pwc-archive/papers-with-abstracts huggingface dataset:
A bottom-up quantification of flexibility potential from the thermal energy storage in electric space heating
Authors: Lars Herre, Behrouz Nourozi, Mohammad Reza Hesamzadeh, Qian Wang, Lennart Söder
Date: 2021-03-11 00:00:00

Non-generating resources such as thermostatically controlled loads (TCLs) can arbitrage energy prices and provide balancing reserves when aggregated due to their thermal energy storage capacity. Based on a performed survey of Swedish single- and two-family dwellings with electric heating, this paper quantifies the potential of TCLs to provide reserves to the power system in Sweden. To this end, dwellings with heat pumps and direct electric heaters are modeled as thermal energy storage equivalents that can be included in a linear two-stage problem formulation. We approach the operational flexibility of the TCLs by modeling a risk-averse aggregator that controls decentralized TCLs and aims to maximize its own profit. The results show a potential of 2 GW/0.1Hz averaged over a year, and up to 6.4 GW/0.1Hz peak capacity. Based on a sensitivity analysis we derive policy implications regarding market timing and activation signal.
Plane Spiral OAM Mode-Group Based MIMO Communications: An Experimental Study
Authors: Xiaowen Xiong, Shilie Zheng, Zelin Zhu, Yuqi Chen, Hongzhe Shi, Bingchen Pan, Cheng Ren, Xianbin Yu, Xiaofeng Jin, Wei E. I. Sha, Xianmin Zhang
Date: 2021-03-11 00:00:00

Spatial division multiplexing using conventional orbital angular momentum (OAM) has become a well-known physical layer transmission method over the past decade. The mode-group (MG) superposed by specific single mode plane spiral OAM (PSOAM) waves has been proved to be a flexible beamforming method to achieve the azimuthal pattern diversity, which inherits the spiral phase distribution of conventional OAM wave. Thus, it possesses both the beam directionality and vorticity. In this paper, it's the first time to show and verify novel PSOAM MG based multiple-in-multiple-out (MIMO) communication link (MG-MIMO) experimentally in a line-of-sight (LoS) scenario. A compact multi-mode PSOAM antenna is demonstrated experimentally to generate multiple independent controllable PSOAM waves, which can be used for constructing MGs. After several proof-of-principle tests, it has been verified that the beam directionality gain of MG can improve the receiving signal-to-noise (SNR) level in an actual system, meanwhile, the vorticity can provide another degree of freedom (DoF) to reduce the spatial correlation of MIMO system. Furthermore, a tentative long-distance transmission experiment operated at 10.2 GHz has been performed successfully at a distance of 50 m with a single-way spectrum efficiency of 3.7 bits/s/Hz/stream. The proposed MG-MIMO may have potential in the long-distance LoS back-haul scenario.
Open GOP Resolution Switching in HTTP Adaptive Streaming with VVC
Authors: Robert Skupin, Christian Bartnik, Adam Wieckowski, Yago Sanchez, Benjamin Bross, Cornelius Hellge, Thomas Schierl
Date: 2021-03-11 00:00:00

The user experience in adaptive HTTP streaming relies on offering bitrate ladders with suitable operation points for all users and typically involves multiple resolutions. While open GOP coding structures are generally known to provide substantial coding efficiency benefit, their use in HTTP streaming has been precluded through lacking support of reference picture resampling (RPR) in AVC and HEVC. The newly emerging Versatile Video Coding (VVC) standard supports RPR, but only conversational scenarios were primarily investigated during the design of VVC. This paper aims at enabling usage of RPR in HTTP streaming scenarios through analysing the drift potential of VVC coding tools and presenting a constrained encoding method that avoids severe drift artefacts in resolution switching with open GOP coding in VVC. In typical live streaming configurations, the presented method achieves -8.57% BD-rate reduction compared to closed GOP coding while in a typical Video on Demand configuration, -1.89% BD-rate reduction is reported. The constraints penalty compared to regular open GOP coding is 0.65% BD-rate in the worst case. The presented method was integrated into the publicly available open source VVC encoder VVenC v0.3.
Optimizing the Level of Challenge in Stroke Rehabilitation using Iterative Learning Control: a Simulation
Authors: Sandra-Carina Noble, Tomas Ward, John V. Ringwood
Date: 2021-03-11 00:00:00

The level of challenge in stroke rehabilitation has to be carefully chosen to keep the patient engaged and motivated while not frustrating them. This paper presents a simulation where this level of challenge is automatically optimized using iterative learning control. An iterative learning controller provides a simulated stroke patient with a target task that the patient then learns to execute. Based on the error between the target task and the execution, the controller adjusts the difficulty of the target task for the next trial. The patient is simulated by a nonlinear autoregressive network with exogenous inputs to mimic their sensorimotor system and a second-order model to approximate their elbow joint dynamics. The results of the simulations show that the rehabilitation approach proposed in this paper results in more difficult tasks and a smoother difficulty progression as compared to a rehabilitation approach where the difficulty of the target task is updated according to a threshold.
UAV Deployment, Device Scheduling and Resource Allocation for Energy-Efficient UAV-Aided IoT Networks with NOMA
Authors: Jingjing Zhao, Kaiquan Cai, YanBo Zhu
Date: 2021-03-11 00:00:00
Tasks: Scheduling

This article investigates the energy efficiency issue in non-orthogonal multiple access (NOMA)-enhanced Internet-of-Things (IoT) networks, where a mobile unmanned aerial vehicle (UAV) is exploited as a flying base station to collect data from ground devices via the NOMA protocol. With the aim of maximizing network energy efficiency, we formulate a joint problem of UAV deployment, device scheduling and resource allocation. First, we formulate the joint device scheduling and spectrum allocation problem as a three-sided matching problem, and propose a novel low-complexity near-optimal algorithm. We also introduce the novel concept of `exploration' into the matching game for further performance improvement. By algorithm analysis, we prove the convergence and stability of the final matching state. Second, in an effort to allocate proper transmit power to IoT devices, we adopt the Dinkelbach's algorithm to obtain the optimal power allocation solution. Furthermore, we provide a simple but effective approach based on disk covering problem to determine the optimal number and locations of UAV's stop points to ensure that all IoT devices can be fully covered by the UAV via line-of-sight (LoS) links for the sake of better channel condition. Numerical results unveil that: i) the proposed joint UAV deployment, device scheduling and resource allocation scheme achieves much higher EE compared to predefined stationary UAV deployment case and fixed power allocation scheme, with acceptable complexity; and ii) the UAV-aided IoT networks with NOMA greatly outperforms the OMA case in terms of number of accessed devices.
Probabilistic Scheduling of UFLS to Secure Credible Contingencies in Low Inertia Systems
Authors: Cormac O'Malley, Luis Badesa, Fei Teng, Goran Strbac
Date: 2021-03-11 00:00:00
Tasks: Scheduling

The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency's nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel nadir constraint from the swing equation that, for the first time, provides a framework for the optimal comparison of all these services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone, resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to {\pounds}559m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored.
Uncoordinated and Decentralized Processing in Extra-Large MIMO Arrays
Authors: Abolfazl Amiri, Carles Navarro Manch'on, Elisabeth de Carvalho
Date: 2021-03-11 00:00:00
Tasks: CPU

We propose a decentralized receiver for extra-large multiple-input multiple-output (XL-MIMO) arrays. Our method operates with no central processing unit (CPU) and all the signal detection tasks are done in distributed nodes. We exploit a combined message-passing framework to design an uncoordinated detection scheme that overcomes three major challenges in the XL-MIMO systems: computational complexity, scalability and non-stationarities in user energy distribution. Our numerical evaluations show a significant performance improvement compared to benchmark distributed methods while operating very close to the centralized receivers.
Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detection
Authors: Janek Ebbers, Reinhold Haeb-Umbach
Date: 2021-03-11 00:00:00
Tasks: Event Detection, Sound Event Detection, TAG

In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domestic environments. We introduce two new models: the forward-backward convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers sharing the same CNN for preprocessing. With one RNN processing a recording in forward direction and the other in backward direction, the two networks are trained to jointly predict audio tags, i.e., weak labels, at each time step within a recording, given that at each time step they have jointly processed the whole recording. The proposed training encourages the classifiers to tag events as soon as possible. Therefore, after training, the networks can be applied to shorter audio segments of, e.g., 200 ms, allowing sound event detection (SED). Further, we propose a tag-conditioned CNN to complement SED. It is trained to predict strong labels while using (predicted) tags, i.e., weak labels, as additional input. For training pseudo strong labels from a FBCRNN ensemble are used. The presented system scored the fourth and third place in the systems and teams rankings, respectively. Subsequent improvements allow our system to even outperform the challenge baseline and winner systems in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source code is publicly available at https://github.com/fgnt/pb_sed.
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Authors: Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama
Date: 2012-03-02 00:00:00
Tasks: Change Point Detection, Density Ratio Estimation, Time Series, Time Series Analysis

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.
ODAS: Open embeddeD Audition System
Authors: François Grondin, Dominic Létourneau, Cédric Godin, Jean-Samuel Lauzon, Jonathan Vincent, Simon Michaud, Samuel Faucher, François Michaud
Date: 2021-03-05 00:00:00
Tasks: Sound Source Localization

Artificial audition aims at providing hearing capabilities to machines, computers and robots. Existing frameworks in robot audition offer interesting sound source localization, tracking and separation performance, although involve a significant amount of computations that limit their use on robots with embedded computing capabilities. This paper presents ODAS, the Open embeddeD Audition System framework, which includes strategies to reduce the computational load and perform robot audition tasks on low-cost embedded computing systems. It presents key features of ODAS, along with cases illustrating its uses in different robots and artificial audition applications.
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning
Authors: Jianan Jiang, Zhenpeng Li, Yuhong Guo, Jieping Ye
Date: 2020-06-08 00:00:00
Tasks: Cross-Domain Few-Shot, cross-domain few-shot learning, Data Augmentation, Few-Shot Learning

In this paper, we present a new method, Transductive Multi-Head Few-Shot learning (TMHFS), to address the Cross-Domain Few-Shot Learning (CD-FSL) challenge. The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i.e, an instance-wise global classification network based on semantic information, after the common feature embedding network. We train the embedding network with the multiple heads, i.e,, the MCT loss, the DFMN loss and the semantic classifier loss, simultaneously in the source domain. For the few-shot learning in the target domain, we first perform fine-tuning on the embedding network with only the semantic global classifier and the support instances, and then use the MCT part to predict labels of the query set with the fine-tuned embedding network. Moreover, we further exploit data augmentation techniques during the fine-tuning and test stages to improve the prediction performance. The experimental results demonstrate that the proposed methods greatly outperform the strong baseline, fine-tuning, on four different target domains.
Statistical Neuroscience in the Single Trial Limit
Authors: Alex H. Williams, Scott W. Linderman
Date: 2021-03-08 00:00:00

Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure -- including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions -- and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits.
UASOL, a large-scale high-resolution outdoor stereo dataset
Authors: Zuria Bauer, Francisco Gomez-Donoso, Edmanuel Cruz, Sergio Orts-Escolano, Miguel Cazorla
Date: 2019-08-29 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Vocal Bursts Intensity Prediction

In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite their already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work.
Non-rationalizable Individuals, Stochastic Rationalizability, and Sampling
Authors: Changkuk Im, John Rehbeck
Date: 2021-02-05 00:00:00

Experimental work regularly finds that individual choices are not deterministically rationalized by well-defined preferences. Nonetheless, recent work shows that data collected from many individuals can be stochastically rationalized by a distribution of well-defined preferences. We study the relationship between deterministic and stochastic rationalizability. We show that a population can be stochastically rationalized even when half of the individuals in the population cannot be deterministically rationalized. We also find the ability to detect individuals who are not deterministically rationalized from population level data can decrease as the number of observations increases.
Quantitative Interpretations of Energetic Features and Key Residues at SARS Coronavirus Spike Receptor-Binding Domain and ACE2 Receptor Interface
Authors: Yanmei Yang, Yunju Zhang, Yuanyuan Qu, Xuewei Liu, Mingwen Zhao, Yuguang Mu, Weifeng Li
Date: 2021-03-11 00:00:00

The wide spread of coronavirus disease 2019 (COVID-19) has declared a global health emergency. As one of the most important targets for antibody and drug developments, Spike RBD-ACE2 interface has received extensive attention. Here, using molecular dynamics simulations, we explicitly evaluated the binding energetic features of the RBD-ACE2 complex of both SARS-CoV and SARS-CoV-2 to find the key residues. Although the overall ACE2-binding mode of the SARS-CoV-2 RBD is nearly identical to that of the SARS-CoV RBD, the difference in binding affinity is as large as -16.35 kcal/mol. Energy decomposition analyses identified three binding patches in the SARS-CoV-2 RBD and eleven key residues (Phe486, Tyr505, Asn501, Tyr489, Gln493, Leu455 and etc) which are believed to be the main targets for drug development. The dominating forces are from van der Waals attractions and dehydration of these residues. It is also worth mention that we found seven mutational sites (Lys417, Leu455, Ala475, Gly476, Glu484, Gln498 and Val503) on SARS-CoV-2 which unexpectedly weakened the RBD-ACE2 binding. Very interestingly, the most repulsive residue at the RBD-ACE2 interface (E484), is found to be mutated in the latest UK variant, B1.1.7, cause complete virus neutralization escapes from highly neutralizing COVID-19 convalescent plasma. Our present results indicate that at least from the energetic point of view such E484 mutation may have beneficial effects on ACE2 binding. The present study provides a systematical understanding, from the energetic point of view, of the binding features of SARS-CoV-2 RBD with ACE2 acceptor. We hope that the present findings of three binding patches, key attracting residues and unexpected mutational sites can provide insights to the design of SARS-CoV-2 drugs and identification of cross-active antibodies.
Adaptive Control for Flow and Volume Regulation in Multi-Producer District Heating Systems
Authors: Juan E. Machado, Michele Cucuzzella, Nina Pronk, Jacquelien M. A. Scherpen
Date: 2021-03-11 00:00:00
Tasks: Management

Flow and storage volume regulation is essential for the adequate transport and management of energy resources in district heating systems. In this letter, we propose a novel and suitably tailored -- decentralized -- adaptive control scheme addressing this problem whilst offering closed-loop stability guarantees. We focus on a system configuration comprising multiple heat producers, consumers and storage tanks exchanging energy through a common distribution network, which are features of modern and prospective district heating installations. {\color{black}The proposed controller is based on passivity, backstepping and (indirect) adaptive control theory.
A learning rule balancing energy consumption and information maximization in a feed-forward neuronal network
Authors: Dmytro Grytskyy, Renaud B. Jolivet
Date: 2021-03-11 00:00:00

Information measures are often used to assess the efficacy of neural networks, and learning rules can be derived through optimization procedures on such measures. In biological neural networks, computation is restricted by the amount of available resources. Considering energy restrictions, it is thus reasonable to balance information processing efficacy with energy consumption. Here, we studied networks of non-linear Hawkes neurons and assessed the information flow through these networks using mutual information. We then applied gradient descent for a combination of mutual information and energetic costs to obtain a learning rule. Through this procedure, we obtained a rule containing a sliding threshold, similar to the Bienenstock-Cooper-Munro rule. The rule contains terms local in time and in space plus one global variable common to the whole network. The rule thus belongs to so-called three-factor rules and the global variable could be related to a number of biological processes. In neural networks using this learning rule, frequent inputs get mapped onto low energy orbits of the network while rare inputs aren't learned.
Advanced Geometry Surface Coding for Dynamic Point Cloud Compression
Authors: Jian Xiong, Hao Gao, Miaohui Wang, Hongliang Li, King Ngi Ngan, Weisi Lin
Date: 2021-03-11 00:00:00
Tasks: Prediction

In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected onto 2D images for compressing with the existing video codecs. However, the existing video codecs are originally designed for natural visual signals, and it fails to account for the characteristics of point clouds. Thus, there are still problems in the compression of geometry information generated from the point clouds. Firstly, the distortion model in the existing rate-distortion optimization (RDO) is not consistent with the geometry quality assessment metrics. Secondly, the prediction methods in video codecs fail to account for the fact that the highest depth values of a far layer is greater than or equal to the corresponding lowest depth values of a near layer. This paper proposes an advanced geometry surface coding (AGSC) method for dynamic point clouds (DPC) compression. The proposed method consists of two modules, including an error projection model-based (EPM-based) RDO and an occupancy map-based (OM-based) merge prediction. Firstly, the EPM model is proposed to describe the relationship between the distortion model in the existing video codec and the geometry quality metric. Secondly, the EPM-based RDO method is presented to project the existing distortion model on the plane normal and is simplified to estimate the average normal vectors of coding units (CUs). Finally, we propose the OM-based merge prediction approach, in which the prediction pixels of merge modes are refined based on the occupancy map. Experiments tested on the standard point clouds show that the proposed method achieves an average 9.84\% bitrate saving for geometry compression.