pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Social Signal to Improve Item Recommendation for Matrix Factorization
Authors: Wang Ze, Li Hong
Date: 2018-05-17 00:00:00
Tasks: Recommendation Systems

Although Recommender Systems have been comprehensively studied in the past
decade both in industry and academia, most of current recommender systems
suffer from the following issues: 1) The data sparsity of the user-item matrix
seriously affect the recommender system quality. As a result, most of
traditional recommender system approaches are not able to deal with the users
who have rated few items, which is known as cold start problem in recommender
system. 2) Traditional recommender systems assume that users are independently
and identically distributed and ignore the social relation between users.
However, in real life scenario, due to the exponential growth of social
networking service, such as facebook and Twitter, social connections between
different users play an significant role for recommender system task. In this
work, aiming at providing a better recommender systems by incorporating user
social network information, we propose a matrix factorization framework with
user social connection constraints. Experimental results on the real-life
dataset shows that the proposed method performs significantly better than the
state-of-the-art approaches in terms of MAE and RMSE, especially for the cold
start users.
Antenna Switching Sequence Design for Channel Sounding in a Fast Time-varying Channel
Date: 2018-05-17 00:00:00
Tasks: parameter estimation

This paper investigates the impact of array switching patterns on the
accuracy of parameter estimation of multipath components for a time division
multiplexed (TDM) channel sounder. To measure fast time-varying channels, the
conventional uniform array switching pattern poses a fundamental limit of the
number of antennas that a TDM channel sounder can utilize. We propose a method,
which is based on the simulated annealing algorithm, to find non-uniform array
switching patterns for realistic antenna arrays, so that we can extend the
Doppler estimation range of the channel sounder by suppressing the high
sidelobes in the spatio-temporal ambiguity function. Monte Carlo simulations
demonstrate that the optimal switching sequence leads to significantly smaller
root mean square errors of both direction of departure and Doppler. Results can
be applied in both vehicle-to-vehicle and mobile millimeter wave MIMO channel
measurements.
Implementation of True Random Number Generator based on Double-Scroll Attractor circuit with GST memristor emulator
Date: 2018-05-19 00:00:00

The cryptographic security provided by various techniques of random number
generator (RNG) construction is one of the developing researches areas today.
Among various types of RNG, the true random bit generator (TRBG) can be
considered as the most unpredictable and most secured because its randomness
seed is generated from chaotic sources. This paper proposes a design of TRBG
model based on double-scroll attractors circuits with GST memristor. After
implementation and simulation of the chaotic circuit with GST memristor
emulator, the chaotic behavior of the output voltage and inductor current were
received. Moreover, their dependence on the input voltage revealed the close to
double-scroll form. The randomness generated from the proposed circuit was
tested by receiving Fast Fourier Transform (FFT) and Lyapunov exponents of the
output voltage.
Widlar Current Mirror Design Using BJT-Memristor Circuits
Date: 2018-05-31 00:00:00

This paper presents a description of basic current mirror (CM), Widlar
current mirror, fourth circuit element (memristor) and an analysis of Widlar
Configuration with integrated memristor. The analysis has been performed by
comparing a modified configuration with a simple circuit of Widlar CM. The
focus of analysis were a power dissipation, a Total Harmonic Distortion and a
chip-surface. The results has shown that a presence of memristor in the Widlar
CM decreases the chip-surface area and the deviation of the signal in the
circuit from a fundamental frequency. Although the analysis of power
dissipation has also been conducted, there is no definite conclusion about the
power losses in the circuit because of the memristor model.
Analyzing order flows in limit order books with ratios of Cox-type intensities
Authors: Ioane Muni Toke, Nakahiro Yoshida
Date: 2018-05-17 00:00:00
Tasks: Model Selection

We introduce a Cox-type model for relative intensities of orders flows in a limit order book. The model assumes that all intensities share a common baseline intensity, which may for example represent the global market activity. Parameters can be estimated by quasi likelihood maximization, without any interference from the baseline intensity. Consistency and asymptotic behavior of the estimators are given in several frameworks, and model selection is discussed with information criteria and penalization. The model is well-suited for high-frequency financial data: fitted models using easily interpretable covariates show an excellent agreement with empirical data. Extensive investigation on tick data consequently helps identifying trading signals and important factors determining the limit order book dynamics. We also illustrate the potential use of the framework for out-of-sample predictions.
A new $\kappa$-deformed parametric model for the size distribution of wealth
Date: 2018-05-17 00:00:00

It has been pointed out by Patriarca et al. (2005) that the power-law tailed
equilibrium distribution in heterogeneous kinetic exchange models with a
distributed saving parameter can be resolved as a mixture of Gamma
distributions corresponding to particular subsets of agents. Here, we propose a
new four-parameter statistical distribution which is a $\kappa$-deformation of
the Generalized Gamma distribution with a power-law tail, based on the deformed
exponential and logarithm functions introduced by Kaniadakis(2001). We found
that this new distribution is also an extension to the $\kappa$-Generalized
distribution proposed by Clementi et al. (2007), with an additional shape
parameter $\nu$, and properly reproduces the whole range of the distribution of
wealth in such heterogeneous kinetic exchange models. We also provide various
associated statistical measures and inequality measures.
The sequencing and interpretation of the genome obtained from a Serbian individual
Date: 2018-05-17 00:00:00

Recent genetic studies and whole-genome sequencing projects have greatly
improved our understanding of human variation and clinically actionable genetic
information. Smaller ethnic populations, however, remain underrepresented in
both individual and large-scale sequencing efforts and hence present an
opportunity to discover new variants of biomedical and demographic
significance. This report describes the sequencing and analysis of a genome
obtained from an individual of Serbian origin, introducing tens of thousands of
previously unknown variants to the currently available pool. Ancestry analysis
places this individual in close proximity of the Central and Eastern European
populations; i.e., closest to Croatian, Bulgarian and Hungarian individuals
and, in terms of other Europeans, furthest from Ashkenazi Jewish, Spanish,
Sicilian, and Baltic individuals. Our analysis confirmed gene flow between
Neanderthal and ancestral pan-European populations, with similar contributions
to the Serbian genome as those observed in other European groups. Finally, to
assess the burden of potentially disease-causing/clinically relevant variation
in the sequenced genome, we utilized manually curated genotype-phenotype
association databases and variant-effect predictors. We identified several
variants that have previously been associated with severe early-onset disease
that is not evident in the proband, as well as variants that could yet prove to
be clinically relevant to the proband over the next decades. The presence of
numerous private and low-frequency variants along with the observed and
predicted disease-causing mutations in this genome exemplify some of the global
challenges of genome interpretation, especially in the context of understudied
ethnic groups.
Subchannel Allocation for Vehicle-to-Vehicle Broadcast Communications in Mode-3
Date: 2018-05-18 00:00:00

Conversely to mainstream cellular networks where uplink / downlink data
traffic is centrally managed by eNodeBs, in vehicle-to-vehicle (V2V) broadcast
communications \textit{mode-3} eNodeBs engage solely in subchannel assignment
but ultimately do not intervene in data traffic control. Accordingly, vehicles
communicate directly with their counterparts utilizing the allotted
subchannels. Due to its loosely controlled one-to-all nature, V2V
\textit{mode-3} is advantageous for time-critical applications. Nevertheless,
it is imperative that the assignment of subchannels is accomplished without
conflicts while at the same time satisfying quality of service (QoS)
requirements. To the best of our knowledge, there exists no unified framework
for V2V \textit{mode-3} that contemplates both prevention of allocation
conflicts and fulfillment of QoS. Thus, four types of conditions that are of
forceful character for attaining QoS-aware conflict-free allocations have been
identified: $(i)$ assure differentiated QoS per vehicle, $(ii)$ preclude
intra-cluster subframe conflicts, $(iii)$ secure minimal time dispersion of
allotted subchannels and $(iv)$ forestall one-hop inter-cluster subchannel
conflicts. Such conditions have been systematized and merged in an holistic
manner allowing non-complex manipulation to perform subchannel allocation
optimization. In addition, we propose a surrogate relaxation of the problem
that does not affect optimality provided that certain requisites are satisfied.
Parallel and Successive Resource Allocation for V2V Communications in Overlapping Clusters
Date: 2018-05-18 00:00:00
Tasks: Graph Matching

The 3rd Generation Partnership Project (3GPP) has introduced in Rel. 14 a
novel technology referred to as vehicle--to--vehicle (V2V) \textit{mode-3}.
Under this scheme, the eNodeB assists in the resource allocation process
allotting sidelink subchannels to vehicles. Thereupon, vehicles transmit their
signals in a broadcast manner without the intervention of the former one.
eNodeBs will thereby play a determinative role in the assignment of subchannels
as they can effectively manage V2V traffic and prevent allocation conflicts.
The latter is a crucial aspect to be enforced in order for the signals to be
received reliably by other vehicles. To this purpose, we propose two resource
allocation schemes namely bipartite graph matching-based successive allocation
(BGM-SA) and bipartite graph matching-based parallel allocation (BGM-PA) which
are suboptimal approaches with lesser complexity than exhaustive search. Both
schemes incorporate constraints to prevent allocation conflicts from emerging.
In this research, we consider overlapping clusters only, which could be formed
at intersections or merging highways. We show through simulations that BGM-SA
can attain near-optimal performance whereas BGM-PA is subpar but less complex.
Additionally, since BGM-PA is based on inter-cluster vehicle pre-grouping, we
explore different metrics that could effectively portray the overall channel
conditions of pre-grouped vehicles. This is of course not optimal in terms of
maximizing the system capacity---since the allocation process would be based on
simplified surrogate information---but it reduces the computational complexity.
MARS: Memory Attention-Aware Recommender System
Authors: Zheng Lei, Lu Chun-Ta, He Lifang, Xie Sihong, Noroozi Vahid, Huang He, Yu Philip S.
Date: 2018-05-18 00:00:00
Tasks: Recommendation Systems

In this paper, we study the problem of modeling users' diverse interests.
Previous methods usually learn a fixed user representation, which has a limited
ability to represent distinct interests of a user. In order to model users'
various interests, we propose a Memory Attention-aware Recommender System
(MARS). MARS utilizes a memory component and a novel attentional mechanism to
learn deep \textit{adaptive user representations}. Trained in an end-to-end
fashion, MARS adaptively summarizes users' interests. In the experiments, MARS
outperforms seven state-of-the-art methods on three real-world datasets in
terms of recall and mean average precision. We also demonstrate that MARS has a
great interpretability to explain its recommendation results, which is
important in many recommendation scenarios.
A Two-Phase Model of Early Fibrous Cap Formation in Atherosclerosis
Date: 2018-05-18 00:00:00
Tasks: Vocal Bursts Valence Prediction

Atherosclerotic plaque growth is characterised by chronic inflammation that
promotes accumulation of cellular debris and extracellular fat in the inner
artery wall. This material is highly thrombogenic, and plaque rupture can lead
to the formation of blood clots that occlude major arteries and cause
myocardial infarction or stroke. In advanced plaques, vascular smooth muscle
cells (SMCs) migrate from deeper in the artery wall to synthesise a cap of
fibrous tissue that stabilises the plaque and sequesters the thrombogenic
plaque content from the bloodstream. The fibrous cap provides crucial
protection against the clinical consequences of atherosclerosis, but the
mechanisms of cap formation are poorly understood. In particular, it is unclear
why certain plaques become stable and robust while others become fragile and
vulnerable to rupture.
  We develop a multiphase model with non-standard boundary conditions to
investigate early fibrous cap formation in the atherosclerotic plaque. The
model is parameterised using a range of in vitro and in vivo data, and includes
highly nonlinear mechanisms of SMC proliferation and migration in response to
an endothelium-derived chemical signal. We demonstrate that the model SMC
population naturally evolves towards a steady-state, and predict a rate of cap
formation and a final plaque SMC content consistent with experimental
observations in mice. Parameter sensitivity simulations show that SMC
proliferation makes a limited contribution to cap formation, and highlight that
stable cap formation relies on a critical balance between SMC recruitment to
the plaque, SMC migration within the plaque and SMC loss by apoptosis. The
model represents the first detailed in silico study of fibrous cap formation in
atherosclerosis, and establishes a multiphase modelling framework that can be
readily extended to investigate many other aspects of plaque development.
Objective and efficient inference for couplings in neuronal networks
Date: 2018-05-18 00:00:00
Proceeding: NeurIPS 2018 12

Inferring directional couplings from the spike data of networks is desired in
various scientific fields such as neuroscience. Here, we apply a recently
proposed objective procedure to the spike data obtained from the
Hodgkin--Huxley type models and in vitro neuronal networks cultured in a
circular structure. As a result, we succeed in reconstructing synaptic
connections accurately from the evoked activity as well as the spontaneous one.
To obtain the results, we invent an analytic formula approximately implementing
a method of screening relevant couplings. This significantly reduces the
computational cost of the screening method employed in the proposed objective
procedure, making it possible to treat large-size systems as in this study.
Marginal process framework: A model reduction tool for Markov jump processes
Date: 2018-05-18 00:00:00

Markov jump process models have many applications across science. Often,
these models are defined on a state-space of product form and only one of the
components of the process is of direct interest. In this paper, we extend the
marginal process framework, which provides a marginal description of the
component of interest, to the case of fully coupled processes. We use entropic
matching to obtain a finite-dimensional approximation of the filtering
equation, which governs the transition rates of the marginal process. The
resulting equations can be seen as a combination of two projection operations
applied to the full master equation, so that we obtain a principled model
reduction framework. We demonstrate the resulting reduced description on the
totally asymmetric exclusion process. An important class of Markov jump
processes are stochastic reaction networks, which have applications in chemical
and biomolecular kinetics, ecological models and models of social networks. We
obtain a particularly simple instantiation of the marginal process framework
for mass-action systems by using product-Poisson distributions for the
approximate solution of the filtering equation. We investigate the resulting
approximate marginal process analytically and numerically.
Stochastic effects in autoimmune dynamics
Date: 2018-05-11 00:00:00

Among various possible causes of autoimmune disease, an important role is
played by infections that can result in a breakdown of immune tolerance,
primarily through the mechanism of "molecular mimicry". In this paper we
propose and analyse a stochastic model of immune response to a viral infection
and subsequent autoimmunity, with account for the populations of T cells with
different activation thresholds, regulatory T cells, and cytokines. We show
analytically and numerically how stochasticity can result in sustained
oscillations around deterministically stable steady states, and we also
investigate stochastic dynamics in the regime of bi-stability. These results
provide a possible explanation for experimentally observed variations in the
progression of autoimmune disease. Computations of the variance of stochastic
fluctuations provide practically important insights into how the size of these
fluctuations depends on various biological parameters, and this also gives a
headway for comparison with experimental data on variation in the observed
numbers of T cells and organ cells affected by infection.
Effects of viral and cytokine delays on dynamics of autoimmunity
Date: 2018-05-18 00:00:00

A major contribution to the onset and development of autoimmune disease is
known to come from infections. An important practical problem is identifying
the precise mechanism by which the breakdown of immune tolerance as a result of
immune response to infection leads to autoimmunity. In this paper, we develop a
mathematical model of immune response to a viral infection, which includes T
cells with different activation thresholds, regulatory T cells (Tregs), and~a
cytokine mediating immune dynamics. Particular emphasis is made on the role of
time delays associated with the processes of infection and mounting the immune
response. Stability analysis of various steady states of the model allows us to
identify parameter regions associated with different types of immune behaviour,
such as, normal clearance of infection, chronic infection, and autoimmune
dynamics. Numerical simulations are used to illustrate different dynamical
regimes, and to identify basins of attraction of different dynamical states. An
important result of the analysis is that not only the parameters of the system,
but also the initial level of infection and the initial state of the immune
system determine the progress and outcome of the dynamics.
Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains
Date: 2018-05-18 00:00:00

We consider the maximum entropy Markov chain inference approach to
characterize the collective statistics of neuronal spike trains, focusing on
the statistical properties of the inferred model. We review large deviations
techniques useful in this context to describe properties of accuracy and
convergence in terms of sampling size. We use these results to study the
statistical fluctuation of correlations, distinguishability and irreversibility
of maximum entropy Markov chains. We illustrate these applications using simple
examples where the large deviation rate function is explicitly obtained for
maximum entropy models of relevance in this field.
Asset Price Bubbles: An Option-based Indicator
Date: 2018-07-14 00:00:00
Tasks: parameter estimation

We construct a statistical indicator for the detection of short-term asset
price bubbles based on the information content of bid and ask market quotes for
plain vanilla put and call options. Our construction makes use of the
martingale theory of asset price bubbles and the fact that such scenarios where
the price for an asset exceeds its fundamental value can in principle be
detected by analysis of the asymptotic behavior of the implied volatility
surface. For extrapolating this implied volatility, we choose the SABR model,
mainly because of its decent fit to real option market quotes for a broad range
of maturities and its ease of calibration. As main theoretical result, we show
that under lognormal SABR dynamics, we can compute a simple yet powerful
closed-form martingale defect indicator by solving an ill-posed inverse
calibration problem. In order to cope with the ill-posedness and to quantify
the uncertainty which is inherent to such an indicator, we adopt a Bayesian
statistical parameter estimation perspective. We probe the resulting posterior
densities with a combination of optimization and adaptive Markov chain Monte
Carlo methods, thus providing a full-blown uncertainty estimation of all the
underlying parameters and the martingale defect indicator. Finally, we provide
real-market tests of the proposed option-based indicator with focus on tech
stocks due to increasing concerns about a tech bubble 2.0.
Algorithmic Trading with Fitted Q Iteration and Heston Model
Date: 2018-05-18 00:00:00
Tasks: Algorithmic Trading, Q-Learning

We present the use of the fitted Q iteration in algorithmic trading. We show
that the fitted Q iteration helps alleviate the dimension problem that the
basic Q-learning algorithm faces in application to trading. Furthermore, we
introduce a procedure including model fitting and data simulation to enrich
training data as the lack of data is often a problem in realistic application.
We experiment our method on both simulated environment that permits arbitrage
opportunity and real-world environment by using prices of 450 stocks. In the
former environment, the method performs well, implying that our method works in
theory. To perform well in the real-world environment, the agents trained might
require more training (iteration) and more meaningful variables with predictive
value.
Global Ultrasound Elastography Using Convolutional Neural Network
Date: 2018-06-12 00:00:00

Displacement estimation is very important in ultrasound elastography and
failing to estimate displacement correctly results in failure in generating
strain images. As conventional ultrasound elastography techniques suffer from
decorrelation noise, they are prone to fail in estimating displacement between
echo signals obtained during tissue distortions. This study proposes a novel
elastography technique which addresses the decorrelation in estimating
displacement field. We call our method GLUENet (GLobal Ultrasound Elastography
Network) which uses deep Convolutional Neural Network (CNN) to get a coarse
time-delay estimation between two ultrasound images. This displacement is later
used for formulating a nonlinear cost function which incorporates similarity of
RF data intensity and prior information of estimated displacement. By
optimizing this cost function, we calculate the finer displacement by
exploiting all the information of all the samples of RF data simultaneously.
The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain
images from our technique is very much close to that of strain images from
GLUE. While most elastography algorithms are sensitive to parameter tuning, our
robust algorithm is substantially less sensitive to parameter tuning.