pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Extended Object Tracking Using Coupled Velocity Model from WLS Perspective
Authors: Zhifei Li, Yan Liang, Linfeng Xu
Date: 2023-08-24 00:00:00
Tasks: Object Tracking

This study proposes a coupled velocity model (CVM) that establishes the relation between the orientation and velocity using their correlation, avoiding that the existing extended object tracking (EOT) models treat them as two independent quantities. As a result, CVM detects the mismatch between the prior dynamic model and actual motion pattern to correct the filtering gain, and simultaneously becomes a nonlinear and state-coupled model with multiplicative noise. The study considers CVM to design a feasible distributed weighted least squares (WLS) filter. The WLS criterion requires a linear state-space model containing only additive noise about the estimated state. To meet the requirement, we derive such two separate pseudo-linearized models by using the first-order Taylor series expansion. The separation is merely in form, and the estimates of interested states are embedded as parameters into each other's model, which implies that their interdependency is still preserved in the iterative operation of two linear filters. With the two models, we first propose a centralized WLS filter by converting the measurements from all nodes into a summation form. Then, a distributed consensus scheme, which directly performs an inner iteration on the priors across different nodes, is proposed to incorporate the cross-covariances between nodes. Under the consensus scheme, a distributed WLS filter over a realistic network with ``naive'' node is developed by proper weighting of the priors and measurements. Finally, the performance of proposed filters in terms of accuracy, robustness, and consistency is testified under different prior situations.
Loss Measurement of Low RDS Devices Through Thermal Modelling -- The Advantage of Not Turning it Fully On
Authors: Jose Miguel Sanz-Alcaine, Francisco Jose Perez-Cebolla, Carlos Bernal-Ruiz, Asier Arruti, Iosu Aizpuru, Juan Sanchez
Date: 2023-08-24 00:00:00

This paper presents and evaluates a novel method for generating power losses on transistors avoiding high currents. These could heat up the circuit tracks, affecting the accurate thermal modeling of the system. The proposed procedure is based on the transistor current regulation with low gate voltages and the linearity between power and temperature, being useful for all transistor technologies (Si, SiC and GaN). Through this method, low DC currents are enough to bring transistors to their thermal limits. Thermal stability issues and their differences between technologies are discussed and an experimental validation of the method is carried out.
Thermal-aware Workload Distribution for Data Centers with Demand Variations
Authors: Somayye Rostami, Douglas G. Down, George Karakostas
Date: 2023-08-24 00:00:00
Tasks: Model Predictive Control

Thermal-aware workload distribution is a common approach in the literature for power consumption optimization in data centers. However, data centers also have other operational costs such as the cost of equipment maintenance and replacement. It has been shown that server reliability depends on frequency of their temperature variations, arising from workload transitions due to dynamic demands. In this work, we formulate a nonlinear optimization problem that considers the cost of workload transitions in addition to IT and cooling power consumption. To approximate the solution, we first linearize the problem; the result is a mixed integer programming problem. A modified heuristic is then proposed to approximate the solution of the linear problem. Finally, a Model Predictive Control (MPC) approach is integrated with the proposed heuristics for automatic workload reconfiguration when future demand is not known exactly, but predictions are available. Numerical results show that the proposed schemes are attractive in different settings.
Optimal and Robust Fault Tolerant Control of Wind Turbines Working under Sensor, Actuator, and System Faults
Authors: Yashar Mousavi
Date: 2023-08-24 00:00:00
Tasks: Pitch control

Motivated by the increasing concerns over environmental challenges such as global warming and the exhaustion of fossil-fuel reserves, the renewable energy industry has become the most demanded electrical energy production source worldwide. In this context, wind energy conversion systems (WECSs) are the most dominant and fastest-growing alternative energy production technologies, playing an increasingly vital role in renewable power generation. To tackle the pitch control problem of WECSs, this thesis proposes an optimal fault-tolerant fractional-order pitch control strategy for pitch angle regulation of WT blades subjected to sensor, actuator, and system faults. To showcast the effects of faults, changes in the system parameters are considered as a result of sensor, actuator and system faults with various levels of severity.} Furthermore, taking advantage of the favourable merits of higher-order SMCs and fractional calculus, this thesis develops a fault-tolerant fractional-calculus-based higher-order sliding mode controller for optimum rotor speed tracking and power production maximization of WECSs. \textcolor{black}{The partial loss of the generator output torque is considered as an actuator failure, leading to loss of partial actuation power. Moreover, active fault-tolerant fractional-order higher-order SMC strategies are developed for rotor current regulation and speed trajectory tracking of doubly-fed induction generator (DFIG) -driven WECSs subjected to model uncertainties and rotor current sensor faults. The developed controllers are augmented with two state observers, an algebraic state estimator and a sliding mode observer, to estimate the rotor current dynamics during sensors' faults.
Relations Between Generalized JST Algorithm and Kalman Filtering Algorithm for Time Scale Generation
Authors: Yuyue Yan, Takahiro Kawaguchi, Yuichiro Yano, Yuko Hanado, Takayuki Ishizaki
Date: 2023-08-24 00:00:00

In this paper, we present a generalized Japan Standard Time algorithm (JST-algo) for higher-order atomic clock ensembles and mathematically clarify the relations of the (generalized) JST-algo and the conventional Kalman filtering algorithm (CKF-algo) in the averaged atomic time and the clock residuals for time scale generation. In particular, we reveal the fact that the averaged atomic time of the generalized JST-algo does not depend on the observation noise even though the measurement signal is not filtered in the algorithm. Furthermore, the prediction error of CKF- algo is rigorously shown by using the prediction error regarding an observable state space. It is mathematically shown that when the covariance matrices of system noises are identical for all atomic clocks, considering equal averaging weights for the clocks is a necessary and sufficient condition to ensure equivalence between the generalized JST-algo and CKF-algo in averaged atomic time. In such homogeneous systems, a necessary and sufficient condition for observation noises is presented to determine which algorithm can generate the clock residuals with smaller variances. A couple of numerical examples comparing the generalized JST-algo and CKF-algo are provided to illustrate the efficacy of the results.
Safe Non-Stochastic Control of Linear Dynamical Systems
Authors: HongYu Zhou, Vasileios Tzoumas
Date: 2023-08-23 00:00:00
Tasks: Collision Avoidance

We study the problem of \textit{safe control of linear dynamical systems corrupted with non-stochastic noise}, and provide an algorithm that guarantees (i) zero constraint violation of convex time-varying constraints, and (ii) bounded dynamic regret, \ie bounded suboptimality against an optimal clairvoyant controller that knows the future noise a priori. The constraints bound the values of the state and of the control input such as to ensure collision avoidance and bounded control effort. We are motivated by the future of autonomy where robots will safely perform complex tasks despite real-world unpredictable disturbances such as wind and wake disturbances. To develop the algorithm, we capture our problem as a sequential game between a linear feedback controller and an adversary, assuming a known upper bound on the noise's magnitude. Particularly, at each step $t=1,\ldots, T$, first the controller chooses a linear feedback control gain $K_t \in \calK_t$, where $\calK_t$ is constructed such that it guarantees that the safety constraints will be satisfied; then, the adversary reveals the current noise $w_t$ and the controller suffers a loss $f_t(K_t)$ -- \eg $f_t$ represents the system's tracking error at $t$ upon the realization of the noise. The controller aims to minimize its cumulative loss, despite knowing $w_t$ only after $K_t$ has been chosen. We validate our algorithm in simulated scenarios of safe control of linear dynamical systems in the presence of bounded noise
Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models
Authors: Soonwoo Kwon
Date: 2023-08-24 00:00:00

Shrinkage methods are frequently used to estimate fixed effects to reduce the noisiness of the least squares estimators. However, widely used shrinkage estimators guarantee such noise reduction only under strong distributional assumptions. I develop an estimator for the fixed effects that obtains the best possible mean squared error within a class of shrinkage estimators. This class includes conventional shrinkage estimators and the optimality does not require distributional assumptions. The estimator has an intuitive form and is easy to implement. Moreover, the fixed effects are allowed to vary with time and to be serially correlated, and the shrinkage optimally incorporates the underlying correlation structure in this case. In such a context, I also provide a method to forecast fixed effects one period ahead.
Financial Inclusion and Monetary Policy: A Study on the Relationship between Financial Inclusion and Effectiveness of Monetary Policy in Developing Countries
Authors: Gautam Kumar Biswas, Faruque Ahamed
Date: 2023-08-24 00:00:00

The study analyzed the impact of financial inclusion on the effectiveness of monetary policy in developing countries. By using a panel data set of 10 developing countries during 2004-2020, the study revealed that the financial inclusion measured by the number of ATM per 100,000 adults had a significant negative effect on monetary policy, whereas the other measure of financial inclusion i.e. the number of bank accounts per 100,000 adults had a positive impact on monetary policy, which is not statistically significant. The study also revealed that foreign direct investment (FDI), lending rate and exchange rate had a positive impact on inflation, but only the effect of lending rate is statistically significant. Therefore, the governments of these countries should make necessary drives to increase the level of financial inclusion as it stabilizes the price level by reducing the inflation in the economy.
Procurement in welfare programs: Evidence and implications from WIC infant formula contracts
Authors: Yonghong An, David Davis, Yizao Liu, Ruli Xiao
Date: 2023-08-24 00:00:00
Tasks: counterfactual

This paper examines the impact of government procurement in social welfare programs on consumers, manufacturers, and the government. We analyze the U.S. infant formula market, where over half of the total sales are purchased by the Women, Infants, and Children (WIC) program. The WIC program utilizes first-price auctions to solicit rebates from the three main formula manufacturers, with the winner exclusively serving all WIC consumers in the winning state. The manufacturers compete aggressively in providing rebates which account for around 85% of the wholesale price. To rationalize and disentangle the factors contributing to this phenomenon, we model manufacturers' retail pricing competition by incorporating two unique features: price inelastic WIC consumers and government regulation on WIC brand prices. Our findings confirm three sizable benefits from winning the auction: a notable spill-over effect on non-WIC demand, a significant marginal cost reduction, and a higher retail price for the WIC brand due to the price inelasticity of WIC consumers. Our counterfactual analysis shows that procurement auctions affect manufacturers asymmetrically, with the smallest manufacturer harmed the most. More importantly, by switching from the current mechanism to a predetermined rebate procurement, the government can still contain the cost successfully, consumers' surplus is greatly improved, and the smallest manufacturer benefits from the switch, promoting market competition.
Comparing Autoencoder to Geometrical Features for Vascular Bifurcations Identification
Authors: Ibtissam Essadik, Anass Nouri, Raja Touahni, Florent Autrusseau
Date: 2023-08-23 00:00:00

The cerebrovascular tree is a complex anatomical structure that plays a crucial role in the brain irrigation. A precise identification of the bifurcations in the vascular network is essential for understanding various cerebral pathologies. Traditional methods often require manual intervention and are sensitive to variations in data quality. In recent years, deep learning techniques, and particularly autoencoders, have shown promising performances for feature extraction and pattern recognition in a variety of domains. In this paper, we propose two novel approaches for vascular bifurcation identification based respectiveley on Autoencoder and geometrical features. The performance and effectiveness of each method in terms of classification of vascular bifurcations using medical imaging data is presented. The evaluation was performed on a sample database composed of 91 TOF-MRA, using various evaluation measures, including accuracy, F1 score and confusion matrix.
On Chernoff Lower-Bound of Outage Threshold for Non-Central $χ^2$-Distributed MIMO Beamforming Gain
Authors: Jinfei Wang, Yi Ma, Rahim Tafazolli
Date: 2023-08-24 00:00:00

The cumulative distribution function (CDF) of a non-central $\chi^2$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For adaptive transmitters, it is important but mathematically challenging to determine the outage threshold for an extreme target outage probability (e.g., $10^{-5}$ or less). This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most {effective} lower bound. The Cher-LB is then employed to predict the multi-antenna transmitter beamforming-gain in ultra-reliable and low-latency communication, concerning the first-order Markov time-varying channel. It is exhibited that, with the proposed Cher-LB, pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.
Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach
Authors: Bin Li, Rongrong Yang, Lei Liu, Junyi Wang, Ning Zhang, Mianxiong Dong
Date: 2023-08-24 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Robust Design

For multiple Unmanned-Aerial-Vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multi-type tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multi-agent Markov decision process and propose a multi-agent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic algorithms.
Network transformation-based analysis of biochemical systems
Authors: Dylan Antonio Talabis, Eduardo Mendoza
Date: 2023-08-24 00:00:00

A dynamical system obtains a wide variety of kinetic realizations, which is advantageous for the analysis of biochemical systems. A reaction network, derived from a dynamical system, may or may not possess some properties needed for a thorough analysis. We improve and extend the work of M. Johnston \cite{JOHN2014} and Hong et al. \cite{HONG2023} on network translations to network transformations, where the network is modified while preserving the dynamical system. These transformations can shrink, extend, or retain the stoichiometric subspace. Here, we show that positive dependent network can be translated to a weakly reversible network. Using the kinetic realizations of (1) calcium signaling in the olfactory system and (2) metabolic insulin signaling, we demonstrate the benefits of transformed systems with positive deficiency for analyzing biochemical systems. Furthermore, we present an algorithm for a network transformation of a weakly reversible non-complex factorizable kinetic (NFK) system to a weakly reversible complex factorizable kinetic (CFK) system, thereby enhancing the Subspace Coincidence Theorem for NFK systems of Nazareno et al. \cite{NAZA2019}. Finally, using the transformed kinetic realization of monolignol biosynthesis in \textit{Populus xylem}, we study the structural and kinetic properties of transformed systems,including the invariance of concordance and variation of injectivity and mono-/multi-stationarity under network transformation.
Machine Learning Small Molecule Properties in Drug Discovery
Authors: Nikolai Schapin, Maciej Majewski, Alejandro Varela, Carlos Arroniz, Gianni de Fabritiis
Date: 2023-08-02 00:00:00
Tasks: Decision Making, Drug Discovery

Machine learning (ML) is a promising approach for predicting small molecule properties in drug discovery. Here, we provide a comprehensive overview of various ML methods introduced for this purpose in recent years. We review a wide range of properties, including binding affinities, solubility, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity). We discuss existing popular datasets and molecular descriptors and embeddings, such as chemical fingerprints and graph-based neural networks. We highlight also challenges of predicting and optimizing multiple properties during hit-to-lead and lead optimization stages of drug discovery and explore briefly possible multi-objective optimization techniques that can be used to balance diverse properties while optimizing lead candidates. Finally, techniques to provide an understanding of model predictions, especially for critical decision-making in drug discovery are assessed. Overall, this review provides insights into the landscape of ML models for small molecule property predictions in drug discovery. So far, there are multiple diverse approaches, but their performances are often comparable. Neural networks, while more flexible, do not always outperform simpler models. This shows that the availability of high-quality training data remains crucial for training accurate models and there is a need for standardized benchmarks, additional performance metrics, and best practices to enable richer comparisons between the different techniques and models that can shed a better light on the differences between the many techniques.
Genome replication in asynchronously growing microbial populations
Authors: Florian Pflug, Deepak Bhat, Simone Pigolotti
Date: 2023-08-24 00:00:00

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. Qualitatively, in a growing cell culture, one expects that genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory to explain these data is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in Escherichia coli and in budding yeast. In both cases, our approach leads to analytical predictions that are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.
Uncertainty Propagation and Dynamic Robust Risk Measures
Authors: Marlon Moresco, Mélina Mailhot, Silvana M. Pesenti
Date: 2023-08-24 00:00:00

We introduce a framework for quantifying propagation of uncertainty arising in a dynamic setting. Specifically, we define dynamic uncertainty sets designed explicitly for discrete stochastic processes over a finite time horizon. These dynamic uncertainty sets capture the uncertainty surrounding stochastic processes and models, accounting for factors such as distributional ambiguity. Examples of uncertainty sets include those induced by the Wasserstein distance and f-divergences. We further define dynamic robust risk measures as the supremum of all candidates' risks within the uncertainty set. In an axiomatic way, we discuss conditions on the uncertainty sets that lead to well-known properties of dynamic robust risk measures, such as convexity and coherence. Furthermore, we discuss the necessary and sufficient properties of dynamic uncertainty sets that lead to time-consistencies of dynamic robust risk measures. We find that uncertainty sets stemming from f-divergences lead to strong time-consistency while the Wasserstein distance results in a new time-consistent notion of weak recursiveness. Moreover, we show that a dynamic robust risk measure is strong time-consistent or weak recursive if and only if it admits a recursive representation of one-step conditional robust risk measures arising from static uncertainty sets.
DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using Sentence Transformers and Reciprocal-Rank Fusion
Authors: Anthony Yazdani, Hossein Rouhizadeh, David Vicente Alvarez, Douglas Teodoro
Date: 2023-08-15 00:00:00
Tasks: Sentence

This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health (DS4DH) group for the Social Media Mining for Health Applications (SMM4H) 2023 shared task 5. Shared task 5 targeted the normalization of adverse drug event mentions in Twitter to standard concepts of the Medical Dictionary for Regulatory Activities terminology. Our system hinges on a two-stage approach: BERT fine-tuning for entity recognition, followed by zero-shot normalization using sentence transformers and reciprocal-rank fusion. The approach yielded a precision of 44.9%, recall of 40.5%, and an F1-score of 42.6%. It outperformed the median performance in shared task 5 by 10% and demonstrated the highest performance among all participants. These results substantiate the effectiveness of our approach and its potential application for adverse drug event normalization in the realm of social media text mining.