pwc-archive/papers-with-abstracts huggingface dataset:
Improved Row-Column-Addressed Array Imaging by Leveraging Ghost Echoes
Authors: Chung-Shiang Mei, Meng-Lin Li
Date: 2023-08-28 00:00:00

Among various choices of 2-D ultrasound transducer arrays, the row-column-addressed (RCA) 2-D array has shown its promise for 3-D imaging. However, RCA suffers from notable edge effects and thus receives ghost echoes, which result in ghost artifacts showing in the volumetric image. In this research, rather than discarding these ghost echoes, we consider them as supportive signals and incorporate them into a postfiltering-based method to enhance the RCA imaging quality. Field II simulation and results of a single scatterer are presented in this work. The strongest ghost artifact is suppressed by 23 dB and the -6 dB lateral resolution is improved from 1.06 to 0.81 mm. The proposed method shows promising results in suppressing ghost artifacts and enhancing lateral resolution.
Phase Aberration Correction with Adaptive Coherence-Weighted Point Spread Function Restoration Filtering Technique
Authors: Wei-Hsiang Shen, Yu-an Lin, Pai-Chi Li, Meng-Lin Li
Date: 2023-08-28 00:00:00

Phase aberration is an inherent side effect of ultrasound imaging due to the speed of sound inhomogeneity nature of human tissues, resulting in focusing error and reduced image contrast. This work introduces a phase aberration correction technique by leveraging a point spread function (PSF) restoration filter. A convolutional neural network (CNN) is used to estimate phase-aberrated PSFs and design the restoration filter. In addition, we incorporate coherence index weighting, derived from the restoration filtering, to further suppress sidelobe energy. Evaluation using Field II-simulated phantoms showed clearer cyst borders and reduced sidelobe energy leakage after PSF restoration and filter-derived coherence weighting, leading to improvement in image contrast and quality.
Low-complexity Samples versus Symbols-based Neural Network Receiver for Channel Equalization
Authors: Yevhenii Osadchuk, Ognjen Jovanovic, Stenio M. Ranzini, Roman Dischler, Vahid Aref, Darko Zibar, Francesco Da Ros
Date: 2023-08-28 00:00:00

Low-complexity neural networks (NNs) have successfully been applied for digital signal processing (DSP) in short-reach intensity-modulated directly detected optical links, where chromatic dispersion-induced impairments significantly limit the transmission distance. The NN-based equalizers are usually optimized independently from other DSP components, such as matched filtering. This approach may result in lower equalization performance. Alternatively, optimizing a NN equalizer to perform functionalities of multiple DSP blocks may increase transmission reach while keeping the complexity low. In this work, we propose a low-complexity NN that performs samples-to-symbol equalization, meaning that the NN-based equalizer includes match filtering and downsampling. We compare it to a samples-to-sample equalization approach followed by match filtering and downsampling in terms of performance and computational complexity. Both approaches are evaluated using three different types of NNs combined with optical preprocessing. We numerically and experimentally show that the proposed samples-to-symbol equalization approach applied for 32 GBd on-off keying (OOK) signals outperforms the samples-domain alternative keeping the computational complexity low. Additionally, the different types of NN-based equalizers are compared in terms of performance with respect to computational complexity.
Sparse Recovery with Attention: A Hybrid Data/Model Driven Solution for High Accuracy Position and Channel Tracking at mmWave
Authors: Yun Chen, Nuria González-Prelcic, Takayuki Shimizu, Hongshen Lu, Chinmay Mahabal
Date: 2023-08-26 00:00:00
Tasks: Position

In this paper, we propose first a mmWave channel tracking algorithm based on multidimensional orthogonal matching pursuit algorithm (MOMP) using reduced sparsifying dictionaries, which exploits information from channel estimates in previous frames. Then, we present an algorithm to obtain the vehicle's initial location for the current frame by solving a system of geometric equations that leverage the estimated path parameters. Next, we design an attention network that analyzes the series of channel estimates, the vehicle's trajectory, and the initial estimate of the position associated with the current frame, to generate a refined, high accuracy position estimate. The proposed system is evaluated through numerical experiments using realistic mmWave channel series generated by ray-tracing. The experimental results show that our system provides a 2D position tracking error below 20 cm, significantly outperforming previous work based on Bayesian filtering.
Spurious-Free Lithium Niobate Bulk Acoustic Resonator for Piezoelectric Power Conversion
Authors: Kristi Nguyen, Eric Stolt, Weston Braun, Vakhtang Chulukhadze, Jeronimo Segovia-Fernandez, Sombuddha Chakraborty, Juan Rivas-Davila, Ruochen Lu
Date: 2023-08-26 00:00:00

Recently, piezoelectric power conversion has shown great benefits from replacing the bulky and lossy magnetic inductor in a traditional power converter with a piezoelectric resonator due to its compact size and low loss. However, the converter performance is ultimately limited by existing resonator designs, specifically by moderate quality factor (Q), moderate electromechanical coupling (kt2), and spurious modes near resonance. This work reports a spurious-free lithium niobate (LiNbO3) thickness-extensional mode bulk acoustic resonator design, demonstrating Q of 4000 and kt2 of 30% with a fractional suppressed region of 62%. We first propose a novel grounded ring structure for spurious-free resonator design, then validate its performance experimentally. Upon further work, this design could be extended to applications requiring spurious suppression, such as filters, tunable oscillators, transformers, etc.
Accurate EH Modelling and Achievable Information Rate for SLIPT Systems with Multi-Junction Photovoltaic Receivers
Authors: Nikita Shanin, Hedieh Ajam, Vasilis K. Papanikolaou, Laura Cottatellucci, Robert Schober
Date: 2023-08-26 00:00:00

In this paper, we study optical simultaneous lightwave information and power transfer (SLIPT) systems employing photovoltaic optical receivers (RXs). To be able to efficiently harvest energy from broadband light, we propose to employ multi-junction photovoltaic cells at the optical RX. We consider the case, where the optical RX is illuminated by ambient light, an intensity-modulated information free space optical (FSO) signal, and since the ambient light may not be always present, a dedicated energy-providing broadband optical signal. Based on the analysis of the equivalent electrical circuit of the multi-junction photovoltaic RX, we model the current flow through the photovoltaic cell and derive a novel accurate and two novel approximate energy harvesting (EH) models for the two cases, where the optical RX is equipped with a single and multiple p-n junctions, respectively. We derive the optimal distribution of the transmit information signal that maximizes the achievable information rate. We validate the proposed EH models by circuit simulations and show that the photovoltaic RXs saturate for high received signal powers. For single-junction RXs, we compare the proposed EH model with two well-known baseline models and demonstrate that, in contrast to the proposed EH model, they are not able to fully capture the RX non-linearity. Since multi-junction RXs allow a more efficient allocation of the optical power, they are more robust against saturation, and thus, are able to harvest significantly more power and achieve higher data rates than RXs employing a single p-n junction. Finally, we highlight a tradeoff between the information rate and harvested power in SLIPT systems and demonstrate that the proposed optimal distribution yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs.
Sensiverse: A dataset for ISAC study
Authors: Jiajin Luo, Baojian Zhou, Yang Yu, Ping Zhang, Xiaohui Peng, Jianglei Ma, Peiying Zhu, Jianmin Lu, Wen Tong
Date: 2023-08-26 00:00:00
Tasks: ISAC

In order to address the lack of applicable channel models for ISAC research and evaluation, we release Sensiverse, a dataset that can be used for ISAC research. In this paper, we present the method of generating Sensiverse, including the acquisition and formatting of the 3D scene models, the generation of the channel data and associations with Tx/Rx deployment. The file structure and usage of the dataset are also described, and finally the use of the dataset is illustrated with examples through the evaluation of use cases such as 3D environment reconstruction and moving targets.
Integrated Monostatic and Bistatic mmWave Sensing
Authors: Yu Ge, Hyowon Kim, Lennart Svensson, Henk Wymeersch, Sumei Sun
Date: 2023-08-26 00:00:00

Millimeter-wave (mmWave) signals provide attractive opportunities for sensing due to their inherent geometrical connections to physical propagation channels. Two common modalities used in mmWave sensing are monostatic and bistatic sensing, which are usually considered separately. By integrating these two modalities, information can be shared between them, leading to improved sensing performance. In this paper, we investigate the integration of monostatic and bistatic sensing in a 5G mmWave scenario, implement the extended Kalman-Poisson multi-Bernoulli sequential filters to solve the sensing problems, and propose a method to periodically fuse user states and maps from two sensing modalities.
Launch Power Optimization for Dynamic Elastic Optical Networks over C+L Bands
Authors: Farhad Arpanaei, Mahdi Ranjbar Zefreh, José Alberto Hernández, Behnam Shariati, Johannes Fischer, José Manuel Rivas-Moscoso, Filipe Jiménez, Juan Pedro Fernández-Palacios, David Larrabeiti
Date: 2023-08-25 00:00:00

We propose an algorithm for calculating the optimum launch power over the entire C+L bands by maximizing the cumulative link GSNR of a channel plan built upon multiple modulation formats, with application to dynamic EONs. Exact last-fit spectrum assignment proves to outperform exact first-fit in terms of average GSNR at arrival time.
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors
Authors: Jingyun Huang, Rachel C. Amey, Mengting Liu, Chad E. Forbes
Date: 2023-08-22 00:00:00
Tasks: Contrastive Learning, EEG, Eeg Decoding, Electroencephalogram (EEG), Graph Classification

This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects. Leveraging EEG-based hyperscanning technology, we introduced an innovative approach known as the functional Graph Contrastive Learning (fGCL), which extracts subject-invariant representations of neural activity patterns from feedback trials. These representations are further subjected to analysis using the Dynamic Graph Classification (DGC) model, aimed at dissecting the process of emotional contagion along three independent temporal stages. The results underscore the substantial role of emotional contagion in shaping the trajectories of participants' performance during collaborative tasks in the presence of SBS conditions. Overall, our research contributes invaluable insights into the neural underpinnings of emotional contagion, thereby enriching our comprehension of the complexities underlying social interactions and emotional dynamics.
Rare Events Analysis and Computation for Stochastic Evolution of Bacterial Populations
Authors: Yingxue Su, Brett Geiger, Ilya Timofeyev, Andreas Mang, Robert Azencott
Date: 2023-08-27 00:00:00

In this paper, we develop a computational approach for computing most likely trajectories describing rare events that correspond to the emergence of non-dominant genotypes. This work is based on the large deviations approach for discrete Markov chains describing the genetic evolution of large bacterial populations. We demonstrate that a gradient descent algorithm developed in this paper results in the fast and accurate computation of most-likely trajectories for a large number of bacterial genotypes. We supplement our analysis with extensive numerical simulations demonstrating the computational advantage of the designed gradient descent algorithm over other, more simplified, approaches.
Improving antibody language models with native pairing
Authors: Sarah M. Burbach, Bryan Briney
Date: 2023-08-28 00:00:00
Tasks: Language Modeling, Language Modelling, Protein Language Model

Current antibody language models are limited by their use of unpaired antibody sequence data and the biases in publicly available antibody sequence datasets, which are skewed toward antibodies against a relatively small number of pathogens. A recently published dataset (by Jaffe, et al) of approximately 1.6 x 10^6 natively paired human antibody sequences from healthy donors represents by far the largest dataset of its kind and offers a unique opportunity to evaluate how antibody language models can be improved by training with natively paired antibody sequence data. We trained two Baseline Antibody Language Models (BALM), using natively paired (BALM-paired) or unpaired (BALM-unpaired) sequences from the Jaffe dataset. We provide evidence that training with natively paired sequences substantially improves model performance and that this improvement results from the model learning immunologically relevant features that span the light and heavy chains. We also show that ESM-2, a state-of-the-art general protein language model, learns similar cross-chain features when fine-tuned with natively paired antibody sequence data.
How thermal priming of coral gametes shapes fertilization success
Authors: Antoine Puisay, Laetitia Hédouin, Rosanne Pilon, Claire Goiran, Benoit Pujol
Date: 2023-08-28 00:00:00

Seawater temperature rise is damaging coral reef ecosystems. There is growing evidence for the negative impact of rising temperatures on the survival of adult corals and their reproductive success. However, the effect of elevated temperatures on gametes remains scarcely studied. Here we tested the effect of the thermal priming of gametes on the fertilization success in experimentally tested populations of Acropora cytherea corals in French Polynesia. As expected, a temperature of 30 {\textdegree}C (ambient +3 {\textdegree}C) reduces coral fertilization success. However, the thermal exposure of gametes to 30 {\textdegree}C after their release in seawater prior to fertilization limited fertilization failure, with a greater impact of oocytes in comparison to sperm. This temperature is similar to temperatures observed in nature under the changing climate. Our findings imply that the thermal priming of early life stages, such as gametes may play a role in maintaining the coral fertilization success in spite of increasing seawater temperature.
Computational modelling of peritoneal dialysis: an overview
Authors: Sangita Swapnasrita, Joost C de Vries, Carl M Öberg, Aurélie MF Carlier, Karin GF Gerritsen
Date: 2023-08-28 00:00:00
Tasks: Kidney Function

Peritoneal dialysis (PD) is becoming more popular as a result of a rising interest in home dialysis, lower intrusion in social life and longer preservation of residual kidney function. However, PD has several important drawbacks: small solute clearance is relatively low compared to hemodialysis and technique survival is limited. Application of continuous flow, sorbent-based dialysate regeneration and novel glucose-sparing PD solutions are some solutions proposed to address the limitations of PD. To optimize and personalize current and novel PD therapies, patient peritoneal characteristics interacting with PD techniques need to be studied together and separately as they interplay. However, considering the multitude of parameters, it would be difficult, expensive, and time consuming to optimize all parameter settings only with the help of clinical trials. Mathematical modelling is an exciting tool to dissect these interacting processes and comprehend PD techniques better at a patient specific level. In this review, we look at the history of computational PD models, explore the many ways a computational PD model can be constructed and review the various existing PD models that can be used to optimize and personalize PD treatment.
Joint Calibration of Local Volatility Models with Stochastic Interest Rates using Semimartingale Optimal Transport
Authors: Benjamin Joseph, Gregoire Loeper, Jan Obloj
Date: 2023-08-28 00:00:00

We develop and implement a non-parametric method for joint exact calibration of a local volatility model and a correlated stochastic short rate model using semimartingale optimal transport. The method relies on the duality results established in Joseph, Loeper, and Obloj, 2023 and jointly calibrates the whole equity-rate dynamics. It uses an iterative approach which starts with a parametric model and tries to stay close to it, until a perfect calibration is obtained. We demonstrate the performance of our approach on market data using European SPX options and European cap interest rate options. Finally, we compare the joint calibration approach with the sequential calibration, in which the short rate model is calibrated first and frozen.
An Empirical Analysis on Financial Markets: Insights from the Application of Statistical Physics
Authors: Haochen Li, Yi Cao, Maria Polukarov, Carmine Ventre
Date: 2023-08-28 00:00:00

In this study, we introduce a physical model inspired by statistical physics for predicting price volatility and expected returns by leveraging Level 3 order book data. By drawing parallels between orders in the limit order book and particles in a physical system, we establish unique measures for the system's kinetic energy and momentum as a way to comprehend and evaluate the state of limit order book. Our model goes beyond examining merely the top layers of the order book by introducing the concept of 'active depth', a computationally-efficient approach for identifying order book levels that have impact on price dynamics. We empirically demonstrate that our model outperforms the benchmarks of traditional approaches and machine learning algorithm. Our model provides a nuanced comprehension of market microstructure and produces more accurate forecasts on volatility and expected returns. By incorporating principles of statistical physics, this research offers valuable insights on understanding the behaviours of market participants and order book dynamics.
The Potential of Quantum Techniques for Stock Price Prediction
Authors: Naman S, Gaurang B, Neel S, Aswath Babu H
Date: 2023-08-25 00:00:00
Tasks: Binary Classification, Dimensionality Reduction, feature selection, Prediction, Stock Price Prediction

We explored the potential applications of various Quantum Algorithms for stock price prediction by conducting a series of experimental simulations using both Classical as well as Quantum Hardware. Firstly, we extracted various stock price indicators, such as Moving Averages (MA), Average True Range (ATR), and Aroon, to gain insights into market trends and stock price movements. Next, we employed Quantum Annealing (QA) for feature selection and Principal Component Analysis (PCA) for dimensionality reduction. Further, we transformed the stock price prediction task essentially into a classification problem. We trained the Quantum Support Vector Machine (QSVM) to predict price movements (whether up or down) contrasted their performance with classical models and analyzed their accuracy on a dataset formulated using Quantum Annealing and PCA individually. We focused on the stock price prediction and binary classification of stock prices for four different companies, namely Apple, Visa, Johnson and Jonson, and Honeywell. We primarily used the real-time stock data of the raw stock prices of these companies. We compared various Quantum Computing techniques with their classical counterparts in terms of accuracy and F-score of the prediction model. Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.
Portfolios Generated by Contingent Claim Functions, with Applications to Option Pricing
Authors: Ricardo T. Fernholz, Robert Fernholz
Date: 2023-08-26 00:00:00

This paper presents a synthesis of the theories of portfolio generating functions and option pricing. The theory of portfolio generation is extended to measure the value of portfolios generated by positive C^{2,1} functions of asset prices X_1,... , X_n directly, rather than with respect to a numeraire portfolio. If a portfolio generating function satisfies a specific partial differential equation, then the value of the portfolio generated by that function will replicate the value of the function. This differential equation is a general form of the Black-Scholes equation. Similar results apply to contingent claim functions, which are portfolio generating functions that are homogeneous of degree 1. With the addition of a riskless asset, an inhomogeneous portfolio generating function V : R^{+n} x [0, T] \to R^+ can be extended to an equivalent contingent claim function \hat{V} : R^+ x R^{+n} x [0, T] \to R^+ that generates the same portfolio and is replicable if and only if V is replicable. Several examples are presented.
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying
Authors: Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana, Hung Le, Truyen Tran, Sunil Gupta, Svetha Venkatesh
Date: 2023-08-21 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. For example, by observing a partial stack of cubes, LLMs can predict the correct sequence in which the remaining cubes should be stacked by extrapolating the observed patterns (e.g., cube sizes, colors or other attributes) in the partial stack. In this work, we introduce LaGR (Language-Guided Reinforcement learning), which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent, in order to subsequently guide the latter's training. However, as RL training is generally not sample-efficient, deploying this approach would inherently imply that the LLM be repeatedly queried for solutions; a process that can be expensive and infeasible. To address this issue, we introduce SEQ (sample efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions. Specifically, we use the quality of the solutions emanating from the LLM as the reward to train this agent. We show that our proposed framework LaGR-SEQ enables more efficient primary RL training, while simultaneously minimizing the number of queries to the LLM. We demonstrate our approach on a series of tasks and highlight the advantages of our approach, along with its limitations and potential future research directions.