Tootfinder

Sources: WBD initiates a round of layoffs in its cable TV divisions, affecting well under 100 employees and largely impacting the Discovery side of the company (Peter White/Deadline)
https://deadline.com/2025/06/warner-bros-discovery-layoffs-tv-d…

Warner Bros. Discovery Initiates More Layoffs In Cable TV Divisions
Deadline understands that the company is set to layoff a number of people, particularly on the cable television side of its business.

A transiting giant planet in orbit around a 0.2-solar-mass host star: #planet orbiting tiny star challenges theories on planet formation: https://warwick.ac.uk/news/pressreleases/discovery_of_giant_planet_orbiting_tiny_star_challenges_theories_on_planet_formation and https://www.news.uliege.be/cms/c_20434166/en/a-giant-planet-around-a-tiny-star-a-discovery-that-challenges-planet-formation-theories

Nonlinear Causal Discovery for Grouped Data
Konstantin G\"obler, Tobias Windisch, Mathias Drton
https://arxiv.org/abs/2506.05120 https://

Nonlinear Causal Discovery for Grouped Data
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather than individual scalar measurements. Motivated by these applications, we extend nonlinear additive noise models to handle random vectors, establishing a two-step …

Archaeologists have announced the discovery of a 3,500-year-old city in Peru's northern Barranca province.
The city, named Peñico, is believed to have served as a key trading hub connecting early Pacific coast communities with those living in the Andes mountains and Amazon basin.
Located some 200km north of Lima, the site lies about 600 metres (1,970 feet) above sea level
and is thought to have been founded between 1,800 and 1,500 BC
- around the same time that ea…

Archaeologists discover 3,500-year-old city in Peru
Researchers believe Peñico served as a trading hub linking ancient Pacific coast communities with those living in the Andes and Amazon.

A comet the size of a small city... just passing by our neighborhood onto the next solar system in a few million years.
#astronomy #comet #interstellar
https://www.cbc.ca/news/science/a11pl3z-discovery-interstellar-object-1.7575576

😡
https://www.independent.co.uk/news/world/americas/discovery-space-shuttle-smithsonian-texas-trump-bill-b2782278.html

Space Shuttle is set to be moved out of Smithsonian and to Texas thanks to Trump’s tax bill
President Donald Trump’s bill will allocate $85 million to transport the Discovery space shuttle from the Smithsonian’s Air and Space Museum to Space Center Houston

This https://arxiv.org/abs/2506.02779 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Predictions of the LSST Solar System Yield: Discovery Rates and Characterizations of Centaurs
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will start by the end of 2025 and operate for ten years, offering billions of observations of the southern night sky. One of its main science goals is to create an inventory of the Solar System, allowing for a more detailed understanding of small body populations including the Centaurs, which will benefit from the survey's high cadence and depth. In this paper, we establish the first discovery limits for Centaurs throughout th…

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Anagha Savit, Harikrishna Sahu, Shivank Shukla, Wei Xiong, Rampi Ramprasad
https://arxiv.org/abs/2506.04233

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design …

twitter_higgs: Twitter, Higgs boson (2012)
Data on tweets related to the announcement of the discovery of a new fundamental particle with the features of the Higgs boson on 4th July 2012. Data covers 1-7 July 2012, and includes four types of networks: followers, retweets, replies, and mentions.
This network has 304691 nodes and 563069 edges.
Tags: Social, Online, Weighted, Multilayer

Beyond Diamond: Interpretable Machine Learning Discovery of Coherent Quantum Defect Hosts in Semiconductors
Mohammed Mahshook, Rudra Banerjee
https://arxiv.org/abs/2506.03844

Beyond Diamond: Interpretable Machine Learning Discovery of Coherent Quantum Defect Hosts in Semiconductors
Quantum point defects in wide bandgap semiconductors-such as the nitrogen-vacancy (NV) center in diamond are leading candidates for solid-state spin qubits due to their long coherence times and optically addressable spin states. Yet, identifying host materials capable of supporting such deep-level defects remains a significant challenge, owing to the complex interplay between chemical composition, crystal structure, and electronic environment. Here, we present a scalable, interpretable machine …

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
Miguel \'Angel de Carvalho Servia (Mimi), Ilya Orson Sandoval (Mimi), King Kuok (Mimi), Hii, Klaus Hellgardt, Dongda Zhang, Ehecatl Antonio del Rio Chanona
https://arxiv.org/abs/2507.02730

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand extensive domain expertise, while many data-driven approaches often lack interpretability and fail to enforce physical consistency. To overcome these limitations, we propose the Physics-Informed Automated Discovery of Kinetics (PI-ADoK) framework. By integrating physical constraints directly into a symbolic regression app…

#Archaeology #Neanderthals Ran “Fat Factories” 125,000 Years Ago
Groundbreaking discovery in Germany reveals large-scale fat processing by Neanderthals: Fat is a very valuable food, packed with calories, especially important when other resources might be scarce. Our earliest ancestors in Africa…

This https://arxiv.org/abs/2501.17753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Feasibility study of true muonium observation with the existing Belle-II dataset
True muonium ($μ^+μ^-$) is one of the cleanest bound states, being composed only of leptons, along with true tauonium and positronium. Unlike the latter, true muonium and true tauonium have not been observed so far. This article shows that the spin-0 state of true muonium (para-TM), decaying into two photons, can be observed at a discovery level of significance in the dataset already collected by the Belle-II experiment at the $Υ(4S)$ peak, with certain assumptions on systematic uncertaintie…

HMS Endeavour confirmed found in Rhode Island!
https://arstechnica.com/science/2025/06/discovery-of-hms-endeavor-wreck-confirmed/

Discovery of HMS Endeavour wreck confirmed
“The timbers are British timbers. The size of all the timber scantlings are almost identical to Endeavour.”…

Qualcomm releases patches to fix a series of vulnerabilities in dozens of chips, including three zero-days that "may be under limited, targeted exploitation" (Lorenzo Franceschi-Bicchierai/TechCrunch)
https://techcrunch.com/2025/06/03/phon

Phone chipmaker Qualcomm fixes three zero-days exploited by hackers | TechCrunch
Google's Threat Analysis Group, which investigates government-backed hacks, was credited with the discovery of the zero-days.

This https://arxiv.org/abs/2411.09740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Type Ia supernovae
Type Ia supernovae (SNe Ia) correspond to the thermonuclear explosion of a carbon-oxygen white dwarf (C-O WD) star in a binary system, triggered by the accretion of material from another star, or the merger/collision with a secondary WD. Their phenomenal luminosity -- several billion times that of the sun -- has motivated their use as cosmological distance indicators and led to the discovery of the accelerated expansion of the universe. SNe Ia are also the main producers of iron and hence play …

Well this was a fun discovery.
Let's see how many of you get this right.
If you read MDIO address 0x0 of a gigabit Ethernet PHY and read the values in bits 13/6 and 8 (speed and duplex state), do you get:
1) The last value you wrote to the register, regardless of actual link conditions
2) The actual current speed/duplex state the PHY is operating in
3) Something else / undefined

The Centre of Attention: a Powerful Radio Galaxy Pinpoints a NIR-Dark Protocluster at z~3.9
A. J. Hedge, N. Seymour, J. W. Broderick, A. Gupta, J. Afonso, L. Ighina, M. Lehnert, G. Noirot, S. Shabala, D. Stern, R. J. Turner
https://arxiv.org/abs/2506.04747

The Centre of Attention: a Powerful Radio Galaxy Pinpoints a NIR-Dark Protocluster at z~3.9
We report the discovery of a $z\sim3.9$ protocluster identified from Atacama Large Millimetre/sub-millimetre Array Band 3 spectral scans of a bright radio source selected from the GaLactic and Extra-galactic All-sky Murchison Widefield Array (GLEAM) survey. Extended CO(4-3) and [CI](1-0) line emission was detected in GLEAM J005332$-$325630 confirming it to be a $z=3.879$ powerful radio galaxy with luminosity, $L_{500 MHz}=1.3\times10^{28}$ ${W Hz}^{-1}$. This source is part of a sample of candi…

Future Slot Prediction for Unsupervised Object Discovery in Surgical Video
Guiqiu Liao, Matjaz Jogan, Marcel Hussing, Edward Zhang, Eric Eaton, Daniel A. Hashimoto
https://arxiv.org/abs/2507.01882

Future Slot Prediction for Unsupervised Object Discovery in Surgical Video
Object-centric slot attention is an emerging paradigm for unsupervised learning of structured, interpretable object-centric representations (slots). This enables effective reasoning about objects and events at a low computational cost and is thus applicable to critical healthcare applications, such as real-time interpretation of surgical video. The heterogeneous scenes in real-world applications like surgery are, however, difficult to parse into a meaningful set of slots. Current approaches wit…

This https://arxiv.org/abs/2505.14613 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Virtual Cells: Predict, Explain, Discover
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researchers to generate and test large numbers of therapeutic hypotheses safely and economically before initiating costly clinical trials. Even a more specific model that predicts the functional response of cells to a wide range of perturbations would be tremendously valuable for disco…

Discovery of the Type-II Superconductor Ta$_4$Rh$_2$C$_{1-\delta}$ with a High Upper Critical Field
KeYuan Ma, Sara L\'opez-Paz, Karolina Gornicka, Harald O. Jeschke, Tomasz Klimczuk, Fabian O. von Rohr
https://arxiv.org/abs/2506.02209

Discovery of the Type-II Superconductor Ta$_4$Rh$_2$C$_{1-δ}$ with a High Upper Critical Field
We report on the discovery of superconductivity in the previously unknown compound Ta$_4$Rh$_2$C$_{1-δ}$. Ta$_4$Rh$_2$C$_{1-δ}$ crystallizes in the $η$-carbide structure type, in the cubic space group $Fd\bar{3}m$ (No.227) with a unit cell parameter of $a = $ 11.7947 Å. Temperature-dependent magnetic susceptibility, resistivity, and specific heat capacity measurements reveal that Ta$_4$Rh$_2$C$_{1-δ}$ is a type-II bulk superconductor with a critical temperature of $T_{\rm c}$ = 6.4 K, and …

e-GAI: e-value-based Generalized $\alpha$-Investing for Online False Discovery Rate Control
Yifan Zhang, Zijian Wei, Haojie Ren, Changliang Zou
https://arxiv.org/abs/2506.01452

e-GAI: e-value-based Generalized $α$-Investing for Online False Discovery Rate Control
Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art generalized $α$-investing (GAI) algorithms can control online false discovery rate (FDR) on p-values only under specific dependence structures, a situation that rarely occurs in practice. The e-LOND algorithm (Xu & Ramdas, 2024) utilizes e-values to achieve online FDR control under arbitrary dependence but suffers from …

GOTO065054 593624 - an 8.5 mag amplitude dwarf nova identified in real time via Kilonova Seekers: #Kilonova Seekers - the GOTO project for real-time citizen science in time-domain astrophysics: https://academic.oup.com/mnras/article/533/2/2113/7735340?login=false -> A citizen science project has already identified 20 new astronomical discoveries: https://www.port.ac.uk/news-events-and-blogs/news/citizen-science-project-identifies-20-new-astronomical-discoveries

Enhancing Drug Discovery: Autoencoder-Based Latent Space Augmentation for Improved Molecular Solubility Prediction using LatMixSol
Mohammad Saleh Hasankhani
https://arxiv.org/abs/2506.00223

Enhancing Drug Discovery: Autoencoder-Based Latent Space Augmentation for Improved Molecular Solubility Prediction using LatMixSol
Accurate prediction of molecular solubility is a cornerstone of early-stage drug discovery, yet conventional machine learning models face significant challenges due to limited labeled data and the high-dimensional nature of molecular descriptors. To address these issues, we propose LatMixSol, a novel latent space augmentation framework that combines autoencoder-based feature compression with guided interpolation to enrich training data. Our approach first encodes molecular descriptors into a lo…

MIR: Methodology Inspiration Retrieval for Scientific Research Problems
Aniketh Garikaparthi, Manasi Patwardhan, Aditya Sanjiv Kanade, Aman Hassan, Lovekesh Vig, Arman Cohan
https://arxiv.org/abs/2506.00249

MIR: Methodology Inspiration Retrieval for Scientific Research Problems
There has been a surge of interest in harnessing the reasoning capabilities of Large Language Models (LLMs) to accelerate scientific discovery. While existing approaches rely on grounding the discovery process within the relevant literature, effectiveness varies significantly with the quality and nature of the retrieved literature. We address the challenge of retrieving prior work whose concepts can inspire solutions for a given research problem, a task we define as Methodology Inspiration Retr…

Discovery and study of the eclipsing variable star Grigoriev 1
V. S. Grigoriev, D. V. Denisenko
https://arxiv.org/abs/2507.01005 https://

Discovery and study of the eclipsing variable star Grigoriev 1
This work is dedicated to the discovery of the eclipsing variable star Grigoriev 1 and determining the parameters of its binary system. The star was found through the systematic checking of ultraviolet sources of GALEX space observatory in Pegasus constellation. Analysis of the ZTF project data has shown the presence of eclipses with two magnitudes depth and a period of 6.5997 days. The duration of eclipse covers only 1% of the orbital period, and the partial phases are at least 30 times shorte…

In a filing, the Newhouse family, which owns Condé Nast, says it plans to sell 100M WBD shares worth around $1.1B total; $WBD falls 4% (Lucas Manfredi/The Wrap)
https://www.thewrap.com/warner-bros-discovery-stock-sale-newhouse-family/

Warner Bros. Discovery Stock Falls as Newhouse Family Sells $1.1 Billion in Shares
The media giant's shares tumbled over 4% during Tuesday's trading session after the Condé Nast owners sold 100 million shares

"The Hague Declaration on Knowledge Discovery in the Digital Age" was published around 10 years ago. Very interesting, how things have changed since then. TDM for research is still in its infancy, and reuse in the context of #ai has caused a backlash against most of the 6 principles of the Declaration. Tellingly, the Declaration is only accessible via the internet archive:

This https://arxiv.org/abs/2411.16666 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors
We introduce CatNet, an algorithm that effectively controls False Discovery Rate (FDR) and selects significant features in LSTM. CatNet employs the derivative of SHAP values to quantify the feature importance, and constructs a vector-formed mirror statistic for FDR control with the Gaussian Mirror algorithm. To avoid instability due to nonlinear or temporal correlations among features, we also propose a new kernel-based independence measure. CatNet performs robustly on different model settings …

Patterns Within the Markov Tree
Robert A. Gore
https://arxiv.org/abs/2506.04299 https://arxiv.org/pdf/2506.04299

Patterns Within the Markov Tree
An analysis of the Markov tree is presented. Markov triplets, {x,R,z}, are the positive integer solutions to the Diophantine equation x2 + R2 + z2 = 3xRz. Inspired by patterns of the Fibonacci and Pell triplets in Region 1 and Region 2 of the tree, an investigation of interior regions of the Markov tree finds generating functions and sequence functions for all triplets of all regions. These sequence functions lead to the discovery of a Pell equation for the Markov region numbers along the edges…

Automatic discovery of optimal meta-solvers for time-dependent nonlinear PDEs
Youngkyu Lee, Shanqing Liu, Jerome Darbon, George Em Karniadakis
https://arxiv.org/abs/2507.00278

Automatic discovery of optimal meta-solvers for time-dependent nonlinear PDEs
We present a general and scalable framework for the automated discovery of optimal meta-solvers for the solution of time-dependent nonlinear partial differential equations after appropriate discretization. By integrating classical numerical methods (e.g., Krylov-based methods) with modern deep learning components, such as neural operators, our approach enables flexible, on-demand solver design tailored to specific problem classes and objectives. The fast solvers tackle the large linear system r…

Running Quantum Computers in Discovery Mode
Benedikt Placke, G. J. Sreejith, Alessio Lerose, S. L. Sondhi
https://arxiv.org/abs/2507.01013 https://

Running Quantum Computers in Discovery Mode
We propose using quantum computers in conjunction with classical machine learning to discover instances of interesting quantum many-body dynamics. Concretely, an ``interest function'' is defined for a given circuit (family) instance that can be evaluated on a quantum computer. The circuit is then adapted by a classical learning agent to maximize interest. We illustrate this approach using two examples and show numerically that, within a sufficiently general circuit family, two simple interest f…

📝 #NoKings Event in Syracuse
https://indigenousvalues.org/event-the-doctrine-of-christian-discovery-the-jesuits-and-laudato-si-defending-mother-earth/
!Event Flyer (https://cdn.some.pics/adjb/68422c8071720.png)

This https://arxiv.org/abs/2505.05227 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Something about the Mechanism of Induction Phenomena: The forgotten work of Berta de Haas-Lorentz on diamagnetism in superconductors
In 1925, Dr. Geertruida Luberta "Berta" de Haas-Lorentz published the paper "Iets over het mechanisme van inductieverschijnselen" in the journal Physica. Her paper was the first to discuss perfect diamagnetism of superconductors, eight years before the discovery of the Meissner effect, when the essential difference between the two phenomena was not understood. Unfortunately, her work was almost forgotten by the scientific community. To counter this, we translate her seminal 1925 paper from Dutc…

Warner Bros. Discovery: Aufspaltung angekündigt
In einer umfassenden Umstrukturierung trennt der US-Konzern seine Filmstudios samt Streaming vom klassischen Kabelsender-Geschäft.
https://www.

Tunnelling Through Time Series: A Probabilistic Visibility Graph for Local and Global Pattern Discovery
Roberto Sotero, Jose Sanchez-Bornot
https://arxiv.org/abs/2507.01247

Tunnelling Through Time Series: A Probabilistic Visibility Graph for Local and Global Pattern Discovery
The growing availability of high-resolution, long-term time series data has highlighted the need for methods capable of capturing both local and global patterns. To address this, we introduce the Probabilistic Visibility Graph (PVG), a novel approach inspired by the quantum tunnelling phenomenon. The PVG extends the classical Visibility Graph (VG) by introducing probabilistic connections between time points that are obstructed in the VG due to intermediate values. We demonstrate the PVG's effec…

Integrating computational detection and experimental validation for rapid GFRAL-specific antibody discovery
Maria Francesca Abbate, Pierre Toxe, Nicolas Maestrali, Marie Gagnaire, Emmanuelle Vigne, Melody A. Shahsavarian, Thierry Mora, Aleksandra M. Walczak
https://arxiv.org/abs/2506.01995…

Integrating computational detection and experimental validation for rapid GFRAL-specific antibody discovery
The identification and validation of therapeutic antibodies is critical for developing effective treatments for many diseases. We present a computational approach for identifying antibodies targeting GFRAL-specific receptors, receptors implicated in appetite regulation. Using humanized Trianni mice, we conducted a longitudinal study with repeated blood sampling and splenic analysis. We applied the STAR computational method for antibody discovery on bulk antibody repertoire data sampled at key t…

QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration
Junze Hu, Xiangyu Jin, Yizhe Zeng, Yuling Liu, Yunpeng Li, Dan Du, Kaiyu Xie, Hongsong Zhu
https://arxiv.org/abs/2506.23644

QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration
We introduce QLPro, a vulnerability detection framework that systematically integrates LLMs and static analysis tools to enable comprehensive vulnerability detection across entire open-source projects.We constructed a new dataset, JavaTest, comprising 10 open-source projects from GitHub with 62 confirmed vulnerabilities. CodeQL, a state-of-the-art static analysis tool, detected only 24 of these vulnerabilities while QLPro detected 41. Furthermore, QLPro discovered 6 previously unknown vulnerabi…

Predictions of the LSST Solar System Yield: Discovery Rates and Characterizations of Centaurs
Joseph Murtagh, Megan E. Schwamb, Stephanie R. Merritt, Pedro H. Bernardinelli, Jacob A. Kurlander, Samuel Cornwall, Mario Juri\'c, Grigori Fedorets, Matthew J. Holman, Siegfried Eggl, David Nesvorn\'y, Kathryn Volk, R. Lynne Jones, Peter Yoachim, Joachim Moeyens, Jeremy Kubica, Drew Oldag, Maxine West, Colin Orion Chandler

Predictions of the LSST Solar System Yield: Discovery Rates and Characterizations of Centaurs
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will start by the end of 2025 and operate for ten years, offering billions of observations of the southern night sky. One of its main science goals is to create an inventory of the Solar System, allowing for a more detailed understanding of small body populations including the Centaurs, which will benefit from the survey's high cadence and depth. In this paper, we establish the first discovery limits for Centaurs throughout th…

Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics
Mustafa Demir, Jacob Miratsky, Jonathan Nguyen, Chun Kit Chan, Punya Mishra, Abhishek Singharoy
https://arxiv.org/abs/2506.22520

Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics
This study examines the impact of an Artificial Intelligence tutor teammate (AI) on student curiosity-driven engagement and learning effectiveness during Interactive Molecular Dynamics (IMD) tasks on the Visual Molecular Dynamics platform. It explores the role of the AI's curiosity-triggering and response behaviors in stimulating and sustaining student curiosity, affecting the frequency and complexity of student-initiated questions. The study further assesses how AI interventions shape student …

"Comparative review on Primo Research Assistant, Scopus AI, Web of Science Research Assistant and a explainer for AI search for librarians" by Aaron Tay: https://musingsaboutlibrarianship.blogspot.com/2025/05/comparative-review-…

Fast Luminous Extragalactic Transients in the VLA Sky Survey: Implications for the rates of Accretion-Induced Collapse Events, Fast Blue Optical Transients and Gamma Ray Burst Afterglows
Kritti Sharma, Vikram Ravi, Dillon Z. Dong, Gregg Hallinan, Casey Law, Delina Levine, Jean J. Somalwar, Jessie Miller, Nikita Kosogorov, Steven T. Myers
https://

Fast Luminous Extragalactic Transients in the VLA Sky Survey: Implications for the rates of Accretion-Induced Collapse Events, Fast Blue Optical Transients and Gamma Ray Burst Afterglows
Radio wavelengths offer a unique window into high-energy astrophysical phenomena that may be obscured or too rapidly evolving to be captured at other wavelengths. Leveraging data from the Very Large Array Sky Survey, we perform a systematic search for fast, luminous transients with characteristic timescales $\lesssim 3$ years in the nearby universe ($z \leq 0.3$). We report the discovery of five such transients, and classify them based on their synchrotron emission energetics and host galaxy pr…

A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE). XIX. The discovery of a spectacular 230 kpc Halpha tail following NGC 4569 in the Virgo cluster
M. Sun, H. Le, B. Epinat, A. Boselli, R. Luo, K. Hosogi, N. Pichette, W. Forman, C. Sarazin, M. Fossati, H. Chen, E. Sarpa, J. Braine, J. C. Cuillandre, S. Gwyn, G. Hensler, S. Martocchia, B. Vollmer

A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE). XIX. The discovery of a spectacular 230 kpc Halpha tail following NGC 4569 in the Virgo cluster
Context. Galaxies fly inside galaxy clusters and ram pressure by the ICM can remove a large amount of the ISM from the galaxy, and deposit the gas in the ICM. The ISM decoupled from the host galaxy leaves a long trail following the moving galaxy. Such long trails track the galaxy motion and can be detected with sensitive data in Halpha. Aims. We study the Halpha tail trailing NGC 4569 in the Virgo cluster. Methods. The initial discovery was made with the deep Halpha imaging data with CFHT, …

This https://arxiv.org/abs/2502.05206 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Safety at Scale: A Comprehensive Survey of Large Model Safety
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI). These models are now foundational to a wide range of applications, including conversational AI, recommendation systems, autonomous driving, content generation, medical diagnostics, and scientific discovery. However, their widespread deployment also exposes them to significant safety risks, raisin…

Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
Michael A. Kraus, Helen Bartsch
https://arxiv.org/abs/2507.02005

Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
This research introduces a unified approach combining Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to predict fatigue strength in welded transverse stiffener details. It integrates expert-driven feature engineering with algorithmic feature creation to enhance accuracy and explainability. Based on the extensive fatigue test database regression models - gradient boosting, random forests, and neural networks - were trained using AutoML under three feature sc…

STELLA: Towards Protein Function Prediction with Multimodal LLMs Integrating Sequence-Structure Representations
Hongwang Xiao, Wenjun Lin, Xi Chen, Hui Wang, Kai Chen, Jiashan Li, Yuancheng Sun, Sicheng Dai, Boya Wu, Qiwei Ye
https://arxiv.org/abs/2506.03800

STELLA: Towards Protein Function Prediction with Multimodal LLMs Integrating Sequence-Structure Representations
Protein biology focuses on the intricate relationships among sequences, structures, and functions. Deciphering protein functions is crucial for understanding biological processes, advancing drug discovery, and enabling synthetic biology applications. Since protein sequences determine tertiary structures, which in turn govern functions, integrating sequence and structure information is essential for accurate prediction of protein functions. Traditional protein language models (pLMs) have advance…

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Kevin Han, Bowen Deng, Amir Barati Farimani, Gerbrand Ceder
https://arxiv.org/abs/2506.02023

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses a challenging, but promising approach to further extending simulation scales to real-world applications. In this…

Memo: David Leavy, who became CNN's COO in 2023, is leaving; sources say the longtime key lieutenant of David Zaslav is returning to Warner Bros. Discovery (Brian Steinberg/Variety)
https://variety.com/2025/tv/news/david-leavy-cnn…

David Leavy, CNN's COO, Will Return to Warner Bros. Discovery
David Leavy will leave CNN and return to duties at corporate parent Warner Bros. Discovery, where he has long been a key aide to CEO David Zaslav

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Rafael Ferreira da Silva, Milad Abolhasani, Dionysios A. Antonopoulos, Laura Biven, Ryan Coffee, Ian T. Foster, Leslie Hamilton, Shantenu Jha, Theresa Mayer, Benjamin Mintz, Robert G. Moore, Salahudin Nimer, Noah Paulson, Woong Shin, Frederic Suter, Mitra Taheri, Michela Taufer, Newell R. Washburn

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institu…

This https://arxiv.org/abs/1402.6774 has been replaced.
link: https://scholar.google.com/scholar?q=a

Local Electronic Structure around a Single Impurity in an Anderson Lattice Model for Topological Kondo Insulators
Shortly after the discovery of topological band insulators, the topological Kondo insulators (TKIs) have also been theoretically predicted. The latter has ignited revival interest in the properties of Kondo insulators. Currently, the feasibility of topological nature in SmB$_6$ has been intensively analyzed by several complementary probes. Here by starting with a minimal-orbital Anderson lattice model, we explore the local electronic structure in a Kondo insulator. We show that the two strong t…

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
Apoorv Verma, Junaid Jami, Amrita Bhattacharya
https://arxiv.org/abs/2507.01913

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and design of next-generation magnetic materials. In this work, a refined descriptor is proposed that significantly improves the prediction of two critical magnetic properties -- magnetic ordering (Ferromagnetic vs. Ferrimagnetic) and magnetic moment per atom -- using only the stru…

Image-based physical characterization of magnetotactic bacteria from an environmental sample
Mara Smite, Mihails Birjukovs, Mila Sirinelli-Kojadinovic, Sandrine Grosse, Agnese Kokina, Janis Liepins, Dita Gudra, Megija Lunge, Davids Fridmanis, Mihaly Posfai, Andrejs Cebers, Damien Faivre, Guntars Kitenbergs
https://arxiv.org/abs/…

Image-based physical characterization of magnetotactic bacteria from an environmental sample
Magnetotactic bacteria (MTB) are a diverse group of microorganisms that are able to biomineralize magnetic nanoparticles. Most MTB remain uncultured, making population-level characterization from natural environments difficult. We report the discovery of a new and diverse MTB-rich site in the Ogre River, Latvia, and present an integrated approach combining 16S rRNA sequencing, transmission electron microscopy, and novel open-source, automated image-based physical methods to characterize bacteri…

Characterizing circle graphs with binomial partial Petrial polynomials
Ruiqing Feng, Qi Yan, Xuan Zheng
https://arxiv.org/abs/2507.02421 https://

Characterizing circle graphs with binomial partial Petrial polynomials
The partial Petrial polynomial was first introduced by Gross, Mansour, and Tucker as a generating function that enumerates the Euler genera of all possible partial Petrials on a ribbon graph. Yan and Li later extended this polynomial invariant to circle graphs by utilizing the correspondence between circle graphs and bouquets. Their explicit computation demonstrated that paths produce binomial polynomials, specifically those containing exactly two non-zero terms. This discovery led them to pose…

Daydream, which raised a $50M seed in June 2024 to build a generative AI shopping agent for fashion, launches in beta, with an app expected later this summer (Hilary Milnes/Vogue Business)
https://www.voguebusiness.com/story/techno

Is Daydream’s AI platform the answer to fashion’s discovery problem?
Founder Julie Bornstein raised $50 million last year to create the ultimate AI shopping experience. Today, it launches in beta.

A tribute to Albert Hofmann on his 100th birthday, from January 2006.
https://www.researchgate.net/publication/278300661_A_tribute_to_Albert_Hofman…

twitter_higgs: Twitter, Higgs boson (2012)
Data on tweets related to the announcement of the discovery of a new fundamental particle with the features of the Higgs boson on 4th July 2012. Data covers 1-7 July 2012, and includes four types of networks: followers, retweets, replies, and mentions.
This network has 116408 nodes and 150818 edges.
Tags: Social, Online, Weighted, Multilayer

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Harsh Goel, Mohammad Omama, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Sandeep Chinchali
https://arxiv.org/abs/2505.24265

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has achieved significant progress in large-scale traffic control, autonomous vehicles, and robotics. Drawing inspiration from biological systems where roles naturally emerge to enable coordination, role-based MARL methods have been proposed to enhance cooperation learning for complex tasks. However, existing methods exclusively derive roles from an agent's past experience during training, neglecting their influence on its future trajectories. This paper…

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
Madan Krishnamurthy, Daniel Korn, Melissa A Haendel, Christopher J Mungall, Anne E Thessen
https://arxiv.org/abs/2506.02160

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
This research aims to develop a dynamic and scalable framework to facilitate harmonization of Common Data Elements (CDEs) across heterogeneous biomedical datasets by addressing challenges such as semantic heterogeneity, structural variability, and context dependence to streamline integration, enhance interoperability, and accelerate scientific discovery. Our methodology leverages Large Language Models (LLMs) for context-aware text embeddings that convert CDEs into dense vectors capturing semant…

This https://arxiv.org/abs/2505.00393 has been replaced.
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S3AND: Efficient Subgraph Similarity Search Under Aggregated Neighbor Difference Semantics (Technical Report)
For the past decades, the \textit{subgraph similarity search} over a large-scale data graph has become increasingly important and crucial in many real-world applications, such as social network analysis, bioinformatics network analytics, knowledge graph discovery, and many others. While previous works on subgraph similarity search used various graph similarity metrics such as the graph isomorphism, graph edit distance, and so on, in this paper, we propose a novel problem, namely \textit{subgrap…

This https://arxiv.org/abs/2505.11224 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Tracing the formation and migration history: molecular signatures in the atmosphere of misaligned hot Jupiter WASP-94Ab using JWST NIRSpec/G395H
The discovery of hot Jupiters that orbit very close to their host stars has long challenged traditional models of planetary formation and migration. Characterising their atmospheric composition - mainly in the form of the carbon-to-oxygen (C/O) ratio and metallicity - can provide insights into their formation locations and evolution pathways. With JWST we can characterise the atmospheres of these types of planets more precisely than previously possible, primarily because it allows us to determi…

Constraining compact dark matter with time-varying quasar equivalent widths
Georgios Vernardos, James Hung Hsu Chan, Frederic Courbin
https://arxiv.org/abs/2507.02046

Constraining compact dark matter with time-varying quasar equivalent widths
One of the possible explanations for dark matter is that of compact dark objects of baryonic origin, such as black holes or even planets. Accumulating evidence, including the discovery of merging stellar mass black holes through gravitational waves, point to a population of such objects making up at least some fraction of dark matter. We revisit a historically heavily used probe, quasar spectra, from the new perspective of time variability and gravitational lensing. From a sample of 777 quasars…

Dienstag: Warner Bros. Discovery wird aufgespalten, Neuheiten bei Apple
WBD trennt Streaming- und Kabel-Sparte "Liquid Glass"-Design Altersgrenze für Social Media Durchbruch für E-Flugzeuge Leitlinien für Datenübermittlung

Controlling the false discovery rate under a non-parametric graphical dependence model
Drew T. Nguyen, William Fithian
https://arxiv.org/abs/2506.24126 htt…

Controlling the false discovery rate under a non-parametric graphical dependence model
We propose sufficient conditions and computationally efficient procedures for false discovery rate control in multiple testing when the $p$-values are related by a known \emph{dependency graph} -- meaning that we assume independence of $p$-values that are not within each other's neighborhoods, but otherwise leave the dependence unspecified. Our methods' rejection sets coincide with that of the Benjamini--Hochberg (BH) procedure whenever there are no edges between BH rejections, and we find in s…

This https://arxiv.org/abs/2505.24584 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams
Recent advancements in generative AI have accelerated the discovery of novel chemicals and materials; however, transitioning these discoveries to industrial-scale production remains a critical bottleneck, as it requires the development of entirely new chemical manufacturing processes. Current AI methods cannot auto-generate PFDs or PIDs, despite their critical role in scaling chemical processes, while adhering to engineering constraints. We present a closed loop, physics aware framework for the…

This https://arxiv.org/abs/2505.15998 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics
We present a method for the automated discovery of system-level dynamics in Flow-Lenia$-$a continuous cellular automaton (CA) with mass conservation and parameter localization$-$using a curiosity-driven AI scientist. This method aims to uncover processes leading to self-organization of evolutionary and ecosystemic dynamics in CAs. We build on previous work which uses diversity search algorithms in Lenia to find self-organized individual patterns, and extend it to large environments that support…

High-Throughput NEB for Li-Ion Conductor Discovery via Fine-Tuned CHGNet Potential
Jingchen Lian, Xiao Fu, Xuhe Gong, Ruijuan Xiao, Hong Li
https://arxiv.org/abs/2507.02334

High-Throughput NEB for Li-Ion Conductor Discovery via Fine-Tuned CHGNet Potential
Solid-state electrolytes are essential in the development of all-solid-state batteries. While density functional theory (DFT)-based nudged elastic band (NEB) and ab initio molecular dynamics (AIMD) methods provide fundamental insights on lithium-ion migration barriers and ionic conductivity, their computational costs make large-scale materials exploration challenging. In this study, we developed a high-throughput NEB computational framework integrated with the fine-tuned universal machine learn…

Centaur Nuclei: Sizes, Shapes, Spins, and Structure
Y. R. Fernandez, M. W. Buie, P. Lacerda, R. Marschall
https://arxiv.org/abs/2506.04483 https://

Centaur Nuclei: Sizes, Shapes, Spins, and Structure
We present a wide-ranging but in-depth analysis of Centaurs, focusing on their physical and structural aspects. Centaurs, originating from the Scattered Disk and Kuiper Belt, play a crucial role in our understanding of Solar System evolution. We first examine how biases in discovery and measurement affect our understanding of the Centaur size distribution. In particular we address the strong dependence of the census on perihelion distance and the broad distribution of Centaur geometric albedos.…

Canceling the elastic Poynting effect with geometry
M. Destrade, Y. Du, J. Blackwell, N. Colgan, V. Balbi
https://arxiv.org/abs/2506.03821 https://

Canceling the elastic Poynting effect with geometry
The Poynting effect is a paragon of nonlinear soft matter mechanics. It is the tendency (found in all incompressible, isotropic, hyperelastic solids) exhibited by a soft block to expand vertically when sheared horizontally. It can be observed whenever the length of the cuboid is at least four times its thickness. Here we show that the Poynting effect can be easily reversed and the cuboid can shrink vertically, simply by reducing this aspect ratio. In principle, this discovery means that for a g…

Evaluating PDE discovery methods for multiscale modeling of biological signals
Andr\'ea Ducos (AISTROSIGHT), Audrey Denizot (AISTROSIGHT), Thomas Guyet (AISTROSIGHT), Hugues Berry (AISTROSIGHT)
https://arxiv.org/abs/2506.20694

Evaluating PDE discovery methods for multiscale modeling of biological signals
Biological systems are non-linear, include unobserved variables and the physical principles that govern their dynamics are partly unknown. This makes the characterization of their behavior very challenging. Notably, their activity occurs on multiple interdependent spatial and temporal scales that require linking mechanisms across scales. To address the challenge of bridging gaps between scales, we leverage partial differential equations (PDE) discovery. PDE discovery suggests meso-scale dynamic…

Usability Evaluation of Cloud for HPC Applications
Vanessa Sochat, Daniel Milroy, Abhik Sarkar, Aniruddha Marathe
https://arxiv.org/abs/2506.02709 https://…

Usability Evaluation of Cloud for HPC Applications
The rise of AI and the economic dominance of cloud computing have created a new nexus of innovation for high performance computing (HPC), which has a long history of driving scientific discovery. In addition to performance needs, scientific workflows increasingly demand capabilities of cloud environments: portability, reproducibility, dynamism, and automation. As converged cloud environments emerge, there is growing need to study their fit for HPC use cases. Here we present a cross-platform usa…

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
Xiao-Qi Han, Ze-Feng Gao, Xin-De Wang, Zhenfeng Ouyang, Peng-Jie Guo, Zhong-Yi Lu
https://arxiv.org/abs/2506.03837

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of…

Discovery of the Hybrid Response of Photoionized Gases
Zhicheng He, Tinggui Wang, Gary J. Ferland
https://arxiv.org/abs/2505.24242 https://

Discovery of the Hybrid Response of Photoionized Gases
Photoionized gases are prevalent throughout the universe. In such gases, the ion concentration typically exhibits two response modes to radiation: a positive response in the low-ionization state and a negative response in the high-ionization state. Here, we report the discovery of a widespread misalignment at the boundary between the above two response modes, and identify a third mode-the hybrid response-through time-dependent photoionization simulations. This phenomenon arises from the asynchr…

Why is Trump all Stick and no Carrot?

Trump must offer Iran more than bombs, rage and humiliation
https://www.economist…

Trump must offer Iran more than bombs, rage and humiliation
How to prevent an inferno in the Middle East

Data-Driven Self-Supervised Learning for the Discovery of Solution Singularity for Partial Differential Equations
Difeng Cai, Paulina Sep\'ulveda
https://arxiv.org/abs/2506.23344

Data-Driven Self-Supervised Learning for the Discovery of Solution Singularity for Partial Differential Equations
The appearance of singularities in the function of interest constitutes a fundamental challenge in scientific computing. It can significantly undermine the effectiveness of numerical schemes for function approximation, numerical integration, and the solution of partial differential equations (PDEs), etc. The problem becomes more sophisticated if the location of the singularity is unknown, which is often encountered in solving PDEs. Detecting the singularity is therefore critical for developing …

EGNInfoLeaker: Unveiling the Risks of Public Key Reuse and User Identity Leakage in Blockchain
Chenyu Li, Xueping Liang, Xiaorui Gong, Xiu Zhang
https://arxiv.org/abs/2507.01635

EGNInfoLeaker: Unveiling the Risks of Public Key Reuse and User Identity Leakage in Blockchain
While Ethereum's discovery protocols (Discv4/ Discv5) incorporate robust cryptographic designs to protect user privacy, real-world deployment reveals critical vulnerabilities when users deviate from security guidelines. In this paper, we design a system called EGNInfoLeaker. Our study is the first work that uncovers widespread public key reuse across Ethereum's peer-to-peer networks - a practice that fundamentally undermines the protocol's privacy guarantees. Through systematic analysis of 300 …

📝 S05E08: Teaching Uncomfortable History: The Doctrine of Discovery |https://podcast.doctrineofdiscovery.org/season5/episode-08/

Spin-glass state in nickelate superconductors
David R. Saykin, Martin Gonzalez, Jennifer Fowlie, Steven A. Kivelson, Harold Hwang, Aharon Kapitulnik
https://arxiv.org/abs/2506.03262

Spin-glass state in nickelate superconductors
Magneto-optical measurements in La${}_{0.8}$Sr${}_{0.2}$NiO${}_2$ and Nd${}_{0.825}$Sr${}_{0.175}$NiO${}_2$ reveal an intriguing new facet of infinite-layer nickelate superconductors: the onset of spin-glass behavior at a temperature far exceeding the superconducting critical temperature $T_c$. This discovery sharply contrasts with copper oxide superconductors, where magnetism and superconductivity remain largely exclusive. Moreover, the magnitude and onset temperature of the polar Kerr effect …

This https://arxiv.org/abs/2505.20688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data
False discovery rate (FDR) control methods are essential for voxel-wise multiple testing in neuroimaging data analysis, where hundreds of thousands or even millions of tests are conducted to detect brain regions associated with disease-related changes. Classical FDR control methods (e.g., BH, q-value, and LocalFDR) assume independence among tests and often lead to high false non-discovery rates (FNR). Although various spatial FDR control methods have been developed to improve power, they still …

This https://arxiv.org/abs/2505.18492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Enumerate-Conjecture-Prove: Formally Solving Answer-Construction Problems in Math Competitions
Mathematical reasoning lies at the heart of artificial intelligence, underpinning applications in education, program verification, and research-level mathematical discovery. Mathematical competitions, in particular, present two challenging problem types: theorem-proving, requiring rigorous proofs of stated conclusions, and answer-construction, involving hypothesizing and formally verifying mathematical objects. Large Language Models (LLMs) effectively generate creative candidate answers but str…

hqQUBO: A Hybrid-querying Quantum Optimization Model Validated with 16-qubits on an Ion Trap Quantum Computer for Life Science Applications
Rong Chen, Quan-Xin Mei, Wen-Ding Zhao, Lin Yao, Hao-Xiang Yang, Shun-Yao Zhang, Jiao Chen, Hong-Lin Li
https://arxiv.org/abs/2506.01559

hqQUBO: A Hybrid-querying Quantum Optimization Model Validated with 16-qubits on an Ion Trap Quantum Computer for Life Science Applications
AlphaFold has achieved groundbreaking advancements in protein structure prediction, exerting profound influence across biology, medicine, and drug discovery. However, its reliance on multiple sequence alignment (MSA) is inherently time-consuming due to the NP-hard nature of constructing MSAs. Quantum computing emerges as a promising alternative, compared to classical computers, offering the potentials for exponential speedup and improved accuracy on such complex optimization challenges. This wo…

From Bjorken Scaling to Scaling Violations
Giorgio Parisi
https://arxiv.org/abs/2506.03383 https://arxiv.org/pdf/2506.03383

From Bjorken Scaling to Scaling Violations
This paper traces the historical and conceptual journey from Bjorken scaling to the discovery of scaling violations in deep inelastic scattering, culminating in the development of Quantum Chromodynamics (QCD). Beginning with the challenges faced by early strong interaction theories in the 1950s, we explore the emergence of agnostic approaches such as the bootstrap philosophy and current algebra, which sought to describe hadronic phenomena without relying on specific field theories. The pivotal …

This https://arxiv.org/abs/2310.18397 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Detectable and defect-free dark photon dark matter
Ultralight dark photons are compelling dark matter candidates, but their allowed kinetic mixing with the Standard Model photon is severely constrained by requiring that the dark photons do not collapse into a cosmic string network in the early Universe. Direct detection in minimal production scenarios for dark photon dark matter is strongly limited, if not entirely excluded; discovery of sub-meV dark photon dark matter would therefore point to a nonminimal dark sector. We describe a model that …

Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
Samhita Pal, Dhrubajyoti Ghosh, Shu Yang
https://arxiv.org/abs/2507.00173

Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
Parkinson's disease (PD) is a progressive neurodegenerative disorder that lacks reliable early-stage biomarkers for diagnosis, prognosis, and therapeutic monitoring. While cerebrospinal fluid (CSF) biomarkers, such as alpha-synuclein seed amplification assays (alphaSyn-SAA), offer diagnostic potential, their clinical utility is limited by invasiveness and incomplete specificity. Plasma biomarkers provide a minimally invasive alternative, but their mechanistic role in PD remains unclear. A major…

Vivodyne, which uses AI and robots to grow human tissues in the lab for drug discovery and development, raised a $40M Series A, taking its total funding to $78M (Katherine Davis/Axios)
https://www.axios.com/pro/biotech-deals/2025/05…

Vivodyne raises $40M for human tissue drug testing
It's seeing heightened demand following the FDA's plans to phase out animal testing requirements.

Discovery and Preliminary Characterization of a Third Interstellar Object: 3I/ATLAS
Darryl Z. Seligman, Marco Micheli, Davide Farnocchia, Larry Denneau, John W. Noonan, Toni Santana-Ros, Luca Conversi, Maxime Devog\`ele, Laura Faggioli, Adina D. Feinstein, Marco Fenucci, Tessa Frincke, Olivier R. Hainaut, Willem B. Hoogendam, Henry H. Hsieh, Theodore Kareta, Michael S. P. Kelley, Tim Lister, Du\v{s}an Mar\v{c}eta, Karen J. Meech, Francisco Oca\~na, Eloy Pe\~na-Asensio, Benjamin J. Shap…

Discovery and Preliminary Characterization of a Third Interstellar Object: 3I/ATLAS
We report initial observations aimed at the characterization of a third interstellar object candidate. This object, 3I/ATLAS -- also C/2025 N1 (ATLAS) -- , was discovered on 2025 July 1 UT and has an orbital eccentricity of $e\sim6.2$, perihelion of $q\sim 1.35$ au, inclination of $\sim175^\circ$, and hyperbolic velocity of $V_\infty\sim 60$ km s$^{-1}$. 3I/ATLAS has an absolute magnitude of $H_V\sim12$, which corresponds to a nuclear radius of $\sim10\text{ km}$, assuming an asteroid-like albe…

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Cibr\'an L\'opez, Joshua Ojih, Ming Hu, Josep Lluis Tamarit, Edgardo Saucedo, Claudio Cazorla
https://arxiv.org/abs/2506.01449

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal management technologies. However, their prediction is computationally intensive due to the need to account for finite-temperature effects. Here, we present an uncertainty-aware machine-learning-guided framework for high-throughput prediction of temperature-induce…

Internal memo: Warner Bros. Discovery's TNT Sports will stop making content for NBA TV, ending a partnership that has been in place since 2008 (Brian Steinberg/Variety)
https://variety.com/2025/tv/news/warners-tnt-sports-stop-content-for…

Warner's TNT Sports Will Stop Making Content for NBA's Cable Network
Warner Bros. Discovery's TNT Sports will stop making content for NBA TV, ending an nearly two-decade-old TV alliance

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
Maxim Moraru, Weiyi Xia, Zhuo Ye, Feng Zhang, Yongxin Yao, Ying Wai Li, Cai-Zhuang Wang
https://arxiv.org/abs/2506.21449

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from executio…

Active Inference AI Systems for Scientific Discovery
Karthik Duraisamy
https://arxiv.org/abs/2506.21329 https://arxiv.org/pdf/2506.21…

Active Inference AI Systems for Scientific Discovery
The rapid evolution of artificial intelligence has led to expectations of transformative scientific discovery, yet current systems remain fundamentally limited by their operational architectures, brittle reasoning mechanisms, and their separation from experimental reality. Building on earlier work, we contend that progress in AI-driven science now depends on closing three fundamental gaps -- the abstraction gap, the reasoning gap, and the reality gap -- rather than on model size/data/test time …

This https://arxiv.org/abs/2502.01452 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Bayesian Deep-stacking for High-energy Neutrino Searches
Following the discovery of the brightest high-energy neutrino sources in the sky, the further detection of fainter sources is more challenging. A natural solution is to combine fainter source candidates, and instead of individual detections, aim to identify and learn about the properties of a larger population. Due to the discreteness of high-energy neutrinos, they can be detected from distant very faint sources as well, making a statistical search benefit from the combination of a large number…

A Relativistic Jet in the Radio Quiet AGN Mrk 110
Ailing Wang, Tao An, Kenneth I. Kellermann, Hua Feng, Emmanuel K. Bempong-Manful, Roland Timmerman, Shaoguang Guo
https://arxiv.org/abs/2506.03970

A Relativistic Jet in the Radio Quiet AGN Mrk 110
We report the discovery of a relativistic jet in Mrk~110, a narrow-line Seyfert 1 galaxy historically classified as a radio-quiet active galactic nucleus (AGN). Very Long Baseline Interferometry (VLBI) observations reveal intermittent jet activity during 2015--2016 and 2022--2024, with proper motion measurements yielding superluminal velocities of $\sim3.6\pm0.6\,c$ and $\sim2.1\pm0.2\,c$, respectively. The recent jet component decelerates to $\sim 1.5\pm0.2\,c$ at a projected distance of 1.1 p…

Robust PDE discovery under sparse and highly noisy conditions via attention neural networks
Shilin Zhang, Yunqing Huang, Nianyu Yi, shihan Zhang
https://arxiv.org/abs/2506.17908

Robust PDE discovery under sparse and highly noisy conditions via attention neural networks
The discovery of partial differential equations (PDEs) from experimental data holds great promise for uncovering predictive models of complex physical systems. In this study, we introduce an efficient automatic model discovery framework, ANN-PYSR, which integrates attention neural networks with the state-of-the-art PySR symbolic regression library. Our approach successfully identifies the governing PDE in six benchmark examples. Compared to the DLGA framework, numerical experiments demonstrate …

Data-driven high-throughput search for the accelerated discovery of rare-earth-free permanent magnets
Junaid Jami, Nitish Bhagat, Amrita Bhattacharya
https://arxiv.org/abs/2507.01849

Data-driven high-throughput search for the accelerated discovery of rare-earth-free permanent magnets
An integrated data-driven approach combined with a high-throughput framework based on first-principles calculations was used to discover novel rare-earth-free permanent magnets, focusing on binary alloys. Compounds were screened systematically based on their elemental composition, structure, stability, and magnetization. Density functional theory (DFT) calculations were performed on the selected candidates to evaluate their magnetocrystalline anisotropy energy (MAE) and Curie temperature (Tc), …

Filing: WBD says it plans to "significantly reduce" CEO David Zaslav's annual pay, including his cash bonus, after shareholders rejected his 2024 pay package (Todd Spangler/Variety)
https://variety.com/2025/tv/news/warne

Warner Bros Discovery CEO David Zaslav Pay to Drop After Company Split
WBD CEO David Zaslav will see his pay package take a haircut after the proposed separation of Warner Bros. Discovery in 2026. The company granted him $225 million in stock option awards.

Efficient and Reuseable Cloud Configuration Search Using Discovery Spaces
Michael Johnston, Burkhard Ringlein, Christoph Hagleitner, Alessandro Pomponio, Vassilis Vassiliadis, Christian Pinto, Srikumar Venugopal
https://arxiv.org/abs/2506.21467

Efficient and Reuseable Cloud Configuration Search Using Discovery Spaces
Finding the optimal set of cloud resources to deploy a given workload at minimal cost while meeting a defined service level agreement is an active area of research. Combining tens of parameters applicable across a large selection of compute, storage, and services offered by cloud providers with similar numbers of application-specific parameters leads to configuration spaces with millions of deployment options. In this paper, we propose Discovery Space, an abstraction that formalizes the descr…

Forbidden p-d Orbital Coupling Accelerates High-Power-Factor Materials Discovery
Wu Xiong, Zhongjuan Han, Zhonghao Xia, Zhilong Yang, Jiangang He
https://arxiv.org/abs/2507.01256 …

Forbidden p-d Orbital Coupling Accelerates High-Power-Factor Materials Discovery
The intrinsic entanglement between electrical conductivity ($σ$) and the Seebeck coefficient ($S$) significantly constrains power factor (PF) enhancement in thermoelectric (TE) materials. While high valley degeneracy ($N_{\mathrm{vk}}$) effectively balances $σ$ and $S$ to improve PF, identifying compounds with high $N_{\mathrm{vk}}$ remains challenging. In this study, we develop an effective approach to rapid discover $p$-type semiconductors with high $N_{\mathrm{vk}}$ through manipulating an…

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa RYu
https://arxiv.org/abs/2506.00056

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Artificial intelligence (AI) is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic settings characterized by sparse data, high-dimensional design spaces, and nontrivial physical constraints. This perspective argues for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and in…

Discovery of Volatile Gas in the Giant Impact Disk around the 150-Myr old HD 23514
Kate Y. L. Su, Attila Mo\'or, Chengyan Xie, Ilaria Pascucci, George H. Rieke, \'Agnes K\'osp\'al, Mark C. Wyatt, P\'eter \'Abrah\'am, Luca Matr\`a, Zoe Roumeliotis, D. J. Wilner
https://arxiv.org/abs/2506.20919

Discovery of Volatile Gas in the Giant Impact Disk around the 150-Myr old HD 23514
We report the discovery of CO$_2$ gas emission around HD 23514, an F5V star in the $\sim$150 Myr-old Pleiades cluster, hosting one of the rare giant-impact disks with unique mineralogy dominated by silica dust. We show that the dust feature remains stable over several decades, and that the sub-$μ$m grains, which give rise to the $\sim$9 $μ$m feature, are co-spatial with the hot CO$_2$ molecules within the sub-au vicinity of the star. Examining the Spitzer spectrum taken 15 years earlier, we s…

First-Principles and Machine Learning Investigation of the Structural and Optoelectronic Properties of Dodecaphenylyne: A Novel Carbon Allotrope
Kleuton A. L. Lima, Jose A. S. Laranjeira, Nicolas F. Martins, Julio R. Sambrano, Alexandre C. Dias, Luiz A. Ribeiro Junior, Douglas S. Galvao
https://arxiv.org/abs/2506.02218

First-Principles and Machine Learning Investigation of the Structural and Optoelectronic Properties of Dodecaphenylyne: A Novel Carbon Allotrope
We report the computational discovery and characterization of Dodecaphenylyne (DP), a novel carbon allotrope with a distinctive geometric arrangement. DP structural, thermodynamic, mechanical, electronic, and optical properties were evaluated using density functional theory and a machine learning interatomic potential trained explicitly for this material. The formation energy of -7.98 eV/atom indicates high thermodynamic stability, further supported by the absence of imaginary phonon modes and …

Constraining nearby substellar companion architectures using High Contrast Imaging, Radial Velocity and Astrometry data
L. F. Sartori, M. J. Bonse, Y. Li, F. A. Dannert, S. P. Quanz, A. Boehle
https://arxiv.org/abs/2507.02455

Constraining nearby substellar companion architectures using High Contrast Imaging, Radial Velocity and Astrometry data
Nearby stars offer prime opportunities for exoplanet discovery and characterization through various detection methods. By combining HCI, RV, and astrometry, it is possible to better constrain the presence of substellar companions, as each method probes different regions of their parameter space. A detailed census of planets around nearby stars is essential to guide the selection of targets for future space missions seeking to identify Earth-like planets and potentially habitable worlds. In addi…