Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinicopathological study of intracranial fusiform and dolichoectatic aneurysms : insight on the mechanism of growth.

BACKGROUND AND PURPOSE Intracranial fusiform aneurysms can be divided into 2 clinically different subtypes: acute dissecting aneurysms and chronic fusiform or dolichoectatic aneurysms. Of these 2, the natural history and growth mechanism of chronic fusiform aneurysms remains unknown. METHODS A consecutive series of 16 patients with chronic fusiform aneurysms was studied retrospectively to clarify patient clinical and neuroradiological features. Aneurysm tissues were obtained from 8 cases and were examined to identify histological features that could correspond to the radiological findings. RESULTS Four histological features were found: (1) fragmentation of internal elastic lamina (IEL), (2) neoangiogenesis within the thickened intima, (3) intramural hemorrhage (IMH) and thrombus formation, and (4) repetitive intramural hemorrhages from the newly formed vessels within thrombus. IEL fragmentation was found in all cases, which suggests that this change may be one of the earliest processes of aneurysm formation. MRI or CT detected IMH, and marked contrast enhancement of the inside of the aneurysm wall (CEI) on MRI corresponded well with intimal thickening. Eight of 9 symptomatic cases but none of 7 asymptomatic cases presented with both radiological features. CONCLUSIONS Data suggest that chronic fusiform aneurysms are progressive lesions that start with IEL fragmentation. Formation of IMH seems to be a critical event necessary for lesions to become symptomatic and progress, and this can be monitored on MRI. Knowledge of this possible mechanism of progression and corresponding MRI characteristics could help determine timing of surgical intervention.
ANALYSIS OF A MIXED FINITE ELEMENT METHOD FOR A PHASE FIELD BENDING ELASTICITY MODEL OF VESICLE

In this paper, we study numerical approximations of a recently proposed phase field model for the vesicle membrane deformation governed by the variation of the elastic bending energy. To overcome the challenges of high order nonlinear differential systems and the nonlinear constraints associated with the problem, we present the phase field bending elasticity model in a nested saddle point formulation. A mixed finite element method is then employed to compute the equilibrium configuration of a vesicle membrane with prescribed volume and surface area. Coupling the approximation results for a related linearized problem and the general theory of Brezzi-Rappaz-Raviart, optimal order error estimates for the finite element approximations of the phase field model are obtained. Numerical results are provided to substantiate the derived estimates.
Iodine excretion in school children in Copenhagen.

INTRODUCTION Studies of dietary habits show a high iodine intake in children in Denmark. Iodine excretion in children has not previously been assessed. Iodine excretion in adults is below the recommended threshold, and it is therefore being discussed to increase the fortification level. The main objective of this study was to assess iodine excretion in children living in Copenhagen to establish whether a moderate increase in iodine fortification would lead to excess iodine intake in this group. METHODS Children in first and fifth grade were recruited through schools in Copenhagen. In total, 244 children de-ivered a urine sample. Urine samples were analysed for iodine and creatinine, and the results were expressed as urinary iodine concentration (UIC) and as estimated 24-h iodine excretion. Iodine excretion in children was also compared with that of adults living in the same area, investigated in a prior study. RESULTS The median UIC was within the recommended level; 145 (range: 116-201) µg/l for boys and 128 (range: 87-184) µg/l for girls, and was lower in fifth grade students than in first grade students. Estimated 24-h iodine excretion was higher in boys than in girls, but did not differ according to grade. The UIC was higher in children than in adults from the same area. CONCLUSIONS The iodine excretion among schoolchildren in Copenhagen, an area with a relatively high iodine content in tap water, was within the recommended range as assessed by the UIC. An increased iodine fortification will not have negative consequences for this group. FUNDING The Ministry of Food, Agriculture and Fisheries. TRIAL REGISTRATION not relevant.
Single‐center analysis of early recurrence of nephrotic syndrome following renal transplantation in children

Abstract: Recurrence of nephrotic syndrome (NS) after transplantation (Tx) occurs in 20–50% of renal transplant recipients, with a median time to recurrence of 14 days and a 50% rate of graft loss. We performed a retrospective analysis of 22 pediatric patients with NS who received renal transplants at the Children's Hospital, Boston, between 1982 and 1999. During the first 14 days following Tx, 13 (59%) patients developed clinical recurrent nephrotic syndrome (RNS). RNS developed in 50% of living donor recipients and in 70% of cadaveric donor recipients (p= non‐significant). Seven of the 13 patients with RNS were treated with plasmapheresis, while six received standard immunosuppressive induction therapy only. Two of the seven treated patients and one of the six untreated patients lost their grafts to RNS, yielding a total RNS graft loss rate of 23%. However, patients with RNS who achieved remission had significantly higher cumulative graft survival at 5 yr than did RNS patients who did not achieve remission (p< 0.001). Overall cumulative graft survival at 5 yr was not significantly different between the two groups: 67% in those with non‐recurrent nephrotic syndrome (NRNS) vs. 64% in those with RNS, p= non‐significant. We conclude that successful reversal of early RNS improves long‐term graft survival in pediatric RNS. Multi‐center studies are sorely needed to develop novel, less toxic therapies for native and recurrent NS.
Thrombin-Stimulated Myosin Phosphorylation in Intact Platelets and its Possible Involvement Secretion

Summary A 20,000 dalton polypeptide, which is phosphorylated in intact platelets pre-incubated with 32P-P04, has been identified as a platelet myosin light chain. Stimulation of intact platelets with thrombin produced a 5-fold increase in the amount of radioactive phosphate incorporated into the light chain. Myosin phosphorylation preceeded acid hydrolase secretion and occurred concomitantly with adenine nucleotide secretion. These results are suggestive of participation of contractile mechanisms in platelet secretion.
Borna Disease Virus infection in young children

Borna Disease Virus infection (BDV) is a well known animal disease. It got its name from a disease outbreak among horses around the small town ‘‘Borna’’, 26 km south-east of Leipzig in the late 19th century. The symptoms in horses have been described in detail and involve neurologic as well as mental and behavioral disorders. A long debate exists whether or not human infections do exist. Several papers reported an association with psyciatric diseases (1–4) but were disputed by others (5). Reports on young animals are rare but the reports yielded interesting insights into disturbed adaptation to social interactions of the affected animals (6–8). Similar reports on children are lacking. The aim of the present study was therefore to investigate the prevalence of serological BDV-markers in children.
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation

With the development of neural radiance fields and generative models, numerous methods have been proposed for learning 3D human generation from 2D images. These methods allow control over the pose of the generated 3D human and enable rendering from different viewpoints. However, none of these methods explore semantic disentanglement in human image synthesis, i.e., they can not disentangle the generation of different semantic parts, such as the body, tops, and bottoms. Furthermore, existing methods are limited to synthesize images at $512^2$ resolution due to the high computational cost of neural radiance fields. To address these limitations, we introduce SemanticHuman-HD, the first method to achieve semantic disentangled human image synthesis. Notably, SemanticHuman-HD is also the first method to achieve 3D-aware image synthesis at $1024^2$ resolution, benefiting from our proposed 3D-aware super-resolution module. By leveraging the depth maps and semantic masks as guidance for the 3D-aware super-resolution, we significantly reduce the number of sampling points during volume rendering, thereby reducing the computational cost. Our comparative experiments demonstrate the superiority of our method. The effectiveness of each proposed component is also verified through ablation studies. Moreover, our method opens up exciting possibilities for various applications, including 3D garment generation, semantic-aware image synthesis, controllable image synthesis, and out-of-domain image synthesis.
Real-Time Image Segmentation via Hybrid Convolutional-Transformer Architecture Search

Image segmentation is one of the most fundamental problems in computer vision and has drawn a lot of attentions due to its vast applications in image understanding and autonomous driving. However, designing effective and efficient segmentation neural architectures is a labor-intensive process that may require lots of trials by human experts. In this paper, we address the challenge of integrating multi-head self-attention into high resolution representation CNNs efficiently, by leveraging architecture search. Manually replacing convolution layers with multi-head self-attention is non-trivial due to the costly overhead in memory to maintain high resolution. By contrast, we develop a multi-target multi-branch supernet method, which not only fully utilizes the advantages of high-resolution features, but also finds the proper location for placing multi-head self-attention module. Our search algorithm is optimized towards multiple objective s (e.g., latency and mIoU) and capable of finding architectures on Pareto frontier with arbitrary number of branches in a single search. We further present a series of model via Hybrid Convolutional-Transformer Architecture Search (HyCTAS) method that searched for the best hybrid combination of light-weight convolution layers and memory-efficient self-attention layers between branches from different resolutions and fuse to high resolution for both efficiency and effectiveness. Extensive experiments demonstrate that HyCTAS outperforms previous methods on semantic segmentation task. Code and models are available at \url{https://github.com/MarvinYu1995/HyCTAS}.
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness

Machine Learning (ML) is susceptible to adversarial attacks that aim to trick ML models, making them produce faulty predictions. Adversarial training was found to increase the robustness of ML models against these attacks. However, in network and cybersecurity, obtaining labeled training and adversarial training data is challenging and costly. Furthermore, concept drift deepens the challenge, particularly in dynamic domains like network and cybersecurity, and requires various models to conduct periodic retraining. This letter introduces Adaptive Continuous Adversarial Training (ACAT) to continuously integrate adversarial training samples into the model during ongoing learning sessions, using real-world detected adversarial data, to enhance model resilience against evolving adversarial threats. ACAT is an adaptive defense mechanism that utilizes periodic retraining to effectively counter adversarial attacks while mitigating catastrophic forgetting. Our approach also reduces the total time required for adversarial sample detection, especially in environments such as network security where the rate of attacks could be very high. Traditional detection processes that involve two stages may result in lengthy procedures. Experimental results using a SPAM detection dataset demonstrate that with ACAT, the accuracy of the SPAM filter increased from 69% to over 88% after just three retraining sessions. Furthermore, ACAT outperforms conventional adversarial sample detectors, providing faster decision times, up to four times faster in some cases.
Protocol-level cooperation in wireless networks: Stable throughput and delay analysis

We study the impact of user cooperation in wireless networks on improving the stable throughput and delay performance. Specifically, we consider a multiaccess system in which a set of source users generate packets to deliver to a common destination. A cooperation strategy is proposed at the protocol level, where users with a better channel to the destination have the option to relay packets from users that are farther afield. For the case of erasure channels with single-packet reception, we derive the stable throughput regions under different multiple access policies based on such cooperation strategy. Then we prove that the stable throughput region of the cooperative system strictly contains the stable throughput region achieved without cooperation. We also assess the delay performance, and show that cooperation significantly reduces the delay of all users. Finally, we characterize the effect of inter-user channel quality on performance, and show that the gain in performance through cooperation increases as the channel quality improves. Our work offers an innovative perspective by implementing cooperation at the network protocol level, while taking into consideration of fading and attenuation at the physical layer as well as the nature of traffic burstiness in a network.
Correlation between structure and mass distribution of the nuclear pore complex and of distinct pore complex components

Nuclear pore complexes (NPCs) prepared from Xenopus laevis oocyte nuclear envelopes were studied in "intact" form (i.e., unexposed to detergent) and after detergent treatment by a combination of conventional transmission electron microscopy (CTEM) and quantitative scanning transmission electron microscopy (STEM). In correlation- averaged CTEM pictures of negatively stained intact NPCs and of distinct NPC components (i.e., "rings," "spoke" complexes, and "plug- spoke" complexes), several fine structural features arranged with octagonal symmetry about a central axis could reproducibly be identified. STEM micrographs of unstained/freeze-dried intact NPCs as well as of their components yielded comparable but less distinct features. Mass determination by STEM revealed the following molecular masses: intact NPC with plug, 124 +/- 11 MD; intact NPC without plug, 112 +/- 11 MD; heavy ring, 32 +/- 5 MD; light ring, 21 +/- 4 MD; plug- spoke complex, 66 +/- 8 MD; and spoke complex, 52 +/- 3 MD. Based on these combined CTEM and STEM data, a three-dimensional model of the NPC exhibiting eightfold centrosymmetry about an axis perpendicular to the plane of the nuclear envelope but asymmetric along this axis is proposed. This structural polarity of the NPC across the nuclear envelope is in accord with its well-documented functional polarity facilitating mediated nucleocytoplasmic exchange of molecules and particles.
Quantization Avoids Saddle Points in Distributed Optimization

Distributed nonconvex optimization underpins key functionalities of numerous distributed systems, ranging from power systems, smart buildings, cooperative robots, vehicle networks to sensor networks. Recently, it has also merged as a promising solution to handle the enormous growth in data and model sizes in deep learning. A fundamental problem in distributed nonconvex optimization is avoiding convergence to saddle points, which significantly degrade optimization accuracy. We discover that the process of quantization, which is necessary for all digital communications, can be exploited to enable saddle-point avoidance. More specifically, we propose a stochastic quantization scheme and prove that it can effectively escape saddle points and ensure convergence to a second-order stationary point in distributed nonconvex optimization. With an easily adjustable quantization granularity, the approach allows a user to control the number of bits sent per iteration and, hence, to aggressively reduce the communication overhead. Numerical experimental results using distributed optimization and learning problems on benchmark datasets confirm the effectiveness of the approach.
ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Image

In this work, we present ThermoHands, a new benchmark for thermal image-based egocentric 3D hand pose estimation, aimed at overcoming challenges like varying lighting and obstructions (e.g., handwear). The benchmark includes a diverse dataset from 28 subjects performing hand-object and hand-virtual interactions, accurately annotated with 3D hand poses through an automated process. We introduce a bespoken baseline method, TheFormer, utilizing dual transformer modules for effective egocentric 3D hand pose estimation in thermal imagery. Our experimental results highlight TheFormer's leading performance and affirm thermal imaging's effectiveness in enabling robust 3D hand pose estimation in adverse conditions.
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning

Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have been successful in solving partially observable MDPs, of which meta-RL is a special case. In this work, we leverage this success and propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods. We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency (up to $15\times$) while requiring very little hyperparameter tuning. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents.
A Face and Speech Biometric Verification System Using a Simple Bayesian Structure

Identity verification systems that use a mono modal biometric always have to contend with sensor noise and limitations of the feature extractor and matcher, while combining information from different biometrics modalities may well provide higher and more consistent performance levels. However, an intelligent scheme is required to fuse the decisions produced by the individual sensors. This paper presents a decision fusion technique for a bimodal biometric verification system that makes use of facial and speech biometrics. The decision fusion schemes considered have simple Bayesian structures (SBS) that particularize the univariat Gaussian density function, Beta density function or Parzen window density estimation. SBS has advantages in terms of computation speed, storage space and its open framework. The performances of SBS is evaluated and compared with that of other classical classification approaches, such as sum rule and Multilayer Perceptron, on a bimodal database.
Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting

While text-to-3D and image-to-3D generation tasks have received considerable attention, one important but under-explored field between them is controllable text-to-3D generation, which we mainly focus on in this work. To address this task, 1) we introduce Multi-view ControlNet (MVControl), a novel neural network architecture designed to enhance existing pre-trained multi-view diffusion models by integrating additional input conditions, such as edge, depth, normal, and scribble maps. Our innovation lies in the introduction of a conditioning module that controls the base diffusion model using both local and global embeddings, which are computed from the input condition images and camera poses. Once trained, MVControl is able to offer 3D diffusion guidance for optimization-based 3D generation. And, 2) we propose an efficient multi-stage 3D generation pipeline that leverages the benefits of recent large reconstruction models and score distillation algorithm. Building upon our MVControl architecture, we employ a unique hybrid diffusion guidance method to direct the optimization process. In pursuit of efficiency, we adopt 3D Gaussians as our representation instead of the commonly used implicit representations. We also pioneer the use of SuGaR, a hybrid representation that binds Gaussians to mesh triangle faces. This approach alleviates the issue of poor geometry in 3D Gaussians and enables the direct sculpting of fine-grained geometry on the mesh. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content.
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image

Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB image. However, existing monocular approaches still struggle to recover fine geometric details such as face, hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships, mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy, which improves the accuracy of the signed distance field estimation of points that lie on the reconstructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB, surface normals, point cloud or RGB-D data as input. In addition, we introduce ANIM-Real, a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera, and our protocol to fine-tune ANIM, enabling high-quality reconstruction from real-world human capture.
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects

We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in model-based 6D object pose estimation from an RGB/RGB-D image and related tasks. Besides the three tasks from 2022 (model-based 2D detection, 2D segmentation, and 6D localization of objects seen during training), the 2023 challenge introduced new variants of these tasks focused on objects unseen during training. In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, 1 GPU) from provided 3D object models. The best 2023 method for 6D localization of unseen objects (GenFlow) notably reached the accuracy of the best 2020 method for seen objects (CosyPose), although being noticeably slower. The best 2023 method for seen objects (GPose) achieved a moderate accuracy improvement but a significant 43% run-time improvement compared to the best 2022 counterpart (GDRNPP). Since 2017, the accuracy of 6D localization of seen objects has improved by more than 50% (from 56.9 to 85.6 AR_C). The online evaluation system stays open and is available at: http://bop.felk. cvut.cz/.