Zobrazeno 1 - 10
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pro vyhledávání: '"SHEN, Ke"'
In this study, energy-energy correlator (EEC) distributions for the $\rm D^0$, $\rm B^0$-tagged, inclusive, and the PYTHIA generated pure quark jets are computed in pp, p+Pb, and Pb+Pb collisions at \sqrts =5.02 TeV for a same jet transverse momentum
Externí odkaz:
http://arxiv.org/abs/2410.05081
The energy-energy correlator (EEC) is considered as a powerful probe of jet substructure, especially a better probe of certain soft and collinear features. To study the utility of such observable for quark vs gluon discrimination of jet quenching phe
Externí odkaz:
http://arxiv.org/abs/2409.13996
Autor:
Shen, Ke, Kejriwal, Mayank
In recent years,Text-to-SQL, the problem of automatically converting questions posed in natural language to formal SQL queries, has emerged as an important problem at the intersection of natural language processing and data management research. Large
Externí odkaz:
http://arxiv.org/abs/2409.10007
Autor:
Shen, Ke, Kejriwal, Mayank
Despite their impressive performance, large language models (LLMs) such as ChatGPT are known to pose important risks. One such set of risks arises from misplaced confidence, whether over-confidence or under-confidence, that the models have in their i
Externí odkaz:
http://arxiv.org/abs/2408.01935
Words of estimative probability (WEPs), such as ''maybe'' or ''probably not'' are ubiquitous in natural language for communicating estimative uncertainty, compared with direct statements involving numerical probability. Human estimative uncertainty,
Externí odkaz:
http://arxiv.org/abs/2405.15185
We investigate the thermoelectric effect, which describes the generation of an electric field induced by temperature and conserved charge chemical potential gradients, in the hot and dense hadronic matter created in heavy-ion collisions. Utilizing th
Externí odkaz:
http://arxiv.org/abs/2403.02705
Autor:
Shen, Ke, Kejriwal, Mayank
Large Language Models (LLMs), such as ChatGPT, have achieved impressive milestones in natural language processing (NLP). Despite their impressive performance, the models are known to pose important risks. As these models are deployed in real-world ap
Externí odkaz:
http://arxiv.org/abs/2310.03283
The paper describes a new supply capacity evaluation model based on the non-extensive statistical entropy. The traditional EW-TOPSIS model is selected as baseline and the GRA method is used to modify it. The correction results in the non-extensive pa
Externí odkaz:
http://arxiv.org/abs/2303.12190
Autor:
Shen, Ke, Kejriwal, Mayank
Acquiring commonsense knowledge and reasoning is an important goal in modern NLP research. Despite much progress, there is still a lack of understanding (especially at scale) of the nature of commonsense knowledge itself. A potential source of struct
Externí odkaz:
http://arxiv.org/abs/2210.01263
Autor:
Shen, Ke, Kejriwal, Mayank
Recent work on transformer-based neural networks has led to impressive advances on multiple-choice natural language understanding (NLU) problems, such as Question Answering (QA) and abductive reasoning. Despite these advances, there is limited work s
Externí odkaz:
http://arxiv.org/abs/2210.01258