Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of increased body mass index on orthodontic tooth movement and related parameters in children and adolescents: A systematic review of longitudinal controlled clinical studies

Objectives: To assess the impact of increased body mass index (BMI) on orthodontic tooth movement (OTM) and related parameters in children and adolescents. Search sources: A search of six electronic databases and manual searching were performed up to June 2019 without language and time restrictions. Data selection: Eligibility criteria were as follows: (1) longitudinal controlled clinical studies; (2) children and adolescents undergoing orthodontic therapy (OT); (3) no systemic diseases; (4) experimental group: patients with increased BMI; and (5) control group: patients with normal BMI. Data extraction: Screening, study selection and data extraction were performed; bias within studies was assessed using the Risk of Bias In Non-randomised Studies (ROBINS-I) tool. Results: Seven studies were included. One study showed that an increased BMI is associated with less wear-time of removable orthodontic appliances and one study found no significant association. One study showed that an increased BMI is associated with less cooperation during OT; however, not with the treatment results. One study reported higher pain experience during OT in adolescents with than without increased BMI. Two studies showed that increased BMI in adolescents is related to OTM, one with increased and one with decreased rates of OTM, respectively. One study reported an association between increased BMI and incidence of white spot lesions and gingivitis during OT. The ROBINS-I tool showed low to moderate risk of bias within studies. Conclusions: The influence of BMI on OTM and related parameters in children and adolescents remains debatable.
Radiation from Medical Procedures in the Pathogenesis of Cancer and Ischemic Heart Disease: Dose-Response Studies with Physicians per 100,000 Population

5 Chap.1 Executive Summary of This Book . 7 2 Pre-1960 and Post-1960 Uses of Medical Radiation, and Its Carcinogenic Action . 27 3 PhysPops --The Doses in Some Massive Studies of Dose-Response . 53 4 Mortality Rates --The Responses in the Dose-Response Studies . 77 5 Dose-Response, Linear Regression, and Some Other Key Concepts in Our Analyses . 89
Comparison of early versus standard timing for silicone stent removal following External Dacrocystorhinostomy under local anaesthesia.

INTRODUCTION The first line treatment for nasolacrimal duct obstruction (NLDO) is external dacrocystorhinostomy (DCR). Following DCR, patients are required to return to Tilganga Institute of Ophthalmology (TIO) six weeks postoperatively for the removal of a silicone stent. As the majority of patients travel large distances at significant cost to reach TIO, most often patients remain within Kathmandu during this six weeks interval. This places a large financial burden on patients. METHODS A randomized controlled trial was designed to compare patient outcomes after early (two weeks postoperatively) versus standard (six weeks postoperatively) removal of silicone stents. 50 selected patients were randomized into two equal groups. RESULTS At the time of publication, 31 patients (14 in group A and 17 in group B) had completed three months follow up. A success rate of 92.9% was noted in Group A and a success rate of 94.1% observed in group B. No significant difference was found between the two groups for success rate and rate of complications. CONCLUSION Early tube removal post DCR appears to cause no significant difference in outcome or complication rates compared to standard tube removal.
Elevated Levels of TNF-α and Decreased Levels of CD68-Positive Macrophages in Primary Tumor Tissues Are Unfavorable for the Survival of Patients With Nasopharyngeal Carcinoma

Due to the critical role of inflammation in nasopharyngeal carcinoma, we aim to investigate the correlation between nasopharyngeal carcinoma prognosis and the levels of tumor necrosis factor α and macrophages for the development of new prognostic models. The levels of tumor necrosis factor-α and CD68-positive macrophages were measured in 111 primary nasopharyngeal carcinoma specimens by immunohistochemistry. Kaplan-Meier analysis showed that, compared with nonelevated tumor necrosis factor-α levels, elevated tumor necrosis factor α levels were correlated with poorer 10-year distant metastasis-free survival (24.5% vs 5.2%, P = .004) and bone metastasis-free survival (17.0% vs 0.0%, P = .001). Multivariate analysis revealed that tumor necrosis factor α level was an independent prognostic factor for distant metastasis-free survival (hazard ratio = 16.765, P = .001), while the level of CD68-positive macrophages was a favorable independent prognostic factor for cancer-specific survival (hazard ratio = 0.481, P = .023) and disease-free survival (hazard ratio = 0.403, P = .010). Additionally, several prognostic models that considered tumor-node-metastasis stage alone or in combination with tumor necrosis factor α and/or CD68-positive macrophage levels were compared by receiver operating characteristic curve analysis. Interestingly, the T_score model, which considered the tumor necrosis factor α level alone, could better predict the distant metastasis-free survival and bone metastasis-free survival, whereas the MT model, which considered the combination of T stage and CD68-positive macrophage level, could better predict the cancer-specific survival and disease-free survival of patients with nasopharyngeal carcinoma. Elevated tumor necrosis factor-α levels and decreased CD68-positive macrophage levels in primary nasopharyngeal carcinoma tissues are unfavorable prognostic indicators in nasopharyngeal carcinoma. The T_score model or the MT model could be better prognostic models than those currently available for nasopharyngeal carcinoma and could be used to select high-risk patients and aid in the design of individualized immunotherapy.
!MDP Playground: Meta-Features in Reinforcement Learning

Reinforcement Learning (RL) algorithms usually do not try to identify specific features of environments which could help them perform better. Here, we present a few key \textit{meta-features} of environments: delayed rewards, specific reward sequences, sparsity of rewards, and stochasticity of environments, adapting to which should help RL agents perform better. While it is very time consuming to run RL algorithms on standard benchmarks, we define a parameterised collection of fast-to-run toy benchmarks in OpenAI Gym by varying these meta-features. Despite their toy nature and low compute requirements, we show that these benchmarks present substantial difficulties to current RL algorithms. Furthermore, since we can generate environments with a desired value for each of the meta-features, we have fine-grained control over the environments' \textit{difficulty} and also have the ground truth available for evaluating algorithms. We believe that devising algorithms that can detect such meta-features of environments and adapt to them will be key to creating robust RL algorithms that work in a variety of different real-world problems.
Unraveling the topology of 
ZrTe5
 by changing temperature

We study the effects of temperature on the topological nature of ZrTe$_5$, which sits near the phase boundary between strong and weak topological insulating orders. Using first-principles calculations, we show that the band gap and the topological indices of ZrTe$_5$ are extremely sensitive to thermal expansion, and that the electron-phonon interaction accounts for more than a third of the total temperature-dependent effects in ZrTe$_5$. We find that the temperature dependence of the band gap has an opposite sign in the strong and weak topological insulator phases. Based on this insight, we propose a robust and unambiguous method to determine the topological nature of any given sample of ZrTe$_5$: if the band gap decreases with temperature it is a strong topological insulator, and if it increases with temperature it is a weak topological insulator. An analogous strategy is expected to be generally applicable to other materials and to become particularly important in the vicinity of topological phase boundaries where other methods provide ambiguous results.
Global Optimal Path-Based Clustering Algorithm

Combinatorial optimization problems for clustering are known to be NP-hard. Most optimization methods are not able to find the global optimum solution for all datasets. To solve this problem, we propose a global optimal path-based clustering (GOPC) algorithm in this paper. The GOPC algorithm is based on two facts: (1) medoids have the minimum degree in their clusters; (2) the minimax distance between two objects in one cluster is smaller than the minimax distance between objects in different clusters. Extensive experiments are conducted on synthetic and real-world datasets to evaluate the performance of the GOPC algorithm. The results on synthetic datasets show that the GOPC algorithm can recognize all kinds of clusters regardless of their shapes, sizes, or densities. Experimental results on real-world datasets demonstrate the effectiveness and efficiency of the GOPC algorithm. In addition, the GOPC algorithm needs only one parameter, i.e., the number of clusters, which can be estimated by the decision graph. The advantages mentioned above make GOPC a good candidate as a general clustering algorithm. Codes are available at this https URL.
Coherence Statistics of Structured Random Ensembles and Support Detection Bounds for OMP

A structured random matrix ensemble that maintains constant modulus entries and unit-norm columns, often called a random phase-rotated (RPR) matrix, is considered in this letter. We analyze the coherence statistics of RPR measurement matrices and apply them to acquire probabilistic performance guarantees of orthogonal matching pursuit (OMP) for support detection (SD). It is revealed via numerical simulations that the SD performance guarantee provides a tight characterization, especially when the signal is sparse.
Algebraic and Combinatorial Tools for State Complexity : Application to the Star-Xor Problem

We investigate the state complexity of the star of symmetrical differences using modifiers and monsters. A monster is an automaton in which every function from states to states is represented by at least one letter. A modifier is a set of functions allowing one to transform a set of automata into one automaton. These recent theoretical concepts allow one to find easily the desired state complexity. We then exhibit a witness with a constant size alphabet.
Multiple Birds with One Stone: Beating 1/2 for EFX and GMMS via Envy Cycle Elimination

Several relaxations of envy-freeness, tailored to fair division in settings with indivisible goods, have been introduced within the last decade. Due to the lack of general existence results for most of these concepts, great attention has been paid to establishing approximation guarantees. In this work, we propose a simple algorithm that is universally fair in the sense that it returns allocations that have good approximation guarantees with respect to four such fairness notions at once. In particular, this is the first algorithm achieving a (φ−1)-approximation of envy-freeness up to any good (EFX) and a 2/φ+2 -approximation of groupwise maximin share fairness (GMMS), where φ is the golden ratio. The best known approximation factor, in polynomial time, for either one of these fairness notions prior to this work was 1/2. Moreover, the returned allocation achieves envy-freeness up to one good (EF1) and a 2/3-approximation of pairwise maximin share fairness (PMMS). While EFX is our primary focus, we also exhibit how to fine-tune our algorithm and improve further the guarantees for GMMS or PMMS.Finally, we show that GMMS—and thus PMMS and EFX—allocations always exist when the number of goods does not exceed the number of agents by more than two.
Fault-Tolerant Gates on Hypergraph Product Codes

Hypergraph product codes are a class of quantum low density parity check (LDPC) codes discovered by Tillich and Zemor. These codes have a constant encoding rate and were recently shown to have a constant fault-tolerant error threshold. With these features, they asymptotically offer a smaller overhead compared to topological codes. However, existing techniques to perform logical gates in hypergraph product codes require many separate code blocks, so only becomes effective with a very large number of logical qubits. Here, we demonstrate how to perform Clifford gates on this class of codes using code deformation. To this end, we generalize punctures and wormhole defects, the latter introduced in a companion paper. Together with state injection, we can perform a universal set of gates within a single block of the class of hypergraph product codes.
Z-Net: an Asymmetric 3D DCNN for Medical CT Volume Segmentation

Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). The use of 3D Deep Convolutional Neural Network (DCNN) is a viable solution for this task but is memory intensive. The use of patch division can mitigate this issue in practice, but can cause discontinuities between the adjacent patches and severe class-imbalances within individual sub-volumes. This paper presents a new patch division approach - Patch-512 to tackle the class-imbalance issue by preserving a full field-of-view of the objects in the XY planes. To achieve better segmentation results based on these asymmetric patches, a 3D DCNN architecture using asymmetrical separable convolutions is proposed. The proposed network, called Z-Net, can be seamlessly integrated into existing 3D DCNNs such as 3D U-Net and V-Net, for improved volume segmentation. Detailed validation of the method is provided for CT aortic, liver and lung segmentation, demonstrating the effectiveness and practical value of the method for intra-operative 3D navigation in robot-assisted MIS.
The orientation of planes of dwarf galaxies in the quasi-linear Universe

To date at least 10 highly flattened planes of dwarf galaxies are claimed to have been discovered in the Local Universe. The origin of these planes of galaxies remains unknown. One suggestion is that they are related to the large-scale structure of the cosmic web. A recent study found that the normal of a number of these dwarf galaxy planes is very closely aligned with the eigenvector of the shear tensor corresponding to the direction of greatest collapse obtained by reconstructing the full velocity field in the linear regime. Here we extend that work by both considering an additional 5 planes beyond the five examined previously and examining the alignment with respect to the quasi-linear field, a more sophisticated reconstruction, which is a better approximation on smaller (quasi-linear) scales. Our analysis recovers the previous result while not finding a significantly tight alignment with the additional five planes. However, the additional five plane normals also do not appear to be randomly oriented. We conclude that this could be due either to the normals of the new planes being poorly defined and described; the quasi-linear field at those locations being poorly constrained; or different formation mechanisms for the orientation of planes of dwarf galaxies.
Agent-Based Simulation of the Two-Dimensional Patlak-Keller-Segel Model

The Patlak-Keller-Segel equation describes the chemotactic interactions of small organisms in the continuum limit, and a singular peak appears through spontaneous aggregation when the total mass of the organisms exceeds a critical value. To deal with this singular behavior numerically, we propose an agent-based simulation method in which both the organisms and the chemicals are represented as particles. Our numerical estimates for the threshold behavior are consistent with the analytic predictions.
HAD-GAN: A Human-perception Auxiliary Defense GAN model to Defend Adversarial Examples

Adversarial examples reveal the vulnerability and unexplained nature of neural networks. It is of great practical significance to study the defense of adversarial examples. In fact, most adversarial examples that misclassify networks are often undetectable by humans. In this paper, we propose a defense model to train the classifier into a human-perception classification model with shape preference. The proposed model consisting of a TTN (Texture Transfer Network) and an auxiliary defense GAN (Generative Adversarial Networks) is called HAD-GAN (Human-perception Auxiliary Defense GAN). The TTN is used to extend the texture samples of a clean image and makes classifiers more focused on its shape. And GAN is utilized to form a training framework for the model and generate the images we need. A series of experiments conducted on MNIST, Fashion-MNIST and CIFAR10 show that the proposed model outperforms the state-of-the-art defense methods for network robust, and have a significant improvement on defense ability of adversarial examples.
What Are You Looking at? Detecting Human Intention in Gaze based Human-Robot Interaction

In gaze based Human-Robot Interaction (HRI), it is important to determine the human intention for further interaction. The gaze intention is often modelled as fixation. However, when looking at an object, it is not natural and it is difficult to maintain the gaze fixating on one point for a long time. Saccades may happen while a human is still focusing on the object. The prediction of human intention will be lost during saccades. In addition, while the human intention is on object, the gazes may be located outside of the object bounding box due to different noise sources, which would cause false negative predictions. In this work, we propose a novel approach to detect whether a human is focusing on an object in HRI application. We determine the gaze intention by comparing the similarity between the hypothetic gazes on objects and the actual gazes. We use Earth Mover's Distance (EMD) to measure the similarity and 1 Nearest Neighbour to classify which object a human is looking at. Our experimental results indicate that, compare to fixation, our method can successfully determine the human intention even during saccadic eye movements and increase the classification accuracy with noisy gaze data. We also demonstrate that, in the interaction with a robot, the proposed approach can obtain a high accuracy of object selection within successful predictions.
The most massive, passive, and oldest galaxies at 0.5 < z < 2.1: Downsizing signature from galaxies selected from MgUV index

Aims. We seek is to identify old and massive galaxies at 0.5 < z < 2.1 on the basis of the magnesium index MgUV and then study their physical properties. Methods. We computed the MgUV index based on the best spectral fitting template of ∼3700 galaxies using data from the VLT VIMOS Deep Survey (VVDS) and VIMOS Ultra Deep Survey (VUDS) galaxy redshift surveys. Based on galaxies with the largest signal to noise and the best fit spectra we selected 103 objects with the highest spectral MgUV signature. We performed an independent fit of the photometric data of these galaxies and computed their stellar masses, star formation rates, extinction by dust and age, and we related these quantities to the MgUV index. Results. We find that the MgUV index is a suitable tracer of early-type galaxies at an advanced stage of evolution. Selecting galaxies with the highest MgUV index allows us to choose the most massive, passive, and oldest galaxies at any epoch. The formation epoch tf computed from the fitted age as a function of the total mass in stars supports the downsizing formation paradigm in which galaxies with the highest mass formed most of their stars at an earlier epoch.
Short-Text Classification Using Unsupervised Keyword Expansion

Short-text classification, like all data science, struggles to achieve high performance using limited data. As a solution, a short sentence may be expanded with new and relevant feature words to form an artificially enlarged dataset, and add new features to testing data. This paper applies a novel approach to text expansion by generating new words directly for each input sentence, thus requiring no additional datasets or previous training. In this unsupervised approach, new keywords are formed within the hidden states of a pre-trained language model and then used to create extended pseudo documents. The word generation process was assessed by examining how well the predicted words matched to topics of the input sentence. It was found that this method could produce 3-10 relevant new words for each target topic, while generating just 1 word related to each non-target topic. Generated words were then added to short news headlines to create extended pseudo headlines. Experimental results have shown that models trained using the pseudo headlines can improve classification accuracy when limiting the number of training examples.
Inferring and Learning Multi-Robot Policies by Observing an Expert

In this paper we present a technique for learning how to solve a multi-robot mission that requires interaction with an external environment by repeatedly observing an expert system executing the same mission. We define the expert system as a team of robots equipped with a library of controllers, each designed to solve a specific task, supervised by an expert policy that appropriately selects controllers based on the states of robots and environment. The objective is for an un-trained team of robots equipped with the same library of controllers, but agnostic to the expert policy, to execute the mission, with performances comparable to those of the expert system. From observations of the expert system, the Interactive Multiple Model technique is used to estimate individual controllers executed by the expert policy. Then, the history of estimated controllers and environmental state is used to learn a policy for the un-trained robots. Considering a perimeter protection scenario on a team of simulated differential-drive robots, we show that the learned policy endows the un-trained team with performances comparable to those of the expert system.
FPR—Fast Path Risk Algorithm to Evaluate Collision Probability

As mobile robots and autonomous vehicles become increasingly prevalent in human-centred environments, there is a need to control the risk of collision. Perceptual modules, for example machine vision, provide <italic>uncertain</italic> estimates of object location. In that context, the frequently made assumption of an exactly known free-space is invalid. Clearly, no paths can be <italic>guaranteed</italic> to be collision free. Instead, it is necessary to compute the probabilistic <italic>risk of collision</italic> on any proposed path. The FPR algorithm, proposed here, efficiently calculates an upper bound on the risk of collision for a robot moving on the plane. That computation orders candidate trajectories according to (the bound on) their degree of risk. Then paths within a user-defined threshold of primary risk could be selected according to secondary criteria such as comfort and efficiency. The key contribution of this letter is the FPR algorithm and its ‘convolution trick’ to factor the integrals used to bound the risk of collision. As a consequence of the convolution trick, given <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> obstacles and <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> candidate paths, the computational load is reduced from the naive <inline-formula><tex-math notation="LaTeX">$O(NK)$</tex-math></inline-formula>, to the qualitatively faster <inline-formula><tex-math notation="LaTeX">$O(N+K)$</tex-math></inline-formula>.