pwc-archive/papers-with-abstracts huggingface dataset:
Lower semicontinuity of monotone functionals in the mixed topology on $C_b$
Authors: Max Nendel
Date: 2022-10-17 00:00:00

The main result of this paper characterizes the continuity from below of monotone functionals on the space $C_b$ of bounded continuous functions on an arbitrary Polish space as lower semicontinuity in the mixed topology. In this particular situation, the mixed topology coincides with the Mackey topology for the dual pair $(C_b,{\rm ca})$, where ${\rm ca}$ denotes the space of all countably additive signed Borel measures of finite variation. Hence, lower semicontinuity in the mixed topology of convex monotone maps $C_b\to \mathbb R$ is equivalent to a dual representation in terms of countably additive measures. Such representations are of fundamental importance in finance, e.g., in the context of risk measures and super hedging problems. Based on the main result, regularity properties of capacities and dual representations of Choquet integrals in terms of countably additive measures for $2$-alternating capacities are studied. In a second step, the paper provides a characterization of equicontinuity in the mixed topology for families of convex monotone maps. As a consequence, for every convex monotone map on $C_b$ taking values in a locally convex vector lattice, continuity in the mixed topology is equivalent to continuity on norm bounded sets.
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
Date: 2022-10-15 00:00:00
Proceeding: NeurIPS 2023 11

In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires significant domain knowledge. To automate this process from a data-driven perspective, we propose a disentangled Wasserstein autoencoder with an auxiliary classifier, which isolates the function-related patterns from the rest with theoretical guarantees. This enables one-pass protein sequence editing and improves the understanding of the resulting sequences and editing actions involved. To demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a well-studied structure-function case. We show that our method can be used to alter the function of TCRs without changing the structural backbone, outperforming several competing methods in generation quality and efficiency, and requiring only 10% of the running time needed by baseline models. To our knowledge, this is the first approach that utilizes disentangled representations for TCR engineering.
Challenging targets or describing mismatches? A comment on Common Decoy Distribution by Madej et al
Authors: Lucas Etourneau, Thomas Burger
Date: 2022-10-17 00:00:00

In their recent article, Madej et al. 1 proposed an original way to solve the recurrent issue of controlling for the false discovery rate (FDR) in peptide-spectrum-match (PSM) validation. Briefly, they proposed to derive a single precise distribution of decoy matches termed the Common Decoy Distribution (CDD) and to use it to control for FDR during a target-only search. Conceptually, this approach is appealing as it takes the best of two worlds, i.e., decoy-based approaches (which leverage a large-scale collection of empirical mismatches) and decoy-free approaches (which are not subject to the randomness of decoy generation while sparing an additional database search). Interestingly, CDD also corresponds to a middle-of-the-road approach in statistics with respect to the two main families of FDR control procedures: Although historically based on estimating the falsepositive distribution, FDR control has recently been demonstrated to be possible thanks to competition between the original variables (in proteomics, target sequences) and their fictional counterparts (in proteomics, decoys). Discriminating between these two theoretical trends is of prime importance for computational proteomics. In addition to highlighting why proteomics was a source of inspiration for theoretical biostatistics, it provides practical insights into the improvements that can be made to FDR control methods used in proteomics, including CDD.
Protein-to-genome alignment with miniprot
Authors: Heng Li
Date: 2022-10-14 00:00:00

Motivation: Protein-to-genome alignment is critical to annotating genes in non-model organisms. While there are a few tools for this purpose, all of them were developed over ten years ago and did not incorporate the latest advances in alignment algorithms. They are inefficient and could not keep up with the rapid production of new genomes and quickly growing protein databases. Results: Here we describe miniprot, a new aligner for mapping protein sequences to a complete genome. Miniprot integrates recent techniques such as k-mer sketch and SIMD-based dynamic programming. It is tens of times faster than existing tools while achieving comparable accuracy on real data. Availability and implementation: https://github.com/lh3/miniprot
From time-series transcriptomics to gene regulatory networks: a review on inference methods
Authors: Malvina Marku, Vera Pancaldi
Date: 2022-10-16 00:00:00
Tasks: Time Series, Time Series Analysis

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the always increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated overview of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims and experimental data.
Analysis of Microstate Organization During Emotional Events
Authors: Sudhakar Mishra, Narayanan Srinivasan, Uma Shanker Tiwary
Date: 2022-10-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Understanding the dynamics of emotional experience is an old problem. However, a clear understanding of the mechanism of emotional experience is still far away. In the presented work, we tried to address this problem using a well-established method called microstate analysis using multichannel electroencephalography (EEG). We recorded the brain activity of spontaneous emotional experiences while participants were watching multimedia emotional stimuli. The time duration where the participants spontaneously felt an emotion, we termed it an emotional event. Microstate segmentation is performed for all emotional events to calculate the set of microstates (MS). Followed by a comparison of calculated statistical parameters and transition probabilities for the emotional and non-emotional conditions. We found a set of MS (four MS) for emotional and non-emotional conditions that differ from each other. We observed that MS1 has a higher value of occurrence, duration and coverage for emotional conditions. In addition, the transition to MS1 for the emotional condition was higher. On the other hand, for non-emotion (or neutral) condition, transition to MS3 was higher. A set of MS related to neutral condition was source localized to brain regions involved in higher-level sensory feature processing. On the other hand, for emotional conditions, MS1 \& MS2 are localized to sensory feature processing regions, and MS3 \& MS4 are additionally localized to regions related to socio-emotional processing. Our results hint toward the constructionist mechanism, which has an asymmetric contribution from bottom-up and top-down processing during an emotional experience.
Grip force as a functional window to somatosensory cognition
Authors: Birgitta Dresp-Langley
Date: 2022-10-16 00:00:00

Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provide unprecedented functional insight into somatosensory cognition. Somatosensory cognition is the basis of our ability to act upon and to transform the physical world around us, to recognize objects on the basis of touch alone, and to grasp them with the right amount of force for lifting and manipulating them. Recent technology has permitted the wireless monitoring of grip force signals recorded from biosensors in the palm of the human hand to track and trace human grip forces deployed in cognitive tasks executed under conditions of variable sensory (visual, auditory) input. Non-invasive multi-finger grip force sensor technology can be exploited to explore functional interactions between somatosensory brain mechanisms and motor control, in particular during learning a novel and complex tasks where the planning and strategic execution of hand movements is essential. Under the light of a comprehensive overview of recent discoveries into the functional significance of human grip force variations, perspectives for future studies in cognition, in particular the cognitive control of strategic hand movements in robot-assisted precision tasks, are pointed out.
Does a larger neural network mean greater information transmission efficiency?
Authors: Bartosz Paprocki, Agnieszka Pregowska, Janusz Szczepanski
Date: 2022-10-15 00:00:00

Realistic modeling of brain involves large number of neurons. The important question is how this size affects transmission efficiency? Here, these issue is studied in terms of Shannon's Theory. Mutual Information between input and output signals for simple class of networks with an increasing number of neurons is analyzed theoretically and numerically. Levy-Baxter neural model is applied. It turned out, that for these networks the Mutual Information converges, with increasing size, asymptotically very slowly to saturation level. This suggests that from certain level, the increase of neurons number does not imply significant increase in transmission efficiency, contributes rather to reliability.
Perspectives for self-driving labs in synthetic biology
Authors: Hector Garcia Martin, Tijana Radivojevic, Jeremy Zucker, Kristofer Bouchard, Jess Sustarich, Sean Peisert, Dan Arnold, Nathan Hillson, Gyorgy Babnigg, Jose Manuel Marti, Christopher J. Mungall, Gregg T. Beckham, Lucas Waldburger, James Carothers, Shivshankar Sundaram, Deb Agarwal, Blake A. Simmons, Tyler Backman, Deepanwita Banerjee, Deepti Tanjore, Lavanya Ramakrishnan, Anup Singh
Date: 2022-10-14 00:00:00

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.
Modelling the impact of repeat asymptomatic testing policies for staff on SARS-CoV-2 transmission potential
Authors: Carl A Whitfield, University of Manchester COVID-19 Modelling Group, Ian Hall
Date: 2022-10-17 00:00:00

Repeat asymptomatic testing in order to identify and quarantine infectious individuals has become a widely-used intervention to control SARS-CoV-2 transmission. In some workplaces, and in particular health and social care settings with vulnerable patients, regular asymptomatic testing has been deployed to staff to reduce the likelihood of workplace outbreaks. We have developed a model based on data available in the literature to predict the potential impact of repeat asymptomatic testing on SARS-CoV-2 transmission. The results highlight features that are important to consider when modelling testing interventions, including population heterogeneity of infectiousness and correlation with test-positive probability, as well as adherence behaviours in response to policy. Furthermore, the model based on the reduction in transmission potential presented here can be used to parameterise existing epidemiological models without them having to explicitly simulate the testing process. Overall, we find that even with different model paramterisations, in theory, regular asymptomatic testing is likely to be a highly effective measure to reduce transmission in workplaces, subject to adherence.
Graph identification of proteins in tomograms (GRIP-Tomo)
Authors: August George, Doo Nam Kim, Trevor Moser, Ian T. Gildea, James E. Evans, Margaret S. Cheung
Date: 2022-10-15 00:00:00

In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three-dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo-multimeric complexes from ten topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single-domain protein native topologies as well as distinct single-domain proteins from multimeric complexes within noisy volumes.
In-vitro to in-vivo acetaminophen hepatotoxicity extrapolation using classical schemes, pharmaco-dynamic models and a multiscale spatial-temporal liver twin
Authors: Jules Dichamp, Geraldine Cellière, Ahmed Ghallab, Reham Hassan, Noemie Boissier, Ute Hofmann, Joerg Reinders, Selahaddin Sezgin, Sebastian Zühlke, Jan Hengstler, Dirk Drasdo
Date: 2022-10-17 00:00:00

In vitro to in vivo extrapolation represents a critical challenge in toxicology. In this paper we explore extrapolation strategies for acetaminophen (APAP) based on mechanistic models, comparing classical homogeneous compartment pharmaco-dynamic (PD) models and a multiscale digital twin model resolving liver microarchitecture at cellular resolution. The models integrate consensus detoxification reactions in each individual hepatocyte. We study the consequences of the two model types on the extrapolation and show in which cases these models perform better than the classical extrapolation strategy that is based either on the maximal drug concentration (Cmax) or the area under the pharmaco-kinetic curve (AUC) of the drug blood concentration.
The impact of big winners on passive and active equity investment strategies
Authors: Maxime Markov, Vladimir Markov
Date: 2022-10-17 00:00:00

We investigate the impact of big winner stocks on the performance of active and passive investment strategies using a combination of numerical and analytical techniques. Our analysis is based on historical stock price data from 2006 to 2021 for a large variety of global indexes. We show that the log-normal distribution provides a reasonable fit for total returns for the majority of world stock indexes but highlight the limitations of this model. Using an analytical expression for a finite sum of log-normal random variables, we show that the typical return of a concentrated portfolio is less than that of an equally weighted index. This finding indicates that active managers face a significant risk of underperforming due to the potential for missing out on the substantial returns generated by big winner stocks. Our results suggest that passive investing strategies, that do not involve the selection of individual stocks, are likely to be more effective in achieving long-term financial goals.
A new nonparametric interpoint distance-based measure for assessment of clustering
Authors: Soumita Modak
Date: 2022-10-01 00:00:00
Tasks: Clustering

A new interpoint distance-based measure is proposed to identify the optimal number of clusters present in a data set. Designed in nonparametric approach, it is independent of the distribution of given data. Interpoint distances between the data members make our cluster validity index applicable to univariate and multivariate data measured on arbitrary scales, or having observations in any dimensional space where the number of study variables can be even larger than the sample size. Our proposed criterion is compatible with any clustering algorithm, and can be used to determine the unknown number of clusters or to assess the quality of the resulting clusters for a data set. Demonstration through synthetic and real-life data establishes its superiority over the well-known clustering accuracy measures of the literature.
Conversion of Legal Agreements into Smart Legal Contracts using NLP
Authors: Eason Chen, Niall Roche, Yuen-Hsien Tseng, Walter Hernandez, Jiangbo Shangguan, Alastair Moore
Date: 2022-08-27 00:00:00
Tasks: NER, Question Answering

A Smart Legal Contract (SLC) is a specialized digital agreement comprising natural language and computable components. The Accord Project provides an open-source SLC framework containing three main modules: Cicero, Concerto, and Ergo. Currently, we need lawyers, programmers, and clients to work together with great effort to create a usable SLC using the Accord Project. This paper proposes a pipeline to automate the SLC creation process with several Natural Language Processing (NLP) models to convert law contracts to the Accord Project's Concerto model. After evaluating the proposed pipeline, we discovered that our NER pipeline accurately detects CiceroMark from Accord Project template text with an accuracy of 0.8. Additionally, our Question Answering method can extract one-third of the Concerto variables from the template text. We also delve into some limitations and possible future research for the proposed pipeline. Finally, we describe a web interface enabling users to build SLCs. This interface leverages the proposed pipeline to convert text documents to Smart Legal Contracts by using NLP models.
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
Authors: Hao Xue, Flora D. Salim
Date: 2022-09-20 00:00:00
Tasks: Language Modelling, Representation Learning, Sentence, Text Generation, Time Series, Time Series Analysis, Time Series Forecasting, Traffic Prediction, Weather Forecasting

This paper presents a new perspective on time series forecasting. In existing time series forecasting methods, the models take a sequence of numerical values as input and yield numerical values as output. The existing SOTA models are largely based on the Transformer architecture, modified with multiple encoding mechanisms to incorporate the context and semantics around the historical data. Inspired by the successes of pre-trained language foundation models, we pose a question about whether these models can also be adapted to solve time-series forecasting. Thus, we propose a new forecasting paradigm: prompt-based time series forecasting (PromptCast). In this novel task, the numerical input and output are transformed into prompts and the forecasting task is framed in a sentence-to-sentence manner, making it possible to directly apply language models for forecasting purposes. To support and facilitate the research of this task, we also present a large-scale dataset (PISA) that includes three real-world forecasting scenarios. We evaluate different SOTA numerical-based forecasting methods and language generation models. The benchmark results with various forecasting settings demonstrate the proposed PromptCast with language generation models is a promising research direction. Additionally, in comparison to conventional numerical-based forecasting, PromptCast shows a much better generalization ability under the zero-shot setting.
A Low-cost Humanoid Prototype Intended to assist people with disability using Raspberry Pi
Authors: Md. Nayem Hasan Muntasir, Tariqul Islam Siam, Md. Kamruzzaman Sarker
Date: 2022-10-04 00:00:00

This paper will try to delineate the making of a Humanoid prototype intended to assist people with disability (PWD). The assistance that this prototype will offer is rather rudimentary. However, our key focus is to make the prototype cost-friendly while pertaining to its humanoid-like functionalities. Considering growing needs of Robots, facilities for further installment of features have been made available in this project. The prototype will be of humanoid shape harnessing the power of Artificial Neural Network (ANN) to converse with the users. The prototype uses a raspberry pi and as the computational capability of a raspberry pi is minimal, we cut corners to squeeze the last drop of performance and make it as efficient as possible.
Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing
Authors: Michalis Piponidis, Panayiotis Aristodemou, Theocharis Theocharides
Date: 2022-09-30 00:00:00
Tasks: Reinforcement Learning (RL)

While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several missions, their inability of reliable and consistent autonomous landing poses a major setback for deploying such systems truly autonomously. In this paper we present an autonomous UAV landing system for landing on a moving platform. In contrast to existing attempts, the proposed system relies only on the camera sensor, and has been designed as lightweight as possible. The proposed system can be deployed on a low power platform as part of the drone payload, whilst being indifferent to any external communication or any other sensors. The system relies on a Neural Network (NN) based controller, for which a target and environment agnostic simulator was created, used in training and testing of the proposed system, via Reinforcement Learning (RL) and Proximal Policy optimization (PPO) to optimally control and steer the drone towards landing on the target. Through real-world testing, the system was evaluated with an average deviation of 15cm from the center of the target, for 40 landing attempts.