Evaluating ChatGPT's Multilingual Performance in Clinical Nutrition Advice Using Synthetic Medical Text: Insights from Central Asia.

Background: While large language models like ChatGPT-4 have demonstrated competency in English, their performance for minority groups speaking underrepresented languages, as well as their ability to adapt to specific socio-cultural nuances and regional cuisines, such as those in Central Asia (e.g., Kazakhstan), still requires further investigation.

Stakeholder perspectives on the costs and benefits of circular construction.

The construction industry significantly impacts the environment through natural resource depletion and energy consumption, leading to environmental degradation. Circular Economy (CE) material efficiency strategies-such as material reuse, design for disassembly, prefabrication, and recycling-offer promising solutions for reducing resource consumption and waste. This paper explores stakeholders' perspectives on the costs and benefits of implementing CE material efficiency strategies in the construction industry, using the 3-R (Reduce, Reuse, Recycle) framework.

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously.

Faces in Event Streams (FES): An Annotated Face Dataset for Event Cameras.

The use of event-based cameras in computer vision is a growing research direction. However, despite the existing research on face detection using the event camera, a substantial gap persists in the availability of a large dataset featuring annotations for faces and facial landmarks on event streams, thus hampering the development of applications in this direction. In this work, we address this issue by publishing the first large and varied dataset (Faces in Event Streams) with a duration of 689 min for face and facial landmark detection in direct event-based camera outputs.

Image Captioning for the Visually Impaired and Blind: A Recipe for Low-Resource Languages.

Visually impaired and blind people often face a range of socioeconomic problems that can make it difficult for them to live independently and participate fully in society. Advances in machine learning pave new venues to implement assistive devices for the visually impaired and blind. In this work, we combined image captioning and text-to-speech technologies to create an assistive device for the visually impaired and blind.

A Central Asian Food Dataset for Personalized Dietary Interventions.

Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region.

Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images.

Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes on the basis of structural MRI and diffusion tensor imaging (DTI) scans from the OASIS-3 dataset.

COVID-19 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation.

Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and simulation can be used to predict the effects of different vaccination strategies.

A Particle-Based COVID-19 Simulator With Contact Tracing and Testing.

The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths.

A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases.

In this work, we present a particle-based SEIR epidemic simulator as a tool to assess the impact of different vaccination strategies on viral propagation and to model sterilizing and effective immunization outcomes. The simulator includes modules to support contact tracing of the interactions amongst individuals and epidemiological testing of the general population. The particles are distinguished by age to represent more accurately the infection and mortality rates.

SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams.

We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction, biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases. Data were collected from 142 subjects, yielding over 13,000 instances of synchronized data (∼3.

End-to-End Deep Diagnosis of X-ray Images.

We present an end-to-end deep learning frame-work for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs the dedicated abnormality classification network.

A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 With Region-Specific Policies.

In this work, we present an open-source stochastic epidemic simulator calibrated with extant epidemic experience of COVID-19. The simulator models a country as a network representing each node as an administrative region. The transportation connections between the nodes are modeled as the edges of this network.

Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation.

Autonomous dexterous manipulation relies on the ability to recognize an object and detect its slippage. Dynamic tactile signals are important for object recognition and slip detection. An object can be identified based on the acquired signals generated at contact points during tactile interaction.

Erratum: Human grasping database for activities of daily living with depth, color and kinematic data streams.

This corrects the article DOI: 10.1038/sdata.2018.

User-Independent Intent Recognition for Lower Limb Prostheses Using Depth Sensing.

Objective: The intent recognizers of advanced lower limb prostheses utilize mechanical sensors on the prosthesis and/or electromyographic measurements from the residual limb. Besides the delay caused by these signals, such systems require user-specific databases to train the recognizers. In this paper, our objective is the development and validation of a user-independent intent recognition framework utilizing depth sensing.

Human grasping database for activities of daily living with depth, color and kinematic data streams.

This paper presents a grasping database collected from multiple human subjects for activities of daily living in unstructured environments. The main strength of this database is the use of three different sensing modalities: color images from a head-mounted action camera, distance data from a depth sensor on the dominant arm and upper body kinematic data acquired from an inertial motion capture suit. 3826 grasps were identified in the data collected during 9-hours of experiments.

A feasibility study of depth image based intent recognition for lower limb prostheses.

This paper presents our preliminary work on a depth camera based intent recognition system intended for future use in robotic prosthetic legs. The approach infers the activity mode of the subject for standing, walking, running, stair ascent and stair descent modes only using data from the depth camera. Depth difference images are also used to increase the performance of the approach by discriminating between static and dynamic instances.

Vital sign monitoring utilizing Eulerian video magnification and thermography.

In this paper we present a proof of concept for non-contact extraction of vital signs using RGB and thermal images obtained from a smart phone. Using our method, heart rate, respiratory rate and forehead temperature can be measured concurrently. Face detection and tracking is leveraged in order to allow natural motion of patients.

MOSES: A Matlab-based open-source stochastic epidemic simulator.

This paper presents an open-source stochastic epidemic simulator. Discrete Time Markov Chain based simulator is implemented in Matlab. The simulator capable of simulating SEQIJR (susceptible, exposed, quarantined, infected, isolated and recovered) model can be reduced to simpler models by setting some of the parameters (transition probabilities) to zero.

Medical decision support using machine learning for early detection of late-onset neonatal sepsis.

Objective: The objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR).

Design: The data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children's Hospital at Vanderbilt and evaluated for late-onset sepsis.

Control of stair ascent and descent with a powered transfemoral prosthesis.

This paper presents a finite state-based control system for a powered transfemoral prosthesis that provides stair ascent and descent capability. The control system was implemented on a powered prosthesis and evaluated by a unilateral, transfemoral amputee subject. The ability of the powered prosthesis to provide stair ascent and descent capability was assessed by comparing the gait kinematics, as recorded by a motion capture system, with the kinematics provided by a passive prosthesis, in addition to those recorded from a set of healthy subjects.

Multigrasp myoelectric control for a transradial prosthesis.

This paper presents the design and preliminary experimental verification of a multigrasp myoelectric controller. The controller enables the direct and proportional control of a multigrasp transradial prosthesis through a set of nine postures using two surface EMG electrodes. Five healthy subjects utilized the multigrasp controller to manipulate a virtual prosthesis to experimentally quantify the performance of the controller in terms of real time performance metrics.

Ground adaptive standing controller for a powered transfemoral prosthesis.

The scope of this work is the design and verification of a new standing controller for a powered knee and ankle prosthesis. The controller is based upon a finite-state impedance control approach previously developed by the authors. The controller provides a comprehensive standing behavior that incorporates ground adaptation for unlevel terrain.

A method for the control of multigrasp myoelectric prosthetic hands.

This paper presents the design and preliminary experimental validation of a multigrasp myoelectric controller. The described method enables direct and proportional control of multigrasp prosthetic hand motion among nine characteristic postures using two surface electromyography electrodes. To assess the efficacy of the control method, five nonamputee subjects utilized the multigrasp myoelectric controller to command the motion of a virtual prosthesis between random sequences of target hand postures in a series of experimental trials.