Fujifilm announced a new camera this week, the X-E5, the latest in its X-E rangefinder-style mirrorless camera series. Think of the X-E as an interchangeable lens version of the X100. The big news in the X-E5 is Fujifilm's latest 40-megapixel APS-C sensor and 7-stop in-body image stabilization (IBIS). This is the first X-E series camera with IBIS, which Fujifilm says will gain you about 7 stops of handholding. The new sensor also means video specs jump to 6.2K at 30 frames per second (with a 1.23 crop) and 4K 30 fps full sensor video. The X-E5 regains the focus mode switch on the side of the body (notably absent from the X-E4), and adds a new film simulation dial.

Rhythm is a fundamental aspect of human behaviour, present from infancy and deeply embedded in cultural practices. Rhythm anticipation is a spontaneous cognitive process that typically occurs before the onset of actual beats. While most research in both neuroscience and artificial intelligence has focused on metronome-based rhythm tasks, studies investigating the perception of complex musical rhythm patterns remain limited. To address this gap, we propose a hierarchical oscillator-based model to better understand the perception of complex musical rhythms in biological systems. The model consists of two types of coupled neurons that generate oscillations, with different layers tuned to respond to distinct perception levels. We evaluate the model using several representative rhythm patterns spanning the upper, middle, and lower bounds of human musical perception. Our findings demonstrate that, while maintaining a high degree of synchronization accuracy, the model exhibits human-like rhythmic behaviours. Additionally, the beta band neuronal activity in the model mirrors patterns observed in the human brain, further validating the biological plausibility of the approach.

In this paper, we propose a novel approach to Bayesian Experimental Design (BED) for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC^2 algorithm that uses a nested sequential Monte Carlo (SMC) estimator of the expected information gain and embeds it into a particle Markov chain Monte Carlo (pMCMC) framework to perform gradient-based policy optimization. This is in contrast to recent approaches that rely on biased estimators of the expected information gain (EIG) to amortize the cost of experiments by learning a design policy in advance. Numerical validation on a set of dynamical systems showcases the efficacy of our method in comparison to other state-of-the-art strategies.

Large language models (LLMs) can capture rich representations of concepts that are useful for real-world tasks. However, language alone is limited. While existing LLMs excel at text-based inferences, health applications require that models be grounded in numerical data (e.g., vital signs, laboratory values in clinical domains; steps, movement in the wellness domain) that is not easily or readily expressed as text in existing training corpus. We demonstrate that with only few-shot tuning, a large language model is capable of grounding various physiological and behavioral time-series data and making meaningful inferences on numerous health tasks for both clinical and wellness contexts. Using data from wearable and medical sensor recordings, we evaluate these capabilities on the tasks of cardiac signal analysis, physical activity recognition, metabolic calculation (e.g., calories burned), and estimation of stress reports and mental health screeners.

Physiological responses to pain have received increasing attention among researchers for developing an automated pain recognition sensing system. Though less explored, Blood Volume Pulse (BVP) is one of the candidate physiological measures that could help objective pain assessment. In this study, we applied machine learning techniques on BVP signals to device a non-invasive modality for pain sensing. Thirty-two healthy subjects participated in this study. First, we investigated a novel set of time-domain, frequency-domain and nonlinear dynamics features that could potentially be sensitive to pain. These include 24 features from BVP signals and 20 additional features from Inter-beat Intervals (IBIs) derived from the same BVP signals. Utilizing these features, we built machine learning models for detecting the presence of pain and its intensity. We explored different machine learning models, including Logistic Regression, Random Forest, Support Vector Machines, Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost). Among them, we found that the XGBoost offered the best model performance for both pain classification and pain intensity estimation tasks. The ROC-AUC of the XGBoost model to detect low pain, medium pain and high pain with no pain as the baseline were 80.06 %, 85.81 %, and 90.05 % respectively. Moreover, the XGboost classifier distinguished medium pain from high pain with ROC-AUC of 91%. For the multi-class classification among three pain levels, the XGBoost offered the best performance with an average F1-score of 80.03%. Our results suggest that BVP signal together with machine learning algorithms is a promising physiological measurement for automated pain assessment. This work will have a national impact on accurate pain assessment, effective pain management, reducing drug-seeking behavior among patients, and addressing national opioid crisis.

For many bird species, scientists don't have much information to figure out whether their populations are rising, falling or staying about the same. To get a better sense, researchers turned to a combination of big data and machine learning. Using over 10,000 species for which information was available, their model looked at correlations to predict population trends for the species they didn't have data for. They found that almost half of the birds with unknown population trends are declining, likely due to having severely fragmented populations. "I see endless possibilities for conservation biology when artificial intelligence is brought into the picture, and we are still not exploring enough," said Xuan Zhang, of Bird Ecology and Conservation Ontario, lead author of the study published in Ibis.

Piali De is a physicist who worked on creating AI systems for the U.S. defense departments, including programs after 9/11. She found the work meaningful, but in 2010, her father-in-law became ill and needed a heart valve replacement. After the procedure, he picked up an infection and his health went downhill. "In front of our eyes, he became reduced to a man who was very capable of taking care of himself to one who couldn't," De said in a phone interview. She and her husband Hugh Stoddart, who is also a physicist, came to a realization: "The [healthcare] system gets exponentially complicated if you have anything other than pristine health."

Artificial Intelligence is the hot topic of 2017. It can build cars, detect cancer, and Elon Musk created a company that will utilize AI to make us smarter. AI even made it onto the big screen in Hollywood starring Scarlett Johansen in "Ghost in the Shell." How is such an exciting and futuristic technology like AI ever really going to impact people on a practical level? Dr. Piali De is the Brown University physicist recognized for inventing the AI system that our U.S. Intelligence Agencies used after 9/11 to detect airplanes that could be a terrorist threat.