pwc-archive/papers-with-abstracts huggingface dataset:
Market-Based Coordination of Price-Responsive Demand Using Dantzig-Wolfe Decomposition Method
Authors: Foad Najafi, Matthias Fripp
Date: 2023-02-01 00:00:00

With the increased share of Distributed Generation (DG) and Demand Responsive (DR) loads in the power systems, new approaches based on the game theory framework have been proposed to tackle the problem of coordination of Price Responsive Devices (PRD). The PRDs are modeled as self-benefiting players who try to optimize their consumption based on the price. In this paper, for the first time, a new algorithm based on the Dantzig-Wolfe (DW) Decomposition method to solve the coordination problem of self-benefiting PRDs in a distributed fashion has been proposed. By utilizing the distributed nature of Dantzig-Wolfe, the PRD's self-benefiting algorithms are modeled as sub-problems of the DW, and the coordinator (or the grid operator) who collects energy consumption of PRDs (their energy bid), solves the master problem of the DW and calculate the price signal accordingly. The proposed algorithm is fast since the subproblem in DW (which could be millions of PRDs) can be solved simultaneously. Furthermore, based on the DW theory, if the PRDs subproblems are convex, reaching the optimal point (Equal to Nash Equilibrium) in limited iterations is guaranteed. A simulation with 8 participant households has been conducted to evaluate the model. Each house is equipped with two types of loads: an Electric Vehicle (EV) as a sample of interruptible loads and an Electric Water Heater (EWH) as a sample of Thermostatically Control Loads (TCA). The results show that when the algorithm reaches the optimal point, the generation cost and the user payment (based on the marginal cost of generation) decrease. Furthermore, the aggregate load's Peak to Average (PAR) reduces significantly.
Hybrid Cathode Lithium Battery Discharge Simulation for Implantable Cardioverter Defibrillators Using a Coupled Electro-Thermal Dynamic Model
Authors: Mahsa Doosthosseini, Mahdi Khajeh Talkhoncheh, Jeffrey L. Silberberg, Sandy Weininger, Hamed Ghods
Date: 2023-01-31 00:00:00
Tasks: Rhythm

This paper investigates the impact of implantable cardioverter defibrillator (ICD)'s load on its lithium battery power sources through a coupled electro-thermal dynamic model simulation. ICDs are one of the effective treatments available to significantly improve survival of patients with fatal arrhythmia (abnormal heart rhythm) disorders. Using a lithium battery power source, this life-saving device sends electrical shocks or pulses to regulate the heartbeat. The service life and reliability of an ICD is primarily expressed by its battery's lifespan and performance. In this paper we investigate the terminal voltage, depth of discharge (DOD) and temperature dynamics of the implantable lithium battery with a combined cathode material, namely carbon-monofluoride and silver vanadium oxide (Li/CFx-SVO). Modeling the implantable batteries characteristics is a well-established topic in literature; however, to the best of the author's knowledge, the impact of the high-energy shocks (defibrillation) and low-energy device power supply (housekeeping) on the ICD's battery operation is relatively less-explored. Our analysis reveals that the battery terminal voltage is primarily affected by small but continuous housekeeping discharge current in the range of uA, rather than intermittent high defibrillation current demand in the range of several amps. The results can be used to improve the device design control and operation, thus extending the service life in patients and reducing the need for invasive replacement surgery.
Differential Analysis for Networks Obeying Conservation Laws
Authors: Anirudh Rayas, Rajasekhar Anguluri, Jiajun Cheng, Gautam Dasarathy
Date: 2023-01-30 00:00:00

Networked systems that occur in various domains, such as the power grid, the brain, and opinion networks, are known to obey conservation laws. For instance, electric networks obey Kirchoff's laws, and social networks display opinion consensus. Such conservation laws are often modeled as balance equations that relate appropriate injected flows and potentials at the nodes of the networks. A recent line of work considers the problem of estimating the unknown structure of such networked systems from observations of node potentials (and only the knowledge of the statistics of injected flows). Given the dynamic nature of the systems under consideration, an equally important task is estimating the change in the structure of the network from data -- the so called differential network analysis problem. That is, given two sets of node potential observations, the goal is to estimate the structural differences between the underlying networks. We formulate this novel differential network analysis problem for systems obeying conservation laws and devise a convex estimator to learn the edge changes directly from node potentials. We derive conditions under which the estimate is unique in the high-dimensional regime and devise an efficient ADMM-based approach to perform the estimation. Finally, we demonstrate the performance of our approach on synthetic and benchmark power network data.
3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations
Authors: Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junichiro Makino
Date: 2023-01-31 00:00:00

Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for SNe are serious bottlenecks. This is an urgent issue that needs to be resolved for future higher-resolution galaxy simulations. One possible solution would be to use the Hamiltonian splitting method, in which regions requiring short timesteps are integrated separately from the entire system. To apply this method to the particles affected by SNe in a smoothed-particle hydrodynamics simulation, we need to detect the shape of the shell on and within which such SN-affected particles reside during the subsequent global step in advance. In this paper, we develop a deep learning model, 3D-MIM, to predict a shell expansion after a SN explosion. Trained on turbulent cloud simulations with particle mass $m_{\rm gas}=1$M$_\odot$, the model accurately reproduces the anisotropic shell shape, where densities decrease by over 10 per cent by the explosion. We also demonstrate that the model properly predicts the shell radius in the uniform medium beyond the training dataset of inhomogeneous turbulent clouds. We conclude that our model enables the forecast of the shell and its interior where SN-affected particles will be present.
Breathing cluster in complex neuron-astrocyte networks
Authors: Ya Wang, Liang Wang, Huawei Fan, Jun Ma, Hui Cao, Xingang Wang
Date: 2023-01-26 00:00:00

Brain activities are featured by spatially distributed neural clusters of coherent firings and a spontaneous switching of the clusters between the synchrony and asynchrony states. Evidences from {\it in vivo} experiments suggest that astrocytes, a type of glial cell regarded previously as providing only structural and metabolic supports to neurons, participate actively in brain functions and play a crucial role in regulating the neural firing activities, yet the mechanism remains unknown. Introducing astrocyte as a reservoir of the glutamate released from neuron synapses, here we propose the model of complex neuron-astrocyte network and employ it to explore the roles of astrocyte in regulating the synchronization behaviors of networked neurons. It is found that a fraction of neurons on the network can be synchronized as a cluster, while the remaining neurons are kept as desynchronized. Moreover, during the course of network evolution, the cluster is switching between the synchrony and asynchrony states intermittently, henceforth the phenomenon of ``breathing cluster". By the method of symmetry-based analysis, we conduct a theoretical investigation on the stability of the cluster and the mechanism generating the breathing activities. It is revealed that the contents of the cluster are determined by the network symmetry and the breathing activities are due to the interplay between the neural network and the astrocyte. The breathing phenomenon is demonstrated in network models of different structures and neural dynamics. The studies give insights into the cellular mechanism of astrocytes in regulating neural activities, and shed lights onto the spontaneous state switching of the neocortex.
Leveraging Interactions in Microfluidic Droplets for Enhanced Biotechnology Screens
Authors: Carolus Vitalis, Tobias Wenzel
Date: 2023-01-31 00:00:00

Microfluidic droplet screens serve as an innovative platform for high-throughput biotechnology, enabling significant advancements in discovery, product optimization, and analysis. This review sheds light on the emerging trend of interaction assays in microfluidic droplets, underscoring the unique suitability of droplets for these applications. Encompassing a diverse range of biological entities such as antibodies, enzymes, DNA, RNA, various microbial and mammalian cell types, drugs, and other molecules, these assays demonstrate their versatility and scope. Recent methodological breakthroughs have escalated these screens to novel scales of bioanalysis and biotechnological product design. Moreover, we highlight pioneering advancements that extend droplet-based screens into new domains: cargo delivery within human bodies, application of synthetic gene circuits in natural environments, 3D-printing, and the development of droplet structures responsive to environmental signals. The potential of this field is profound and only set to increase.
Regression Adjustment, Cross-Fitting, and Randomized Experiments with Many Controls
Authors: Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
Date: 2023-02-01 00:00:00
Tasks: regression

This paper studies estimation and inference for average treatment effects in randomized experiments with many covariates, under a design-based framework with a deterministic number of treated units. We show that a simple yet powerful cross-fitted regression adjustment achieves bias-correction and leads to sharper asymptotic properties than existing alternatives. Specifically, we derive higher-order stochastic expansions, analyze associated inference procedures, and propose a modified HC3 variance estimator that accounts for up to second-order. Our analysis reveals that cross-fitting permits substantially faster growth in the covariate dimension $p$ relative to sample size $n$, with asymptotic normality holding under favorable designs when $p = o(n^{3/4}/(\log n)^{1/2})$, improving on standard rates. We also explain and address the poor size performance of conventional variance estimators. The methodology extends naturally to stratified experiments with many strata. Simulations confirm that the cross-fitted estimator, combined with the modified HC3, delivers accurate estimation and reliable inference across diverse designs.
Adaptive hedging horizon and hedging performance estimation
Authors: Wang Haoyu, Junpeng Di, Qing Han
Date: 2023-02-01 00:00:00
Tasks: Time Series, Time Series Analysis

In this study, we constitute an adaptive hedging method based on empirical mode decomposition (EMD) method to extract the adaptive hedging horizon and build a time series cross-validation method for robust hedging performance estimation. Basing on the variance reduction criterion and the value-at-risk (VaR) criterion, we find that the estimation of in-sample hedging performance is inconsistent with that of the out-sample hedging performance. The EMD hedging method family exhibits superior performance on the VaR criterion compared with the minimum variance hedging method. The matching degree of the spot and futures contracts at the specific time scale is the key determinant of the hedging performance in the corresponding hedging horizon.
How exporters neutralized an increase in tariffs
Authors: Asier Minondo
Date: 2023-02-01 00:00:00

I use the unanticipated and large additional tariffs the US imposed on European Union products due to the Airbus-Boeing conflict to analyze how exporters reacted to a change in trade policy. Using firm-level data for Spain and applying a difference-in-differences methodology, I show that the export revenue in the US of the firms affected by the tariff hike did not significantly decrease relative to the one of other Spanish exporters to the US. I show that Spanish exporters were able to neutralize the increase in tariffs by substituting Spanish products with products originated in countries unaffected by tariffs and shifting to varieties not affected by tariffs. My results show that tariff avoidance is another margin exporters can use to counteract the effects of a tariff hike.
Time-warped Trials
Authors: Eric Easthope
Date: 2023-02-01 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

I outline a signal resampling strategy for aligning event times between time series trials in contexts where significant event times like onsets and offsets vary between trials. These variations prevent direct comparisons of trials in practical contexts as comparisons require equal-length time series (Salari et al., 2019). Algorithms like dynamic time warping help us quantify these variations locally but do not apply well to continuous transformations of time series signals without interpolating or downsampling to add or remove samples (Jamid, 2004; Eckner, 2014). I show that with consideration for padding and sampling frequency that sinc interpolation is sufficient to resample parts of trial intervals to produce equal-length time-locked trials that correlate to and strongly approximate their unwarped counterparts with minimal interpolation effects. Specifically I show that interpolation effects can be minimized by oversampling, selectively interpolating mis-aligned parts of trials with respect to mean mis-aligned event lengths, and interpolating mis-aligned events with sufficient zero-padding. Interpolated signals then have a bandlimit well below the Nyquist frequency and satisfy the Nyquist-Shannon sampling theorem ensuring perfect reconstructions, and I find that I can track and potentially counteract resampling effects on signal energy quantities.
Integrated Sensing and Communication for Joint GPS Spoofing and Jamming Detection in Vehicular V2X Networks
Authors: Ali Krayani, Gabriele Barabino, Lucio Marcenaro, Carlo Regazzoni
Date: 2023-02-01 00:00:00
Tasks: Autonomous Vehicles, Integrated sensing and communication

Vehicle-to-everything (V2X) communication is expected to be a prominent component of the sixth generation (6G) to accomplish intelligent transportation systems (ITS). Autonomous vehicles relying only on onboard sensors cannot bypass the limitations of safety and reliability. Thus, integrated sensing and communication is proposed as an effective way to achieve high situational- and self-awareness levels, enabling V2X to perceive the physical world and adjust its behaviour to emergencies. Secure navigation through the Global Positioning System (GPS) is essential in ITS for safe operation. Nevertheless, due to the lack of encryption and authentication mechanisms of civil GPS receivers, spoofers can easily replicate satellite signals by launching GPS spoofing attacks to deceive the vehicle and manipulate navigation data. In addition, due to its shared nature, V2X links are prone to jamming attacks which might endanger vehicular safety. This paper proposes a method to jointly detect GPS spoofing and jamming attacks in a V2X network. Simulation results demonstrate that the proposed method can detect spoofers and jammers with high detection probabilities.
Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attacks
Authors: Huan Huang, Ying Zhang, Hongliang Zhang, Yi Cai, A. Lee Swindlehurst, Zhu Han
Date: 2023-02-01 00:00:00
Tasks: Quantization

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users' (LUs') channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a "disco" IRS (DIRS), that acts like a "disco ball" to actively age the LUs' channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ.
A Projective Geometric View for 6D Pose Estimation in mmWave MIMO Systems
Authors: Shengqiang Shen, Henk Wymeersch
Date: 2023-02-01 00:00:00
Tasks: 6D Pose Estimation, Pose Estimation

Millimeter-wave (mmWave) systems in the 30--300 GHz bands are among the fundamental enabling technologies of 5G and beyond 5G, providing large bandwidths, not only for high data rate communication, but also for precise positioning services, in support of high accuracy demanding applications such as vehicle positioning. With the possibility to introduce relatively large arrays on user devices with a small footprint, the ability to determine the user orientation becomes unlocked. The estimation of the full user pose (joint 3D position and 3D orientation) is referred to as 6D localization. Conventionally, the problem of 6D localization using antenna arrays has been considered difficult and was solved through a combination of heuristics and optimization. In this paper, we reveal a close connection between the AoA and AoD and the well-studied perspective projection model from computer vision. This connection allows us to solve the 6D localization problem, by adapting state-of-the-art methods from computer vision. More specifically, two problems, namely 6D pose estimation from AoA from multiple single-antenna base stations and 6D SLAM based on single-BS mmWave communication, are first modeled with the perspective projection model, and then solved. Numerical simulations show that the proposed estimators operate close to the theoretical performance bounds. Moreover, the proposed SLAM method is effective even in the absence of the LoS path, or knowledge of the LoS/NLoS condition.
Deep Reinforcement Learning for Energy-Efficient on the Heterogeneous Computing Architecture
Authors: Zheqi Yu, Chao Zhang, Pedro Machado, Adnan Zahid, Tim. Fernandez-Hart, Muhammad A. Imran, Qammer H. Abbasi
Date: 2023-02-01 00:00:00
Tasks: Deep Reinforcement Learning, Management

The growing demand for optimal and low-power energy consumption paradigms for IOT devices has garnered significant attention due to their cost-effectiveness, simplicity, and intelligibility. In this article, an AI hardware energy-efficient framework to achieve optimal energy savings in heterogeneous computing through appropriate power consumption management is proposed. The deep reinforcement learning framework is employed, utilising the Actor-Critic architecture to provide a simple and precise method for power saving. The results of the study demonstrate the proposed approach's suitability for different hardware configurations, achieving notable energy consumption control while adhering to strict performance requirements. The evaluation of the proposed power-saving framework shows that it is more stable, and has achieved more than 34.6% efficiency improvement, outperforming other methods by more than 16%.
f-Betas and Portfolio Optimization with f-Divergence induced Risk Measures
Authors: Rui Ding
Date: 2023-02-01 00:00:00
Tasks: Portfolio Optimization

In this paper, we build on using the class of f-divergence induced coherent risk measures for portfolio optimization and derive its necessary optimality conditions formulated in CAPM format. We derive a new f-Beta similar to the Standard Betas and also extended it to previous works in Drawdown Betas. The f-Beta evaluates portfolio performance under an optimally perturbed market probability measure, and this family of Beta metrics gives various degrees of flexibility and interpretability. We conduct numerical experiments using selected stocks against a chosen S\&P 500 market index as the optimal portfolio to demonstrate the new perspectives provided by Hellinger-Beta as compared with Standard Beta and Drawdown Betas. In our experiments, the squared Hellinger distance is chosen to be the particular choice of the f-divergence function in the f-divergence induced risk measures and f-Betas. We calculate Hellinger-Beta metrics based on deviation measures and further extend this approach to calculate Hellinger-Betas based on drawdown measures, resulting in another new metric which is termed Hellinger-Drawdown Beta. We compare the resulting Hellinger-Beta values under various choices of the risk aversion parameter to study their sensitivity to increasing stress levels.
Molecular Graph Generation by Decomposition and Reassembling
Authors: Masatsugu Yamada, Mahito Sugiyama
Date: 2022-12-11 00:00:00
Tasks: Drug Discovery, Graph Generation, Molecular Graph Generation, valid

Designing molecular structures with desired chemical properties is an essential task in drug discovery and material design. However, finding molecules with the optimized desired properties is still a challenging task due to combinatorial explosion of candidate space of molecules. Here we propose a novel \emph{decomposition-and-reassembling} based approach, which does not include any optimization in hidden space and our generation process is highly interpretable. Our method is a two-step procedure: In the first decomposition step, we apply frequent subgraph mining to a molecular database to collect smaller size of subgraphs as building blocks of molecules. In the second reassembling step, we search desirable building blocks guided via reinforcement learning and combine them to generate new molecules. Our experiments show that not only can our method find better molecules in terms of two standard criteria, the penalized $\log P$ and drug-likeness, but also generate drug molecules with showing the valid intermediate molecules.