pwc-archive/papers-with-abstracts huggingface dataset:
Efficient identification of myocardial material parameters and the stress-free reference configuration for patient-specific human heart models
Authors: Laura Marx, Justyna A. Niestrawska, Matthias A. F. Gsell, Federica Caforio, Gernot Plank, Christoph M. Augustin
Date: 2021-01-12 00:00:00

Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, a stress-free reference configuration is generated, where a novel fail-safe feature to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters.
Dynamics of contentment
Authors: Alexey A. Burluka
Date: 2021-01-14 00:00:00

A continuous variable changing between 0 and 1 is introduced to characterise contentment, or satisfaction with life, of an individual and an equation governing its evolution is postulated from analysis of several factors likely to affect the contentment. As contentment is strongly affected by material well-being, a similar equation is formulated for wealth of an individual and from these two equations derived an evolution equation for the joint distribution of individuals' wealth and contentment within a society. The equation so obtained is used to compute evolution of this joint distribution in a society with initially low variation of wealth and contentment over a long period time. As illustration of this model capabilities, effects of the wealth tax rate are simulated and it is shown that a higher taxation in the longer run may lead to a wealthier and more content society. It is also shown that lower rates of the wealth tax lead to pronounced stratification of the society in terms of both wealth and contentment and that there is no direct relationship between the average values of these two variables.
The backpropagation-based recollection hypothesis: Backpropagated action potentials mediate recall, imagination, language understanding and naming
Authors: Zied Ben Houidi
Date: 2021-01-11 00:00:00
Tasks: Language Acquisition, Retrieval

Ever since the advent of the neuron doctrine more than a century ago, information processing in the brain is widely believed to mainly follow the forward pre to post-synaptic neurons direction. Challenging this prevalent view, in this paper, we emit the backpropagation-based recollection hypothesis as follows: weak and fast fading Action Potentials following the (highest weight) post to pre-synaptic backward pathways, mediate explicit cue-based memory recall. This includes also the tasks of imagination, future episodic thinking, language understanding and associating names to various stimuli. These signals originate in highly invariant neurons, which uniquely respond to some specific stimuli (e.g. image of a cat). They then travel backwards to reactivate the same populations of neurons that uniquely respond to this specific stimuli during perception, thus recreating "offline" an experience that is similar. After stating our hypothesis in details, we challenge its assumptions through a thorough literature review. We find abundant evidence that supports most of its assumptions, including the existence of backpropagating signals that have interesting properties. We then leverage simulations based on existing spiking neural network models with STDP learning to show the computational feasibility of using such a mechanism to map the image of an object to its name with the same high accuracy as a state of the art machine learning classifier. Although not yet a theory, we believe this hypothesis presents a paradigm shift that is worth further investigating: it opens the way, among others, to new interpretations of language acquisition and understanding, the interplay between memories encoding and retrieval, as well as reconciling the apparently opposed views between sparse coding and distributed representations.
Calibration based Minimalistic Multi-Exposure Digital Sensor Camera Robust Linear High Dynamic Range Enhancement Technique Demonstration
Authors: Nabeel A. Riza, Nazim Ashraf
Date: 2021-01-14 00:00:00

Demonstrated for a digital image sensor based camera is a calibration target optimized method for finding the Camera Response Function (CRF). The proposed method uses localized known target zone pixel outputs spatial averaging and histogram analysis for saturated pixel detection. Using the proposed CRF generation method with a 87 dB High Dynamic Range (HDR) silicon CMOS image sensor camera viewing a 90 dB HDR calibration target, experimentally produced is a non-linear CRF with a limited 40 dB linear CRF zone. Next, a 78 dB test target is deployed to test the camera with this measured CRF and its restricted 40 dB zone. By engaging the proposed minimal exposures, weighting free, multi-exposure imaging method with 2 images, demonstrated is a highly robust recovery of the test target. In addition, the 78 dB test target recovery with 16 individual DR value patches stays robust over a factor of 20 change in test target illumination lighting. In comparison, a non-robust test target image recovery is produced by 5 leading prior-art multi-exposure HDR recovery algorithms using 16 images having 16 different exposure times, with each consecutive image having a sensor dwell time increasing by a factor of 2. Further validation of the proposed HDR image recovery method is provided using two additional experiments, the first using a 78 dB calibrated target combined with a natural indoor scene to form a hybrid design target and a second experiment using an uncalibrated indoor natural scene. The proposed technique applies to all digital image sensor based cameras having exposure time and illumination controls. In addition, the proposed methods apply to various sensor technologies, spectral bands, and imaging applications.
Fast offline Transformer-based end-to-end automatic speech recognition for real-world applications
Authors: Yoo Rhee Oh, Kiyoung Park, Jeon Gyu Park
Date: 2021-01-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

With the recent advances in technology, automatic speech recognition (ASR) has been widely used in real-world applications. The efficiency of converting large amounts of speech into text accurately with limited resources has become more important than ever. This paper proposes a method to rapidly recognize a large speech database via a Transformer-based end-to-end model. Transformers have improved the state-of-the-art performance in many fields. However, they are not easy to use for long sequences. In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested, including decoding via multiple-utterance batched beam search, detecting end-of-speech based on a connectionist temporal classification (CTC), restricting the CTC prefix score, and splitting long speeches into short segments. Experiments are conducted with the Librispeech English and the real-world Korean ASR tasks to verify the proposed methods. From the experiments, the proposed system can convert 8 hours of speeches spoken at real-world meetings into text in less than 3 minutes with a 10.73% character error rate, which is 27.1% relatively lower than that of conventional systems.
Outlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning
Authors: M. A. Bhatti, R. Riaz, S. S. Rizvi, S. Shokat, F. Riaz and S. J. Kwon
Date: 2020-06-03 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning, Indoor Localization, Outlier Detection

In Internet of things (IoT) millions of devices are intelligently connected for providing smart services. Especially in indoor localization environment, that is one of the most concerning
topic of smart cities, internet of things and wireless sensor networks. Many technologies are being used for localization purpose
in indoor environment and Wi-Fi using received signal strengths
(RSSs) is one of them. Wi-Fi RSSs are sensitive to reflection, refraction, interference and channel noise that cause irregularity in
signal strengths. The irregular and anomalous RSS values, used in
a Wi-Fi indoor localization environment, cannot define the location
of any unknown node correctly. Therefore, this research has developed an outlier detection technique named as iF_Ensemble
for Wi-Fi indoor localization environment by analyzing RSSs using the combination of supervised, unsupervised and ensemble machine learning methods. In this research isolation forest (iForest)
is used as an unsupervised learning method. Supervised learning
method includes support vector machine (SVM), K-nearest neighbor (KNN) and random forest (RF) classifiers with stacking that
is an ensemble learning method. For the evaluation purpose accuracy, precision, recall, F-score and ROC-AUC curve are used. The
evaluation of used machine learning method provides high accuracy of 97.8 percent with proposed outlier detection methods and
almost 2 percent improvement in the accuracy of localization process in indoor environment after eliminating outliers.
Dynamic network analysis of a target defense differential game with limited observations
Authors: Sharad Kumar Singh, Puduru Viswanadha Reddy
Date: 2021-01-14 00:00:00

In this paper, we study a Target-Attacker-Defender (TAD) differential game involving one attacker, one target and multiple defenders. We consider two variations where (a) the attacker and the target have unlimited observation range and the defenders are visibility constrained (b) only the attacker has unlimited observation range and the remaining players are visibility constrained. We model the players' interactions as a dynamic game with asymmetric information. Here, the visibility constraints of the players induce a visibility network which encapsulates the visibility information during the evolution of the game. Based on this observation, we introduce network adapted feedback or implementable strategies for visibility constrained players. Using inverse game theory approach we obtain network adapted feedback Nash equilibrium strategies. We introduce a consistency criterion for selecting a subset (or refinement) of network adapted feedback Nash strategies, and provide an optimization based approach for computing them. Finally, we illustrate our results with numerical experiments.
Design of false data injection attack on distributed process estimation
Authors: Moulik Choraria, Arpan Chattopadhyay, Urbashi Mitra, Erik Strom
Date: 2021-01-14 00:00:00

Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation based update of a Lagrange multiplier, and an optimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions or online stochastic gradient descent. The problem turns out to be convex for some special cases. Desired convergence of the proposed algorithms are proved by exploiting the convexity and properties of stochastic approximation algorithms. Finally, numerical results demonstrate the efficacy of the attack.
Phenotypic variation modulates the growth dynamics and response to radiotherapy of solid tumours under normoxia and hypoxia
Authors: Giulia L. Celora, Helen M. Byrne, Christos Zois, Panos G. Kevrekidis
Date: 2021-01-14 00:00:00

In cancer, treatment failure and disease recurrence have been associated with small subpopulations of cancer cells with a stem-like phenotype. In this paper, we develop and investigate a phenotype-structured model of solid tumour growth in which cells are structured by a stemness level, which varies continuously between stem-like and terminally differentiated behaviours. Cell evolution is driven by proliferation and apoptosis, as well as advection and diffusion with respect to the stemness structure variable. We use the model to investigate how the environment, in particular oxygen levels, affects the tumour's population dynamics and composition, and its response to radiotherapy. We use a combination of numerical and analytical techniques to quantify how under physiological oxygen levels the cells evolve to a differentiated phenotype and under low oxygen level (i.e., hypoxia) they de-differentiate. Under normoxia, the proportion of cancer stem cells is typically negligible and the tumour may ultimately become extinct whereas under hypoxia cancer stem cells comprise a dominant proportion of the tumour volume, enhancing radio-resistance and favouring the tumour's long-term survival. We then investigate how such phenotypic heterogeneity impacts the tumour's response to treatment with radiotherapy under normoxia and hypoxia. Of particular interest is establishing how the presence of radio-resistant cancer stem cells can facilitate a tumour's regrowth following radiotherapy. We also use the model to show how radiation-induced changes in tumour oxygen levels can give rise to complex re-growth dynamics. For example, transient periods of hypoxia induced by damage to tumour blood vessels may rescue the cancer cell population from extinction and drive secondary regrowth. Further model extensions to account for spatial variation are also discussed briefly.
Constructed wetlands operated as bioelectrochemical systems for the removal of organic micropollutants
Authors: M. Hartl, M. J. García-Galán, V. Matamoros, M. Fernández-Gatell, D. P. L. Rousseau, G. Du Laing, M. Garfí, J. Puigagut
Date: 2021-01-14 00:00:00

The removal of organic micropollutants (OMPs) has been investigated in constructed wetlands (CWs) operated as bioelectrochemical systems (BES). The operation of CWs as BES (CW-BES), either in the form of microbial fuel cells (MFC) or microbial electrolysis cells (MEC), has only been investigated in recent years. The presented experiment used CW meso-scale systems applying a realistic horizontal flow regime and continuous feeding of real urban wastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ), diclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the removal efficiency of conventional CW systems (CW-control) as well as CW systems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although a few positive trends were identified for the CW-BES compared to the CW-control (higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC), these proved to be not statistically significantly different. Mesoscale experiments with real wastewater could thus not confirm earlier positive effects of CW-BES found under strictly controlled laboratory conditions with synthetic wastewaters.
Computational Model of Motion Sickness Describing the Effects of Learning Exogenous Motion Dynamics
Authors: Takahiro Wada
Date: 2020-11-25 00:00:00
Tasks: Future prediction

The existing computational models used to estimate motion sickness are incapable of describing the fact that the predictability of motion patterns affects motion sickness. Therefore, the present study proposes a computational model to describe the effect of the predictability of dynamics or the pattern of motion stimuli on motion sickness. In the proposed model, a submodel, in which a recursive Gaussian process regression is used to represent human features of online learning and future prediction of motion dynamics, is combined with a conventional model of motion sickness based on an observer theory. A simulation experiment was conducted in which the proposed model predicted motion sickness caused by a 900 s horizontal movement. The movement was composed of a 9 m repetitive back-and-forth movement pattern with a pause. Regarding the motion condition, the direction and timing of the motion were varied as follows: a) Predictable motion (M_P): the direction of the motion and duration of the pause were set to 8 s; b) Motion with unpredicted direction (M_dU): the pause duration was fixed as in (P), but the motion direction was randomly determined; c) Motion with unpredicted timing (M_tU): the motion direction was fixed as in (M_P), but the pause duration was randomly selected from 4 to 12 s. The results obtained using the proposed model demonstrated that the predicted motion sickness incidence for (M_P) was smaller than those for (M_dU) and (M_tU). This tendency agrees with the sickness patterns observed in a previous experimental study in which the human participants were subject to motion conditions similar to those used in our simulations. Moreover, no significant differences were found in the predicted motion sickness incidences at different conditions when the conventional model was used.
Speaker activity driven neural speech extraction
Authors: Marc Delcroix, Katerina Zmolikova, Tsubasa Ochiai, Keisuke Kinoshita, Tomohiro Nakatani
Date: 2021-01-14 00:00:00
Tasks: Speech Extraction

Target speech extraction, which extracts the speech of a target speaker in a mixture given auxiliary speaker clues, has recently received increased interest. Various clues have been investigated such as pre-recorded enrollment utterances, direction information, or video of the target speaker. In this paper, we explore the use of speaker activity information as an auxiliary clue for single-channel neural network-based speech extraction. We propose a speaker activity driven speech extraction neural network (ADEnet) and show that it can achieve performance levels competitive with enrollment-based approaches, without the need for pre-recordings. We further demonstrate the potential of the proposed approach for processing meeting-like recordings, where speaker activity obtained from a diarization system is used as a speaker clue for ADEnet. We show that this simple yet practical approach can successfully extract speakers after diarization, which leads to improved ASR performance when using a single microphone, especially in high overlapping conditions, with a relative word error rate reduction of up to 25 %.
Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey
Authors: Benyamin Ghojogh, Ali Ghodsi
Date: 2020-11-17 00:00:00
Tasks: Decoder, Deep Attention, Natural Language Inference, Survey, Text Generation

This is a tutorial and survey paper on the attention mechanism, transformers, BERT, and GPT. We first explain attention mechanism, sequence-to-sequence model without and with attention, self-attention, and attention in different areas such as natural language processing and computer vision. Then, we explain transformers which do not use any recurrence. We explain all the parts of encoder and decoder in the transformer, including positional encoding, multihead self-attention and cross-attention, and masked multihead attention. Thereafter, we introduce the Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) as the stacks of encoders and decoders of transformer, respectively. We explain their characteristics and how they work.
A shifted multiplicative model fusion method for grouping environments without cultivar rank change.
Authors: José Crossa  Paul L. Cornelius  Ken Sayre  R.J. Iván Ortiz‐Monasterio
Date: 1995-01-01 00:00:00

Genotype × environment interactions of most concern to plant breeders involve cultivar rank change across environments, i.e., crossover interaction (COl). When COls are present, cluster strategies can be used to group environments without significant changes in cultivar ranks. Several methods for classifying environments without COI have recently been proposed. In this study, the shifted multiplicative model (SHMM) fusion method based on a building block principle is used for grouping environments which rank cultivars similarly. The SHMM fusion selects the best action by computing for each new cluster formed, a new set of distance measures with the other clusters and with the unclustered individuals. Experimental data were collected on 55 cultivars evaluated under five irrigation levels in each of two trials (I0 environments). Groups of environments obtained with SHMM fusion were compared with those obtained using cluster analysis based on Euclidean distances computed from unstandardized and standardized data. The SHMM fusion formed four final clusters of environments. The final clusters grouped environments with similar irrigation levels. Conventional cluster analysis based on Euclidean distances produced final clusters with much larger percentage of COls than SHMM fusion. Results show that SHMM fusion is a useful strategy for classifying environments without cultivar rank change.