pwc-archive/papers-with-abstracts huggingface dataset:
Trustworthy Federated Learning via Blockchain
Authors: Zhanpeng Yang, Yuanming Shi, Yong Zhou, Zixin Wang, Kai Yang
Date: 2022-08-13 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Federated Learning, Privacy Preserving, reinforcement-learning, Reinforcement Learning (RL)

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms.
Energy Shaping Control of a Muscular Octopus Arm Moving in Three Dimensions
Authors: Heng-Sheng Chang, Udit Halder, Chia-Hsien Shih, Noel Naughton, Mattia Gazzola, Prashant G. Mehta
Date: 2022-09-09 00:00:00

Flexible octopus arms exhibit an exceptional ability to coordinate large numbers of degrees of freedom and perform complex manipulation tasks. As a consequence, these systems continue to attract the attention of biologists and roboticists alike. In this paper, we develop a three-dimensional model of a soft octopus arm, equipped with biomechanically realistic muscle actuation. Internal forces and couples exerted by all major muscle groups are considered. An energy shaping control method is described to coordinate muscle activity so as to grasp and reach in 3D space. Key contributions of this paper are: (i) modeling of major muscle groups to elicit three-dimensional movements; (ii) a mathematical formulation for muscle activations based on a stored energy function; and (iii) a computationally efficient procedure to design task-specific equilibrium configurations, obtained by solving an optimization problem in the Special Euclidean group SE(3). Muscle controls are then iteratively computed based on the co-state variable arising from the solution of the optimization problem. The approach is numerically demonstrated in the physically accurate software environment Elastica. Results of numerical experiments mimicking observed octopus behaviors are reported.
Longitudinal Acoustic Speech Tracking Following Pediatric Traumatic Brain Injury
Authors: Camille Noufi, Adam C. Lammert, Daryush D. Mehta, James R. Williamson, Gregory Ciccarelli, Douglas Sturim, Jordan R. Green, Thomas F. Quatieri, Thomas F. Campbell
Date: 2022-09-09 00:00:00

Recommendations for common outcome measures following pediatric traumatic brain injury (TBI) support the integration of instrumental measurements alongside perceptual assessment in recovery and treatment plans. A comprehensive set of sensitive, robust and non-invasive measurements is therefore essential in assessing variations in speech characteristics over time following pediatric TBI. In this article, we study the changes in the acoustic speech patterns of a pediatric cohort of ten subjects diagnosed with severe TBI. We extract a diverse set of both well-known and novel acoustic features from child speech recorded throughout the year after the child produced intelligible words. These features are analyzed individually and by speech subsystem, within-subject and across the cohort. As a group, older children exhibit highly significant (p<0.01) increases in pitch variation and phoneme diversity, shortened pause length, and steadying articulation rate variability. Younger children exhibit similar steadied rate variability alongside an increase in formant-based articulation complexity. Correlation analysis of the feature set with age and comparisons to normative developmental data confirm that age at injury plays a significant role in framing the recovery trajectory. Nearly all speech features significantly change (p<0.05) for the cohort as a whole, confirming that acoustic measures supplementing perceptual assessment are needed to identify efficacious treatment targets for speech therapy following TBI.
Streaming Target-Speaker ASR with Neural Transducer
Authors: Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Takahiro Shinozaki
Date: 2022-09-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Extraction, speech-recognition, Speech Recognition, Speech Separation

Although recent advances in deep learning technology have boosted automatic speech recognition (ASR) performance in the single-talker case, it remains difficult to recognize multi-talker speech in which many voices overlap. One conventional approach to tackle this problem is to use a cascade of a speech separation or target speech extraction front-end with an ASR back-end. However, the extra computation costs of the front-end module are a critical barrier to quick response, especially for streaming ASR. In this paper, we propose a target-speaker ASR (TS-ASR) system that implicitly integrates the target speech extraction functionality within a streaming end-to-end (E2E) ASR system, i.e. recurrent neural network-transducer (RNNT). Our system uses a similar idea as adopted for target speech extraction, but implements it directly at the level of the encoder of RNNT. This allows TS-ASR to be realized without placing extra computation costs on the front-end. Note that this study presents two major differences between prior studies on E2E TS-ASR; we investigate streaming models and base our study on Conformer models, whereas prior studies used RNN-based systems and considered only offline processing. We confirm in experiments that our TS-ASR achieves comparable recognition performance with conventional cascade systems in the offline setting, while reducing computation costs and realizing streaming TS-ASR.
Open-loop contraction design
Authors: Jin Gyu Lee, Thiago B. Burghi, Rodolphe Sepulchre
Date: 2022-09-09 00:00:00
Tasks: parameter estimation

Given a non-contracting trajectory of a nonlinear system, we consider the question of designing an input perturbation that makes the perturbed trajectory contracting. This paper stresses the analogy of this question with the classical question of feedback stabilization. In particular, it is shown that the existence of an output variable that ensures contraction of the inverse system facilitates the design of a contracting input perturbation. We illustrate the relevance of this question in parameter estimation.
Restoring AC Power Flow Feasibility from Relaxed and Approximated Optimal Power Flow Models
Authors: Babak Taheri, Daniel K. Molzahn
Date: 2022-09-09 00:00:00
Tasks: State Estimation

To address computational challenges associated with power flow nonconvexities, significant research efforts over the last decade have developed convex relaxations and approximations of optimal power flow (OPF) problems. However, benefits associated with the convexity of these relaxations and approximations can have tradeoffs in terms of solution accuracy since they may yield voltage phasors that are inconsistent with the power injections and line flows, limiting their usefulness for some applications. Inspired by state estimation (SE) techniques, this paper proposes a new method for obtaining an AC power flow feasible point from the solution to a relaxed or approximated optimal power flow (OPF) problem. By treating the inconsistent voltage phasors, power injections, and line flows analogously to noisy measurements in a state estimation algorithm, the proposed method yields power injections and voltage phasors that are feasible with respect to the AC power flow equations while incorporating information from many quantities in the solution to a relaxed or approximated OPF problem. We improve this method by adjusting weighting terms with an approach inspired by algorithms used to train machine learning models. We demonstrate the proposed method using several relaxations and approximations. The results show up to several orders of magnitude improvement in accuracy over traditional methods.
Integrated storage assignment for an e-grocery fulfilment centre: Accounting for day-of-week demand patterns
Authors: David Winkelmann, Frederik Tolkmitt, Matthias Ulrich, Michael Römer
Date: 2022-09-08 00:00:00

In this paper, we deal with a storage assignment problem arising in a fulfilment centre of a major European e-grocery retailer. The centre can be characterised as a hybrid warehouse consisting of a highly efficient and partially automated fast-picking area designed as a pick-and-pass system with multiple stations, and a picker-to-parts area. The storage assignment problem considered in this paper comprises the decisions to select the products to be allocated to the fast-picking area, the assignment of the products to picking stations and the determination of a shelf within the assigned station. The objective is to achieve a high level of picking efficiency while respecting station workload balancing and precedence order constraints. We propose to solve this three-level problem using an integrated MILP model. In computational experiments with real-world data, we show that using the proposed integrated approach yields significantly better results than a sequential approach in which the selection of products to be included in the fast-picking area is solved before assigning station and shelf. Furthermore, we provide an extension to the integrated storage assignment model that explicitly accounts for within-week demand variation. In a set of experiments with day-of-week-dependent demands we show that while a storage assignment that is based on average demand figures tends to exhibit a highly imbalanced workload on certain days of the week, the augmented model yields storage assignments that are well balanced on each day of the week without compromising the quality of the solutions in terms of picking efficiency.
Singular Secular Kuznets-like Period Realized Amid Industrial Transformation in US FDA Medical Devices: A Perspective on Innovation from 1976 to 2020
Authors: Iraj Daizadeh
Date: 2022-07-11 00:00:00
Tasks: Marketing

Introduction: Since inception, the United States (US) Food and Drug Administration (FDA) has kept a robust record of regulated medical devices (MDs). Based on these data, can we gain insight into the innovation dynamics of the industry, including the potential for industrial transformation? Areas Covered: Using Premarket Notifications (PMNs) and Approvals (PMAs) data, it is shown that from 1976 to 2020 the total composite (PMN + PMA) metric follows a single secular period: 20.5 years (applications peak-to-peak: 1992-2012; trough: 2002) and 26.5 years (registrations peak to peak: 1992 to 2019; trough: 2003), with a peak to trough relative percentage difference of 24% and 28%, respectively. Importantly, PMNs and PMAs independently present as an inverse structure. Expert Opinion: The evidence suggests: MD innovation is driven by a singular secular Kutnets-like cyclic phenomenon (independent of economic crises) derived from a fundamental shift from simple (PMNs) to complex (PMAs) MDs. Portentously, while the COVID-19 crisis may not affect the overriding dynamic, the anticipated yet significant (~25%) MD innovation drop may be potentially attenuated with attentive measures by MD stakeholders. Limitations of this approach and further thoughts complete this perspective.
The Existence of Equilibrium Flows
Authors: Alfred Galichon, Larry Samuelson, Lucas Vernet
Date: 2022-09-09 00:00:00

Galichon, Samuelson and Vernet (2022) introduced a class of problems, equilibrium flow problems, that nests several classical economic models such as bipartite matching models, minimum-cost flow problems and hedonic pricing models. We establish conditions for the existence of equilibrium prices in the equilibrium flow problem, in the process generalizing Hall's theorem.
IEEE 802.11ad Based Joint Radar Communication Transceiver: Design, Prototype and Performance Analysis
Authors: Akanksha Sneh, Soumya Jain, V Sri Sindhu, Shobha Sundar Ram, Sumit Darak
Date: 2022-09-09 00:00:00
Tasks: Joint Radar-Communication

Rapid beam alignment is required to support high gain millimeter wave (mmW) communication links between a base station (BS) and mobile users (MU). The standard IEEE 802.11ad protocol enables beam alignment at the BS and MU through a lengthy beam training procedure accomplished through additional packet overhead. However, this results in reduced latency and throughput. Auxiliary radar functionality embedded within the communication protocol has been proposed in prior literature to enable rapid beam alignment of communication beams without the requirement of channel overheads. In this work, we propose a complete architectural framework of a joint radar-communication wireless transceiver wherein radar based detection of MU is realized to enable subsequent narrow beam communication. We provide a software prototype implementation with transceiver design details, signal models and signal processing algorithms. The prototype is experimentally evaluated with realistic simulations in free space and Rician propagation conditions and demonstrated to accelerate the beam alignment by a factor of four while reducing the overall bit error rate (BER) resulting in significant improvement in throughput with respect to standard 802.11ad. Likewise, the radar performance is found to be comparable to commonly used mmW radars.
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics
Authors: Xiping Wang, Zhao Zhang, Danping He, Ke Guan, Dongliang Liu, Jianwu Dou
Date: 2022-09-09 00:00:00
Tasks: Multi-Task Learning, Super-Resolution

Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel characteristics data. The model is based on multi-task learning (MTL) convolutional neural networks (CNN) with residual connection. Experiments demonstrate that the proposed SR model could achieve excellent performances in mean absolute error and standard deviation of error. Advantages of the proposed model are demonstrated in comparisons with other state-of-the-art deep learning models. Ablation study also proved the necessity of multi-task learning and techniques in model design. The contribution in this paper could be helpful in channel modeling, network optimization, positioning and other wireless channel characteristics related work by largely reducing workload of simulation or measurement.
Distributed Complementary Fusion for Connected Vehicles
Authors: James Klupacs, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Jennifer Palmer, Reza Hosseinezhad
Date: 2022-09-09 00:00:00

We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled multi-Bernoulli filters running in each vehicle. It involves complementary fusion of sensor information locally running through consensus iterations. We introduce a novel label merging algorithm to eliminate double counting. We also extend the label space to incorporate sensor identities. This helps to overcome label inconsistencies. We show that the proposed algorithm is able to outperform the standard LMB filter using a distributed complementary approach with limited fields of view.
Optimal Bubble Riding: A Mean Field Game with Varying Entry Times
Authors: Ludovic Tangpi, Shichun Wang
Date: 2022-09-08 00:00:00

Recent financial bubbles such as the emergence of cryptocurrencies and "meme stocks" have gained increasing attention from both retail and institutional investors. In this paper, we propose a game-theoretic model on optimal liquidation in the presence of an asset bubble. Our setup allows the influx of players to fuel the price of the asset. Moreover, traders will enter the market at possibly different times and take advantage of the uptrend at the risk of an inevitable crash. In particular, we consider two types of crashes: an endogenous burst which results from excessive selling, and an exogenous burst which cannot be anticipated and is independent from the actions of the traders. The popularity of asset bubbles suggests a large-population setting, which naturally leads to a mean field game (MFG) formulation. We introduce a class of MFGs with varying entry times. In particular, an equilibrium will depend on the entry-weighted average of conditional optimal strategies. To incorporate the exogenous burst time, we adopt the method of progressive enlargement of filtrations. We prove existence of MFG equilibria using the weak formulation in a generalized setup, and we show that the equilibrium strategy can be decomposed into before-and-after-burst segments, each part containing only the market information. We also perform numerical simulations of the solution, which allow us to provide some intriguing results on the relationship between the bubble burst and equilibrium strategies.
Biology-inspired geometric representation of probability and applications to completion and options' pricing
Authors: Felix Polyakov
Date: 2022-09-09 00:00:00

Geometry constitutes a core set of intuitions present in all humans, regardless of their language or schooling [1]. Could brain's built in machinery for processing geometric information take part in uncertainty representation? For decades already traders have been citing the price of uncertainty based FX optional contracts in terms of implied volatility, a dummy variable related to the standard deviation, instead of pricing with units of money. This work introduces a methodology for geometric representation of probability in terms of implied volatility and attempts to find ways to approximate certain probability distributions using intuitive geometric symmetry. In particular, it is shown how any probability distribution supported on $\mathbb{R}_{+}$ and having finite expectation may be represented with a planar curve whose geometric characteristics can be further analyzed. Log-normal distributions are represented with circles centered at the origin. Certain non-log-normal distributions with bell-shaped density profiles are represented by curves that can be closely approximated with circles whose centers are translated away from the origin. Only three points are needed to define a circle while it represents the candidate probability density approximating the distribution along the entire $\mathbb{R}_{+}$. Just three numbers: scaling and translations along the $x$ and $y$ axes map one circle to another. It is possible to introduce equivalence classes whose member distributions can be obtained by transitive actions of geometric transformations on any of corresponding representations. Approximate completion of probability with non-circular shapes and cases when probability is supported outside of $\mathbb{R}_{+}$ are considered too. Proposed completion of implied volatility is compared to the vanna-volga method.
Analytical Formulation and Field-Theoretic Simulation of Sequence-Specific Phase Separation of Proteinlike Heteropolymers with Short- and Long-Spatial-Range Interactions
Authors: Jonas Wessén, Suman Das, Tanmoy Pal, Hue Sun Chan
Date: 2022-09-08 00:00:00

A theory for sequence dependent liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs) in the study of biomolecular condensates is formulated by extending the random phase approximation (RPA) and field-theoretic simulation (FTS) of heteropolymers with spatially long-range Coulomb interactions to include the fundamental effects of short-range, hydrophobic-like interactions between amino acid residues. To this end, short-range effects are modeled by Yukawa interactions between multiple nonelectrostatic charges derived from an eigenvalue decomposition of pairwise residue-residue contact energies. Chain excluded volume is afforded by incompressibility constraints. A mean-field approximation leads to an effective Flory $\chi$ parameter, which, in conjunction with RPA, accounts for the contact-interaction effects of amino acid composition and the sequence-pattern effects of long-range electrostatics in IDP LLPS, whereas FTS based on the formulation provides full sequence dependence for both short- and long-range interactions. This general approach is illustrated here by applications to variants of a natural IDP in the context of several different amino-acid interaction schemes as well as a set of different model hydrophobic-polar sequences sharing the same composition. Effectiveness of the methodology is verified by coarse-grained explicit-chain molecular dynamics simulations.
Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles
Authors: Robin Gutzen, Sonja Grün, Michael Denker
Date: 2022-09-08 00:00:00

Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The "eigenangle test" quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov-Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.