pwc-archive/papers-with-abstracts huggingface dataset:
Translocation of a single Arg9 peptide across a DOPC/DOPG(4:1) model membrane using the weighted ensemble method
Authors: Seungho Choe
Date: 2022-06-29 00:00:00

It is difficult to observe a spontaneous translocation of cell-penetrating peptides(CPPs) within a short time scale (e.g., a few hundred ns) in all-atom molecular dynamics(MD) simulations because the time required for the translocation of usual CPPs is on the order of minutes or so. In this work, we report a spontaneous translocation of a single Arg$_9$(R9) across a DOPC/DOPG(4:1) model membrane within an order of a few tens ns scale by using the weighted ensemble(WE) method. We identify how water molecules and the orientation of Arg$_9$ play a role in translocation. We also show how lipid molecules are transported along with Arg$_9$. In addition, we present free energy profiles of the translocation across the membrane using umbrella sampling and show that a single Arg$_9$ translocation is energetically unfavorable. We expect that the WE method can help study interactions of CPPs with various model membranes within MD simulation approaches.
A local continuum model of cell-cell adhesion
Authors: Carles Falcó, Ruth E. Baker, José A. Carrillo
Date: 2022-06-29 00:00:00
Tasks: Diversity, model

Cell-cell adhesion is one the most fundamental mechanisms regulating collective cell migration during tissue development, homeostasis and repair, allowing cell populations to self-organize and eventually form and maintain complex tissue shapes. Cells interact with each other via the formation of protrusions or filopodia and they adhere to other cells through binding of cell surface proteins. The resulting adhesive forces are then related to cell size and shape and, often, continuum models represent them by nonlocal attractive interactions. In this paper, we present a new continuum model of cell-cell adhesion which can be derived from a general nonlocal model in the limit of short-range interactions. This new model is local, resembling a system of thin-film type equations, with the various model parameters playing the role of surface tensions between different cell populations. Numerical simulations in one and two dimensions reveal that the local model maintains the diversity of cell sorting patterns observed both in experiments and in previously used nonlocal models. In addition, it also has the advantage of having explicit stationary solutions, which provides a direct link between the model parameters and the differential adhesion hypothesis.
Modeling in higher dimensions to improve diagnostic testing accuracy: theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays
Authors: Rayanne A. Luke, Anthony J. Kearsley, Nora Pisanic, Yukari C. Manabe, David L. Thomas, Christopher D. Heaney, Paul N. Patrone
Date: 2022-06-28 00:00:00
Tasks: Classification, Diagnostic

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the importance and challenges of correctly interpreting antibody test results. Identification of positive and negative samples requires a classification strategy with low error rates, which is hard to achieve when the corresponding measurement values overlap. Additional uncertainty arises when classification schemes fail to account for complicated structure in data. We address these problems through a mathematical framework that combines high dimensional data modeling and optimal decision theory. Specifically, we show that appropriately increasing the dimension of data better separates positive and negative populations and reveals nuanced structure that can be described in terms of mathematical models. We combine these models with optimal decision theory to yield a classification scheme that better separates positive and negative samples relative to traditional methods such as confidence intervals (CIs) and receiver operating characteristics. We validate the usefulness of this approach in the context of a multiplex salivary SARS-CoV-2 immunoglobulin G assay dataset. This example illustrates how our analysis: (i) improves the assay accuracy (e.g. lowers classification errors by up to 42 % compared to CI methods); (ii) reduces the number of indeterminate samples when an inconclusive class is permissible (e.g. by 40 % compared to the original analysis of the example multiplex dataset); and (iii) decreases the number of antigens needed to classify samples. Our work showcases the power of mathematical modeling in diagnostic classification and highlights a method that can be adopted broadly in public health and clinical settings.
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet
Authors: Ziyan Chen, Zhentao Liu, Chenyu Hu, Heng Wu, Jianrong Wu, Jinda Lin, Zhishen Tong, Hong Yu, Shensheng Han
Date: 2022-06-28 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

Spectral camera based on ghost imaging via sparsity constraints (GISC spectral camera) obtains three-dimensional (3D) hyperspectral information with two-dimensional (2D) compressive measurements in a single shot, which has attracted much attention in recent years. However, its imaging quality and real-time performance of reconstruction still need to be further improved. Recently, deep learning has shown great potential in improving the reconstruction quality and reconstruction speed for computational imaging. When applying deep learning into GISC spectral camera, there are several challenges need to be solved: 1) how to deal with the large amount of 3D hyperspectral data, 2) how to reduce the influence caused by the uncertainty of the random reference measurements, 3) how to improve the reconstructed image quality as far as possible. In this paper, we present an end-to-end V-DUnet for the reconstruction of 3D hyperspectral data in GISC spectral camera. To reduce the influence caused by the uncertainty of the measurement matrix and enhance the reconstructed image quality, both differential ghost imaging results and the detected measurements are sent into the network's inputs. Compared with compressive sensing algorithm, such as PICHCS and TwIST, it not only significantly improves the imaging quality with high noise immunity, but also speeds up the reconstruction time by more than two orders of magnitude.
Dynamic CoVaR Modeling and Estimation
Authors: Timo Dimitriadis, Yannick Hoga
Date: 2022-06-28 00:00:00
Tasks: regression

The popular systemic risk measure CoVaR (conditional Value-at-Risk) and its variants are widely used in economics and finance. In this article, we propose joint dynamic forecasting models for the Value-at-Risk (VaR) and CoVaR. The CoVaR version we consider is defined as a large quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank's shares) being in distress. We introduce a two-step M-estimator for the model parameters drawing on recently proposed bivariate scoring functions for the pair (VaR, CoVaR). We prove consistency and asymptotic normality of our parameter estimator and analyze its finite-sample properties in simulations. Finally, we apply a specific subclass of our dynamic forecasting models, which we call CoCAViaR models, to log-returns of large US banks. A formal forecast comparison shows that our CoCAViaR models generate CoVaR predictions which are superior to forecasts issued from current benchmark models.
Schools as a Safety-net: The Impact of School Closures and Reopenings on Rates of Reporting of Violence Against Children
Authors: Damian Clarke, Pilar Larroulet, Daniel Pailañir, Daniela Quintana
Date: 2022-06-29 00:00:00

Ongoing school closures and gradual reopenings have been occurring since the beginning of the COVID-19 pandemic. One substantial cost of school closure is breakdown in channels of reporting of violence against children, in which schools play a considerable role. There is, however, little evidence documenting how widespread such a breakdown in reporting of violence against children has been, and scant evidence exists about potential recovery in reporting as schools re-open. We study all formal criminal reports of violence against children occurring in Chile up to December 2021, covering physical, psychological, and sexual violence. This is combined with administrative records of school re-opening, attendance, and epidemiological and public health measures. We observe sharp declines in violence reporting at the moment of school closure across all classes of violence studied. Estimated reporting declines range from -17% (rape), to -43% (sexual abuse). While reports rise with school re-opening, recovery of reporting rates is slow. Conservative projections suggest that reporting gaps remained into the final quarter of 2021, nearly two years after initial school closures. Our estimates suggest that school closure and incomplete re-opening resulted in around 2,800 `missing' reports of intra-family violence, 2,000 missing reports of sexual assault, and 230 missing reports of rape against children, equivalent to between 10-25 weeks of reporting in baseline periods. The immediate and longer term impacts of school closures account for between 40-70% of `missing' reports in the post-COVID period.
Environmental-Social-Governance Preferences and Investments in Crypto-Assets
Authors: Pavel Ciaian, Andrej Cupak, Pirmin Fessler, d'Artis Kancs
Date: 2022-06-29 00:00:00

Individuals invest in Environmental-Social-Governance (ESG)-assets not only because of (higher) expected returns but also driven by ethical and social considerations. Less is known about ESG-conscious investor subjective beliefs about crypto-assets and how do these compare to traditional assets. Controversies surrounding the ESG footprint of certain crypto-asset classes - mainly on grounds of their energy-intensive crypto mining - offer a potentially informative object of inquiry. Leveraging a unique representative household finance survey for the Austrian population, we examine whether investors' ESG preferences can explain cross-sectional differences in individual portfolio exposure to crypto-assets. We find a strong association between investors' ESG preferences and the crypto-investment exposure. The ESG-conscious investor attention is higher for crypto-assets compared to traditional asset classes such as bonds and shares.
The benefits of coordination in (over)adaptive virtual teams
Authors: Darío Blanco-Fernández, Stephan Leitner, Alexandra Rausch
Date: 2022-06-29 00:00:00

The emergence of new organizational forms--such as virtual teams--has brought forward some challenges for teams. One of the most relevant challenges is coordinating the decisions of team members who work from different time zones. Intuition suggests that task performance should improve if the team members' decisions are coordinated. However, previous research suggests that the effect of coordination on task performance is ambiguous. Specifically, the effect of coordination on task performance depends on aspects such as the team members' learning and the changes in team composition over time. This paper aims to understand how individual learning and team composition moderate the relationship between coordination and task performance. We implement an agent-based modeling approach based on the NK-framework to fulfill our research objective. Our results suggest that both factors have moderating effects. Specifically, we find that excessively increasing individual learning is harmful for the task performance of fully autonomous teams, but less detrimental for teams that coordinate their decisions. In addition, we find that teams that coordinate their decisions benefit from changing their composition in the short-term, but fully autonomous teams do not. In conclusion, teams that coordinate their decisions benefit more from individual learning and dynamic composition than teams that do not coordinate. Nevertheless, we should note that the existence of moderating effects does not imply that coordination improves task performance. Whether coordination improves task performance depends on the interdependencies between the team members' decisions.
Reducing US Biofuels Requirements Mitigates Short-term Impacts of Global Population and Income Growth on Agricultural Environmental Outcomes
Authors: David R. Johnson, Nathan B. Geldner, Jing Liu, Uris Lantz Baldos, Thomas Hertel
Date: 2022-06-28 00:00:00

Biobased energy, particularly corn starch-based ethanol and other liquid renewable fuels, are a major element of federal and state energy policies in the United States. These policies are motivated by energy security and climate change mitigation objectives, but corn ethanol does not substantially reduce greenhouse gas emissions when compared to petroleum-based fuels. Corn production also imposes substantial negative externalities (e.g., nitrogen leaching, higher food prices, water scarcity, and indirect land use change). In this paper, we utilize a partial equilibrium model of corn-soy production and trade to analyze the potential of reduced US demand for corn as a biobased energy feedstock to mitigate increases in nitrogen leaching, crop production and land use associated with growing global populations and income from 2020 to 2050. We estimate that a 23% demand reduction would sustain land use and nitrogen leaching below 2020 levels through the year 2025, and a 41% reduction would do so through 2030. Outcomes are similar across major watersheds where corn and soy are intensively farmed.
Spinoza, Leibniz, Kant, and Weyl
Authors: Michael H. Freedman
Date: 2022-06-29 00:00:00

The paper describes a funding mechanism called Quadratic Finance (QF) and deploys a bit of calculus to show that within a very clean and simple linear model QF maximizes social utility. They differentiate the social utility function. The mathematical content of this note is that by taking one further derivative, one may also deduce that QF is the unique solution.
Nextformer: A ConvNeXt Augmented Conformer For End-To-End Speech Recognition
Authors: Yongjun Jiang, Jian Yu, Wenwen Yang, Bihong Zhang, Yanfeng Wang
Date: 2022-06-29 00:00:00
Tasks: speech-recognition, Speech Recognition

Conformer models have achieved state-of-the-art(SOTA) results in end-to-end speech recognition. However Conformer mainly focuses on temporal modeling while pays less attention on time-frequency property of speech feature. In this paper we augment Conformer with ConvNeXt and propose Nextformer structure. We use stacks of ConvNeXt block to replace the commonly used subsampling module in Conformer for utilizing the information contained in time-frequency speech feature. Besides, we insert an additional downsampling module in middle of Conformer layers to make our model more efficient and accurate. We conduct experiments on two opening datasets, AISHELL-1 and WenetSpeech. On AISHELL-1, compared to Conformer baselines, Nextformer obtains 7.3% and 6.3% relative CER reduction in non-streaming and streaming mode respectively, and on a much larger WenetSpeech dataset, Nextformer gives 5.0%~6.5% and 7.5%~14.6% relative CER reduction in non-streaming and streaming mode, while keep the computational cost FLOPs comparable to Conformer. To the best of our knowledge, the proposed Nextformer model achieves SOTA results on AISHELL-1(CER 4.06%) and WenetSpeech(CER 7.56%/11.29%).
DeepMB: Deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound
Authors: Christoph Dehner, Guillaume Zahnd, Vasilis Ntziachristos, Dominik Jüstel
Date: 2022-06-29 00:00:00
Tasks: Image Reconstruction

Multispectral optoacoustic tomography (MSOT) is a high-resolution functional imaging modality that can non-invasively access a broad range of pathophysiological phenomena by quantifying the contrast of endogenous chromophores in tissue. Real-time imaging is imperative to translate MSOT into clinical imaging, visualize dynamic pathophysiological changes associated with disease progression, and enable in situ diagnoses. Model-based reconstruction affords state-of-the-art optoacoustic images; however, the image quality provided by model-based reconstruction remains inaccessible during real-time imaging because the algorithm is iterative and computationally demanding. Deep learning affords faster reconstruction, but the lack of ground truth training data can lead to reduced image quality for in vivo data. We introduce a framework, termed DeepMB, that achieves accurate optoacoustic image reconstruction for arbitrary input data in 31 ms per image by expressing model-based reconstruction with a deep neural network. DeepMB facilitates accurate generalization to experimental test data through training on signals synthesized from real-world images and ground truth images generated by model-based reconstruction. The framework affords in-focus images for a broad range of anatomical locations because it supports dynamic adjustment of the reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible with the data rates and image sizes of modern multispectral optoacoustic tomography scanners. We evaluate DeepMB on a diverse dataset of in vivo images and demonstrate that the framework reconstructs images 1000 times faster than the iterative model-based reference method while affording near-identical image qualities. Accurate and real-time image reconstructions with DeepMB can enable full access to the high-resolution and multispectral contrast of handheld optoacoustic tomography.
GreenBIQA: A Lightweight Blind Image Quality Assessment Method
Authors: Zhanxuan Mei, Yun-Cheng Wang, Xingze He, C. -C. Jay Kuo
Date: 2022-06-29 00:00:00
Tasks: Blind Image Quality Assessment, feature selection, Image Quality Assessment

Deep neural networks (DNNs) achieve great success in blind image quality assessment (BIQA) with large pre-trained models in recent years. Their solutions cannot be easily deployed at mobile or edge devices, and a lightweight solution is desired. In this work, we propose a novel BIQA model, called GreenBIQA, that aims at high performance, low computational complexity and a small model size. GreenBIQA adopts an unsupervised feature generation method and a supervised feature selection method to extract quality-aware features. Then, it trains an XGBoost regressor to predict quality scores of test images. We conduct experiments on four popular IQA datasets, which include two synthetic-distortion and two authentic-distortion datasets. Experimental results show that GreenBIQA is competitive in performance against state-of-the-art DNNs with lower complexity and smaller model sizes.
Correctly Modeling TX and RX Chain in (Distributed) Massive MIMO -- New Fundamental Insights on Coherency
Authors: Ronald Nissel
Date: 2022-06-29 00:00:00

This letter shows that the TX and RX models commonly used in literature for downlink (distributed) massive MIMO are inaccurate, leading also to inaccurate conclusions. In particular, the Local Oscillator (LO) effect should be modeled as $+\varphi$ in the transmitter chain and $-\varphi$ in the receiver chain, i.e., different signs. A common misconception in literature is to use the same sign for both chains. By correctly modeling TX and RX chain, one realizes that the LO phases are included in the reciprocity calibration and whenever the LO phases drift apart, a new reciprocity calibration becomes necessary (the same applies to time drifts). Thus, free-running LOs and the commonly made assumption of perfect reciprocity calibration (to enable blind DL channel estimation) are both not that useful, as they would require too much calibration overhead. Instead, the LOs at the base stations should be locked and relative reciprocity calibration in combination with downlink demodulation reference symbols should be employed.
Generative Adversarial Networks for Pseudo-Radio-Signal Synthesis
Authors: Haythem Chaker, Soumaya Hamouda, Nicola Michailow
Date: 2022-06-29 00:00:00
Tasks: Generative Adversarial Network

For many wireless communication applications, traffic pattern modeling of radio signals combined with channel effects is much needed. While analytical models are used to capture these phenomena, real world non-linear effects (e.g. device responses, interferences, distortions, noise) and especially the combination of such effects can be difficult to capture by these models. This is simply due to their complexity and degrees of freedom which can be hard to explicitize in compact expressions. In this paper, we propose a more model-free approach to jointly approximate an end-to-end black-boxed wireless communication scenario using software-defined radio platforms and optimize for an efficient synthesis of subsequently similar 'pseudo-radio-signals'. More precisely, we implement a generative adversarial network based solution that automatically learns radio properties from recorded prototypes in specific scenarios. This allows for a high degree of expressive freedom. Numerical results show that the prototypes' traffic patterns jointly with channel effects are learned without the introduction of assumptions about the scenario or the simplification to a parametric model.
Robust Unlimited Sampling Beyond Modulo
Authors: Eyar Azar, Satish Mulleti, Yonina C. Eldar
Date: 2022-06-29 00:00:00

Analog to digital converters (ADCs) act as a bridge between the analog and digital domains. Two important attributes of any ADC are sampling rate and its dynamic range. For bandlimited signals, the sampling should be above the Nyquist rate. It is also desired that the signals' dynamic range should be within that of the ADC's; otherwise, the signal will be clipped. Nonlinear operators such as modulo or companding can be used prior to sampling to avoid clipping. To recover the true signal from the samples of the nonlinear operator, either high sampling rates are required or strict constraints on the nonlinear operations are imposed, both of which are not desirable in practice. In this paper, we propose a generalized flexible nonlinear operator which is sampling efficient. Moreover, by carefully choosing its parameters, clipping, modulo, and companding can be seen as special cases of it. We show that bandlimited signals are uniquely identified from the nonlinear samples of the proposed operator when sampled above the Nyquist rate. Furthermore, we propose a robust algorithm to recover the true signal from the nonlinear samples. We show that our algorithm has the lowest mean-squared error while recovering the signal for a given sampling rate, noise level, and dynamic range of the compared to existing algorithms. Our results lead to less constrained hardware design to address the dynamic range issues while operating at the lowest rate possible.
Power-efficient Joint Link Selection and Multi-hop Routing for Throughput Maximization in UAV Assisted FANETs
Authors: Payal Mittal, Santosh Shah, Anirudh Agarwal
Date: 2022-06-29 00:00:00

This paper considers a multi-UAV network with a ground station (GS) that uses multi-hop relaying structure for data transmission in a power-efficient manner. The objective is to investigate the best possible multi-hop routing structure for data transmission to maximize the overall network throughput of a flying ad-hoc network (FANET) of UAVs. We formulate a problem to jointly optimize the multi-hop routing structure with the communication link selection for a given power budget so that the overall network throughput can be maximized. It appears that the formulated problem belongs to a class of nonconvex and integer optimization problems, thus making it NP-hard. To solve this problem efficiently, it is decoupled into two subproblems $\textbf{i)}$ power allocation with known Bellman Ford-based multi-hop routing structure and $\textbf{ii)}$ link selection problem. Further, these two subproblems are independently converted into convex problems by relaxation and solved in tandem for the best suboptimal solution to the main problem. Simulation results indicate that the proposed multi-hop routing schemes can achieve a significant improvement in network throughput compared to the other benchmark scheme.