Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Corporate Social Responsibility dengan Cause Branding dan Venture Philanthrophy terhadap Profitabilitas Bank Syariah

Profitabilitas bank syariah yang cenderung menurun dari tahun 2010 hingga 2014 mengindikasikan manajemen untuk mengevaluasi dan menentukan langkah strategis termasuk dalam pelaksanaan CSR ( Corporate Social Responsibility ) sebagai salah satu faktor determinannya. Pelaksanaan fungsi sosial dan fungsi edukasi diasosiasikan sebagai CSR bank syariah. Penelitian ini menganalisis dan membahas kinerja CSR dengan cause branding dan venture philanthropy melalui fungsi sosial dan fungsi edukasi serta profitabilitas bank syariah dari tahun 2010 hingga 2015 dan terutama pengaruh kedua fungsi tersebut terhadap profitabilitas. Data diambil dari laporan keuangan tahunan 6 Bank Umum Syariah (BUS) selama 6 tahun (2010-2015). Teknik analisis yang digunakan adalah statistik deskriptif dan regresi linier berganda. Rasio Pelaksanaan Fungsi Sosial (RPFS) dan Rasio Edukasi sebagai variabel independen diuji pengaruhnya terhadap Return on Asset (ROA) sebagai variabel dependen setelah sebelumnya dilakukan analisis deskriptif pada masing-masing variabel tersebut. Hasil dari penelitian ini menunjukkan bahwa CSR melalui fungsi sosial sudah maksimal sementara fungsi edukasi dan profitabilitas bank syariah masih perlu ditingkatkan. Dalam hal ini, fungsi sosial dan fungsi edukasi masing-masing berpengaruh positif dan signifikan terhadap profitabilitas bank syariah. Kata Kunci: Cause Branding, Venture Philanthropy, Fungsi Sosial, Fungsi Edukasi, Profitabilitas, Bank Syariah.
Monedas virtuales se suman al comercio electrónico

This study aims to show the dynamic of the use of Bitcoin has had as a main crypto-currency and how it has been penetrating the domestic and international markets since it was created in 2009 until the present, analyzing certain advantages and disadvantages from an objective point of view that will allow to demonstrate that this virtual currency can be classified as the new currency that will evolve the way we see the money.
Improvement of GRBM Based on Activation Function

In this paper, inspired by ReLu and Softplus activation function, we propose two improved models of GRBM, called SPC-GRBM and RPC-GRBM, to obtain better recognition results. Different from the traditional activation-functionimproved models, SPC-GRBM and RPC-GRBM focus on the visual layer activation function, which is trained by CBCL database and is finally used for image classification with the help of the k-Nearest Neighbor (KNN) method. Experimental results show that the recognition accuracy of SPC-GRBM and RPCGRBM are both enhanced and SPC-GRBM has achieved the highest recognition rate among the several models particularly, of which the recognition accuracy is 20.10% higher than the original GRBM. In addition, the reconstruction error is apparently reduced and its performance keeps well. Keywords-component; image recognition; Gaussian Boltzmann machine; ReLu activation function; Softplus activation function; parallel tempering
MR Findings in Palatal Myoclonus

Purpose: Palatal myoclonus(PM) is an involuntary cyclic movement ofthe soft palate and is known to be caused bythe hypertrophic degeneration ofthe inferior olivary nucleus of the medulla , secondary to the dentato-rubro-tegmental tract lesions. Our purpose is to describe the brain magnetic resonance(MR) imaging findings in palatal myoclonus. Materials and Methods: We retrospectively analyzed the locations and causes of primary lesions in the dentato-rubro-tegmental tract and correlated them with changes in MR signal intensity and size of the inferior olive on T2-weighted MR images in 1 0 patients with palatal myoclonus. Results: AII cases showed hyperintensity in one or both inferior olive and six cases showed accompaning enlargement . The locations of the primary lesions were dorsal aspect of pons(central tegmental tract) in eight patients and the dentate nucleus in one, and no lesion was found in the dentato-rubro-tegmental tract in one . Among eight patients who had lesions in the pons, the ipsilateral inferior 이 ivary nuclei showed changes in five and bilateral olive were involved in three . ln the case with a lesion in the dentate nucleus, the contralateral inferior 이ivary nucleus was involved. The causes of primary lesions were hemorrhage in eight cases and traumatic brain stem injury in one. Conclusion: MR specifically showed enlargement and hyperintensity of the inferior olivary nucleus on T2-weighted images and matching lesions in the pons or the dentate nucleus in patients with palatal myoclonus.
Molecular identification of streptomycetes by exploiting RNA polymerase beta-subunit (rpoB) gene in Saudi arabia

This study was designed with the aim of exploring the efficacy of RNA polymerase beta subunit, rpoB gene analysis for the identification of streptomycetes. A characteristic marine environment distinguished by their content of streptomycetes were targeted, and two organisms of the genus Streptomyces were isolated in Saudi Arabia. The two Streptomyces spp. , named isolates EH1 and EH2 were isolated and subjected to the regular phenotypic characterization including morphological and physiological studies, analyses of cells hydrolysates, detecting their antibacterial activities and pocks formation ability. These isolates showed inhibitory activity of gram-positive bacteria Staphylococcus aureus and Bacillus subtilis. The two isolates gave positive results in pocks formation test, which indicated that both must belong to the genus Streptomyces. Molecular identification of both isolates was accomplished through analysis of the beta-subunit of the RNA polymerase gene (rpoB) to assist in species identification. The target fragments 352 bp were amplified and detected in both isolates using agarose gel electrophoresis, indicating that they belong to the genus Streptomyces. The amplified product of the isolate EH1 was sequenced and 296 bp continuous sequence was determined (GenBank accession MK569762.1). By matching the obtained sequence with the Streptomyces DNA sequence databases, the isolate EH1 was found very similar to Streptomyces labedae. The study concluded with the possibility of using molecular analysis of RNA polymerase beta subunit gene to correctly identify streptomycetes to the species level.
Tele-cottage approach to the community development in the rural Karnataka, India

ABSTRACT This paper describes the concept of telecottage. Tale-cottage is co-operative approach. It involves local residents, representatives of government at all levels, educators, and business people. It is financially supported by the national state and local government. There are participation of the representatives in the design and establishment of the program. It represents a community, response to a local problem. It will enhance communication among people and interaction networking. It facilitates the social and cultural acceptance of information technology. Further suggested a model for developing a telecottage system; for the rural Karnataka under the public and mandal libraries. INTRODUCTION The Tele cottage approach to community development began as a pragmatic empirical attempt to solve a practical problem. The first Tele cottage was “Harjedalens Telestuga” 1 , established in a mountainous area in the north of Sweden. The Tele cottage concept accords well with accepted principles of education and training and with the requirements of the Internet age. It is no accident that it has been emulated and adopted in many countries of Europe and beyond. Networking and sharing of experience, learning together is no less important for organizations then for individuals. In the present study an attempt has been made to give a model plan for the Telecottage approach to community development in rural Karnataka state of India.
NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing

Dementia related cognitive impairment (CI) is a neurodegenerative disorder, affecting over 55 million people worldwide and growing rapidly at the rate of one new case every 3 seconds. 75% cases go undiagnosed globally with up to 90% in low-and-middle-income countries, leading to an estimated annual worldwide cost of USD 1.3 trillion, forecasted to reach 2.8 trillion by 2030. With no cure, a recurring failure of clinical trials, and a lack of early diagnosis, the mortality rate is 100%. Information in electronic health records (EHR) can provide vital clues for early detection of CI, but a manual review by experts is tedious and error prone. Several computational methods have been proposed, however, they lack an enhanced understanding of the linguistic context in complex language structures of EHR. Therefore, I propose a novel and more accurate framework, NeuraHealth, to identify patients who had no earlier diagnosis. In NeuraHealth, using patient EHR from Mass General Brigham BioBank, I fine-tuned a bi-directional attention-based deep learning natural language processing model to classify sequences. The sequence predictions were used to generate structured features as input for a patient level regularized logistic regression model. This two-step framework creates high dimensionality, outperforming all existing state-of-the-art computational methods as well as clinical methods. Further, I integrate the models into a real-world product, a web app, to create an automated EHR screening pipeline for scalable and high-speed discovery of undetected CI in EHR, making early diagnosis viable in medical facilities and in regions with scarce health services.
Rank Diminishing in Deep Neural Networks

The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In particular, the assumption of low-rank feature representations leads to algorithmic developments in many architectures. For neural networks, however, the intrinsic mechanism that yields low-rank structures remains vague and unclear. To fill this gap, we perform a rigorous study on the behavior of network rank, focusing particularly on the notion of rank deficiency. We theoretically establish a universal monotonic decreasing property of network rank from the basic rules of differential and algebraic composition, and uncover rank deficiency of network blocks and deep function coupling. By virtue of our numerical tools, we provide the first empirical analysis of the per-layer behavior of network rank in practical settings, i.e., ResNets, deep MLPs, and Transformers on ImageNet. These empirical results are in direct accord with our theory. Furthermore, we reveal a novel phenomenon of independence deficit caused by the rank deficiency of deep networks, where classification confidence of a given category can be linearly decided by the confidence of a handful of other categories. The theoretical results of this work, together with the empirical findings, may advance understanding of the inherent principles of deep neural networks.
Toward Real-world Single Image Deraining: A New Benchmark and Beyond

—Single image deraining (SID) in real scenarios at- tracts increasing attention in recent years. Due to the difﬁculty in obtaining real-world rainy/clean image pairs, previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs, resulting in incomprehensive evaluation of SID methods. To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of 1 , 120 high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively. Images in RealRain-1k are automatically generated from a large number of real-world rainy video clips through a simple yet effective rain density-controllable ﬁltering method, and have good properties of high image resolution, background diversity, rain streaks variety, and strict spatial alignment. RealRain-1k also provides abundant rain streak layers as a byproduct, enabling us to build a large-scale synthetic dataset named SynRain-13k by pasting the rain streak layers on abundant natural images. Based on them and existing datasets, we benchmark more than 10 representative SID methods on three tracks: (1) fully supervised learning on RealRain-1k, (2) domain generalization to real datasets, and (3) syn-to-real transfer learning. The experimental results (1) show the difference of representative methods in image restoration performance and model complexity, (2) validate the signiﬁcance of the proposed datasets for model generalization, and (3) provide useful insights on the superiority of learning from diverse domains and shed lights on the future research on real-world SID. The datasets have been released at https://github.com/hiker-lw/RealRain-1k.
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

This paper presents a method to explain how the information of each input variable is gradually discarded during the forward propagation in a deep neural network (DNN), which provides new perspectives to explain DNNs. We define two types of entropy-based metrics, i.e. (1) the discarding of pixel-wise information used in the forward propagation, and (2) the uncertainty of the input reconstruction, to measure input information contained by a specific layer from two perspectives. Unlike previous attribution metrics, the proposed metrics ensure the fairness of comparisons between different layers of different DNNs. We can use these metrics to analyze the efficiency of information processing in DNNs, which exhibits strong connections to the performance of DNNs. We analyze information discarding in a pixel-wise manner, which is different from the information bottleneck theory measuring feature information w.r. t. the sample distribution. Experiments have shown the effectiveness of our metrics in analyzing classic DNNs and explaining existing deep-learning techniques.
Human mobility prediction with causal and spatial-constrained multi-task network

Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the “ when → what → where ”, a.k. a. “ time → activity → location ” decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers’ destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show promising performance improvements of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL .
Latent Diffusion Energy-Based Model for Interpretable Text Modeling

Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and strong modeling power of the latent space, recent works built upon it have made interesting attempts aiming at the interpretability of text modeling. However, latent space EBMs also inherit some flaws from EBMs in data space; the degenerate MCMC sampling quality in practice can lead to poor generation quality and instability in training, especially on data with complex latent structures. Inspired by the recent efforts that leverage diffusion recovery likelihood learning as a cure for the sampling issue, we introduce a novel symbiosis between the diffusion models and latent space EBMs in a variational learning framework, coined as the latent diffusion energy-based model. We develop a geometric clustering-based regularization jointly with the information bottleneck to further improve the quality of the learned latent space. Experiments on several challenging tasks demonstrate the superior performance of our model on interpretable text modeling over strong counterparts.
InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness

Humans rely less on spurious correlations and trivial cues, such as texture, compared to deep neural networks which lead to better generalization and robustness. It can be attributed to the prior knowledge or the high-level cognitive inductive bias present in the brain. Therefore, introducing meaningful inductive bias to neural networks can help learn more generic and high-level representations and alleviate some of the shortcomings. We propose InBiaseD to distill inductive bias and bring shape-awareness to the neural networks. Our method includes a bias alignment objective that enforces the networks to learn more generic representations that are less vulnerable to unintended cues in the data which results in improved generalization performance. InBiaseD is less susceptible to shortcut learning and also exhibits lower texture bias. The better representations also aid in improving robustness to adversarial attacks and we hence plugin InBiaseD seamlessly into the existing adversarial training schemes to show a better trade-off between generalization and robustness.
Ubiquitous stripe phase and enhanced electron pairing in interfacial high-Tc superconductor FeSe/BaTiO$_3$

Monolayer FeSe grown on SrTiO 3 provides a new playground for high- T c superconductivity. A recent study shows the importance of stripy instability for superconductivity enhancement in FeSe/SrTiO 3 . However, it is still under debate whether such a stripe phase is ubiquitously bound to the interfacial superconductivity. Here, we report on molecular beam epitaxy growth and low-temperature scanning tunneling microscopy study of FeSe films on BaTiO 3 (001). We find that the stripe phase is more prominent in FeSe/BaTiO 3 . Long-range stripes exist in bilayer and triple-layer FeSe films at 4 K and even persist up to 77 K in the case of bilayer FeSe, with enlarged superconducting gaps upon alkali-metal deposition. The results point out an intimate correlation between the interfacial superconductivity and the stripe phase.
GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) is an inter-esting technology that enables massive connectivity as required in future 5G and 6G networks. While purely linear processing already achieves good performance in NOMA systems, in certain scenarios, non-linear processing is mandatory to ensure accept-able performance. In this paper, we propose a neural network architecture that combines the advantages of both linear and non-linear processing. Its real-time detection performance is demonstrated by a highly efficient implementation on a graphics processing unit (GPU). Using real measurements in a laboratory environment, we show the superiority of our approach over conventional methods.
Visual Transformer for Object Detection

Convolutional Neural networks (CNN) have been the first choice of paradigm in many computer vision applications. The convolution operation however has a significant weakness which is it only operates on a local neighborhood of pixels, thus it misses global information of the surrounding neighbors. Transformers, or Self-attention networks to be more specific, on the other hand, have emerged as a recent advance to capture long range interactions of the input, but they have mostly been applied to sequence modeling tasks such as Neural Machine Translation, Image captioning and other Natural Language Processing tasks. Transformers has been applied to natural language related tasks and achieved promising results. However, its applications in visual related tasks are far from being satisfying. Taking into consideration of both the weaknesses of Convolutional Neural Networks and those of the Transformers, in this paper, we consider the use of self-attention for discriminative visual tasks, object detection, as an alternative to convolutions. In this paper, we propose our model: DetTransNet. Extensive experiments show that our model leads to consistent improvements in object detection on COCO across many different models and scales, including ResNets, while keeping the number of parameters similar. In particular, our method achieves a 1.2% Average Precision improvement on COCO object detection task over other baseline models.
Semi-Supervised Hierarchical Graph Classification

Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, or a document in a document citation network. In this work, we consider a more challenging but practically useful setting, in which a node itself is a graph instance. This leads to a hierarchical graph perspective which arises in many domains such as social network, biological network and document collection. We study the node classification problem in the hierarchical graph where a “node” is a graph instance. As labels are usually limited, we design a novel semi-supervised solution named SEAL-CI. SEAL-CI adopts an iterative framework that takes turns to update two modules, one working at the graph instance level and the other at the hierarchical graph level. To enforce a consistency among different levels of hierarchical graph, we propose the Hierarchical Graph Mutual Information (HGMI) and further present a way to compute HGMI with theoretical guarantee. We demonstrate the effectiveness of this hierarchical graph modeling and the proposed SEAL-CI method on text and social network data.
Learning Joint Surface Atlases

This paper describes new techniques for learning atlas-like representations of 3D surfaces, i.e. homeomorphic transformations from a 2D domain to surfaces. Compared to prior work, we propose two major contributions. First, instead of mapping a fixed 2D domain, such as a set of square patches, to the surface, we learn a continuous 2D domain with arbitrary topology by optimizing a point sampling distribution represented as a mixture of Gaussians. Second, we learn consistent mappings in both directions: charts, from the 3D surface to 2D domain, and parametrizations, their inverse. We demonstrate that this improves the quality of the learned surface representation, as well as its consistency in a collection of related shapes. It thus leads to improvements for applications such as correspondence estimation, texture transfer, and consistent UV mapping. As an additional technical contribution, we outline that, while incorporating normal consistency has clear benefits, it leads to issues in the optimization, and that these issues can be mitigated using a simple repulsive regularization. We demonstrate that our contributions provide better surface representation than existing baselines.