pwc-archive/papers-with-abstracts huggingface dataset:
Energy-preserving Indirect-feedback for Wireless Power Transfer
Authors: Siddhartha Sarma, Rahul Shrestha, Rohit B. Chaurasiya
Date: 2023-05-11 00:00:00

Recognising the limitations of various existing channel-estimation schemes for energy beamforming, we propose an energy-preserving indirect feedback-based approach for finding the optimal beamforming vector. Upon elaborating on the key ideas behind the proposed approach -- dynamics of the harvest-then-transmit protocol and the latency associated with the charging process -- we present an algorithm and its hardware architecture to concretise the proposed approach. The algorithm and the hardware architecture are supplemented by mathematical analysis, numerical simulation and hardware utilisation details, ASIC synthesis and post-layout simulation details, respectively. We firmly believe this paper, due to its unified algorithm-hardware design, will open up new avenues for research in radio frequency (RF) wireless power transfer.
Input Distribution Optimization in OFDM Dual-Function Radar-Communication Systems
Authors: Yumeng Zhang, Sundar Aditya, Bruno Clerckx
Date: 2023-05-11 00:00:00

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in dual-function radar-communication (DFRC) systems, where radar and communications are performed simultaneously with a common signal. However, with random communication symbols (CS) in DFRC, the transmit signal has a random ambiguity function that affects the radar's range-velocity estimation performance, whose influence is remained uncovered. Hence, this paper focuses on minimizing the outlier probability (OP) -- the probability of incorrectly estimating a target's range-velocity bin -- in OFDM DFRC w.r.t the CS probability distribution (i.e., the \emph{input distribution}). Conditioned on the CSs, the OP only depends on the CS magnitudes. Hence, we consider the following two schemes for the above optimization: CSs with (1) constant magnitude (phase shift keying input), and (2) random magnitude (Gaussian input). For (1), the problem reduces to the familiar power allocation design across OFDM's subcarriers and symbols, with uniform power allocation across subcarriers and a \emph{windowed} power allocation across symbols being near-optimal. For (2), the mean and variance of the Gaussian distribution at each subcarrier is optimized, with an additional communication constraint to avoid the zero-variance solution where no CSs are carried. We observe that subcarriers with strong communication channels feature strong variance (i.e., favour communications) while the others are characterized by a strong mean (favouring radar). However, the overall power allocation (i.e., the sum of mean and variance) across the OFDM subcarriers and symbols is similar to (1). Simulations show that CSs with random magnitudes degrade the sensing performance, but can be compensated significantly with the proposed input distribution optimization.
Array-Informed Waveform Design for Active Sensing: Diversity, Redundancy, and Identifiability
Authors: Robin Rajamäki, Piya Pal
Date: 2023-05-10 00:00:00
Tasks: Diversity

This paper investigates the combined role of transmit waveforms and (sparse) sensor array geometries in active sensing multiple-input multiple-output (MIMO) systems. Specifically, we consider the fundamental identifiability problem of uniquely recovering the unknown scatterer angles and coefficients from noiseless spatio-temporal measurements. Assuming a sparse scene, identifiability is determined by the Kruskal rank of a highly structured sensing matrix, which depends on both the transmitted waveforms and the array configuration. We derive necessary and sufficient conditions that the array geometry and transmit waveforms need to satisfy for the Kruskal rank -- and hence identifiability -- to be maximized. Moreover, we propose waveform designs that maximize identifiability for common array configurations. We also provide novel insights on the interaction between the waveforms and array geometry. A key observation is that waveforms should be matched to the pattern of redundant transmit-receive sensor pairs. Redundant array configurations are commonly employed to increase noise resilience, robustify against sensor failures, and improve beamforming capabilities. Our analysis also clearly shows that a redundant array is capable of achieving its maximum identifiability using fewer linearly independent waveforms than transmitters. This has the benefit of lowering hardware costs and transmission time. We illustrate our findings using multiple examples with unit-modulus waveforms, which are often preferred in practice.
D-Band RIS as a Reflect Array: Characterization and Hardware Impairments Study
Authors: Ehsan Tohidi, Robert Stoecker, Julia-Marie Koeszegi, Slawomir Stanczak
Date: 2023-05-10 00:00:00

Reflecting intelligent surface (RIS) has emerged as a promising technology for enhancing wireless communication performance and enabling new applications in 6G networks with potentially low energy consumption and hardware complexity thanks to their passive nature. Despite the significant growth of the literature on RIS in recent years, covering various aspects of this technology, challenges and issues regarding the practical implementation of RIS have been less addressed. This issue is even more severe at D-band frequencies due to the inherent challenges. This paper aims to connect the requirements and aspirations from the link-level side to the actually achievable RIS hardware with the focus on the various models for RIS. In order to obtain a realistic hardware scenario while maintaining a manageable parameter set, a static reflect array with similar reflection behavior as the reconfigurable one is employed. The results of the study enable an improved RIS design process due to more realistic models. The special focus of this paper is on the hardware impairments, in particular, (i) the effect of specular reflection, and (ii) the beam squint effect.
Efficient Beamforming Designs for IRS-Aided DFRC Systems
Authors: Yi-Kai Li, Athina Petropulu
Date: 2023-05-10 00:00:00

This short tutorial presents several ideas for designing dual function radar communication (DFRC) systems aided by intelligent reflecting surfaces (IRS). These problems are highly nonlinear in the IRS parameter matrix, and further, the IRS parameters are subject to non-convex unit modulus constraints. We present classical semidefinite relaxation based methods, low-complexity minorization based optimization methods, low-complexity Riemannian manifold optimization methods, and near optimal branch and bound based methods.
Beampattern design for radars with reconfigurable intelligent surfaces
Authors: Emanuele Grossi, Luca Venturino
Date: 2023-05-10 00:00:00

We consider a radar architecture where an illuminator composed of few sources is used as a feeder for a (passive) reconfigurable intelligent surface (RIS), so as to mimic the behavior of a multiple-input multiple-output (MIMO) radar composed of as many active elements as the RIS. In this framework, we study the problem of beampattern design in the space-frequency domain, and we propose to choose the source signals and the RIS adjustable phases in order to minimize the weighted squared error between the desired (amplitude) beampattern and the synthesized one. A low complexity iterative algorithm is proposed to solve the resulting non-convex least square problem. An example is provided to show the merits of the proposed approach.
Biophysical Model for Signal-Embedded Droplet Soaking into 2D Cell Culture
Authors: Ibrahim Isik, Hamidreza Arjmandi, Christophe Corre, Adam Noel
Date: 2023-05-10 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Drug Discovery

Using agar plates hosting a 2D cell population stimulated with signaling molecules is crucial for experiments such as gene regulation and drug discovery in a wide range of biological studies. In this paper, a biophysical model is proposed that incorporates droplet soaking, diffusion of molecules within agar, cell growth over an agar surface, and absorption of signaling molecules by cells. The proposed model describes the channel response and provides valuable insights for designing experiments more efficiently and accurately. The molecule release rate due to droplet soaking into agar, which is characterized and modeled as the source term for the diffusion model, is derived. Furthermore, cell growth is considered over the surface, which dictates the dynamics of signaling molecule reactions and leads to a variable boundary condition. As a case study, genetically-modified $E.\,coli$ bacteria are spread over the surface of agar and Isopropyl-beta-D thiogalactopyranoside (IPTG) is considered as a signaling molecule. IPTG droplets are dropped onto the bacteria-covered agar surface. The parameters for the IPTG molecule release rate as a diffusion source into the agar are estimated from this experiment. Then, a particle-based simulator is used to obtain the spatio-temporal profile of the signaling molecules received by the surface bacteria. The results indicate that the number of molecules reacting with or absorbed by bacteria at different locations on the surface could be widely different, which highlights the importance of taking this variation into account for biological inferences.
Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets
Authors: Sean K. Maden, Sang Ho Kwon, Louise A. Huuki-Myers, Leonardo Collado-Torres, Stephanie C. Hicks, Kristen R. Maynard
Date: 2023-05-10 00:00:00

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a "gold standard" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.
Reproducible biomarkers: Leveraging nonlinear descriptors in the face of non-ergodicity
Authors: Madhur Mangalam, Arash Sadri, Junichiro Hayano, Eiichi Watanabe, Ken Kiyono, Damian G. Kelty-Stephen
Date: 2023-05-11 00:00:00
Tasks: Heart Rate Variability

Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with non-ergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of non-ergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are non-ergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the non-ergodic heart rate variability ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.
Comparative Analysis of Machine Learning Algorithms for Predicting On-Target and Off-Target Effects of CRISPR-Cas13d for gene editing
Authors: Jingze Liu, Jiahao Ma
Date: 2023-05-11 00:00:00

CRISPR-Cas13 is a system that utilizes single stranded RNAs for RNA editing. Prediction of on-target and off-target effects for the CRISPR-Cas13d dependency enables us to design specific single guide RNAs (sgRNAs) that help locate the desired RNA target positions. In this study, we compared the performance of multiple machine learning algorithms in predicting these effects using a reported dataset. Our results show that Catboost is the most accurate model with high sensitivity. This finding represents a significant advancement in our understanding of how to chose modeling methods to deal with RNA sequence feaatures effictivelys. Furthermore, our approach can potentially be applied to other CRISPR systems and genetic engineering techniques. Overall, this work has important implications for developing safer and more effective gene therapies and biotechnological applications.
A Platform for the Biomedical Application of Large Language Models
Authors: Sebastian Lobentanzer, Shaohong Feng, The BioChatter Consortium, Andreas Maier, Cankun Wang, Jan Baumbach, Nils Krehl, Qin Ma, Julio Saez-Rodriguez
Date: 2023-05-10 00:00:00
Tasks: Benchmarking, Privacy Preserving, Retrieval, Retrieval-augmented Generation

Current-generation Large Language Models (LLMs) have stirred enormous interest in recent months, yielding great potential for accessibility and automation, while simultaneously posing significant challenges and risk of misuse. To facilitate interfacing with LLMs in the biomedical space, while at the same time safeguarding their functionalities through sensible constraints, we propose a dedicated, open-source framework: BioChatter. Based on open-source software packages, we synergise the many functionalities that are currently developing around LLMs, such as knowledge integration / retrieval-augmented generation, model chaining, and benchmarking, resulting in an easy-to-use and inclusive framework for application in many use cases of biomedicine. We focus on robust and user-friendly implementation, including ways to deploy privacy-preserving local open-source LLMs. We demonstrate use cases via two multi-purpose web apps (https://chat.biocypher.org), and provide documentation, support, and an open community.
Copula-Based Trading of Cointegrated Cryptocurrency Pairs
Authors: Masood Tadi, Jiří Witzany
Date: 2023-05-11 00:00:00

This research introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs, the study employs linear and non-linear cointegration tests along with a correlation coefficient measure and fits different copula families to generate trading signals formulated from a reference asset for analyzing the mispricing index. The strategy's performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms buy-and-hold trading strategies in terms of both profitability and risk-adjusted returns.
Backward Hedging for American Options with Transaction Costs
Authors: Ludovic Goudenège, Andrea Molent, Antonino Zanette
Date: 2023-05-10 00:00:00

In this article, we introduce an algorithm called Backward Hedging, designed for hedging European and American options while considering transaction costs. The optimal strategy is determined by minimizing an appropriate loss function, which is based on either a risk measure or the mean squared error of the hedging strategy at maturity. The proposed algorithm moves backward in time, determining, for each time-step and different market states, the optimal hedging strategy that minimizes the loss function at the time the option is exercised, by assuming that the strategy used in the future for hedging the liability is the one determined at the previous steps of the algorithm. The approach avoids machine learning and instead relies on classic optimization techniques, Monte Carlo simulations, and interpolations on a grid. Comparisons with the Deep Hedging algorithm in various numerical experiments showcase the efficiency and accuracy of the proposed method.
Unequal Error Protection for Radiography Image Transmission Using Protograph Double LDPC Codes
Authors: Liangliang Xu, Huihui Wu, Jiguang He and Lin Wang, Senior Member
Date: 2013-01-23 00:00:00
Proceeding: r, IEEE Dept. of Communication Engineering 2013 1

In this paper, a novel unequal error protection (UEP)
scheme, which is constructed from protograph double low-density
parity-check (PD-LDPC) codes, and tailored for medical images is
proposed to minimize the end to end image distortion over additive
white Gaussian noise (AWGN) channel. The UEP scheme is carried
out by adopting different coding strategies in terms of the different
significant levels and different entropies of image data streams from
discrete cosine transform (DCT). A typical radiography image and
other two gray-scale images are exploited in the simulation. The
experimental results show that the proposed scheme can evidently
increase the peak signal to noise ratio (PSNR) value of the received
images and reconstruct the transmitted images with good quality
even at a very low signal-to-noise ratio (SNR). In particular, the
proposed scheme outperforms the other UEP schemes based on
regular D-LDPC codes and turbo codes by up to 0.5 and 2.5 dB
in the SNR when the PSNR of the reconstructed images reach the
expected upper bound, respectively.
Cognition Guided Human-Object Relationship Detection
Authors: Zhitao Zeng, Pengwen Dai, Xuan Zhang, Lei Zhang, Xiaochun Cao
Date: 2023-05-06 00:00:00
Proceeding: journal 2023 5
Tasks: Decoder, Human-Object Relationship Detection, Object, Relation

Human-object relationship detection reveals the fine-grained relationship between humans and objects, helping the comprehensive understanding of videos. Previous human-object relationship detection approaches are mainly developed with object features and relation features without exploring the specific information of humans. In this paper, we propose a novel Relation-Pose Transformer (RPT) for human-object relationship detection. Inspired by the coordination of eye-head-body movements in cognitive science, we employ the head pose to find those crucial objects that humans focus on and use the body pose with skeleton information to represent multiple actions. Then, we utilize the spatial encoder to capture spatial contextualized information of the relation pair, which integrates the relation features and pose features. Next, the temporal decoder aims to model the temporal dependency of the relationship. Finally, we adopt multiple classifiers to predict different types of relationships. Extensive experiments on the benchmark Action Genome validate the effectiveness of our proposed method and show the state-of-the-art performance compared with related methods.
Deep learning models for plant disease detection and diagnosis
Authors: Konstantinos P. Ferentinos
Date: 2018-02-05 00:00:00
Proceeding: The 22nd International Joint Conference on Artificial Intelligence 2018 2
Tasks: Deep Learning

In this paper, convolutional neural network models were developed to perform plant disease detection and
diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies.
Training of the models was performed with the use of an open database of 87,848 images, containing 25 different plants in a set of 58 distinct classes of [plant, disease] combinations, including healthy plants. Several
model architectures were trained, with the best performance reaching a 99.53% success rate in identifying the
corresponding [plant, disease] combination (or healthy plant). The significantly high success rate makes the
model a very useful advisory or early warning tool, and an approach that could be further expanded to support
an integrated plant disease identification system to operate in real cultivation conditions
Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning
Authors: Gorkem Polat, Haluk Tarik Kani, Ilkay Ergenc, Yesim Ozen Alahdab, Alptekin Temizel, Ozlen Atug
Date: 2022-11-16 00:00:00
Proceeding: Inflammatory Bowel Diseases 2022 11
Tasks: Classification, Image Classification, regression

Background:  Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment. Methods: The cohort comprises 11276 images from 564 patients who underwent colonoscopy for UC. We propose a regression-based deep learning approach for the endoscopic evaluation of UC according to the Mayo endoscopic score (MES). Five state-of-the-art convolutional neural network (CNN) architectures were used for the performance measurements and comparisons. Ten-fold cross-validation was used to train the models and objectively benchmark them. Model performances were assessed using quadratic weighted kappa and macro F1 scores for full Mayo score classification and kappa statistics and F1 score for remission classification. Results: Five classification-based CNNs used in the study were in excellent agreement with the expert annotations for all Mayo subscores and remission classification according to the kappa statistics. When the proposed regression-based approach was used, (1) the performance of most of the models statistically significantly increased, and (2) the same model trained on different cross-validation folds produced more robust results on the test set in terms of deviation between different folds. Conclusions: Comprehensive experimental evaluations show that commonly used classification-based  CNN  architectures have successful performance in evaluating the endoscopic disease activity of UC. Integration of domain knowledge into these architectures further increases performance and robustness, accelerating their translation into clinical use.