pwc-archive/papers-with-abstracts huggingface dataset:
Tree balance indices: a comprehensive survey
Authors: Mareike Fischer, Lina Herbst, Sophie Kersting, Luise Kühn, Kristina Wicke
Date: 2021-09-25 00:00:00
Tasks: Survey

Tree balance plays an important role in phylogenetics and other research areas, which is why several indices to measure tree balance have been introduced over the years. Nevertheless, a formal definition of what a balance index actually is and what makes it a useful measure of balance (or, in other cases, imbalance), has so far not been introduced in the literature. While the established indices all summarize the (im)balance of a tree in a single number, they vary in their definitions and underlying principles. It is the aim of the present manuscript to introduce formal definitions of balance and imbalance indices that classify desirable properties of such indices and to analyze and categorize established indices accordingly. In this regard, we review 19 established (im)balance indices from the literature, summarize their general, statistical and combinatorial properties (where known), prove numerous additional results and indicate directions for future research by making explicit open questions and gaps in the literature. We also prove that a few tree shape statistics that have been used to measure tree balance in the literature do not fulfill our definition of an (im)balance index, which might indicate that their properties are not as useful for practical purposes. Moreover, we show that five additional tree shape statistics from other contexts actually are tree (im)balance indices according to our definition. The manuscript is accompanied by the website \url{treebalance.wordpress.com} containing fact sheets of the discussed indices. Moreover, we introduce the software package \verb|treebalance| implemented in $\mathsf{R}$ that can be used to calculate all indices discussed.
Deep learning tackles single-cell analysis A survey of deep learning for scRNA-seq analysis
Authors: Mario Flores, Zhentao Liu, Ting-He Zhang, Md Musaddaqui Hasib, Yu-Chiao Chiu, Zhenqing Ye, Karla Paniagua, Sumin Jo, Jianqiu Zhang, Shou-Jiang Gao, Yu-Fang Jin, Yidong Chen, Yufei Huang
Date: 2021-09-25 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Survey

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioning as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline. Specifically, we establish a unified mathematical representation of all variational autoencoder, autoencoder, and generative adversarial network models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative use of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.
Information transmission in a two-step cascade: Interplay of activation and repression
Authors: Tuhin Subhra Roy, Mintu Nandi, Ayan Biswas, Pinaki Chaudhury, Suman K Banik
Date: 2021-09-27 00:00:00

We present an information-theoretic formalism to study signal transduction in four architectural variants of a model two-step cascade with increasing input population. Our results categorize these four types into two classes depending upon the effect played out by activation and repression on mutual information, net synergy, and signal-to-noise ratio. Within the Gaussian framework and using the linear noise approximation, we derive the analytic expressions for these metrics to establish their underlying relationships in terms of the biochemical parameters. We also verify our approximations through stochastic simulations.
Effect of Dormant Spare Capacity on the Attack Tolerance of Complex Networks
Authors: Sai Saranga Das M, Karthik Raman
Date: 2021-09-25 00:00:00

The vulnerability of networks to targeted attacks is an issue of widespread interest for policymakers, military strategists, network engineers and systems biologists alike. Current approaches to circumvent targeted attacks seek to increase the robustness of a network by changing the network structure in one way or the other, leading to a higher size of the largest connected component for a given fraction of nodes removed. In this work, we propose a strategy in which there is a pre-existing, dormant spare capacity already built into the network for an identified vulnerable node, such that the traffic of the disrupted node can be diverted to another pre-existing node/set of nodes in the network. Using our algorithm, the increase in robustness of canonical scale-free networks was nearly 16-fold. We also analysed real-world networks using our algorithm, where the mean increase in robustness was nearly five-fold. To our knowledge, these numbers are significantly higher than those hitherto reported in literature. The normalized cost of this spare capacity and its effect on the operational parameters of the network have also been discussed. Instances of spare capacity in biological networks, termed as distributed robustness, are also presented.
Toward Personalized Medicine in Connectomic Deep Brain Stimulation
Authors: Barbara Hollunder, Nanditha Rajamani, Shan H. Siddiqi, Carsten Finke, Andrea A. Kühn, Helen S. Mayberg, Michael D. Fox, Clemens Neudorfer, Andreas Horn
Date: 2021-09-25 00:00:00
Tasks: Template Matching

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top responding patients. Here, we propose a three-step, circuit-based framework that aims to develop patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.
COVID-19 in low-tolerance border quarantine systems: impact of the Delta variant of SARS-CoV-2
Authors: Cameron Zachreson, Freya M. Shearer, David J. Price, Michael J. Lydeamore, Jodie McVernon, James McCaw, Nicholas Geard
Date: 2021-09-27 00:00:00

In controlling transmission of COVID-19, the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large scale lockdown policies. Here, we examine to what degree vaccination status of incoming arrivals and the quarantine workforce can allow relaxation of quarantine requirements. To do so, we develop and apply a detailed model of COVID-19 disease progression and transmission taking into account nuanced timing factors. Key among these are disease incubation periods and the progression of infection detectability during incubation. Using the disease characteristics associated with the ancestral lineage of SARS-CoV-2 to benchmark the level of acceptable risk, we examine the performance of the border quarantine system for vaccinated arrivals. We examine disease transmission and vaccine efficacy parameters over a wide range, covering plausible values for the Delta variant currently circulating globally. Our results indicate a threshold in outbreak potential as a function of vaccine efficacy, with the time until an outbreak increasing by up to two orders of magnitude as vaccine efficacy against transmission increases from 70% to 90%. For parameters corresponding to the Delta variant, vaccination is able to maintain the capacity of quarantine systems to reduce case importation and outbreak risk, by counteracting the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.
Uncertainty quantification in covid-19 spread: lockdown effects
Authors: A. Carpio, E. Pierret
Date: 2021-09-25 00:00:00
Tasks: Bayesian Inference, Uncertainty Quantification

We develop a Bayesian inference framework to quantify uncertainties in epidemiological models. We use SEIJR and SIJR models involving populations of susceptible, exposed, infective, diagnosed, dead and recovered individuals to infer from covid-19 data rate constants, as well as their variations in response to lockdown measures. To account for confinement, we distinguish two susceptible populations at different risk: confined and unconfined. We show that transmission and recovery rates within them vary in response to facts. A key unknown to predict the evolution of the epidemic is the fraction of the population affected by the virus, including asymptomatic subjects. Our study tracks its time evolution with quantified uncertainty from available official data from the onset of the epidemic, limited, however, by the data quality. We exemplify the technique with data from Spain, country in which late drastic lockdowns were enforced for months. In late actions and in the absence of other measures, spread is delayed but not stopped unless a large enough fraction of the population is confined until the asymptomatic population is depleted. To some extent, confinement could be replaced by strong distancing through masks in adequate circumstances.
Human genetic admixture through the lens of population genomics
Authors: Shyamalika Gopalan, Samuel Patillo Smith, Katharine Korunes, Iman Hamid, Sohini Ramachandran, Amy Goldberg
Date: 2021-09-24 00:00:00
Tasks: Diversity

Over the last fifty years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, 'The Apportionment of Human Diversity'. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces unique patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification genetic targets of selection, and prediction of phenotypes. In this review, we highlight recent studies and methodological advances that have leveraged genomic signatures of admixture to gain insights into human history, natural selection, and complex trait architecture. We also outline some challenges for admixture population genetics, including limitations of applying methods designed for single-ancestry populations to the study of admixed populations.
Vaccine efficacy and SARS CoV 2 control in California and USA during the session 2020 2026: A modeling study
Authors: Md. Shahriar Mahmud, Md. Kamrujjaman, Md. Mashih Ibn Yasin Adan, Md. Alamgir Hossain, Md. Mizanur Rahman, Md. Shahidul Islam, Muhammad Mohebujjaman, Md. Mamun Molla
Date: 2021-09-15 00:00:00

Besides maintaining health precautions, vaccination has been the only prevention from SARS-CoV-2, though no clinically proved 100% effective vaccine has been developed till date. At this stage, to withhold the debris of this pandemic, experts need to know the impact of the vaccine efficacy rate's threshold and how long this pandemic may extent with vaccines that have different efficacy rates. In this article, a mathematical model study has been done on the importance of vaccination and vaccine efficiency rate during an ongoing pandemic. We simulated a five compartment mathematical model to analyze the pandemic scenario in both California, and whole U.S. We considered four vaccines, Pfizer, Moderna, AstraZeneca, and Johnson and Johnson, which are being used rigorously to control the COVID-19 pandemic, in addition with two special cases: a vaccine with 100% efficacy rate and no vaccine under use. Both the infection and death rates are very high in California. Our model suggests that the pandemic situation in California will be under control in the last quartile of the year 2023 if frequent vaccination is continued with the Pfizer vaccine. During this time, six waves will happen from the beginning of the immunization where the case fatality and recovery rates will be 1.697% and 98.30%, respectively. However, according to the considered model, this period might be extended to the mid of 2024 when vaccines with lower efficacy rates are used. The more effective a vaccine, the less people suffer from this malign infection. Although specific groups of people get prioritized initially, mass vaccination is needed to control the spread of the disease.
On the nature of monetary and price inflation and hyperinflation
Authors: Laurence Francis Lacey
Date: 2021-08-18 00:00:00

Monetary inflation is a sustained increase in the money supply than can result in price inflation, which is a rise in the general level of prices of goods and services. The objectives of this paper were to develop economic models to (1) predict the annual rate of growth in the US consumer price index (CPI), based on the annual growth in the US broad money supply (BMS), the annual growth in US real GDP, and the annual growth in US savings, over the time period 2001 to 2019; (2) investigate the means by which monetary and price inflation can develop into monetary and price hyperinflation. The hypothesis that the annual rate of growth in the US CPI is a function of the annual growth in the US BMS minus the annual growth in US real GDP minus the annual growth in US savings, over the time period investigated, has been shown to be the case. However, an exact relationship required the use of a non-zero residual term. A mathematical statistical formulation of a hyperinflationary process has been provided and used to quantify the period of hyperinflation in the Weimar Republic, from July 1922 until the end of November 1923.
Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets
Authors: G. Mazzei, F. G. Bellora, J. A. Serur
Date: 2021-09-25 00:00:00

In most real scenarios the construction of a risk-neutral portfolio must be performed in discrete time and with transaction costs. Two human imposed constraints are the risk-aversion and the profit maximization, which together define a nonlinear optimization problem with a model-dependent solution. In this context, an optimal fixed frequency hedging strategy can be determined a posteriori by maximizing a sharpe ratio simil path dependent reward function. Sampling from Heston processes, a convolutional neural network was trained to infer which period is optimal using partial information, thus leading to a dynamic hedging strategy in which the portfolio is hedged at various frequencies, each weighted by the probability estimate of that frequency being optimal.
Automated Market-Making for Fiat Currencies
Authors: Alex Lipton, Artur Sepp
Date: 2021-09-24 00:00:00

We present an automated market-making (AMM) cross-settlement mechanism for digital assets on interoperable blockchains, focusing on central bank digital currencies (CBDCs) and stable coins. We develop an innovative approach for generating fair exchange rates for on-chain assets consistent with traditional off-chain markets. We illustrate the efficacy of our approach on realized FX rates for G-10 currencies.
Psychological dimension of adaptive trading in cryptocurrency markets
Authors: Misha Perepelitsa
Date: 2021-09-24 00:00:00
Tasks: parameter estimation

In this paper we extend the analysis of an agent-based model for adaptive trading, called asynchronous stochastic price pump (ASPP) introduced by Perepelitsa and Timofeyev (2019), to the model with heterogeneous distribution of psychological parameters of speculative optimism and pessimism across the population of traders. We show that the new model has a range of qualitatively different dynamics when the correlation between those factors ranges from low negative to large positive values. A statistical parameter estimation suggests a heterogeneous ASPP with negative correlation as a model of price variations of Bitcoin.
MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition
Authors: Hanting Li, Mingzhe Sui, Zhaoqing Zhu, Feng Zhao
Date: 2021-09-20 00:00:00
Tasks: 3D Facial Expression Recognition, Facial Expression Recognition, Facial Expression Recognition (FER)

Vision transformer (ViT) has been widely applied in many areas due to its self-attention mechanism that help obtain the global receptive field since the first layer. It even achieves surprising performance exceeding CNN in some vision tasks. However, there exists an issue when leveraging vision transformer into 2D+3D facial expression recognition (FER), i.e., ViT training needs mass data. Nonetheless, the number of samples in public 2D+3D FER datasets is far from sufficient for evaluation. How to utilize the ViT pre-trained on RGB images to handle 2D+3D data becomes a challenge. To solve this problem, we propose a robust lightweight pure transformer-based network for multimodal 2D+3D FER, namely MFEViT. For narrowing the gap between RGB and multimodal data, we devise an alternative fusion strategy, which replaces each of the three channels of an RGB image with the depth-map channel and fuses them before feeding them into the transformer encoder. Moreover, the designed sample filtering module adds several subclasses for each expression and move the noisy samples to their corresponding subclasses, thus eliminating their disturbance on the network during the training stage. Extensive experiments demonstrate that our MFEViT outperforms state-of-the-art approaches with an accuracy of 90.83% on BU-3DFE and 90.28% on Bosphorus. Specifically, the proposed MFEViT is a lightweight model, requiring much fewer parameters than multi-branch CNNs. To the best of our knowledge, this is the first work to introduce vision transformer into multimodal 2D+3D FER. The source code of our MFEViT will be publicly available online.
What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020
Authors: Jamell Dacon, Jiliang Tang
Date: 2021-09-20 00:00:00
Tasks: Sentence

Since the fatal shooting of 17-year old Black teenager Trayvon Martin in February 2012 by a White neighborhood watchman, George Zimmerman in Sanford, Florida, there has been a significant increase in digital activism addressing police-brutality related and racially-motivated incidents in the United States. In this work, we administer an innovative study of digital activism by exploiting social media as an authoritative tool to examine and analyze the linguistic cues and thematic relationships in these three mediums. We conduct a multi-level text analysis on 36,984,559 tweets to investigate users' behaviors to examine the language used and understand the impact of digital activism on social media within each social movement on a sentence-level, word-level, and topic-level. Our results show that excessive use of racially-related or prejudicial hashtags were used by the counter protests which portray potential discriminatory tendencies. Consequently, our findings highlight that social activism done by Black Lives Matter activists does not diverge from the social issues and topics involving police-brutality related and racially-motivated killings of Black individuals due to the shape of its topical graph that topics and conversations encircling the largest component directly relate to the topic of Black Lives Matter. Finally, we see that both Blue Lives Matter and All Lives Matter movements depict a different directive, as the topics of Blue Lives Matter or All Lives Matter do not reside in the center. These findings suggest that topics and conversations within each social movement are skewed, random or possessed racially-related undertones, and thus, deviating from the prominent social injustice issues.
Recurrent Recommender Networks
Authors: Chao-yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, How Jing
Date: 2017-02-06 00:00:00
Proceeding: WSDM 2017 2
Tasks: Recommendation Systems

Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.