pwc-archive/papers-with-abstracts huggingface dataset:
Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning
Authors: Yongsheng Cao, Hao Wang, Demin Li, Guanglin Zhang
Date: 2021-06-01 00:00:00
Tasks: Computational Efficiency, energy management, Management, Scheduling

With the advances in the Internet of Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This paper investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor-critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor-critic learning charging algorithm (CALC) by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs' expected cost by 24.03%, 21.49%, 13.80%, compared with the Eagerly Charging Algorithm, Online Charging Algorithm, RL-based Adaptive Energy Management Algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.
Resource allocation for D2D-Based AMI Communications Underlaying LTE Cellular Networks
Authors: H. H. Esmat, Mahmoud M. Elmesalawy, I. I. Ibrahim
Date: 2021-06-01 00:00:00
Tasks: Management

Smart meters are utilized to transmit the consumption information to the metering data management system for observing and management in smart grid advanced metering infrastructure systems. In the meantime, for efficient utilization for spectrum, Device-to-Device (D2D) communications underlaying LTE networks are a promising wireless communication technology for advanced metering infrastructure which supporting a technique for reusing the same radio resources (RRs) of LTE networks. Therefore, we examine the utilization of D2D communication technology for advanced metering infrastructure communications underlaying LTE networks. A novel approach is suggested for provisioning the mandatory communication between serving data concentrator and its set of smart meters using this technology. The suggested approach is dependent on two main stages. The group of permissible cellular user equipment reuse candidates for every smart meter is calculated with taking the quality of service demands for cellular user devices and smart meters into consideration in the first stage. The optimal RR allocation for every smart meter is determined based on maximizing the access rate of smart meters which can be accepted and operated in D2D reuse mode in the second stage. Simulation results prove the efficacy of the suggested approach for efficient advanced metering infrastructure communication underlaying LTE systems with accepting remarkable number of SMs and accomplishing outstanding throughput gain.
On Optimizing the Conditional Value-at-Risk of a Maximum Cost for Risk-Averse Safety Analysis
Authors: Margaret P. Chapman, Michael Fauss, Kevin M. Smith
Date: 2021-06-01 00:00:00

The popularity of Conditional Value-at-Risk (CVaR), a risk functional from finance, has been growing in the control systems community due to its intuitive interpretation and axiomatic foundation. We consider a nonstandard optimal control problem in which the goal is to minimize the CVaR of a maximum random cost subject to a Borel-space Markov decision process. The objective represents the maximum departure from a desired operating region averaged over a given fraction of the worst cases. This problem provides a safety criterion for a stochastic system that is informed by both the probability and severity of the potential consequences of the system's behavior. In contrast, existing safety analysis frameworks apply stage-wise risk constraints or assess the probability of constraint violation without quantifying the potential severity of the violation. To the best of our knowledge, the problem of interest has not been solved. To solve the problem, we propose and study a family of stochastic dynamic programs on an augmented state space. We prove that the optimal CVaR of a maximum random cost enjoys an equivalent representation in terms of the solutions to these dynamic programs under appropriate assumptions. For each dynamic program, we show the existence of an optimal policy that depends on the dynamics of an augmented state under the assumptions. In a numerical example, we illustrate how our safety analysis framework is useful for assessing the severity of combined sewer overflows under precipitation uncertainty.
A Simulation-Optimization Technique for Service Level Analysis in Conjunction with Reorder Point Estimation and Lead-Time consideration: A Case Study in Sea Port
Authors: Mohammad Arani, Saeed Abdolmaleki, Maryam Maleki, Mohsen Momenitabar, Xian Liu
Date: 2021-05-28 00:00:00
Tasks: Diversity

This study offers a step-by-step practical procedure from the analysis of the current status of the spare parts inventory system to advanced service-level analysis by virtue of simulation-optimization technique for a real-world case study associated with a seaport. The remarkable variety and immense diversity on one hand, and extreme complexities not only in consumption patterns but in the supply of spare parts in an international port with technically advance port operator machinery, on the other hand, have convinced the managers to deal with this issue in a structural framework. The huge available data require cleaning and classification to properly process them and derive reorder point (ROP) estimation, reorder quantity (ROQ) estimation, and associated service level analysis. Finally, from 247000 items used in 9 years long, 1416 inventory items are elected as a result of ABC analysis integrating with the Analytic Hierarchy Process (AHP), which led to the main items that need to be kept under strict inventory control. The ROPs and the pertinent quantities are simulated by Arena software for all the main items, each of which took approximately 30 minutes run-time on a personal computer to determine near-optimal estimations.
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
Authors: Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi
Date: 2021-05-23 00:00:00
Tasks: Active Learning, Bayesian Optimisation, Bayesian Optimization, Benchmarking, Materials Screening, Model Selection, Relevance Detection

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and shapes. By defining acceleration and enhancement metrics for general materials optimization objectives, we find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD. We discuss the implicit distributional assumptions of RF and GP, and the benefits of using GP with anisotropic kernels in detail. We provide practical insights for experimentalists on surrogate model selection of BO during materials optimization campaigns.
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series
Authors: Mohsin Munir, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed
Date: 2018-12-19 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis, Time Series Anomaly Detection, Unsupervised Anomaly Detection

Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap
in time series anomaly detection in the current era of the IoT. To address this problem, we present a novel deep
learning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to
the non-streaming cases. DeepAnT is capable of detecting a wide range of anomalies, i.e., point anomalies,
contextual anomalies, and discords in time series data. In contrast to the anomaly detection methods where
anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to
forecast the normal behavior of a time series. DeepAnT consists of two modules: time series predictor and
anomaly detector. The time series predictor module uses deep convolutional neural network (CNN) to predict
the next time stamp on the dened horizon. This module takes a window of time series (used as a context)
and attempts to predict the next time stamp. The predicted value is then passed to the anomaly detector
module, which is responsible for tagging the corresponding time stamp as normal or abnormal. DeepAnT
can be trained even without removing the anomalies from the given data set. Generally, in deep learning based
approaches, a lot of data are required to train a model. Whereas in DeepAnT, a model can be trained
on relatively small data set while achieving good generalization capabilities due to the effective parameter
sharing of the CNN. As the anomaly detection in DeepAnT is unsupervised, it does not rely on anomaly
labels at the time of model generation. Therefore, this approach can be directly applied to real-life scenarios
where it is practically impossible to label a big stream of data coming from heterogeneous sensors comprising
of both normal as well as anomalous points. We have performed a detailed evaluation of 15 algorithms on
10 anomaly detection benchmarks, which contain a total of 433 real and synthetic time series. Experiments
show that DeepAnT outperforms the state-of-the-art anomaly detection methods in most of the cases, while
performing on par with others.
Transmitter IQ Skew Calibration using Direct Detection
Authors: Wing Chau Ng, Xuefeng Tang, Zhuhong Zhang
Date: 2021-06-01 00:00:00

We propose a transmitter skew calibration based on direct detection of coherent signals with estimation errors of +/-0.2ps, providing a reliable, accurate and low-cost scheme to calibrate skew for coherent transceivers. In October 2019, this work was submitted / was exposed to Optical Fiber Communication Conference 2020 but an acceptance was not granted. We claimed the first time to use a direct detection-based feedback method for coherent transmitter calibration.
Noncompliance in randomized control trials without exclusion restrictions
Authors: Masayuki Sawada
Date: 2019-10-08 00:00:00

This study proposes a method to identify treatment effects without exclusion restrictions in randomized experiments with noncompliance. Exploiting a baseline survey commonly available in randomized experiments, I decompose the intention-to-treat effects conditional on the endogenous treatment status. I then identify these parameters to understand the effects of the assignment and treatment. The key assumption is that a baseline variable maintains rank orders similar to the control outcome. I also reveal that the change-in-changes strategy may work without repeated outcomes. Finally, I propose a new estimator that flexibly incorporates covariates and demonstrate its properties using two experimental studies.
Justice as a Social Bargain and Optimization Problem
Authors: Andreas Siemoneit
Date: 2021-06-01 00:00:00
Tasks: Philosophy

The question of "Justice" still divides social research and moral philosophy. Several Theories of Justice and conceptual approaches compete here, and distributive justice remains a major societal controversy. From an evolutionary point of view, fair and just exchange can be nothing but "equivalent", and this makes "strict" reciprocity (merit, equity) the foundational principle of justice, both theoretically and empirically. But besides being just, justice must be effective, efficient, and communicable. Moral reasoning is a communicative strategy for resolving conflict, enhancing status, and maintaining cooperation, thereby making justice rather a social bargain and an optimization problem. Social psychology (intuitions, rules of thumb, self-bindings) can inform us when and why the two auxiliary principles equality and need are more likely to succeed than merit would. Nevertheless, both equality and need are governed by reciprocal considerations, and self-bindings help to interpret altruism as "very generalized reciprocity". The Meritocratic Principle can be implemented, and its controversy avoided, by concentrating on "non-merit", i.e., institutionally draining the wellsprings of undeserved incomes (economic rents). Avoiding or taxing away economic rents is an effective implementation of justice in liberal democracies. This would enable market economies to bring economic achievement and income much more in line, thus becoming more just.
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition
Authors: Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Takafumi Moriya, Naoyuki Kamo
Date: 2021-06-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

Although recent advances in deep learning technology improved automatic speech recognition (ASR), it remains difficult to recognize speech when it overlaps other people's voices. Speech separation or extraction is often used as a front-end to ASR to handle such overlapping speech. However, deep neural network-based speech enhancement can generate `processing artifacts' as a side effect of the enhancement, which degrades ASR performance. For example, it is well known that single-channel noise reduction for non-speech noise (non-overlapping speech) often does not improve ASR. Likewise, the processing artifacts may also be detrimental to ASR in some conditions when processing overlapping speech with a separation/extraction method, although it is usually believed that separation/extraction improves ASR. In order to answer the question `Do we always have to separate/extract speech from mixtures?', we analyze ASR performance on observed and enhanced speech at various noise and interference conditions, and show that speech enhancement degrades ASR under some conditions even for overlapping speech. Based on these findings, we propose a simple switching algorithm between observed and enhanced speech based on the estimated signal-to-interference ratio and signal-to-noise ratio. We demonstrated experimentally that such a simple switching mechanism can improve recognition performance when processing artifacts are detrimental to ASR.
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising
Authors: Kaicong Sun, Sven Simon
Date: 2021-06-01 00:00:00
Tasks: Denoising, Image Denoising, Image Super-Resolution, Super-Resolution

In this paper, we propose a regularization technique for noisy-image super-resolution and image denoising. Total variation (TV) regularization is adopted in many image processing applications to preserve the local smoothness. However, TV prior is prone to oversmoothness, staircasing effect, and contrast losses. Nonlocal TV (NLTV) mitigates the contrast losses by adaptively weighting the smoothness based on the similarity measure of image patches. Although it suppresses the noise effectively in the flat regions, it might leave residual noise surrounding the edges especially when the image is not oversmoothed. To address this problem, we propose the bilateral spectrum weighted total variation (BSWTV). Specially, we apply a locally adaptive shrink coefficient to the image gradients and employ the eigenvalues of the covariance matrix of the weighted image gradients to effectively refine the weighting map and suppress the residual noise. In conjunction with the data fidelity term derived from a mixed Poisson-Gaussian noise model, the objective function is decomposed and solved by the alternating direction method of multipliers (ADMM) algorithm. In order to remove outliers and facilitate the convergence stability, the weighting map is smoothed by a Gaussian filter with an iteratively decreased kernel width and updated in a momentum-based manner in each ADMM iteration. We benchmark our method with the state-of-the-art approaches on the public real-world datasets for super-resolution and image denoising. Experiments show that the proposed method obtains outstanding performance for super-resolution and achieves promising results for denoising on real-world images.
Asymptotic Performance of TDOA Estimation using Satellites
Authors: Hodaya Halevi, Itsik Bergel, Yair Noam
Date: 2021-06-02 00:00:00

We present novel lower bounds on the localization error using a network of satellites randomly deployed on a sphere around Earth. Our new analysis approach characterizes the localization performance by its asymptotic behavior as the number of satellites gets large while assuming a dense network. Using the law of large numbers, we derive closed-form expressions for the asymptotic Cramer Rao bound (CRB) from which we draw valuable insights. The resulting expressions depend solely on the network statistics and are not a function of a particular network configuration. We consider two types of estimators. The first uses the exact statistical model, and hence employs both timing and amplitude information. The second estimator ignores the amplitudes and hence uses only time difference of arrival (TDOA) information. The asymptotic CRB indicates that for practical system setup, a TDOA estimator approaches the performance of the ideal estimator. For both estimators, the localization accuracy improves as satellites get closer to Earth. The latter finding is essential in light of the proliferation of low-Earth-orbit (LEO) satellites and motivates a further study of localization-performance in such networks. Besides, we show that the vertical localization accuracy is lower than the horizontal accuracy and is also more sensitive to the receiver field-of-view.
Passive Beamforming Design for Intelligent Reflecting Surface Assisted MIMO Systems
Authors: Chenghao Feng, Wenqian Shen, Xinyu Gao, Jianping An
Date: 2021-06-02 00:00:00
Tasks: Quantization

Intelligent reflecting surfaces (IRSs) constitute passive devices, which are capable of adjusting the phase shifts of their reflected signals, and hence they are suitable for passive beamforming. In this paper, we conceive their design with the active beamforming action of multiple-input multipleoutput (MIMO) systems used at the access points (APs) for improving the beamforming gain, where both the APs and users are equipped with multiple antennas. Firstly, we decouple the optimization problem and design the active beamforming for a given IRS configuration. Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program (QCQP). For solving the transformed problem, we give an approximate solution based on the technique of widely used semidefinite relaxation (SDR). We also propose a low-complexity iterative solution. We further prove that it can converge to a locally optimal value. Finally, considering the practical scenario of discrete phase shifts at the IRS, we give the quantization design for IRS elements on basis of the two solutions. Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks.
Law-invariant functionals that collapse to the mean: Beyond convexity
Authors: Felix-Benedikt Liebrich, Cosimo Munari
Date: 2021-06-02 00:00:00

We establish general "collapse to the mean" principles that provide conditions under which a law-invariant functional reduces to an expectation. In the convex setting, we retrieve and sharpen known results from the literature. However, our results also apply beyond the convex setting. We illustrate this by providing a complete account of the "collapse to the mean" for quasiconvex functionals. In the special cases of consistent risk measures and Choquet integrals, we can even dispense with quasiconvexity. In addition, we relate the "collapse to the mean" to the study of solutions of a broad class of optimisation problems with law-invariant objectives that appear in mathematical finance, insurance, and economics. We show that the corresponding quantile formulations studied in the literature are sometimes illegitimate and require further analysis.
On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins
Authors: Kresten Lindorff-Larsen, Birthe B. Kragelund
Date: 2021-06-01 00:00:00

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.
Neural message passing for joint paratope-epitope prediction
Authors: Alice Del Vecchio, Andreea Deac, Pietro Liò, Petar Veličković
Date: 2021-05-31 00:00:00
Tasks: Prediction

Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them. The binding sites in an antibody-antigen interaction are known as the paratope and epitope, respectively, and the prediction of these regions is key to vaccine and synthetic antibody development. Contrary to prior art, we argue that paratope and epitope predictors require asymmetric treatment, and propose distinct neural message passing architectures that are geared towards the specific aspects of paratope and epitope prediction, respectively. We obtain significant improvements on both tasks, setting the new state-of-the-art and recovering favourable qualitative predictions on antigens of relevance to COVID-19.