Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of homogenisation pressure on the microstructure of milk during evaporation and drying: particle-size distribution, electronic scanning microscopy, water activity and isotherm

Abstract Homogenisation is a widely used technique in manufacturing powdered milk with a direct impact on product solubility, and the homogenisation pressure is a central attribute of this process. We aimed to understand the effect of increasing homogenisation pressures (0/0, 15/5, and 75/5 MPa, 1st/2nd stages) on particle-size distribution during homogenised whole milk powder manufacture and rehydration of the final product. The fluid milk was thermally treated, homogenised, concentrated by rotary evaporation, and then dried using a spray dryer. Particle size (Dv90) was monitored at all stages of the manufacturing process. The final product (milk powder) was analysed using particle-size distribution, electronic scanning microscopy, water activity, and isotherms. The results demonstrated that increasing the homogenisation pressure leads to milk powder with smaller particle size when rehydrated (Dv90 values: 6.08, 1.48 and 0.64 μm for 0, 20 and 80 MPa, respectively). Furthermore, the volume (%) of the particles in the ‘sub-micro’ region (smaller than 1.0 μm) presented an inversely proportional profile to the homogenisation pressure (homogenised fluid milk: 86.1, 29.3 and 2.4%; concentrated milk: 86.1, 26.5 and 5.7%, and reconstituted milk powder: 84.2, 31.8 and 10.9%). Surprisingly, this pattern was not observed in the SPAN value (which corresponds to the width or range of the size distribution based on the volume). Additionally, the increase in the homogenisation pressure did not affect the sorption isotherm pattern. These results demonstrate that increasing the homogenisation pressure decreases the particle size of the reconstituted powdered milk, indicating the potential for future studies on how this phenomenon affects its physicochemical and final product properties.
Would You Screen This Patient for Cognitive Impairment?

Dementia, according to the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, is defined by a significant decline in 1 or more cognitive domains that interferes with a person's independence in daily activities. Mild cognitive impairment (MCI) differs from dementia in that the impairment is not sufficient to interfere with independence. For the purposes of this discussion, cognitive impairment (CI) includes both dementia and MCI. Various screening tests are available for CI. These tests ask patients to perform a series of tasks that assess 1 or more domains of cognitive function or ask a caregiver to report on the patient's abilities. A positive result on a screening test does not equate to a diagnosis of CI; rather, it should lead to additional testing to confirm the diagnosis. On review of the evidence, the U.S. Preventive Services Task Force (USPSTF) concluded in 2020 that the evidence was insufficient to assess the balance of benefits and harms of screening for CI in older adults ("I statement"). The USPSTF did clarify that although there is insufficient evidence, there may be important reasons to identify CI. In this article, 2 experts review the available evidence to answer the following questions: What screening tools are available, and how effective are they in identifying patients with CI? What interventions are available for patients found to have CI, to what extent do they improve patient outcomes, and what, if any, negative effects occur? And, would they recommend screening for CI, and why or why not?
Calibrating 3D tomograms of a granitic pluton

First arrival‐time P‐waves from offset and azimuth variable vertical seismic reflection records are used to tomographically reconstruct the 3D P‐wave velocity structure around a 500 m deep vertical borehole. Comparisons with sonic and fracture index logs and core samples indicates that low velocity anomalies in the tomograms correlate with surface mapped fracture zones. Results also show that low velocity zones intersect the borehole in areas characterized by relatively low values for Vp and Vs determined from sonic logs. These zones are characterized by Poisson's ratio (σ) close to 0.3, which are indicative of altered or fractured rock and supported by the fracture index and core samples. The high velocity anomalies intersect the well in areas characterized by low values of the fracture index and σ = 0.25 or lower. This suggests that the relatively high Vp velocities are indicative of unfractured rock. This study reveals that high resolution tomography produces results consistent with direct observations in a well‐sampled borehole.
Some paleomagnetic constraints on the tectonic evolution of the coastal cordillera of central Chile

Paleomagnetic samples collected from 19 sites in radioisotopically dated Jurassic mafic dykes and plutons in the coastal cordillera of Chile between latitude 32°15′ and 33°30′ S yield Paleomagnetic poles (calculated by averaging the virtual geomagnetic poles from approximately coeval sites) at latitude 85.9°S, longitude 36.7°E (N = 8, a95 = 11.9°) for sites dated at close to 170 Ma and at latitude 80.3°S, longitude 207.8°E (N = 10, a95 = 9.5°) for sites dated at close to 145 Ma. These paleomagnetic poles from the coast of Chile coincide with poles from coeval sequences within the South American craton. Some of the igneous rocks that constitute these sampling sites crosscut a high-grade metamorphic complex that is distinct from other segments of the coastal cordillera in age, structural style, and metamorphic grade. This complex cannot be correlated with metamorphic rocks anywhere else in central western South America and could be considered a potentially “exotic” allochthonous terrane. If this terrane has been added to the western South American margin, then accretion occurred prior to 170 Ma. Paleomagnetic results presented here indicate that no major rotation or large-scale latitudinal translation of crustal blocks has taken place in this part of central Chile since Middle Jurassic time.
Card9/neutrophil signalling axis promotes IL-17A-mediated ankylosing spondylitis

Objective Polymorphisms in the antifungal signalling molecule CARD9 are associated with ankylosing spondylitis (AS). Here, we investigated the cellular mechanism by which CARD9 controls pathogenic Th17 responses and the onset of disease in both experimental murine AS and patients. Methods Experiments in SKG, Card9−/−SKG, neutrophil-deplete SKG mice along with in vitro murine, neutrophil and CD4+ T cell cocultures examined Card9 function in neutrophil activation, Th17 induction and arthritis in experimental AS. In AS patients the neutrophil: Bath Ankylosing Spondylitis Functional Index relationship was analysed. In vitro studies with autologous neutrophil: T cell cocultures examined endogenous CARD9 versus the AS-associated variant (rs4075515) of CARD9 in T cellular production of IL-17A. Results Card9 functioned downstream of Dectin-1 and was essential for induction of Th17 cells, arthritis and spondylitis in SKG mice. Card9 expression within T cells was dispensable for arthritis onset in SKG mice. Rather, Card9 expression controlled neutrophil function; and neutrophils in turn, were responsible for triggering Th17 expansion and disease in SKG mice. Mechanistically, cocultures of zymosan prestimulated neutrophils and SKG T cells revealed a direct cellular function for Card9 within neutrophils in the potentiation of IL-17 production by CD4+ T cells on TCR-ligation. The clinical relevance of the neutrophil-Card9-coupled mechanism in Th17-mediated disease is supported by a similar observation in AS patients. Neutrophils from HLA-B27+ AS patients expanded autologous Th17 cells in vitro, and the AS-associated CARD9S12N variant increased IL-17A. Conclusions These data reveal a novel neutrophil-intrinsic role for Card9 in arthritogenic Th17 responses and AS pathogenesis. These data provide valuable utility in our future understanding of CARD9-specific mechanisms in spondyloarthritis .
Towards the generation of synchronized and believable non-verbal facial behaviors of a talking virtual agent

This paper introduces a new model to generate rhythmically relevant non-verbal facial behaviors for virtual agents while they speak. The model demonstrates perceived performance comparable to behaviors directly extracted from the data and replayed on a virtual agent, in terms of synchronization with speech and believability. Interestingly, we found that training the model with two different sets of data, instead of one, did not necessarily improve its performance. The expressiveness of the people in the dataset and the shooting conditions are key elements. We also show that employing an adversarial model, in which fabricated fake examples are introduced during the training phase, increases the perception of synchronization with speech. A collection of videos demonstrating the results and code can be accessed at: https://github.com/aldelb/non_verbal_facial_animation.
Sickness Reduction in FPV Drone Control: Improved Effects of Reverse Optical Flow with Static Landmarks Only

Drones are controlled remotely through the on-board live camera often using a headset to immersively (without external distraction) situate the drone operator with a first-person view. The highly dynamic imagery of drone piloting can elicit significant VR sickness. In this poster, we demonstrate the application of mixing the reverse optical flow pattern into the drone piloting imagery for mitigating VR sickness, using only the features of the static landmarks in the scene. We compare it to the cases of applying no mitigation technique (baseline) and mixing in the optical flow pattern from “all” object features. The results show that the suggested method was significantly more effective in reducing sickness than when considering all object features, and had a higher preference for improved visibility and controllability.
Redirecting Rays: Evaluation of Assistive Raycasting Techniques in Virtual Reality

Raycasting-based interaction techniques are widely used for object selection in immersive environments. Despite their intuitive use, they come with challenges due to small or far away objects, hand tremor, and tracking inaccuracies. Previous adaptations for raycasting, such as directly snapping the ray to the closest target, extruding the ray to a cone, or multi-step selection techniques, require additional time for users to become familiar with them. To address these issues, we propose three assistive techniques in which the visible selection ray is subtly redirected towards a target, with a proximity and gain based increase in the redirection amount. In a user study (N = 26), we compared these redirection techniques with a baseline condition based on a Fitts’ law task and collected performance measures as well as comprehensive subjective feedback. The results indicate that the three redirection techniques are significantly faster and have higher effective throughput than the baseline condition. Participants retained a high sense of agency with all redirection techniques and reported significantly lower total workload compared to the baseline. The majority of participants preferred selection with assistive ray redirection and perceived it as not distracting or intrusive. Our findings support that assistive redirected raycasting techniques can improve object selection performance and user experience in virtual environments.
Designing Deep Learning Models on FPGA with Multiple Heterogeneous Engines

Deep learning models are becoming more complex and heterogeneous with new layer types to improve their accuracy. This brings a considerable challenge to the designers of accelerators of deep neural networks. There have been several architectures and design flows to map deep learning models on hardware, but they are limited to a particular model and/or layer types. Also, the architectures generated by these tools target, in general, high-performance devices, not appropriate for embedded computing. This paper proposes a multi-engine architecture and a design flow to implement deep learning models on FPGA. The hardware design uses high-level synthesis to allow design space exploration. The architecture is scalable and therefore applicable to any density FPGAs. The architecture and design flow were applied to the development of a hardware/software system for image classification with ResNet50, object detection with YOLOv3-Tiny and image segmentation with Deeplabv3+. The system was tested in a low-density Zynq UltraScale+ ZU3EG FPGA to show its scalability. The results show that the proposed multi-engine architecture generates efficient accelerators. An accelerator of ResNet50 with a 4-bit quantization achieves 67 FPS, and the object detector with YOLOv3-Tiny with a throughput of 36 FPS and the image segmentation application achieves 1.4 FPS.
Towards Automated Pain Assessment using Embodied Conversational Agents

Narrative accounts are the ultimate authoritative source for pain assessment, and face-to-face encounters provide a rich context in which nonverbal conversational behavior can be used to enrich the detail in these descriptions. Embodied Conversational Agents—animated characters that simulate face-to-face conversation—can provide a medium for automated pain assessment in which multimodal pain narratives are elicited, clarified, and grounded. These agents can also use facial displays and nonverbal behavior to provide simulated empathic responses to help patients feel understood. We describe work towards a conversational agent that elicits various aspects of a pain experience, followed by an empathic summary. Our preliminary findings indicate that individuals are comfortable reporting their pain experiences to the agent, and are largely satisfied with this assessment methodology. We also find evidence that patients prefer the conversational empathic summary to standard self-report measures.
Latent L-systems: Transformer-based Tree Generator

We show how a Transformer can encode hierarchical tree-like string structures by introducing a new deep learning-based framework for generating 3D biological tree models represented as Lindenmayer system (L-system) strings. L-systems are string-rewriting procedural systems that encode tree topology and geometry. L-systems are efficient, but creating the production rules is one of the most critical problems precluding their usage in practice. We substitute the procedural rules creation with a deep neural model. Instead of writing the rules, we train a deep neural model that produces the output strings. We train our model on 155k tree geometries that are encoded as L-strings, de-parameterized, and converted to a hierarchy of linear sequences corresponding to branches. An end-to-end deep learning model with an attention mechanism then learns the distributions of geometric operations and branches from the input, effectively replacing the L-system rewriting rule generation. The trained deep model generates new L-strings representing 3D tree models in the same way L-systems do by providing the starting string. Our model allows for the generation of a wide variety of new trees, and the deep model agrees with the input by 93.7% in branching angles, 97.2% in branch lengths, and 92.3% in an extracted list of geometric features. We also validate the generated trees using perceptual metrics showing 97% agreement with input geometric models.
Risk factors and biomarkers for post-tuberculosis lung damage in a Chinese cohort of male smokers and non-smokers: protocol for a prospective observational study

Introduction Post-tuberculosis lung damage (PTLD) refers to the residual pulmonary impairment following the completion of antituberculosis (TB) therapy, characterised by persistent respiratory symptoms and abnormal pulmonary function. The risk factors and biomarkers for PTLD have been scarcely investigated. More importantly, whether and to what extent cigarette smoking is involved in PTLD remain to be known. Methods and analysis This prospective observational study will enrol 400 male smoking or non-smoking patients aged 25–65 years, with newly confirmed active TB between 2022 and 2024, from the Department of Respiratory and Critical Care Medicine at Peking University Third Hospital and the Tuberculosis Department at Beijing Geriatric Hospital. Because females rarely smoke in China, we will enrol only males in this study. Demographic data, smoking history and amount, clinical symptoms, lung function, and chest CT findings will be prospectively collected. Respiratory questionnaires, lung function measurements and chest CT examinations will be performed immediately after, and 1 year, 2 years and 3 years after the completion of TB treatment. Peripheral blood samples will be obtained at baseline and at the end of anti-TB therapy, and a Luminex xMAP-based multiplex immunoassay will be used to measure inflammatory mediators and cytokines in serum. The collected data will be analysed to determine the incidence and factors/biomarkers of PTLD according to smoking status. Ethics and dissemination The study was approved by the Ethics Committee of Peking University Third Hospital (approval number: (2022)271-03; approval date: 8 June 2022). The research results will be disseminated through scientific and medical conferences and will be published in an academic journal. Trial registration number NCT04966052.
An Adaptive Virtual Agent Platform for Automated Social Skills Training

Interlocutors adapt their verbal and nonverbal behaviors as signs of engagement during face-to-face interaction. We aim to build engaging Socially Interactive Agents, SIAs, that can adapt their behaviors during interaction. With an adaptive behavior generation model, we drive SIAs’ upper face and head movements in real-time. We evaluate this platform through a scenario for Social Skills Training, SST.
Multimodal Synchronization in Musical Ensembles: Investigating Audio and Visual Cues

Multimodal interaction is a dynamic process and in certain situations where there is a repetitive aspect, it can be modelled as a set of coupled oscillators. This has applications in musical interfaces. A key metric of interactivity is synchronization where events occur with reference to each other. This synchronization is often not instantaneous among participants and different models of how convergence occurs exist. This study will investigate two particular mathematical models- Kuramoto and Swarmalator - used for musical phase synchronization amongst an ensemble, where each musician is represented as an individual oscillator communicating within a closely connected graph network. Research assesses the dynamic response of these models to tempo changes, initially derived from audio feedback alone, but then subsequently incorporating additional information via visual cues, such as body sway. We employed the URMP dataset, featuring multimodal music performance data, for the experiments. Our results reveal that Kuramoto’s model outperforms the Swarmalator approach in predicting the synchronization behavior of the ensemble for both the audio-only and audio-visual conditions, with the combined audio-visual approach yielding superior results. Additionally, we observed that larger ensembles provided more visual sway information, leading to a higher mean accuracy in Kuramoto’s results for the audio-visual model. These findings underscore the significance of considering both auditory and visual cues when studying musical phase synchronization in ensembles and suggest that Kuramoto’s model presents a promising avenue for modelling synchronous behaviour in more sophisticated group musical performances.
Study of User Training Methods Using Onomatopoeia in Brain Computer Interfaces Based on Mental Imagery

Mental-imagery-based brain-computer interfaces (MI-BCI) control external devices using specific thoughts or mental imagery. MI-BCIs are promising for many applications, but because they lack reliability, they are rarely used outside laboratories. Therefore, user training is critical for controlling an MI-BCI, and users must stably be able to generate specific EEG patterns. However, optimal training methods for acquiring this ability are still being investigated. Onomatopoeias are sensory expressions that symbolically describe sounds, scenes, and feelings. A multimodal approach for training with visual and auditory imagery using onomatopoeias was used to investigate its effects on how user perform.
Increasing Web3D Accessibility with Audio Captioning

Situational awareness plays a critical role in daily life, enabling individuals to comprehend their surroundings, make informed decisions, and navigate safely. However, individuals with low vision or visual impairments face difficulties in perceiving their real or virtual environment. In order to address this challenge, we propose a 3D computer vision-based accessibility solution, empowered by object-detection and text-to-speech technology. Our application describes the visual content of a Web3D scene from the user’s perspective through auditory channels, thereby enhancing situational awareness for individuals with visual impairments in virtual and physical environments. We conducted a user study of 44 participants to compare a set of algorithms for specific tasks, such as Search or Summarize, and assessed the effectiveness of our captioning algorithms based on user ratings of naturalness, correctness, and satisfaction. Our study results indicate positive subjective results in accessibility for both normal and visually-impaired subjects and also distinguish significant effects between the task and the captioning algorithm.
Effects of work-related electronic communication during non-working hours after work from home and office on fatigue, psychomotor vigilance performance and actigraphic sleep: observational study on information technology workers

Objectives This study examined the effects of work-related electronic communication (WREC) during non-working hours in the work from home or office setting on health. Methods The study recruited 98 information technology workers in a 9-day observational study. They recorded work–life events (eg, work style (working mostly from home or the office) and duration of WREC during non-working hours) and subjective ratings (eg, current fatigue, sleepiness and depression) and wore a sleep actigraph to measure objective sleep variables before bedtime every day. They completed the Brief Psychomotor Vigilance Test (PVT-B) before bedtime for 4 days. Results The frequency of WREC was significantly higher when working mostly from home than in the office (p<0.01). In addition, the duration of WREC was longer when working mostly from home than in the office (p<0.001). Linear or generalised linear mixed model analysis for fatigue, depression and PVT lapse revealed significant interaction effects between work style and WREC (all p<0.05). Post hoc analysis showed that the longer the WREC, the worse the fatigue and depression and the lower the lapse on working mostly from the office (all p<0.05). Conclusions Longer WREC is associated with worse fatigue and depression and lower lapse of PVT (higher alertness) before bedtime for working mostly from the office. Workers, especially those working from the office, should minimise WREC during non-working hours to maintain good health. Therefore, companies, managers and other relevant stakeholders should refrain from contacting workers during non-working hours.
Enhancing User Experience in VR using Wearable Olfactory System

This paper introduces a mounted olfactory device prototype that instantly emits and quickly switches scents. Earlier approaches, such as fixed olfactory devices, have limitations in terms of the time it took for users to perceive a scent in Virtual Reality (VR) due to dissipating lingering scents in physical space, as well as the lack of instantaneous scent switching. To evaluate its effectiveness, we conducted a pilot user study comparing mounted and fixed olfactory devices.The results show that the mounted olfactory device provides a better VR experience than the fixed olfactory device.