pwc-archive/papers-with-abstracts huggingface dataset:
Carica papaya Leaf Extract Inhibits SARS-CoV-2 Main Proteases but not Human TMPRSS2: An In-vitro and In-silico Study
Authors: Maywan Hariono, Irwan Hidayat, Ipang Djunarko, Jeffry Julianus, Fadi G. Saqallah, Muhammad Hidhir Khawory, Nurul Hanim Salin, Habibah Abdul Wahab
Date: 2022-12-01 00:00:00

Carica papaya (CP) leaf is long known for its traditional pharmacological effects against dengue virus and malaria. Therefore, CP could also be a potential solution for the treatment of other infectious diseases, such as coronavirus. In this study, we evaluate the potential effect of the ethanolic CP leaf extract in inhibiting the enzymatic activity of three protein targets in SARS-CoV-2 life cycle, which include the 3-chymotrypsin-like protease (3CLpro), papain-like protease (PLpro) and the human transmembrane protein serine 2 (TMPRSS2). Results demonstrate that CP leaf extract inhibits 3CLpro and PLpro significantly with IC50 of 0.02 microgram/mL and 0.06 microgram/mL, respectively, but it is inactive towards TMPRSS2. Phenol, 2-methyl-5-(1,2,2-trimethylcyclopentyl)-(S)- (17a) and beta-mannofuranoside, farnesyl- (21a) were identified in the extract using GC-MS. These two compounds demonstrated a stronger binding affinity towards the main proteases than TMPRSS2 during the docking simulation, which agrees with the in-vitro study. Further pharmacophore mapping suggests that 17a has a fit score higher than 21a to the SARS-CoV-2 3CLpro pharmacophore model concluding that CP leaf extract has the potential to be developed as a herbal SARS-CoV-2 antiviral agent.
Robust Circuitry-Based Scores of Structural Importance of Human Brain Areas
Authors: Daniel Hegedus, Vince Grolmusz
Date: 2022-11-30 00:00:00
Tasks: Anatomy, Diffusion  MRI

We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors, connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others.
Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition
Authors: Melani Sanchez-Garcia, Tushar Chauhan, Benoit R. Cottereau, Michael Beyeler
Date: 2022-11-30 00:00:00
Tasks: All, Object, Object Recognition

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity (STDP). We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.
Cellular Automata Model for Non-Structural Proteins Comparing Transmissibility and Pathogenesis of SARS Covid (CoV-2, CoV) and MERS Covid
Authors: Raju Hazari, Parimal Pal Chaudhuri
Date: 2022-11-25 00:00:00
Tasks: Blocking

Significantly higher transmissibility of SARS CoV-2 (2019) compared to SARS CoV (2003) can be attributed to mutations of structural proteins (Spike S, Nucleocapsid N, Membrane M, and Envelope E) and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly and shedding. The non-structural proteins (nsps) avail host protein synthesis machinery to initiate viral replication, along with neutralization of host immune defense. The key protein out of the 16 nsps, is the non-structural protein nsp1, also known as the leader protein. Nsp1 leads the process of hijacking host resources by blocking host translation. This paper concentrates on the analysis of nsps of SARS covid (CoV-2, CoV) and MERS covid based on Cellular Automata enhanced Machine Learning (CAML) model developed for study of biological strings. This computational model compares deviation of structure - function of CoV-2 from that of CoV employing CAML model parameters derived out of CA evolution of amino acid chains of nsps. This comparative analysis points to - (i) higher transmissibility of CoV-2 compared to CoV for major nsps, and (ii) deviation of MERS covid from SARS CoV in respect of virulence and pathogenesis. A Machine Learning (ML) framework has been designed to map the CAML model parameters to the physical domain features reported in in-vitro/in-vivo/in-silico experimental studies. The ML framework enables us to learn the permissible range of model parameters derived out of mutational study of sixteen nsps of three viruses.
A Speed Limit for Evolution: Postscript
Authors: Robert Worden
Date: 2022-12-01 00:00:00

In 1995 I wrote a paper: "A Speed Limit for Evolution" whose main result was that evolution must proceed rather slowly, in accordance with the earlier views and intuitions of many authors. The abstract of the paper said: "The genetic information expressed in some part of the phenotype of a species cannot increase faster than a given rate, determined by the selection pressure on that part. This rate is typically a small fraction of a bit per generation". This result was derived in the presence of sexual reproduction and other effects such as temporarily isolated sub-populations. In 1999 David Mackay published a paper which apparently contradicted this result. In the abstract, he wrote "We find striking differences between populations that have recombination and populations that do not. If variation is produced by mutation alone, then the entire population gains up to roughly 1 bit per generation. If variation is created by recombination, the population can gain of the order of sqrt(G) bits per generation." Mackay proposed that there were outstanding evolutionary benefits to sexual reproduction, and that my result was too low by a very large factor. He later repeated this result in a textbook he wrote in 2003. The purpose of this note is to show that the key assumption of Mackays model, that "fitness is a strictly additive trait" is so unrealistic as to render his results irrelevant to any actual life form. In consequence, the speed limit I derived is still valid, and has important consequences for human cognitive evolution.
An optimization based 3D-1D coupling strategy for tissue perfusion and chemical transport during tumor-induced angiogenesis
Authors: Stefano Berrone, Chiara Giverso, Denise Grappein, Luigi Preziosi, Stefano Scialò
Date: 2022-11-17 00:00:00

A new mathematical model and numerical approach are proposed for the simulation of fluid and chemical exchanges between a growing capillary network and the surrounding tissue, in the context of tumor-induced angiogenesis. Thanks to proper modeling assumptions the capillaries are reduced to their centerline: a well posed mathematical model is hence worked out, based on the coupling between a three-dimensional and a one-dimensional equation (3D-1D coupled problem). Also the application of a PDE-constrained optimization formulation is here proposed for the first time for angiogenesis simulations. Under this approach no mesh conformity is required, thus making the method particularly suitable for this kind of application, since no remeshing is required as the capillary network grows. In order to handle both the evolution of the quantities of interest and the changes in the geometry, a discrete-hybrid strategy is adopted, combining a continuous modeling of the tissue and of the chemicals with a discrete tip-tracking model to account for the vascular network growth. The tip-tracking strategy, together with some proper rules for branching and anastomosis, is able to provide a realistic representation of the capillary network.
Dynamic and static fund separations and their stability for long-term optimal investments
Authors: Hyungbin Park, Heejun Yeo
Date: 2022-12-01 00:00:00

This paper investigates dynamic and static fund separations and their stability for long-term optimal investments under three model classes. An investor maximizes the expected utility with constant relative risk aversion under an incomplete market consisting of a safe asset, several risky assets, and a single state variable. The state variables in two of the model classes follow a 3/2 process and an inverse Bessel process, respectively. The other market model has the partially observed state variable modeled as an Ornstein-Uhlenbeck state process. We show that the dynamic optimal portfolio of this utility maximization consists of m+3 portfolios: the safe asset, the myopic portfolio, the m time-independent portfolios, and the intertemporal portfolio. Over time, the intertemporal portfolio eventually vanishes, leading the dynamic portfolio to converge to m+2 portfolios, referred to as the static portfolio. We also prove that the convergence is stable under model parameter perturbations. In addition, sensitivities of the intertemporal portfolio with respect to small parameters perturbations also vanish in the long run. The convergence rate for the intertemporal portfolio and its sensitivities are computed explicitly for the presented models.
Ranking Critical Tools in the Implementation of Lean Six Sigma as an Integrated Management System in Portugal
Authors: David Ferreira, Pedro Cunha
Date: 2022-11-30 00:00:00
Tasks: Management

Lean Six Sigma (LSS) is a comprehensive and powerful strategy for processes improvement and products. There is a cornucopia of tools for its implementation and 37 among them were selected to carry out an evaluation based on three factors, namely: Frequency of use of the tool, difficulty in implementing, importance and impact of the tool in the implementation of LSS. An online survey was conducted with Portuguese consultants and it included questions on the profile, and the companies they worked, as well as the degree of impact of the tools used. Consultants were asked to choose ten tools, ranking them in order of importance. The frequencies with which each tool had been cited were counted. A procedure was then developed to identify the know-how of consultants to establish a ranking of LSS tools. It was created an ordering list of tools, which emphasized in: Honshin Kanri, VOC, VSM. The results presented are particularly relevant when is considered the importance of understanding the requirements for a successful implementation of Lean Six Sigma management system in the organizations.
Automated Market Makers: Mean-Variance Analysis of LPs Payoffs and Design of Pricing Functions
Authors: Philippe Bergault, Louis Bertucci, David Bouba, Olivier Guéant
Date: 2022-12-01 00:00:00

With the emergence of decentralized finance, new trading mechanisms called Automated Market Makers have appeared. The most popular Automated Market Makers are Constant Function Market Makers. They have been studied both theoretically and empirically. In particular, the concept of impermanent loss has emerged and explains part of the profit and loss of liquidity providers in Constant Function Market Makers. In this paper, we propose another mechanism in which price discovery does not solely rely on liquidity takers but also on an external exchange rate or price oracle. We also propose to compare the different mechanisms from the point of view of liquidity providers by using a mean / variance analysis of their profit and loss compared to that of agents holding assets outside of Automated Market Makers. In particular, inspired by Markowitz' modern portfolio theory, we manage to obtain an efficient frontier for the performance of liquidity providers in the idealized case of a perfect oracle. Beyond that idealized case, we show that even when the oracle is lagged and in the presence of adverse selection by liquidity takers and systematic arbitrageurs, optimized oracle-based mechanisms perform better than popular Constant Function Market Makers.
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Authors: Tommaso Salvatori, Yuhang Song, Yordan Yordanov, Beren Millidge, Zhenghua Xu, Lei Sha, Cornelius Emde, Rafal Bogacz, Thomas Lukasiewicz
Date: 2022-11-16 00:00:00
Tasks: Distributed Computing, image-classification, Image Classification, Scheduling

Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one, and has theoretical guarantees in terms of convergence. The proposed algorithm, that we call incremental predictive coding (iPC) is also more biologically plausible than the original one, as it it fully automatic. In an extensive set of experiments, we show that iPC constantly performs better than the original formulation on a large number of benchmarks for image classification, as well as for the training of both conditional and masked language models, in terms of test accuracy, efficiency, and convergence with respect to a large set of hyperparameters.
Using Gradient to Boost the Generalization Performance of Deep Learning Models for Fluid Dynamics
Authors: Eduardo Vital Brasil
Date: 2022-10-09 00:00:00

Nowadays, Computational Fluid Dynamics (CFD) is a fundamental tool for industrial design. However, the computational cost of doing such simulations is expensive and can be detrimental for real-world use cases where many simulations are necessary, such as the task of shape optimization. Recently, Deep Learning (DL) has achieved a significant leap in a wide spectrum of applications and became a good candidate for physical systems, opening perspectives to CFD. To circumvent the computational bottleneck of CFD, DL models have been used to learn on Euclidean data, and more recently, on non-Euclidean data such as unstuctured grids and manifolds, allowing much faster and more efficient (memory, hardware) surrogate models. Nevertheless, DL presents the intrinsic limitation of extrapolating (generalizing) out of training data distribution (design space). In this study, we present a novel work to increase the generalization capabilities of Deep Learning. To do so, we incorporate the physical gradients (derivatives of the outputs w.r.t. the inputs) to the DL models. Our strategy has shown good results towards a better generalization of DL networks and our methodological/ theoretical study is corroborated with empirical validation, including an ablation study.
A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification
Authors: Jingwei Zhang, Ke Ma, John Van Arnam, Rajarsi Gupta, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 6
Tasks: Deep Learning

Deep learning has achieved great success in process-
ing large size medical images such as histopathology slides.
However, conventional deep learning methods cannot han-
dle the enormous image sizes; instead, they split the im-
age into patches which are exhaustively processed, usually
through multi-instance learning approaches. Moreover and
especially in histopathology, determining the most appro-
priate magnification to generate these patches is also ex-
haustive: a model needs to traverse all the possible magnifi-
cations to select the optimal one. These limitations make the
application of deep learning on large medical images and
in particular histopathological images markedly inefficient.
To tackle these problems, we propose a novel spatial and
magnification based attention sampling strategy. First, we
use a down-sampled large size image to estimate an atten-
tion map that represents a spatial probability distribution
of informative patches at different magnifications. Then a
small number of patches are cropped from the large size
medical image at certain magnifications based on the ob-
tained attention. The final label of the large size image is
predicted solely by these patches using an end-to-end train-
ing strategy. Our experiments on two different histopathol-
ogy datasets, the publicly available BACH and a subset of
the TCGA-PRAD dataset, demonstrate that the proposed
method runs 2.5 times faster with automatic magnification
selection in training and at least 1.6 times faster than us-
ing all patches in inference as the most of state-of-the-art
methods do, without loosing in performance.
Deep sector rotation swing trading
Authors: Joel R. Bock, Akhilesh Maewal
Date: 2022-11-22 00:00:00
Proceeding: SSRN Electronic Journal 2022 11

A system for sector rotation swing trading of exchange-traded funds (ETFs) using deep learning is presented. Weekly trades are made on funds representing 11 major sectors of the U.S. economy. The trading system was backtested for the period January 2012-October 2022. Annualized CAGR returns exceeded the benchmark buy-and-hold strategy by an average 12.28% (median 7.63%). Over the studied period, Sharpe ratios averaged 1.41, and the mean maximum drawdown was 10%. The deep model design is multiple-input, multiple output, and can be easily extended to include other factors that may influence predictability of future price movements. Results presented here are preliminary, and are exclusive of trading costs. Analysis of these costs is prerequisite to deployment as a semi-mechanical swing trading system.
Forget-free Continual Learning with Winning Subnetworks
Authors: Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, Chang D. Yoo
Date: 2022-07-01 00:00:00
Proceeding: International Conference on Machine Learning 2022 7
Tasks: Continual Learning

Inspired by Lottery Ticket Hypothesis that competitive subnetworks exist within a dense network, we propose a continual learning method referred to as Winning SubNetworks (WSN), which sequentially learns and selects an optimal subnetwork for each task. Specifically, WSN jointly learns the model weights and task-adaptive binary masks pertaining to subnetworks associated with each task whilst attempting to select a small set of weights to be activated (winning ticket) by reusing weights of the prior subnetworks. The proposed method is inherently immune to catastrophic forgetting as each selected subnetwork model does not infringe upon other subnetworks. Binary masks spawned per winning ticket are encoded into one N-bit binary digit mask, then compressed using Huffman coding for a sub-linear increase in network capacity with respect to the number of tasks.
Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification
Authors: Yuanzhi Liang, Linchao Zhu, Xiaohan Wang, Yi Yang
Date: 2022-11-21 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2022 11
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Recognition

Though significant progress has been achieved on fine-grained visual classification (FGVC), severe overfitting still hinders model generalization. A recent study shows that hard samples in the training set can be easily fit, but most existing FGVC methods fail to classify some hard examples in the test set. The reason is that the model overfits those hard examples in the training set, but does not learn to generalize to unseen examples in the test set. In this article, we propose a moderate hard example modulation (MHEM) strategy to properly modulate the hard examples. MHEM encourages the model to not overfit hard examples and offers better generalization and discrimination. First, we introduce three conditions and formulate a general form of a modulated loss function. Second, we instantiate the loss function and provide a strong baseline for FGVC, where the performance of a naive backbone can be boosted and be comparable with recent methods. Moreover, we demonstrate that our baseline can be readily incorporated into the existing methods and empower these methods to be more discriminative. Equipped with our strong baseline, we achieve consistent improvements on three typical FGVC datasets, i.e., CUB-200-2011, Stanford Cars, and FGVC-Aircraft. We hope the idea of moderate hard example modulation will inspire future research work toward more effective fine-grained visual recognition.
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations
Authors: Sudarshan Pant, Eunchae Lim, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim, Young-Shin Kang, Hyerim Jang
Date: 2022-11-11 00:00:00
Proceeding: IEEE Access 2022 11
Tasks: Emotion Recognition, Emotion Recognition in Conversation

Understanding emotions in conversation is a challenging task, as the sentences often have an implied meaning that is not generally understood in isolation. Efficient use of contextual information is essential for emotion recognition in conversations. Many published datasets provide contextual information for situations such as text-based online messaging, chatbots, and movie dialogues. However, such dialogue-based datasets are collected by selecting ideal conversational situations and thus do not include many variations in dialogue length and number of participants. Therefore, such datasets may not be applicable for emotion recognition in text-based movie transcripts, where scenes contain variations in the number of speakers and length of spoken sentences. We present a conversation dataset based on the Korean television show transcripts to analyze the emotions in presence of scene context. The Korean Drama Scene Transcript dataset for Emotion Recognition (KD-EmoR) is a text-based conversation dataset. We analyze three classes of complex emotions: euphoria, dysphoria, and neutral, in the scenes of a television drama to build a publicly available dataset for further research. We developed a context-aware deep learning model to classify emotions using the speaker-level context and scene context and achieved an F1-score of 0.63 on the proposed dataset.