Tootfinder

On embedding theorems in analytic and harmonic function spaces of several complex variables in some domains in $C^n$
R. F. Shamoyan, M. G. Bashmakova
https://arxiv.org/abs/2507.16980

On embedding theorems in analytic and harmonic function spaces of several complex variables in some domains in $C^n$
In this survey we collect some recent advances concerning embedding theorems in analytic and harmonic function spaces of several complex variables in various domains in $C^n$. The paper is based mainly only on recent results in this research area of the first author, but some recent new interesting nice, results of other authors will also be added in this survey in addition. Some rather short, but valuable discussions of related to this area of research problems in analytic and harmonic functio…

StarStream: Live Video Analytics over Space Networking
Miao Zhang, Jiaxing Li, Haoyuan Zhao, Linfeng Shen, Jiangchuan Liu
https://arxiv.org/abs/2508.14222 https://

StarStream: Live Video Analytics over Space Networking
Streaming videos from resource-constrained front-end devices over networks to resource-rich cloud servers has long been a common practice for surveillance and analytics. Most existing live video analytics (LVA) systems, however, have been built over terrestrial networks, limiting their applications during natural disasters and in remote areas that desperately call for real-time visual data delivery and scene analysis. With the recent advent of space networking, in particular, Low Earth Orbit (L…

Deep Research: A Survey of Autonomous Research Agents
Wenlin Zhang, Xiaopeng Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Huifeng Guo, Yong Liu, Xiangyu Zhao
https://arxiv.org/abs/2508.12752

Deep Research: A Survey of Autonomous Research Agents
The rapid advancement of large language models (LLMs) has driven the development of agentic systems capable of autonomously performing complex tasks. Despite their impressive capabilities, LLMs remain constrained by their internal knowledge boundaries. To overcome these limitations, the paradigm of deep research has been proposed, wherein agents actively engage in planning, retrieval, and synthesis to generate comprehensive and faithful analytical reports grounded in web-based evidence. In this…

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Festus Adedoyin, Huseyin Dogan
https://arxiv.org/abs/2506.15325

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Advancements in Artificial Intelligence (AI) have significantly transformed the financial industry, enabling the development of more personalised and adaptable financial products and services. This research paper explores various instances where Human-Centred AI (HCAI) has facilitated these advancements, drawing from contemporary studies and industry progress. The paper examines how the application of HCAI-powered data analytics, machine learning, and natural language processing enables financi…

MapIQ: Benchmarking Multimodal Large Language Models for Map Question Answering
Varun Srivastava, Fan Lei, Srija Mukhopadhyay, Vivek Gupta, Ross Maciejewski
https://arxiv.org/abs/2507.11625

MapIQ: Benchmarking Multimodal Large Language Models for Map Question Answering
Recent advancements in multimodal large language models (MLLMs) have driven researchers to explore how well these models read data visualizations, e.g., bar charts, scatter plots. More recently, attention has shifted to visual question answering with maps (Map-VQA). However, Map-VQA research has primarily focused on choropleth maps, which cover only a limited range of thematic categories and visual analytical tasks. To address these gaps, we introduce MapIQ, a benchmark dataset comprising 14,70…

Terabyte-Scale Analytics in the Blink of an Eye
Bowen Wu, Wei Cui, Carlo Curino, Matteo Interlandi, Rathijit Sen
https://arxiv.org/abs/2506.09226 https://

Terabyte-Scale Analytics in the Blink of an Eye
For the past two decades, the DB community has devoted substantial research to take advantage of cheap clusters of machines for distributed data analytics -- we believe that we are at the beginning of a paradigm shift. The scaling laws and popularity of AI models lead to the deployment of incredibly powerful GPU clusters in commercial data centers. Compared to CPU-only solutions, these clusters deliver impressive improvements in per-node compute, memory bandwidth, and inter-node interconnect pe…

A 20-Year Retrospective on Power and Thermal Modeling and Management
David Atienza, Kai Zhu, Darong Huang, Luis Costero
https://arxiv.org/abs/2508.05495 https://

A 20-Year Retrospective on Power and Thermal Modeling and Management
As processor performance advances, increasing power densities and complex thermal behaviors threaten both energy efficiency and system reliability. This survey covers more than two decades of research on power and thermal modeling and management in modern processors. We start by comparing analytical, regression-based, and neural network-based techniques for power estimation, then review thermal modeling methods, including finite element, finite difference, and data-driven approaches. Next, we c…

Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics
Yongjie Li, Ruilin Nong, Jianan Liu, Lucas Evans
https://arxiv.org/abs/2507.18949 https://

Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics
Large language models (LLMs) are revolutionizing the field of education by enabling personalized learning experiences tailored to individual student needs. In this paper, we introduce a framework for Adaptive Learning Systems that leverages LLM-powered analytics for personalized curriculum design. This innovative approach uses advanced machine learning to analyze real-time data, allowing the system to adapt learning pathways and recommend resources that align with each learner's progress. By co…

EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Issa Sugiura, Takashi Ishida, Taro Makino, Chieko Tazuke, Takanori Nakagawa, Kosuke Nakago, David Ha
https://arxiv.org/abs/2506.08762

EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Financial analysis presents complex challenges that could leverage large language model (LLM) capabilities. However, the scarcity of challenging financial datasets, particularly for Japanese financial data, impedes academic innovation in financial analytics. As LLMs advance, this lack of accessible research resources increasingly hinders their development and evaluation in this specialized domain. To address this gap, we introduce EDINET-Bench, an open-source Japanese financial benchmark design…

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
Jun-Hsiang Yao, Mingzheng Li, Jiayi Liu, Yuxiao Li, Jielin Feng, Jun Han, Qibao Zheng, Jianfeng Feng, Siming Chen
https://arxiv.org/abs/2505.23730

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges,…