pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying Effective Noise Sources in Coupled Resonating MEMS sensors
Authors: Vinayak Pachkawade
Date: 2021-09-10 00:00:00

This paper presents realistic system-level modelling and simulation of effective noise sources in a coupled resonating MEMS sensors. A governing set of differential equations are used to build a numerical model of a mechanical noise source in a coupled-resonator sensor. An effective thermomechanical noise is then quantified through the system-level simulation obtained via Simulink. On a similar note, various noise sources in electronic readout are identified and the contribution of each is quantified to determine an effective noise that stems from the electronic readout. A comparison between an effective mechanical and electronic noise aids in identifying the dominant noise source in a sensor system. A method to optimize the system noise floor for an amplitude-based the readout is presented. The proposed models present a variety of operating conditions, such as finite quality factor, varying coupled electric spring strength, and operation with in-phase and out-of-phase mode. The proposed models aim to determine the impact of fundamental noise processes and thus quantify the ultimate detection limit into a coupled resonating system used for various sensing applications.
Design of input for data-driven simulation with Hankel and Page matrices
Authors: Andrea Iannelli, Mingzhou Yin, Roy S. Smith
Date: 2021-09-10 00:00:00

The paper deals with the problem of designing informative input trajectories for data-driven simulation. First, the excitation requirements in the case of noise-free data are discussed and new weaker conditions, which assume the simulated input to be known in advance, are provided. Then, the case of noisy data trajectories is considered and an input design problem based on a recently proposed maximum likelihood estimator is formulated. A Bayesian interpretation is provided, and the implications of using Hankel and Page matrix representations are demonstrated. Numerical examples show the impact of the designed input on the predictive accuracy.
Observability-Blocking Control using Sparser and Regional Feedback for Network Synchronization Processes
Authors: Abdullah Al Maruf, Sandip Roy
Date: 2021-03-15 00:00:00
Tasks: Blocking

The design of feedback control systems to block observability in a network synchronization model, i.e. to make the dynamics unobservable from measurements at a subset of the network's nodes, is studied. First, a general design algorithm is presented for blocking observability at any specified group of $m$ nodes, by applying state feedback controls at $m+2$ specified actuation nodes. The algorithm is based on a method for eigenstructure assignment, which allows surgical modification of particular eigenvectors to block observability while preserving the remaining open-loop eigenstructure. Next, the topological structure of the network is exploited to reduce the number of controllers required for blocking observability; the result is based on blocking observability on the nodes associated with a vertex-cutset separating the actuation and measurement locations. Also, the design is modified to encompass regional feedback controls, which only use data from a subset of accessible nodes. The regional feedback design does not maintain the open-loop eigenstructure, but can be guaranteed to preserve stability via a time-scale argument. The results are illustrated with numerical examples.
STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks
Authors: Wataru Hashimoto, Kazumune Hashimoto, Shigemasa Takai
Date: 2021-09-10 00:00:00

In this paper, a method for learning a recurrent neural network (RNN) controller that maximizes the robustness of signal temporal logic (STL) specifications is presented. In contrast to previous methods, we consider synthesizing the RNN controller for which the user is able to select an STL specification arbitrarily from multiple STL specifications. To obtain such a controller, we propose a novel notion called STL2vec, which represents a vector representation of the STL specifications and exhibits their similarities. The construction of the STL2vec is useful since it allows us to enhance the efficiency and performance of the RNN controller. We validate our proposed method through the examples of the path planning problem.
Universality of noise-induced resilience restoration in spatially-extended ecological systems
Authors: Cheng Ma, Gyorgy Korniss, Boleslaw K. Szymanski, Jianxi Gao
Date: 2020-11-24 00:00:00

Many systems may switch to an undesired state due to internal failures or external perturbations, of which critical transitions toward degraded ecosystem states are a prominent example. Resilience restoration focuses on the ability of spatially-extended systems and the required time to recover to their desired states under stochastic environmental conditions. While mean-field approaches may guide recovery strategies by indicating the conditions needed to destabilize undesired states, these approaches are not accurately capturing the transition process toward the desired state of spatially-extended systems in stochastic environments. The difficulty is rooted in the lack of mathematical tools to analyze systems with high dimensionality, nonlinearity, and stochastic effects. We bridge this gap by developing new mathematical tools that employ nucleation theory in spatially-embedded systems to advance resilience restoration. We examine our approach on systems following mutualistic dynamics and diffusion models, finding that systems may exhibit single-cluster or multi-cluster phases depending on their sizes and noise strengths, and also construct a new scaling law governing the restoration time for arbitrary system size and noise strength in two-dimensional systems. This approach is not limited to ecosystems and has applications in various dynamical systems, from biology to infrastructural systems.
When Two Worlds Collide: Using Particle Physics Tools to Visualize the Limit Order Book
Authors: Marjolein E. Verhulst, Philippe Debie, Stephan Hageboeck, Joost M. E. Pennings, Cornelis Gardebroek, Axel Naumann, Paul van Leeuwen, Andres A. Trujillo-Barrera, Lorenzo Moneta
Date: 2021-09-10 00:00:00

We introduce a methodology to visualize the limit order book (LOB) using a particle physics lens. Open-source data-analysis tool ROOT, developed by CERN, is used to reconstruct and visualize futures markets. Message-based data is used, rather than snapshots, as it offers numerous visualization advantages. The visualization method can include multiple variables and markets simultaneously and is not necessarily time dependent. Stakeholders can use it to visualize high-velocity data to gain a better understanding of markets or effectively monitor markets. In addition, the method is easily adjustable to user specifications to examine various LOB research topics, thereby complementing existing methods.
Mid-wave infrared super-resolution imaging based on compressive calibration and sampling
Authors: Xiao-Peng Jin, Qing Zhao, Xue-Feng Liu, An-Dong Xiong
Date: 2021-09-08 00:00:00
Tasks: compressed sensing, Super-Resolution

Mid-wave infrared (MWIR) cameras for large number pixels are extremely expensive compared with their counterparts in visible light, thus, super-resolution imaging (SRI) for MWIR by increasing imaging pixels has always been a research hotspot in recent years. Over the last decade, with the extensively investigation of the compressed sensing (CS) method, focal plane array (FPA) based compressive imaging in MWIR developed rapidly for SRI. This paper presents a long-distance super-resolution FPA compressive imaging in MWIR with improved calibration method and imaging effect. By the use of CS, we measure and calculate the calibration matrix of optical system efficiently and precisely, which improves the imaging contrast and signal-to-noise ratio(SNR) compared with previous work. We also achieved the 4x4 times super-resolution reconstruction of the long-distance objects which reaches the limit of the system design in our experiment.
On the Size Control of the Hybrid Test for Predictive Ability
Authors: Deborah Kim
Date: 2020-08-05 00:00:00

We analyze theoretical properties of the hybrid test for superior predictability. We demonstrate with a simple example that the test may not be pointwise asymptotically of level $\alpha$ at commonly used significance levels and may lead to rejection rates over $11\%$ when the significance level $\alpha$ is $5\%$. Generalizing this observation, we provide a formal result that pointwise asymptotic invalidity of the hybrid test persists in a setting under reasonable conditions. As an easy alternative, we propose a modified hybrid test based on the generalized moment selection method and show that the modified test enjoys pointwise asymptotic validity. Monte Carlo simulations support the theoretical findings.
Fair Compensation
Authors: John E. Stovall
Date: 2021-09-09 00:00:00
Tasks: Fairness

We introduce a novel framework that considers how a firm could fairly compensate its workers. A firm has a group of workers, each of whom has varying productivities over a set of tasks. After assigning workers to tasks, the firm must then decide how to distribute its output to the workers. We first consider three compensation rules and various fairness properties they may satisfy. We show that among efficient and symmetric rules: the Egalitarian rule is the only rule that does not decrease a worker's compensation when every worker becomes weakly more productive (Group Productivity Monotonicity); the Shapley Value rule is the only rule that, for any two workers, equalizes the impact one worker has on the other worker's compensation (Balanced Impact); and the Individual Contribution rule is the only rule that is invariant to the removal of workers and their assigned tasks (Consistency). We introduce other rules and axioms, and relate each rule to each axiom.
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing
Authors: Jacopo Pegoraro, Jesus Omar Lacruz, Francesca Meneghello, Enver Bashirov, Michele Rossi, Joerg Widmer
Date: 2021-09-10 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Human Detection, Person Identification

In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated from reflected packets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of 94% and 90%, respectively.
Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
Authors: Austin Clyde, Ashka Shah, Max Zvyagin, Arvind Ramanathan, Rick Stevens
Date: 2021-09-10 00:00:00
Tasks: Drug Discovery, Graph Sampling

Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoods. We formalize scaffold based drug discovery into a network design. Utilizing docking data from SARS-CoV-2 virtual screening studies and JAK2 kinase assay data, we showcase how a scaffold based conception of chemical space is intuitive for design. Lastly, we highlight the utility of scaffold based networks for chemical space as a potential solution to the intractable enumeration problem of chemical space by working inductively on local neighborhoods.
Emerging vaccine-breakthrough SARS-CoV-2 variants
Authors: Rui Wang, Jiahui Chen, Yuta Hozumi, Changchuan Yin, Guo-Wei Wei
Date: 2021-09-09 00:00:00
Tasks: Topological Data Analysis

The recent global surge in COVID-19 infections has been fueled by new SARS-CoV-2 variants, namely Alpha, Beta, Gamma, Delta, etc. The molecular mechanism underlying such surge is elusive due to 4,653 non-degenerate mutations on the spike protein, which is the target of most COVID-19 vaccines. The understanding of the molecular mechanism of transmission and evolution is a prerequisite to foresee the trend of emerging vaccine-breakthrough variants and the design of mutation-proof vaccines and monoclonal antibodies. We integrate the genotyping of 1,489,884 SARS-CoV-2 genomes isolates, 130 human antibodies, tens of thousands of mutational data points, topological data analysis, and deep learning to reveal SARS-CoV-2 evolution mechanism and forecast emerging vaccine-escape variants. We show that infectivity-strengthening and antibody-disruptive co-mutations on the S protein RBD can quantitatively explain the infectivity and virulence of all prevailing variants. We demonstrate that Lambda is as infectious as Delta but is more vaccine-resistant. We analyze emerging vaccine-breakthrough co-mutations in 20 countries, including the United Kingdom, the United States, Denmark, Brazil, and Germany, etc. We envision that natural selection through infectivity will continue to be the main mechanism for viral evolution among unvaccinated populations, while antibody disruptive co-mutations will fuel the future growth of vaccine-breakthrough variants among fully vaccinated populations. Finally, we have identified the co-mutations that have the great likelihood of becoming dominant: [A411S, L452R, T478K], [L452R, T478K, N501Y], [V401L, L452R, T478K], [K417N, L452R, T478K], [L452R, T478K, E484K, N501Y], and [P384L, K417N, E484K, N501Y]. We predict they, particularly the last four, will break through existing vaccines. We foresee an urgent need to develop new vaccines that target these co-mutations.
Robust haplotype-resolved assembly of diploid individuals without parental data
Authors: Haoyu Cheng, Erich D. Jarvis, Olivier Fedrigo, Klaus-Peter Koepfli, Lara Urban, Neil J. Gemmell, Heng Li
Date: 2021-09-10 00:00:00

Routine single-sample haplotype-resolved assembly remains an unresolved problem. Here we describe a new algorithm that combines PacBio HiFi reads and Hi-C chromatin interaction data to produce a haplotype-resolved assembly without the sequencing of parents. Applied to human and other vertebrate samples, our algorithm consistently outperforms existing single-sample assembly pipelines and generates assemblies of comparable quality to the best pedigree-based assemblies.
HELIX: Data-driven characterization of Brazilian land snails
Authors: Marcelo N. Almeida, Rodolfo Alves de Oliveira, Luiz Olmes, Gustavo S. Semaan, Daniel de Oliveira, Lucio Santos, Marcos Bedo
Date: 2021-09-10 00:00:00
Tasks: feature selection

Decision-support systems benefit from hidden patterns extracted from digital information. In the specific domain of gastropod characterization, morphometrical measurements support biologists in the identification of land snail specimens. Although snails can be easily identified by their excretory and reproductive systems, the after-death mollusk body is commonly inaccessible because of either soft material deterioration or fossilization. This study aims at characterizing Brazilian land snails by morphometrical data features manually taken from the shells. In particular, we examined a dataset of shells by using different learning models that labeled snail specimens with a precision up to 97.5% (F1-Score = .975, CKC = .967 and ROC Area = .998). The extracted patterns describe similarities and trends among land snail species and indicates possible outliers physiologies due to climate traits and breeding. Finally, we show some morphometrical characteristics dominate others according to different feature selection biases. Those data-based patterns can be applied to fast land snail identification whenever their bodies are unavailable, as in the recurrent cases of lost shells in nature or private and museum collections.
Risk-Adjusted Valuation for Real Option Decisions
Authors: Carol Alexander, Xi Chen, Charles Ward
Date: 2021-09-10 00:00:00
Tasks: Decision Making

We model investor heterogeneity using different required returns on an investment and evaluate the impact on the valuation of an investment. By assuming no disagreement on the cash flows, we emphasize how risk preferences in particular, but also the costs of capital, influence a subjective evaluation of the decision to invest now or retain the option to invest in future. We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity. The investor's subjective assessment arises from their perceived misvaluation of the investment by the market, so projected cash flows are discounted using two different rates representing the investor's and the market's view. This liberates our model from perfect or imperfect hedging assumptions and instead, we are able to illustrate the hedging effect on the real option value when perceptions of risk premia diverge. During crises periods, delaying an investment becomes more valuable as the idiosyncratic risk of future cash flows increases, but the decision-maker may rush to invest too quickly when the risk level is exceptionally high. Our model verifies features established by classical real-option valuation models and provides many new insights about the importance of modelling divergences in decision-makers risk premia, especially during crisis periods. It also has many practical advantages because it requires no more parameter inputs than basic discounted cash flow approaches, such as the marketed asset disclaimer method, but the outputs are much richer. They allow for complex interactions between cost and revenue uncertainties as well as an easy exploration of the effects of hedgeable and un-hedgeable risks on the real option value. Furthermore, we provide fully-adjustable Python code in which all parameter values can be chosen by the user.
An Alternative Approach to Evaluate American Options Price Using HJM Approach
Authors: Kushantha Fernando, Vajira Manathunga
Date: 2021-09-10 00:00:00

Developments in finance industry and academic research has led to innovative financial products. This paper presents an alternative approach to price American options. Our approach utilizes famous \cite{heath1992bond} ("HJM") technique to calculate American option written on an asset. Originally, HJM forward modeling approach was introduced as an alternative approach to bond pricing in fixed income market. Since then, \cite{schweizer2008term} and \cite{carmona2008infinite} extended HJM forward modeling approach to equity market by capturing dynamic nature of volatility. They modeled the term structure of volatility, which is commonly observed in the market place as opposed to constant volatility assumption under Black - Scholes framework. Using this approach, we propose an alternative value function, a stopping criteria and a stopping time. We give an example of how to price American put option using proposed methodology.
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation
Authors: Mohammad Abuzar Hashemi, Zhanghexuan Li, Mihir Chauhan, Yan Shen, Abhishek Satbhai, Mir Basheer Ali, Mingchen Gao, Sargur Srihari
Date: 2021-09-04 00:00:00
Tasks: Caption Generation, Image Captioning, Image Generation, Representation Learning, Self-Supervised Learning

Pre-training visual and textual representations from large-scale image-text pairs is becoming a standard approach for many downstream vision-language tasks. The transformer-based models learn inter and intra-modal attention through a list of self-supervised learning tasks. This paper proposes LAViTeR, a novel architecture for visual and textual representation learning. The main module, Visual Textual Alignment (VTA) will be assisted by two auxiliary tasks, GAN-based image synthesis and Image Captioning. We also propose a new evaluation metric measuring the similarity between the learnt visual and textual embedding. The experimental results on two public datasets, CUB and MS-COCO, demonstrate superior visual and textual representation alignment in the joint feature embedding space
PERC: Posit Enhanced Rocket Chip
Authors: Arunkumar M Vaidyanathan, Sai Ganesh Bhairathi, Harshal Ganpatrao Hayatnagarkar
Date: 2020-05-29 00:00:00
Proceeding: Computer Architecture Research with RISC-V 2020 5

Balancing between precision and performance is a known trade-off in system design. Universal Number system attempts to dissolve this trade-off with its flexible arbitrary precision bound within fixed bit length. Its Type-III class, also known as Posits, has been especially introduced to be hardware implementation friendly. Posit is a dynamic floating-point representation that ensures better accuracy and precision using a system that minimizes the number of unusable representations and introduces a higher dynamic range which can serve as a substitute for the IEEE-754 2008 floating-point standard. In this paper, we share our experience with implementation and integration of a Posit Processing Unit (PPU) into the Rocket Chip SoC generator. This PPU replaces the IEEE-754 2008 FPU inside the chip, and supports both of RISC-V ISA floating-point extensions namely 'F' for single precision and 'D' for double precision using 32-bit and 64-bit posits respectively. We discuss various design choices that were available to us and the decisions made in this work. We elaborate our use of Chisel, a Scala embedded hardware construction DSL, to describe our design. Later we observe how various constructs in Chisel help not only to describe a product but also aids the description process in a robust, flexible and efficient manner. We further delve into how the design has been tested using a version of the RISC-V ISA test suite which has been modified for Posit arithmetic numbers. The paper also discusses the scope for future work that can be done, including a posit arithmetic accelerator and higher-level toolchain support for posits.