Tootfinder

Morgan Stanley says xAI raised $5B of equity in addition to $5B in debt; the company raised $6B in December from a16z and others (Miles Kruppa/The Information)
https://www.theinformation.com/briefings/elon-musks-xai-raises-10-billion-debt-equi…

Elon Musk’s xAI Raises $10 Billion in Debt, Equity
Elon Musk’s xAI raised $5 billion of equity in addition to $5 billion in debt, according to Morgan Stanley, which advised on the debt transactions. “The combination of debt and equity reduces the overall cost of capital and substantially expands pools of capital available to xAI,” Morgan Stanley wrote in a post on X , which did not name any investors in the debt or equity. XAI raised $6

Variability-finding in Rubin Data Preview 1 with LSDB
Konstantin Malanchev, Melissa DeLucchi, Neven Caplar, Alex I. Malz, Wilson Beebe, Doug Branton, Sandro Campos, Andrew Connolly, Mi Dai, Jeremy Kubica, Olivia Lynn, Rachel Mandelbaum, Sean McGuire, Eric Aubourg, Robert David Blum, Jeffrey L. Carlin, Francisco Delgado, Emmanuel Gangler, Buell T. Jannuzi, Tim Jenness, Yijung Kang, Arun Kannawadi, Marc Moniez, Andr\'es A. Plazas Malag\'on, Wouter van Reeven, David Sanmartim, Ela…

Variability-finding in Rubin Data Preview 1 with LSDB
The Vera C. Rubin Observatory recently released Data Preview 1 (DP1) in advance of the upcoming Legacy Survey of Space and Time (LSST), which will enable boundless discoveries in time-domain astronomy over the next ten years. DP1 provides an ideal sandbox for validating innovative data analysis approaches for the LSST mission, whose scale challenges established software infrastructure paradigms. This note presents a pair of such pipelines for variability-finding using powerful software infrastr…

Characterizing Small Circuit Classes from FAC^0 to FAC^1 via Discrete Ordinary Differential Equations
Melissa Antonelli, Arnaud Durand, Juha Kontinen
https://arxiv.org/abs/2506.23404

Characterizing Small Circuit Classes from FAC^0 to FAC^1 via Discrete Ordinary Differential Equations
In this paper, we provide a uniform framework for investigating small circuit classes and bounds through the lens of ordinary differential equations (ODEs). Following an approach recently introduced to capture the class of polynomial-time computable functions via ODE-based recursion schemas and later applied to the context of functions computed by unbounded fan-in circuits of constant depth (FAC^0), we study multiple relevant small circuit classes. In particular, we show that natural restrictio…

Unconventional superlattice ordering in intercalated transition metal dichalcogenide V$_{1/3}$NbS$_2$
Shannon S. Fender, Noah Schnitzer, Wuzhang Fang, Lopa Bhatt, Dingbin Huang, Amani Malik, Oscar Gonzalez, Veronika Sunko, Lilia S. Xie, David A. Muller, Joseph Orenstein, Yuan Ping, Berit H. Goodge, D. Kwabena Bediako
https://arx…

Unconventional superlattice ordering in intercalated transition metal dichalcogenide V$_{1/3}$NbS$_2$
The interplay between symmetry and topology in magnetic materials makes it possible to engineer exotic phases and technologically useful properties. A key requirement for these pursuits is achieving control over local crystallographic and magnetic structure, usually through sample morphology (such as synthesis of bulk crystals versus thin-films) and application of magnetic or electric fields. Here we show that V$_{1/3}$NbS$_2$ can be crystallized in two ordered superlattices, distinguished by t…

Unified Memcapacitor-Memristor Memory for Synaptic Weights and Neuron Temporal Dynamics
Simone D'Agostino, Marco Massarotto, Tristan Torchet, Filippo Moro, Niccol\`o Castellani, Laurent Grenouillet, Yann Beilliard, David Esseni, Melika Payvand, Elisa Vianello
https://arxiv.org/abs/2506.22227

Unified Memcapacitor-Memristor Memory for Synaptic Weights and Neuron Temporal Dynamics
We present a fabricated and experimentally characterized memory stack that unifies memristive and memcapacitive behavior. Exploiting this dual functionality, we design a circuit enabling simultaneous control of spatial and temporal dynamics in recurrent spiking neural networks (RSNNs). Hardware-aware simulations highlight its promise for efficient neuromorphic processing.

Ranked choice voting expansion in Maine sent to Gov. Mills
Proponents say a court case in Alaska casts doubt on a Maine Law Court advisory opinion that the voting method violates the Maine Constitution
https://www.pressherald.com/2025/06/18/ran

Break-in reported at home of slain Minnesota state Rep. Melissa Hortman (David K. Li/NBC News)
https://www.nbcnews.com/news/us-news/break-reported-home-slain-minnesota-state-rep-melissa-hortman-rcna213780
http://www.memeorandum.com/250618/p109#a250618p109

The DECam MAGIC Survey: Spectroscopic Follow-up of the Most Metal-Poor Stars in the Distant Milky Way Halo
Vinicius M. Placco, Guilherme Limberg, Anirudh Chiti, Deepthi S. Prabhu, Alexander P. Ji, Fabr\'icia O. Barbosa, William Cerny, Andrew B. Pace, Guy S. Stringfellow, David J. Sand, Clara E. Mart\'inez-V\'azquez, Alexander H. Riley, Silvia Rossi, Noelia E. D. No\"el, A. Katherina Vivas, Gustavo E. Medina, Alex Drlica-Wagner, Joanna D. Sakowska, Bur\c{c}in Mutlu-Pakd…

The DECam MAGIC Survey: Spectroscopic Follow-up of the Most Metal-Poor Stars in the Distant Milky Way Halo
In this work, we present high-resolution spectroscopic observations for six metal-poor stars with [Fe/H]<-3 (including one with [Fe/H]<-4), selected using narrow-band Ca II HK photometry from the DECam MAGIC Survey. The spectroscopic data confirms the accuracy of the photometric metallicities and allows for the determination of chemical abundances for 16 elements, from carbon to barium. The program stars have chemical abundances consistent with this metallicity range. A kinematic/dynamical anal…

Richard David #Precht dilettiert in der #taz zur Weltordnung 🤣
„Ich habe mich noch nie vor Russen gefürchtet“ meint er. Das ist wirklich eine über alles dämliche Aussage, zumal für einen, der sich selbst Philosoph nennt.
Er ist in der Philosophie das, was

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
Dmitry Kondratyev, Benedikt Riedel, Yuan-Tang Chou, Miles Cochran-Branson, Noah Paladino, David Schultz, Mia Liu, Javier Duarte, Philip Harris, Shih-Chieh Hsu
https://arxiv.org/abs/2506.20657

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
The increasing computational demand from growing data rates and complex machine learning (ML) algorithms in large-scale scientific experiments has driven the adoption of the Services for Optimized Network Inference on Coprocessors (SONIC) approach. SONIC accelerates ML inference by offloading it to local or remote coprocessors to optimize resource utilization. Leveraging its portability to different types of coprocessors, SONIC enhances data processing and model deployment efficiency for cuttin…