Tootfinder

Online Conformal Selection with Accept-to-Reject Changes
Kangdao Liu, Huajun Xi, Chi-Man Vong, Hongxin Wei
https://arxiv.org/abs/2508.13838 https://arxiv.o…

Online Conformal Selection with Accept-to-Reject Changes
Selecting a subset of promising candidates from a large pool is crucial across various scientific and real-world applications. Conformal selection offers a distribution-free and model-agnostic framework for candidate selection with uncertainty quantification. While effective in offline settings, its application to online scenarios, where data arrives sequentially, poses challenges. Notably, conformal selection permits the deselection of previously selected candidates, which is incompatible with…

Bend It, Aim It, Tap It: Designing an On-Body Disambiguation Mechanism for Curve Selection in Mixed Reality
Xiang Li, Per Ola Kristensson
https://arxiv.org/abs/2508.13748 https:…

Bend It, Aim It, Tap It: Designing an On-Body Disambiguation Mechanism for Curve Selection in Mixed Reality
Object selection in Mixed Reality (MR) becomes particularly challenging in dense or occluded environments, where traditional mid-air ray-casting often leads to ambiguity and reduced precision. We present two complementary techniques: (1) a real-time Bezier Curve selection paradigm guided by finger curvature, enabling expressive one-handed trajectories, and (2) an on-body disambiguation mechanism that projects the four nearest candidates onto the user's forearm via proximity-based mapping. Toget…

Toward Practical Fluid Antenna Systems: Co-Optimizing Hardware and Software for Port Selection and Beamforming
Sai Xu, Kai-Kit Wong, Yanan Du, Hanjiang Hong, Chan-Byoung Chae, Baiyang Liu, Kin-Fai Tong
https://arxiv.org/abs/2507.14035

Toward Practical Fluid Antenna Systems: Co-Optimizing Hardware and Software for Port Selection and Beamforming
This paper proposes a hardware-software co-design approach to efficiently optimize beamforming and port selection in fluid antenna systems (FASs). To begin with, a fluid-antenna (FA)-enabled downlink multi-cell multiple-input multiple-output (MIMO) network is modeled, and a weighted sum-rate (WSR) maximization problem is formulated. Second, a method that integrates graph neural networks (GNNs) with random port selection (RPS) is proposed to jointly optimize beamforming and port selection, while…

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
Jos\'e Eduardo Zerna Torres, Marios Avgeris, Chrysa Papagianni, Gergely Pongr\'acz, Istv\'an G\'odor, Paola Grosso
https://arxiv.org/abs/2508.13806

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 sw…

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
Jianfei Li, Kevin Kam Fung Yuen
https://arxiv.org/abs/2507.14022

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient B…

Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities
Muhammad Z. Haq, Nadia N. Qadri, Omer Chughtai, Sadiq A. Ahmad, Waqas Khalid, Heejung Yu
https://arxiv.org/abs/2508.14471

Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities
This paper presents ANS-V2X, an Adaptive Network Selection framework tailored for latency-aware V2X systems operating under varying vehicle densities and heterogeneous network conditions. Modern vehicular environments demand low-latency and high-throughput communication, yet real-time network selection is hindered by diverse application requirements and the coexistence of multiple Radio Access Technologies (RATs) such as 4G, 5G, and ad hoc links. ANS-V2X employs a heuristic-driven approach to a…

Diffusion-Driven High-Dimensional Variable Selection
Minjie Wang, Xiaotong Shen, Wei Pan
https://arxiv.org/abs/2508.13890 https://arxiv.org/pdf/2508.13890

Diffusion-Driven High-Dimensional Variable Selection
Variable selection for high-dimensional, highly correlated data has long been a challenging problem, often yielding unstable and unreliable models. We propose a resample-aggregate framework that exploits diffusion models' ability to generate high-fidelity synthetic data. Specifically, we draw multiple pseudo-data sets from a diffusion model fitted to the original data, apply any off-the-shelf selector (e.g., lasso or SCAD), and store the resulting inclusion indicators and coefficients. Aggregat…

Online 3D Gaussian Splatting Modeling with Novel View Selection
Byeonggwon Lee, Junkyu Park, Khang Truong Giang, Soohwan Song
https://arxiv.org/abs/2508.14014 https://

Online 3D Gaussian Splatting Modeling with Novel View Selection
This study addresses the challenge of generating online 3D Gaussian Splatting (3DGS) models from RGB-only frames. Previous studies have employed dense SLAM techniques to estimate 3D scenes from keyframes for 3DGS model construction. However, these methods are limited by their reliance solely on keyframes, which are insufficient to capture an entire scene, resulting in incomplete reconstructions. Moreover, building a generalizable model requires incorporating frames from diverse viewpoints to ac…

Optimizing Scalar Selection in Elliptic Curve Cryptography Using Differential Evolution for Enhanced Security
Takreem Haider
https://arxiv.org/abs/2508.13520 https://

Optimizing Scalar Selection in Elliptic Curve Cryptography Using Differential Evolution for Enhanced Security
Elliptic Curve Cryptography (ECC) is a fundamental component of modern public-key cryptosystems that enable efficient and secure digital signatures, key exchanges, and encryption. Its core operation, scalar multiplication, denoted as $k \cdot P$, where $P$ is a base point and $k$ is a private scalar, relies heavily on the secrecy and unpredictability of $k$. Conventionally, $k$ is selected using user input or pseudorandom number generators. However, in resource-constrained environments with wea…

Comparing Model-agnostic Feature Selection Methods through Relative Efficiency
Chenghui Zheng, Garvesh Raskutti
https://arxiv.org/abs/2508.14268 https://ar…

Comparing Model-agnostic Feature Selection Methods through Relative Efficiency
Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest. Wrapper methods are commonly used because they are typically model-agnostic, even though they are computationally intensive. In this paper, we focus on feature selection methods related to the Generalized Covariance Measure (GCM) and Leave-One-Covariate-Out (LOCO) estimation, and provide a comparison based on relative efficiency. In particular, we present a theoretical compar…

Quantum Gambling: Best-Arm Strategies for Generator Selection in Adaptive Variational Algorithms
Rick Huang, Artur F. Izmaylov
https://arxiv.org/abs/2509.14917 https://

Quantum Gambling: Best-Arm Strategies for Generator Selection in Adaptive Variational Algorithms
Adaptive variational algorithms suffer from prohibitively high measurement costs during the generator selection step, since energy gradients must be estimated for a large operator pool. This scaling bottleneck limits their applicability to larger molecular systems on near-term quantum devices. We address this challenge by reformulating generator selection as a Best Arm Identification (BAI) problem, where the goal is to identify the generator with the largest energy gradient using as few measure…

Each week Metacurity offers our free and paid subscribers a run-down of the best infosec-related long reads.
This week's selection covers
--Scattered Spider's Urban speaks,
--EU nations subsidize spyware makers,
--AI chatbots fool senior citizens with phishing,
--Scam compounds endanger job seekers and tourists,
--Meat and dairy companies spy on activists,
--Texas wants in on Chihuahua's massive surveillance,
--Cyber outsourcing thr…

Best infosec-related long reads for the week of 9/13/25
Scattered Spider's Urban speaks, EU nations subsidize spyware makers, AI chatbots fool senior citizens with phishing, Scam compounds endanger job seekers and tourists, Meat and dairy companies spy on activists, Texas wants in on Chihuahua's massive surveillance, Cyber outsourcing threatens the UK

Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms
Nizheen A. Ali, Ramadhan J. Mstafa
https://arxiv.org/abs/2508.13710

Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms
With the widespread use of the internet, there is an increasing need to ensure the security and privacy of transmitted data. This has led to an intensified focus on the study of video steganography, which is a technique that hides data within a video cover to avoid detection. The effectiveness of any steganography method depends on its ability to embed data without altering the original video quality while maintaining high efficiency. This paper proposes a new method to video steganography, whi…

Boosting Payment Channel Network Liquidity with Topology Optimization and Transaction Selection
Krishnendu Chatterjee, Jan Maty\'a\v{s} K\v{r}i\v{s}\v{t}an, Stefan Schmid, Jakub Svoboda, Michelle Yeo
https://arxiv.org/abs/2508.14524

Boosting Payment Channel Network Liquidity with Topology Optimization and Transaction Selection
Payment channel networks (PCNs) are a promising technology that alleviates blockchain scalability by shifting the transaction load from the blockchain to the PCN. Nevertheless, the network topology has to be carefully designed to maximise the transaction throughput in PCNs. Additionally, users in PCNs also have to make optimal decisions on which transactions to forward and which to reject to prolong the lifetime of their channels. In this work, we consider an input sequence of transactions over…

How can we measure the information created by natural selection?
Joel R. Peck, David Waxman
https://arxiv.org/abs/2508.13373 https://arxiv.org/pdf/2508.133…

How can we measure the information created by natural selection?
Natural selection can create information. In particular, because of the action of natural selection, we can often learn something about an environment by examining local organisms, and vice versa. For example, the characteristics of a cactus suggest that the local environment is relatively dry, and if a natural terrestrial environment is dry, then we will generally have an enhanced probability of finding drought-resistant plants (like cacti). Here, we propose a measure that can be used to quant…

Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
Kartik Pandey, Arun Balasubramanian, Debasis Samanta
https://arxiv.org/abs/2508.14060

Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
Electroencephalogram (EEG) signals have gained widespread adoption in brain-computer interface (BCI) applications due to their non-invasive, low-cost, and relatively simple acquisition process. The demand for higher spatial resolution, particularly in clinical settings, has led to the development of high-density electrode arrays. However, increasing the number of channels introduces challenges such as cross-channel interference and computational overhead. To address these issues, modern BCI sys…

A selection of pictures from yesterday
#iceland #reykjavik

AEFS: Adaptive Early Feature Selection for Deep Recommender Systems
Fan Hu, Gaofeng Lu, Jun Chen, Chaonan Guo, Yuekui Yang, Xirong Li
https://arxiv.org/abs/2509.12076 https://…

AEFS: Adaptive Early Feature Selection for Deep Recommender Systems
Feature selection has emerged as a crucial technique in refining recommender systems. Recent advancements leveraging Automated Machine Learning (AutoML) has drawn significant attention, particularly in two main categories: early feature selection and late feature selection, differentiated by whether the selection occurs before or after the embedding layer. The early feature selection selects a fixed subset of features and retrains the model, while the late feature selection, known as adaptive f…

🔊 #NowPlaying on #BBCRadio3:
#Unclassified
- Wave Patterns
Elizabeth Alker with a selection of ambient and experimental sounds from genre-defying artists including cellist Okyyung Lee and producer Ben Lucas Boysen.
Relisten now 👇
https://www.bbc.co.uk/programmes/m0022c52

WISE-FUSE: Efficient Whole Slide Image Encoding via Coarse-to-Fine Patch Selection with VLM and LLM Knowledge Fusion
Yonghan Shin, SeungKyu Kim, Won-Ki Jeong
https://arxiv.org/abs/2508.14537

WISE-FUSE: Efficient Whole Slide Image Encoding via Coarse-to-Fine Patch Selection with VLM and LLM Knowledge Fusion
Whole slide images (WSIs) in computational pathology (CPath) pose a major computational challenge due to their gigapixel scale, often requiring the processing of tens to hundreds of thousands of high-resolution patches per slide. This results in prohibitive encoding costs, with preprocessing and training times extending to days or even weeks-making WSI encoding the most significant bottleneck in real-world deployment. In this work, we propose WISE-FUSE, an adaptive WSI encoding framework that l…

The Milky Way is a less massive galaxy--new estimates of the Milky Way's local and global stellar masses
Jianhui Lian (Yunnan Uni.), Tao Wang (Yunnan Uni.), Qikang Feng (Peking Uni.), Yang Huang (U of Chinese Academy of Sciences), Helong Guo (Yunnan Uni.)
https://arxiv.org/abs/2508.13665

The Milky Way is a less massive galaxy--new estimates of the Milky Way's local and global stellar masses
Stellar mass is the most fundamental property of a galaxy. While it has been robustly measured for millions of external galaxies, it remains poorly constrained for the Milky Way because of the strong selection effect from our inside perspective. In this work, we reconstruct the intrinsic vertical mass density profile in the solar neighborhood and the radial mass density profile across the entire Galaxy using data from the Gaia and APOGEE surveys, following careful correction for the selection f…

Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
Mengke Li, Yukun Liu, Pengfei Li, Jing Qin
https://arxiv.org/abs/2507.13615

Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
Meta analysis is commonly-used to synthesize multiple results from individual studies. However, its validation is usually threatened by publication bias and between-study heterogeneity, which can be captured by the Copas selection model. Existing inference methods under this model are all based on conditional likelihood and may not be fully efficient. In this paper, we propose a full likelihood approach to meta analysis by integrating the conditional likelihood and a marginal semi-parametric em…

LOVE-LORE is a 35-minute, 43 song live-in-the-studio medley of covers unlike anything #Deerhoof or anyone else has ever put together. Love-Lore forages a wide-ranging selection of music from the 50s through the 80s to reflect upon the overly optimistic promises of progress that humanity was fed across the culture and music of the post-war era.
Instead of the projected 21st century in which…

A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security
Mehrab Hosain, Rajiv Kapoor
https://arxiv.org/abs/2507.13367

A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security
Steganography is the process of embedding secret information discreetly within a carrier, ensuring secure exchange of confidential data. The Adaptive Pixel Value Differencing (APVD) steganography method, while effective, encounters certain challenges like the "unused blocks" issue. This problem can cause a decrease in security, compromise the embedding capacity, and lead to lower visual quality. This research presents a novel steganographic strategy that integrates APVD with pseudorandom pixel …

MAGNeT: Multimodal Adaptive Gaussian Networks for Intent Inference in Moving Target Selection across Complex Scenarios
Xiangxian Li, Yawen Zheng, Baiqiao Zhang, Yijia Ma, XianhuiCao XianhuiCao, Juan Liu, Yulong Bian, Jin Huang, Chenglei Yang
https://arxiv.org/abs/2508.12992

MAGNeT: Multimodal Adaptive Gaussian Networks for Intent Inference in Moving Target Selection across Complex Scenarios
Moving target selection in multimedia interactive systems faces unprecedented challenges as users increasingly interact across diverse and dynamic contexts-from live streaming in moving vehicles to VR gaming in varying environments. Existing approaches rely on probabilistic models that relate endpoint distribution to target properties such as size and speed. However, these methods require substantial training data for each new context and lack transferability across scenarios, limiting their pr…

NFL predictions: Jets beaten out for No. 1 overall pick, coin flip for a top-three selection

https://www.cbssports.com/betting/news/nfl-…

Humans learn to prefer trustworthy AI over human partners
Yaomin Jiang, Levin Brinkmann, Anne-Marie Nussberger, Ivan Soraperra, Jean-Fran\c{c}ois Bonnefon, Iyad Rahwan
https://arxiv.org/abs/2507.13524

Humans learn to prefer trustworthy AI over human partners
Partner selection is crucial for cooperation and hinges on communication. As artificial agents, especially those powered by large language models (LLMs), become more autonomous, intelligent, and persuasive, they compete with humans for partnerships. Yet little is known about how humans select between human and AI partners and adapt under AI-induced competition pressure. We constructed a communication-based partner selection game and examined the dynamics in hybrid mini-societies of humans and b…

Agentic DraCor and the Art of Docstring Engineering: Evaluating MCP-empowered LLM Usage of the DraCor API
Peer Trilcke, Ingo B\"orner, Henny Sluyter-G\"athje, Daniil Skorinkin, Frank Fischer, Carsten Milling
https://arxiv.org/abs/2508.13774

Agentic DraCor and the Art of Docstring Engineering: Evaluating MCP-empowered LLM Usage of the DraCor API
This paper reports on the implementation and evaluation of a Model Context Protocol (MCP) server for DraCor, enabling Large Language Models (LLM) to autonomously interact with the DraCor API. We conducted experiments focusing on tool selection and application by the LLM, employing a qualitative approach that includes systematic observation of prompts to understand how LLMs behave when using MCP tools, evaluating "Tool Correctness", "Tool-Calling Efficiency", and "Tool-Use Reliability". Our find…

Joint AP Selection and Power Allocation for Unicast-Multicast Cell-Free Massive MIMO
Mustafa S. Abbas, Zahra Mobini, Hien Quoc Ngo, Hyundong Shin, Michail Matthaiou
https://arxiv.org/abs/2508.13771

Joint AP Selection and Power Allocation for Unicast-Multicast Cell-Free Massive MIMO
Joint unicast and multicast transmissions are becoming increasingly important in practical wireless systems, such as Internet of Things networks. This paper investigates a cell-free massive multiple-input multiple-output system that simultaneously supports both transmission types, with multicast serving multiple groups. Exact closed-form expressions for the achievable downlink spectral efficiency (SE) of both unicast and multicast users are derived for zero-forcing and maximum ratio precoding d…

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
Qiyue Li, Yingxin Liu, Hang Qi, Jieping Luo, Zhizhang Liu, Jingjin Wu
https://arxiv.org/abs/2509.13933

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
We consider the client selection problem in wireless Federated Learning (FL), with the objective of reducing the total required time to achieve a certain level of learning accuracy. Since the server cannot observe the clients' dynamic states that can change their computation and communication efficiency, we formulate client selection as a restless multi-armed bandit problem. We propose a scalable and efficient approach called the Whittle Index Learning in Federated Q-learning (WILF-Q), which us…

Extending a Phylogeny-based Method for Detecting Signatures of Multi-level Selection for Applications in Artificial Life
Matthew Andres Moreno, Sanaz Hasanzadeh Fard, Luis Zaman, Emily Dolson
https://arxiv.org/abs/2508.14232

Extending a Phylogeny-based Method for Detecting Signatures of Multi-level Selection for Applications in Artificial Life
Multilevel selection occurs when short-term individual-level reproductive interests conflict with longer-term group-level fitness effects. Detecting and quantifying this phenomenon is key to understanding evolution of traits ranging from multicellularity to pathogen virulence. Multilevel selection is particularly important in artificial life research due to its connection to major evolutionary transitions, a hallmark of open-ended evolution. Bonetti Franceschi & Volz (2024) proposed to detect m…

Mean field social optimization: feedback person-by-person optimality and the dynamic programming equation
Minyi Huang, Shuenn-Jyi Sheu, Li-Hsien Sun
https://arxiv.org/abs/2508.14236

Mean field social optimization: feedback person-by-person optimality and the dynamic programming equation
We consider mean field social optimization in nonlinear diffusion models. By dynamic programming with a representative agent employing cooperative optimizer selection, we derive a new Hamilton--Jacobi--Bellman (HJB) equation to be called the master equation of the value function. Under some regularity conditions, we establish $ε$-person-by-person optimality of the master equation-based control laws, which may be viewed as a necessary condition for nearly attaining the social optimum. A major c…

He's no martyr...
Liberals need to stop spreading this myth about Charlie Kirk - Alternet.org
https://archive.ph/8Wksb#selection-1013.98-1013.146

Replaced article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- Bounds on Covert Capacity with Sub-Exponential Random Slot Selection
Shi-Yuan Wang, Keerthi S. K. Arumugam, Matthieu R. Bloch

Non-Invertible Selection Rules on Heterotic Non-Abelian Orbifolds
Tatsuo Kobayashi, Ryusei Nishida, Hajime Otsuka
https://arxiv.org/abs/2509.10019 https://…

Non-Invertible Selection Rules on Heterotic Non-Abelian Orbifolds
We investigate coupling selection rules in heterotic string theory on non-Abelian orbifolds. Since boundary conditions on the orbifolds are classified by conjugacy classes of space group elements, non-Abelian orbifolds give rise to non-invertible selection rules on couplings among twisted sectors as well as ones including untwisted sectors. Furthermore, we find that non-invertible selection rules lead to characteristic patterns of Yukawa matrices.

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
Yuqi Luan
https://arxiv.org/abs/2508.14656 https://arxiv.org…

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
This study proposes a behaviorally-informed multi-factor stock selection framework that integrates short-cycle technical alpha signals with deep learning. We design a dual-task multilayer perceptron (MLP) that jointly predicts five-day future returns and directional price movements, thereby capturing nonlinear market behaviors such as volume-price divergence, momentum-driven herding, and bottom reversals. The model is trained on 40 carefully constructed factors derived from price-volume pattern…

A Catalog of M&M Eclipsing Binaries with TESS
Dominic Oddo, Diana Dragomir, Brian P. Powell, Veselin B. Kostov, Te Han
https://arxiv.org/abs/2508.13941 https://

A Catalog of M&M Eclipsing Binaries with TESS
We present a catalog of 1292 low-mass (M&M) short-period eclipsing binaries observed by the TESS mission. Eclipsing binaries are useful for many aspects of stellar astrophysics, including calibrating stellar models. Catalogs of eclipsing binary properties provide context for population-level inferences. In this work, we present our selection criteria for our catalog, along with steps taken to characterize both the orbital and physical properties of our EBs. We further detail crucial steps in ve…

Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
Jan Krej\v{c}\'i, Oliver Kost, Yuxuan Xia, Lennart Svensson, Ond\v{r}ej Straka
https://arxiv.org/abs/2508.13647

Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
This paper uses multi-object tracking methods known from the radar tracking community to address the problem of pedestrian tracking using 2D bounding box detections. The standard point-object (SPO) model is adopted, and the posterior density is computed using the Poisson multi-Bernoulli mixture (PMBM) filter. The selection of the model parameters rooted in continuous time is discussed, including the birth and survival probabilities. Some parameters are selected from the first principles, while …

Do you know of any blog posts that writes about the small quality of life shortcuts, or cool small "secret" things in the Helix editor? Or do you have some cool snippets or configs you'd like to share? E.g. I didn't know about `x_`. The underscore trims any newlines from the selection, when `x` selects the whole line (including the newline). This makes pasting the selected text on the same line, without the need to backspace the newline. How did I never know about this unti…

Variable Selection for Additive Global Fr\'echet Regression
Haoyi Yang, Satarupa Bhattacharjee, Lingzhou Xue, Bing Li
https://arxiv.org/abs/2509.13685 https://

Variable Selection for Additive Global Fréchet Regression
We present a novel framework for variable selection in Fréchet regression with responses in general metric spaces, a setting increasingly relevant for analyzing non-Euclidean data such as probability distributions and covariance matrices. Building on the concept of (weak) Fréchet conditional means, we develop an additive regression model that represents the metric-based discrepancy of the response as a sum of covariate-specific nonlinear functions in reproducing kernel Hilbert spaces (RKHS). …

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Bogdan Burlacu
https://arxiv.org/abs/2508.13859 https://arxiv.org/pdf/2508.13859

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Symbolic regression encompasses a family of search algorithms that aim to discover the best fitting function for a set of data without requiring an a priori specification of the model structure. The most successful and commonly used technique for symbolic regression is Genetic Programming (GP), an evolutionary search method that evolves a population of mathematical expressions through the mechanism of natural selection. In this work we analyze the efficiency of the evolutionary search in GP and…

A Durrett-Remenik particle system in $\mathbb{R}^d$
Rami Atar
https://arxiv.org/abs/2507.13990 https://arxiv.org/pdf/2507.13990

A Durrett-Remenik particle system in $\mathbb{R}^d$
This paper studies a branching-selection model of motionless particles in $\mathbb{R}^d$, with nonlocal branching, introduced by Durrett and Remenik in dimension $1$. The assumptions on the fitness function, $F$, and on the inhomogeneous branching distribution, are mild. The evolution equation for the macroscopic density is given by an integro-differential free boundary problem in $\mathbb{R}^d$, in which the free boundary represents the least $F$-value in the population. The main result is the…

AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications
Fabio Tarocco, Pi A. B. Haase, Fabijan Pavo\v{s}evi\'c, Vijay Krishna, Leonardo Guidoni, Stefan Knecht, Martina Stella
https://arxiv.org/abs/2…

AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications
The selection of a balanced active space is a critical step in multi-reference quantum chemistry calculations, particularly for systems with strong electron correlation. Likewise, active space selection is a key to unlock the potential of contemporary quantum computing in quantum chemistry. Albeit recent progress, there remains a lack of a unified, robust, and fully automated framework for active space selection that performs reliably across a wide range of molecular systems. In this work, we…

Replaced article(s) found for hep-ex. https://arxiv.org/list/hep-ex/new
[1/1]:
- Learning Selection Cuts With Gradients
Mike Hance, Juan Robles
https://

From Firms to Computation: AI Governance and the Evolution of Institutions
Michael S. Harre
https://arxiv.org/abs/2507.13616 https://…

From Firms to Computation: AI Governance and the Evolution of Institutions
The integration of agential artificial intelligence into socioeconomic systems requires us to reexamine the evolutionary processes that describe changes in our economic institutions. This article synthesizes three frameworks: multi-level selection theory, Aoki's view of firms as computational processes, and Ostrom's design principles for robust institutions. We develop a framework where selection operates concurrently across organizational levels, firms implement distributed inference via game-…

Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learning
Zongxin Shen, Yanyong Huang, Bin Wang, Jinyuan Chang, Shiyu Liu, Tianrui Li
https://arxiv.org/abs/2509.13763

Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learning
Multi-view unsupervised feature selection (MUFS) has recently received increasing attention for its promising ability in dimensionality reduction on multi-view unlabeled data. Existing MUFS methods typically select discriminative features by capturing correlations between features and clustering labels. However, an important yet underexplored question remains: \textit{Are such correlations sufficiently reliable to guide feature selection?} In this paper, we analyze MUFS from a causal perspectiv…

Things Spirit Airlines make their passengers pay for:
▫️Drinks & snacks
▫️Checked luggage
▫️Carry-ons
▫️Seat selection
▫️Changing flights
▫️Printing boarding pass at airport
Customers joke about the lavatory being coin-op. I'd rather die than fly #Spirit.
From: @…

Smudge The Insult Cat 🐀 (@SmudgeTheInsultCat@mas.to)
Attached: 1 image

Novel Knockoff Generation and Importance Measures with Heterogeneous Data via Conditional Residuals and Local Gradients
Evan Mason, Zhe Fei
https://arxiv.org/abs/2508.14882 http…

Novel Knockoff Generation and Importance Measures with Heterogeneous Data via Conditional Residuals and Local Gradients
Knockoff variable selection is a powerful framework that creates synthetic knockoff variables to mirror the correlation structure of the observed features, enabling principled control of the false discovery rate in variable selection. However, existing methods often assume homogeneous data types or known distributions, limiting their applicability in real-world settings with heterogeneous, distribution-free data. Moreover, common variable importance measures rely on linear outcome models, hinde…

ConPoSe: LLM-Guided Contact Point Selection for Scalable Cooperative Object Pushing
Noah Steinkr\"uger, Nisarga Nilavadi, Wolfram Burgard, Tanja Katharina Kaiser
https://arxiv.org/abs/2510.08705

ConPoSe: LLM-Guided Contact Point Selection for Scalable Cooperative Object Pushing
Object transportation in cluttered environments is a fundamental task in various domains, including domestic service and warehouse logistics. In cooperative object transport, multiple robots must coordinate to move objects that are too large for a single robot. One transport strategy is pushing, which only requires simple robots. However, careful selection of robot-object contact points is necessary to push the object along a preplanned path. Although this selection can be solved analytically, …

Who Gets the Mic? Investigating Gender Bias in the Speaker Assignment of a Speech-LLM
Dariia Puhach, Amir H. Payberah, \'Eva Sz\'ekely
https://arxiv.org/abs/2508.13603 h…

Who Gets the Mic? Investigating Gender Bias in the Speaker Assignment of a Speech-LLM
Similar to text-based Large Language Models (LLMs), Speech-LLMs exhibit emergent abilities and context awareness. However, whether these similarities extend to gender bias remains an open question. This study proposes a methodology leveraging speaker assignment as an analytic tool for bias investigation. Unlike text-based models, which encode gendered associations implicitly, Speech-LLMs must produce a gendered voice, making speaker selection an explicit bias cue. We evaluate Bark, a Text-to-Sp…

Microsoft adds auto model selection to Visual Studio Code that primarily favors Claude Sonnet 4 over GPT-5 for paid GitHub Copilot users (Tom Warren/The Verge)
https://www.theverge.com/report/778641/microsoft-visual-studio-code-anthropic-claude-4…

Microsoft favors Anthropic over OpenAI for Visual Studio Code
Microsoft is favoring Anthropic’s AI models over OpenAI for Visual Studio Code. It’s also recommending that Microsoft’s developers to use Claude 4.

Electrically pumped ultrabright entangled photons on chip
Xu-Feng Jiao, Ming-Yang Zheng, Yi-Hang Chen, Bo Cao, Xina Wang, Yang Liu, Cheng-Ao Yang, Xiu-Ping Xie, Chao-Yang Lu, Zhi-Chuan Niu, Qiang Zhang, Jian-Wei Pan
https://arxiv.org/abs/2508.14566

Electrically pumped ultrabright entangled photons on chip
Entangled photon sources (EPS) are essential for quantum science and technology. Despite advancements in integrated optical platforms like thin-film lithium niobate, a scalable, high-performance, chip-scale EPS has remained elusive. We address this by demonstrating an electrically pumped, post-selection-free polarization-EPS, achieved through hybrid integration of a distributed feedback laser with thin-film lithium niobate chip which integrates periodically poled lithium niobate waveguides, bea…

Metacurity is pleased to offer our free and premium subscribers a weekly digest of the best long-form (and longish) infosec-related pieces we couldn't properly fit into our daily news crush.
This week's selection covers
--The time is now to end cybersecurity with AI,
--Prosecutors say PowerSchool hacker was skilled,
--People with facial differences struggle with digital identity systems,
--Autistic teens can't grasp ramifications of digital crimes, …

Best infosec-related long reads for the week of 10/11/25
The time is now to end cybersecurity with AI, Prosecutors say PowerSchool hacker was skilled, People with facial differences struggle with digital identity systems, Autistic teens can't grasp ramifications of digital crimes, Online predator groups can kill teens, Crypto ATMs rip off scam victims

Selection Procedures in Competitive Admission
Nathan Hancart
https://arxiv.org/abs/2510.12653 https://arxiv.org/pdf/2510.12653

Selection Procedures in Competitive Admission
Two identical firms compete to attract and hire from a pool of candidates of unknown productivity. Firms simultaneously post a selection procedure which consists of a test and an acceptance probability for each test outcome. After observing the firms' selection procedures, each candidate can apply to one of them. Both firms have access to a limited set of feasible tests. The firms face two key considerations when choosing their selection procedure: the statistical properties of their test and t…

Multi-Condition Conformal Selection
Qingyang Hao, Wenbo Liao, Bingyi Jing, Hongxin Wei
https://arxiv.org/abs/2510.08075 https://arxiv.org/pdf/2510.08075

Multi-Condition Conformal Selection
Selecting high-quality candidates from large-scale datasets is critically important in resource-constrained applications such as drug discovery, precision medicine, and the alignment of large language models. While conformal selection methods offer a rigorous solution with False Discovery Rate (FDR) control, their applicability is confined to single-threshold scenarios (i.e., y > c) and overlooks practical needs for multi-condition selection, such as conjunctive or disjunctive conditions. In th…

The music selection at the @… social event was on point all night long. Too bad I was too busy socialising to actually enjoy it 😅
#FrOSCon #OpenSource

Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
Lei Xu, Torkel B Brismar
https://arxiv.org/abs/2507.13901 …

Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
We have developed a novel CT image analysis package named AnatomyArchive, built on top of the recent full body segmentation model TotalSegmentator. It provides automatic target volume selection and deselection capabilities according to user-configured anatomies for volumetric upper- and lower-bounds. It has a knowledge graph-based and time efficient tool for anatomy segmentation mask management and medical image database maintenance. AnatomyArchive enables automatic body volume cropping, as wel…

Rethinking Technology Stack Selection with AI Coding Proficiency
Xiaoyu Zhang, Weipeng Jiang, Juan Zhai, Shiqing Ma, Qingshuang Bao, Chenhao Lin, Chao Shen, Tianlin Li, Yang Liu
https://arxiv.org/abs/2509.11132

Rethinking Technology Stack Selection with AI Coding Proficiency
Large language models (LLMs) are now an integral part of software development workflows and are reshaping the whole process. Traditional technology stack selection has not caught up. Most of the existing selection methods focus solely on the inherent attributes of the technology, overlooking whether the LLM can effectively leverage the chosen technology. For example, when generating code snippets using popular libraries like Selenium (one of the most widely used test automation tools with over …

Mitigating the Impact of Location Uncertainty on Radio Map-Based Predictive Rate Selection via Noisy-Input Gaussian Process
Koya Sato
https://arxiv.org/abs/2509.14710 https://…

Mitigating the Impact of Location Uncertainty on Radio Map-Based Predictive Rate Selection via Noisy-Input Gaussian Process
This paper proposes a predictive rate-selection framework based on Gaussian process (GP)-based radio map construction that is robust to location uncertainty. Radio maps are a promising tool for improving communication efficiency in 6G networks. Although they enable the design of location-based maximum transmission rates by exploiting statistical channel information, existing discussions often assume perfect (i.e., noiseless) location information during channel sensing. Since such information mu…

Note on Selection Bias in Observational Estimates of Algorithmic Progress
Parker Whitfill
https://arxiv.org/abs/2508.11033 https://arxiv.org/pdf/2508.11033…

Note on Selection Bias in Observational Estimates of Algorithmic Progress
Ho et. al (2024) is an interesting paper that attempts to estimate the degree of algorithmic progress from language models. They collect observational data on language models' loss and compute over time, and argue that as time has passed, language models' algorithmic efficiency has been rising. That is, the loss achieved for fixed compute has been dropping over time. In this note, I want to raise one potential methodological problem with the estimation strategy. Intuitively, if part of algorith…

Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network
Mehrshad Eskandarpour, Saba Pirahmadian, Parham Soltani, Hossein Soleimani
https://arxiv.org/abs/2508.12707

Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network
Energy in Wireless Sensor Networks (WSNs) is critical to network lifetime and data delivery. However, the primary impediment to the durability and dependability of these sensor nodes is their short battery life. Currently, power-saving algorithms such as clustering and routing algorithms have improved energy efficiency in standard protocols. This paper proposes a clustering-based routing approach for creating an adaptive, energy-efficient mechanism. Our system employs a multi-step clustering st…

VLA-Mark: A cross modal watermark for large vision-language alignment model
Shuliang Liu, Qi Zheng, Jesse Jiaxi Xu, Yibo Yan, He Geng, Aiwei Liu, Peijie Jiang, Jia Liu, Yik-Cheung Tam, Xuming Hu
https://arxiv.org/abs/2507.14067

VLA-Mark: A cross modal watermark for large vision-language alignment model
Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable. We propose VLA-Mark, a vision-aligned framework that embeds detectable watermarks while preserving semantic fidelity through cross-modal coordination. Our approach integrates multiscale visual-textua…

The C-index Multiverse
Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Catalina A. Vallejos
https://arxiv.org/abs/2508.14821 https://arxiv.org/pdf/2508.14…

The C-index Multiverse
Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between proposed C-index estimators (e.g. Harrell's, Uno's and Antolini's), we demonstrate the existence of a C-index multiverse among available R and pyth…

🔊 #NowPlaying on #BBCRadio3:
#RecordReview
- Ravel's Piano Concerto in G major in Building a Library with Yshani Perinpanayagam and Andrew McGregor
Ben Gernon joins Andrew McGregor to discuss his selection of the best new releases, and Building a Library on Maurice Ravel's Piano concerto in G major with Yshani Perinpanayagam.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002kq61

In-Context Decision Making for Optimizing Complex AutoML Pipelines
Amir Rezaei Balef, Katharina Eggensperger
https://arxiv.org/abs/2508.13657 https://arxiv…

In-Context Decision Making for Optimizing Complex AutoML Pipelines
Combined Algorithm Selection and Hyperparameter Optimization (CASH) has been fundamental to traditional AutoML systems. However, with the advancements of pre-trained models, modern ML workflows go beyond hyperparameter optimization and often require fine-tuning, ensembling, and other adaptation techniques. While the core challenge of identifying the best-performing model for a downstream task remains, the increasing heterogeneity of ML pipelines demands novel AutoML approaches. This work extend…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- MAGNeT: Multimodal Adaptive Gaussian Networks for Intent Inference in Moving Target Selection acr...
Li, Zheng, Zhang, Ma, Cao, Liu, Bian, Huang, Yang

A Collaborative Framework for Quantum Optimisation and Quantum Neural Networks: Credit Feature Selection and Image Classification
JiaNing Long, Xuechen Liang
https://arxiv.org/abs/2509.11110

A Collaborative Framework for Quantum Optimisation and Quantum Neural Networks: Credit Feature Selection and Image Classification
This paper investigates the efficacy of quantum computing in two distinct machine learning tasks: feature selection for credit risk assessment and image classification for handwritten digit recognition. For the first task, we address the feature selection challenge of the German Credit Dataset by formulating it as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is solved using quantum annealing to identify the optimal feature subset. Experimental results show that the result…

Towards PerSense : Advancing Training-Free Personalized Instance Segmentation in Dense Images
Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid, Kevin Henry, Muhammad Haris Khan
https://arxiv.org/abs/2508.14660

Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images
Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance Segmentation in dense images. PerSense employs a novel Instance Detection Module (IDM) that leverages density maps (DMs) to generate instance-level candidate point prompts, followed by a Point Prompt Selection Module (PPSM) that filters fa…

PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
Pei Liu, Terry Zhuo, Jiawei Deng, Zhenchang Xing, Qinghua Lu, Xiaoning Du, Hongyu Zhan
https://arxiv.org/abs/2508.11179

PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
The rapid emergence of pretrained models (PTMs) has attracted significant attention from both Deep Learning (DL) researchers and downstream application developers. However, selecting appropriate PTMs remains challenging because existing methods typically rely on keyword-based searches in which the keywords are often derived directly from function descriptions. This often fails to fully capture user intent and makes it difficult to identify suitable models when developers also consider factors s…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models
Borui Tang, Jin Zhu, Junxian Zhu, Xueqin Wang, Heping Zhang

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
Ashish Jha, Anh huy Phan, Razan Dibo, Valentin Leplat
https://arxiv.org/abs/2508.13653 https://

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
Training modern neural networks on large datasets is computationally and environmentally costly. We introduce GRAFT, a scalable in-training subset selection method that (i) extracts a low-rank feature representation for each batch, (ii) applies a Fast MaxVol sampler to select a small, diverse subset that spans the batch's dominant subspace, and (iii) dynamically adjusts the subset size using a gradient-approximation criterion. By operating in low-rank subspaces and training on carefully chosen …

QuizRank: Picking Images by Quizzing VLMs
Tenghao Ji, Eytan Adar
https://arxiv.org/abs/2509.15059 https://arxiv.org/pdf/2509.15059

QuizRank: Picking Images by Quizzing VLMs
Images play a vital role in improving the readability and comprehension of Wikipedia articles by serving as `illustrative aids.' However, not all images are equally effective and not all Wikipedia editors are trained in their selection. We propose QuizRank, a novel method of image selection that leverages large language models (LLMs) and vision language models (VLMs) to rank images as learning interventions. Our approach transforms textual descriptions of the article's subject into multiple-cho…

Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
Victor Dh\'edin, Haizhou Zhao, Majid Khadiv
https://arxiv.org/abs/2508.12928 https://

Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
Legged robots have the potential to traverse highly constrained environments with agile maneuvers. However, planning such motions requires solving a highly challenging optimization problem with a mixture of continuous and discrete decision variables. In this paper, we present a full pipeline based on Monte-Carlo tree search (MCTS) and whole-body trajectory optimization (TO) to perform simultaneous contact sequence and patch selection on highly challenging environments. Through extensive simulat…

Post-selection inference with a single realization of a network
Ethan Ancell, Daniela Witten, Daniel Kessler
https://arxiv.org/abs/2508.11843 https://arxiv…

Post-selection inference with a single realization of a network
Given a dataset consisting of a single realization of a network, we consider conducting inference on a parameter selected from the data. In particular, we focus on the setting where the parameter of interest is a linear combination of the mean connectivities within and between estimated communities. Inference in this setting poses a challenge, since the communities are themselves estimated from the data. Furthermore, since only a single realization of the network is available, sample splitting …

Energy-Efficient Routing Algorithm for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach
Parham Soltani, Mehrshad Eskandarpour, Amir Ahmadizad, Hossein Soleimani
https://arxiv.org/abs/2508.14679

Energy-Efficient Routing Algorithm for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach
Efficient energy management is essential in Wireless Sensor Networks (WSNs) to extend network lifetime and ensure reliable data transmission. This paper presents a novel method using reinforcement learning-based cluster-head selection and a hybrid multi-hop routing algorithm, which leverages Q-learning within a multi-agent system to dynamically adapt transmission paths based on the energy distribution across sensor nodes. Each sensor node is modeled as an autonomous agent that observes local st…

Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition
Navid Hasanzadeh, Shahrokh Valaee
https://arxiv.org/abs/2509.15129

Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition
With the IEEE 802.11bf Task Group introducing amendments to the WLAN standard for advanced sensing, interest in using Wi-Fi Channel State Information (CSI) for remote sensing has surged. Recent findings indicate that learning a unified three-dimensional motion representation through Doppler Radiance Fields (DoRFs) derived from CSI significantly improves the generalization capabilities of Wi-Fi-based human activity recognition (HAR). Despite this progress, CSI signals remain affected by asynchro…

Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation
Liang Qu, Jianxin Li, Wei Yuan, Penghui Ruan, Yuhui Shi, Hongzhi Yin
https://arxiv.org/abs/2508.10401

Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation
Federated recommender systems have emerged as a promising privacy-preserving paradigm, enabling personalized recommendation services without exposing users' raw data. By keeping data local and relying on a central server to coordinate training across distributed clients, FedRSs protect user privacy while collaboratively learning global models. However, most existing FedRS frameworks adopt fully random client selection strategy in each training round, overlooking the statistical heterogeneity of…

Proceedings 18th Interaction and Concurrency Experience
Cl\'ement Aubert (Augusta University), Cinzia Di Giusto (Universit\'e C\^ote d'Azur, CNRS, I3SSophia Antipolis), Simon Fowler (University of Glasgow School of Computing Science), Violet Ka I Pun (Western Norway University of Applied Sciences)
https://arxiv.org/abs/2508.123…

Proceedings 18th Interaction and Concurrency Experience
This volume contains the proceedings of ICE'25, the 18th Interaction and Concurrency Experience, which was held on Friday 20th June 2025 at the École National Supérieure des Arts et Métiers in Lille, France, as a satellite workshop of DisCoTec 2025. The ICE workshop series features a distinguishing review and selection procedure: PC members are encouraged to interact, anonymously, with authors. The 2025 edition of ICE received 7 submissions, each reviewed by three PC members, and about 75 co…

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
Kartik Pandey, Arun Balasubramanian, Debasis Samanta

Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon
https://arxiv.org/abs/2508.13715 https://

Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address several key challenges, including privacy, information silos, class imbalance, non-identically and independently distributed (Non-IID) data, and model interpretability in supply chain credit assessment. We introduce a performance-based client selection strategy (PBCS) to tackle class imbalance and Non-IID problems. T…

Targeted Test Selection Approach in Continuous Integration
Pavel Plyusnin, Aleksey Antonov, Vasilii Ermakov, Aleksandr Khaybriev, Margarita Kikot, Ilseyar Alimova, Stanislav Moiseev
https://arxiv.org/abs/2509.10279

Targeted Test Selection Approach in Continuous Integration
In modern software development change-based testing plays a crucial role. However, as codebases expand and test suites grow, efficiently managing the testing process becomes increasingly challenging, especially given the high frequency of daily code commits. We propose Targeted Test Selection (T-TS), a machine learning approach for industrial test selection. Our key innovation is a data representation that represent commits as Bags-of-Words of changed files, incorporates cross-file and addition…

Variable Selection Using Relative Importance Rankings
Tien-En Chang, Argon Chen
https://arxiv.org/abs/2509.10853 https://arxiv.org/pdf/2509.10853

Variable Selection Using Relative Importance Rankings
Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature. While RI is typically used for post-hoc model explanation, this paper explores its potential for variable ranking and filter-based selection before model creation. Specifically, we anticipate strong performance from the RI measures because they incorporate both direct and combined effects of predictors, addressing a key limitation of marginal correlation…

Bridge Sampling Diagnostics
Giorgio Micaletto, Aki Vehtari
https://arxiv.org/abs/2508.14487 https://arxiv.org/pdf/2508.14487…

Bridge Sampling Diagnostics
In Bayesian statistics, the marginal likelihood is used for model selection and averaging, yet it is often challenging to compute accurately for complex models. Approaches such as bridge sampling, while effective, may suffer from issues of high variability of the estimates. We present how to estimate Monte Carlo standard error (MCSE) for bridge sampling, and how to diagnose the reliability of MCSE estimates using Pareto-$\hat{k}$ and block reshuffling diagnostics without the need to repeatedly …

Each week, Metacurity offers our free and paid subscribers a run-down of the best infosec-related long reads.
This week's selection covers
--A twisted tale of how two men tortured someone for his crypto account passwords,
--Russia's cyber sector supports Putin's Ukraine war,
--A brain-reading implant requires a password,
--Social media algorithms didn't cause America's woes,
--The internet is really bad for children,
--More
…

Best infosec-related long reads for the week of 8/9/25
A twisted tale of how two men tortured someone for his crypto account passwords, Russia's cyber sector supports Putin's Ukraine war, A brain-reading implant requires a password, Social media algorithms didn't cause America's woes, The internet is really bad for children, more

Active Model Selection for Large Language Models
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch, Torsten Hoefler, Nezihe Merve G\"urel
https://arxiv.org/abs/2510.09418 …

Active Model Selection for Large Language Models
We introduce LLM SELECTOR, the first framework for active model selection of Large Language Models (LLMs). Unlike prior evaluation and benchmarking approaches that rely on fully annotated datasets, LLM SELECTOR efficiently identifies the best LLM with limited annotations. In particular, for any given task, LLM SELECTOR adaptively selects a small set of queries to annotate that are most informative about the best model for the task. To further reduce annotation cost, we leverage a judge-based or…

Communication-Efficient Federated Learning with Adaptive Number of Participants
Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov, Aram Avetisyan, Dmitry Bylinkin, Daniil Medyakov, Aleksandr Beznosikov
https://arxiv.org/abs/2508.13803

Communication-Efficient Federated Learning with Adaptive Number of Participants
Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framework to address these concerns by enabling decentralized training. Nevertheless, communication efficiency remains a key bottleneck in FL, particularly under heterogeneous and dynamic client participation. Existing methods, such as FedAvg and FedProx, or other approaches, including client selection strategies, attempt to…

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
Kartik Pandey, Arun Balasubramanian, Debasis Samanta

The invariance and non-decreasing expectation of an evolutionary path characteristic under weak selection
Yun-Yun Yu, Cang Hui, Tian-Jiao Feng, Cong Li, Chao Wang, Yi Tao, Rui-Wu Wang
https://arxiv.org/abs/2508.11221

The invariance and non-decreasing expectation of an evolutionary path characteristic under weak selection
Fisher's fundamental theorem of natural selection states that the rate of change in a population's mean fitness equals its additive genetic variance in fitness. This implies that mean fitness should not decline in a constant environment, thereby positioning it as an indicator of evolutionary progression. However, this theorem has been shown to lack universality. Here, we derive the Fokker-Planck equation that describes the stochastic frequency dynamics of two phenotypes in a large population un…

Contrastive CUR: Interpretable Joint Feature and Sample Selection for Case-Control Studies
Eric Zhang, Michael Love, Didong Li
https://arxiv.org/abs/2508.11557 https://

Contrastive CUR: Interpretable Joint Feature and Sample Selection for Case-Control Studies
Dimension reduction is an essential tool for analyzing high dimensional data. Most existing methods, including principal component analysis (PCA), as well as their extensions, provide principal components that are often linear combinations of features, which are often challenging to interpret. CUR decomposition, another matrix decomposition technique, is a more interpretable and efficient alternative, offers simultaneous feature and sample selection. Despite this, many biomedical studies involv…

Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality
Chao-Jung Lai, Mauricio Sousa, Tianyu Zhang, Ludwig Sidenmark, Tovi Grossman
https://arxiv.org/abs/2508.08505

Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality
Selection is a fundamental task that is challenging in virtual reality due to issues such as distant and small targets, occlusion, and target-dense environments. Previous research has tackled these challenges through various selection techniques, but complicates selection and can be seen as tedious outside of their designed use case. We present Adaptique, an adaptive model that infers and switches to the most optimal selection technique based on user and environmental information. Adaptique con…

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…

Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection
Andrea Castellani, Zacharias Papadovasilakis, Giorgos Papoutsoglou, Mary Cole, Brian Bautsch, Tobias Rodemann, Ioannis Tsamardinos, Angela Harden
https://arxiv.org/abs/2508.11504

Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection
Motor vehicle crashes remain a leading cause of injury and death worldwide, necessitating data-driven approaches to understand and mitigate crash severity. This study introduces a curated dataset of more than 3 million people involved in accidents in Ohio over six years (2017-2022), aggregated to more than 2.3 million vehicle-level records for predictive analysis. The primary contribution is a transparent and reproducible methodology that combines Automated Machine Learning (AutoML) and explain…

The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
Argimiro Arratia, Alejandra Caba\~na, Ernesto Mordecki, Gerard Rovira-Parra
https://arxiv.org/abs/2509.12185

The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
Model selection in non-linear models often prioritizes performance metrics over statistical tests, limiting the ability to account for sampling variability. We propose the use of a statistical test to assess the equality of variances in forecasting errors. The test builds upon the classic Morgan-Pitman approach, incorporating enhancements to ensure robustness against data with heavy-tailed distributions or outliers with high variance, plus a strategy to make residuals from machine learning mode…

$\Delta$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Jucheng Hu, Suorong Yang, Dongzhan Zhou
https://arxiv.org/abs/2508.09199 h…

$Δ$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Visual Instruction Finetuning (VIF) is pivotal for post-training Vision-Language Models (VLMs). Unlike unimodal instruction finetuning in plain-text large language models, which mainly requires instruction datasets to enable model instruction-following ability, VIF also requires multimodal data to enable joint visual and textual understanding; therefore, it typically requires more data. Consequently, VIF imposes stricter data selection challenges: the method must scale efficiently to handle lar…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Comparing Model-agnostic Feature Selection Methods through Relative Efficiency
Chenghui Zheng, Garvesh Raskutti

CARGO: A Framework for Confidence-Aware Routing of Large Language Models
Amine Barrak, Yosr Fourati, Michael Olchawa, Emna Ksontini, Khalil Zoghlami
https://arxiv.org/abs/2509.14899

CARGO: A Framework for Confidence-Aware Routing of Large Language Models
As large language models (LLMs) proliferate in scale, specialization, and latency profiles, the challenge of routing user prompts to the most appropriate model has become increasingly critical for balancing performance and cost. We introduce CARGO (Category-Aware Routing with Gap-based Optimization), a lightweight, confidence-aware framework for dynamic LLM selection. CARGO employs a single embedding-based regressor trained on LLM-judged pairwise comparisons to predict model performance, with a…

Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis
Yi Wang, Chetan Arora, Xiao Liu, Thuong Hoang, ZHengxin Zhang, Henry Been Lirn Duh, John Grundy
https://arxiv.org/abs/2508.13051

Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis
Accessibility reviews provide valuable insights into both the limitations and benefits experienced by users with disabilities when using virtual reality (VR) applications. However, a comprehensive investigation into VR accessibility for users with disabilities is still lacking. To fill this gap, this study analyzes user reviews from the Meta and Steam stores of VR apps, focusing on the reported issues affecting users with disabilities. We applied selection criteria to 1,367,419 reviews from the…

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 …

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…

Bayesian Models for Joint Selection of Features and Auto-Regressive Lags: Theory and Applications in Environmental and Financial Forecasting
Alokesh Manna, Sujit K. Ghosh
https://arxiv.org/abs/2508.10055

Bayesian Models for Joint Selection of Features and Auto-Regressive Lags: Theory and Applications in Environmental and Financial Forecasting
We develop a Bayesian framework for variable selection in linear regression with autocorrelated errors, accommodating lagged covariates and autoregressive structures. This setting occurs in time series applications where responses depend on contemporaneous or past explanatory variables and persistent stochastic shocks, including financial modeling, hydrological forecasting, and meteorological applications requiring temporal dependency capture. Our methodology uses hierarchical Bayesian models w…