pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Self-Adaptive Synchronous Triggering System in Superconducting Quantum Computing
Date: 2018-06-11 00:00:00

Superconducting quantum computers (SQC) can solve some specific problems
which are deeply believed to be intractable for classical computers. The
control and measurement of qubits can't go on without the synchronous operation
of digital-to-analog converters (DAC) array and the controlled sampling of
analog-to-digital converters (ADC). In this paper, a scalable self-adaptive
synchronous triggering system is proposed to ensure the synchronized operation
of multiple qubits. The skew of the control signal between different qubits is
less than 25 ps. After upgrading the clock design, the 250 MHz single-tone
phase noise of DAC has been increased about 15 dB. The phase noise of the 6.25
GHz qubit control signal has an improvement of about 6 dB.
Approximation Methods for Analyzing Multiscale Stochastic Vector-borne Epidemic Models
Date: 2018-06-11 00:00:00

Stochastic epidemic models, generally more realistic than deterministic
counterparts, have often been seen too complex for rigorous mathematical
analysis because of level of details it requires to comprehensively capture the
dynamics of diseases. This problem further becomes intense when complexity of
diseasees increases as in the case of vector-borne diseases (VBD). The VBDs are
human illnesses caused by pathogens transmitted among humans by intermediate
species, which are primarily arthropods. In this study, a stochastic VBD model
is developed and novel mathematical methods are described and evaluated to
systematically analyze the model and understand its complex dynamics. The VBD
model incorporates some relevant features of the VBD transmission process
including demographical, ecological and social mechanisms. The analysis is
based on dimensional reductions and model simplications via scaling limit
theorems. The results suggest that the dynamics of the stochastic VBD depends
on a threshold quantity R_0, the initial size of infectives, and the type of
scaling in terms of host population size. The quantity R_0 for deterministic
counterpart of the model, interpreted as threshold condition for infection
persistence as is mentioned in the literature for many infectious disease
models, can be computed. Different scalings yield different approximations of
the model, and in particular, if vectors have much faster dynamics, the effect
of the vector dynamics on the host population averages out, which largely
reduces the dimension of the model.
Distributed Evaluations: Ending Neural Point Metrics
Authors: Cohen Daniel, Jordan Scott M., Croft W. Bruce
Date: 2018-06-11 00:00:00
Tasks: Information Retrieval, Retrieval

With the rise of neural models across the field of information retrieval,
numerous publications have incrementally pushed the envelope of performance for
a multitude of IR tasks. However, these networks often sample data in random
order, are initialized randomly, and their success is determined by a single
evaluation score. These issues are aggravated by neural models achieving
incremental improvements from previous neural baselines, leading to multiple
near state of the art models that are difficult to reproduce and quickly become
deprecated. As neural methods are starting to be incorporated into low resource
and noisy collections that further exacerbate this issue, we propose evaluating
neural models both over multiple random seeds and a set of hyperparameters
within $\epsilon$ distance of the chosen configuration for a given metric.
SVA Based Beamforming
Date: 2018-06-11 00:00:00
Tasks: Direction of Arrival Estimation

A frequency domain method is proposed to reduce the sidelobe level of a
uniformly weighted uniform linear array in direction-of-arrival estimation. The
development is based on the nonlinear method of spatially variant apodization
originally proposed for spectral analysis and synthetic aperture radar imagery.
Optimizing sequential decisions in the drift-diffusion model
Date: 2018-06-11 00:00:00
Tasks: Decision Making, model

To make decisions organisms often accumulate information across multiple
timescales. However, most experimental and modeling studies of decision-making
focus on sequences of independent trials. On the other hand, natural
environments are characterized by long temporal correlations, and evidence used
to make a present choice is often relevant to future decisions. To understand
decision-making under these conditions we analyze how a model ideal observer
accumulates evidence to freely make choices across a sequence of correlated
trials. We use principles of probabilistic inference to show that an ideal
observer incorporates information obtained on one trial as an initial bias on
the next. This bias decreases the time, but not the accuracy of the next
decision. Furthermore, in finite sequences of trials the rate of reward is
maximized when the observer deliberates longer for early decisions, but
responds more quickly towards the end of the sequence. Our model also explains
experimentally observed patterns in decision times and choices, thus providing
a mathematically principled foundation for evidence-accumulation models of
sequential decisions.
Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code
Authors: Michael Schmuker, Rüdiger Kupper, Ad Aertsen, Thomas Wachtler, Marc-Oliver Gewaltig
Date: 2018-06-11 00:00:00
Tasks: Image Segmentation, Low-latency processing, Semantic Segmentation

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.
Nutrients and biomass dynamics in photo-sequencing batch reactors treating wastewater with high nutrients loadings
Date: 2018-06-11 00:00:00

The present study investigates different strategies for the treatment of a
mixture of digestate from an anaerobic digester diluted and secondary effluent
from a high rate algal pond. To this aim, the performance of two
photo-sequencing batch reactors (PSBRs) operated at high nutrients loading
rates and different solids retention times (SRTs) were compared with a
semi-continuous photobioreactor (SC). Performances were evaluated in terms of
wastewater treatment, biomass composition and biopolymers accumulation during
30 days of operation. PSBRs were operated at a hydraulic retention time (HRT)
of 2 days and SRTs of 10 and 5 days (PSBR2-10 and PSBR2-5, respectively),
whereas the semi-continuous reactor was operated at a coupled HRT/SRT of 10
days (SC10-10). Results showed that PSBR2-5 achieved the highest removal rates
in terms of TN (6.7 mg L-1 d-1), TP (0.31 mg L-1 d-1), TOC (29.32 mg L-1 d-1)
and TIC (3.91 mg L-1 d-1). These results were in general 3-6 times higher than
the removal rates obtained in the SC10-10 (TN 29.74 mg L-1 d-1, TP 0.96 mg L-1
d-1, TOC 29.32 mg L-1 d-1 and TIC 3.91 mg L-1 d-1). Furthermore, both PSBRs
were able to produce biomass up to 0.09 g L-1 d-1, more than twofold the
biomass produced by the semi-continuous reactor (0.04 g L-1 d-1), and achieved
a biomass settleability of 86-92%. This study also demonstrated that the
microbial composition could be controlled by the nutrients loads, since the
three reactors were dominated by different species depending on the nutritional
conditions. Concerning biopolymers accumulation, carbohydrates concentration
achieved similar values in the three reactors (11%), whereas <0.5 % of
polyhydrohybutyrates (PHB) was produced. These low values in biopolymers
production could be related to the lack of microorganisms as cyanobacteria that
are able to accumulate carbohydrates/PHB.
Statistical theory of phenotype abundance distributions: a test through exact enumeration of genotype spaces
Date: 2018-07-25 00:00:00

The evolutionary dynamics of molecular populations are strongly dependent on
the structure of genotype spaces. The map between genotype and phenotype
determines how easily genotype spaces can be navigated and the accessibility of
evolutionary innovations. In particular, the size of neutral networks
corresponding to specific phenotypes and its statistical counterpart, the
distribution of phenotype abundance, have been studied through multiple
computationally tractable genotype-phenotype maps. In this work, we test a
theory that predicts the abundance of a phenotype and the corresponding
asymptotic distribution (given the compositional variability of its genotypes)
through the exact enumeration of several GP maps. Our theory predicts with high
accuracy phenotype abundance, and our results show that, in navigable genotype
spaces ---characterised by the presence of large neutral networks---, phenotype
abundance converges to a log-normal distribution.
Versatile Mobile Communications Simulation: The Vienna 5G Link Level Simulator
Date: 2018-06-11 00:00:00

Research and development of mobile communications systems require a detailed
analysis and evaluation of novel technologies to further enhance spectral
efficiency, connectivity and reliability. Due to the exponentially increasing
demand of mobile broadband data rates and challenging requirements for latency
and reliability, mobile communications specifications become increasingly
complex to support ever more sophisticated techniques. For this reason,
analytic analysis as well as measurement based investigations of link level
methods soon encounter feasibility limitations. Therefore, computer aided
numeric simulation is an important tool for investigation of wireless
communications standards and is indispensable for analysis and developing
future technologies. In this contribution, we introduce the Vienna 5G Link
Level Simulator, a Matlab-based link level simulation tool to facilitate
research and development of 5G and beyond mobile communications. Our simulator
enables standard compliant setups according to 4G Long Term Evolution, 5G new
radio and even beyond, making it a very flexible simulation tool. Offered under
an academic use license to fellow researchers it considerably enhances
reproducibility in wireless communications research. We give a brief overview
of our simulation platform and introduce unique features of our link level
simulator in more detail to outline its versatile functionality.
Learning Linear Temporal Properties
Authors: Neider Daniel, Gavran Ivan
Date: 2018-09-20 00:00:00

We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol.
Low-Complexity Decoder for Overloaded Uniquely Decodable Synchronous CDMA
Authors: Michel Kulhandjian, Claude D'Amours, Hovannes Kulhandjian, Halim Yanikomeroglu, Dimitris A. Pados, Gurgen Khachatrian
Date: 2018-06-11 00:00:00
Tasks: Decoder

We consider the problem of designing a low-complexity decoder for antipodal uniquely decodable (UD) /errorless code sets for overloaded synchronous code-division multiple access (CDMA) systems, where the number of signals Kamax is the largest known for the given code length L. In our complexity analysis, we illustrate that compared to maximum-likelihood (ML) decoder, which has an exponential computational complexity for even moderate code lengths, the proposed decoder has a quasi-quadratic computational complexity. Simulation results in terms of bit-error-rate (BER) demonstrate that the performance of the proposed decoder has only a 1-2 dB degradation in signal-to-noise ratio (SNR) at a BER of 10^-3 when compared to ML. Moreover, we derive the proof of the minimum Manhattan distance of such UD codes and we provide the proofs for the propositions; these proofs constitute the foundation of the formal proof for the maximum number users Kamax for L=8 .
Reconciling Multiple Genes Trees via Segmental Duplications and Losses
Date: 2018-06-11 00:00:00

Reconciling gene trees with a species tree is a fundamental problem to
understand the evolution of gene families. Many existing approaches reconcile
each gene tree independently. However, it is well-known that the evolution of
gene families is interconnected. In this paper, we extend a previous approach
to reconcile a set of gene trees with a species tree based on segmental
macro-evolutionary events, where segmental duplication events and losses are
associated with cost $\delta$ and $\lambda$, respectively. We show that the
problem is polynomial-time solvable when $\delta \leq \lambda$ (via
LCA-mapping), while if $\delta > \lambda$ the problem is NP-hard, even when
$\lambda = 0$ and a single gene tree is given, solving a long standing open
problem on the complexity of the reconciliation problem. On the positive side,
we give a fixed-parameter algorithm for the problem, where the parameters are
$\delta/\lambda$ and the number $d$ of segmental duplications, of time
complexity $O(\lceil \frac{\delta}{\lambda} \rceil^{d} \cdot n \cdot
\frac{\delta}{\lambda})$. Finally, we demonstrate the usefulness of this
algorithm on two previously studied real datasets: we first show that our
method can be used to confirm or refute hypothetical segmental duplications on
a set of 16 eukaryotes, then show how we can detect whole genome duplications
in yeast genomes.
PubMed Labs: An experimental platform for improving biomedical literature search
Authors: Fiorini Nicolas, Canese Kathi, Bryzgunov Rostyslav, Radetska Ievgeniia, Gindulyte Asta, Latterner Martin, Miller Vadim, Osipov Maxim, Kholodov Michael, Starchenko Grisha, Kireev Evgeny, Lu Zhiyong
Date: 2018-06-11 00:00:00

PubMed is a freely accessible system for searching the biomedical literature,
with approximately 2.5 million users worldwide on an average workday. We have
recently developed PubMed Labs (www.pubmed.gov/labs), an experimental platform
for users to test new features/tools and provide feedback, which enables us to
make more informed decisions about potential changes to improve the search
quality and overall usability of PubMed. In doing so, we hope to better meet
our user needs in an era of information overload. Another novel aspect of
PubMed Labs lies in its mobile-first and responsive layout, which offers better
support for accessing PubMed on the increasingly popular use of mobile and
small-screen devices. Currently, PubMed Labs only includes a core subset of
PubMed functionalities, e.g. search, facets. We encourage users to test PubMed
Labs and share their experience with us, based on which we expect to
continuously improve PubMed Labs with more advanced features and better user
experience.
Digital compensation of the side-band-rejection ratio in a fully analog 2SB sub-millimeter receiver
Date: 2018-06-11 00:00:00
Tasks: Astronomy

In observational radio astronomy, sideband-separating receivers are
preferred, particularly under high atmospheric noise, which is usually the case
in the sub-millimeter range. However, obtaining a good rejection ratio between
the two sidebands is difficult since, unavoidably, imbalances in the different
analog components appear. We describe a method to correct these imbalances
without making any change in the analog part of the sideband-separating
receiver, specifically, keeping the intermediate-frequency hybrid in place.
This opens the possibility of implementing the method in any existing receiver.
We have built hardware to demonstrate the validity of the method and tested it
on a fully analog receiver operating between 600 and 720GHz. We have tested the
stability of calibration and performance vs time and after full resets of the
receiver. We have performed an error analysis to compare the digital
compensation in two configurations of analog receivers, with and without
intermediate frequency (IF) hybrid. An average compensated sideband rejection
ratio of 46dB is obtained. Degradation of the compensated sideband rejection
ratio on time and after several resets of the receiver is minimal. A receiver
with an IF hybrid is more robust to systematic errors. Moreover, we have shown
that the intrinsic random errors in calibration have the same impact for
configuration without IF hybrid and for a configuration with IF hybrid with
analog rejection ratio better than 10dB. Compensated rejection ratios above
40dB are obtained even in the presence of high analog rejection. The method is
robust allowing its use under normal operational conditions at any telescope.
We also demonstrate that a full analog receiver is more robust against
systematic errors. Finally, the error bars associated to the compensated
rejection ratio are almost independent of whether IF hybrid is present or not.
Fairness-Aware Scheduling in Multi-Numerology Based 5G New Radio
Date: 2019-06-13 00:00:00
Tasks: Fairness, Scheduling

Multi-numerology waveform based 5G New Radio (NR) systems offer great
flexibility for different requirements of users and services. Providing
fairness between users is not an easy task due to inter-numerology interference
(INI) between multiple numerologies. This paper proposes two novel scheduling
algorithms to provide fairness for all users, especially at the edges of
numerologies. Signal-to-noise ratio (SIR) results for multi-numerology systems
are obtained through computer simulations.
Evidence accumulation in a Laplace domain decision space
Date: 2018-10-23 00:00:00
Tasks: Decision Making, Hippocampus

Evidence accumulation models of simple decision-making have long assumed that
the brain estimates a scalar decision variable corresponding to the
log-likelihood ratio of the two alternatives. Typical neural implementations of
this algorithmic cognitive model assume that large numbers of neurons are each
noisy exemplars of the scalar decision variable. Here we propose a neural
implementation of the diffusion model in which many neurons construct and
maintain the Laplace transform of the distance to each of the decision bounds.
As in classic findings from brain regions including LIP, the firing rate of
neurons coding for the Laplace transform of net accumulated evidence grows to a
bound during random dot motion tasks. However, rather than noisy exemplars of a
single mean value, this approach makes the novel prediction that firing rates
grow to the bound exponentially, across neurons there should be a distribution
of different rates. A second set of neurons records an approximate inversion of
the Laplace transform, these neurons directly estimate net accumulated
evidence. In analogy to time cells and place cells observed in the hippocampus
and other brain regions, the neurons in this second set have receptive fields
along a "decision axis." This finding is consistent with recent findings from
rodent recordings. This theoretical approach places simple evidence
accumulation models in the same mathematical language as recent proposals for
representing time and space in cognitive models for memory.
Simulation of morphogen and tissue dynamics
Date: 2018-06-11 00:00:00

Morphogenesis, the process by which an adult organism emerges from a single
cell, has fascinated humans for a long time. Modelling this process can provide
novel insights into development and the principles that orchestrate the
developmental processes. This chapter focusses on the mathematical description
and numerical simulation of developmental processes. In particular, we discuss
the mathematical representation of morphogen and tissue dynamics on static and
grow- ing domains, as well as the corresponding tissue mechanics. In addition,
we give an overview of numerical methods that are routinely used to solve the
resulting systems of partial differential equations. These include the finite
element method and the Lattice Boltzmann method for the discretisation as well
as the arbitrary Lagrangian-Eulerian method and the Diffuse-Domain method to
numerically treat deforming domains.
Inference under Covariate-Adaptive Randomization with Multiple Treatments
Date: 2019-01-17 00:00:00
Tasks: regression

This paper studies inference in randomized controlled trials with
covariate-adaptive randomization when there are multiple treatments. More
specifically, we study inference about the average effect of one or more
treatments relative to other treatments or a control. As in Bugni et al.
(2018), covariate-adaptive randomization refers to randomization schemes that
first stratify according to baseline covariates and then assign treatment
status so as to achieve balance within each stratum. In contrast to Bugni et
al. (2018), we not only allow for multiple treatments, but further allow for
the proportion of units being assigned to each of the treatments to vary across
strata. We first study the properties of estimators derived from a fully
saturated linear regression, i.e., a linear regression of the outcome on all
interactions between indicators for each of the treatments and indicators for
each of the strata. We show that tests based on these estimators using the
usual heteroskedasticity-consistent estimator of the asymptotic variance are
invalid; on the other hand, tests based on these estimators and suitable
estimators of the asymptotic variance that we provide are exact. For the
special case in which the target proportion of units being assigned to each of
the treatments does not vary across strata, we additionally consider tests
based on estimators derived from a linear regression with strata fixed effects,
i.e., a linear regression of the outcome on indicators for each of the
treatments and indicators for each of the strata. We show that tests based on
these estimators using the usual heteroskedasticity-consistent estimator of the
asymptotic variance are conservative, but tests based on these estimators and
suitable estimators of the asymptotic variance that we provide are exact. A
simulation study illustrates the practical relevance of our theoretical
results.