pwc-archive/papers-with-abstracts huggingface dataset:
Logic Programming as a Service
Authors: Calegari Roberta, Denti Enrico, Mariani Stefano, Omicini Andrea
Date: 2018-09-25 00:00:00

New generations of distributed systems are opening novel perspectives for
logic programming (LP): on the one hand, service-oriented architectures
represent nowadays the standard approach for distributed systems engineering;
on the other hand, pervasive systems mandate for situated intelligence. In this
paper we introduce the notion of Logic Programming as a Service (LPaaS) as a
means to address the needs of pervasive intelligent systems through logic
engines exploited as a distributed service. First we define the abstract
architectural model by re-interpreting classical LP notions in the new context;
then we elaborate on the nature of LP interpreted as a service by describing
the basic LPaaS interface. Finally, we show how LPaaS works in practice by
discussing its implementation in terms of distributed tuProlog engines,
accounting for basic issues such as interoperability and configurability.
From single-cell variability to population growth
Date: 2020-01-13 00:00:00

Single-cell experiments have revealed cell-to-cell variability in generation
times and growth rates for genetically identical cells. Theoretical models
relating the fluctuating generation times of single cells to the population
growth rate are usually based on the assumption that the generation times of
mother and daughter cells are uncorrelated. This assumption, however, is
inconsistent with the exponential growth of cell volume in time observed for
many cell types. Here we develop a more general and biologically relevant model
in which cells grow exponentially and generation times are correlated in a
manner which controls cell size. In addition to the fluctuating generation
times, we also allow the single-cell growth rates to fluctuate and account for
their correlations across the lineage tree. Surprisingly, we find that the
population growth rate only depends on the distribution of single-cell growth
rates and their correlations.
An O(n) method of calculating Kendall correlations of spike trains
Date: 2020-02-25 00:00:00

The ability to record from increasingly large numbers of neurons, and the
increasing attention being paid to large scale neural network simulations,
demands computationally fast algorithms to compute relevant statistical
measures. We present an O(n) algorithm for calculating the Kendall correlation
of spike trains, a correlation measure that is becoming especially recognized
as an important tool in neuroscience. We show that our method is around 50
times faster than the O (n ln n) method which is a current standard for quickly
computing the Kendall correlation. In addition to providing a faster algorithm,
we emphasize the role that taking the specific nature of spike trains had on
reducing the run time. We imagine that there are many other useful algorithms
that can be even more significantly sped up when taking this into
consideration. A MATLAB function executing the method described here has been
made freely available on-line.
Stochastic Deflator for an Economic Scenario Generator with Five Factors
Date: 2019-02-15 00:00:00

In this paper, we implement a stochastic deflator with five economic and
financial risk factors: interest rates, market price of risk, stock prices,
default intensities, and convenience yields. We examine the deflator with
different financial assets, such as stocks, zero-coupon bonds, vanilla options,
and corporate coupon bonds. We find required regularity conditions to implement
our stochastic deflator. Our numerical results show the reliability of the
deflator approach in pricing financial derivatives.
Production of polyhydroxybutyrates and carbohydrates in a mixed cyanobacterial culture: effect of nutrients limitation and photoperiods
Date: 2018-06-11 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

In the present study, different photoperiods and nutritional conditions were
applied to a mixed wastewater-borne cyanobacterial culture in order to enhance
the intracellular accumulation of polyhydroxybutyrates (PHBs) and
carbohydrates. Two different experimental set-ups were used. In the first, the
culture was permanently exposed to illumination, while in the second; it was
submitted to light/dark alternation (12h cycles). In both cases, two different
nutritional regimes were also evaluated, N-limitation and P-limitation. Results
showed that the highest PHB concentration (104 mg L-1) was achieved under P
limited conditions and permanent illumination, whereas the highest carbohydrate
concentration (838 mg L-1) was obtained under N limited condition and
light/dark alternation. With regard to bioplastics and biofuel generation, this
study demonstrates that the accumulation of PHBs (bioplastics) and
carbohydrates (potential biofuel substrate) is favored in wastewater-borne
cyanobacteria under conditions where nutrients are limited.
CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq
Date: 2019-05-23 00:00:00
Tasks: Imputation

Direct cDNA preamplification protocols developed for single-cell RNA-seq have
enabled transcriptome profiling of precious clinical samples and rare cells
without sample pooling or RNA extraction. Currently, there is no algorithm
optimized to reveal and remove noisy transcripts in limiting-cell RNA-seq
(lcRNA-seq) data for downstream analyses. Herein, we present CLEAR, a workflow
that identifies reliably quantifiable transcripts in lcRNA-seq data for
differentially expressed gene (DEG) analysis. Libraries at three input amounts
of FACS-derived CD5+ and CD5- cells from a chronic lymphocytic leukemia patient
were used to develop CLEAR. When using CLEAR transcripts vs. using all
transcripts, downstream analyses revealed more shared transcripts across
different input RNA amounts, improved Principal Component Analysis (PCA)
separation, and yielded more DEGs between cell types. As proof-of-principle,
CLEAR was applied to an in-house lcRNA-seq dataset and two public datasets.
When imputation is used, CLEAR is also adaptable to large clinical studies and
for single cell analyses.
A Markov Variation Approach to Smooth Graph Signal Interpolation
Date: 2020-01-28 00:00:00

In this paper we present the Markov variation, a smoothness measure which
offers a probabilistic interpretation of graph signal smoothness. This measure
is then used to develop an optimization framework for graph signal
interpolation. Our approach is based on diffusion embedding vectors and the
connection between diffusion maps and signal processing on graphs. As diffusion
embedding vectors may be expensive to compute for large graphs, we present a
computationally efficient method, based on the Nystr\"{o}m extension, for
interpolation of signals over a graph. We demonstrate our approach on the MNIST
dataset and a dataset of daily average temperatures around the US. We show that
our method outperforms state of the art graph signal interpolation techniques
on both datasets, and that our computationally efficient reconstruction
achieves slightly reduced accuracy with a large computational speedup.
Analysis of Length Normalization in End-to-End Speaker Verification System
Date: 2018-06-11 00:00:00
Tasks: Speaker Verification

The classical i-vectors and the latest end-to-end deep speaker embeddings are
the two representative categories of utterance-level representations in
automatic speaker verification systems. Traditionally, once i-vectors or deep
speaker embeddings are extracted, we rely on an extra length normalization step
to normalize the representations into unit-length hyperspace before back-end
modeling. In this paper, we explore how the neural network learns
length-normalized deep speaker embeddings in an end-to-end manner. To this end,
we add a length normalization layer followed by a scale layer before the output
layer of the common classification network. We conducted experiments on the
verification task of the Voxceleb1 dataset. The results show that integrating
this simple step in the end-to-end training pipeline significantly boosts the
performance of speaker verification. In the testing stage of our L2-normalized
end-to-end system, a simple inner-product can achieve the state-of-the-art.
Multidimensional Analysis of Psychological Factors affecting Students Academic Performance
Authors: Khanna Leena, Singh Shailendra Narayan, Alam Mansaf
Date: 2018-05-26 00:00:00

Academic performance of any individual is dependent upon numerous aspects
regarding the day to day life of the individual under consideration. Academic
performance is measured in terms of the grade point average or GPA as it is
called. Grade point average is dependent not only on the faculty but also on
various psychological parameters including the study habits, social anxiety and
allied. In this study, a detail analysis of numerous psychological factors
impacting the grade point was carried and based upon various psychological
factors the performance for the student in forth coming examination was
forecasted.
Full Optical Fiber Link Characterization with the BSS-Lasso
Date: 2018-12-29 00:00:00

Manipulation of the detected backscattered Rayleigh signal inside the
bandwidth of a frequency-swept optical sub-carrier propagating into an optical
fiber permits an efficient localization of faults through a Fourier operator.
When the bandwidth is restricted, analysis in the frequency domain can overcome
the spatial resolution limitation while also inducing a high-dimensional
problem. Introducing the Lasso as a signal processing technique paired with the
Baseband Subcarrier Sweep (BSS) framework allows for a methodology to
consistently evaluate fiber defects. In this work, a novel technique for
optical fiber monitoring within the BSS framework, hereinafter called the
BSS-Lasso, is proposed and tested in simulated and real-world environments,
taking into account both reflective and non-reflective events. The results show
that, for fiber links ranging from 2 to 15 km with up to 3 faults, over 80% of
faults are detected within a 50 m range, and indicate that the proposed
methodology significantly outperforms current state-of-the-art BSS-based
supervision techniques. Finally, the BSS-Lasso allows for precise, low-cost,
transmitter-embedded full characterization of optical fiber links.
Polygenic adaptation in changing environments
Date: 2018-10-18 00:00:00

Although many phenotypic traits are determined by a large number of genetic
variants, how a polygenic trait adapts in response to the changes in the
environment is still poorly understood. Here we study the adaptation dynamics
of a polygenic trait that is determined by a finite number of genetic loci in
an infinitely large population which is evolving under stabilising selection
and recurrent mutations. We find that in a changing environment, modeled here
by a linearly moving phenotypic optimum, the mean trait also moves linearly
with time. But its speed is smaller than that of the phenotypic optimum when
the effect sizes of the genetic variants are small and approaches that of the
environmental change for larger effect sizes. Our study thus highlights the
influence of the genetic architecture of a polygenic trait on its adaptability.
Hierarchical Information Quadtree: Efficient Spatial Temporal Image Search for Multimedia Stream
Authors: Zhang Chengyuan, Chen Ruipeng, Zhu Lei, Liu Anfeng, Lin Yunwu, Huang Fang
Date: 2018-08-15 00:00:00
Tasks: Image Retrieval

Massive amount of multimedia data that contain times- tamps and geographical
information are being generated at an unprecedented scale in many emerging
applications such as photo sharing web site and social networks applications.
Due to their importance, a large body of work has focused on efficiently
computing various spatial image queries. In this paper,we study the spatial
temporal image query which considers three important constraints during the
search including time recency, spatial proximity and visual relevance. A novel
index structure, namely Hierarchical Information Quadtree(\hiq), to efficiently
insert/delete spatial temporal images with high arrive rates. Base on \hiq an
efficient algorithm is developed to support spatial temporal image query. We
show via extensive experimentation with real spatial databases clearly
demonstrate the efficiency of our methods.
Perturbation analysis of sub/super hedging problems
Authors: Sergey Badikov, Mark H. A. Davis, Antoine Jacquier
Date: 2018-06-09 00:00:00

We investigate the links between various no-arbitrage conditions and the existence of pricing functionals in general markets, and prove the Fundamental Theorem of Asset Pricing therein. No-arbitrage conditions, either in this abstract setting or in the case of a market consisting of European Call options, give rise to duality properties of infinite-dimensional sub- and super-hedging problems. With a view towards applications, we show how duality is preserved when reducing these problems over finite-dimensional bases. We finally perform a rigorous perturbation analysis of those linear programming problems, and highlight numerically the influence of smile extrapolation on the bounds of exotic options.
Scanning Test System Prototype of p/sFEB for the ATLAS Phase-I sTGC Trigger Upgrade
Date: 2018-06-10 00:00:00

The Pad Front End Board (pFEB) and the Strip Front End Board (sFEB) are
developed for the ATLAS Phase-I sTGC Trigger Upgrade. The pFEB is used to to
gather and analyze pads trigger, and the sFEB is developed to accept the pad
trigger to define the regions-of-interest for strips readout. The performance
of p/sFEBs must be confirmed before they are mounted on the sTGC detector. We
will present the scanning test system prototype which is designed according to
the test requirements of the p/sFEB. In this test system prototype, a
simulation signal board is developed to generate different types of signal to
the p/sFEB. PC software and FPGA XADC cooperate to achieve the scan test of
analog parameter.
Optimal Control of Constrained Stochastic Linear-Quadratic Model with Applications
Date: 2018-06-10 00:00:00

This paper studies a class of continuous-time scalar-state stochastic
Linear-Quadratic (LQ) optimal control problem with the linear control
constraints. Applying the state separation theorem induced from its special
structure, we develop the explicit solution for this class of problem. The
revealed optimal control policy is a piece-wise affine function of system
state. This control policy can be computed efficiently by solving two Riccati
equations off-line. Under some mild conditions, the stationary optimal control
policy can be also derived for this class of problem with infinite horizon.
This result can be used to solve the constrained dynamic mean-variance
portfolio selection problem. Examples shed light on the solution procedure of
implementing our method.
High Performance and Scalable AWG for Superconducting Quantum Computing
Date: 2018-06-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Superconducting quantum computer is manufactured based on semiconductor
process which makes qubits integration possible. At the same time, this kind of
qubit exhibits high performance fidelity, de-coherence time, scalability and
requires a programmable arbitrary waveform generator (AWG). This paper presents
implementation of an AWG which composed of two gigabit samples per second
(GSPS) sampling rate, 16 bit vertical resolution digital to analog converters
(DACs). The AWG integrated with separate microwave devices onto a metal plate
for the scale-up consideration. A special waveform sequence output controller
is designed to realize seamless waveform switching and arbitrary waveform
generator. The jitter in multiple AWG channels is around 10ps, Integral
nonlinearity (INL) as well as differential nonlinearity (DNL) is about 2 LSB,
and the qubit performance of the de-coherence time (T2*) achieved 33% promotion
over that of a commercial 1 GSPS, 14 bit AWG.
Technique of active phase stabilization for the interferometer with 128 actively selectable paths
Date: 2018-06-10 00:00:00

A variable-delay optical interferometer with 128 actively selectable delays
and a technique of active phase stabilization are innovatively designed and
applied for the first time in the experiment of round-robin differential phase
shift quantum key distribution (RRDPS-QKD). According to the RRDPS protocol,
larger number of delay channels in interferometer can ensure higher tolerance
of bit errors, eliminating the fundamental threshold of bit error rate of 11%
of traditional BB84 protocol. Considering that the implementation of RRDPS
protocol relys on the realizaiton of the interferometer, therefore, an
interferometer with 128 selectable delay paths is constructed and demands the
ability of fast switching at the rate of 10 kHz, which requires dynamic
stability of multiple paths. Thus, a specific designed phase stabilization
technique with closed real time feedback loop is introduced to guarantee the
high visibility of interferometer selections dynamically. The active phase
stabilization technique employs a phase modulator (PM) driven by a DAC to
adjust the relative phase between the two arms of the interferometer. By
monitoring photon counting rates of two Up-Conversion Detectors (UCD) at two
output ports of the interferometer, a Field Programmable Gate Array (FPGA)
calculates and finds the optimal code value for the DAC, maintaining a high
visibility of the interferometer every time a new light path is selected. The
visibility of most of the 128 interferometer selections can simultaneously be
maintained over 96% during the QKD, which lays the foundation for the RRDPS
experiment.
On The Calibration of Short-Term Interest Rates Through a CIR Model
Date: 2018-06-10 00:00:00

It is well known that the Cox-Ingersoll-Ross (CIR) stochastic model to study
the term structure of interest rates, as introduced in 1985, is inadequate for
modelling the current market environment with negative short interest rates.
Moreover, the diffusion term in the rate dynamics goes to zero when short rates
are small; both volatility and long-run mean do not change with time; they do
not fit with the skewed (fat tails) distribution of the interest rates, etc.
The aim of the present work is to suggest a new framework, which we call the
CIR\# model, that well fits the term structure of short interest rates so that
the market volatility structure is preserved as well as the analytical
tractability of the original CIR model.
Smart GSM Based Home Automation System
Date: 2018-06-10 00:00:00

This research work investigates the potential of Full Home Control, which is
the aim of the Home Automation Systems in near future. The analysis and
implementation of the home automation technology using Global System for Mobile
Communication (GSM) modem to control home appliances such as light, conditional
system, and security system via Short Message Service (SMS) text messages is
presented in this paper. The proposed research work is focused on the
functionality of the GSM protocol, which allows the user to control the target
system away from residential using the frequency bandwidths. The concept of
serial communication and AT-commands has been applied towards the development
of the smart GSM-based home automation system. Homeowners will be able to
receive feedback status of any home appliances under control whether switched
on or off remotely from their mobile phones. PIC16F887 microcontroller with the
integration of GSM provides the smart automated house system with the desired
baud rate of 9600 bps. The proposed prototype of GSM based home automation
system was implemented and tested with a maximum of four loads and shows the
accuracy of greater or equal 98%.
Static Quantized Radix-2 FFT/IFFT Processor for Constraints Analysis
Date: 2018-06-10 00:00:00
Tasks: Quantization

This research work focuses on the design of a high-resolution fast Fourier
transform (FFT) /inverse fast Fourier transform (IFFT) processors for
constraints analysis purpose. Amongst the major setbacks associated with such
high resolution, FFT processors are the high power consumption resulting from
the structural complexity and computational inefficiency of floating-point
calculations. As such, a parallel pipelined architecture was proposed to
statically scale the resolution of the processor to suite adequate trade-off
constraints. The quantization was applied to provide an approximation to
address the finite word-length constraints of digital signal processing (DSP).
An optimum operating mode was proposed, based on the
signal-to-quantization-noise ratio (SQNR) as well as the statistical theory of
quantization, to minimize the tradeoff issues associated with selecting the
most application-efficient floating-point processing capability in contrast to
their resolution quality.
VLSI Design Of Advanced Digital Filters
Date: 2018-06-10 00:00:00
Tasks: Unity

The Cascaded Integrator Comb filters (CIC) find many applications in recent
electronic devices such as frequency selection functions in a digital radio or
modem and any filter structure that is required to efficiently process large
sample rate factor. These filters are normally located after the sigma-delta
modulator and have a regular structure. These types of filters do not require
multipliers and the coefficient storage unlike in the normal digital FIR and
IIR filters because of all filter coefficients are unity. Hence, it can be
efficiently implemented to operate at high speed. Hence, this book describes
the Very Large Scale Integration (VLSI) implementation of the CIC filters that
are suitable for high-performance audio applications.
DG-GL: Differential geometry based geometric learning of molecular datasets
Date: 2018-06-11 00:00:00
Tasks: Descriptive, Dimensionality Reduction, Drug Discovery

Motivation: Despite its great success in various physical modeling,
differential geometry (DG) has rarely been devised as a versatile tool for
analyzing large, diverse and complex molecular and biomolecular datasets due to
the limited understanding of its potential power in dimensionality reduction
and its ability to encode essential chemical and biological information in
differentiable manifolds. Results: We put forward a differential geometry based
geometric learning (DG-GL) hypothesis that the intrinsic physics of
three-dimensional (3D) molecular structures lies on a family of low-dimensional
manifolds embedded in a high-dimensional data space. We encode crucial
chemical, physical and biological information into 2D element interactive
manifolds, extracted from a high-dimensional structural data space via a
multiscale discrete-to-continuum mapping using differentiable density
estimators. Differential geometry apparatuses are utilized to construct element
interactive curvatures %Gaussian curvature, mean curvature, minimum curvature
and maximum curvature in analytical forms for certain analytically
differentiable density estimators. These low-dimensional differential geometry
representations are paired with a robust machine learning algorithm to showcase
their descriptive and predictive powers for large, diverse and complex
molecular and biomolecular datasets. Extensive numerical experiments are
carried out to demonstrated that the proposed DG-GL strategy outperforms other
advanced methods in the predictions of drug discovery related protein-ligand
binding affinity, drug toxicity, and molecular solvation free energy.