Tootfinder

Entropy Engineered Middle-In Synthesis of Dual Single-Atom Compounds for Nitrate Reduction Reaction
Yao Hu, Haihui Lan, Junjun He, Wenjing Fang, Wen-Da Zhang, Shuanglong Lu, Fang Duan, Mingliang Du
https://arxiv.org/abs/2404.05075

Entropy Engineered Middle-In Synthesis of Dual Single-Atom Compounds for Nitrate Reduction Reaction
Despite the immense potential of Dual Single-Atom Compounds (DSACs), the challenges in their synthesis process, including complexity, stability, purity, and scalability, remain primary concerns in current research. Here, we present a general strategy, termed "Entropy-Engineered Middle-In Synthesis of Dual Single-Atom Compounds" (EEMIS-DSAC), which is meticulously crafted to produce a diverse range of DSACs, effectively addressing the aforementioned issues. Our strategy integrates the advantages…

I’m reminded of a very old Slashdot comment that I return to which I will share in its entirety. Not the article, not the source, but this single insightful comment from Shannon Love that still rings true:
“I can relate to Zawinski's frustration and many others do as well. I notice that it seems to effect those with more experience than those newer to computing.
When one first acquires a new tool, whether it is hardware, software or a woodworking plane, the very act of learning how use the tool itself works is highly engaging. Just futzing about figuring out how the new tool works is an end in itself.
However, after one has spent 20 years learning the ends and out of each season's new tools the joy fades. One becomes progressively less interested in the tools itself and more interested in product you want to use the tool to make. The time spent futzing with the tool is not engaging but frustrating and wasteful. You want to get the primary work done not spend all your time adjusting your tools.
How many times over the years has Zawinski wrestled with a problem similar to his Linux sound issue? The thrill of solving such a problem is long gone, baby.
The Linux community is dominated by people who enjoy the process of learning and using the tool itself. They are the kind of people who take the toaster apart to see how it works. The vast majority of desktop users, however, just want to make toast.
People like Zawinski, who have taken apart their fair share of toasters, also now just want to make toast. At present, Linux doesn't let him do that.”
https://apple.slashdot.org/story/05/06/12/1315235/jamie-zawinski-switches-to-mac-os-x#

HalluVault: A Novel Logic Programming-aided Metamorphic Testing Framework for Detecting Fact-Conflicting Hallucinations in Large Language Models
Ningke Li, Yuekang Li, Yi Liu, Ling Shi, Kailong Wang, Haoyu Wang
https://arxiv.org/abs/2405.00648

HalluVault: A Novel Logic Programming-aided Metamorphic Testing Framework for Detecting Fact-Conflicting Hallucinations in Large Language Models
Large language models (LLMs) have transformed the landscape of language processing, yet struggle with significant challenges in terms of security, privacy, and the generation of seemingly coherent but factually inaccurate outputs, commonly referred to as hallucinations. Among these challenges, one particularly pressing issue is Fact-Conflicting Hallucination (FCH), where LLMs generate content that directly contradicts established facts. Tackling FCH poses a formidable task due to two primary ob…

Gamu Blue: A Practical Tool for Game Theory Security Equilibria
Ameer Taweel, Burcu Y{\i}ld{\i}z, Alptekin K\"up\c{c}\"u
https://arxiv.org/abs/2403.19130

Gamu Blue: A Practical Tool for Game Theory Security Equilibria
The application of game theory in cybersecurity enables strategic analysis, adversarial modeling, and optimal decision-making to address security threats' complex and dynamic nature. Previous studies by Abraham et al. and Biçer et al. presented various definitions of equilibria to examine the security aspects of games involving multiple parties. Nonetheless, these definitions lack practical and easy-to-use implementations. Our primary contribution is addressing this gap by developing Gamu Blue…

Hulu changed subscriber ageeement
Account sharing on you to comply with..
"You will be responsible for any use of your account by your household, including compliance with this section."
However Hulu can analyze it and come to a judgment.
It also seems like they may make a service tier sometime into the future that makes this a paid thing... Which is an interesting idea.
"Unless otherwise permitted by your Service Tier, you may not share your subs…

A Model of Solar Magnetic Flux Rope Eruption Initiated Primarily by Magnetic Reconnection
Qingjun Liu, Chaowei Jiang, Xinkai Bian, Xueshang Feng, Pingbing Zuo, Yi Wang
https://arxiv.org/abs/2402.11172

A Model of Solar Magnetic Flux Rope Eruption Initiated Primarily by Magnetic Reconnection
There is a heated debate regarding the specific roles played by ideal magnetohydrodynamic (MHD) instability and magnetic reconnection in the causes of solar eruptions. In the context with a pre-existing magnetic flux rope (MFR) before an eruption, it is widely believed that an ideal MHD instability, in particular, the torus instability, is responsible for triggering and driving the eruption, while reconnection, as invoked in the wake of the erupting MFR, plays a secondary role. Here we present …

Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
Wei Xu, Derek Freeman DeSantis, Xihaier Luo, Avish Parmar, Klaus Tan, Balu Nadiga, Yihui Ren, Shinjae Yoo
https://arxiv.org/abs/2404.06418

Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network) with implicit neural networks. In this work, we design additional studies leveraging explainability methods to complement the previous experiments and further enhance the understanding of latent representations generated by the model. The adopted …

Latent Distance Guided Alignment Training for Large Language Models
Haotian Luo, Wenhao Zheng, Huaxiu Yao
https://arxiv.org/abs/2404.06390 https://<…

Latent Distance Guided Alignment Training for Large Language Models
Ensuring alignment with human preferences is a crucial characteristic of large language models (LLMs). Presently, the primary alignment methods, RLHF and DPO, require extensive human annotation, which is expensive despite their efficacy. The significant expenses associated with current alignment techniques motivate researchers to investigate the development of annotation-free alignment training methods. In pursuit of improved alignment without relying on external annotation, we introduce Latent…

Record-high Anomalous Ettingshausen effect in a micron-sized magnetic Weyl semimetal on-chip cooler
Mohammadali Razeghi, Jean Spiece, Valentin Fonck, Yao Zhang, Michael Rohde, Rikkie Joris, Philip S. Dobson, Jonathan M. R. Weaver, Lino da Costa Pereira, Simon Granville, Pascal Gehring
https://arxiv.org/abs/2403.13598

Record-high Anomalous Ettingshausen effect in a micron-sized magnetic Weyl semimetal on-chip cooler
Solid-state cooling devices offer compact, quiet, reliable and environmentally friendly solutions that currently rely primarily on the thermoelectric (TE) effect. Despite more than two centuries of research, classical thermoelectric coolers suffer from low efficiency which hampers wider application. In this study, the less researched Anomalous Ettingshausen effect (AEE), a transverse thermoelectric phenomenon, is presented as a new approach for on-chip cooling. This effect can be boosted in mat…

Emerging Platforms Meet Emerging LLMs: A Year-Long Journey of Top-Down Development
Siyuan Feng, Jiawei Liu, Ruihang Lai, Charlie F. Ruan, Yong Yu, Lingming Zhang, Tianqi Chen
https://arxiv.org/abs/2404.09151

Emerging Platforms Meet Emerging LLMs: A Year-Long Journey of Top-Down Development
Deploying machine learning (ML) on diverse computing platforms is crucial to accelerate and broaden their applications. However, it presents significant software engineering challenges due to the fast evolution of models, especially the recent \llmfull{s} (\llm{s}), and the emergence of new computing platforms. Current ML frameworks are primarily engineered for CPU and CUDA platforms, leaving a big gap in enabling emerging ones like Metal, Vulkan, and WebGPU. While a traditional bottom-up dev…