Miracle-dz/UniFAR-FTUs huggingface dataset:
Amphibolite facies metamorphism in lower Witwatersrand Supergroup rocks exposed in the Vredefort Dome – a Ventersdorp LIP connection

Metamorphism in the lower Witwatersrand Supergroup exposed in the Vredefort Dome has previously been proposed to be related to elevated heat flow linked to the 2.06 Ga Bushveld magmatic event; however, there are no unambiguous chronological data to confirm this timing. Microtextural and mineral compositional analysis of a garnet-bearing metapelite in the northwestern collar of the Vredefort Dome suggests at least two metamorphic events with distinctly different P-T conditions preceding the Dome-forming meteorite impact at 2.02 Ga. THERMOCALC mineral equilibrium calculations yield P-T conditions of 500°C, 3.1 kbar for the M1 mineral assemblage garnet1-plagioclase-muscovite-biotite-chlorite-ilmenite. Thin, discontinuous, garnet2 overgrowths on the garnet1 porphyroblasts define a subsequent, M2, event with P-T conditions of 530°C, 5 kbar. Garnet Sm-Nd chronology yields an isochron age of 2 796.0 ± 1.5 Ma, indicating an early Ventersdorp (Klipriviersberg) timing of M1 metamorphism. Although the M2 garnet overgrowths are volumetrically too small to date, the calculated M2 pressure is consistent with the predicted overburden thickness above the lower Witwatersrand Supergroup during emplacement of the Bushveld Complex. While elevated, the M2 apparent geotherm (30°C/km) is significantly lower than the M1 apparent geotherm (46°C/km); however, thermal modelling suggests both events benefitted from local perturbations caused by contemporaneous sill emplacement. Our results thus show that the initial garnet-forming, mid-amphibolite facies metamorphism in the collar of the Vredefort Dome is not related to the emplacement of the Bushveld Complex, but rather to the early stages of magmatism associated with the Ventersdorp Large Igneous Province (LIP). Nonetheless, elevated heat flow associated with the Bushveld LIP also reached comparable amphibolite facies conditions.
Slacktivisme : cliquer ici pour sauver le monde

Le Slacktivisme est une forme d’action militante accomplie majoritairement dansl’espace digital. Souvent critiqué pour le manque d’engagement qui en découle, lescampagnes slacktivistes permettent de créer des buzz autour de sujets et d’augmenterla visibilité de certaines problématiques ou actions. Toutefois, ces actions militantesdigitales restent largement dépendantes des fonctionnalités des plateformes surlesquelles elles prennent place ainsi que des algorithmes qui les régissent.
Rigorous Modeling and Investigation of Low-Field Hole Mobility in Silicon and Germanium Gate-All-Around Nanosheet Transistors

The low-field hole mobility in p-type inversion-mode silicon (Si) and germanium (Ge) nanosheet (NS) transistors is rigorously calculated by a physics-based theoretical model, where momentum scatterings from phonons, charged impurities, and surface roughness are all considered. With a holistic physical picture of carrier scattering mechanisms, the effects of material, crystal orientation, channel width, strain/stress, and temperature are investigated comprehensively by the in-house developed numerical simulator. This sound and prospective theoretical work paves the way for clarifying the physics of achieving high hole mobility in ultrascaled NS channels, presenting some valuable insights and guidelines about the device design and fabrication for the next-generation CMOS technology.
Survey Method of Vascular Surgery on the Onset of Ischemic Steal Syndrome Following Vascular Access in Dialysis Patients

Background: Access to arteries is one of the most appropriate approaches for helping chronic kidney diseases. Objectives: The aim of the present study was to evaluate the survey method of vascular surgery on the onset of ischemic limb syndrome following vascular access in dialysis patients. Methods: In this cross-sectional study, the population study was patients with end-stage renal disease (ESRD) referring to Ahvaz Golestan Hospital in 2018. Control of limbs temperature and comparison with the other side, before and after surgery, feeling cold by the patient, control of organs pulse and comparison with the other side, pain of organs comparing to the other side, pain during the dialysis, paresthesia, and movement failure and during dialysis, and organs atrophy-ulcer were measured. Results: In the present study, 294 patients, including 139 women (46.94%) and 156 men (53.06%), were studied. Ischemic steal syndrome (ISS) was observed in 20 patients, that 13 cases of them had benefited from fistula approach and 7 patients used graft approach. The most common observed ISS was related to brachiocephalic fistula approach (AVF-Brachiocephalic) and then graft in arm. In non-steal syndrome group, brachiocephalic fistula (AVF-Brachiocephalic) had the most common application. In the present study, there was no significant correlation between access method and underlying diseases of high blood pressure, diabetes, and cardiac ischemic diseases. Conclusions: One of the most important results of the present study was the highest existence of steal syndrome in brachiocephalic approach, which is confirmed by other studies.
ANN Optimization of Weighting Factors Using Genetic Algorithm for Model Predictive Control of PMSM Drives

Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for permanent magnet synchronous motor (PMSM) drives. However, the tuning of weighting factors for the cost function of MPC is a time-consuming procedure and weighting factors can significantly affect torque ripples of PMSM drives. For the purpose of optimizing the weighting factors in cost functions of MPC, this article proposes an artificial neural network (ANN) based method, which applies genetic algorithm as the back propagation algorithm. Since this method is trained offline, it does not increase any computation complexity of MPC. Consequently, any optimization targets that combine the performance metrics of MPC are defined and the optimal weighting factors for the given targets can be fast and precisely found through the proposed method. Besides, this method is robust to the variations of motor parameters like stator resistance and permanent magnet flux. The superiority of the proposed method over conventional ANN is evaluated by comparative simulations, in terms of current total harmonic distortion, tracking performance, and neutral-point potential balance. Finally, the feasibility and robustness of the proposed method are verified on the platform of PMSM drives fed by three-level T-type inverter.
Single-Phase Transformer-based HF-Isolated Impedance Source Inverters With Voltage Clamping Techniques

In this paper, a new family of impedance source inverters is presented. It employs high-frequency electrical isolation between the inverter bridge switches and the load along with voltage clamping across the dc-link voltage. Conventional Z-source inverters (ZSIs) employs an impedance network that consists of inductors and capacitors. It has unique features that realize both step-up/step-down functions and eliminates the need of dead/overlap times. This paper extends this novel concept by using electrical isolation in impedance source inverters. High-frequency isolation has many advantages in terms of immunity and reliability; when applied with impedance source inverters this makes ZSIs a preferable choice for industrial applications. In photo-voltaic systems, the addition of the high-frequency transformer provides safety by avoiding the injection of dc circulating current into the grid, without the need of an external bulky line frequency transformer. The gain of the proposed inverter design can be accurately selected by choosing the turns ratio of the high frequency transformer (HFT) or by adjusting the shoot-through duty cycle (STDC) to the inverter. This allows for greater freedom especially when utilizing a higher modulation index, with the STDC allowing dynamic gain adjusts to be done speedily during operation of the inverter. Additionally, a dc-rail voltage clamping technique for the proposed class of isolated ZSIs is also discussed. This technique provides benefits not only in improving the output voltage quality, but also in reducing voltage stress of the active and passive components by minimizing the voltage spikes across the switching devices. In this paper, several high-frequency isolated ZSIs are presented, and an example isolated improved ΓZSI design is shown and discussed in detail. Simulations are provided for the proposed class of isolated inverters to verify their working. Further experimental investigation has been done for which results for the isolated improved ΓZSI are reported. These empirical results have largely confirmed the expected benefits that were determined through simulation and accurate model-based testing.
Longitudinal Mapping of White Matter Tractography Changes in Huntington’s Disease: The IMAGE-HD Study.

The striatum is the principal site of disease pathology in Huntington’s disease and contains neural connections to numerous cortical brain regions. Studies examining abnormalities to neural connections find that white matter integrity is compromised in HD; however, further regional, and longitudinal investigation is required. This paper is the first longitudinal investigation into region-based white-matter integrity changes in Huntington’s Disease. The aim of this study was to better understand how disease progression impacts white matter tracts connecting the striatum to the prefrontal and motor cortical regions in HD. We used existing neuroimaging data from IMAGE-HD, comprised of 25 pre-symptomatic, 27 symptomatic, and 25 healthy controls at three separate time points (baseline, 18-months, 30-months). Fractional anisotropy, axial diffusivity and radial diffusivity were derived as measures of white matter microstructure. The anatomical regions of interest were identified using the Desikan-Killiany brain atlas. A Group by Time repeated measures ANCOVA was conducted for each tract of interest and for each measure. We found significantly lower fractional anisotropy and significantly higher radial diffusivity in the symptomatic group , compared to both the pre-symptomatic group and controls (the latter two groups did not differ from each other), in the rostral middle frontal and superior frontal tracts; as well as significantly higher axial diffusivity in the rostral middle tracts only. We did not find a Group by Time interaction for any of the white matter integrity measures. These findings demonstrate that whilst the microstructure of white matter tracts, extending from the striatum to these regions of interest, are compromised during the symptomatic stages of Huntington’s disease, 36-month follow-up did not show progressive changes in these measures. Additionally, no correlations were found between clinical measures and tractography changes, indicating further investigations into the relationship between tractography changes and clinical symptoms in Huntington’s disease are required.
E ciency of Donepezil in Elderly Patients Undergoing Orthopaedic Surgery Due To Underlying Post-Operative cognitive Dysfunction: Study Protocol for A Multicentre Randomised Controlled Trial

Huichen Zhu Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Lu Cong Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Yi Chen Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Shaoyi Chen Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Lingke Chen Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Zhenling Huang Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Jie Zhou Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Jie Xiao Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Yonglei Huang Shanghai Jiao Tong University School of Medicine A liated Renji Hospital Diansan Su (  diansansu@yahoo.com ) Department of Anesthesiology, Renji Hospital, Shanghai Jiaotong University, School of Medicine https://orcid.org/0000-0001-9755-1025
Outcomes of Decompressive Hemicraniectomy for Spontaneous Intracerebral Hemorrhage: A Case-Control Study

Introduction: Intracerebral hemorrhage (ICH) remains a devastating type of stroke. Besides blood pressure control, treatment options including surgery remain limited. Decompressive hemicraniectomy (DHC) in patients with spontaneous ICH may be considered as salvage therapy. The purpose of this study is to evaluate the role of DHC for the management of primary ICH associated with malignant cerebral edema and its effect on case-fatality and long-term functional outcome. Methods: A matched case control study was conducted at Thomas Jefferson University. Patient records were reviewed for age, sex; Glasgow Coma Scale (GCS) and ICH score on admission, on discharge and at follow-up. For those patients that underwent DHC, the time to surgery was documented. Computed tomography scans were then reviewed at the time of admission, allowing calculation of ICH scores. Functional outcome was assessed over the phone using the Simplified Modified Rankin Scale Questionnaire (smRSq). We matched (1:1) our cases to controls (ICH without DHC) by age and ICH score. The primary outcome of interest was survival over time. Secondary outcomes were discharge GCS and long-term functional outcome measured by the Modified Rankin Scale (mRS). Results: There were 17 patients with DHC who were matched (1:1). The mean time to DHC was 61.3 ± 101 hours. Median mRS at follow-up was similar in DHC group vs. controls (4, IQR 4-6 vs. 5, IQR 3-6, p = 0.7). In multivariable analysis, predictors of death overtime were ICH score > 2, and female gender. Predictors of survival was DHC but only in patients with ICH score > 2 (HR 0.83, 95% CI 0.7-0.9, p = 0.04 for interaction). Conclusion: This case-control study suggests that patients with ICH associated with significant cerebral edema and an ICH score greater than 2 may potentially benefit from DHC. Further research in a larger patient population conducted prospectively is warranted to assess the potential benefit of DHC in ICH.
in How

The consumer purchasing journey has evolved. The current paper revisits the practitioner-led “Moments of Truth” model used by a number of successful multinationals (initially Procter & Gamble and subsequently Google). “Moments of Truth” (MOT) describe key instances of contact between a potential customer and a brand. The new model integrates variables such as shared brand experience and searchable electronic word of mouth (e-WOM). This short paper establishes the cycle of e-WOM influence as recommendations are shared and searched among digitally connected consumers and explains how marketers can successfully manage these MOTs.
SDPA-C (SemiDefinite Programming Algorithm - Completion method) User's Manual — Version 6.2.0

Classiﬁcation: Mathematical ProgrammingAugust 2004Abstract. The SDPA-C (SemiDeﬁnite Programming Algorithm – Completion method) is a soft-ware package designed for solving large scale sparse SDPs (semideﬁnite programming problems). In particular, the SDPA-C solves an SDP quite eﬃciently in computational time and memory ifthe aggregated sparsity pattern of the data matrices induces a sparse chordal extension of theaggregate sparsity pattern matrix; if not, the standard version of the SemiDeﬁnite ProgrammingAlgorithm, SDPA solves the SDP faster. Using the positive deﬁnite matrix completion theory,the SDPA-C stores only sparse matrices and perform matrix computations that take advantagesof their sparsity. For theoretical and technical details of the SDPA-C, see the papers Fukuda-Kojima-Murota-Nakata (2000) and Nakata-Fujisawa-Fukuda-Kojima-Murota (2003) listed in thereferences of this manual.This manual, the SDPA-C, and the instructions for its installation can be found in the WWWsitehttp://www.is. titech.ac. jp/˜kojima/sdpa/index.html. Key words Semideﬁnite Programming, Interior-Point Method, Matrix Completion,Computer Software
Seroprevalence of Hepatitis B&C in coronary artery disease patients in Urmia, Northwest of Iran

Background: Coronary artery disease (CAD) is one of the main causes of death all over the world. Predisposing factors comprise some infectious etiologies with systemic effects such as the hepatitis C virus and HBV-infection might be related to CAD from chronic inflammation process. The relationships between hepatitis B&C virus (HBV&HCV) infections and CAD considered a noticeable health problem. In the present study, we evaluated the seropositivity of HCV&HCB in CAD patients from the Iranian population. Materials and Methods: We conducted a cohort study including 192 CAD subjects. To identify HCV&HCB -infected subjects, a serologic examination comprising Hepatitis B surface antigen (HBsAg) and anti-HCV antibodies (HCV-Ab) tests performed by ELISA method. Also, we examined the biochemical blood parameters such as lipid profile, glycemic parameters, and blood inflammatory factor (C-reactive protein). Results: We identified a very low percent of HBV-infected cases (1.04%). All examined CAD patients indicate the HCV Ab-negative results. The HBV-infected subject had not, hyperlipidemia, and diabetes. HBV infection was not related to the increased risk of CAD. Also, CAD risk factors were not associated with the prevalence of HBV&HCV. Conclusion: According to the results, HCV&HCB infections had very low prevalence in examined subjects and there was not any association between CAD and prevalence of HCV&HCB infections.
M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer

In this study, we propose a three-dimensional Medical image classifier using Multi-plane and Multi-slice Trans-former (M3T) network to classify Alzheimer's disease (AD) in 3D MRI images. The proposed network synergically com-bines 3D CNN, 2D CNN, and Transformer for accurate AD classification. The 3D CNN is used to perform natively 3D representation learning, while 2D CNN is used to utilize the pre-trained weights on large 2D databases and 2D repre-sentation learning. It is possible to efficiently extract the lo-cality information for AD-related abnormalities in the local brain using CNN networks with inductive bias. The trans-former network is also used to obtain attention relationships among multi-plane (axial, coronal, and sagittal) and multi-slice images after CNN. It is also possible to learn the ab-normalities distributed over the wider region in the brain using the transformer without inductive bias. In this ex-periment, we used a training dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) which contains a total of 4,786 3D T1-weighted MRI images. For the validation data, we used dataset from three different institutions: The Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL), The Open Access Series of Imaging Studies (OASIS), and some set of ADNI data indepen-dent from the training dataset. Our proposed M3T is compared to conventional 3D classification networks based on an area under the curve (AUC) and classification accuracy for AD classification. This study represents that the pro-posed network M3T achieved the highest performance in multi-institutional validation database, and demonstrates the feasibility of the method to efficiently combine CNN and Transformer for 3D medical images.
Optimal DR Activation Strategy for Risk Aversion Considering Hourly Loads and Locational Prices

Demand response (DR) activation and dispatch introduce additional economic and security benefits to the power system operation and control. The strategies for DR activation and compensation are among critical market operation issues which continue to raise concerns among stakeholders. In this respect, DR cost allocation would be required to guarantee DR benefits to the independent system operator and individual participating consumers. This paper proposes a DR activation strategy considering hourly system load and locational marginal prices (LMPs) in which payoffs are optimized at effective LMP where DR dispatch occurs. This paper presents a closed-form solution for the DR activation strategy according to the FERC’s Net Benefits Test. A DR cost reallocation method is proposed using the contribution factor theory for managing financial risks which ensures market participants’ optimal payoff corresponding to the DR dispatch. The proposed framework is extended to consider practical DR activation strategies and cost reallocations. Case studies based on the IEEE 6-bus system and the IEEE 118-bus system illustrate the validity and effectiveness of the proposed DR activation strategy and cost reallocation method.
Data Synthesis for Large-scale Supermarket Product Recognition

—Training data acquisition for deep learning-based visual product recognition systems on a large scale is laborious and often infeasible due to the vast product assortments containing tens of thousands of products and the densely packed scenes. In this work, we propose a potential solution to this problem in the form of an automatic data synthesis pipeline that can generate training data for product detectors and classiﬁers on a large scale. To demonstrate that our synthesis pipeline can produce realistic data, we train a product detector using only synthetic data and measure its generalization to real data. A detection accuracy of 0.832 mAP@0.50 is achieved on real data, showing that the model can learn from our synthetic data.
Effective Offline RL Needs Going Beyond Pessimism: Representations and Distributional Shift

Standard off-policy reinforcement learning (RL) methods based on temporal dif-1 ference (TD) learning generally fail to learn good policies when applied to static 2 offline datasets. Conventionally, this is attributed to distribution shift, where the 3 Bellman backup queries high-value out-of-distribution (OOD) actions for the next 4 time step, which then leads to systematic overestimation. However, this expla-5 nation is incomplete, as conservative offline RL methods that directly address 6 overestimation still suffer from stability problems in practice. This suggests that 7 although OOD actions may account for part of the challenge, the difficulties with 8 TD learning in the offline setting are also deeply connected to other aspects such 9 as the quality of representations of learned function approximators. In this work, 10 we demonstrate that merely imposing pessimism is not sufficient for good per-11 formance, and demonstrate empirically that regularizing representations actually 12 accounts for a large part of the improvement observed in modern offline RL meth-13 ods. Building on this insight, we identify concrete metrics that enable effective 14 diagnosis of the quality of the learned representation, and are able to adequately 15 predict performance of the underlying method. Finally, we show that a simple 16 approach for handling representations, without any changing any other aspect of 17 conservative offline RL algorithms, can lead to better performance in several offline 18 RL problems. 19
Supplementary Materials for gDNA: Towards Generative Detailed Neural Avatars

1. Implementation Details 1.1. Network Architecture We implement our models in PyTorch [18]. Our architectures for the networks are illustrated in Fig. 1. Note that we choose a relatively small network size for the skinning weight network and warping network, as the skinning weight field and warping field are typically smooth in 3D space and thus do not require high-capacity networks to be modeled well. We use a positional encoding [16] with 4 frequency components. The shape codes and details codes are 64-dimensional each.