Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Kai Wan, Hua Sun, Shiqiang Wang, Mingyue Ji | Summary: Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their summation. It finds broad applications in distributed […]


Continue.. Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion

Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Kai Wan, Hua Sun, Shiqiang Wang, Mingyue Ji | Summary: Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their summation. It finds broad applications in distributed […]


Continue.. Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion

Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Kai Wan, Hua Sun, Shiqiang Wang, Mingyue Ji | Summary: Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their summation. It finds broad applications in distributed […]


Continue.. Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion

GRB Redshift Estimation using Machine Learning and the Associated Web-App

Kavli Affiliate: Vahe Petrosian | Summary:Context. Gamma-ray bursts (GRBs), observed at redshifts as high as 9.4, could serve as valuable probes for investigating the distant Universe. However, this necessitates an increase in the number of GRBs with determined redshifts, as currently, only 12% of GRBs have known redshifts due to observational biases. Aims. We aim […]


Continue.. GRB Redshift Estimation using Machine Learning and the Associated Web-App

Trojan Prompt Attacks on Graph Neural Networks

Kavli Affiliate: Xiang Zhang | First 5 Authors: Minhua Lin, Zhiwei Zhang, Enyan Dai, Zongyu Wu, Yilong Wang | Summary: Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning of the entire model. Despite the advantages of GPL, […]


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Role of dopamine in reward expectation and predictability during execution of action sequences

Kavli Affiliate: Patricia Janak | Authors: Robin Magnard, Yifeng Cheng, Joanna Zhou, Haley Province, Nathalie Thiriet, Patricia H Janak and Youna Vandaele | Summary: Reward-associated cues serve different functions depending on whether they precede or terminate action sequences. Cues that precede action sequences and signal opportunity for reward could serve as GO signals to initiate […]


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LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation

Kavli Affiliate: Wei Gao| Summary:Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. Motivated by the efficiency gains of hybrid models and the broad availability of pretrained large transformer backbones, we explore transitioning transformer models into hybrid architectures for a more efficient generation. In […]


Continue.. LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation

Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

Kavli Affiliate: Carlos Wagner | Summary:The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector […]


Continue.. Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

Kavli Affiliate: Carlos E. M. Wagner | First 5 Authors: , , , , | Summary: The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter […]


Continue.. Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC