Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncontrolled DCDs: The Next Step?

An increasing percentage of kidney transplants in the United States come from donors after cardiac death (DCD). In 2004, 537 DCD kidney transplants were performed, which represents 5.8% of all kidney transplants and an increase of 39% over 2003 (1). Recent analyses have shown that despite higher rates of delayed graft function (DGF), long-term graft survival for DCD kidney transplants is similar to that of kidneys from brain dead donors (DBD) (2). An increasing number of donor hospitals, OPOs and transplant centers have developed DCD protocols. These are mostly controlled DCDs (cDCD) with severe neurological injury for whom withdrawal of mechanical support is planned.
Sonography of Thyroid Nodules: A “Classic Pattern” Diagnostic Approach

This article describes an approach to some of the commonly encountered, “classic pattern,” appearances of both benign and malignant thyroid nodules that are seen in day-to-day practice. These appearances include specific nodules that commonly need fine needle aspiration (FNA)/biopsy, and other nodules that do not usually need FNA/biopsy.
Developmental stages of the third molar in Israeli children.

PURPOSE The purpose of this study was to examine the developmental stages of the third molar in Israeli children, compare its development in boys and girls, in the maxilla and mandible, right to left sides in various age groups, and relate the developmental stage to chronological age. METHODS Panoramic radiographs of 693 healthy children and adolescents (328 boys and 365 girls) ages 7 to 16 were analyzed using a modified 6-stage method. Since the 16-year-old group was too small for statistical analysis, it was later excluded from the study. Radiographs were obtained from patient files. RESULTS There were no significant differences in the development of the third molar between boys and girls and between right and left sides. Slight differences were found between the maxilla and mandible. The first appearance of a radiolucent bud was at age 8.7 in the mandible and 9.3 in the maxilla. Crown completion was observed on average at age 11.7 in the maxilla and 11.8 in the mandible. CONCLUSIONS There was a high correlation between third molar development and chronological age. Agenesis can be determined conclusively if no radiolucent bud is present beyond age 14. The modified 6-stage method allowed more accurate determination of developmental stages.
Development and Testing of a New Instrument for Researching on Cancer Treatment Technologies Based on Magnetic Hyperthermia

A power electronics circuit allows the generation of high-frequency magnetic field that can be used to increase the temperature of cancer cells previously invaded with the magnetic nanoparticles. The circuit designed for this purpose is a high-frequency phase-shift inverter implemented with SiC devices and natural zero voltage switching. The inductive load has been optimized to increase as much as possible the magnetic field at the center of it considering the physical restrictions. Into this inductor, an adiabatic probe filled with nanoparticles is placed being the main objective to increase its temperature. The control of the inverter has been designed in such a way that it is easy to try waveforms different from the classical sine waves to see its effect on the temperature of the sample. Although the research is in one of the early stages, the first conclusions about the optimal frequency and field have been obtained showing that this technique could be a real option in the future.
The interplay between iron accumulation, mitochondrial dysfunction, and inflammation during the execution step of neurodegenerative disorders

A growing set of observations points to mitochondrial dysfunction, iron accumulation, oxidative damage and chronic inflammation as common pathognomonic signs of a number of neurodegenerative diseases that includes Alzheimer’s disease, Huntington disease, amyotrophic lateral sclerosis, Friedrich’s ataxia and Parkinson’s disease. Particularly relevant for neurodegenerative processes is the relationship between mitochondria and iron. The mitochondrion upholds the synthesis of iron–sulfur clusters and heme, the most abundant iron-containing prosthetic groups in a large variety of proteins, so a fraction of incoming iron must go through this organelle before reaching its final destination. In turn, the mitochondrial respiratory chain is the source of reactive oxygen species (ROS) derived from leaks in the electron transport chain. The co-existence of both iron and ROS in the secluded space of the mitochondrion makes this organelle particularly prone to hydroxyl radical-mediated damage. In addition, a connection between the loss of iron homeostasis and inflammation is starting to emerge; thus, inflammatory cytokines like TNF-alpha and IL-6 induce the synthesis of the divalent metal transporter 1 and promote iron accumulation in neurons and microglia. Here, we review the recent literature on mitochondrial iron homeostasis and the role of inflammation on mitochondria dysfunction and iron accumulation on the neurodegenerative process that lead to cell death in Parkinson’s disease. We also put forward the hypothesis that mitochondrial dysfunction, iron accumulation and inflammation are part of a synergistic self-feeding cycle that ends in apoptotic cell death, once the antioxidant cellular defense systems are finally overwhelmed.
Regulation of a Rho-associated kinase expression during the corneal epithelial cell cycle.

PURPOSE It has been recognized that an increased expression of the Rho-associated kinase (ROCK-I), a downstream target of Rho (a Ras-related small guanosine triphosphatase [GTPase]), is associated with limbal-to-corneal epithelial transition. The purpose of the present study was to determine whether the expression of ROCK-I is regulated during the cell cycle of corneal epithelial cells. METHODS Rabbit corneal epithelial cells in culture were subjected to different culture conditions to enrich them in the G0, G1, and S phases of the cell cycle. Indirect immunofluorescence staining and western blot techniques were used for analyzing the changes in the relative intracellular concentrations of ROCK-I. Northern blot analysis of the isolated cellular RNA was performed to estimate the relative concentrations of ROCK-I mRNA. RESULTS Serum deprivation did not cause all the corneal epithelial cells in culture to be arrested in the G0 phase of the cell cycle. However, the cells could be arrested in G0 by treating them with culture medium supplemented with transforming growth factor (TGF)-beta1. The relative concentration of ROCK-I in the G0-arrested cells was higher than in the corresponding control untreated cultures. G0-arrested cells were induced to enter G1, followed by the S phase of the cell cycle, by refeeding them with the medium devoid of TGF-beta1. The total intracellular concentration of ROCK-I significantly decreased during the G1 phase of the cell cycle and increased again during the S phase. The decrease in intracellular ROCK-I during the G1 phase was confirmed by arresting the cells in G1 with isoleucine deprivation and thymidine-mimosine treatments. ROCK-I mRNA levels were also found to be decreased during the G1 phase of the cell cycle. CONCLUSIONS The levels of ROCK-I in the corneal epithelial cells were significantly lower in the G1 phase than those in the S and G0 phases of the cell cycle. Therefore, a Rho signaling pathway(s) involving ROCK-I may be regulated during the corneal epithelial cell cycle. The downregulation of ROCK-I during the G1 phase, at least in part, is due to the decreased levels of its mRNA. Based on these findings, ROCK-I may have a role in the progression of the cell cycle in the corneal epithelial cells as they migrate centripetally from the limbal to the corneal surface.
Contextualising e-learning services and content for computing course in higher education based on learning style and competency level

Delivering e-learning services and content according to learner profiles is the main requirement towards developing context-adaptive e-learning systems. The work presented in this paper is part of an ongoing effort towards personalisation of e-learning services and content based on learner attributes such as learning styles and competency levels. Towards that aim, we formalise a context model which serves as the foundation for delineating the learner's profile for service and content adaptation. In this paper, the learning styles and competency levels of a cohort of engineering students undertaking a computing course were identified. The students were asked to rate a list of e-learning services and content personalisation approaches. The result shows there is significant variation of preferences and rating of e-learning services among learners of different profiles. This work serves as a reference for research on learner context profiling in context adaptive e-learning, particularly in computing courses.
LightIt: Illumination Modeling and Control for Diffusion Models

We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations, we propose to condition the generation on shading and normal maps. We model the lighting with single bounce shading, which includes cast shadows. We first train a shading estimation module to generate a dataset of real-world images and shading pairs. Then, we train a control network using the estimated shading and normals as input. Our method demonstrates high-quality image generation and lighting control in numerous scenes. Additionally, we use our generated dataset to train an identity-preserving relighting model, conditioned on an image and a target shading. Our method is the first that enables the generation of images with controllable, consistent lighting and performs on par with specialized relighting state-of-the-art methods.
Combined pancreas and liver therapies: Resection and ablation in hepato‐pancreatico‐biliary malignancies

Combined pancreatic and liver resection for hepato‐pancreatico‐biliary disease is generally considered contraindicated since it is thought to provide little if any survival benefit with high risk of morbidity. Our goal was to review our experience with combined pancreatic and liver resections to better define characteristics that increase risk for perioperative complications after combined resections.
Reinforcement Learning with Options and State Representation

The current thesis aims to explore the reinforcement learning field and build on existing methods to produce improved ones to tackle the problem of learning in high-dimensional and complex environments. It addresses such goals by decomposing learning tasks in a hierarchical fashion known as Hierarchical Reinforcement Learning. We start in the first chapter by getting familiar with the Markov Decision Process framework and presenting some of its recent techniques that the following chapters use. We then proceed to build our Hierarchical Policy learning as an answer to the limitations of a single primitive policy. The hierarchy is composed of a manager agent at the top and employee agents at the lower level. In the last chapter, which is the core of this thesis, we attempt to learn lower-level elements of the hierarchy independently of the manager level in what is known as the"Eigenoption". Based on the graph structure of the environment, Eigenoptions allow us to build agents that are aware of the geometric and dynamic properties of the environment. Their decision-making has a special property: it is invariant to symmetric transformations of the environment, allowing as a consequence to greatly reduce the complexity of the learning task.
A Lie Group Approach to Riemannian Batch Normalization

Manifold-valued measurements exist in numerous applications within computer vision and machine learning. Recent studies have extended Deep Neural Networks (DNNs) to manifolds, and concomitantly, normalization techniques have also been adapted to several manifolds, referred to as Riemannian normalization. Nonetheless, most of the existing Riemannian normalization methods have been derived in an ad hoc manner and only apply to specific manifolds. This paper establishes a unified framework for Riemannian Batch Normalization (RBN) techniques on Lie groups. Our framework offers the theoretical guarantee of controlling both the Riemannian mean and variance. Empirically, we focus on Symmetric Positive Definite (SPD) manifolds, which possess three distinct types of Lie group structures. Using the deformation concept, we generalize the existing Lie groups on SPD manifolds into three families of parameterized Lie groups. Specific normalization layers induced by these Lie groups are then proposed for SPD neural networks. We demonstrate the effectiveness of our approach through three sets of experiments: radar recognition, human action recognition, and electroencephalography (EEG) classification. The code is available at https://github.com/GitZH-Chen/LieBN.git.
Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies

In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, contingent upon the volume of simulations conducted. Notwithstanding its broad applicability, the algorithm's performance can be adversely impacted in certain scenarios, particularly within the domain of game strategy development. This research posits that the inherent branch divergence within the Da Vinci Code board game significantly impedes parallelism when executed on Graphics Processing Units (GPUs). To investigate this hypothesis, we implemented and meticulously evaluated two variants of the MCTS algorithm, specifically designed to assess the impact of branch divergence on computational performance. Our comparative analysis reveals a linear improvement in performance with the CPU-based implementation, in stark contrast to the GPU implementation, which exhibits a non-linear enhancement pattern and discernible performance troughs. These findings contribute to a deeper understanding of the MCTS algorithm's behavior in divergent branch scenarios, highlighting critical considerations for optimizing game strategy algorithms on parallel computing architectures.
Systematic shell-model study of structure and isomeric states in $^{204-213}$Bi isotopes

In this work, we have performed systematic shell-model calculations for Bi isotopes with $A=$ 204-213 using KHH7B and KHM3Y effective interactions. We have reported yrast and non-yrast shell-model states corresponding to the available experimental data. From the comparison with the experimental data, we could assign spin and parity of several unconfirmed states. We have also calculated electromagnetic properties and compared them with the available experimental data and predicted where experimental data are not available. This study also includes a detailed discussion of multiple isomeric states based on computed shell-model configurations and their respective half-lives.
Leveraging CLIP for Inferring Sensitive Information and Improving Model Fairness

Performance disparities across sub-populations are known to exist in deep learning-based vision recognition models, but previous work has largely addressed such fairness concerns assuming knowledge of sensitive attribute labels. To overcome this reliance, previous strategies have involved separate learning structures to expose and adjust for disparities. In this work, we explore a new paradigm that does not require sensitive attribute labels, and evades the need for extra training by leveraging the vision-language model, CLIP, as a rich knowledge source to infer sensitive information. We present sample clustering based on similarity derived from image and attribute-specified language embeddings and assess their correspondence to true attribute distribution. We train a target model by re-sampling and augmenting under-performed clusters. Extensive experiments on multiple benchmark bias datasets show clear fairness gains of the model over existing baselines, which indicate that CLIP can extract discriminative sensitive information prompted by language, and used to promote model fairness.
SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images

This research paper presents an innovative multi-task learning framework that allows concurrent depth estimation and semantic segmentation using a single camera. The proposed approach is based on a shared encoder-decoder architecture, which integrates various techniques to improve the accuracy of the depth estimation and semantic segmentation task without compromising computational efficiency. Additionally, the paper incorporates an adversarial training component, employing a Wasserstein GAN framework with a critic network, to refine model's predictions. The framework is thoroughly evaluated on two datasets - the outdoor Cityscapes dataset and the indoor NYU Depth V2 dataset - and it outperforms existing state-of-the-art methods in both segmentation and depth estimation tasks. We also conducted ablation studies to analyze the contributions of different components, including pre-training strategies, the inclusion of critics, the use of logarithmic depth scaling, and advanced image augmentations, to provide a better understanding of the proposed framework. The accompanying source code is accessible at \url{https://github.com/PardisTaghavi/SwinMTL}.
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment

Alignment with human preference prevents large language models (LLMs) from generating misleading or toxic content while requiring high-cost human feedback. Assuming resources of human annotation are limited, there are two different ways of allocating considered: more diverse PROMPTS or more diverse RESPONSES to be labeled. Nonetheless, a straightforward comparison between their impact is absent. In this work, we first control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their influence. We find that instead of numerous prompts, more responses but fewer prompts better trigger LLMs for human alignment. Additionally, the concept of diversity for prompts can be more complex than responses that are typically quantified by single digits. Consequently, a new formulation of prompt diversity is proposed, further implying a linear correlation with the final performance of LLMs after fine-tuning. We also leverage it on data augmentation and conduct experiments to show its effect on different algorithms.
Computational thermoforming

We propose a method to fabricate textured 3D models using thermoforming. Differently from industrial techniques, which target mass production of a specific shape, we propose a combined hardware and software solution to manufacture customized, unique objects. Our method simulates the forming process and converts the texture of a given digital 3D model into a pre-distorted image that we transfer onto a plastic sheet. During thermoforming, the sheet deforms to create a faithful physical replica of the digital model. Our hardware setup uses off-the-shelf components and can be calibrated with an automatic algorithm that extracts the simulation parameters from a single calibration object produced by the same process.
On the injectivity of the Kudla-Millson lift and surjectivity of the Borcherds lift.

We consider the Kudla-Millson lift from elliptic modular forms of weight (p + q)/2 to closed q-forms on locally symmetric spaces corresponding to the orthogonal group O(p,q). We study the L 2 -norm of the lift following the Rallis inner product formula. We compute the contribution at the Archimedian place. For locally symmetric spaces associated to even unimodular lattices, we obtain an explicit formula for the L 2 -norm of the lift, which often implies that the lift is in- jective. For O(p,2) we discuss how such injectivity results imply the surjectivity of the Borcherds lift.
Well-balanced path-conservative discontinuous Galerkin methods with equilibrium preserving space for two-layer shallow water equations

This paper introduces well-balanced path-conservative discontinuous Galerkin (DG) methods for two-layer shallow water equations, ensuring exactness for both still water and moving water equilibrium steady states. The approach involves approximating the equilibrium variables within the DG piecewise polynomial space, while expressing the DG scheme in the form of path-conservative schemes. To robustly handle the nonconservative products governing momentum exchange between the layers, we incorporate the theory of Dal Maso, LeFloch, and Murat (DLM) within the DG method. Additionally, linear segment paths connecting the equilibrium functions are chosen to guarantee the well-balanced property of the resulting scheme. The simple ``lake-at-rest"steady state is naturally satisfied without any modification, while a specialized treatment of the numerical flux is crucial for preserving the moving water steady state. Extensive numerical examples in one and two dimensions validate the exact equilibrium preservation of the steady state solutions and demonstrate its high-order accuracy. The performance of the method and high-resolution results further underscore its potential as a robust approach for nonconservative hyperbolic balance laws.
N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields

Understanding complex scenes at multiple levels of abstraction remains a formidable challenge in computer vision. To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a single feature field, wherein different dimensions within the same high-dimensional feature encode scene properties at varying granularities. Our method allows for a flexible definition of hierarchies, tailored to either the physical dimensions or semantics or both, thereby enabling a comprehensive and nuanced understanding of scenes. We leverage a 2D class-agnostic segmentation model to provide semantically meaningful pixel groupings at arbitrary scales in the image space, and query the CLIP vision-encoder to obtain language-aligned embeddings for each of these segments. Our proposed hierarchical supervision method then assigns different nested dimensions of the feature field to distill the CLIP embeddings using deferred volumetric rendering at varying physical scales, creating a coarse-to-fine representation. Extensive experiments show that our approach outperforms the state-of-the-art feature field distillation methods on tasks such as open-vocabulary 3D segmentation and localization, demonstrating the effectiveness of the learned nested feature field.