Miracle-dz/UniFAR-FTUs huggingface dataset:
An RCT of Virtual Reality Job Interview Training for Individuals With Serious Mental Illness in IPS Supported Employment.

OBJECTIVE Virtual Reality Job Interview Training (VR-JIT) is a computerized interview simulator with efficacy at enhancing interview skills and employment outcomes. A randomized controlled trial assessed VR-JIT effectiveness for participants in individual placement and support (IPS), in which approximately 55% of individuals with serious mental illness obtain employment. METHODS Ninety participants with serious mental illness were randomly assigned to IPS+VR-JIT (N=54) or IPS as usual (N=36), completing pretest-posttest assessments and an employment evaluation at 9 months. Intent-to-treat chi-square analysis, multivariable logistic regression, Cox proportional hazards models, and mixed-effects linear regressions were conducted. Fifty-one percent were IPS nonresponders (i.e., no employment within the first 90 days of IPS). RESULTS IPS+VR-JIT participants did not have significantly higher employment rates, compared with IPS-as-usual participants (43% versus 28%). IPS nonresponders (N=46) in the IPS+VR-JIT group had greater odds of obtaining employment (odds ratio [OR]=5.82, p=0.014) and shorter time to employment (hazard ratio=2.70, p=0.044) compared with IPS nonresponders in the IPS-as-usual group. Intent-to-treat mixed-effects linear analyses indicated that IPS+VR-JIT, compared with IPS as usual, significantly improved interview skills (p=0.006), interview confidence (p=0.013), and interview anxiety (p=0.019). CONCLUSIONS VR-JIT's potential benefits (increased employment in a shorter time) appeared to be specific to IPS nonresponders, whereas employment outcomes for recent IPS enrollees were not affected. VR-JIT could be a valuable resource for employment specialists to support IPS nonresponders, because 47% of participants engaged in mock interview training with their specialist. Future research should focus on evaluating the effectiveness and implementation of VR-JIT among IPS nonresponders.
Nurse leader agency: Creating an environment conducive to support for graduate nurses

Abstract Aim The aim of the study was to gain insight on how nurse leaders manage a culture of safety for graduate nurses. Background Current theoretical approaches to safety culture tend towards a checklist approach that focuses on institutional characteristics, failing to examine the quality of interpersonal relationships. These interpersonal interactions are often seen as separate from the institutional realities of resource allocation, nurse–patient ratios, patient acuity or throughput. A theoretical approach is required to illuminate the dialectic between the structure of an organisation and the agency created by nurse leaders to promote patient safety. Design Qualitative exploratory descriptive study. Methods Semi‐structured interviews were undertaken with 24 nurse leaders from hospital and aged care settings. Thematic analysis and Giddens structuration theory was used to describe the findings. Results Nurse leaders identified a range of reciprocal communicative and cultural norms and values, decision‐making processes, personal nursing philosophies, strategies and operational procedures to foster patient safety and mentor graduate nurses. The mentoring of graduate nurses included fostering critical thinking, building and affirming formal structural practices such as handover, teamwork, medication protocols and care plans. Conclusions The study provides insight into how nurse leaders foster a culture of safety. Emphasis is placed on how agency in nurse leaders creates an environment conducive to learning and support for graduate nurses. Implications for Nursing Management Nurse leadership functions and decision‐making capacity hinges on multiple factors including practicing agency and aspects of the social structure such as the rules for safe communication, and the various institutional protocols. Nurse leaders enforce these forms of engagement and practice through their legitimation as leaders. They have both allocative and authoritative resources; they can command resources, direct staff to attend to patients and/or clinical tasks, mentor, guide, assign, correct and encourage with the authority vested in them by the formal structure of the organisation. In doing so, they sustain the structure and reinforce it.
Sex Determination in Nematode Germ Cells

Background: Animal germ cells differentiate as sperm or as oocytes. These sexual fates are controlled by complex regulatory pathways to ensure that the proper gametes are made at the appropriate times. Summary: Nematodes like Caenorhabditis elegans and its close relatives are ideal models for studying how this regulation works, because the XX animals are self-fertile hermaphrodites that produce both sperm and oocytes. In these worms, germ cells use the same signal transduction pathway that functions in somatic cells. This pathway determines the activity of the transcription factor TRA-1, a Gli protein that can repress male genes. However, the pathway is extensively modified in germ cells, largely by the action of translational regulators like the PUF proteins. Many of these modifications play critical roles in allowing the XX hermaphrodites to make sperm in an otherwise female body. Finally, TRA-1 cooperates with chromatin regulators in the germ line to control the activity of fog-1 and fog-3, which are essential for spermatogenesis. FOG-1 and FOG-3 work together to determine germ cell fates by blocking the translation of oogenic transcripts. Key Messages: Although there is great diversity in how germ cell fates are controlled in other animals, many of the key nematode genes are conserved, and the critical role of translational regulators may be universal.
The effectiveness of nutritional interventions on COVID-19 outcomes: A protocol for systematic review and meta-analysis

Background: Despite global efforts to treat and alleviate the outcomes of COVID-19, no consensus has been reached regarding the optimal approach. Some nutrients have been known to be vital for the integrity of the immune system. Although limited interventional studies support this idea, the findings of some cross-sectional and case studies have been encouraging. Aim: The present study will seek to determine the effectiveness of nutritional interventions on the outcomes of COVID-19. Methods: Electronic databases including PubMed/MEDLINE, Web of Science, Scopus, and Google Scholar will be searched for articles published from October 1st, 2019, to January 12th, 2022. Different study designs, both randomized and nonrandomized trials, cross-sectional or cohort studies, and pre and post-interventions will be included. Screening, selection, and extraction of data as well as quality assessment of included studies, will be carried out by two separate reviewers. Any potential conflicts will be resolved through discussion. An appropriate risk of bias assessment tool will be used to appraise the included studies. Then, the results will be synthesized and pooled for meta-analysis. If the meta-analysis is not performed, the reason will be provided. After summarizing the results and providing conclusions, the specific features associated with effective interventions will be presented based on the power of each study.
Community Transmission of SARS-CoV-2 Omicron Variant, South Korea, 2021

In South Korea, a November 2021 outbreak caused by severe acute respiratory syndrome coronavirus 2 Omicron variant originated from 1 person with an imported case and spread to households, kindergartens, workplaces, restaurants, and hospitals, resulting in 11 clusters within 3 weeks. An epidemiologic curve indicated rapid community transmission of the Omicron variant.
2.4-Å structure of the double-ring Gemmatimonas phototrophica photosystem

Phototrophic Gemmatimonadetes evolved the ability to use solar energy following horizontal transfer of photosynthesis-related genes from an ancient phototrophic proteobacterium. The electron cryo-microscopy structure of the Gemmatimonas phototrophica photosystem at 2.4 Å reveals a unique, double-ring complex. Two unique membrane-extrinsic polypeptides, RC-S and RC-U, hold the central type 2 reaction center (RC) within an inner 16-subunit light-harvesting 1 (LH1) ring, which is encircled by an outer 24-subunit antenna ring (LHh) that adds light-gathering capacity. Femtosecond kinetics reveal the flow of energy within the RC-dLH complex, from the outer LHh ring to LH1 and then to the RC. This structural and functional study shows that G. phototrophica has independently evolved its own compact, robust, and highly effective architecture for harvesting and trapping solar energy.
Using High-Fidelity Avatars to Advance Camera-Based Cardiac Pulse Measurement

Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show strong performance on three large benchmark datasets and improved robustness to skin type and motion. These results highlight the promise of synthetic data for training camera-based pulse measurement; however, further research and validation is needed to establish whether synthetic data alone could be sufficient for training models.
Reliability of cephalometric superimposition for the assessment of craniofacial changes: a systematic review.

BACKGROUND Superimposition of serial cephalometric radiographs enables the assessment of craniofacial changes over time, and therefore, several methods have been suggested in the literature. OBJECTIVE The aim of the present study is to summarize and critically evaluate the available evidence on the reliability of methods used to superimpose serial cephalometric radiographs. SEARCH METHODS Electronic searches were performed in MEDLINE, EMBASE, Google Scholar, and Cochrane Databases, without time limit (last update: 1 November 2020). Unpublished literature was searched on the Open Grey and Grey Literature Report databases. SELECTION CRITERIA Studies that tested the accuracy, precision, or agreement between different cephalometric superimposition techniques, used to evaluate the craniofacial changes due treatment or growth. DATA COLLECTION AND ANALYSIS Reference lists of relevant articles were screened and authors were contacted, if needed. All study selection steps, data extraction, and risk of bias (QUADAS-2 tool) assessments were performed independently by two authors on predefined forms. RESULTS There were 27 eligible studies. From these, 17 tested superimpositions methods on the anterior cranial base, 10 on the maxilla and 12 on the mandible. There were three studies that compared superimpositions on the cranial base with those on the maxilla and one that compared the cranial base with the mandibular superimposition. There was high heterogeneity among studies in terms of sample size, growth, radiographic machines, selection criteria, superimposition methods, references, and outcomes measured. Furthermore, almost all studies presented important methodological limitations, with only two studies having unclear risk of bias and the rest 25 presenting high risk. CONCLUSIONS Currently, there is no cephalometric superimposition method that has been proved to deliver accurate results. There is an urgent need for further research in this topic, since this is a primary assessment method to assess craniofacial changes over time for several relevant disciplines. REGISTRATION PROSPERO (CRD42020200349).
Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics

Numerous toolkits have been developed to support ethical AI development. However, toolkits, like all tools, encode assumptions in their design about what work should be done and how. In this paper, we conduct a qualitative analysis of 27 AI ethics toolkits to critically examine how the work of ethics is imagined and how it is supported by these toolkits. Specifically, we examine the discourses toolkits rely on when talking about ethical issues, who they imagine should do the work of ethics, and how they envision the work practices involved in addressing ethics. Among the toolkits, we identify a mismatch between the imagined work of ethics and the support the toolkits provide for doing that work. In particular, we identify a lack of guidance around how to navigate labor, organizational, and institutional power dynamics as they relate to performing ethical work. We use these omissions to chart future work for researchers and designers of AI ethics toolkits.
Should I send this notification? Optimizing push notifications decision making by modeling the future

Most recommender systems are myopic, that is they optimize based on the immediate response of the user. This may be misaligned with the true objective, such as creating long term user satisfaction. In this work we focus on mobile push notifications, where the long term effects of recommender system decisions can be particularly strong. For example, sending too many or irrelevant notifications may annoy a user and cause them to disable notifications. However, a myopic system will always choose to send a notification since negative effects occur in the future. This is typically mitigated using heuristics. However, heuristics can be hard to reason about or improve, require retuning each time the system is changed, and may be suboptimal. To counter these drawbacks, there is significant interest in recommender systems that optimize directly for long-term value (LTV). Here, we describe a method for maximising LTV by using model-based reinforcement learning (RL) to make decisions about whether to send push notifications. We model the effects of sending a notification on the user's future behavior. Much of the prior work applying RL to maximise LTV in recommender systems has focused on session-based optimization, while the time horizon for notification decision making in this work extends over several days. We test this approach in an A/B test on a major social network. We show that by optimizing decisions about push notifications we are able to send less notifications and obtain a higher open rate than the baseline system, while generating the same level of user engagement on the platform as the existing, heuristic-based, system.
Sampling and Reconstructing Angular Domains With Uniform Arrays

The surge of massive antenna arrays in wireless networks calls for the adoption of analog/hybrid array solutions, where multiple antenna elements are driven by a common radio front end to form a beam along a specific angle in order to maximize the beamforming gain. Many heuristics have been proposed to sample the angular domain by trading off between sampling step size and overhead, where arbitrarily small angular step size is only attainable with infinite sampling overhead. We show that, for uniform linear and rectangular arrays, lossless reconstruction of the array’s angular responses at arbitrary angular precision is possible using a finite number of samples without resorting to assumptions of angular sparsity. The proposed method, (SARA), defines how many and which angles to be sampled and the corresponding reconstruction. This general solution to scan the angular domain can therefore be applied not only to beam acquisition and channel estimation, but also to radio imaging techniques, making it a candidate for future integrated sensing and communications (ISAC). Extensive simulation results for target detection and radio imaging have demonstrated clear advantages of SARA over other considered baselines, both in terms of angular reconstruction performance and computational complexity.
A Survey of Explainable Reinforcement Learning

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of learning agents in sequential decision-making settings. In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting. We overview techniques according to this taxonomy. We point out gaps in the literature, which we use to motivate and outline a roadmap for future work.
An overview of deep learning in medical imaging

Machine learning (ML) has seen enormous consideration during the most recent decade. This success started in 2012 when an ML model accomplished a remarkable triumph in the ImageNet Classification, the world's most famous competition for computer vision. This model was a kind of convolutional neural system (CNN) called deep learning (DL). Since then, researchers have started to participate efficiently in DL's fastest developing area of research. These days, DL systems are cutting-edge ML systems spanning a broad range of disciplines, from human language processing to video analysis, and commonly used in the scholarly world and enterprise sector. Recent advances can bring tremendous improvement to the medical field. Improved and innovative methods for data processing, image analysis and can significantly improve the diagnostic technologies and medicinal services gradually. A quick review of current developments with relevant problems in the field of DL used for medical imaging has been provided. The primary purposes of the review are four: (i) provide a brief prolog to DL by discussing different DL models, (ii) review of the DL usage for medical image analysis (classification, detection, segmentation, and registration), (iii) review seven main application fields of DL in medical imaging, (iv) give an initial stage to those keen on adding to the research area about DL in clinical imaging by providing links of some useful informative assets, such as freely available DL codes, public datasets Table 7, and medical imaging competition sources Table 8 and end our survey by outlining distinct continuous difficulties, lessons learned and future of DL in the field of medical science.
The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks

It is currently known how to characterize functions that neural networks can learn with SGD for two extremal parameterizations: neural networks in the linear regime, and neural networks with no structural constraints. However, for the main parametrization of interest (non-linear but regular networks) no tight characterization has yet been achieved, despite significant developments. We take a step in this direction by considering depth-2 neural networks trained by SGD in the mean-field regime. We consider functions on binary inputs that depend on a latent low-dimensional subspace (i.e., small number of coordinates). This regime is of interest since it is poorly understood how neural networks routinely tackle high-dimensional datasets and adapt to latent low-dimensional structure without suffering from the curse of dimensionality. Accordingly, we study SGD-learnability with $O(d)$ sample complexity in a large ambient dimension $d$. Our main results characterize a hierarchical property, the"merged-staircase property", that is both necessary and nearly sufficient for learning in this setting. We further show that non-linear training is necessary: for this class of functions, linear methods on any feature map (e.g., the NTK) are not capable of learning efficiently. The key tools are a new"dimension-free"dynamics approximation result that applies to functions defined on a latent space of low-dimension, a proof of global convergence based on polynomial identity testing, and an improvement of lower bounds against linear methods for non-almost orthogonal functions.
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks

The spread of an epidemic is often modeled by an SIR random process on a social network graph. The MININFEDGE problem for optimal social distancing involves minimizing the expected number of infections, when we are allowed to break at most B edges;similarly the MININFNODE problem involves removing at most B vertices. These are fundamental problems in epidemiology and network science. While a number of heuristics have been considered, the complexity of these problems remains generally open. In this paper, we present two bicriteria approximation algorithms for MININFEDGE, which give the first non-trivial approximations for this problem. The first is based on the cut sparsification result of Karger (1999), and works when the transmission probabilities are not too small. The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model. We also extend some of our results to tackle the MININFNODE problem.
Real Time Motion Planning Using Constrained Iterative Linear Quadratic Regulator for On-Road Self-Driving

Collision avoidance is one of the most challenging tasks people need to consider for developing the self-driving technology. In this paper we propose a new spatiotemporal motion planning algorithm that efficiently solves a constrained nonlinear optimal control problem using the iterative linear quadratic regulator (iLQR), which takes into account the uncertain driving behaviors of the traffic vehicles and minimizes the collision risks between the self-driving vehicle (referred to as the"ego"vehicle) and the traffic vehicles such that the ego vehicle is able to maintain sufficiently large distances to all the surrounding vehicles for achieving the desired collision avoidance maneuver in traffic. To this end, we introduce the concept of the"collision polygon"for computing the minimum distances between the ego vehicle and the traffic vehicles, and provide two different solutions for designing the constraints of the motion planning problem by properly modeling the behaviors of the traffic vehicles in order to evaluate the collision risk. Finally, the iLQR motion planning algorithm is validated in multiple real-time tasks for collision avoidance using both a simulator and a level-3 autonomous driving test platform.
A hybrid 2-stage vision transformer for AI-assisted 5 class pathologic diagnosis of gastric endoscopic biopsies

Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. Although artiﬁcial intelligence (AI) has brought a great promise to assist pathologist to screen digitalized whole slide images, automatic classiﬁcation systems for guiding proper GC treatment based on clinical guideline are still lacking. Here, we propose an AI system classifying 5 classes of GC histology, which can be perfectly matched to general treatment guidance. The AI system, mimicking the way pathologist understand slides through multi-scale self-attention mechanism using a 2-stage Vision Transformer, demonstrates clinical capability by achieving diagnostic sensitivity of above 85% for both internal and external cohort analysis. Furthermore, AI-assisted pathologists showed signiﬁcantly improved diagnostic sensitivity by 10% within 18% saved screening time compared to human pathologists. Our AI system has a great potential for providing presumptive pathologic opinion for deciding proper treatment for early GC patients.