Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation between cognitive impairment and cerebral hemodynamic disturbances on perfusion magnetic resonance imaging in European adults with moyamoya disease

753 MoyaMoya disease is a rare chronic cerebrovascular disease characterized by progressive narrowing of supraclinoid ICAs and proximal middle and anterior cerebral arteries, resulting in the development of an abnormal anastomotic basal vascular network.41,42 Pial and transdural collaterals develop later in the course of the disease. Moyamoya disease primarily affects children during the 1st decade but it can also appear in young adults during the 3rd or 4th decades.28,41 The clinical presentation of MMD in adults depends on the ethnicity of the individual. In Asian adults, the disease usually manifests as cerebral hemorrhages, mainly caused by rupture of the basal collateral network.19,41 In contrast, cerebral ischemia is the most common complication of MMD in Caucasian adults.8,18,27 Cerebral ischemia is presumably caused by progressive hemodynamic failure, although thromboembolism has been occasionally demonstrated.22,23,30,31 Loss of intellectual capacity is commonly described in children with MMD.24,33 Cognitive dysfunction has been less well studied in adults with MMD. A few reports suggest that executive function may be impaired in these patients.3,20,25,26,32,34 Nevertheless, the pathophysiology of this cognitive impairment is poorly understood. Correlation between cognitive impairment and cerebral hemodynamic disturbances on perfusion magnetic resonance imaging in European adults with moyamoya disease
Modeling of Flow and Heat Transfer in a Molten Glass Mini-Film for High Temperature Heat Collection in a Falling-Film Solar Central Receiver

ABSTRACT In concentrating solar power plants, there is a strong incentive to increase the collection temperature and the overall exergy efficiency of the system. Some molten glass mixtures are attractive working fluids for high temperature solar thermal heat collection because optimized glass mixtures can be more stable, less-toxic, and less-corrosive than, for example, molten salts at high temperatures (≥1000°C). A specific phosphorous pentoxide glass mixture is considered in this study to explore its performance in a molten glass falling film central receiver design for collection of heat at conditions resulting in a mini-film with a thickness less than 3mm. In our falling molten glass thin film, the phosphate glass flow is treated as a laminar, Newtonian and gravity-driven flow over a slightly inclined flat plate using an explicit finite difference scheme to evaluate its heat transfer performance for a direct absorption receiver concept. One of the main challenges of modeling transport in the molten glass is the strong dependence of its viscosity on temperature. To incorporate this effect in our numerical analysis, a temperature-dependent viscosity model is used in the momentum equation to model the fluid behavior as it flows down the surface and is progressively heated. An exponential function is used to model the viscosity as it changes with temperature to properly fit the measured the viscosity data provided by Halotechnics. Also, a variable film thickness model analysis is implemented to treat the variation in film thickness that results from the viscosity variation with temperature. In order to avoid stability issues, the finite difference scheme is organized in terms of nondimensional parameters that include all important properties that govern the system. The results of our model indicate that thinning of the film as it flows over the heated surface enhances the heat transfer performance on the lower portion of the receiver system. The heat transfer coefficient increases instead of remaining constant (as normally expected for fully developed laminar flows) on the lower portion of the heated surface. The results further indicate that using a thin mini-film of molten glass for solar thermal heat collection provides high heat transfer performance and enhances the exergy collection.
N 1 × N 1 Generalized Laplacian P × T 1 × N 1 × 3 Concatenated Sequence

With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skeletons based on RNN or CNN, which has limited expressive power for irregular joints. In this paper, we represent skeletons naturally on graphs and propose a generalized graph convolutional neural networks (GGCN) for skeleton-based action recognition, aiming to capture space-time variation via spectral graph theory. In particular, we construct a generalized graph over consecutive frames, where each joint is not only connected to its neighboring joints in the same frame strongly or weakly, but also linked with relevant joints in the previous and subsequent frames. The generalized graphs are then fed into GGCN along with the coordinate matrix of the skeleton sequence for feature learning, where we deploy high-order and fast Chebyshev approximation of spectral graph convolution in the network. Experiments show that we achieve the state-of-the-art performance on the widely used NTU RGB+D, UT-Kinect and SYSU 3D datasets.
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations — – Supplementary Material — –

As our framework aims to generate synthetic labels for instance and semantic segmentation, we evaluated the efficacy of our framework by learning fully supervised models for the two tasks with our synthetic labels. Specifically, we adopt Mask R-CNN [5] for instance segmentation and DeepLab v2 [1] for semantic segmentation. Both of them are first pretrained on ImageNet [2] then finetuned with the synthetic labels instead of groundtruth segmentation masks. The rest of this section describes details of the two models.
International Dental Research

Aim: This case report demonstrates the application of piezocision in correcting persistent crowding in the lower anterior region of dental arch. Methodology: A female aged 14 years and 3 months presented to the Selçuk University, Faculty of Dentistry, Department of Orthodontics due to esthetic complaints. Clinical and radiological examination revealed crowding in the upper and lower dental arches as well as skeletal and dental Class I malocclusion. First, bonding and arch wire were applied to the patient’s upper jaw. The patient then had check-up appointments at 4-week intervals. In the second session, bonding was applied to the lower jaw. As the leveling was completed in the upper jaw, thicker arch wires were used. However, 4 months after the bonding of the lower anterior region, leveling did not occur. Consequently, piezocision was applied to the lower anterior region. Then, two applications of 0.014'' and 0.016'' NiTi wire were carried out, respectively. After the completion of the leveling, intrusion was provided with the utility arch placed on the lower incisor brackets. Orthodontic treatment was concluded with the use of NiTi and steel angle wires. Conclusion: Efficient leveling was achieved in the lower anterior region with piezocision applications.
Bohr and the Measurement Problem: Moving towards Holism in Copenhagen

Bohr and the Measurement Problem: Moving towards Holism in Copenhagen John Lawrence Benich Advisor: University of Guelph, 2014 Professor A. Wayne This thesis is an examination of the measurement problem from within the Copenhagen interpretation of quantum mechanics where I take issue with von Neumann’s theory of measurement which argues for the subjective collapse of the wave function by a conscious observer. I argue that von Neumann’s postulate of collapse has led to several intractable philosophical and conceptual problems in its account of quantum measurement. I argue that these problems arise from von Neumann’s misreading of Bohr’s complementarity regarding the relationship between subject and object as distinct metaphysical entities. Instead, I offer a new way of defining the subject-object relation based on Don Howard’s reading of Bohr’s complementarity as subject-object nonseparability (SON). I argue that the SON principle has the potential of placing Copenhagen on more solid philosophical footing by viewing the subjectobject relationship not as a causal relation, but as a relation of ontological dependence to avoid the idea of collapse altogether.
ALFAA: Active Learning Fingerprint Based Anti-Aliasing for Correcting Developer Identity Errors in Version Control Data

Graphs of developer networks are important for software engineering research and practice. For these graphs to realistically represent the networks, accurate developer identities are imperative. We aim to identify developer identity errors from open source software repositories in VCS, investigate the nature of these errors, design corrective algorithms, and estimate the impact of the errors on networks inferred from this data. We investigate these questions using over 1B Git commits with over 23M recorded author identities. By inspecting the author strings that occur most frequently, we group identity errors into categories. We then augment the author strings with 3 behavioral fingerprints: time-zone frequencies, the set of files modified, and a vector embedding of the commit messages. We create a manually validated set of identities for a subset of OpenStack developers using an active learning approach and use it to fit supervised learning models to predict the identities for the remaining author strings in OpenStack. We compare these predictions with a commercial effort and a leading research method. Finally, we compare network measures for file-induced author networks based on corrected and raw data. We find commits done from different environments, misspellings, organizational IDs, default values, and anonymous IDs to be the major sources of errors. We also find supervised learning methods to reduce errors by several times in comparison to existing methods and the active learning approach to be an effective way to create validated datasets and that correction of developer identity has a large impact on the inference of the social network. We believe that our proposed Active Learning Fingerprint Based Anti-Aliasing (ALFAA) approach will expedite research progress in the software engineering domain for applications that depend upon graphs of developers or other social networks.
Recent advances in deep learning applied for skin cancer detection

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take.
Dissipation Limit for the Compressible Navier-Stokes to Euler Equations in One-Dimensional Domains

We prove that as the viscosity and heat-conductivity coefficients tend to zero, respectively, the global solution of the Navier-Stokes equations for one-dimensional compressible heat-conducting fluids with centered rarefaction data of small strength converges to the centered rarefaction wave solution of the corresponding Euler equations uniformly away from the initial discontinuity.
Statistical assessment of the relation between the inferred morphological type and the emission-line activity type of a large sample of galaxies

Abstract We quantify the statistical evidence of the relation between the inferred morphology and the emission-line activity type of galaxies for a large sample of galaxies. We compare the distribution of the inferred morphologies of galaxies of different dominant activity types, showing that the difference in the median morphological type between the samples of different activity types is significant. We also test the significance of the difference in the mean morphological type between all the activity-type samples using an ANOVA model with a modified Tukey test that takes into account heteroscedasticity and the unequal sample sizes. We show this test in the form of simultaneous confidence intervals for all pairwise comparisons of the mean morphological types of the samples. Using this test, scarcely applied in astronomy, we conclude that there are statistically significant differences in the inferred morphologies of galaxies of different dominant activity types.
Strongly coupled coherent phonons in single-layer MoS$_2$.

We present a transient absorption setup combining broadband detection over the visible-UV range with high temporal resolution ($\sim$20fs) which is ideally suited to trigger and detect vibrational coherences in different classes of materials. We generate and detect coherent phonons (CPs) in single layer (1L) MoS$_2$, as a representative semiconducting 1L-transition metal dichalcogenide (TMD), where the confined dynamical interaction between excitons and phonons is unexplored. The coherent oscillatory motion of the out-of-plane $A'_{1}$ phonons, triggered by the ultrashort laser pulses, dynamically modulates the excitonic resonances on a timescale of few tens fs. We observe an enhancement by almost two orders of magnitude of the CP amplitude when detected in resonance with the C exciton peak, combined with a resonant enhancement of CP generation efficiency. Ab initio calculations of the change in 1L-MoS$_2$ band structure induced by the $A'_{1}$ phonon displacement confirm a strong coupling with the C exciton. The resonant behavior of the CP amplitude follows the same spectral profile of the calculated Raman susceptibility tensor. This demonstrates that CP excitation in 1L-MoS$_2$ can be described as a Raman-like scattering process. These results explain the CP generation process in 1L-TMDs, paving the way for coherent all-optical control of excitons in layered materials in the THz frequency range.
Image Inpainting by Minimizing Energy Function Based on Pattern Similarity Considering Brightness Change and Spatial Locality

あらまし 本論文では,写真についた傷など画像内の不要な部分を取り除き,その欠損領域を自動的に修復す る新たな手法を提案する. 従来,欠損領域の修復に関しては,欠損領域と欠損領域以外のデータ領域のパターン 類似度を用いて欠損領域全体のもっともらしさを表す目的関数を定義し,それを最適化することで画像修復を行 う手法が提案されている. しかし,従来提案されている目的関数の最適化による修復では,以下に挙げる二つの 問題により不自然なテクスチャが生じやすい. ( 1)データ領域におけるテクスチャパターンの種類に限りがある ため,単純なテクスチャのコピーによる修復を前提とした場合には,違和感のない修復に最低限必要なパターン がデータ領域に存在しないことが多い. ( 2)データ領域と欠損領域間のパターン類似度のみによる指標は自然な テクスチャの再現条件としては不十分である. 本研究ではこのような問題に対して,テクスチャの明度変化を許 容したパターン類似度を用いることで表現できるテクスチャパターンの拡張を行う. また,パターン類似度以外 にテクスチャの局所性を表すコスト関数を用いることで不自然なテクスチャの生成を抑制する. 実験では,様々 な画像に対して欠損領域の修復を行い,従来手法との比較及びアンケート評価に基づく定性的評価により提案手 法の有効性を示す. また,従来行われてきた評価方法である少人数・少数の画像による主観評価及び結果画像と 原画像の差分に基づく定量的評価の問題点を実験により明らかにする. キーワード 画像修復,画像補間,Image Inpainting,エネルギー最小化,パターン類似度
One of these (Few) Things is Not Like the Others

To perform well, most deep learning based image classification systems require large amounts of data and computing resources. These constraints make it difficult to quickly personalize to individual users or train models outside of fairly powerful machines. To deal with these problems, there has been a large body of research into teaching machines to learn to classify images based on only a handful of training examples, a field known as few-shot learning. Few-shot learning research traditionally makes the simplifying assumption that all images belong to one of a fixed number of previously seen groups. However, many image datasets, such as a camera roll on a phone, will be noisy and contain images that may not be relevant or fit into any clear group. We propose a model which can both classify new images based on a small number of examples and recognize images which do not belong to any previously seen group. We adapt previous few-shot learning work to include a simple mechanism for learning a cutoff that determines whether an image should be excluded or classified. We examine how well our method performs in a realistic setting, benchmarking the approach on a noisy and ambiguous dataset of images. We evaluate performance over a spectrum of model architectures, including setups small enough to be run on low powered devices, such as mobile phones or web browsers. We find that this task of excluding irrelevant images poses significant extra difficulty beyond that of the traditional few-shot task. We decompose the sources of this error, and suggest future improvements that might alleviate this difficulty.
Arylthienyl-vinyl-benzothiazoles as Efficient Second Harmonic Generators (SHG) for Nonlinear Optics

New push-pull second harmonic generation (SHG) chromophores 2 were synthesized in order to study their linear and nonlinear optical properties. The donor-acceptor π-conjugated systems 2 were prepared in good to excellent yields by a simple aldol-type condensation of the precursor aldehydes 1 with 6-nitro-2-methyl-1,3-benzothiazole in the presence of NaOH. Hyper-Rayleigh scattering in dioxane solutions was used to evaluate their second-order nonlinear optical properties. The experimental results showed that the optical (linear and nonlinear) properties are influenced by the electron-donor strength of the groups of the π-conjugated bridge. The vinyl-benzothiazole derivative 2b, with methoxyl as the donor group, exhibited the largest first hyperpolarizability β (1660 × 10−30 esu, 40 times greater than the standard reference pNA molecule) and the highest decomposition temperature (Td = 360 °C). The good balance between nonlinearity and thermal stability makes this compound a promising candidate as second harmonic generator (SHG) for nonlinear optics.
Child behaviors associated with childhood obesity and parents ’ self-efficacy to handle them : Confirmatory factor analysis of the Lifestyle Behavior Checklist

Child behaviors associated with childhood obesity and parents’ self-efficacy to handle them: Confirmatory factor analysis of the Lifestyle Behavior Checklist International Journal of Behavioral Nutrition and Physical Activity Sample doi:10.1186/s12966-015-0194-4 Anna Ek (anna.ek@ki.se) Kimmo Sorjonen (kimmo.sorjonen@ki.se) Jonna Nyman (jonna.nyman@ki.se) Claude Marcus (claude.marcus@ki.se) Paulina Nowicka (paulina.nowicka@ki.se) Sample
Model predictive force control of hardware implementation for electro-hydraulic servo system

This manuscript introduces the application of Model Predictive Control (MPC) for high force control precision in a real industrial electro-hydraulic servo system (EHSS). Moreover, it presents a fractional order control (FOC) and conventional controllers (CC) based on genetic algorithm (GA). The GA technique has been used to tune the parameters of FOC and CC approach. In order to verify the ability of the proposed controller applied to the hydraulic press machines emulator using EHSS, a hardware implementation of a test press system is also suggested and setup to be used in this research. As a result, the study has been investigated using a simulation model then verified via the experimental implementation. In fact, the EHSS plays an important role in many industrial applications, especially in flight simulators, aircraft landing gear system, material testing machine and hydraulic press machines for which the high accuracy and fast response of the force or pressure control are exceedingly necessary. Real-time experiments on the EHSS are carried out to evaluate the proposed control approach in a large system parameters variation of working environments. Considerable improvement in the performance generated by the designed MPC controller is compared with the traditional and fractional order controllers. Moreover, the results show that the performance criteria in terms of settling, rise times, system overshoots, system parameters variation and applying different test signals are good values in case of applying MPC over using FOC and CC in this study. As a general conclusion, one can conclude that the MPC has the priority of applying it in the field of the industrial EHSS. The obtained results are promising in the field of mechatronic.
Crowd motion and evolution PDEs under density constraints

This is a survey about the theory of density-constrained evolutions in theWasserstein space developed by B. Maury, the author, and their collaborators as a model for crowd motion. Connections with microscopic models and other PDEs are presented, as well as several time-discretization schemes based on variational techniques, together with the main theorems guaranteeing their convergence as a tool to prove existence results. Then, a section is devoted to the uniqueness question, and a last one to different numerical methods inspired by optimal transport.
A Unified Approach to Dynamic Decision Problems with Asymmetric Information - Part I: Non-Strategic Agents

We study a general class of dynamic games with asymmetric information where agents' beliefs are strategy dependent, i.e. signaling occurs. We show that the notion of sufficient information, introduced in the companion paper team, can be used to effectively compress the agents' information in a mutually consistent manner that is sufficient for decision-making purposes. We present instances of dynamic games with asymmetric information where we can characterize a time-invariant information state for each agent. Based on the notion of sufficient information, we define a class of equilibria for dynamic games called Sufficient Information Based Perfect Bayesian Equilibrium (SIB-PBE). Utilizing the notion of SIB-PBE, we provide a sequential decomposition of dynamic games with asymmetric information over time; this decomposition leads to a dynamic program that determines SIB-PBE of dynamic games. Furthermore, we provide conditions under which we can guarantee the existence of SIB-PBE.
A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

Solar Photovoltaic (PV) system is one of the most signiﬁcant forms of renewable energy resources, and it requires accuracy to assess, design, and extraction of its parameters. Several methods have been extensively applied to mimic the nonlinearity and multi-model behavior of the PV system. However, there is no method to date that can guarantee the extracted parameter of the PV model is the most accurate one. Therefore, this paper presents a unique approach known as Hybridized Arithmetic Operation Algorithm based on Efﬁcient Newton Raphson (HAOA ENR ) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. Firstly, the objective function is efﬁciently designed to roughly predict the initial root values of the PV equation. Secondly, the Lévy ﬂight and Brownian strategies are integrated in the four operators of AOA to thoroughly analyze the feature space of this problem. Additionally, the four operators of the AOA is divided into two phases to equilibrium between the exploration and exploitation tendencies. Furthermore, the chaotic map and robust mutation techniques are systematically employed in the beginning and halves of generations to ensure the algorithm can reach globally at few numbers iterations. Finally, a nonlinearly adjustable damping parameter of the Levenberg-Marquardt technique is linked with the NR method to replicate the ﬂuctuation behaviours of the PV models. The experimental ﬁndings revealed that the proposed HAOA ENR outperformed all other methods found in the literature, with average RMSE values close to zero values for both PV models.