Miracle-dz/UniFAR-FTUs huggingface dataset:
DISPERSION ANALYSIS OF ACOUSTIC WAVES IN FLUID MEDIA DISCRETIZED BY ENERGY-ORTHOGONAL FINITE ELEMENTS

Abstract. This paper studies the dispersion of acoustic waves in fluid media discretized by the finite element method. The element stiffness matrix is split into basic and higher order components which are respectively related to the mean and deviatoric components of the gradient of acoustic pressure. This decomposition is applied to the kinetic energy. The dispersion analysis yields a correlation between the higher order kinetic energy and the kinetic energy error. The use of this correlation as a reference to apply the higher order energy as an error indicator for the acoustic cavity modes computed by the finite element method is explored.
Heart transplantation and anti‐HLA antibodY: myocardial dysfunction and prognosis ‐ HeartLAy study

The presence of anti‐human leucocyte antigen (HLA) antibodies has been implicated in a higher incidence of complications as well as mortality rate in heart transplantation. The aim of the study was to identify through non‐invasive parameters early signs of myocardial dysfunction in the presence of anti‐HLA antibodies but without evidence of antibody‐mediated rejection (AMR) and its possible prognostic impact.
Massive stellar systems: observational challenges and perspectives in the E-ELT era

Abstract We introduce the empirical framework concerning optical and near-infrared (NIR) photometry of crowded stellar fields. In particular, we address the impact that linear detectors and analytical PSF played in improving the accuracy and the precision of multi-band color-magnitude diagrams (CMDs). We focus our attention on recent findings based on deep NIR images collected with Adaptive Optics (AO) systems at the 8-10m class telescopes and discuss pros and cons of the different approaches. We also discuss the estimate of the absolute age of globular clusters using a well defined knee along the lower main sequence. We mention the role which the current AO-assisted instruments will have in addressing longstanding astrophysical problems of the Galactic center. Finally, we outline the role of first generation of E-ELT instruments upon photometry and spectroscopy of crowded stellar fields.
Could normalization improve robustness of abdominal MRI radiomic features?

Radiomics-based systems could improve the management of oncological patients by supporting cancer diagnosis, treatment planning, and response assessment. However, one of the main limitations of these systems is the generalizability and reproducibility of results when they are applied to images acquired in different hospitals by different scanners. Normalization has been introduced to mitigate this issue, and two main approaches have been proposed: one rescales the image intensities (image normalization), the other the feature distributions for each center (feature normalization). The aim of this study is to evaluate how different image and feature normalization methods impact the robustness of 93 radiomics features acquired using a multicenter and multi-scanner abdominal Magnetic Resonance Imaging (MRI) dataset. To this scope, 88 rectal MRIs were retrospectively collected from 3 different institutions (4 scanners), and for each patient, six 3D regions of interest on the obturator muscle were considered. The methods applied were min-max, 1st-99th percentiles and 3-Sigma normalization, z-score standardization, mean centering, histogram normalization, Nyul-Udupa and ComBat harmonization. The Mann-Whitney U-test was applied to assess features repeatability between scanners, by comparing the feature values obtained for each normalization method, including the case in which no normalization was applied. Most image normalization methods allowed to reduce the overall variability in terms of intensity distributions, while worsening or showing unpredictable results in terms of feature robustness, except for the z-score, which provided a slight improvement by increasing the number of statistically similar features from 9/93 to 10/93. Conversely, feature normalization methods positively reduced the overall variability across the scanners, in particular, 3sigma, z_score and ComBat that increased the number of similar features (79/93). According to our results, it emerged that none of the image normalization methods was able to strongly increase the number of statistically similar features.
Evaluation of an organisational-level monetary incentive to promote the health and wellbeing of workers in small and medium-sized enterprises: A mixed-methods cluster randomised controlled trial

We conducted an independent evaluation on the effectiveness of an organisational-level monetary incentive to encourage small and medium-sized enterprises (SMEs) to improve employees’ health and wellbeing. This was A mixed-methods cluster randomised trial with four arms: high monetary incentive, low monetary incentive, and two no monetary incentive controls (with or without baseline measurements to examine ‘reactivity’ The consequence of particpant awareness of being studied, and potential impact on participant behavior effects). SMEs with 10–250 staff based in West Midlands, England were eligible. We randomly selected up to 15 employees at baseline and 11 months post-intervention. We elicited employee perceptions of employers’ actions to improve health and wellbeing; and employees’ self-reported health behaviours and wellbeing. We also interviewed employers and obtained qualitative data. One hundred and fifty-two SMEs were recruited. Baseline assessments were conducted in 85 SMEs in three arms, and endline assessments in 100 SMEs across all four arms. The percentage of employees perceiving “positive action” by their employer increased after intervention (5 percentage points, pp [95% Credible Interval -3, 21] and 3pp [–9, 17], in models for high and low incentive groups). Across six secondary questions about specific issues the results were strongly and consistently positive, especially for the high incentive. This was consistent with qualitative data and quantitative employer interviews. However, there was no evidence of any impact on employee health behaviour or wellbeing outcomes, nor evidence of ‘reactivity’. An organisational intervention (a monetary incentive) changed employee perceptions of employer behaviour but did not translate into changes in employees’ self-reports of their own health behaviours or wellbeing. Trial registration: AEARCTR-0003420, registration date: 17.10. 2018, retrospectively registered (delays in contracts and identfying a suitable trial registry). The authors confirm that there are no ongoing and related trials for this intervention.
Diagnostic test accuracy of ultrasound for orbital cellulitis: A systematic review

Background Periorbital and orbital cellulitis are inflammatory conditions of the eye that can be difficult to distinguish using clinical examination alone. Computer tomography (CT) scans are often used to differentiate these two infections and to evaluate for complications. Orbital ultrasound (US) could be used as a diagnostic tool to supplement or replace CT scans as the main diagnostic modality. No prior systematic review has evaluated the diagnostic test accuracy (DTA) of ultrasound compared to cross-sectional imaging. Objective To conduct a systematic review of studies evaluating the DTA of orbital ultrasound compared with cross-sectional imaging, to diagnose orbital cellulitis. Methods MEDLINE, EMBASE, CENTRAL, and Web of Science were searched from inception to August 10, 2022. All study types were included that enrolled patients of any age with suspected or diagnosed orbital cellulitis who underwent ultrasound and a diagnostic reference standard (i.e., CT or magnetic resonance imaging [MRI]). Two authors screened titles/abstracts for inclusion, extracted data, and assessed the risk of bias. Results Of the 3548 studies identified, 20 were included: 3 cohort studies and 17 case reports/series. None of the cohort studies directly compared the diagnostic accuracy of ultrasound with CT or MRI, and all had high risk of bias. Among the 46 participants, diagnostic findings were interpretable in 18 (39%) cases which reported 100% accuracy. We were unable to calculate sensitivity and specificity due to limited data. In the descriptive analysis of the case reports, ultrasound was able to diagnose orbital cellulitis in most (n = 21/23) cases. Conclusion Few studies have evaluated the diagnostic accuracy of orbital ultrasound for orbital cellulitis. The limited evidence based on low quality studies suggests that ultrasound may provide helpful diagnostic information to differentiate orbital inflammation. Future research should focus studies to determine the accuracy of orbital US and potentially reduce unnecessary exposure to radiation.
The Use of Virtual Reality Interventions to Promote Positive Mental Health: Systematic Literature Review

Background A large body of research has documented the efficacy of psychological interventions integrated with virtual reality (VR) therapies in treating psychiatric disorders. However, the concept of positive mental health calls for a 2-fold approach in which both symptoms and positive functioning should be addressed by modern interventions. Objective This review aimed to summarize studies that applied VR therapies by embracing the positive mental health perspective. Methods A literature search was conducted by entering the following keywords—“virtual reality” AND “intervention” OR “treatment” OR “therapy” AND “mental health” NOT “systematic review or meta-analysis”—and limiting it to “journal article” and the English language. To be included in this review, articles had to present at least one quantitative measure of positive functioning and one quantitative measure of symptoms or distress and had to investigate adult populations, including populations with psychiatric disorders. Results A total of 20 articles were included. They described various VR protocols that were applied for the treatment of anxiety disorders (5/20, 25%), depression (2/20, 10%), posttraumatic stress disorder (3/20, 15%), psychosis (3/20, 15%), and stress (7/20, 35%). Most of the studies (13/20, 65%) showed the beneficial effects of VR therapies in improving stress and negative symptoms. However, 35% (7/20) of the studies showed no or a small effect on the various dimensions of positivity, particularly in clinical samples. Conclusions VR interventions might be cost-effective and largely scalable, but further research is needed to develop existing VR software and treatments according to the modern positive mental health approach.
3D-Printed Soft Wearable Electronics: Techniques, Materials, and Applications

Wearable electronics with soft features are emerging in recent years, enabling longterm applications on the human body. For example, wearable sensors can provide accurate monitoring of human physiological signals; [1–6] wearable drug delivery systems and wearable heaters can ensure precise and timely therapy; [7–10] and wearable displays with the visual interface can clearly inform the monitoring status [11]. Not only for human health, wearable devices can also be used on plants to monitor plant health for smart agriculture [12, 13]. Wearable electronics that feature gas permeability [14] and recyclability [15] have emerged, which further facilitate practical application of these wearable devices. Relatively simple and cost-effective fabrication of soft wearable electronics can greatly facilitate their deployment. Topdown microfabrication, e.g., deposition/evaporation, lithography, and etching, are conventional approaches for device fabrication [16–20]. They are widely used in the fabrication of electronics with high resolution and high integration levels [21– 24]. However, they are typically complicated and expensive. Instead of top-down approaches, bottom-up approaches including coating and printing techniques are widely used to process nanomaterials with high throughput and low costs [25– 28]. However, coating techniques and conventional printing techniques (e.g., inkjet printing, screen printing, and gravure printing) are typically limited to 2D planar fabrication; hence, most of the devices, including wearable soft sensors, actuators,
A Scientometric Analysis: The Application of Big Data in the Enterprise Supply Chain

Using big data analytics to deliver services to businesses is becoming increasingly popular among professionals. At the same time, the application in the supply chain is very important for practitioners. As a part of enterprises, the supply chain plays an important role in enterprises. Although many domestic and foreign researchers mainly discuss the impact of big data and supply chain on the internal environment, external environment, and the whole enterprise, few researchers have linked big data with the enterprise supply chain. Based on the 140 articles retrieved by Web of Science, this paper gives a systematic review, which provides directions for the application research of supply chain in the future, and makes up the research gap. Research papers published between 1989 and 2021 weregroupedaccordingtotheirsourceandyearofpublication.Theresultsshowed thatinrecentyears,theresearchﬁeldsinvolveﬁnance,business,management,and otherdisciplines.Inaddition,ChineseandU.S. institutionsaremoreprominentin theﬁeldofresearch. Inaddition,bigdataanddifferentkeywordsshowimportant interactionsinvisualizations:(a)researchbasedonbigdata,(b)bigdataand enterprisemodels,(c)bigdataandsupplychains,and(d)bigdataanddesign.
12 Computational Cognitive Neuroscience Models of Categorization

Categorization is the process of assigning an object or event to a class or group – typically one that is behaviorally relevant. It is a vitally important skill that is required of all animals, because it allows nutrients and prey to be approached and poisons and predators to be avoided. Interest in how humans categorize dates back at least to Aristotle. For almost all of this long history, theorizing was dominated by purely cognitive approaches. The past few decades however, have seen an explosion of new results that collectively are beginning to paint a detailed picture of the neural mechanisms and pathways that mediate human categorization. These results come from a wide variety of sources, including human behavioral experiments, animal lesion studies, single-cell recordings, neuroimaging experiments, and neuropsychological patient studies. Lagging somewhat behind this avalanche of new data has been the development of new theories that can account for the traditional cognitive results as well as for these newer neuroscience results. Even so, a number of such theories have been proposed. This chapter reviews those theories.
Autonomous Justification for Enabling Explainable Decision Support in Human-Robot Teaming

—Justification is an important facet of policy explanation, a process for describing the behavior of an autonomous system. In human-robot collaboration, an autonomous agent can attempt to justify distinctly important decisions by offering explanations as to why those decisions are right or reasonable, leveraging a snapshot of its internal reasoning to do so. With-out sufficient insight into a robot’s decision-making process, it becomes challenging for users to trust or comply with those important decisions, especially when they are viewed as confusing or contrary to the user’s expectations (e.g., when decisions change as new information is introduced to the agent’s decision-making process). In this work we characterize the benefits of justification within the context of decision-support during human-robot teaming (i.e., agents giving recommendations to human teammates). We introduce a formal framework using value of information theory to strategically time justifications during periods of misaligned expectations for greater effect. We also characterize four different types of counterfactual justification derived from established explainable AI literature and evaluate them against each other in a human-subjects study involving a collaborative, partially observable search task. Based on our findings, we present takeaways on the effective use of different types of justifications in human-robot teaming scenarios, to improve user compliance and decision-making by strategically influencing human teammate thinking patterns. Finally, we present an augmented reality system incorporating these findings into a real-world decision-support system for human-robot teaming.
Privacy in the Interpolation Regime

Interpolation Regime: Modern machine learning models are often overparametrized, i.e., the number of model parameters typically far exceed the number of data points by orders of magnitude. In the over-parametrized setting, learning algorithms typically achieve close to 100% training accuracy and are said to be in the “interpolation regime” where the algorithm almost perfectly fits all the training data [1]. Conventional statistical wisdom suggests that such overfitting may inhibit the generalization capacity of learning algorithm i.e. the ability of an algorithm to perform well on unseen data outside of the training set. However, recent progress on over-parametrized models challenge such wisdom and indicate that generalization and overfitting may not be in conflict [2]. For long-tail data distributions (which are common in image and text data), interpolation may be necessary for generalization [3]. In addition, there are practical advantages to interpolation. Recent work shows that optimization algorithms like the Stochastic Gradient Descent (SGD) converge faster in the interpolation regime [4]. Privacy: Recent research shows that deep learning models are vulnerable to a variety of privacy attacks. In a membership inference attack, an adversary is able to identify if a specific data point is used in training the DL model [5]. In a reconstruction attack, an adversary is able to approximately reconstruct the data used for training the model [6]. Such attacks pose a serious privacy risk to the use of DL models, especially when it comes to sensitive applications like healthcare.
Poster: Resiliency in Low Earth Orbit Satellite Routing

As satellite constellations become more widely adopted for observation, navigation, and communication applications, resiliency measures must be in place to ensure these constellations and the applications they support are not compromised. This adoption of satellite constellations for numerous applications coincides with the trend of constellations moving out of Geosynchronous Orbit (GEO) to Low Earth Orbit (LEO). LEO satellites have advantages: launching to orbit is less expensive, less complicated, inexpensive components can be used, and communication latency is lower. LEO satellites also have drawbacks: they do not fly above a consistent point on earth’s surface and they often times leverage commodity hardware and software, potentially opening the satellites to unanticipated vulnerabilities. So, LEO constellations spanning multiple orbital planes necessarily forms a changing topology. Our goal is to improve the resiliency of satellite routing when some nodes are compromised. For our first step towards this goal, we created a testbed in Mininet [1] to simulate a satellite network in a Walker-Delta constellation. We then use this testbed to assess the impact of a satellite network where some nodes fail to forward packets, either due to an attack or to environmental conditions such as radiation. Our results will be used to guide future work to improve satellite communication security.
Investigating the conformational dynamics of protein-RNA complexes

The main goal of this PhD project is to elucidate the conformational dynamics of protein-RNA complexes and to develop a method for understanding their allosteric communications. To achieve this objective, the candidate will develop a deep learning model to predict communication pathways. They will investigate several facets of the algorithms and ultimately provide this application to the broad scientific community. The proposed deep learning model will be set up to address the characterization of dynamic properties of protein-RNA interactions at the 3D level. This approach is a prerequisite for the design of new therapeutics that target protein-RNA complexes. The PhD candidate will be hosted in the CAPSID team within LORIA at the Inria, Nancy - Grand Est site. The candidate will be supervised by Yasaman Karami (Inria researcher) with expertise in analyzing proteins conformational dynamics and drug design [1,2], and Malika Smail-Tabonne (associate professor, University of Lorraine) expert in machine learning. CAPSID team ( https://capsid.loria.fr/ ) is directed by Dr. Marie-Dominique Devignes and provides a multidisciplinary and international environment for students. The team benefits from experts in RNA-protein interactions and structural bioinformatics, as well as in computer science. The group is equipped with a computational platform, MBI-DS4H ( https://mbi-ds4h.loria.fr/ ) composed of 8 nodes and 12 GPUs, and provides technical support to the users.
Poster: Towards Refinement Types in Rust

—The Rust programming language comes with memory safety by default due to its unique Ownership and Borrowing System (OBS). But Rust provides only limited possibilities to restrict interfaces at compile time so that they only accept well-formed input. To increase Rust’s capabilities to precisely specify valid inputs, we propose the introduction of refinement types to Rust which can be checked by SMT solvers.
3D Finite Element Models Reconstructed From 2D Dual‐Energy X‐Ray Absorptiometry (DXA) Images Improve Hip Fracture Prediction Compared to Areal BMD in Osteoporotic Fractures in Men (MrOS) Sweden Cohort

Bone strength is an important contributor to fracture risk. Areal bone mineral density (aBMD) derived from dual‐energy X‐ray absorptiometry (DXA) is used as a surrogate for bone strength in fracture risk prediction tools. 3D finite element (FE) models predict bone strength better than aBMD, but their clinical use is limited by the need for 3D computed tomography and lack of automation. We have earlier developed a method to reconstruct the 3D hip anatomy from a 2D DXA image, followed by subject‐specific FE‐based prediction of proximal femoral strength. In the current study, we aim to evaluate the method's ability to predict incident hip fractures in a population‐based cohort (Osteoporotic Fractures in Men [MrOS] Sweden). We defined two subcohorts: (i) hip fracture cases and controls cohort: 120 men with a hip fracture (<10 years from baseline) and two controls to each hip fracture case, matched by age, height, and body mass index; and (ii) fallers cohort: 86 men who had fallen the year before their hip DXA scan was acquired, 15 of which sustained a hip fracture during the following 10 years. For each participant, we reconstructed the 3D hip anatomy and predicted proximal femoral strength in 10 sideways fall configurations using FE analysis. The FE‐predicted proximal femoral strength was a better predictor of incident hip fractures than aBMD for both hip fracture cases and controls (difference in area under the receiver operating characteristics curve, ΔAUROC = 0.06) and fallers (ΔAUROC = 0.22) cohorts. This is the first time that FE models outperformed aBMD in predicting incident hip fractures in a population‐based prospectively followed cohort based on 3D FE models obtained from a 2D DXA scan. Our approach has potential to notably improve the accuracy of fracture risk predictions in a clinically feasible manner (only one single DXA image is needed) and without additional costs compared to the current clinical approach. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).