'Thank God they're still alive': Kaiser therapists claim its new screening system puts patients at higher risk by delaying their care Kaiser pushed back on striking workers' claims and AI fears, saying it delivers'timely, high-quality care to meet members' needs' I lana Marcucci-Morris is worried about the patients she treats and how long it took for them to arrive in her office. At Kaiser Permanente's psychiatry outpatient clinic in Oakland, California, she says she increasingly finds herself assessing people experiencing severe mental health issues whom she believes should have been sent to the emergency room weeks earlier. For those who do make it to their appointments, she thinks: "Thank God they're still alive." It wasn't always this way, according to Marcucci-Morris, a licensed clinical social worker. Licensed professionals used to almost always be the first point of contact for patients with behavioral health issues at Kaiser, she said. She has noticed a change since January 2024, after the healthcare giant introduced a new screening process for first-time patients.

Claire chatted to Tanja Katharina Kaiser from the University of Technology Nuremberg about how applying evolutionary principles can help robot teams make better decisions. Tanja Katharina Kaiser is a senior researcher heading the Multi-Robot Systems Satellite Lab at the University of Technology Nuremberg (UTN) in Germany. She and her team focus on the development of adaptive multi-robot systems to solve complex real-world tasks using artificial intelligence. Tanja received her doctorate in robotics from the University of Lübeck in Germany in 2022. Before joining UTN, she held postdoctoral research positions at the Technical University of Dresden and the University of Konstanz.

The paper presents a framework of microservices-based architecture dedicated to enhancing the performance of real-time travel reservation systems using the power of predictive analytics. Traditional monolithic systems are bad at scaling and performing with high loads, causing backup resources to be underutilized along with delays. To overcome the above-stated problems, we adopt a modularization approach in decoupling system components into independent services that can grow or shrink according to demand. Our framework also includes real-time predictive analytics, through machine learning models, that optimize forecasting customer demand, dynamic pricing, as well as system performance. With an experimental evaluation applying the approach, we could show that the framework impacts metrics of performance such as response time, throughput, transaction rate of success, and prediction accuracy compared to their conventional counterparts. Not only does the microservices approach improve scalability and fault tolerance like a usual architecture, but it also brings along timely and accurate predictions, which imply a greater customer satisfaction and efficiency of operation. The integration of real-time analytics would lead to more intelligent decision-making, thereby improving the response of the system along with the reliability it holds. A scalable, efficient framework is offered by such a system to address the modern challenges imposed by any form of travel reservation system while considering other complex, data-driven industries as future applications. Future work will be an investigation of advanced AI models and edge processing to further improve the performance and robustness of the systems employed.

The rapid growth of the travel industry has increased the need for real-time optimization in reservation systems that could take care of huge data and transaction volumes. This study proposes a hybrid framework that ut folds an Artificial Intelligence and a Microservices approach for the performance optimization of the system. The AI algorithms forecast demand patterns, optimize the allocation of resources, and enhance decision-making driven by Microservices architecture, hence decentralizing system components for scalability, fault tolerance, and reduced downtime. The model provided focuses on major problems associated with the travel reservation systems such as latency of systems, load balancing and data consistency. It endows the systems with predictive models based on AI improved ability to forecast user demands. Microservices would also take care of different scales during uneven traffic patterns. Hence, both aspects ensure better handling of peak loads and spikes while minimizing delays and ensuring high service quality. A comparison was made between traditional reservation models, which are monolithic and the new model of AI-Microservices. Comparatively, the analysis results state that there is a drastic improvement in processing times where the system uptime and resource utilization proved the capability of AI and the microservices in transforming the travel industry in terms of reservation. This research work focused on AI and Microservices towards real-time optimization, providing critical insight into how to move forward with practical recommendations for upgrading travel reservation systems with this technology.

In the spring of 2017, in a room on the second floor of Google's Building 1965, a college intern named Aidan Gomez stretched out, exhausted. It was three in the morning, and Gomez and Ashish Vaswani, a scientist focussed on natural language processing, were working on their team's contribution to the Neural Information Processing Systems conference, the biggest annual meeting in the field of artificial intelligence. Along with the rest of their eight-person group at Google, they had been pushing flat out for twelve weeks, sometimes sleeping in the office, on couches by a curtain that had a neuron-like pattern. They were nearing the finish line, but Gomez didn't have the energy to go out to a bar and celebrate. He couldn't have even if he'd wanted to: he was only twenty, too young to drink in the United States.

Predicting the outcome of antiretroviral therapies for HIV-1 is a pressing clinical challenge, especially when the treatment regimen includes drugs for which limited effectiveness data is available. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN). The FC network employs tabular data with a feature vector made up of viral mutations identified in the most recent genotypic resistance test, along with the drugs used in therapy. Conversely, the GNN leverages knowledge derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence, to build informative graphs. We evaluated these models' robustness against Out-of-Distribution drugs in the test set, with a specific focus on the GNN's role in handling such scenarios. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model, especially when considering Out-of-Distribution drugs. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in real-world applications with limited data availability. This research highlights the potential of our approach to inform antiretroviral therapy outcome prediction and contribute to more informed clinical decisions.

This article's objective is to present a hand-wavy understanding of how Performers [1] work. Transformers dominate the deep-learning literature in 2022. Unfortunately, Transformers suffer quadratic complexity in the self-attention layer. This has hindered transformers for long-input signals, i.e., large sequence L. Large sequences are not critical in NLP applications since most sentences have less than 40 words. Yet, large sequences are abundant in other applications such as protein sequencing [1] and high-resolution medical images [4].

The mutual incompatibility of distinct spectroscopic systems is among the most limiting factors in Laser-Induced Breakdown Spectroscopy (LIBS). The cost related to setting up a new LIBS system is increased, as its extensive calibration is required. Solving the problem would enable inter-laboratory reference measurements and shared spectral libraries, which are fundamental for other spectroscopic techniques. In this work, we study a simplified version of this challenge where LIBS systems differ only in used spectrometers and collection optics but share all other parts of the apparatus, and collect spectra simultaneously from the same plasma plume. Extensive datasets measured as hyperspectral images of heterogeneous specimens are used to train machine learning models that can transfer spectra between systems. The transfer is realized by a pipeline that consists of a variational autoencoder (VAE) and a fully-connected artificial neural network (ANN). In the first step, we obtain a latent representation of the spectra which were measured on the Primary system (by using the VAE). In the second step, we map spectra from the Secondary system to corresponding locations in the latent space (by the ANN). Finally, Secondary system spectra are reconstructed from the latent space to the space of the Primary system. The transfer is evaluated by several figures of merit (Euclidean and cosine distances, both spatially resolved; k-means clustering of transferred spectra). The methodology is compared to several baseline approaches.

Attending ICRA is a great opportunity to see many state-of-the-art (and famous?) robots in a single venue. Indeed, a quick trip to the exhibitors' booths is enough to get introduced to the large and diverse group of commercial robots we have today. Yet, one can easily notice that these amazing state-of-the-art robots do not interact with each other. At least they do not do it without human mediation. Although in the exhibitions one can find two or three robots that appear to be joyfully playing together, the reality is that their operators are creating these inter-robot interactions.

Coastal communities around the world are especially vulnerable to flooding, storms, hurricanes and heavy rainfall. Now, scientists are studying whether artificial intelligence can better predict the impact of the storms. More information would help areas like New Orleans, Louisiana, which is forced to fix and rebuild after severe flooding. Clint Dawson, a professor at the University of Texas Austin, is part of a team of investigators working on a project funded by the Department of Energy's Office of Advanced Scientific Computing Research. "The only reason that place still exists is because there is fairly extensive levy system that protects it," Dawson said.