pwc-archive/papers-with-abstracts huggingface dataset:
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training
Authors: Zehua Chen, Xu Tan, Ke Wang, Shifeng Pan, Danilo Mandic, Lei He, Sheng Zhao
Date: 2022-02-08 00:00:00
Tasks: Denoising

Denoising diffusion probabilistic models (diffusion models for short) require a large number of iterations in inference to achieve the generation quality that matches or surpasses the state-of-the-art generative models, which invariably results in slow inference speed. Previous approaches aim to optimize the choice of inference schedule over a few iterations to speed up inference. However, this results in reduced generation quality, mainly because the inference process is optimized separately, without jointly optimizing with the training process. In this paper, we propose InferGrad, a diffusion model for vocoder that incorporates inference process into training, to reduce the inference iterations while maintaining high generation quality. More specifically, during training, we generate data from random noise through a reverse process under inference schedules with a few iterations, and impose a loss to minimize the gap between the generated and ground-truth data samples. Then, unlike existing approaches, the training of InferGrad considers the inference process. The advantages of InferGrad are demonstrated through experiments on the LJSpeech dataset showing that InferGrad achieves better voice quality than the baseline WaveGrad under same conditions while maintaining the same voice quality as the baseline but with $3$x speedup ($2$ iterations for InferGrad vs $6$ iterations for WaveGrad).
CALM: Contrastive Aligned Audio-Language Multirate and Multimodal Representations
Authors: Vin Sachidananda, Shao-Yen Tseng, Erik Marchi, Sachin Kajarekar, Panayiotis Georgiou
Date: 2022-02-08 00:00:00
Tasks: Emotion Recognition, Natural Language Understanding

Deriving multimodal representations of audio and lexical inputs is a central problem in Natural Language Understanding (NLU). In this paper, we present Contrastive Aligned Audio-Language Multirate and Multimodal Representations (CALM), an approach for learning multimodal representations using contrastive and multirate information inherent in audio and lexical inputs. The proposed model aligns acoustic and lexical information in the input embedding space of a pretrained language-only contextual embedding model. By aligning audio representations to pretrained language representations and utilizing contrastive information between acoustic inputs, CALM is able to bootstrap audio embedding competitive with existing audio representation models in only a few hours of training time. Operationally, audio spectrograms are processed using linearized patches through a Spectral Transformer (SpecTran) which is trained using a Contrastive Audio-Language Pretraining objective to align audio and language from similar queries. Subsequently, the derived acoustic and lexical tokens representations are input into a multimodal transformer to incorporate utterance level context and derive the proposed CALM representations. We show that these pretrained embeddings can subsequently be used in multimodal supervised tasks and demonstrate the benefits of the proposed pretraining steps in terms of the alignment of the two embedding spaces and the multirate nature of the pretraining. Our system shows 10-25\% improvement over existing emotion recognition systems including state-of-the-art three-modality systems under various evaluation objectives.
Analysis of Voltage Stability in Terms of Interactions of Q(U)-Characteristic Control in Distribution Grids
Authors: Sebastian Krahmer, Stefan Ecklebe, Peter Schegner, Klaus Röbenack
Date: 2022-02-08 00:00:00

As the amount of volatile, renewable energy sources in power distribution grids is increasing, the stability of the latter is a vital aspect for grid operators. Within the STABEEL project, the authors develop rules on how to parametrize the reactive power control of distributed energy resources to increase the performance while guaranteeing stability. The work focuses on distribution grids with a high penetration of distributed energy resources equipped with Q(U)-characteristic. This contribution is based on the stability assessment of previous work and introduces a new approach utilizing the circle criterion. With the aim of extending existing technical guidelines, stability assessment methods are applied to various distribution grids - including those from the SimBench project. Herein, distributed energy resources can be modelled as detailed control loops or as approximations, derived from technical guidelines.
Communication-Control Co-design in Wireless Edge Industrial Systems
Authors: Mark Eisen, Santosh Shukla, Dave Cavalcanti, Amit S. Baxi
Date: 2022-02-08 00:00:00
Tasks: Edge-computing

We consider the problem of controlling a series of industrial systems, such as industrial robotics, in a factory environment over a shared wireless channel leveraging edge computing capabilities. The wireless control system model supports the offloading of computational intensive functions, such as perception workloads, to an edge server. However, wireless communications is prone to packet loss and latency and can lead to instability or task failure if the link is not kept sufficiently reliable. Because maintaining high reliability and low latency at all times prohibits scalability due to resource limitations, we propose a communication-control co-design paradigm that varies the network quality of service (QoS) and resulting control actions to the dynamic needs of each plant. We further propose a modular learning framework to solve the complex learning task without knowledge of plant or communication models in a series of learning steps and demonstrate its effectiveness in learning resource-efficient co-design policies in a robotic conveyor belt task.
Coordination of resources at the edge of the electricity grid: systematic review and taxonomy
Authors: Flora Charbonnier, Thomas Morstyn, Malcolm McCulloch
Date: 2022-02-08 00:00:00
Tasks: Systematic Literature Review

This paper proposes a novel taxonomy of coordination strategies for distributed energy resources at the edge of the electricity grid, based on a systematic analysis of key literature trends. The coordination of distributed energy resources such as decentralised generation and flexibility sources is critical for decarbonising electricity and achieving climate goals. The literature on the topic is growing exponentially; however, there is ambiguity in the terminology used to date. We seek to resolve this lack of clarity by synthesising the categories of coordination strategies in a novel exhaustive, mutually exclusive taxonomy based on agency, information and game type. The relevance of these concepts in the literature is illustrated through a systematic literature review of 84,741 publications using a structured topic search query. Then 93 selected coordination strategies are analysed in more detail and mapped onto this framework. Clarity on structural assumptions is key for selecting appropriate coordination strategies for differing contexts within energy systems. We argue that a plurality of complementary strategies is needed to coordinate energy systems' different components and achieve deep decarbonisation.
Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park
Authors: Dafeng Zhu, Bo Yang, Yuxiang Liu, Zhaojian Wang, Kai Ma, Xinping Guan
Date: 2022-02-08 00:00:00
Tasks: counterfactual, Deep Reinforcement Learning, energy management, Management, Reinforcement Learning (RL)

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands.
Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network
Authors: Han Hu, Weiwei Song, Qun Wang, Rose Qingyang Hu, Hongbo Zhu
Date: 2022-02-08 00:00:00
Tasks: CPU, Edge-computing, Stochastic Optimization

Mobile Edge Computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation-intensive and delay critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering non-stationary computation demands, user mobility, and wireless fading channels. This paper aims to study the tradeoff between energy efficiency (EE) and service delay for multi-user multi-server MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.
An Exact Method for the Daily Package Shipment Problem with Outsourcing
Authors: Zhuolin Wang, Rongping Zhu, Jian-Ya Ding, Yu Yang, Keyou You
Date: 2022-02-08 00:00:00

The package shipment problem requires to optimally co-design paths for both packages and a heterogeneous fleet in a transit center network (TCN). Instances arising from the package delivery industry in China usually involve more than ten thousand origin-destination (OD) pairs and have to be solved daily within an hour. Motivated by the fact that there is no interaction among different origin centers due to their competitive relationship, we propose a novel two-layer localized package shipment on a TCN (LPS-TCN) model that exploits outsourcing for cost saving. Consequently, the original problem breaks into a set of much smaller shipment problems, each of which has hundreds of OD pairs and is subsequently modelled as a mixed integer program (MIP). Since the LPS-TCN model is proved to be Strongly NP-hard and contains tens of thousands of feasible paths, an off-the-shelf MIP solver cannot produce a reliable solution in a practically acceptable amount of time. We develop a column generation based algorithm that iteratively adds "profitable" paths and further enhance it by problem-specific cutting planes and variable bound tightening techniques. Computational experiments on realistic instances from a major Chinese package express company demonstrate that the LPS-TCN model can yield solutions that bring daily economic cost reduction up to 1 million CNY for the whole TCN. In addition, our proposed algorithm solves the LPS-TCN model substantially faster than CPLEX, one of the state-of-the-art commercial MIP solvers.
Representative Scenarios to Capture Renewable Generation Stochasticity and Cross-Correlations
Authors: Dhaval Dalal, Anamitra Pal, Philip Augustin
Date: 2022-02-08 00:00:00

Generating representative scenarios for power system planning in which the stochasticity of renewable generation and cross-correlations between renewables and load are fully captured, is a challenging problem. Traditional methods for scenario generation often fail to generate diverse scenarios that include both seasonal (frequently occurring) and atypical (extreme) days required for planning purposes. This paper presents a methodical approach to generate representative scenarios. It also proposes new metrics that are more relevant for evaluating the generated scenarios from an applications perspective. When applied to historical data from a power utility, the proposed approach resulted in scenarios that included a good mix of seasonal and atypical days. The results also demonstrated pertinence of the proposed cluster validation metrics. Finally, the paper presents a trade-off for determining optimal number of scenarios for a given application.
Integration of Clinical, Biological, and Computational Perspectives to Support Cerebral Autoregulatory Informed Clinical Decision Making Decomposing Cerebral Autoregulation using Mechanistic Timescales to Support Clinical Decision-Making
Authors: J. K. Briggs, J. N. Stroh, T. D. Bennett, S. Park, D. J. Albers
Date: 2022-02-07 00:00:00
Tasks: Decision Making

Adequate brain perfusion is required for proper brain function and life. Maintaining optimal brain perfusion to avoid secondary brain injury is one of the main concerns of neurocritical care. Cerebral autoregulation is responsible for maintaining optimal brain perfusion despite pressure derangements. Knowledge of cerebral autoregulatory function should be a key factor in clinical decision-making, yet it is often insufficiently and incorrectly applied. Multiple physiologic mechanisms impact cerebral autoregulation, each of which operate on potentially different and incompletely understood timescales confounding conclusions drawn from observations. Because of such complexities, clinical conceptualization of cerebral autoregulation has been distilled into practical indices defined by multimodal neuromonitoring, which removes mechanistic information and limits decision options. The next step towards cerebral autoregulatory-informed clinical decision-making is to quantify cerebral autoregulation mechanistically, which requires decomposing cerebral autoregulation into its fundamental processes and partitioning those processes into the timescales at which each operates. In this review, we scrutinize biologically, clinically, and computationally focused literature to build a timescales-based framework around cerebral autoregulation. This new framework will allow us to quantify mechanistic interactions and directly infer which mechanism(s) are functioning based only on current monitoring equipment, paving the way for a new frontier in cerebral autoregulatory-informed clinical decision-making.
Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning
Authors: Søren Taverniers, Svyatoslav Korneev, Kyle M. Pietrzyk, Morad Behandish
Date: 2022-02-02 00:00:00
Tasks: Operator learning

Predicting part quality for additive manufacturing (AM) processes requires high-fidelity numerical simulation of partial differential equations (PDEs) governing process multiphysics on a scale of minimum manufacturable features. This makes part-scale predictions computationally demanding, especially when they require many small-scale simulations. We consider drop-on-demand liquid metal jetting (LMJ) as an illustrative example of such computational complexity. A model describing droplet coalescence for LMJ may include coupled incompressible fluid flow, heat transfer, and phase change equations. Numerically solving these equations becomes prohibitively expensive when simulating the build process for a full part consisting of thousands to millions of droplets. Reduced-order models (ROMs) based on neural networks (NN) or k-nearest neighbor (kNN) algorithms have been built to replace the original physics-based solver and are computationally tractable for part-level simulations. However, their quick inference capabilities often come at the expense of accuracy, robustness, and generalizability. We apply an operator learning (OL) approach to learn a mapping between initial and final states of the droplet coalescence process for enabling rapid and accurate part-scale build simulation. Preliminary results suggest that OL requires order-of-magnitude fewer data points than a kNN approach and is generalizable beyond the training set while achieving similar prediction error.
COVID-19 Hospitalizations Forecasts Using Internet Search Data
Authors: Tao Wang, Simin Ma, Soobin Baek, Shihao Yang
Date: 2022-02-03 00:00:00
Tasks: Decision Making, Time Series, Time Series Analysis

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decision on medical resources allocations such as ICU beds, ventilators, and personnel to prepare for the surge of COVID-19 pandemics. Inspired by the strong association between public search behavior and hospitalization admission, we extended previously-proposed influenza tracking model, ARGO (AutoRegression with GOogle search data), to predict future 2-week national and state-level COVID-19 new hospital admissions. Leveraging the COVID-19 related time series information and Google search data, our method is able to robustly capture new COVID-19 variants' surges, and self-correct at both national and state level. Based on our retrospective out-of-sample evaluation over 12-month comparison period, our method achieves on average 15\% error reduction over the best alternative models collected from COVID-19 forecast hub. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist health-care officials and decision making for the current and future infectious disease outbreak.
Continuous permanent unobserved heterogeneity in dynamic discrete choice models
Authors: Jackson Bunting
Date: 2022-02-08 00:00:00
Tasks: Discrete Choice Models

In dynamic discrete choice (DDC) analysis, it is common to use mixture models to control for unobserved heterogeneity. However, consistent estimation typically requires both restrictions on the support of unobserved heterogeneity and a high-level injectivity condition that is difficult to verify. This paper provides primitive conditions for point identification of a broad class of DDC models with multivariate continuous permanent unobserved heterogeneity. The results apply to both finite- and infinite-horizon DDC models, do not require a full support assumption, nor a long panel, and place no parametric restriction on the distribution of unobserved heterogeneity. In addition, I propose a seminonparametric estimator that is computationally attractive and can be implemented using familiar parametric methods.
Cuierzhuang Phenomenon: A model of rural industrialization in north China
Authors: Jinghan Tian, Jianhua Wang
Date: 2022-02-08 00:00:00

Cuierzhuang Phenomenon (or Cuierzhuang Model) is a regional development phenomenon or rural revitalization model driven by ICT in the information era, characterized by the storage and transportation, processing, packaging and online sales of agricultural products, as well as online and offline coordination, long-distance and cross-regional economic cooperation, ethnic blending, equality, and mutual benefit. Unlike the Wenzhou Model, South Jiangsu Model, and Pearl River Model in the 1980s and 1990s, the Cuierzhuang Model is not only a rural revitalization brought about by the industrialization and modernization of northern rural areas with the characteristics of industrial development in the information age, but also an innovative regional economic cooperation and development model with folk nature, spontaneous formation, equality, and mutual benefit. Taking southern Xinjiang as the production base, Xinjiang jujubes from Hotan and Ruoqiang are continuously transported to Cuierzhuang, Cangzhou City, Hebei Province, where they are transferred, cleaned, dried and packaged, and finally sold all over the country. With red dates as a link, the eastern town of Cuierzhuang, which is more than 4,000 kilometers apart, connected with Xinjiang in the western region. Along the ancient Silk Road, the farthest route can reach as far as Kashgar through the southern Xinjiang route. Then, how did this long-distance and cross-regional economic cooperation channel form, what are the regional economics or economic geography principles of Cuierzhuang attracting Xinjiang jujube, and the challenges and opportunities faced by Cuierzhuang phenomenon, etc. A preliminary economic analysis has been carried out in this paper.
The Legacy of Authoritarianism in a Democracy
Authors: Pramod Kumar Sur
Date: 2022-02-08 00:00:00

Recent democratic backsliding and the rise of authoritarian regimes worldwide have rekindled interest in understanding the causes and consequences of such authoritarian rule in democracies. In this paper, I study the long-run political consequences of authoritarianism in the world's largest democracy. Exploiting the unexpected timing of the authoritarian rule imposed in India in the 1970s and using a difference-in-difference (DID), triple difference (DDD), and a regression discontinuity design (RDD) estimation approach, I document a sharp decline in the then-dominant incumbent, the Indian National Congress party's political dominance in subsequent years. I also present evidence that the decline in political dominance was not at the expense of a lower voter turnout rate. Instead, a sharp rise in the number of opposition candidates contesting elections in subsequent years played an important role. Finally, I examine the enduring consequences, revealing that confidence in politicians remains low in states where the draconian policy was high.
US Salary History Bans -- Strategic Disclosure by Job Applicants and the Gender Pay Gap
Authors: Sourav Sinha
Date: 2022-02-08 00:00:00

I study the effects of US salary history bans which restrict employers from inquiring about job applicants' pay history during the hiring process, but allow candidates to voluntarily share information. Using a difference-in-differences design, I show that these policies narrowed the gender pay gap significantly by 2 p.p., driven almost entirely by an increase in female earnings. The bans were also successful in weakening the auto-correlation between current and future earnings, especially among job-changers. I provide novel evidence showing that when employers could no longer nudge candidates for information, the likelihood of voluntarily disclosing salary history decreased among job applicants and by 2 p.p. more among women. I then develop a salary negotiation model with asymmetric information, where I allow job applicants to choose whether to reveal pay history, and use this framework to explain my empirical findings on disclosure behavior and gender pay gap.