pwc-archive/papers-with-abstracts huggingface dataset:
Defining binary phylogenetic trees using parsimony: new bounds
Authors: Mirko Wilde, Mareike Fischer
Date: 2023-03-06 00:00:00
Tasks: 2k, 4k

Phylogenetic trees are frequently used to model evolution. Such trees are typically reconstructed from data like DNA, RNA, or protein alignments using methods based on criteria like maximum parsimony (amongst others). Maximum parsimony has been assumed to work well for data with only few state changes. Recently, some progress has been made to formally prove this assertion. For instance, it has been shown that each binary phylogenetic tree $T$ with $n \geq 20k$ leaves is uniquely defined by the set $A_k(T)$, which consists of all characters with parsimony score $k$ on $T$. In the present manuscript, we show that the statement indeed holds for all $n \geq 4k$, thus drastically lowering the lower bound for $n$ from $20k$ to $4k$. However, it has been known that for $n \leq 2k$ and $k \geq 3$, it is not generally true that $A_k(T)$ defines $T$. We improve this result by showing that the latter statement can be extended from $n \leq 2k$ to $n \leq 2k+2$. So we drastically reduce the gap of values of $n$ for which it is unknown if trees $T$ on $n$ taxa are defined by $A_k(T)$ from the previous interval of $[2k+1,20k-1]$ to the interval $[2k+3,4k-1]$. Moreover, we close this gap completely for the nearest neighbor interchange (NNI) neighborhood of $T$ in the following sense: We show that as long as $n\geq 2k+3$, no tree that is one NNI move away from $T$ (and thus very similar to $T$) shares the same $A_k$-alignment.
The effect of a linear feedback mechanism in a homeostasis model
Authors: Antonio Francesco Zirattu, Marta Biondo, Matteo Osella, Michele Caselle
Date: 2023-03-06 00:00:00

Feedback loops are essential for regulating cell proliferation and maintaining the delicate balance between cell division and cell death. Thanks to the exact solution of a few simple models of cell growth it is by now clear that stochastic fluctuations play a central role in this process and that cell growth (and in particular the robustness and stability of homeostasis) can be properly addressed only as a stochastic process. Using epidermal homeostasis as a prototypical example, we show that it is possible to discriminate among different feedback strategies which turn out to be characterized by different, experimentally testable, behaviours. In particular, we focus on the so-called Dynamical Heterogeneity model, an epidermal homeostasis model that takes into account two well known cellular features: the plasticity of the cells and their adaptability to face environmental stimuli. We show that specific choices of the parameter on which the feedback is applied may decrease the fluctuations of the homeostatic population level and improve the recovery of the system after an external perturbation.
Chronic Kidney Disease of Unknown Aetiolgy (CKDu)-the search for causes and the impact of its politicization
Authors: Chandre Dharma-wardana
Date: 2023-03-04 00:00:00

Kidney disease of unknown aetiology (CKDu) has been identified in many countries extending from MesoAmerica and Egypt, to South-east Asia and China. Although CKDu has been linked by various authors to farming, it is an artifact of treating multi-modal disease distributions as unimodal. There is NO correlation of CKDu with agriculture since affected farming villages are often surrounded by other farming villages free of CKDu. Initial studies looked for a correlation of CKDu with toxic heavy metal residues of arsenic, cadmium etc., or herbicides like glyphosate that may be present in the environment, as the causative factors. There is now considerable consensus that their concentrations are below danger thresholds, be it in Mesoamerica or south-east Asia. The conceptual basis of a search for etiology within a systems approach is discussed, and attempts to name the disease to bias the identification of its etiology are reviewed. Current research has narrowed down the etiology to geochemical electrolytic contaminants like fluorides and ionic components in hard water, nanosilica (found in water as well as in the air), as well as renal toxins similar to indoxyl sulphates that may arise from interactions of ions with humic acids contained in aqueous organic matter. However, while agrochemical toxins are increasingly considered less relevant to the etiology of CKDu, it has become a firm public belief. In Sri Lanka this has spawned ideology-based agricultural policies for partial and complete banning of agrochemicals (2014-2021), followed by some back-tracking, disrupting the economy and the food supply. A farmer's uprising in 2022 was spawned by poor harvests. It triggered a larger popular uprising that led to the collapse of a government wedded to romanticized eco-extremist agricultural policies in a country already facing difficulties in the wake of Covid and Ukraine.
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy
Authors: Ziqi Chen, Martin Renqiang Min, Hongyu Guo, Chao Cheng, Trevor Clancy, Xia Ning
Date: 2023-03-02 00:00:00
Tasks: Reinforcement Learning (RL), valid

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery.
Comparison of different versions of SignalP and TargetP for diatom plastid protein predictions with ASAFind
Authors: Ansgar Gruber, Cedar McKay, Gabrielle Rocap, Miroslav Oborník
Date: 2023-03-04 00:00:00
Tasks: Prediction, Specificity

Plastid targeted proteins of diatoms and related algae can be predicted with high sensitivity and specificity using the ASAFind method published in 2015. ASAFind predictions rely on SignalP predictions of endoplasmic reticulum (ER) targeting signal peptides. Recently (in 2019), a new version of SignalP was released, SignalP 5.0. We tested the ability of SignalP 5.0 to recognize signal peptides of nucleus-encoded, plastid-targeted diatom pre-proteins, and to identify the signal peptide cleavage site. The results were compared to manual predictions of the characteristic cleavage site motif, and to previous versions of SignalP. SignalP 5.0 is less sensitive than the previous versions of SignalP in this specific task, and also in the detection of signal peptides of non-plastid proteins in diatoms. However, in combination with ASAFind, the resulting prediction performance for plastid proteins is high. In addition, we tested the multi-location prediction tool TargetP for its suitability to provide signal peptide information to ASAFind. The newest version, TargetP 2.0, had the highest prediction performances for diatom signal peptides and mitochondrial transit peptides compared to other versions of SignalP and TargetP, thus it provides a good basis for ASAFind predictions.
Multi class intracellular protein targeting predictions in diatoms and other algae with complex plastids: ASAFind 2.0
Authors: Ansgar Gruber, Cedar McKay, Miroslav Oborník, Gabrielle Rocap
Date: 2023-03-04 00:00:00

Cells of diatoms and related algae with complex plastids of red algal origin are highly compartmentalized. These plastids are surrounded by four envelope membranes, which also define the periplastidic compartment (PPC), the space between the second and third membranes. The PPC corresponds to the cytosol of the eukaryotic alga that was the ancestor of the complex plastid. Metabolic reactions as well as cell biological processes take place in this compartment; however, its exact function remains elusive. Automated predictions of protein locations proved useful for genome wide explorations of metabolism in the case of plastid proteins, but until now, no automated method for the prediction of PPC proteins was available. Here, we present an updated version of the plastid protein predictor ASAFind, which includes optional prediction of PPC proteins. The new ASAFind version also accepts the output of the most recent versions of SignalP (5.0) and TargetP (2.0) input data. Furthermore, we release a Python script to calculate custom scoring matrices for adjustment of the ASAFind method to other groups of algae, and included the option to run the predictions with custom scoring matrices in a simplified score cut-off mode.
The Economics of the DeLend Project: Agent-based Simulations
Authors: Frederico Dutilh Novaes, Gabriel de Abreu Madeira, Aurimar Cerqueira
Date: 2023-03-06 00:00:00

This paper presents our methodology to simulate the behavior of the DeLend Platform. Such simulations are important to verify if the system is able to connect the different sets of agents linked to the platform in a functional manner. They also provide inputs to guide the choices of operational parameters, such as the platform spread, and strategies by DeLend, since they estimate how the key variables of interest respond to different policies. We discuss the methodology and provide examples meant to clarify the approach and to how we intend to use the tool in practice -- they should not be interpreted as representative of real life scenarios.
Electricity Virtual Bidding Strategy Via Entropy-Regularized Stochastic Control Method
Authors: Zhou Fang
Date: 2023-03-04 00:00:00
Tasks: Management

We propose a virtual bidding strategy by modeling the price differences between the day-ahead market and the real-time market as Brownian motion with drift, where the drift rate and volatility are functions of meteorological variables. We then transform the virtual bidding problem into a mean-variance portfolio management problem, where we approach the mean-variance portfolio management problem by using the exploratory mean-variance portfolio management framework
Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction
Authors: Zhou Fang
Date: 2023-03-04 00:00:00
Tasks: Clustering, Management

In this paper, we present an extended exploratory continuous-time mean-variance framework for portfolio management. Our strategy involves a new clustering method based on simulated annealing, which allows for more practical asset selection. Additionally, we consider past wealth evolution when constructing the mean-variance portfolio. We found that our strategy effectively learns from the past and performs well in practice.
Defining and comparing SICR-events for classifying impaired loans under IFRS 9
Authors: Arno Botha, Esmerelda Oberholzer, Janette Larney, Riaan de Jongh
Date: 2023-03-06 00:00:00

The IFRS 9 accounting standard requires the prediction of credit deterioration in financial instruments, i.e., significant increases in credit risk (SICR). However, the definition of such a SICR-event is inherently ambiguous, given its current reliance on evaluating the change in the estimated probability of default (PD) against some arbitrary threshold. We examine the shortcomings of this PD-comparison approach and propose an alternative framework for generating SICR-definitions based on three parameters: delinquency, stickiness, and the outcome period. Having varied these framework parameters, we obtain 27 unique SICR-definitions and fit logistic regression models accordingly using rich South African mortgage and macroeconomic data. For each definition and corresponding model, the resulting SICR-rates are analysed at the portfolio-level on their stability over time and their responsiveness to economic downturns. At the account-level, we compare both the accuracy and dynamicity of the SICR-predictions, and discover several interesting trends and trade-offs. These results can help any bank with appropriately setting the three framework parameters in defining SICR-events for prediction purposes. We demonstrate this process by comparing the best-performing SICR-model to the PD-comparison approach, and show the latter's inferiority as an early-warning system. Our work can therefore guide the formulation, modelling, and testing of any SICR-definition, thereby promoting the timeous recognition of credit losses; the main imperative of IFRS 9.
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
Authors: Rui Xu, Zhi Liu, Yong Luo, Han Hu, Li Shen, Bo Du, Kaiming Kuang, Jiancheng Yang
Date: 2023-03-07 00:00:00
Tasks: Computed Tomography (CT)

Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective. However, the current pulmonary nodule detection methods are usually domain-specific, and cannot satisfy the requirement of working in diverse real-world scenarios. To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks. This attention module works in the axial, coronal, and sagittal directions. In each direction, we divide the input feature into groups, and for each group, we utilize a universal adapter bank to capture the feature subspaces of the domains spanned by all pulmonary nodule datasets. Then the bank outputs are combined from the perspective of domain to modulate the input group. Extensive experiments demonstrate that SGDA enables substantially better multi-domain pulmonary nodule detection performance compared with the state-of-the-art multi-domain learning methods.
Deciphering a Sleeping Pathogen: Uncovering Novel Transcriptional Regulators of Hypoxia-Induced Dormancy in Mycobacterium Tuberculosis
Authors: Rohak Jain
Date: 2023-03-04 00:00:00
Tasks: Prognosis

Along the pathogenesis of Mycobacterium Tuberculosis (MTB), hypoxia-induced dormancy is a process involving the oxygen-depleted environment encountered inside the lung granuloma, where bacilli enter a viable, non-replicating state termed as latency. Affecting nearly two billion people, latent TB can linger in the host for indefinite periods of time before resuscitating, which significantly strains the accuracy of treatment options and patient prognosis. Transcriptional factors thought to mediate this process have only conferred mild growth defects, signaling that our current understanding of the MTB genetic architecture is highly insufficient. In light of these inconsistencies, the objective of this study was to characterize regulatory mechanisms underlying the transition of MTB into dormancy. The project methodology involved a three-part approach - constructing an aggregate hypoxia dataset, inferring a gene regulatory network based on those observations, and leveraging several downstream network analyses to make sense of it all. Results indicated dormancy to be functionally associated with cell redox homeostasis, metal ion cycling, and cell wall metabolism, all of which modulate essential host-pathogen interactions. Additionally, the crosstalk between individual regulons (Rv0821c and Rv0144; Rv1152 and Rv2359) was shown to be critical in facilitating bacterial persistence and allowing MTB to gain control over key micronutrients within the cell. Defense antioxidants and nutritional immunity were also identified as future avenues to explore further. In providing some of the first insights into the methods utilized by MTB to endure in a hypoxic state, this research suggests a range of strategies that might aid in improved clinical outcomes of TB treatment.
Evolutionary Deep Nets for Non-Intrusive Load Monitoring
Authors: Jinsong Wang, Kenneth A. Loparo
Date: 2023-03-06 00:00:00
Tasks: Deep Learning, Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is an energy efficiency technique to track electricity consumption of an individual appliance in a household by one aggregated single, such as building level meter readings. The goal of NILM is to disaggregate the appliance from the aggregated singles by computational method. In this work, deep learning approaches are implemented to operate the desegregations. Deep neural networks, convolutional neural networks, and recurrent neural networks are employed for this operation. Additionally, sparse evolutionary training is applied to accelerate training efficiency of each deep learning model. UK-Dale dataset is used for this work.
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Authors: Maxence Faldor, Félix Chalumeau, Manon Flageat, Antoine Cully
Date: 2023-03-07 00:00:00
Tasks: Deep Reinforcement Learning

Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfully applied to a variety of domains and particularly in evolutionary robotics. However, MAP-Elites performs a divergent search based on random mutations originating from Genetic Algorithms, and thus, is limited to evolving populations of low-dimensional solutions. PGA-MAP-Elites overcomes this limitation by integrating a gradient-based variation operator inspired by Deep Reinforcement Learning which enables the evolution of large neural networks. Although high-performing in many environments, PGA-MAP-Elites fails on several tasks where the convergent search of the gradient-based operator does not direct mutations towards archive-improving solutions. In this work, we present two contributions: (1) we enhance the Policy Gradient variation operator with a descriptor-conditioned critic that improves the archive across the entire descriptor space, (2) we exploit the actor-critic training to learn a descriptor-conditioned policy at no additional cost, distilling the knowledge of the archive into one single versatile policy that can execute the entire range of behaviors contained in the archive. Our algorithm, DCG-MAP-Elites improves the QD score over PGA-MAP-Elites by 82% on average, on a set of challenging locomotion tasks.
Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation
Authors: Adam N. McCaughan, Bakhrom G. Oripov, Natesh Ganesh, Sae Woo Nam, Andrew Dienstfrey, Sonia M. Buckley
Date: 2023-03-05 00:00:00
Tasks: GPU

We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware. MGD utilizes zero-order optimization techniques for online training of hardware neural networks. We demonstrate its ability to train neural networks on modern machine learning datasets, including CIFAR-10 and Fashion-MNIST, and compare its performance to backpropagation. Assuming realistic timescales and hardware parameters, our results indicate that these optimization techniques can train a network on emerging hardware platforms orders of magnitude faster than the wall-clock time of training via backpropagation on a standard GPU, even in the presence of imperfect weight updates or device-to-device variations in the hardware. We additionally describe how it can be applied to existing hardware as part of chip-in-the-loop training, or integrated directly at the hardware level. Crucially, the MGD framework is highly flexible, and its gradient descent process can be optimized to compensate for specific hardware limitations such as slow parameter-update speeds or limited input bandwidth.
Spelling convention sensitivity in neural language models
Authors: Elizabeth Nielsen, Christo Kirov, Brian Roark
Date: 2023-03-06 00:00:00
Tasks: Language Modeling, Language Modelling, Sensitivity

We examine whether large neural language models, trained on very large collections of varied English text, learn the potentially long-distance dependency of British versus American spelling conventions, i.e., whether spelling is consistently one or the other within model-generated strings. In contrast to long-distance dependencies in non-surface underlying structure (e.g., syntax), spelling consistency is easier to measure both in LMs and the text corpora used to train them, which can provide additional insight into certain observed model behaviors. Using a set of probe words unique to either British or American English, we first establish that training corpora exhibit substantial (though not total) consistency. A large T5 language model does appear to internalize this consistency, though only with respect to observed lexical items (not nonce words with British/American spelling patterns). We further experiment with correcting for biases in the training data by fine-tuning T5 on synthetic data that has been debiased, and find that finetuned T5 remains only somewhat sensitive to spelling consistency. Further experiments show GPT2 to be similarly limited.
Two-stage Pipeline for Multilingual Dialect Detection
Authors: Ankit Vaidya, Aditya Kane
Date: 2023-03-06 00:00:00
Tasks: Classification, Dialect Identification, Vocal Bursts Valence Prediction

Dialect Identification is a crucial task for localizing various Large Language Models. This paper outlines our approach to the VarDial 2023 shared task. Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively. Our proposed approach consists of a two-stage system and outperforms other participants' systems and previous works in this domain. We achieve a score of 58.54% for Track-1 and 85.61% for Track-2. Our codebase is available publicly (https://github.com/ankit-vaidya19/EACL_VarDial2023).
Guilt Detection in Text: A Step Towards Understanding Complex Emotions
Authors: Abdul Gafar Manuel Meque, Nisar Hussain, Grigori Sidorov, Alexander Gelbukh
Date: 2023-03-06 00:00:00

We introduce a novel Natural Language Processing (NLP) task called Guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts from three existing emotion detection datasets that we binarized into guilt and no-guilt classes. We experimented with traditional machine learning methods using bag-of-words and term frequency-inverse document frequency features, achieving a 72% f1 score with the highest-performing model. Our study provides a first step towards understanding guilt in text and opens the door for future research in this area.