Zobrazeno 1 - 10
of 16 671
pro vyhledávání: '"P Tandon"'
In our previous works, we defined Local Information Privacy (LIP) as a context-aware privacy notion and presented the corresponding privacy-preserving mechanism. Then we claim that the mechanism satisfies epsilon-LIP for any epsilon>0 for arbitrary P
Externí odkaz:
http://arxiv.org/abs/2410.12309
Deep Neural Network (DNN) based classifiers have recently been used for the modulation classification of RF signals. These classifiers have shown impressive performance gains relative to conventional methods, however, they are vulnerable to impercept
Externí odkaz:
http://arxiv.org/abs/2410.06339
Autor:
Kashyap, Pankhi, Tandon, Pavni, Gupta, Sunny, Tiwari, Abhishek, Kulkarni, Ritwik, Jadhav, Kshitij Sharad
Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of precise and dependable classification methods. Traditional loss funct
Externí odkaz:
http://arxiv.org/abs/2410.04084
Low Rank Adaptation (LoRA) is a popular Parameter Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. LoRA parameterizes model updates using low-rank matrices at each layer, significantly reducin
Externí odkaz:
http://arxiv.org/abs/2410.04060
Autor:
Bhattacharjee, Payel, Tandon, Ravi
Causal Graph Discovery (CGD) is the process of estimating the underlying probabilistic graphical model that represents joint distribution of features of a dataset. CGD-algorithms are broadly classified into two categories: (i) Constraint-based algori
Externí odkaz:
http://arxiv.org/abs/2409.19060
This paper considers the $\varepsilon$-differentially private (DP) release of an approximate cumulative distribution function (CDF) of the samples in a dataset. We assume that the true (approximate) CDF is obtained after lumping the data samples into
Externí odkaz:
http://arxiv.org/abs/2409.18573
Autor:
Nakajima, R., Arai, S., Aoyama, K., Utsumi, Y., Tamba, T., Odaka, H., Tanaka, M., Yorita, K., Aramaki, T., Asaadi, J., Bamba, A., Cannady, N., Coppi, P., De Nolfo, G., Errando, M., Fabris, L., Fujiwara, T., Fukazawa, Y., Ghosh, P., Hagino, K., Hakamata, T., Hijikata, U., Hiroshima, N., Ichihashi, M., Ichinohe, Y., Inoue, Y., Ishikawa, K., Ishiwata, K., Iwata, T., Karagiorgi, G., Kato, T., Kawamura, H., Krizmanic, J., Leyva, J., Malige, A., Mitchell, J. G., Mitchell, J. W., Mukherjee, R., Nakazawa, K., Okuma, K., Perez, K., Poudyal, N., Safa, I., Sasaki, M., Seligman, W., Shirahama, K., Shiraishi, T., Smith, S., Suda, Y., Suraj, A., Takahashi, H., Takashima, S., Tandon, S., Tatsumi, R., Tomsick, J., Tsuji, N., Uchida, Y., Watanabe, S., Yano, Y., Yawata, K., Yoneda, H., Yoshimoto, M., Zeng, J.
GRAMS (Gamma-Ray and AntiMatter Survey) is a next-generation balloon/satellite experiment utilizing a LArTPC (Liquid Argon Time Projection Chamber), to simultaneously target astrophysical observations of cosmic MeV gamma-rays and conduct an indirect
Externí odkaz:
http://arxiv.org/abs/2409.13209
Autor:
Moreno, Esperanza, Kumar, Piyush, Adansi, Richard O, Moreno, Dorothy, Rodriguez, Demy, Ramirez, Raul Baez, Kapsa, Audrey R, Rodriguez, Arturo, Agarwal, Neelam, Kumar, Vinod, Calvo, Beverley A, Tandon, Vivek
The ExploreSTEM Summer Camps 2023 were designed to deliver inclusive STEM education to students aged 14 to 22 years with disabilities. This paper presents a thorough examination of the 2023 camp program, emphasizing the pivotal role of inclusive STEM
Externí odkaz:
http://arxiv.org/abs/2409.12251
Autor:
George, Koshy, Poggianti, B. M., Omizzolo, A., Vulcani, B., Côté, P., Postma, J., Smith, R., Jaffe, Y. L., Gullieuszik, M., Moretti, A., Subramaniam, A., Sreekumar, P., Ghosh, S. K., Tandon, S. N., Hutchings, J. B.
The assembly of galaxy clusters is understood to be a hierarchical process with a continuous accretion of galaxies over time, which increases the cluster size and mass. Late-type galaxies that fall into clusters can undergo ram-pressure stripping, fo
Externí odkaz:
http://arxiv.org/abs/2409.10586
Autor:
K, Haritha, Burra, Ramya, Mittal, Srishti, Sharma, Sarthak, Venkatesh, Abhilash, Tandon, Anshoo
This work contributes towards the development of an efficient and scalable open-source Secure Multi-Party Computation (SMPC) protocol on machines with moderate computational resources. We use the ABY2.0 SMPC protocol implemented on the C++ based MOTI
Externí odkaz:
http://arxiv.org/abs/2408.16387