Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a 3D composite training model for cranial remodeling.

OBJECTIVE Craniosynostosis correction, including cranial vault remodeling, fronto-orbital advancement (FOA), and endoscopic suturectomy, requires practical experience with complex anatomy and tools. The infrequent exposure to complex neurosurgical procedures such as these during residency limits extraoperative training. Lack of cadaveric teaching tools given the pediatric nature of synostosis compounds this challenge. The authors sought to create lifelike 3D printed models based on actual cases of craniosynostosis in infants and incorporate them into a practical course for endoscopic and open correction. The authors hypothesized that this training tool would increase extraoperative facility and familiarity with cranial vault reconstruction to better prepare surgeons for in vivo procedures. METHODS The authors utilized representative craniosynostosis patient scans to create 3D printed models of the calvaria, soft tissues, and cranial contents. Two annual courses implementing these models were held, and surveys were completed by participants (n = 18, 5 attending physicians, 4 fellows, 9 residents) on the day of the course. These participants were surveyed during the course and 1 year later to assess the impact of this training tool. A comparable cohort of trainees who did not participate in the course (n = 11) was also surveyed at the time of the 1-year follow-up to assess their preparation and confidence with performing craniosynostosis surgeries. RESULTS An iterative process using multiple materials and the various printing parameters was used to create representative models. Participants performed all major surgical steps, and we quantified the fidelity and utility of the model through surveys. All attendees reported that the model was a valuable training tool for open reconstruction (n = 18/18 [100%]) and endoscopic suturectomy (n = 17/18 [94%]). In the first year, 83% of course participants (n = 14/17) agreed or strongly agreed that the skin and bone materials were realistic and appropriately detailed; the second year, 100% (n = 16/16) agreed or strongly agreed that the skin material was realistic and appropriately detailed, and 88% (n = 14/16) agreed or strongly agreed that the bone material was realistic and appropriately detailed. All participants responded that they would use the models for their own personal training and the training of residents and fellows in their programs. CONCLUSIONS The authors have developed realistic 3D printed models of craniosynostosis including soft tissues that allow for surgical practice simulation. The use of these models in surgical simulation provides a level of preparedness that exceeds what currently exists through traditional resident training experience. Employing practical modules using such models as part of a standardized resident curriculum is a logical evolution in neurosurgical education and training.
A Guide to Effective Care in Pregnancy and Childbirth

Effective care in pregnancy and childbirth evaluating care in pregnancy and childbirth social, financial and psychological support during pregnancy and childbirth advice for pregnancy dietary modification in pregnancy antenatal classes formal risk scoring screening for genetic disease screening for pre-eclampsia gestational diabetes assessment of fetal size and growth ultrasound in pregnancy assessment of fetal wellbeing suspected fetopelvic disproportion and abnormal lie symptoms in pregnancy hypertension in pregnancy infection in pregnancy rhesus isoimmunization diabetes in pregnancy bleeding in the latter half of pregnancy maintaining pregnancy and promoting fetal growth promoting pulmonary maturity preterm labour prelabour rupture of the membranes post-term pregnancy - induction or surveillance? fetal death care of the bereaved social and professional support in childbirth hospital policies and admission practices nutrition and position in labour monitoring the fetus during labour monitoring the progress of labour prolonged labour control of pain in labour the second stage of labour the third stage of labour repair of perineal trauma instrumental vaginal delivery labour and delivery after previous Caesarean section Caesarean section prophylactic antibiotics with Caesarean section preparing for induction of labour methods of inducing labour preterm delivery immediate care of the newborn infant care of the new mother and baby breastfeeding perineal pain and discomfort breast symptoms in women who are not breastfeeding effective care in pregnancy and childbirth - a synopsis.
Body Burden — The Pollution in Newborns

Not long ago scientists thought that the placenta shielded cord blood — and the developing baby — from most chemicals and pollutants in the environment. But now we know that at this critical time when organs, vessels, membranes and systems are knit together from single cells to finished form in a span of weeks, the umbilical cord carries not only the building blocks of life, but also a steady stream of industrial chemicals, pollutants and pesticides that cross the placenta as readily as residues from cigarettes and alcohol. This is the human "body burden" — the pollution in people that permeates everyone in the world, including babies in the womb.
Priorities for action

The past forty years have witnessed many cases of remarkably rapid economic advance among the countries of the developing worldso rapid that some countries are on the point of "graduating" to the ranks of the high-income industrial countries. Most developing countries have made progress at a slower pace. Even so, in comparison with the industrial countries at a comparable stage of development, these countries have still done well, and their standards of living have improved greatly. Unfortunately, however, in some countries, especially in Sub-Saharan Africa, development is moving too slowly to make much difference in people's lives. For them, better economic performance is not merely very desirable. It is literally a matter of life and death. This wide range of experience has told us much, though by no means everything, about what works in development and what does not. Development has emerged as a fragile and multidimensional process. It depends on complex interactions among institutions, policies, and the global economic climate. There have been no shortcuts. Neither forced modernization of industry nor massive inflows of external resources led to the gains expected a generation ago. But steadyeven exceptionalprogress has come through actions that foster competitive markets, private initiative, and investment in physical and human capital. It is true that many countries must overcome tremendous obstaclesadverse natural conditions, poor infrastructure, weak administrative capacity, entrenched special interests, and inadequate financial resources. Experience is nevertheless turning policy reform into the art of the possible. Comprehensive, market-friendly re-
Impact of ER gene polymorphisms on overweight and obesity in Down Syndrome

The impact of ER XbaI and PvuII α gene polymorphisms on overweight and obesity were studied in 77 subjects with Down Syndrome (DS), of which 32 were children (18 boys, 14 girls), mean age 8.7 ± 2.3 years, and 45 adolescents (28 boys, 17 girls) mean age 14 ± 2.5 years. Their lifestyle was compared to 40 healthy age-matched controls. DS subjects had significant lesser physical activity than controls (p<0.05) and a lower caloric intake than the recommended requirements, which was significantly lesser than controls (p<0.05). Body Mass Index (BMI), Arm Circumference (AC) and Triceps Skinfold Thickness (TST) were significantly higher in DS subjects than controls (p<0.05), while metabolic and cardiovascular parameters were not significantly different between the groups (p>0.05). The frequency of ER genotypes in DS subjects was compared with the healthy controls, finding that there was a high prevalence of XXER genotype in DS subjects. Children and adolescents with DS, lacking ER XbaI site, showed significantly higher BMI and body fat distribution than other XbaI genotypes. The lack of ER XbaI site can indicate added risk of obesity in DS. No differences in metabolic and cardiovascular parameters were observed among ER genotypes. However, childhood obesity is associated with increased cardiovascular risk.
Carina’s pillars of destruction: the view from ALMA

Forming high-mass stars have a significant effect on their natal environment. Their feedback pathways, including winds, outflows, and ionizing radiation, shape the evolution of their surroundings which impacts the formation of the next generation of stars. They create or reveal dense pillars of gas and dust towards the edges of the cavities they clear. They are modelled in feedback simulations, and the sizes and shapes of the pillars produced are consistent with those observed. However, these models predict measurably different kinematics which provides testable discriminants. Here we present the first ALMA Compact Array (ACA) survey of 13 pillars in Carina, observed in 12CO, 13CO, and C18O J = 2–1, and the 230 GHz continuum. The pillars in this survey were chosen to cover a wide range in properties relating to the amount and direction of incident radiation, proximity to nearby irradiating clusters and cloud rims, and whether they are detached from the cloud. With these data, we are able to discriminate between models. We generally find pillar velocity dispersions of <1 km s−1 and that the outer few layers of molecular emission in these pillars show no significant offsets from each other, suggesting little bulk internal motions within the pillars. There are instances where the pillars are offset in velocity from their parental cloud rim, and some with no offset, hinting at a stochastic development of these motions.
S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization

In this article, we study convex optimization problems where agents of a network cooperatively minimize the global objective function which consists of multiple local objective functions. The intention of this work is to solve large-scale optimization problems where the local objective function is complicated, and numerous. Different from most of the existing works, the local objective function of each agent is presented as the average of finite instantaneous functions. Integrating the gradient tracking algorithm with stochastic averaging gradient technology, a distributed stochastic gradient tracking (termed as S-DIGing) algorithm is proposed. At each time instant, only one randomly selected gradient of an instantaneous function is computed, and applied to approximate the local batch gradient for each agent. Based on a novel primal-dual interpretation of the S-DIGing algorithm, it is shown that the S-DIGing algorithm linearly converges to the global optimal solution when step-size do not exceed an explicit upper bound, and the instantaneous functions are strongly convex with Lipschitz continuous gradients. Numerical experiments are presented to demonstrate the practicability of the S-DIGing algorithm, and correctness of the theoretical results.
Complexity of the steady state of weakly symmetric open quantum lattices

We investigate the properties of Lindblad equations on $d$-dimensional lattices supporting a unique steady-state configuration. We consider the case of a time evolution weakly symmetric under the action of a finite group $G$, which is also a symmetry group for the lattice structure. We show that in such case the steady-state belongs to a relevant subspace, and provide an explicit algorithm for constructing an orthonormal basis of such set. As explicitly shown for a spin-1/2 system, the dimension of such subspace is extremely smaller than the dimension of the set of square operators. As a consequence, by projecting the dynamics within such set, the steady-state configuration can be determined with a considerably reduced amount of resources. We demonstrate the validity of our theoretical results by determinining the \emph{exact} structure of the steady-state configuration of the two dimensional XYZ model in the presence of uniform dissipation, with and without magnetic fields, up to a number of sites equal to 12. As far as we know, this is the first time one is capable of determining the steady-state structure of such model for the 12 sites cluster exactly. Altough in this work we consider explicitly only spin-1/2 systems, our approach can be exploited in the characterisation of arbitrary spin systems, fermion and boson systems (with truncated Fock space), as well as many-particle systems with degrees of freedom having different statistical properties.
Self-Educated Language Agent with Hindsight Experience Replay for Instruction Following

Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. These properties make it a natural fit to guide the training of interactive agents as it could ease recurrent challenges in Reinforcement Learning such as sample complexity, generalization, or multi-tasking. Yet, it remains an open-problem to relate language and RL in even simple instruction following scenarios. Current methods rely on expert demonstrations, auxiliary losses, or inductive biases in neural architectures. In this paper, we propose an orthogonal approach called Textual Hindsight Experience Replay (THER) that extends the Hindsight Experience Replay approach to the language setting. Whenever the agent does not fulfill its instruction, THER learns to output a new directive that matches the agent trajectory, and it relabels the episode with a positive reward. To do so, THER learns to map a state into an instruction by using past successful trajectories, which removes the need to have external expert interventions to relabel episodes as in vanilla HER. We observe that this simple idea also initiates a learning synergy between language acquisition and policy learning on instruction following tasks in the BabyAI environment.
Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.
Pricing Mechanism for Resource Sustainability in Competitive Online Learning Multi-Agent Systems

In this paper, we consider the problem of resource congestion control for competing online learning agents. On the basis of non-cooperative game as the model for the interaction between the agents, and the noisy online mirror ascent as the model for rational behavior of the agents, we propose a novel pricing mechanism which gives the agents incentives for sustainable use of the resources. Our mechanism is distributed and resource-centric, in the sense that it is done by the resources themselves and not by a centralized instance, and that it is based rather on the congestion state of the resources than the preferences of the agents. In case that the noise is persistent, and for several choices of the intrinsic parameter of the agents, such as their learning rate, and of the mechanism parameters, such as the learning rate of -, the progressivity of the price-setters, and the extrinsic price sensitivity of the agents, we show that the accumulative violation of the resource constraints of the resulted iterates is sub-linear w.r. t. the time horizon. Moreover, we provide numerical simulations to support our theoretical findings.
Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach

A salient approach to interpretable machine learning is to restrict modeling to simple and hence understandable models. In the Bayesian framework, this can be pursued by restricting the model structure and prior to favor interpretable models. Fundamentally, however, interpretability is about users' preferences, not the data generation mechanism: it is more natural to formulate interpretability as a utility function. In this work, we propose an interpretability utility, which explicates the trade-off between explanation fidelity and interpretability in the Bayesian framework. The method consists of two steps. First, a reference model, possibly a black-box Bayesian predictive model compromising no accuracy, is constructed and fitted to the training data. Second, a proxy model from an interpretable model family that best mimics the predictive behaviour of the reference model is found by optimizing the interpretability utility function. The approach is model agnostic - neither the interpretable model nor the reference model are restricted to be from a certain class of models - and the optimization problem can be solved using standard tools in the chosen model family. Through experiments on real-word data sets using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the earlier alternative of restricting the prior. We also propose a systematic way to measure stabilities of interpretabile models constructed by different interpretability approaches and show that our proposed approach generates more stable models.
Singularity for a multidimensional variational wave equation arising from nematic liquid crystals.

This article is focused on a multidimensional nonlinear variational wave equation which is the Euler-Lagrange equation of a variational principle arising form the theory of nematic liquid crystals. By using the method of characteristics, we show that the smooth solutions for the spherically-symmetric variational wave equation breakdown in finite time, even for the arbitrarily small initial energy.
Improving Privacy in Graphs Through Node Addition

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users’ identity. Since structure-based de-anonymization attacks can reveal users’ identity’s even when the graph is simply anonymized by employing naïve ID removal, recently, k- anonymity is proposed to secure users’ privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides k- anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have k- anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.
Stochastic Recursive Gradient-Based Methods for Projection-Free Online Learning

This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds or have high per-iteration computational costs. To fill this gap, two efficient projection-free online methods called ORGFW and MORGFW are proposed for solving stochastic and adversarial OCO problems, respectively. By employing a recursive gradient estimator, our methods achieve optimal regret bounds (up to a logarithmic factor) while possessing low per-iteration computational costs. Experimental results demonstrate the efficiency of the proposed methods compared to state-of-the-arts.
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance

We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and global information for each robot from motion information maps. We use a three-layer CNN that takes these maps as input to generate a suitable action to drive each robot to its goal position. Our approach is general, learns an optimal policy using a multi-scenario, multi-state training algorithm, and can directly handle raw sensor measurements for local observations. We demonstrate the performance on dense, complex benchmarks with narrow passages and environments with tens of agents. We highlight the algorithm’s benefits over prior learning methods and geometric decentralized algorithms in complex scenarios.
Batch Face Alignment using a Low-rank GAN

This paper studies the problem of aligning a set of face images of the same individual into a normalized image while removing the outliers like partial occlusion, extreme facial expression as well as significant illumination variation. Our model seeks an optimal image domain transformation such that the matrix of misaligned images can be decomposed as the sum of a sparse matrix of noise and a rank-one matrix of aligned images. The image transformation is learned in an unsupervised manner, which means that ground-truth aligned images are unnecessary for our model. Specifically, we make use of the remarkable non-linear transforming ability of generative adversarial network(GAN) and guide it with low-rank generation as well as sparse noise constraint to achieve the face alignment. We verify the efficacy of the proposed model with extensive experiments on real-world face databases, demonstrating higher accuracy and efficiency than existing methods.
Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration

Medical image registration is an important task in automated analysis of multi-modal images and temporal data involving multiple patient visits. Conventional approaches, although useful for different image types, are time consuming. Of late, deep learning (DL) based image registration methods have been proposed that outperform traditional methods in terms of accuracy and time. However,DL based methods are heavily dependent on training data and do not generalize well when presented with images of different scanners or anatomies. We present a DL based approach that can perform medical image registration of one image type despite being trained with images of a different type. This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive this http URL achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders. The resultant encoded feature space is used to generate the registered images with the help of generative adversarial networks (GANs). This feature transformation ensures greater invariance to the input image type. Experiments on chest Xray, retinal and brain MR images show that our method, trained on one dataset gives better registration performance for other datasets, outperforming conventional methods that do not incorporate domain adaptation