pwc-archive/papers-with-abstracts huggingface dataset:
A N-Path Receiver With Harmonic Response Suppression
Authors: Venkata Suresh Rayudu, Ki Yong Kim, Ranjit Gharpurey
Date: 2021-07-04 00:00:00
Tasks: Translation

A downconversion receiver employing a switch-based N-path filter with reduced harmonic response around the third- and fifth- LO harmonics is presented. The N-path filter is placed in a frequency-translation feedback loop that is effective at the 3rd and the 5th LO harmonics to mitigate harmonic downconversion. A pulse-width-modulated LO (PWM-LO) clocking scheme is used in the feedback upconverter to reduce the noise injected around the LO harmonic at the input of N-path downconverter. The compression resulting from blockers around the 3rd and the 5th LO harmonics is also suppressed as a result of reduced harmonic response. Compensation of peak frequency shift of the N-path response due to parasitic input capacitance is also described.
Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
Authors: Zhiyang Wang, Mark Eisen, Alejandro Ribeiro
Date: 2021-07-03 00:00:00

We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.
Deployment Optimization for Meta-material Based Internet of Things
Authors: Xu Liu, Jingzhi Hu, Hongliang Zhang, Boya Di, Lingyang Song
Date: 2021-07-03 00:00:00

In this paper, we propose a Meta-IoT system to achieve ubiquitous deployment and pervasive sensing for future Internet of Things (IoT). In such a system, sensors are composed of dedicated meta-materials whose frequency response of wireless signal is sensitive to environmental conditions. Therefore, we can obtain sensing results from reflected signals through Meta-IoT devices and the energy supplies for IoT devices can be removed. Nevertheless, in the Meta-IoT system, because the positions of the Meta-IoT devices decide the interference among the reflected signals, which may make the sensing results of different positions hard to be distinguished and the estimation function should integrate the results to reconstruct 3D distribution. It is a challenge to optimize the positions of the Meta-IoT devices to ensure sensing accuracy of 3D environmental conditions. To handle this challenge, we establish a mathematical model of Meta-IoT devices' sensing and transmission to calculate the interference between Meta-IoT devices. Then, an algorithm is proposed to jointly minimize the interference and reconstruction error by optimizing the Meta-IoT devices' position and the estimation function. The simulation results verify that the proposed system can obtain a 3D environmental conditions' distribution with high accuracy.
Meta-material Sensors based Internet of Things for 6G Communications
Authors: Jingzhi Hu, Hongliang Zhang, Boya Di, Kaigui Bian, Lingyang Song
Date: 2021-07-03 00:00:00

In the coming 6G communications, the internet of things (IoT) serves as a key enabler to collect environmental information and is expected to achieve ubiquitous deployment. However, it is challenging for traditional IoT sensors to meet this expectation because of their requirements of power supplies and frequent maintenance, which are due to their power-demanding sense and transmit modules. To address this challenge, we propose a meta-IoT sensing system, where the IoT sensors are based on specially designed meta-materials. The meta-IoT sensors achieve simultaneous sensing and transmission by physical reflection and require no power supplies. In order to design a meta-IoT sensing system with optimal sensing accuracy, we jointly consider the sensing and transmission of meta-IoT sensors and propose efficient algorithms to optimize the meta-IoT structure and the sensing function at the receiver. As an example, we apply the meta-IoT system to sensing environmental temperature and humidity levels. Simulation results show that by using the proposed algorithm, the sensing accuracy can be largely increased.
Optimal transport for model calibration
Authors: Ivan Guo, Gregoire Loeper, Jan Obloj, Shiyi Wang
Date: 2021-07-05 00:00:00
Tasks: model, Survey

We provide a survey of recent results on model calibration by Optimal Transport. We present the general framework and then discuss the calibration of local, and local-stochastic, volatility models to European options, the joint VIX/SPX calibration problem as well as calibration to some path-dependent options. We explain the numerical algorithms and present examples both on synthetic and market data.
Asymptotic Analysis of Risk Premia Induced by Law-Invariant Risk Measures
Authors: Thomas Knispel, Roger J. A. Laeven, Gregor Svindland
Date: 2021-07-04 00:00:00

We analyze the limiting behavior of the risk premium associated with the Pareto optimal risk sharing contract in an infinitely expanding pool of risks under a general class of law-invariant risk measures encompassing rank-dependent utility preferences. We show that the corresponding convergence rate is typically only $n^{1/2}$ instead of the conventional $n$, with $n$ the multiplicity of risks in the pool, depending upon the precise risk preferences.
Exploring generative atomic models in cryo-EM reconstruction
Authors: Ellen D. Zhong, Adam Lerer, Joseph H. Davis, Bonnie Berger
Date: 2021-07-03 00:00:00
Tasks: Protein Folding

Cryo-EM reconstruction algorithms seek to determine a molecule's 3D density map from a series of noisy, unlabeled 2D projection images captured with an electron microscope. Although reconstruction algorithms typically model the 3D volume as a generic function parameterized as a voxel array or neural network, the underlying atomic structure of the protein of interest places well-defined physical constraints on the reconstructed structure. In this work, we exploit prior information provided by an atomic model to reconstruct distributions of 3D structures from a cryo-EM dataset. We propose Cryofold, a generative model for a continuous distribution of 3D volumes based on a coarse-grained model of the protein's atomic structure, with radial basis functions used to model atom locations and their physics-based constraints. Although the reconstruction objective is highly non-convex when formulated in terms of atomic coordinates (similar to the protein folding problem), we show that gradient descent-based methods can reconstruct a continuous distribution of atomic structures when initialized from a structure within the underlying distribution. This approach is a promising direction for integrating biophysical simulation, learned neural models, and experimental data for 3D protein structure determination.
Discovering Sex and Age Implicator Edges in the Human Connectome
Authors: Laszlo Keresztes, Evelin Szogi, Balint Varga, Vince Grolmusz
Date: 2021-07-04 00:00:00

Determining important vertices in large graphs (e.g., Google's PageRank in the case of the graph of the World Wide Web) facilitated the construction of excellent web search engines, returning the most important hits corresponding to the submitted user queries. Interestingly, finding important edges -- instead of vertices -- in large graphs has received much less attention until now. Here we examine the human structural braingraph (or connectome), identified by diffusion magnetic resonance imaging (dMRI) methods, with edges connecting cortical and subcortical gray matter areas and weighted by fiber strengths, measured by the number of the discovered fiber tracts along the edge. We identify several "single" important edges in these braingraphs, whose high or low weights imply the sex or the age of the subject observed. We call these edges implicator edges since solely from their weight, one can infer the sex of the subject with more than 67 \% accuracy or their age group with more than 62\% accuracy. We argue that these brain connections are the most important ones characterizing the sex or the age of the subjects. Surprisingly, the edges implying the male sex are mostly located in the anterior parts of the brain, while those implying the female sex are mostly in the posterior regions. Additionally, most of the inter-hemispheric implicator edges are male ones, while the intra-hemispheric ones are predominantly female edges. Our pioneering method for finding the sex- or age implicator edges can also be applied for characterizing other biological and medical properties, including neurodegenerative- and psychiatric diseases besides the sex or the age of the subject, if large and high-quality neuroimaging datasets become available.
A look into the future of the COVID-19 pandemic in Europe: an expert consultation
Authors: Emil Nafis Iftekhar, Viola Priesemann, Rudi Balling, Simon Bauer, Philippe Beutels, André Calero Valdez, Sarah Cuschieri, Thomas Czypionka, Uga Dumpis, Enrico Glaab, Eva Grill, Claudia Hanson, Pirta Hotulainen, Peter Klimek, Mirjam Kretzschmar, Tyll Krüger, Jenny Krutzinna, Nicola Low, Helena Machado, Carlos Martins, Martin McKee, Sebastian Bernd Mohr, Armin Nassehi, Matjaž Perc, Elena Petelos, Martyn Pickersgill, Barbara Prainsack, Joacim Rocklöv, Eva Schernhammer, Anthony Staines, Ewa Szczurek, Sotirios Tsiodras, Steven Van Gucht, Peter Willeit
Date: 2021-07-04 00:00:00

How will the coronavirus disease 2019 (COVID-19) pandemic develop in the coming months and years? Based on an expert survey, we examine key aspects that are likely to influence COVID-19 in Europe. The future challenges and developments will strongly depend on the progress of national and global vaccination programs, the emergence and spread of variants of concern, and public responses to nonpharmaceutical interventions (NPIs). In the short term, many people are still unvaccinated, VOCs continue to emerge and spread, and mobility and population mixing is expected to increase over the summer. Therefore, policies that lift restrictions too much and too early risk another damaging wave. This challenge remains despite the reduced opportunities for transmission due to vaccination progress and reduced indoor mixing in the summer. In autumn 2021, increased indoor activity might accelerate the spread again, but a necessary reintroduction of NPIs might be too slow. The incidence may strongly rise again, possibly filling intensive care units, if vaccination levels are not high enough. A moderate, adaptive level of NPIs will thus remain necessary. These epidemiological aspects are put into perspective with the economic, social, and health-related consequences and thereby provide a holistic perspective on the future of COVID-19.
Study of conduction, block and reflection at the excitable tissues boundary in terms of the interval model of action potential
Authors: Sergii Kovalchuk
Date: 2021-07-03 00:00:00

Some mechanisms of cardiac arrhythmias can be presented as a composition of elementary acts of block and reflection on the contacts of homogeneous areas of the conducting tissue. For study this phenomena we use an axiomatic one-dimensional model of interaction of cells of excitable tissue. The model has four functional parameters that determine the functional states durations of the cell. We show that the cells of a homogeneous excitable tissue, depending on the ratio of the durations of the functional intervals, can operate in the mode of solitary waves conduction or in one of three modes of selfgeneration. It is proved that the propagation of a solitary wave through the boundary of homogeneous conducting tissues can be accompanied by a block or multiplex reflection. Block and reflection are unidirectional phenomena, and there are not compatible on the same boundary. Systematized rules of transmitting, block and reflection waves at the boundary of homogeneous conducting tissues open up new possibilities for design mechanisms of generation and analyzing complex heart rate patterns.
Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis
Authors: Gabriel W. Hassler, Brigida Gallone, Leandro Aristide, William L. Allen, Max R. Tolkoff, Andrew J. Holbrook, Guy Baele, Philippe Lemey, Marc A. Suchard
Date: 2021-07-02 00:00:00

Biological phenotypes are products of complex evolutionary processes in which selective forces influence multiple biological trait measurements in unknown ways. Phylogenetic factor analysis disentangles these relationships across the evolutionary history of a group of organisms. Scientists seeking to employ this modeling framework confront numerous modeling and implementation decisions, the details of which pose computational and replicability challenges. General and impactful community employment requires a data scientific analysis plan that balances flexibility, speed and ease of use, while minimizing model and algorithm tuning. Even in the presence of non-trivial phylogenetic model constraints, we show that one may analytically address latent factor uncertainty in a way that (a) aids model flexibility, (b) accelerates computation (by as much as 500-fold) and (c) decreases required tuning. We further present practical guidance on inference and modeling decisions as well as diagnosing and solving common problems in these analyses. We codify this analysis plan in an automated pipeline that distills the potentially overwhelming array of modeling decisions into a small handful of (typically binary) choices. We demonstrate the utility of these methods and analysis plan in four real-world problems of varying scales.
Preventing Covid59
Authors: Yaniv Erlich, Daniel Douek
Date: 2021-07-01 00:00:00

SARS-CoV-2 is the third betacoronavirus to enter the human population in the past 20 years, revealing a concerning pattern. Clearly, preventing a future pandemic from such viruses is a critical priority. Previous studies have shown that shRNAs can be powerful suppressors of RNA viruses in transgenic animals and substantially reduce transmission. Thus, we propose the introduction of anti-betacoronavirus shRNAs using CRISPR/CAS9 gene drive into the horseshoe bat population, the natural reservoir of those viruses, to combat this pandemic threat at its source. Importantly, our approach is not expected to create any harm to bats and can benefit other animals in the ecosystem that contract betacoronaviruses from bats. We map the ethical and the technical aspects and suggest guidelines for moving forward with this proposal.
In vitro reconstitution of microtubule dynamics and severing imaged by label-free interference reflection microscopy
Authors: Yin-wei Kuo, Jonathon Howard
Date: 2021-07-03 00:00:00

The dynamic architecture of the microtubule cytoskeleton is crucial for cell division, motility and morphogenesis. The dynamic properties of microtubules - growth, shrinkage, nucleation and severing - are regulated by an arsenal of microtubule-associated proteins (MAPs). The activities of many of these MAPs have been reconstituted in vitro using microscope assays. As an alternative to fluorescence microscopy, interference-reflection microscopy (IRM) has been introduced as an easy-to-use, wide-field imaging technique that allows label-free visualization of microtubules with high contrast and speed. IRM circumvents several problems associated with fluorescence microscopy including the high concentrations of tubulin required for fluorescent labeling, the potential perturbation of function caused by the fluorophores, and the risks of photodamage. IRM can be implemented on a standard epifluorescence microscope at low cost and can be combined with fluorescence techniques like total-internal-reflection-fluorescence (TIRF) microscopy. Here we describe the experimental procedure to image microtubule dynamics and severing using IRM, providing practical tips and guidelines to resolve possible experimental hurdles.
Vestibular Drop Attacks and Meniere's Disease as Results of Otolithic Membrane Damage -- A Numerical Model
Authors: Nicholas Senofsky, Justin Faber, Dolores Bozovic
Date: 2021-07-03 00:00:00
Tasks: Position

BACKGROUND: Meniere's Disease (MD) is a condition of the inner ear with symptoms affecting both vestibular and hearing functions. Some patients with MD experience vestibular drop attacks (VDAs), which are violent falls caused by spurious vestibular signals from the utricle and/or saccule. Recent surgical work has shown that patients who experience VDAs also show distrupted utricular otolithic membranes. OBJECTIVE: The objective of this study is to determine if otolithic membrane damage alone is sufficient to induce spurious vestibular signals, thus potentially eliciting VDAs and the vestibular dysfunction seen in patients with MD. METHODS: We use a previously developed numerical model to describe the nonlinear dynamics of an array of active, elastically coupled hair cells. We then reduce the coupling strength of a selected region of the membrane to model the effects of tissue damage. RESULTS: As we reduce the coupling strength, we observe large and abrupt spikes in hair bundle position. As bundle displacements from the equilibrium position have been shown to lead to depolarization of the hair-cell soma and hence trigger neural activity, this spontaneous activity could elicit false detection of a vestibular signal. CONCLUSIONS: The results of this numerical model suggest that otolithic membrane damage alone may be sufficient to induce VDAs and the vestibular dysfunction seen in patients with MD. Future experimental work is needed to confirm these results in vitro.
Deep calibration of the quadratic rough Heston model
Authors: Mathieu Rosenbaum, Jianfei Zhang
Date: 2021-07-04 00:00:00

The quadratic rough Heston model provides a natural way to encode Zumbach effect in the rough volatility paradigm. We apply multi-factor approximation and use deep learning methods to build an efficient calibration procedure for this model. We show that the model is able to reproduce very well both SPX and VIX implied volatilities. We typically obtain VIX option prices within the bid-ask spread and an excellent fit of the SPX at-the-money skew. Moreover, we also explain how to use the trained neural networks for hedging with instantaneous computation of hedging quantities.
A Note on Utility Maximization with Proportional Transaction Costs and Stability of Optimal Portfolios
Authors: Erhan Bayraktar, Christoph Czichowsky, Leonid Dolinskyi, Yan Dolinsky
Date: 2021-07-04 00:00:00
Tasks: Open-Ended Question Answering

The aim of this short note is to establish a limit theorem for the optimal trading strategies in the setup of the utility maximization problem with proportional transaction costs. This limit theorem resolves the open question from [4]. The main idea of our proof is to establish a uniqueness result for the optimal strategy. The proof of the uniqueness is heavily based on the dual approach which was developed recently in [6,7,8].
Explainability via Interactivity? Supporting Nonexperts' Sensemaking of Pretrained CNN by Interacting with Their Daily Surroundings
Authors: Chao Wang, Pengcheng An
Date: 2021-05-31 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

Current research on Explainable AI (XAI) heavily targets on expert users (data scientists or AI developers). However, increasing importance has been argued for making AI more understandable to nonexperts, who are expected to leverage AI techniques, but have limited knowledge about AI. We present a mobile application to support nonexperts to interactively make sense of Convolutional Neural Networks (CNN); it allows users to play with a pretrained CNN by taking pictures of their surrounding objects. We use an up-to-date XAI technique (Class Activation Map) to intuitively visualize the model's decision (the most important image regions that lead to a certain result). Deployed in a university course, this playful learning tool was found to support design students to gain vivid understandings about the capabilities and limitations of pretrained CNNs in real-world environments. Concrete examples of students' playful explorations are reported to characterize their sensemaking processes reflecting different depths of thought.
Unsupervised Video Summarization via Attention-Driven Adversarial Learning
Authors: Evlampios Apostolidis, Eleni Adamantidou, Alexandros I. Metsai, Vasileios Mezaris, Ioannis Patras
Date: 2019-12-24 00:00:00
Proceeding: MultiMedia Modeling (MMM) 2019 12
Tasks: Unsupervised Video Summarization, Video Summarization

This paper presents a new video summarization approach that integrates an attention mechanism to identify the significant parts of the video, and is trained unsupervisingly via generative adversarial learning. Starting from the SUM-GAN model, we first develop an improved version of it (called SUM-GAN-sl) that has a significantly reduced number of learned parameters, performs incremental training of the model’s components, and applies a stepwise label-based strategy for updating the adversarial part. Subsequently, we introduce an attention mechanism to SUM-GAN-sl in two ways: (i) by integrating an attention layer within the variational auto-encoder (VAE) of the architecture (SUM-GAN-VAAE), and (ii) by replacing the VAE with a deterministic attention auto-encoder (SUM-GAN-AAE). Experimental evaluation on two datasets (SumMe and TVSum) documents the contribution of the attention auto-encoder to faster and more stable training of the model, resulting in a significant performance improvement with respect to the original model and demonstrating the competitiveness of the proposed SUM-GAN-AAE against the state of the art.
A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization
Authors: Evlampios Apostolidis, Alexandros I. Metsai, Eleni Adamantidou, Vasileios Mezaris, Ioannis Patras
Date: 2019-10-21 00:00:00
Proceeding: AI4TV 2019 10
Tasks: Benchmarking, Unsupervised Video Summarization, Video Summarization

In this paper we present our work on improving the efficiency of adversarial training for unsupervised video summarization. Our starting point is the SUM-GAN model, which creates a representative summary based on the intuition that such a summary should make it possible to reconstruct a video that is indistinguishable from the original one. We build on a publicly available implementation of a variation of this model, that includes a linear compression layer to reduce the number of learned parameters and applies an incremental approach for training the different components of the architecture. After assessing the impact of these changes to the model's performance, we propose a stepwise, label-based learning process to improve the training efficiency of the adversarial part of the model. Before evaluating our model's efficiency, we perform a thorough study with respect to the used evaluation protocols and we examine the possible performance on two benchmarking datasets, namely SumMe and TVSum. Experimental evaluations and comparisons with the state of the art highlight the competitiveness of the proposed method. An ablation study indicates the benefit of each applied change on the model's performance, and points out the advantageous role of the introduced stepwise, label-based training strategy on the learning efficiency of the adversarial part of the architecture.