pwc-archive/papers-with-abstracts huggingface dataset:
The known unknowns of the Hsp90 chaperone
Authors: Laura-Marie Silbermann, Benjamin Vermeer, Sonja Schmid, Katarzyna, Tych
Date: 2023-08-31 00:00:00
Tasks: Drug Design, Known Unknowns, Protein Folding

Molecular chaperones are vital proteins that maintain protein homeostasis by assisting in protein folding, activation, degradation, and stress protection. Among them, heat-shock protein 90 (Hsp90) stands out as an essential proteostasis hub in eukaryotes, chaperoning hundreds of "clients" (substrates). After decades of research, several "known unknowns" about the molecular function of Hsp90 remain unanswered, hampering rational drug design for the treatment of cancers, neurodegenerative and other diseases. We highlight three fundamental open questions, reviewing the current state of the field for each, and discuss new opportunities, including single-molecule technologies, to answer the known unknowns of the Hsp90 chaperone.
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Authors: Luke Lozenski, Hanchen Wang, Fu Li, Mark A. Anastasio, Brendt Wohlberg, Youzuo Lin, Umberto Villa
Date: 2023-08-30 00:00:00
Tasks: Image Reconstruction, Lesion Detection, SSIM

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost of FWI reconstruction represents a significant burden for its widespread application in a clinical setting. The research reported here investigates the use of a convolutional neural network (CNN) to learn a mapping from USCT waveform data to speed of sound estimates. The CNN was trained using a supervised approach with a task-informed loss function aiming at preserving features of the image that are relevant to the detection of lesions. A large set of anatomically and physiologically realistic numerical breast phantoms (NBPs) and corresponding simulated USCT measurements was employed during training. Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data. The performance of the proposed method was assessed and compared against FWI using a hold-out sample of 41 NBPs and corresponding USCT data. Accuracy was measured using relative mean square error (RMSE), structural self-similarity index measure (SSIM), and lesion detection performance (DICE score). This numerical experiment demonstrates that a supervised learning model can achieve accuracy comparable to FWI in terms of RMSE and SSIM, and better performance in terms of task performance, while significantly reducing computational time.
Target PCA: Transfer Learning Large Dimensional Panel Data
Authors: Junting Duan, Markus Pelger, Ruoxuan Xiong
Date: 2023-08-29 00:00:00
Tasks: Transfer Learning

This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a large fraction of missing observations and weak signals in the target panel. We show that our estimator is more efficient and can consistently estimate weak factors, which are not identifiable with conventional methods. We provide the asymptotic inferential theory for target-PCA under very general assumptions on the approximate factor model and missing patterns. In an empirical study of imputing data in a mixed-frequency macroeconomic panel, we demonstrate that target-PCA significantly outperforms all benchmark methods.
Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?
Authors: Kian Tehranian
Date: 2023-08-28 00:00:00
Tasks: Decision Making, Time Series

Quantitative models are an important decision-making factor for policy makers and investors. Predicting an economic recession with high accuracy and reliability would be very beneficial for the society. This paper assesses machine learning technics to predict economic recessions in United States using market sentiment and economic indicators (seventy-five explanatory variables) from Jan 1986 - June 2022 on a monthly basis frequency. In order to solve the issue of missing time-series data points, Autoregressive Integrated Moving Average (ARIMA) method used to backcast explanatory variables. Analysis started with reduction in high dimensional dataset to only most important characters using Boruta algorithm, correlation matrix and solving multicollinearity issue. Afterwards, built various cross-validated models, both probability regression methods and machine learning technics, to predict recession binary outcome. The methods considered are Probit, Logit, Elastic Net, Random Forest, Gradient Boosting, and Neural Network. Lastly, discussed different models performance based on confusion matrix, accuracy and F1 score with potential reasons for their weakness and robustness.
DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing
Authors: Dongheon Lee, Jung-Woo Choi
Date: 2023-08-30 00:00:00
Tasks: Speech Enhancement

In this work, we present DeFTAN-II, an efficient multichannel speech enhancement model based on transformer architecture and subgroup processing. Despite the success of transformers in speech enhancement, they face challenges in capturing local relations, reducing the high computational complexity, and lowering memory usage. To address these limitations, we introduce subgroup processing in our model, combining subgroups of locally emphasized features with other subgroups containing original features. The subgroup processing is implemented in several blocks of the proposed network. In the proposed split dense blocks extracting spatial features, a pair of subgroups is sequentially concatenated and processed by convolution layers to effectively reduce the computational complexity and memory usage. For the F- and T-transformers extracting temporal and spectral relations, we introduce cross-attention between subgroups to identify relationships between locally emphasized and non-emphasized features. The dual-path feedforward network then aggregates attended features in terms of the gating of local features processed by dilated convolutions. Through extensive comparisons with state-of-the-art multichannel speech enhancement models, we demonstrate that DeFTAN-II with subgroup processing outperforms existing methods at significantly lower computational complexity. Moreover, we evaluate the model's generalization capability on real-world data without fine-tuning, which further demonstrates its effectiveness in practical scenarios.
Twofold Structured Features-Based Siamese Network for Infrared Target Tracking
Authors: Wei-Jie Yan, Yun-Kai Xu, Qian Chen, Xiao-Fang Kong, Guo-Hua Gu, A-Jun Shao, Min-Jie Wan
Date: 2023-08-31 00:00:00

Nowadays, infrared target tracking has been a critical technology in the field of computer vision and has many applications, such as motion analysis, pedestrian surveillance, intelligent detection, and so forth. Unfortunately, due to the lack of color, texture and other detailed information, tracking drift often occurs when the tracker encounters infrared targets that vary in size or shape. To address this issue, we present a twofold structured features-based Siamese network for infrared target tracking. First of all, in order to improve the discriminative capacity for infrared targets, a novel feature fusion network is proposed to fuse both shallow spatial information and deep semantic information into the extracted features in a comprehensive manner. Then, a multi-template update module based on template update mechanism is designed to effectively deal with interferences from target appearance changes which are prone to cause early tracking failures. Finally, both qualitative and quantitative experiments are carried out on VOT-TIR 2016 dataset, which demonstrates that our method achieves the balance of promising tracking performance and real-time tracking speed against other out-of-the-art trackers.
Research on Image Stitching Based on Invariant Features of Reconstructed Plane
Authors: Qi Liu, Xiyu Tang, Ju Huo
Date: 2023-08-30 00:00:00
Tasks: Image Stitching

Generating high-quality stitched images is a challenging task in computer vision. The existing feature-based image stitching methods commonly only focus on point and line features, neglecting the crucial role of higher-level planar features in image stitching. This paper proposes an image stitching method based on invariant planar features, which uses planar features as constraints to improve the overall effect of natural image stitching. Initially, our approach expands the quantity of matched feature points and lines through straight-line procedures, advancing alignment quality and reducing artifacts in overlapping areas. Then, uncertain planes are described by known matching points and matching lines, and plane features are introduced to preserve energy items, which improves the overall appearance of stitched images while reducing distortion and guarantees a more natural stitched image. Furthermore, to demonstrate the superiority of our approach, we also propose several evaluation indexes related to planar features to quantify the detailed changes of planar features. An extensive set of experiments validates the effectiveness of our approach in stitching natural images with a larger field of view. Compared with the most advanced methods, our method retains the texture and structure of the image better, and produces less unnatural distortion. Multiple quantitative evaluations illustrate that our approach outperforms existing methods with significant improvements, further validating the effectiveness and superiority of our proposed method.
Channel Estimation Using RIDNet Assisted OMP for Hybrid-field THz Massive MIMO Systems
Authors: Hasan Nayir, Erhan Karakoca, Ali Görçin, Khalid Qaraqe
Date: 2023-08-31 00:00:00

The terahertz (THz) band radio access with larger available bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modelling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a recursive information distillation network (RIDNet) together with orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including both far-field and near-field components. The simulation experiments are performed using the ray-tracing tool. The results indicate that the proposed RIDNet-based method consistently provides lower channel estimation errors compared to the conventional OMP algorithm for all signal-to-noise ratio (SNR) regimes, and the performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.
Data-Aided Channel Estimation Utilizing Gaussian Mixture Models
Authors: Franz Weißer, Nurettin Turan, Dominik Semmler, Wolfgang Utschick
Date: 2023-08-31 00:00:00

In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind channel estimation. To this end, a subspace is estimated based on all received symbols and utilized to improve the estimation quality of a Gaussian mixture model-based channel estimator, which solely uses pilot symbols for channel estimation. Both of the proposed approaches allow for parallelization. Even the precomputation of estimation filters, which is beneficial in terms of computational complexity, is enabled by one of the proposed methods. Numerical simulations for real channel measurement data available to us show that the proposed methods outperform the studied state-of-the-art channel estimators.
Open and Closed-Loop Weight Selection for Pattern Control of Paraboloidal Reflector Antennas with Reconfigurable Rim Scattering
Authors: R. Michael Buehrer, William W. Howard, Steven Ellingson
Date: 2023-08-30 00:00:00

It has been demonstrated that modifying the rim scattering of a paraboloidal reflector antenna through the use of reconfigurable elements along the rim facilitates sidelobe modification including cancelling sidelobes. In this work we investigate several open questions with respect to algorithms for determining the weights. First, we derive the general weight values needed at each reconfigurable element to place nulls at arbitrary angles. Second, since in many cases these weights require gains other than one, we develop a technique for determining unit-modulus weights so as to allow for surfaces which merely modify the phase of the scattered field, while substantially reducing the gain at arbitrary angles. Specifically, it is shown that despite the large search space (and non-convexity in the presence of discrete weights), weights can be found with reasonable computational complexity which provide useful cancellation capability. Third, it is demonstrated that this can be done using open-loop (i.e., with pattern knowledge), closed-loop (without pattern knowledge), or hybrid (with inexact pattern knowledge) techniques both quantized and unquantized phases. A primary finding is that sufficiently deep nulls are possible with essentially no change in the main lobe gain with practical (binary or quaternary) phase-only weights.
A Radiological Clip Design Using Ultrasound Identification to Improve Localization
Authors: Jenna Cario, Zhengchang Kou, Rita J. Miller, April Dickenson, Christine U. Lee, Michael L. Oelze
Date: 2023-08-30 00:00:00

Objective: We demonstrate the use of ultrasound to receive an acoustic signal transmitted from a radiological clip designed from a custom circuit. This signal encodes an identification number and is localized and identified wirelessly by the ultrasound imaging system. Methods: We designed and constructed the test platform with a Teensy 4.0 microcontroller core to detect ultrasonic imaging pulses received by a transducer embedded in a phantom, which acted as the radiological clip. Ultrasound identification (USID) signals were generated and transmitted as a result. The phantom and clip were imaged using an ultrasonic array (Philips L7-4) connected to a Verasonics Vantage 128 system operating in pulse inversion (PI) mode. Cross-correlations were performed to localize and identify the code sequences in the PI images. Results: USID signals were detected and visualized on B-mode images of the phantoms with up to sub-millimeter localization accuracy. The average detection rate across 4,800 frames of ultrasound data was 93.0%. Tested ID values exhibited differences in detection rates. Conclusion: The USID clip produced identifiable, distinguishable, and localizable signals when imaged. Significance: Radiological clips are used to mark breast cancer being treated by neoadjuvant chemotherapy (NAC) via implant in or near treated lesions. As NAC progresses, available marking clips can lose visibility in ultrasound, the imaging modality of choice for monitoring NAC-treated lesions. By transmitting an active signal, more accurate and reliable ultrasound localization of these clips could be achieved and multiple clips with different ID values could be imaged in the same field of view.
Stable estimation of pulses of unknown shape from multiple snapshots via ESPRIT
Authors: Meghna Kalra, Kiryung Lee
Date: 2023-08-30 00:00:00

We consider the problem of resolving overlapping pulses from noisy multi-snapshot measurements, which has been a problem central to various applications including medical imaging and array signal processing. ESPRIT algorithm has been used to estimate the pulse locations. However, existing theoretical analysis is restricted to ideal assumptions on signal and measurement models. We present a novel perturbation analysis that overcomes the previous theoretical limitation, which is derived without a stringent assumption on the signal model. Our unifying analysis applies to various sub-array designs of the ESPRIT algorithm. We demonstrate the usefulness of the perturbation analysis by specifying the result in two practical scenarios. In the first scenario, we quantify how the number of snapshots for stable recovery scales when the number of Fourier measurements per snapshot is sufficiently large. In the second scenario, we propose compressive blind array calibration by ESPRIT with random sub-arrays and provide the corresponding non-asymptotic error bound. Furthermore, we demonstrate that the empirical performance of ESPRIT corroborates the theoretical analysis through extensive numerical results.
A Neural-enhanced Factor Graph-based Algorithm for Robust Positioning in Obstructed LOS Situations
Authors: Alexander Venus, Erik Leitinger, Stefan Tertinek, Klaus Witrisal
Date: 2023-08-30 00:00:00
Tasks: Position

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models. The proposed algorithm uses radio signal measurements from multiple base stations to robustly estimate the mobile agent's position together with all model parameters. It provides high localization accuracy by exploiting the position-related information of the LOS component via the physics-based model and robustness by exploiting the geometric imprint of multipath components independent of the propagation channel via the data-driven model. In a challenging numerical experiment involving obstructed LOS situations to all anchors, we show that the proposed sequential algorithm significantly outperforms state-of-the-art methods and attains the posterior Cramer-Rao lower bound even with training data limited to local regions.
Structure-Aware Parametric Representations for Time-Resolved Light Transport
Authors: Diego Royo, Zesheng Huang, Yun Liang, Boyan Song, Adolfo Muñoz, Diego Gutierrez, Julio Marco
Date: 2023-08-30 00:00:00
Tasks: Depth Estimation

Time-resolved illumination provides rich spatio-temporal information for applications such as accurate depth sensing or hidden geometry reconstruction, becoming a useful asset for prototyping and as input for data-driven approaches. However, time-resolved illumination measurements are high-dimensional and have a low signal-to-noise ratio, hampering their applicability in real scenarios. We propose a novel method to compactly represent time-resolved illumination using mixtures of exponentially-modified Gaussians that are robust to noise and preserve structural information. Our method yields representations two orders of magnitude smaller than discretized data, providing consistent results in applications such as hidden scene reconstruction and depth estimation, and quantitative improvements over previous approaches.
Improved Ultrasound Attenuation Coefficient Estimation Using Spectral Normalization on Local Interference-Free Single-Scatterer Power Spectrum
Authors: Kun-Lin Liu, Yu-Heng Chen, Chiao-Yin Wang, Po-Hsiang Tsui, Meng-Lin Li
Date: 2023-08-30 00:00:00
Tasks: Diagnostic

Ultrasound attenuation coefficient estimation (ACE) can be utilized to quantify liver fat content, offering significant diagnostic potential in addressing the growing global public health issue of non-alcoholic fatty liver and other chronic liver diseases. Among ACE methods, the reference frequency method (RFM) proposed recently possesses the advantages of being system-independent and not requiring reference phantom. However, the presence of large oscillations in frequency power ratio decay curves leads to unstable ACE results with RFM, originating from noise as well as constructive and destructive interference in the backscattered signal's power spectrum. To address this issue, we propose an improved RFM version where a single-scatterer power spectrum estimator is incorporated to restore interference free single-scatterer power spectrum, thereby reducing oscillations in the frequency power ratio decay curves and greatly improving the accuracy of ACE.
Anti-Jamming Precoding Against Disco Intelligent Reflecting Surfaces Based Fully-Passive Jamming Attacks
Authors: Huan Huang, Lipeng Dai, Hongliang Zhang, Zhongxing Tian, Yi Cai, Chongfu Zhang, A. Lee Swindlehurst, Zhu Han
Date: 2023-08-30 00:00:00

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, but also pose a huge risk for physical layer security. Existing works have illustrated that a disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties (like a "disco ball"), can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS can be employed to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by DIRS-based fully-passive jammers (FPJs), an anti-jamming precoder is proposed that requires only the statistical characteristics of the DIRS-based ACA channels instead of their CSI. The statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is derived based on the statistical characteristics. Furthermore, we prove that the anti-jamming precoder can achieve the maximum signal-to-jamming-plus-noise ratio (SJNR). To acquire the ACA statistics without changing the system architecture or cooperating with the illegitimate DIRS, we design a data frame structure that the legitimate access point (AP) can use to estimate the statistical characteristics. During the designed data frame, the LUs only need to feed back their received power to the legitimate AP when they detect jamming attacks. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJs and the feasibility of the designed data frame used by the legitimate AP to estimate the statistical characteristics.