Tootfinder

Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering
Karthik Menon, Batool Arhamna Haider, Muhammad Arham, Kanwal Mehreen, Ram Mohan Rao Kadiyala, Hamza Farooq
https://arxiv.org/abs/2508.04683

Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering
This study introduces Query Attribute Modeling (QAM), a hybrid framework that enhances search precision and relevance by decomposing open text queries into structured metadata tags and semantic elements. QAM addresses traditional search limitations by automatically extracting metadata filters from free-form text queries, reducing noise and enabling focused retrieval of relevant items. Experimental evaluation using the Amazon Toys Reviews dataset (10,000 unique items with 40,000+ reviews and d…

"Climate change: new method can more accurately attribute environmental harm to individual polluter"
#Climate #ClimateChange

Climate change: new method can more accurately attribute environmental harm to individual polluters
Claimants in climate liability cases will benefit from a new emissions attribution method.

We need to develop good practices for bots and crawlers that are acceptable for all parties involved. Otherwise AI will suffocate the open web.
https://www.eff.org/deeplinks/2025/06/keeping-web-under-weight-ai-crawlers

Keeping the Web Up Under the Weight of AI Crawlers
If you run a site on the open web, chances are you've noticed a big increase in traffic over the past few months, whether or not your site has been getting more viewers, and you're not alone. Operators everywhere have observed a drastic increase in automated traffic—bots—and in most cases attribute...

I am ignorant on what it is to be trans or a woman or a world-class athlete, but I THINK the problem here is a doomed project of representing a multidimensional continuously variable attribute with a single Boolean https://infosec.exchange/@JessTheUnstill/114639211101368863

i have a weird bug: https://godbolt.org/z/vo1qYed8c
doesn't compile for me on arm-none-eabi-gcc-15.1.0 (and probably also earlier versions) because the assembler complains: "Error: registers may not be the same" - line 64 in the godbolt output. godbolt does not complain however …

Compiler Explorer - C++ (ARM GCC 14.3.0 (unknown-eabi))
#define MALIGN(x) __attribute__((aligned(x))) #define MROUND(x) (((uint32_t)(x)+3) & (~((uint32_t)3))) void mem_init(void *mem_pool, uint16_t mem_size, uint16_t mem_count, void **mem_head) { *mem_head = mem_pool; /* Store free mem_count after the list's next pointer at an 32-bit * aligned memory location to ensure atomic read/write (in ARM for now). */ *((uint16_t *)MROUND((uint8_t *)mem_pool + sizeof(mem_pool))) = mem_count; /* Initialize next pointers to form a free list, …

Stravinsky:
“…Expression has never been an inherent property of music. That is by no means the purpose of its existence. If, as is nearly always the case, music appears to express something, this is only an illusion and not a reality. It is simply an additional attribute which, by tacit and inveterate agreement, we have lent it, thrust upon it, as a label, a convention - in short, an aspect which, unconsciously or by force of habit, we have come to confuse with its essential being.”…

Towards Better Attribute Inference Vulnerability Measures
Paul Francis, David Wagner
https://arxiv.org/abs/2507.01710 https://arxiv.o…

Towards Better Attribute Inference Vulnerability Measures
The purpose of anonymizing structured data is to protect the privacy of individuals in the data while retaining the statistical properties of the data. An important class of attack on anonymized data is attribute inference, where an attacker infers the value of an unknown attribute of a target individual given knowledge of one or more known attributes. A major limitation of recent attribute inference measures is that they do not take recall into account, only precision. It is often the case tha…

Think Twice Before You Judge: Mixture of Dual Reasoning Experts for Multimodal Sarcasm Detection
Soumyadeep Jana, Abhrajyoti Kundu, Sanasam Ranbir Singh
https://arxiv.org/abs/2507.04458

Think Twice Before You Judge: Mixture of Dual Reasoning Experts for Multimodal Sarcasm Detection
Multimodal sarcasm detection has attracted growing interest due to the rise of multimedia posts on social media. Understanding sarcastic image-text posts often requires external contextual knowledge, such as cultural references or commonsense reasoning. However, existing models struggle to capture the deeper rationale behind sarcasm, relying mainly on shallow cues like image captions or object-attribute pairs from images. To address this, we propose \textbf{MiDRE} (\textbf{Mi}xture of \textbf{D…

HiPerMotif: Novel Parallel Subgraph Isomorphism in Large-Scale Property Graphs
Mohammad Dindoost, Oliver Alvarado Rodriguez, Bartosz Bryg, Ioannis Koutis, David A. Bader
https://arxiv.org/abs/2507.04130

HiPerMotif: Novel Parallel Subgraph Isomorphism in Large-Scale Property Graphs
Subgraph isomorphism, essential for pattern detection in large-scale graphs, faces scalability challenges in attribute-rich property graphs used in neuroscience, systems biology, and social network analysis. Traditional algorithms explore search spaces vertex-by-vertex from empty mappings, leading to extensive early-stage exploration with limited pruning opportunities. We introduce HiPerMotif, a novel hybrid parallel algorithm that fundamentally shifts the search initialization strategy. After …

Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding
Weiqing Xiao, Hao Huang, Chonghao Zhong, Yujie Lin, Nan Wang, Xiaoxue Chen, Zhaoxi Chen, Saining Zhang, Shuocheng Yang, Pierre Merriaux, Lei Lei, Hao Zhao
https://arxiv.org/abs/2506.03134

Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding
We present SA-Radar (Simulate Any Radar), a radar simulation approach that enables controllable and efficient generation of radar cubes conditioned on customizable radar attributes. Unlike prior generative or physics-based simulators, SA-Radar integrates both paradigms through a waveform-parameterized attribute embedding. We design ICFAR-Net, a 3D U-Net conditioned on radar attributes encoded via waveform parameters, which captures signal variations induced by different radar configurations. Th…

Robust Multi-generation Learned Compression of Point Cloud Attribute
Xiangzuo Liu, Zhikai Liu, PengPeng Yu, Ruishan Huang, Fan Liang
https://arxiv.org/abs/2507.01320

Robust Multi-generation Learned Compression of Point Cloud Attribute
Existing learned point cloud attribute compression methods primarily focus on single-pass rate-distortion optimization, while overlooking the issue of cumulative distortion in multi-generation compression scenarios. This paper, for the first time, investigates the multi-generation issue in learned point cloud attribute compression. We identify two primary factors contributing to quality degradation in multi-generation compression: quantization-induced non-idempotency and transformation irrevers…

Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: A Case for Human-Guided k-Anonymity
Sri Harsha Gajavalli
https://arxiv.org/abs/2507.04104

Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: A Case for Human-Guided k-Anonymity
Privacy-preserving machine learning (ML) seeks to balance data utility and privacy, especially as regulations like the GDPR mandate the anonymization of personal data for ML applications. Conventional anonymization approaches often reduce data utility due to indiscriminate generalization or suppression of data attributes. In this study, we propose an interactive approach that incorporates human input into the k-anonymization process, enabling domain experts to guide attribute preservation based…

PSA: attrs 25.4 will change how it handles class-level kw_only to conform with dataclass transform rules (IOW act like type-checkers ex Mypy expect).
We found 0 cases in the wild where this would break anything so it’s on for NG APIs. Now is the time to stop us. 🤓
https://github.com/python-attrs/attrs/

Make `kw_only=True` behavior consistent with dataclasses by huzecong · Pull Request #1457 · python-attrs/attrs
Summary This PR resolve #980. Before this change When kw_only=True is set on a class, all attributes are made keyword-only, including those from base classes. If an attribute sets kw_only=False, th...

X-ray Polarimetry of Accreting White Dwarfs: A Case Study of EX Hydrae
Sean J. Gunderson, Swati Ravi, Herman L. Marshall, Dustin K. Swarm, Richard Ignace, Yael Naze, David P. Huenemoerder, Pragati Pradhan
https://arxiv.org/abs/2508.03412

X-ray Polarimetry of Accreting White Dwarfs: A Case Study of EX Hydrae
We present the first first X-ray polarization measurements of a white dwarf, the intermediate polar EX Hya. We measured significant polarization only in the 2 -- 3 keV energy band with a polarization degree of 8 percent at a $3σ$ significance. No significant polarization was detected above 3 keV, which we attribute to the higher energy bands having lower signal-to-noise. We found that the scattering surface detected by the IXPE is nearly perpendicular to the optical scattering plane, showing t…

Joe Burrow says this one attribute separates good NFL quarterbacks from great ones

https://www.cbssports.com/nfl/news/joe-burrow-says-this-one-attr…

SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions
Jessica Bader, Leander Girrbach, Stephan Alaniz, Zeynep Akata
https://arxiv.org/abs/2507.23784 https:/…

SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions
Concept Bottleneck Models (CBMs) and other concept-based interpretable models show great promise for making AI applications more transparent, which is essential in fields like medicine. Despite their success, we demonstrate that CBMs struggle to reliably identify the correct concepts under distribution shifts. To assess the robustness of CBMs to concept variations, we introduce SUB: a fine-grained image and concept benchmark containing 38,400 synthetic images based on the CUB dataset. To create…

This https://arxiv.org/abs/2503.11207 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?
This work presents a first evaluation of two state-of-the-art Large Reasoning Models (LRMs), OpenAI's o3-mini and DeepSeek R1, on analogical reasoning, focusing on well-established nonverbal human IQ tests based on Raven's progressive matrices. We benchmark with the I-RAVEN dataset and its extension, I-RAVEN-X, which tests the ability to generalize to longer reasoning rules and ranges of the attribute values. To assess the influence of visual uncertainties on these symbolic analogical reasoning…

Evaluating Structured Output Robustness of Small Language Models for Open Attribute-Value Extraction from Clinical Notes
Nikita Neveditsin, Pawan Lingras, Vijay Mago
https://arxiv.org/abs/2507.01810

Evaluating Structured Output Robustness of Small Language Models for Open Attribute-Value Extraction from Clinical Notes
We present a comparative analysis of the parseability of structured outputs generated by small language models for open attribute-value extraction from clinical notes. We evaluate three widely used serialization formats: JSON, YAML, and XML, and find that JSON consistently yields the highest parseability. Structural robustness improves with targeted prompting and larger models, but declines for longer documents and certain note types. Our error analysis identifies recurring format-specific fail…

If Trump embodies the theory of the “unitary executive,” it’s because conservative jurists like John Roberts and Clarence Thomas and Samuel Alito have
— along with their Republican-appointed colleagues
— effectively rewritten the Constitution to allow for the exercise of virtually unaccountable power by Republican presidents.
Yes, you can attribute some of the worst of this administration to the specific authoritarian vision of Trump and his allies.
But a good deal of…

Opinion | Now Can We Stop Talking About What a Maverick Trump Is?
The president is a normie Republican. Stop thinking otherwise.

The Triangle Friendship Paradox
Bishakh Bhattacharya, Nitya Gadhiwala, Frank den Hollander, Pradeeptha R Jain, Tejashree Subramanya
https://arxiv.org/abs/2507.02627

The Triangle Friendship Paradox
We consider the generalised friendship paradox, focussing on the number of triangles at a vertex as the relevant attribute. We show that, contrary to the setting where the attribute is the number of edges at a vertex or the number of wedges at a vertex, the average friendship-bias of the number of triangles at a vertex is not always non-negative. We identify classes of finite deterministic graphs for which the bias is non-negative, and provide examples of finite deterministic graphs for which i…

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Emmanouil Anagnostakis, Polyvios Pratikakis
https://arxiv.org/abs/2506.03854 https:…

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Managed big data frameworks, such as Apache Spark and Giraph demand a large amount of memory per core to process massive volume datasets effectively. The memory pressure that arises from the big data processing leads to high garbage collection (GC) overhead. Big data analytics frameworks attempt to remove this overhead by offloading objects to storage devices. At the same time, infrastructure providers, trying to address the same problem, attribute more memory to increase memory per instance le…

Putting Perspective into OWL [sic]: Complexity-Neutral Standpoint Reasoning for Ontology Languages via Monodic S5 over Counting Two-Variable First-Order Logic (Extended Version with Appendix)
Luc\'ia G\'omez \'Alvarez, Sebastian Rudolph
https://arxiv.org/abs/2508.00653

Putting Perspective into OWL [sic]: Complexity-Neutral Standpoint Reasoning for Ontology Languages via Monodic S5 over Counting Two-Variable First-Order Logic (Extended Version with Appendix)
Standpoint extensions of knowledge representation formalisms have been recently introduced as a means to incorporate multi-perspective modelling and reasoning through modal operators that attribute pieces of knowledge to specific entities or agents. In these extensions, the integration between conceptual modelling and perspective annotations can vary in strength, with monodic standpoint extensions offering a well-balanced approach. They allow for advanced modelling features, such as the express…

Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
Basudha Pal, Rama Chellappa, Muhammad Umair
https://arxiv.org/abs/2506.03192

Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
While echocardiography and MRI are clinical standards for evaluating cardiac structure, their use is limited by cost and accessibility.We introduce a direct classification framework that predicts severe left ventricular hypertrophy from chest X-rays, without relying on anatomical measurements or demographic inputs. Our approach achieves high AUROC and AUPRC, and employs Mutual Information Neural Estimation to quantify feature expressivity. This reveals clinically meaningful attribute encoding a…

WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Shaozhen Ma, Hanchen Wang, Dong Wen, Wenjie Zhang, Wei Huang, Ying Zhang
https://arxiv.org/abs/2508.00927

WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Overlapping community detection (OCD) is a fundamental graph data analysis task for extracting graph patterns. Traditional OCD methods can be broadly divided into node clustering and link clustering approaches, both of which rely solely on link information to identify overlapping communities. In recent years, deep learning-based methods have made significant advancements for this task. However, existing GNN-based approaches often face difficulties in effectively integrating link, attribute, and…

This https://arxiv.org/abs/2502.04049 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components
We propose an explainable probabilistic framework for characterizing spoofed speech by decomposing it into probabilistic attribute embeddings. Unlike raw high-dimensional countermeasure embeddings, which lack interpretability, the proposed probabilistic attribute embeddings aim to detect specific speech synthesizer components, represented through high-level attributes and their corresponding values. We use these probabilistic embeddings with four classifier back-ends to address two downstream t…

Dallas Cowboys QB ranked No. 1 by NFL Network analysts in key attribute over MVP https://www.si.com/nfl/cowboys/news/dallas-cowboys-qb-ranked-top-nfl-network-key-attribute-over-josh-allen

A Group Consensus-Driven Auction Algorithm for Cooperative Task Allocation Among Heterogeneous Multi-Agents
Gang Wang, Hongfang Han, Xiaowei Liu, Hanfeng Jiang, Ming Zhang
https://arxiv.org/abs/2508.02015

A Group Consensus-Driven Auction Algorithm for Cooperative Task Allocation Among Heterogeneous Multi-Agents
In scenarios like automated warehouses, assigning tasks to robots presents a heterogeneous multi-task and multi-agent task allocation problem. However, existing task allocation study ignores the integration of multi-task and multi-attribute agent task allocation with heterogeneous task allocation. In addition, current algorithms are limited by scenario constraints and can incur significant errors in specific contexts. Therefore, this study proposes a distributed heterogeneous multi-task and mul…

Supporting architecture evaluation for ATAM scenarios with LLMs
Rafael Capilla, J. Andr\'es D\'iaz-Pace, Yamid Ram\'irez, Jennifer P\'erez, Vanessa Rodr\'iguez-Horcajo
https://arxiv.org/abs/2506.00150

Supporting architecture evaluation for ATAM scenarios with LLMs
Architecture evaluation methods have long been used to evaluate software designs. Several evaluation methods have been proposed and used to analyze tradeoffs between different quality attributes. Having competing qualities leads to conflicts for selecting which quality-attribute scenarios are the most suitable ones that an architecture should tackle and for prioritizing the scenarios required by the stakeholders. In this context, architecture evaluation is carried out manually, often involving …

Spin vs. position conjugation in quantum simulations with atoms: application to quantum chemistry
N. A. Moroz (Quantum Technology Centre, M.V. Lomonosov Moscow State University), K. S. Tikhonov (St. Petersburg State University and Russian Quantum Center, Russian Quantum Center), L. V. Gerasimov (Quantum Technology Centre, M.V. Lomonosov Moscow State University, Centre for Interdisciplinary Basic Research, HSE University), A. D. Manukhova (Department of Optics, Palacky University), I. B…

Spin vs. position conjugation in quantum simulations with atoms: application to quantum chemistry
The permutation symmetry is a fundamental attribute of the collective wavefunction of indistinguishable particles. It makes a difference for the behavior of collective systems having different quantum statistics but existing in the same environment. Here we show that for some specific quantum conjugation between the spin and spatial degrees of freedom the indistinguishable particles can behave similarly for either quantum statistics. In particular, a mesoscopically scaled collection of atomic q…

VecFlow: A High-Performance Vector Data Management System for Filtered-Search on GPUs
Jingyi Xi, Chenghao Mo, Benjamin Karsin, Artem Chirkin, Mingqin Li, Minjia Zhang
https://arxiv.org/abs/2506.00812

VecFlow: A High-Performance Vector Data Management System for Filtered-Search on GPUs
Vector search and database systems have become a keystone component in many AI applications. While many prior research has investigated how to accelerate the performance of generic vector search, emerging AI applications require running more sophisticated vector queries efficiently, such as vector search with attribute filters. Unfortunately, recent filtered-ANNS solutions are primarily designed for CPUs, with few exploration and limited performance of filtered-ANNS that take advantage of the m…

Ah fuck, people are using LLMs for kernel code. They really are going to fuck over everything, aren't they?
https://lwn.net/SubscriberLink/1026558/9f3079fb392c4a9a/

Google DeepMind unveils Aeneas, an AI model for contextualizing ancient Latin inscriptions, to help historians interpret and restore fragmentary texts (Google DeepMind)
https://deepmind.google/discover/blog/aeneas-transforms-how-historians-connect-the…

Aeneas transforms how historians connect the past
Introducing the first model for contextualizing ancient inscriptions, designed to help historians better interpret, attribute and restore fragmentary texts.

This https://arxiv.org/abs/2412.13147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Are Your LLMs Capable of Stable Reasoning?
The rapid advancement of large language models (LLMs) has shown remarkable progress in complex reasoning tasks. However, a significant disparity exists between benchmark performances and real-world applications. We attribute this gap primarily to current evaluation protocols and metrics, which inadequately capture the full spectrum of LLM capabilities, especially in complex reasoning tasks where both accuracy and consistency are essential. In this paper, we introduce G-Pass@$k$, a novel evaluat…

A Unified Framework for Interactive Visual Graph Matching via Attribute-Structure Synchronization
Yuhua Liu, Haoxuan Wang, Jiajia Kou, Ling Sun, Heyu Wang, Yongheng Wang, Yigang Wang, Jinchang Lic, Zhiguang Zhou
https://arxiv.org/abs/2507.19750

A Unified Framework for Interactive Visual Graph Matching via Attribute-Structure Synchronization
In traditional graph retrieval tools, graph matching is commonly used to retrieve desired graphs from extensive graph datasets according to their structural similarities. However, in real applications, graph nodes have numerous attributes which also contain valuable information for evaluating similarities between graphs. Thus, to achieve superior graph matching results, it is crucial for graph retrieval tools to make full use of the attribute information in addition to structural information. W…

Bias Analysis for Synthetic Face Detection: A Case Study of the Impact of Facial Attribute
Asmae Lamsaf, Lucia Cascone, Hugo Proen\c{c}a, Jo\~ao Neves
https://arxiv.org/abs/2507.19705

Bias Analysis for Synthetic Face Detection: A Case Study of the Impact of Facial Attribute
Bias analysis for synthetic face detection is bound to become a critical topic in the coming years. Although many detection models have been developed and several datasets have been released to reliably identify synthetic content, one crucial aspect has been largely overlooked: these models and training datasets can be biased, leading to failures in detection for certain demographic groups and raising significant social, legal, and ethical issues. In this work, we introduce an evaluation framew…

This https://arxiv.org/abs/2506.01483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Inter-Speaker Relative Cues for Text-Guided Target Speech Extraction
We propose a novel approach that utilize inter-speaker relative cues for distinguishing target speakers and extracting their voices from mixtures. Continuous cues (e.g., temporal order, age, pitch level) are grouped by relative differences, while discrete cues (e.g., language, gender, emotion) retain their categories. Relative cues offers greater flexibility than fixed speech attribute classification, facilitating much easier expansion of text-guided target speech extraction datasets. Our exper…

Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiatio
Yaowen Hu, Wenxuan Tu, Yue Liu, Xinhang Wan, Junyi Yan, Taichun Zhou, Xinwang Liu
https://arxiv.org/abs/2507.13368

Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiatio
Deep graph clustering (DGC), which aims to unsupervisedly separate the nodes in an attribute graph into different clusters, has seen substantial potential in various industrial scenarios like community detection and recommendation. However, the real-world attribute graphs, e.g., social networks interactions, are usually large-scale and attribute-missing. To solve these two problems, we propose a novel DGC method termed \underline{\textbf{C}}omplementary \underline{\textbf{M}}ulti-\underline{\te…

DisProtEdit: Exploring Disentangled Representations for Multi-Attribute Protein Editing
Max Ku, Sun Sun, Hongyu Guo, Wenhu Chen
https://arxiv.org/abs/2506.14853

DisProtEdit: Exploring Disentangled Representations for Multi-Attribute Protein Editing
We introduce DisProtEdit, a controllable protein editing framework that leverages dual-channel natural language supervision to learn disentangled representations of structural and functional properties. Unlike prior approaches that rely on joint holistic embeddings, DisProtEdit explicitly separates semantic factors, enabling modular and interpretable control. To support this, we construct SwissProtDis, a large-scale multimodal dataset where each protein sequence is paired with two textual descr…

@… @… @… I attribute the failure to revoke the holiday to incompetence

SPARK: Graph-Based Online Semantic Integration System for Robot Task Planning
Mimo Shirasaka, Yuya Ikeda, Tatsuya Matsushima, Yutaka Matsuo, Yusuke Iwasawa
https://arxiv.org/abs/2506.20394

SPARK: Graph-Based Online Semantic Integration System for Robot Task Planning
The ability to update information acquired through various means online during task execution is crucial for a general-purpose service robot. This information includes geometric and semantic data. While SLAM handles geometric updates on 2D maps or 3D point clouds, online updates of semantic information remain unexplored. We attribute the challenge to the online scene graph representation, for its utility and scalability. Building on previous works regarding offline scene graph representations, …

5 Days, 5 Stories: Using Technology to Promote Empathy in the Workplace
Russell Beale, Eugenia Sergueeva
https://arxiv.org/abs/2506.19495 https://

5 Days, 5 Stories: Using Technology to Promote Empathy in the Workplace
Empathy is widely recognized as a vital attribute for effective collaboration and communication in the workplace, yet developing empathic skills and fostering it among colleagues remains a challenge. This study explores the potential of a collaborative digital storytelling platform - In Your Shoes - designed to promote empathic listening and interpersonal understanding through the structured exchange of personal narratives. A one-week intervention was conducted with employees from multiple orga…

Frequency Estimation of Correlated Multi-attribute Data under Local Differential Privacy
Shafizur Rahman Seeam, Ye Zheng, Yidan Hu
https://arxiv.org/abs/2507.17516 https://

Frequency Estimation of Correlated Multi-attribute Data under Local Differential Privacy
Large-scale data collection, from national censuses to IoT-enabled smart homes, routinely gathers dozens of attributes per individual. These multi-attribute datasets are vital for analytics but pose significant privacy risks. Local Differential Privacy (LDP) is a powerful tool to protect user data privacy by allowing users to locally perturb their records before releasing to an untrusted data aggregator. However, existing LDP mechanisms either split the privacy budget across all attributes or t…

ESG Risk: Lessons Learned from Utility Theory
Sebastian Geissel, Christoph Knochenhauer
https://arxiv.org/abs/2507.23496 https://arxiv.org/pdf/2507.23496…

ESG Risk: Lessons Learned from Utility Theory
We propose a new class of monetary risk measures capable of assessing financial and ESG risk. The construction of these risk measures is based on an extension of classical shortfall risk measures in which the loss function is replaced by a multi-attribute utility function. We present an extensive theoretical analysis of these risk measures, showing in particular how properties of the utility function translate into properties of the associated risk measure. We furthermore discuss how these mult…

Rule-Assisted Attribute Embedding
Sibo Zhao, Michael Bewong, Selasi Kwashie, Junwei Hu, Zaiwen Feng
https://arxiv.org/abs/2506.08314 https://

Rule-Assisted Attribute Embedding
Recommendation systems often overlook the rich attribute information embedded in property graphs, limiting their effectiveness. Existing graph convolutional network (GCN) models either ignore attributes or rely on simplistic triples, failing to capture deeper semantic structures. We propose RAE (Rule- Assisted Approach for Attribute Embedding), a novel method that improves recommendations by mining semantic rules from property graphs to guide attribute embedding. RAE per…

This https://arxiv.org/abs/2412.12690 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Preference Robust Ordinal Priority Approach with Preference Elicitation under Incomplete Information for Multi-Attribute Robust Ranking and Selection
Ordinal Priority Approach (OPA) has recently been proposed to determine the weights of experts, attributes, and alternatives using ordinal preference without precise information for multi-attribute ranking and selection (MARS). This study extends OPA with preference elicitation under incomplete information to counter the parametric and preference uncertainty within MARS. Specifically, we propose Preference Robust Ordinal Priority Approach (OPA-PR) within a two-stage optimization framework to ge…

Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling
David Palzer, Matthew Maciejewski, Eric Fosler-Lussier
https://arxiv.org/abs/2506.05593

Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling
In recent years, end-to-end approaches have made notable progress in addressing the challenge of speaker diarization, which involves segmenting and identifying speakers in multi-talker recordings. One such approach, Encoder-Decoder Attractors (EDA), has been proposed to handle variable speaker counts as well as better guide the network during training. In this study, we extend the attractor paradigm by moving beyond direct speaker modeling and instead focus on representing more detailed `speake…

Stylometry recognizes human and LLM-generated texts in short samples
Karol Przystalski, Jan K. Argasi\'nski, Iwona Grabska-Gradzi\'nska, Jeremi K. Ochab
https://arxiv.org/abs/2507.00838

Stylometry recognizes human and LLM-generated texts in short samples
The paper explores stylometry as a method to distinguish between texts created by Large Language Models (LLMs) and humans, addressing issues of model attribution, intellectual property, and ethical AI use. Stylometry has been used extensively to characterise the style and attribute authorship of texts. By applying it to LLM-generated texts, we identify their emergent writing patterns. The paper involves creating a benchmark dataset based on Wikipedia, with (a) human-written term summaries, (b) …

Evaluating Attribute Confusion in Fashion Text-to-Image Generation
Ziyue Liu, Federico Girella, Yiming Wang, Davide Talon
https://arxiv.org/abs/2507.07079 …

Evaluating Attribute Confusion in Fashion Text-to-Image Generation
Despite the rapid advances in Text-to-Image (T2I) generation models, their evaluation remains challenging in domains like fashion, involving complex compositional generation. Recent automated T2I evaluation methods leverage pre-trained vision-language models to measure cross-modal alignment. However, our preliminary study reveals that they are still limited in assessing rich entity-attribute semantics, facing challenges in attribute confusion, i.e., when attributes are correctly depicted but as…

Hill: Podcast shows why Raiders lineman could emerge as team leader https://www.reviewjournal.com/sports/sports-columns/adam-hill/hill-podcast-shows-why-raiders-lineman-could-emerge-as-team-leader-…

$(\ell,\delta)$-Diversity: Linkage-Robustness via a Composition Theorem
V. Arvind Rameshwar, Anshoo Tandon
https://arxiv.org/abs/2506.18405 https://…

$(\ell,δ)$-Diversity: Linkage-Robustness via a Composition Theorem
In this paper, we consider the problem of degradation of anonymity upon linkages of anonymized datasets. We work in the setting where an adversary links together $t\geq 2$ anonymized datasets in which a user of interest participates, based on the user's known quasi-identifiers, which motivates the use of $\ell$-diversity as the notion of dataset anonymity. We first argue that in the worst case, such linkage attacks can reveal the exact sensitive attribute of the user, even when each dataset res…

This https://arxiv.org/abs/2405.12042 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Attribute-Based Authentication in Secure Group Messaging for Distributed Environments
Messaging Layer security (MLS) and its underlying Continuous Group Key Agreement (CGKA) protocol allows a group of users to share a cryptographic secret in a dynamic manner, such that the secret is modified in member insertions and deletions. Although this flexibility makes MLS ideal for implementations in distributed environments, a number of issues need to be overcome. Particularly, the use of digital certificates for authentication in a group goes against the group members' privacy. In this …

TRUST: Test-time Resource Utilization for Superior Trustworthiness
Haripriya Harikumar, Santu Rana
https://arxiv.org/abs/2506.06048 https://

TRUST: Test-time Resource Utilization for Superior Trustworthiness
Standard uncertainty estimation techniques, such as dropout, often struggle to clearly distinguish reliable predictions from unreliable ones. We attribute this limitation to noisy classifier weights, which, while not impairing overall class-level predictions, render finer-level statistics less informative. To address this, we propose a novel test-time optimization method that accounts for the impact of such noise to produce more reliable confidence estimates. This score defines a monotonic subs…

Room temperature spin injection into commercial VCSELs at non-resonant wavelengths
Timur Almabetov, Petros Androvitsaneas, Zhongze Ren, Andrew Young, Ruth Oulton, Edmund Harbord
https://arxiv.org/abs/2506.18376

Room temperature spin injection into commercial VCSELs at non-resonant wavelengths
Spin lasers leverage electron spin-polarisation to control photon polarisation, offering the potential for lower thresholds, rapid modulation, and all-optical data processing. We report successful spin injection into a commercial Vertical Cavity Surface Emitting Laser (VCSEL) using optical pumping at wavelengths of 794 nm and 810 nm. Maximum circular polarisation achieved was about 20% at 794 nm, whereas this falls to 5% with exciting at 810 nm. We attribute this to reduced spin injection due t…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Ka...
Anthony Richardson, Felix Putze

You Only Render Once: Enhancing Energy and Computation Efficiency of Mobile Virtual Reality
Xingyu Chen, Xinmin Fang, Shuting Zhang, Xinyu Zhang, Liang He, Zhengxiong Li
https://arxiv.org/abs/2506.15183

You Only Render Once: Enhancing Energy and Computation Efficiency of Mobile Virtual Reality
Mobile Virtual Reality (VR) is essential to achieving convenient and immersive human-computer interaction and realizing emerging applications such as Metaverse. However, existing VR technologies require two separate renderings of binocular images, causing a significant bottleneck for mobile devices with limited computing capability and power supply. This paper proposes an approach to rendering optimization for mobile VR called EffVR. By utilizing the per-pixel attribute, EffVR can generate bino…

Vo-Ve: An Explainable Voice-Vector for Speaker Identity Evaluation
Jaejun Lee, Kyogu Lee
https://arxiv.org/abs/2506.19446 https://arx…

Vo-Ve: An Explainable Voice-Vector for Speaker Identity Evaluation
In this paper, we propose Vo-Ve, a novel voice-vector embedding that captures speaker identity. Unlike conventional speaker embeddings, Vo-Ve is explainable, as it contains the probabilities of explicit voice attribute classes. Through extensive analysis, we demonstrate that Vo-Ve not only evaluates speaker similarity competitively with conventional techniques but also provides an interpretable explanation in terms of voice attributes. We strongly believe that Vo-Ve can enhance evaluation schem…

Inter-Speaker Relative Cues for Text-Guided Target Speech Extraction
Wang Dai, Archontis Politis, Tuomas Virtanen
https://arxiv.org/abs/2506.01483 https://…

Inter-Speaker Relative Cues for Text-Guided Target Speech Extraction
We propose a novel approach that utilize inter-speaker relative cues for distinguishing target speakers and extracting their voices from mixtures. Continuous cues (e.g., temporal order, age, pitch level) are grouped by relative differences, while discrete cues (e.g., language, gender, emotion) retain their categories. Relative cues offers greater flexibility than fixed speech attribute classification, facilitating much easier expansion of text-guided target speech extraction datasets. Our exper…

Filter-Centric Vector Indexing: Geometric Transformation for Efficient Filtered Vector Search
Alireza Heidari, Wei Zhang
https://arxiv.org/abs/2506.15987 h…

Filter-Centric Vector Indexing: Geometric Transformation for Efficient Filtered Vector Search
The explosive growth of vector search applications demands efficient handling of combined vector similarity and attribute filtering; a challenge where current approaches force an unsatisfying choice between performance and accuracy. We introduce Filter-Centric Vector Indexing (FCVI), a novel framework that transforms this fundamental trade-off by directly encoding filter conditions into the vector space through a mathematically principled transformation $ψ(v, f, α)$. Unlike specialized soluti…

Efficient Learning for Product Attributes with Compact Multimodal Models
Mandar Kulkarni
https://arxiv.org/abs/2507.19679 https://arxiv.org/pdf/2507.19679

Efficient Learning for Product Attributes with Compact Multimodal Models
Image-based product attribute prediction in e-commerce is a crucial task with numerous applications. The supervised fine-tuning of Vision Language Models (VLMs) faces significant scale challenges due to the cost of manual or API based annotation. In this paper, we investigate label-efficient semi-supervised fine-tuning strategies for compact VLMs (2B-3B parameters) that leverage unlabeled product listings through Direct Preference Optimization (DPO). Beginning with a small, API-based, annotated…

Surface phase behavior, not hydrogen bonding, governs hydrophobic attraction between extended solutes
Nigel B. Wilding, Francesco Turci
https://arxiv.org/abs/2506.09858

Surface phase behavior, not hydrogen bonding, governs hydrophobic attraction between extended solutes
Hydrophobic interactions are central to biological self-assembly and soft matter organization, yet their microscopic origins remain debated. Traditional explanations often attribute the increase in attraction between hydrophobic solutes with increasing temperature to entropy changes resulting from disrupted hydrogen bonding in water. Here, we present a different perspective based on the physics of surface phase transitions, supported by extensive molecular dynamics simulations. Using well-tempe…

Spin-split superconductivity in spin-orbit coupled hybrid nanowires with ferromagnetic barriers
J. Zhao, A. Mazanik, D. Razmadze, Y. Liu, P. Krogstrup, F. S. Bergeret, S. Vaitiek\.enas
https://arxiv.org/abs/2506.08247

Spin-split superconductivity in spin-orbit coupled hybrid nanowires with ferromagnetic barriers
We report transport studies of hybrid Josephson junctions based on semiconducting InAs nanowires with fully overlapping epitaxial ferromagnetic insulator EuS and superconducting Al partial shells. Current-biased measurements reveal a hysteretic superconducting window with a sizable supercurrent near the coercive field of the ferromagnetic insulator, accompanied by multiple Andreev reflections. Tunneling spectroscopy shows a superconducting gap characterized by three peaks, which we attribute to…

Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs
Feng Hong, Geng Yu, Yushi Ye, Haicheng Huang, Huangjie Zheng, Ya Zhang, Yanfeng Wang, Jiangchao Yao
https://arxiv.org/abs/2507.18578

Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs
Diffusion Large Language Models (DLLMs) have emerged as a compelling alternative to Autoregressive models, designed for fast parallel generation. However, existing DLLMs are plagued by a severe quality-speed trade-off, where faster parallel decoding leads to significant performance degradation. We attribute this to the irreversibility of standard decoding in DLLMs, which is easily polarized into the wrong decoding direction along with early error context accumulation. To resolve this, we introd…

Poor example to introduce the `disabled` HTML attribute:
Lesson: Don't use the same/similar name as the concept being introduced.
```html
<label>
<input type="checkbox" name="ch-box" value="disabled" disabled />
disabled
</label>
```
#writing #scholarship #examples #technicalWriting

CUHK-EE Systems for the vTAD Challenge at NCMMSC 2025
Aemon Yat Fei Chiu, Jingyu Li, Yusheng Tian, Guangyan Zhang, Tan Lee
https://arxiv.org/abs/2507.23266 https://

CUHK-EE Systems for the vTAD Challenge at NCMMSC 2025
This paper presents the Voice Timbre Attribute Detection (vTAD) systems developed by the Digital Signal Processing & Speech Technology Laboratory (DSP&STL) of the Department of Electronic Engineering (EE) at The Chinese University of Hong Kong (CUHK) for the 20th National Conference on Human-Computer Speech Communication (NCMMSC 2025) vTAD Challenge. The proposed systems leverage WavLM-Large embeddings with attentive statistical pooling to extract robust speaker representations, followed by two…

Measuring how changes in code readability attributes affect code quality evaluation by Large Language Models
Igor Regis da Silva Simoes, Elaine Venson
https://arxiv.org/abs/2507.05289

Measuring how changes in code readability attributes affect code quality evaluation by Large Language Models
Code readability is one of the main aspects of code quality, influenced by various properties like identifier names, comments, code structure, and adherence to standards. However, measuring this attribute poses challenges in both industry and academia. While static analysis tools assess attributes such as code smells and comment percentage, code reviews introduce an element of subjectivity. This paper explores using Large Language Models (LLMs) to evaluate code quality attributes related to its…

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Bijay Gurung, David T. Hoffmann, Thomas Brox
https://arxiv.org/abs/2507.07985

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Contrastive vision-language models like CLIP are used for a large variety of applications, such as zero-shot classification or as vision encoder for multi-modal models. Despite their popularity, their representations show major limitations. For instance, CLIP models learn bag-of-words representations and, as a consequence, fail to distinguish whether an image is of "a yellow submarine and a blue bus" or "a blue submarine and a yellow bus". Previous attempts to fix this issue added hard negative…

A family of graph GOSPA metrics for graphs with different sizes
Jinhao Gu, \'Angel F. Garc\'ia-Fern\'andez, Robert E. Firth, Lennart Svensson
https://arxiv.org/abs/2506.17316

A family of graph GOSPA metrics for graphs with different sizes
This paper proposes a family of graph metrics for measuring distances between graphs of different sizes. The proposed metric family defines a general form of the graph generalised optimal sub-pattern assignment (GOSPA) metric and is also proved to satisfy the metric properties. Similarly to the graph GOSPA metric, the proposed graph GOSPA metric family also penalises the node attribute costs for assigned nodes between the two graphs, and the number of unassigned nodes. However, the proposed fam…

Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
Hitomi Yanaka, Xinqi He, Jie Lu, Namgi Han, Sunjin Oh, Ryoma Kumon, Yuma Matsuoka, Katsuhiko Watabe, Yuko Itatsu
https://arxiv.org/abs/2506.12327

Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
An growing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias …

Data-Semantics-Aware Recommendation of Diverse Pivot Tables
Whanhee Cho, Anna Fariha
https://arxiv.org/abs/2507.06171 https://arxiv.o…

Data-Semantics-Aware Recommendation of Diverse Pivot Tables
Data summarization is essential to discover insights from large datasets. In a spreadsheets, pivot tables offer a convenient way to summarize tabular data by computing aggregates over some attributes, grouped by others. However, identifying attribute combinations that will result in useful pivot tables remains a challenge, especially for high-dimensional datasets. We formalize the problem of automatically recommending insightful and interpretable pivot tables, eliminating the tedious manual pro…

MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable Speech Emotion Recognition
Hyo Jin Jon, Longbin Jin, Hyuntaek Jung, Hyunseo Kim, Donghun Min, Eun Yi Kim
https://arxiv.org/abs/2506.19887

MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable Speech Emotion Recognition
This paper presents our contributions to the Speech Emotion Recognition in Naturalistic Conditions (SERNC) Challenge, where we address categorical emotion recognition and emotional attribute prediction. To handle the complexities of natural speech, including intra- and inter-subject variability, we propose Multi-level Acoustic-Textual Emotion Representation (MATER), a novel hierarchical framework that integrates acoustic and textual features at the word, utterance, and embedding levels. By fusi…

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin
https://arxiv.org/abs/2507.08459

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
With the widespread application of Large Language Models (LLMs) in various tasks, the mainstream LLM platforms generate massive user-model interactions daily. In order to efficiently analyze the performance of models and diagnose failures in their answers, it is essential to develop an automated framework to systematically categorize and attribute errors. However, existing evaluation models lack error attribution capability. In this work, we establish a comprehensive Misattribution Framework wi…

This https://arxiv.org/abs/2505.20124 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing benchmarks for video understanding often treat these properties separately or narrowly focus on specific aspects, overlooking the holistic nature of video content. To address this, we introduce TUNA, a temporal-oriented benchmark for fine-grained understanding on dense dynamic videos, with two complementary tasks: captio…

This https://arxiv.org/abs/2506.01483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Inter-Speaker Relative Cues for Text-Guided Target Speech Extraction
We propose a novel approach that utilize inter-speaker relative cues for distinguishing target speakers and extracting their voices from mixtures. Continuous cues (e.g., temporal order, age, pitch level) are grouped by relative differences, while discrete cues (e.g., language, gender, emotion) retain their categories. Relative cues offers greater flexibility than fixed speech attribute classification, facilitating much easier expansion of text-guided target speech extraction datasets. Our exper…