Tootfinder

‘Only Embrace’ was actually called ‘Only Envelope’ for the longest time, partly because along with the emotional layer I was interested in breaking free of any use of percussion - rather implying rhythm through envelopes programmed into the patches (along with musical use of shifting compression flaring in response to these, and then saturation that would “bloom” out in various M/S configurations.)

"This single task of managing memory has proven to be one of the most difficult, let alone to grasp and understand, but most importantly, to get right.
Because not getting this right meant crashes, security issues, resource shortages, unhappy customers, and lots of white hair. To make things worse, pretty much every programming language comes these days with their own ideas of how to keep track of things on the heap."

Souvenir
On Monday, June 6th, 2011, after Steve Jobs' last public appearance as a keynote speaker, took place the "Developer Tools Kickoff" session at Apple’s annual Worldwide Developers Conference, also known as WWDC. That day, Chris Lattner, creator of the LLVM compiler infrastructure and the Swift programming language, introduced a new feature of the Objective-C language to thunderous applause. This feature, still present in Swift, is known as "Automatic Reference Counting", or ARC.

Disable JavaScript — How to disable JavaScript in your browser
Nowadays almost all web pages contain JavaScript, a scripting programming language that runs arbitrary code, through the web browser, on the visitor's computer. It is supposed to make web pages functional for specific purposes but it has proven its potential to cause significant harm to users time and time again. […]
✋ …

Disable JavaScript
Instructions on how to disable (deactivate) JavaScript in web browser and why.

I became a programmer because I found it much easier to program computers than to talk to people. Why would anyone in their sane mind claim that I'd be better off talking in human language to machines that pretend to be the kind of smug humans who have no clue about coding, but are going to fulfill all the assignments given by me by googling and copy-pasting whatever they can find?!
#NoAI #AI #LLM

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
https://arxiv.org/abs/2504.03790 https://mastoxiv.page/@arXiv_csCL_bot/114301112970577326
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
https://arxiv.org/abs/2504.12392 https://mastoxiv.page/@arXiv_statME_bot/114357826909813483
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
https://arxiv.org/abs/2505.11622 https://mastoxiv.page/@arXiv_statML_bot/114539065460187982
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
https://arxiv.org/abs/2505.12289 https://mastoxiv.page/@arXiv_mathNA_bot/114539035462135281
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
https://arxiv.org/abs/2505.15215 https://mastoxiv.page/@arXiv_statML_bot/114550346291754635
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
https://arxiv.org/abs/2506.11683 https://mastoxiv.page/@arXiv_statML_bot/114692410563481289
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
https://arxiv.org/abs/2506.14850 https://mastoxiv.page/@arXiv_physicschemph_bot/114709402590755731
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
https://arxiv.org/abs/2506.18283 https://mastoxiv.page/@arXiv_statML_bot/114738165218533987
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
https://arxiv.org/abs/2506.20915 https://mastoxiv.page/@arXiv_csCR_bot/114754394485208892
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
https://arxiv.org/abs/2507.01939 https://mastoxiv.page/@arXiv_astrophIM_bot/114788369702591337
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
https://arxiv.org/abs/2507.17860 https://mastoxiv.page/@arXiv_csCV_bot/114912976717523345
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501 https://mastoxiv.page/@arXiv_csAI_bot/115032101532614110
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
https://arxiv.org/abs/2508.20717 https://mastoxiv.page/@arXiv_csSD_bot/115111255835875066
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
https://arxiv.org/abs/2509.11512 https://mastoxiv.page/@arXiv_csDC_bot/115213444524490263
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
https://arxiv.org/abs/2509.16187 https://mastoxiv.page/@arXiv_csSE_bot/115247172280557686
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115286737423175311
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
https://arxiv.org/abs/2509.21791 https://mastoxiv.page/@arXiv_csCL_bot/115287166674809413
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
https://arxiv.org/abs/2510.24830 https://mastoxiv.page/@arXiv_csCV_bot/115462527449411627
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
https://arxiv.org/abs/2511.04461 https://mastoxiv.page/@arXiv_eessSY_bot/115507785247809767
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
toXiv_bot_toot

🔊 #NowPlaying on #BBCRadio3:
#Unclassified
- Peace on Earth
Elizabeth Alker presents a mix of calm wintery sounds, ambient dreamscapes and tranquil tracks to soundtrack the longest night of the year.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002ngbm

https://www.instagram.com/p/DTy4y3VDpS0/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

Japan Daily on Instagram: "Abandoned suitcases are becoming a growing issue at Narita Airport, where nearly 900 pieces of luggage have already been collected this fiscal year—about nine times more than four years ago. Police say around 10% of the bags are empty, suggesting intentional disposal, with storage and disposal costs ultimately borne by taxpayers. The problem extends beyond airports to train stations and public spaces, often linked to travelers avoiding disposal fees after shopping. In response, Narita Airport is testing solutions such as clothing compression machines to help travelers save space and avoid buying new luggage. Retailers and repair businesses are also encouraging reuse through free take-back and resale programs, aiming to reduce waste and promote more sustainable travel habits."
49 likes, 0 comments - japandaily_jp on January 21, 2026: "Abandoned suitcases are becoming a growing issue at Narita Airport, where nearly 900 pieces of luggage have already been collected this fiscal year—about nine times more than four years ago. Police say around 10% of the bags are empty, suggesting intentional disposal, with storage and disposal costs ultimately borne by taxpayers. The problem extends beyond airports to train stations and public spaces, often linked to travelers avoid…

Our second episode is the first one on a more specific topic. This time we talk about knowing your Language. In order to be able to communication effectively through your code, you have to know your programming language, its features, and its idioms.
As always, enjoy, subscribe, and give us constructive feedback.
https…

UC Berkeley Postdoctoral Fellow in Buddhist Studies
https://ift.tt/DhQaOFx
CFP: [FRISTVERLÄNGERUNG] Variations 28/2021: Gender through technology (25.09.2021) Call…
via Input 4 RELCFP https:…

Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju, Tong Zhao, Donald Loveland, Leonardo Neves, Neil Shah
https://arxiv.org/abs/2512.17820 https://arxiv.org/pdf/2512.17820 https://arxiv.org/html/2512.17820
arXiv:2512.17820v1 Announce Type: new
Abstract: Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling. To do so, several works completely replace ID embeddings with modality embeddings, claiming that modality embeddings render ID embeddings unnecessary because they can match or even exceed ID embedding performance. On the other hand, many works jointly utilize ID and modality features, but posit that complex fusion strategies, such as multi-stage training and/or intricate alignment architectures, are necessary for this joint utilization. However, underlying both these lines of work is a lack of understanding of the complementarity of ID and modality features. In this work, we address this gap by studying the complementarity of ID- and text-based SR models. We show that these models do learn complementary signals, meaning that either should provide performance gain when used properly alongside the other. Motivated by this, we propose a new SR method that preserves ID-text complementarity through independent model training, then harnesses it through a simple ensembling strategy. Despite this method's simplicity, we show it outperforms several competitive SR baselines, implying that both ID and text features are necessary to achieve state-of-the-art SR performance but complex fusion architectures are not.
toXiv_bot_toot