pwc-archive/papers-with-abstracts huggingface dataset:
Deleting object selective units in a fully-connected layer of deep convolutional networks improves classification performance
Date: 2020-01-21 00:00:00
Tasks: Object Recognition

Neurons in the primate visual cortices show a wide range of stimulus
selectivity. Some neurons respond to only a small fraction of stimulus images,
whereas others respond to many stimulus images in a non-selective manner. It is
unclear how stimulus selective and non-selective neurons contribute to visual
object recognition. Herein, we examined the relationship between stimulus
selectivity and the effect of deletion of units on task performance using fully
a connected layer of two types of deep convolutional neural networks (DCNNs).
Deleting a stimulus selective unit caused slight improvements of task
performance, whereas deleting stimulus non-selective units caused a significant
decrease in task performance. However, these findings do not imply that
stimulus selective units have no use for the task. Indeed, better performance
was obtained when the networks consisted of both stimulus selective and
non-selective units.
Progesterone: An Enigmatic Ligand for the Mineralocorticoid Receptor
Date: 2020-01-22 00:00:00

The progesterone receptor (PR) mediates progesterone regulation of female
reproductive physiology, as well as gene transcription in non-reproductive
tissues, such as brain, bone, lung and vasculature, in both women and men. An
unusual property of progesterone is its high affinity for the mineralocorticoid
receptor (MR), which regulates electrolyte transport in the kidney in humans
and other terrestrial vertebrates. In humans, rats, alligators and frogs,
progesterone antagonizes activation of the MR by aldosterone, the physiological
mineralocorticoid in terrestrial vertebrates. In contrast, in elephant shark,
ray-finned fishes and chickens, progesterone activates the MR. Interestingly,
cartilaginous fishes and ray-finned fishes do not synthesize aldosterone,
raising the question of which steroid(s) activate the MR in cartilaginous
fishes and ray-finned fishes. The simpler synthesis of progesterone, compared
to cortisol and other corticosteroids, makes progesterone a candidate
physiological activator of the MR in elephant sharks and ray-finned fishes.
Elephant shark and ray-finned fish MRs are expressed in diverse tissues,
including heart, brain and lung, as well as, ovary and testis, two reproductive
tissues that are targets for progesterone, which together suggests a
multi-faceted physiological role for progesterone activation of the MR in
elephant shark and ray-finned fish. The functional consequences of progesterone
as an antagonist of some terrestrial vertebrate MRs and as an agonist of fish
and chicken MRs are not fully understood. Indeed, little is known of
physiological activities of progesterone via any vertebrate MR.
Simultaneous Localization and Parameter Estimation for Single Particle Tracking via Sigma Points based EM
Date: 2020-01-22 00:00:00
Tasks: parameter estimation

Single Particle Tracking (SPT) is a powerful class of tools for analyzing the
dynamics of individual biological macromolecules moving inside living cells.
The acquired data is typically in the form of a sequence of camera images that
are then post-processed to reveal details about the motion. In this work, we
develop an algorithm for jointly estimating both particle trajectory and motion
model parameters from the data. Our approach uses Expectation Maximization (EM)
combined with an Unscented Kalman filter (UKF) and an Unscented
Rauch-Tung-Striebel smoother (URTSS), allowing us to use an accurate, nonlinear
model of the observations acquired by the camera. Due to the shot noise
characteristics of the photon generation process, this model uses a Poisson
distribution to capture the measurement noise inherent in imaging. In order to
apply a UKF, we first must transform the measurements into a model with
additive Gaussian noise. We consider two approaches, one based on variance
stabilizing transformations (where we compare the Anscombe and Freeman-Tukey
transforms) and one on a Gaussian approximation to the Poisson distribution.
Through simulations, we demonstrate efficacy of the approach and explore the
differences among these measurement transformations.
NJst and ASTRID are not statistically consistent under a random model of missing data
Date: 2020-01-22 00:00:00

Species tree estimation from multi-locus datasets is statistically
challenging for multiple reasons, including gene tree heterogeneity across the
genome due to incomplete lineage sorting (ILS). Species tree estimation methods
have been developed that operate by estimating gene trees and then using those
gene trees to estimate the species tree. Several of these methods (e.g.,
ASTRAL, ASTRID, and NJst) are provably statistically consistent under the
multi-species coalescent (MSC) model, provided that the gene trees are
estimated correctly, and there is no missing data. Recently, Nute et al. (BMC
Genomics 2018) addressed the question of whether these methods remain
statistically consistent under random models of taxon deletion, and asserted
that they do so. Here we provide a counterexample to one of these theorems, and
establish that ASTRID and NJst are not statistically consistent under an i.i.d.
model of taxon deletion.
Inference over Wireless IoT Links with Importance-Filtered Updates
Authors: Ivana Nikoloska, Josefine Holm, Anders Kalør, Petar Popovski, Nikola Zlatanov
Date: 2020-01-22 00:00:00
Tasks: BIG-bench Machine Learning

We consider a communication cell comprised of Internet-of-Things (IoT) nodes transmitting to a common Access Point (AP). The nodes in the cell are assumed to generate data samples periodically, which are to be transmitted to the AP. The AP hosts a machine learning model, such as a neural network, which is trained on the received data samples to make accurate inferences. We address the following tradeoff: The more often the IoT nodes transmit, the higher the accuracy of the inference made by the AP, but also the higher the energy expenditure at the IoT nodes. We propose a data filtering scheme employed by the IoT nodes, which we refer to as distributed importance filtering in order to filter out redundant data samples already at the IoT nodes. The IoT nodes do not have large on-device machine learning models and the data filtering scheme operates under periodic instructions from the model placed at the AP. The proposed scheme is evaluated using neural networks on a benchmark machine vision dataset, as well as in two practical scenarios: leakage detection in water distribution networks and air-pollution detection in urban areas. The results show that the proposed scheme offers significant benefits in terms of network longevity as it preserves the devices' resources, whilst maintaining high inference accuracy. Our approach reduces the the computational complexity for training the model and obviates the need for data pre-processing, which makes it highly applicable in practical IoT scenarios.
Efficient Bound for Conditional Min-Entropy of Physical Unclonable Functions Beyond IID
Date: 2020-01-22 00:00:00
Tasks: Side Channel Analysis

The remaining min-entropy of a secret generated by fuzzy extraction from a
Physical Unclonable Function is typically estimated under the assumption of
independent and identically distributed PUF responses, but this assumption does
not hold in practice. This work analyzes the more realistic case that the
responses are independent but not necessarily identically distributed. For this
case, we extend the (n-k) bound and a tighter bound by Delvaux et al. In
particular, we suggest a grouping bound which provides a trade off for accuracy
vs computational effort. Comparison to previous bounds shows the accuracy and
efficiency of our bound. We also adapt the key rank (a tool from side-channel
analysis) to cross-validate the state-of-the-art and our proposed min-entropy
bounds based on publicly available PUF data from real hardware.
Neural dynamics under active inference: plausibility and efficiency of information processing
Authors: Lancelot Da Costa, Thomas Parr, Biswa Sengupta, Karl Friston
Date: 2020-01-22 00:00:00
Tasks: State Estimation

Active inference is a normative framework for explaining behaviour under the free energy principle -- a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy -- a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error -- plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance traveled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.
Removing Multi-frame Gaussian Noise by Combining Patch-based Filters with Optical Flow
Authors: Kireeti Bodduna, Joachim Weickert
Date: 2020-01-22 00:00:00
Tasks: Optical Flow Estimation

Patch-based approaches such as 3D block matching (BM3D) and non-local Bayes (NLB) are widely accepted filters for removing Gaussian noise from single-frame images. In this work, we propose three extensions for these filters when there exist multiple frames of the same scene. The first of them employs reference patches on every frame instead of a commonly used single reference frame method, thus utilizing the complete available information. The remaining two techniques use a separable spatio-temporal filter to reduce interactions between dissimilar regions, hence mitigating artifacts. In order to deal with non-registered datasets we combine all our extensions with robust optical flow computation. Two of our proposed multi-frame filters outperform existing extensions on most occasions by a significant margin while also being competitive with a state-of-the-art neural network-based technique. Moreover, one of these two strategies is the fastest among all due to its separable design.
Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping
Date: 2020-02-18 00:00:00
Tasks: Management, Scheduling

Modern embedded computing platforms consist of a high amount of heterogeneous
resources, which allows executing multiple applications on a single device. The
number of running application on the system varies with time and so does the
amount of available resources. This has considerably increased the complexity
of analysis and optimization algorithms for runtime mapping of firm real-time
applications. To reduce the runtime overhead, researchers have proposed to
pre-compute partial mappings at compile time and have the runtime efficiently
compute the final mapping. However, most existing solutions only compute a
fixed mapping for a given set of running applications, and the mapping is
defined for the entire duration of the workload execution. In this work we
allow applications to adapt to the amount of available resources by using
mapping segments. This way, applications may switch between different
configurations with varied degree of parallelism. We present a runtime manager
for firm real-time applications that generates such mapping segments based on
partial solutions and aims at minimizing the overall energy consumption without
deadline violations. The proposed algorithm outperforms the state-of-the-art
approaches on the overall energy consumption by up to 13% while incurring an
order of magnitude less scheduling overhead.
Stability of a non-local kinetic model for cell migration with density dependent orientation bias
Authors: Nadia Loy, Luigi Preziosi
Date: 2020-01-21 00:00:00

The aim of the article is to study the stability of a non-local kinetic model proposed by Loy and Preziosi (2019a). We split the population in two subgroups and perform a linear stability analysis. We show that pattern formation results from modulation of one non-dimensional parameter that depends on the tumbling frequency, the sensing radius, the mean speed in a given direction, the uniform configuration density and the tactic response to the cell density. Numerical simulations show that our linear stability analysis predicts quite precisely the ranges of parameters determining instability and pattern formation. We also extend the stability analysis in the case of different mean speeds in different directions. In this case, for parameter values leading to instability travelling wave patterns develop.
A growth adjusted price-earnings ratio
Authors: Graham Baird, James Dodd, Lawrence Middleton
Date: 2020-01-22 00:00:00

The purpose of this paper is to introduce a new growth adjusted price-earnings measure (GA-P/E) and assess its efficacy as measure of value and predictor of future stock returns. Taking inspiration from the interpretation of the traditional price-earnings ratio as a period of time, the new measure computes the requisite payback period whilst accounting for earnings growth. Having derived the measure, we outline a number of its properties before conducting an extensive empirical study utilising a sorted portfolio methodology. We find that the returns of the low GA-P/E stocks exceed those of the high GA-P/E stocks, both in an absolute sense and also on a risk-adjusted basis. Furthermore, the returns from the low GA-P/E porfolio was found to exceed those of the value portfolio arising from a P/E sort on the same pool of stocks. Finally, the returns of our GA-P/E sorted porfolios were subjected to analysis by conducting regressions against the standard Fama and French risk factors.
Energy-Efficient Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning
Authors: Rafia Malik, Mai Vu
Date: 2020-01-22 00:00:00
Tasks: CPU, Edge-computing

We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multi-access edge computing (MEC) server. Massive-MIMO enables simultaneous uplink transmissions from all users, significantly shortening the data offloading time compared to sequential protocols, and makes the three phases of data offloading, computing, and downloading have comparable durations. Based on this three-phase structure, we formulate a novel problem to minimize a weighted sum of the energy consumption at both the users and the MEC server under a round-trip latency constraint, using a combination of data partitioning, transmit power control and CPU frequency scaling at both the user and server ends. We design a novel nested primal-dual algorithm using two different methods to solve this problem efficiently. Optimized solutions show that for larger requests, more data is offloaded to the MECs to reduce local computation time in order to meet the latency constraint, despite higher energy cost of wireless transmissions. Massive-MIMO channel estimation errors under pilot contamination also causes more data to be offloaded to the MECs. Compared to binary offloading, partial offloading with data partitioning is superior and leads to significant reduction in the overall energy consumption.
The kinetic space of multistationarity in dual phosphorylation
Authors: E. Feliu, N. Kaihnsa, T. de Wolff, O. Yürük
Date: 2020-01-22 00:00:00
Tasks: Decision Making

Multistationarity in molecular systems underlies switch-like responses in cellular decision making. Determining whether and when a system displays multistationarity is in general a difficult problem. In this work we completely determine the set of kinetic parameters that enable multistationarity in a ubiquitous motif involved in cell signaling, namely a dual phosphorylation cycle. In addition we show that the regions of multistationarity and monostationarity are both path connected. We model the dynamics of the concentrations of the proteins over time by means of a parametrized polynomial ordinary differential equation (ODE) system arising from the mass-action assumption. Since this system has three linear first integrals defined by the total amounts of the substrate and the two enzymes, we study for what parameter values the ODE system has at least two positive steady states after suitably choosing the total amounts. We employ a suite of techniques from (real) algebraic geometry, which in particular concern the study of the signs of a multivariate polynomial over the positive orthant and sums of nonnegative circuit polynomials.
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI
Date: 2020-01-22 00:00:00
Tasks: Denoising, Diffusion  MRI

We propose a model-based deep learning architecture for the reconstruction of
highly accelerated diffusion magnetic resonance imaging (MRI) that enables high
resolution imaging. The proposed reconstruction jointly recovers all the
diffusion weighted images in a single step from a joint k-q under-sampled
acquisition in a parallel MRI setting. We propose the novel use of a
pre-trained denoiser as a regularizer in a model-based reconstruction for the
recovery of highly under-sampled data. Specifically, we designed the denoiser
based on a general diffusion MRI tissue microstructure model for
multi-compartmental modeling. By using a wide range of biologically plausible
parameter values for the multi-compartmental microstructure model, we simulated
diffusion signal that spans the entire microstructure parameter space. A neural
network was trained in an unsupervised manner using an autoencoder to learn the
diffusion MRI signal subspace. We employed the autoencoder in a model-based
reconstruction and show that the autoencoder provides a strong denoising prior
to recover the q-space signal. We show reconstruction results on a simulated
brain dataset that shows high acceleration capabilities of the proposed method.
Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models
Authors: Takahiro Ezaki, Yu Himeno, Takamitsu Watanabe, Naoki Masuda
Date: 2020-01-23 00:00:00
Tasks: Time Series, Time Series Analysis

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants, and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition.
Comments are welcome
Date: 2020-02-14 00:00:00

Scholars present their new research at seminars and conferences, and send
drafts to peers, hoping to receive comments and suggestions that will improve
the quality of their work. Using a dataset of papers published in economics
journals, this article measures how much peers' individual and collective
comments improve the quality of research. Controlling for the quality of the
research idea and author, I find that a one standard deviation increase in the
number of peers' individual and collective comments increases the quality of
the journal in which the research is published by 47%.
Epileptic Seizure Detection and Classification using Time-Frequency Features in EEG Signals
Date: 2020-01-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

The use of EEG signal to diagnose several brain abnormalities is
well-established in the literature. Particularly, epileptic seizure can be
detected using EEG signals and several works were done in this field. The joint
time-frequency domain features proved to be an improvement in classification
results. This can be attributed to the non-stationarity inherent feature of the
EEG signals. Therefore, shifting to the joint time-frequency domain benefits in
utilizing the variations in time with respect to frequency and vice versa to
obtain better outcomes. The Bayesian classifier is utilized to obtain the
ranking of the best features. The information gain criterion was built to test
the system and obtain the results.
Effects of the institutional change based on democratization on origin and diffusion of technological innovation
Date: 2020-01-23 00:00:00

Political systems shape institutions and govern institutional change
supporting economic performance, production and diffusion of technological
innovation. This study shows, using global data of countries, that
institutional change, based on a progressive democratization of countries, is a
driving force of inventions, adoption and diffusion of innovations in society.
The relation between technological innovation and level of democracy can be
explained with following factors: higher economic freedom in society, effective
regulation, higher economic and political stability, higher investments in R&D
and higher education, good economic governance and higher level of education
system for training high-skilled human resources. Overall, then, the positive
associations between institutional change, based on a process of
democratization, and paths of technological innovation can sustain best
practices of political economy for the development of economies in the presence
of globalization and geographical expansion of markets.