pwc-archive/papers-with-abstracts huggingface dataset:
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death Processes
Authors: Linh Huynh, Jacob G. Scott, Peter J. Thomas
Date: 2022-05-10 00:00:00
Tasks: Time Series, Time Series Analysis

Density dependence is important in the ecology and evolution of microbial and cancer cells. Typically, we can only measure net growth rates, but the underlying density-dependent mechanisms that give rise to the observed dynamics can manifest in birth processes, death processes, or both. Therefore, we utilize the mean and variance of cell number fluctuations to separately identify birth and death rates from time series that follow stochastic birth-death processes with logistic growth. Our method provides a novel perspective on stochastic parameter identifiability, which we validate by analyzing the accuracy in terms of the discretization bin size. We apply our method to the scenario where a homogeneous cell population goes through three stages: (1) grows naturally to its carrying capacity, (2) is treated with a drug that reduces its carrying capacity, and (3) overcomes the drug effect to restore its original carrying capacity. In each stage, we disambiguate whether it happens through the birth process, death process, or some combination of the two, which contributes to understanding drug resistance mechanisms. In the case of limited data sets, we provide an alternative method based on maximum likelihood and solve a constrained nonlinear optimization problem to identify the most likely density dependence parameter for a given cell number time series. Our methods can be applied to other biological systems at different scales to disambiguate density-dependent mechanisms underlying the same net growth rate.
Coronavirus RNA Sensor Using Single-Stranded DNA Bonded to Sub-Percolated Gold Films on Monolayer Graphene Field-Effect Transistors
Authors: Nicholas E. Fuhr, Mohamed Azize, David J. Bishop
Date: 2022-05-10 00:00:00
Tasks: Sensitivity

Electrical detection of messenger ribonucleic acid (mRNA) is a promising approach to enhancing transcriptomics and disease diagnostics because of its sensitivity, rapidity, and modularity. Reported here is a fast SARS-CoV-2 mRNA biosensor (<1 minute) with a limit of detection of 1 aM, and dynamic range of 4 orders of magnitude and a linear sensitivity of 22 mV per molar decade. These figures of merit were obtained on photoresistlessly patterned monolayer graphene field-effect transistors (FETs) derived from commercial four-inch graphene on 90 nm of silicon dioxide on p-type silicon. Then, to facilitate mRNA hybridization, graphene sensing mesa were coated with an ultrathin sub-percolation threshold gold film for bonding 3'-thiolated single-stranded deoxyribonucleic acid (ssDNA) probes complementary to SARS-CoV-2 nucleocapsid phosphoprotein (N) gene. Sub-percolated gold was used to minimize the distance between the graphene material and surface hybridization events. The liquid-transfer characteristics of the graphene FETs repeatedly shows correlation between the Dirac voltage and the copy number of polynucleotide. Ultrathin percolated gold films on graphene FETs facilitate two-dimensional electron gas (2DEG) mRNA biosensors for transcriptomic profiling.
A Lipid-Structured Model of Atherosclerotic Plaque Macrophages with Lipid-Dependent Kinetics
Authors: M. G. Watson, K. L. Chambers, M. R. Myerscough
Date: 2022-05-11 00:00:00

Atherosclerotic plaques are fatty growths in artery walls that cause heart attacks and strokes. Plaque formation is orchestrated by macrophages that are recruited to the artery wall to consume and remove blood-derived lipids, such as low-density lipoprotein (LDL). Ineffective lipid removal, due to macrophage death and other factors, leads to the accumulation of lipid-loaded macrophages and formation of a necrotic core. Experimental observations suggest that macrophage functionality varies with the extent of lipid loading. However, little is known about the resultant influence on plaque fate. Extending work by Ford et al. (2019) and Chambers et al. (2022), we develop a plaque model in which macrophages are classified by their ingested lipid content and behave in a lipid-dependent manner. The model, a system of partial-integro differential equations, considers several macrophage behaviours. These include: recruitment to the artery wall; proliferation and apoptosis; ingestion of LDL, apoptotic cells and necrotic lipid; emigration from the artery wall; and necrosis of apoptotic cells. Here, we consider apoptosis, emigration and proliferation to be lipid-dependent. We model lipid-dependence in these behaviours with experimentally-informed functions of the internalised lipid load. Our results demonstrate that lipid-dependent macrophage behaviour can substantially alter plaque fate by changing both the total quantity of lipid in the plaque and the distribution of lipid between the live cells, dead cells and necrotic core. For lipid-dependent apoptosis and lipid-dependent emigration simulations, we find significant differences in outcomes for cases that ultimately converge on the same net rate of apoptosis or emigration.
Enzyme Similarity Networks
Authors: Renan dos Reis, Luciano da F. Costa
Date: 2022-05-10 00:00:00

There is a crescent use of enzymes in multiple industries and sciences, ranging from materials and fuel synthesis to pharmaceutical and food production. Their applicability in this variety of fields depends not only on their biochemical function but also on their physicochemical properties. In the present work, we describe how the coincidence methodology can be employed to construct similarity networks of seventy well-studied enzymes of the Glycoside Hydrolase Family 13 and to identify communities of physicochemically related enzymes. More specifically, each of the selected enzymes is mapped into a network node, while the links between pairs of enzymes are determined by the coincidence similarity between selected physicochemical features of interest. The obtained networks have modularity and number of isolated nodes optimized respectively to two parameters involved in the coincidence methodology, resulting in highly modular networks. In order to investigate the effect of the considered physicochemical features on the enzymes relationships, the coincidence-based method also is applied to create a meta-network, in which the enzymes similarity networks obtained by the combination of every possible feature becomes nodes of a feature combination network, and the coincidence similarity between those networks defines the respective links. The obtained feature combination network systematically and comprehensively indicates the impact of the selected physicochemical features on enzyme similarity. Several interesting results are reported and discussed, including the identification of subgroups of enzymes with similar physicochemical features within catalytical classes, providing important information for the selection and design of enzymes for targeted biotechnological applications.
Dendritic predictive coding: A theory of cortical computation with spiking neurons
Authors: Fabian A. Mikulasch, Lucas Rudelt, Michael Wibral, Viola Priesemann
Date: 2022-05-11 00:00:00

Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive, and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition, and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. The implied model shows a remarkable correspondence to experimentally observed cortical connectivity patterns, plasticity and dynamics, and at the same time can explain hallmarks of predictive processing, such as mismatch responses, in cortex. We thus propose dendritic predictive coding as one of the main organizational principles of cortex.
Evolution of viral pathogens follows a linear order
Authors: Zi Hian Tan, Kian Yan Yong, Jian-Jun Shu
Date: 2022-05-11 00:00:00

Although lessons have been learned from previous severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) outbreaks, the rapid evolution of the viruses means that future outbreaks of a much larger scale are possible, as shown by the current coronavirus disease 2019 (COVID-19) outbreak. Therefore, it is necessary to better understand the evolution of coronaviruses as well as viruses in general. This study reports a comparative analysis of the amino acid usage within several key viral families and genera that are prone to triggering outbreaks, including coronavirus (SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-HKU1, HCoV-OC43, HCoV-NL63, HCoV-229E), influenza A (H1N1, H3N2), flavivirus (dengue virus serotypes 1-4, Zika) and ebolavirus (Zaire, Sudan, Bundibugyo ebolavirus). Our analysis reveals that the distribution of amino acid usage in the viral genome is constrained to follow a linear order, and the distribution remains closely related to the viral species within the family or genus. This constraint can be adapted to predict viral mutations and future variants of concern. By studying previous SARS and MERS outbreaks, we have adapted this naturally occurring pattern to determine that although pangolin plays a role in the outbreak of COVID-19, it may not be the sole agent as an intermediate animal. In addition to this study, our findings contribute to the understanding of viral mutations for subsequent development of vaccines and toward developing a model to determine the source of the outbreak.
Mathematical Modeling and Investigation on the Role of Demography and Contact Patterns in Social Distancing Measures Effectiveness in COVID-19 Dissemination
Authors: M. A. Ridenti, L. K. Teles, A. Maranhão, V. K. Teles
Date: 2022-05-10 00:00:00

In this article, we investigate the importance of demographic and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), infected and asymptomatic (A), hospitalized (H), infected and symptomatic (I), and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones which produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.
FlavorDB2: An Updated Database of Flavor Molecules
Authors: Nishant Grover, Mansi Goel, Devansh Batra, Neelansh Garg, Rudraksh Tuwani, Apuroop Sethupathy, Ganesh Bagler
Date: 2022-05-10 00:00:00

Flavor is expressed through interaction of molecules via gustatory and olfactory mechanisms. Knowing the utility of flavor molecules in food and fragrances, it is valuable to add a comprehensive repository of flavor compounds characterizing their flavor profile, chemical properties, regulatory status, consumption statistics, taste/aroma threshold values, reported uses in food categories, and synthesis. FlavorDB2 (https://cosylab.iiitd.edu.in/flavordb2/) is an updated database of flavor molecules with an user-friendly interface. This repository simplifies the search for flavor molecules, their attributes and offers a range of applications including food pairing. FlavorDB2 serves as a standard repository of flavor compounds.
Hull and White and Alòs type formulas for barrier options in stochastic volatility models with nonzero correlation
Authors: Frido Rolloos
Date: 2022-04-30 00:00:00
Tasks: Form

Two novel closed-form formulas for the price of barrier options in stochastic volatility models with zero interest rate and dividend yield but nonzero correlation between the asset and its instantaneous volatility are derived. The first is a Hull and White type formula, and the second is a decomposition formula similar in form to the Al\`os decomposition for vanilla options. A model-free approximation is also given.
NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
Authors: Shuang Wu, Xiaoning Song, ZhenHua Feng, Xiao-Jun Wu
Date: 2022-05-12 00:00:00
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Recently, Flat-LAttice Transformer (FLAT) has achieved great success in Chinese Named Entity Recognition (NER). FLAT performs lexical enhancement by constructing flat lattices, which mitigates the difficulties posed by blurred word boundaries and the lack of word semantics. In FLAT, the positions of starting and ending characters are used to connect a matching word. However, this method is likely to match more words when dealing with long texts, resulting in long input sequences. Therefore, it significantly increases the memory and computational costs of the self-attention module. To deal with this issue, we advocate a novel lexical enhancement method, InterFormer, that effectively reduces the amount of computational and memory costs by constructing non-flat lattices. Furthermore, with InterFormer as the backbone, we implement NFLAT for Chinese NER. NFLAT decouples lexicon fusion and context feature encoding. Compared with FLAT, it reduces unnecessary attention calculations in "word-character" and "word-word". This reduces the memory usage by about 50% and can use more extensive lexicons or higher batches for network training. The experimental results obtained on several well-known benchmarks demonstrate the superiority of the proposed method over the state-of-the-art hybrid (character-word) models.
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
Authors: Ahmed El-Kishky, Thomas Markovich, Kenny Leung, Frank Portman, Aria Haghighi, Ying Xiao
Date: 2022-05-12 00:00:00
Tasks: Diversity, Graph Mining, Recommendation Systems, Representation Learning, Retrieval

Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in later stages of recommender systems using a more complex ranking model. As the top of the recommendation funnel, it is important to retrieve a high-recall candidate set to feed into downstream ranking models. A common approach is to leverage approximate nearest neighbor (ANN) search from a single dense query embedding; however, this approach this can yield a low-diversity result set with many near duplicates. As users often have multiple interests, candidate retrieval should ideally return a diverse set of candidates reflective of the user's multiple interests. To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval. kNN-Embed represents each user as a smoothed mixture over learned item clusters that represent distinct "interests" of the user. By querying each of a user's mixture component in proportion to their mixture weights, we retrieve a high-diversity set of candidates reflecting elements from each of a user's interests. We experimentally compare kNN-Embed to standard ANN candidate retrieval, and show significant improvements in overall recall and improved diversity across three datasets. Accompanying this work, we open source a large Twitter follow-graph dataset (https://huggingface.co/datasets/Twitter/TwitterFollowGraph), to spur further research in graph-mining and representation learning for recommender systems.
Surface Representation for Point Clouds
Authors: Haoxi Ran, Jun Liu, Chengjie Wang
Date: 2022-05-11 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Object Detection, 3D Point Cloud Classification, 3D Semantic Segmentation, Semantic Segmentation, Supervised Only 3D Point Cloud Classification, Surface Reconstruction

Most prior work represents the shapes of point clouds by coordinates. However, it is insufficient to describe the local geometry directly. In this paper, we present \textbf{RepSurf} (representative surfaces), a novel representation of point clouds to \textbf{explicitly} depict the very local structure. We explore two variants of RepSurf, Triangular RepSurf and Umbrella RepSurf inspired by triangle meshes and umbrella curvature in computer graphics. We compute the representations of RepSurf by predefined geometric priors after surface reconstruction. RepSurf can be a plug-and-play module for most point cloud models thanks to its free collaboration with irregular points. Based on a simple baseline of PointNet++ (SSG version), Umbrella RepSurf surpasses the previous state-of-the-art by a large margin for classification, segmentation and detection on various benchmarks in terms of performance and efficiency. With an increase of around \textbf{0.008M} number of parameters, \textbf{0.04G} FLOPs, and \textbf{1.12ms} inference time, our method achieves \textbf{94.7\%} (+0.5\%) on ModelNet40, and \textbf{84.6\%} (+1.8\%) on ScanObjectNN for classification, while \textbf{74.3\%} (+0.8\%) mIoU on S3DIS 6-fold, and \textbf{70.0\%} (+1.6\%) mIoU on ScanNet for segmentation. For detection, previous state-of-the-art detector with our RepSurf obtains \textbf{71.2\%} (+2.1\%) mAP$\mathit{_{25}}$, \textbf{54.8\%} (+2.0\%) mAP$\mathit{_{50}}$ on ScanNetV2, and \textbf{64.9\%} (+1.9\%) mAP$\mathit{_{25}}$, \textbf{47.7\%} (+2.5\%) mAP$\mathit{_{50}}$ on SUN RGB-D. Our lightweight Triangular RepSurf performs its excellence on these benchmarks as well. The code is publicly available at \url{https://github.com/hancyran/RepSurf}.
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction
Authors: Shivam Duggal, Deepak Pathak
Date: 2022-05-12 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Reconstruction, Object, Single-View 3D Reconstruction

We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned image collections and their poses without any 3D supervision. Generally, image collections on the internet contain several intra-category geometric and topological variations, for example, different chairs can have different topologies, which makes the task of joint shape and correspondence estimation much more challenging. Because of this, prior works either focus on learning each 3D object shape individually without modeling cross-instance correspondences or perform joint shape and correspondence estimation on categories with minimal intra-category topological variations. We overcome these restrictions by learning a topologically-aware implicit deformation field that maps a 3D point in the object space to a higher dimensional point in the category-specific canonical space. At inference time, given a single image, we reconstruct the underlying 3D shape by first implicitly deforming each 3D point in the object space to the learned category-specific canonical space using the topologically-aware deformation field and then reconstructing the 3D shape as a canonical signed distance field. Both canonical shape and deformation field are learned end-to-end in an inverse-graphics fashion using a learned recurrent ray marcher (SRN) as a differentiable rendering module. Our approach, dubbed TARS, achieves state-of-the-art reconstruction fidelity on several datasets: ShapeNet, Pascal3D+, CUB, and Pix3D chairs. Result videos and code at https://shivamduggal4.github.io/tars-3D/
Computing Programs for Generalized Planning as Heuristic Search
Authors: Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson
Date: 2022-05-12 00:00:00
Tasks: Heuristic Search

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a program-based solution space for GP that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines the BFGP algorithm for GP, that implements a best-first search in our program-based solution space, and that is guided by different evaluation and heuristic functions.
Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that?
Authors: Marko Tesic, Ulrike Hahn
Date: 2022-05-12 00:00:00
Tasks: counterfactual, Misinformation

Counterfactual (CF) explanations have been employed as one of the modes of explainability in explainable AI-both to increase the transparency of AI systems and to provide recourse. Cognitive science and psychology, however, have pointed out that people regularly use CFs to express causal relationships. Most AI systems are only able to capture associations or correlations in data so interpreting them as casual would not be justified. In this paper, we present two experiment (total N = 364) exploring the effects of CF explanations of AI system's predictions on lay people's causal beliefs about the real world. In Experiment 1 we found that providing CF explanations of an AI system's predictions does indeed (unjustifiably) affect people's causal beliefs regarding factors/features the AI uses and that people are more likely to view them as causal factors in the real world. Inspired by the literature on misinformation and health warning messaging, Experiment 2 tested whether we can correct for the unjustified change in causal beliefs. We found that pointing out that AI systems capture correlations and not necessarily causal relationships can attenuate the effects of CF explanations on people's causal beliefs.