pwc-archive/papers-with-abstracts huggingface dataset:
Frames and vertex-frequency representations in graph fractional Fourier domain
Authors: Linbo Shang, Zhichao Zhang
Date: 2024-12-28 00:00:00
Tasks: Anomaly Detection, Computational Efficiency

Vertex-frequency analysis, particularly the windowed graph Fourier transform (WGFT), is a significant challenge in graph signal processing. Tight frame theories is known for its low computational complexity in signal reconstruction, while fractional order methods shine at unveil more detailed structural characteristics of graph signals. In the graph fractional Fourier domain, we introduce multi-windowed graph fractional Fourier frames (MWGFRFF) to facilitate the construction of tight frames. This leads to developing the multi-windowed graph fractional Fourier transform (MWGFRFT), enabling novel vertex-frequency analysis methods. A reconstruction formula is derived, along with results concerning dual and tight frames. To enhance computational efficiency, a fast MWGFRFT (FMWGFRFT) algorithm is proposed. Furthermore, we define shift multi-windowed graph fractional Fourier frames (SMWGFRFF) and their associated transform (SMWGFRFT), exploring their dual and tight frames. Experimental results indicate that FMWGFRFT and SMWGFRFT excel in extracting vertex-frequency features in the graph fractional Fourier domain, with their combined use optimizing analytical performance. Applications in signal anomaly detection demonstrate the advantages of FMWGFRFT.
Amplitude-to-Phase Conversion in Injection-Locked CMOS Ring Oscillators
Authors: Zhaowen Wang
Date: 2024-12-28 00:00:00

Injection-locked ring oscillators (ILROs) are extensively employed for multi-phase clock generation in wireline and optical links. However, existing injection-locking theorems primarily rely on linearized phase-domain or nonlinear time-domain models, which fail to account for amplitude-to-phase conversion effects inherent in ILROs. This paper introduces an enhanced analytical model based on an extension of Adler's equation, explicitly incorporating amplitude-to-phase conversion. Simulation results demonstrate strong alignment with the proposed analytical predictions, validating the model's accuracy in capturing the locking range and phasor relationships.
On Random Sampling of Diffused Graph Signals with Sparse Inputs on Vertex Domain
Authors: YingCheng Lai, Li Chai, Jinming Xu
Date: 2024-12-28 00:00:00

The sampling of graph signals has recently drawn much attention due to the wide applications of graph signal processing. While a lot of efficient methods and interesting results have been reported to the sampling of band-limited or smooth graph signals, few research has been devoted to non-smooth graph signals, especially to sparse graph signals, which are also of importance in many practical applications. This paper addresses the random sampling of non-smooth graph signals generated by diffusion of sparse inputs. We aim to present a solid theoretical analysis on the random sampling of diffused sparse graph signals, which can be parallel to that of band-limited graph signals, and thus present a sufficient condition to the number of samples ensuring the unique recovery for uniform random sampling. Then, we focus on two classes of widely used binary graph models, and give explicit and tighter estimations on the sampling numbers ensuring unique recovery. We also propose an adaptive variable-density sampling strategy to provide a better performance than uniform random sampling. Finally, simulation experiments are presented to validate the effectiveness of the theoretical results.
Exploiting Movable-Element STARS for Wireless Communications
Authors: Jingjing Zhao, Quan Zhou, Xidong Mu, Kaiquan Cai, YanBo Zhu, Yuanwei Liu
Date: 2024-12-28 00:00:00
Tasks: Position

A novel movable-element enabled simultaneously transmitting and reflecting surface (ME-STARS) communication system is proposed, where ME-STARS elements positions can be adjusted to enhance the degress-of-freedom for transmission and reflection. For each ME-STARS operating protocols, namely energy-splitting (ES), mode switching (MS), and time switching (TS), a weighted sum rate (WSR) maximization problem is formulated to jointly optimize the active beamforming at the base station (BS) as well as the elements positions and passive beamforming at the ME-STARS. An alternative optimization (AO)-based iterative algorithm is developed to decompose the original non-convex problem into three subproblems. Specifically, the gradient descent algorithm is employed for solving the ME-STARS element position optimization subproblem, and the weighted minimum mean square error and the successive convex approximation methods are invoked for solving the active and passive beamforming subproblems, respectively. It is further demonstrated that the proposed AO algorithm for ES can be extended to solve the problems for MS and TS. Numerical results unveil that: 1) the ME-STARS can significantly improve the WSR compared to the STARS with fixed position elements and the conventional reconfigurable intelligent surface with movable elements, thanks to the extra spatial-domain diversity and the higher flexibility in beamforming; and 2) the performance gain of ME-STARS is significant in the scenarios with larger number of users or more scatterers.
Fusion of Deep Learning and GIS for Advanced Remote Sensing Image Analysis
Authors: Sajjad Afroosheh, Mohammadreza Askari
Date: 2024-12-25 00:00:00

This paper presents an innovative framework for remote sensing image analysis by fusing deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, with Geographic Information Systems (GIS). The primary objective is to enhance the accuracy and efficiency of spatial data analysis by overcoming challenges associated with high dimensionality, complex patterns, and temporal data processing. We implemented optimization algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), to fine-tune model parameters, resulting in improved performance metrics. Our findings reveal a significant increase in classification accuracy from 78% to 92% and a reduction in prediction error from 12% to 6% after optimization. Additionally, the temporal accuracy of the models improved from 75% to 88%, showcasing the frameworks capability to monitor dynamic changes effectively. The integration of GIS not only enriched the spatial analysis but also facilitated a deeper understanding of the relationships between geographical features. This research demonstrates that combining advanced deep learning methods with GIS and optimization strategies can significantly advance remote sensing applications, paving the way for future developments in environmental monitoring, urban planning, and resource management.
Revealing the Shape of Genome Space via K-mer Topology
Authors: Yuta Hozumi, Guo-Wei Wei
Date: 2024-12-28 00:00:00
Tasks: Diversity

Despite decades of effort, understanding the shape of genome space in biology remains a challenge due to the similarity, variability, diversity, and plasticity of evolutionary relationships among species, genes, or other biological entities. We present a k-mer topology method, the first of its kind, to delineate the shape of the genome space. K-mer topology examines the topological persistence and the evolution of the homotopic shape of the sequences of k nucleotides in species, organisms, and genes using persistent Laplacians, a new multiscale combinatorial approach. We also propose a topological genetic distance between species by their topological invariants and non-harmonic spectra over scales. This new metric defines the topological phylogenetic trees of genomes, facilitating species classification and clustering. K-mer topology substantially outperforms state-of-the-art methods on a variety of benchmark datasets, including mammalian mitochondrial genomes, Rhinovirus, SARS-CoV-2 variants, Ebola virus, Hepatitis E virus, Influenza hemagglutinin genes, and whole bacterial genomes. K-mer topology reveals the intrinsic shapes of the genome space and can be directly applied to the rational design of viral vaccines.
Validation of Subject-Specific Knee Models from In Vivo Measurements
Authors: Thor E. Andreassen, Donald R. Hume, Landon D. Hamilton, Stormy L. Hegg, Sean E. Higinbotham, Kevin B. Shelburne
Date: 2024-12-27 00:00:00
Tasks: Computed Tomography (CT)

Calibration to experimental data is vital when developing subject-specific models towards developing digital twins. Yet, to date, subject-specific models are largely based on cadaveric testing, as in vivo data to calibrate against has been difficult to obtain until recently. To support our overall goal of building subject-specific models of the living knee, we aimed to show that subject-specific computational models built and calibrated using in vivo measurements would have accuracy comparable to models built using in vitro measurements. Two knee specimens were imaged using a combination of computed tomography (CT), and surface scans. Knee laxity measurements were made with a custom apparatus used for the living knee and from a robotic knee simulator. Models of the knees were built using the CT geometry and surface scans, and then calibrated with either laxity data from the robotic knee simulator or from the knee laxity apparatus. Model performance was compared by simulation of passive flexion, knee laxity and a clinically relevant pivot shift. Performance was similar with differences during simulated anterior-posterior laxity tests of less than 2.5 mm. Additionally, model predictions of a pivot shift were similar with differences less than 3 deg or 3 mm for rotations and translations, respectively. Still, differences in the predicted ligament loads and calibrated material properties emerged, highlighting a need for methods to include ligament load as part of the underlying calibration process. Overall, the results showed that currently available methods of measuring knee laxity in vivo are sufficient to calibrate models comparable with existing in vitro techniques, and the workflows described here may provide a basis for modeling the living knee. The models, data, and code are publicly available.
Rough differential equations for volatility
Authors: Ofelia Bonesini, Emilio Ferrucci, Ioannis Gasteratos, Antoine Jacquier
Date: 2024-12-30 00:00:00

We introduce a canonical way of performing the joint lift of a Brownian motion $W$ and a low-regularity adapted stochastic rough path $\mathbf{X}$, extending [Diehl, Oberhauser and Riedel (2015). A L\'evy area between Brownian motion and rough paths with applications to robust nonlinear filtering and rough partial differential equations]. Applying this construction to the case where $\mathbf{X}$ is the canonical lift of a one-dimensional fractional Brownian motion (possibly correlated with $W$) completes the partial rough path of [Fukasawa and Takano (2024). A partial rough path space for rough volatility]. We use this to model rough volatility with the versatile toolkit of rough differential equations (RDEs), namely by taking the price and volatility processes to be the solution to a single RDE. We argue that our framework is already interesting when $W$ and $X$ are independent, as correlation between the price and volatility can be introduced in the dynamics. The lead-lag scheme of [Flint, Hambly, and Lyons (2016). Discretely sampled signals and the rough Hoff process] is extended to our fractional setting as an approximation theory for the rough path in the correlated case. Continuity of the solution map transforms this into a numerical scheme for RDEs. We numerically test this framework and use it to calibrate a simple new rough volatility model to market data.
Tractable fish growth models considering individual differences with an application to the fish Plecoglossus altivelis altivelis
Authors: H. Yoshioka, Y. Yoshioka, M. Tsujimura
Date: 2024-12-28 00:00:00

Modeling fish growth is an important research topic in ecological and fishery sciences because body weight statistics directly affect the total biomass of fish in a habitat, which in turn affects their population dynamics. Many models of fish growth assume that the fish population in a habitat is homogenous, meaning that there is no physiological spectrum and, therefore, no size spectrum. Moreover, models that account for the size spectrum are not always analytically tractable. We present novel mathematical models of fish growth in which the body weight of each fish is assumed to follow a von Bertalanffy-type model whose proportionality coefficient, representing the maximum body weight, may differ among individual fish. This probabilistic description introduces the size spectrum into the model, owing to which the time-dependent probability density of this model is obtained explicitly. We also consider a misspecified version and a stochastic version of the model as advanced cases. We apply the first model to the real growth data of Plecoglossus altivelis altivelis as a keystone fish species in Japan. The model successfully reproduces the skewed size spectrum of this fish species over multiple years. We further use the stochastic model to investigate how fish growth dynamics are affected by environmental fluctuations.
Molecular Communication-Based Quorum Sensing Disruption for Enhanced Immune Defense
Authors: Shees Zulfiqar, Ozgur B. Akan
Date: 2024-12-29 00:00:00

Molecular Communication (MC) utilizes chemical molecules to transmit information, introducing innovative strategies for pharmaceutical interventions and enhanced immune system monitoring. This paper explores Molecular communication based approach to disrupt Quorum Sensing (QS) pathways to bolster immune defenses against antimicrobial-resistant bacteria. Quorum Sensing enables bacteria to coordinate critical behaviors, including virulence and antibiotic resistance, by exchanging chemical signals, known as autoinducers. By interfering with this bacterial communication, we can disrupt the synchronization of activities that promote infection and resistance. The study focuses on RNAIII inhibiting peptide (RIP), which blocks the production of critical transcripts, RNAII and RNAIII, within the Accessory Gene Regulator (AGR) system, thereby weakening bacterial virulence and enhancing host immune responses. The synergistic effects of combining QS inhibitors like RIP with traditional antimicrobial treatments reduce the need for highdose antibiotics, offering a potential solution to antibiotic resistance. This molecular communication-based approach presents a promising path to improved treatment efficacy and more robust immune responses against bacterial infections by targeting bacterial communication.
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks
Authors: Regina Ibragimova, Dimitrios Iliadis, Willem Waegeman
Date: 2024-12-11 00:00:00
Tasks: Drug Discovery, Prediction, Transfer Learning

Recently, machine learning (ML) has gained popularity in the early stages of drug discovery. This trend is unsurprising given the increasing volume of relevant experimental data and the continuous improvement of ML algorithms. However, conventional models, which rely on the principle of molecular similarity, often fail to capture the complexities of chemical interactions, particularly those involving activity cliffs (ACs) - compounds that are structurally similar but exhibit evidently different activity behaviors. In this work, we address two distinct yet related tasks: (1) activity cliff (AC) prediction and (2) drug-target interaction (DTI) prediction. Leveraging insights gained from the AC prediction task, we aim to improve the performance of DTI prediction through transfer learning. A universal model was developed for AC prediction, capable of identifying activity cliffs across diverse targets. Insights from this model were then incorporated into DTI prediction, enabling better handling of challenging cases involving ACs while maintaining similar overall performance. This approach establishes a strong foundation for integrating AC awareness into predictive models for drug discovery. Scientific Contribution This study presents a novel approach that applies transfer learning from AC prediction to enhance DTI prediction, addressing limitations of traditional similarity-based models. By introducing AC-awareness, we improve DTI model performance in structurally complex regions, demonstrating the benefits of integrating compound-specific and protein-contextual information. Unlike previous studies, which treat AC and DTI predictions as separate problems, this work establishes a unified framework to address both data scarcity and prediction challenges in drug discovery.
Pharmacophore-guided de novo drug design with diffusion bridge
Authors: Conghao Wang, Jagath C. Rajapakse
Date: 2024-12-10 00:00:00
Tasks: Drug Design, Drug Discovery

De novo design of bioactive drug molecules with potential to treat desired biological targets is a profound task in the drug discovery process. Existing approaches tend to leverage the pocket structure of the target protein to condition the molecule generation. However, even the pocket area of the target protein may contain redundant information since not all atoms in the pocket is responsible for the interaction with the ligand. In this work, we propose PharmacoBridge, a phamacophore-guided de novo design approach to generate drug candidates inducing desired bioactivity via diffusion bridge. Our method adapts the diffusion bridge to effectively convert pharmacophore arrangements in the spatial space into molecular structures under the manner of SE(3)-equivariant transformation, providing sophisticated control over optimal biochemical feature arrangements on the generated molecules. PharmacoBridge is demonstrated to generate hit candidates that exhibit high binding affinity with potential protein targets.
Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
Authors: Shriprakash Sinha
Date: 2024-12-29 00:00:00

Achaete-scute complex homolog 2 (ASCL2) codes a part of the basic helix-loop-helix (BHLH) transcription factor family. WNTs have been found to directly affect the stemness of the tumor cells via regulation of ASCL2. Switching off the ASCL2 literally blocks the stemness process of the tumor cells and vice versa. In colorectal cancer (CRC) cells treated with ETC-1922159, ASCL2 was found to be down regulated along with other genes. A recently developed search engine ranked combinations of ASCL2-X (X, a particular gene/protein) at 2nd order level after drug administration. Some rankings confirm the already tested combinations, while others point to those that are untested/unexplored. These rankings reveal which ASCL2-X combinations might be working synergistically in CRC. In this research work, I cover combinations of ASCL2 with WNT, transforming growth factor beta (TGFB), interleukin (IL), leucine rich repeat containing G protein-coupled receptor (LGR), NOTCH, solute carrier family (SLC), SRY-box transcription factor (SOX), small nucleolar RNA host gene (SNHG), KIAA, F-box protein (FBXO), family with sequence similarity (FAM), B cell CLL/lymphoma (BCL), autophagy related (ATG) and Rho GTPase activating protein (ARHGAP) family.
Intrinsic meaning, perception, and matching
Authors: William G. P. Mayner, Bjørn Erik Juel, Giulio Tononi
Date: 2024-12-30 00:00:00
Tasks: Diversity

Integrated information theory (IIT) argues that the substrate of consciousness is a maximally irreducible complex of units. Together, subsets of the complex specify a cause-effect structure, composed of distinctions and their relations, which accounts in full for the quality of experience. The feeling of a specific experience is also its meaning for the subject, which is thus defined intrinsically, regardless of whether the experience occurs in a dream or is triggered by processes in the environment. Here we extend IIT's framework to characterize the relationship between intrinsic meaning, extrinsic stimuli, and causal processes in the environment, illustrated using a simple model of a sensory hierarchy. We argue that perception should be considered as a structured interpretation, where a stimulus from the environment acts merely as a trigger for the complex's state and the structure is provided by the complex's intrinsic connectivity. We also propose that perceptual differentiation - the richness and diversity of structures triggered by representative sequences of stimuli - quantifies the meaningfulness of different environments to a complex. In adaptive systems, this reflects the "matching" between intrinsic meanings and causal processes in an environment.
Neurophenomenal Structuralism and the Role of Computational Context
Authors: Marlo Paßler, Adrien Doerig
Date: 2024-12-30 00:00:00

Neurophenomenal structuralism posits that conscious experiences are defined relationally and that their phenomenal structures are mirrored by neural structures. While this approach offers a promising framework for identifying neural correlates of contents of consciousness (NCCCs), we argue that merely establishing structural correspondences between neural and phenomenal structures is insufficient. This paper emphasizes the critical role of computational context in determining the content of neural structures. We introduce four criteria - Sensitivity, Organization, Exploitation, and Contextualization - to evaluate which neural structures are viable NCCC candidates. These criteria highlight that, for neural structures to meaningfully mirror phenomenal structures they have to be actively exploited and be able to influence behavior in a structure-preserving way. Our analysis demonstrates that anatomical and causal neural structures fail to meet certain criteria, whereas activation structures can, provided they are embedded within the appropriate computational context. Our findings challenge both local and rich global structuralist theories for overlooking the content-constituting role of computational context, leading to proposed NCCCs that fail to fully account for conscious content. We conclude that incorporating computational context is essential for any structuralist account of consciousness, as it determines the nature of dimensions within neural activation spaces and, consequently, the content of conscious experiences.