-
Comparison of extrafine and non-extrafine inhaled corticosteroids/long-acting beta2-agonists as maintenance and reliever therapy in asthma: a randomized open-label crossover trial
CONCLUSION: Our study demonstrated that extrafine ICS treatment provided no significant advantage over non-extrafine ICS in improving small airway obstruction or overall asthma control in moderate asthma. This suggests that factors other than particle size may contribute to treatment outcomes. →
-
Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data
CONCLUSIONS: The AU-ROC values indicated moderately meaningful effect sizes in identifying prescriptive predictors within multivariable models for clients with SAD. Focusing on the identified notable client strengths, weaknesses, and Chinese ethnicity may enhance our ability to predict future responses to scalable treatments. Estimating the likelihood of SAD remission with a «prescriptive predictor calculator» for each… →
-
A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChain
In this tutorial, we lean hard on Together AI’s growing ecosystem to show how quickly we can turn unstructured text into a question-answering service that cites its sources. We’ll scrape a handful of live web pages, slice them into coherent chunks, and feed those chunks to the togethercomputer/m2-bert-80M-8k-retrieval embedding model. Those vectors land in a… →
-
Agent-Based Debugging Gets a Cost-Effective Alternative: Salesforce AI Presents SWERank for Accurate and Scalable Software Issue Localization
Identifying the exact location of a software issue—such as a bug or feature request—remains one of the most labor-intensive tasks in the development lifecycle. Despite advances in automated patch generation and code assistants, the process of pinpointing where in the codebase a change is needed often consumes more time than determining how to fix it.… →
-
This AI Paper Investigates Test-Time Scaling of English-Centric RLMs for Enhanced Multilingual Reasoning and Domain Generalization
Reasoning language models, or RLMs, are increasingly used to simulate step-by-step problem-solving by generating long, structured reasoning chains. These models break down complex questions into simpler parts and build logical steps to reach answers. This chain-of-thought (CoT) approach has proven effective in improving output quality, especially in mathematical and logical tasks. Despite multilingual capabilities in… →
-
Rethinking Toxic Data in LLM Pretraining: A Co-Design Approach for Improved Steerability and Detoxification
In the pretraining of LLMs, the quality of training data is crucial in determining model performance. A common strategy involves filtering out toxic content from the training corpus to minimize harmful outputs. While this approach aligns with the principle that neural networks reflect their training data, it introduces a tradeoff. Removing toxic content can reduce… →
-
Time Well Spent: A New Way to Value Time Could Change Your Life
Jon Krause/theispot.com The Research The researchers conducted multiple studies — with diverse samples of working professionals — to understand how people use their time, what is important to them in life, and what factors lead people to live more fulfilling and satisfying lives. Their samples included over 2,000 Harvard Business School alumni, from fresh graduates… →
-
Building Better Answers With AI Agents: SAP’s Walter Sun
The suggestions and support offered by AI are helpful only if they’re relevant. On today’s episode, Walter Sun, senior vice president and global head of artificial intelligence at SAP, joins hosts Sam Ransbotham and Shervin Khodabandeh to share how his organization is helping employees get smarter about artificial intelligence through the company’s AI Days. Additionally,… →
-
Podcast: Preventing a Potential Culture Clash at Work
When Chinese manufacturer Fuyao Glass opened a plant in Moraine, Ohio, it hadn’t anticipated that its newly hired American workforce would be less productive than its Chinese workers. That’s partially because it made a classic mistake, says Cynthia Wang, clinical professor of management and organizations. It forgot to consider the culture of its new workforce—specifically,… →