pwc-archive/papers-with-abstracts huggingface dataset:
User Activity Detection for Irregular Repetition Slotted Aloha based MMTC
Authors: Chirag Ramesh Srivatsa, Chandra R. Murthy
Date: 2021-11-11 00:00:00
Tasks: Action Detection, Activity Detection, compressed sensing

Irregular repetition slotted aloha (IRSA) is a grant-free random access protocol for massive machine-type communications, where a large number of users sporadically send their data packets to a base station (BS). IRSA is a completely distributed multiple access protocol: in any given frame, a small subset of the users, i.e., the active users, transmit replicas of their packet in randomly selected resource elements (REs). The first step in the decoding process at the BS is to detect which users are active in each frame. To this end, a novel Bayesian user activity detection (UAD) algorithm is developed, which exploits both the sparsity in user activity as well as the underlying structure of IRSA-based transmissions. Next, the Cramer-Rao bound (CRB) on the mean squared error in channel estimation is derived. It is empirically shown that the channel estimates obtained as a by-product of the proposed UAD algorithm achieves the CRB. Then, the signal to interference plus noise ratio achieved by the users is analyzed, accounting for UAD, channel estimation errors, and pilot contamination. The impact of these non-idealities on the throughput of IRSA is illustrated via Monte Carlo simulations. For example, in a system with 1500 users and 10% of the users being active per frame, a pilot length of as low as 20 symbols is sufficient for accurate user activity detection. In contrast, using classical compressed sensing approaches for UAD would require a pilot length of about 346 symbols. The results also reveal crucial insights into dependence of UAD errors and throughput on parameters such as the length of the pilot sequence, the number of antennas at the BS, the number of users, and the signal to noise ratio.
Processing of large sets of stochastic signals: filtering based on piecewise interpolation technique
Authors: Anatoli Torokhti
Date: 2021-11-11 00:00:00

Suppose $K_{_Y}$ and $K_{_X}$ are large sets of observed and reference signals, respectively, each containing $N$ signals. Is it possible to construct a filter $F$ that requires a priori information only on few signals, $p\ll N$, from $K_{_X}$ but performs better than the known filters based on a priori information on every reference signal from $K_{_X}$? It is shown that the positive answer is achievable under quite unrestrictive assumptions. The device behind the proposed method is based on a special extension of the piecewise linear interpolation technique to the case of random signal sets. The proposed technique provides a single filter to process any signal from the arbitrarily large signal set. The filter is determined in terms of pseudo-inverse matrices so that it always exists.
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
Authors: Thomas Lew, Apoorva Sharma, James Harrison, Edward Schmerling, Marco Pavone
Date: 2021-11-11 00:00:00

We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation. Specifically, we discuss the approximation that replaces a model with fixed but uncertain parameters (a source of epistemic uncertainty) with a model subject to external disturbances modeled as a Brownian motion (corresponding to aleatoric uncertainty).
Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation
Authors: Yihui Fu, Yun Liu, Jingdong Li, Dawei Luo, Shubo Lv, Yukai Jv, Lei Xie
Date: 2021-11-11 00:00:00
Tasks: Decoder, Speech Enhancement

Complex spectrum and magnitude are considered as two major features of speech enhancement and dereverberation. Traditional approaches always treat these two features separately, ignoring their underlying relationship. In this paper, we propose Uformer, a Unet based dilated complex & real dual-path conformer network in both complex and magnitude domain for simultaneous speech enhancement and dereverberation. We exploit time attention (TA) and dilated convolution (DC) to leverage local and global contextual information and frequency attention (FA) to model dimensional information. These three sub-modules contained in the proposed dilated complex & real dual-path conformer module effectively improve the speech enhancement and dereverberation performance. Furthermore, hybrid encoder and decoder are adopted to simultaneously model the complex spectrum and magnitude and promote the information interaction between two domains. Encoder decoder attention is also applied to enhance the interaction between encoder and decoder. Our experimental results outperform all SOTA time and complex domain models objectively and subjectively. Specifically, Uformer reaches 3.6032 DNSMOS on the blind test set of Interspeech 2021 DNS Challenge, which outperforms all top-performed models. We also carry out ablation experiments to tease apart all proposed sub-modules that are most important.
Robust Moving Target Defence Against False Data Injection Attacks in Power Grids
Authors: Wangkun Xu, Imad M. Jaimoukha, Fei Teng
Date: 2021-11-11 00:00:00
Tasks: State Estimation

Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices. One of the key challenges for MTD in power grid is to design its real-time implementation with performance guarantees against unknown attacks. Converting from the noiseless assumptions in the literature, this paper investigates the MTD design problem in a noisy environment and proposes, for the first time, the concept of robust MTD to guarantee the worst-case detection rate against all unknown attacks. We theoretically prove that, for any given MTD strategy, the minimal principal angle between the Jacobian subspaces corresponds to the worst-case performance against all potential attacks. Based on this finding, robust MTD algorithms are formulated for the systems with both complete and incomplete configurations. Extensive simulations using standard IEEE benchmark systems demonstrate the improved average and worst-case performances of the proposed robust MTD against state-of-the-art algorithms. All codes are available at https://github.com/xuwkk/Robust_MTD.
System Parameter Exploration of Ship Maneuvering Model for Automatic Docking / Berthing using CMA-ES
Authors: Yoshiki Miyauchi, Atsuo Maki, Naoya Umeda, Dimas M. Rachman, Youhei Akimoto
Date: 2021-11-11 00:00:00
Tasks: global-optimization

Accurate maneuvering estimation is essential to establish autonomous berthing control. The system-based mathematical model is widely used to estimate the ship's maneuver. Commonly, the system parameters of the mathematical model are obtained by the captive model test (CMT), which is time-consuming to construct an accurate model suitable for complex berthing maneuvers. System identification (SI) is an alternative to constructing the mathematical model. However, SI on the mathematical model of ship's maneuver has been only conducted on much simpler maneuver: turning and zig-zag. Therefore, this study investigates the SI on a mathematical model capable of berthing maneuver. The main contributions of this study are as follows: (i) construct the system-based mathematical model on berthing by optimizing system parameters with a reduced amount of model tests than the CMT-based scheme; (ii) Find the favorable choice of objective function and type of training data for optimization. Global optimization scheme CMA-ES explored the system parameters of the MMG model from the free-running model's trajectories. The berthing simulation with the parameters obtained by the proposed method showed better agreement with the free-running model test than parameters obtained by the CMT. Furthermore, the proposed method required fewer data amounts than a CMT-based scheme.
On Neural Network Identification for Low-Speed Ship Maneuvering Model
Authors: Kouki Wakita, Atsuo Maki, Umeda Naoya, Yoshiki Miyauchi, Tohga Shimoji, Dimas M. Rachman, Youhei Akimoto
Date: 2021-11-11 00:00:00
Tasks: Network Identification

Several studies on ship maneuvering models have been conducted using captive model tests or computational fluid dynamics (CFD) and physical models, such as the maneuvering modeling group (MMG) model. A new system identification method for generating a low-speed maneuvering model using recurrent neural networks (RNNs) and free running model tests is proposed in this study. We especially focus on a low-speed maneuver such as the final phase in berthing to achieve automatic berthing control. Accurate dynamic modeling with minimum modeling error is highly desired to establish a model-based control system. We propose a new loss function that reduces the effect of the noise included in the training data. Besides, we revealed the following facts - an RNN that ignores the memory before a certain time improved the prediction accuracy compared with the "standard" RNN, and the random maneuver test was effective in obtaining an accurate berthing maneuver model. In addition, several low-speed free running model tests were performed for the scale model of the M.V. Esso Osaka. As a result, this paper showed that the proposed method using a neural network model could accurately represent low-speed maneuvering motions.
Socially-Aware Evaluation Framework for Transportation
Authors: Anu Kuncheria, Joan L. Walker, Jane Macfarlane
Date: 2021-11-11 00:00:00
Tasks: Management

Technological advancements are rapidly changing traffic management in cities. Massive adoption of mobile devices and cloud-based applications have created new mechanisms for urban traffic control and management. Specifically, navigation applications have impacted cities in multiple ways by rerouting traffic on their streets. As different routing strategies distribute traffic differently across the city network, understanding these differences across multiple dimensions is highly relevant for policymakers. In this paper, we develop a holistic framework of indicators, called Socially-Aware Evaluation Framework for Transportation (SAEF), that will assist in understanding how traffic routing and the resultant traffic dynamics impact city metrics, with the intent of avoiding unintended consequences and adhering to city objectives. SAEF is a holistic decision framework formed as an assembled set of city performance indicators grounded in the literature. The selected indicators can be evaluated for cities of various sizes and at the urban scale. The SAEF framework is presented for four Bay Area cities, for which we compare three different routing strategies. Our intent with this work is to provide an evaluation framework that enables reflection on the consequence of policies, traffic management strategies and network changes. With an ability to model out proposed traffic management strategies, the policymaker can consider the trade-offs and potential unintended consequences.
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends
Authors: Lingying Huang, Xiaomeng Chen, Wei Huo, Jiazheng Wang, Fan Zhang, Bo Bai, Ling Shi
Date: 2021-11-05 00:00:00
Tasks: Variable Selection

In this paper, we surveyed the existing literature studying different approaches and algorithms for the four critical components in the general branch and bound (B&B) algorithm, namely, branching variable selection, node selection, node pruning, and cutting-plane selection. However, the complexity of the B&B algorithm always grows exponentially with respect to the increase of the decision variable dimensions. In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently. We further surveyed how machine learning can be used to improve the four critical components in B&B algorithms. In general, a supervised learning method helps to generate a policy that mimics an expert but significantly improves the speed. An unsupervised learning method helps choose different methods based on the features. In addition, models trained with reinforcement learning can beat the expert policy, given enough training and a supervised initialization. Detailed comparisons between different algorithms have been summarized in our survey. Finally, we discussed some future research directions to accelerate and improve the algorithms further in the literature.
Environmental variation compensated damage classification and localization in ultrasonic guided wave SHM using self-learnt features and Gaussian mixture models
Authors: Shruti Sawant, Sheetal Patil, Jeslin Thalapil, Sauvik Banerjee, Siddharth Tallur
Date: 2021-11-11 00:00:00
Tasks: Structural Health Monitoring

Conventional damage localization algorithms used in ultrasonic guided wave-based structural health monitoring (GW-SHM) rely on physics-defined features of GW signals. In addition to requiring domain knowledge of the interaction of various GW modes with various types of damages, they also suffer from errors due to variations in environmental and operating conditions (EOCs) in practical use cases. While several machine learning tools have been reported for EOC compensation, they need to be custom-designed for each combination of damage and structure due to their dependence on physics-defined feature extraction. In this work, we propose a CNN-based automated feature extraction framework coupled with Gaussian mixture model (GMM) based EOC compensation and damage classification and localization method. Features learnt by the CNNs are used for damage classification and localization of damage by modeling the probability distribution of the features using GMMs. The Kullback-Leibler (KL) divergence of these GMMs with respect to corresponding baseline GMMs are used as signal difference coefficients (SDCs) to compute damage indices (DIs) along various GW sensor paths, and thus for damage localization. The efficacy of the proposed method is demonstrated using FE generated GW-data for an aluminum plate with a network of six lead zirconate titanate (PZT) sensors, for three different types of damages (rivet hole, added mass, notch) at various temperatures, with added white noise and pink noise to incorporate errors due to EOCs. We also present experimental validation of the method through characterization of notch damage in an aluminum panel under varying and non-uniform temperature profiles, using a portable custom-designed field programmable gate array (FPGA) based signal transduction and data acquisition system.
The Supply of Motivated Beliefs
Authors: Michael Thaler
Date: 2021-11-11 00:00:00

When people choose what messages to send to others, they often consider how others will interpret the messages. A sender may expect a receiver to engage in motivated reasoning, leading the receiver to trust good news more than bad news, relative to a Bayesian. This paper experimentally studies how motivated reasoning affects information transmission in political settings. Senders are randomly matched with receivers whose political party's stances happen to be aligned or misaligned with the truth, and either face incentives to be rated as truthful or face no incentives. Incentives to be rated as truthful cause senders to be less truthful; when incentivized, senders send false information to align messages with receivers' politically-motivated beliefs. The adverse effect of incentives is not appreciated by receivers, who rate senders in both conditions as being equally likely to be truthful. A complementary experiment further identifies senders' beliefs about receivers' motivated reasoning as the mechanism driving these results. Senders are additionally willing to pay to learn the politics of their receivers, and use this information to send more false messages.
Grid Tariffs Based on Capacity Subscription: Multi Year Analysis on Metered Consumer Data
Authors: Sigurd Bjarghov, Hossein Farahmand, Gerard Doorman
Date: 2021-11-05 00:00:00

While volume-based grid tariffs have been the norm for residential consumers, capacity-based tariffs will become more relevant with the increasing electrification of society. A further development is capacity subscription, where consumers are financially penalised for exceeding their subscribed capacity, or alternatively their demand is limited to the subscribed level. The penalty or limitation can either be static (always active) or dynamic, meaning that it is only activated when there are active grid constraints. We investigate the cost impact for static and dynamic capacity subscription tariffs, for 84 consumers based on six years of historical load data. We use several approaches for finding the optimal subscription level ex ante. The results show that annual costs remain both stable and similar for most consumers, with a few exceptions for those that have high peak demand. In the case of a physical limitation, it is important to use a stochastic approach for the optimal subscription level to avoid excessive demand limitations. Facing increased peak loads due to electrification, regulators should consider a move to capacity-based tariffs in order to reduce cross-subsidisation between consumers and increase cost reflectivity without impacting the DSO cost recovery.
Long Run Law and Entropy
Authors: Weidong Tian
Date: 2021-11-11 00:00:00

This paper demonstrates the additive and multiplicative version of a long-run law of unexpected shocks for any economic variable. We derive these long-run laws by the martingale theory without relying on the stationary and ergodic conditions. We apply these long-run laws to asset return, risk-adjusted asset return, and the pricing kernel process and derive new asset pricing implications. Moreover, we introduce several dynamic long-term measures on the pricing kernel process, which relies on the sample data of asset return. Finally, we use these long-term measures to diagnose leading asset pricing models.
Detecting COVID-19 from Chest Computed Tomography Scans using AI-Driven Android Application
Authors: Aryan Verma, Sagar B. Amin, Muhammad Naeem, Monjoy Saha
Date: 2021-11-06 00:00:00
Tasks: Computed Tomography (CT), Diagnostic

The COVID-19 (coronavirus disease 2019) pandemic affected more than 186 million people with over 4 million deaths worldwide by June 2021. The magnitude of which has strained global healthcare systems. Chest Computed Tomography (CT) scans have a potential role in the diagnosis and prognostication of COVID-19. Designing a diagnostic system which is cost-efficient and convenient to operate on resource-constrained devices like mobile phones would enhance the clinical usage of chest CT scans and provide swift, mobile, and accessible diagnostic capabilities. This work proposes developing a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It further creates an attention heatmap, augmented on the segmented lung parenchyma region in the CT scans through an algorithm developed as a part of this work, which shows the regions of infection in the lungs. We propose a selection approach combined with multi-threading for a faster generation of heatmaps on Android Device, which reduces the processing time by about 93%. The neural network trained to detect COVID-19 in this work is tested with F1 score and accuracy, both of 99.58% and sensitivity of 99.69%, which is better than most of the results in the domain of COVID diagnosis from CT scans. This work will be beneficial in high volume practices and help doctors triage patients in the early diagnosis of the COVID-19 quickly and efficiently.
Heteroclinic cycling and extinction in May-Leonard models with demographic stochasticity
Authors: Nicholas W. Barendregt, Peter J. Thomas
Date: 2021-11-10 00:00:00
Tasks: Math, Unity

May and Leonard (SIAM J. Appl. Math 1975) introduced a three-species Lotka-Volterra type population model that exhibits heteroclinic cycling. Rather than producing a periodic limit cycle, the trajectory takes longer and longer to complete each "cycle", passing closer and closer to unstable fixed points in which one population dominates and the others approach zero. Aperiodic heteroclinic dynamics have subsequently been studied in ecological systems (side-blotched lizards; colicinogenic E. coli), in the immune system, in neural information processing models ("winnerless competition"), and in models of neural central pattern generators. Yet as May and Leonard observed "Biologically, the behavior (produced by the model) is nonsense. Once it is conceded that the variables represent animals, and therefore cannot fall below unity, it is clear that the system will, after a few cycles, converge on some single population, extinguishing the other two." Here, we explore different ways of introducing discrete stochastic dynamics based on May and Leonard's ODE model, with application to ecological population dynamics, and to a neuromotor central pattern generator system. We study examples of several quantitatively distinct asymptotic behaviors, including total extinction of all species, extinction to a single species, and persistent cyclic dominance with finite mean cycle length.
ParaFold: Paralleling AlphaFold for Large-Scale Predictions
Authors: Bozitao Zhong, Xiaoming Su, Minhua Wen, Sichen Zuo, Liang Hong, James Lin
Date: 2021-11-11 00:00:00
Tasks: CPU, GPU, Protein Folding

AlphaFold predicts protein structures from the amino acid sequence at or near experimental resolution, solving the 50-year-old protein folding challenge, leading to progress by transforming large-scale genomics data into protein structures. AlphaFold will also greatly change the scientific research model from low-throughput to high-throughput manner. The AlphaFold framework is a mixture of two types of workloads: MSA construction based on CPUs and model inference on GPUs. The first CPU stage dominates the overall runtime, taking hours for a single protein due to the large database sizes and I/O bottlenecks. However, GPUs in this CPU stage remain idle, resulting in low GPU utilization and restricting the capacity of large-scale structure predictions. Therefore, we proposed ParaFold, an open-source parallel version of AlphaFold for high throughput protein structure predictions. ParaFold separates the CPU and GPU parts to enable large-scale structure predictions. ParaFold also effectively reduces the CPU and GPU runtime with two optimizations without compromising the quality of prediction results: using multi-threaded parallelism on CPUs and using optimized JAX compilation on GPUs. We evaluated ParaFold with three datasets of different size and protein lengths. We evaluated the accuracy and efficiency of optimizations on CPUs and GPUs, and showed the large-scale prediction capability by running ParaFold inferences of 19,704 small proteins in five hours on one NVIDIA DGX-2. Using the JAX compile optimization, ParaFold attained a 13.8X average speedup over AlphaFold. ParaFold offers a rapid and effective approach for high-throughput structure predictions, leveraging the predictive power by running on supercomputers, with shorter time, and at a lower cost. The development of ParaFold will greatly speed up high-throughput studies and render the protein "structure-omics" feasible.