Tootfinder

Through the Looking Glass: Bitcoin Treasury Companies
B K Meister
https://arxiv.org/abs/2507.14910 https://arxiv.org/pdf/2507.14910

Through the Looking Glass: Bitcoin Treasury Companies
Bitcoin treasury companies have taken stock markets by storm amassing billions of dollars worth of tokens. More than a hundred companies active in various crypto-tokens, not just Bitcoin, are listed. The ability to generate leverage, for example stemming from borrowing collateralized by stock portfolios, helps to understand this phenomenon. In addition, the extension of the binary-choice Kelly criterion to incorporate uncertainty in the form of the Kullback-Leibler divergence or more generall…

Can you share your favorite #Nature channels on #PeerTube? I'm looking for more content to watch.
#Forest

If anyone I have a connection with has any thoughts on part or full time design/illustration roles, I would LOVE to hear from you.
18 years experience and am open to new challenges!
#lookingforwork #getfedihired
My most recently uploaded work can be found in my portfolio at https://www.andycarolan.com
If you wish to find out more about me as a person, my strengths, skills and what I'm like to work with, please take a look at my personal user manual at https://www.andycarolan.com/myusermanual
CV/Resume on request

Solvable Models of Heat Transport in Quantum Mechanics
R Loganayagam, Prithvi Narayan, Swathi T S
https://arxiv.org/abs/2508.09253 https://arxiv.org/pdf/25…

Solvable Models of Heat Transport in Quantum Mechanics
We investigate solvable models of heat transport between a pair of quantum mechanical systems initialized at two different temperatures. At time $t=0$, a weak interaction is switched on between the systems, and we study the resulting energy transport. Focusing on the heat current as the primary observable, we analyze both the transient dynamics and the long-time behavior of the system. We demonstrate that simple toy models - including Random Matrix Theory like models ({\it RMT models}) and Schw…

Now that my game released on steam, I am looking for a new long term opportunity to work as a game designer.
If you need a (freelance) Game designer, hit me up!
here you can see my cool stuff
http://dominik-dammer.de/

Dominik Dammer - Game Designer & Philosopher
Check out my portfolio and other work. Or just get to know me!

SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows
Jingwen Zhou, Jieshan Chen, Qinghua Lu, Dehai Zhao, Liming Zhu
https://arxiv.org/abs/2508.07935 https:…

SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows
Large Language Model (LLM) agentic systems are software systems powered by LLMs that autonomously reason, plan, and execute multi-step workflows to achieve human goals, rather than merely executing predefined steps. During execution, these workflows frequently encounter exceptions. Existing exception handling solutions often treat exceptions superficially, failing to trace execution-phase exceptions to their reasoning-phase root causes. Furthermore, their recovery logic is brittle, lacking stru…

Portable Targeted Sampling Framework Using LLVM
Zhantong Qiu, Mahyar Samani, Jason Lowe-Power
https://arxiv.org/abs/2509.02873 https://arxiv.org/pdf/2509.0…

Portable Targeted Sampling Framework Using LLVM
Comprehensive architectural evaluation of full workloads is throttled by slow simulation and per-binary sampling pipelines. We present Nugget, a flexible framework for portable sampling across simulators and real hardware, ISAs, and libraries. Nugget operates at the LLVM IR level to perform binary-agnostic interval analysis, then emits lightweight, cross-platform executables--nuggets--that can be validated on real machines before driving simulation. Across SPEC CPU2017, NPB, and LSMS, Nugget cu…

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Jialiang Shi, Yaguang Dou, Tian Qi
https://arxiv.org/abs/2508.09090 https:…

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Modeling multi-interests has arisen as a core problem in real-world RS. Current multi-interest retrieval methods pose three major challenges: 1) Interests, typically extracted from predefined external knowledge, are invariant. Failed to dynamically evolve with users' real-time consumption preferences. 2) Online inference typically employs an over-exploited strategy, mainly matching users' existing interests, lacking proactive exploration and discovery of novel and long-tail interests. To addres…

If you're writing a library, you should really avoid #CMake. CMake is designed to lock you in. As in, once you release a #FreeSoftware project using CMake, you can't switch to another build system with causing real trouble to your users. And if you support multiple build systems, as soon as you start supporting CMake, some of your users are going to start locking everyone else in.
That's because CMake uses a custom package discovery mechanism that's hardly compatible with anything else, and that is so complex that it's very hard to reimplement it with any other build system. So when others start relying on the CMake config files being installed (and they naturally will, since that's how CMake does things), you can't stop installing them without actually breaking stuff. And if you want to preserve them without actually using CMake, well, good luck with that.
And if CMake is one of the options you support, then some of your consumers will accidentally start relying on it anyway. And this will be much worse for everyone, because now their projects won't work for people who build your project with any other build system. Which in turn will force more projects to use CMake anyway. Which in turn will make more people rely on CMake being used…
Use #Meson as the build system, it's clean and not designed to lock you in. Use pkg-config for library data; it's simple and portable.
#OpenSource

Crosslisted article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Dynamic Factor Models with Forward-Looking Views
Anas Abdelhakmi, Andrew E. B. Lim