (EMG)

The aim of this work was to identify six basic movements of the hand using
two systems. Being an interdisciplinary topic, there has been conducted
studying in the anatomy of forearm muscles, biosignals, the method of
electromyography (EMG) and methods of pattern recognition. Moreover, the signal
contained enough noise and had to be analyzed, using EMD, to extract features
and to reduce its dimensionality, using RELIEF and PCA, to improve the success
rate of classification. The first part uses an EMG system of Delsys initially
for an individual and then for six people with the average successful
classification, for these six movements at rates of over 80%. The second part
involves the construction of an autonomous system EMG using an Arduino
microcontroller, EMG sensors and electrodes, which are arranged in an elastic
glove. Classification results in this case reached 75% of success.
A Proximal Zeroth-Order Algorithm for Nonconvex Nonsmooth Problems
Authors: Kazemi Ehsan, Wang Liqiang
Date: 2018-10-17 00:00:00

In this paper, we focus on solving an important class of nonconvex
optimization problems which includes many problems for example signal
processing over a networked multi-agent system and distributed learning over
networks. Motivated by many applications in which the local objective function
is the sum of smooth but possibly nonconvex part, and non-smooth but convex
part subject to a linear equality constraint, this paper proposes a proximal
zeroth-order primal dual algorithm (PZO-PDA) that accounts for the information
structure of the problem. This algorithm only utilize the zeroth-order
information (i.e., the functional values) of smooth functions, yet the
flexibility is achieved for applications that only noisy information of the
objective function is accessible, where classical methods cannot be applied. We
prove convergence and rate of convergence for PZO-PDA. Numerical experiments
are provided to validate the theoretical results.
History by Diversity: Helping Historians search News Archives
Authors: Singh Jaspreet, Nejdl Wolfgang, Anand Avishek
Date: 2018-10-24 00:00:00
Tasks: Articles, Diversity

Longitudinal corpora like newspaper archives are of immense value to
historical research, and time as an important factor for historians strongly
influences their search behaviour in these archives. While searching for
articles published over time, a key preference is to retrieve documents which
cover the important aspects from important points in time which is different
from standard search behavior. To support this search strategy, we introduce
the notion of a Historical Query Intent to explicitly model a historian's
search task and define an aspect-time diversification problem over news
archives.
  We present a novel algorithm, HistDiv, that explicitly models the aspects and
important time windows based on a historian's information seeking behavior. By
incorporating temporal priors based on publication times and temporal
expressions, we diversify both on the aspect and temporal dimensions. We test
our methods by constructing a test collection based on The New York Times
  Collection with a workload of 30 queries of historical intent assessed
manually. We find that HistDiv outperforms all competitors in subtopic recall
with a slight loss in precision. We also present results of a qualitative user
study to determine wether this drop in precision is detrimental to user
experience. Our results show that users still preferred HistDiv's ranking.
Discovering Entities with Just a Little Help from You
Authors: Singh Jaspreet, Hoffart Johannes, Anand Avishek
Date: 2018-10-24 00:00:00
Tasks: Entity Disambiguation, Retrieval

Linking entities like people, organizations, books, music groups and their
songs in text to knowledge bases (KBs) is a fundamental task for many
downstream search and mining applications. Achieving high disambiguation
accuracy crucially depends on a rich and holistic representation of the
entities in the KB. For popular entities, such a representation can be easily
mined from Wikipedia, and many current entity disambiguation and linking
methods make use of this fact. However, Wikipedia does not contain long-tail
entities that only few people are interested in, and also at times lags behind
until newly emerging entities are added. For such entities, mining a suitable
representation in a fully automated fashion is very difficult, resulting in
poor linking accuracy.
  What can automatically be mined, though, is a high-quality representation
given the context of a new entity occurring in any text. Due to the lack of
knowledge about the entity, no method can retrieve these occurrences
automatically with high precision, resulting in a chicken-egg problem. To
address this, our approach automatically generates candidate occurrences of
entities, prompting the user for feedback to decide if the occurrence refers to
the actual entity in question. This feedback gradually improves the knowledge
and allows our methods to provide better candidate suggestions to keep the user
engaged. We propose novel human-in-the-loop retrieval methods for generating
candidates based on gradient interleaving of diversification and textual
relevance approaches.
  We conducted extensive experiments on the FACC dataset, showing that our
approaches convincingly outperform carefully selected baselines in both
intrinsic and extrinsic measures while keeping users engaged.
Designing Search Tasks for Archive Search
Authors: Singh Jaspreet, Anand Avishek
Date: 2018-10-24 00:00:00

Longitudinal corpora like legal, corporate and newspaper archives are of
immense value to a variety of users, and time as an important factor strongly
influences their search behavior in these archives. While many systems have
been developed to support users temporal information needs, questions remain
over how users utilize these advances to satisfy their needs. Analyzing their
search behavior will provide us with novel insights into search strategy, guide
better interface and system design and highlight new problems for further
research. In this paper we propose a set of search tasks, with varying
complexity, that IIR researchers can utilize to study user search behavior in
archives. We discuss how we created and refined these tasks as the result of a
pilot study using a temporal search engine. We not only propose task
descriptions but also pre and post-task evaluation mechanisms that can be
employed for a large-scale study (crowdsourcing). Our initial findings show the
viability of such tasks for investigating search behavior in archives.
The Losses from Integration in Matching Markets can be Large
Date: 2018-10-24 00:00:00

Although the integration of two-sided matching markets using stable
mechanisms generates expected gains from integration, I show that there are
worst-case scenarios in which these are negative. The losses from integration
can be large enough that the average rank of an agent's spouse decreases by
37.5% of the length of their preference list in any stable matching mechanism.
TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets
Authors: Fafalios Pavlos, Iosifidis Vasileios, Ntoutsi Eirini, Dietze Stefan
Date: 2018-10-23 00:00:00
Tasks: Data Integration, Sociology

Publicly available social media archives facilitate research in a variety of
fields, such as data science, sociology or the digital humanities, where
Twitter has emerged as one of the most prominent sources. However, obtaining,
archiving and annotating large amounts of tweets is costly. In this paper, we
describe TweetsKB, a publicly available corpus of currently more than 1.5
billion tweets, spanning almost 5 years (Jan'13-Nov'17). Metadata information
about the tweets as well as extracted entities, hashtags, user mentions and
sentiment information are exposed using established RDF/S vocabularies. Next to
a description of the extraction and annotation process, we present use cases to
illustrate scenarios for entity-centric information exploration, data
integration and knowledge discovery facilitated by TweetsKB.