pwc-archive/papers-with-abstracts huggingface dataset:
E-CONDOR: Efficient Contour-Based Detection Of Random Spatial Signals From UAV Observations Using Dual Stochastic Gradient
Authors: Maryam Zahra, Homa Tajiani, Hadi Alasti
Date: 2024-11-26 00:00:00

This paper presents a novel efficient method for spatial monitoring of the distribution of correlated field signals, such as temperature, humidity, etc. using unmanned aerial vehicles (UAVs). The spatial signal is compressed to its iso-contour lines at a number of known levels that are introduced by data fusion center (DFC). The UAV traces a contour line of the field signal at a time, and reports the coordinates of its own traces to the DFC for spatial modeling. The DFC iteratively improves the spatial model of the field signal and assigns a new contour level to each UAV to trace and report its coordinates for spatial model improvement. The selected batch of levels and the start point of the search are introduced by the DFC. In order to reduce the required data for spatial modeling, and accordingly improve the algorithm data efficiency, dual stochastic gradient routines are used at the DFC to find a next proper number of contour levels in the batch, and to eliminate the redundant contour levels, in each iteration. The performance evaluation of the proposed algorithm based on computer simulations demonstrates significantly faster convergence, better signal estimation, and a higher data efficiency against when the stochastic gradient is not used.
Decoupling Networks and Super-Quadratic Gains for RIS Systems with Mutual Coupling
Authors: Dominik Semmler, Josef A. Nossek, Michael Joham, Benedikt Böck, Wolfgang Utschick
Date: 2024-11-26 00:00:00

We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particular, we show that when incorporating these networks, the system model reduces to the same structure as if no mutual coupling is present. Hence, all algorithms and theoretical discussions neglecting mutual coupling can be directly applied when mutual coupling is present by utilizing our proposed decoupling networks. For example, by including decoupling networks, the channel gain maximization in RIS-aided single-input single-output (SISO) systems does not require an iterative algorithm but is given in closed form as opposed to using no decoupling network. In addition, this closed-form solution allows to analytically analyze scenarios under mutual coupling resulting in novel connections to the conventional transmit array gain. In particular, we show that super-quadratic (up to quartic) channel gains w.r.t. the number of RIS elements are possible and, therefore, the system with mutual coupling performs significantly better than the conventional uncoupled system in which only squared gains are possible. We consider diagonal as well as beyond diagonal (BD)-RISs and give various analytical and numerical results, including the inevitable losses at the RIS array. In addition, simulation results validate the superior performance of decoupling networks w.r.t. the channel gain compared to other state-of-the-art methods.
Deep Unfolding-Empowered MmWave Massive MIMO Joint Communications and Sensing
Authors: Nhan Thanh Nguyen, Ly V. Nguyen, Nir Shlezinger, Yonina C. Eldar, A. Lee Swindlehurst, Markku Juntti
Date: 2024-11-25 00:00:00

In this paper, we propose a low-complexity and fast hybrid beamforming design for joint communications and sensing (JCAS) based on deep unfolding. We first derive closed-form expressions for the gradients of the communications sum rate and sensing beampattern error with respect to the analog and digital precoders. Building on this, we develop a deep neural network as an unfolded version of the projected gradient ascent algorithm, which we refer to as UPGANet. This approach efficiently optimizes the communication-sensing performance tradeoff with fast convergence, enabled by the learned step sizes. UPGANet preserves the interpretability and flexibility of the conventional PGA optimizer while enhancing performance through data training. Our simulations show that UPGANet achieves up to a 33.5% higher communications sum rate and 2.5 dB lower beampattern error compared to conventional designs based on successive convex approximation and Riemannian manifold optimization. Additionally, it reduces runtime and computational complexity by up to 65% compared to PGA without unfolding.
A Low-Cost Monopulse Receiver with Enhanced Estimation Accuracy Via Deep Neural Network
Authors: Hanxiang Zhang, Saeed Zolfaghary Pour, Hao Yan, Powei Liu, Bayaner Arigong
Date: 2024-11-22 00:00:00

In this paper, a low-cost monopulse receiver with an enhanced direction of arrival (DoA) estimation accuracy via deep neural network (DNN) is proposed. The entire system is composed of a 4-element patch array, a fully planar symmetrical monopulse comparator network, and a down conversion link. Unlike the conventional design topology, the proposed monopulse comparator network is configured by four novel port-transformation rat-race couplers. In specific, the proposed coupler is designed to symmetrically allocate the sum ({\Sigma}) / delta ({\Delta}) ports with input ports, where a 360{\deg} phase delay crossover is designed to transform the unsymmetrical ports in the conventional rat-race coupler. This new rat-race coupler resolves the issues in conventional monopulse receiver comparator network design using multilayer and expensive fabrication technology. To verify the design theory, a prototype of the proposed planar monopulse comparator network operating at 2 GHz is designed, simulated, and measured. In addition, the monopulse radiation patterns and direction of arrival are also decently evaluated. To further boost the accuracy of angular information, a deep neural network is introduced to map the misaligned target angular positions in the measurement to the actual physical location under detection.
Efficient Channel Prediction for Beyond Diagonal RIS-Assisted MIMO Systems with Channel Aging
Authors: Nipuni Ginige, Arthur Sousa de Sena, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho
Date: 2024-11-21 00:00:00

Novel reconfigurable intelligent surface (RIS) architectures, known as beyond diagonal RISs (BD-RISs), have been proposed to enhance reflection efficiency and expand RIS capabilities. However, their passive nature, non-diagonal reflection matrix, and the large number of coupled reflecting elements complicate the channel state information (CSI) estimation process. The challenge further escalates in scenarios with fast-varying channels. In this paper, we address this challenge by proposing novel joint channel estimation and prediction strategies with low overhead and high accuracy for two different RIS architectures in a BD-RIS-assisted multiple-input multiple-output system under correlated fast-fading environments with channel aging. The channel estimation procedure utilizes the Tucker2 decomposition with bilinear alternative least squares, which is exploited to decompose the cascade channels of the BD-RIS-assisted system into effective channels of reduced dimension. The channel prediction framework is based on a convolutional neural network combined with an autoregressive predictor. The estimated/predicted CSI is then utilized to optimize the RIS phase shifts aiming at the maximization of the downlink sum rate. Insightful simulation results demonstrate that our proposed approach is robust to channel aging, and exhibits a high estimation accuracy. Moreover, our scheme can deliver a high average downlink sum rate, outperforming other state-of-the-art channel estimation methods. The results also reveal a remarkable reduction in pilot overhead of up to 98\% compared to baseline schemes, all imposing low computational complexity.
Reaction-conditioned De Novo Enzyme Design with GENzyme
Authors: Chenqing Hua, Jiarui Lu, Yong liu, Odin Zhang, Jian Tang, Rex Ying, Wengong Jin, Guy Wolf, Doina Precup, Shuangjia Zheng
Date: 2024-11-10 00:00:00

The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interaction prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.
Energy Consumption Optimization, Response Time Differences and Indicators in Cortical Working Memory Revealed by Nonequilibrium
Authors: Xiaochen Wang, Yuxuan Wu, Feng Zhang, Jin Wang
Date: 2024-11-26 00:00:00

The neocortex, a complex system driving multi-region interactions, remains a core puzzle in neuroscience. Despite quantitative insights across brain scales, understanding the mechanisms underlying neural activities is challenging. Advances from Hopfield networks to large-scale cortical models have deepened neural network theory, yet these models often fall short of capturing global brain functions. In large-scale cortical networks, an intriguing hierarchy of timescales reflects diverse information processing speeds across spatial regions. As a non-equilibrium system, the brain incurs significant energy costs, with long-distance connectivity suggesting an evolutionary spatial organization. To explore these complexities, we introduce a nonequilibrium landscape flux approach to analyze cortical networks. This allows us to quantify potential landscapes and principal transition paths, uncovering dynamical characteristics across timescales. We examine whether temporal hierarchies correlate with stimuli distribution and how hierarchical networks exhibit differential responses. Furthermore, our analysis quantifies the thermodynamic cost of sustaining cognition, highlighting a link to network connectivity. These findings provide insights into energy consumption during cognitive processes and emphasize the spatial benefits for working memory tasks. Experimental validation is challenging due to evolutionary variability, making our theoretical approach valuable for quantifying complex dynamics. By assessing time irreversibility and critical slowdown, we gain predictive insights into network bifurcations and state transitions, offering practical tools for identifying cortical state changes. These results advance our understanding of cortical dynamics.
Assessing microbiome engraftment extent following fecal microbiota transplant with q2-fmt
Authors: Chloe Herman, Evan Bolyen, Anthony Simard, Liz Gehret, J. Gregory Caporaso
Date: 2024-11-26 00:00:00

We present q2-fmt, a QIIME 2 plugin that provides diverse methods for assessing the extent of microbiome engraftment following fecal microbiota transplant. The methods implemented here were informed by a recent literature review on approaches for assessing FMT engraftment, and cover aspects of engraftment including Chimeric Asymmetric Community Coalescence, Donated Microbiome Indicator Features, and Temporal Stability. q2-fmt is free for all use, and detailed documentation illustrating worked examples on a real-world data set are provided in the project's documentation.
Determinants of Longevity amongst SSNIT Pensioners in Ghana
Authors: B. Gyamfi
Date: 2024-11-25 00:00:00

This paper statistically analysed pensioner longevity in Ghana. It fundamentally sought to ascertain the significant determinants of longevity amongst Ghanaian pensioners, specifically, SSNIT pensioners by estimating the mortality rate of SSNIT pensioners, determining the factors that significantly affect the longevity of the average SSNIT pensioner and constructing a predictive model for predicting the longevity of SSNIT pensioners in Ghana. Secondary data was obtained from the leading pension provider in Ghana, SSNIT. The results of the study revealed that the total number of male deaths was significantly greater, about four times more, than the total number of female deaths. There was sufficient evidence that there exists a lower rate of death among female pensioners as compared to male pensioners. Furthermore, a significant number of the SSNIT pensioners used in the analysis survived less than 8 years after retirement before their death and the average basic salary of the pensioners who lived less than 8 years after retirement was GH 7741.827 cedis and served for 35.65 years on average. On the contrary, it was observed that pensioners who lived more than 8 years after retirement had an average basic salary of GH 5544.20 cedis and served for 31.49 years on average. In conclusion, predictors such as basic salary, the number of years of service in the workforce, the age of the pensioner before death and the gender of the pensioner were statistically significant in predicting the number of years a SSNIT pensioner survived after retirement before death that is, whether a particular SSNIT pensioner survived less than 8 years or 8 years and above after retirement. The authors recommend to employees that, their health should be of great priority as every increase in the service year increases the likelihood of a SSNIT pensioner surviving less than 8 years after retirement by 22.36%.
Critically assessing atavism, an evolution-centered and deterministic hypothesis on cancer
Authors: Bertrand Daignan-Fornier, Thomas Pradeu
Date: 2024-11-26 00:00:00

Cancer is most commonly viewed as resulting from somatic mutations enhancing proliferation and invasion. Some hypotheses further propose that these new capacities reveal a breakdown of multicellularity allowing cancer cells to escape proliferation and cooperation control mechanisms that were implemented during evolution of multicellularity. Here we critically review one such hypothesis, named ``atavism,'' which puts forward the idea that cancer results from the re-expression of normally repressed genes forming a program, or toolbox, inherited from unicellular or simple multicellular ancestors. This hypothesis places cancer in an interesting evolutionary perspective that has not been widely explored and deserves attention. Thinking about cancer within an evolutionary framework, especially the major transitions to multicellularity, offers particularly promising perspectives. It is therefore of the utmost important to analyze why one approach that tries to achieve this aim, the atavism hypothesis, has not so far emerged as a major theory on cancer. We outline the features of the atavism hypothesis that, would benefit from clarification and, if possible, unification.
Understanding Immune Dynamics in Liver Transplant Through Mathematical Modeling
Authors: Julia Bruner, Kyle Adams, Skylar Grey, Mahya Aghaee, Sergio Duarte, Ali Zarrinpar, Helen Moore
Date: 2024-11-26 00:00:00

Liver transplant can be a life-saving procedure for patients with end-stage liver disease. With the introduction of modern immunosuppressive therapies, short-term survival has significantly improved. However, long-term survival has not substantially improved in decades. Consequently, causes of death are now more likely to be due to the toxicities and side-effects of long-term immunosuppression rather than rejection. In order to study the balance of immunosuppression and rejection, we developed the first mechanistic mathematical model of liver transplant and immune system dynamics. We determined key cells and interactions in the model using literature information; we then used sensitivity analysis to determine key pathways driving the health status of the transplanted liver. We found that dynamics related to cytotoxic T cells and IL-2, in addition to the liver itself, are key determinants of liver graft injury. This has significant implications for the use of tests to monitor patients, and therapeutic strategies to prevent or treat liver transplantation rejection. Future work to collect appropriate data and parametrize the model would be valuable in improving our understanding of the dynamics of this system. We also note that our model could be tailored to model transplant of other organs.
Studying self-organisation across the biosphere with process-enablement graphs
Authors: Emmy Brown, Sean T. Vittadello
Date: 2024-11-26 00:00:00

At the heart of many contemporary theories of life is the concept of biological self-organisation: organisms have to continuously produce and maintain the conditions of their own existence in order to stay alive. The way in which these accounts articulate this concept, however, differs quite significantly. As a result, it can be difficult to identify self-organising features within biological systems, and to compare different descriptions of such features. In this paper, we develop a graph theoretic formalism -- process-enablement graphs -- to study the organisational structure of living systems. Cycles within these graphs capture self-organising components of a system in a general and abstract way. We build the mathematical tools needed to compare biological models as process-enablement graphs, facilitating a comparison of their corresponding descriptions of self-organisation in a consistent and precise manner. We apply our formalism to a range of classical theories of life and demonstrate exactly how these models are similar, and where they differ, with respect to their organisational structure. While our current framework does not demarcate living systems from non-living ones, it does allow us to better study systems that lie in the grey area between life and non-life.
Semiclassical CEV Option Pricing Model: an Analytical Approach
Authors: Jose A. Capitán, Jose Lope-Alba, Juan J. Morales-Ruiz
Date: 2024-11-27 00:00:00
Tasks: model

This paper is devoted to obtain closed form solutions for the semiclassical (or WKB) approximation of the heat kernel propagator of the diffusion equation defined by the constant elasticity variance (CEV) option pricing model. One of the key points is that our calculations are based on the Van Vleck-Morette determinant instead of the Van Vleck determinant used by other authors. In fact, we compute this determinant in two different ways: by means of the solution of the classical Hamiltonian equations, and by solving the variational equations. Furthermore, the calculation reveals an exponential factor in the prefactor of the kernel not considered in previous works.
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning
Authors: Yeachan Kim, Junho Kim, SangKeun Lee
Date: 2024-10-31 00:00:00
Tasks: parameter-efficient fine-tuning

Parameter-efficient fine-tuning (PEFT) has enabled the efficient optimization of cumbersome language models in real-world settings. However, as datasets in such environments often contain noisy labels that adversely affect performance, PEFT methods are inevitably exposed to noisy labels. Despite this challenge, the adaptability of PEFT to noisy environments remains underexplored. To bridge this gap, we investigate various PEFT methods under noisy labels. Interestingly, our findings reveal that PEFT has difficulty in memorizing noisy labels due to its inherently limited capacity, resulting in robustness. However, we also find that such limited capacity simultaneously makes PEFT more vulnerable to interference of noisy labels, impeding the learning of clean samples. To address this issue, we propose Clean Routing (CleaR), a novel routing-based PEFT approach that adaptively activates PEFT modules. In CleaR, PEFT modules are preferentially exposed to clean data while bypassing the noisy ones, thereby minimizing the noisy influence. To verify the efficacy of CleaR, we perform extensive experiments on diverse configurations of noisy labels. The results convincingly demonstrate that CleaR leads to substantially improved performance in noisy environments.
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention
Authors: Omar El-Khashab; Alaa Hamdy; Ayman Mahmoud
Date: 2023-10-20 00:00:00
Proceeding: Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International 2023 10
Tasks: Deep Learning, Emotion Classification, Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Marketing

Facial expression recognition has contributed significantly to various domains of life from healthcare and education to marketing and sales. This has led to extensive research in trying to improve recognition methods using deep learning. These methods aim to integrate advanced feature extraction techniques with enhanced classification accuracy. In this paper, a method for facial expression detection called (FerNeXt) was proposed which is based on the already existing ConvNeXt network architecture. An Efficient Channel Attention (ECA) block was introduced within the ConvNeXt architecture to enhance the model's attention to important channel features. Additionally, an Affinity loss function was incorporated to optimize class separability. The method was experimented on two large datasets: AffectNet and RAF-DB to show the proposed technique's capability. The results obtained show that the proposed model demonstrates good performance, consistently achieving higher accuracy and outperforming state of the art facial expression recognition approaches.