pwc-archive/papers-with-abstracts huggingface dataset:
Superiorized Adaptive Projected Subgradient Method with Application to MIMO Detection
Authors: Jochen Fink, Renato L. G. Cavalcante, Slawomir Stanczak
Date: 2022-03-02 00:00:00

In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient. To illustrate a potential application of this result, we propose a set-theoretic framework for MIMO detection, and we devise algorithms based on a superiorized APSM. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models (e.g., correlated channels) are considered. Compared to existing low-complexity iterative detectors such as individually optimal large-MIMO approximate message passing (IO-LAMA), the proposed algorithms can achieve considerably lower symbol error ratios over correlated channels. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to IO-LAMA.
Splitting Receiver with Joint Envelope and Coherent Detection
Authors: Yanyan Wang, Wanchun Liu, Xiangyun Zhou
Date: 2022-03-02 00:00:00

This letter proposes a new splitting receiver design with joint envelope detection (ED) and coherent detection (CD). To characterize its fundamental performance limit, we conduct high signal-to-noise ratio (SNR) analysis on the proposed ED-CD splitting receiver and obtain closed-form approximations of both the achievable mutual information and the optimal splitting ratio (i.e., a key design parameter of the receiver). Our numerical results show that these high SNR approximations are accurate over a wide range of moderate SNR values, signifying the usefulness of the obtained analytical results. We also provide insights on the conditions at which the proposed splitting receiver has significant performance advantages over the traditional receivers.
Modeling and Control of Smart Standalone Microgrids within Cyber Physical System Frameworks
Authors: Meher Preetam Korukonda
Date: 2022-03-02 00:00:00

The ability of grid-connected microgrids (MG) to operate in islanded mode makes them an efficient solution for improving power quality and reliability. This property of MG is very much beneficial for remote and undeveloped areas in progressing countries. Moreover, ICT technology has led to the development of Smart Standalone Microgrids (SSMG), which are inundated with a plethora of sensors. This allows multiple microgrids to be controlled in a coordinated way to achieve self-sufficiency in power. In such systems, there is much interdependency between various power, control and communication parameters. Owing to these developments, the control of physical variables like voltage get affected by cyber parameters like, communication structure, delay and link loss. Moreover, due to isolation from main grid and abundance of renewable power generation units like solar PV, the inertia of these standalone grids is reduced greatly and calls for advanced control algorithms which use an abundance of sensors. Hence, the stability of these systems is greatly affected by sensor failures apart from many physical parameters like load and environmental conditions. In this thesis, a generic structure of an AC-DC hybrid microgrid is considered which is subdivided into various AC and DC counterparts. The AC and DC SSMGs are separately modeled and control solutions are proposed to improve their stability. The first two contributions propose adaptive control schemes on the primary level of control in the hybrid microgrid. Their function is to provide fast and stable parameters regulation in the DCSSMG when subjected to atmospheric changes along with faults in sensor readings. The third and fourth contributions cater to development of coordinated control cyber physical frameworks in the ACSSMG to handle the presence of simultaneous disturbances from both cyber/physical domains.
Defining a synthetic data generator for realistic electric vehicle charging sessions
Authors: Manu Lahariya, Dries Benoit, Chris Develder
Date: 2022-02-28 00:00:00

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Analysis of EV charging sessions is useful for flexibility analysis, load balancing, offering incentives to customers, etc. Yet, the limited availability of such EV sessions data hinders further development in these fields. Addressing this need for publicly available and realistic data, we develop a synthetic data generator (SDG) for EV charging sessions. Our SDG assumes the EV inter-arrival time to follow an exponential distribution. Departure times are modeled by defining a conditional probability density function (pdf) for connection times. This pdf for connection time and required energy is fitted by Gaussian mixture models. Since we train our SDG using a large real-world dataset, its output is realistic.
MuSE-SVS: Multi-Singer Emotional Singing Voice Synthesizer that Controls Emotional Intensity
Authors: Sungjae Kim, Yewon Kim, Jewoo Jun, Injung Kim
Date: 2022-03-02 00:00:00

We propose a multi-singer emotional singing voice synthesizer, Muse-SVS, that expresses emotion at various intensity levels by controlling subtle changes in pitch, energy, and phoneme duration while accurately following the score. To control multiple style attributes while avoiding loss of fidelity and expressiveness due to interference between attributes, Muse-SVS represents all attributes and their relations together by a joint embedding in a unified embedding space. Muse-SVS can express emotional intensity levels not included in the training data through embedding interpolation and extrapolation. We also propose a statistical pitch predictor to express pitch variance according to emotional intensity, and a context-aware residual duration predictor to prevent the accumulation of variances in phoneme duration, which is crucial for synchronization with instrumental parts. In addition, we propose a novel ASPP-Transformer, which combines atrous spatial pyramid pooling (ASPP) and Transformer, to improve fidelity and expressiveness by referring to broad contexts. In experiments, Muse-SVS exhibited improved fidelity, expressiveness, and synchronization performance compared with baseline models. The visualization results show that Muse-SVS effectively express the variance in pitch, energy, and phoneme duration according to emotional intensity. To the best of our knowledge, Muse-SVS is the first neural SVS capable of controlling emotional intensity.
Real time spectrogram inversion on mobile phone
Authors: Oleg Rybakov, Marco Tagliasacchi, Yunpeng Li, Liyang Jiang, Xia Zhang, Fadi Biadsy
Date: 2022-03-01 00:00:00
Tasks: CPU

We present two methods of real time magnitude spectrogram inversion: streaming Griffin Lim(GL) and streaming MelGAN. We demonstrate the impact of looking ahead on perceptual quality of MelGAN. As little as one hop size (12.5ms) of lookahead is able to significantly improve perceptual quality in comparison to its causal version. We compare streaming GL with the streaming MelGAN and show different trade-offs in terms of perceptual quality, on-device latency, algorithmic delay, memory footprint and noise sensitivity. For fair quality assessment of the GL approach, we use input log magnitude spectrogram without mel transformation. We evaluate presented real time spectrogram inversion approaches on clean, noisy and atypical speech. We specified conditions when streaming GL has comparable quality with MelGAN: noisy audio and no mel transformation. Streaming GL is 2.4x faster than real time on the ARM CPU of a Pixel4 and it uses 4.5x times less memory than MelGAN.
Speaker recognition improvement using blind inversion of distortions
Authors: Marcos Faundez-Zanuy, Jordi Sole-Casals
Date: 2022-02-23 00:00:00
Tasks: Speaker Recognition

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results shows that a combination of data fusion with and without nonlinear distortion compensation can improve the recognition rates with saturated test sentences from 80% to 88.57%, while the results with clean speech (without saturation) is 87.76% for one microphone.
Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems
Authors: Hantao Cui, Yichen Zhang
Date: 2022-03-02 00:00:00
Tasks: Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents Andes_gym, a versatile and high-performance reinforcement learning environment for power system studies. The environment leverages the modeling and simulation capability of ANDES and the reinforcement learning (RL) environment OpenAI Gym to enable the prototyping and demonstration of RL algorithms for power systems. The architecture of the proposed software tool is elaborated to provide the observation and action interfaces for RL algorithms. An example is shown to rapidly prototype a load-frequency control algorithm based on RL trained by available algorithms. The proposed environment is highly generalized by supporting all the power system dynamic models available in ANDES and numerous RL algorithms available for OpenAI Gym.
Recursively feasible stochastic predictive control using an interpolating initial state constraint -- extended version
Authors: Johannes Köhler, Melanie N. Zeilinger
Date: 2022-03-02 00:00:00
Tasks: Model Predictive Control

We present a stochastic model predictive control (SMPC) framework for linear systems subject to possibly unbounded disturbances. State of the art SMPC approaches with closed-loop chance constraint satisfaction recursively initialize the nominal state based on the previously predicted nominal state or possibly the measured state under some case distinction. We improve these initialization strategies by allowing for a continuous optimization over the nominal initial state in an interpolation of these two extremes. The resulting SMPC scheme can be implemented as one standard quadratic program and is more flexible compared to state-of-the-art initialization strategies. As the main technical contribution, we show that the proposed SMPC framework also ensures closed-loop satisfaction of chance constraints and suitable performance bounds.
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Authors: Simon Muntwiler, Kim P. Wabersich, Lukas Hewing, Melanie N. Zeilinger
Date: 2020-04-06 00:00:00
Tasks: Model Predictive Control

Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and optimization methods. In this work, we propose a distributed stochastic model predictive control (DSMPC) scheme for dynamically coupled linear discrete-time systems subject to unbounded additive disturbances that are potentially correlated in time. An indirect feedback formulation ensures recursive feasibility of the DSMPC problem, and a data-driven, distributed and optimization-free constraint tightening approach allows for exact satisfaction of chance constraints during closed-loop control, addressing typical sources of conservatism. The computational complexity of the proposed controller is similar to nominal distributed MPC. The approach is demonstrated in simulation for the temperature control of a large-scale data center subject to randomly varying computational loads.
Smart Tracking Tray System for A Smart and Sustainable Wet Lab Community
Authors: Nan Xu, Jingchen Li, Yue Yu, Yang Li, Jinglei Yang
Date: 2022-03-02 00:00:00
Tasks: Data Visualization, Experimental Design

The laboratories and research institutes are the major places for cutting-edge scientific exploration. Hundreds of millions of research papers were formed from front-line labs. Behind this glorious achievement were unsustainable facts. More and more human investment is required in innovative experimental design and analysis of results. However, the laboratory operating environment has not been subversively transformed for centuries. This abstract proposed a smart tracking system, consisting of IoT and Data Visualization technologies, to track the chemicals in an automatic and timely approach. Positive feedback has been collected from pilot tests in several labs. The system benefits various lab users in their daily work and improves their working efficiency. In the long run, it will play an essential role in promoting the efficient use of lab resources and achieving the goal of sustainable labs.
Distributed goal assignment strategy for improving leader-following formation control performance
Authors: Yun Ho Choi, Doik Kim
Date: 2022-03-02 00:00:00

This paper investigates a distributed goal assignment problem in leader-following formation control of second-order multi-agent systems. It is assumed that each agent can communicate with nearby agents within the communication range and the leader information is only available to a subset of agents. Compared with existing formation control schemes addressing the goal assignment issue, the main contribution of this paper is to construct a novel distributed assignment strategy allotting appropriate goal positions of agents in the leader-following formation control framework. Based on the rigorous analysis using the Lyapunov stability theory, the enhancement of the control performance is proved via the proposed assignment strategy. To demonstrate the effectiveness of our theoretical results, two examples including multiple quadrotors are simulated.
Using Geographic Load Shifting to Reduce Carbon Emissions
Authors: Julia Lindberg, Bernard C. Lesieutre, Line A. Roald
Date: 2022-03-02 00:00:00
Tasks: Cloud Computing

An increasing focus on the electricity use and carbon emissions associated with computing has lead to pledges by major cloud computing companies to lower their carbon footprint. Data centers have a unique ability to shift computing load between different geographical locations, giving rise to geographic load flexibility that can be employed to reduce carbon emissions. In this paper, we present a model where data centers shift load independently of the ISOs. We first consider the impact of load shifting guided by locational marginal carbon emissions, denoted by $\lambda_{\text{CO}_2}$, a sensitivity metric that measures the impact of incremental load shifts. Relative to previous models for data center load shifting, the presented model improves accuracy and include more realistic assumptions regarding the operation of both data centers and the electricity market. Further, we introduce a new benchmark model in which data centers have access to the full information about the power system and can identify optimal shifts for the current time period. We demonstrate the efficacy of our model on the IEEE RTS GMLC system using 5 minute load and generation data for an entire year. Our results show that the proposed accuracy improvements for the shifting model based on $\lambda_{\text{CO}_2}$ are highly effective, leading to results that outperform the benchmark model.
Computing Bounds on $L_{\infty}$-induced Norm for Linear Time-Invariant Systems Using Homogeneous Lyapunov Functions
Authors: Hassan Abdelraouf, Gidado-Yisa Immanuel, Eric Feron
Date: 2022-03-01 00:00:00

Quadratic Lyapunov function has been widely used in the analysis of linear time invariant (LTI) systems ever since it has shown that the existence of such quadratic Lyapunov function certifies the stability of the LTI system. In this work, the problem of finding upper and lower bounds for the $L_{\infty}$-induced norm of the LTI system is considered. Quadratic Lyapunov functions are used to find the star norm, the best upper on the $L_{\infty}$-induced norm, by bounding the unit peak input reachable sets by inescapable ellipsoids. Instead, a more general class of homogeneous Lyapunov functions is used to get less conservative upper bounds on the $L_{\infty}$-induced norm and better conservative approximations for the reachable sets than those obtained using standard quadratic Lyapunov functions. The homogeneous Lyapunov function for the LTI system is considered to be a quadratic Lyapunov function for a higher-order system obtained by Lifting the LTI system via Kronecker product. Different examples are provided to show the significant improvements on the bounds obtained by using Homogeneous Lyapunov functions.
ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views
Authors: Yuhe Zhang, Zisheng Yao, Tobias Ritschel, Pablo Villanueva-Perez
Date: 2022-03-01 00:00:00
Tasks: 3D Object Reconstruction, 3D Reconstruction

Three-dimensional (3D) X-ray imaging techniques like tomography and confocal microscopy are crucial for academic and industrial applications. These approaches access 3D information by scanning the sample with respect to the X-ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for some applications, such as surgical guidance in medical applications. Alternatives to obtaining 3D information when scanning is not possible are X-ray stereoscopy and multi-projection imaging. However, these approaches suffer from limited volumetric information as they only acquire a small number of views or projections compared to traditional 3D scanning techniques. Here, we present ONIX (Optimized Neural Implicit X-ray imaging), a deep-learning algorithm capable of retrieving 3D objects with arbitrary large resolution from only a set of sparse projections. ONIX, although it does not have access to any volumetric information, outperforms current 3D reconstruction approaches because it includes the physics of image formation with X-rays, and it generalizes across different experiments over similar samples to overcome the limited volumetric information provided by sparse views. We demonstrate the capabilities of ONIX compared to state-of-the-art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. We anticipate that ONIX will become a crucial tool for the X-ray community by i) enabling the study of fast dynamics not possible today when implemented together with X-ray multi-projection imaging, and ii) enhancing the volumetric information and capabilities of X-ray stereoscopic imaging in medical applications.
Is Our Research Productivity In Decline? A New Approach in Resolving the Controversy
Authors: Gennady Shkliarevsky
Date: 2022-03-02 00:00:00

This contribution examines the current controversy over research productivity. There are two sides in this controversy. Using extensive data from several industries and areas of research, one side argues that research productivity is currently in decline. The other side disputes this conclusion. It contends that the data used in making this argument are selective and limited; they do not reflect the overall state of research. The conclusion that follows from this critique is that the indicators of research productivity we currently use are not reliable and do not warrant a definitive answer to the problem. The article agrees that we need a new set of indicators in assessing research productivity. It proposes that we should look at global indicators related to knowledge production in general, rather than look at selective data that are inevitably limited in their scope. The article argues that the process of creation plays the essential role in knowledge production. Therefore, the perspective that uses the process of creation as its central organizing principle offers a unique and global view on the production of knowledge and makes a definitive resolution of the controversy possible. The article also outlines some steps for improving research productivity and realizing the full potential of the human capacity to produce knowledge. Key words: Research productivity, knowledge growth, the process of creation, levels of organization, equilibration and the production of disequilibrium.
The Economics and Econometrics of Gene-Environment Interplay
Authors: Pietro Biroli, Titus J. Galama, Stephanie von Hinke, Hans van Kippersluis, Cornelius A. Rietveld, Kevin Thom
Date: 2022-03-01 00:00:00
Tasks: Econometrics

Economists and social scientists have debated the relative importance of nature (one's genes) and nurture (one's environment) for decades, if not centuries. This debate can now be informed by the ready availability of genetic data in a growing number of social science datasets. This paper explores the potential uses of genetic data in economics, with a focus on estimating the interplay between nature (genes) and nurture (environment). We discuss how economists can benefit from incorporating genetic data into their analyses even when they do not have a direct interest in estimating genetic effects. We argue that gene--environment (GxE) studies can be instrumental for (i) testing economic theory, (ii) uncovering economic or behavioral mechanisms, and (iii) analyzing treatment effect heterogeneity, thereby improving the understanding of how (policy) interventions affect population subgroups. We introduce the reader to essential genetic terminology, develop a conceptual economic model to interpret gene-environment interplay, and provide practical guidance to empirical researchers.
Awareness and use of quantitative decision-making methods in pharmaceutical development
Authors: Guido Thömmes, Martin Oliver Sailer, Nicolas Bonnet, Alex Carlton, Juan J. Abellan, Veronique Robert
Date: 2022-03-01 00:00:00
Tasks: Decision Making

The pharmaceutical industry has experienced increasing costs and sustained high attrition rates in drug development over the last years. One proposal that addresses this challenge from a statistical perspective is the use of quantitative decision-making (QDM) methods to support a data-driven, objective appraisal of the evidence that forms the basis of decisions at different development levels. Growing awareness among statistical leaders in the industry has led to the creation of the European EFSPI/PSI special interest group (ESIG) on quantitative decision making to share experiences, collect best practices, and promote the use of QDM. In this paper, we introduce key components of QDM and present examples of QDM methods on trial, program, and portfolio level. The ESIG created a questionnaire to learn how and to what extent QDM methods are currently used in the different development phases. We present the main questionnaire findings, and we show where QDM is already used today but also where areas for future improvement can be identified. In particular, statisticians should increase their visibility, involvement, and leadership in cross-functional decision-making.
Optimal Defaults, Limited Enforcement and the Regulation of Contracts
Authors: Zoë Hitzig, Benjamin Niswonger
Date: 2022-03-02 00:00:00

We study how governments promote social welfare through the design of contracting environments. We model the regulation of contracting as default delegation: the government chooses a delegation set of contract terms it is willing to enforce, and influences the default terms that serve as outside options in parties' negotiations. Our analysis shows that limiting the delegation set principally mitigates externalities, while default terms primarily achieve distributional objectives. Applying our model to the regulation of labor contracts, we derive comparative statics on the optimal default delegation policy. As equity concerns or externalities increase, in-kind support for workers increases (e.g. through benefits requirements and public health insurance). Meanwhile, when worker bargaining power decreases away from parity, support for workers increases in cash (e.g. through cash transfers and minimum wage laws).