Breakthroughs, discoveries, and DIY tips sent every weekday. Making new friends (especially as an adult) can be challenging. When new birds are introduced to a group, monk parakeets will "test the waters" to avoid getting injured by defensive strangers. The parakeets will gradually approach the new bird, taking some time to get familiar before ramping up to more risky or vulnerable interactions that are needed to form the bonds necessary for survival. "There can be a lot of benefits to being social, but these friendships have to start somewhere," said Claire O'Connell, a study co-author and a doctoral student in the University of Cincinnati, said in a statement .

Autonomous space robotics is poised to play a vital role in future space missions, particularly for In-space Servicing, Assembly, and Manufacturing (ISAM). A key capability in such missions is the Robot-to-Robot (R2R) handover of mission-critical objects. This work presents a dynamic model of a dual-arm space manipulator system and compares various tracking control laws. The key contributions of this work are the development of a cooperative manipulator dynamic model and the comparative analysis of control laws to support autonomous R2R handovers in ISAM scenarios. INTRODUCTION The global space industry has grown significantly over the past decade and is expected to continue expanding. In-space Servicing, Assembly, and Manufacturing (ISAM) is emerging as a transfor-mative approach to space operations.

We present a reinforcement learning method for training neuro-fuzzy controllers using Proximal Policy Optimization (PPO). Unlike prior approaches that used Deep Q-Networks (DQN) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS), our PPO-based framework leverages a stable on-policy actor-critic setup. Evaluated on the CartPole-v1 environment across multiple seeds, PPO-trained fuzzy agents consistently achieved the maximum return of 500 with zero variance after 20, 000 updates, outperforming ANFIS-DQN baselines in both stability and convergence speed. This highlights PPO's potential for training explainable neuro-fuzzy agents in reinforcement learning tasks.

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop a de-biasing method for mental health text data. Methods: We examined classification parity across demographic groups and assessed how gendered language influences model predictions. A data-centric de-biasing method was applied, focusing on neutralizing biased terms while retaining salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients. Results: Our findings revealed a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced diagnostic bias by up to 27%, demonstrating its effectiveness in enhancing equity across demographic groups. Discussion: We developed a data-centric de-biasing framework to address gender-based content disparities within clinical text. By neutralizing biased language and enhancing focus on clinically essential information, our approach demonstrates an effective strategy for mitigating bias in AI healthcare models trained on text.

After a first date it's normal to wonder if those warm, fuzzy feelings are reciprocated. Now, experts are one step closer to an app that will tell you if they're'just not that into you'. Researchers have trained a computer - using data from wearable technology that measures respiration, heart rates and perspiration – to identify the type of conversation two people are having. In experiments with 16 pairs of participants, it was able to differentiate four different conversation scenarios with as much as 75 per cent accuracy. Lead author Iman Chatterjee, from the University of Cincinnati, said the technology could one day give you honest feedback about yourself or your date.

Abstract-- This paper considers cooperative control of robots involving two different testbed systems in remote locations with communication on the internet. This provides us the capability to exchange robots status like positions, velocities and directions needed for the swarming algorithm. The results show that all robots properly follow some leader defined one of the testbeds. Measurement of data exchange rates show no loss of packets, and average transfer delays stay within tolerance limits for practical applications. I. INTRODUCTION The efficient co-operation between multiple agents situated at distinct locations while pursuing common While the topic raises fundamental questions related to a variety of fields such as communication systems and distributed co-operative control, it is of immense practical of California Los Angeles (UCLA) Applied Mathematics interest as well.

We innovate the way brands are built. That means we do things differently so they're better than before. In this way, we make our clients' most important marketing assets--their brands--win in a changing world. Dentsu International is a modern marketing solutions company. Our mission is to help clients navigate, progress, and thrive in a world of change.

Could an AI understand or appreciate art? Is it possible for a computer to form a critical opinion? Or have an aesthetic response? What does it "see" when it looks at a work of art? To explore these and other questions, we enlisted an AI (or, more precisely, several AI models) to co-produce this curatorial project. Complicating matters, there are no images anywhere, either in the gallery or the PDF.

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.

Driverless or autonomous vehicles are currently on the road today making multiple trips in certain test areas. While many of these cars are test vehicles and have a human driver sitting behind the wheel to take over in case a problem happens some of them are completely driverless. Companies like Uber, Apple, Google, Toyota, GM, Tesla and more are researching and investing in driverless car technology. Driverless cars will change the face of real estate as we know it and even the very home and neighborhoods we live in will be impacted by driverless vehicle technologies. This article explores some of the impact driverless vehicles will have on where we live and work.