Tootfinder

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
Jihun Kim, Javad Lavaei
https://arxiv.org/abs/2509.15794 https://

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
System identification in modern engineering systems faces emerging challenges from unanticipated adversarial attacks beyond existing detection mechanisms. In this work, we obtain a provably accurate estimate of the Markov parameter matrix of order $k$ to identify partially observed linear systems, in which the probability of having an attack at each time is $O(1/k)$. We show that given the batch data accumulated up to time $T^*$, the $\ell_2$-norm estimator achieves an error decaying exponentia…

Identification and characterization of nascent planetary nebulae with OH and H$_2$O masers
Rold\'an A. Cala, Jos\'e F. G\'omez, Luis F. Miranda, Hiroshi Imai, Guillem Anglada, Itziar de Gregorio-Monsalvo, Keiichi Ohnaka, Olga Su\'arez, Daniel Tafoya, Lucero Uscanga
https://arxiv.org/abs/2509.15727

Identification and characterization of nascent planetary nebulae with OH and H$_2$O masers
Stars like the Sun expel their outer layers and form planetary nebulae (PNe) as they evolve into white dwarfs. PNe exhibit diverse morphologies, the origins of which are not fully understood. PNe with OH (OHPNe) and H$_{2}$O (H$_{2}$OPNe) masers are thought to be nascent PNe. However, the number of known OHPNe and H$_{2}$OPNe remains small, and only in eight cases the position of the maser emission has been found to coincide with the PN, using the high astrometric accuracy of interferometric ob…

Wonder if Apple will add that as a biometric ID method https://www.vice.com/en/article/stanford-smart-toilet-uses-butthole-for-identification/

Rec-RIR: Monaural Blind Room Impulse Response Identification via DNN-based Reverberant Speech Reconstruction in STFT Domain
Pengyu Wang, Xiaofei Li
https://arxiv.org/abs/2509.15628

Rec-RIR: Monaural Blind Room Impulse Response Identification via DNN-based Reverberant Speech Reconstruction in STFT Domain
Room impulse response (RIR) characterizes the complete propagation process of sound in an enclosed space. This paper presents Rec-RIR for monaural blind RIR identification. Rec-RIR is developed based on the convolutive transfer function (CTF) approximation, which models reverberation effect within narrow-band filter banks in the short-time Fourier transform (STFT) domain. Specifically, we propose a deep neural network (DNN) with cross-band and narrow-band blocks to estimate the CTF filter. The …

Blind Source Separation of Radar Signals in Time Domain Using Deep Learning
Sven Hinderer
https://arxiv.org/abs/2509.15603 https://arxiv.org/pdf/2509.15603…

Blind Source Separation of Radar Signals in Time Domain Using Deep Learning
Identification and further analysis of radar emitters in a contested environment requires detection and separation of incoming signals. If they arrive from the same direction and at similar frequencies, deinterleaving them remains challenging. A solution to overcome this limitation becomes increasingly important with the advancement of emitter capabilities. We propose treating the problem as blind source separation in time domain and apply supervisedly trained neural networks to extract the und…

Numerical Analysis of Simultaneous Reconstruction of Initial Condition and Potential in Subdiffusion
Xu Wu, Jiang Yang, Zhi Zhou
https://arxiv.org/abs/2509.15633 https://…

Numerical Analysis of Simultaneous Reconstruction of Initial Condition and Potential in Subdiffusion
This paper investigates the simultaneous identification of a spatially dependent potential and the initial condition in a subdiffusion model based on two terminal observations. The existence, uniqueness, and conditional stability of the inverse problem are established under weak regularity assumptions through a constructive fixed-point iteration approach. The theoretical analysis further inspires the development of an easy-to-implement iterative algorithm. A fully discrete scheme is then propos…

A statement from iNaturalist on experimenting with “AI-Assisted Identification Summaries” in 'Exploring New Ways to Learn from iNaturalist’s Community Expertise' https://www.inaturalist.org/blog/118695-exploring-new-ways…

Post crisis Strategies: Antifragility Principles as Catalysts for Urban Evolution Towards Sustainability
Joseph Uguet, Nicola Tollin, Jordi Morat\'o
https://arxiv.org/abs/2509.15245

Post crisis Strategies: Antifragility Principles as Catalysts for Urban Evolution Towards Sustainability
Urban crises reveal the true essence of cities: their ability to either withstand disorder or collapse under its pressure. This article explores how antifragility principles can transforms urban disruption into levers for reinforcement and innovation. While resilience seeks to restore a lost balance, antifragility goes further: it pushes cities to improve through shocks. Across a critical analysis of post-crisis strategies and the identification of fifteen fundamental theoretical principles, th…

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
Li, Yuwei, Mao, Wang, Xu, Ma, Yu, Ma, Dou, Wang, Luo, An, Yang, Lv, Liu

Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Yuhao Pan, Xiucheng Wang, Nan Cheng, Wenchao Xu
https://arxiv.org/abs/2510.09405

Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Radio frequency fingerprint identification (RFFI) is a critical technique for wireless network security, leveraging intrinsic hardware-level imperfections introduced during device manufacturing to enable precise transmitter identification. While deep neural networks have shown remarkable capability in extracting discriminative features, their real-world deployment is hindered by receiver-induced variability. In practice, RF fingerprint signals comprise transmitter-specific features as well as c…

🎉 Social identification with a team boosts fans' social well-being
#sports…

Social identification with a team boosts fans' social well-being
Sports fans all know that rosy feeling of happiness when we hang out with others who support our favorite team. A new study conducted with sport consumers in the U.S. and the United Kingdom suggests that organizations that want to enhance their supporters' health and well-being can achieve that by bolstering their social identification with the group.

How to identify the #artist who did a #painting.
#artHistory

How To Recognise Famous Painters According To The Internet
Art history has never been so easy! A redditor DontTacoBoutIt posted this series of famous paintings and gave short but hilariously accurate explanations on how to recognize their authors. Commenters on Reddit and Imgur also started sharing their own ideas for artist identification. Check out the following examples, and don't hesitate to add your own!

After identification of a Thai worker's remains, the first phase of Gaza’s tenuous 8-week-old ceasefire is
a step closer to completion, with one more hostage’s remains still to be returned.
The subsequent phases under a U.S.-drafted, U.N.-backed plan for Gaza remain deeply uncertain.
There has been no word on how provisions for Hamas’ disarmament will be carried out,
or how a planned international administration and security force will be established.

A single hostage remains in Gaza after identification of Thai worker's remains
JERUSALEM (AP) — The subsequent phases under a U.S.-drafted, U.N.-backed plan for Gaza remain deeply uncertain.

Structured identification of multivariable modal systems
Maarten van der Hulst, Rodrigo A. Gonz\'alez, Koen Classens, Paul Tacx, Nick Dirkx, Jeroen van de Wijdeven, Tom Oomen
https://arxiv.org/abs/2510.10820

Structured identification of multivariable modal systems
Physically interpretable models are essential for next-generation industrial systems, as these representations enable effective control, support design validation, and provide a foundation for monitoring strategies. The aim of this paper is to develop a system identification framework for estimating modal models of complex multivariable mechanical systems from frequency response data. To achieve this, a two-step structured identification algorithm is presented, where an additive model is first …

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
Yuhua Zhang, Jukka-Pekka Onnela, Shuo Sun, Ruoyu Wang
https://arxiv.org/abs/2510.10508 …

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
Interference--in which a unit's outcome is affected by the treatment of other units--poses significant challenges for the identification and estimation of causal effects. Most existing methods for estimating interference effects assume that the interference networks are known. In many practical settings, this assumption is unrealistic as such networks are typically latent. To address this challenge, we propose a novel framework for identifying and estimating heterogeneous group-level interferen…

Automatic Music Sample Identification with Multi-Track Contrastive Learning
Alain Riou, Joan Serr\`a, Yuki Mitsufuji
https://arxiv.org/abs/2510.11507 https://

Automatic Music Sample Identification with Multi-Track Contrastive Learning
Sampling, the technique of reusing pieces of existing audio tracks to create new music content, is a very common practice in modern music production. In this paper, we tackle the challenging task of automatic sample identification, that is, detecting such sampled content and retrieving the material from which it originates. To do so, we adopt a self-supervised learning approach that leverages a multi-track dataset to create positive pairs of artificial mixes, and design a novel contrastive lear…

Sensitivity of Perron and Fiedler eigenpairs to structural perturbations of a network
Silvia Noschese, Lothar Reichel
https://arxiv.org/abs/2509.16024 https://

Sensitivity of Perron and Fiedler eigenpairs to structural perturbations of a network
One can estimate the change of the Perron and Fiedler values for a connected network when the weight of an edge is perturbed by analyzing relevant entries of the Perron and Fiedler vectors. This is helpful for identifying edges whose weight perturbation causes the largest change in the Perron and Fiedler values. It also is important to investigate the sensitivity of the Perron and Fiedler vectors to perturbations. Applications of the perturbation analysis include the identification of edges tha…

Cross-Breed Pig Identification Using Auricular Vein Pattern Recognition: A Machine Learning Approach for Small-Scale Farming Applications
Emmanuel Nsengiyumvaa, Leonard Niyitegekaa, Eric Umuhoza
https://arxiv.org/abs/2510.02197

Cross-Breed Pig Identification Using Auricular Vein Pattern Recognition: A Machine Learning Approach for Small-Scale Farming Applications
Accurate livestock identification is a cornerstone of modern farming: it supports health monitoring, breeding programs, and productivity tracking. However, common pig identification methods, such as ear tags and microchips, are often unreliable, costly, target pure breeds, and thus impractical for small-scale farmers. To address this gap, we propose a noninvasive biometric identification approach that leverages uniqueness of the auricular vein patterns. To this end, we have collected 800 ear im…

Enhanced Localization of Dark Lensed Gravitational Wave Events Enables Host Galaxy Identification and Precise Cosmological Inference
Zhiwei Chen, Qingjuan Yu, Youjun Lu, Xiao Guo
https://arxiv.org/abs/2510.12470

Enhanced Localization of Dark Lensed Gravitational Wave Events Enables Host Galaxy Identification and Precise Cosmological Inference
Lensed gravitational wave (GW) events are expected to be powerful new probes of cosmology, contingent on redshift measurement by electromagnetic observations. Host galaxy identification is thus crucial but challenging due to poor localization by GW signal alone. In this paper, we show that the third-generation ground-based GW detectors will detect a population of lensed events with three or more detectable images (including the central one), each arriving at distinct times and Earth locations i…

#uspol

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Bicheng Wang, Jinping Wang, Yibo Sue
https://arxiv.org/abs/2510.09082

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Learning complex network dynamics is fundamental to understanding, modelling and controlling real-world complex systems. There are two main problems in the task of predicting the dynamic evolution of complex networks: on the one hand, existing methods usually use simple graphs to describe the relationships in complex networks; however, this approach can only capture pairwise relationships, while there may be rich non-pairwise structured relationships in the network. First-order GNNs have diffic…

MOLLId: software for automatic identification of spectral molecular lines in the sub-millimeter and millimeter bands and its application to the spectra of protostars from the region RCW 120
A. A. Farafontova, M. S. Kirsanova, S. V. Salii
https://arxiv.org/abs/2510.08306

MOLLId: software for automatic identification of spectral molecular lines in the sub-millimeter and millimeter bands and its application to the spectra of protostars from the region RCW 120
In this work, we present the program MOLLId (MOLecular Line Identification) for automated molecular lines approximation with gaussian profile. Molecular identification was performed using multi-level comparison of the lines' center frequencies and rest frequencies from the spectroscopic database. The program was tested using identification of the molecular lines in observational spectra of young stellar objects RCW 120 YSO S1 and RCW 120 YSO S2, located near the border of the RCW 120 PDR. In th…

Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification
Nabeel Nisar Bhat, Maksim Karnaukh, Jakob Struye, Rafael Berkvens, Jeroen Famaey
https://arxiv.org/abs/2510.08160

Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification
Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person's unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers new opportunities through its finer spatial resolution, though its comparative advantages for person identification remain unexplored. Thi…

FocusMed: A Large Language Model-based Framework for Enhancing Medical Question Summarization with Focus Identification
Chao Liu, Ling Luo, Tengxiao Lv, Huan Zhuang, Lejing Yu, Jian Wang, Hongfei Lin
https://arxiv.org/abs/2510.04671

FocusMed: A Large Language Model-based Framework for Enhancing Medical Question Summarization with Focus Identification
With the rapid development of online medical platforms, consumer health questions (CHQs) are inefficient in diagnosis due to redundant information and frequent non-professional terms. The medical question summary (MQS) task aims to transform CHQs into streamlined doctors' frequently asked questions (FAQs), but existing methods still face challenges such as poor identification of question focus and model hallucination. This paper explores the potential of large language models (LLMs) in the MQS …

Identification of Gamma Ray Pulsar Candidates in the \emph{Fermi}-LAT 4FGL-DR4 Unassociated Sources Using Supervised Machine Learning
A. Pathania, K. K. Singh, S. K. Singh, A. Tolamatti, B. B. Singh, K. K. Yadav
https://arxiv.org/abs/2510.08654

Identification of Gamma Ray Pulsar Candidates in the \emph{Fermi}-LAT 4FGL-DR4 Unassociated Sources Using Supervised Machine Learning
The Large Area Telescope (LAT) on board the \emph{Fermi} Gamma-ray Space Telescope has been continuously providing good quality survey data of the entire sky in the high energy range from 30 MeV to 500 GeV and above since August 2008. A succession of gamma-ray source catalogs is published after a comprehensive analysis of the \emph{Fermi}--LAT data. The most recent release of data in the fourth \emph{Fermi}--LAT catalog of gamma-ray sources (4FGL-DR4), based on the first 14 years of obser…

Parameter Identification of a Differentiable Human Arm Musculoskeletal Model without Deep Muscle EMG Reconstruction
Philip Sanderink, Yingfan Zhou, Shuzhen Luo, Cheng Fang
https://arxiv.org/abs/2509.22825

Parameter Identification of a Differentiable Human Arm Musculoskeletal Model without Deep Muscle EMG Reconstruction
Accurate parameter identification of a subject-specific human musculoskeletal model is crucial to the development of safe and reliable physically collaborative robotic systems, for instance, assistive exoskeletons. Electromyography (EMG)-based parameter identification methods have demonstrated promising performance for personalized musculoskeletal modeling, whereas their applicability is limited by the difficulty of measuring deep muscle EMGs invasively. Although several strategies have been pr…

Efficient Decomposition Identification of Deterministic Finite Automata from Examples
Junjie Meng, Jie An, Yong Li, Andrea Turrini, Fanjiang Xu, Naijun Zhan, Miaomiao Zhang
https://arxiv.org/abs/2509.24347

Efficient Decomposition Identification of Deterministic Finite Automata from Examples
The identification of deterministic finite automata (DFAs) from labeled examples is a cornerstone of automata learning, yet traditional methods focus on learning monolithic DFAs, which often yield a large DFA lacking simplicity and interoperability. Recent work addresses these limitations by exploring DFA decomposition identification problems (DFA-DIPs), which model system behavior as intersections of multiple DFAs, offering modularity for complex tasks. However, existing DFA-DIP approaches dep…

An FPCA-Enhanced Ensemble Learning Framework for Photometric Identification of Type Ia Supernovae
Moonzarin Reza, Lifan Wang, Lei Hu
https://arxiv.org/abs/2510.09990 https://

An FPCA-Enhanced Ensemble Learning Framework for Photometric Identification of Type Ia Supernovae
Type Ia supernovae (SNe Ia) are essential tools for addressing key cosmic questions, including the Hubble tension and the nature of dark energy. Modern surveys are predominantly photometry-based, making the construction of a clean photometric SNe Ia sample crucial. In this study, we investigate whether functional principal component analysis (FPCA) scores derived from photometric light curves, combined with ensemble learning, can reliably distinguish SNe Ia from other transients using the PLAsT…

Ultrafast Grid Impedance Identification in $dq$-Asymmetric Three-Phase Power Systems
Mohamed Abdalmoaty, Verena H\"aberle, Xiuqiang He, Florian D\"orfler
https://arxiv.org/abs/2510.12338

Ultrafast Grid Impedance Identification in $dq$-Asymmetric Three-Phase Power Systems
We propose a non-parametric frequency-domain method to identify small-signal $dq$-asymmetric grid impedances, over a wide frequency band, using grid-connected converters. Existing identification methods are faced with significant trade-offs: e.g., passive approaches rely on ambient harmonics and rare grid events and thus can only provide estimates at a few frequencies, while many active approaches that intentionally perturb grid operation require long time series measurement and specialized equ…

Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at $\sqrt{s}$ = 13 TeV
CMS Collaboration
https://arxiv.org/abs/2510.10228

Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to $\mathrm{b\overline{b}}$ or $\mathrm{c\overline{c}}$, have been developed and deployed across a range of physics analyses. This paper highlights their performance …

Bluetooth Fingerprint Identification Under Domain Shift Through Transient Phase Derivative
Haytham Albousayri, Bechir Hamdaoui, Weng-Keen Wong, Nora Basha
https://arxiv.org/abs/2510.09940

Bluetooth Fingerprint Identification Under Domain Shift Through Transient Phase Derivative
Deep learning-based radio frequency fingerprinting (RFFP) has become an enabling physical-layer security technology, allowing device identification and authentication through received RF signals. This technology, however, faces significant challenges when it comes to adapting to domain variations, such as time, location, environment, receiver and channel. For Bluetooth Low Energy (BLE) devices, addressing these challenges is particularly crucial due to the BLE protocol's frequency-hopping natur…

Kritiek op 'onzichtbare' Nederlandse vissers in Duitse wateren
https://nos.nl/artikel/2594221-kritiek-op-onzichtbare-nederlandse-vissers-in-duitse-wateren
'Het Nederlandse schip had de zogeheten AIS (Automatic Identification System) uitgeschakeld. Dat was waarschijnlijk omdat er op een plek werd gevist waar ze niet mochten zijn. Daar staat een boete op van een paar honderd euro, al zouden Duitse vissers die graag verhoogd willen zien.'

Quantum Network-Based Prediction of Cancer Driver Genes
Patricia Marques, Andreas Wichert, Duarte Magano, Bruno Coutinho
https://arxiv.org/abs/2510.12628 https://

Quantum Network-Based Prediction of Cancer Driver Genes
Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein interaction (PPI) networks offers a promising avenue for their detection [ 1, 2], but scaling to large network datasets is computationally demanding. Quantum computing offers compact representations and potential complexity reductions. Motivated by the classical method of Gumpinger et al. [3], in this work we introduce a supe…

GCVE-BCP-05 - GCVE Vulnerability Format (Updated CVE Record Format) has been published as DRAFT and ready for public review.
The standard is similar to the @… record format with some extensions (via the X_ prefixes) for GCVE format and the reference implementation vulnerability-lookup. This allows some flexibility and innovation in GNA - GCVE space w…

Identification in Auctions with Truncated Transaction Prices
Tonghui Qi
https://arxiv.org/abs/2510.04464 https://arxiv.org/pdf/2510.04464

Identification in Auctions with Truncated Transaction Prices
Many auction datasets with reserve prices do not include bids that fall below the reserve. This paper establishes nonparametric identification results in first- and second-price auctions when transaction prices are truncated by a binding reserve price under a range of information structures. In the simplest case-where the number of potential bidders is fixed and known across all auctions-if only the transaction price is observed, the bidders' private-value distribution is identified in second-p…

Accurate linear modeling of EEG-based cortical activity during a passive motor task with input: a sub-space identification approach
Sanna Bakels, Mark van de Ruit, Matin Jafarian
https://arxiv.org/abs/2510.02596

Accurate linear modeling of EEG-based cortical activity during a passive motor task with input: a sub-space identification approach
This paper studies linear mathematical modeling of brain's cortical dynamics using electroencephalography (EEG) data in an experiment with continuous exogenous input. The EEG data were recorded while participants were seated with their wrist strapped to a haptic manipulator. The manipulator imposed a continuous multisine angular perturbation to the wrist as the exogenous input to the brain. We show that subspace identification, in particular the PO-MOESP algorithm, leads to a linear time-invari…

Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Prediction, and Decision-Making
Rifat Ara Proma, Ghulam Jilani Quadri, Paul Rosen
https://arxiv.org/abs/2510.11912

Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Prediction, and Decision-Making
Overplotted line charts can obscure trends in temporal data and hinder prediction. We conduct a user study comparing three alternatives-aggregated, trellis, and spiral line charts against standard line charts on tasks involving trend identification, making predictions, and decision-making. We found aggregated charts performed similarly to standard charts and support more accurate trend recognition and prediction; trellis and spiral charts generally lag. We also examined the impact on decision-m…

Differential Privacy of Network Parameters from a System Identification Perspective
Andrew Campbell, Anna Scaglione, Hang Liu, Victor Elvira, Sean Peisert, Daniel Arnold
https://arxiv.org/abs/2509.20460

Differential Privacy of Network Parameters from a System Identification Perspective
This paper addresses the problem of protecting network information from privacy system identification (SI) attacks when sharing cyber-physical system simulations. We model analyst observations of networked states as time-series outputs of a graph filter driven by differentially private (DP) nodal excitations, with the analyst aiming to infer the underlying graph shift operator (GSO). Unlike traditional SI, which estimates system parameters, we study the inverse problem: what assumptions prevent…

Identification of low-energy kaons in the ProtoDUNE-SP detector
DUNE Collaboration, et al.
https://arxiv.org/abs/2510.08380 https://arxiv.org/pdf/2510.0838…

Identification of low-energy kaons in the ProtoDUNE-SP detector
The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proton decay. Utilizing liquid-argon time-projection chamber technology, DUNE is expected to achieve world-leading sensitivity in the proton decay channels that involve charged kaons in their final states. The first DUNE demonstrator, ProtoDUNE Single-Phase, was a 0.77~kt detector that operated from 2018 to 2020 at the CER…

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification
Huike Zou, Haiyang Yang, Yindu Su, Liyu Chen, Chengbao Lian, Qingheng Zhang, Shuguang Han, Jufeng Chen
https://arxiv.org/abs/2509.23874

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification
Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generat…

Digital ID – The New Chains of Capitalist Surveillance
[…] From passports to colonial passbooks, from welfare cards to border regimes, the apparatus of identification has always been tied to domination. Digital ID is simply the latest iteration of this long history, but with a scale and sophistication that makes its dangers even more profound. […]
👷

Digital ID – The New Chains of Capitalist Surveillance - Aotearoa Workers Solidarity Movement
The world is entering an era where identity is no longer a matter of personal relationships, lived experience, or even paperwork. Increasingly, it is reduced...

A Source Identification Problem for the Bi-Parabolic Equation Containing a Poly-harmonic Operator
Dang Duc Trong, Bui Thanh Duy, Nguyen Dang Minh
https://arxiv.org/abs/2509.24470

A Source Identification Problem for the Bi-Parabolic Equation Containing a Poly-harmonic Operator
In this paper, we address the source identification problem for the bi-parabolic equation involving a operator. Specifically, we investigate the equation $(\partial_t +\frak A)^2u(t)=ψ(t)f$, where $\frak A$ denotes a poly-harmonic operator. Given the perturbed data of $ψ$ and $u(T)$ (where $T>0$), our objective is to determine $f$. Although several scientific publications have explored regularization techniques for bi-parabolic problems, the existing literature remains limited. By relaxing ce…

A Mathematical Framework for Quantifying Nonlinear Uncertainty Propagation in Eddy Identification Criteria
Charlotte Moser, Nan Chen, Stephen Wiggins
https://arxiv.org/abs/2510.01209

A Mathematical Framework for Quantifying Nonlinear Uncertainty Propagation in Eddy Identification Criteria
Ocean eddies are swirling mesoscale features that play a fundamental role in oceanic transport and mixing. Eddy identification relies on diagnostic criteria that are inherently nonlinear functions of the flow variables. However, estimating the ocean flow field is subject to uncertainty due to its turbulent nature and the use of sparse and noisy observations. This uncertainty interacts with nonlinear diagnostics, complicating its quantification and limiting the accuracy of eddy identification. I…

Identification in source apportionment using geometry
Bora Jin, Abhirup Datta
https://arxiv.org/abs/2510.03616 https://arxiv.org/pdf/2510.03616

Identification in source apportionment using geometry
Source apportionment analysis, which aims to quantify the attribution of observed concentrations of multiple air pollutants to specific sources, can be formulated as a non-negative matrix factorization (NMF) problem. However, NMF is non-unique and typically relies on unverifiable assumptions such as sparsity and uninterpretable scalings. In this manuscript, we establish identifiability of the source attribution percentage matrix under much weaker and more realistic conditions. We introduce the …

Looking Alike From Far to Near: Enhancing Cross-Resolution Re-Identification via Feature Vector Panning
Zanwu Liu, Chao Yuan, Bo Li, Xiaowei Zhang, Guanglin Niu
https://arxiv.org/abs/2510.00936

Looking Alike From Far to Near: Enhancing Cross-Resolution Re-Identification via Feature Vector Panning
In surveillance scenarios, varying camera distances cause significant differences among pedestrian image resolutions, making it hard to match low-resolution (LR) images with high-resolution (HR) counterparts, limiting the performance of Re-Identification (ReID) tasks. Most existing Cross-Resolution ReID (CR-ReID) methods rely on super-resolution (SR) or joint learning for feature compensation, which increases training and inference complexity and has reached a performance bottleneck in recent s…

Identification of molecular line emission using Convolutional Neural Networks
Nina Kessler, Timea Csengeri, David Cornu, Sylvain Bontemps, Laure Bouscasse
https://arxiv.org/abs/2510.09119

Identification of molecular line emission using Convolutional Neural Networks
Complex organic molecules (COMs) are observed to be abundant in various astrophysical environments, in particular toward star forming regions they are observed both toward protostellar envelopes as well as shocked regions. Emission spectrum especially of heavier COMs may consists of up to hundreds of lines, where line blending hinders the analysis. However, identifying the molecular composition of the gas leading to the observed millimeter spectra is the first step toward a quantitative analysi…

MS2toImg: A Framework for Direct Bioactivity Prediction from Raw LC-MS/MS Data
Hansol Hong, Sangwon Lee, Jang-Ho Ha, Sung-June Chu, So-Hee An, Woo-Hyun Paek, Gyuhwa Chung, Kyoung Tai No
https://arxiv.org/abs/2510.09716

MS2toImg: A Framework for Direct Bioactivity Prediction from Raw LC-MS/MS Data
Untargeted metabolomics using LC-MS/MS offers the potential to comprehensively profile the chemical diversity of biological samples. However, the process is fundamentally limited by the "identification bottleneck," where only a small fraction of detected features can be annotated using existing spectral libraries, leaving the majority of data uncharacterized and unused. In addition, the inherently low reproducibility of LC-MS/MS instruments introduces alignment errors between runs, making featu…

A Copula-Based Variational Autoencoder for Uncertainty Quantification in Inverse Problems: Application to Damage Identification in an Offshore Wind Turbine
Ana Fernandez-Navamuel, Matteo Croci, Martin Alberto Diaz Viera
https://arxiv.org/abs/2510.02013

A Copula-Based Variational Autoencoder for Uncertainty Quantification in Inverse Problems: Application to Damage Identification in an Offshore Wind Turbine
Structural Health Monitoring of Floating Offshore Wind Turbines (FOWTs) is critical for ensuring operational safety and efficiency. However, identifying damage in components like mooring systems from limited sensor data poses a challenging inverse problem, often characterized by multimodal solutions where various damage states could explain the observed response. To overcome it, we propose a Variational Autoencoder (VAE) architecture, where the encoder approximates the inverse operator, while t…

Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides
Johnathan Kowalski, Liangbo Liang
https://arxiv.org/abs/2510.09839 https://arxiv.org…

Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides
Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It is thus highly desirable to have a rapid, simple, and in situ characterization technique to monitor, in real time, the conversion process fr…

VL-KnG: Visual Scene Understanding for Navigation Goal Identification using Spatiotemporal Knowledge Graphs
Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov, Arthur Nigmatzyanov, Dmitrii Nalberskii, Sergey Zagoruyko, Gonzalo Ferrer
https://arxiv.org/abs/2510.01483

VL-KnG: Visual Scene Understanding for Navigation Goal Identification using Spatiotemporal Knowledge Graphs
Vision-language models (VLMs) have shown potential for robot navigation but encounter fundamental limitations: they lack persistent scene memory, offer limited spatial reasoning, and do not scale effectively with video duration for real-time application. We present VL-KnG, a Visual Scene Understanding system that tackles these challenges using spatiotemporal knowledge graph construction and computationally efficient query processing for navigation goal identification. Our approach processes vid…

Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
Matteo Fuoli, Weihang Huang, Jeannette Littlemore, Sarah Turner, Ellen Wilding
https://arxiv.org/abs/2509.24866

Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
Metaphor is a pervasive feature of discourse and a powerful lens for examining cognition, emotion, and ideology. Large-scale analysis, however, has been constrained by the need for manual annotation due to the context-sensitive nature of metaphor. This study investigates the potential of large language models (LLMs) to automate metaphor identification in full texts. We compare three methods: (i) retrieval-augmented generation (RAG), where the model is provided with a codebook and instructed to …

Bridging the Prediction Error Method and Subspace Identification: A Weighted Null Space Fitting Method
Jiabao He, S. Joe Qin, H\r{a}kan Hjalmarsson
https://arxiv.org/abs/2510.02529

Bridging the Prediction Error Method and Subspace Identification: A Weighted Null Space Fitting Method
Subspace identification methods (SIMs) have proven to be very useful and numerically robust for building state-space models. While most SIMs are consistent, few if any can achieve the efficiency of the maximum likelihood estimate (MLE). Conversely, the prediction error method (PEM) with a quadratic criteria is equivalent to MLE, but it comes with non-convex optimization problems and requires good initialization points. This contribution proposes a weighted null space fitting (WNSF) approach for…

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination
Karl C. Linne (Kai Li), Sho Uemura, Yue Ji, Andrew Karmen, Allen Zang, Alex Kolar, Ian Chin, Martin Di Federico, Gustavo Cancelo, Orlando Quaranta, Debashri Roy, Tian Zhong
https://arxiv.org/abs/2509.26315

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination
Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons…

Text-Independent Speaker Identification Using Audio Looping With Margin Based Loss Functions
Elliot Q C Garcia, Nic\'eias Silva Vilela, K\'atia Pires Nascimento do Sacramento, Tiago A. E. Ferreira
https://arxiv.org/abs/2509.22838

Text-Independent Speaker Identification Using Audio Looping With Margin Based Loss Functions
Speaker identification has become a crucial component in various applications, including security systems, virtual assistants, and personalized user experiences. In this paper, we investigate the effectiveness of CosFace Loss and ArcFace Loss for text-independent speaker identification using a Convolutional Neural Network architecture based on the VGG16 model, modified to accommodate mel spectrogram inputs of variable sizes generated from the Voxceleb1 dataset. Our approach involves implementin…

Nonlinear System Identification for Model-Based Control of Waked Wind Turbines
Sebastiano Randino, Lorenzo Schena, Nicolas Coudou, Emanuele Garone, Miguel Alfonso Mendez
https://arxiv.org/abs/2510.07336

Nonlinear System Identification for Model-Based Control of Waked Wind Turbines
This work presents a nonlinear system identification framework for modeling the power extraction dynamics of wind turbines, including both freestream and waked conditions. The approach models turbine dynamics using data-driven power coefficient maps expressed as combinations of compact radial basis functions and polynomial bases, parameterized in terms of tip-speed ratio and upstream conditions. These surrogate models are embedded in a first-order dynamic system suitable for model-based control…

Explainability, risk modeling, and segmentation based customer churn analytics for personalized retention in e-commerce
Sanjula De Alwis, Indrajith Ekanayake
https://arxiv.org/abs/2510.11604

Explainability, risk modeling, and segmentation based customer churn analytics for personalized retention in e-commerce
In online retail, customer acquisition typically incurs higher costs than customer retention, motivating firms to invest in churn analytics. However, many contemporary churn models operate as opaque black boxes, limiting insight into the determinants of attrition, the timing of retention opportunities, and the identification of high-risk customer segments. Accordingly, the emphasis should shift from prediction alone to the design of personalized retention strategies grounded in interpretable ev…

Counterfactual Identifiability via Dynamic Optimal Transport
Fabio De Sousa Ribeiro, Ainkaran Santhirasekaram, Ben Glocker
https://arxiv.org/abs/2510.08294 https://

Counterfactual Identifiability via Dynamic Optimal Transport
We address the open question of counterfactual identification for high-dimensional multivariate outcomes from observational data. Pearl (2000) argues that counterfactuals must be identifiable (i.e., recoverable from the observed data distribution) to justify causal claims. A recent line of work on counterfactual inference shows promising results but lacks identification, undermining the causal validity of its estimates. To address this, we establish a foundation for multivariate counterfactual …

A Comprehensive Survey on Smart Home IoT Fingerprinting: From Detection to Prevention and Practical Deployment
Eduardo Baena, Han Yang, Dimitrios Koutsonikolas, Israat Haque
https://arxiv.org/abs/2510.09700

A Comprehensive Survey on Smart Home IoT Fingerprinting: From Detection to Prevention and Practical Deployment
Smart homes are increasingly populated with heterogeneous Internet of Things (IoT) devices that interact continuously with users and the environment. This diversity introduces critical challenges in device identification, authentication, and security, where fingerprinting techniques have emerged as a key approach. In this survey, we provide a comprehensive analysis of IoT fingerprinting specifically in the context of smart homes, examining methods for device and their event detection, classific…

NGTS-EB-8: A double-lined eclipsing M M binary discovered by citizen scientists
Sean M. O'Brien, Megan E. Schwamb, Christopher A. Watson, Louise D. Nielsen, Edward M. Bryant, Sarah L. Casewell, Matthew R. Burleigh, Lucy Fortson, Samuel Gill, Chris J. Lintott, Katlyn L. Hobbs, Ioannis Apergis, Daniel Bayliss, Jorge Fern\'andez Fern\'andez, Maximilian N. G\"unther, Faith Hawthorn, James S. Jenkins, Alicia Kendall, James McCormac, Ernst J. W. de Mooij, Toby Rodel, Suman S…

NGTS-EB-8: A double-lined eclipsing M+M binary discovered by citizen scientists
We report the identification and characterization of a new binary system composed of two near-equal mass M-dwarfs. The binary NGTS-EB-8 was identified as a planet candidate in data from the Next Generation Transit Survey (NGTS) by citizen scientists participating in the Planet Hunters NGTS project. High-resolution spectroscopic observations reveal the system to be a double-lined binary. By modeling the photometric and radial velocity observations, we determine an orbital period of 4.2 days and …

Specific multi-emitter identification via multi-label learning
Yuhao Chen, Boxiang He, Shilian Wang, Jing Lei
https://arxiv.org/abs/2509.22396 https://arxi…

Specific multi-emitter identification via multi-label learning
Specific emitter identification leverages hardware-induced impairments to uniquely determine a specific transmitter. However, existing approaches fail to address scenarios where signals from multiple emitters overlap. In this paper, we propose a specific multi-emitter identification (SMEI) method via multi-label learning to determine multiple transmitters. Specifically, the multi-emitter fingerprint extractor is designed to mitigate the mutual interference among overlapping signals. Then, the m…

Overview of LifeCLEF Plant Identification task 2020
Herve Goeau, Pierre Bonnet, Alexis Joly
https://arxiv.org/abs/2509.19402 https://arxiv.org/pdf/2509.194…

Overview of LifeCLEF Plant Identification task 2020
Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data with more and more photos in the field. However, this profusion of data only concerns a few tens of thousands of species, mostly located in North America and Western Europe, much less in the richest regions in terms of biodiversity such as tropical countries. On the other hand, for several centuries, botanists have collected, catalogued and systematic…

Using gravitational lensing to probe for bright quintessential galaxies in the Epoch of Reionization
Joshua Roberson, Matthew B. Bayliss, M. D. Gladders, Gourav Khullar, Keren Sharon, Keunho Kim, Dan Coe, Lindsey Bleem, Taweewat Somboonpanyakul
https://arxiv.org/abs/2510.12097

Using gravitational lensing to probe for bright quintessential galaxies in the Epoch of Reionization
Understanding the properties of the first generation of galaxies is an ongoing challenge in observational astrophysics. While advances in deep field observation have led to the identification of large numbers of galaxies within the Epoch of Reionization, there are very few observed galaxies at this range that are sufficiently bright for high signal-to-noise spectroscopy. To this end, we analyse HST and ground-based photometry of five candidate strongly lensed galaxies, all projected behind the …

dN/dx Reconstruction with Deep Learning for High-Granularity TPCs
Guang Zhao, Yue Chang, Jinxian Zhang, Linghui Wu, Huirong Qi, Xin She, Mingyi Dong, Shengsen Sun, Jianchun Wang, Yifang Wang, Chunxu Yu
https://arxiv.org/abs/2510.10628

dN/dx Reconstruction with Deep Learning for High-Granularity TPCs
Particle identification (PID) is essential for future particle physics experiments such as the Circular Electron-Positron Collider and the Future Circular Collider. A high-granularity Time Projection Chamber (TPC) not only provides precise tracking but also enables dN/dx measurements for PID. The dN/dx method estimates the number of primary ionization electrons, offering significant improvements in PID performance. However, accurate reconstruction remains a major challenge for this approach. In…

DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching
Jessica Ojo, Zina Kamel, David Ifeoluwa Adelani
https://arxiv.org/abs/2509.17768 https:/…

DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching
Language Identification (LID) is a core task in multilingual NLP, yet current systems often overfit to clean, monolingual data. This work introduces DIVERS-BENCH, a comprehensive evaluation of state-of-the-art LID models across diverse domains, including speech transcripts, web text, social media texts, children's stories, and code-switched text. Our findings reveal that while models achieve high accuracy on curated datasets, performance degrades sharply on noisy and informal inputs. We also in…

Driver Identification and PCA Augmented Selection Shrinkage Framework for Nordic System Price Forecasting
Yousef Adeli Sadabad, Mohammad Reza Hesamzadeh, Gyorgy Dan, Matin Bagherpour, Darryl R. Biggar
https://arxiv.org/abs/2509.18887

Enhancing Particle Identification in Helium-Based Drift Chambers Using Cluster Counting Insights from Beam Test Studies
W. Elmetenawee, M. Abbrescia, M. Anwar, G. Chiarello, A. Corvaglia, F. Cuna, N. De Filippis, F. De Santis, M. Dong, E. Gorini, F Grancagnolo, F. G. Gravili, K. Johnson, S. Liu, M. Louka, A. Miccoli, M. Panareo, M. Primavera, F. M. Procacci, A. Taliercio, G. Tassielli, A. Ventura L. Wu, G. Zhao

Enhancing Particle Identification in Helium-Based Drift Chambers Using Cluster Counting Insights from Beam Test Studies
Particle identification in gaseous detectors traditionally relies on energy loss measurements (dE/dx); however, uncertainties in total energy deposition limit its resolution. The cluster counting technique (dN/dx) offers an alternative approach by exploiting the Poisson-distributed nature of primary ionization, providing a statistically robust method for mass determination. Simulation studies with Garfield++ and Geant4 indicate that dN/dx can achieve twice the resolution of dE/dx in helium-base…

A federal judge in Washington permanently barred the Trump administration on Friday from requiring proof of citizenship on federal voter registration forms,
a change dictated in an executive order Donald Trump signed in March.
The ruling definitively halted the effort to compel the Elections Assistance Commission,
an independent body,
to adopt nationwide changes to voting procedures
at a time when the president has also called for requiring voter identification …

Judge Permanently Bars Trump From Requiring Proof of Citizenship for Voter Registration
The ruling on Friday found that Trump had usurped the power vested in Congress and the states to administer and oversee elections protocols.

An Interpolation-based Scheme for Rapid Frequency-Domain System Identification
Jared Jonas, Bassam Bamieh
https://arxiv.org/abs/2510.00525 https://arxiv.or…

An Interpolation-based Scheme for Rapid Frequency-Domain System Identification
We present a frequency-domain system identification scheme based on barycentric interpolation and weight optimization. The scheme is related to the Adaptive Antoulas-Anderson (AAA) algorithm for model reduction, but uses an adaptive algorithm for selection of frequency points for interrogating the system response, as would be required in identification versus model reduction. The scheme is particularly suited for systems in which any one sinusoidal response run is long or expensive, and thus th…

Computational identification of the source domain in an inverse problem of potential theory
P. N. Vabishchevich
https://arxiv.org/abs/2510.04693 https://ar…

Computational identification of the source domain in an inverse problem of potential theory
The inverse potential problem consists in determining the density of the volume potential from measurements outside the sources. Its ill-posedness is due both to the non-uniqueness of the solution and to the instability of the solution with respect to measurement errors. The inverse problem is solved under additional assumptions about the sources using regularizing algorithms. In this work, an inverse problem is posed for identifying the domain that contains the sources. The computational algor…

Wavefront Error Recovery and Companion Identification with the James Webb Space Telescope
Matthew De Furio, Marie Ygouf, Alexandra Greenbaum, Gra\c{c}a Rocha, Michael Meyer, Charles Beichman, Jorge Llop-Sayson, Gael Roudier, Steph Sallum, Jarron Leisenring, Anand Sivaramakrishnan
https://arxiv.org/abs/2510.05308

Wavefront Error Recovery and Companion Identification with the James Webb Space Telescope
The James Webb Space Telescope is orders of magnitude more sensitive than any other facility across the near to mid-infrared wavelengths. Many approved programs take advantage of its highly stable point spread function (PSF) to directly detect faint companions using diverse high-contrast imaging (HCI) techniques. However, periodic re-phasing of the Optical Telescope Element (OTE) is required due to slow thermal drifts distorting to the primary mirror backplane along with stochastic tilt events …

Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation
Yuchen Yan, Zhihua Liu, Hao Wang, Weiming Li, Xiaoshuai Hao
https://arxiv.org/abs/2510.11541 …

Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple knowledge targets to form a synthesized answer, raise new challenges for RAG systems. Under the multi-hop settings, existing methods often struggle to fully understand the questions with complex semantic structures and are susceptible to irrelevant noise during the retrieval…

Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
Cheng Ouyang, Moeen Ul Islam, Dong Chen, Kaixiang Zhang, Zhaojian Li, Xiaobo Tan
https://arxiv.org/abs/2510.08953

Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
Soft robots offer significant advantages in safety and adaptability, yet achieving precise and dynamic control remains a major challenge due to their inherently complex and nonlinear dynamics. Recently, Data-enabled Predictive Control (DeePC) has emerged as a promising model-free approach that bypasses explicit system identification by directly leveraging input-output data. While DeePC has shown success in other domains, its application to soft robots remains underexplored, particularly for thr…

Feature Identification for Hierarchical Contrastive Learning
Julius Ott, Nastassia Vysotskaya, Huawei Sun, Lorenzo Servadei, Robert Wille
https://arxiv.org/abs/2510.00837 https:…

Feature Identification for Hierarchical Contrastive Learning
Hierarchical classification is a crucial task in many applications, where objects are organized into multiple levels of categories. However, conventional classification approaches often neglect inherent inter-class relationships at different hierarchy levels, thus missing important supervisory signals. Thus, we propose two novel hierarchical contrastive learning (HMLC) methods. The first, leverages a Gaussian Mixture Model (G-HMLC) and the second uses an attention mechanism to capture hierarchy…

Identification and Estimation of Joint Potential Outcome Distributions from a Single Study
Zach Shahn, David Madigan
https://arxiv.org/abs/2509.20506 https://

Identification and Estimation of Joint Potential Outcome Distributions from a Single Study
Most causal inference methods focus on estimating marginal average treatment effects, but many important causal estimands depend on the joint distribution of potential outcomes, including the probability of causation and proportions benefiting from or harmed by treatment. Wu et al (2025) recently established nonparametric identification of this joint distribution for categorical outcomes under binary treatment by leveraging variation across multiple studies. We demonstrate that their multi-stud…

A quest for sulfur-bearing refractory species. Identification of CaS in the interstellar medium
A. Tasa-Chaveli, \'A. S\'anchez-Monge, A. Fuente, A. Ginsburg, H. S. P. M\"uller, Th. M\"oller, P. Rivi\`ere-Marichalar, D. Navarro-Almaida, G. Esplugues, P. Schilke, M. Rodr\'iguez-Baras, S. Thorwirth, L. Beitia-Antero
https://

A quest for sulfur-bearing refractory species. Identification of CaS in the interstellar medium
The recent detection of refractory molecules in massive star-forming regions provides a means of probing the innermost regions of disks around massive stars. These detections also make it possible to explore the chemical composition of refractories through gas-phase observations. In this regard, identifying refractory compounds containing sulfur could reveal potential connections between sulfur and refractories, as well as help determine the sulfur budget in these extreme environments. We find …

Online Specific Emitter Identification via Collision-Alleviated Signal Hash
Hongyu Wang, Wenjia Xu, Guangzuo Li, Siyuan Wan, Yaohua Sun, Jiuniu Wang, Mugen Peng
https://arxiv.org/abs/2509.23807

Online Specific Emitter Identification via Collision-Alleviated Signal Hash
Specific Emitter Identification (SEI) has been widely studied, aiming to distinguish signals from different emitters given training samples from those emitters. However, real-world scenarios often require identifying signals from novel emitters previously unseen. Since these novel emitters only have a few or no prior samples, existing models struggle to identify signals from novel emitters online and tend to bias toward the distribution of seen emitters. To address these challenges, we propose …

Direct Continuous-Time LPV System Identification of Li-ion Batteries via L1-Regularized Least Squares
Yang Wang, Riccardo M. G. Ferrari
https://arxiv.org/abs/2509.21110 https://…

Direct Continuous-Time LPV System Identification of Li-ion Batteries via L1-Regularized Least Squares
Accurate identification of lithium-ion battery parameters is essential for estimating battery states and managing performance. However, the variation of battery parameters over the state of charge (SOC) and the nonlinear dependence of the open-circuit voltage (OCV) on the SOC complicate the identification process. In this work, we develop a continuous-time LPV system identification approach to identify the SOC-dependent battery parameters and the OCV-SOC mapping. We model parameter variations u…

Collusion-Driven Impersonation Attack on Channel-Resistant RF Fingerprinting
Zhou Xu, Guyue Li, Zhe Peng, Aiqun Hu
https://arxiv.org/abs/2509.22154 https://

Collusion-Driven Impersonation Attack on Channel-Resistant RF Fingerprinting
Radio frequency fingerprint (RFF) is a promising device identification technology, with recent research shifting from robustness to security due to growing concerns over vulnerabilities. To date, while the security of RFF against basic spoofing such as MAC address tampering has been validated, its resilience to advanced mimicry remains unknown. To address this gap, we propose a collusion-driven impersonation attack that achieves RF-level mimicry, successfully breaking RFF identification systems…

Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support
Cen (Mia), Zhao (Wayne), Tiantian Zhang (Wayne), Hanchen Su (Wayne), Yufeng (Wayne), Zhang (Elaine), Shaowei Su (Elaine), Mingzhi Xu (Elaine), Yu (Elaine), Liu, Wei Han, Jeremy Werner, Claire Na Cheng, Yashar Mehdad
https://arxiv.org/abs/2510.0…

Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support
We introduce an Agent-in-the-Loop (AITL) framework that implements a continuous data flywheel for iteratively improving an LLM-based customer support system. Unlike standard offline approaches that rely on batch annotations, AITL integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adoption and rationales, (3) knowledge relevance checks, and (4) identification of missing knowledge. These feedback signals seamlessly feed ba…

Automatic Intermodal Loading Unit Identification using Computer Vision: A Scoping Review
Emre G\"ulsoylu, Alhassan Abdelhalim, Derya Kara Boztas, Ole Grasse, Carlos Jahn, Simone Frintrop, Janick Edinger
https://arxiv.org/abs/2509.17707

Automatic Intermodal Loading Unit Identification using Computer Vision: A Scoping Review
The standardisation of Intermodal Loading Units (ILUs), such as containers, semi-trailers and swap bodies, has revolutionised global trade yet their efficient and robust identification remains a critical bottleneck in high-throughput ports and terminals. This paper reviews 63 empirical studies that propose computer vision (CV) based solutions. It covers the last 35 years (1990-2025), tracing the field's evolution from early digital image processing (DIP) and traditional machine learning (ML) to…

Object Classification from JWST Catalogs
B. L. Crompvoets, H. Kirk, R. Gutermuth, J. Di Francesco
https://arxiv.org/abs/2510.07747 https://arxiv.org/pdf/25…

Object Classification from JWST Catalogs
JWST's exquisite data have opened the doors to new possibilities in detecting broad classes of astronomical objects, but also to new challenges in classifying those objects. In this work, we introduce SESHAT, the Stellar Evolutionary Stage Heuristic Assessment Tool for the identification of Young Stellar Objects, field stars (main sequence through asymptotic giant branch), brown dwarfs, white dwarfs, and galaxies, from any JWST observation. This identification is done using the machine learning…

Financial Risk Relation Identification through Dual-view Adaptation
Wei-Ning Chiu, Yu-Hsiang Wang, Andy Hsiao, Yu-Shiang Huang, Chuan-Ju Wang
https://arxiv.org/abs/2509.18775 ht…

Identification and Semiparametric Estimation of Conditional Means from Aggregate Data
Cory McCartan, Shiro Kuriwaki
https://arxiv.org/abs/2509.20194 https://

Identification and Semiparametric Estimation of Conditional Means from Aggregate Data
We introduce a new method for estimating the mean of an outcome variable within groups when researchers only observe the average of the outcome and group indicators across a set of aggregation units, such as geographical areas. Existing methods for this problem, also known as ecological inference, implicitly make strong assumptions about the aggregation process. We first formalize weaker conditions for identification, which motivates estimators that can efficiently control for many covariates. …

Identification and optimal control strategies for the transversal splitting of ultra--cold Bose gases
Nikolaus W\"urkner, Yevhenii Kuriatnikov, Karthikeyan Kumaran, Marupaka Venkat Ramana, J\"org Schmiedmayer, Andreas Kugi, Maximilian Pr\"ufer, Andreas Deutschmann-Olek
https://arxiv.org/abs/2510.07113

Identification and optimal control strategies for the transversal splitting of ultra--cold Bose gases
Splitting a Bose--Einstein condensate (BEC) is a key operation in fundamental physics experiments and emerging quantum technologies, where precise preparation of well--defined initial states requires fast yet coherent control of the condensate's nonlinear dynamics. This work formulates the BEC splitting process as an optimal feedforward control problem based on a physically interpretable, reduced--order model identified from limited experimental data. We introduce a systematic calibration strat…

Replaced article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Optical communication-based identification for multi-UAV systems: theory and practice
Daniel Bonilla Licea, Viktor Walter, Mounir Ghogho, Martin Saska

Beyond the Brightest: A Deep Learning Approach to Identifying Major and Minor Galaxy Mergers in CANDELS at $z \sim 1$
Aimee L. Schechter, Aleksandra \'Ciprijanovi\'c, Xuejian Shen, Rebecca Nevin, Julia M. Comerford, Aaron Stemo, Laura Blecha, Austin Fraley
https://arxiv.org/abs/2510.12173

Beyond the Brightest: A Deep Learning Approach to Identifying Major and Minor Galaxy Mergers in CANDELS at $z \sim 1$
Galaxy mergers play an important role in many aspects of galaxy evolution, therefore, more accurate merger identifications are paramount for achieving a complete understanding of galaxy evolution. As we enter the era of very large imaging surveys, we are able to observe mergers extending to even lower masses and higher redshifts. Despite low-mass galaxies being more common, many previous merger identification methods were mostly calibrated for high-mass, local galaxies, which are easier to iden…

Accurate and Noise-Tolerant Extraction of Routine Logs in Robotic Process Automation (Extended Version)
Massimiliano de Leoni, Faizan Ahmed Khan, Simone Agostinelli
https://arxiv.org/abs/2510.08118

Accurate and Noise-Tolerant Extraction of Routine Logs in Robotic Process Automation (Extended Version)
Robotic Process Mining focuses on the identification of the routine types performed by human resources through a User Interface. The ultimate goal is to discover routine-type models to enable robotic process automation. The discovery of routine-type models requires the provision of a routine log. Unfortunately, the vast majority of existing works do not directly focus on enabling the model discovery, limiting themselves to extracting the set of actions that are part of the routines. They were a…

Leveraging Prior Knowledge of Diffusion Model for Person Search
Giyeol Kim, Sooyoung Yang, Jihyong Oh, Myungjoo Kang, Chanho Eom
https://arxiv.org/abs/2510.01841 https://…

Leveraging Prior Knowledge of Diffusion Model for Person Search
Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncropped scene images. Existing methods predominantly utilize ImageNet pre-trained backbones, which may be suboptimal for capturing the complex spatial context and fine-grained identity cues necessary for person search. Moreover, they rely on a shared backbone feature for both person detection and re-identification, leading to suboptimal features due to …

Detection and Identification of Sensor Attacks Using Data
Takumi Shinohara, Karl H. Johansson, Henrik Sandberg
https://arxiv.org/abs/2510.02183 https://arx…

Detection and Identification of Sensor Attacks Using Data
In this paper, we investigate data-driven attack detection and identification in a model-free setting. Unlike existing studies, we consider the case where the available output data include malicious false-data injections. We aim to detect and identify such attacks solely from the compromised data. We address this problem in two scenarios: (1) when the system operator is aware of the system's sparse observability condition, and (2) when the data are partially clean (i.e., attack-free). In both s…

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Ultrafast Grid Impedance Identification in $dq$-Asymmetric Three-Phase Power Systems
Mohamed Abdalmoaty, Verena H\"aberle, Xiuqiang He, Florian D\"orfler

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Dual Cluster Contrastive learning for Object Re-Identification
Hantao Yao, Changsheng Xu

Electric Vehicle Identification from Behind Smart Meter Data
Ammar Kamoona, Hui Song, Ali Moradi Amani, Mahdi Jalili, Xinghuo Yu, Peter McTaggart
https://arxiv.org/abs/2509.19316

Electric Vehicle Identification from Behind Smart Meter Data
Electric vehicle (EV) charging loads identification from behind smart meter recordings is an indispensable aspect that enables effective decision-making for energy distributors to reach an informed and intelligent decision about the power grid's reliability. When EV charging happens behind the meter (BTM), the charging occurs on the customer side of the meter, which measures the overall electricity consumption. In other words, the charging of the EV is considered part of the customer's load and…

Spectral Flow Learning Theory: Finite-Sample Guarantees for Vector-Field Identification
Chi Ho Leung, Philip E. Par\'e
https://arxiv.org/abs/2509.25000 https://

Spectral Flow Learning Theory: Finite-Sample Guarantees for Vector-Field Identification
We study the identification of continuous-time vector fields from irregularly sampled trajectories. We introduce Spectral Flow Learning (SFL), which learns in a windowed flow space using a lag-linear label operator that aggregates lagged Koopman actions. We provide finite-sample high-probability (FS-HP) guarantees for the class of variable-step linear multistep methods (vLLM). The FS-HP rates are constructed using spectral regularization with qualification-controlled filters for flow predictors…

Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control
Hongyi Zhou, Jingwei Li, Jingzhao Zhang
https://arxiv.org/abs/2509.22741 http…

Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control
Real world evolves in continuous time but computations are done from finite samples. Therefore, we study algorithms using finite observations in continuous-time linear dynamical systems. We first study the system identification problem, and propose a first non-asymptotic error analysis with finite observations. Our algorithm identifies system parameters without needing integrated observations over certain time intervals, making it more practical for real-world applications. Further we propose a…

Generative Adversarial Networks Applied for Privacy Preservation in Biometric-Based Authentication and Identification
Lubos Mjachky, Ivan Homoliak
https://arxiv.org/abs/2509.20024

Generative Adversarial Networks Applied for Privacy Preservation in Biometric-Based Authentication and Identification
Biometric-based authentication systems are getting broadly adopted in many areas. However, these systems do not allow participating users to influence the way their data is used. Furthermore, the data may leak and can be misused without the users' knowledge. In this paper, we propose a new authentication method that preserves the privacy of individuals and is based on a generative adversarial network (GAN). Concretely, we suggest using the GAN for translating images of faces to a visually priva…

Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares
Yang Wang, Riccardo M. G. Ferrari, Michel Verhaegen
https://arxiv.org/abs/2509.21116

Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares
Accurate identification of lithium-ion (Li-ion) battery parameters is essential for managing and predicting battery behavior. However, existing discrete-time methods hinder the estimation of physical parameters and face the fast-slow dynamics problem presented in the battery. In this paper, we developed a continuous-time approach that enables the estimation of battery parameters directly from sampled data. This method avoids discretization errors in converting continuous-time models into discre…

Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Jialin Zheng, Zhong Liu, Xiaonan Lu
https://arxiv.org/abs/2510.09826

Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Stability evaluation of black-box grid-tied inverters is vital for grid reliability, yet identification techniques are both data-hungry and blocked by proprietary internals. {To solve this, this letter proposes a latent-feature-informed neural ordinary differential equation (LFI-NODE) modeling method that can achieve lightweight stability evaluation directly from trajectory data.} LFI-NODE parameterizes the entire system ODE with a single continuous-time neural network, allowing each new sample…

On continuous-time sparse identification of nonlinear polynomial systems
Mazen Alamir
https://arxiv.org/abs/2509.17635 https://arxiv.org/pdf/2509.17635

On continuous-time sparse identification of nonlinear polynomial systems
This paper leverages recent advances in high derivatives reconstruction from noisy-time series and sparse multivariate polynomial identification in order to improve the process of parsimoniously identifying, from a small amount of data, unknown Single-Input/Single-Output nonlinear dynamics of relative degree up to 4. The methodology is illustrated on the Electronic Throttle Controlled automotive system.

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
Amir Bahador Javadi, Philip Pong
https://arxiv.org/abs/2510.09411 https://

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
This study explores data-driven modeling techniques to capture the dynamics of a grid-forming converter-based infinite bus system, critical for renewable-integrated power grids. Using sparse identification of nonlinear dynamics and deep symbolic regression, models were generated from synthetic data simulating key disturbances in active power, reactive power, and voltage references. Deep symbolic regression demonstrated more accuracy in capturing complex system dynamics, though it required subst…