pwc-archive/papers-with-abstracts huggingface dataset:
Inference for High Dimensional Censored Quantile Regression
Authors: Zhe Fei, Qi Zheng, Hyokyoung G. Hong, Yi Li
Date: 2021-07-22 00:00:00
Tasks: Epidemiology, quantile regression, regression, Variable Selection, Vocal Bursts Intensity Prediction

With the availability of high dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inference on the effects of high dimensional predictors for censored quantile regression. This paper proposes a novel procedure to draw inference on all predictors within the framework of global censored quantile regression, which investigates covariate-response associations over an interval of quantile levels, instead of a few discrete values. The proposed estimator combines a sequence of low dimensional model estimates that are based on multi-sample splittings and variable selection. We show that, under some regularity conditions, the estimator is consistent and asymptotically follows a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure can properly quantify the uncertainty of the estimates in high dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in lung cancer pathways on patients' survival, using the Boston Lung Cancer Survival Cohort, a cancer epidemiology study on the molecular mechanism of lung cancer.
Optimization on manifolds: A symplectic approach
Authors: Guilherme França, Alessandro Barp, Mark Girolami, Michael I. Jordan
Date: 2021-07-23 00:00:00

Optimization tasks are crucial in statistical machine learning. Recently, there has been great interest in leveraging tools from dynamical systems to derive accelerated and robust optimization methods via suitable discretizations of continuous-time systems. However, these ideas have mostly been limited to Euclidean spaces and unconstrained settings, or to Riemannian gradient flows. In this work, we propose a dissipative extension of Dirac's theory of constrained Hamiltonian systems as a general framework for solving optimization problems over smooth manifolds, including problems with nonlinear constraints. We develop geometric/symplectic numerical integrators on manifolds that are "rate-matching," i.e., preserve the continuous-time rates of convergence. In particular, we introduce a dissipative RATTLE integrator able to achieve optimal convergence rate locally. Our class of (accelerated) algorithms are not only simple and efficient but also applicable to a broad range of contexts.
Applying Evolutionary Algorithms Successfully: A Guide Gained from Real-world Applications
Authors: Wilfried Jakob
Date: 2021-07-23 00:00:00
Tasks: Evolutionary Algorithms, Management

Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization. This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated.
A Resolution-Adaptive 8 mm$^\text{2}$ 9.98 Gb/s 39.7 pJ/b 32-Antenna All-Digital Spatial Equalizer for mmWave Massive MU-MIMO in 65nm CMOS
Authors: Oscar Castañeda, Zachariah Boynton, Seyed Hadi Mirfarshbafan, Shimin Huang, Jamie C. Ye, Alyosha Molnar, Christoph Studer
Date: 2021-07-23 00:00:00
Tasks: All

All-digital millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) receivers enable extreme data rates but require high power consumption. In order to reduce power consumption, this paper presents the first resolution-adaptive all-digital receiver ASIC that is able to adjust the resolution of the data-converters and baseband-processing engine to the instantaneous communication scenario. The scalable 32-antenna, 65 nm CMOS receiver occupies a total area of 8 mm$^\text{2}$ and integrates analog-to-digital converters (ADCs) with programmable gain and resolution, beamspace channel estimation, and a resolution-adaptive processing-in-memory spatial equalizer. With 6-bit ADC samples and a 4-bit spatial equalizer, our ASIC achieves a throughput of 9.98 Gb/s while being at least 2x more energy-efficient than state-of-the-art designs.
Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO
Authors: Oscar Castañeda, Seyed Hadi Mirfarshbafan, Shahaboddin Ghajari, Alyosha Molnar, Sven Jacobsson, Giuseppe Durisi, Christoph Studer
Date: 2021-07-23 00:00:00
Tasks: All

All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters (ADCs) and spatial equalizer of an all-digital system to the communication scenario (e.g., the number of users, modulation scheme, and propagation conditions) enables orders-of-magnitude power savings for realistic mmWave channels. For example, for a 256-BS-antenna 16-user system supporting 1 GHz bandwidth, a traditional baseline architecture designed for a 64-user worst-case scenario would consume 23 W in 28 nm CMOS for the ADC array and the spatial equalizer, whereas a resolution-adaptive architecture is able to reduce the power consumption by 6.7x.
Using NLP to analyze whether customer statements comply with their inner belief
Authors: Fabian Thaler, Stefan Faußer, Heiko Gewald
Date: 2021-07-22 00:00:00

Customers' emotions play a vital role in the service industry. The better frontline personnel understand the customer, the better the service they can provide. As human emotions generate certain (unintentional) bodily reactions, such as increase in heart rate, sweating, dilation, blushing and paling, which are measurable, artificial intelligence (AI) technologies can interpret these signals. Great progress has been made in recent years to automatically detect basic emotions like joy, anger etc. Complex emotions, consisting of multiple interdependent basic emotions, are more difficult to identify. One complex emotion which is of great interest to the service industry is difficult to detect: whether a customer is telling the truth or just a story. This research presents an AI-method for capturing and sensing emotional data. With an accuracy of around 98 %, the best trained model was able to detect whether a participant of a debating challenge was arguing for or against her/his conviction, using speech analysis. The data set was collected in an experimental setting with 40 participants. The findings are applicable to a wide range of service processes and specifically useful for all customer interactions that take place via telephone. The algorithm presented can be applied in any situation where it is helpful for the agent to know whether a customer is speaking to her/his conviction. This could, for example, lead to a reduction in doubtful insurance claims, or untruthful statements in job interviews. This would not only reduce operational losses for service companies, but also encourage customers to be more truthful.
Semantic Communications for Speech Recognition
Authors: Zhenzi Weng, Zhijin Qin, Geoffrey Ye Li
Date: 2021-07-22 00:00:00
Tasks: Semantic Communication, speech-recognition, Speech Recognition

The traditional communications transmit all the source data represented by bits, regardless of the content of source and the semantic information required by the receiver. However, in some applications, the receiver only needs part of the source data that represents critical semantic information, which prompts to transmit the application-related information, especially when bandwidth resources are limited. In this paper, we consider a semantic communication system for speech recognition by designing the transceiver as an end-to-end (E2E) system. Particularly, a deep learning (DL)-enabled semantic communication system, named DeepSC-SR, is developed to learn and extract text-related semantic features at the transmitter, which motivates the system to transmit much less than the source speech data without performance degradation. Moreover, in order to facilitate the proposed DeepSC-SR for dynamic channel environments, we investigate a robust model to cope with various channel environments without requiring retraining. The simulation results demonstrate that our proposed DeepSC-SR outperforms the traditional communication systems in terms of the speech recognition metrics, such as character-error-rate and word-error-rate, and is more robust to channel variations, especially in the low signal-to-noise (SNR) regime.
Heuristics for Customer-focused Ride-pooling Assignment
Authors: Alexander Sundt, Qi Luo, John Vincent, Mehrdad Shahabi, Yafeng Yin
Date: 2021-07-23 00:00:00

Ride-pooling has become an important service option offered by ride-hailing platforms as it serves multiple trip requests in a single ride. By leveraging customer data, connected vehicles, and efficient assignment algorithms, ride-pooling can be a critical instrument to address driver shortages and mitigate the negative externalities of ride-hailing operations. Recent literature has focused on computationally intensive optimization-based methods that maximize system throughput or minimize vehicle miles. However, individual customers may experience substantial service quality degradation due to the consequent waiting and detour time. In contrast, this paper examines heuristic methods for real-time ride-pooling assignments that are highly scalable and easily computable. We propose a restricted subgraph method and compare it with other existing heuristic and optimization-based matching algorithms using a variety of metrics. By fusing multiple sources of trip and network data in New York City, we develop a flexible, agent-based simulation platform to test these strategies on different demand levels and examine how they affect both the customer experience and the ride-hailing platform. Our results find a trade-off among heuristics between throughput and customer matching time. We show that our proposed ride-pooling strategy maintains system performance while limiting trip delays and improving customer experience. This work provides insight for policymakers and ride-hailing operators about the performance of simpler heuristics and raises concerns about prioritizing only specific platform metrics without considering service quality.
Graphical Nonlinear System Analysis
Authors: Thomas Chaffey, Fulvio Forni, Rodolphe Sepulchre
Date: 2021-07-23 00:00:00

We use the recently introduced concept of a Scaled Relative Graph (SRG) to develop a graphical analysis of input-output properties of feedback systems. The SRG of a nonlinear operator generalizes the Nyquist diagram of an LTI system. In the spirit of classical control theory, important robustness indicators of nonlinear feedback systems are measured as distances between SRGs.
Chance Constrained Economic Dispatch Considering the Capability of Network Flexibility Against Renewable Uncertainties
Authors: Yue Song, Tao Liu, David J. Hill
Date: 2021-07-23 00:00:00
Tasks: Form

This paper incorporates a continuous-type network flexibility into chance constrained economic dispatch (CCED). In the proposed model, both power generations and line susceptances are continuous variables to minimize the expected generation cost and guarantee a low probability of constraint violation in terms of generations and line flows under renewable uncertainties. From the analytical form of CCED, we figure out the mechanism of network flexibility against uncertainties -- while renewable uncertainties shrink the usable line capacities and aggravate transmission congestion, network flexibility mitigates congestion by re-routing the base-case line flows and reducing the line capacity shrinkage caused by uncertainties. Further, we propose an alternate iteration solver for this problem. By duality theory, we set up a master problem in the form of second-order cone programming to optimize generation dispatch scheme and a subproblem in the form of linear programming to optimize line susceptances. A satisfactory solution can be obtained efficiently by alternately solving these two problems. The proposed method applies to both Gaussian uncertainty and non-Gaussian uncertainty by means of Gaussian mixture model. The case studies on the IEEE 14-bus system and IEEE 118-bus system suggest that network flexibility can significantly improve operational economy while ensuring security under uncertainties.
A Hybrid Vehicle Platoon for Connected and Automated Vehicles: Formulation, Stability Analysis, and Applications
Authors: Yuan Zheng, Min Xu, Shining Wu, Shuaian Wang
Date: 2021-07-23 00:00:00

Vehicle platooning has the potential to significantly improve traffic throughput and reduce fuel consumption and emissions and thus has attracted extensive attention recently. In this study, we propose a hybrid vehicle platoon system for the connected and automated vehicles (CAVs). First, a hybrid spacing policy combining the constant time gap (CTG) and constant spacing (CS) is formulated for the proposed platoon system, where the leader adopts the CTG and the followers use the CS policy. Based on the h2-norm string stability criteria, the notions of exogenous-head-to-tail string stability and hybrid string stability are newly introduced, and the sufficient conditions of the hybrid string stability in the frequency domain are derived using the Laplace transform. Numerical experiments are conducted to validate the hybrid string stability. Moreover, two typical scenarios and several measurements of effectiveness (MOE) are adopted to verify the effectiveness of the proposed hybrid platoon system in various aspects. The results show that the hybrid platoon system performs better than the CS-based platoon system. It also indicates that the hybrid platoon system has obvious advantages over the CTG-based platoon system under the periodical fluctuation scenario and it is also comparable to the CTG-based platoon system under the large deceleration and acceleration scenario. The findings have demonstrated the merits of the combined implementation of CTG and CS policy in enhancing the performance and applicability of the platoon system for CAVs.
Synchronization Instability of Inverter-Based Generation During Asymmetrical Grid Faults
Authors: Xiuqiang He, Changjun He, Sisi Pan, Hua Geng, Feng Liu
Date: 2020-11-20 00:00:00

The transient stability of traditional power systems is concerned with the ability of generators to stay synchronized with the positive-sequence voltage of the network, whether for symmetrical or asymmetrical faults. In contrast, both positive- and negative-sequence synchronizations should be of concern for inverter-based generation (IBG) under asymmetrical faults. This is because the latest grid codes stipulate that IBG should inject dual-sequence current when riding through asymmetrical faults. Currently, much less is known about the synchronization stability during asymmetrical faults. This significantly differs from the positive-sequence synchronization alone because the coupled dual-sequence synchronization is involved. This paper aims to fill this gap. Considering the sequence coupling under asymmetrical faults, the dual-sequence synchronization model of IBG is developed. Based on the model, the conditions that steady-state equilibrium points should follow are identified. The conditions throw light on the possible types of synchronization instability, including the positive-sequence dominated instability and the negative-sequence dominated one. For different types of instability, the dominant factors are analyzed quantitatively, which are reflected by the limit on the current injection amplitude. Exceeding the limit will lead to the loss of both positive- and negative-sequence synchronizations. The model and the analysis are verified by simulations and hardware-in-the-loop experiments.
A 60-GHz Radar Sensor for Micron-Scale Motion Detection
Authors: Marcel Balle, Chengkai Zhu, Bin Zhang, Jie Wang, Lixin Ran
Date: 2021-07-23 00:00:00
Tasks: Contact Detection, Motion Detection, Sensitivity

A compact, continuous-wave, mmWave radar sensor is developed for non-contact detection of micron-scale motions. This board-integrated radar system consists of a pair of mmWave transmitter and receiver, two series-fed microstrip patch arrays, an IF subsystem, and a microcontroller. Working at 60-GHz frequency, this super-heterodyne, digital-IF radar sensor exhibits an agile sensitivity and a robust anti-noise performance. Assisted by a gradient-descent DC offset estimation and a phase demodulation algorithm based on extended differential and cross-multiply, a 45-{\mu}m pendulum swing can be experimentally detected with a 20.6-dB SNR, verifying its strong ability in Doppler micro-motion detections.
Modal Decomposition of the Linear Swing Equation in Networks with Symmetries
Authors: Kshitij Bhatta, Majeed Hayat, Francesco Sorrentino
Date: 2021-07-22 00:00:00

Symmetries are widespread in physical, technological, biological, and social systems and networks, including power grids. The swing equation is a classic model for the dynamics of powergrid networks. The main goal of this paper is to explain how network symmetries affect the swing equation transient and steady state dynamics. We introduce a modal decomposition that allows us to study transient effects, such as the presence of overshoots in the system response. This modal decomposition provides insight into the peak flows within the network lines and allows a rigorous characterization of the effects of symmetries in the network topology on the dynamics. Our work applies to both cases of homogeneous and heterogeneous parameters. Further, the model is used to show how small perturbations propagate in networks with symmetries. Finally, we present an application of our approach to a large power grid network that displays symmetries.
Individual crop loads provide local control for collective food intake in ant colonies
Authors: Efrat Greenwald, Lior Baltiansky, Ofer Feinerman
Date: 2021-07-23 00:00:00

Nutritional regulation by ants emerges from a distributed process: food is collected by a small fraction of workers, stored within the crops of individuals, and spreads via local ant-to-ant interactions. The precise individual level underpinnings of this collective regulation have remained unclear mainly due to difficulties in measuring food within ants crops. Here we image fluorescent liquid food in individually tagged Camponotus sanctus ants, and track the real-time food flow from foragers to their gradually satiating colonies. We show how the feedback between colony satiation level and food inflow is mediated by individual crop loads; specifically, the crop loads of recipient ants control food flow rates, while those of foragers regulate the frequency of foraging trips. Interestingly, these effects do not rise from pure physical limitations of crop capacity. Our findings suggest that the emergence of food intake regulation does not require individual foragers to assess the global state of the colony.
Estimation of Conditional Average Treatment Effects with High-Dimensional Data
Authors: Qingliang Fan, Yu-Chin Hsu, Robert P. Lieli, Yichong Zhang
Date: 2019-08-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. We consider two variants of the estimator depending on whether the nuisance functions are estimated over the full sample or over a hold-out sample. Building on Belloni at al. (2017) and Chernozhukov et al. (2018), we derive functional limit theory for the estimators and provide an easy-to-implement procedure for uniform inference based on the multiplier bootstrap. The empirical application revisits the effect of maternal smoking on a baby's birth weight as a function of the mother's age.
Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions
Authors: Nicolas Eschenbaum, Helge Liebert
Date: 2021-07-23 00:00:00

This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reputation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.
Margin trading, short selling and corporate green innovation
Authors: Ge-zhi Wu, Da-ming You
Date: 2021-07-23 00:00:00
Tasks: Management

This paper uses the panel data of Chinese listed companies from 2007 to 2019, uses the relaxation of China's margin trading and short selling restrictions as the basis of quasi experimental research, and then constructs a double difference model to analyze whether the margin trading and short selling will encourage enterprises to engage in green technology innovation activities. Firstly, our research results show that after the implementation of the margin trading and short selling, the green technology innovation behavior of pilot companies will increase significantly. We believe that the short selling threat and pressure brought by short selling to enterprises are the main reasons for pilot enterprises to engage in green technology innovation. Secondly, the empirical results show that the implementation of margin trading and short selling will significantly promote the quantity of green technology innovation of pilot enterprises, but will not significantly promote the quality of green technology innovation of pilot enterprises. Furthermore, we analyze the difference of the impact of margin trading and short selling on the quantity of green technology innovation of pilot enterprises in different periods. Finally, we find that the performance decline, yield gap between financial assets and operating assets, the risk of stock price crash, management shareholding, institutional shareholding ratio, product market competition, short selling intensity, margin trading intensity and formal environmental regulation intensity will affect the role of policy in promoting green technology innovation of pilot enterprises.