Zobrazeno 1 - 10
of 4 166
pro vyhledávání: '"Magar A"'
Autor:
Magar Ashok, Lal Achchhe
Publikováno v:
Curved and Layered Structures, Vol 8, Iss 1, Pp 1-12 (2021)
This paper presents the solution of stress distribution around elliptical cutout in an infinite laminated composite plate. Analysis is done for in plane loading under hygrothermal environment. The formulation to obtain stresses around elliptical hole
Externí odkaz:
https://doaj.org/article/6ac20aa8a89b423bbae3dcf341566d11
Autor:
Jamba Team, Lenz, Barak, Arazi, Alan, Bergman, Amir, Manevich, Avshalom, Peleg, Barak, Aviram, Ben, Almagor, Chen, Fridman, Clara, Padnos, Dan, Gissin, Daniel, Jannai, Daniel, Muhlgay, Dor, Zimberg, Dor, Gerber, Edden M, Dolev, Elad, Krakovsky, Eran, Safahi, Erez, Schwartz, Erez, Cohen, Gal, Shachaf, Gal, Rozenblum, Haim, Bata, Hofit, Blass, Ido, Magar, Inbal, Dalmedigos, Itay, Osin, Jhonathan, Fadlon, Julie, Rozman, Maria, Danos, Matan, Gokhman, Michael, Zusman, Mor, Gidron, Naama, Ratner, Nir, Gat, Noam, Rozen, Noam, Fried, Oded, Leshno, Ohad, Antverg, Omer, Abend, Omri, Lieber, Opher, Dagan, Or, Cohavi, Orit, Alon, Raz, Belson, Ro'i, Cohen, Roi, Gilad, Rom, Glozman, Roman, Lev, Shahar, Meirom, Shaked, Delbari, Tal, Ness, Tal, Asida, Tomer, Gal, Tom Ben, Braude, Tom, Pumerantz, Uriya, Cohen, Yehoshua, Belinkov, Yonatan, Globerson, Yuval, Levy, Yuval Peleg, Shoham, Yoav
We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retai
Externí odkaz:
http://arxiv.org/abs/2408.12570
A detailed study of the structural and magnetic properties of the spin-$3/2$ hyperkagome lattice compound Li$_2$MgMn$_3$O$_8$ is reported. This material shows ferromagnetic response below $T_{\rm C} \simeq 20.6$ K, the temperature almost three times
Externí odkaz:
http://arxiv.org/abs/2406.17623
We present spiral resonators of thin film niobium (Nb) that exhibit large geometric inductance, high critical magnetic fields and high single photon quality factors. These low loss geometric inductors can be a compelling alternative to kinetic induct
Externí odkaz:
http://arxiv.org/abs/2406.10386
Autor:
Mohanty, S., Magar, A., Singh, Vikram, Islam, S. S., Guchhait, S., Jain, A., Yusuf, S. M., Tsirlin, A. A., Nath, R.
A detailed study of the magnetic and magnetocaloric properties of a garnet compound Mn$_{3}$Cr$_{2}$Ge$_{3}$O$_{12}$ is carried out using x-ray diffraction, magnetization, heat capacity, and neutron diffraction measurements as well as \textit{ab init
Externí odkaz:
http://arxiv.org/abs/2403.02082
Autor:
Magar-Sawant, Priyanka
In this paper, the concordance structure set of connected sums of complex and quaternionic projective spaces in the real $n$-dimensional range with $8\leq n\leq 16$ is computed. It is demonstrated that the concordance inertia group of a connected sum
Externí odkaz:
http://arxiv.org/abs/2403.02341
Autor:
Ock, Janghoon, Badrinarayanan, Srivathsan, Magar, Rishikesh, Antony, Akshay, Farimani, Amir Barati
Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determining the lowest energy across various adsorption configurations on a ca
Externí odkaz:
http://arxiv.org/abs/2401.07408
Explanations on relational data are hard to verify since the explanation structures are more complex (e.g. graphs). To verify interpretable explanations (e.g. explanations of predictions made in images, text, etc.), typically human subjects are used
Externí odkaz:
http://arxiv.org/abs/2401.02703
The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks. However, in many cases, we would wan
Externí odkaz:
http://arxiv.org/abs/2312.08287
In-context learning (ICL) has shown impressive results in few-shot learning tasks, yet its underlying mechanism is still not fully understood. A recent line of work suggests that ICL performs gradient descent (GD)-based optimization implicitly. While
Externí odkaz:
http://arxiv.org/abs/2311.07772