pwc-archive/papers-with-abstracts huggingface dataset:
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
Authors: Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, Silvio Savarese
Date: 2018-09-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Form, Robot Navigation, Translation

We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model's performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.
Towards Adapting NMF Dictionaries Using Total Variability Modeling for Noise-Robust Acoustic Features
Authors: Kunal Dhawan, Colin Vaz, Ruchir Travadi, Shrikanth Narayanan
Date: 2019-07-16 00:00:00

We propose an algorithm to extract noise-robust acoustic features from noisy speech. We use Total Variability Modeling in combination with Non-negative Matrix Factorization (NMF) to learn a total variability subspace and adapt NMF dictionaries for each utterance. Unlike several other approaches for extracting noise-robust features, our algorithm does not require a training corpus of parallel clean and noisy speech. Furthermore, the proposed features are produced by an utterance-specific transform, allowing the features to be robust to the noise occurring in each utterance. Preliminary results on the Aurora 4 + DEMAND noise corpus show that our proposed features perform comparably to baseline acoustic features, including features calculated from a convolutive NMF (CNMF) model. Moreover, on unseen noises, our proposed features gives the most similar word error rate to clean speech compared to the baseline features.
Modeling the molecular impact of SARS-CoV-2 infection on the renin-angiotensin system
Authors: Fabrizio Pucci, Philippe Bogaerts, Marianne Rooman
Date: 2020-06-04 00:00:00

SARS-CoV-2 coronavirus infection is mediated by the binding of its spike protein to the angiotensin-converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS). The study of RAS dysregulation due to SARS-CoV-2 infection is fundamentally important for a better understanding of the pathogenic mechanisms and risk factors associated with COVID-19 coronavirus disease, and to design effective therapeutic strategies. In this context, we developed a mathematical model of RAS based on data regarding protein and peptide concentrations; the model was tested on clinical data from healthy normotensive and hypertensive individuals. We then used our model to analyze the impact of SARS-CoV-2 infection on RAS, which we modeled through a down-regulation of ACE2 as a function of viral load. We also used it to predict the effect of RAS-targeting drugs, such as RAS-blockers, human recombinant ACE2, and angiotensin 1-7 peptide, on COVID-19 patients; the model predicted an improvement of the clinical outcome for some drugs and a worsening for others.
Aster Swarming by Collective Mechanics of Dyneins and Kinesins
Authors: Neha Khetan, Chaitanya A. Athale
Date: 2020-06-10 00:00:00

Microtubule (MT) radial arrays or asters establish the internal topology of a cell by interacting with organelles and molecular motors. We proceed to understand the general pattern forming potential of aster-motor systems using a computational model of multiple MT asters interacting with motors in a cellular confinement. In this model dynein motors are attached to the cell cortex and plus-ended motors resembling kinesin-5 diffuse in the cell interior. The introduction of 'noise' in the form of MT length fluctuations spontaneously results in the emergence of coordinated, achiral vortex-like rotation of asters. The coherence and persistence of rotation requires a threshold density of both cortical dyneins and coupling kinesins, while the onset of rotation if diffusion-limited with relation to cortical dynein mobility. The coordinated rotational motion arises due to the resolution of the 'tug-of-war' of the rotational component due to cortical motors by 'noise' in the form of MT dynamic instability, and such transient symmetry breaking is amplified by local coupling by kinesin-5 complexes. The lack of widespread aster rotation across cell types suggests biophysical mechanisms that suppress such intrinsic dynamics may have evolved. This model is analogous to more general models of locally coupled self-propelled particles (SPP) that spontaneously undergo collective transport in presence of 'noise' that have been invoked to explain swarming in birds and fish. However, the aster-motor system is distinct from SPP models with regard to particle density and 'noise' dependence, providing a set of experimentally testable predictions for a novel sub-cellular pattern forming system.
Predictability: Can the turning point and end of an expanding epidemic be precisely forecast?
Authors: Mario Castro, Saúl Ares, José A. Cuesta, Susanna Manrubia
Date: 2020-04-19 00:00:00

No, they can't. Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. An SIR model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slow-down in the number of newly infected individuals during the expansion phase allows to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the mid-term is not due to wrong or incomplete data, since it persists in error-free, synthetically produced data sets, and does not necessarily improve by using larger data sets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective or phenomenological models, and supports that only probabilities of different outcomes can be confidently given.
Translating and Evolving: Towards a Model of Language Change in DisCoCat
Authors: Tai-Danae Bradley, Martha Lewis, Jade Master, Brad Theilman
Date: 2018-11-08 00:00:00
Tasks: Translation

The categorical compositional distributional (DisCoCat) model of meaning developed by Coecke et al. (2010) has been successful in modeling various aspects of meaning. However, it fails to model the fact that language can change. We give an approach to DisCoCat that allows us to represent language models and translations between them, enabling us to describe translations from one language to another, or changes within the same language. We unify the product space representation given in (Coecke et al., 2010) and the functorial description in (Kartsaklis et al., 2013), in a way that allows us to view a language as a catalogue of meanings. We formalize the notion of a lexicon in DisCoCat, and define a dictionary of meanings between two lexicons. All this is done within the framework of monoidal categories. We give examples of how to apply our methods, and give a concrete suggestion for compositional translation in corpora.
Repurposing drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
Authors: Fuhai Li, Andrew P. Michelson, Randi Foraker, Ming Zhan, Philip R. O. Payne
Date: 2020-06-01 00:00:00

The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling of host cells plays critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells, and drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. In this study, we aimed to identify these potential molecular signaling pathways, and repurpose existing drugs as a potentially effective treatment of COVID-19 to facilitate the therapeutic discovery, based on the transcriptional response of host cells. We first identified dysfunctional signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through analysis of the altered gene expression profiles. In addition to the signaling pathway analysis, the activated gene ontologies (GOs) and super gene ontologies were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly identified. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. The dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. The results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.
An India-specific Compartmental Model for Covid-19: Projections and Intervention Strategies by Incorporating Geographical, Infrastructural and Response Heterogeneity
Authors: Sanit Gupta, Sahil Shah, Sumit Chaturvedi, Pranav Thakkar, Parvinder Solanki, Soham Dibyachintan, Sandeepan Roy, M. B. Sushma, Adwait Godbole, Noufal Jaseem, Pradumn Kumar, Sucheta Ravikanti, Aritra Das, Giridhara R. Babu, Tarun Bhatnagar, Avijit Maji, Mithun K. Mitra, Sai Vinjanampathy
Date: 2020-07-28 00:00:00

We present a compartmental meta-population model for the spread of Covid-19 in India. Our model simulates populations at a district or state level using an epidemiological model that is appropriate to Covid-19. Different districts are connected by a transportation matrix developed using available census data. We introduce uncertainties in the testing rates into the model that takes into account the disparate responses of the different states to the epidemic and also factors in the state of the public healthcare system. Our model allows us to generate qualitative projections of Covid-19 spread in India, and further allows us to investigate the effects of different proposed interventions. By building in heterogeneity at geographical and infrastructural levels and in local responses, our model aims to capture some of the complexity of epidemiological modeling appropriate to a diverse country such as India.
Heterogeneity in susceptibility dictates the order of epidemiological models
Authors: Christopher Rose, Andrew J. Medford, C. Franklin Goldsmith, Tejs Vegge, Joshua S. Weitz, Andrew A. Peterson
Date: 2020-05-10 00:00:00
Tasks: Epidemiology

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in disease susceptibility. We show that variation leads to the natural emergence of a power law in the force of infection ($\beta I S^p$), where the order $p$ is a simple function of the distribution shape. $p$ is significantly greater than one for reasonable variances, suggesting that conventional epidemic models make extreme assumptions about the absence of variance in susceptibility. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.
Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization
Authors: Kumi Nakai, Keigo Yamada, Takayuki Nagata, Yuji Saito, Taku Nonomura
Date: 2020-07-10 00:00:00

The selection problem of an optimal set of sensors estimating the snapshot of high-dimensional data is considered. The objective functions based on various criteria of optimal design are adopted to the greedy method: D-optimality, A-optimality, and E-optimality, which maximizes the determinant, minimize the trace of inverse, and maximize the minimum eigenvalue of the Fisher information matrix, respectively. First, the Fisher information matrix is derived depending on the numbers of latent state variables and sensors. Then, the unified formulation of the objective function based on A-optimality is introduced and proved to be submodular, which provides the lower bound on the performance of the greedy method. Next, the greedy methods based on D-, A-, and E-optimality are applied to randomly generated systems and a practical data set of global climates. The sensors selected by the D-optimality objective function works better than those by A- and E-optimality with regard to the determinant, trace of the inverse, and reconstruction error, while those by A-optimality works the best with regard to the minimum eigenvalue. On the other hand, the performance of sensors selected by the E-optimality objective function is worse for all indices and reconstruction error. This might be because of the lack of submodularity as proved in the paper. The results indicate that the greedy method based on D-optimality is the most suitable for high accurate reconstruction with low computational cost.
Finding statistically significant communities in networks
Authors: Andrea Lancichinetti, Filippo Radicchi, Jose' Javier Ramasco, Santo Fortunato
Date: 2010-12-10 00:00:00

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks.
A strategy for finding people infected with SARS-CoV-2: optimizing pooled testing at low prevalence
Authors: Leon Mutesa, Pacifique Ndishimye, Yvan Butera, Jacob Souopgui, Annette Uwineza, Robert Rutayisire, Emile Musoni, Nadine Rujeni, Thierry Nyatanyi, Edouard Ntagwabira, Muhammed Semakula, Clarisse Musanabaganwa, Daniel Nyamwasa, Maurice Ndashimye, Eva Ujeneza, Ivan Emile Mwikarago, Claude Mambo Muvunyi, Jean Baptiste Mazarati, Sabin Nsanzimana, Neil Turok, Wilfred Ndifon
Date: 2020-04-30 00:00:00

Suppressing SARS-CoV-2 will likely require the rapid identification and isolation of infected individuals, on an ongoing basis. RT-PCR (reverse transcription polymerase chain reaction) tests are accurate but costly, making regular testing of every individual expensive. The costs are a challenge for all countries and particularly for developing countries. Cost reductions can be achieved by combining samples and testing them in groups. We propose an algorithm for grouping subsamples, prior to testing, based on the geometry of a hypercube. At low prevalence, this testing procedure uniquely identifies infected individuals in a small number of tests. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, parallel searches are preferred. We report proof of concept experiments in which a positive sample was detected even when diluted a hundred-fold with negative samples. Using these methods, the costs of mass testing could be reduced by a factor of ten to a hundred or more. If infected individuals are quickly and effectively quarantined, the prevalence will fall and so will the costs of regularly testing everyone. Such a strategy provides a possible pathway to the longterm elimination of SARS-CoV-2. Field trials of our approach are now under way in Rwanda and initial data from these are reported here.
Traffic Simulator for Multibeam Satellite Communication Systems
Authors: Hayder Al-Hraishawi, Eva Lagunas, Symeon Chatzinotas
Date: 2020-07-14 00:00:00
Tasks: Management

Assume that a multibeam satellite communication system is designed from scratch to serve a particular area with maximal resource utilization and to satisfactorily accommodate the expected traffic demand. The main design challenge here is setting optimal system parameters such as number of serving beams, beam directions and sizes, and transmit power. This paper aims at developing a tool, multibeam satellite traffic simulator, that helps addressing these fundamental challenges, and more importantly, provides an understanding to the spatial-temporal traffic pattern of satellite networks in large-scale environments. Specifically, traffic demand distribution is investigated by processing credible datasets included three major input categories of information: (i) population distribution for broadband Fixed Satellite Services (FSS), (ii) aeronautical satellite communications, and (iii) vessel distribution for maritime services. This traffic simulator combines this three-dimensional information in addition to time, locations of terminals, and traffic demand. Moreover, realistic satellite beam patterns have been considered in this work, and thus, an algorithm has been proposed to delimit the coverage boundaries of each satellite beam, and then compute the heterogeneous traffic demand at the footprint of each beam. Furthermore, another algorithm has been developed to capture the inherent attributes of satellite channels and the effects of multibeam interference. Data-driven modeling for satellite traffic is crucial nowadays to design innovative communication systems, e.g., precoding and beam hopping, and to devise efficient resource management algorithms.
Climate & BCG: Effects on COVID-19 Death Growth Rates
Authors: Chris Finlay, Bruce A. Bassett
Date: 2020-07-10 00:00:00

Multiple studies have suggested the spread of COVID-19 is affected by factors such as climate, BCG vaccinations, pollution and blood type. We perform a joint study of these factors using the death growth rates of 40 regions worldwide with both machine learning and Bayesian methods. We find weak, non-significant (< 3$\sigma$) evidence for temperature and relative humidity as factors in the spread of COVID-19 but little or no evidence for BCG vaccination prevalence or $\text{PM}_{2.5}$ pollution. The only variable detected at a statistically significant level (>3$\sigma$) is the rate of positive COVID-19 tests, with higher positive rates correlating with higher daily growth of deaths.
Testing biological network motif significance with exponential random graph models
Authors: Alex Stivala, Alessandro Lomi
Date: 2020-01-29 00:00:00

Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over ten years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.
MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity
Authors: F. P. Spitzner, J. Dehning, J. Wilting, A. Hagemann, J. P. Neto, J. Zierenberg, V. Priesemann
Date: 2020-07-07 00:00:00
Tasks: Time Series, Time Series Analysis

Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling -- the difficulty to observe the whole system in full detail -- limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.
Online Visual Analytics of Text Streams
Authors: Shixia Liu, Jialun Yin, Xiting Wang, Weiwei Cui, Kelei Cao, Jian Pei
Date: 2015-12-13 00:00:00

We present an online visual analytics approach to helping users explore and understand hierarchical topic evolution in high-volume text streams. The key idea behind this approach is to identify representative topics in incoming documents and align them with the existing representative topics that they immediately follow (in time). To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes. A dynamic Bayesian network model has been developed to derive the tree cuts in the incoming topic trees to balance the fitness of each tree cut and the smoothness between adjacent tree cuts. By connecting the corresponding topics at different times, we are able to provide an overview of the evolving hierarchical topics. A sedimentation-based visualization has been designed to enable the interactive analysis of streaming text data from global patterns to local details. We evaluated our method on real-world datasets and the results are generally favorable.