pwc-archive/papers-with-abstracts huggingface dataset:
Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump
Authors: Simon Thorsteinsson, Alex Arash Sand Kalaee, Pierre Vogler-Finck, Henrik Lund Stærmose, Ivan Katic, Jan Dimon Bendtsen
Date: 2023-03-28 00:00:00
Tasks: Management, Model Predictive Control

The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-33% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready.
Neural spikes as rare events
Authors: Siddharth Kackar
Date: 2023-03-29 00:00:00

We consider the information transmission problem in neurons and its possible implications for learning in neural networks. Our approach is based on recent developments in statistical physics and complexity science. We also develop a method to select statistically significant neural responses from the background activity and consider its wider applications. This would support temporal coding theory as a model for neural coding.
Power sector effects of alternative options for de-fossilizing heavy-duty vehicles -- go electric, and charge smartly
Authors: Carlos Gaete-Morales, Julius Jöhrens, Florian Heining, Wolf-Peter Schill
Date: 2023-03-29 00:00:00

Various options are discussed to de-fossilize heavy-duty vehicles (HDV), including battery-electric vehicles (BEV), electric road systems (ERS), and indirect electrification via hydrogen fuel cells or e-fuels. We investigate their power sector implications in future scenarios of Germany with high renewable energy shares, using an open-source capacity expansion model and route-based truck traffic data. Power sector costs are lowest for flexibly charged BEV that also carry out vehicle-to-grid operations, and highest for e-fuels. If BEV and ERS-BEV are not optimally charged, power sector costs increase, but are still substantially lower than in scenarios with hydrogen or e-fuels. This is because indirect electrification is less energy efficient, which outweighs potential flexibility benefits. BEV and ERS-BEV favor solar photovoltaic energy, while hydrogen and e-fuels favor wind power and increase fossil electricity generation. Results remain qualitatively robust in sensitivity analyses.
A general equilibrium model for multi-passenger ridesharing systems with stable matching
Authors: Rui Yao, Shlomo Bekhor
Date: 2023-03-29 00:00:00

This paper proposes a general equilibrium model for multi-passenger ridesharing systems, in which interactions between ridesharing drivers, passengers, platforms, and transportation networks are endogenously captured. Stable matching is modeled as an equilibrium problem in which no ridesharing driver or passenger can reduce ridesharing disutility by unilaterally switching to another matching sequence. This paper is one of the first studies that explicitly integrates the ridesharing platform multi-passenger matching problem into the model. By integrating matching sequence with hyper-network, ridesharing-passenger transfers are avoided in a multi-passenger ridesharing system. Moreover, the matching stability between the ridesharing drivers and passengers is extended to address the multi-OD multi-passenger case in terms of matching sequence. The paper provides a proof for the existence of the proposed general equilibrium. A sequence-bush algorithm is developed for solving the multi-passenger ridesharing equilibrium problem. This algorithm is capable to handle complex ridesharing constraints implicitly. Results illustrate that the proposed sequence-bush algorithm outperforms general-purpose solver, and provides insights into the equilibrium of the joint stable matching and route choice problem. Numerical experiments indicate that ridesharing trips are typically longer than average trip lengths. Sensitivity analysis suggests that a properly designed ridesharing unit price is necessary to achieve network benefits, and travelers with relatively lower values of time are more likely to participate in ridesharing.
Critical Thinking Via Storytelling: Theory and Social Media Experiment
Authors: Brian Jabarian, Elia Sartori
Date: 2023-03-29 00:00:00

In a stylized voting model, we establish that increasing the share of critical thinkers -- individuals who are aware of the ambivalent nature of a certain issue -- in the population increases the efficiency of surveys (elections) but might increase surveys' bias. In an incentivized online social media experiment on a representative US population (N = 706), we show that different digital storytelling formats -- different designs to present the same set of facts -- affect the intensity at which individuals become critical thinkers. Intermediate-length designs (Facebook posts) are most effective at triggering individuals into critical thinking. Individuals with a high need for cognition mostly drive the differential effects of the treatments.
Joint unsupervised and supervised learning for context-aware language identification
Authors: Jinseok Park, Hyung Yong Kim, Jihwan Park, Byeong-Yeol Kim, Shukjae Choi, Yunkyu Lim
Date: 2023-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Identification, Language Modeling, Language Modelling, Masked Language Modeling, speech-recognition, Speech Recognition

Language identification (LID) recognizes the language of a spoken utterance automatically. According to recent studies, LID models trained with an automatic speech recognition (ASR) task perform better than those trained with a LID task only. However, we need additional text labels to train the model to recognize speech, and acquiring the text labels is a cost high. In order to overcome this problem, we propose context-aware language identification using a combination of unsupervised and supervised learning without any text labels. The proposed method learns the context of speech through masked language modeling (MLM) loss and simultaneously trains to determine the language of the utterance with supervised learning loss. The proposed joint learning was found to reduce the error rate by 15.6% compared to the same structure model trained by supervised-only learning on a subset of the VoxLingua107 dataset consisting of sub-three-second utterances in 11 languages.
Kernel-interpolation-based spatial active noise control with exterior radiation suppression
Authors: Kazuyuki Arikawa, Shoichi Koyama, Hiroshi Saruwatari
Date: 2023-03-29 00:00:00

A spatial active noise control (ANC) method based on kernel interpolation of a sound field with exterior radiation suppression is proposed. The aim of spatial ANC is to reduce incoming noise over a target region by using multiple secondary sources and microphones. The method based on kernel interpolation of a sound field allows noise attenuation in a regional space with an array of arbitrary geometry. The cost function is defined as the acoustic potential energy, i.e., the regional integral of the power distribution inside the target region. However, this cost function does not take into consideration the exterior radiation of secondary sources. Thus, the acoustic power in the exterior region can be amplified by the output of the secondary sources. We propose two spatial ANC methods with exterior radiation suppression. The first approach is based on the minimization of the cost function formulated as a sum of the interior acoustic potential energy and exterior radiation power. The second approach is based on the minimization of the interior acoustic potential energy with inequality constraints on the exterior radiation power. Adaptive algorithms for minimizing the cost function are derived for the two approaches. Numerical experimental results indicate that the proposed methods can reduce the interior regional noise while suppressing the exterior radiation.
bAIoimage analysis: elevating the rate of scientific discovery -- as a community
Authors: Damian Edward Dalle Nogare, Matthew Hartley, Joran Deschamps, Jan Ellenberg, Florian Jug
Date: 2023-03-29 00:00:00
Tasks: scientific discovery

The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it will require our multidisciplinary community to work together, establish FAIR data sharing and deliver usable, reproducible analytical tools.
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
Authors: Alexander Ulrichsen, Paul Murray, Stephen Marshall, Moncef Gabbouj, Serkan Kiranyaz, Mehmet Yamac, Nour Aburaed
Date: 2023-03-29 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information comes at the cost of spatial resolution. Super-resolution is a popular technique where the goal is to generate a high-resolution version of a given low-resolution input. The majority of modern super-resolution approaches use convolutional neural networks. However, convolution itself is a linear operation and the networks rely on the non-linear activation functions after each layer to provide the necessary non-linearity to learn the complex underlying function. This means that convolutional neural networks tend to be very deep to achieve the desired results. Recently, self-organized operational neural networks have been proposed that aim to overcome this limitation by replacing the convolutional filters with learnable non-linear functions through the use of MacLaurin series expansions. This work focuses on extending the convolutional filters of a popular super-resolution model to more powerful operational filters to enhance the model performance on hyperspectral images. We also investigate the effects that residual connections and different normalization types have on this type of enhanced network. Despite having fewer parameters than their convolutional network equivalents, our results show that operational neural networks achieve superior super-resolution performance on small hyperspectral image datasets. Our code is made available on Github: https://github.com/aulrichsen/SRONN.
Pushing The Limits of the Wiener Filter in Image Denoising
Authors: Clément Bled, François Pitié
Date: 2023-03-27 00:00:00
Tasks: Denoising, Image Denoising

As modern image denoiser networks have grown in size, their reported performance in popular real noise benchmarks such as DND and SIDD have now long outperformed classic non-deep learning denoisers such as Wiener and Wavelet-based methods. In this paper, we propose to revisit the Wiener filter and re-assess its potential performance. We show that carefully considering the implementation of the Wiener filter can yield similar performance to popular networks such as DnCNN.
Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review
Authors: Irad Mwendo, Kinyua Gikunda, Anthony Maina
Date: 2023-03-26 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

Chest X-rays remains to be the most common imaging modality used to diagnose lung diseases. However, they necessitate the interpretation of experts (radiologists and pulmonologists), who are few. This review paper investigates the use of deep transfer learning techniques to detect COVID-19, pneumonia, and tuberculosis in chest X-ray (CXR) images. It provides an overview of current state-of-the-art CXR image classification techniques and discusses the challenges and opportunities in applying transfer learning to this domain. The paper provides a thorough examination of recent research studies that used deep transfer learning algorithms for COVID-19, pneumonia, and tuberculosis detection, highlighting the advantages and disadvantages of these approaches. Finally, the review paper discusses future research directions in the field of deep transfer learning for CXR image classification, as well as the potential for these techniques to aid in the diagnosis and treatment of lung diseases.
Channel Phase Processing in Wireless Networks for Human Activity Recognition
Authors: Guillermo Diaz, Iker Sobron, Iñaki Eizmendi, Iratxe landa, Manuel Velez
Date: 2023-03-29 00:00:00
Tasks: Activity Recognition, Denoising, Few-Shot Learning, Human Activity Recognition

The phase of the channel state information (CSI) is underutilized as a source of information in wireless sensing due to its sensitivity to synchronization errors of the signal reception. A linear transformation of the phase is commonly applied to correct linear offsets and, in a few cases, some filtering in time or frequency is carried out to smooth the data. This paper presents a novel processing method of the CSI phase to improve the accuracy of human activity recognition (HAR) in indoor environments. This new method, coined Time Smoothing and Frequency Rebuild (TSFR), consists of performing a CSI phase sanitization method to remove phase impairments based on a linear regression transformation method, then a time domain filtering stage with a Savitzy-Golay (SG) filter for denoising purposes and, finally, the phase is rebuilt, eliminating distortions in frequency caused by SG filtering. The TSFR method has been tested on five datasets obtained from experimental measurements, using three different deep learning algorithms, and compared against five other types of CSI phase processing. The results show an accuracy improvement using TSFR in all the cases. Concretely, accuracy performance higher than 90\% in most of the studied scenarios has been achieved with the proposed solution. In few-shot learning strategies, TSFR outperforms the state-of-the-art performance from 35% to 85%.
Sensing with OFDM Waveform at mmWave Band based on Micro-Doppler Analysis
Authors: Mingqing Liu, Fei Gao, Zhuangzhuang Cui, Sofie Pollin, Qingwen Liu
Date: 2023-03-29 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Joint communication and sensing (JCAS) technology has been regarded as one of the innovations in the 6G network. With the channel modeling proposed by the 3rd Generation Partnership Project (3GPP) TR 38.901, this paper investigates the sensing capability using the millimeter-wave (mmWave) band with an orthogonal frequency division multiplexing (OFDM) waveform. Based on micro-Doppler (MD) analysis, we present two case studies, i.e., fan speed detection and human activity recognition, to demonstrate the target modeling with micro-motions, backscattering signal construction, and MD signature extraction using an OFDM waveform at 28 GHz. Simulated signatures demonstrate distinct fan rotation or human motion, and waveform parameters that affect the MD signature extraction are analyzed. Simulation results draw the validity of the proposed modeling and simulation methods, which also aim to facilitate the generation of data sets for various JCAS applications.
Efficient LOS Channel Estimation for RIS-Aided Communications Under Non-Stationary Mobility
Authors: Mehdi Haghshenas, Parisa Ramezani, Emil Björnson
Date: 2023-03-29 00:00:00

Reconfigurable intelligent surface (RIS) is a newly-emerged technology that, with its unique features, is considered to be a game changer for future wireless networks. Channel estimation is one of the most critical challenges for the realization of RIS-assisted communications. Non-parametric channel estimation techniques are inefficient due to the huge pilot dimensionality that stems from the large number of RIS elements. The challenge becomes more serious if we consider the mobility of the users where the channel needs to be re-estimated whenever the user moves to a new location. This paper develops a novel maximum likelihood estimator (MLE) for jointly estimating the line-of-sight (LOS) channel from the user to the RIS and the direct channel between the user and the base station. By smartly refining the RIS configuration during the channel estimation procedure, we show that the channels can be accurately estimated with only a few pilot transmissions -- much fewer than the number of RIS elements. The proposed scheme is also shown to be capable of effectively tracking the channel when the user moves around in a continuous but non-stationary manner with varying LOS angles.
One-Bit Covariance Reconstruction with Non-zero Thresholds: Algorithm and Performance Analysis
Authors: Yu-Hang Xiao, Lei Huang, David Ramírez, Cheng Qian, Hing Cheung So
Date: 2023-03-29 00:00:00
Tasks: Direction of Arrival Estimation

Covariance matrix reconstruction is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this paper, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to use a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to enhance the estimation performance. Our simulations, including the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated.
Hybrid RIS-Assisted MIMO Dual-Function Radar-Communication System
Authors: Zhuoyang Liu, Haiyang Zhang, Tianyao Huang, Feng Xu, Yonina C. Eldar
Date: 2023-03-28 00:00:00

Dual-function radar-communication (DFRC) technology is emerging in next-generation wireless systems. Reconfigurable intelligent surface (RIS) arrays have been suggested as a crucial sensor component of the DFRC. In this paper, we propose a hybrid RIS (HRIS)-assisted multiple-input multiple-output (MIMO) DFRC system, where the HRIS is capable of reflecting communication signals to mobile users and receiving the scattering signal reflected from the radar target simultaneously. Under such a scenario, we are interested in characterizing the fundamental trade-off between radar sensing and communication. Specifically, we study the joint design of the beamforming vectors at the base station (BS) and the parameter configuration of the HRIS so as to maximize the signal-to-interference-and-noise ratio (SINR) of the radar while guaranteeing a communication SINR requirement. To solve the formulated non-convex beamforming design problem, we propose an efficient alternating optimization approach. In particular, for fixed beams at the BS, we use a fast grid search-assisted auto gradient descent (FGS-AGD) algorithm to seek the best HRIS configuration; Then, a closed-form BS beamforming solution is obtained using semidefinite relaxation. Numerical results indicate that compared with benchmark schemes, the proposed approach is capable of improving the radar performance and communication quality significantly and simultaneously.
On the number of equilibria of the replicator-mutator dynamics for noisy social dilemmas
Authors: L. Chen, C. Deng, M. H. Duong, T. A. Han
Date: 2023-03-29 00:00:00
Tasks: Diversity

In this paper, we consider the replicator-mutator dynamics for pairwise social dilemmas where the payoff entries are random variables. The randomness is incorporated to take into account the uncertainty, which is inevitable in practical applications and may arise from different sources such as lack of data for measuring the outcomes, noisy and rapidly changing environments, as well as unavoidable human estimate errors. We analytically and numerically compute the probability that the replicator-mutator dynamics has a given number of equilibria for four classes of pairwise social dilemmas (Prisoner's Dilemma, Snow-Drift Game, Stag-Hunt Game and Harmony Game). As a result, we characterise the qualitative behaviour of such probabilities as a function of the mutation rate. Our results clearly show the influence of the mutation rate and the uncertainty in the payoff matrix definition on the number of equilibria in these games. Overall, our analysis has provided novel theoretical contributions to the understanding of the impact of uncertainty on the behavioural diversity in a complex dynamical system.
Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in Computational Biology
Authors: Tiago Lubiana, Rafael Lopes, Pedro Medeiros, Juan Carlo Silva, Andre Nicolau Aquime Goncalves, Vinicius Maracaja-Coutinho, Helder I Nakaya
Date: 2023-03-29 00:00:00
Tasks: Chatbot, Prompt Engineering, scientific discovery

The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the scientific community. ChatGPT is a general-purpose chatbot powered by large language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous fields, including computational biology. In this article, we offer ten tips based on our experience with ChatGPT to assist computational biologists in optimizing their workflows. We have collected relevant prompts and reviewed the nascent literature in the field, compiling tips we project to remain pertinent for future ChatGPT and LLM iterations, ranging from code refactoring to scientific writing to prompt engineering. We hope our work will help bioinformaticians to complement their workflows while staying aware of the various implications of using this technology. Additionally, to track new and creative applications for bioinformatics tools such as ChatGPT, we have established a GitHub repository at https://github.com/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical adherence to ChatGPT and other LLMs will increase the efficiency of computational biologists, ultimately advancing the pace of scientific discovery in the life sciences.