pwc-archive/papers-with-abstracts huggingface dataset:
Logit-based alternatives to two-stage least squares
Authors: Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Shuyang Sheng
Date: 2023-12-16 00:00:00

We propose logit-based IV and augmented logit-based IV estimators that serve as alternatives to the traditionally used 2SLS estimator in the model where both the endogenous treatment variable and the corresponding instrument are binary. Our novel estimators are as easy to compute as the 2SLS estimator but have an advantage over the 2SLS estimator in terms of causal interpretability. In particular, in certain cases where the probability limits of both our estimators and the 2SLS estimator take the form of weighted-average treatment effects, our estimators are guaranteed to yield non-negative weights whereas the 2SLS estimator is not.
A new behavioral model for portfolio selection using the Half-Full/Half-Empty approach
Authors: Francesco Cesarone, Massimiliano Corradini, Lorenzo Lampariello, Jessica Riccioni
Date: 2023-12-17 00:00:00

We focus on a behavioral model, that has been recently proposed in the literature, whose rational can be traced back to the Half-Full/Half-Empty glass metaphor. More precisely, we generalize the Half-Full/Half-Empty approach to the context of positive and negative lotteries and give financial and behavioral interpretations of the Half-Full/Half-Empty parameters. We develop a portfolio selection model based on the Half-Full/Half-Empty strategy, resulting in a nonconvex optimization problem, which, nonetheless, is proven to be equivalent to an alternative Mixed-Integer Linear Programming formulation. By means of the ensuing empirical analysis, based on three real-world datasets, the Half-Full/Half-Empty model is shown to be very versatile by appropriately varying its parameters, and to provide portfolios displaying promising performances in terms of risk and profitability, compared with Prospect Theory, risk minimization approaches and Equally-Weighted portfolios.
The Fallacy of Borda Count Method -- Why it is Useless with Group Intelligence and Shouldn't be Used with Big Data including Banking Customer Services
Authors: Hao Wang
Date: 2023-12-16 00:00:00

Borda Count Method is an important theory in the field of voting theory. The basic idea and implementation methodology behind the approach is simple and straight forward. Borda Count Method has been used in sports award evaluations and many other scenarios, and therefore is an important aspect of our society. An often ignored ground truth is that online cultural rating platforms such as Douban.com and Goodreads.com often adopt integer rating values for large scale public audience, and therefore leading to Poisson/Pareto behavior. In this paper, we rely on the theory developed by Wang from 2021 to 2023 to demonstrate that online cultural rating platform rating data often evolve into Poisson/Pareto behavior, and individualistic voting preferences are predictable without any data input, so Borda Count Method (or, Range Voting Method) has intrinsic fallacy and should not be used as a voting theory method.
Forecasting skill of a crowd-prediction platform: A comparison of exchange rate forecasts
Authors: Niklas Valentin Lehmann
Date: 2023-12-14 00:00:00
Tasks: Prediction

Open online crowd-prediction platforms are increasingly used to forecast trends and complex events. Despite the large body of research on crowd-prediction and forecasting tournaments, online crowd-prediction platforms have never been directly compared to other forecasting methods. In this analysis, exchange rate crowd-predictions made on Metaculus are compared to predictions made by the random-walk, a statistical model considered extremely hard-to-beat. The random-walk provides less erroneous forecasts, but the crowd-prediction does very well. By using the random-walk as a benchmark, this analysis provides a rare glimpse into the forecasting skill displayed on open online crowd-prediction platforms.
SELM: Speech Enhancement Using Discrete Tokens and Language Models
Authors: Ziqian Wang, Xinfa Zhu, Zihan Zhang, YuanJun Lv, Ning Jiang, Guoqing Zhao, Lei Xie
Date: 2023-12-15 00:00:00
Tasks: Self-Supervised Learning, Speech Enhancement

Language models (LMs) have shown superior performances in various speech generation tasks recently, demonstrating their powerful ability for semantic context modeling. Given the intrinsic similarity between speech generation and speech enhancement, harnessing semantic information holds potential advantages for speech enhancement tasks. In light of this, we propose SELM, a novel paradigm for speech enhancement, which integrates discrete tokens and leverages language models. SELM comprises three stages: encoding, modeling, and decoding. We transform continuous waveform signals into discrete tokens using pre-trained self-supervised learning (SSL) models and a k-means tokenizer. Language models then capture comprehensive contextual information within these tokens. Finally, a detokenizer and HiFi-GAN restore them into enhanced speech. Experimental results demonstrate that SELM achieves comparable performance in objective metrics alongside superior results in subjective perception. Our demos are available https://honee-w.github.io/SELM/.
A Deep Representation Learning-based Speech Enhancement Method Using Complex Convolution Recurrent Variational Autoencoder
Authors: Yang Xiang, Jingguang Tian, Xinhui Hu, Xinkang Xu, ZhaoHui Yin
Date: 2023-12-15 00:00:00
Tasks: Representation Learning, Speech Enhancement

Generally, the performance of deep neural networks (DNNs) heavily depends on the quality of data representation learning. Our preliminary work has emphasized the significance of deep representation learning (DRL) in the context of speech enhancement (SE) applications. Specifically, our initial SE algorithm employed a gated recurrent unit variational autoencoder (VAE) with a Gaussian distribution to enhance the performance of certain existing SE systems. Building upon our preliminary framework, this paper introduces a novel approach for SE using deep complex convolutional recurrent networks with a VAE (DCCRN-VAE). DCCRN-VAE assumes that the latent variables of signals follow complex Gaussian distributions that are modeled by DCCRN, as these distributions can better capture the behaviors of complex signals. Additionally, we propose the application of a residual loss in DCCRN-VAE to further improve the quality of the enhanced speech. {Compared to our preliminary work, DCCRN-VAE introduces a more sophisticated DCCRN structure and probability distribution for DRL. Furthermore, in comparison to DCCRN, DCCRN-VAE employs a more advanced DRL strategy. The experimental results demonstrate that the proposed SE algorithm outperforms both our preliminary SE framework and the state-of-the-art DCCRN SE method in terms of scale-invariant signal-to-distortion ratio, speech quality, and speech intelligibility.
Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments
Authors: Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Date: 2023-12-14 00:00:00
Tasks: Data Augmentation

Hearables with integrated microphones may offer communication benefits in noisy working environments, e.g. by transmitting the recorded own voice of the user. Systems aiming at reconstructing the clean and full-bandwidth own voice from noisy microphone recordings are often based on supervised learning. Recording a sufficient amount of noise required for training such a system is costly since noise transmission between outer and inner microphones varies individually. Previously proposed methods either do not consider noise, only consider noise at outer microphones or assume inner and outer microphone noise to be independent during training, and it is not yet clear whether individualized noise can benefit the training of and own voice reconstruction system. In this paper, we investigate several noise data augmentation techniques based on measured transfer functions to simulate multi-microphone noise. Using augmented noise, we train a multi-channel own voice reconstruction system. Experiments using real noise are carried out to investigate the generalization capability. Results show that incorporating augmented noise yields large benefits, in particular considering individualized noise augmentation leads to higher performance.
System Integration of Xilinx DPU and HDMI for Real-Time inference in PYNQ Environment with Image Enhancement
Authors: Jonathan Sanderson, Syed Rafay Hasan
Date: 2023-12-15 00:00:00
Tasks: Edge-computing, Image Enhancement

Use of edge computing in application of Computer Vision (CV) is an active field of research. Today, most CV applications make use of Convolutional Neural Networks (CNNs) to inference on and interpret video data. These edge devices are responsible for several CV related tasks, such as gathering, processing and enhancing, inferencing on, and displaying video data. Due to ease of reconfiguration, computation on FPGA fabric is used to achieve such complex computation tasks. Xilinx provides the PYNQ environment as a user-friendly interface that facilitates in Hardware/Software system integration. However, to the best of authors' knowledge there is no end-to-end framework available for the PYNQ environment that allows Hardware/Software system integration and deployment of CNNs for real-time input feed from High Definition Multimedia Interface (HDMI) input to HDMI output, along with insertion of customized hardware IPs. In this work we propose an integration of rea\textbf{L}-time image \textbf{E}nancement IP with \textbf{A}I inferencing engine in the \textbf{P}ynq environment (\textbf{LEAP}), that integrates HDMI, AI acceleration, image enhancement in the PYNQ environment for Xilinx's Microprocessor on Chip (MPSoC) platform. We evaluate our methodology with two well know CNN models, Resnet50 and YOLOv3. To validate our proposed methodology, LEAP, a simple image enhancement algorithm, histogram equalization, is designed and integrated in the FPGA fabric along with Xilinx's Deep Processing Unit (DPU). Our results show successful implementation of end-to-end integration using completely open source information.
Geometry-Corrected Geodesic Motion Modeling with Per-Frame Camera Motion for 360-Degree Video Compression
Authors: Andy Regensky, André Kaup
Date: 2023-12-14 00:00:00
Tasks: Motion Compensation, Video Compression

The large amounts of data associated with 360-degree video require highly effective compression techniques for efficient storage and distribution. The development of improved motion models for 360-degree motion compensation has shown significant improvements in compression efficiency. A geodesic motion model representing translational camera motion proved to be one of the most effective models. In this paper, we propose an improved geometry-corrected geodesic motion model that outperforms the state of the art at reduced complexity. We additionally propose the transmission of per-frame camera motion information, where prior work assumed the same camera motion for all frames of a sequence. Our approach yields average Bj{\o}ntegaard Delta rate savings of 2.27% over H.266/VVC, outperforming the original geodesic motion model by 0.32 percentage points at reduced computational complexity.
A Guided Upsampling Network for Short Wave Infrared Images Using Graph Regularization
Authors: Frank Sippel, Jürgen Seiler, André Kaup
Date: 2023-12-14 00:00:00

Exploiting the infrared area of the spectrum for classification problems is getting increasingly popular, because many materials have characteristic absorption bands in this area. However, sensors in the short wave infrared (SWIR) area and even higher wavelengths have a very low spatial resolution in comparison to classical cameras that operate in the visible wavelength area. Thus, in this paper an upsampling method for SWIR images guided by a visible image is presented. For that, the proposed guided upsampling network (GUNet) uses a graph-regularized optimization problem based on learned affinities is presented. The evaluation is based on a novel synthetic near-field visible-SWIR stereo database. Different guided upsampling methods are evaluated, which shows an improvement of nearly 1 dB on this database for the proposed upsampling method in comparison to the second best guided upsampling network. Furthermore, a visual example of an upsampled SWIR image of a real-world scene is depicted for showing real-world applicability.
Color Agnostic Cross-Spectral Disparity Estimation
Authors: Frank Sippel, Nils Genser, Hannah Och, Jürgen Seiler, André Kaup
Date: 2023-12-14 00:00:00
Tasks: Disparity Estimation, Image Generation, Stereo Matching

Since camera modules become more and more affordable, multispectral camera arrays have found their way from special applications to the mass market, e.g., in automotive systems, smartphones, or drones. Due to multiple modalities, the registration of different viewpoints and the required cross-spectral disparity estimation is up to the present extremely challenging. To overcome this problem, we introduce a novel spectral image synthesis in combination with a color agnostic transform. Thus, any recently published stereo matching network can be turned to a cross-spectral disparity estimator. Our novel algorithm requires only RGB stereo data to train a cross-spectral disparity estimator and a generalization from artificial training data to camera-captured images is obtained. The theoretical examination of the novel color agnostic method is completed by an extensive evaluation compared to state of the art including self-recorded multispectral data and a reference implementation. The novel color agnostic disparity estimation improves cross-spectral as well as conventional color stereo matching by reducing the average end-point error by 41% for cross-spectral and by 22% for mono-modal content, respectively.
Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering
Authors: Siddhant Gautam, Angqi Li, Saiprasad Ravishankar
Date: 2023-12-13 00:00:00
Tasks: Clustering, Image Reconstruction

There has been much recent interest in adapting undersampled trajectories in MRI based on training data. In this work, we propose a novel patient-adaptive MRI sampling algorithm based on grouping scans within a training set. Scan-adaptive sampling patterns are optimized together with an image reconstruction network for the training scans. The training optimization alternates between determining the best sampling pattern for each scan (based on a greedy search or iterative coordinate descent (ICD)) and training a reconstructor across the dataset. The eventual scan-adaptive sampling patterns on the training set are used as labels to predict sampling design using nearest neighbor search at test time. The proposed algorithm is applied to the fastMRI knee multicoil dataset and demonstrates improved performance over several baselines.
Study of cognitive component of auditory attention to natural speech events
Authors: Nhan D. T. Nguyen, Kaare Mikkelsen, Preben Kidmose
Date: 2023-12-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), ERP

Event-related potentials (ERP) have been used to address a wide range of research questions in neuroscience and cognitive psychology including selective auditory attention. The recent progress in auditory attention decoding (AAD) methods is based on algorithms that find a relation between the audio envelope and the neurophysiological response. The most popular approach is based on the reconstruction of the audio envelope based on EEG signals. However, these methods are mainly based on the neurophysiological entrainment to physical attributes of the sensory stimulus and are generally limited by a long detection window. This study proposes a novel approach to auditory attention decoding by looking at higher-level cognitive responses to natural speech. To investigate if natural speech events elicit cognitive ERP components and how these components are affected by attention mechanisms, we designed a series of four experimental paradigms with increasing complexity: a word category oddball paradigm, a word category oddball paradigm with competing speakers, and competing speech streams with and without specific targets. We recorded the electroencephalogram (EEG) from 32 scalp electrodes and 12 in-ear electrodes (ear-EEG) from 24 participants. A cognitive ERP component, which we believe is related to the well-known P3b component, was observed at parietal electrode sites with a latency of approximately 620 ms. The component is statistically most significant for the simplest paradigm and gradually decreases in strength with increasing complexity of the paradigm. We also show that the component can be observed in the in-ear EEG signals by using spatial filtering. The cognitive component elicited by auditory attention may contribute to decoding auditory attention from electrophysiological recordings and its presence in the ear-EEG signals is promising for future applications within hearing aids.
Multi-Target Two-way Integrated Sensing and Communications with Full Duplex MIMO Radios
Authors: Muhammad Talha, Besma Smida, Md Atiqul Islam, George C. Alexandropoulos
Date: 2023-12-16 00:00:00
Tasks: Integrated sensing and communication, parameter estimation

In this paper, we propose a multiple input multiple output (MIMO) Full-Duplex Integrated Sensing and Communication System consisting of multiple targets, a single downlink, and a single uplink user. We employed signal-to-interference plus noise ratio (SINR) as the performance metric for radar, downlink, and uplink communication. We use a communication-centric approach in which communication waveform is used for both communication and sensing of the environment. We develop a sensing algorithm capable of estimating the direction of arrival (DoA), range, and velocity of each target. We also propose a joint optimization framework for designing A/D transmit and receive beamformers to improve radar, downlink, and uplink SINRs while minimizing self-interference (SI) leakage. We also propose a null space projection (NSP) based approach to improve the uplink rate. Our simulation results, considering orthogonal frequency division multiplexing (OFDM) waveform, show accurate radar parameter estimation with improved downlink and uplink rate.
Methodologies for Future Vehicular Digital Twins
Authors: Danilo Radovic, Markus Hofer, Faruk Pasic, Enrico M. Vitucci, Aleksei Fedorov, Thomas Zemen
Date: 2023-12-15 00:00:00
Tasks: Position

The role of wireless communications in various domains of intelligent transportation systems is significant; it is evident that dependable message exchange between nodes (cars, bikes, pedestrians, infrastructure, etc.) has to be guaranteed to fulfill the stringent requirements for future transportation systems. A precise site-specific digital twin is seen as a key enabler for the cost-effective development and validation of future vehicular communication systems. Furthermore, achieving a realistic digital twin for dependable wireless communications requires accurate measurement, modeling, and emulation of wireless communication channels. However, contemporary approaches in these domains are not efficient enough to satisfy the foreseen needs. In this position paper, we overview the current solutions, indicate their limitations, and discuss the most prospective paths for future investigation.
RIS-Enabled NLoS Near-Field Joint Position and Velocity Estimation under User Mobility
Authors: Moustafa Rahal, Benoit Denis, Musa Furkan Keskin, Bernard Uguen, Henk Wymeersch
Date: 2023-12-15 00:00:00
Tasks: Position

In the context of single-base station (BS) non-line-of-sight (NLoS) single-epoch localization with the aid of a reflective reconfigurable intelligent surface (RIS), this paper introduces a novel three-step algorithm that jointly estimates the position and velocity of a mobile user equipment (UE), while compensating for the Doppler effects observed in near-field (NF) at the RIS elements over the short transmission duration of a sequence of downlink (DL) pilot symbols. First, a low-complexity initialization procedure is proposed, relying in part on far-field (FF) approximation and a static user assumption. Then, an alternating optimization procedure is designed to iteratively refine the velocity and position estimates, as well as the channel gain. The refinement routines leverage small angle approximations and the linearization of the RIS response, accounting for both NF and mobility effects. We evaluate the performance of the proposed algorithm through extensive simulations under diverse operating conditions with regard to signal-to-noise ratio (SNR), UE mobility, uncontrolled multipath and RIS-UE distance. Our results reveal remarkable performance improvements over the state-of-the-art (SoTA) mobility-agnostic benchmark algorithm, while indicating convergence of the proposed algorithm to respective theoretical bounds on position and velocity estimation.
Brain-scale Theta Band Functional Connectivity As A Signature of Slow Breathing and Breath-hold Phases
Authors: Anusha A. S., Pradeep Kumar G., A. G. Ramakrishnan
Date: 2023-12-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG), feature selection, Functional Connectivity

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain during consciously controlled breathing at 2 cycles per minute (cpm), and breath-hold have been investigated and reported here. An experimental protocol involving controlled breathing and breath-hold sessions, synchronized to a visual metronome, was designed and administered to 20 healthy subjects (9 females and 11 males). EEG data were collected during these sessions using the 61-channel eego mylab system from ANT Neuro. Further, FC was estimated for all possible pairs of EEG time series data, for 7 EEG bands. Feature selection using a genetic algorithm (GA) was performed to identify a subset of functional connections that would best distinguish the inhale, exhale, inhale-hold, and exhale-hold phases using a random committee classifier. The best accuracy of 93.36 % was obtained when 1161 theta-band functional connections were fed as input to the classifier, highlighting the efficacy of the theta-band functional connectome in distinguishing these phases of the respiratory cycle. This functional network was further characterized using graph measures, and observations illustrated a statistically significant difference in the efficiency of information exchange through the network during different respiratory phases.
Towards Neuromorphic Compression based Neural Sensing for Next-Generation Wireless Implantable Brain Machine Interface
Authors: Vivek Mohan, Wee Peng Tay, Arindam Basu
Date: 2023-12-15 00:00:00
Tasks: Data Compression

This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless iBMI. The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio and spike information preservation. For the latter, we use metrics such as root-mean-square error and correlation coefficient between the original and recovered signal to assess the effect of neuromorphic compression on spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data compression ratio of $50-100$ can be achieved, $5-18\times$ more than prior work, by selective transmission of event pulses corresponding to neural spikes. A correlation coefficient of $\approx0.9$ and spike detection accuracy of over $90\%$ for the worst-case analysis involving $10K$-channel simulated recording and typical analysis using $100$ or $384$-channel real neural recordings. We also analyze the collision handling capability and scalability of the proposed pipeline.