pwc-archive/papers-with-abstracts huggingface dataset:
Final Topology for Preference Spaces
Authors: Pablo Schenone
Date: 2020-04-06 00:00:00

We say a model is continuous in utilities (resp., preferences) if small perturbations of utility functions (resp., preferences) generate small changes in the model's outputs. While similar, these two questions are different. They are only equivalent when the following two sets are isomorphic: the set of continuous mappings from preferences to the model's outputs, and the set of continuous mappings from utilities to the model's outputs. In this paper, we study the topology for preference spaces defined by such an isomorphism. This study is practically significant, as continuity analysis is predominantly conducted through utility functions, rather than the underlying preference space. Our findings enable researchers to infer continuity in utility as indicative of continuity in underlying preferences.
Volume of hyperintense inflammation (VHI): a deep learning-enabled quantitative imaging biomarker of inflammation load in spondyloarthritis
Authors: Carolyna Hepburn, Alexis Jones, Alan Bainbridge, Coziana Ciurtin, Juan Eugenio Iglesias, HUI ZHANG, Margaret A. Hall-Craggs, Timothy JP Bray, . Joint senior authorship.
Date: 2021-06-21 00:00:00
Tasks: Lesion Segmentation, Segmentation

Short inversion time inversion recovery (STIR) MRI is widely used in clinical practice to identify and quantify inflammation in axial spondyloarthritis. However, assessment of STIR images is limited by the need for qualitative evaluation, which depends on observer experience and expertise, creating substantial variability in inflammation assessments. To address this problem, we developed a deep learning-enabled, semiautomated workflow for segmentation of inflammatory lesions, whereby an initial segmentation is generated automatically and a radiologist then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which we propose as a quantitative imaging biomarker of inflammation load in spondyloarthritis. The data, code and models used in the study are available at https://github.com/c-hepburn/Bone_MRI.
Over-the-Air Computation via Cloud Radio Access Networks
Authors: Lukuan Xing, Yong Zhou, Yuanming Shi
Date: 2021-06-22 00:00:00
Tasks: Quantization

Over-the-air computation (AirComp) has recently been recognized as a promising scheme for a fusion center to achieve fast distributed data aggregation in wireless networks via exploiting the superposition property of multiple-access channels. Since it is challenging to provide reliable data aggregation for a large number of devices using AirComp, in this paper, we propose to enable AirComp via the cloud radio access network (Cloud-RAN) architecture, where a large number of antennas are deployed at separate sites called remote radio heads (RRHs). However, the potential densification gain provided by Cloud-RAN is generally bottlenecked by the limited capacity of the fronthaul links connecting the RRHs and the fusion center. To this end, we formulate a joint design problem for AirComp transceivers and quantization bits allocation and propose an efficient algorithm to tackle this problem. Our numerical results shows the advantages of the proposed architecture compared with the state-of-the-art solutions.
On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization
Authors: Neil Irwin Bernardo, Jingge Zhu, Jamie Evans
Date: 2021-06-22 00:00:00
Tasks: Diversity, Quantization

We analyze the symbol error probability (SEP) of $M$-ary pulse amplitude modulation ($M$-PAM) receivers equipped with optimal low-resolution quantizers. We first show that the optimum detector can be reduced to a simple decision rule. Using this simplification, an exact SEP expression for quantized $M$-PAM receivers is obtained when Nakagami-$m$ fading channel is considered. The derived expression enables the optimization of the quantizer and/or constellation under the minimum SEP criterion. Our analysis of optimal quantization for equidistant $M$-PAM receiver reveals the existence of error floor which decays at a double exponential rate with increasing quantization bits, $b$. Moreover, by also allowing the transmitter to optimize the constellation based on the statistics of the fading channel, we prove that the error floor can be eliminated but at a lower decay exponent than the unquantized case. Characterization of this decay exponent is provided in this paper. We also expose the outage performance limitations of SEP-optimal uniform quantizers. To be more precise, its decay exponent does not improve with $b$. Lastly, we demonstrate that the decay exponent of a quantized receiver can be complemented by receive antenna diversity techniques.
Sub- and Super-solution Approach to Accuracy Analysis of Portfolio Optimization Asymptotics in Multiscale Stochastic Factor Market
Authors: Jean-Pierre Fouque, Ruimeng Hu, Ronnie Sircar
Date: 2021-06-22 00:00:00
Tasks: Portfolio Optimization

The problem of portfolio optimization when stochastic factors drive returns and volatilities has been studied in previous works by the authors. In particular, they proposed asymptotic approximations for value functions and optimal strategies in the regime where these factors are running on both slow and fast timescales. However, the rigorous justification of the accuracy of these approximations has been limited to power utilities and a single factor. In this paper, we provide an accurate analysis for cases with general utility functions and two timescale factors by constructing sub- and super-solutions to the fully nonlinear problem so that their difference is at the desired level of accuracy. This approach will be valuable in various related stochastic control problems.
BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes
Authors: Mosè Manni, Matthew R Berkeley, Mathieu Seppey, Felipe A Simao, Evgeny M Zdobnov
Date: 2021-06-22 00:00:00

Methods for evaluating the quality of genomic and metagenomic data are essential to aid genome assembly and to correctly interpret the results of subsequent analyses. BUSCO estimates the completeness and redundancy of processed genomic data based on universal single-copy orthologs. Here we present new functionalities and major improvements of the BUSCO software, as well as the renewal and expansion of the underlying datasets in sync with the OrthoDB v10 release. Among the major novelties, BUSCO now enables phylogenetic placement of the input sequence to automatically select the most appropriate dataset for the assessment, allowing the analysis of metagenome-assembled genomes of unknown origin. A newly-introduced genome workflow increases the efficiency and runtimes especially on large eukaryotic genomes. BUSCO is the only tool capable of assessing both eukaryotic and prokaryotic species, and can be applied to various data types, from genome assemblies and metagenomic bins, to transcriptomes and gene sets.
Advance in Reversible Covalent Kinase Inhibitors
Authors: Zheng Zhao, Philip E. Bourne
Date: 2021-06-22 00:00:00

Reversible covalent kinase inhibitors (RCKIs) are a class of novel kinase inhibitors attracting increasing attention because they simultaneously show the selectivity of covalent kinase inhibitors, yet avoid permanent protein-modification-induced adverse effects. Over the last decade, RCKIs have been reported to target different kinases, including atypical kinases. Currently, three RCKIs are undergoing clinical trials to treat specific diseases, for example, Pemphigus, an autoimmune disorder. In this perspective, first, RCKIs are systematically summarized, including characteristics of electrophilic groups, chemical scaffolds, nucleophilic residues, and binding modes. Second, we provide insights into privileged electrophiles, the distribution of nucleophiles and hence effective design strategies for RCKIs. Finally, we provide a brief perspective on future design strategies for RCKIs, including those that target proteins other than kinases.
A platform for cognitive monitoring of neurosurgical patients during hospitalization
Authors: Omer E Ashmaig, Liberty S Hamilton, Pradeep Modur, Robert J Buchanan, Alison R Preston, Andrew J Watrous
Date: 2021-06-22 00:00:00

Intracranial recordings in epilepsy patients are increasingly utilized to gain insight into the electrophysiological mechanisms of human cognition. There are currently several practical limitations to conducting research with these patients, including patient and researcher availability and the cognitive abilities of patients, which limit the amount of task-related data that can be collected. Prior studies have synchronized clinical audio, video, and neural recordings to understand naturalistic behaviors, but these recordings are centered on the patient to understand their seizure semiology and thus do not capture and synchronize audiovisual stimuli from tasks. Here, we describe a platform for cognitive monitoring of neurosurgical patients during their hospitalization that benefits both patients and researchers alike. We provide the full specifications for this system and describe some example use cases in perception, memory, and sleep research. Our system opens up new avenues to collect more data per patient using real-world tasks, affording new possibilities to conduct longitudinal studies of the electrophysiological basis of human cognition under naturalistic conditions.
Sectoral portfolio optimization by judicious selection of financial ratios via PCA
Authors: Vrinda Dhingra, Amita Sharma, Shiv K. Gupta
Date: 2021-06-22 00:00:00
Tasks: Portfolio Optimization

Embedding value investment in portfolio optimization models has always been a challenge. In this paper, we attempt to incorporate it by employing principal component analysis to filter out dominant financial ratios from each sector and thereafter, use the portfolio optimization model incorporating second-order stochastic dominance criteria to derive an optimal investment. We consider a total of $11$ financial ratios corresponding to each sector representing four categories of ratios, namely liquidity, solvency, profitability, and valuation. PCA is then applied over a period of 10 years to extract dominant ratios from each sector in two ways, one from the component solution and the other from each category on the basis of their communalities. The two-step Sectoral Portfolio Optimization (SPO) model is then utilized to build an optimal portfolio. The strategy formed using the formerly extracted ratios is termed PCA-SPO(A) and the latter PCA-SPO(B). The results obtained from the proposed strategies are compared with those from mean-variance, minimum variance, SPO, and nominal SSD models, with and without financial ratios. The empirical performance of proposed strategies is analyzed in two ways, viz., using a rolling window scheme and using market trend scenarios for S\&P BSE 500 (India) and S\&P 500 (U.S.) markets. We observe that the proposed strategy PCA-SPO(B) outperforms all other models in terms of downside deviation, CVaR, VaR, Sortino, Rachev, and STARR ratios over almost all out-of-sample periods. This highlights the importance of value investment where ratios are carefully selected and embedded quantitatively in portfolio selection process.
Two Price Regimes in Limit Order Books: Liquidity Cushion and Fragmented Distant Field
Authors: Sebastian M. Krause, Edgar Jungblut, Thomas Guhr
Date: 2021-06-22 00:00:00

The distribution of liquidity within the limit order book is essential for the impact of market orders on the stock price and the emergence of price shocks. Limit orders are characterized by stylized facts: The number of inserted limit orders declines with the price distance from the quotes following a power law and limit order lifetimes and volumes are power law distributed. Strong dependencies among these quantities add to the complexity of limit order books. Here we analyze the limit order book in the dimensions of price, time, limit order lifetime and volume altogether. This allows us to identify regularities which are not visible in marginal distributions. Particularly we find that the limit order book is divided into two regimes. Around the quotes we find a densely filled regime with mostly short living limit orders closely adapting to the price. Far away from the quotes we find a sparse filling with long living limit orders, mostly inserted at particular times of the day being prone to flash crashes. We determine the characteristics of those two regimes and point out the main differences. Based on our research we propose a model for simulating the regime around the quotes.
Understanding top-down attention using task-oriented ablation design
Authors: Freddie Bickford Smith, Brett D Roads, Xiaoliang Luo, Bradley C Love
Date: 2021-06-08 00:00:00

Top-down attention allows neural networks, both artificial and biological, to focus on the information most relevant for a given task. This is known to enhance performance in visual perception. But it remains unclear how attention brings about its perceptual boost, especially when it comes to naturalistic settings like recognising an object in an everyday scene. What aspects of a visual task does attention help to deal with? We aim to answer this with a computational experiment based on a general framework called task-oriented ablation design. First we define a broad range of visual tasks and identify six factors that underlie task variability. Then on each task we compare the performance of two neural networks, one with top-down attention and one without. These comparisons reveal the task-dependence of attention's perceptual boost, giving a clearer idea of the role attention plays. Whereas many existing cognitive accounts link attention to stimulus-level variables, such as visual clutter and object scale, we find greater explanatory power in system-level variables that capture the interaction between the model, the distribution of training data and the task format. This finding suggests a shift in how attention is studied could be fruitful. We make publicly available our code and results, along with statistics relevant to ImageNet-based experiments beyond this one. Our contribution serves to support the development of more human-like vision models and the design of more informative machine-learning experiments.
From SIR to SEAIRD: a novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19
Authors: Komi Midzodzi Pékpé, Djamel Zitouni, Gilles Gasso, Wajdi Dhifli, Benjamin C. Guinhouya
Date: 2021-05-29 00:00:00

Common compartmental modeling for COVID-19 is based on a priori knowledge and numerous assumptions. Additionally, they do not systematically incorporate asymptomatic cases. Our study aimed at providing a framework for data-driven approaches, by leveraging the strengths of the grey-box system theory or grey-box identification, known for its robustness in problem solving under partial, incomplete, or uncertain data. Empirical data on confirmed cases and deaths, extracted from an open source repository were used to develop the SEAIRD compartment model. Adjustments were made to fit current knowledge on the COVID-19 behavior. The model was implemented and solved using an Ordinary Differential Equation solver and an optimization tool. A cross-validation technique was applied, and the coefficient of determination $R^2$ was computed in order to evaluate the goodness-of-fit of the model. %to the data. Key epidemiological parameters were finally estimated and we provided the rationale for the construction of SEAIRD model. When applied to Brazil's cases, SEAIRD produced an excellent agreement to the data, with an %coefficient of determination $R^2$ $\geq 90\%$. The probability of COVID-19 transmission was generally high ($\geq 95\%$). On the basis of a 20-day modeling data, the incidence rate of COVID-19 was as low as 3 infected cases per 100,000 exposed persons in Brazil and France. Within the same time frame, the fatality rate of COVID-19 was the highest in France (16.4\%) followed by Brazil (6.9\%), and the lowest in Russia ($\leq 1\%$). SEAIRD represents an asset for modeling infectious diseases in their dynamical stable phase, especially for new viruses when pathophysiology knowledge is very limited.
On Prediction Using Variable Order Markov Models
Authors: R. Begleiter, R. El-Yaniv, G. Yona
Date: 2011-06-30 00:00:00
Tasks: Classification, Prediction

This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a "decomposed" CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.
Efficient detection of online communities and social bot activity during electoral campaigns
Authors: Ludovic Rheault, Andreea Musulan
Date: 2021-02-02 00:00:00
Proceeding: Journal of Information Technology & Politics 2021 2

Threats of social media manipulation during elections have become a central concern for modern democracies. This study tackles the problem of identifying the purpose and origins of social bots during electoral campaigns. We propose a methodology – uniform manifold approximation and projection combined with user-level document embeddings – that efficiently reveals the community structure of social media users. We show that this method can be used to predict the partisan affiliation of social media users with high accuracy, detect anomalous concentrations of social bots, and infer their geographical origin. We illustrate the methodology using Twitter data from the 2019 Canadian electoral campaign. Our evidence supports the thesis that social bots have become an integral component of campaign strategy for national actors. We also demonstrate how the methodology can be deployed to identify clusters of foreign bots, and we show that such accounts were used to share far-right and environment-related content during the campaign.
Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora
Authors: Ludovic Rheault, Christopher Cochrane
Date: 2019-07-03 00:00:00
Proceeding: Political Analysis 2019 7
Tasks: Word Embeddings

Word embeddings, the coefficients from neural network models predicting the use of words in context, have now become inescapable in applications involving natural language processing. Despite a few studies in political science, the potential of this methodology for the analysis of political texts has yet to be fully uncovered. This paper introduces models of word embeddings augmented with political metadata and trained on large-scale parliamentary corpora from Britain, Canada, and the United States. We fit these models with indicator variables of the party affiliation of members of parliament, which we refer to as party embeddings. We illustrate how these embeddings can be used to produce scaling estimates of ideological placement and other quantities of interest for political research. To validate the methodology, we assess our results against indicators from the Comparative Manifestos Project, surveys of experts, and measures based on roll-call votes. Our findings suggest that party embeddings are successful at capturing latent concepts such as ideology, and the approach provides researchers with an integrated framework for studying political language.
DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network
Authors: Limeng Pu, Rajiv Gandhi Govindaraj, Jeffrey Mitchell Lemoine, Hsiao-Chun Wu, Michal Brylinski
Date: 2019-02-04 00:00:00
Proceeding: Plos Computational Biology 2019 2
Tasks: Benchmarking, Specificity

Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of Deep-Drug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.
Real-time Instance Segmentation with Discriminative Orientation Maps
Authors: Wentao Du, Zhiyu Xiang, Shuya Chen, Chengyu Qiao, Yiman Chen, Tingming Bai
Date: 2021-06-23 00:00:00
Proceeding: ICCV 2021 10
Tasks: Foreground Segmentation, Instance Segmentation, Real-time Instance Segmentation, Segmentation, Semantic Segmentation

Although instance segmentation has made considerable advancement over recent years, it's still a challenge to design high accuracy algorithms with real-time performance. In this paper, we propose a real-time instance segmentation framework termed OrienMask. Upon the one-stage object detector YOLOv3, a mask head is added to predict some discriminative orientation maps, which are explicitly defined as spatial offset vectors for both foreground and background pixels. Thanks to the discrimination ability of orientation maps, masks can be recovered without the need for extra foreground segmentation. All instances that match with the same anchor size share a common orientation map. This special sharing strategy reduces the amortized memory utilization for mask predictions but without loss of mask granularity. Given the surviving box predictions after NMS, instance masks can be concurrently constructed from the corresponding orientation maps with low complexity. Owing to the concise design for mask representation and its effective integration with the anchor-based object detector, our method is qualified under real-time conditions while maintaining competitive accuracy. Experiments on COCO benchmark show that OrienMask achieves 34.8 mask AP at the speed of 42.7 fps evaluated with a single RTX 2080 Ti. The code is available at https://github.com/duwt/OrienMask.