pwc-archive/papers-with-abstracts huggingface dataset:
PV-VLM: A Multimodal Vision-Language Approach Incorporating Sky Images for Intra-Hour Photovoltaic Power Forecasting
Authors: Huapeng Lin, Miao Yu
Date: 2025-04-18 00:00:00
Tasks: energy management, Language Modeling, Language Modelling, Large Language Model, Scheduling

The rapid proliferation of solar energy has significantly expedited the integration of photovoltaic (PV) systems into contemporary power grids. Considering that the cloud dynamics frequently induce rapid fluctuations in solar irradiance, accurate intra-hour forecasting is critical for ensuring grid stability and facilitating effective energy management. To leverage complementary temporal, textual, and visual information, this paper has proposed PV-VLM, a multimodal forecasting framework that integrates temporal, textual, and visual information by three modules. The Time-Aware Module employed a PatchTST-inspired Transformer to capture both local and global dependencies in PV power time series. Meanwhile, the Prompt-Aware Module encodes textual prompts from historical statistics and dataset descriptors via a large language model. Additionally, the Vision-Aware Module utilizes a pretrained vision-language model to extract high-level semantic features from sky images, emphasizing cloud motion and irradiance fluctuations. The proposed PV-VLM is evaluated using data from a 30-kW rooftop array at Stanford University and through a transfer study on PV systems at the University of Wollongong in Australia. Comparative experiments reveal an average RMSE reduction of approximately 5% and a MAE improvement of nearly 6%, while the transfer study shows average RMSE and MAE reductions of about 7% and 9.5%, respectively. Overall, PV-VLM leverages complementary modalities to provide a robust solution for grid scheduling and energy market participation, enhancing the stability and reliability of PV integration.
Design Of a Dual-band Patch Antenna with Stacked Structure
Authors: Xunchi Ma
Date: 2025-04-18 00:00:00

This project presents the design, simulation, and fabrication of a compact dual-band patch antenna using a stacked structure, targeting 2.25 to 2.35 GHz and 5.6 to 5.8 GHz ISM bands. A stacked configuration was chosen over a slotted design for its better performance, featuring two copper patches separated by dielectrics, with a single feed line and a 5 mm patch offset to enhance coupling. Simulations showed strong return loss and directional gain in both bands. Although physical testing was limited due to equipment constraints, the prototype met design expectations, demonstrating potential for use in compact wireless and embedded systems.
Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm
Authors: Jingran Xu, Hongqi Min, Yong Zeng
Date: 2025-04-18 00:00:00
Tasks: Benchmarking, Super-Resolution

Low-altitude unmanned aerial vehicle (UAV) swarms are expected to play important role for future intelligent aerial systems due to their great potential to cooperatively accomplish complicated missions effectively. However, there are important challenges to be addressed to enable their efficient operation: the large-scale nature of swarms usually leads to excessive spectrum consumption, and ultra-low cost requirements for individual UAVs renders it necessary to develop more cost-effective communication modules. In addition, the densely located swarm UAVs require high resolution for localization and sensing. To address the above challenges and simultaneously achieve spectrum and energy-efficient communication and accurate sensing, we investigate low-altitude UAV swarm with integrated super-resolution sensing and symbiotic communication technology. Specifically, one leading UAV may act as a primary transmitter (PT) to transmit communication signals to the base station (BS), and the remaining nearby UAVs in the swarm act as passive backscatter devices (BDs), which can modulate their information by efficiently backscattering the radio frequency (RF) signals from the PT without consuming extra spectrum or power. In addition, to achieve efficient three-dimensional (3D) super-resolution sensing for the densely located UAV swarm, 3D sparse multiple-input multiple-output (MIMO) technology and super-resolution signal processing algorithms are further exploited, where both L-shaped nested array (LNA) and planar nested arrays (PNA) are considered at the BS. To evaluate the communication and sensing performance for the UAV-symbiotic radio (SR) system, the achievable rates of UAV swarm are derived and the beam patterns of sparse LNA, PNA and the benchmarking compact uniform planar array (UPA) are compared.
Block-Weighted Lasso for Joint Optimization of Memory Depth and Kernels in Wideband DPD
Authors: Jinfei Wang, Yi Ma, Fei Tong, Ziming He
Date: 2025-04-18 00:00:00

The optimizations of both memory depth and kernel functions are critical for wideband digital pre-distortion (DPD). However, the memory depth is usually determined via exhaustive search over a wide range for the sake of linearization optimality, followed by the kernel selection of each memory depth, yielding excessive computational cost. In this letter, we aim to provide an efficient solution that jointly optimizes the memory depth and kernels while preserving reasonable linearization performance. Specifically, we propose to formulate this optimization as a blockweighted least absolute shrinkage and selection operator (Lasso) problem, where kernels are assigned regularization weights based on their polynomial orders. Then, a block coordinate descent algorithm is introduced to solve the block-weighted Lasso problem. Measurement results on a generalized memory polynomial (GMP) model demonstrates that our proposed solution reduces memory depth by 31.6% and kernel count by 85% compared to the full GMP, while achieving -46.4 dB error vector magnitude (EVM) for signals of 80 MHz bandwidth. In addition, the proposed solution outperforms both the full GMP and the GMP pruned by standard Lasso by at least 0.7 dB in EVM.
Modular XL-Array-Enabled 3-D Localization based on Hybrid Spherical-Planar Wave Model in Terahertz Systems
Authors: Yang Zhang, Ruidong Li, Cunhua Pan, Hong Ren, Tuo Wu, Changhong Wang
Date: 2025-04-18 00:00:00
Tasks: compressed sensing, Position

This work considers the three-dimensional (3-D) positioning problem in a Terahertz (THz) system enabled by a modular extra-large (XL) array with sub-connected architecture. Our purpose is to estimate the Cartesian Coordinates of multiple user equipments (UEs) with the received signal of the RF chains while considering the spatial non-stationarity (SNS). We apply the hybrid spherical-planar wave model (HSPWM) as the channel model owing to the structual feature of the modular array, and propose a 3-D localization algorithm with relatively high accuracy and low complexity. Specifically, we first distinguish the visible sub-arrays (SAs) located in the VR and estimate the angles-of-arrival (AoAs) from each UE to typical visible SAs with the largest receive power via compressed sensing (CS) method. In addition, we apply the weighted least square (WLS) method to obtain a coarse 3-D position estimation of each UE according to the AoA estimations. Then, we estimate the AoAs of the other SAs with a reduced dictionary (RD)-CS-based method for lower computational complexity, and utilize all the efficient AoA estimations to derive a fine position estimation. Simulation results indicate that the proposed positioning framework based on modular XL-array can achieve satisfactory accuracy with evident reduction in complexity. Furthermore, the deployment of SAs and the allocation of antenna elements need to be specially designed for better positioning performance.
A Deep Learning-Based Supervised Transfer Learning Framework for DOA Estimation with Array Imperfections
Authors: Bo Zhou, Kaijie Xu, Yinghui Quan, Mengdao Xing
Date: 2025-04-18 00:00:00
Tasks: Deep Learning, Transfer Learning

In practical scenarios, processes such as sensor design, manufacturing, and installation will introduce certain errors. Furthermore, mutual interference occurs when the sensors receive signals. These defects in array systems are referred to as array imperfections, which can significantly degrade the performance of Direction of Arrival (DOA) estimation. In this study, we propose a deep-learning based transfer learning approach, which effectively mitigates the degradation of deep-learning based DOA estimation performance caused by array imperfections. In the proposed approach, we highlight three major contributions. First, we propose a Vision Transformer (ViT) based method for DOA estimation, which achieves excellent performance in scenarios with low signal-to-noise ratios (SNR) and limited snapshots. Second, we introduce a transfer learning framework that extends deep learning models from ideal simulation scenarios to complex real-world scenarios with array imperfections. By leveraging prior knowledge from ideal simulation data, the proposed transfer learning framework significantly improves deep learning-based DOA estimation performance in the presence of array imperfections, without the need for extensive real-world data. Finally, we incorporate visualization and evaluation metrics to assess the performance of DOA estimation algorithms, which allow for a more thorough evaluation of algorithms and further validate the proposed method. Our code can be accessed at https://github.com/zzb-nice/DOA_est_Master.
Traffic Flow and Speed Monitoring Based On Optical Fiber Distributed Acoustic Sensor
Authors: LinLin Wang, Shixin Wang, Peng Wang, Wei Wang, Dezhao Wang, Yongcai Wang, Shanwen Wang
Date: 2024-01-20 00:00:00

In the realm of intelligent transportation systems, accurate and reliable traffic monitoring is crucial. Traditional devices, such as cameras and lidars, face limitations in adverse weather conditions and complex traffic scenarios, prompting the need for more resilient technologies. Thispaperpresentstrafficflowmonitoringmethodusingopticalfiber-baseddistributedacoustic sensors(DAS).Aninnovativevehicletrajectoryextractionalgorithmisproposedtoderivetraffic flow statistics. In the processing of optical fiber waterfall diagrams, Butterworth low-pass filter and peaks location search algorithm are employed to determine the entry position of vehicles. Subsequently, line-by-line matching algorithm is proposed to effectively track the trajectories. Experiments were conducted in highway, tunnel and city scenarios. Visualizations show that ourapproachnotonlyextractsvehicletrajectoriesmoreaccuratelythantheclassicalHoughand Radon transform-based methods and MUSIC beamforming algorithm, but also facilitates the calculation of traffic flow information using the low-cost acoustic sensors. It provides a new reliable means for traffic flow monitoring which can be integrated with existing methods like vision-based method.
Use of Topological Data Analysis for the Detection of Phenomenological Bifurcations in Stochastic Epidemiological Models
Authors: Sunia Tanweer, Konstantinos Mamis, Firas A. Khasawneh
Date: 2025-04-16 00:00:00
Tasks: Topological Data Analysis

We investigate predictions of stochastic compartmental models on the severity of disease outbreaks. The models we consider are the Susceptible-Infected-Susceptible (SIS) for bacterial infections, and the Susceptible -Infected-Removed (SIR) for airborne diseases. Stochasticity enters the compartmental models as random fluctuations of the contact rate, to account for uncertainties in the disease spread. We consider three types of noise to model the random fluctuations: the Gaussian white and Ornstein-Uhlenbeck noises, and the logarithmic Ornstein-Uhlenbeck (logOU). The advantages of logOU noise are its positivity and its ability to model the presence of superspreaders. We utilize homological bifurcation plots from Topological Data Analysis to automatically determine the shape of the long-time distributions of the number of infected for the SIS, and removed for the SIR model, over a range of basic reproduction numbers and relative noise intensities. LogOU noise results in distributions that stay close to the endemic deterministic equilibrium even for high noise intensities. For low reproduction rates and increasing intensity, the distribution peak shifts towards zero, that is, disease eradication, for all three noises; for logOU noise the shift is the slowest. Our study underlines the sensitivity of model predictions to the type of noise considered in contact rate.
Sensitivity analysis enlightens effects of connectivity in a Neural Mass Model under Control-Target mode
Authors: Anaïs Vallet, Stéphane Blanco, Coline Chevallier, Francis Eustache, Jacques Gautrais, Jean-Yves Grandpeix, Jean-Louis Joly, Shailendra Segobin, Pierre Gagnepain
Date: 2025-04-18 00:00:00
Tasks: Sensitivity

Biophysical models of human brain represent the latter as a graph of inter-connected neural regions. Building from the model by Naskar et al. (Network Neuroscience 2021), our motivation was to understand how these brain regions can be connected at neural level to implement some inhibitory control, which calls for inhibitory connectivity rarely considered in such models. In this model, regions are made of inter-connected excitatory and inhibitory pools of neurons, but are long-range connected only via excitatory pools (mutual excitation). We thus extend this model by generalizing connectivity, and we analyse how connectivity affects the behaviour of this model. Focusing on the simplest paradigm made of a Control area and a Target area, we explore four typical kinds of connectivity: mutual excitation, Target inhibition by Control, Control inhibition by Target, and mutual inhibition. For this, we build an analytical sensitivity framework, nesting up sensitivities of isolated pools, of isolated regions, and of the full system. We show that inhibitory control can emerge only in Target inhibition by Control and mutual inhibition connectivities. We next offer an analysis of how the model sensitivities depends on connectivity structure, depending on a parameter controling the strength of the self-inhibition within Target region. Finally, we illustrate the effect of connectivity structure upon control effectivity in response to an external forcing in the Control area. Beyond the case explored here, our methodology to build analytical sensitivities by nesting up levels (pool, region, system) lays the groundwork for expressing nested sensitivities for more complex network configurations, either for this model or any other one.
Adaptive modelling of anti-tau treatments for neurodegenerative disorders based on the Bayesian approach with physics-informed neural networks
Authors: Swadesh Pal, Roderick Melnik
Date: 2025-04-18 00:00:00
Tasks: Bayesian Inference, parameter estimation, Uncertainty Quantification

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the accumulation of amyloid-beta (A$\beta$) and phosphorylated tau (p-tau) proteins, leading to cognitive decline measured by the Alzheimer's Disease Assessment Scale (ADAS) score. In this study, we develop and analyze a system of ordinary differential equation models to describe the interactions between A$\beta$, p-tau, and ADAS score, providing a mechanistic understanding of disease progression. To ensure accurate model calibration, we employ Bayesian inference and Physics-Informed Neural Networks (PINNs) for parameter estimation based on Alzheimer's Disease Neuroimaging Initiative data. The data-driven Bayesian approach enables uncertainty quantification, improving confidence in model predictions, while the PINN framework leverages neural networks to capture complex dynamics directly from data. Furthermore, we implement an optimal control strategy to assess the efficacy of an anti-tau therapeutic intervention aimed at reducing p-tau levels and mitigating cognitive decline. Our data-driven solutions indicate that while optimal drug administration effectively decreases p-tau concentration, its impact on cognitive decline, as reflected in the ADAS score, remains limited. These findings suggest that targeting p-tau alone may not be sufficient for significant cognitive improvement, highlighting the need for multi-target therapeutic strategies. The integration of mechanistic modelling, advanced parameter estimation, and control-based therapeutic optimization provides a comprehensive framework for improving treatment strategies for AD.
Synaptic Spine Head Morphodynamics from Graph Grammar Rules for Actin Dynamics
Authors: Matthew Hur, Thomas Bartol, Padmini Rangamani, Terrence Sejnowski, Eric Mjolsness
Date: 2025-04-18 00:00:00

There is a morphodynamic component to synaptic learning by which changes in dendritic spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons, in response to the temporal correlation of local presynaptic and postsynaptic signals. Morphological factors are in turn sculpted by the dynamics of the actin cytoskeleton. We use Dynamical Graph Grammars (DGGs) implemented within a computer algebra system to model how networks of actin filaments can dynamically grow or shrink, reshaping the spine head. DGGs provide a well-defined way to accommodate dynamically changing system structure such as active cytoskeleton represented using dynamic graphs, within nonequilibrium statistical physics under the master equation. We show that DGGs can also incorporate biophysical forces between graph-connected objects at a finer time scale, with specialized DGG kinetic rules obeying biophysical constraints of Galilean invariance, conservation of momentum, and dissipation of conserved global energy. We use graph-local energy functions for cytoskeleton networks interacting with membranes, and derive DGG rules from the specialization of dissipative stochastic dynamics to a mutually exclusive and exhaustive collection of graph-local neighborhood types for the rule left hand sides. Dissipative rules comprise a stochastic version of gradient descent dynamics. Thermal noise rules use a Gaussian approximation of each position coordinate to sample jitter-like displacements. We designed and implemented DGG grammar sub-models including actin network growth, non-equilibrium statistical mechanics, and filament-membrane mechanical interaction to regulate the re-writing of graph objects. From a biological perspective, we observe regulatory effects of three actin-binding proteins on the membrane size and find evidence supporting mechanisms of membrane growth.
Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers
Authors: Xinjie Shen, Zhichao Geng, Yang Yang
Date: 2025-04-21 00:00:00
Tasks: Computational Efficiency, Information Retrieval, Retrieval, Zero Shot on BEIR (Inference Free Model)

With increasing demands for efficiency, information retrieval has developed a branch of sparse retrieval, further advancing towards inference-free retrieval where the documents are encoded during indexing time and there is no model-inference for queries. Existing sparse retrieval models rely on FLOPS regularization for sparsification, while this mechanism was originally designed for Siamese encoders, it is considered to be suboptimal in inference-free scenarios which is asymmetric. Previous attempts to adapt FLOPS for inference-free scenarios have been limited to rule-based methods, leaving the potential of sparsification approaches for inference-free retrieval models largely unexplored. In this paper, we explore $\ell_0$ inspired sparsification manner for inference-free retrievers. Through comprehensive out-of-domain evaluation on the BEIR benchmark, our method achieves state-of-the-art performance among inference-free sparse retrieval models and is comparable to leading Siamese sparse retrieval models. Furthermore, we provide insights into the trade-off between retrieval effectiveness and computational efficiency, demonstrating practical value for real-world applications.
Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search
Authors: Kou Misaki, Yuichi Inoue, Yuki Imajuku, So Kuroki, Taishi Nakamura, Takuya Akiba
Date: 2025-03-06 00:00:00
Tasks: Diversity

Recent advances demonstrate that increasing inference-time computation can significantly boost the reasoning capabilities of large language models (LLMs). Although repeated sampling (i.e., generating multiple candidate outputs) is a highly effective strategy, it does not leverage external feedback signals for refinement, which are often available in tasks like coding. In this work, we propose $\textit{Adaptive Branching Monte Carlo Tree Search (AB-MCTS)}$, a novel inference-time framework that generalizes repeated sampling with principled multi-turn exploration and exploitation. At each node in the search tree, AB-MCTS dynamically decides whether to "go wider" by expanding new candidate responses or "go deeper" by revisiting existing ones based on external feedback signals. We evaluate our method on complex coding and engineering tasks using frontier models. Empirical results show that AB-MCTS consistently outperforms both repeated sampling and standard MCTS, underscoring the importance of combining the response diversity of LLMs with multi-turn solution refinement for effective inference-time scaling.
Self-Evolved Preference Optimization for Enhancing Mathematical Reasoning in Small Language Models
Authors: Joykirat Singh, Tanmoy Chakraborty, Akshay Nambi
Date: 2025-03-04 00:00:00
Tasks: GSM8K, Math, Mathematical Problem-Solving, Mathematical Reasoning, Prompt Engineering

Large language models (LLMs) have significantly improved their reasoning capabilities; however, they still struggle with complex multi-step mathematical problem-solving due to error propagation, lack of self-correction, and limited adaptability to diverse reasoning styles. Existing methods rely on static fine-tuning or prompt engineering, which fail to generalize across problem complexities, while the scarcity of high-quality preference data further hinders reliable reasoning. We introduce SPHERE, a self-evolving data generation pipeline that enhances reasoning in small language models (SLMs) by iteratively generating, correcting, and diversifying reasoning chains. SPHERE operates in three stages: (i) Self-Generation, where the model autonomously constructs problem-solving steps; (ii) Self-Correction, enabling it to identify and rectify errors; and (iii) Diversity Induction, improving robustness through multiple valid reasoning trajectories. This self-evolution mechanism strengthens mathematical reasoning and enhances model reliability. Evaluations on MATH 500, GSM8K, AIME, AMC, and Olympiad show that SPHERE-trained models achieve significant gains over their base versions and match/surpass GPT-4o on certain benchmarks. Our findings demonstrate that self-evolving models can close the reasoning gap between SLMs and state-of-the-art LLMs, making mathematical AI more reliable, scalable, and efficient.