pwc-archive/papers-with-abstracts huggingface dataset:
Wideband mmWave Massive MIMO Channel Estimation and Localization
Authors: Shudi Weng, Fan Jiang, Henk Wymeersch
Date: 2022-09-18 00:00:00

Spatial wideband effects are known to affect channel estimation and localization performance in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Based on perturbation analysis, we show that the spatial wideband effect is in fact more pronounced than previously thought and significantly degrades performance, even at moderate bandwidths, if it is not properly considered in the algorithm design. We propose a novel channel estimation method based on multidimensional ESPRIT per subcarrier, combined with unsupervised learning for pairing across subcarriers, which shows significant performance gain over existing schemes under wideband conditions.
Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO
Authors: Minghui Wu, Zhen Gao, Yang Huang, Zhenyu Xiao, Derrick Wing Kwan Ng, Zhaoyang Zhang
Date: 2022-09-18 00:00:00

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme. Furthermore, for better precoding performance as well as lower computational complexity, a model-driven deep unfolding active precoding network (DFAPN) is also designed by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead, where the downlink pilot transmission, CSI feedback at the user equipments (UEs), and CSI reconstruction at the BS are modeled as an end-to-end neural network based on Transformer.
How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction
Authors: Yutong Sun, Jianhua Zhang, Li Yu, Zhen Zhang, Ping Zhang
Date: 2022-09-17 00:00:00
Tasks: Beam Prediction, Semantic Communication

In view of the propagation environment directly determining the channel fading, the application tasks can also be solved with the aid of the environment information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, this work aims to provide a new view of the environment from the semantic level, which defines the propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS) for diverse application tasks. The PESS is extracted oriented to the tasks with channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is presented in non-line-of-sight (NLOS). The PESS of environment features and graphs are given for the semantic actions of channel quality evaluation and target scatterer detection of maximum power, which can obtain 0.92 and 0.9 precision, respectively, and save over 87% of time cost.
Zigzag persistence for coral reef resilience using a stochastic spatial model
Authors: Robert A. McDonald, Rosanna Neuhausler, Martin Robinson, Laurel G. Larsen, Heather A. Harrington, Maria Bruna
Date: 2022-09-19 00:00:00
Tasks: Topological Data Analysis

A complex interplay between species governs the evolution of spatial patterns in ecology. An open problem in the biological sciences is characterising spatio-temporal data and understanding how changes at the local scale affect global dynamics/behaviour. Here, we extend a well-studied temporal mathematical model of coral reef dynamics to include stochastic and spatial interactions and generate data to study different ecological scenarios. We present descriptors to characterise patterns in heterogeneous spatio-temporal data surpassing spatially averaged measures. We apply these descriptors to simulated coral data and demonstrate the utility of two topological data analysis techniques--persistent homology and zigzag persistence--for characterising mechanisms of reef resilience. We show that the introduction of local competition between species leads to the appearance of coral clusters in the reef. We use our analyses to distinguish temporal dynamics stemming from different initial configurations of coral, showing that the neighbourhood composition of coral sites determines their long-term survival. Using zigzag persistence, we determine which spatial configurations protect coral from extinction in different environments. Finally, we apply this toolkit of multi-scale methods to empirical coral reef data, which distinguish spatio-temporal reef dynamics in different locations, and demonstrate the applicability to a range of datasets.
Pairwise independent correlation gap
Authors: Arjun Ramachandra, Karthik Natarajan
Date: 2022-09-18 00:00:00
Tasks: 4k

In this paper, we introduce the notion of a ``pairwise independent correlation gap'' for set functions with random elements. The pairwise independent correlation gap is defined as the ratio of the maximum expected value of a set function with arbitrary dependence among the elements with fixed marginal probabilities to the maximum expected value with pairwise independent elements with the same marginal probabilities. We show that for any nonnegative monotone submodular set function defined on $n$ elements, this ratio is upper bounded by $4/3$ in the following two cases: (a) $n = 3$ for all marginal probabilities and (b) all $n$ for small marginal probabilities (and similarly large marginal probabilities). This differs from the bound on the ``correlation gap'' which holds with mutual independence and showcases the fundamental difference between pairwise independence and mutual independence. We discuss the implication of the results with two examples and end the paper with a conjecture.
A Stochastic Model for the Early Stages of Highly Contagious Epidemics by using a State-Dependent Point Process
Authors: Jonathan A. Chávez Casillas
Date: 2022-09-18 00:00:00

The recent COVID-19 pandemic has shown that when the reproduction number is high and there are no proper measurements in place, the number of infected people can increase dramatically in a short time, producing a phenomenon that many stochastic SIR-like models cannot describe: overdispersion of the number of infected people (i.e., the variance of the number of infected people during any interval is very high compared to the average). To address this issue, in this paper we explore the possibility of modeling the total number of infections as a state dependent self-exciting point process. In this way, infections are not independent among themselves, but any infection will increase the likelihood of a new infection while also the number of currently infected and recovered individuals are included into determining the likelihood of new infections, Since long term simulation is extremely computationally intensive, exact expressions for the moments of the processes determining the number of infected and recovered individuals are computed, while also simulation algorithms for these state-dependent processes are provided.
Coarse-Grained Molecular Simulation of Extracellular Vesicles Squeezing for Drug Loading
Authors: Khayrul Islam, Meghdad Razizadeh, Yaling Liu
Date: 2022-09-19 00:00:00

In recent years, extracellular vesicles such have become promising carriers as the next-generation drug delivery platforms. Effective loading of exogenous cargos without compromising the extracellular vesicle membrane is a major challenge. Rapid squeezing through nanofluidic channels is a widely used approach to load exogenous cargoes into the EV through the nanopores generated temporarily on the membrane. However, the exact mechanism and dynamics of nanopores opening, as well as cargo loading through nanopores during the squeezing process remains unknown and is impossible to be visualized or quantified experimentally due to the small size of the EV and the fast transient process. This paper developed a systemic algorithm to simulate nanopore formation and predict drug loading during extracellular vesicle (EV) squeezing by leveraging the power of coarse-grain (CG) molecular dynamics simulations with fluid dynamics. The EV CG beads are coupled with implicit Fluctuating Lattice Boltzmann solvent. Effects of EV property and various squeezing test parameters, such as EV size, flow velocity, channel width, and length, on pore formation and drug loading efficiency are analyzed. Based on the simulation results, a phase diagram is provided as a design guidance for nanochannel geometry and squeezing velocity to generate pores on membrane without damaging the EV. This method can be utilized to optimize the nanofluidic device configuration and flow setup to obtain desired drug loading into EVs
HLA predictions from long sequence read alignments, streamed directly into HLAminer
Authors: René L. Warren
Date: 2022-09-19 00:00:00

The rapidly changing landscape of sequencing technologies brings new opportunities to genomics research. Longer sequence reads and higher sequence throughput coupled with ever-improving base accuracy and decreasing per-base cost is now making long reads suitable for analyzing polymorphic regions of the human genome, such as those of the human leucocyte antigen (HLA) gene complex. Here I present a simple protocol for predicting HLA signatures from whole genome shotgun (WGS) long sequencing reads, by directly streaming sequence alignments into HLAminer. The method is as simple as running minimap2, it scales with the number of sequences to align, and can be used with any read aligner capable of sam format output without the need to store bulky alignment files to disk. I show how the predictions are robust even with older and less [base] accurate WGS nanopore datasets and relatively low (10X) sequence coverage and present a step-by-step protocol to predict HLA class I and II genes from the long sequencing reads of modern third-generation technologies.
Self-Organized Criticality Explains Readiness Potential
Authors: Katsushi Kagaya, Tomoyuki Kubota, Kohei Nakajima
Date: 2022-09-19 00:00:00
Tasks: Relation

Readiness potential is a widely observed brain activity in several species including crayfish before the spontaneous behavioral initiation. However, it is poorly understood how this spontaneous activity is generated. The hypothesis that some specific, dedicated site is responsible for the spontaneity has been questioned. Here, by using intracellular recording and staining of the brain neurons in crayfish and modeling using the sandpile, which is the original model of self-organized criticality (SOC), we show that readiness potential can emerge everywhere in the brain because it is a SOC system. Despite the diversity in neurons and their morphology, brain neurons showed signatures of criticality and readiness potential. We find that the previously known readiness potential in a neuron is a consequence of the critical behavior of the entire network. Indeed, seemingly unrelated membrane potential activity in neurons in different animals can shape readiness potential when its time series are averaged after their alignment with respect to the spontaneous behavioral initiation. We show that the sandpile model not made for the potential, can form the premovement buildup activity similar to readiness potential. Scaling properties of the synaptic avalanches are in line with those of vertebrate species; thus, not only is the critical brain hypothesis supported in crayfish, but our findings might also provide a unified view of the basis of spontaneity in animal behavior.
In vivo whole-cell recording from morphologically identified mouse superior colliculus neurons
Authors: R. Broersen, G. J. Stuart
Date: 2022-09-19 00:00:00

In vivo whole-cell recording when combined with morphological characterization after biocytin labeling is a powerful technique to study subthreshold synaptic processing in cell-type-identified neuronal populations. Here, we provide a step-by-step procedure for performing whole-cell recordings in the superior colliculus of urethane-anesthetized mice, a major visual processing region in the rodent brain. Two types of visual stimulation methods are described. While we focus on superior colliculus neurons, this protocol is applicable to other brain areas.
Modelling the impact of vaccination on the COVID-19 pandemic in African countries
Authors: Dephney Mathebula, Abigail Amankwah, Kossi Amouzouvi, Kétévi A. Assamagan, Somiéalo Azote, Jesutofunmi Ayo Fajemisin, Jean Baptiste Fankam Fankam, Aluwani Guga, Moses Kamwela, Toivo S. Mabote, Mulape M Kanduza, Francisco Fenias Macucule, Azwinndini Muronga, Ann Njeri, Michael Oluwole, Cláudio Moisés Paulo
Date: 2022-09-19 00:00:00

The rapid development of vaccines to combat the spread of COVID-19 disease caused by the SARS-CoV-2 virus is a great scientific achievement. Before the development of the COVID-19 vaccines, most studies capitalized on the available data that did not include pharmaceutical measures. Such studies focused on the impact of non-pharmaceutical measures (e.g social distancing, sanitation, wearing of face masks, and lockdown) to study the spread of COVID-19. In this study, we used the SIDARTHE-V model which is an extension of the SIDARTHE model wherein we include vaccination roll outs. We studied the impact of vaccination on the severity (deadly nature) of the virus in African countries. Model parameters were extracted by fitting simultaneously the COVID-19 cumulative data of deaths, recoveries, active cases, and full vaccinations reported by the governments of Ghana, Kenya, Mozambique, Nigeria, South Africa, Togo, and Zambia. With countries having some degree of variation in their vaccination programs, we considered the impact of vaccination campaigns on the death rates in these countries. The study showed that the cumulative death rates declined drastically with the increased extent of vaccination in each country; while infection rates were sometimes increasing with the arrival of new waves, the death rates did not increase as we saw before vaccination.
Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types
Authors: Heyrim Cho, Allison L. Lewis, Kathleen M. Storey, Helen M. Byrne
Date: 2022-09-17 00:00:00
Tasks: Experimental Design

The Lotka-Volterra model is widely used to model interactions between two species. Here, we generate synthetic data mimicking competitive, mutualistic and antagonistic interactions between two tumor cell lines, and then use the Lotka-Volterra model to infer the interaction type. Structural identifiability of the Lotka-Volterra model is confirmed, and practical identifiability is assessed for three experimental designs: (a) use of a single data set, with a mixture of both cell lines observed over time, (b) a sequential design where growth rates and carrying capacities are estimated using data from experiments in which each cell line is grown in isolation, and then interaction parameters are estimated from an experiment involving a mixture of both cell lines, and (c) a parallel experimental design where all model parameters are fitted to data from two mixtures simultaneously. In addition to assessing each design for practical identifiability, we investigate how the predictive power of the model-i.e., its ability to fit data for initial ratios other than those to which it was calibrated-is affected by the choice of experimental design. The parallel calibration procedure is found to be optimal and is further tested on in silico data generated from a spatially-resolved cellular automaton model, which accounts for oxygen consumption and allows for variation in the intensity level of the interaction between the two cell lines. We use this study to highlight the care that must be taken when interpreting parameter estimates for the spatially-averaged Lotka-Volterra model when it is calibrated against data produced by the spatially-resolved cellular automaton model, since baseline competition for space and resources in the CA model may contribute to a discrepancy between the type of interaction used to generate the CA data and the type of interaction inferred by the LV model.
Multi-modal Protein Knowledge Graph Construction and Applications
Authors: Siyuan Cheng, Xiaozhuan Liang, Zhen Bi, Huajun Chen, Ningyu Zhang
Date: 2022-05-27 00:00:00
Tasks: graph construction

Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences, respectively, to GO terms and protein entities. ProteinKG65 is mainly dedicated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. We also illustrate the potential applications of ProteinKG65 with a prototype. Our dataset can be downloaded at https://w3id.org/proteinkg65.
SEC Form 13F-HR: Statistical investigation of trading imbalances and profitability analysis
Authors: Deborah Miori, Mihai Cucuringu
Date: 2022-09-19 00:00:00
Tasks: Form, Management

US Institutions with more than $100 million assets under management must disclose part of their long positions into the SEC Form 13F-HR on a quarterly basis. We consider the number of variations in holdings between consecutive reporting periods, and compute imbalances in buying versus selling behaviour for the assets under consideration. A significant opportunity for profit arises if an external investor is willing to trade contrarian to the 13F filings imbalances. Indeed, imbalances capture the amount of information already consumed in the market and the related trades tend to be inflated by crowding and herding. Betting on a relatively short-term movement of prices against the sign of imbalances results in a profitable strategy especially when using a time horizon between 21 and 42 trading days (corresponding to 1-2 calendar months) after each financial quarter ends.
Price Formation in Field Prediction Markets: the Wisdom in the Crowd
Authors: Frederik Bossaerts, Nitin Yadav, Peter Bossaerts, Chad Nash, Torquil Todd, Torsten Rudolf, Rowena Hutchins, Anne-Louise Ponsonby, Karl Mattingly
Date: 2022-09-19 00:00:00
Tasks: Prediction

Prediction markets are a popular, prominent, and successful structure for a collective intelligence platform. However the exact mechanism by which information known to the participating traders is incorporated into the market price is unknown. Kyle (1985) detailed a model for price formation in continuous auctions with information distributed heterogeneously amongst market participants. This paper demonstrates a novel method derived from the Kyle model applied to data from a field experiment prediction market. The method is able to identify traders whose trades have price impact that adds a significant amount of information to the market price. Traders who are not identified as informed in aggregate have price impact consistent with noise trading. Results are reproduced on other prediction market datasets. Ultimately the results provide strong evidence in favor of the Kyle model in a field market setting, and highlight an under-discussed advantage of prediction markets over alternative group forecasting mechanisms: that the operator of the market does not need to have information on the distribution of information amongst participating traders.
Asymptotic Normality for the Fourier spot volatility estimator in the presence of microstructure noise
Authors: Maria Elvira Mancino, Tommaso Mariotti, Giacomo Toscano
Date: 2022-09-19 00:00:00

The main contribution of the paper is proving that the Fourier spot volatility estimator introduced in [Malliavin and Mancino, 2002] is consistent and asymptotically efficient if the price process is contaminated by microstructure noise. Specifically, in the presence of additive microstructure noise we prove a Central Limit Theorem with the optimal rate of convergence $n^{1/8}$. The result is obtained without the need for any manipulation of the original data or bias correction. Moreover, we complete the asymptotic theory for the Fourier spot volatility estimator in the absence of noise, originally presented in [Mancino and Recchioni, 2015], by deriving a Central Limit Theorem with the optimal convergence rate $n^{1/4}$. Finally, we propose a novel feasible adaptive method for the optimal selection of the parameters involved in the implementation of the Fourier spot volatility estimator with noisy high-frequency data and provide support to its accuracy both numerically and empirically.
Analyzing the Impact of Sentiments of Scientific Articles on COVID-19 Vaccination Rates
Authors: Sean Eugene G. Chua, Kevin Anthony S. Sison
Date: 2022-08-29 00:00:00
Tasks: Articles, Sentiment Analysis

At the peak of the COVID-19 pandemic, numerous countries worldwide sought to mobilize vaccination campaigns in an attempt to curb the spread and number of deaths caused by the virus. One avenue in which information regarding COVID vaccinations is propagated is that of scientific articles, which provide a certain level of credibility regarding this. Hence, this increases the probability that people who view these articles would get vaccinated if the articles convey a positive message on vaccinations and conversely decreases the probability of vaccinations if the articles convey a negative message. This being said, this study aims to investigate the correlation between article sentiments and the corresponding increase or decrease in vaccinations in the United States. To do this, a lexicon-based sentiment analysis was performed in two steps: first, article content was scraped via a Python library called BeautifulSoup, and second, VADER was used to obtain the sentiment analysis scores for each article based on the scraped text content. Results suggest that there was a relatively weak correlation between the average sentiment score of articles and the corresponding increase or decrease in COVID vaccination rates in the US.