pwc-archive/papers-with-abstracts huggingface dataset:
Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables
Authors: Dmitry Arkhangelsky, David Hirshberg
Date: 2023-11-22 00:00:00

We analyze the synthetic control (SC) method in panel data settings with many units. We assume the treatment assignment is based on unobserved heterogeneity and pre-treatment information, allowing for both strictly and sequentially exogenous assignment processes. We show that the critical property that determines the behavior of the SC method is the ability of input features to approximate the unobserved heterogeneity. Our results imply that the SC method delivers asymptotically normal estimators for a large class of linear panel data models as long as the number of pre-treatment periods is sufficiently large, making it a natural alternative to the Difference-in-Differences.
Regressions under Adverse Conditions
Authors: Timo Dimitriadis, Yannick Hoga
Date: 2023-11-22 00:00:00
Tasks: regression, Time Series

We introduce a new regression method that relates the mean of an outcome variable to covariates, under the "adverse condition" that a distress variable falls in its tail. This allows to tailor classical mean regressions to adverse scenarios, which receive increasing interest in economics and finance, among many others. In the terminology of the systemic risk literature, our method can be interpreted as a regression for the Marginal Expected Shortfall. We propose a two-step procedure to estimate the new models, show consistency and asymptotic normality of the estimator, and propose feasible inference under weak conditions that allow for cross-sectional and time series applications. Simulations verify the accuracy of the asymptotic approximations of the two-step estimator. Two empirical applications show that our regressions under adverse conditions are a valuable tool in such diverse fields as the study of the relation between systemic risk and asset price bubbles, and dissecting macroeconomic growth vulnerabilities into individual components.
Belief identification by proxy
Authors: Elias Tsakas
Date: 2023-11-22 00:00:00
Tasks: Econometrics

It is well known that individual beliefs cannot be identified using traditional choice data, unless we exogenously assume state-independent utilities. In this paper, I propose a novel methodology that solves this long-standing identification problem in a simple way. This method relies on the extending the state space by introducing a proxy, for which the agent has no stakes conditional on the original state space. The latter allows us to identify the agent's conditional beliefs about the proxy given each state realization, which in turn suffices for indirectly identifying her beliefs about the original state space. This approach is analogous to the one of instrumental variables in econometrics. Similarly to instrumental variables, the appeal of this method comes from the flexibility in selecting a proxy.
Sparsity-Driven EEG Channel Selection for Brain-Assisted Speech Enhancement
Authors: Jie Zhang, Qing-Tian Xu, Zhen-Hua Ling, Haizhou Li
Date: 2023-11-22 00:00:00
Tasks: channel selection, EEG, Electroencephalogram (EEG), Speech Enhancement

Speech enhancement is widely used as a front-end to improve the speech quality in many audio systems, while it is hard to extract the target speech in multi-talker conditions without prior information on the speaker identity. It was shown that the auditory attention on the target speaker can be decoded from the electroencephalogram (EEG) of the listener implicitly. In this work, we therefore propose a novel end-to-end brain-assisted speech enhancement network (BASEN), which incorporates the listeners' EEG signals and adopts a temporal convolutional network together with a convolutional multi-layer cross attention module to fuse EEG-audio features. Considering that an EEG cap with sparse channels exhibits multiple benefits and in practice many electrodes might contribute marginally, we further propose two channel selection methods, called residual Gumbel selection and convolutional regularization selection. They are dedicated to tackling training instability and duplicated channel selections, respectively. Experimental results on a public dataset show the superiority of the proposed BASEN over existing approaches. The proposed channel selection methods can significantly reduce the amount of informative EEG channels with a negligible impact on the performance.
Performance Analysis Of Binaural Signal Matching (BSM) in the Time-Frequency Domain
Authors: Ami Berger, Vladimir Tourbabin, Jacob Donley, Zamir Ben-Hur, Boaz Rafaely
Date: 2023-11-22 00:00:00

The capture and reproduction of spatial audio is becoming increasingly popular, with the mushrooming of applications in teleconferencing, entertainment and virtual reality. Many binaural reproduction methods have been developed and studied extensively for spherical and other specially designed arrays. However, the recent increased popularity of wearable and mobile arrays requires the development of binaural reproduction methods for these arrays. One such method is binaural signal matching (BSM). However, to date this method has only been investigated with fixed matched filters designed for long audio recordings. With the aim of making the BSM method more adaptive to dynamic environments, this paper analyzes BSM with a parameterized sound-field in the time-frequency domain. The paper presents results of implementing the BSM method on a sound-field that was decomposed into its direct and reverberant components, and compares this implementation with the BSM computed for the entire sound-field, to compare performance for binaural reproduction of reverberant speech in a simulated environment.
Deep Audio Zooming: Beamwidth-Controllable Neural Beamformer
Authors: Meng Yu, Dong Yu
Date: 2023-11-22 00:00:00

Audio zooming, a signal processing technique, enables selective focusing and enhancement of sound signals from a specified region, attenuating others. While traditional beamforming and neural beamforming techniques, centered on creating a directional array, necessitate the designation of a singular target direction, they often overlook the concept of a field of view (FOV), that defines an angular area. In this paper, we proposed a simple yet effective FOV feature, amalgamating all directional attributes within the user-defined field. In conjunction, we've introduced a counter FOV feature capturing directional aspects outside the desired field. Such advancements ensure refined sound capture, particularly emphasizing the FOV's boundaries, and guarantee the enhanced capture of all desired sound sources inside the user-defined field. The results from the experiment demonstrate the efficacy of the introduced angular FOV feature and its seamless incorporation into a low-power subband model suited for real-time applica?tions.
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection
Authors: Wenqing Wei, Zhengdong Yang, Yuan Gao, Jiyi Li, Chenhui Chu, Shogo Okada, Sheng Li
Date: 2023-11-21 00:00:00
Tasks: Contrastive Learning, Federated Learning, Privacy Preserving

The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practice is to use federated learning to protect the patients' data privacy. However, its distributed learning process also causes performance reduction. To alleviate this problem while protecting user privacy, we propose a federated contrastive pre-training (FedCPC) performed before federated training for AD speech detection, which can learn a better representation from raw data and enables different clients to share data in the pre-training and training stages. Experimental results demonstrate that the proposed methods can achieve satisfactory performance while preserving data privacy.
Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction
Authors: Tam Thuc Do, Philip A. Chou, Gene Cheung
Date: 2023-11-22 00:00:00
Tasks: Attribute, Decoder

We extend a previous study on 3D point cloud attribute compression scheme that uses a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \mapsto \mathbb{R}$, we quantize and encode parameters $\theta$ that characterize $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf(x))$ at known 3D points $\mathbf(x)$ at the decoder. Specifically, parameters $\theta$ are quantized coefficients of B-spline basis vectors $\mathbf{\Phi}_l$ (for order $p \geq 2$) that span the function space $\mathcal{F}_l^{(p)}$ at a particular resolution $l$, which are coded from coarse to fine resolutions for scalability. In this work, we focus on the prediction of finer-grained coefficients given coarser-grained ones by learning parameters of a polynomial bilateral filter (PBF) from data. PBF is a pseudo-linear filter that is signal-dependent with a graph spectral interpretation common in the graph signal processing (GSP) field. We demonstrate PBF's predictive performance over a linear predictor inspired by MPEG standardization over a wide range of point cloud datasets.
Real-time unobtrusive sleep monitoring of in-patients with affective disorders: a feasibility study
Authors: Samuel Askjer, Kim Mathiasen, Ali Amidi, Christine Parsons, Nicolai Ladegaard
Date: 2023-11-22 00:00:00
Tasks: Descriptive

Sleep and mental health are highly related concepts, and it is an important research and clinical priority to understand their interactions. In-bed sensors using ballistocardiography provide the possibility of unobtrusive measurements of sleep. In this study, we examined the feasibility of ballistocardiography in measuring key aspects of sleep in psychiatric in-patients. Specifically, we examined a sample of patients diagnosed with depression and bipolar disorder. The subjective experiences of the researchers conducting the study are explored and descriptive analyses of patient sleep are subsequently presented. The practicalities of using the ballistocardiography device seem to be favourable. There were no apparent issues regarding data quality or data integrity. Of clinical interest, we found no link between length of stay and reduced time in bed (b = -0.06, SE = 0.03, t = -1.76, p = .08). Using ballistocardiography for measurements on in-patients with affective disorders seems to be a feasible approach.
Millimeter Wave Thin-Film Bulk Acoustic Resonator in Sputtered Scandium Aluminum Nitride Using Platinum Electrodes
Authors: Sinwoo Cho, Omar Barrera, Pietro Simeoni, Ellie Y. Wang, Jack Kramer, Vakhtang Chulukhadze, Joshua Campbell, Matteo Rinaldi, Ruochen Lu
Date: 2023-11-22 00:00:00

This work describes sputtered scandium aluminum nitride (ScAlN) thin-film bulk acoustic resonators (FBAR) at millimeter wave (mmWave) with high quality factor (Q) using platinum (Pt) electrodes. FBARs with combinations of Pt and aluminum (Al) electrodes, i.e., Al top Al bottom, Pt top Al bottom, Al top Pt bottom, and Pt top Pt bottom, are built to study the impact of electrodes on mmWave FBARs. The demonstrated FBAR with Pt top and bottom electrodes achieve electromechanical coupling (k2) of 4.0% and Q of 116 for the first-order symmetric (S1) mode at 13.7 GHz, and k2 of 1.8% and Q of 94 for third-order symmetric (S3) mode at 61.6 GHz. Through these results, we confirmed that even in the frequency band of approximately 60 GHz, ScAlN FBAR can achieve a Q factor approaching 100 with optimized fabrication and acoustic/EM design. Further development calls for stacks with better quality in piezoelectric and metallic layers.
Modulation For Modulo: A Sampling-Efficient High-Dynamic Range ADC
Authors: Satish Mulleti, Resham Yashwanth Kumar, Laxmeesha Somappa
Date: 2023-11-22 00:00:00
Tasks: Quantization

In high-dynamic range (HDR) analog-to-digital converters (ADCs), having many quantization bits minimizes quantization errors but results in high bit rates, limiting their application scope. A strategy combining modulo-folding with a low-DR ADC can create an efficient HDR-ADC with fewer bits. However, this typically demands oversampling, increasing the overall bit rate. An alternative method using phase modulation (PM) achieves HDR-ADC functionality by modulating the phase of a carrier signal with the analog input. This allows a low-DR ADC with fewer bits. We've derived identifiability results enabling reconstruction of the original signal from PM samples acquired at the Nyquist rate, adaptable to various signals and non-uniform sampling. Using discrete phase demodulation algorithms for practical implementation, our PM-based approach doesn't require oversampling in noise-free conditions, contrasting with modulo-based ADCs. With noise, our PM-based HDR method demonstrates efficiency with lower reconstruction errors and reduced sampling rates. Our hardware prototype illustrates reconstructing signals ten times greater than the ADC's DR from Nyquist rate samples, potentially replacing high-bit rate HDR-ADCs while meeting existing bit rate needs.
Transferred Thin Film Lithium Niobate as Millimeter Wave Acoustic Filter Platforms
Authors: Omar Barrera, Sinwoo Cho, Kenny Hyunh, Jack Kramer, Michael Liao, Vakhtang Chulukhadze, Lezli Matto, Mark S. Goorsky, Ruochen Lu
Date: 2023-11-21 00:00:00

This paper reports the first high-performance acoustic filters toward millimeter wave (mmWave) bands using transferred single-crystal thin film lithium niobate (LiNbO3). By transferring LiNbO3 on the top of silicon (Si) and sapphire (Al2O3) substrates with an intermediate amorphous Si (aSi) bonding and sacrificial layer, we demonstrate compact acoustic filters with record-breaking performance beyond 20 GHz. In the LN-aSi-Al2O3 platform, the third-order ladder filter exhibits low insertion loss (IL) of 1.62 dB and 3-dB fractional bandwidth (FBW) of 19.8% at 22.1 GHz, while in the LN-aSi-Si platform, the filter shows low IL of 2.38 dB and FBW of 18.2% at 23.5 GHz. Material analysis validates the great crystalline quality of the stacks. The high-resolution x-ray diffraction (HRXRD) shows full width half maximum (FWHM) of 53 arcsec for Al2O3 and 206 arcsec for Si, both remarkably low compared to piezoelectric thin films of similar thickness. The reported results bring the state-of-the-art (SoA) of compact acoustic filters to much higher frequencies, and highlight transferred LiNbO3 as promising platforms for mmWave filters in future wireless front ends.
Antenna Selection For Receive Spatial Modulation System Empowered By Reconfigurable Intelligent Surface
Authors: Burak Ahmet Ozden, Erdogan Aydin
Date: 2023-11-21 00:00:00

Reconfigurable intelligent surface (RIS) enhances signal quality by adjusting the phase of electromagnetic waves in wireless communication. Spatial modulation (SM), a prominent index modulation (IM) technique, provides high spectral efficiency and low energy consumption. In this article, a new wireless communication system is proposed by combining capacity-optimized antenna selection (COAS), antenna correlation antenna selection (ACAS), and Euclidean distance-optimized antenna selection (EDAS)-supported RIS-empowered receive SM (RIS-RSM) system (AS-RIS-RSM) in a single-input multiple-output (SIMO) structure. The proposed AS-RIS-RSM schemes (COAS-RIS-RSM, ACAS-RIS-RSM, and EDAS-RIS-RSM) have superior features such as high spectral efficiency, high energy efficiency, and low error data transmission. Integrating COAS, ACAS, and EDAS techniques into the system enables the selection of the channel with the best conditions, thus increasing the error performance of the proposed system. Also, using RIS increases the error performance of the system by controlling the transmitted signal to a certain extent. The analytical ABER results of the proposed AS-RIS-RSM systems are derived and shown to overlap with simulation results. For the proposed systems, an optimal maximum likelihood (ML) detector and a sub-optimal low-complexity greedy detector (GD) are offered. Also, capacity analyses of the proposed AS-RIS-RSM systems are derived and it is observed that they have higher capacity compared to RIS-QAM/PSK and RIS-RSM systems. Then, computational complexity analyses of the proposed COAS-RIS-RSM, ACAS-RIS-RSM, and EDAS-RIS-RSM systems are presented. The proposed systems have been compared to counterpart wireless communication systems including RIS-RSM, RIS-QAM, and RIS-PSK under equivalent conditions, demonstrating that the proposed systems achieve better error performance.
Piecewise Polynomial Regression of Tame Functions via Integer Programming
Authors: Gilles Bareilles, Johannes Aspman, Jiri Nemecek, Jakub Marecek
Date: 2023-11-22 00:00:00
Tasks: regression

Tame functions are a class of nonsmooth, nonconvex functions, which feature in a wide range of applications: functions encountered in the training of deep neural networks with all common activations, value functions of mixed-integer programs, or wave functions of small molecules. We consider approximating tame functions with piecewise polynomial functions. We bound the quality of approximation of a tame function by a piecewise polynomial function with a given number of segments on any full-dimensional cube. We also present the first mixed-integer programming formulation of piecewise polynomial regression. Together, these can be used to estimate tame functions. We demonstrate promising computational results.
Computational Explorations in Biomedicine: Unraveling Molecular Dynamics for Cancer, Drug Delivery, and Biomolecular Insights using LAMMPS Simulations
Authors: Reza Bozorgpour
Date: 2023-11-21 00:00:00
Tasks: Computational Efficiency, Drug Design, Protein Folding

With the rapid advancement of computational techniques, Molecular Dynamics (MD) simulations have emerged as powerful tools in biomedical research, enabling in-depth investigations of biological systems at the atomic level. Among the diverse range of simulation software available, LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) has gained significant recognition for its versatility, scalability, and extensive range of functionalities. This literature review aims to provide a comprehensive overview of the utilization of LAMMPS in the field of biomedical applications. This review begins by outlining the fundamental principles of MD simulations and highlighting the unique features of LAMMPS that make it suitable for biomedical research. Subsequently, a survey of the literature is conducted to identify key studies that have employed LAMMPS in various biomedical contexts, such as protein folding, drug design, biomaterials, and cellular processes. The reviewed studies demonstrate the remarkable contributions of LAMMPS in understanding the behavior of biological macromolecules, investigating drug-protein interactions, elucidating the mechanical properties of biomaterials, and studying cellular processes at the molecular level. Additionally, this review explores the integration of LAMMPS with other computational tools and experimental techniques, showcasing its potential for synergistic investigations that bridge the gap between theory and experiment. Moreover, this review discusses the challenges and limitations associated with using LAMMPS in biomedical simulations, including the parameterization of force fields, system size limitations, and computational efficiency. Strategies employed by researchers to mitigate these challenges are presented, along with potential future directions for enhancing LAMMPS capabilities in the biomedical field.
Multiscale cortical morphometry reveals pronounced regional and scale-dependent variations across the lifespan
Authors: Karoline Leiberg, Timo Blattner, Bethany Little, Victor B. B. Mello, Fernanda H. P. de Moraes, Christian Rummel, Peter N. Taylor, Bruno Mota, Yujiang Wang
Date: 2023-11-22 00:00:00

Motivation: Characterising the changes in cortical morphology across the lifespan is fundamental for a range of research and clinical applications. Most studies to date have found a monotonic decrease in commonly used morphometrics, such as cortical thickness and volume, across the entire brain with increasing age. Any regional variations reported are subtle changes in the rate of decrease. However, these descriptions of morphological changes have been limited to a single length scale. Here, we delineate the morphological changes associated with the healthy lifespan in multiscale morphometrics. Methods: We applied multiscale morphometric analysis to structural MRI from subjects aged 6-88 years from NKI (n=833) and CamCAN (n=641). These multiscale morphometrics were obtained at both the cortical hemisphere and lobe level. Results: On the level of whole cortical hemispheres, lifespan trajectories show diverging and even opposing trends at different spatial scales, in contrast to the monotonic decreases of volume and thickness described so far. Importantly, larger scales displayed most dramatic changes across the lifespan (up to 60%). More pronounced lobal differences in lifespan trajectories also became apparent in scales over 0.7mm. In a proof-of-principle application in brain age prediction, we also demonstrate added information contributed by multiscale morphometrics. Conclusion: Our study provides a comprehensive multiscale description of lifespan effects on cortical morphology in an age range from 6-88~years. In future, this can form the foundations for a normative model to compare individuals or cohorts, hence identifying multiscale morphological abnormalities. Our results reveal the complementary information contained in different spatial scales, suggesting that morphometrics should not be considered on a single scale, but as functions of length scale.
Non-deterministic linear thresholding systems reveal their deterministic origins
Authors: Anna Laddach, Michael Shapiro
Date: 2023-11-22 00:00:00

Linear thresholding systems have been used as a model of neural activation and have more recently been proposed as a model of gene activation. Deterministic linear thresholding systems can be turned into non-deterministic systems by the introduction of noise. Under mild conditions on the noise, we show that the deterministic model can be deduced from the probabilities of the non-deterministic model.
Reproducible image-based profiling with Pycytominer
Authors: Erik Serrano, Srinivas Niranj Chandrasekaran, Dave Bunten, Kenneth I. Brewer, Jenna Tomkinson, Roshan Kern, Michael Bornholdt, Stephen Fleming, Ruifan Pei, John Arevalo, Hillary Tsang, Vincent Rubinetti, Callum Tromans-Coia, Tim Becker, Erin Weisbart, Charlotte Bunne, Alexandr A. Kalinin, Rebecca Senft, Stephen J. Taylor, Nasim Jamali, Adeniyi Adeboye, Hamdah Shafqat Abbasi, Allen Goodman, Juan C. Caicedo, Anne E. Carpenter, Beth A. Cimini, Shantanu Singh, Gregory P. Way
Date: 2023-11-22 00:00:00

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as image-based profiling. We demonstrate Pycytominers usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries.