Tootfinder

PIC12F683 SRAM row address decoding.
The physical memory array is 32 x 32 bitcells (1 kbit / 128 bytes), logically 32 rows x 4-way column mux x 8 bit data bus.
The row logic decodes the 5-bit row address into ten one-hot signals for X and !X. Each wordline is driven by a 5-input AND gate, where each row selects a different one of the 32 combinations.
The PIC12F683 uses a very straightforward address order with MSB at left and LSB at right, and addresses counting in what see…

MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
Soheil Mahdizadeh, Elyas Oustad, Mohsen Ansari
https://arxiv.org/abs/2507.12028

MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
Task offloading in three-layer fog computing environments presents a critical challenge due to user equipment (UE) mobility, which frequently triggers costly service migrations and degrades overall system performance. This paper addresses this problem by proposing MOFCO, a novel Mobility- and Migration-aware Task Offloading algorithm for Fog Computing environments. The proposed method formulates task offloading and resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem and…

Inverse regression for causal inference with multiple outcomes
Wei Zhang, Qizhai Li, Peng Ding
https://arxiv.org/abs/2509.12587 https://arxiv.org/pdf/2509.…

Inverse regression for causal inference with multiple outcomes
With multiple outcomes in empirical research, a common strategy is to define a composite outcome as a weighted average of the original outcomes. However, the choices of weights are often subjective and can be controversial. We propose an inverse regression strategy for causal inference with multiple outcomes. The key idea is to regress the treatment on the outcomes, which is the inverse of the standard regression of the outcomes on the treatment. Although this strategy is simple and even counte…

Kalman Filtering of Stationary Graph Signals
Yang Chen, Yeonju Lee, Yao Shi, Qiyu Sun
https://arxiv.org/abs/2509.12605 https://arxiv.org/pdf/2509.12605

Kalman Filtering of Stationary Graph Signals
In this paper, we propose a novel definition of stationary graph signals, formulated with respect to a symmetric graph shift, such as the graph Laplacian. We show that stationary graph signals can be generated by transmitting white noise through polynomial graph channels, and that their stationarity is preserved under polynomial channel transmission. In this paper, we also investigate Kalman filtering to dynamical systems characterized by polynomial state and observation matrices. We demonstr…

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- A Training-Free Approach for Music Style Transfer with Latent Diffusion Models
Heehwan Wang, Joonwoo Kwon, Sooyoung Kim, Shinjae Yoo, Yuewei Lin, Jiook Cha

Fernando Mendoza at No. 1 ... to the Jets? Reid predicts Round 1 in his midseason NFL mock draft https://www.espn.com/nfl/draft2026/story/_/id/46583887/2026-nfl-mock-draft-first-round-predictions-32-picks-jordan-reid

Oh, did you think the israel model would be confined to the Middle East? Why should it? Didn’t all our Western governments just declare it kosher?
First, they came for the Palestinians… https://kolektiva.social/@DoomsdaysCW/115034832497483253

So my Windows 10 box will no long eat updates unless I upgrade to Win11?
Guess I'll just stick to doing everything on Android. 😞

“Liquid Glass—at least it’s not Microsoft Teams!”

ぐ-すくま

An investment consortium including BlackRock, Nvidia, xAI, and Microsoft plans to acquire Texas-based Aligned Data Centers from Macquarie in a $40B deal (Financial Times)
https://www.ft.com/content/f7f0457a-df43-44a2-bfa8-4e3d7dbcf2b8

Zug hat Reh überfahren. Und dann gebremst und steht jetzt.

I could make an entire conference talk solely about Web-Server-On-A-Disposable-Vape and how it’s really a metaphor for the Great Divide

Before you head out for the weekend, don't miss today's Metacurity -- our last daily newsletter before a two-week summer break -- for the most critical infosec developments you should know, including
--US feds crack down on Russian cryptocurrency exchange Garantex,
--US seeks to seize $2.8m from ransomware actor's wallet,
--Polish city fended off likely Russian attack on water supply,
--Iranian hackers infiltrated former Israeli justice minister's phone…

US feds crack down on Russian cryptocurrency exchange Garantex
US seeks to seize $2.8m from ransomware actor's wallet, Polish city fended off likely Russian attack on water supply, Iranian hackers infiltrated former Israeli justice minister's phone, BtcTurk lost $48m in hack, Odin.fun exploited for $7m, Duo pleads guilty in stolen data pandemic fraud, much more

Statistical Model Checking Beyond Means: Quantiles, CVaR, and the DKW Inequality (extended version)
Carlos E. Budde, Arnd Hartmanns, Tobias Meggendorfer, Maximilian Weininger, Patrick Wienh\"oft
https://arxiv.org/abs/2509.11859

Statistical Model Checking Beyond Means: Quantiles, CVaR, and the DKW Inequality (extended version)
Statistical model checking (SMC) randomly samples probabilistic models to approximate quantities of interest with statistical error guarantees. It is traditionally used to estimate probabilities and expected rewards, i.e. means of different random variables on paths. In this paper, we develop methods using the Dvoretzky-Kiefer-Wolfowitz-Massart inequality (DKW) to extend SMC beyond means to compute quantities such as quantiles, conditional value-at-risk, and entropic risk. The DKW provides conf…

High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic
George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos
https://arxiv.org/abs/2507.12418

High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic
The Number Theoretic Transform (NTT) is a fundamental operation in privacy-preserving technologies, particularly within fully homomorphic encryption (FHE). The efficiency of NTT computation directly impacts the overall performance of FHE, making hardware acceleration a critical technology that will enable realistic FHE applications. Custom accelerators, in FPGAs or ASICs, offer significant performance advantages due to their ability to exploit massive parallelism and specialized optimizations. …

Replaced article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Ambiguity Function Analysis of Affine Frequency Division Multiplexing for Integrated Sensing and ...
Ebrahim Bedeer

Crosslisted article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Beyond Hard Sharing: Efficient Multi-Task Speech-to-Text Modeling with Supervised Mixture of Experts
Hojun Jin, Eunsoo Hong, Ziwon Hyung, Sungjun Lim, Seungjin Lee, Keunseok Cho

Crosslisted article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Edge-weighted Online Stochastic Matching: Beating $1-\frac1e$
Shuyi Yan
https://…

Covering Unknown Correlations in Bayesian Priors by Inflating Uncertainties
Lukas Koch
https://arxiv.org/abs/2509.11821 https://arxiv.org/pdf/2509.11821

Covering Unknown Correlations in Bayesian Priors by Inflating Uncertainties
Bayesian analyses require that all variable model parameters are given a prior probability distribution. This can pose a challenge for analyses where multiple experiments are combined if these experiments use different parametrisations for their nuisance parameters. If the parameters in the two models describe exactly the same physics, they should be 100% correlated in the prior. If the parameters describe independent physics, they should be uncorrelated. But if they describe related or overlap…

Meeting at Google: “We haven’t fucked up the Web enough with our AMP shit, but the AI shit is doing pretty good work. What about if we also fuck up RSS?”
What an utterly shit company. I hope the AI hype destroys them.
(Just in case you think they're "just asking questions", they already opened a removal tracker before listening to replies: https://issues.chromium.org/issues/435623334)
https://github.com/whatwg/html/issues/11523

@… 🎂 🎉 おめっ！
カレー送っといたよー

Find someone that loves you like this tortie loves this blanket.

Salesforce says it expects FY 2030 revenue of $60B , vs. $58.37B est., excluding impact from the $8B pending acquisition of data management company Informatica (Jordan Novet/CNBC)
https://www.cnbc.com/2025/10/15/salesforce-stock-jumps-a…

Salesforce stock jumps after company offers rosy forecast for 2030
Salesforce is counting on Agentforce artificial intelligence software to pull in more revenue from customers.

Double Duty: FPGA Architecture to Enable Concurrent LUT and Adder Chain Usage
Junius Pun, Xilai Dai, Grace Zgheib, Mahesh A. Iyer, Andrew Boutros, Vaughn Betz, Mohamed S. Abdelfattah
https://arxiv.org/abs/2507.11709

Double Duty: FPGA Architecture to Enable Concurrent LUT and Adder Chain Usage
Flexibility and customization are key strengths of Field-Programmable Gate Arrays (FPGAs) when compared to other computing devices. For instance, FPGAs can efficiently implement arbitrary-precision arithmetic operations, and can perform aggressive synthesis optimizations to eliminate ineffectual operations. Motivated by sparsity and mixed-precision in deep neural networks (DNNs), we investigate how to optimize the current logic block architecture to increase its arithmetic density. We find that…

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- MIMOSA: Multi-parametric Imaging using Multiple-echoes with Optimized Simultaneous Acquisition fo...
Chen, Jun, Heydari, Yong, Kim, Lee, Liu, Ye, Gagoski, Fujita, Bilgic

https://www.nbcnews.com/world/asia/cambodia-makes-1000-arrests-latest-crackdown-cybercrime-rcna219141
Cambodia makes 1,000 arrests in latest crackdown on cybercrime

Cambodia makes 1,000 arrests in latest crackdown on cybercrime
Cambodia crackdown on criminal cybercrime operations being run in the country had resulted in the arrest of more than 1,000 suspects so far this week.

[2025-07-17 Thu (UTC), 4 new articles found for cs.AR Hardware Architecture]
toXiv_bot_toot

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Joint Optimization and Statistical Inference for Zero-th Order Simulation Optimization
Yuhang Wu, Zeyu Zheng, Yingfei Wang, Guangyu Zhang, Zuohua Zhang, Chu Wang

Replaced article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
Chen, Wang, Chen, Zhao, Li, Piovesan, Zhu, Shi

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length
Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon
https://arxiv.org/abs/2507.12442

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length
The demand for machine intelligence capable of processing continuous, long-context inputs on local devices is growing rapidly. However, the quadratic complexity and memory requirements of traditional Transformer architectures make them inefficient and often unusable for these tasks. This has spurred a paradigm shift towards new architectures like State Space Models (SSMs) and hybrids, which promise near-linear scaling. While most current research focuses on the accuracy and theoretical throughp…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- ALIGNS: Unlocking nomological networks in psychological measurement through a large language model
Kai R. Larsen, Sen Yan, Roland M\"uller, Lan Sang, Mikko R\"onkk\"o, Ravi Starzl, Donald …

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- SoilSound: Smartphone-based Soil Moisture Estimation
Yixuan Gao, Tanvir Ahmed, Shuang He, Zhongqi Cheng, Rajalakshmi Nandakumar

Learning Discrete Bayesian Networks with Hierarchical Dirichlet Shrinkage
Alexander Dombowsky, David B. Dunson
https://arxiv.org/abs/2509.13267 https://arx…

Learning Discrete Bayesian Networks with Hierarchical Dirichlet Shrinkage
Discrete Bayesian networks (DBNs) provide a broadly useful framework for modeling dependence structures in multivariate categorical data. There is a vast literature on methods for inferring conditional probabilities and graphical structure in DBNs, but data sparsity and parametric assumptions are major practical issues. In this article, we detail a comprehensive Bayesian framework for learning DBNs. First, we propose a hierarchical prior for the conditional probabilities that enables complicate…

Towards personalized, precise and survey-free environment recognition: AI-enhanced sensor fusion without pre-deployment
Ruichen Wang, Zhikang Ni, Pengzhou Wang, Xiya Cao, Zhi Li, Bao Zhang
https://arxiv.org/abs/2509.12870

Towards personalized, precise and survey-free environment recognition: AI-enhanced sensor fusion without pre-deployment
Accurate and personalized environment recognition is essential for seamless indoor positioning and optimized connectivity, yet traditional fingerprinting requires costly site surveys and lacks user-level adaptation. We present a survey-free, on-device sensor-fusion framework that builds a personalized, lightweight multi-source fingerprint (FP) database from pedestrian dead reckoning (PDR), WiFi/cellular, GNSS, and interaction time tags. Matching is performed by an AI-enhanced dynamic time warpi…

Semiparametric Causal Inference for Right-Censored Outcomes with Many Weak Invalid Instruments
Qiushi Bu, Wen Su, Xingqiu Zhao, Zhonghua Liu
https://arxiv.org/abs/2509.13176 htt…

Semiparametric Causal Inference for Right-Censored Outcomes with Many Weak Invalid Instruments
We propose a semiparametric framework for causal inference with right-censored survival outcomes and many weak invalid instruments, motivated by Mendelian randomization in biobank studies where classical methods may fail. We adopt an accelerated failure time model and construct a moment condition based on augmented inverse probability of censoring weighting, incorporating both uncensored and censored observations. Under a heteroscedasticity-based condition on the treatment model, we establish p…

Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling
Yi Zhang, Rui Guo, Yonina C. Eldar
https://arxiv.org/abs/2509.12857 https://ar…

Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling
We propose a posterior sampling algorithm for the problem of estimating multiple independent source signals from their noisy superposition. The proposed algorithm is a combination of Gibbs sampling method and plug-and-play (PnP) diffusion priors. Unlike most existing diffusion-model-based approaches for signal separation, our method allows source priors to be learned separately and flexibly combined without retraining. Moreover, under the assumption of perfect diffusion model training, the prop…

Robust Sensitivity Analysis via Augmented Percentile Bootstrap under Simultaneous Violations of Unconfoundedness and Overlap
Han Cui, Xinran Li
https://arxiv.org/abs/2509.13169 …

Robust Sensitivity Analysis via Augmented Percentile Bootstrap under Simultaneous Violations of Unconfoundedness and Overlap
The identification of causal effects in observational studies typically relies on two standard assumptions: unconfoundedness and overlap. However, both assumptions are often questionable in practice: unconfoundedness is inherently untestable, and overlap may fail in the presence of extreme unmeasured confounding. While various approaches have been developed to address unmeasured confounding and extreme propensity scores separately, few methods accommodate simultaneous violations of both assumpt…

A Statistical Benchmark for Diffusion Posterior Sampling Algorithms
Martin Zach, Youssef Haouchat, Michael Unser
https://arxiv.org/abs/2509.12821 https://a…

A Statistical Benchmark for Diffusion Posterior Sampling Algorithms
We propose a statistical benchmark for diffusion posterior sampling (DPS) algorithms for Bayesian linear inverse problems. The benchmark synthesizes signals from sparse Lévy-process priors whose posteriors admit efficient Gibbs methods. These Gibbs methods can be used to obtain gold-standard posterior samples that can be compared to the samples obtained by the DPS algorithms. By using the Gibbs methods for the resolution of the denoising problems in the reverse diffusion, the framework also is…

Scale-Location-Truncated Beta Regression: Expanding Beta Regression to Accommodate 0 and 1
Mingang Kim, Brent A. Kaplan, Mikhail N. Koffarnus, Christopher T. Franck
https://arxiv.org/abs/2509.13167

Scale-Location-Truncated Beta Regression: Expanding Beta Regression to Accommodate 0 and 1
Beta regression is frequently used when the outcome variable y is bounded within a specific interval, transformed to the (0, 1) domain if necessary. However, standard beta regression cannot handle data observed at the boundary values of 0 or 1, as the likelihood function takes on values of either 0 or infinity. To address this issue, we propose the Scale-Location-Truncated beta (SLTB) regression model, which extends the beta distribution's domain to the [0, 1] interval. By using scale-location …

EMC Limit Level Guidelines for In-System Interference with GPS Receivers
Giorgi Tsintsadze, Haran Manoharan, Aaron Harmon, Daniel Commerou, Connor Buneta, Brian Booth, Daryl Beetner
https://arxiv.org/abs/2509.12770

EMC Limit Level Guidelines for In-System Interference with GPS Receivers
Because GPS signals are weak, electronic systems and components that are placed near GPS receivers can easily cause disruptive electromagnetic interference through their unintended radiated emissions. In this paper, EMC limit level guidelines are presented for electronics that are intended to be placed near to GPS receivers, as often happens in automotive and other applications. One of the challenges of defining limit-levels for systems intended to be integrated with GPS receivers is that the i…

Efficient estimation for flexible spatial zero-inflated models with environmental applications
Chung-Wei Shen (Department of Mathematics, National Chung Cheng University, Chia-Yi, Taiwan, R.O.C), Bu-Ren Hsu (Graduate Institute of Statistics, National Central University, Taoyuan, Taiwan, R.O.C), Chia-Ming Hsu (Graduate Institute of Statistics, National Central University, Taoyuan, Taiwan, R.O.C), Chun-Shu Chen (Graduate Institute of Statistics, National Central University, Taoyuan, Taiw…

Efficient estimation for flexible spatial zero-inflated models with environmental applications
Spatial two-component mixture models offer a robust framework for analyzing spatially correlated data with zero inflation. To circumvent potential biases introduced by assuming a specific distribution for the response variables, we employ a flexible spatial zero-inflated model. Despite its flexibility, this model poses significant computational challenges, particularly with large datasets, due to the high dimensionality of spatially dependent latent variables, the complexity of matrix operation…

NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems
Haiyang Li, Tianqi Mao, Pengyu Wang, Ruiqi Liu, Shunyu Li, Zhaocheng Wang
https://arxiv.org/abs/2509.12748

NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, operating in the near-field region due to their massive antenna arrays, are a key enabler of next-generation wireless communications but face significant challenges in channel state information (CSI) feedback. Deep learning has emerged as a powerful tool by learning compact CSI representations for feedback. However, existing methods struggle to capture the intricate structure of near-field CSI while incurring prohibitive co…

Least squares estimation of the transition density in bifurcating Markov models
S. Val\`ere Bitseki Penda
https://arxiv.org/abs/2509.12906 https://arxiv.or…

Least squares estimation of the transition density in bifurcating Markov models
In this article, we propose a least squares method for the estimation of the transition density in bifurcating Markov models. Unlike the kernel estimation, this method do not use the quotient which can be a source of errors. In order to study the rate of convergence for least squares estimators, we develop exponential inequalities for empirical process of bifurcating Markov chain under bracketing assumption. Unlike the classical processes, we observe that for bifurcating Markov chains, the comp…

Low-Altitude UAV Tracking via Sensing-Assisted Predictive Beamforming
Yifan Jiang, Qingqing Wu, Hongxun Hui, Wen Chen, Derrick Wing Kwan Ng
https://arxiv.org/abs/2509.12698 http…

Low-Altitude UAV Tracking via Sensing-Assisted Predictive Beamforming
Sensing-assisted predictive beamforming, as one of the enabling technologies for emerging integrated sensing and communication (ISAC) paradigm, shows significant promise for enhancing various future unmanned aerial vehicle (UAV) applications. However, current works predominately emphasized on spectral efficiency enhancement, while the impact of such beamforming techniques on the communication reliability was largely unexplored and challenging to characterize. To fill this research gap and tackl…

Modeling nonstationary spatial processes with normalizing flows
Pratik Nag, Andrew Zammit-Mangion, Ying Sun
https://arxiv.org/abs/2509.12884 https://arxiv.…

Modeling nonstationary spatial processes with normalizing flows
Nonstationary spatial processes can often be represented as stationary processes on a warped spatial domain. Selecting an appropriate spatial warping function for a given application is often difficult and, as a result of this, warping methods have largely been limited to two-dimensional spatial domains. In this paper, we introduce a novel approach to modeling nonstationary, anisotropic spatial processes using neural autoregressive flows (NAFs), a class of invertible mappings capable of generat…

Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI
Po-Heng Chou, Jiun-Jia Wu, Wan-Jen Huang, Ronald Y. Chang
https://arxiv.org/abs/2509.12658 h…

Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI
In this paper, we propose a sustainable long short-term memory (LSTM)-based precoding framework for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) MIMO systems. Instead of explicit channel state information (CSI) estimation, the framework exploits uplink pilot sequences to implicitly learn channel characteristics, reducing both pilot overhead and inference complexity. Practical hardware constraints are addressed by incorporating the phase-dependent amplitude model of…

Multivariate Low-Rank State-Space Model with SPDE Approach for High-Dimensional Data
Jacopo Rodeschini, Lorenzo Tedesco, Francesco Finazzi, Philipp Otto, Alessandro Fass\`o
https://arxiv.org/abs/2509.12825

Multivariate Low-Rank State-Space Model with SPDE Approach for High-Dimensional Data
This paper proposes a novel low-rank approximation to the multivariate State-Space Model. The Stochastic Partial Differential Equation (SPDE) approach is applied component-wise to the independent-in-time Matérn Gaussian innovation term in the latent equation, assuming component independence. This results in a sparse representation of the latent process on a finite element mesh, allowing for scalable inference through sparse matrix operations. Dependencies among observed components are introduc…

Data Fusion for BS-UE Cooperative MIMO-OFDM ISAC
Yixin Ding, Haoyu Jiang, Xiaoli Xu, Yanan Liang, Yong Zeng
https://arxiv.org/abs/2509.12646 https://arxiv.…

Data Fusion for BS-UE Cooperative MIMO-OFDM ISAC
Integrated sensing and communication (ISAC) is a promising technique for expanding the functionalities of wireless networks with enhanced spectral efficiency. The 3rd Generation Partnership Project (3GPP) has defined six basic sensing operation modes in wireless networks. To further enhance the sensing capability of wireless networks, this paper proposes a new sensing operation mode, i.e., the base station (BS) and user equipment (UE) cooperative sensing. Specifically, after decoding the commun…

Power-Dominance in Estimation Theory: A Third Pathological Axis
Sri Satish Krishna Chaitanya Bulusu, Mikko Sillanp\"a\"a
https://arxiv.org/abs/2509.12691 https://

Power-Dominance in Estimation Theory: A Third Pathological Axis
This paper introduces a novel framework for estimation theory by introducing a second-order diagnostic for estimator design. While classical analysis focuses on the bias-variance trade-off, we present a more foundational constraint. This result is model-agnostic, domain-agnostic, and is valid for both parametric and non-parametric problems, Bayesian and frequentist frameworks. We propose to classify the estimators into three primary power regimes. We theoretically establish that any estimator o…

Generalizable Blood Pressure Estimation from Multi-Wavelength PPG Using Curriculum-Adversarial Learning
Zequan Liang, Ruoyu Zhang, Wei Shao, Mahdi Pirayesh Shirazi Nejad, Ehsan Kourkchi, Setareh Rafatirad, Houman Homayoun
https://arxiv.org/abs/2509.12518

Generalizable Blood Pressure Estimation from Multi-Wavelength PPG Using Curriculum-Adversarial Learning
Accurate and generalizable blood pressure (BP) estimation is vital for the early detection and management of cardiovascular diseases. In this study, we enforce subject-level data splitting on a public multi-wavelength photoplethysmography (PPG) dataset and propose a generalizable BP estimation framework based on curriculum-adversarial learning. Our approach combines curriculum learning, which transitions from hypertension classification to BP regression, with domain-adversarial training that co…

Instrument, Variable and Model Selection with Nonignorable Nonresponse
Ji Chen, Jun Shao
https://arxiv.org/abs/2509.12557 https://arxiv.org/pdf/2509.12557

Instrument, Variable and Model Selection with Nonignorable Nonresponse
With nonignorable nonresponse, an effective method to construct valid estimators of population parameters is to use a covariate vector called instrument that can be excluded from the nonresponse propensity but are still useful covariate even when other covariates are conditioned. The existing work in this approach assumes such an instrument is given, which is frequently not the case in applications. In this paper we investigate how to search for an instrument from a given set of covariates. The…

Rapid Adaptation of SpO2 Estimation to Wearable Devices via Transfer Learning on Low-Sampling-Rate PPG
Zequan Liang, Ruoyu Zhang, Wei Shao, krishna Karthik, Ehsan Kourkchi, Setareh Rafatirad, Houman Homayoun
https://arxiv.org/abs/2509.12515

Rapid Adaptation of SpO2 Estimation to Wearable Devices via Transfer Learning on Low-Sampling-Rate PPG
Blood oxygen saturation (SpO2) is a vital marker for healthcare monitoring. Traditional SpO2 estimation methods often rely on complex clinical calibration, making them unsuitable for low-power, wearable applications. In this paper, we propose a transfer learning-based framework for the rapid adaptation of SpO2 estimation to energy-efficient wearable devices using low-sampling-rate (25Hz) dual-channel photoplethysmography (PPG). We first pretrain a bidirectional Long Short-Term Memory (BiLSTM) m…

Cox Regression on the Plane
Yael Travis-Lumer, Micha Mandel, Rebecca A. Betensky, Malka Gorfine
https://arxiv.org/abs/2509.12473 https://arxiv.org/pdf/2509…

Cox Regression on the Plane
The Cox proportional hazards model is the most widely used regression model in univariate survival analysis. Extensions of the Cox model to bivariate survival data, however, remain scarce. We propose two novel extensions based on a Lehmann-type representation of the survival function. The first, the simple Lehmann model, is a direct extension that retains a straightforward structure. The second, the generalized Lehmann model, allows greater flexibility by incorporating three distinct regression…

Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device
Wei Shao, Ruoyu Zhang, Zequan Liang, Ehsan Kourkchi, Setareh Rafatirad, Houman Homayoun
https://arxiv.org/abs/2509.12510

Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device
Wearable photoplethysmography (PPG) is embedded in billions of devices, yet its optical waveform is easily corrupted by motion, perfusion loss, and ambient light, jeopardizing downstream cardiometric analytics. Existing signal-quality assessment (SQA) methods rely either on brittle heuristics or on data-hungry supervised models. We introduce the first fully unsupervised SQA pipeline for wrist PPG. Stage 1 trains a contrastive 1-D ResNet-18 on 276 h of raw, unlabeled data from heterogeneous sour…

A computational method for type I error rate control in power-maximizing response-adaptive randomization
Stef Baas, Lukas Pin, Sof\'ia S. Villar, William F. Rosenberger
https://arxiv.org/abs/2509.12448

A computational method for type I error rate control in power-maximizing response-adaptive randomization
Maximizing statistical power in experimental design often involves imbalanced treatment allocation, but several challenges hinder its practical adoption: (1) the misconception that equal allocation always maximizes power, (2) when only targeting maximum power, more than half the participants may be expected to obtain inferior treatment, and (3) response-adaptive randomization (RAR) targeting maximum statistical power may inflate type I error rates substantially. Recent work identified issue (3)…

Partial Secrecy Analysis in Wireless Systems: Diversity-Enhanced PLS over Generalized Fading Channels
Henry Carvajal Mora, Nathaly Orozco, Fernando Almeida Garc\'ia, Jos\'e Vega-S\'anchez, Felipe Grijalva, Edgar Benitez Olivo
https://arxiv.org/abs/2509.12359

Partial Secrecy Analysis in Wireless Systems: Diversity-Enhanced PLS over Generalized Fading Channels
Securing information in future mobile networks is challenging, especially for devices with limited computational resources. Physical layer security (PLS) offers a viable solution by leveraging wireless channel randomness. When full secrecy is unattainable, the partial secrecy regime provides a realistic alternative. This work analyzes partial secrecy performance under the generalized multicluster fluctuating two-ray (MFTR) fading model, which subsumes many classical fading cases. We study a sys…

System Reliability Estimation via Shrinkage
Beidi Qiang, Edsel Pena
https://arxiv.org/abs/2509.12420 https://arxiv.org/pdf/2509.12420

System Reliability Estimation via Shrinkage
In a coherent reliability system composed of multiple components configured according to a specific structure function, the distribution of system time to failure, or system lifetime, is often of primary interest. Accurate estimation of system reliability is critical in a wide range of engineering and industrial applications, forming decisions in system design, maintenance planning, and risk assessment. The system lifetime distribution can be estimated directly using the observed system failure…

FAS-ARIS: Turning Multipath Challenges Into Localization Opportunities
Hua Chen, Tao Gong, Tuo Wu, Maged Elkashlan, Baiyang Liu, Chan-Byoung Chae, Kin-Fai Tong, Kai-Kit Wong
https://arxiv.org/abs/2509.12348

FAS-ARIS: Turning Multipath Challenges Into Localization Opportunities
Traditional single-input single-output (SISO) systems face fundamental limitations in achieving accurate three-dimensional (3D) localization due to limited spatial degrees of freedom (DoF) and the adverse impact of multipath propagation. This paper proposes a novel fluid antenna system (FAS)-active reconfigurable intelligent surface (ARIS) framework that transforms multipath effects from a hindrance into a resource for enhanced localization. By synergistically combining the signal amplification…

Significant inference and confidence sets for graphical models
P. A. Koldanov, A. P. Koldanov
https://arxiv.org/abs/2509.12292 https://arxiv.org/pdf/2509.1…

Significant inference and confidence sets for graphical models
The problem of identifying statistically significant inferences about the structure of the graphical model is considered, along with the related task of constructing a confidence set for a graphical model. It has been proven that the procedure for constructing such set is equivalent to the procedure for simultaneous testing of hypotheses and alternatives regarding the composition of the graphical model. Some variants of the simultaneous testing of hypotheses and alternatives are discussed. It i…

[2025-09-17 Wed (UTC), 20 new articles found for eess.SP Signal Processing]
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New generalized unit distributions based on order statistics
Iman Mohamed Attia
https://arxiv.org/abs/2509.12276 https://arxiv.org/pdf/2509.12276

New generalized unit distributions based on order statistics
In the present paper, the author discusses the derivation of unit distributions and the derivation of the generalized form using the order statistics. The author discusses the Kumaraswamy as the smallest order statistic of the unit power distribution derived from the inverse Weibull distribution. The author discusses the unit Rayleigh distribution and how it can be generalized using the smallest, largest, and kth order statistics. Using the order statistics to generalize a distribution differs …

Replaced article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- A Note on Shift Retrieval Problems
Cristian Rusu
https://

[2025-09-17 Wed (UTC), 16 new articles found for stat.ME Methodology]
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Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Synergetic Empowerment: Wireless Communications Meets Embodied Intelligence
Hongtao Liang, Yihe Diao, YuHang Wu, Fuhui Zhou, Qihui Wu

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[2/2]:
- Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with ...
Soojin Park, Suyeon Kang, Chioun Lee

When marine radar target detection meets pretrained large language models
Qiying Hu, Linping Zhang, Xueqian Wang, Gang Li, Yu Liu, Xiao-Ping Zhang
https://arxiv.org/abs/2509.12110

When marine radar target detection meets pretrained large language models
Deep learning (DL) methods are widely used to extract high-dimensional patterns from the sequence features of radar echo signals. However, conventional DL algorithms face challenges such as redundant feature segments, and constraints from restricted model sizes. To address these issues, we propose a framework that integrates feature preprocessing with large language models (LLMs). Our preprocessing module tokenizes radar sequence features, applies a patch selection algorithm to filter out uninf…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/2]:
- Semiparametric sensitivity analysis: unmeasured confounding in observational studies
Razieh Nabi, Matteo Bonvini, Edward H. Kennedy, Ming-Yueh Huang, Marcela Smid, Daniel O. Scharfstein

RadarLLM: Adapting Pretrained Large Language Models for Marine Radar Target Detection with Preference-aware Loss
Qiying Hu
https://arxiv.org/abs/2509.12089 https://

RadarLLM: Adapting Pretrained Large Language Models for Marine Radar Target Detection with Preference-aware Loss
Recent advances in pre-trained large language models (LLMs) have demonstrated their capacities to capture universal knowledge, making them promising general-purpose optimization solvers for wireless signal processing. Motivated by these findings, we take the first step towards fine-tuning pre-trained LLMs for the effective analysis of radar signal features in marine target detection tasks. Nevertheless, directly fine-tuning pre-trained LLMs on marine target detection tasks tends to suffer from …

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Cond...
Byeonghee Lee, Joonsung Kang

Meta Fluid Antenna: Architecture Design, Performance Analysis, Experimental Examination
Baiyang Liu, Jiewei Huang, Tuo Wu, Huan Meng, Fengcheng Mei, Lei Ning, Kai-Kit Wong, Hang Wong, Kin-Fai Tong, Kwai-Man Luk
https://arxiv.org/abs/2509.12032

Meta Fluid Antenna: Architecture Design, Performance Analysis, Experimental Examination
Fluid antenna systems (FAS) have recently emerged as a promising solution for sixth-generation (6G) ultra-dense connectivity. These systems utilize dynamic radiating and/or shaping techniques to mitigate interference and improve spectral efficiency without relying on channel state information (CSI). The reported improvements achieved by employing a single dynamically activated radiating position in fluid antenna multiple access (FAMA) are significant. To fully realize the potential of FAMA in m…

Least squares-based methods to bias adjustment in scalar-on-function regression model using a functional instrumental variable
Xiwei Chen, Ufuk Beyaztas, Caihong Qin, Heyang Ji, Gilson Honvoh, Roger S. Zoh, Lan Xue, Carmen D. Tekwe
https://arxiv.org/abs/2509.12122

Least squares-based methods to bias adjustment in scalar-on-function regression model using a functional instrumental variable
Instrumental variables are widely used to adjust for measurement error bias when assessing associations of health outcomes with ME prone independent variables. IV approaches addressing ME in longitudinal models are well established, but few methods exist for functional regression. We develop two methods to adjust for ME bias in scalar on function linear models. We regress a scalar outcome on an ME prone functional variable using a functional IV for model identification and propose two least squ…

Optimized Sparse Network Coverage via L1-norm Minimization
Souvik Paul, Iv\'an Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu
https://arxiv.org/abs/2509.11994 …

Optimized Sparse Network Coverage via L1-norm Minimization
The selection of nodes that can serve as cluster heads, local sinks and gateways is a critical challenge in distributed sensor and communication networks. This paper presents a novel framework for identifying a minimal set of nexus nodes to ensure full network coverage while minimizing cost. By formulating the problem as a convex relaxation of the NP-hard set cover problem, we integrate the graph theoretic centrality measures of node degree and betweenness centrality into a cost function optimi…

Modeling Non-Uniform Hypergraphs Using Determinantal Point Processes
Yichao Chen, Jingfei Zhang, Ji Zhu
https://arxiv.org/abs/2509.12028 https://arxiv.org/…

Modeling Non-Uniform Hypergraphs Using Determinantal Point Processes
Most statistical models for networks focus on pairwise interactions between nodes. However, many real-world networks involve higher-order interactions among multiple nodes, such as co-authors collaborating on a paper. Hypergraphs provide a natural representation for these networks, with each hyperedge representing a set of nodes. The majority of existing hypergraph models assume uniform hyperedges (i.e., edges of the same size) or rely on diversity among nodes. In this work, we propose a new hy…

Multi-Stage Location Optimization Through Power Delay Profile Alignment Using Site-Specific Wireless Ray Tracing
Mingjun Ying, Peijie Ma, Dipankar Shakya, Theodore S. Rappaport
https://arxiv.org/abs/2509.11923

Multi-Stage Location Optimization Through Power Delay Profile Alignment Using Site-Specific Wireless Ray Tracing
Ray tracing (RT) simulations require accurate transmitter (TX) and receiver (RX) location information from real-world measurements to accurately characterize wireless propagation behavior in an environment. Such wireless propagation measurements typically employ GPS-based logging for TX/RX locations, which can produce meter-level errors that lead to unreliable RT calibration and validation. These location misalignments cause inaccurate interactions between RT-generated multipath components (MPC…

Scalable Variable Selection and Model Averaging for Latent Regression Models Using Approximate Variational Bayes
Gregor Zens, Mark F. J. Steel
https://arxiv.org/abs/2509.11751 h…

Scalable Variable Selection and Model Averaging for Latent Regression Models Using Approximate Variational Bayes
We propose a fast and theoretically grounded method for Bayesian variable selection and model averaging in latent variable regression models. Our framework addresses three interrelated challenges: (i) intractable marginal likelihoods, (ii) exponentially large model spaces, and (iii) computational costs in large samples. We introduce a novel integrated likelihood approximation based on mean-field variational posterior approximations and establish its asymptotic model selection consistency under …

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, Markku Juntti
https://arxiv.org/abs/2509.11725

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context, infrastructure-mounted cameras can capture rich environmental information that can facilitate beam tracking design. In this work, we develop an efficient attention-enhanced machine learning model for long-term beam tracking built upon convolutional neural networks and gated recurrent units to predict both current and …

Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference
Minhao Yao, Anqi Wang, Xihao Li, Zhonghua Liu
https://arxiv.org/abs/2509.11519 http…

Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference
Mendelian randomization (MR) has become an essential tool for causal inference in biomedical and public health research. By using genetic variants as instrumental variables, MR helps address unmeasured confounding and reverse causation, offering a quasi-experimental framework to evaluate causal effects of modifiable exposures on health outcomes. Despite its promise, MR faces substantial methodological challenges, including invalid instruments, weak instrument bias, and design complexities acros…

Low-Altitude Wireless Networks: A Survey
Jun Wu, Yaoqi Yang, Weijie Yuan, Wenchao Liu, Jiacheng Wang, Tianqi Mao, Lin Zhou, Yuanhao Cui, Fan Liu, Geng Sun, Nan Wu, Dezhi Zheng, Jindan Xu, Nan Ma, Zhiyong Feng, Wei Xu, Dusit Niyato, Chau Yuen, Xiaojun Jing, Zhiguo Shi, Yingchang Liang, Shi Jin, Dong In Kim, Jiangzhou Wang, Ping Zhang, Hao Yin, Jun Zhang
https://…

Low-Altitude Wireless Networks: A Survey
The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering fu…

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials
Ante Bing (Department of Mathematics,Statistics, Boston University), Donna Spiegelman (Department of Biostatistics, Yale University), Judith J. Lok (Department of Mathematics,Statistics, Boston University)
https://arxiv.org/abs/2509.11479

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials
The Learn-As-you-GO (LAGO) design provides a rigorous framework for adapting the intervention package based on accumulating data while the trial is ongoing. This article improves the flexibility of the LAGO design by incorporating statistical power as an optimization criterion (power goal) in LAGO optimizations. We propose the unconditional and conditional power approaches to add a power goal. Both approaches estimate the power at the end of the LAGO trial using data from prior stages, and incr…

RadioLAM: A Large AI Model for Fine-Grained 3D Radio Map Estimation
Zhiyuan Liu, Qingyu Liu, Shuhang Zhang, Hongliang Zhang, Lingyang Song
https://arxiv.org/abs/2509.11571 https…

RadioLAM: A Large AI Model for Fine-Grained 3D Radio Map Estimation
A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained three-dimensional (3D) radio map estimation involves inferring a high-resolution radio map for the two-dimensional (2D) area at an arbitrary target height within a 3D region of interest, using radio samples collected by sensors sparsely distributed in that 3D region. Solutions to the problem are crucial for efficient spe…

A New Class of Mark-Specific Proportional Hazards Models for Recurrent Events: Application to Opioid Refills Among Post-Surgical Patients
Eileen Yang, Donglin Zeng, Mark Bicket, Yi Li
https://arxiv.org/abs/2509.11472

A New Class of Mark-Specific Proportional Hazards Models for Recurrent Events: Application to Opioid Refills Among Post-Surgical Patients
Prescription opioids relieve moderate-to-severe pain after surgery, but overprescription can lead to misuse and overdose. Understanding factors associated with post-surgical opioid refills is crucial for improving pain management and reducing opioid-related harms. Conventional methods often fail to account for refill size or dosage and capture patient risk dynamics. We address this gap by treating dosage as a continuously varying mark for each refill event and proposing a new class of mark-spec…

Stacked Intelligent Metasurface for End-to-End OFDM System
Yida Zhang, Qiuyan Liu, Hongtao Luo, Yuqi Xia, Qiang Wang
https://arxiv.org/abs/2509.11551 https://

Stacked Intelligent Metasurface for End-to-End OFDM System
Stacked intelligent metasurface (SIM) and dual-polarized SIM (DPSIM) enabled wave-domain signal processing have emerged as promising research directions for offloading baseband digital processing tasks and efficiently simplifying transceiver design. However, existing architectures are limited to employing SIM (DPSIM) for a single communication function, such as precoding or combining. To further enhance the overall performance of SIM (DPSIM)-assisted systems and achieve end-to-end (E2E) joint o…

Unified Distributed Estimation Framework for Sufficient Dimension Reduction Based on Conditional Moments
Hongying Li, Minyi Zhu, Yaqi Cao, Xinyi Xu
https://arxiv.org/abs/2509.11455

Unified Distributed Estimation Framework for Sufficient Dimension Reduction Based on Conditional Moments
Nowadays, massive datasets are typically dispersed across multiple locations, encountering dual challenges of high dimensionality and huge sample size. Therefore, it is necessary to explore sufficient dimension reduction (SDR) methods for distributed data. In this paper, we first propose an exact distributed estimation of sliced inverse regression, which substantially improves computational efficiency while obtaining identical estimation as that on the full sample. Then, we propose a unified di…

Simplified Design Approach for Via Transitions up to 67 GHz
Giorgi Tsintsadze, Reza Vahdani, James L. Drewniak, Richard Zai
https://arxiv.org/abs/2509.11542 https://

Simplified Design Approach for Via Transitions up to 67 GHz
A systematic approach for high-speed via transition design is proposed. The effects of via barrel radius, anti-pad size, and the distance from adjacent stitching (GND) vias on bandwidth are analyzed and characterized. Guidelines for selecting parameter values are provided and validated by correlating 3D full-wave FEM simulation results with actual measurements of the coupon board. When a sufficient number of stitching vias are used, the via structure can be approximated as a coaxial transmissio…

BE-BOIN: A Dose Optimization Design Accommodating Backfill and Late-Onset Toxicity
Kai Chen, Yixuan Zhao, Kentaro Takeda, Ying Yuan
https://arxiv.org/abs/2509.11333 https://

BE-BOIN: A Dose Optimization Design Accommodating Backfill and Late-Onset Toxicity
The US Food and Drug Administration (FDA) launched Project Optimus and issued guidance to reform dose-finding and selection trials, shifting the paradigm from identifying the maximum tolerable dose (MTD) to determining the optimal biological dose (OBD), which optimizes the risk and benefit of treatments. The FDA's guidance emphasizes the importance of collecting sufficient toxicity and efficacy data across multiple doses and considering late-onset cumulative toxicity that often results in toler…

Cooperative UAV-mounted RISs-assisted Energy-efficient Communications
Hongyang Pan, Yanheng Liu, Geng Sun, Qingqing Wu, Tierui Gong, Pengfei Wang, Dusit Niyato, Chau Yuen
https://arxiv.org/abs/2509.11533

Cooperative UAV-mounted RISs-assisted Energy-efficient Communications
Cooperative reconfigurable intelligent surfaces (RISs) are promising technologies for 6G networks to support a great number of users. Compared with the fixed RISs, the properly deployed RISs may improve the communication performance with less communication energy consumption, thereby improving the energy efficiency. In this paper, we consider a cooperative unmanned aerial vehicle-mounted RISs (UAV-RISs)-assisted cellular network, where multiple RISs are carried and enhanced by UAVs to serve mul…

KOO Method-based Consistent Clustering for Group-wise Linear Regression with Graph Structure
M. Ohishi, R. Oda
https://arxiv.org/abs/2509.11103 https://arx…

KOO Method-based Consistent Clustering for Group-wise Linear Regression with Graph Structure
The kick-one-out (KOO) method is a variable selection method based on a model selection criterion. The method is very simple, and yet it has consistency in variable selection under a high-dimensional asymptotic framework with a specific model selection criterion. This paper proposes the join-twotogether (JTT) method, which is a clustering method based on the KOO method for group-wise linear regression with graph structure. The JTT method formulates the clustering problem as an edge selection pr…

Radio Frequency Amplitude-Modulation to Frequency-Modulation Signal Converter
Rishab Parthasarathy, Michael Popik, Noah Haefner
https://arxiv.org/abs/2509.11510 https://

Radio Frequency Amplitude-Modulation to Frequency-Modulation Signal Converter
In this project, we wanted to discover an analog topology that could effectively convert amplitude-modulated (AM) signals to frequency-modulated (FM) signals, while also ensuring that both sets of signals were within their respective radio frequency (RF) bands. To that end, an effective topology for doing so was developed, characterized, and demonstrated, requiring the ability to de-modulate incoming signals from the AM radio band--spanning from 530 kHz to 1700 kHz--and re-modulate these signal…

Varying-Coefficient Fr\'echet Regression
Yanzhao Wang, Jianqiang Zhang, Wangli Xu
https://arxiv.org/abs/2509.11061 https://arxiv.org/pdf/2509.11061

Varying-Coefficient Fréchet Regression
As a growing number of problems involve variables that are random objects, the development of models for such data has become increasingly important. This paper introduces a novel varying-coefficient Fréchet regression model that extends the classical varying-coefficient framework to accommodate random objects as responses. The proposed model provides a unified methodology for analyzing both Euclidean and non-Euclidean response variables. We develop a comprehensive estimation procedure that ac…

Dynamic Length FSK Waveforms for Joint Communications and Radar
Tian Han, Peter J Smith, Urbashi Mitra, Jamie S Evans, Robin Evans, Rajitha Senanayake
https://arxiv.org/abs/2509.11500

Dynamic Length FSK Waveforms for Joint Communications and Radar
Motivated by the constant modulus property of frequency shift keying (FSK) based waveforms and the stabilisation of its radar performance with an increase in the number of subpulses, in this paper an FSK-based dynamic subpulse number joint communications and radar waveform design is proposed. From a communications point of view, the system operates based on traditional FSK modulation. From a sensing point of view, although the subpulses are continuously generated and transmitted, radar waveform…

A Latent Factor Panel Approach to Spatiotemporal Causal Inference
Jiaxi Wu, Alexander Franks
https://arxiv.org/abs/2509.10974 https://arxiv.org/pdf/2509.10…

A Latent Factor Panel Approach to Spatiotemporal Causal Inference
Unmeasured confounding can severely bias causal effect estimates from spatiotemporal observational data, especially when the confounders do not vary smoothly in time and space. In this work, we develop a method for addressing unmeasured confounding in spatiotemporal contexts by building on models from the panel data literature and methods in multivariate causal inference. Our method is based on a factor confounding assumption, which posits that effects of unmeasured confounders on exposures and…

Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, A. Lee Swindlehurst, Markku Juntti
https://arxiv.org/abs/2509.11419

Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an efficient sensing-assisted long-term beam tracking framework that selects optimal beams from a codebook for current and multiple future time slots. We first design a large attention-enhanced neural network (NN) to fully exploit past visual observations for beam tracking. A convolutional NN extracts compact image features…

A Tutorial on Conducting Mediation Analysis with Exposure Mixtures
Yiran Wang, Yi-Ting Lin, Sean McGrath, John D. Meeker, Sung Kyun Park, Joshua L. Warren, Bhramar Mukherjee
https://arxiv.org/abs/2509.10916

A Tutorial on Conducting Mediation Analysis with Exposure Mixtures
Causal mediation analysis is a powerful tool in environmental health research, allowing researchers to uncover the pathways through which exposures influence health outcomes. While traditional mediation methods have been widely applied to individual exposures, real-world scenarios often involve complex mixtures. Such mixtures introduce unique methodological challenges, including multicollinearity, sparsity of active exposures, and potential nonlinear and interactive effects. This paper provides…

Solving ill-conditioned polynomial equations using score-based priors with application to multi-target detection
Rafi Beinhorn, Shay Kreymer, Amnon Balanov, Michael Cohen, Alon Zabatani, Tamir Bendory
https://arxiv.org/abs/2509.11397

Solving ill-conditioned polynomial equations using score-based priors with application to multi-target detection
Recovering signals from low-order moments is a fundamental yet notoriously difficult task in inverse problems. This recovery process often reduces to solving ill-conditioned systems of polynomial equations. In this work, we propose a new framework that integrates score-based diffusion priors with moment-based estimators to regularize and solve these nonlinear inverse problems. This introduces a new role for generative models: stabilizing polynomial recovery from noisy statistical features. As a…

Two-Stage Least Squares Instrumental Variable Estimation for Semiparametric Accelerated Failure Time Models with Right-Censored Data
Zian Zhuang, Hua Zhou, Jin Zhou, Gang Li
https://arxiv.org/abs/2509.10905

Two-Stage Least Squares Instrumental Variable Estimation for Semiparametric Accelerated Failure Time Models with Right-Censored Data
Instrumental variable (IV) analysis is widely used in fields such as economics and epidemiology to address unobserved confounding and measurement error when estimating the causal effects of intermediate covariates on outcomes. However, extending the commonly used two-stage least squares (TSLS) approach to survival settings is nontrivial due to censoring. This paper introduces a novel extension of TSLS to the semiparametric accelerated failure time (AFT) model with right-censored data, supported…

Closed-form parameter estimation for the bivariate gamma distribution: New approaches
Roberto Vila, Helton Saulo
https://arxiv.org/abs/2509.10794 https://a…

Closed-form parameter estimation for the bivariate gamma distribution: New approaches
We propose new closed-form estimators for the parameters of McKay's bivariate gamma distribution by exploiting monotone transformations of the likelihood equations. As a special case, our framework recovers the estimators recently introduced by Zhao et al. (2022) [Zhao, J., Jang, Y.-H., and Kim, H. (2022). Closed-form and bias-corrected estimators for the bivariate gamma distribution. Journal of Multivariate Analysis, 191:105009]. Theoretical properties, including strong consistency and asympto…

Outlier-Resistant Heterogeneous Treatment Effect Estimation in HDLSS Settings via GAT--CVAE Framework
Byeonghee Lee, Joonsung Kang
https://arxiv.org/abs/2509.10787 https://

Outlier-Resistant Heterogeneous Treatment Effect Estimation in HDLSS Settings via GAT--CVAE Framework
We introduce a robust framework for heterogeneous treatment effect (HTE) estimation tailored to high-dimensional low sample size (HDLSS) settings. By combining Graph Attention Networks (GAT) to capture structural dependencies among confounders with a Conditional Variational Autoencoder (CVAE) for latent representation learning, our method expands the sample space and performs clustering that integrates even outlier sets into coherent subgroups. Clusterwise causal effects are then estimated usin…

[2025-09-16 Tue (UTC), 17 new articles found for stat.ME Methodology]
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