Zobrazeno 1 - 10
of 12 378
pro vyhledávání: '"Siddhartha, P."'
In this article, we study discrete maximal function associated with the Birch-Magyar averages over sparse sequences. We establish sparse domination principle for such operators. As a consequence, we obtain $\ell^p$-estimates for such discrete maximal
Externí odkaz:
http://arxiv.org/abs/2412.06348
Model finding, as embodied by SAT solvers and similar tools, is used widely, both in embedding settings and as a tool in its own right. For instance, tools like Alloy target SAT to enable users to incrementally define, explore, verify, and diagnose s
Externí odkaz:
http://arxiv.org/abs/2412.03310
Extracting work from a physical system is one of the cornerstones of quantum thermodynamics. The extractable work, as quantified by ergotropy, necessitates a complete description of the quantum system. This is significantly more challenging when the
Externí odkaz:
http://arxiv.org/abs/2412.02673
Rapid detection of foodborne bacteria is critical for food safety and quality, yet traditional culture-based methods require extended incubation and specialized sample preparation. This study addresses these challenges by i) enhancing the generalizab
Externí odkaz:
http://arxiv.org/abs/2411.19514
In this article, we have studied the dissociation temperature of 1S and 2S states of heavy quarkonium in the presence of anisotropy and a strong magnetic field background using the dissociation energy criterion. We utilized the medium-modified form o
Externí odkaz:
http://arxiv.org/abs/2411.18937
Autor:
Diesing, Rebecca, Gupta, Siddhartha
Near the ends of their lives, supernova remnants (SNRs) enter a "radiative phase," when efficient cooling of the postshock gas slows expansion. Understanding SNR evolution at this stage is crucial for estimating feedback in galaxies, as SNRs are expe
Externí odkaz:
http://arxiv.org/abs/2411.18679
In many real-world applications, continuous machine learning (ML) systems are crucial but prone to data drift, a phenomenon where discrepancies between historical training data and future test data lead to significant performance degradation and oper
Externí odkaz:
http://arxiv.org/abs/2411.15616
Autor:
Roh, Younghun, Wei, Yuanhao, Ruppert, Eric, Fatourou, Panagiota, Jayanti, Siddhartha, Shun, Julian
Many concurrent algorithms require processes to perform fetch-and-add operations on a single memory location, which can be a hot spot of contention. We present a novel algorithm called Aggregating Funnels that reduces this contention by spreading the
Externí odkaz:
http://arxiv.org/abs/2411.14420
Autor:
Ruis, Laura, Mozes, Maximilian, Bae, Juhan, Kamalakara, Siddhartha Rao, Talupuru, Dwarak, Locatelli, Acyr, Kirk, Robert, Rocktäschel, Tim, Grefenstette, Edward, Bartolo, Max
The capabilities and limitations of Large Language Models have been sketched out in great detail in recent years, providing an intriguing yet conflicting picture. On the one hand, LLMs demonstrate a general ability to solve problems. On the other han
Externí odkaz:
http://arxiv.org/abs/2411.12580
We consider a mesh network at the edge of a wireless network that connects users with the core network via multiple base stations. For this scenario we present a novel tree-search based algorithm that determines the optimal communication path to the
Externí odkaz:
http://arxiv.org/abs/2411.10228