pwc-archive/papers-with-abstracts huggingface dataset:
Retention time trajectory matching for target compound peak identification in chromatographic analysis
Authors: Wenzhe Zang, Ruchi Sharma, Maxwell Wei-Hao Li, Xudong Fan
Date: 2021-07-16 00:00:00

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak identification and alignment across different chromatograms is critical prior to any subsequent analysis. This work introduces a peak identification method called retention time trajectory (RTT) matching, which uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. RTT matching is also capable of reporting interferents. An RTT is a 2-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a test sample and of pre-characterized library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak alignment methods, no mathematical warping or transformations are involved. Based on the experimentally characterized RTT, an RTT hybridization method is developed to rapidly generate more RTTs without performing actual time-consuming chromatographic measurements. This enables successful identification even for chromatograms with serious retention time drift. Experimentally obtained gas chromatograms and publicly available fruit metabolomics liquid chromatograms are used to generate over two trillions of tests that validate the proposed method, demonstrating real-time peak/interferent identification.
A Preliminary Study on Automatic Motion Artifacts Detection in Electrodermal Activity Data Using Machine Learning
Authors: Md Billal Hossain, Hugo Fernando Posada-Quintero, Youngsun Kong, Riley McNaboe, Ki Chon
Date: 2021-07-16 00:00:00
Tasks: Artifact Detection, feature selection

The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. Using these data, we proposed a cross-correlation-based approach for non-biased labeling of EDA data segments. A set of statistical, spectral and model-based features were calculated which were then subjected to a feature selection algorithm. Finally, we trained and validated several machine learning methods using a leave-one-subject-out approach. The classification accuracy of the developed model was 83.85% with a standard deviation of 4.91%, which was better than a recent standard method that we considered for comparison to our algorithm.
Resource Allocation in a Quantum Key Distribution Network with LEO and GEO trusted-repeaters
Authors: Milo Grillo, Alexis A. Dowhuszko, Mohammad-Ali Khalighi, Jyri Hämäläinen
Date: 2021-07-15 00:00:00

Quantum Key Distribution~(QKD) is a technology that enables the exchange of private encryption keys between two legitimate parties, using protocols that involve quantum mechanics principles. The rate at which secret keys can be exchanged depends on the attenuation that is experienced. Therefore, it is more convenient to replace many terrestrial fiber segments (and repeaters) by just few optical satellite links that would enable flexible global coverage. Then, the satellite nodes can take the role of trusted-relays, forwarding the secret keys from source to destination. However, since the rate at which secret keys can be generated in each quantum link is limited, it is very important to select the intermediate satellite nodes to inter-connect ground stations efficiently. This paper studies the most convenient allocation of resources in a QKD network that combines complementary connectivity services of GEO and LEO satellites. The aim of the centralized routing algorithm is to select the most convenient trusted-relays to forward the secret keys between pairs of ground stations, verifying the constraints that satellite-to-ground and inter-satellite quantum channels have.
Strong, recent selective sweeps reshape genetic diversity in freshwater bivalve Megalonaias nervosa
Authors: Rebekah L. Rogers, Stephanie L. Grizzard, Jeffrey T. Garner
Date: 2021-07-16 00:00:00
Tasks: Diversity, Management

Freshwater Unionid bivalves have recently faced ecological upheaval through pollution, barriers to dispersal, human harvesting, and changes in fish-host prevalence. Currently, over 70% of species are threatened, endangered or extinct. To characterize the genetic response to these recent selective pressures, we collected population genetic data for one successful bivalve species, Megalonaias nervosa. We identify megabase sized regions that are nearly monomorphic across the population, a signal of strong, recent selection reshaping genetic diversity. These signatures of selection encompass a total of 73Mb, greater response to selection than is commonly seen in population genetic models. We observe 102 duplicate genes with high dN/dS on terminal branches among regions with sweeps, suggesting that gene duplication is a causative mechanism of recent adaptation in M. nervosa. Genes in sweeps reflect functional classes known to be important for Unionid survival, including anticoagulation genes important for fish host parasitization, detox genes, mitochondria management, and shell formation. We identify selective sweeps in regions with no known functional impacts, suggesting mechanisms of adaptation that deserve greater attention in future work on species survival. In contrast, polymorphic transposable element insertions appear to be detrimental and underrepresented among regions with sweeps. TE site frequency spectra are skewed toward singleton variants, and TEs among regions with sweeps are present only at low frequency. Our work suggests that duplicate genes are an essential source of genetic novelty that has helped this successful species succeed in environments where others have struggled. These results suggest that gene duplications deserve greater attention in non-model population genomics, especially in species that have recently faced sudden environmental challenges.
A Graph-based Molecular Communications Model Analysis of the Human Gut Bacteriome
Authors: Samitha Somathilaka, Daniel P. Martins, Wiley Barton, Orla O'Sullivan, Paul D. Cotter, Sasitharan Balasubramaniam
Date: 2021-07-16 00:00:00

Alterations in the human gut bacteriome can be associated with human health issues, such as type-2 diabetes and cardiovascular disease. Both external and internal factors can drive changes in the composition and in the interactions of the human gut bacteriome, impacting negatively on the host cells. In this paper, we focus on the human gut bacteriome metabolism and we propose a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in the human gut bacteriome network system. Our results show that the regulation of molecular inputs can strongly affect bacterial population growth and create an unbalanced network, as shown by the shift in the node weights based on the molecular signals that are produced. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human gut bacteriome network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviors of the human gut bacteriome depending on the molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial growth and exchange of metabolites.
Modeling cooperation and competition in biological communities
Authors: F. Meloni, G. Nakamura, B. Grammaticos, A. S. Martinez, M. Badoual
Date: 2021-07-16 00:00:00

The far-reaching consequences of ecological interactions in the dynamics of biological communities remain an intriguing subject. For decades, competition has been a cornerstone in ecological processes, but mounting evidence shows that cooperation does also contribute to the structure of biological communities. Here, we propose a simple deterministic model for the study of the effects of facilitation and competition in the dynamics of such systems. The simultaneous inclusion of both effects produces rich dynamics and captures the context-dependence observed in the formation of ecological communities. The approach reproduces relevant aspects of primary and secondary plant succession, the effect invasive species, and the survival of rare species. The model also takes into account the role of the ecological priority effect and stress the crucial role of facilitation in conservation efforts and species coexistence.
On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks
Authors: Magnus Bordewich, Charles Semple, Kristina Wicke
Date: 2021-07-16 00:00:00
Tasks: Diversity

Phylogenetic Diversity (PD) is a prominent quantitative measure of the biodiversity of a collection of present-day species (taxa). This measure is based on the evolutionary distance among the species in the collection. Loosely speaking, if $\mathcal{T}$ is a rooted phylogenetic tree whose leaf set $X$ represents a set of species and whose edges have real-valued lengths (weights), then the PD score of a subset $S$ of $X$ is the sum of the weights of the edges of the minimal subtree of $\mathcal{T}$ connecting the species in $S$. In this paper, we define several natural variants of the PD score for a subset of taxa which are related by a known rooted phylogenetic network. Under these variants, we explore, for a positive integer $k$, the computational complexity of determining the maximum PD score over all subsets of taxa of size $k$ when the input is restricted to different classes of rooted phylogenetic networks
Unravelling Heterogeneous Transport of Endosomes
Authors: Nickolay Korabel, Daniel Han, Alessandro Taloni, Gianni Pagnini, Sergei Fedotov, Viki Allan, Thomas Andrew Waigh
Date: 2021-07-16 00:00:00

A major open problem in biophysics is to understand the highly heterogeneous transport of many structures inside living cells, such as endosomes. We find that mathematically it is described by spatio-temporal heterogeneous fractional Brownian motion (hFBM) which is defined as FBM with a randomly switching anomalous exponent and random generalized diffusion coefficient. Using a comprehensive local analysis of a large ensemble of experimental endosome trajectories (> 10^5), we show that their motion is characterized by power-law probability distributions of displacements and displacement increments, exponential probability distributions of local anomalous exponents and power-law probability distributions of local generalized diffusion coefficients of endosomes which are crucial ingredients of spatio-temporal hFBM. The increased sensitivity of deep learning neural networks for FBM characterisation corroborates the development of this multi-fractal analysis. Our findings are an important step in understanding endosome transport. We also provide a powerful tool for studying other heterogeneous cellular processes.
Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging
Authors: John W. Hickey, Elizabeth K. Neumann, Andrea J. Radtke, Jeannie M. Camarillo, Rebecca T. Beuschel, Alexandre Albanese, Elizabeth McDonough, Julia Hatler, Anne E. Wiblin, Jeremy Fisher, Josh Croteau, Eliza C. Small, Anup Sood, Richard M. Caprioli, R. Michael Angelo, Garry P. Nolan, Kwanghun Chung, Stephen M. Hewitt, Ronald N. Germain, Jeffrey M. Spraggins, Emma Lundberg, Michael P. Snyder, Neil L. Kelleher, Sinem K. Saka
Date: 2021-07-16 00:00:00

Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells are largely based on transcriptomic single-cell approaches that lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient coverage depth, several multiplexed protein imaging methods have recently been developed. Though these antibody-based technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this perspective, we provide essential resources and key considerations for obtaining robust and reproducible multiplexed antibody-based imaging data compiling specialized knowledge from domain experts and technology developers.
Modelling risk for commodities in Brazil: An application to live cattle spot and futures prices
Authors: R. G. Alcoforado, W. Bernardino, A. D. Egídio dos Reis, J. A. C. Santos
Date: 2021-07-15 00:00:00
Tasks: Management

This study analysed a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective was to develop a model that best portrays this commodity's behaviour to estimate futures prices more accurately. The database created contained 2,010 daily entries in which trade in futures contracts occurred, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of the most important reasons why this type of risk needs to be measured is to set loss limits. To identify patterns in price behaviour in order to improve future transactions' results, investors must analyse fluctuations in assets' value for longer periods. Bibliographic research revealed that no other study has conducted a comprehensive analysis of this commodity using this approach. Cattle ranching is big business in Brazil given that in 2017, this sector moved 523.25 billion Brazilian reals (about 130.5 billion United States dollars). In that year, agribusiness contributed 22% of Brazil's total gross domestic product. Using the proposed risk modelling technique, economic agents can make the best decision about which options within these investors' reach produce more effective risk management. The methodology was based on Holt-Winters exponential smoothing algorithm, autoregressive integrated moving average (ARIMA), ARIMA with exogenous inputs, generalised autoregressive conditionally heteroskedastic and generalised autoregressive moving average (GARMA) models. More specifically, 5 different methods were applied that allowed a comparison of 12 different models as ways to portray and predict the BGI commodity's behaviour. The results show that GARMA with order c(2,1) and without intercept is the best model.
Controllable Transmission Networks under Demand Uncertainty with Modular FACTS
Authors: Alireza Soroudi
Date: 2021-03-01 00:00:00

The transmission system operators (TSOs) are responsible to provide secure and efficient access to the transmission system for all stakeholders. This task is gradually getting challenging due to the demand growth, demand uncertainty, rapid changes in generation mix, and market policies. Traditionally, the TSOs try to maximize the technical performance of the transmission network via building new overhead lines or physical hardening. However, obtaining public acceptance for building new lines is not an easy step in this procedure. For this reason, the TSOs try to capture the capabilities of existing assets. This paper investigates the use of modular FACTS devices (to alter the line characteristics) for improving the capability of transmission network in serving the uncertain demand without the need for building new overhead lines. The proposed method considers the uncertainty of demands and controls the utilization of existing transmission assets. The mathematical results obtained are validated with a complete non-linear simulation model of three transmission networks.
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative Model
Authors: Jaeyoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain
Date: 2021-07-17 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

The high-dimensional or sparse reward task of a reinforcement learning (RL) environment requires a superior potential controller such as hierarchical reinforcement learning (HRL) rather than an atomic RL because it absorbs the complexity of commands to achieve the purpose of the task in its hierarchical structure. One of the HRL issues is how to train each level policy with the optimal data collection from its experience. That is to say, how to synchronize adjacent level policies optimally. Our research finds that a HRL model through the off-policy correction technique of HRL, which trains a higher-level policy with the goal of reflecting a lower-level policy which is newly trained using the off-policy method, takes the critical role of synchronizing both level policies at all times while they are being trained. We propose a novel HRL model supporting the optimal level synchronization using the off-policy correction technique with a deep generative model. This uses the advantage of the inverse operation of a flow-based deep generative model (FDGM) to achieve the goal corresponding to the current state of the lower-level policy. The proposed model also considers the freedom of the goal dimension between HRL policies which makes it the generalized inverse model of the model-free RL in HRL with the optimal synchronization method. The comparative experiment results show the performance of our proposed model.
Learning Attributed Graph Representations with Communicative Message Passing Transformer
Authors: Jianwen Chen, Shuangjia Zheng, Ying Song, Jiahua Rao, Yuedong Yang
Date: 2021-07-19 00:00:00
Tasks: Inductive Bias, molecular representation, Representation Learning

Constructing appropriate representations of molecules lies at the core of numerous tasks such as material science, chemistry and drug designs. Recent researches abstract molecules as attributed graphs and employ graph neural networks (GNN) for molecular representation learning, which have made remarkable achievements in molecular graph modeling. Albeit powerful, current models either are based on local aggregation operations and thus miss higher-order graph properties or focus on only node information without fully using the edge information. For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture. Unlike the previous transformer-style GNNs that treat molecules as fully connected graphs, we introduce a message diffusion mechanism to leverage the graph connectivity inductive bias and reduce the message enrichment explosion. Extensive experiments demonstrated that the proposed model obtained superior performances (around 4$\%$ on average) against state-of-the-art baselines on seven chemical property datasets (graph-level tasks) and two chemical shift datasets (node-level tasks). Further visualization studies also indicated a better representation capacity achieved by our model.
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning
Authors: Sayak Paul, Siddha Ganju
Date: 2021-07-18 00:00:00
Proceeding: NeurIPS Workshop AI4Scien 2021 12
Tasks: Disaster Response, Semantic Segmentation

Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Flood Detection competition tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, progressively improving accuracy. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the provided high confidence hand-labeled data and, generated pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combine the generated labels with the previously available high confidence hand-labeled dataset. This assimilated dataset is used for the next round of training ensemble models and the cyclical process is repeated until the performance improvement plateaus. We post process our results with Conditional Random Fields. Our approach sets a new state-of-the-art on the Sentinel-1 dataset with 0.7654 IoU, an impressive improvement over the 0.60 IoU baseline. Our method, which we release with all the code and models, can also be used as an open science benchmark for the Sentinel-1 dataset.
DiscFace: Minimum Discrepancy Learning for Deep Face Recognition
Authors: Insoo Kim, Seungju Han, Seong-Jin Park, Ji-won Baek, Jinwoo Shin, Jae-Joon Han, Changkyu Choi
Date: 2020-11-30 00:00:00
Proceeding: Asian Conference on Computer Vision (ACCV) 2020 11
Tasks: Face Recognition, Face Verification

Softmax-based learning methods have shown state-of-the-art performances on large-scale face recognition tasks. In this paper, we discover an important issue of softmax-based approaches: the sample features around the corresponding class weight are similarly penalized in the training phase even though their directions are different from each other. This directional discrepancy, i.e., process discrepancy leads to performance degradation at the evaluation phase. To mitigate the issue, we propose a novel training scheme, called minimum discrepancy learning that enforces directions of intra-class sample features to be aligned toward an optimal direction by using a single learnable basis. Furthermore, the single learnable basis facilitates disentangling the so-called class-invariant vectors from sample features, such that they are effective to train under class-imbalanced datasets.