pwc-archive/papers-with-abstracts huggingface dataset:
RIS-Enhanced MIMO Channels in Urban Environments: Experimental Insights
Authors: James Rains, Anvar Tukmanov, Qammer Abbasi, Muhammad Imran
Date: 2023-11-28 00:00:00

Can the smart radio environment paradigm measurably enhance the performance of contemporary urban macrocells? In this study, we explore the impact of reconfigurable intelligent surfaces (RISs) on a real-world sub-6 GHz MIMO channel. A rooftop-mounted macrocell antenna has been adapted to enable frequency domain channel measurements to be ascertained. A nature-inspired beam search algorithm has been employed to maximize channel gain at user positions, revealing a potential 50% increase in channel capacity in certain circumstances. Analysis reveals, however, that the spatial characteristics of the channel can be adversely affected through the introduction of a RIS in these settings. The RIS prototype schematics, Gerber files, and source code have been made available to aid in future experimental efforts of the wireless research community.
A Novel 3D Non-stationary Localization-assisted ISAC Channel Model
Authors: Runruo Yang, Yang Wu, Jie Huang, Cheng-Xiang Wang
Date: 2023-11-28 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has attracted wide attention as an emerging application scenario for the sixth generation (6G) wireless communication system. In this paper, a novel three-dimensional (3D) non-stationary localization-assisted ISAC geometry-based stochastic model (GBSM) is proposed. The locations of the first-bounce scatterer and last-bounce scatterer in the communication channel can be estimated by the particle filter with the assistance of backscattering sensing. The important channel statistical properties of the proposed channel model are simulated and compared with the ray tracing (RT) results, including the delay spread, azimuth angle of departure/arrival (AAoD/AAoA) spread, and elevation angle of departure/arrival (EAoD/EAoA) spread. The simulation results of the proposed channel model show a good agreement with the RT results, which proves the correctness of the proposed channel model. Utilizing the localization parameters of scatterers, the proposed ISAC channel model can better map the real environment.
A General 3D Non-Stationary 5G Wireless Channel Model
Authors: Shangbin Wu, Cheng-Xiang Wang, el-Hadi M. Aggoune, Mohammed M. Alwakeel, Xiao-Hu You
Date: 2023-11-28 00:00:00

A novel unified framework of geometry-based stochastic models (GBSMs) for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output (MIMO), high-speed train (HST), vehicle-to-vehicle (V2V), and millimeter wave (mmWave) communication scenarios. It is a three-dimensional (3D) non-stationary channel model based on the WINNER II and Saleh-Valenzuela (SV) channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.
Energy Efficiency Optimization in Active Reconfigurable Intelligent Surface-Aided Integrated Sensing and Communication Systems
Authors: Junjie Ye, Mohamed Rihan, Peichang Zhang, Lei Huang, Stefano Buzzi, Zhen Chen
Date: 2023-11-28 00:00:00
Tasks: Integrated sensing and communication, ISAC

Energy efficiency (EE) is a challenging task in integrated sensing and communication (ISAC) systems, where high spectral efficiency and low energy consumption appear as conflicting requirements. Although passive reconfigurable intelligent surface (RIS) has emerged as a promising technology for enhancing the EE of the ISAC system, the multiplicative fading feature hinders its effectiveness. This paper proposes the use of active RIS with its amplification gains to assist the ISAC system for EE improvement. Specifically, we formulate an EE optimization problem in an active RIS-aided ISAC system under system power budgets, considering constraints on user communication quality of service and sensing signal-to-noise ratio (SNR). A novel alternating optimization algorithm is developed to address the highly non-convex problem by leveraging a combination of the generalized Rayleigh quotient optimization approach, semidefinite relaxation (SDR), and the majorization-minimization (MM) framework. Furthermore, to accelerate the algorithm and reduce computational complexity, we derive a semi-closed form for eigenvalue determination. Numerical results demonstrate the effectiveness of the proposed approach, showcasing significant improvements in EE compared to both passive RIS and spectrum efficiency optimization cases.
What Really is `Molecule' in Molecular Communications? The Quest for Physics of Particle-based Information Carriers
Authors: Hanlin Xiao, Kamela Dokaj, Ozgur B. Akan
Date: 2023-11-27 00:00:00

Molecular communication, as implied by its name, uses molecules as information carriers for communication between objects. It has an advantage over traditional electromagnetic-wave-based communication in that molecule-based systems could be biocompatible, operable in challenging environments, and energetically undemanding. Consequently, they are envisioned to have a broad range of applications, such as in the Internet of Bio-nano Things, targeted drug delivery, and agricultural monitoring. Despite the rapid development of the field, with an increasing number of theoretical models and experimental testbeds established by researchers, a fundamental aspect of the field has often been sidelined, namely, the nature of the molecule in molecular communication. The potential information molecules could exhibit a wide range of properties, making them require drastically different treatments when being modeled and experimented upon. Therefore, in this paper, we delve into the intricacies of commonly used information molecules, examining their fundamental physical characteristics, associated communication systems, and potential applications in a more realistic manner, focusing on the influence of their own properties. Through this comprehensive survey, we aim to offer a novel yet essential perspective on molecular communication, thereby bridging the current gap between theoretical research and real-world applications.
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
Authors: Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, Serguei Barannikov, Irina Piontkovskaya, Sergey Nikolenko, Evgeny Burnaev
Date: 2023-06-07 00:00:00
Proceeding: NeurIPS 2023 11

Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly important to study the properties of human texts that are invariant over different text domains and varying proficiency of human writers, can be easily calculated for any language, and can robustly separate natural and AI-generated texts regardless of the generation model and sampling method. In this work, we propose such an invariant for human-written texts, namely the intrinsic dimensionality of the manifold underlying the set of embeddings for a given text sample. We show that the average intrinsic dimensionality of fluent texts in a natural language is hovering around the value $9$ for several alphabet-based languages and around $7$ for Chinese, while the average intrinsic dimensionality of AI-generated texts for each language is $\approx 1.5$ lower, with a clear statistical separation between human-generated and AI-generated distributions. This property allows us to build a score-based artificial text detector. The proposed detector's accuracy is stable over text domains, generator models, and human writer proficiency levels, outperforming SOTA detectors in model-agnostic and cross-domain scenarios by a significant margin.
Curvature Filtrations for Graph Generative Model Evaluation
Authors: Joshua Southern, Jeremy Wayland, Michael Bronstein, Bastian Rieck
Date: 2023-01-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: model, Topological Data Analysis

Graph generative model evaluation necessitates understanding differences between graphs on the distributional level. This entails being able to harness salient attributes of graphs in an efficient manner. Curvature constitutes one such property that has recently proved its utility in characterising graphs. Its expressive properties, stability, and practical utility in model evaluation remain largely unexplored, however. We combine graph curvature descriptors with emerging methods from topological data analysis to obtain robust, expressive descriptors for evaluating graph generative models.
Algorithmic approach for an unique definition of the next generation matrix
Authors: Florin Avram, Rim Adenane, Lasko Basnarkov, Matthew Johnston
Date: 2023-11-28 00:00:00
Tasks: Epidemiology

The basic reproduction number R0 is a concept which originated in population dynamics, mathematical epidemiology, and ecology and is closely related to the mean number of children in branching processes.We offer below three new contributions to the literature: 1) We order a universal algorithmic definition of a (F, V) gradient decomposition (and hence of the resulting R0), which requires a minimal input from the user, namely the specification of an admissible set of disease/infection variables. We also present examples where other choices may be more reasonable, with more terms in F, or more terms in V . 2) We glean out from the works of Bacaer a fixed point equation (8) for the extinction probabilities of a stochastic model associated to a deterministic ODE model, which may be expressed in terms of the (F, V ) decomposition. The fact that both R0 and the extinction probabilities are functions of (F, V ) underlines the centrality of this pair, which may be viewed as more fundamental than the famous next generation matrix FV^{-1}. 3) We suggest introducing a new concept of sufficient/minimal disease/infection set (sufficient for determining R0). More precisely, our universal recipe of choosing "new infections" once the "infections" are specified suggests focusing on the choice of the latter, which is also not unique. The maximal choice of choosing all compartments which become 0 at the given boundary point seems to always work, but is the least useful for analytic computations, therefore we propose to investigate the minimal one. As a bonus, this idea seems useful for understanding the Jacobian factorization approach for computing R0 . Last but not least, we offer Mathematica scripts and implement them for a large variety of examples, which illustrate that our recipe others always reasonable results, but that sometimes other reasonable (F, V ) decompositions are available as well.
GeoTop: Advancing Image Classification with Geometric-Topological Analysis
Authors: Mariem Abaach, Ian Morilla
Date: 2023-11-08 00:00:00
Tasks: Classification, image-classification, Image Classification, Topological Data Analysis

In this study, we explore the application of Topological Data Analysis (TDA) and Lipschitz-Killing Curvatures (LKCs) as powerful tools for feature extraction and classification in the context of biomedical multiomics problems. TDA allows us to capture topological features and patterns within complex datasets, while LKCs provide essential geometric insights. We investigate the potential of combining both methods to improve classification accuracy. Using a dataset of biomedical images, we demonstrate that TDA and LKCs can effectively extract topological and geometrical features, respectively. The combination of these features results in enhanced classification performance compared to using each method individually. This approach offers promising results and has the potential to advance our understanding of complex biological processes in various biomedical applications. Our findings highlight the value of integrating topological and geometrical information in biomedical data analysis. As we continue to delve into the intricacies of multiomics problems, the fusion of these insights holds great promise for unraveling the underlying biological complexities.
From Byte to Bench to Bedside: Molecular Dynamics Simulations and Drug Discovery
Authors: Mayar Ahmed, Alex M. Maldonado, Jacob D. Durrant
Date: 2023-11-28 00:00:00
Tasks: Drug Design, Drug Discovery

Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware and software improvements. Given these advancements, MD simulations are poised to become even more powerful tools for investigating the dynamic interactions between potential small-molecule drugs and their target proteins, with significant implications for pharmacological research.
Protein-ligand binding representation learning from fine-grained interactions
Authors: Shikun Feng, Minghao Li, Yinjun Jia, WeiYing Ma, Yanyan Lan
Date: 2023-11-09 00:00:00
Tasks: Drug Discovery, Prediction, Protein-Ligand Affinity Prediction, Representation Learning, Self-Supervised Learning

The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional computationally intensive methods, but resulting in poor generalization due to limited supervised data. In this paper, we propose to learn protein-ligand binding representation in a self-supervised learning manner. Different from existing pre-training approaches which treat proteins and ligands individually, we emphasize to discern the intricate binding patterns from fine-grained interactions. Specifically, this self-supervised learning problem is formulated as a prediction of the conclusive binding complex structure given a pocket and ligand with a Transformer based interaction module, which naturally emulates the binding process. To ensure the representation of rich binding information, we introduce two pre-training tasks, i.e.~atomic pairwise distance map prediction and mask ligand reconstruction, which comprehensively model the fine-grained interactions from both structure and feature space. Extensive experiments have demonstrated the superiority of our method across various binding tasks, including protein-ligand affinity prediction, virtual screening and protein-ligand docking.
A novel RNA pseudouridine site prediction model using Utility Kernel and data-driven parameters
Authors: Sourabh Patil, Archana Mathur, Raviprasad Aduri, Snehanshu Saha
Date: 2023-11-02 00:00:00

RNA protein Interactions (RPIs) play an important role in biological systems. Recently, we have enumerated the RPIs at the residue level and have elucidated the minimum structural unit (MSU) in these interactions to be a stretch of five residues (Nucleotides/amino acids). Pseudouridine is the most frequent modification in RNA. The conversion of uridine to pseudouridine involves interactions between pseudouridine synthase and RNA. The existing models to predict the pseudouridine sites in a given RNA sequence mainly depend on user-defined features such as mono and dinucleotide composition/propensities of RNA sequences. Predicting pseudouridine sites is a non-linear classification problem with limited data points. Deep Learning models are efficient discriminators when the data set size is reasonably large and fail when there is a paucity of data ($<1000$ samples). To mitigate this problem, we propose a Support Vector Machine (SVM) Kernel based on utility theory from Economics, and using data-driven parameters (i.e. MSU) as features. For this purpose, we have used position-specific tri/quad/pentanucleotide composition/propensity (PSPC/PSPP) besides nucleotide and dineculeotide composition as features. SVMs are known to work well in small data regimes and kernels in SVM are designed to classify non-linear data. The proposed model outperforms the existing state-of-the-art models significantly (10%-15% on average).
FABind: Fast and Accurate Protein-Ligand Binding
Authors: Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan
Date: 2023-10-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Blind Docking, Drug Discovery, Pose Estimation, regression

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose $\mathbf{FABind}$, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. $\mathbf{FABind}$ incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed $\mathbf{FABind}$ demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at https://github.com/QizhiPei/FABind
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths
Authors: Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling
Date: 2022-06-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Dimensionality Reduction

We consider the problem of sampling transition paths between two given metastable states of a molecular system, e.g. a folded and unfolded protein or products and reactants of a chemical reaction. Due to the existence of high energy barriers separating the states, these transition paths are unlikely to be sampled with standard Molecular Dynamics (MD) simulation. Traditional methods to augment MD with a bias potential to increase the probability of the transition rely on a dimensionality reduction step based on Collective Variables (CVs). Unfortunately, selecting appropriate CVs requires chemical intuition and traditional methods are therefore not always applicable to larger systems. Additionally, when incorrect CVs are used, the bias potential might not be minimal and bias the system along dimensions irrelevant to the transition. Showing a formal relation between the problem of sampling molecular transition paths, the Schr\"odinger bridge problem and stochastic optimal control with neural network policies, we propose a machine learning method for sampling said transitions. Unlike previous non-machine learning approaches our method, named PIPS, does not depend on CVs. We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Authors: Tianyu Liu, Yuge Wang, Rex Ying, Hongyu Zhao
Date: 2023-09-29 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Benchmarking, Contrastive Learning, Graph Neural Network, Representation Learning

Discovering genes with similar functions across diverse biomedical contexts poses a significant challenge in gene representation learning due to data heterogeneity. In this study, we resolve this problem by introducing a novel model called Multimodal Similarity Learning Graph Neural Network, which combines Multimodal Machine Learning and Deep Graph Neural Networks to learn gene representations from single-cell sequencing and spatial transcriptomic data. Leveraging 82 training datasets from 10 tissues, three sequencing techniques, and three species, we create informative graph structures for model training and gene representations generation, while incorporating regularization with weighted similarity learning and contrastive learning to learn cross-data gene-gene relationships. This novel design ensures that we can offer gene representations containing functional similarity across different contexts in a joint space. Comprehensive benchmarking analysis shows our model's capacity to effectively capture gene function similarity across multiple modalities, outperforming state-of-the-art methods in gene representation learning by up to 97.5%. Moreover, we employ bioinformatics tools in conjunction with gene representations to uncover pathway enrichment, regulation causal networks, and functions of disease-associated or dosage-sensitive genes. Therefore, our model efficiently produces unified gene representations for the analysis of gene functions, tissue functions, diseases, and species evolution.
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Authors: Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang, Guy Wolf, Ian Adelstein, Smita Krishnaswamy
Date: 2023-05-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Denoising, Dimensionality Reduction, Representation Learning

Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).