pwc-archive/papers-with-abstracts huggingface dataset:
Top of the Batch: Interviews and the Match
Authors: Federico Echenique, Ruy Gonzalez, Alistair Wilson, Leeat Yariv
Date: 2020-02-13 00:00:00
Tasks: Position

Most doctors in the NRMP are matched to one of their most-preferred internship programs. Since various surveys indicate similarities across doctors' preferences, this suggests a puzzle. How can nearly everyone get a position in a highly-desirable program when positions in each program are scarce? We provide one possible explanation for this puzzle. We show that the patterns observed in the NRMP data may be an artifact of the interview process that precedes the match. Our analysis highlights the importance of interactions occurring outside of a matching clearinghouse for resulting outcomes, and casts doubts on analysis of clearinghouses that take reported preferences at face value.
Resilient Multi-Dimensional Consensus in Adversarial Environment
Authors: Jiaqi Yan, Xiuxian Li, Yilin Mo, Changyun Wen
Date: 2020-01-03 00:00:00

This paper considers the multi-dimensional consensus in networked systems, where some of the agents might be misbehaving (or faulty). Despite the influence of these misbehaviors, the benign agents aim to reach an agreement while avoiding being seriously influenced by the faulty ones. To this end, this paper first considers a general class of consensus algorithms, where each benign agent computes an "auxiliary point" based on the received values and moves its state toward this point. Concerning this generic form, we present conditions for achieving resilient consensus and obtain a lower bound on the exponential convergence rate. Assuming that the number of malicious agents is upper bounded, two specific resilient consensus algorithms are further developed based on the obtained conditions. Particularly, the first solution, based on Helly's Theorem, achieves the consensus within the convex hull formed by the benign agents' initial states, where the auxiliary point can be efficiently computed through linear programming. On the other hand, the second algorithm serves as a "built-in" security guarantee for standard average consensus algorithms, in the sense that its performance coincides exactly with that of the standard ones in the absence of faulty nodes while also resisting the serious influence of the misbehaving ones in adversarial environment. Some numerical examples are provided in the end to verify the theoretical results.
Conversational Exploratory Search via Interactive Storytelling
Authors: Svitlana Vakulenko, Ilya Markov, Maarten de Rijke
Date: 2017-09-15 00:00:00
Tasks: Conversational Search, Navigate, Question Answering

Conversational interfaces are likely to become more efficient, intuitive and engaging way for human-computer interaction than today's text or touch-based interfaces. Current research efforts concerning conversational interfaces focus primarily on question answering functionality, thereby neglecting support for search activities beyond targeted information lookup. Users engage in exploratory search when they are unfamiliar with the domain of their goal, unsure about the ways to achieve their goals, or unsure about their goals in the first place. Exploratory search is often supported by approaches from information visualization. However, such approaches cannot be directly translated to the setting of conversational search. In this paper we investigate the affordances of interactive storytelling as a tool to enable exploratory search within the framework of a conversational interface. Interactive storytelling provides a way to navigate a document collection in the pace and order a user prefers. In our vision, interactive storytelling is to be coupled with a dialogue-based system that provides verbal explanations and responsive design. We discuss challenges and sketch the research agenda required to put this vision into life.
Multi-Season Analysis Reveals the Spatial Structure of Disease Spread
Authors: Inbar Seroussi, Nir Levy, Elad Yom-Tov
Date: 2019-02-11 00:00:00

Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies. Here, we develop a novel tensor-driven multi-compartment version of the classic Susceptible-Infected-Recovered (SIR) model and apply it to Internet data to reveal information about the complex spatial structure of disease spread. The model is used to analyze state-level Google search data from the US pertaining to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV). We fit the data with correlations of $R^2=0.70$, and $0.52$ for RSV and WNV, respectively. Although no prior assumptions on spatial structure are made, human movement patterns in the US explain 27-30\% of the estimated inter-state transmission rates. The transmission rates within states are correlated with known demographic indicators, such as population density and average age. Finally, we show that the patterns of disease load for subsequent seasons can be predicted using the model parameters estimated for previous seasons and as few as $7$ weeks of data from the current season. Our results are applicable to other countries and similar viruses, allowing the identification of disease spread parameters and prediction of disease load for seasonal viruses earlier in season.
Ad Hoc Table Retrieval using Semantic Similarity
Authors: Shuo Zhang, Krisztian Balog
Date: 2018-02-16 00:00:00
Tasks: Retrieval, Semantic Similarity, Semantic Textual Similarity, Table Retrieval

We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline.
A Short Note on Proximity-based Scoring of Documents with Multiple Fields
Authors: Tomohiro Manabe, Sumio Fujita
Date: 2017-09-11 00:00:00

The BM25 ranking function is one of the most well known query relevance document scoring functions and many variations of it are proposed. The BM25F function is one of its adaptations designed for modeling documents with multiple fields. The Expanded Span method extends a BM25-like function by taking into considerations of the proximity between term occurrences. In this note, we combine these two variations into one scoring method in view of proximity-based scoring of documents with multiple fields.
A Brief History of Excitable Map-Based Neurons and Neural Networks
Authors: M. Girardi-Schappo, M. H. R. Tragtenberg, O. Kinouchi
Date: 2013-03-01 00:00:00

This review gives a short historical account of the excitable maps approach for modeling neurons and neuronal networks. Some early models, due to Pasemann (1993), Chialvo (1995) and Kinouchi and Tragtenberg (1996), are compared with more recent proposals by Rulkov (2002) and Izhikevich (2003). We also review map-based schemes for electrical and chemical synapses and some recent findings as critical avalanches in map-based neural networks. We conclude with suggestions for further work in this area like more efficient maps, compartmental modeling and close dynamical comparison with conductance-based models.
Book-Ahead & Supply Management for Ridesourcing Platforms
Authors: Cesar N. Yahia, Gustavo de Veciana, Stephen D. Boyles, Jean Abou Rahal, Michael Stecklein
Date: 2020-02-22 00:00:00
Tasks: Management

Ridesourcing platforms recently introduced the ``schedule a ride'' service where passengers may reserve (book-ahead) a ride in advance of their trip. Reservations give platforms precise information that describes the start time and location of anticipated future trips; in turn, platforms can use this information to adjust the availability and spatial distribution of the driver supply. In this article, we propose a framework for modeling/analyzing reservations in time-varying stochastic ridesourcing systems. We consider that the driver supply is distributed over a network of geographic regions and that book-ahead rides have reach time priority over non-reserved rides. First, we propose a state-dependent admission control policy that assigns drivers to passengers; this policy ensures that the reach time service requirement would be attained for book-ahead rides. Second, given the admission control policy and reservations information in each region, we predict the ``target" number of drivers that is required (in the future) to probabilistically guarantee the reach time service requirement for stochastic non-reserved rides. Third, we propose a reactive dispatching/rebalancing mechanism that determines the adjustments to the driver supply that are needed to maintain the targets across regions. For a specific reach time quality of service, simulation results using data from Lyft rides in Manhattan exhibit how the number of idle drivers decreases with the fraction of book-ahead rides. We also observe that the non-stationary demand (ride request) rate varies significantly across time; this rapid variation further illustrates that time-dependent models are needed for operational analysis of ridesourcing systems.
MENSADB: A Thorough Structural Analysis of Membrane Protein Dimers
Authors: Pedro Matos-Filipe, António J. Preto, Panagiotis I. Koukos, Joana Mourão, Alexandre M. J. J. Bonvin, Irina S. Moreira
Date: 2019-02-06 00:00:00

Membrane Proteins (MPs) account for around 15-39% of the human proteome and assume a critical role in a vast set of cellular and physiological mechanisms, including molecular transport, nutrient uptake, toxin and waste product clearance, respiration, and signaling. While roughly 60% of all FDA-approved drugs target MPs, there is a shortage of structural and biochemical data on them mainly hindered by their localization in the lipid bilayer. We present here MEmbrane protein dimer Novel Structure Analyser database (MENSAdb), a real time web-application exposing a broad array of fundamental features about MPs surface and their interfacial regions. In particular, we present conservation, four distinctive Accessible Solvent Area (ASA) descriptors, average and environment-specific B-factors, intermolecular contacts at 2.5 and 4.0 angstroms distance cutoffs, salt-bridges, hydrogen-bonds, hydrophobic, pi-pi interactions, t-stacking and cation-pi interactions. Additionally, users can closely inspect differences in values between three distinctive residues classes: i) non-surface, ii) surface and non-interfacial and iii) interfacial. The database is freely available at www.moreiralab.com/resources/mensadb.
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Authors: Paul Fearnhead, Zhen Liu
Date: 2009-10-16 00:00:00

We consider Bayesian analysis of a class of multiple changepoint models. While there are a variety of efficient ways to analyse these models if the parameters associated with each segment are independent, there are few general approaches for models where the parameters are dependent. Under the assumption that the dependence is Markov, we propose an efficient online algorithm for sampling from an approximation to the posterior distribution of the number and position of the changepoints. In a simulation study, we show that the approximation introduced is negligible. We illustrate the power of our approach through fitting piecewise polynomial models to data, under a model which allows for either continuity or discontinuity of the underlying curve at each changepoint. This method is competitive with, or out-performs, other methods for inferring curves from noisy data; and uniquely it allows for inference of the locations of discontinuities in the underlying curve.
Non-parametric Bayesian modelling of digital gene expression data
Authors: Dimitrios V. Vavoulis, Julian Gough
Date: 2013-01-17 00:00:00
Tasks: Clustering, TAG

Next-generation sequencing technologies provide a revolutionary tool for generating gene expression data. Starting with a fixed RNA sample, they construct a library of millions of differentially abundant short sequence tags or "reads", which constitute a fundamentally discrete measure of the level of gene expression. A common limitation in experiments using these technologies is the low number or even absence of biological replicates, which complicates the statistical analysis of digital gene expression data. Analysis of this type of data has often been based on modified tests originally devised for analysing microarrays; both these and even de novo methods for the analysis of RNA-seq data are plagued by the common problem of low replication. We propose a novel, non-parametric Bayesian approach for the analysis of digital gene expression data. We begin with a hierarchical model for modelling over-dispersed count data and a blocked Gibbs sampling algorithm for inferring the posterior distribution of model parameters conditional on these counts. The algorithm compensates for the problem of low numbers of biological replicates by clustering together genes with tag counts that are likely sampled from a common distribution and using this augmented sample for estimating the parameters of this distribution. The number of clusters is not decided a priori, but it is inferred along with the remaining model parameters. We demonstrate the ability of this approach to model biological data with high fidelity by applying the algorithm on a public dataset obtained from cancerous and non-cancerous neural tissues.
Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza Data
Authors: Timothy Kinyanjui, Thomas House
Date: 2019-11-27 00:00:00
Tasks: Model Selection, Uncertainty Quantification

Much traditional statistical modelling assumes that the outcome variables of interest are independent of each other when conditioned on the explanatory variables. This assumption is strongly violated in the case of infectious diseases, particularly in close-contact settings such as households, where each individual's probability of infection is strongly influenced by whether other household members experience infection. On the other hand, general multi-type transmission models of household epidemics quickly become unidentifiable from data as the number of types increases. This has led to a situation where it is has not been possible to draw consistent conclusions from household studies of infectious diseases, for example in the event of an influenza pandemic. Here, we present a generalised linear modelling framework for binary outcomes in sub-units that can (i) capture the effects of non-independence arising from a transmission process and (ii) adjust estimates of disease risk and severity for differences in study population characteristics. This model allows for computationally fast estimation, uncertainty quantification, covariate choice and model selection. In application to real pandemic influenza household data, we show that it is formally favoured over existing modelling approaches.
Collaborative Filtering and the Missing at Random Assumption
Authors: Benjamin Marlin, Richard S. Zemel, Sam Roweis, Malcolm Slaney
Date: 2012-06-20 00:00:00
Tasks: Collaborative Filtering

Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a user study in which we collect a random sample of ratings from current users of an online radio service. An analysis of the rating data collected in the study shows that the sample of random ratings has markedly different properties than ratings of user-selected songs. When asked to report on their own rating behaviour, a large number of users indicate they believe their opinion of a song does affect whether they choose to rate that song, a violation of the MAR condition. Finally, we present experimental results showing that incorporating an explicit model of the missing data mechanism can lead to significant improvements in prediction performance on the random sample of ratings.
Interplay between competitive and cooperative interactions in a three-player pathogen system
Authors: Francesco Pinotti, Fakhteh Ghanbarnejad, Philipp Hövel, Chiara Poletto
Date: 2019-12-16 00:00:00
Tasks: Epidemiology

In ecological systems heterogeneous interactions between pathogens take place simultaneously. This occurs, for instance, when two pathogens cooperate, while at the same time multiple strains of these pathogens co-circulate and compete. Notable examples include the cooperation of HIV with antibiotic-resistant and susceptible strains of tuberculosis, or some respiratory infections with Streptococcus pneumoniae strains. Models focusing on competition or cooperation separately fail to describe how these concurrent interactions shape the epidemiology of such diseases. We studied this problem considering two cooperating pathogens, where one pathogen is further structured in two strains. The spreading follows a susceptible-infected-susceptible process and the strains differ in transmissibility and extent of cooperation with the other pathogen. We combined a mean-field stability analysis with stochastic simulations on networks considering both well-mixed and structured populations. We observed the emergence of a complex phase diagram, where the conditions for the less transmissible, but more cooperative strain to dominate are non-trivial, e.g. non-monotonic boundaries and bistability. Coupled with community structure, the presence of the cooperative pathogen enables the co-existence between strains by breaking the spatial symmetry and dynamically creating different ecological niches. These results shed light on ecological mechanisms that may impact the epidemiology of diseases of public health concern.
Geometric Invariants for Sparse Unknown View Tomography
Authors: Mona Zehni, Shuai Huang, Ivan Dokmanić, Zhizhen Zhao
Date: 2018-11-25 00:00:00

In this paper, we study a 2D tomography problem for point source models with random unknown view angles. Rather than recovering the projection angles, we reconstruct the model through a set of rotation-invariant features that are estimated from the projection data. For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances. This establishes a connection between unknown view tomography and unassigned distance geometry problem (uDGP). We propose new methods to extract the distances and approximate the pairwise distance distribution of the underlying points. We then use the recovered distribution to estimate the locations of the points through constrained non-convex optimization. Our simulation results verify the robustness of our point source reconstruction pipeline to noise and error in the estimation of the features.
Structural thermokinetic modelling
Authors: Wolfram Liebermeister
Date: 2013-09-01 00:00:00

Translating metabolic networks into dynamic models is difficult if kinetic constants are unknown. Structural Kinetic Modelling (SKM) replaces reaction elasticities by independent random numbers. Here I propose a variant that accounts for reversible reactions and thermodynamics: in Structural Thermokinetic Modelling (STM), correlated elasticities are computed from enzyme saturation values and thermodynamic forces, which are physically independent. STM relies on a dependency schema in which basic variables can be sampled, fitted to data, or optimised, while all other variables are computed from them. Probability distributions in the dependency schema define a model ensemble, which leads to probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies and covariances of biological variables. By choosing or sampling the basic variables, we can convert metabolic networks into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations as well as enzyme synergies, metabolite correlations, and metabolic fluctuations arising from chemical noise. By comparing model variants with different network structures, fluxes, thermodynamic forces, regulation, or types of rate laws, we can quantify the effects of these model features. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Thermodynamics can be used to obtain more precise predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and of the emergence and propagation of metabolic noise.
Enzyme economy and metabolic control
Authors: Wolfram Liebermeister
Date: 2014-04-21 00:00:00

The metabolic state of a cell, comprising fluxes, metabolite concentrations and enzyme levels, is shaped by a compromise between metabolic benefit and enzyme cost. This hypothesis and its consequences can be studied by computational models and using a theory of metabolic value. In optimal metabolic states, any increase of an enzyme level must improve the metabolic performance to justify its own cost, so each active enzyme must contribute to the cell's benefit by producing valuable products. This principle of value production leads to variation rules that relate metabolic fluxes and reaction elasticities to enzyme costs. Metabolic value theory provides a language to describe this. It postulates a balance of local values, which I derive here from concepts of metabolic control theory. Economic state variables, called economic potentials and loads, describe how metabolites, reactions, and enzymes contribute to metabolic performance. Economic potentials describe the indirect value of metabolite production, while economic loads describe the indirect value of metabolite concentrations. These economic variables, and others, are linked by local balance equations. These laws for optimal metabolic states define conditions for metabolic fluxes that hold for a wide range of rate laws. To produce metabolic value, fluxes run from lower to higher economic potentials, must be free of futile cycles, and satisfy a principle of minimal weighted fluxes. Given an economical flux mode, one can systematically construct kinetic models in which all enzymes have positive effects on metabolic performance.