pwc-archive/papers-with-abstracts huggingface dataset:
A Consolidated Approach to Convolutional Neural Networks and the Kolmogorov Complexity
Authors: Yomba D Yoan L. Mekontchou
Date: 2018-11-26 00:00:00
Tasks: Classification, General Classification

The ability to precisely quantify similarity between various entities has
been a fundamental complication in various problem spaces specifically in the
classification of cellular images. Contemporary similarity measures applied in
the domain of image processing proposed by the scientific community are mainly
pursued in supervised settings. In this work, we will explore the innovative
algorithmic normalized compression distance metric based on the information
theoretic concept of Kolmogorov Complexity. Additionally we will observe its
possible implementation in Convolutional Neural Networks to facilitate and
automate the classification of Retinal Pigment Epithelial cell cultures for use
in Age Related Macular Degeneration Stem Cell therapy in an unsupervised
setting.
Numerical Frequency Transfer Function Analysis of a Leaky Integrate-and-Fire Neuron
Date: 2019-10-22 00:00:00

This work reports a transfer function-based approach to characterizing the
operation of single neuronal cells in terms of the instantaneous frequency of
the input and output signals. We adopt the leaky integrate-and-fire model. The
transfer relationship is obtained by performing successive
numeric-computational simulations and statistical regressions. Several
interesting results are reported, including the identification and
characterization of linearity in the transfer relationship, as well as the
identification of regions in the parameter space characterized by sharper
transfer functions. These properties can be used to facilitate simulations
under certain circumstances and to validate the model by comparison with
biological results.
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds
Authors: Kirczenow Greg, Hashemi Masoud, Fathi Ali, Davison Matt
Date: 2018-11-23 00:00:00
Tasks: BIG-bench Machine Learning, Denoising

This paper studies an application of machine learning in extracting features
from the historical market implied corporate bond yields. We consider an
example of a hypothetical illiquid fixed income market. After choosing a
surrogate liquid market, we apply the Denoising Autoencoder (DAE) algorithm to
learn the features of the missing yield parameters from the historical data of
the instruments traded in the chosen liquid market. The DAE algorithm is then
challenged by two "point-in-time" inpainting algorithms taken from the image
processing and computer vision domain. It is observed that, when tested on
unobserved rate surfaces, the DAE algorithm exhibits superior performance
thanks to the features it has learned from the historical shapes of yield
curves.
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure
Authors: Derevyanko Georgy, Lamoureux Guillaume
Date: 2018-11-23 00:00:00
Tasks: Protein Folding, Reinforcement Learning (RL)

Predicting the structure of a protein from its sequence is a cornerstone task
of molecular biology. Established methods in the field, such as homology
modeling and fragment assembly, appeared to have reached their limit. However,
this year saw the emergence of promising new approaches: end-to-end protein
structure and dynamics models, as well as reinforcement learning applied to
protein folding. For these approaches to be investigated on a larger scale, an
efficient implementation of their key computational primitives is required. In
this paper we present a library of differentiable mappings from two standard
dihedral-angle representations of protein structure (full-atom representation
"$\phi,\psi,\omega,\chi$" and backbone-only representation
"$\phi,\psi,\omega$") to atomic Cartesian coordinates. The source code and
documentation can be found at https://github.com/lupoglaz/TorchProteinLibrary.
Boundary Vibration Control of Strain Gradient Timoshenko Micro-Cantilevers Using Piezoelectric Actuators
Date: 2019-05-21 00:00:00

In this paper the problem of boundary control of vibration in a clamped-free
strain gradient Timoshenko micro-cantilever is studied. For getting systems
closer to reality, the force or moment exertion conditions should be modeled.
To this end, a piezoelectric layer is laminated on one side of the beam and the
controlling actuation is applied through the piezoelectric voltage. The beam
and piezoelectric layer are coupled and modeled at the same time and the
dynamic equations and boundary conditions of the system are achieved using the
Hamilton principle. To achieve the purpose of eliminating vibration of the
system, the control law is obtained from a Lyapunov function using Lasalle
invariant set theorem. The control law has a form of feedback from the spatial
derivatives of boundary states of the beam. The finite element method using the
strain gradient Timoshenko beam element has been used and then the simulation
is performed to illustrate the impact of the proposed controller to the micro
beam.
The Dynamic Shift of Neuron Excitability Observed with Enveloped High Frequency Stimulation
Date: 2019-08-22 00:00:00

Neuronal excitability is known to be affected by transcranial electrical
stimulation. However, due to the existence of both excitatory and inhibitory
neurons in the cortex, the mechanism of neuronal excitability shift is still
not clear. Here, we study electrical stimulation disturbance on neuronal
excitability in the neuromuscular system on an acute rat model. We design a
special stimulation waveform of enveloped high frequency stimulation (EHFS).
With modeling and in vivo measurements, EHFS is confirmed to synchronize the
neuronal recruitment at the positive and negative electrode. This unique
synchronization property of EHFS amplifies the electrical stimulation
disturbance on neuronal excitability, to enable the macroscopic observations of
large increase and decrease of force output. By eliminating the complicated
inhibitory interneuron effects in the cortex, our observations on the
neuromuscular system supports the argument that electrical stimulation
dynamically shifts neuronal excitability, by inducing depolarization during
anodal stimulation and hyperpolarization during cathodal stimulation.
Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions
Date: 2019-01-21 00:00:00

Genetic regulatory networks (GRNs) have been widely studied, yet there is a
lack of understanding with regards to the final size and properties of these
networks, mainly due to no network currently being complete. In this study, we
analyzed the distribution of GRN structural properties across a large set of
distinct prokaryotic organisms and found a set of constrained characteristics
such as network density and number of regulators. Our results allowed us to
estimate the number of interactions that complete networks would have, a
valuable insight that could aid in the daunting task of network curation,
prediction, and validation. Using state-of-the-art statistical approaches, we
also provided new evidence to settle a previously stated controversy that
raised the possibility of complete biological networks being random and
therefore attributing the observed scale-free properties to an artifact
emerging from the sampling process during network discovery. Furthermore, we
identified a set of properties that enabled us to assess the consistency of the
connectivity distribution for various GRNs against different alternative
statistical distributions. Our results favor the hypothesis that highly
connected nodes (hubs) are not a consequence of network incompleteness.
Finally, an interaction coverage computed for the GRNs as a proxy for
completeness revealed that high-throughput based reconstructions of GRNs could
yield biased networks with a low average clustering coefficient, showing that
classical targeted discovery of interactions is still needed.
Optical Camera Communications: Survey, Use Cases, Challenges, and Future Trends
Date: 2019-10-16 00:00:00
Tasks: Indoor Localization, Survey

Recently, a camera or an image sensor receiver based optical wireless
communications (OWC) techniques have attracted particular interest in areas
such as the internet of things, indoor localization, motion capture, and
intelligent transportation systems. As a supplementary technique of high-speed
OWC based on photo-detectors, communications hinging on image sensors as
receivers do not need much modification to the current infrastructure, such
that the implementation complexity and cost are quite low. Therefore, in this
paper, we present a comprehensive survey of optical camera communication (OCC)
techniques, and their use in localization, navigation, and motion capture. This
survey is distinguishable from the existing reviews on this topic by covering
multiple aspects of OCC and its various applications. The first part of the
paper focuses on the standardization, channel characterization, modulation,
coding, and synchronization of OCC systems while the second part of the article
presents the literature on OCC based localization, navigation, and motion
capture. Finally, in the last part of the paper, we present the challenges and
future research directions of OCC.
Parsimonious evolutionary scenario for the origin of allostery and coevolution patterns in proteins
Date: 2019-07-12 00:00:00

Proteins display generic properties that are challenging to explain by direct
selection, notably allostery, the capacity to be regulated through long-range
effects, and evolvability, the capacity to adapt to new selective pressures. An
evolutionary scenario is proposed where proteins acquire these two features
indirectly as a by-product of their selection for a more fundamental property,
exquisite discrimination, the capacity to bind discriminatively very similar
ligands. Achieving this task is shown to typically require proteins to undergo
a conformational change. We argue that physical and evolutionary constraints
impel this change to be controlled by a group of sites extending from the
binding site. Proteins can thus acquire a latent potential for allosteric
regulation and evolutionary adaptation because of long-range effects that
initially arise as evolutionary spandrels. This scenario accounts for the
groups of conserved and coevolving residues observed in multiple sequence
alignments. However, we propose that most pairs of coevolving and contacting
residues inferred from such alignments have a different origin, related to
thermal stability. A physical model is presented that illustrates this
evolutionary scenario and its implications. The scenario can be implemented in
experiments of protein evolution to directly test its predictions.
Optimal models of decision-making in dynamic environments
Date: 2018-12-21 00:00:00
Tasks: Decision Making

Nature is in constant flux, so animals must account for changes in their
environment when making decisions. How animals learn the timescale of such
changes and adapt their decision strategies accordingly is not well understood.
Recent psychophysical experiments have shown humans and other animals can
achieve near-optimal performance at two alternative forced choice (2AFC) tasks
in dynamically changing environments. Characterization of performance requires
the derivation and analysis of computational models of optimal decision-making
policies on such tasks. We review recent theoretical work in this area, and
discuss how models compare with subjects' behavior in tasks where the correct
choice or evidence quality changes in dynamic, but predictable, ways.
Enriching Article Recommendation with Phrase Awareness
Authors: Chen Chia-Wei, Chou Sheng-Chuan, Ku Lun-Wei
Date: 2018-12-12 00:00:00
Tasks: Informativeness, Recommendation Systems, Sentence

Recent deep learning methods for recommendation systems are highly
sophisticated. For article recommendation task, a neural network encoder which
generates a latent representation of the article content would prove useful.
However, using raw text with embedding for models could degrade sentence
meanings and deteriorate performance. In this paper, we propose PhrecSys
(Phrase-based Recommendation System), which injects phrase-level features into
content-based recommendation systems to enhance feature informativeness and
model interpretability. Experiments conducted on six months of real-world data
demonstrate that phrase features boost content-based models in predicting both
user click and view behavior. Furthermore, the attention mechanism illustrates
that phrase awareness benefits the learning of textual focus by putting the
model's attention on meaningful text spans, which leads to interpretable
article recommendation.
Fine-Grained User Profiling for Personalized Task Matching in Mobile Crowdsensing
Authors: Yang Shuo, Zheng Zhenzhe, Tang Shaojie, Wu Fan, Chen Guihai
Date: 2018-11-14 00:00:00
Tasks: Recommendation Systems

In mobile crowdsensing, finding the best match between tasks and users is
crucial to ensure both the quality and effectiveness of a crowdsensing system.
Existing works usually assume a centralized task assignment by the crowdsensing
platform, without addressing the need of fine-grained personalized task
matching. In this paper, we argue that it is essential to match tasks to users
based on a careful characterization of both the users' preference and
reliability. To that end, we propose a personalized task recommender system for
mobile crowdsensing, which recommends tasks to users based on a recommendation
score that jointly takes each user's preference and reliability into
consideration. We first present a hybrid preference metric to characterize
users' preference by exploiting their implicit feedback. Then, to profile
users' reliability levels, we formalize the problem as a semi-supervised
learning model, and propose an efficient block coordinate descent algorithm to
solve the problem. For some tasks that lack users' historical information, we
further propose a matrix factorization method to infer the users' reliability
levels on those tasks. We conduct extensive experiments to evaluate the
performance of our system, and the evaluation results demonstrate that our
system can achieve superior performance to the benchmarks in both user
profiling and personalized task recommendation.
Estimation of the mortality rate functions from time series field data in a stage-structured demographic model for Lobesia botrana
Date: 2020-10-25 00:00:00
Tasks: Time Series, Time Series Analysis

Simulating the population dynamics of a stage-structured population requires
the knowledge of the biodemographic functions characterizing the species,
namely development, mortality and fecundity. In general, development and
fecundity can satisfactorily be estimated starting from literature data.
Unfortunately, this is often not the case of the mortality function, because of
the lack of experimental data. To overcome this problem we estimate the
mortality rate function from field data on the abundance of the species. The
mortality is expressed as a linear combination of cubic splines and the
estimation method allows to determine its coefficients taking into account the
observations measurement error. Moreover, the variability in the estimate is
quantified by means of the confidence bands for both mortality and dynamics.
The presented method allows to obtain a more flexible shape for the mortality
rate functions compared with previous methods applied to the same pest. The
method is applied to the case of Lobesia botrana, the main pest in the European
vineyards, with abundance data collected in a location in the North of Italy
for five consecutive years. Data collected over three years are used to
estimate the mortality and to analyze the variability in the estimate and its
effects on the population dynamics. Other two datasets are used to validate the
model simulating the dynamics using the estimated mortality.
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition
Authors: Kremer Jan, Borgholt Lasse, Maaløe Lars
Date: 2018-11-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Inductive Bias, Multi-Task Learning, speech-recognition, Speech Recognition

End-to-end automatic speech recognition (ASR) commonly transcribes audio
signals into sequences of characters while its performance is evaluated by
measuring the word-error rate (WER). This suggests that predicting sequences of
words directly may be helpful instead. However, training with word-level
supervision can be more difficult due to the sparsity of examples per label
class. In this paper we analyze an end-to-end ASR model that combines a
word-and-character representation in a multi-task learning (MTL) framework. We
show that it improves on the WER and study how the word-level model can benefit
from character-level supervision by analyzing the learned inductive preference
bias of each model component empirically. We find that by adding
character-level supervision, the MTL model interpolates between recognizing
more frequent words (preferred by the word-level model) and shorter words
(preferred by the character-level model).
The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain
Authors: Abdeddaïm Saïd, Vimard Sylvestre, Soualmia Lina Fatima
Date: 2018-11-28 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Eliciting semantic similarity between concepts in the biomedical domain
remains a challenging task. Recent approaches founded on embedding vectors have
gained in popularity as they risen to efficiently capture semantic
relationships The underlying idea is that two words that have close meaning
gather similar contexts. In this study, we propose a new neural network model
named MeSH-gram which relies on a straighforward approach that extends the
skip-gram neural network model by considering MeSH (Medical Subject Headings)
descriptors instead words. Trained on publicly available corpus PubMed MEDLINE,
MeSH-gram is evaluated on reference standards manually annotated for semantic
similarity. MeSH-gram is first compared to skip-gram with vectors of size 300
and at several windows contexts. A deeper comparison is performed with tewenty
existing models. All the obtained results of Spearman's rank correlations
between human scores and computed similarities show that MeSH-gram outperforms
the skip-gram model, and is comparable to the best methods but that need more
computation and external resources.
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft
Authors: Fournier Edouard  ENAC, Grihon Stéphane  IMT, Klein Thierry  IMT
Date: 2018-11-16 00:00:00
Tasks: Dimensionality Reduction, Math

In aircraft industry, market needs evolve quickly in a high competitiveness
context. This requires adapting a given aircraft model in minimum time
considering for example an increase of range or the number of passengers (cf
A330 NEO family). The computation of loads and stress to resize the airframe is
on the critical path of this aircraft variant definition: this is a consuming
and costly process, one of the reason being the high dimen-sionality and the
large amount of data. This is why Airbus has invested since a couple of years
in Big Data approaches (statistic methods up to machine learning) to improve
the speed, the data value extraction and the responsiveness of this process.
This paper presents recent advances in this work made in cooperation between
Airbus, ENAC and Institut de Math{\'e}-matiques de Toulouse in the framework of
a proof of value sprint project. It compares the influence of three dimensional
reduction techniques (PCA, polynomial fitting, combined) on the extrapolation
capabilities of Regression Trees based algorithms for loads prediction. It
shows that AdaBoost with Random Forest offers promising results in average in
terms of accuracy and computational time to estimate loads on which a PCA is
applied only on the outputs.
Improved Inference on the Rank of a Matrix
Date: 2019-03-25 00:00:00
Tasks: valid

This paper develops a general framework for conducting inference on the rank
of an unknown matrix $\Pi_0$. A defining feature of our setup is the null
hypothesis of the form $\mathrm H_0: \mathrm{rank}(\Pi_0)\le r$. The problem is
of first order importance because the previous literature focuses on $\mathrm
H_0': \mathrm{rank}(\Pi_0)= r$ by implicitly assuming away
$\mathrm{rank}(\Pi_0)<r$, which may lead to invalid rank tests due to
over-rejections. In particular, we show that limiting distributions of test
statistics under $\mathrm H_0'$ may not stochastically dominate those under
$\mathrm{rank}(\Pi_0)<r$. A multiple test on the nulls
$\mathrm{rank}(\Pi_0)=0,\ldots,r$, though valid, may be substantially
conservative. We employ a testing statistic whose limiting distributions under
$\mathrm H_0$ are highly nonstandard due to the inherent irregular natures of
the problem, and then construct bootstrap critical values that deliver size
control and improved power. Since our procedure relies on a tuning parameter, a
two-step procedure is designed to mitigate concerns on this nuisance. We
additionally argue that our setup is also important for estimation. We
illustrate the empirical relevance of our results through testing
identification in linear IV models that allows for clustered data and inference
on sorting dimensions in a two-sided matching model with transferrable utility.
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks
Authors: Kozlowski Michal, McConville Ryan, Santos-Rodriguez Raul, Piechocki Robert
Date: 2018-11-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As sensor networks for health monitoring become more prevalent, so will the
need to control their usage and consumption of energy. This paper presents a
method which leverages the algorithm's performance and energy consumption. By
utilising Reinforcement Learning (RL) techniques, we provide an adaptive
framework, which continuously performs weak training in an energy-aware system.
We motivate this using a realistic example of residential localisation based on
Received Signal Strength (RSS). The method is cheap in terms of work-hours,
calibration and energy usage. It achieves this by utilising other sensors
available in the environment. These other sensors provide weak labels, which
are then used to employ the State-Action-Reward-State-Action (SARSA) algorithm
and train the model over time. Our approach is evaluated on a simulated
localisation environment and validated on a widely available pervasive health
dataset which facilitates realistic residential localisation using RSS. We show
that our method is cheaper to implement and requires less effort, whilst at the
same time providing a performance enhancement and energy savings over time.
Can Artificial Intelligence Do Everything That We Can?
Authors: Conitzer Vincent
Date: 2018-11-26 00:00:00

In this article, I discuss what AI can and cannot yet do, and the
implications for humanity.