pwc-archive/papers-with-abstracts huggingface dataset:
Portfolio Construction Matters
Date: 2018-10-19 00:00:00

The role of portfolio construction in the implementation of equity market
neutral factors is often underestimated. Taking the classical momentum strategy
as an example, we show that one can significantly improve the main strategy's
features by properly taking care of this key step. More precisely, an optimized
portfolio construction algorithm allows one to significantly improve the Sharpe
Ratio, reduce sector exposures and volatility fluctuations, and mitigate the
strategy's skewness and tail correlation with the market. These results are
supported by long-term, world-wide simulations and will be shown to be
universal. Our findings are quite general and hold true for a number of other
"equity factors". Finally, we discuss the details of a more realistic set-up
where we also deal with transaction costs.
Convolutional group-sparse coding and source localization
Date: 2018-10-19 00:00:00

In this paper, we present a new interpretation of non-negatively constrained
convolutional coding problems as blind deconvolution problems with spatially
variant point spread function. In this light, we propose an optimization
framework that generalizes our previous work on non-negative group sparsity for
convolutional models. We then link these concepts to source localization
problems that arise in scientific imaging and provide a visual example on an
image derived from data captured by the Hubble telescope.
ALM for insurers with multiple underwriting lines and portfolio constraints: a Lagrangian duality approach
Authors: Rafael Serrano, Camilo Castillo
Date: 2018-10-19 00:00:00

We study a continuous-time asset-allocation problem for an insurance firm that backs up liabilities from multiple non-life business lines with underwriting profits and investment income. The insurance risks are captured via a multidimensional jump-diffusion process with a multivariate compound Poisson process with dependent components, which allows to model claims that occur in different lines simultaneously. Using Lagrangian convex duality techniques, we provide a general verification-type result for investment-underwriting strategies that maximize expected utility from the dividend payout rate and final wealth over a finite-time horizon. We also study the precautionary effect on earnings retention of risk aversion, prudence, portfolio constraints and multivariate insurance risk. We find an explicit characterization of optimal strategies under CRRA preferences. Numerical results for two-dimensional examples with policy limits illustrate the impact of co-integration for ALM with multiple (dependent and independent) sources of insurance risk.
Forecasting Time Series with VARMA Recursions on Graphs
Date: 2019-07-10 00:00:00
Tasks: Time Series, Time Series Analysis

Graph-based techniques emerged as a choice to deal with the dimensionality
issues in modeling multivariate time series. However, there is yet no complete
understanding of how the underlying structure could be exploited to ease this
task. This work provides contributions in this direction by considering the
forecasting of a process evolving over a graph. We make use of the
(approximate) time-vertex stationarity assumption, i.e., timevarying graph
signals whose first and second order statistical moments are invariant over
time and correlated to a known graph topology. The latter is combined with VAR
and VARMA models to tackle the dimensionality issues present in predicting the
temporal evolution of multivariate time series. We find out that by projecting
the data to the graph spectral domain: (i) the multivariate model estimation
reduces to that of fitting a number of uncorrelated univariate ARMA models and
(ii) an optimal low-rank data representation can be exploited so as to further
reduce the estimation costs. In the case that the multivariate process can be
observed at a subset of nodes, the proposed models extend naturally to Kalman
filtering on graphs allowing for optimal tracking. Numerical experiments with
both synthetic and real data validate the proposed approach and highlight its
benefits over state-of-the-art alternatives.
CoHSI III: Long proteins and implications for protein evolution
Authors: Les Hatton, Gregory Warr
Date: 2018-10-19 00:00:00
Tasks: Protein Folding

The length distribution of proteins measured in amino acids follows the CoHSI (Conservation of Hartley-Shannon Information) probability distribution. In previous papers we have verified various predictions of this using the Uniprot database but here we explore a novel predicted relationship between the longest proteins and evolutionary time. We demonstrate from both theory and experiment that the longest protein and the total number of proteins are intimately related by Information Theory and we give a simple formula for this. We stress that no evolutionary explanation is necessary; it is an intrinsic property of a CoHSI system. While the CoHSI distribution favors the appearance of proteins with fewer than 750 amino acids (characteristic of most functional proteins or their constituent domains) its intrinsic asymptotic power-law also favors the appearance of unusually long proteins; we predict that there are as yet undiscovered proteins longer than 45,000 amino acids. In so doing, we draw an analogy between the process of protein folding driven by favorable pathways (or funnels) through the energy landscape of protein conformations, and the preferential information pathways through which CoHSI exerts its constraints in discrete systems. Finally, we show that CoHSI predicts the recent appearance in evolutionary time of the longest proteins, specifically in eukaryotes because of their richer unique alphabet of amino acids, and by merging with independent phylogenetic data, we confirm a predicted consistent relationship between the longest proteins and documented and potential undocumented mass extinctions.
Towards automated neural design: An open source, distributed neural architecture research framework
Authors: Kyriakides George, Margaritis Konstantinos
Date: 2018-09-20 00:00:00
Tasks: Distributed Computing

NORD (Neural Operations Research & Development) is an open source distributed
deep learning architectural research framework, based on PyTorch, MPI and
Horovod. It aims to make research of deep architectures easier for experts of
different domains, in order to accelerate the process of finding better
architectures, as well as study the best architectures generated for different
datasets. Although currently under heavy development, the framework aims to
allow the easy implementation of different design and optimization method
families (optimization algorithms, meta-heuristics, reinforcement learning
etc.) as well as the fair comparison between them. Furthermore, due to the
computational resources required in order to optimize and evaluate network
architectures, it leverage the use of distributed computing, while aiming to
minimize the researcher's overhead required to implement it. Moreover, it
strives to make the creation of architectures more intuitive, by implementing
network descriptors, allowing to separately define the architecture's nodes and
connections. In this paper, we present the framework's current state of
development, while presenting its basic concepts, providing simple examples as
well as their experimental results.
The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations Trained on Scarce Data
Authors: Bagińska Marta, Srokosz Piotr E.
Date: 2018-09-22 00:00:00

This study is focused on determining the potential of using deep neural
networks (DNNs) to predict the ultimate bearing capacity of shallow foundation
in situations when the experimental data which may be used to train networks is
scarce. Two experiments involving testing over 17000 networks were conducted.
The first experiment was aimed at comparing the accuracy of shallow neural
networks and DNNs predictions. It shows that when the experimental dataset used
for preparing models is small then DNNs have a significant advantage over
shallow networks. The second experiment was conducted to compare the
performance of DNNs consisting of different number of neurons and layers.
Obtained results indicate that the optimal number of layers varies between 5 to
7. Networks with less and - surprisingly - more layers obtain lower accuracy.
Moreover, the number of neurons in DNN has a lower impact on the prediction
accuracy than the number of DNN's layers. DNNs perform very well, even when
trained with only 6 samples. Basing on the results it seems that when
predicting the ultimate bearing capacity with ANN models obtaining small but
high-quality experimental training datasets instead of large training datasets
affected by a higher error is an advisable approach.
Design Space Exploration of Neural Network Activation Function Circuits
Authors: Yang Tao, Wei Yadong, Tu Zhijun, Zeng Haolun, Kinsy Michel A., Zheng Nanning, Ren Pengju
Date: 2018-09-22 00:00:00

The widespread application of artificial neural networks has prompted
researchers to experiment with FPGA and customized ASIC designs to speed up
their computation. These implementation efforts have generally focused on
weight multiplication and signal summation operations, and less on activation
functions used in these applications. Yet, efficient hardware implementations
of nonlinear activation functions like Exponential Linear Units (ELU), Scaled
Exponential Linear Units (SELU), and Hyperbolic Tangent (tanh), are central to
designing effective neural network accelerators, since these functions require
lots of resources. In this paper, we explore efficient hardware implementations
of activation functions using purely combinational circuits, with a focus on
two widely used nonlinear activation functions, i.e., SELU and tanh. Our
experiments demonstrate that neural networks are generally insensitive to the
precision of the activation function. The results also prove that the proposed
combinational circuit-based approach is very efficient in terms of speed and
area, with negligible accuracy loss on the MNIST, CIFAR-10 and IMAGENET
benchmarks. Synopsys Design Compiler synthesis results show that circuit
designs for tanh and SELU can save between 3.13-7.69 and 4.45-8:45 area
compared to the LUT/memory-based implementations, and can operate at 5.14GHz
and 4.52GHz using the 28nm SVT library, respectively. The implementation is
available at: https://github.com/ThomasMrY/ActivationFunctionDemo.
How can deep learning advance computational modeling of sensory information processing?
Authors: Thompson Jessica A. F., Bengio Yoshua, Formisano Elia, Schönwiesner Marc
Date: 2018-09-25 00:00:00

Deep learning, computational neuroscience, and cognitive science have
overlapping goals related to understanding intelligence such that perception
and behaviour can be simulated in computational systems. In neuroimaging,
machine learning methods have been used to test computational models of sensory
information processing. Recently, these model comparison techniques have been
used to evaluate deep neural networks (DNNs) as models of sensory information
processing. However, the interpretation of such model evaluations is muddied by
imprecise statistical conclusions. Here, we make explicit the types of
conclusions that can be drawn from these existing model comparison techniques
and how these conclusions change when the model in question is a DNN. We
discuss how DNNs are amenable to new model comparison techniques that allow for
stronger conclusions to be made about the computational mechanisms underlying
sensory information processing.
Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors
Authors: Zhang Yanjun, Li Tie, Na Guangyu, Li Guoqing, Li Yang
Date: 2018-09-27 00:00:00

A new optimized extreme learning machine- (ELM-) based method for power
system transient stability prediction (TSP) using synchrophasors is presented
in this paper. First, the input features symbolizing the transient stability of
power systems are extracted from synchronized measurements. Then, an ELM
classifier is employed to build the TSP model. And finally, the optimal
parameters of the model are optimized by using the improved particle swarm
optimization (IPSO) algorithm. The novelty of the proposal is in the fact that
it improves the prediction performance of the ELM-based TSP model by using IPSO
to optimize the parameters of the model with synchrophasors. And finally, based
on the test results on both IEEE 39-bus system and a large-scale real power
system, the correctness and validity of the presented approach are verified.
Compressive Imaging with Stochastic Spatial Light Modulator
Date: 2018-10-19 00:00:00

We present a stochastic analog spatial light modulator designed for
compressive imaging applications. We rely on the unpredictable nature of
multi-particle collisions to provide randomization for the particle location.
We demonstrate this concept in an optical imaging system using a single-pixel
camera. This design can be applied to imaging or spectroscopic systems in which
no analog to optical spatial light modulators currently exist or in non-optical
lensless imaging systems.
Neural wave interference and intrinsic tuning in distributed excitatory-inhibitory networks
Date: 2018-10-20 00:00:00

We developed a model of cortical computation that implements key features of
cortical circuitry and is capable of describing propagation of neural signals
between cortical locations in response to spatially distributed stimuli. The
model is based on the canonical neural circuit that consists of excitatory and
inhibitory cells interacting through reciprocal connections, with recurrent
feedback. The canonical circuit is used as a node in a distributed network with
nearest neighbor coupling between the nodes. We find that this system is
characterized by intrinsic preference for spatial frequency. The value of
preferred frequency depends on the relative weights of excitatory and
inhibitory connections between cells. This balance of excitation and inhibition
changes as stimulus contrast increases, which is why intrinsic spatial
frequency is predicted to change with contrast in a manner determined by
stimulus temporal frequency. The dynamics of network preference is consistent
with properties of the cortical area MT in alert macaque monkeys.
Temporal Proximity induces Attributes Similarity
Authors: Kumar Arun, Aggarwal Karan, Schrater Paul
Date: 2018-10-20 00:00:00
Tasks: Attribute, Recommendation Systems

Users consume their favorite content in temporal proximity of consumption
bundles according to their preferences and tastes. Thus, the underlying
attributes of items implicitly match user preferences, however, current
recommender systems largely ignore this fundamental driver in identifying
matching items. In this work, we introduce a novel temporal proximity filtering
method to enable items-matching. First, we demonstrate that proximity
preferences exist. Second, we present an induced similarity metric in temporal
proximity driven by user tastes and third, we show that this induced similarity
can be used to learn items pairwise similarity in attribute space. The proposed
model does not rely on any knowledge outside users' consumption bundles and
provide a novel way to devise user preferences and tastes driven novel items
recommender.
Reconstructing 3D Motion Trajectory of Large Swarm of Flying Objects
Date: 2018-10-20 00:00:00
Tasks: Stereo Matching

This paper addresses the problem of reconstructing the motion trajectories of
the individuals in a large collection of flying objects using two temporally
synchronized and geometrically calibrated cameras. The 3D trajectory
reconstruction problem involves two challenging tasks - stereo matching and
temporal tracking. Existing methods separate the two and process them one at a
time sequentially, and suffer from frequent irresolvable ambiguities in stereo
matching and in tracking. We unify the two tasks, and propose an optimization
approach to solving stereo matching and temporal tracking simultaneously. It
treats 3D trajectory acquisition problem as selecting appropriate stereo
correspondence out of all possible ones for each object via minimizing a cost
function. Experiment results show that the proposed method offers significant
performance advantage over existing approaches. The proposed method has
successfully been applied to reconstruct 3D motion trajectories of hundreds of
simultaneously flying fruit flies (Drosophila Melanogaster), which could
facilitate the study the insect's collective behavior.
Learning differentially reorganizes brain activity and connectivity
Date: 2020-09-10 00:00:00
Tasks: Functional Connectivity

Human learning is a complex process in which future behavior is altered via
the reorganization of brain activity and connectivity. It remains unknown
whether activity and connectivity differentially reorganize during learning,
and, if so, how that differential reorganization tracks stages of learning
across distinct brain areas. Here, we address this gap in knowledge by
measuring brain activity and functional connectivity in a longitudinal fMRI
experiment in which healthy adult human participants learn the values of novel
objects over the course of four days. An increasing similarity in activity or
functional connectivity across subjects during learning reflects reorganization
toward a common functional architecture. We assessed the presence of
reorganization in activity and connectivity both during value learning and
during the resting-state, allowing us to differentiate common elicited
processes from intrinsic processes. We found a complex and dynamic
reorganization of brain connectivity and activity--as a function of time,
space, and performance--that occurs while subjects learn. Spatially localized
brain activity reorganizes across the brain to a common functional architecture
early in learning, and this reorganization tracks early learning performance.
In contrast, spatially distributed connectivity reorganizes across the brain to
a common functional architecture as training progresses, and this
reorganization tracks later learning performance. Particularly good performance
is associated with a sticky connectivity, that persists into the resting state.
Broadly, our work uncovers distinct principles of reorganization in activity
and connectivity at different phases of value learning, which inform the
ongoing study of learning processes more generally.
A Hardware Realization of Superresolution Combining Random Coding and Blurring
Date: 2018-10-20 00:00:00

Resolution enhancements are often desired in imaging applications where
high-resolution sensor arrays are difficult to obtain. Many computational
imaging methods have been proposed to encode high-resolution scene information
on low-resolution sensors by cleverly modulating light from the scene before it
hits the sensor. These methods often require movement of some portion of the
imaging apparatus or only acquire images up to the resolution of a modulating
element. Here a technique is presented for resolving beyond the resolutions of
both a pointwise-modulating mask element and a sensor array through the
introduction of a controlled blur into the optical pathway. The analysis
contains an intuitive and exact expression for the overall superresolvability
of the system, and arguments are presented to explain how the combination of
random coding and blurring makes the superresolution problem well-posed.
Experimental results demonstrate that a resolution enhancement of approximately
$4\times$ is possible in practice using standard optical components, without
mechanical motion of the imaging apparatus, and without any a priori
assumptions on scene structure.
Optimal Transmit Antenna Selection Algorithm in Massive MIMOME Channels
Date: 2019-01-19 00:00:00

This paper studies the transmit antenna selection in massive multiple-input
multiple-output (MIMO) system over wiretap channel. The transmitter, equipped
with a large-scale antenna array whose size is much larger than that of the
eavesdropper and legitimate receiver, selects a subset of antennas to transmit
messages. An branch-and-bound (BAB) search based algorithm for antenna
selection in independent and identical Rayleigh flat fading channel is proposed
to maximize the secrecy capacity between the transmitter and the legitimate
receiver when the transmit power is equally allocated into the active antennas.
Furthermore, the proposed algorithm is separately applied to two scenarios
which is based on whether the channel side information of the eavesdropper
(CSIE) is available. Simulation results show that the proposed algorithm has
the same performance as the exhaustive search under both scenarios but with
much lower complexity.
Modeling Oral Multispecies Biofilm Recovery After Antibacterial Treatment
Date: 2018-10-21 00:00:00

Recovery of multispecies oral biofilms is investigated following treatment by
chlorhexidine gluconate (CHX), iodine-potassium iodide (IPI) and Sodium
hypochlorite (NaOCl) both experimentally and theoretically. Experimentally,
biofilms taken from two donors were exposed to the three antibacterial
solutions (irrigants) for 10 minutes, respectively. We observe that (a) live
bacterial cell ratios decline for a week after the exposure and the trend
reverses beyond a week; after fifteen weeks, live bacterial cell ratios in
biofilms fully return to their pretreatment levels; (b) NaOCl is shown as the
strongest antibacterial agent for the oral biofilms; (c) multispecies oral
biofilms from different donors showed no difference in their susceptibility to
all the bacterial solutions. Guided by the experiment, a mathematical model for
biofilm dynamics is developed, accounting for multiple bacterial phenotypes,
quorum sensing, and growth factor proteins, to describe the nonlinear time
evolutionary behavior of the biofilms. The model captures time evolutionary
dynamics of biofilms before and after antibacterial treatment very well. It
reveals the crucial role played by quorum sensing molecules and growth factors
in biofilm recovery and verifies that the source of biofilms has a minimal to
their recovery. The model is also applied to describe the state of biofilms of
various ages treated by CHX, IPI and NaOCl, taken from different donors. Good
agreement with experimental data predicted by the model is obtained as well,
confirming its applicability to modeling biofilm dynamics in general.
Analog-to-digital conversion revolutionized by deep learning
Date: 2018-10-21 00:00:00
Tasks: Deep Learning

As the bridge between the analog world and digital computers,
analog-to-digital converters are generally used in modern information systems
such as radar, surveillance, and communications. For the configuration of
analog-to-digital converters in future high-frequency broadband systems, we
introduce a revolutionary architecture that adopts deep learning technology to
overcome tradeoffs between bandwidth, sampling rate, and accuracy. A photonic
front-end provides broadband capability for direct sampling and speed
multiplication. Trained deep neural networks learn the patterns of system
defects, maintaining high accuracy of quantized data in a succinct and adaptive
manner. Based on numerical and experimental demonstrations, we show that the
proposed architecture outperforms state-of-the-art analog-to-digital
converters, confirming the potential of our approach in future
analog-to-digital converter design and performance enhancement of future
information systems.