Log in Sign up
Your paper timeline
Scroll AI takes the way you would scroll a great paper aggregator: quick signal first, deeper critique when something earns your attention, and challenges when a claim feels off.
Trending Newest Top
5 papers in eess.SP
Trending mixes fresh papers with community signal.
0
Cluster-Specific Predictive Modeling: A Scalable Solution for Resource-Constrained Wi-Fi Controllers
eess.SPcs.LG Gianluca Fontanesi, Luca Barbieri, Lorenzo Galati Giordano et al. · Mar 23, 2026

This paper tackles the challenge of deploying traffic forecasting models in resource-constrained Wi-Fi controllers that manage thousands of access points (APs). The core idea is to use feature-based clustering (k-means on PCA-reduced features) to group APs by traffic behavior, then deploy cluster-specific LSTM models only to high-activity clusters while using a lightweight global model for low-activity clusters. The approach reduces memory footprint by approximately 40% compared to deploying complex models for all clusters, while preserving prediction accuracy through selective specialization.

This manuscript presents a comprehensive analysis of predictive modeling optimization in managed Wi-Fi networks through the integration of clustering algorithms and model evaluation techniques. The study addresses the challenges of deploying forecasting algorithms in large-scale environments managed by a central controller constrained by memory and computational resources. Feature-based clustering, supported by Principal Component Analysis (PCA) and advanced feature engineering, is employed to group time series data based on shared characteristics, enabling the development of cluster-specific predictive models. Comparative evaluations between global models (GMs) and cluster-specific models demonstrate that cluster-specific models consistently achieve superior accuracy in terms of Mean Absolute Error (MAE) values in high-activity clusters. The trade-offs between model complexity (and accuracy) and resource utilization are analyzed, highlighting the scalability of tailored modeling approaches. The findings advocate for adaptive network management strategies that optimize resource allocation through selective model deployment, enhance predictive accuracy, and ensure scalable operations in large-scale, centrally managed Wi-Fi environments.
AI reviewed
arXivPDF
0
Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff
cs.ITcs.LGeess.SP Zijun Qin, Jingxuan Huang, Zesong Fei et al. · Mar 23, 2026

The paper addresses adaptive broadcast of data-intensive sensory streams (e.g., camera/LiDAR) to heterogeneous edge devices with diverse channel conditions and computational budgets. It proposes Nonlinear Transform Rateless Source-Channel Coding (NTRSCC), integrating learned nonlinear transforms with physical-layer Luby Transform (LT) codes to enable receivers to adaptively adjust the number of received symbols and belief propagation iterations. This achieves an explicit, controllable tradeoff between distortion, transmission rate, and decoding complexity—addressing key limitations of fixed-rate DeepJSCC schemes that either underserve capable devices or require costly retransmissions.

In recent years, numerous data-intensive broadcasting applications have emerged at the wireless edge, calling for a flexible tradeoff between distortion, transmission rate, and processing complexity. While deep learning-based joint source-channel coding (DeepJSCC) has been identified as a potential solution to data-intensive communications, most of these schemes are confined to worst-case solutions, lack adaptive complexity, and are inefficient in broadcast settings. To overcome these limitations, this paper introduces nonlinear transform rateless source-channel coding (NTRSCC), a variable-length JSCC framework for broadcast channels based on rateless codes. In particular, we integrate learned source transformations with physical-layer LT codes, develop unequal protection schemes that exploit decoder side information, and devise approximations to enable end-to-end optimization of rateless parameters. Our framework enables heterogeneous receivers to adaptively adjust their received number of rateless symbols and decoding iterations in belief propagation, thereby achieving a controllable tradeoff between distortion, rate, and decoding complexity. Simulation results demonstrate that the proposed method enhances image broadcast quality under stringent communication and processing budgets over heterogeneous edge devices.
AI reviewed
arXivPDF
0
Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG
cs.LGeess.SP Mohammad Moulaeifard, Philip J. Aston, Peter H. Charlton et al. · Mar 23, 2026

This paper establishes a comprehensive benchmark for photoplethysmography (PPG)-based clinical prediction using the large-scale MIMIC-III-Ext-PPG dataset, evaluating multi-task learning across arrhythmia classification (13 classes) and physiological regression (blood pressure, heart rate, respiratory rate). The core contribution is demonstrating robust atrial fibrillation detection (AUROC 0.96) with strong cross-dataset generalizability, alongside the first systematic assessment of fine-grained arrhythmia classification from PPG alone. It matters because PPG sensors are ubiquitous in wearables and ICUs, yet standardized, large-scale, multi-task benchmarks have been lacking, hindering meaningful algorithm comparison and clinical deployment.

Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based algorithms are typically trained on small-scale datasets of uncertain quality, which hinders meaningful algorithm comparisons. We present a comprehensive benchmark for PPG-based clinical prediction using the \dbname~dataset, establishing baselines across the full spectrum of clinically relevant applications: multi-class heart rhythm classification, and regression of physiological parameters including respiratory rate (RR), heart rate (HR), and blood pressure (BP). Most notably, we provide the first comprehensive assessment of PPG for general arrhythmia detection beyond atrial fibrillation (AF) and atrial flutter (AFLT), with performance stratified by BP, HR, and demographic subgroups. Using established deep learning architectures, we achieved strong performance for AF detection (AUROC = 0.96) and accurate physiological parameter estimation (RR MAE: 2.97 bpm; HR MAE: 1.13 bpm; SBP/DBP MAE: 16.13/8.70 mmHg). Cross-dataset validation demonstrates excellent generalizability for AF detection (AUROC = 0.97), while clinical subgroup analysis reveals marked performance differences across subgroups by BP, HR, and demographic strata. These variations appear to reflect population-specific waveform differences rather than systematic bias in model behavior. This framework establishes the first integrated benchmark for multi-task PPG-based clinical prediction, demonstrating that PPG signals can effectively support multiple simultaneous monitoring tasks and providing essential baselines for future algorithm development.
AI reviewed
arXivPDF
0
Amortized Variational Inference for Logistic Regression with Missing Covariates
cs.LGeess.SP M. Cherifi, Aude Sportisse, Xujia Zhu et al. · Mar 22, 2026

The paper proposes AV-LR, a lightweight amortized variational inference framework for logistic regression with missing covariates that eliminates latent variables entirely. Unlike VAE-based competitors, it directly models the posterior over missing values using a single neural network coupled with a linear classification layer, enabling joint optimization of imputation and prediction. The approach extends naturally to MNAR settings and claims substantial computational speedups over EM-based methods while maintaining comparable statistical accuracy.

Missing covariate data pose a significant challenge to statistical inference and machine learning, particularly for classification tasks like logistic regression. Classical iterative approaches (EM, multiple imputation) are often computationally intensive, sensitive to high missingness rates, and limited in uncertainty propagation. Recent deep generative models based on VAEs show promise but rely on complex latent representations. We propose Amortized Variational Inference for Logistic Regression (AV-LR), a unified end-to-end framework for binary logistic regression with missing covariates. AV-LR integrates a probabilistic generative model with a simple amortized inference network, trained jointly by maximizing the evidence lower bound. Unlike competing methods, AV-LR performs inference directly in the space of missing data without additional latent variables, using a single inference network and a linear layer that jointly estimate regression parameters and the missingness mechanism. AV-LR achieves estimation accuracy comparable to or better than state-of-the-art EM-like algorithms, with significantly lower computational cost. It naturally extends to missing-not-at-random settings by explicitly modeling the missingness mechanism. Empirical results on synthetic and real-world datasets confirm its effectiveness and efficiency across various missing-data scenarios.
AI reviewed
arXivPDF
0
B-jet Tagging Using a Hybrid Edge Convolution and Transformer Architecture
hep-phcs.AIeess.SP Diego F. Vasquez Plaza, Vidya Manian · Mar 22, 2026

This paper tackles the challenging problem of b-jet tagging at the LHC, particularly the difficult discrimination between bottom-quark jets (b-jets) and charm-quark jets (c-jets). The authors propose ECT (Edge Convolution Transformer), a hybrid deep learning architecture that combines local feature extraction via EdgeConv blocks with global context modeling through transformer self-attention. The work is motivated by the need for real-time flavor tagging in high-level trigger systems, where both accuracy and inference latency are critical.

Jet flavor tagging plays an important role in precise Standard Model measurement enabling the extraction of mass dependence in jet-quark interaction and quark-gluon plasma (QGP) interactions. They also enable inferring the nature of particles produced in high-energy particle collisions that contain heavy quarks. The classification of bottom jets is vital for exploring new Physics scenarios in proton-proton collisions. In this research, we present a hybrid deep learning architecture that integrates edge convolutions with transformer self-attention mechanisms, into one single architecture called the Edge Convolution Transformer (ECT) model for bottom-quark jet tagging. ECT processes track-level features (impact parameters, momentum, and their significances) alongside jet-level observables (vertex information and kinematics) to achieve state-of-the-art performance. The study utilizes the ATLAS simulation dataset. We demonstrate that ECT achieves 0.9333 AUC for b-jet versus combined charm and light jet discrimination, surpassing ParticleNet (0.8904 AUC) and the pure transformer baseline (0.9216 AUC). The model maintains inference latency below 0.060 ms per jet on modern GPUs, meeting the stringent requirements for real-time event selection at the LHC. Our results demonstrate that hybrid architectures combining local and global features offer superior performance for challenging jet classification tasks. The proposed architecture achieves good results in b-jet tagging, particularly excelling in charm jet rejection (the most challenging task), while maintaining competitive light-jet discrimination comparable to pure transformer models.
AI reviewed
arXivPDF