Tootfinder

Revisiting symbiotic binaries with interferometry: II. New PIONIER data
Henri M. J. Boffin, Jaroslav Merc
https://arxiv.org/abs/2508.01304 https://arxiv.or…

Revisiting symbiotic binaries with interferometry: II. New PIONIER data
Symbiotic stars, which generally comprise a red giant and an accreting white dwarf, are excellent laboratories to understand mass transfer in wide binaries, with application to a wide family of systems. One of the fundamental questions is how mass is transferred from the red giant to the white dwarf. We use interferometric measurements made with the VLTI/PIONIER instrument, combined with Gaia data, to measure the radius of the giant in seven symbiotic systems. We further place the giants in the…

The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications
Bryan M. Gee
https://arxiv.org/abs/2507.01228

The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications
The ability to find data is central to the FAIR principles underlying research data stewardship. As with the ability to reuse data, efforts to ensure and enhance findability have historically focused on discoverability of data by other researchers, but there is a growing recognition of the importance of stewarding data in a fashion that makes them FAIR for a wide range of potential reusers and stakeholders. Research institutions are one such stakeholder and have a range of motivations for disco…

DynoStore: A wide-area distribution system for the management of data over heterogeneous storage
Dante D. Sanchez-Gallegos, J. L. Gonzalez-Compean, Maxime Gonthier, Valerie Hayot-Sasson, J. Gregory Pauloski, Haochen Pan, Kyle Chard, Jesus Carretero, Ian Foster
https://arxiv.org/abs/2507.00576

DynoStore: A wide-area distribution system for the management of data over heterogeneous storage
Data distribution across different facilities offers benefits such as enhanced resource utilization, increased resilience through replication, and improved performance by processing data near its source. However, managing such data is challenging due to heterogeneous access protocols, disparate authentication models, and the lack of a unified coordination framework. This paper presents DynoStore, a system that manages data across heterogeneous storage systems. At the core of DynoStore are data …

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…

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…

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…

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…

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…

Shaping circuit for improving linearity, bandwidth, and dynamic range in ToT-based detectors
J. Pe\~na-Rodr\'iguez, J. F\"ortsch, C. Pauly, K. -H. Kampert
https://arxiv.org/abs/2508.00739

Shaping circuit for improving linearity, bandwidth, and dynamic range in ToT-based detectors
The quantitative measurement of energy deposits in particle detectors, particularly in calorimeters, is usually accomplished with the help of Analog-to-Digital converters (ADCs) due to their precision, wide measurement range, and good linearity. However, drawbacks such as power consumption, data volume, and bandwidth limit their use in the next generation of high-energy physics experiments. Time-over-threshold (ToT) systems offer simplicity, low power consumption, easy integrability, and wide b…

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…

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…

Observation of orbitally excited $B_{c}^{ }$ states
LHCb collaboration, et al.
https://arxiv.org/abs/2507.02149 https://arxiv.org/pdf…

Observation of orbitally excited $B_{c}^{+}$ states
The observation of a wide peaking structure in the $B_{c}^{+} γ$ mass spectrum is reported using proton-proton collision data collected by the LHCb detector at center-of-mass energies of $7$, $8$ and $13~\text{TeV}$, corresponding to a total integrated luminosity of $9~\text{fb}^{-1}$. The statistical significance over the background-only hypothesis exceeds seven standard deviations. The width of the observed structure is larger than the expectation from a single-peak hypothesis, and is well d…

“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…

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…

Functional Renormalization for Signal Detection: Dimensional Analysis and Dimensional Phase Transition for Nearly Continuous Spectra Effective Field Theory
Riccardo Finotello, Vincent Lahoche, Dine Ousmane Samary
https://arxiv.org/abs/2507.01064

Functional Renormalization for Signal Detection: Dimensional Analysis and Dimensional Phase Transition for Nearly Continuous Spectra Effective Field Theory
Signal detection is one of the main challenges of data science. According to the nature of the data, the presence of noise may corrupt measurements and hinder the discovery of significant patterns. A wide range of techniques aiming at extracting the relevant degrees of freedom from data has been thus developed over the years. However, signal detection in almost continuous spectra, for small signal-to-noise ratios, remains a known difficult issue. This paper develops over recent advancements pro…

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-…

My slides on The Paradoxes of Open Data in Libraries, Archives and Museums #DH2025 panel on Openness in GLAM: Analysing, Reflecting, and D…

Study of $B_{c}(1P)^{ }$ states in the $B_{c}^{ } \gamma$ mass spectrum
LHCb collaboration, et al.
https://arxiv.org/abs/2507.02142 https://

Study of $B_{c}(1P)^{+}$ states in the $B_{c}^{+} γ$ mass spectrum
The study of a wide peaking structure in the $B_{c}^{+} γ$ mass spectrum is reported using a data sample of proton-proton collisions collected by the LHCb detector at center-of-mass energies of $7$, $8$ and $13~\text{TeV}$, corresponding to an integrated luminosity of $9~\text{fb}^{-1}$. The observed structure is consistent with the lowest excited $P$-wave $B_{c}^{+}$ states and exhibits a statistical significance exceeding seven standard deviations relative to the background-only hypothesis. …

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
Yating Wang, Haoyi Zhu, Mingyu Liu, Jiange Yang, Hao-Shu Fang, Tong He
https://arxiv.org/abs/2507.01016

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
In this paper, we introduce an innovative vector quantization based action tokenizer built upon the largest-scale action trajectory dataset to date, leveraging over 100 times more data than previous approaches. This extensive dataset enables our tokenizer to capture rich spatiotemporal dynamics, resulting in a model that not only accelerates inference but also generates smoother and more coherent action outputs. Once trained, the tokenizer can be seamlessly adapted to a wide range of downstream…

Uniform Validity of the Subset Anderson-Rubin Test under Heteroskedasticity and Nonlinearity
Atsushi Inoue, \`Oscar Jord\`a, Guido M. Kuersteiner
https://arxiv.org/abs/2507.01167 …

Uniform Validity of the Subset Anderson-Rubin Test under Heteroskedasticity and Nonlinearity
We consider the Anderson-Rubin (AR) statistic for a general set of nonlinear moment restrictions. The statistic is based on the criterion function of the continuous updating estimator (CUE) for a subset of parameters not constrained under the Null. We treat the data distribution nonparametrically with parametric moment restrictions imposed under the Null. We show that subset tests and confidence intervals based on the AR statistic are uniformly valid over a wide range of distributions that incl…

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei, Ryan Wang, James Flemings, Krishna Gummadi, Willie Neiswanger, Robin Jia
https://arxiv.org/abs/2507.19419

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often inaccessible to researchers. We present TokenSmith, an open-source library for interactive editing, inspection, and analysis of datasets used in Megatron-style pretraining frameworks such as GPT-NeoX, Megatron, and NVIDIA NeMo. TokenSmith supports a wide range of operations including searching, viewing, ingesting, expor…

Topological Data Analysis and Topological Deep Learning Beyond Persistent Homology - A Review
Zhe Su, Xiang Liu, Layal Bou Hamdan, Vasileios Maroulas, Jie Wu, Gunnar Carlsson, Guo-Wei Wei
https://arxiv.org/abs/2507.19504

Topological Data Analysis and Topological Deep Learning Beyond Persistent Homology - A Review
Topological data analysis (TDA) is a rapidly evolving field in applied mathematics and data science that leverages tools from topology to uncover robust, shape-driven insights in complex datasets. The main workhorse is persistent homology, a technique rooted in algebraic topology. Paired with topological deep learning (TDL) or topological machine learning, persistent homology has achieved tremendous success in a wide variety of applications in science, engineering, medicine, and industry. Howev…

Learning Constraints Directly from Network Data
Hongyu H\`e, Minhao Jin, Maria Apostolaki
https://arxiv.org/abs/2506.23964 https://ar…

Learning Constraints Directly from Network Data
Network data conforms to a wide range of rules that arise from protocols, design principles, and deployment decisions (e.g., a packet's queuing delay must be less than its end-to-end delay). Formalizing such rules as logic constraints can (i) improve the quality of synthetic data, (ii) reduce the brittleness of machine learning (ML) models, and (iii) improve semantic understanding of network measurements. However, these benefits remain out of reach if rule extraction is manual or solely reliant…

MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines
Yaolun Zhang, Xiaogeng Liu, Chaowei Xiao
https://arxiv.org/abs/2507.22606 https://

MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines
Large Language Models (LLMs) have demonstrated the ability to solve a wide range of practical tasks within multi-agent systems. However, existing human-designed multi-agent frameworks are typically limited to a small set of pre-defined scenarios, while current automated design methods suffer from several limitations, such as the lack of tool integration, dependence on external training data, and rigid communication structures. In this paper, we propose MetaAgent, a finite state machine based fr…

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
Yueh-Po Peng, Vincent K. M. Cheung, Li Su
https://arxiv.org/abs/2507.22378

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture brain-wide neural dynamics with high spatial precision, decoding from fMRI data -- particularly from task-evoked activity -- remains challenging due to its high dimensionality, low signal-to-noise ratio, and limited within-subject data. Here, we leverage recent advances in computer vision and propose STDA-SwiFT, a tr…

A CPFSK Transceiver with Hybrid CSS-DSSS Spreading for LPWAN PHY Communication
Wenkun Wen, Ruiqi Zhang, Peiran Wu, Tierui Min, Minghua Xia
https://arxiv.org/abs/2507.23029 https…

A CPFSK Transceiver with Hybrid CSS-DSSS Spreading for LPWAN PHY Communication
Traditional low-power wide-area network (LPWAN) transceivers typically compromise data rates to achieve deep coverage. This paper presents a novel transceiver that achieves high receiver sensitivity and low computational complexity. At the transmitter, we replace the conventional direct sequence spread spectrum (DSSS) preamble with a chirp spread spectrum (CSS) preamble, consisting of a pair of down-chirp and up-chirp signals that are conjugate to each other, simplifying packet synchronization.…

Piezoelectric truss metamaterials: data-driven design and additive manufacturing
Saurav Sharma, Satya K. Ammu, Prakash Thakolkaran, Jovana Jovanova, Kunal Masania, Siddhant Kumar
https://arxiv.org/abs/2506.22451

Piezoelectric truss metamaterials: data-driven design and additive manufacturing
In the development of active animate materials, electromechanical coupling is highly attractive to realize mechanoresponsive functionality. Piezoelectricity is the most utilized electromechanical phenomenon due to the wide availability of materials that display precise and reliable coupling. However, the inherent directionality of these materials is constrained by the symmetry of their crystal structure, which limits the choice of available properties in natural piezoelectric materials. A solut…

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Terahertz for Radar applications and Wireless Communication
Sofiane Latreche, Hocine Bellahsene, Abdelmalik Taleb-Ahmed
https://arxiv.org/abs/2507.23076 https://

Terahertz for Radar applications and Wireless Communication
Technological advancements in the design of electronic and optical materials have opened up the possibility of utilizing the latest available Radio Frequency spectrum the Terahertz (THz) band. This band holds great promise for next-generation wireless systems, which are poised to seamlessly integrate a wide array of data-intensive and time-sensitive applications. In this article, we delve into the Terahertz band, providing insights into its properties and showcasing examples of its applications…

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Sarah Pungitore, Shashank Yadav, David Maughan, Vignesh Subbian
https://arxiv.org/abs/2507.23146

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Objective: Although computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications, it is time-intensive because of manual data review. We previously assessed the ability of LLMs to perform computational phenotyping tasks using computable phenotypes for ARF respiratory support therapies. They successfully performed concept classification and classification of single-therapy phenotypes, but underperformed on multiple-therapy phenotype…

How Does Empirical Research Facilitate Creation Tool Design? A Data Video Perspective
Leixian Shen, Leni Yang, Haotian Li, Yun Wang, Yuyu Luo, Huamin Qu
https://arxiv.org/abs/2507.15244

How Does Empirical Research Facilitate Creation Tool Design? A Data Video Perspective
Empirical research in creative design deepens our theoretical understanding of design principles and perceptual effects, offering valuable guidance for innovating creation tools. However, how these empirical insights currently influence the development of creation tools, and how their integration can be enhanced in the future, remains insufficiently understood. In this paper, we aim to unveil the gap through a case study on data videos, a prominent and wide-spread medium for effective data stor…

BlockFIFO & MultiFIFO: Scalable Relaxed Queues
Stefan Koch, Peter Sanders, Marvin Williams
https://arxiv.org/abs/2507.22764 https://arxiv.org/pdf/2507.…

BlockFIFO & MultiFIFO: Scalable Relaxed Queues
FIFO queues are a fundamental data structure used in a wide range of applications. Concurrent FIFO queues allow multiple execution threads to access the queue simultaneously. Maintaining strict FIFO semantics in concurrent queues leads to low throughput due to high contention at the head and tail of the queue. By relaxing the FIFO semantics to allow some reordering of elements, it becomes possible to achieve much higher scalability. This work presents two orthogonal designs for relaxed concurre…

Scalable Subset Selection in Linear Mixed Models
Ryan Thompson, Matt P. Wand, Joanna J. J. Wang
https://arxiv.org/abs/2506.20425 https://

Scalable Subset Selection in Linear Mixed Models
Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalized medicine or adaptive marketing. Nowadays, this type of data is increasingly wide, sometimes containing thousands of candidate predictors, necessitating sparsity for prediction and interpretation. However, existing sparse learning methods for LMMs do not scale well beyond tens or hundreds of predictors, leaving a large gap compared with sparse methods fo…

Glyph-Based Multiscale Visualization of Turbulent Multi-Physics Statistics
Arisa Cowe, Tyson Neuroth, Qi Wu, Martin Rieth, Jacqueline Chen, Myoungkyu Lee, Kwan-Liu Ma
https://arxiv.org/abs/2506.23092

Glyph-Based Multiscale Visualization of Turbulent Multi-Physics Statistics
Many scientific and engineering problems involving multi-physics span a wide range of scales. Understanding the interactions across these scales is essential for fully comprehending such complex problems. However, visualizing multivariate, multiscale data within an integrated view where correlations across space, scales, and fields are easily perceived remains challenging. To address this, we introduce a novel local spatial statistical visualization of flow fields across multiple fields and tur…

DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges
Zhengtong Yan, Gongsheng Yuan, Qingsong Guo, Jiaheng Lu
https://arxiv.org/abs/2507.19254

DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges
Modern enterprises are increasingly driven by the DATA+AI paradigm, in which Database Management Systems (DBMSs) and Large Language Models (LLMs) have become two foundational infrastructures powering a wide range of industrial and business applications, such as enterprise analytics, intelligent customer service, and data-driven decision-making. The efficient integration of DBMSs and LLMs within a unified system offers significant opportunities but also introduces new technical challenges. This …

Distributions of wide binary stars in theory and in Gaia data: II. Reconstruction of sample probability density of true orbit sizes
Valeri V. Makarov
https://arxiv.org/abs/2507.19273

Distributions of wide binary stars in theory and in Gaia data: II. Reconstruction of sample probability density of true orbit sizes
Wide binary stars are important for testing alternative models of gravitation in the weak-field regime and understanding the statistical outcomes of dynamical interactions in the general Galactic field. The Gaia mission's collection of weakly bound pairs of stars offers a unique opportunity to estimate the rate of survivors at separations above 7 KAU, where non-Newtonian components of gravitation may become important. The available Gaia-based catalogs of resolved binaries provide the projected …

Boundary-layer transition in the age of data: from a comprehensive dataset to fine-grained prediction
Wenhui Chang, Hongyuan Hu, Youcheng Xi, Markus Kloker, Honghui Teng, Jie Ren
https://arxiv.org/abs/2507.19120

Boundary-layer transition in the age of data: from a comprehensive dataset to fine-grained prediction
The laminar-to-turbulent transition remains a fundamental and enduring challenge in fluid mechanics. Its complexity arises from the intrinsic nonlinearity and extreme sensitivity to external disturbances. This transition is critical in a wide range of applications, including aerospace, marine engineering, geophysical flows, and energy systems. While the governing physics can be well described by the Navier-Stokes equations, practical prediction efforts often fall short due to the lack of compre…

CMOS X: Stacking Persistent Embedded Memories based on Oxide Transistors upon GPGPU Platforms
Faaiq Waqar, Ming-Yen Lee, Seongwon Yoon, Seongkwang Lim, Shimeng Yu
https://arxiv.org/abs/2506.23405

CMOS+X: Stacking Persistent Embedded Memories based on Oxide Transistors upon GPGPU Platforms
In contemporary general-purpose graphics processing units (GPGPUs), the continued increase in raw arithmetic throughput is constrained by the capabilities of the register file (single-cycle) and last-level cache (high bandwidth), which require the delivery of operands at a cadence demanded by wide single-instruction multiple-data (SIMD) lanes. Enhancing the capacity, density, or bandwidth of these memories can unlock substantial performance gains; however, the recent stagnation of SRAM bit-cell…

Optimization of Flying Ad Hoc Network Topology and Collaborative Path Planning for Multiple UAVs
Ming He, Peizhao Wang, Haihua Chen, Bin Sun, Hongpeng Wang
https://arxiv.org/abs/2506.17945

Optimization of Flying Ad Hoc Network Topology and Collaborative Path Planning for Multiple UAVs
Multiple unmanned aerial vehicles (UAVs) play a vital role in monitoring and data collection in wide area environments with harsh conditions. In most scenarios, issues such as real-time data retrieval and real-time UAV positioning are often disregarded, essentially neglecting the communication constraints. In this paper, we comprehensively address both the coverage of the target area and the data transmission capabilities of the flying ad hoc network (FANET). The data throughput of the network …

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows
Vladislav Esaulov, Jieyang Chen, Norbert Podhorszki, Fred Suter, Scott Klasky, Anu G Bourgeois, Lipeng Wan
https://arxiv.org/abs/2506.17084

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows
In modern science, the growing complexity of large-scale projects has increased reliance on cross-facility workflows, where institutions share resources and expertise to accelerate discovery. These workflows often involve transferring massive data over wide-area networks. While high-speed networks like ESnet and data transfer services like Globus have improved data mobility, challenges remain. Large data volumes can strain bandwidth, TCP suffers from retransmissions due to packet loss, and trad…

pySpainMobility: a Python Package to Access and Manage Spanish Open Mobility Data
Ciro Beneduce, Tania Gull\'on Mu\~noz-Repiso, Bruno Lepri, Massimiliano Luca
https://arxiv.org/abs/2506.13385

pySpainMobility: a Python Package to Access and Manage Spanish Open Mobility Data
Mobility patterns play a critical role in a wide range of societal challenges, from epidemic modeling and emergency response to transportation planning and regional development. Yet, access to high-quality, timely, and openly available mobility data remains limited. In response, the Spanish Ministry of Transportation and Sustainable Mobility has released daily mobility datasets based on anonymized mobile phone data, covering districts, municipalities, and greater urban areas from February 2020 …

From Chase to London, ranking the most reliable TD scorers among WRs https://www.espn.com/fantasy/football/story/_/id/45748000/fantasy-football-ranking-most-reliable-touchdown-scorers-wide-r…

Tightening constraints on primordial oscillations with latest ACT and SPT data
Ze-Yu Peng, Yun-Song Piao
https://arxiv.org/abs/2507.17276 https://arxiv.org…

Tightening constraints on primordial oscillations with latest ACT and SPT data
The oscillation in primordial power spectrum (PPS), a fingerprint of not only a wide class of models of inflation but new physics, is of significant theoretical interest, and can be imprinted on the cosmic microwave background (CMB). In this work, we present constraints on periodic oscillations in the PPS using the latest ACT DR6 and SPT-3G D1 CMB data with the precise measurements at high multipoles beyond the Planck angular resolution and sensitivity. It is found that the combination of SPT a…

No tracking across the web. No surveillance. No selling your data. That's it—that's the privacy policy.
"I fly with Gander. Because ragebait isn’t very Canadian."
#Gander Social Inc

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…

Chain Table: Protecting Table-Level Data Integrity by Digital Ledger Technology
Feng Yu, Ryan Laird
https://arxiv.org/abs/2507.13932 https://

Chain Table: Protecting Table-Level Data Integrity by Digital Ledger Technology
The rise of blockchain and Digital Ledger Technology (DLT) has gained wide traction. Instead of relying on a traditional centralized data authority, a blockchain system consists of digitally entangled block data shared across a distributed network. The specially designed chain data structure and its consensus mechanism protect blockchain data from being tampered by unauthorized adversaries. However, implementing a full-fledged blockchain system to protect a database can be technically cumbersom…

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…

Recurrent Event Analysis with Ordinary Differential Equations
Bo Meng, Weijing Tang, Gongjun Xu, Ji Zhu
https://arxiv.org/abs/2507.20396 https://arxiv.org/…

Recurrent Event Analysis with Ordinary Differential Equations
This paper introduces a general framework for analyzing recurrent event data by modeling the conditional mean function of the recurrent event process as the solution to an Ordinary Differential Equation (ODE). This approach not only accommodates a wide range of semi-parametric recurrent event models, including both non-homogeneous Poisson processes (NHPPs) and non-Poisson processes, but also is scalable and easy-to-implement. Based on this framework, we propose a Sieve Maximum Pseudo-Likelihood…

Signed Higher-Order Interactions for Brain Disorder Diagnosis via Multi-Channel Transformers
Dengyi Zhao, Zhiheng Zhou, Guiying Yan, Dongxiao Yu, Xingqi Qi
https://arxiv.org/abs/2507.20205

Signed Higher-Order Interactions for Brain Disorder Diagnosis via Multi-Channel Transformers
Accurately characterizing higher-order interactions of brain regions and extracting interpretable organizational patterns from Functional Magnetic Resonance Imaging data is crucial for brain disease diagnosis. Current graph-based deep learning models primarily focus on pairwise or triadic patterns while neglecting signed higher-order interactions, limiting comprehensive understanding of brain-wide communication. We propose HOI-Brain, a novel computational framework leveraging signed higher-orde…

Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing
Chenqi Lin, Kang Yang, Tianshi Xu, Ling Liang, Yufei Wang, Zhaohui Chen, Runsheng Wang, Mingyu Gao, Meng Li
https://arxiv.org/abs/2507.16391

Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing
With the wide application of machine learning (ML), privacy concerns arise with user data as they may contain sensitive information. Privacy-preserving ML (PPML) based on cryptographic primitives has emerged as a promising solution in which an ML model is directly computed on the encrypted data to provide a formal privacy guarantee. However, PPML frameworks heavily rely on the oblivious transfer (OT) primitive to compute nonlinear functions. OT mainly involves the computation of single-point co…

Probing Equatorial Ionospheric TEC at Sub-GHz Frequencies with Wide-Band (B4) uGMRT Interferometric Data
Dipanjan Banerjee, Abhik Ghosh, Sushanta K Mondal, Parimal Ghosh
https://arxiv.org/abs/2506.20690

Probing Equatorial Ionospheric TEC at Sub-GHz Frequencies with Wide-Band (B4) uGMRT Interferometric Data
Phase stability at low radio frequencies is severely impacted by ionospheric propagation delays. Radio interferometers such as the Giant Metrewave Radio Telescope (GMRT) are capable of detecting changes in the ionosphere's total electron content (TEC) over larger spatial scales and with greater sensitivity compared to conventional tools like the Global Navigation Satellite System (GNSS). Thanks to its unique design featuring both a dense central array and long outer arms-and its strategic locat…

Constraining the Stellar-to-Halo Mass Relation with Galaxy Clustering and Weak Lensing from DES Year 3 Data
G. Zacharegkas, C. Chang, J. Prat, W. Hartley, S. Mucesh, A. Alarcon, O. Alves, A. Amon, K. Bechtol, M. R. Becker, G. Bernstein, J. Blazek, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, R. Chen, A. Choi, J. Cordero, C. Davis, J. Derose, H. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, K. Eckert, T. F. Eifler, J. Elvin-Poole, S. Everett, X. Fang, A. Ferte, M. Gatti,…

Constraining the Stellar-to-Halo Mass Relation with Galaxy Clustering and Weak Lensing from DES Year 3 Data
We develop a framework to study the relation between the stellar mass of a galaxy and the total mass of its host dark matter halo using galaxy clustering and galaxy-galaxy lensing measurements. We model a wide range of scales, roughly from $\sim 100 \; {\rm kpc}$ to $\sim 100 \; {\rm Mpc}$, using a theoretical framework based on the Halo Occupation Distribution and data from Year 3 of the Dark Energy Survey (DES) dataset. The new advances of this work include: 1) the generation and validation o…

J'ai découvert les noms "Wide data" et "Long data" : #BuisnessIntelligence

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling
Leandro Von Krannichfeldt, Kristina Orehounig, Olga Fink
https://arxiv.org/abs/2507.17526

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling
Building energy modeling is a key tool for optimizing the performance of building energy systems. Historically, a wide spectrum of methods has been explored -- ranging from conventional physics-based models to purely data-driven techniques. Recently, hybrid approaches that combine the strengths of both paradigms have gained attention. These include strategies such as learning surrogates for physics-based models, modeling residuals between simulated and observed data, fine-tuning surrogates with…

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…

Towards Domain Specification of Embedding Models in Medicine
Mohammad Khodadad, Ali Shiraee, Mahdi Astaraki, Hamidreza Mahyar
https://arxiv.org/abs/2507.19407 https://

Towards Domain Specification of Embedding Models in Medicine
Medical text embedding models are foundational to a wide array of healthcare applications, ranging from clinical decision support and biomedical information retrieval to medical question answering, yet they remain hampered by two critical shortcomings. First, most models are trained on a narrow slice of medical and biological data, beside not being up to date in terms of methodology, making them ill suited to capture the diversity of terminology and semantics encountered in practice. Second, ex…

Dynamite: Real-Time Debriefing Slide Authoring through AI-Enhanced Multimodal Interaction
Panayu Keelawat, David Barron, Kaushik Narasimhan, Daniel Manesh, Xiaohang Tang, Xi Chen, Sang Won Lee, Yan Chen
https://arxiv.org/abs/2507.20137

Dynamite: Real-Time Debriefing Slide Authoring through AI-Enhanced Multimodal Interaction
Facilitating class-wide debriefings after small-group discussions is a common strategy in ethics education. Instructor interviews revealed that effective debriefings should highlight frequently discussed themes and surface underrepresented viewpoints, making accurate representations of insight occurrence essential. Yet authoring presentations in real time is cognitively overwhelming due to the volume of data and tight time constraints. We present Dynamite, an AI-assisted system that enables sem…

Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Yiqi Wang, Mrinal Verghese, Jeff Schneider
https://arxiv.org/abs/2507.13340 https:/…

Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Learning visuomotor policies via imitation has proven effective across a wide range of robotic domains. However, the performance of these policies is heavily dependent on the number of training demonstrations, which requires expensive data collection in the real world. In this work, we aim to reduce data collection efforts when learning visuomotor robot policies by leveraging existing or cost-effective data from a wide range of embodiments, such as public robot datasets and the datasets of huma…

Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts
Matthew Sprintson, Edward Oughton
https://arxiv.org/abs/2507.19989

Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts
It is estimated that over one-fourth of US households experienced a power outage in 2023, costing on average US $\$150$ Bn annually, with $87\%$ of outages caused by natural hazards. Indeed, numerous studies have examined the macroeconomic impact of power network interruptions, employing a wide variety of modeling methods and data parameterization techniques, which warrants further investigation. In this paper, we quantify the macroeconomic effects of three significant natural hazard-induced US…

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu, Qidong Liu, Xinhang Li, Wenlin Zhang, Feng Li, Pengjie Wang, Jian Xu, Bo Zheng, Xiangyu Zhao
https://arxiv.org/abs/2506.16114

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios. The majority of existing research efforts primarily concentrate on developing powerful item tokenizers or advancing LLM decoding strategies to attain superior performance. However, the critical fine-tuning step in GR frameworks, which is essential for adapting LLMs to recommendation data, remains largely unexpl…

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
Mohsen Jenadeleh, Jon Sneyers, Davi Lazzarotto, Shima Mohammadi, Dominik Keller, Atanas Boev, Rakesh Rao Ramachandra Rao, Ant\'onio Pinheiro, Thomas Richter, Alexander Raake, Touradj Ebrahimi, Jo\~ao Ascenso, Dietmar Saupe
https://arxiv…

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual…

evortran: a modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction
Thomas Biek\"otter
https://arxiv.org/abs/2507.06082

evortran: a modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction
evortran is a modern Fortran library designed for high-performance genetic algorithms and evolutionary optimization. evortran can be used to tackle a wide range of problems in high-energy physics and beyond, such as derivative-free parameter optimization, complex search taks, parameter scans and fitting experimental data under the presence of instrumental noise. The library is built as an fpm package with flexibility and efficiency in mind, while also offering a simple installation process, use…

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
Amoy Ashesh, Harsimran Kaur, Sandeep Aashish
https://arxiv.org/abs/2506.19942

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
We present a machine learning (ML) framework for the detection of wide binary star systems and the identification of deviations from standard gravity using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. We recast the problem of identifying systematic deviations from standard gravity as a supervised anomaly detection. Our approach incorporate…

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…

Origin of Suppressed Ferroelectricity in k-Ga$_2$O$_3$: Interplay Between Polarization and Lattice Domain Walls
Yonghao Zhu, Zhi Wang, Junwei Luo, Lin-Wang Wang
https://arxiv.org/abs/2507.16167

Origin of Suppressed Ferroelectricity in k-Ga$_2$O$_3$: Interplay Between Polarization and Lattice Domain Walls
The large discrepancy between experimental and theoretical remanent polarization and coercive field limits the applications of wide-band-gap ferroelectric materials. Here, using a machine-learning potential trained on ab-initio molecular dynamics data, we identify a new mechanism of the interplay between polarization domain wall (PDW) and lattice domain wall (LDW) in ferroelectric k-phase gallium oxide (Ga2O3), which reconciles predictions with experimental observations. Our results reveal that…

An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management
Simin Sun, Yuchuan Jin, Miroslaw Staron
https://arxiv.org/abs/2506.11659

An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management
Developing autonomous driving systems (ADSs) involves generating and storing extensive log data from test drives, which is essential for verification, research, and simulation. However, these high-frequency logs, recorded over varying durations, pose challenges for developers attempting to locate specific driving scenarios. This difficulty arises due to the wide range of signals representing various vehicle components and driving conditions, as well as unfamiliarity of some developers' with the…

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…

Infrared Variability of Carbon Stars in the LMC Observed with NEOWISE-R
Kyung-Won Suh
https://arxiv.org/abs/2506.22100 https://arxiv.…

Infrared Variability of Carbon Stars in the LMC Observed with NEOWISE-R
We investigate the infrared variability of carbon stars in the Large Magellanic Cloud (LMC). Our sample consists of 11,134 carbon stars identified in both visual and infrared bands. Among these, 1,184 objects are known Mira variables based on the Optical Gravitational Lensing Experiment III (OGLE-III) observations. We study the infrared variability of the entire sample using the Wide-field Infrared Survey Explorer (WISE) photometric data spanning the past 16 years, including the AllWISE multiep…

AKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes
Zhenwei Dai, Chuan Lei, Asterios Katsifodimos, Xiao Qin, Christos Faloutsos, Huzefa Rangwala
https://arxiv.org/abs/2507.04687

AKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes
How to generate a large, realistic set of tables along with joinability relationships, to stress-test dataset discovery methods? Dataset discovery methods aim to automatically identify related data assets in a data lake. The development and evaluation of such solutions for customers from a wide range of business domains, relies on diverse, high quality and domain-specific tabular benchmarks. Large language models (LLMs) are trained on a wide variety of text data, which can provide a strong foun…

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…

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, …

A Survey of Dimension Estimation Methods
James A. D. Binnie, Pawe{\l} D{\l}otko, John Harvey, Jakub Malinowski, Ka Man Yim
https://arxiv.org/abs/2507.13887

A Survey of Dimension Estimation Methods
It is a standard assumption that datasets in high dimension have an internal structure which means that they in fact lie on, or near, subsets of a lower dimension. In many instances it is important to understand the real dimension of the data, hence the complexity of the dataset at hand. A great variety of dimension estimators have been developed to find the intrinsic dimension of the data but there is little guidance on how to reliably use these estimators. This survey reviews a wide range o…

Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures
Suman Deb, Emil Lupu, Emm Mic Drakakis, Anil Anthony Bharath, Zhen Kit Leung, Guang Rui Ma, Anupam Chattopadhyay
https://arxiv.org/abs/2507.19609

Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures
The Internet of Medical Things (IoMT) has the potential to radically improve healthcare by enabling real-time monitoring, remote diagnostics, and AI-driven decision making. However, the connectivity, embedded intelligence, and inclusion of a wide variety of novel sensors expose medical devices to severe cybersecurity threats, compromising patient safety and data privacy. In addition, many devices also have direct capacity - individually or in conjunction with other IoMT devices - to perform act…

Arcsecond-Scale X-ray Imaging and Spectroscopy of SS 433 with Chandra HETG
Yusuke Sakai, Shinya Yamada, Haruka Sakemi, Mami Machida, Taichi Igarashi, Ryota Hayakawa, Miho Tan, Taisei Furuyama
https://arxiv.org/abs/2507.19042

Arcsecond-Scale X-ray Imaging and Spectroscopy of SS 433 with Chandra HETG
We present a spatial and spectral analysis of arcsecond-scale X-ray emission in SS433 using zeroth-order data from Chandra High-Energy Transmission Grating (HETG) observations. The analysis is based on 24 observations acquired between 1999 and 2024, comprising a total exposure of $\sim$850 ks and covering a wide range of orbital and precessional phases. Among these, the $\sim$140 ks observation from 2014 was analyzed in detail for this study. This data provides the best statistics and was taken…

SOAP: A Python Package for Calculating the Properties of Galaxies and Halos Formed in Cosmological Simulations
Robert McGibbon, John C. Helly, Joop Schaye, Matthieu Schaller, Bert Vandenbroucke
https://arxiv.org/abs/2507.22669

SOAP: A Python Package for Calculating the Properties of Galaxies and Halos Formed in Cosmological Simulations
Modern large scale cosmological hydrodynamic simulations require robust tools capable of analysing their data outputs in a parallel and efficient manner. We introduce SOAP (Spherical Overdensity and Aperture Processor), a Python package designed to compute halo and galaxy properties from SWIFT simulations after being post-processed with a subhalo finder. SOAP takes a subhalo catalogue as input and calculates a wide array of properties for each object. SOAP offers parallel processing capabilitie…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- The World Wide recipe: A community-centred framework for fine-grained data collection and regiona...
Jabez Magomere, et al.

Generating real-time detailed ground visualisations from sparse aerial point clouds
Aidan Murray, Eddie Waite, Caleb Ross, Scarlet Mitchell, Alexander Bradley, Joanna Jamrozy, Kenny Mitchell
https://arxiv.org/abs/2507.18664

Generating real-time detailed ground visualisations from sparse aerial point clouds
Building realistic wide scale outdoor 3D content with sufficient visual quality to observe at walking eye level or from driven vehicles is often carried out by large teams of artists skilled in modelling, texturing, material shading and lighting, which typically leads to both prohibitive costs and reduced accuracy honoring the variety of real world ground truth landscapes. In our proposed method, we define a process to automatically amplify real-world scanned data and render real-time in animat…

Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
Yuto Harada, Yusuke Yamauchi, Yusuke Oda, Yohei Oseki, Yusuke Miyao, Yu Takagi
https://arxiv.org/abs/2506.14681

Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We trained a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks, resulting in 1,000+ SFT models under controlled conditions. We then identified the dataset properties that matter most and examined the layer-wise modifications introduced by SFT. Our…

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Sarah Pungitore, Shashank Yadav, Molly Douglas, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2506.16359

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To suppor…

Radio Emission from a Nearby M dwarf Binary
Kelvin Wandia, Michael A. Garrett, Robert J. Beswick, Jack F. Radcliffe, Vishal Gajjar, David Williams-Baldwin, Chenoa Tremblay, Iain McDonald, Alex Andersson, Andrew Siemion
https://arxiv.org/abs/2507.20681

Radio Emission from a Nearby M dwarf Binary
We present the detection of the binary system 2MASS J02132062+3648506 AB using the Karl G. Jansky Very Large Array (VLA) archive data observed at 4-8 GHz. The system is a triple consisting of a tight binary ($\sim0.2"$) of two M dwarfs of spectral class M4.5 and M6.5 and a wide T3 brown dwarf companion ($\sim$16.4"). The binary displays coronal and chromospheric activity as traced by previously measured X-ray flux and H$α$ emission. We detect the unresolved binary at a peak flux density of $\s…

mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
Elvis Nava, Victoriano Montesinos, Erik Bauer, Benedek Forrai, Jonas Pai, Stefan Weirich, Stephan-Daniel Gravert, Philipp Wand, Stephan Polinski, Benjamin F. Grewe, Robert K. Katzschmann
https://arxiv.org/abs/2506.11916…

mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
We present a diffusion-based model recipe for real-world control of a highly dexterous humanoid robotic hand, designed for sample-efficient learning and smooth fine-motor action inference. Our system features a newly designed 16-DoF tendon-driven hand, equipped with wide angle wrist cameras and mounted on a Franka Emika Panda arm. We develop a versatile teleoperation pipeline and data collection protocol using both glove-based and VR interfaces, enabling high-quality data collection across dive…

Towards channel foundation models (CFMs): Motivations, methodologies and opportunities
Jun Jiang, Yuan Gao, Xinyi Wu, Shugong Xu
https://arxiv.org/abs/2507.13637

Towards channel foundation models (CFMs): Motivations, methodologies and opportunities
Artificial intelligence (AI) has emerged as a pivotal enabler for next-generation wireless communication systems. However, conventional AI-based models encounter several limitations, such as heavy reliance on labeled data, limited generalization capability, and task-specific design. To address these challenges, this paper introduces, for the first time, the concept of channel foundation models (CFMs)-a novel and unified framework designed to tackle a wide range of channel-related tasks through …

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
Bokai Yang
https://arxiv.org/abs/2506.14244 https://

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
In the network literature, a wide range of statistical models have been proposed to exploit structural patterns in the data. Therefore, model selection between different models is a fundamental problem. Cross-validation is a powerful candidate to solve this problem, and Li et al. have already proposed an edge-sampling procedure to choose the number of communities in the block model framework. In this paper, we propose a penalized edge-sampling cross-validation framework for nested network model…

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu, Nicholas Konz, Yaqian Chen, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2506.12186

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or object segmentation. However, training models for specific new tasks often requires large amounts of labeled data, which is difficult to obtain due to high annotation costs and data privacy concerns. To circumvent this issue, we introduce MRI-CORE (MRI COmprehensive Representat…

Working with AI: Measuring the Occupational Implications of Generative AI
Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri
https://arxiv.org/abs/2507.07935

Working with AI: Measuring the Occupational Implications of Generative AI
Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society's most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users an…

Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations
Xizheng Yu, Justin Torok, Sneha Pandya, Sourav Pal, Vikas Singh, Ashish Raj
https://arxiv.org/abs/2506.11158

Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations
In this paper, we study the efficacy and utility of recent advances in non-local, non-linear image interpolation and extrapolation algorithms, specifically, ideas based on Implicit Neural Representations (INR), as a tool for analysis of spatial transcriptomics data. We seek to utilize the microarray gene expression data sparsely sampled in the healthy human brain, and produce fully resolved spatial maps of any given gene across the whole brain at a voxel-level resolution. To do so, we first obt…

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 …

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo, Taehyun Yang, Daniel Archambault, Sungahn Ko, Takanori Fujiwara, Kwan-Liu Ma, Jinwook Seo
https://arxiv.org/abs/2506.14820

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual ana…

An H$\alpha$ Cloud in the HI Tail: Recent Star Formation in the Outskirts of NGC 4258 Revealed by Nanshan 1-m Telescope
Cheng Cheng, Jia-Sheng Huang, Peng Wei, Ali Esamdin, Guojie Feng, Zhi-Xiang Zhang, Haojing Yan, Wei Du, Pei Zuo, Zi-Jian Li, Gustavo Orellana, Letian Wang, Yong Wang, Abdusamatjan Iskandar, Shahidin Yaqup
https://

An H$α$ Cloud in the HI Tail: Recent Star Formation in the Outskirts of NGC 4258 Revealed by Nanshan 1-m Telescope
We present first-light deep H$α$ imaging taken with the Nanshan 1-meter wide-field telescope on the local galaxy NGC 4258, alongside archival data from Hubble Space telescope (HST), Westerbork Synthesis Radio Telescope, and The Dark Energy Camera Legacy Survey. The H$α$ image shows ongoing star formation not only inside the galaxy but also in an HI cloud in the eastern HI tail, which is roughly 16 kpc away from the main galaxy. The HST images reveal several ultra-blue compact objects ($\rm F5…

A real-time metric of online engagement monitoring
Laura J. Johnston, Jim E. Griffin, Ioanna Manolopoulou, Takoua Jendoubi
https://arxiv.org/abs/2507.12162

A real-time metric of online engagement monitoring
Measuring online behavioural student engagement often relies on simple count indicators or retrospective, predictive methods, which present challenges for real-time application. To address these limitations, we reconceptualise an existing course-wide engagement metric to create a chapter-based version that aligns with the weekly structure of online courses. Derived directly from virtual learning environment log data, the new metric allows for cumulative, real-time tracking of student activity w…

Watermarking LLM-Generated Datasets in Downstream Tasks
Yugeng Liu, Tianshuo Cong, Michael Backes, Zheng Li, Yang Zhang
https://arxiv.org/abs/2506.13494 ht…

Watermarking LLM-Generated Datasets in Downstream Tasks
Large Language Models (LLMs) have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated data to train their models. However, the inability to track content produced by LLMs poses a significant challenge, potentially leading to copyright infringement for the LLM owners. In this paper, we pr…

A $6.37\,{\rm Hz}$ quasi-periodic oscillating photospheric emission in GRB~240825A
Guo-Yu Li, Da-Bin Lin, Zhi-Lin Chen, Bao-Quan Huang, Tong Liu, En-Wei Liang
https://arxiv.org/abs/2507.16538

A $6.37\,{\rm Hz}$ quasi-periodic oscillating photospheric emission in GRB~240825A
Using data from Swift-BAT and Fermi-GBM, we report the first detection of a high-confidence quasi-periodic oscillation (QPO) in the thermal emission of gamma-ray burst GRB~240825A. The spectral analysis of the burst reveals two radiation components, including a thermal emission dominant in $100 \text{--} 300\,\mathrm{keV}$ and a non-thermal emission spanning a wide energy range. During the interval $2.07 \text{--} 3.25\,\mathrm{s}$ post-trigger, a strong QPO signal at $6.37\,\mathrm{Hz}$ ($\gtr…

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Huihan Liu, Rutav Shah, Shuijing Liu, Jack Pittenger, Mingyo Seo, Yuchen Cui, Yonatan Bisk, Roberto Mart\'in-Mart\'in, Yuke Zhu
https://arxiv.org/abs/2506.14727

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A central challenge in real-world assistive teleoperation is for the robot to infer a wide range of human intentions from user control inputs and to assist users with correct actions. Existing methods are either confined to simple, predefined scenarios or restricted to task-specific data distributions at training, limit…

COS2A: Conversion from Sentinel-2 to AVIRIS Hyperspectral Data Using Interpretable Algorithm With Spectral-Spatial Duality
Chia-Hsiang Lin, Jui-Ting Chen, Zi-Chao Leng, Jhao-Ting Lin
https://arxiv.org/abs/2507.06575

COS2A: Conversion from Sentinel-2 to AVIRIS Hyperspectral Data Using Interpretable Algorithm With Spectral-Spatial Duality
The Sentinel-2 satellite, launched by the European Space Agency (ESA), offers extensive spatial coverage and has become indispensable in a wide range of remote sensing applications. However, it just has 12 spectral bands, making substances/objects identification less effective, not mentioning the varying spatial resolutions (10/20/60 m) across the 12 bands. If such a multi-resolution 12-band image can be computationally converted into a hyperspectral image with uniformly high resolution (i.e., …

The PGPUC horizontal branch evolutionary tracks
A. A. R. Valcarce (UTA), M. Catelan (PUC-Chile, MAS), S. Alves (UTA), F. Gonzalez-Bordon (UTA)
https://arxiv.org/abs/2506.16562

The PGPUC horizontal branch evolutionary tracks
The horizontal branch (HB) phase of stellar evolution plays a critical role in understanding the life cycle of stars, particularly in the context of low-mass stars. However, generating theoretical evolutionary tracks for HB stars is computationally intensive, especially when attempting to match a wide range of observational data. This work presents an extensive grid of HB evolutionary tracks computed using the PGPUC code, covering a broad range of chemical compositions, progenitor masses, and a…

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…

Watermarking LLM-Generated Datasets in Downstream Tasks
Yugeng Liu, Tianshuo Cong, Michael Backes, Zheng Li, Yang Zhang
https://arxiv.org/abs/2506.13494 ht…

Watermarking LLM-Generated Datasets in Downstream Tasks
Large Language Models (LLMs) have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated data to train their models. However, the inability to track content produced by LLMs poses a significant challenge, potentially leading to copyright infringement for the LLM owners. In this paper, we pr…

Interplanetary magnetic correlation and low-frequency spectrum over many solar rotations
Jiaming Wang, Francesco Pecora, Rohit Chhiber, Sohom Roy, William H. Matthaeus
https://arxiv.org/abs/2507.16053

Interplanetary magnetic correlation and low-frequency spectrum over many solar rotations
Fluctuations and structure across a wide range of spatial and temporal scales are frequently studied in the solar wind. The properties of the low-frequency fluctuations are of relevance to turbulent energy injection into the plasma and the transport of high-energy cosmic rays. Correlation analysis of decade-long intervals of interplanetary data permits study of fluctuations at time scales much longer than suitably defined correlation times, and therefore at frequencies well below those associat…

Stellar Population Astrophysics (SPA) with the TNG: NLTE atmospheric parameters and abundances of giant stars in 33 Open Clusters
M. Dal Ponte, V. D'Orazi, A. Bragaglia, A. R. Casey, N. Storm, L. Spina, J. Alonso-Santiago, G. Andreuzzi, A. Frasca, J. Kos, S. Lucatello, D. Romano, A. Vallenari, N. Vernekar
https://arxiv.org/a…

Stellar Population Astrophysics (SPA) with the TNG: NLTE atmospheric parameters and abundances of giant stars in 33 Open Clusters
Open clusters serve as important tools for accurately studying the chemical evolution of the Milky Way. By combining precise chemical data from high-resolution spectra with information on their distances and ages, we can effectively uncover the processes that have shaped our Galaxy. This study aims to derive NLTE atmospheric parameters and chemical abundances for approximately one hundred giant stars across 33 open clusters with near-solar metallicity. The clusters span a wide range of ages, en…

A Spectroscopic Search for Dormant Black Holes in Low-Metallicity Binaries
Pranav Nagarajan, Kareem El-Badry, Henrique Reggiani, Casey Y. Lam, Joshua D. Simon, Johanna M\"uller-Horn, Rhys Seeburger, Hans-Walter Rix, Howard Isaacson, Jessica Lu, Vedant Chandra, Rene Andrae
https://arxiv.org/abs/2507.12532

A Spectroscopic Search for Dormant Black Holes in Low-Metallicity Binaries
The discovery of the massive black hole (BH) system Gaia BH3 in pre-release Gaia DR4 data suggests that wide BH binaries with luminous companions may be significantly overrepresented at low metallicities. Motivated by this finding, we have initiated a spectroscopic survey of low-metallicity stars exhibiting elevated RUWE values in Gaia DR3, using the FEROS and APF spectrographs. We identify promising BH binary candidates as objects with instantaneously measured radial velocities (RVs) that are …