Tootfinder

The Cost Advantage of Virtual Machine Migrations: Empirical Insights into Amazon's EC2 Marketspace
Benedikt Pittl, Werner Mach, Erich Schikuta
https://arxiv.org/abs/2508.14883

The Cost Advantage of Virtual Machine Migrations: Empirical Insights into Amazon's EC2 Marketspace
In recent years, cloud providers have introduced novel approaches for trading virtual machines. For example, Virtustream introduced so-called muVMs to charge cloud computing resources while other providers such as Google, Microsoft, or Amazon re-invented their marketspaces. Today, the market leader Amazon runs six marketspaces for trading virtual machines. Consumers can purchase bundles of virtual machines, which are called cloud-portfolios, from multiple marketspaces and providers. An industry…

Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection
Nils Dengler, Jesper M\"ucke, Rohit Menon, Maren Bennewitz
https://arxiv.org/abs/2506.02286

Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection
Service robots operating in cluttered human environments such as homes, offices, and schools cannot rely on predefined object arrangements and must continuously update their semantic and spatial estimates while dealing with possible frequent rearrangements. Efficient and accurate mapping under such conditions demands selecting informative viewpoints and targeted manipulations to reduce occlusions and uncertainty. In this work, we present a manipulation-enhanced semantic mapping framework for oc…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang, Alexandra F Dobrita, Yingfu Xu, Kanishkan Vadivel, Kevin Shidqi, Paul Detterer Mario Konijnenburg, Gert-Jan van Schaik, Manolis Sifalakis, Zaid Al-Ars, Amirreza Yousefzadeh
https://…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design optimized for executing edge AI SNN/ANN inference applications efficiently. To speed up the silicon tape-out and chip design for SENECA, an accurate emulator, SENSIM, was designed. While SENSIM supports direct mapping of SNNs on neuromorphic architectures, as the SNN and ANNs grow in size, achieving optimal mapping fo…

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Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Sha Zhao, Song Yi, Yangxuan Zhou, Jiadong Pan, Jiquan Wang, Jie Xia, Shijian Li, Shurong Dong, Gang Pan
https://arxiv.org/abs/2508.04723

Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Emotions critically influence mental health, driving interest in music-based affective computing via neurophysiological signals with Brain-computer Interface techniques. While prior studies leverage music's accessibility for emotion induction, three key limitations persist: \textbf{(1) Stimulus Constraints}: Music stimuli are confined to small corpora due to copyright and curation costs, with selection biases from heuristic emotion-music mappings that ignore individual affective profiles. \text…

Large-scale portfolio optimization with variational neural annealing
Nishan Ranabhat, Behnam Javanparast, David Goerz, Estelle Inack
https://arxiv.org/abs/2507.07159

Large-scale portfolio optimization with variational neural annealing
Portfolio optimization is a routine asset management operation conducted in financial institutions around the world. However, under real-world constraints such as turnover limits and transaction costs, its formulation becomes a mixed-integer nonlinear program that current mixed-integer optimizers often struggle to solve. We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), via its classical formulation implemented using …

Toward Supporting Narrative-Driven Data Exploration: Barriers and Design Opportunities
Oliver Huang, Carolina Nobre
https://arxiv.org/abs/2508.04920 https://

Toward Supporting Narrative-Driven Data Exploration: Barriers and Design Opportunities
Analysts increasingly explore data through evolving, narrative-driven inquiries, moving beyond static dashboards and predefined metrics as their questions deepen and shift. As these explorations progress, insights often become dispersed across views, making it challenging to maintain context or clarify how conclusions arise. Through a formative study with 48 participants, we identify key barriers that hinder narrative-driven exploration, including difficulty maintaining context across views, tr…

RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats
Arjun Juneja
https://arxiv.org/abs/2508.03879 https://arxiv.org/pdf/2508.…

RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats
Malware and cheat developers use fileless execution techniques to evade traditional, signature-based security products. These methods include various types of manual mapping, module stomping, and threadless injection which work entirely within the address space of a legitimate process, presenting a challenge for detection due to ambiguity between what is legitimate and what isn't. Existing tools often have weaknesses, such as a dependency on Portable Executable (PE) structures or a vulnerabilit…

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Tianyi Ding, Hongli Chen, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Hongfu Sun
https://arxiv.org/abs/2507.03369

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by matching signal evolutions to a predefined dictionary. However, conventional dictionary matching suffers from exponential growth in computational cost and memory usage as the number of parameters increases, limiting its scalability to multi-parametric mapping. To address this, recent work has explored deep learning-based approaches as alternatives to DM. We propose GAST-Mamba, an end-to-end framework that combines a du…

Tactile-Guided Robotic Ultrasound: Mapping Preplanned Scan Paths for Intercostal Imaging
Yifan Zhang, Dianye Huang, Nassir Navab, Zhongliang Jiang
https://arxiv.org/abs/2507.20282

Tactile-Guided Robotic Ultrasound: Mapping Preplanned Scan Paths for Intercostal Imaging
Medical ultrasound (US) imaging is widely used in clinical examinations due to its portability, real-time capability, and radiation-free nature. To address inter- and intra-operator variability, robotic ultrasound systems have gained increasing attention. However, their application in challenging intercostal imaging remains limited due to the lack of an effective scan path generation method within the constrained acoustic window. To overcome this challenge, we explore the potential of tactile c…