Tootfinder

Quantifying Data Requirements for EEG Independent Component Analysis Using AMICA
Gwenevere Frank, Seyed Yahya Shirazi, Jason Palmer, Gert Cauwenberghs, Scott Makeig, Arnaud Delorme
https://arxiv.org/abs/2506.10156

Quantifying Data Requirements for EEG Independent Component Analysis Using AMICA
Independent Component Analysis (ICA) is an important step in EEG processing for a wide-ranging set of applications. However, ICA requires well-designed studies and data collection practices to yield optimal results. Past studies have focused on quantitative evaluation of the differences in quality produced by different ICA algorithms as well as different configurations of parameters for AMICA, a multimodal ICA algorithm that is considered the benchmark against which other algorithms are measure…

Upcoming GÉANT infoshare 🚀
Earth Observations Support Through NRENs - Part 1
Explore the multi-national policy context which supports earth observation, the complimentary RREN research engagement approaches employed, and discover the Group on Earth Observations (GEO).
👥 Hosted by Daniela Brauner, Chris Atherton and Hendrik Ike (GÉANT), with presentations from Paola De Salvo (GEO Secretariat), Yousef Torman (ASREN), and Mark Urban (RedCLARA).
🔗 Register here:

Infoshare Series on Earth Observations Support Through NRENs - Part 1
Earth observation has a direct impact on societal ambitions to reduce the effects of climate change, monitor the achievement of the UN sustainable development goals, and for its use in disaster and emergency response.  As a wide-ranging topic area, Earth observation is reliant upon ever increasing volumes of data for achieving the latest scientific discoveries.  It is because of the societal impact, and large-scale data transit to universities and national institutions which makes this an...

Natural Language Guided Ligand-Binding Protein Design
Zhenqiao Song, Ramith Hettiarachchi, Chuan Li, Jianwen Xie, Lei Li
https://arxiv.org/abs/2506.09332 h…

Natural Language Guided Ligand-Binding Protein Design
Can AI protein models follow human language instructions and design proteins with desired functions (e.g. binding to a ligand)? Designing proteins that bind to a given ligand is crucial in a wide range of applications in biology and chemistry. Most prior AI models are trained on protein-ligand complex data, which is scarce due to the high cost and time requirements of laboratory experiments. In contrast, there is a substantial body of human-curated text descriptions about protein-ligand interac…

EasyDRAM: An FPGA-based Infrastructure for Fast and Accurate End-to-End Evaluation of Emerging DRAM Techniques
O\u{g}uzhan Canpolat, Ataberk Olgun, David Novo, O\u{g}uz Ergin, Onur Mutlu
https://arxiv.org/abs/2506.10441

EasyDRAM: An FPGA-based Infrastructure for Fast and Accurate End-to-End Evaluation of Emerging DRAM Techniques
DRAM is a critical component of modern computing systems. Recent works propose numerous techniques (that we call DRAM techniques) to enhance DRAM-based computing systems' throughput, reliability, and computing capabilities (e.g., in-DRAM bulk data copy). Evaluating the system-wide benefits of DRAM techniques is challenging as they often require modifications across multiple layers of the computing stack. Prior works propose FPGA-based platforms for rapid end-to-end evaluation of DRAM techniques…

Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das
https://arxiv.org/abs/2506.09234

Transaction Categorization with Relational Deep Learning in QuickBooks
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in this domain stem from the unique formatting of transaction descriptions, the wide variety of transaction categories, and the vast scale of the data involved. Furthermore, organizing transaction data in a relational database creates difficulties in developing a unified model that covers the entire database. In this work, …

LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
Sahar Abdelnabi, Aideen Fay, Ahmed Salem, Egor Zverev, Kai-Chieh Liao, Chi-Huang Liu, Chun-Chih Kuo, Jannis Weigend, Danyael Manlangit, Alex Apostolov, Haris Umair, Jo\~ao Donato, Masayuki Kawakita, Athar Mahboob, Tran Huu Bach, Tsun-Han Chiang, Myeongjin Cho, Hajin Choi, Byeonghyeon Kim, Hyeonjin Lee, Benjamin Pannell, Conor McCauley, Mark Russinovich, Andrew Paverd, Giovanni Cherubin

LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
Indirect Prompt Injection attacks exploit the inherent limitation of Large Language Models (LLMs) to distinguish between instructions and data in their inputs. Despite numerous defense proposals, the systematic evaluation against adaptive adversaries remains limited, even when successful attacks can have wide security and privacy implications, and many real-world LLM-based applications remain vulnerable. We present the results of LLMail-Inject, a public challenge simulating a realistic scenario…

Understanding the Performance and Power of LLM Inferencing on Edge Accelerators
Mayank Arya, Yogesh Simmhan
https://arxiv.org/abs/2506.09554 https://

Understanding the Performance and Power of LLM Inferencing on Edge Accelerators
Large Language Models (LLMs) have demonstrated exceptional benefits to a wide range of domains, for tasks as diverse as code generation and robot navigation. While LLMs are usually served from cloud data centers, mission-critical and privacy-sensitive applications may require local hosting of open LLM models. Given the large GPU memory footprint needed for LLMs, edge accelerators such as Nvidia Jetson Orin AGX with 64GB of shared GPU-CPU RAM are a compelling choice. However, the feasibility and…

RIDEN pilot survey: broad-band selection of candidate quasars with extended Lyman-$\alpha$ nebulae using CLAUDS-HSC-SSP-DUNES$^2$ joint data
Rhythm Shimakawa, Satoshi Kikuta, Haruka Kusakabe, Marcin Sawicki, Yongming Liang, Rieko Momose, Stephen Gwyn, Guillaume Desprez
https://arxiv.org/abs/2506.04570

RIDEN pilot survey: broad-band selection of candidate quasars with extended Lyman-$α$ nebulae using CLAUDS-HSC-SSP-DUNES$^2$ joint data
The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), delivering deep, multi-band ($ugrizy$) imaging data across 18,000 square degrees over the next decade. Before this ultra-wide-field survey, we constructed a broad-band Ly$α$ imaging toward 483 SDSS/BOSS quasars at $z=$ 1.9-3.0, using deep, wide-field ultraviolet to near-infrared ($u$-to-$K$) data from the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP), the CFHT Large Area U-band Deep Survey (CLAUDS), th…

The Curse of Dimensionality: De-identification Challenges in the Sharing of Highly Dimensional Datasets
https://fpf.org/blog/the-curse-of-dimensionality-de-identification-challenges-in-the-sharing-of-highl…

Stability and Extension of Steady and Ranging Persistence
Yann-Situ Gazull
https://arxiv.org/abs/2506.07911 https://arxiv.org/pdf/250…

Stability and Extension of Steady and Ranging Persistence
Persistent homology is a topological data analysis tool that has been widely generalized, extending its scope outside the field of topology. Among its extensions, steady and ranging persistence was developed to study a wide variety of graph properties. Precisely, given a feature of interest on graphs, it is possible to build two types of persistence (steady and ranging persistence) that follow the evolution of the feature along graph filtrations. This study extends steady and ranging persistenc…

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems
Arthur A. B. Pessa, Leonardo G. J. M. Voltarelli, Lucio Cardozo-Filho, Andres G. M. Tamara, Claudio Dariva, Papa M. Ndiaye, Matjaz Perc, Haroldo V. Ribeiro
https://arxiv.org/abs/2506.06516

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems
Understanding the high-pressure phase behavior of carbon dioxide-hydrocarbon mixtures is of considerable interest owing to their wide range of applications. Under certain conditions, these systems are not amenable to direct visual monitoring, and experimentalists often rely on spectrophotometric data to infer phase behavior. Consequently, developing computationally efficient and robust methods to leverage such data is crucial. Here, we combine nearest neighbor permutation entropy, computed dire…

“Pivot to AI itself got hit by an AI scraper bot over the weekend! Thankfully the scoundrels who vibe-code these things are idiots.”
➡️ https://pivot-to-ai.com/2025/06/02/fighting-the-ai-scraper-bots-at-pivot-to-ai-and-rationalwiki/…

Fighting the AI scraper bots at Pivot to AI and RationalWiki
We’ve covered the AI scraper bots before. These just hit web pages over and over, at high speed, to scrape new training data for LLMs. They’re an absolute plague across the whole World Wide Web and…

Pluto Geologic Map: Use of Crater Data to Understand Age Relationships
Kelsi N. Singer, Oliver L. White, Sarah Greenstreet, Jeffrey M. Moore, David A. Williams, Rosaly M. C. Lopes
https://arxiv.org/abs/2506.00254

Pluto Geologic Map: Use of Crater Data to Understand Age Relationships
Pluto's surface displays a wide variety of geologic units from smooth plains to extremely rugged mountainous expanses. These terrains range in age from young, actively resurfaced regions (no observable craters even in the highest-resolution New Horizons images) to old, heavily cratered, eroded regions. Here we expand upon the crater data analysis and the independent crater data set used in the production of a 1:7M scale geologic map of Pluto that is to be published by the United States Geologic…

Real-time Light Curve Classification Framework for the Wide Field Survey Telescope Using Modified Semi-supervised Variational Auto-Encoder
Yongling Tang, Lulu Fan, Zhen Wan, Yating Liu, Yan Lu
https://arxiv.org/abs/2506.01216

Real-time Light Curve Classification Framework for the Wide Field Survey Telescope Using Modified Semi-supervised Variational Auto-Encoder
Modern time-domain astronomy will benefit from the vast data collected by survey telescopes. The 2.5 m Wide Field Survey Telescope (WFST), with its powerful capabilities, is promising to make significant contributions in the era of large sky surveys. To harness the full potential of the enormous amount of unlabeled light curve data that the WFST will collect, we have developed a semisupervised light curve classification framework. This framework showcases several unique features. First, it is o…

ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
Armand Kassa\"i Koupa\"i, Lise Le Boudec, Louis Serrano, Patrick Gallinari
https://arxiv.org/abs/2506.06158

ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
Solving time-dependent parametric partial differential equations (PDEs) remains a fundamental challenge for neural solvers, particularly when generalizing across a wide range of physical parameters and dynamics. When data is uncertain or incomplete-as is often the case-a natural approach is to turn to generative models. We introduce ENMA, a generative neural operator designed to model spatio-temporal dynamics arising from physical phenomena. ENMA predicts future dynamics in a compressed latent …

Exploring the Non-uniqueness of Node Co-occurrence Matrices of Hypergraphs
Timothy LaRock, Renaud Lambiotte
https://arxiv.org/abs/2506.01479 https://

Exploring the Non-uniqueness of Node Co-occurrence Matrices of Hypergraphs
Hypergraphs extend traditional networks by capturing multi-way or group interactions. Given the complexity of hypergraph data and the wide range of methodology available for pairwise network analysis, hypergraph data is often projected onto a weighted and undirected network. The simplest of these projections, often referred to as a node co-occurrence matrix, is known to be non-unique, as distinct non-isomorphic hypergraphs can produce the same weighted adjacency matrix. This non-uniqueness rais…

This https://arxiv.org/abs/2504.02628 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Towards Computation- and Communication-efficient Computational Pathology
Despite the impressive performance across a wide range of applications, current computational pathology models face significant diagnostic efficiency challenges due to their reliance on high-magnification whole-slide image analysis. This limitation severely compromises their clinical utility, especially in time-sensitive diagnostic scenarios and situations requiring efficient data transfer. To address these issues, we present a novel computation- and communication-efficient framework called Mag…

Memory Hierarchy Design for Caching Middleware in the Age of NVM
Shahram Ghandeharizadeh, Sandy Irani, Jenny Lam
https://arxiv.org/abs/2506.05071 https://

Memory Hierarchy Design for Caching Middleware in the Age of NVM
Advances in storage technology have introduced Non-Volatile Memory, NVM, as a new storage medium. NVM, along with Dynamic Random Access Memory (DRAM), Solid State Disk (SSD), and Disk present a system designer with a wide array of options in designing caching middleware. Moreover, design decisions to replicate a data item in more than one level of a caching memory hierarchy may enhance the overall system performance with a faster recovery time in the event of a memory failure. Given a fixed bud…

This https://arxiv.org/abs/2506.00834 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Söze: One Network Telemetry Is All You Need for Per-flow Weighted Bandwidth Allocation at Scale
Weighted bandwidth allocation is a powerful abstraction that has a wide range of use cases in modern data center networks. However, realizing highly agile and precise weighted bandwidth allocation for large-scale cloud environments is fundamentally challenging. In this paper, we propose Söze, a lightweight decentralized weighted bandwidth allocation system that leverages simple network telemetry features of commodity Ethernet switches. Given the flow weights, Söze can effectively use the tele…

This https://arxiv.org/abs/2505.24586 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

All-sky search for individual Primordial Black Hole bursts with LHAASO
Primordial Black Holes~(PBHs) are hypothetical black holes with a wide range of masses that formed in the early universe. As a result, they may play an important cosmological role and provide a unique probe of the early universe. A PBH with an initial mass of approximately $10^{15}$~g is expected to explode today in a final burst of Hawking radiation. In this work, we conduct an all-sky search for individual PBH burst events using the data collected from March 2021 to July 2024 by the Water Che…

Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
M. Archipley, A. Hryciuk, L. E. Bleem, K. Kornoelje, M. Klein, A. J. Anderson, B. Ansarinejad, M. Aravena, L. Balkenhol, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, S. Bocquet, F. R. Bouchet, E. Camphuis, M. G. Campitiello, J. E. Carlstrom, J. Cathey, C. L. Chang, S. C. Chapman, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou…

Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
Context. The South Pole Telescope third-generation camera (SPT-3G) has observed over 10,000 square degrees of sky at 95, 150, and 220 GHz (3.3, 2.0, 1.4 mm, respectively) overlapping the ongoing 14,000 square-degree Euclid Wide Survey. The Euclid collaboration recently released Euclid Deep Field observations in the first quick data release (Q1). Aims. With the goal of releasing complementary millimeter-wave data and encouraging legacy science, we performed dedicated observations of a 57-square-…

This https://arxiv.org/abs/2409.18858 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Predicting memorization within Large Language Models fine-tuned for classification
Large Language Models have received significant attention due to their abilities to solve a wide range of complex tasks. However these models memorize a significant proportion of their training data, posing a serious threat when disclosed at inference time. To mitigate this unintended memorization, it is crucial to understand what elements are memorized and why. This area of research is largely unexplored, with most existing works providing a posteriori explanations. To address this gap, we pro…

This https://arxiv.org/abs/2502.04434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Searching for Inflationary Physics with the CMB Trispectrum: 1. Primordial Theory & Optimal Estimators
The primordial four-point function encodes a wealth of information about the inflationary Universe. Despite extensive theoretical work, most models of four-point physics have never been compared to data. In this series, we conduct a detailed analysis of Cosmic Microwave Background temperature and polarization trispectra, searching for a wide variety of phenomena including local effects, self-interactions, curvatons, DBI inflation, gauge fields, solid inflation, scalar field exchange, spinning m…

Self-Sustaining Multi-Sensor LoRa-Based Activity Monitoring for Community Workout Parks
Victor Luder, Michele Magno
https://arxiv.org/abs/2506.03203 https:…

Self-Sustaining Multi-Sensor LoRa-Based Activity Monitoring for Community Workout Parks
With the rise of the Internet of Things (IoT), more sensors are deployed around us, covering a wide range of applications from industry and agriculture to urban environments such as smart cities. Throughout these applications the sensors collect data of various characteristics and support city planners and decision-makers in their work processes, ultimately maximizing the impact of public funds. This paper introduces the design and implementation of a self-sustaining wireless sensor node design…

RUBIES: A Spectroscopic Census of Little Red Dots; All V-Shaped Point Sources Have Broad Lines
Raphael E. Hviding, Anna de Graaff, Tim B. Miller, David J. Setton, Jenny E. Greene, Ivo Labb\'e, Gabriel Brammer, Rachel Bezanson, Leindert A. Boogaard, Nikko J. Cleri, Joel Leja, Michael V. Maseda, Ian McConachie, Jorryt Matthee, Rohan P. Naidu, Pascal A. Oesch, Bingjie Wang, Katherine E. Whitaker, Christina Williams

RUBIES: A Spectroscopic Census of Little Red Dots; All V-Shaped Point Sources Have Broad Lines
The physical nature of Little Red Dots (LRDs) - a population of compact, red galaxies revealed by JWST - remains unclear. Photometric samples are constructed from varying selection criteria with limited spectroscopic follow-up available to test intrinsic spectral shapes and prevalence of broad emission lines. We use the RUBIES survey, a large spectroscopic program with wide color-morphology coverage and homogeneous data quality, to systematically analyze the emission-line kinematics, spectral s…

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Anum Nawaz, Hafiz Humza Mahmood Ramzan, Xianjia Yu, Zhuo Zou, Tomi Westerlund
https://arxiv.org/abs/2506.02038

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralized network model. However, the operational architecture of such systems introduces inherent vulnerabil…

All-sky search for individual Primordial Black Hole bursts with LHAASO
Cao, Aharonian, Bai, Bao, Bastieri, Bi, Bi, Bian, Bukevich, Cai, Cao, Cao, Chang, Chang, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Cheng, Cheng, Chu, Cui, Cui, Cui, Cui, Dai, Dai, Dai, , Diao, Dong, Duan, Fan, Fan, Fang, Fang, Fang, Feng, Feng, Feng, Feng, Feng, Feng, Feng, Gabici, Gao, Gao, Gao, Gao, Gao, Ge, Ge, Geng, Giacinti, Gong, Gou, Gu, Guo, Guo, Guo, Guo…

All-sky search for individual Primordial Black Hole bursts with LHAASO
Primordial Black Holes~(PBHs) are hypothetical black holes with a wide range of masses that formed in the early universe. As a result, they may play an important cosmological role and provide a unique probe of the early universe. A PBH with an initial mass of approximately $10^{15}$~g is expected to explode today in a final burst of Hawking radiation. In this work, we conduct an all-sky search for individual PBH burst events using the data collected from March 2021 to July 2024 by the Water Che…

A Wide Field Map of Ultra-Compact Dwarfs in the Coma Cluster
Richard T. Pomeroy, Juan P. Madrid, Conor R. O'Neill, Alexander T. Gagliano
https://arxiv.org/abs/2506.02296

A Wide Field Map of Ultra-Compact Dwarfs in the Coma Cluster
A dataset of 23,351 globular clusters (GCs) and ultra-compact dwarfs (UCDs) in the Coma cluster of galaxies was built using Hubble Space Telescope Advanced Camera for Surveys data. Based on the standard magnitude cut of $M_V \leq -11$, a total of 523 UCD candidates are found within this dataset of Compact Stellar Systems (CSS). From a color-magnitude diagram (CMD) analysis built using this catalog, we find a clear mass-magnitude relation extending marginally into the UCD parameter space. The lu…

Genetic Influences on Brain Aging: Analyzing Sex Differences in the UK Biobank using Structural MRI
Karen Ardila, Aashka Mohite, Abdoljalil Addeh, Amanda V. Tyndall, Cindy K. Barha, Quan Long, M. Ethan MacDonald
https://arxiv.org/abs/2505.20344

Genetic Influences on Brain Aging: Analyzing Sex Differences in the UK Biobank using Structural MRI
Brain aging trajectories differ between males and females, yet the genetic factors underlying these differences remain underexplored. Using structural MRI and genotyping data from 40,940 UK Biobank participants (aged 45-83), we computed Brain Age Gap Estimates (BrainAGE) for total brain, hippocampal, and ventricular volumes. We conducted sex-stratified genome-wide association studies (GWAS) and Post-GWAS analyses to identify genetic variants associated with accelerated brain aging. Distinct gen…

S\"{o}ze: One Network Telemetry Is All You Need for Per-flow Weighted Bandwidth Allocation at Scale
Weitao Wang, T. S. Eugene Ng
https://arxiv.org/abs/2506.00834

Söze: One Network Telemetry Is All You Need for Per-flow Weighted Bandwidth Allocation at Scale
Weighted bandwidth allocation is a powerful abstraction that has a wide range of use cases in modern data center networks. However, realizing highly agile and precise weighted bandwidth allocation for large-scale cloud environments is fundamentally challenging. In this paper, we propose Söze, a lightweight decentralized weighted bandwidth allocation system that leverages simple network telemetry features of commodity Ethernet switches. Given the flow weights, Söze can effectively use the tele…