Miracle-dz/UniFAR-FTUs huggingface dataset:
WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration

In this work, we present a novel method called WSDesc to learn 3D local descriptors in a weakly supervised manner for robust point cloud registration. Our work builds upon recent 3D CNN-based descriptor extractors, which leverage a voxel-based representation to parameterize local geometry of 3D points. Instead of using a predefined fixed-size local support in voxelization, we propose to learn the optimal support in a data-driven manner. To this end, we design a novel differentiable voxelization layer that can back-propagate the gradient to the support size optimization. To train the extracted descriptors, we propose a novel registration loss based on the deviation from rigidity of 3D transformations, and the loss is weakly supervised by the prior knowledge that the input point clouds have partial overlap, without requiring ground-truth alignment information. Through extensive experiments, we show that our learned descriptors yield superior performance on existing geometric registration benchmarks.
Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning

Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a local patch and aggregates them by an adaptive inherent relation based on the attention mechanism. The subpixel coordinate of each landmark is predicted independently based on the aggregated feature. Moreover, a coarse-to-fine framework is further introduced to incorporate with the SLPT, which enables the initial landmarks to gradually converge to the target facial landmarks using fine-grained features from dynamically resized local patches. Extensive experiments carried out on three popular benchmarks, including WFLW, 300W and COFW, demonstrate that the proposed method works at the state-of-the-art level with much less computational complexity by learning the inherent relation between facial landmarks. The code is available at the project website11https://github.com/Jiahao-UTS/SLPT-master.
Few-Shot Real Image Super-resolution via Distortion-Relation Guided Transfer Learning

Collecting large clean-distorted training image pairs in real world is non-trivial, which seriously limits the practical applications of these supervised learning based image super-resolution (SR) methods. Previous works attempt to address this problem by leveraging unsupervised learning technologies to alleviate the dependency for paired training samples. However, these methods typically suffer from unsat-isfactory textures synthesis due to the lack of clean image supervision. Compared with purely unsupervised solution, the under-explored scheme with F ew-S hot clean images (FS-RSR) is more feasible to tackle this challenging R eal Image S uper-R esolution task. In this paper, we are the first to investigate the few-shot real image super-resolution and pro-pose a D istortion-R elation guided T ransfer L earning (termed as DRTL) framework. DRTL assigns a knowledge graph to capture the distortion relation between auxiliary tasks ( i . e ., synthetic distortions) and target tasks ( i . e ., real distortions with few images), and then adopt a gradient weighting strategy to guide the knowledge transfer from auxiliary task to target task. In this way, DRTL could quickly learn the most relevant knowledge from the prior distortions for target distortion. We instantiate DRTL integrated with pre-training and meta-learning pipelines as an embodiment to realize a distortion-relation aware FS-RSR. Extensive experiments on multiple benchmarks demonstrate the effectiveness of DRTL on few-shot real image super-resolution.
Cite as :Paper Title; Vol. ...|Issue ....|Pg:102-107

Universities as part of higher education have a strategic role in producing quality, professional and highly competitive resources. To realize the role of universities in producing superior and ethical human resources, the accountability of universities for compliance with government regulations is a major factor. However, the reality of the existence of private universities in Denpasar City shows that there are still relatively low academic positions of lecturers, low ratings of research and service performance, low ratings of accreditation scores and also universities that have not been accredited. This study aims to answer the main problem whether an organizational culture based on Tri Hita Karana (THK) can moderate the relationship of intellectual capital through organizational learning to the performance of private universities. The number of respondents who are willing and participating in the survey are 41 heads of study programs. The results showed that high intellectual capital was able to improve the performance of private universities in Denpasar City through the full effectiveness of organizational learning. However, other findings indicate that organizational culture based on Tri Hita Karana (THK) has not been able to streng then the relationship between intellectual capital and organizational learning on the performance of private universities in Denpasar City. KEYWORDSIntellectual Capital, Organizational Learning, Organizational Culture Tri Hita Karana, Private University Performance. INTRODUCTION In the era of globalization and openness as well as various disruptions that befall organizations, the demand for reliable and highly competitive human resources professionalism is absolutely necessary. Responding to these conditions, universities have a very strategic role in producing human resources who can meet these demands. But on the other hand, the existence of private universities, especially in Bali and West Nusa Tenggara, shows that performance is still low in the form of accreditation ratings results from the regulator of the National Accreditation Board for Higher Education as shown in table 1 below: Table 1. Accreditation Value of Colleges and Study Programs. No Accreditation Score 1 College Accreditation Information Amount Percentage
Comparison of the diagnostic efficacies of 18F-FDG PET / MRI versus contrast enhanced MRI in the detection of brain metastases

Purpose: The aim of our study was to determine which imaging method was superior in detecting brain metastases by comparing contrast-enhanced MRI and 18F FDG PET / MRI which were obtained simultaneously. Methods: From August 2015 to December 2018, 480 consecutive patients with histopathologic proven primary malignancy (188 men and 292 women; mean age+ standard deviation, 45 years+ 25,4) were retrospectively evaluated. All patients underwent 18F-FDG PET/MRI during follow-up of their malignancies. Results: Brain metastases (BM) were found in 33 (25 Female and 8 Male) of 480 patients. Contrast-enhanced MRI revealed 190 brain metastases (BM) in 33 patients (100%), while 18F-FDG PET data was accurately identified 50 (26.3%) BM lesions in 16 (48.5%) patients. Based on measurments on contrast enhanced MR images, maximum BM lesion size was 40.1mm (mean: 6.64mm±6.39mm, range: 0.9mm-40.1mm). Mean lesion size ± SD was 12.88±8.13mm in 18F-FDG PET/MRI detectable lesions. Conclusion: Contrast-enhanced brain MR is more likely to detect brain metastases than 18F-FDG PET / MRI, and is still the gold standard imaging method for the detection of brain metastases.
Effectiveness of Cooperative Learning Model Type Stad-Based on Skills 4c-On Education Training for Strengthening of Principals of Batch 18-In the Development Agency of Human Resources of Jakarta Province 2019

This study aims to determine the effectiveness of STAD type cooperative learning models with 4C skills in PKS Training for Class 18 of 2019 Fiscal Year organized by BPSDM DKI Jakarta Province. The subjects of this study were 30 PKS PKS training participants. In the cooperative learning process the training participants were divided into 6 groups, each group consisting of 5 training participants with random shares. In one day of learning widyaiswara delivered two different types of training courses to participants through presentation activities. Training participants then gather with their respective groups to discuss each of the training courses, through this group each group member ensures that his group members master the material presented by widyaiswara. After that, then each individual works on the quiz, the value of this individual quiz will then be accumulated into group scores, therefore each individual is responsible for the points earned by his group.The effectiveness of the learning model is measured through the learning outcomes of training participants in cycle I and cycle II which are assessed through worksheets provided by widyaiswara on each training material and through increasing the activity of training participants in 4C skills assessed from the results of questionnaires, observations and interviews with training participants and learning outcomes of training participants. Achievement of the average percentage of learning activeness of training participants in the first cycle was 78.6% and in the second cycle increased to 91.6%. The percentage increase in training participants' activity from cycle I to cycle II was 16.5%. The positive impact of the increased learning activeness of training participants is an increase in learning outcomes. The average value of the pre-stage cycle is 38, the average value in the first cycle adal ah 63.8 and the average value in the second cycle is 77.1. The percentage increase in learning outcomes from cycle I to cycle II was 13.7%.
EFFECT OF HCl CONCENTRATION ON MICROSTRUCTURE AND MAGNETIC PROPERTIES OF Fe3O4 NANOPARTICLES SYNTHESIZED FROM IRON SAND IN GLAGAH BEACH KULONPROGO

Iron oxide (Fe3O4) nanoparticles had been successfully synthesized from natural iron sand of Glagah Beach Kulon Progo, by using co-precipitation method with variation of HCl concentration (10%, 15%, 25% and 37%). The effect of HCl concentration on the microstructure and magnetic properties of Fe3O4 was carried out using XRD, SEM-EDX and VSM. The XRD patterns showed that the Fe3O4 nanoparticles sample had invers spinel structure and the degree of crystalinity increased with increasing HCl concentration. The sample with 37% of HCl concentration contains 72,42% Fe and 27,58% O with homogeneous grain size. Hysteresis loop showed that the Fe3O4 sample exhibited soft magnetic behavior. Mmax, Hc, and Mr of Fe3O4 nanoparticles with 25% HCl concentration were 0,0215 T, 11,60 emu/gr, and 36,51 emu/gr, respectively. While Mmax, Hc and Mr of Fe3O4 nanoparticles with 37% HCl concentration were 0,0017 T, 10,83 emu/gr and 36,82 emu/gr, respectively.
The effect of risperidone and electroconvulsive therapy on corrected QT interval in electrocardiogram of psychiatric patients

BACKGROUND Some types of antidepressants and antipsychotic medications have cardiovascular side effects that can be life-threatening. Electroconvulsive therapy (ECT) is capable of generating physiological stress and may lead to increased QT interval followed by arrhythmias. Risperidone can also increase the risk of arrhythmia by increasing the corrected QT (QTc) interval. Since many patients require co-administration of risperidone and ECT, this study aimed to investigate the concurrent effect of ECT and risperidone administration on the QTc interval. METHODS For this cross-sectional study, 60 patients (18-65 years) admitted in 22 Bahman Psychiatric Hospital (Qazvin, Iran) that were candidate for treatment with risperidone, ECT, or both methods were concurrently divided into three groups. The groups included patients treated with ECT, risperidone, and combination treatment (risperidone and ECT). At the beginning of the study, electrocardiogram (ECG) was obtained for all patients and QT was performed manually, and finally, QTc interval was measured two times for each group. Required information was collected through medical records. Then, inferential statistics, analysis of variance (ANOVA), was used to determine differences between different variables. RESULTS A significant increase in heart rate (HR) in the third group compared to first and second groups was observed. None of the treatments had a significant effect on QTc interval, but the QTc interval increased slightly in groups treated with the ECT alone and particularly, the ECT plus risperidone in comparison to the baseline values. CONCLUSION Our study showed that risperidone, ECT, and their concomitant combination did not affect the QTc interval. Therefore, risperidone and ECT are safe and their combination can also be a good option for refractory patients undergoing ECG monitoring and cardiopulmonary devices.
Review of Spectrum Allocation of Secondary Users over Cognitive Radio

—With the rapid growth in the wireless communication during last decade. The demand of spectrum resources increases day by day. Cognitive Radio can successfully deal with the growing demand. CR systems are radios with ability to exploit their environment to increase spectral efficiency and capacity. To exploit the limited spectrum efficiency, CR system allows unlicensed users (secondary users) to access licensed spectrum band of primary user when they are not occupied. To do this the secondary users need to continuously monitor the licensed user’s activity to find the unused band. Spectrum sensing is the basic and essential method to find the unused spectrum. It is one of the most challenging issues of CR process to prevent interference between primary users and secondary users. This paper present the overview of the available spectrum sensing techniques such as matched filtering, energy detection, cyclostationary feature detection, and cooperative spectrum sensing techniques.
Review of: "Calculating dissolved marine oxygen values based on an enhanced Benthic Foraminifera Oxygen Index"

Review of: “Calculating Dissolved Marine Oxygen Values Based on an Enhanced Benthic Foraminifera Oxygen Index” Jitendra Pandey Environmental Science Division, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi-221005, India Human-induced changes have triggered a shift in ocean chemistry leading to reduced concentration of dissolved oxygen (DO) in a large part of the world ocean. The areas with very low DO values often called as oxygen minimum zones (OMZs) are rapidly expanding throughout the world ocean leading to adverse effects on ocean metabolic cycle and severe reduction in ocean biodiversity especially the benthic macroand microorganisms. The hypoxic areas have expanded to over 2.45×105 km2 covering over 400 different areas worldwide (Diaz and Rosenberg, 2008). This embarked the reconstruction of marine oxygen status of recent and geological past. The foraminifers are among the most abundant single celled benthic protists present in deep sea fossil record. Being highly sensitive to environmental fluctuations (temperature, salinity, pH, dissolved oxygen, food resources etc.), foraminifers are widely used for palaeoceanographic reconstructions (Tetard et al., 2021). By combining multiple proxies, Kranner et al. (2022) have developed Enhanced Benthic Foraminfera Oxygen Index (EBFOI) by modifying Kaiho’s (Kaiho, 1994) original BFOI. Descriptive information on bottom water oxygen (BWO) based on samples from different depositional environment together with some measured DO (mDO) values have been used to validate EBFOI and new transfer function. The authors used correlative evidences to ascertain the magnitude of synchronization between the calculated DO (cDO) and mDO values. The resulting offset has been linked with the caveats associated with the distance of sampling locations and other causes of variability between mDO and cDO, especially to those related with the lack of PWO measurements. Kranner’s index merits attention contexting three major issues: The new calculations (this ref.) also consider transitional statethe sub-oxic indicators to remove constraints associated with considering samples with oxicand lack of oxic indicators only. Furthermore, in addition to calcareous foraminifers, it also includes agglutinated genera. This large group, which account for about two-third of all foraminiferal genera in geographical record, inhabit the ocean floor below the calcium compensation depth. The findings Qeios, CC-BY 4.0 · Review, March 2, 2022
Deep Learning-Based Crack Detection Using Mask R-CNN Technique

Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site.
Was Chinese Green-Credit Policy Driving the Transformation or Transfer of High-Polluting Enterprises?

This paper investigates the regional differences and spatial effects of Chinese green credit policy. Our research is based on the transmission mechanisms of green credit in promoting industrial structural upgrading. And a number of conclusions are drawn. First of all, green credit could promote the regional industrial structure through two major mechanisms, namely, the financial orientation approach and the technological incentive approach. What is more, this effect however differed in the Eastern, Central and Western regions of China. Furthermore, the impact of green credit on the regional industrial structure had a two-way spatial spillover effect. On one hand, green credit directly promote the upgrading of Chinese industrial structures in both local and surrounding regions, on the other hand, it inhibited the upgrading of industrial structures in the surrounding areas due to the increasing cost to high-polluting enterprises brought by green credit policy. Therefore, it is crucial to be vigilant in either the simultaneous promotion and suppression of green credit within the surrounding areas. Due to our conclusion, this paper suggests government to set higher entry standards for the transfer of high-polluting enterprises between provinces.
Developments in the treatment of postoperative pain in paediatrics

Pain treatment in children is a rather large and complicated topic, which has recently been further developed and enriched by interesting research studies that have been reported in the medical literature. We therefore decided to restrict this paper to postoperative pain control and the problems related to procedural pain. The undertreatment of children who are in pain following surgery has been recognised. Inadequate treatment of neonates and babies was widespread earlier. However, the 1990s saw a change in physicians’ perceptions of neonatal pain and refinement of modern analgesic techniques for use in children [1]. The increased understanding of the neurophysiology of pain and the concept that inadequate treatment of pain can have an impact on outcome and lead to long-term behavioural changes have prompted anaesthetists to study new techniques of pain management in children. In addition, developments are currently in train in clinical pain services, with new, sophisticated analgesia delivery devices and monitoring protocols. A multimodal approach using locoregional anaesthesia combined with opioids and nonsteroidal anti-inflammatory drugs (NSAIDs) or paracetamol is now widely accepted. The emotional component of pain must also be addressed in all aspects of paediatric practice, and it must be recognised that there is a place for instinctive comforting measures, distraction techniques and new nonpharmacological treatments, all of which should be applied to complement safe and effective use of analgesic drugs.
SHANGHAITECH SEM WORKING PAPER SERIES No. 2020-009 Quality Differentiation and Optimal Pricing Strategy in Multi-sided Markets

This paper analyzes the generalized quality differentiation model in multi-sided markets with positive externalities, which leads to new insights into the optimal pricing structure of the firm. We find that quality differentiation for users on one side leads to a decrease in the price charged to users on the other side due to cross-subsidization, thereby affecting the pricing structure of multi-sided firms. In addition, quality differentiation affects the strategic relationships among the choice variables for the platform, so that the platform strategically uses quality differentiation to raise its profits.
A DESIGN OF A NEW HASH FUNCTION BASED ON CELLULAR AUTOMATA

Cryptographic hash functions play an important role in information security. They are used in several cryptographic applications to verify the integrity and authenticity of data. The hash functions are also the basis of blockchain technology. Many hash function constructions are inspired by boolean functions. The proposed hash function algorithm in this paper is based on Elementary Cellular Automata (ECA) and 2-dimensional Cellular Automata (2DCA), which are another type of boolean functions that have excellent cryptographic properties. This algorithm has a sponge construction as such as SHA-3. The strict avalanche criterion (SAC) and NIST statistical tests suite (STS) were used to measure the security of this algorithm. The obtained results demonstrate that the proposed algorithm exhibit high sensitivity to input changes.
Bioactive Materials in Restorative Dentistry: A Literature Review

Bioactive materials have had a great impact on restorative dentistry, favoring the longevity of restorations, cell stimulation for dentin repair, increasing adhesive resistance, and reducing the recurrence of cavities and bacterial microfiltration. Treatment with bioactive materials has covered various fields in restorative dentistry, generating direct interactions are the substrate and remineralization by initiating precipitation and ion exchange that allow the development of a hydroxyapatite layer favoring the dental remineralization process. Its antimicrobial action has an impact on the protection of recurrent cavities and bacterial microfiltration. Besides, interdigitation with the collagen mesh and apatite crystallization in the dentinal tubules favors the treatment of tooth sensitivity. This article provides a review of the characteristics of bioactive materials used in restorative dentistry.
Energy Efficient Approximate Storing to MRAM for Deep Neural Network Tasks in Edge Computing

- On-chip learning is gaining attention in edge devices. In addition, a magnetic RAM (MRAM) is a promising memory technology for edge devices because of low leakage energy. However, the high write energy is a disadvantage of MRAM. For minimizing the write energy, we propose an approximate storing approach to MRAM for learning tasks of deep neural networks (DNN). The proposed approach writes the weight and bias data to NVM approximately on each epoch with the fine-grained adjusted write time. Simulation results with image recognition DNN applications have demonstrated the write energy can be reduced range from 9% to 37% while negligible (< 0.5%) accuracy loss.