pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Global Building Stocks by 2070: Unlocking Renovation Potential
Authors: Shufan Zhang, Minda Ma, Nan Zhou, Jinyue Yan, Wei Feng, Ran Yan, Kairui You, Jingjing Zhang, Jing Ke
Date: 2024-06-06 00:00:00

Buildings produce one-third of carbon emissions globally, however, data absence regarding global floorspace poses challenges in advancing building carbon neutrality. We compile the measured building stocks for 14 major economies and apply our global building stock model, GLOBUS, to evaluate future trends in stock turnover. Based on a scenario not considering renovation, by 2070 the building stock in developed economies will be ~1.4 times that of 2020 (100 billion m2); in developing economies it is expected to be 2.2 times that of 2020 (313 billion m2). Based on a techno-economic potential scenario, however, stocks in developed economies will decline to approximately 0.8 times the 2020 level, while stocks in developing economies will increase to nearly twice the 2020 level due to their fewer buildings currently. Overall, GLOBUS provides a way of calculating the global building stock, helping scientists, engineers, and policymakers conduct a range of investigation across various future scenarios.
Views about ChatGPT: Are human decision making and human learning necessary?
Authors: Eiji Yamamura, Fumio Ohtake
Date: 2024-06-06 00:00:00
Tasks: Decision Making

Using individual-level survey data from 2024, this study investigated how respondent characteristics are associated with a subjective view of generative artificial intelligence (GAI). We asked 14 questions concerning respondents view about GAI, such as general view, faulty GAI, autonomous GEI, GAI replacing humans, and importance of human learning. Regression analysis based on the ordered logit model revealed that: (1) In some cases, the results of smartphone and computer usage times differed. Smartphone usage time was negatively correlated with the importance of human learning, whereas computer usage was not negatively correlated. (2) Managers and ordinary businesspeople have positive views of GAI. However, managers do not show a positive view about GAI being responsible for human decision making. (3) Teachers generally have a negative view about GAI replacing humans and no need of learning. They do not have negative views about GAI producing documents unless GAI is faulty. (4) Medical industry workers positively view GAI if it operates following their direction. However, they do not agree with the view that GAI replaces humans, and that human learning is unnecessary. (5) Females are less likely than men to have a positive view of GAI. In summary, views about GAI vary widely by the individual characteristics and condition of GAI, and by the question set.
Evaluating Feature Selection Methods for Macro-Economic Forecasting, Applied for Inflation Indicator of Iran
Authors: Mahdi Goldani
Date: 2024-06-06 00:00:00
Tasks: feature selection

This study explores various feature selection techniques applied to macro-economic forecasting, using Iran's World Bank Development Indicators. Employing a comprehensive evaluation framework that includes Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) within a 10-fold cross-validation setup, this research systematically analyzes and ranks different feature selection methodologies. The study distinctly highlights the efficiency of Stepwise Selection, Tree-based methods, Hausdorff distance, Euclidean distance, and Mutual Information (MI) Score, noting their superior performance in reducing predictive errors. In contrast, methods like Recursive Feature Elimination with Cross-Validation (RFECV) and Variance Thresholding showed relatively lower effectiveness. The results underline the robustness of similarity-based approaches, particularly Hausdorff and Euclidean distances, which consistently performed well across various datasets, achieving an average rank of 9.125 out of a range of tested methods. This paper provides crucial insights into the effectiveness of different feature selection methods, offering significant implications for enhancing the predictive accuracy of models used in economic analysis and planning. The findings advocate for the prioritization of stepwise and tree-based methods alongside similarity-based techniques for researchers and practitioners working with complex economic datasets.
Total-Duration-Aware Duration Modeling for Text-to-Speech Systems
Authors: Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker, Chung-Hsien Tsai, Canrun Li, Zhen Xiao, Hemin Yang, Zirun Zhu, Min Tang, Jinyu Li, Sheng Zhao, Naoyuki Kanda
Date: 2024-06-06 00:00:00
Tasks: Diversity, text-to-speech, Text to Speech

Accurate control of the total duration of generated speech by adjusting the speech rate is crucial for various text-to-speech (TTS) applications. However, the impact of adjusting the speech rate on speech quality, such as intelligibility and speaker characteristics, has been underexplored. In this work, we propose a novel total-duration-aware (TDA) duration model for TTS, where phoneme durations are predicted not only from the text input but also from an additional input of the total target duration. We also propose a MaskGIT-based duration model that enhances the diversity and quality of the predicted phoneme durations. Our results demonstrate that the proposed TDA duration models achieve better intelligibility and speaker similarity for various speech rate configurations compared to the baseline models. We also show that the proposed MaskGIT-based model can generate phoneme durations with higher quality and diversity compared to its regression or flow-matching counterparts.
Helsinki Speech Challenge 2024
Authors: Martin Ludvigsen, Elli Karvonen, Markus Juvonen, Samuli Siltanen
Date: 2024-06-06 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

The Helsinki Speech Challenge 2024 (HSC2024) invites researchers to enhance and deconvolve speech audio recordings. We recorded a dataset that challenges participants to apply speech enhancement and inverse problems techniques to recorded speech data. This dataset includes paired samples of AI-generated clean speech and corresponding recordings, which feature varying levels of corruption, including frequency attenuation and reverberation. The challenge focuses on developing innovative deconvolution methods to accurately recover the original audio. The effectiveness of these methods will be quantitatively assessed using a speech recognition model, providing a relevant metric for evaluating enhancements in real-world scenarios.
PLDNet: PLD-Guided Lightweight Deep Network Boosted by Efficient Attention for Handheld Dual-Microphone Speech Enhancement
Authors: Nan Zhou, Youhai Jiang, Jialin Tan, Chongmin Qi
Date: 2024-06-06 00:00:00
Tasks: Speech Enhancement

Low-complexity speech enhancement on mobile phones is crucial in the era of 5G. Thus, focusing on handheld mobile phone communication scenario, based on power level difference (PLD) algorithm and lightweight U-Net, we propose PLD-guided lightweight deep network (PLDNet), an extremely lightweight dual-microphone speech enhancement method that integrates the guidance of signal processing algorithm and lightweight attention-augmented U-Net. For the guidance information, we employ PLD algorithm to pre-process dual-microphone spectrum, and feed the output into subsequent deep neural network, which utilizes a lightweight U-Net with our proposed gated convolution augmented frequency attention (GCAFA) module to extract desired clean speech. Experimental results demonstrate that our proposed method achieves competitive performance with recent top-performing models while reducing computational cost by over 90%, highlighting the potential for low-complexity speech enhancement on mobile phones.
NeuRO: An Application for Code-Switched Autism Detection in Children
Authors: Mohd Mujtaba Akhtar, Girish, Orchid Chetia Phukan, Muskaan Singh
Date: 2024-06-05 00:00:00
Tasks: Autism detection

Code-switching is a common communication phenomenon where individuals alternate between two or more languages or linguistic styles within a single conversation. Autism Spectrum Disorder (ASD) is a developmental disorder posing challenges in social interaction, communication, and repetitive behaviors. Detecting ASD in individuals with code-switch scenario presents unique challenges. In this paper, we address this problem by building an application NeuRO which aims to detect potential signs of autism in code-switched conversations, facilitating early intervention and support for individuals with ASD.
Machine Learning-Driven Microwave Imaging for Soil Moisture Estimation near Leaky Pipe
Authors: Mohammad Ramezaninia, Mohammadreza Shams, Mohammad Zoofaghari
Date: 2024-06-06 00:00:00
Tasks: Soil moisture estimation

Characterizing soil moisture (SM) around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated by two different microwave imaging techniques based on spatio-temporal measurements at various frequency bands. The study investigates the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN) algorithms for moisture classification based on these images, both before and after performing soil clutter reduction. We also explore the potentials of CNN and KNN for moisture estimation around the plant roots and in the presence of pebbles. The results demonstrate the more accurate moisture estimation using CNN when it is applied after clutter reduction considering back projection algorithm (BPA) as the imaging technique.
Towards robust radiomics and radiogenomics predictive models for brain tumor characterization
Authors: Maria Nadeem, Asma Shaheen, Muhammad F. A. Chaudhary, Hassan Mohy-ud-Din
Date: 2024-06-05 00:00:00
Tasks: Segmentation

In the context of brain tumor characterization, we focused on two key questions: (a) stability of radiomics features to variability in multiregional segmentation masks obtained with fully-automatic deep segmentation methods and (b) subsequent impact on predictive performance on downstream tasks: IDH prediction and Overall Survival (OS) classification. We further constrained our study to limited computational resources setting which are found in underprivileged, remote, and (or) resource-starved clinical sites in developing countries. We employed seven SOTA CNNs which can be trained with limited computational resources and have demonstrated superior segmentation performance on BraTS challenge. Subsequent selection of discriminatory features was done with RFE-SVM and MRMR. Our study revealed that highly stable radiomics features were: (1) predominantly texture features (79.1%), (2) mainly extracted from WT region (96.1%), and (3) largely representing T1Gd (35.9%) and T1 (28%) sequences. Shape features and radiomics features extracted from the ENC subregion had the lowest average stability. Stability filtering minimized non-physiological variability in predictive models as indicated by an order-of-magnitude decrease in the relative standard deviation of AUCs. The non-physiological variability is attributed to variability in multiregional segmentation maps obtained with fully-automatic CNNs. Stability filtering significantly improved predictive performance on the two downstream tasks substantiating the inevitability of learning novel radiomics and radiogenomics models with stable discriminatory features. The study (implicitly) demonstrates the importance of suboptimal deep segmentation networks which can be exploited as auxiliary networks for subsequent identification of radiomics features stable to variability in automatically generated multiregional segmentation maps.
Near-field Beam Training with Sparse DFT Codebook
Authors: Cong Zhou, Chenyu Wu, Changsheng You, Shuo Shi
Date: 2024-06-06 00:00:00

Extremely large-scale array (XL-array) has emerged as one promising technology to improve the spectral efficiency and spatial resolution of future sixth generation (6G) wireless systems.The upsurge in the antenna number antennas renders communication users more likely to be located in the near-field region, which requires a more accurate spherical (instead of planar) wavefront propagation modeling.This inevitably incurs unaffordable beam training overhead when performing a two-dimensional (2D) beam-search in both the angular and range domains.To address this issue, we first introduce a new sparse discrete Fourier transform (DFT) codebook, which exhibits the angular periodicity in the received beam pattern at the user, which motivates us to propose a three-phase beam training scheme.Specifically, in the first phase, we utilize the sparse DFT codebook for beam sweeping in an angular subspace and estimate candidate user angles according to the received beam pattern.Then, a central sub-array is activated to scan specific candidate angles for resolving the issue of angular ambiguity and identity the user angle.In the third phase, the polar-domain codebook is applied in the estimated angle to search the best effective range of the user.Finally, numerical results show that the proposed beam training scheme enabled by sparse DFT codebook achieves 98.67% reduction as compared with the exhaustive-search scheme, yet without compromising rate performance in the high signal-to-ratio (SNR) regime.
Beyond Diagonal RIS-Aided Networks: Performance Analysis and Sectorization Tradeoff
Authors: Mostafa Samy, Hayder Al-Hraishawi, Abuzar B. M. Adam, Konstantinos Ntontin, Symeon Chatzinotas, Björn Otteresten
Date: 2024-06-06 00:00:00
Tasks: Diversity

Reconfigurable intelligent surfaces (RISs) have emerged as a spectrum- and energy-efficient technology to enhance the coverage of wireless communications within the upcoming 6G networks. Recently, novel extensions of this technology, referred to as multi-sector beyond diagonal RIS (BD-RIS), have been proposed, where the configurable elements are divided into $L$ sectors $(L \geq 2)$ and arranged as a polygon prism, with each sector covering $1/L$ space. This paper presents a performance analysis of a multi-user communication system assisted by a multi-sector BD-RIS operating in time-switching (TS) mode. Specifically, we derive closed-form expressions for the moment-generating function (MGF), probability density function (PDF), and cumulative density function (CDF) of the signal-to-noise ratio (SNR) per user. Furthermore, closed-form expressions for the outage probability, achievable spectral and energy efficiency, symbol error probability, and diversity order for the proposed system model are derived. Moreover, a comparison is performed with the simultaneously transmitting and reflecting (STAR)-RISs, a special case of multi-sector BD-RIS with two sectors. Our analysis shows that for a fixed number of elements, increasing the sectors improves outage performance at the expense of reduced diversity order compared to STAR-RIS. This trade-off is influenced by the Rician factors of the cascaded channel and the number of configurable elements per sector. However, this superiority in slope is observed at outage probability values below $10^{-5}$, which remains below practical operating ranges of communication systems. Additionally, simulations are provided to validate the accuracy of our theoretical analyses showing a notable $182\%$ increase in spectral efficiency and a $238\%$ increase in energy efficiency when transitioning from a 2-sector to a 6-sector configuration.
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation
Authors: Keivan Faghih Niresi, Lucas Kuhn, Gaëtan Frusque, Olga Fink
Date: 2024-06-06 00:00:00
Tasks: Denoising, Graph Learning

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across various domains. However, acquiring the underlying graphs for specific applications remains a challenging task. While graph inference based on smooth graph signal representation has become one of the state-of-the-art methods, these approaches usually overlook the unique properties of networks, which are generally derived from domain-specific knowledge. Overlooking this information could make the approaches less interpretable and less effective overall. In this study, we propose a new graph inference method that leverages available domain knowledge. The proposed methodology is evaluated on the task of denoising and imputing missing sensor data, utilizing graph signal reconstruction techniques. The results demonstrate that incorporating domain knowledge into the graph inference process can improve graph signal reconstruction in district heating networks. Our code is available at \href{https://github.com/Keiv4n/IGL}{github.com/Keiv4n/IGL}.
BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
Authors: Dimitrios Samakovlis, Stefano Albini, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Pasquale Davide Schiavone, Miguel Peón Quirós, David Atienza
Date: 2024-06-06 00:00:00

The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despite advances in application and hardware design research, the domain lacks a systematic approach to hardware evaluation. In this work, we propose BiomedBench, a new benchmark suite composed of complete end-to-end TinyML biomedical applications for real-time monitoring of patients using wearable devices. Each application presents different requirements during typical signal acquisition and processing phases, including varying computational workloads and relations between active and idle times. Furthermore, our evaluation of five state-of-the-art low-power platforms in terms of energy efficiency shows that modern platforms cannot effectively target all types of biomedical applications. BiomedBench is released as an open-source suite to standardize hardware evaluation and guide hardware and application design in the TinyML wearable domain.
Iterative Sparse Identification of Nonlinear Dynamics
Authors: Jinho Choi
Date: 2024-06-06 00:00:00
Tasks: Compressive Sensing, Time Series

In order to extract governing equations from time-series data, various approaches are proposed. Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal number of terms, utilizing the principles of compressive sensing. This feature, which relies on a small number of terms, is crucial for interpretability. The effectiveness of SINDy hinges on the choice of candidate functions within its dictionary to extract governing equations of dynamical systems. A larger dictionary allows for more terms, enhancing the quality of approximations. However, the computational complexity scales with dictionary size, rendering SINDy less suitable for high-dimensional datasets, even though it has been successfully applied to low-dimensional datasets. To address this challenge, we introduce iterative SINDy in this paper, where the dictionary undergoes expansion and compression through iterations. We also conduct an analysis of the convergence properties of iterative SINDy. Simulation results validate that iterative SINDy can achieve nearly identical performance to SINDy, while significantly reducing computational complexity. Notably, iterative SINDy demonstrates effectiveness with high-dimensional time-series data without incurring the prohibitively high computational cost associated with SINDy.
Energy-Efficient Hybrid Beamforming for Integrated Sensing and Communication Enabled mmWave MIMO Systems
Authors: Jitendra Singh, Suraj Srivastava, Aditya K. Jagannatham
Date: 2024-06-06 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper conceives a hybrid beamforming design (HBF) that maximizes the energy efficiency (EE) of an integrated sensing and communication (ISAC)-enabled millimeter wave (mmWave) multiple-input multiple-output (MIMO) system. In the system under consideration, an ISAC base station (BS) with the hybrid MIMO architecture communicates with multiple users and simultaneously detects multiple targets. The proposed scheme seeks to maximize the EE of the system, considering the signal-to-interference and noise ratio (SINR) as the user's quality of service (QoS) and the sensing beampattern gain of the targets as constraints. To solve this non-convex problem, we initially adopt Dinkelbach's method to convert the fractional objective function to subtractive form and subsequently obtain the sub-optimal fully-digital transmit beamformer by leveraging the principle of semi-definite relaxation. Subsequently, we propose a penalty-based manifold optimization scheme in conjunction with an alternating minimization method to determine the baseband (BB) and analog beamformers based on the designed fully-digital transmit beamformer. Finally, simulation results are given to demonstrate the efficacy of our proposed algorithm with respect to the benchmarks.
Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation
Authors: JunJie Wee, Guo-Wei Wei
Date: 2024-06-06 00:00:00
Tasks: Benchmarking, Drug Discovery, Prediction, Protein Structure Prediction

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step towards comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. Evaluated using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations, AF3 complex structures give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.
A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems
Authors: Theo Guyard, Cédric Herzet, Clément Elvira, Ayşe-Nur Arslan
Date: 2024-06-03 00:00:00

We consider the resolution of learning problems involving $\ell_0$-regularization via Branch-and-Bound (BnB) algorithms. These methods explore regions of the feasible space of the problem and check whether they do not contain solutions through "pruning tests". In standard implementations, evaluating a pruning test requires to solve a convex optimization problem, which may result in computational bottlenecks. In this paper, we present an alternative to implement pruning tests for some generic family of $\ell_0$-regularized problems. Our proposed procedure allows the simultaneous assessment of several regions and can be embedded in standard BnB implementations with a negligible computational overhead. We show through numerical simulations that our pruning strategy can improve the solving time of BnB procedures by several orders of magnitude for typical problems encountered in machine-learning applications.