pwc-archive/papers-with-abstracts huggingface dataset:
FDH knockout and TsFDH transformation lead to enhanced growth rate of Escherichia coli
Authors: Roya Razavipour, Saman Hosseini Ashtiani, Abbas Akhavan Sepahy, Mohammad Hossein Modarressi, Bijan Bambai
Date: 2021-10-03 00:00:00

Increased Atmospheric CO2 to over 400 ppm has prompted global climate irregularities. Reducing the released CO2 from biotechnological processes could remediate these phenomena. In this study, we sought to find a solution to reduce the amount of CO2 in the process of growth and reproduction by preventing the conversion of formic acid into CO2. The (bio)chemical conversion of formic acid to CO2 is a key reaction. Therefore, we compared the growth of BL21, being a subfamily of K12, alongside two strains in which two different genes related to the formate metabolism were deleted, in complex and simple media. Experimental results were entirely consistent with metabolic predictions. Subsequently, the knockout bacteria grew more efficiently than BL21. Interestingly, TsFDH, a formate dehydrogenase with the tendency of converting CO2 to formate, increased the growth of all strains compared with cells without the TsFDH. Most mutants grew in a simple medium containing glycerol, which showed that glycerol is the preferred carbon source compared to glucose for the growth of E. coli. These results explain the reasons for the inconsistency of predictions in previous metabolic models that declared glycerol as a suitable carbon source for the growth of E. coli but failed to achieve it in practice. To conduct a more mechanistic evaluation of our observations, RNA sequencing data analysis was conducted on an E. coli RNA-seq dataset. The gene expression correlation outcome revealed the increased expression levels of several genes related to protein biosynthesis and glycerol degradation as a possible explanation of our observations.
A mixture model for determining SARS-Cov-2 variant composition in pooled samples
Authors: Renan Valieris, Rodrigo Drummond, Alexandre Defelicibus, Emannuel Dias-Neto, Rafael A. Rosales, Israel Tojal da Silva
Date: 2021-10-03 00:00:00

Despite of the fast development of highly effective vaccines to control the current COVID$-$19 pandemic, the unequal distribution and availability of these vaccines worldwide and the number of people infected in the world lead to the continuous emergence of SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) variants of concern. It is likely that real-time genomic surveillance will be continuously needed as an unceasing monitoring tool, necessary to follow the spillover of the disease spread and the evolution of the virus. In this context, new genomic variants of SARS-CoV-2 that may emerge as a response to selective pressure, including variants refractory to current vaccines, makes genomic surveillance programs tools of utmost importance. Here propose a statistical model for the estimation of the relative frequencies of SARS-CoV-2 variants in pooled samples. This model is built by considering a previously defined selection of genomic polymorphisms that characterize SARS-CoV-2 variants. The methods described here support both raw sequencing reads for polymorphisms-based markers calling and predefined markers in the VCF format. Results obtained by using simulated data show that our method is quite effective in recovering the correct variant proportions. Further, results obtained by considering longitudinal data from wastewater samples of two locations in Switzerland agree well with those describing the epidemiological evolution of COVID-19 variants in clinical samples of these locations. Our results show that the described method can be a valuable tool for tracking the proportions of SARS-CoV-2 variants.
Cognitive control as a multivariate optimization problem
Authors: Harrison Ritz, Xiamin Leng, Amitai Shenhav
Date: 2021-10-01 00:00:00
Tasks: Decision Making

Research has characterized the various forms cognitive control can take, including enhancement of goal-relevant information, suppression of goal-irrelevant information, and overall inhibition of potential responses, and has identified computations and neural circuits that underpin this multitude of control types. Studies have also identified a wide range of situations that elicit adjustments in control allocation (e.g., those eliciting signals indicating an error or increased processing conflict), but the rules governing when a given situation will give rise to a given control adjustment remain poorly understood. Significant progress has recently been made on this front by casting the allocation of control as a decision-making problem, and developing unifying and normative models that prescribe when and how a change in incentives and task demands will result in changes in a given form of control. Despite their successes, these models, and the experiments that have been developed to test them, have yet to face their greatest challenge: deciding how to allocate control across the multiplicity of control signals that one could engage at any given time. Here, we will lay out the complexities of the inverse problem inherent to cognitive control allocation, and their close parallels to inverse problems within motor control (e.g., choosing between redundant limb movements). We discuss existing solutions to motor control`s inverse problems drawn from optimal control theory, which have proposed that effort costs act to regularize actions and transform motor planning into a well-posed problem. These same principles may help shed light on how our brains optimize over complex control configuration, while providing a new normative perspective on the origins of mental effort.
Calcium imaging and analysis of the mouse hippocampus or neocortex using miniature microendoscopes
Authors: Jessica Winne, George Nascimento, Ingrid Nogueira, Richardson N. Leao, Katarina E. Leao
Date: 2021-10-01 00:00:00
Tasks: Hippocampus

Calcium imaging using miniscopes are becoming increasingly popular in the neuroscience community with a multitude of microendoscope versions, lens systems and packages for analysis available as open source. Here we describe in detail how to implant lenses and baseplates for the University of California at Los Angeles (UCLA) miniscope V3 and V4 using a Gradient Index (GRIN) lens for the hippocampus or a combination of prism and GRIN lens for recording in the neocortex. Our protocols contain adaptations from several published protocols however the GRIN-relay-Prism lens system protocol is developed in-house and not previously described. This chapter also suggests how lenses may be re-used and how to test the quality of a reused lens. Lastly, we comment on the difference between analysis software and the computational needs for different packages.
The winter dilemma
Authors: Sebastian Contreras, Philipp Dönges, Joel Wagner, Simon Bauer, Sebastian B. Mohr, Emil N. Iftekhar, Mirjam Kretzschmar, Michael Maes, Kai Nagel, André Calero Valdez, Viola Priesemann
Date: 2021-10-04 00:00:00

With winter coming in the northern hemisphere, disadvantageous seasonality of SARS-CoV-2 requires high immunity levels in the population or increasing non-pharmaceutical interventions (NPIs), compared to summer. Otherwise intensive care units (ICUs) might fill up. However, compliance with mandatory NPIs, vaccine uptake, and individual protective measures depend on individuals' opinions and behavior. Opinions, in turn, depend on information, e.g., about vaccine safety or current infection levels. Therefore, understanding how information about the pandemic affects its spread through the modulation of voluntary protection-seeking behaviors is crucial for better preparedness this winter and for future crises.
Backward bifurcation of a disease-severity-structured epidemic model with treatment
Authors: Hiromu Gion, Yasuhisa Saito
Date: 2021-10-04 00:00:00

This paper presents a disease-severity-structured epidemic model with treatment necessary only to severe infective individuals to discuss the effect of the treatment capacity on the disease transmission. It is shown that a backward bifurcation occurs in the basic reproduction number R0, where a stable endemic equilibrium co-exists with a stable disease-free equilibrium when R0 <1, if the capacity is relatively small. This epidemiological implication is that, when there is not enough capacity for treatment, the requirement R0 <1 is not sufficient for effective disease control and disease outbreak can happen to a high endemic level even though R0 <1.
Influence of Allee Effect on Extreme Events in Coupled Three Species Systems
Authors: Deeptajyoti Sen, Sudeshna Sinha
Date: 2021-10-04 00:00:00

We consider the dynamics of two coupled three-species population patches, incorporating the Allee Effect, focussing on the onset of extreme events in the coupled system. First we show that the interplay between coupling and the Allee effect may change the nature of the dynamics, with regular periodic dynamics becoming chaotic in a range of Allee parameters and coupling strengths. Further, the growth in the vegetation population displays an explosive blow-up beyond a critical value of coupling strength and Allee parameter. Most interestingly, we observe that beyond a threshold of coupling strength and Allee parameter, the population densities of all three species exhibit non-zero probability of yielding extreme events. The emergence of extreme events in the predator populations in the patches is the most prevalent, and the probability of obtaining large deviations in the predator populations is not affected significantly by either the coupling strength or the Allee effect. In the absence of the Allee effect the prey population in the coupled system exhibits no extreme events for low coupling strengths, but yields a sharp increase in extreme events after a critical strength of coupling. The vegetation population in the patches display a small finite probability of extreme events for strong enough coupling, only in the presence of Allee effect. Lastly we consider the influence of additive noise on the continued prevalence of extreme events. Very significantly, we find that noise suppresses the unbounded vegetation growth that was induced by a combination of Allee effect and coupling. Further, we demonstrate that noise mitigates extreme events in all three populations, and beyond a noise level we do not observe any extreme events in the system at all. This finding has important bearing on the potential observability of extreme events in natural and laboratory systems.
Inferring the Localization of White-Matter Tracts using Diffusion Driven Label Fusion
Authors: Guillermo Gallardo, Gaston Zanitti, Mat Higger, Sylvain Bouix, Demian Wassermann
Date: 2021-10-04 00:00:00

Inferring which pathways are affected by a brain lesion is key for both pre and post-treatment planning. However, many disruptive lesions cause changes in the tissue that interrupt tractography algorithms. In such cases, aggregating information from healthy subjects can provide a solution to inferring the affected pathways. In this paper, we introduce a novel label fusion technique that leverages diffusion information to locate brain pathways. Through simulations and experiments in publicly available data we show that our method is able to correctly reconstruct brain pathways, even if they are affected by a focal lesion.
DRP-VEM: Drug repositioning prediction using voting ensemble
Authors: Zahra Ghorbanali, Fatemeh Zare-Mirakabad, Bahram Mohammadpour
Date: 2021-10-01 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, Prediction

Traditional drug discovery methods are costly and time-consuming. Drug repositioning (DR) is a common strategy to overcome these issues. Recently, machine learning methods have been used extensively in DR problem. The performance of these methods depends on the features, representations and training dataset. In this problem, feature sets include many redundant features, which have a negative effect on the performance of methods. Moreover, selecting an appropriate training set is influential in the rise of machine learning method accuracy. However, in this problem, we face two obstacles to find the proper training set. First, most methods employ known and unknown drug-disease pairs as positive and negative sets, respectively. While the number of known pairs is much less than unknowns, it leads to machine learning performance error because of biasing to the majority group. Second, the absence of a drug-disease association means this association has not been approved experimentally and may be changed. In this paper, DRP-VEM framework is proposed to overcome the challenges. We assess DRP-VEM based on different parameters: disease and drug feature representations, classification methods, and voting ensemble training approaches. DRP-VEM is evaluated using heterogenous evaluation criteria. Moreover, we compare DRP-VEM using the best combination of parameters with DisDrugPred.
Liquidity Stress Testing in Asset Management -- Part 3. Managing the Asset-Liability Liquidity Risk
Authors: Thierry Roncalli
Date: 2021-10-04 00:00:00
Tasks: Asset Management, Management

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market liquidity), whereas the third dimension considers the management of the asset-liability liquidity risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. In this third and last research paper focused on managing the asset-liability liquidity risk, we explore the ALM tools that can be put in place to control the liquidity gap. These ALM tools can be split into three categories: measurement tools, management tools and monitoring tools. In terms of measurement tools, we focus on the computation of the redemption coverage ratio (RCR), which is the central instrument of liquidity stress testing programs. We also study the redemption liquidation policy and the different implementation methodologies, and we show how reverse stress testing can be developed. In terms of liquidity management tools, we study the calibration of liquidity buffers, the pros and cons of special arrangements (redemption suspensions, gates, side pockets and in-kind redemptions) and the effectiveness of swing pricing. In terms of liquidity monitoring tools, we compare the macro- and micro-approaches of liquidity monitoring in order to identify the transmission channels of liquidity risk.
Non-average price impact in order-driven markets
Authors: Claudio Bellani, Damiano Brigo, Mikko Pakkanen, Leandro Sanchez-Betancourt
Date: 2021-10-02 00:00:00
Tasks: Clustering

We present a measurement of price impact in order-driven markets that does not require averages across executions or scenarios. Given the order book data associated with one single execution of a sell metaorder, we measure its contribution to price decrease during the trade. We do so by modelling the limit order book using state-dependent Hawkes processes, and by defining the price impact profile of the execution as a function of the compensator of a stochastic process in our model. We apply our measurement to a data set from NASDAQ, and we conclude that the clustering of sell child orders has a bigger impact on price than their sizes.
Concentrated Liquidity in Automated Market Makers
Authors: Robin Fritsch
Date: 2021-10-04 00:00:00

We examine how the introduction of concentrated liquidity has changed the liquidity provision market in automated market makers such as Uniswap. To this end, we compare average liquidity provider returns from trading fees before and after its introduction. Furthermore, we quantify the performance of a number of fundamental concentrated liquidity strategies using historical trade data. We estimate their possible returns and evaluate which perform best for certain trading pairs and market conditions.
Coupled Fixed Points for Hardy-Rogers Type of Maps and Their Applications in the Investigations of Market Equilibrium in Duopoly Markets for Non-Differentiable, Nonlinear Response Functions
Authors: S. Kabaivanov, V. Zhelinski, B. Zlatanov
Date: 2021-09-09 00:00:00

In this paper we generalize Hardy-Rogers maps in the context of coupled fixed points. We generalizes with the help of the obtained main theorem some known results about existence and uniqueness of market equilibrium in duopoly markets. We investigate some recent results about market equilibrium in duopoly markets with the help of the main theorem and we enrich them. We define a generalized response function including production and surpluses. Finally we illustrate a possible application of the main result in the investigation of market equilibrium, when the pay off functions are non differentiable.
Development of an autism screening classification model for toddlers
Authors: Afef Saihi, Hussam Alshraideh
Date: 2021-09-29 00:00:00
Tasks: BIG-bench Machine Learning, Classification

Autism spectrum disorder ASD is a neurodevelopmental disorder associated with challenges in communication, social interaction, and repetitive behaviors. Getting a clear diagnosis for a child is necessary for starting early intervention and having access to therapy services. However, there are many barriers that hinder the screening of these kids for autism at an early stage which might delay further the access to therapeutic interventions. One promising direction for improving the efficiency and accuracy of ASD detection in toddlers is the use of machine learning techniques to build classifiers that serve the purpose. This paper contributes to this area and uses the data developed by Dr. Fadi Fayez Thabtah to train and test various machine learning classifiers for the early ASD screening. Based on various attributes, three models have been trained and compared which are Decision tree C4.5, Random Forest, and Neural Network. The three models provided very good accuracies based on testing data, however, it is the Neural Network that outperformed the other two models. This work contributes to the early screening of toddlers by helping identify those who have ASD traits and should pursue formal clinical diagnosis.
CR-Fill: Generative Image Inpainting With Auxiliary Contextual Reconstruction
Authors: Yu Zeng, Zhe Lin, Huchuan Lu, Vishal M. Patel
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Inpainting

    Recent deep generative inpainting methods use attention layers to allow the generator to explicitly borrow feature patches from the known region to complete a missing region. Due to the lack of supervision signals for the correspondence between missing regions and known regions, it may fail to find proper reference features, which often leads to artifacts in the results. Also, it computes pair-wise similarity across the entire feature map during inference bringing a significant computational overhead. To address this issue, we propose to teach such patch-borrowing behavior to an attention-free generator by joint training of an auxiliary contextual reconstruction task, which encourages the generated output to be plausible even when reconstructed by surrounding regions. The auxiliary branch can be seen as a learnable loss function, i.e. named as contextual reconstruction (CR) loss, where query-reference feature similarity and reference-based reconstructor are jointly optimized with the inpainting generator. The auxiliary branch (i.e. CR loss) is required only during training, and only the inpainting generator is required during the inference. Experimental results demonstrate that the proposed inpainting model compares favourably against the state-of-the-art in terms of quantitative and visual performance. Code is available at https://github.com/zengxianyu/crfill.
Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance
Authors: Xiaohan Yu, Yang Zhao, Yongsheng Gao, Xiaohui Yuan, Shengwu Xiong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Fine-Grained Visual Categorization

    Deep learning methods have achieved remarkable success in fine-grained visual categorization. Such successful categorization at sub-ordinate level, e.g., different animal or plant species, however relies heavily on the visual differences that human can observe and the ground-truths are labelled on the basis of such human visual observation. In contrast, few research has been done for visual categorization at the ultra-fine-grained level, i.e., a granularity where even human experts can hardly identify the visual differences or are not yet able to give affirmative labels by inferring observed pattern differences. This paper reports our efforts towards mitigating this research gap. We introduce the ultra-fine-grained (UFG) image dataset, a large collection of 47,114 images from 3,526 categories. All the images in the proposed UFG image dataset are grouped into categories with different confirmed cultivar names. In addition, we perform an extensive evaluation of state-of-the-art fine-grained classification methods on the proposed UFG image dataset as comparative baselines. The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI). The dataset is available online at https://github.com/XiaohanYu-GU/Ultra-FGVC.
Channel Augmented Joint Learning for Visible-Infrared Recognition
Authors: Mang Ye, Weijian Ruan, Bo Du, Mike Zheng Shou
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Data Augmentation, Diversity, Metric Learning

    This paper introduces a powerful channel augmented joint learning strategy for the visible-infrared recognition problem. For data augmentation, most existing methods directly adopt the standard operations designed for single-modality visible images, and thus do not fully consider the imagery properties in visible to infrared matching. Our basic idea is to homogenously generate color-irrelevant images by randomly exchanging the color channels. It can be seamlessly integrated into existing augmentation operations without modifying the network, consistently improving the robustness against color variations. Incorporated with a random erasing strategy, it further greatly enriches the diversity by simulating random occlusions. For cross-modality metric learning, we design an enhanced channel-mixed learning strategy to simultaneously handle the intra- and cross-modality variations with squared difference for stronger discriminability. Besides, a channel-augmented joint learning strategy is further developed to explicitly optimize the outputs of augmented images. Extensive experiments with insightful analysis on two visible-infrared recognition tasks show that the proposed strategies consistently improve the accuracy. Without auxiliary information, it improves the state-of-the-art Rank-1/mAP by 14.59%/13.00% on the large-scale SYSU-MM01 dataset.