pwc-archive/papers-with-abstracts huggingface dataset:
Maximum Likelihood Alternating Summation for Multistatic Angle-based Multitarget Localization
Authors: Martin Willame, Laurent Storrer, Hasan Can Yildirim, François Horlin, Jérôme Louveaux
Date: 2024-03-20 00:00:00

Recent advancements in Wi-Fi sensing have sparked interest in exploiting OFDM modulated communication signals for target detection and tracking. In this study, we address the angle-based localization of multiple targets using a multistatic OFDM radar. While the maximum likelihood approach optimally merges data from each radar pair comprised by the system, it entails a complex multi-dimensional search process. Leveraging pre-estimation of the targets' parameters obtained via the MUSIC algorithm, our method decouples this multi-dimensional search into a single two-dimensional estimator per target. The proposed alternating summation method allows the computation of a combined likelihood map aggregating contributions from each radar pair, enabling target detection via peak selection. Besides reducing computational complexity, the method effectively captures target interactions and accommodates varying radar pair localization abilities. Also, it requires transmitting only the estimated channel covariance matrices of each radar pair to the central processor. Numerical simulations demonstrate superior performance over existing approaches.
Superposed IM-OFDM (S-IM-OFDM): An Enhanced OFDM for Integrated Sensing and Communications
Authors: Zonghui Yang, Shijian Gao, Xiang Cheng, Liuqing Yang
Date: 2024-03-20 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) is a critical enabler for emerging 6G applications, and at its core lies in the dual-functional waveform design. While orthogonal frequency division multiplexing (OFDM) has been a popular basic waveform, its primitive version falls short in sensing due to the inherent unregulated auto-correlation properties. Furthermore, the sensitivity to Doppler shift hinders its broader applications in dynamic scenarios. To address these issues, we propose a superposed index-modulated OFDM (S-IM-OFDM). The proposed scheme improves the sensing performance without excess power consumption by translating the energy efficiency of IM-OFDM onto sensing-oriented signals over OFDM. Also, it maintains excellent communication performance in time-varying channels by leveraging the sensed parameters to compensate for Doppler. Compared to conventional OFDM, the proposed S-IM-OFDM waveform exhibits better sensing capabilities and wider applicability in dynamic scenarios. Both theoretical analyses and simulations corroborate its dual benefits.
Modeling RIS from Electromagnetic Principles to Communication Systems--Part II: System-Level Simulation, Ray Tracing, and Measurement
Authors: Le Hao, Sravan K. R. Vuyyuru, Sergei A. Tretyakov, Artan Salihu, Markus Rupp, Risto Valkonen
Date: 2024-03-20 00:00:00

In this paper, we systematically study the electromagnetic (EM) and communication aspects of an RIS through EM simulations, system-level and ray-tracing simulations, and finally measurements. We simulate a nearly perfect, lossless RIS, and a realistic lossy anomalous reflector (AR) in different ray tracers and analyze the large-scale fading of simple RIS-assisted links. We also compare the results with continuous and quantized unit cell reflection phases with one to four-bit resolutions. Finally, we perform over-the-air communication link measurements in an indoor setting with a manufactured sample of a wide-angle AR. The EM, system-level, and ray-tracing simulation results show good agreement with the measurement results. It is proved that the introduced macroscopic model of RIS from the EM aspects is consistent with our proposed communication models, both for an ideal RIS and a realistic AR.
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
Authors: Jingwei Zhang, Lauren Swinnen, Christos Chatzichristos, Victoria Broux, Renee Proost, Katrien Jansen, Benno Mahler, Nicolas Zabler, Nino Epitashvilli, Matthias Dümpelmann, Andreas Schulze-Bonhage, Elisabeth Schriewer, Ummahan Ermis, Stefan Wolking, Florian Linke, Yvonne Weber, Mkael Symmonds, Arjune Sen, Andrea Biondi, Mark P. Richardson, Abuhaiba Sulaiman I, Ana Isabel Silva, Francisco Sales, Gergely Vértes, Wim Van Paesschen, Maarten De Vos
Date: 2024-03-19 00:00:00
Tasks: Brain Decoding, EEG, Electrocardiography (ECG), Electromyography (EMG), Seizure Detection, Sensitivity

Objective: Most current wearable tonic-clonic seizure (TCS) detection systems are based on extra-cerebral signals, such as electromyography (EMG) or accelerometry (ACC). Although many of these devices show good sensitivity in seizure detection, their false positive rates (FPR) are still relatively high. Wearable EEG may improve performance; however, studies investigating this remain scarce. This paper aims 1) to investigate the possibility of detecting TCSs with a behind-the-ear, two-channel wearable EEG, and 2) to evaluate the added value of wearable EEG to other non-EEG modalities in multimodal TCS detection. Method: We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The multimodal wearable detection system Sensor Dot (Byteflies) was used to measure two-channel, behind-the-ear EEG, EMG, electrocardiography (ECG), ACC and gyroscope (GYR). First, we evaluated automatic unimodal detection of TCSs, using performance metrics such as sensitivity, precision, FPR and F1-score. Secondly, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to a neurologist and a wearable data expert, who reviewed and annotated the true positive TCSs, and discarded false positives (FPs). Results: Wearable EEG outperformed the other modalities in unimodal TCS detection by achieving a sensitivity of 100.0% and a FPR of 10.3/24h (compared to 97.7% sensitivity and 30.9/24h FPR for EMG; 95.5% sensitivity and 13.9 FPR for ACC). The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97.7%, a FPR of 0.4/24 h, a precision of 43.0%, and a F1-score of 59.7%. Subsequent visual review of the automated detections resulted in maximal sensitivity and zero FPs.
Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy
Authors: Govind Vashishtha, Sumika Chauhan, Surinder Kumar, Rajesh Kumar, Radoslaw Zimroz, Anil Kumar
Date: 2024-03-19 00:00:00
Tasks: Diagnostic, Fault Diagnosis

The worm gearbox is a high-speed transmission system that plays a vital role in various industries. Therefore it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis purposes. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. Further, the search space of each Hp is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy and is stable and robust while diagnosing the worm gearbox.
Fundamental Components of Deep Learning: A category-theoretic approach
Authors: Bruno Gavranović
Date: 2024-03-13 00:00:00
Tasks: Deep Learning, Descriptive, In-Context Learning

Deep learning, despite its remarkable achievements, is still a young field. Like the early stages of many scientific disciplines, it is marked by the discovery of new phenomena, ad-hoc design decisions, and the lack of a uniform and compositional mathematical foundation. From the intricacies of the implementation of backpropagation, through a growing zoo of neural network architectures, to the new and poorly understood phenomena such as double descent, scaling laws or in-context learning, there are few unifying principles in deep learning. This thesis develops a novel mathematical foundation for deep learning based on the language of category theory. We develop a new framework that is a) end-to-end, b) unform, and c) not merely descriptive, but prescriptive, meaning it is amenable to direct implementation in programming languages with sufficient features. We also systematise many existing approaches, placing many existing constructions and concepts from the literature under the same umbrella. In Part I we identify and model two main properties of deep learning systems parametricity and bidirectionality by we expand on the previously defined construction of actegories and Para to study the former, and define weighted optics to study the latter. Combining them yields parametric weighted optics, a categorical model of artificial neural networks, and more. Part II justifies the abstractions from Part I, applying them to model backpropagation, architectures, and supervised learning. We provide a lens-theoretic axiomatisation of differentiation, covering not just smooth spaces, but discrete settings of boolean circuits as well. We survey existing, and develop new categorical models of neural network architectures. We formalise the notion of optimisers and lastly, combine all the existing concepts together, providing a uniform and compositional framework for supervised learning.
A necessary condition for non-monotonic dose response, with an application to a kinetic proofreading model -- Extended version
Authors: Polly Y. Yu, Eduardo D. Sontag
Date: 2024-03-20 00:00:00

Steady state nonmonotonic ("biphasic") dose responses are often observed in experimental biology, which raises the control-theoretic question of identifying which possible mechanisms might underlie such behaviors. It is well known that the presence of an incoherent feedforward loop (IFFL) in a network may give rise to a nonmonotonic response. It has been conjectured that this condition is also necessary, i.e. that a nonmonotonic response implies the existence of an IFFL. In this paper, we show that this conjecture is false, and in the process prove a weaker version: that either an IFFL must exist or both a positive feedback loop and a negative feedback loop must exist. Towards this aim, we give necessary and sufficient conditions for when minors of a symbolic matrix have mixed signs. Finally, we study in full generality when a model of immune T-cell activation could exhibit a steady state nonmonotonic dose response.
Max- and min-stability under first-order stochastic dominance
Authors: Christopher Chambers, Alan Miller, Ruodu Wang, Qinyu Wu
Date: 2024-03-19 00:00:00

Max-stability is the property that taking a maximum between two inputs results in a maximum between two outputs. We study max-stability with respect to first-order stochastic dominance, the most fundamental notion of stochastic dominance in decision theory. Under two additional standard axioms of nondegeneracy and lower semicontinuity, we establish a representation theorem for functionals satisfying max-stability, which turns out to be represented by the supremum of a bivariate function. A parallel characterization result for min-stability, that is, with the maximum replaced by the minimum in max-stability, is also established. By combining both max-stability and min-stability, we obtain a new characterization for a class of functionals, called the Lambda-quantiles, that appear in finance and political science.
Optimal State Estimation in the Presence of Non-Gaussian Uncertainty via Wasserstein Distance Minimization
Authors: Himanshu Prabhat, Raktim Bhattacharya
Date: 2024-03-06 00:00:00
Tasks: State Estimation

This paper presents a novel distribution-agnostic Wasserstein distance-based estimation framework. The goal is to determine an optimal map combining prior estimate with measurement likelihood such that posterior estimation error optimally reaches the Dirac delta distribution with minimal effort. The Wasserstein metric is used to quantify the effort of transporting from one distribution to another. We hypothesize that minimizing the Wasserstein distance between the posterior error and the Dirac delta distribution results in optimal information fusion and posterior state uncertainty. Framework validation is demonstrated by the successful recovery of the classical Kalman filter for linear systems with Gaussian uncertainties. Notably, the proposed Wasserstein filter does not rely on particle representation of uncertainty. Furthermore, the classical result for the Gaussian Sum Filter (GSF) is retrieved from the Wasserstein framework. This approach analytically exhibits the suboptimality of GSF and enables the use of nonlinear optimization techniques to enhance the accuracy of the Gaussian sum estimator.
Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule
Authors: Yi Xiao, Xiangxin Zhou, Qiang Liu, Liang Wang
Date: 2024-03-07 00:00:00
Tasks: Drug Discovery

Artificial intelligence has demonstrated immense potential in scientific research. Within molecular science, it is revolutionizing the traditional computer-aided paradigm, ushering in a new era of deep learning. With recent progress in multimodal learning and natural language processing, an emerging trend has targeted at building multimodal frameworks to jointly model molecules with textual domain knowledge. In this paper, we present the first systematic survey on multimodal frameworks for molecules research. Specifically,we begin with the development of molecular deep learning and point out the necessity to involve textual modality. Next, we focus on recent advances in text-molecule alignment methods, categorizing current models into two groups based on their architectures and listing relevant pre-training tasks. Furthermore, we delves into the utilization of large language models and prompting techniques for molecular tasks and present significant applications in drug discovery. Finally, we discuss the limitations in this field and highlight several promising directions for future research.
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
Authors: Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu
Date: 2024-03-07 00:00:00
Tasks: Drug Design, Drug Discovery

Recently, 3D generative models have shown promising performances in structure-based drug design by learning to generate ligands given target binding sites. However, only modeling the target-ligand distribution can hardly fulfill one of the main goals in drug discovery -- designing novel ligands with desired properties, e.g., high binding affinity, easily synthesizable, etc. This challenge becomes particularly pronounced when the target-ligand pairs used for training do not align with these desired properties. Moreover, most existing methods aim at solving \textit{de novo} design task, while many generative scenarios requiring flexible controllability, such as R-group optimization and scaffold hopping, have received little attention. In this work, we propose DecompOpt, a structure-based molecular optimization method based on a controllable and decomposed diffusion model. DecompOpt presents a new generation paradigm which combines optimization with conditional diffusion models to achieve desired properties while adhering to the molecular grammar. Additionally, DecompOpt offers a unified framework covering both \textit{de novo} design and controllable generation. To achieve so, ligands are decomposed into substructures which allows fine-grained control and local optimization. Experiments show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines, and demonstrate great potential in controllable generation tasks.
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
Authors: Bowen Gao, Minsi Ren, Yuyan Ni, Yanwen Huang, Bo Qiang, Zhi-Ming Ma, Wei-Ying Ma, Yanyan Lan
Date: 2024-03-04 00:00:00
Tasks: Contrastive Learning, Drug Design, Specificity

In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. To address this, we introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. To further incorporate this insight for generation, we develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity. Our empirical results show that this method not only enhances the delta score but also maintains or improves traditional docking scores, successfully bridging the gap between SBDD and real-world needs.
Genetic diversity of barley accessions and their response under abiotic stresses using different approaches
Authors: Djshwar Dhahir Lateef, Nawroz Abdul-razzak Tahir
Date: 2024-03-21 00:00:00
Tasks: Diversity

In this investigation, five separate experiments were carried out. The first experiments were examined the molecular characteristics of 59 barley accessions collected from different regions in Iraq using three different molecular markers (ISSR, CDDP, and Scot). A total of 391 amplified polymorphic bands were generated using forty-four ISSR, nine CDDP, and twelve Scot primers, which they totally observed 255, 35, and 101 polymorphic bands respectively. The mean values of PIC for ISSR, CDDP, and Scot markers were 0.74, 0.63, and 0.80, respectively, indicating the efficiency of the underlying markers in detecting polymorphic status among the studied barley accessions. Based on the respective markers, the barley accessions were classified and clustered into two main groups using the UPGMA and population structure analysis. Results of claustral analyses showed that the variation patterns corresponded with the geographical distribution of barley accessions.
Exact analytic expressions for discrete first-passage time probability distributions in Markov networks
Authors: Jaroslav Albert
Date: 2024-03-21 00:00:00

The first-passage time (FPT) is the time it takes a system variable to cross a given boundary for the first time. In the context of Markov networks, the FPT is the time a random walker takes to reach a particular node (target) by hopping from one node to another. If the walker pauses at each node for a period of time drawn from a continuous distribution, the FPT will be a continuous variable; if the pauses last exactly one unit of time, the FPT will be discrete and equal to the number of hops. We derive an exact analytical expression for the discrete first-passage time (DFPT) in Markov networks. Our approach is as follows: first, we divide each edge (connection between two nodes) of the network into $h$ unidirectional edges connecting a cascade of $h$ fictitious nodes and compute the continuous FPT (CFPT). Second, we set the transition rates along the edges to $h$, and show that as $h\to\infty$, the distribution of travel times between any two nodes of the original network approaches a delta function centered at 1, which is equivalent to pauses lasting 1 unit of time. Using this approach, we also compute the joint-probability distributions for the DFPT, the target node, and the node from which the target node was reached. A comparison with simulation confirms the validity of our approach.
Desiderata of evidence for representation in neuroscience
Authors: Stephan Pohl, Edgar Y. Walker, David L. Barack, Jennifer Lee, Rachel N. Denison, Ned Block, Florent Meyniel, Wei Ji Ma
Date: 2024-03-21 00:00:00

This paper develops a systematic framework for the evidence neuroscientists use to establish whether a neural response represents a feature. Researchers try to establish that the neural response is (1) sensitive and (2) specific to the feature, (3) invariant to other features, and (4) functional, which means that it is used downstream in the brain. We formalize these desiderata in information-theoretic terms. This formalism allows us to precisely state the desiderata while unifying the different analysis methods used in neuroscience under one framework. We discuss how common methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence are used to evaluate the desiderata. In doing so, we provide a common terminology to researchers that helps to clarify disagreements, to compare and integrate results across studies and research groups, and to identify when evidence might be missing and when evidence for some representational conclusion is strong. We illustrate the framework with several canonical examples, including the representation of orientation, numerosity, faces, and spatial location. We end by discussing how the framework can be extended to cover models of the neural code, multi-stage models, and other domains.
A systematic review on visual-processing deficits in Neurofibromatosis type 1: what possible impact on learning to read?
Authors: Marie Vernet, Stéphanie Ducrot, Yves Chaix
Date: 2024-03-19 00:00:00

This systematic review aimed to examine the possible implication of visual-perceptual, visuo-attentional and oculomotor processing in the reading deficits frequently experienced by children with Neurofibromatosis type 1 (NF1), as previously shown in dyslexia. Using PRISMA methodological guidelines, we examined 49 studies; most of these reported visual-processing deficits in this population, raising the importance of directly studying the visuo-perceptual and visuo-attentional processes and eye-movement control involved in the learning-to-read process in NF1. The discussion provides a reflection for a better understanding of how visual-processing skills interact with reading deficits in NF1, as well as new avenues for their screening and care.