pwc-archive/papers-with-abstracts huggingface dataset:
Spatio-Spectral Radar Beampattern Design for Co-existence with Wireless Communication Systems
Date: 2018-11-28 00:00:00

We address the problem of designing a transmit beampattern for multiple-input
multiple-output (MIMO) radar considering co-existence with wireless
communication systems. The designed beampattern is able to manage the transmit
energy in spatial directions as well as in spectral frequency bands of interest
by minimizing the deviation of the designed beampattern versus a desired one
under a spectral constraint as well as the constant modulus constraint. While
unconstrained beampattern design is straightforward, a key open challenge is
jointly enforcing the spectral constraint in addition to the constant modulus
constraint on the radar waveform. A new approach is proposed in our work, which
involves solving a sequence of constrained quadratic programs such that
constant modulus is achieved at convergence. Further, we show that each problem
in the sequence has a closed form solution leading to analytical tractability.
We evaluate the proposed beampattern with interference control (BIC) algorithm
against the state-of-the-art MIMO beampattern design techniques and show that
BIC achieves closeness to an idealized beampattern along with desired spectral
shaping.
Reduced structural connectivity between left auditory thalamus and the motion-sensitive planum temporale in developmental dyslexia
Date: 2018-11-28 00:00:00

Developmental dyslexia is characterized by the inability to acquire typical
reading and writing skills. Dyslexia has been frequently linked to cerebral
cortex alterations; however recent evidence also points towards sensory
thalamus dysfunctions: dyslexics showed reduced responses in the left auditory
thalamus (medial geniculate body, MGB) during speech processing in contrast to
neurotypical readers. In addition, in the visual modality, dyslexics have
reduced structural connectivity between the left visual thalamus (lateral
geniculate nucleus, LGN) and V5/MT, a cerebral cortex region involved in visual
movement processing. Higher LGN-V5/MT connectivity in dyslexics was associated
with the faster rapid naming of letters and numbers (RANln), a measure that is
highly correlated with reading proficiency. We here tested two hypotheses that
were directly derived from these previous findings. First, we tested the
hypothesis that dyslexics have reduced structural connectivity between the left
MGB and the auditory motion-sensitive part of the left planum temporale (mPT).
Second, we hypothesized that the amount of left mPT-MGB connectivity correlates
with dyslexics RANln scores. Using diffusion tensor imaging based probabilistic
tracking we show that male adults with developmental dyslexia have reduced
structural connectivity between the left MGB and the left mPT, confirming the
first hypothesis. Stronger left mPT-MGB connectivity was not associated with
faster RANnl scores in dyslexics, but in neurotypical readers. Our findings
provide first evidence that reduced cortico-thalamic connectivity in the
auditory modality is a feature of developmental dyslexia, and that it may also
impact on reading related cognitive abilities in neurotypical readers.
SVD-PHAT: A Fast Sound Source Localization Method
Date: 2019-02-11 00:00:00
Tasks: Sound Source Localization

This paper introduces a new localization method called SVD-PHAT. The SVD-PHAT
method relies on Singular Value Decomposition of the SRP-PHAT projection
matrix. A k-d tree is also proposed to speed up the search for the most likely
direction of arrival of sound. We show that this method performs as accurately
as SRP-PHAT, while reducing significantly the amount of computation required.
A Study of the Complexity and Accuracy of Direction of Arrival Estimation Methods Based on GCC-PHAT for a Pair of Close Microphones
Date: 2018-11-28 00:00:00
Tasks: C++ code, Direction of Arrival Estimation

This paper investigates the accuracy of various Generalized Cross-Correlation
with Phase Transform (GCC-PHAT) methods for a close pair of microphones. We
investigate interpolation-based methods and also propose another approach based
on Singular Value Decomposition (SVD). All investigated methods are implemented
in C code, and the execution time is measured to determine which approach is
the most appealing for real-time applications on low-cost embedded hardware.
Fast Proteome Identification and Quantification from Data-Dependent Acquisition - Tandem Mass Spectrometry using Free Software Tools
Date: 2018-11-28 00:00:00

Identification of nearly all proteins in a system using data-dependent
acquisition (DDA) mass spectrometry has become routine for simple organisms,
such as bacteria and yeast. Still, quantification of the identified proteins
may be a complex process and require multiple different software packages. This
protocol describes identification and label-free quantification of proteins
from bottom-up proteomics experiments. This method can be used to quantify all
the detectable proteins in any DDA dataset collected with high-resolution
precursor scans. This protocol may be used to quantify proteome remodeling in
response to a drug treatment or a gene knockout. Notably, the method uses the
latest and fastest freely-available software, and the entire protocol can be
completed in a few hours with data from organisms with relatively small
genomes, such as yeast or bacteria.
Estimating of the inertial manifold dimension for a chaotic attractor of complex Ginzburg-Landau equation using a neural network
Authors: Kuptsov Pavel V., Kuptsova Anna V.
Date: 2018-11-23 00:00:00

Dimension of an inertial manifold for a chaotic attractor of spatially
distributed system is estimated using autoencoder neural network. The inertial
manifold is a low dimensional manifold where the chaotic attractor is embedded.
The autoencoder maps system state vectors onto themselves letting them pass
through an inner state with a reduced dimension. The training processes of the
autoencoder is shown to depend dramatically on the reduced dimension: a
learning curve saturates when the dimension is too small and decays if it is
sufficient for a lossless information transfer. The smallest sufficient value
is considered as a dimension of the inertial manifold, and the autoencoder
implements a mapping onto the inertial manifold and back. The correctness of
the computed dimension is confirmed by its remarkable coincidence with the one
obtained as a number of covariant Lyapunov vectors with vanishing pairwise
angles. These vectors are called physical modes. Unlike never having zero
angles residual ones they are known to span a tangent subspace for the inertial
manifold.
A Review on Recommendation Systems: Context-aware to Social-based
Authors: Seyednezhad S. M. Mahdi, Cozart Kailey Nobuko, Bowllan John Anthony, Smith Anthony O.
Date: 2018-11-28 00:00:00
Tasks: Recommendation Systems

The number of Internet users had grown rapidly enticing companies and
cooperations to make full use of recommendation infrastructures. Consequently,
online advertisement companies emerged to aid us in the presence of numerous
items and users. Even as a user, you may find yourself drowned in a set of
items that you think you might need, but you are not sure if you should try
them. Those items could be online services, products, places or even a person
for a friendship. Therefore, we need recommender systems that pave the way and
help us making good decisions. This paper provides a review on traditional
recommendation systems, recommendation system evaluations and metrics,
context-aware recommendation systems, and social-based recommendation systems.
While it is hard to include all the information in a brief review paper, we try
to have an introductory review over the essentials of recommendation systems.
More detailed information on each chapter will be found in the corresponding
references. For the purpose of explaining the concept in a different way, we
provided slides available on
https://www.slideshare.net/MahdiSeyednejad/recommender-systems-97094937.
Optimizing Throughput in a MIMO System with a Self-sustained Relay and Non-uniform Power Splitting
Date: 2018-11-28 00:00:00

We present a novel approach to maximizing the transmission rate in a MIMO
relay system, where all nodes are equipped with multiple antennas and the relay
is self-sustained by harvesting energy. We formulate an optimization problem
and use dual-characterization to derive a closed-form solution for the optimal
power splitting ratio and precoding design. We propose an efficient primal-dual
algorithm to jointly optimize the power allocation at source and relay for
transmission and the power splitting at relay for energy harvesting, and show
that using non-uniform power splitting is optimal. Numerical results
demonstrate the significant rate gain of non-uniform power splitting over
traditional uniform splitting especially at low source transmit power. We also
analyze our algorithm numerically and demonstrate its efficiency at reducing
the run-time by several orders of magnitudes compared to a standard solver,
\textcolor{blue}{and existing algorithms in literature
Performance Analysis of Deep Learning based on Recurrent Neural Networks for Channel Coding
Date: 2018-11-29 00:00:00
Tasks: Decoder

Channel Coding has been one of the central disciplines driving the success
stories of current generation LTE systems and beyond. In particular, turbo
codes are mostly used for cellular and other applications where a reliable data
transfer is required for latency-constrained communication in the presence of
data-corrupting noise. However, the decoding algorithm for turbo codes is
computationally intensive and thereby limiting its applicability in hand-held
devices. In this paper, we study the feasibility of using Deep Learning (DL)
architectures based on Recurrent Neural Networks (RNNs) for encoding and
decoding of turbo codes. In this regard, we simulate and use data from various
stages of the transmission chain (turbo encoder output, Additive White Gaussian
Noise (AWGN) channel output, demodulator output) to train our proposed RNN
architecture and compare its performance to the conventional turbo
encoder/decoder algorithms. Simulation results show, that the proposed RNN
model outperforms the decoding performance of a conventional turbo decoder at
low Signal to Noise Ratio (SNR) regions
Stochastic resonance in three-neuron motifs
Date: 2018-11-29 00:00:00
Tasks: Sensitivity

Stochastic resonance is a non-linear phenomenon, in which the sensitivity of
signal detectors can be enhanced by adding random noise to the detector input.
Here, we demonstrate that noise can also improve the information flux in
recurrent neural networks. In particular, we show for the case of three-neuron
motifs that the mutual information between successive network states can be
maximized by adding a suitable amount of noise to the neuron inputs. This
striking result suggests that noise in the brain may not be a problem that
needs to be suppressed, but indeed a resource that is dynamically regulated in
order to optimize information processing.
Stationary-State Statistics of a Binary Neural Network Model with Quenched Disorder
Date: 2018-11-29 00:00:00

We study the statistical properties of the stationary firing-rate states of a
neural network model with quenched disorder. The model has arbitrary size,
discrete-time evolution equations and binary firing rates, while the topology
and the strength of the synaptic connections are randomly generated from known,
generally arbitrary, probability distributions. We derived semi-analytical
expressions of the occurrence probability of the stationary states and the mean
multistability diagram of the model, in terms of the distribution of the
synaptic connections and of the external stimuli to the network. Our
calculations rely on the probability distribution of the bifurcation points of
the stationary states with respect to the external stimuli, which can be
calculated in terms of the permanent of special matrices, according to extreme
value theory. While our semi-analytical expressions are exact for any size of
the network and for any distribution of the synaptic connections, we also
specialized our calculations to the case of statistically-homogeneous
multi-population networks. In the specific case of this network topology, we
calculated analytically the permanent, obtaining a compact formula that
outperforms of several orders of magnitude the
Balasubramanian-Bax-Franklin-Glynn algorithm. To conclude, by applying the
Fisher-Tippett-Gnedenko theorem, we derived asymptotic expressions of the
stationary-state statistics of multi-population networks in the
large-network-size limit, in terms of the Gumbel (double exponential)
distribution. We also provide a Python implementation of our formulas and some
examples of the results generated by the code.
Managed Forgetting to Support Information Management and Knowledge Work
Authors: Jilek Christian, Runge Yannick, Niederée Claudia, Maus Heiko, Tempel Tobias, Dengel Andreas, Frings Christian
Date: 2018-11-17 00:00:00
Tasks: Management

Trends like digital transformation even intensify the already overwhelming
mass of information knowledge workers face in their daily life. To counter
this, we have been investigating knowledge work and information management
support measures inspired by human forgetting. In this paper, we give an
overview of solutions we have found during the last five years as well as
challenges that still need to be tackled. Additionally, we share experiences
gained with the prototype of a first forgetful information system used 24/7 in
our daily work for the last three years. We also address the untapped potential
of more explicated user context as well as features inspired by Memory
Inhibition, which is our current focus of research.
Universal Network Representation for Heterogeneous Information Networks
Authors: Hu Ruiqi, Yu Celina Ping, Fung Sai-Fu, Pan Shirui, Wang Haishuai, Long Guodong
Date: 2018-09-26 00:00:00
Tasks: Node Classification

Network representation aims to represent the nodes in a network as continuous
and compact vectors, and has attracted much attention in recent years due to
its ability to capture complex structure relationships inside networks.
However, existing network representation methods are commonly designed for
homogeneous information networks where all the nodes (entities) of a network
are of the same type, e.g., papers in a citation network. In this paper, we
propose a universal network representation approach (UNRA), that represents
different types of nodes in heterogeneous information networks in a continuous
and common vector space. The UNRA is built on our latest mutually updated
neural language module, which simultaneously captures inter-relationship among
homogeneous nodes and node-content correlation. Relationships between different
types of nodes are also assembled and learned in a unified framework.
Experiments validate that the UNRA achieves outstanding performance, compared
to six other state-of-the-art algorithms, in node representation, node
classification, and network visualization. In node classification, the UNRA
achieves a 3\% to 132\% performance improvement in terms of accuracy.
Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning
Authors: Chen Jianguo, Li Kenli, Bilal Kashif, Metwally Ahmed A., Li Keqin, Yu Philip S.
Date: 2018-10-17 00:00:00
Tasks: Cloud Computing, Community Detection

Protein interactions constitute the fundamental building block of almost
every life activity. Identifying protein communities from Protein-Protein
Interaction (PPI) networks is essential to understand the principles of
cellular organization and explore the causes of various diseases. It is
critical to integrate multiple data resources to identify reliable protein
communities that have biological significance and improve the performance of
community detection methods for large-scale PPI networks. In this paper, we
propose a Multi-source Learning based Protein Community Detection (MLPCD)
algorithm by integrating Gene Expression Data (GED) and a parallel solution of
MLPCD using cloud computing technology. To effectively discover the biological
functions of proteins that participating in different cellular processes, GED
under different conditions is integrated with the original PPI network to
reconstruct a Weighted-PPI (WPPI) network. To flexibly identify protein
communities of different scales, we define community modularity and functional
cohesion measurements and detect protein communities from WPPI using an
agglomerative method. In addition, we respectively compare the detected
communities with known protein complexes and evaluate the functional enrichment
of protein function modules using Gene Ontology annotations. Moreover, we
implement a parallel version of the MLPCD algorithm on the Apache Spark
platform to enhance the performance of the algorithm for large-scale realistic
PPI networks. Extensive experimental results indicate the superiority and
notable advantages of the MLPCD algorithm over the relevant algorithms in terms
of accuracy and performance.
Effective Resistance-based Germination of Seed Sets for Community Detection
Authors: Eskreis-Winkler Jonathan, Kondor Risi
Date: 2018-10-31 00:00:00
Tasks: Community Detection

Community detection is, at its core, an attempt to attach an interpretable
function to an otherwise indecipherable form. The importance of labeling
communities has obvious implications for identifying clusters in social
networks, but it has a number of equally relevant applications in product
recommendations, biological systems, and many forms of classification. The
local variety of community detection starts with a small set of labeled seed
nodes, and aims to estimate the community containing these nodes. One of the
most ubiquitous methods - due to its simplicity and efficiency - is
personalized PageRank. The most obvious bottleneck for deploying this form of
PageRank successfully is the quality of the seeds. We introduce a "germination"
stage for these seeds, where an effective resistance-based approach is used to
increase the quality and number of seeds from which a community is detected. By
breaking seed set expansion into a two-step process, we aim to utilize two
distinct random walk-based approaches in the regimes in which they excel. In
synthetic and real network data, a simple, greedy algorithm which minimizes the
effective resistance diameter combined with PageRank achieves clear
improvements in precision and recall over a standalone PageRank procedure.
Advanced Memory Buoyancy for Forgetful Information Systems
Authors: Jilek Christian, Chwalek Jessica, Schwarz Sven, Schröder Markus, Maus Heiko, Dengel Andreas
Date: 2018-11-17 00:00:00
Tasks: Management

Knowledge workers face an ever increasing flood of information in their daily
lives. To counter this and provide better support for information management
and knowledge work in general, we have been investigating solutions inspired by
human forgetting since 2013. These solutions are based on Semantic Desktop (SD)
and Managed Forgetting (MF) technology. A key concept of the latter is the
so-called Memory Buoyancy (MB), which is intended to represent an information
item's current value for the user and allows to employ forgetting mechanisms.
The SD thus continuously performs information value assessment updating MB and
triggering respective MF measures. We extended an SD-based organizational
memory system, which we have been using in daily work for over seven years now,
with MF mechanisms directly embedding them in daily activities, too, and
enabling us to test and optimize them in real-world scenarios. In this paper,
we first present our initial version of MB and discuss success and failure
stories we have been experiencing with it during three years of practical
usage. We learned from cognitive psychology that our previous research on
context can be beneficial for MF. Thus, we created an advanced MB version
especially taking user context, and in particular context switches, into
account. These enhancements as well as a first prototypical implementation are
presented, too.
Design of a Highly Reliable Wireless Module for Ultra-Low-Latency Short Range Applications
Date: 2018-11-29 00:00:00

Current radio systems are currently optimized for capacity and range.
However, certain applications of wireless systems require fast and reliable
communication over short distances. The challenge of these systems is to
communicate with a minimum time delay (latency) while at the same time being
very reliable and resilient to interference. This paper describes the concept
and the proposed abstract architecture of a wireless technology that allows
highly reliable and ultra low latency transmission of data between moving units
over a few meters, the applications of which can be found in multitude of
domains such as Train Communicaion Networks (TCNs), Truck/Tractor - Trailer
Communication, Platooning, and in smart industry in the form of co-ordinating
machines. The paper also describes the set of novelties that were planned to be
realized as part of the final demo hardware
Machine Learning Based Obstacle Detection for Automatic Train Pairing
Date: 2018-11-29 00:00:00
Tasks: BIG-bench Machine Learning

Short Range wireless devices are becoming more and more popular for
ubiquitous sensor and actuator connectivity in industrial communication
scenarios. Apart from communication-only scenarios, there are also
mission-critical use cases where the distance between the two communicating
nodes needs to be determined precisely. Applications such as Automatic Guided
Vehicles (AGV's), Automatic Train Pairing additionally require the devices to
scan the environment and detect any potential humans/obstacles. Ultra-Wide Band
(UWB) has emerged as a promising candidate for Real-Time Ranging and
Localization (RTRL) due to advantages such as large channel capacity, better
co-existence with legacy systems due to low transmit power, better performance
in multipath environments etc. In this paper, we evaluate the performance of a
UWB COTS device - TimeDomain P440 which can operate as a ranging radio and a
monostatic radar simultaneously. To this end, we evaluate the possibility of
using Supervised Learning based estimators for predicting the presence of
obstacles by constructing a multiclass hypothesis. Simulation results show that
the Ensemble tree based methods are able to calculate the likelihood of
obstacle collision with accuracies close to 95%.
Effect of time of day on reward circuitry: Further thoughts on methods, prompted by Steel et al 2018
Date: 2018-12-11 00:00:00
Tasks: Rhythm

The interplay between circadian and reward function is well understood in
animal models, and is of growing interest as an aetiological explanation in
psychopathologies. Circadian modulation of reward function has been
demonstrated in human behavioural data, but understanding at the neural level
is limited. In 2017, our group published results of a first step in addressing
this deficit, demonstrating a diurnal rhythm in fMRI-measured reward
activation. In 2018, Steel et al wrote a constructive critique of our findings,
and the aim of this paper is to outline how future research could improve on
our first proof-of-concept study. Key challenges include addressing divergent
and convergent validity (by addressing non-reward neural variation, and testing
for absence of variation in threat-related pathways), preregistration and power
analysis to protect against false positives, wider range of fMRI methods (to
directly test our post-hoc hypothesis of some form of reward prediction error,
and multiple phases of reward), the parallel collection of behavioural data
(particularly self-reported positive affect, and actigraphically measured
activity) to illuminate the nature of the reward activation across the day, and
some attempt to parse out circadian versus homeostatic/masking influences on
any observed diurnal rhythm in neural reward activation.