pwc-archive/papers-with-abstracts huggingface dataset:
Mode Selection in Cognitive Radar Networks
Authors: William W. Howard, Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer
Date: 2023-11-30 00:00:00

Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations. These limitations include large physical size, power consumption, fixed operating parameters, and single point vulnerabilities. Cognitive radar solves part of this problem through adaptability, using biologically inspired techniques to observe the environment and adjust operation accordingly. Cognitive radar networks (CRNs) extend the capabilities of cognitive radar spatially, providing the opportunity to observe targets from multiple angles to mitigate stealth effects; distribute resources over space and in time; obtain better tracking performance; and gain more information from a scene. Often, problems of cognition in CRNs are viewed through the lens of iterative learning problems - one or multiple cognitive processes are implemented in the network, where each process first observes the environment, then selects operating parameters (from discrete or continuous options) using the history of observations and previous rewards, then repeats the cycle. Further, cognitive radar networks often are modeled with a flexible architecture and wide-bandwidth front-ends, enabling the addition of electronic support measures such as passive signal estimation. In this work we consider questions of the form "How should a cognitive radar network choose when to observe targets?" and "How can a cognitive radar network reduce the amount of energy it uses?". We implement tools from the multi-armed bandit and age of information literature to select modes for the network, choosing either an active radar mode or a passive signal estimation mode. We show that through the use of target classes, the network can determine how often each target should be observed to optimize tracking performance.
Integration of Robotics, Computer Vision, and Algorithm Design: A Chinese Poker Self-Playing Robot
Authors: Kuan-Huang Yu
Date: 2023-11-28 00:00:00
Tasks: object-detection, Object Detection

This paper presents Chinese Poker Self-Playing Robot, an integrated system enabling a TM5-900 robotic arm to independently play the four-person card game Chinese poker. The robot uses a custom sucker mechanism to pick up and play cards. An object detection model based on YOLOv5 is utilized to recognize the suit and number of 13 cards dealt to the robot. A greedy algorithm is developed to divide the 13 cards into optimal hands of 3, 5, and 5 cards to play. Experiments demonstrate that the robot can successfully obtain the cards, identify them using computer vision, strategically select hands to play using the algorithm, and physically play the selected cards in the game. The system showcases effective integration of mechanical design, computer vision, algorithm design, and robotic control to accomplish the complex task of independently playing cards.
Riemannian Prediction of Anatomical Diagnoses in Congenital Heart Disease based on 12-lead ECGs
Authors: Muhammet Alkan, Gruschen Veldtman, Fani Deligianni
Date: 2023-11-27 00:00:00
Tasks: Prognosis, Time Series

Congenital heart disease (CHD) is a relatively rare disease that affects patients at birth and results in extremely heterogeneous anatomical and functional defects. 12-lead ECG signal is routinely collected in CHD patients because it provides significant biomarkers for disease prognosis. However, developing accurate machine learning models is challenging due to the lack of large available datasets. Here, we suggest exploiting the Riemannian geometry of the spatial covariance structure of the ECG signal to improve classification. Firstly, we use covariance augmentation to mix samples across the Riemannian geodesic between corresponding classes. Secondly, we suggest to project the covariance matrices to their respective class Riemannian mean to enhance the quality of feature extraction via tangent space projection. We perform several ablation experiments and demonstrate significant improvement compared to traditional machine learning models and deep learning on ECG time series data.
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
Authors: Yan Pei, Jiahui Xu, Qianhao Chen, Chenhao Wang, Feng Yu, Lisan Zhang, Wei Luo
Date: 2023-11-27 00:00:00
Tasks: Brain Computer Interface, Denoising, EEG, EEG Denoising, Electroencephalogram (EEG), Representation Learning

Electroencephalography (EEG) signals are easily corrupted by various artifacts, making artifact removal crucial for improving signal quality in scenarios such as disease diagnosis and brain-computer interface (BCI). In this paper, we present a fully convolutional neural architecture, called DTP-Net, which consists of a Densely Connected Temporal Pyramid (DTP) sandwiched between a pair of learnable time-frequency transformations for end-to-end electroencephalogram (EEG) denoising. The proposed method first transforms a single-channel EEG signal of arbitrary length into the time-frequency domain via an Encoder layer. Then, noises, such as ocular and muscle artifacts, are extracted by DTP in a multi-scale fashion and reduced. Finally, a Decoder layer is employed to reconstruct the artifact-reduced EEG signal. Additionally, we conduct an in-depth analysis of the representation learning behavior of each module in DTP-Net to substantiate its robustness and reliability. Extensive experiments conducted on two public semi-simulated datasets demonstrate the effective artifact removal performance of DTP-Net, which outperforms state-of-art approaches. Experimental results demonstrate cleaner waveforms and significant improvement in Signal-to-Noise Ratio (SNR) and Relative Root Mean Square Error (RRMSE) after denoised by the proposed model. Moreover, the proposed DTP-Net is applied in a specific BCI downstream task, improving the classification accuracy by up to 5.55% compared to that of the raw signals, validating its potential applications in the fields of EEG-based neuroscience and neuro-engineering.
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics
Authors: Tristan Shah, Feruza Amirkulova, Stas Tiomkin
Date: 2023-11-27 00:00:00
Tasks: Interpretable Machine Learning

Controlling systems governed by partial differential equations is an inherently hard problem. Specifically, control of wave dynamics is challenging due to additional physical constraints and intrinsic properties of wave phenomena such as dissipation, attenuation, reflection, and scattering. In this work, we introduce an environment designed for the study of the control of acoustic waves by actuated metamaterial designs. We utilize this environment for the development of a novel machine-learning method, based on deep neural networks, for efficiently learning the dynamics of an acoustic PDE from samples. Our model is fully interpretable and maps physical constraints and intrinsic properties of the real acoustic environment into its latent representation of information. Within our model we use a trainable perfectly matched layer to explicitly learn the property of acoustic energy dissipation. Our model can be used to predict and control scattered wave energy. The capabilities of our model are demonstrated on an important problem in acoustics, which is the minimization of total scattered energy. Furthermore, we show that the prediction of scattered energy by our model generalizes in time and can be extended to long time horizons. We make our code repository publicly available.
Deep Learning-Enabled Swallowing Monitoring and Postoperative Recovery Biosensing System
Authors: Chih-Ning Tsai, Pei-Wen Yang, Tzu-Yen Huang, Jung-Chih Chen, Hsin-Yi Tseng, Che-Wei Wu, Amrit Sarmah, Tzu-En Lin
Date: 2023-11-24 00:00:00
Tasks: Deep Learning

This study introduces an innovative 3D printed dry electrode tailored for biosensing in postoperative recovery scenarios. Fabricated through a drop coating process, the electrode incorporates a novel 2D material.
Joint Alignment of Multivariate Quasi-Periodic Functional Data Using Deep Learning
Authors: Vi Thanh Pham, Jonas Bille Nielsen, Klaus Fuglsang Kofoed, Jørgen Tobias Kühl, Andreas Kryger Jensen
Date: 2023-11-14 00:00:00

The joint alignment of multivariate functional data plays an important role in various fields such as signal processing, neuroscience and medicine, including the statistical analysis of data from wearable devices. Traditional methods often ignore the phase variability and instead focus on the variability in the observed amplitude. We present a novel method for joint alignment of multivariate quasi-periodic functions using deep neural networks, decomposing, but retaining all the information in the data by preserving both phase and amplitude variability. Our proposed neural network uses a special activation of the output that builds on the unit simplex transformation, and we utilize a loss function based on the Fisher-Rao metric to train our model. Furthermore, our method is unsupervised and can provide an optimal common template function as well as subject-specific templates. We demonstrate our method on two simulated datasets and one real example, comprising data from 12-lead 10s electrocardiogram recordings.
Convergence of Heavy-Tailed Hawkes Processes and the Microstructure of Rough Volatility
Authors: Ulrich Horst, Wei Xu, Rouyi Zhang
Date: 2023-12-14 00:00:00

We establish the weak convergence of the intensity of a nearly-unstable Hawkes process with heavy-tailed kernel. Our result is used to derive a scaling limit for a financial market model where orders to buy or sell an asset arrive according to a Hawkes process with power-law kernel. After suitable rescaling the price-volatility process converges weakly to a rough Heston model. Our convergence result is much stronger than previously established ones that have either focused on light-tailed kernels or the convergence of integrated volatility process. The key is to establish the tightness of the family of rescaled volatility processes. This is achieved by introducing a new methods to establish the $C$-tightness of c\`adl\`ag processes based on the classical Kolmogorov-Chentsov tightness criterion for continuous processes.
Optimized Parallelization of Boundary Integral Poisson-Boltzmann Solvers
Authors: Xin Yang, Elyssa Sliheet, Reece Iriye, Daniel Reynolds, Weihua Geng
Date: 2023-11-30 00:00:00
Tasks: CPU, GPU

The Poisson-Boltzmann (PB) model governs the electrostatics of solvated biomolecules, i.e., potential, field, energy, and force. These quantities can provide useful information about protein properties, functions, and dynamics. By considering the advantages of current algorithms and computer hardware, we focus on the parallelization of the treecode-accelerated boundary integral (TABI) PB solver using the Message Passing Interface (MPI) on CPUs and the direct-sum boundary integral (DSBI) PB solver using KOKKOS on GPUs. We provide optimization guidance for users when the DSBI solver on GPU or the TABI solver with MPI on CPU should be used depending on the size of the problem. Specifically, when the number of unknowns is smaller than a predetermined threshold, the GPU-accelerated DSBI solver converges rapidly thus has the potential to perform PB model-based molecular dynamics or Monte Carlo simulation. As practical appliations, our parallelized boundary integral PB solvers are used to solve electrostatics on selected proteins that play significant roles in the spread, treatment, and prevention of COVID-19 virus diseases. For each selected protein, the simulation produces the electrostatic solvation energy as a global measurement and electrostatic surface potential for local details.
Interpretable Online Network Dictionary Learning for Inferring Long-Range Chromatin Interactions
Authors: Vishal Rana, Jianhao Peng, Chao Pan, Hanbaek Lyu, Albert Cheng, Minji Kim, Olgica Milenkovic
Date: 2023-12-16 00:00:00
Tasks: Dictionary Learning

Dictionary learning (DL) is commonly used in computational biology to tackle ubiquitous clustering problems due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability and are not optimized for large-scale graph-structured data. We propose a novel DL algorithm called online convex network dictionary learning (online cvxNDL) that can handle extremely large datasets and enables the interpretation of dictionary elements, which serve as cluster representatives, through convex combinations of real measurements. Moreover, the algorithm can be applied to network-structured data via specialized subnetwork sampling techniques. To demonstrate the utility of our approach, we apply cvxNDL on 3D-genome RNAPII ChIA-Drop data to identify important long-range interaction patterns. ChIA-Drop probes higher-order interactions, and produces hypergraphs whose nodes represent genomic fragments. The hyperedges represent observed physical contacts. Our hypergraph model analysis creates an interpretable dictionary of long-range interaction patterns that accurately represent global chromatin physical contact maps. Using dictionary information, one can also associate the contact maps with RNA transcripts and infer cellular functions. Our results offer two key insights. First, we demonstrate that online cvxNDL retains the accuracy of classical DL methods while simultaneously ensuring unique interpretability and scalability. Second, we identify distinct collections of proximal and distal interaction patterns involving chromatin elements shared by related processes across different chromosomes, as well as patterns unique to specific chromosomes. To associate the dictionary elements with biological properties of the corresponding chromatin regions, we employ Gene Ontology enrichment analysis and perform RNA coexpression studies.
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data
Authors: Hongsong Feng, Sean Cottrell, Yuta Hozumi, Guo-Wei Wei
Date: 2023-12-15 00:00:00
Tasks: Diversity

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expression dynamics. The analysis of scRNA-seq data poses challenges attributed to both sparsity and the extensive number of genes implicated. An increasing number of computational tools are devised for analyzing and interpreting scRNA-seq data. We present a multiscale differential geometry (MDG) strategy to exploit the geometric and biological properties inherent in scRNA-seq data. We assume that those intrinsic properties of cells lies on a family of low-dimensional manifolds embedded in the high-dimensional space of scRNA-seq data. Subsequently, we explore these properties via multiscale cell-cell interactive manifolds. Our multiscale curvature-based representation serves as a powerful approach to effectively encapsulate the complex relationships in the cell-cell network. We showcase the utility of our novel approach by demonstrating its effectiveness in classifying cell types. This innovative application of differential geometry in scRNA-seq analysis opens new avenues for understanding the intricacies of biological networks and holds great potential for network analysis in other fields.
Representational constraints underlying similarity between task-optimized neural systems
Authors: Tahereh Toosi
Date: 2023-12-13 00:00:00
Tasks: Object Recognition

Neural systems, artificial and biological, show similar representations of inputs when optimized to perform similar tasks. In visual systems optimized for tasks similar to object recognition, we propose that representation similarities arise from the constraints imposed by the development of abstractions in the representation across the processing stages. To study the effect of abstraction hierarchy of representations across different visual systems, we constructed a two-dimensional space in which each neural representation is positioned based on its distance from the pixel space and the class space. Trajectories of representations in all the task-optimized visual neural networks start close to the pixel space and gradually move towards higher abstract representations, such as object categories. We also observe that proximity in this abstraction space predicts the similarity of neural representations between visual systems. The gradual similar change of the representations suggests that the similarity across different task-optimized systems could arise from constraints on representational trajectories.
Phylogeny of Twenty-One Mammals
Authors: Ray Han
Date: 2023-12-12 00:00:00

Phylogeny can be inferred using two sources of data from an organism: morphological data and molecular data. Historically, phylogenies were usually inferred using morphological characters, but some morphological features may not necessarily indicate shared heritage. With the introduction of molecular phylogenies, the base sequence of genes, or amino acid sequence of proteins can be compared to find the number of similarities or differences to ascertain levels of relatedness between species. These two types of phylogenies are to be taken as a data-driven hypothesis about the evolutionary history of the studied organisms, and a consensus is drawn from the comparison between the different phylogenies built from the two sources of data, utilizing different methods.
Illness perception and self-management in patients with type 2 diabetes
Authors: A. Y. Timashkov, I. N. Abrosimov, V. M. Yaltonsky
Date: 2023-12-15 00:00:00
Tasks: Management

This paper presents the results of a study on the perception of illness and adaptation parameters in patients with type 2 diabetes. The study involved 173 patients diagnosed with "Type 2 Diabetes" (ICD-11 code 5 A 11). The average age of the patients was 55.21+/-13.47 and the average duration of the disease was 11.79+/-8.16. Two profiles of illness perception were identified: Profile 1 - "Perception of illness threat" and Profile 2 - "Perception of illness and treatment controllability". Three types of illness perception were also identified: Type 1 - "Formed illness threat and negative beliefs about illness and treatment control" (Group 1); Type 2 - "Unformed illness threat and neutral beliefs about illness and treatment control" (Group 2); Type 3 - "Formed illness threat and positive beliefs about illness and treatment control" (Group 3). Targets for further psychological interventions were formulated for each identified type.
Comments on "Climbing Escher's stairs: A way to approximate stability landscapes in multidimensional systems"
Authors: Jingmeng Cui, Anna Lichtwarck-Aschoff, Fred Hasselman
Date: 2023-12-15 00:00:00

The article under discussion, titled "Climbing Escher's stairs: A way to approximate stability landscapes in multidimensional systems" (doi: 10.1371/journal.pcbi.1007788), has captured our attention due to important methodological limitations that we believe warrant discussion. Our aim in writing this Formal Comment is to bring to the attention of potential readers and users the following key points: 1. The construction of the potential landscape function necessitates the global integrability of the velocity functions. However, the decomposition method outlined in this article is only applied to the Jacobian of the velocity functions instead of the velocity functions themselves, which leads to path-dependent integrals. Such a characteristic is not desirable for a potential landscape function. 2. In the article's implementation, integration is conducted along the x- and y-axes. This approach renders the decomposition method, which primarily affects the non-diagonal elements in the Jacobian matrix, ineffective. 3. We provide evidence that removing the core step of the method, the decomposition process, in the rolldown package (now renamed as waydown), results in identical output. This finding highlights the ineffectiveness of this crucial implementation step. 4. In our Comment, we also offer recommendations for potential alternatives for readers interested in constructing potential landscape functions.
A parallelized cellular Potts model that enables simulations at tissue scale
Authors: Shabaz Sultan, Sapna Devi, Scott N. Mueller, Johannes Textor
Date: 2023-12-14 00:00:00
Tasks: GPU

The Cellular Potts Model (CPM) is a widely used simulation paradigm for systems of interacting cells that has been used to study scenarios ranging from plant development to morphogenesis, tumour growth and cell migration. Despite their wide use, CPM simulations are considered too computationally intensive for three-dimensional (3D) models at organ scale. CPMs have been difficult to parallelise because of their inherently sequential update scheme. Here, we present a Graphical Processing Unit (GPU)-based parallelisation scheme that preserves local update statistics and is up to 3-4 orders of magnitude faster than serial implementations. We show several examples where our scheme preserves simulation behaviors that are drastically altered by existing parallelisation methods. We use our framework to construct tissue-scale models of liver and lymph node environments containing millions of cells that are directly based on microscopy-imaged tissue structures. Thus, our GPU-based CPM framework enables in silico studies of multicellular systems of unprecedented scale.
Managing ESG Ratings Disagreement in Sustainable Portfolio Selection
Authors: Francesco Cesarone, Manuel Luis Martino, Federica Ricca, Andrea Scozzari
Date: 2023-12-17 00:00:00

Sustainable Investing identifies the approach of investors whose aim is twofold: on the one hand, they want to achieve the best compromise between portfolio risk and return, but they also want to take into account the sustainability of their investment, assessed through some Environmental, Social, and Governance (ESG) criteria. The inclusion of sustainable goals in the portfolio selection process may have an actual impact on financial portfolio performance. ESG indices provided by the rating agencies are generally considered good proxies for the performance in sustainability of an investment, as well as, appropriate measures for Socially Responsible Investments (SRI) in the market. In this framework of analysis, the lack of alignment between ratings provided by different agencies is a crucial issue that inevitably undermines the robustness and reliability of these evaluation measures. In fact, the ESG rating disagreement may produce conflicting information, implying a difficulty for the investor in the portfolio ESG evaluation. This may cause underestimation or overestimation of the market opportunities for a sustainable investment. In this paper, we deal with a multi-criteria portfolio selection problem taking into account risk, return, and ESG criteria. For the ESG evaluation of the securities in the market, we consider more than one agency and propose a new approach to overcome the problem related to the disagreement between the ESG ratings by different agencies. We propose a nonlinear optimization model for our three-criteria portfolio selection problem. We show that it can be reformulated as an equivalent convex quadratic program by exploiting a technique known in the literature as the k-sum optimization strategy. An extensive empirical analysis of the performance of this model is provided on real-world financial data sets.