Authors: Mengying Yan, Meng Xia, Wei A. Huang, Chuan Hong, Benjamin A. Goldstein, Matthew M. Engelhard Affiliations: Duke AI Health, Department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering, Duke University School of Medicine Adversarial-Positive-Unlabeled-Domain=Adaptation Understanding the Research This research addresses a real-world challenge in healthcare: predicting long-term patient outcomes (e.g., 1-year mortality) in recent patient cohorts for whom such outcomes are not yet fully available. Traditional predictive models struggle when applied across time due to changes in clinical practice, patient populations, and label availability. To overcome this, the authors introduce an approach that combines adversarial domain adaptation and positive-unlabeled (PU) learning to enable prediction using partially labeled data. Motivation Predicting long-term outcomes is crucial in clinical settings, yet:

Authors: Zeyu Yang, Han Yu, Akane Sano Affiliations: Rice University, Department of Electrical and Computer Engineering; Plaid Contrastive-Pretraining for-StressDetection Understanding the Research This study explores a novel approach to detecting psychological stress by combining wearable time series data (e.g., heart rate, step count) with tabular survey data (e.g., personality traits, sleep quality). The research leverages a contrastive self-supervised learning framework to align the two modalities without requiring labeled stress data during pretraining. The method was evaluated on two real-world datasets—LifeSnaps and PMData—to show how multimodal fusion and pretraining can improve stress detection, especially in data-scarce scenarios. Motivation Detecting stress in everyday life is a critical yet challenging task due to:

Authors Arnav Pillai, Dimitris Spathis, Sujibya Nepal, Amanda C. Collins, Daniel M. Mackin, Michael V. Heinz, Tess Z. Griffin, Nicholas C. Jacobson, Andrew Campbell Affiliations: Dartmouth College, University of Cambridge, Stanford University, Google Research 🧠 Understanding the Research time2 Time2Lang is a novel framework designed to connect time-series data from sensors (like wearables or smartphones) with the capabilities of Large Language Models (LLMs), such as LLaMA or GPT. The research addresses a key technical challenge: how can you efficiently feed long, structured numerical data (e.g., heart rate, activity, temperature) into a language model that’s designed for text?

Introduction I'm aiming to develop a mobile application in which requires the realtime 3D rendering from the UDP packet that flows in 60fps. In this phase, I would like to discuss about how should I define the appropriate tech stacks from several perspective. Identifying Prerequisites Business persepective The first thing we have to work on is about business plans, since the capability of the application will affect out development phase, in cost, delivery, and quality. We should decide with careful manners. Currently, we are in the Proof of Concept phase, so we are more likely to be able to limit the range of devices to support, which means we can solely develop for iOS devices. We still have advantages if we choose languages or frameworks in which mak us easy to develop for both iOS and Android, therefore, let's analyze the pros and cons about them.

Overview Packages to use and configuring settings.py djangorestframework-simplejwt Simple JWT provides a JSON Web Token authentication backend for the Django REST Framework. It aims to cover the most common use cases of JWTs by offering a conservative set of default features. It also aims to be easily extensible in case a desired feature is not present Add following configurations: REST_FRAMEWORK = { ... 'DEFAULT_AUTHENTICATION_CLASSES': (

Overview This model is used for a general view of the data and is independent of a specific DBMS. The Object-based Logical Model represents this model. Conceptual data model is a mental image of a familiar physical object and are not specific to a database. To design a conceptual data model, you will follow steps: Draw an Entity-Relationship Diagram: To identify attributes and to establish the proper relationship sets. Define integrity constraints: Identify and document integriry constraints Review the final model: Remove M:N relationships, recursive relationships, super types and so on.

Simple Attributes Composite Attributes Single Valued Attributes Multi-Valued Attributes Derived Attributes Simple Attributes Simple attributes in an ER model diagram are independent attributes that can't be classified further and also, can't be subdivided into any other component. These attributes are also known as atomic attributes. Composite Attributes

Explanation Failed to sign in with Google account as you do not have permission. Background I was creating a blog site with Django+Next.js with Google Authentication watching an Tutorial video on Youtube. After I coded [...nextauth].js, _app.js, and navigation.js files, I got the title's error. The thing is that I developed with Next-auth v4 while the tutorial video used v3, so I had to modify some codes by referencing the official document.

Error explanation Tells us that some of components or classes are not correctly imported. For example import { ... } from "next-auth/client" #This importing may be failed Background

JSON Web Token JSON Web Tokens are an open, industry standard RFC 7519 method for representing claims securely between two parties (auth0)