Tootfinder

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
Jiahao Yang, Ran Fang, Ming Zhang, Jun Zhou
https://arxiv.org/abs/2505.22678

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes. However, this task is challenging due to the high-dimensional and volatile nature of the original data. Even recent deep learning models often struggle to capture price movement patterns effectively, particularly without well-designed features. We observed that raw LOB data exhibits inherent symmetry between the ask and bid sides, and the bid-ask diffe…

Thermodynamic Constraints in DRAM cells: Experimental Verification of Energy Efficiency Limits in Information Erasure
Takase Shimizu, Kensaku Chida, Gento Yamahata, Katsuhiko Nishiguchi
https://arxiv.org/abs/2505.23087

Thermodynamic Constraints in DRAM cells: Experimental Verification of Energy Efficiency Limits in Information Erasure
We measured the energy efficiency of information erasure using silicon DRAM cells capable of counting charges on capacitors at the single-electron level. Our measurements revealed that the efficiency decreased as the erasure error rate decreased, and notably, the Landauer limit was not achieved even under effectively infinite-time operation. By comparing the measured efficiency with the Landauer limit, we identified a thermodynamic constraint that prevents DRAM from reaching this limit: the ina…

Nonparametric Estimation of Conditional Survival Function with Time-Varying Covariates Using DeepONet
Bingqing Hu, Bin Nan
https://arxiv.org/abs/2505.22748

Nonparametric Estimation of Conditional Survival Function with Time-Varying Covariates Using DeepONet
Traditional survival models often rely on restrictive assumptions such as proportional hazards or instantaneous effects of time-varying covariates on the hazard function, which limit their applicability in real-world settings. We consider the nonparametric estimation of the conditional survival function, which leverages the flexibility of neural networks to capture the complex, potentially long-term non-instantaneous effects of time-varying covariates. In this work, we use Deep Operator Network…

Profiling and optimization of multi-card GPU machine learning jobs
Marcin Lawenda, Kyrylo Khloponin, Krzesimir Samborski, {\L}ukasz Szustak
https://arxiv.org/abs/2505.22905

Profiling and optimization of multi-card GPU machine learning jobs
The effectiveness and efficiency of machine learning methodologies are crucial, especially with respect to the quality of results and computational cost. This paper discusses different model optimization techniques, providing a comprehensive analysis of key performance indicators. Several parallelization strategies for image recognition, adapted to different hardware and software configurations, including distributed data parallelism and distributed hardware processing, are analyzed. Selected o…

The second order Huang-Yang approximation to the Fermi thermodynamic pressure
Xuwen Chen, Jiahao Wu, Zhifei Zhang
https://arxiv.org/abs/2505.23136 https://…

The second order Huang-Yang approximation to the Fermi thermodynamic pressure
We consider a dilute Fermi gas in the thermodynamic limit with interaction potential scattering length $\mathfrak{a}_0$ at temperature $T>0$. We prove the 2nd order Huang-Yang approximation for the Fermi pressure of the system, in which there is a 2nd order term carrying the positive temperature efffect.Our formula is valid up to the temperature $T<ρ^{\frac{2}{3}+\frac{1}{6}}$, which is, by scaling, also necessary for the Huang-Yang formula to hold. Here, $T_F\simρ^{\frac{2}{3}}$ is the Fermi…

How #Vietnam bets on serving the #Trump organization. Despicable, including land evictions—not particularly a thing the Vietnamese people fancy.

Video: Inside a Trump Family Project in Vietnam
As President Trump blurs the lines between politics and business — and threatens steep tariffs on trade partners — governments feel compelled to favor Trump-related projects. Damien Cave, Vietnam bureau chief for The New York Times, and Katrin Bennhold, a senior writer on the international desk, discuss how this dynamic has played out in Vietnam, at the groundbreaking of a fast-tracked $1.5 billion Trump golf complex.

So the basic idea is that we first compute a "level" for whatever interaction, by adding beneficial modifiers and subtracting harmful ones. Imagine most modifiers are smallish integers like 2 or -3 (though they can be non-integers too). Each level can be thought of as making things twice as good/bad, although this only applies directly when they're balanced. The actual formula starts with a 50/50 chance of "success" at level 0, and then each positive level halves the chance of failure, or if the levels are negative, each negative level halves the chance of success (note that halving the chance of failure is not the same as doubling the chance of success).
The intuitive explanation is that you start with a coin flip. Then if the level is positive, you flip that many additional coins and succeed if any single coin succeeds, but it the level is negative, you have to flip that many additional coins and succeed only if *all* flips succeed.
For example, if I have a dagger with 5 crit chance, and I attack an opponent with no armor modifiers, I'd have to win any 1 of 6 coin flips to score a crit (p = 1 - (1/(2^6)) = 63/64. Increasing my crit modifier by 1 ups my chances only slightly, to 127/128. This is obviously pretty poor return, indicating that the 5 I already have is very strong. If the opponent had armor with -3 to crits, the interaction is now level 2, so the crit chance is 7/8, which is still pretty good. We can see from these examples that the basic system
rewards a small level advantage a lot, but the rewards diminish rapidly. The system has a few avenues for tweaking how it works though, that can let us modify this. There's also a potential benefit (though sometimes drawback) that no matter what the level gap, there's an effective limit to how much the interaction swings.