Deep learning techniques have greatly improved the ability to perform automatic diagnosis of breast cancer through use of computers; however, existing techniques are generally complex and costly, do not well incorporate multiple data modalities, and cannot be deployed clinically in real time. This paper proposes SQCDL-WIoT, which is a scalable, wearable-based internet of things (IoT)-enabled quantum convolutional deep learning (C-DL) framework capable of multi-class breast cancer screening. The proposed method includes two VQCs with five qubits each that can be embedded into a ResNet-50 back bone. In addition to being able to process images from histopathology and wearable-based bio sensor signals as input, the method also enables the incorporation of both wearable based bio sensor signal processing (i.e., wearable-IoT) and clinical variables via the Squeeze-and-Excitation (SE) attention mechanism. Thus, this proposed method is capable of hybrid quantum-classical training. Using stratified k-folds cross-validation (where k = 5) on the BreaKHis dataset and zero-shot transfer learning on the CBIS-DDSM dataset, SQCDL-WIoT demonstrated the highest accuracy (98.14%) with the highest sensitivity (macro-sensitivity = 97.63%), specificity (macro-specificity = 98.87%), and area under curve (AUC = 0.9891) when compared to all other baseline methods at 40x magnification on BreaKHis. Additionally, SQCDL-WIoT demonstrated 93.41% accuracy on...

Job Description The lab is part of the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) and is based at SciLifeLab Stockholm and KTH. We develop and apply computational a

Learn how AssemblyAI runs cost-optimized speech-to-text inference at scale on AWS, using Amazon ECS, Amazon EC2 Spot GPU instances, and an Amazon SQS-based retry architecture to achieve 70% cost savings while maintaining SLA commitments across European Regions.

Groups Conversations All groups and messages Sign in Machine Learning, Deep Learning and AI Conversations About Privacy • Terms     Taking AI-based transformations to the next level with prompt markup languages 0 views Skip to first unread message Eyal Estrin unread, 10:19 PM (15 minutes ago)    to https://www.cio.com/article/4246977/taking-ai-based-transformations-to-the-next-level-with-prompt-markup-languages.html Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

ITanaka. Nanoinformatics, 75–95 (Springer, SINGAPORE, 2018). Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: A review on methods and applications of machine learning for materials design. Engineering. 2019;5:1017–26. https://doi.org/10.1016/j.eng.2019.02.011. Chen C-T, Gu GX. Machine learning for composite materials. MRS Commun. 2019;9:556566 https://doi.org/10.1557/mrc.2019.32. Chen L, Pilania G, Batra R, Huan TD, Kim C, Kuenneth C, et al. Polymer informatics: Current status and critical next steps. Mater Sci Eng R: Rep. 2021;144:100595. https://doi.org/10.1016/j.mser.2020.100595. Doan Tran H, Kim C, Chen L, Chandrasekaran A, Batra R, Venkatram S, et al. Machine-learning predictions of polymer properties with polymer genome. J Appl Phys. 2020;128:171104. https://doi.org/10.1063/5.0023759. Tanaka I, Rajan K, Wolverton C. Data-centric science for materials innovation. MRS Bull. 2018;43:659663 https://doi.org/10.1557/mrs.2018.205. von Lilienfeld OA, Ramakrishnan R, Rupp M, Knoll A. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties. Int J Quantum Chem. 2015;115:1084–93. https://doi.org/10.1002/qua.24912. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: recent applications and prospects. npj Comput Mater. 2017;3:54 https://doi.org/10.1038/s41524-017-0056-5. Wang CC, Pilania G, Boggs SA, Kumar S, Breneman C, Ramprasad R. Computational strategies for polymer dielectrics design. Polymer. 2014;55:979–88. https://doi.org/10.1016/j.polymer.2013.12.069. Kadupitiya J,...

AI agents now run multi-step workloads for hours and act on behalf of users, raising the bar for secure, scalable agent compute. Learn how the next generation of Amazon Bedrock AgentCore runtime delivers hardware-isolated sessions with faster starts, more compute, usage-based billing, and isolation that now extends to your data.

This study focuses on incorporating a Neutrosophic-embedded fusion framework into deep learning networks for uncertainty modeling. The proposed framework introduces a learnable Neutrosophic module placed at the mid-level of the network, meaning it is embedded between the early feature extraction layers and the final classification layers. In this work, the network refers to backbone deep learning models, where the Neutrosophic module operates on intermediate feature representations rather than raw input images or final prediction scores. The framework is trained end-to-end. The aim of this work is to enhance multi-class classification of underwater images for bathymetry mapping. Bathymetry mapping refers to the process of mapping underwater terrain and measuring water depth. The proposed framework is evaluated using three representative architectures, namely a CNN-based model, a Vision Mamba-based sequence-modeling architecture, and a Transformer-based model. A fair comparison is ensured by comparing each enhanced model with its corresponding baseline. N-ResNet18, N-Mamba, and N-SAEViT are the enhanced versions of their respective baseline models. While ResNet18 and SAEViT are used for the detailed analyses as representative CNN and Transformer-based architectures, the Vision Mamba results are presented as additional validation of the framework on a sequence-modeling backbone. Each image input is explicitly divided into non-overlapping patches...

Learn how to expose an Amazon Bedrock Managed Knowledge Base to AI agents as MCP tools through Amazon Bedrock AgentCore Gateway while keeping per-user access control. A request interceptor injects verified identity, Policy in AgentCore validates it independently, and a resource-based policy restricts direct access to the knowledge base.

This post shows how to keep the AWS Agent Registry current automatically. Using AWS CloudTrail, Amazon EventBridge, AWS Lambda, and AgentCore Identity, an event-driven pipeline detects MCP server changes and pushes updated tool metadata to the registry with no stored secrets, across single- and multi-account environments.

AWS Marketplace now supports usage-based metering for Amazon SageMaker bidirectional streaming inference, so model providers can charge for what customers actually consume during a live stream. Deepgram is the launch partner, bringing usage-based speech-to-text and text-to-speech billing to AWS Marketplace.

Approach could provide scientists with a quicker, gentler way to gauge the health of bird populations

A comparison of Nanbeige4.3-33B, a top agentic coding model, and Arete-27B, a model fine-tuned for Western values. Choose based on your task and values.

Background: Maintaining functional ability is essential for healthy aging. The Five Times Chair Rise Test (5CRT) is widely used to assess lower-extremity function, yet conventional stopwatch timing provides limited insight into movement quality and compensatory strategies. Sensor-based biomechanics combined with machine learning (ML) enables detailed analysis of movement dynamics and supports digital health approaches for early detection and monitoring of functional decline. However, the clinical relevance of ML-based 5CRT movement patterns and their relationship to established functional outcomes and aging trajectories remains insufficiently understood. Objective: This study aimed (1) to classify 5CRT movement patterns using an established dynamic time warping (DTW)-based clustering workflow; (2) to examine associations between ML-based movement clusters and multidimensional indicators of strength, mobility, and functional performance across 24 months; and (3) to evaluate whether ML-based movement classification provides clinically meaningful differentiation beyond conventional stopwatch-based 5CRT timing within a digital health assessment context. Methods: In this longitudinal observational study, community-dwelling adults aged ≥70 years completed standardized functional assessments at baseline, 6 months, and 24 months, including Handgrip Strength, Stair Climb Power Test, Timed “Up & Go” Test, Short Physical Performance Battery (including 5CRT), 4-meter Gait Speed, Balance Testing, 6-minute Walking Test, and Countermovement Jump. The assessments were recorded using a force plate and depth camera to capture biomechanical time-series data. DTW and k-means clustering were applied to ground reaction force signals to identify movement patterns. Results: Among 172 participants included in the final analysis (mean age 76.9, SD 3.40 years; 96/172, 56% female), 2 distinct movement clusters were identified. Cluster 1 demonstrated faster chair rise performance and lower stabilization forces, whereas cluster 2 exhibited slower execution and increased stabilization demands. Across baseline, 6-month, and 24-month follow-up assessments, cluster 1 consistently showed higher muscle strength, greater leg power, faster mobility, higher gait speed, and longer walking distance than cluster 2 (all

About Clipboard Our mission is to uplift as many communities as possible. We do this through our app-based marketplace that connects healthcare professionals with the...