Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions

Synthetic data generation is of great interest in diverse applications, such as for privacy protection. Deep generative models, such as variational autoencoders (VAEs), are a popular approach for creating such synthetic datasets from original data. Despite the success of VAEs, there are limitations when it comes to the bimodal and skewed marginal distributions. These deviate from the unimodal symmetric distributions that are encouraged by the normality assumption typically used for the latent representations in VAEs. While there are extensions that assume other distributions for the latent space, this does not generally increase flexibility for data with many different distributions. Therefore, we propose a novel method, pre-transformation variational autoencoders (PTVAEs), to specifically address bimodal and skewed data, by employing pre-transformations at the level of original variables. Two types of transformations are used to bring the data close to a normal distribution by a separate parameter optimization for each variable in a dataset. We compare the performance of our method with other state-of-the-art methods for synthetic data generation. In addition to the visual comparison, we use a utility measurement for a quantitative evaluation. The results show that the PTVAE approach can outperform others in both bimodal and skewed data generation. Furthermore, the simplicity of the approach makes it usable in combination with other extensions of VAE.
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations

—Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture graph information from the topology view but consistently ignore the feature view. To circumvent this issue, we propose a novel approach by exploiting mutual information maximization across feature and topology views. Speciﬁcally, we ﬁrst utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we then develop a common representation learning module via using mutual information maximization and reconstruction loss minimization. Here, minimizing reconstruction loss forces the model to learn the shared information of feature and topology spaces. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, our proposed method can achieve comparable or even better performance under the unsupervised representation and linear evaluation protocol.
Desain Antena Vivaldi Untuk Teknologi 5G

Teknologi 5G membutuhkan antena yang memiliki karakteristik bandwidth yang lebar dan beamwidth yang sempit. Dalam hal ini, antena vivaldi merupakan salah satu jenis antena yang dapat memenuhi kebutuhan untuk pengaplikasian teknologi 5G karena antena vivaldi memiliki lebar bandwidth yang cukup luas dan pola radiasi yang terarah karena beam yang sempit. Penelitian ini mendesain sebuah antena mikrostrip vivaldi yang dirancang dan dianalisis menggunakan software CST Suite Studio 2018 sebagai software simulasi dan direalisasikan atau dipabrikasi menggunakan substrate Rogers RT/Duroid 5880 series double side dengan konstanta dielektrik (ε r) = 2,2 dan ketebalan 1,575 mm untuk frekuensi mmWave. Rancangan desain antena mikrostrip vivaldi pada frekuensi tengah atau frekuensi resonansi di frekuensi 42 GHz yang menghasilkan nilai parameter antena sebagai berikut : spektrum frekuensi kerja antena di frekuensi 40,5 GHz – 43,5 GHz, bandwidth 3 GHz, s-parameter atau return loss -24,137 dB, Voltage Stading Wave Rasio (VSWR) 1,1324, gain 7,94 dBi, dan pola radiasi Linear Unidirectional, dimana hasil tersebut adalah hasil simulasi menggunakan software CST Suite Studio 2018. Pengujian dan implementasi dengan aplikasi Speed Test dengan hasil nilai parameter kecepatan koneksi internet Wifi USB dengan antena sendiri lebih baik dari antena vivaldi yang dipasangkan pada Wifi USB akan tetapi hasil dari penggunaan antena vivaldi yang dipasangkan pada Wifi USB koneksi internet masih dapat terhubung dan digunakan.
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement

Network Slice placement with the problem of allocation of resources from a virtualized substrate network is an optimization problem which can be formulated as a multi-objective Integer Linear Programming (ILP) problem. However, to cope with the complexity of such a continuous task and seeking for optimality and automation, the use of Machine Learning (ML) techniques appear as a promising approach. We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization heuristic based on the “Power of Two Choices” principle. The DRL algorithm uses the so-called Asynchronous Advantage Actor Critic (A3C) algorithm for fast learning, and Graph Convolutional Networks (GCN) to automate feature extraction from the physical substrate network. The proposed Heuristically-Assisted DRL (HA-DRL) allows for the acceleration of the learning process and substantial gain in resource usage when compared against other state-of-the-art approaches, as evidenced by evaluation results.
An improved efficiency model for ACE/SWICS : determination of the carbon isotopic ratio 13C/12C in the solar wind from ACE/SWICS measurements

The elemental and isotopic composition of heavy ions in the solar wind is a source of information about the composition of the presolar nebula because there is no synthesis of heavy ions from helium due to nuclear burning in the Sun. Thus, the solar composition can be used as a baseline for the detection of eventual spatial deviations in the solar system. Especially the isotopic composition of different extraterrestrial samples compared to the composition of the presolar nebula can provide information about the early evolution of the solar system. The focus of this work is to study the solar isotopic composition of the fourth most abundant solar wind ion, carbon, especially the ratio of the two stable isotopes 12C and 13C. For that we have used measurements of ACE/SWICS, a linear Time-of-Flight mass spectrometer which is positioned at L1. For the purpose of a detailed analysis of these data we have developed an advanced efficiency model of the instrument. Carbon is a highly volatile element and therefore, the terrestrial abundance with respect to non-volatile elements is lower than is observed for solar elemental composition. Nevertheless, it is assumed that the solar carbon isotopic composition as well as the composition of the solar wind is similar to the terrestrial one. A possible deviation between the solar and the solar-wind composition would indicate mass dependent fractionation processes in solar-wind evolution. All previous measurements of the solar carbon-isotopic ratio were accomplished via spectroscopic observations. These determinations are based on measurements of CO absorption lines in the solar electromagnetic radiation spectrum. Most of the previous results show that the solar value is similar to the terrestrial one, e.g. (Harris et.al [1987]) found 12C/13C=84 +/- 5. For the first time we have determined that ratio in the solar wind by in-situ measurements and found 12C/13C=97.7 +10.3/-9.3. Within the error bars both ratios are consistent with the terrestrial ratio of about 12C/13C = 89.
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems

Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setup used for data acquisition. This bias can seriously impair the perception models’ transferability to new sensor setups, which continuously occur due to the market's competitive nature. We envision sensor data abstraction as an interface between sensor data and machine learning applications for highly automated vehicles (HAD). For this purpose, we review the primary sensor modalities, camera, lidar, and radar, published in autonomous-driving related datasets, examine single sensor abstraction and abstraction of sensor setups, and identify critical paths towards an abstraction of sensor data from multiple perception configurations.
BERT Busters: Outlier LayerNorm Dimensions that Disrupt BERT

Multiple studies have shown that BERT is remarkably robust to pruning, yet few if any of its components retain high importance across downstream tasks. Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization ( < 0.0001% of model weights). These are high-magnitude normalization parameters that emerge early in pre-training and show up consistently in the same dimensional position throughout the model. They are present in all six models of BERT family that we examined and removing them signiﬁcantly degrades both the MLM perplexity and the downstream task performance. Our results suggest that layer normalization plays a much more important role than usually assumed.
Axes for Sociotechnical Inquiry in AI Research

The development of artificial intelligence (AI) technologies has far exceeded the investigation of their relationship with society. Sociotechnical inquiry is needed to mitigate the harms of new technologies whose potential impacts remain poorly understood. To date, subfields of AI research develop primarily individual views on their relationship with sociotechnics, while tools for an external investigation, comparison, and cross-pollination are lacking. In this article, we propose four directions for inquiry into new and evolving areas of technological development: 1) value—what progress and direction does a field promote; 2) optimization—how the defined system within a problem formulation relates to broader dynamics; 3) consensus—how the agreement is achieved and who is included in building it; and 4) failure—what methods are pursued when the problem specification is found wanting. This article provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.
Posterior Regularization on Bayesian Hierarchical Mixture Clustering

Bayesian hierarchical mixture clustering (BHMC) improves traditionalBayesian hierarchical clustering by replacing conventional Gaussian-to-Gaussian kernels with a Hierarchical Dirichlet Process Mixture Model(HDPMM) for parent-to-child diffusion in the generative process. However,BHMC may produce trees with high nodal variance, indicating weak separation between nodes at higher levels. To address this issue, we employ Posterior Regularization, which imposes max-margin constraints on nodes at every level to enhance cluster separation. We illustrate how to apply PR toBHMC and demonstrate its effectiveness in improving the BHMC model.
On Inhibition of Rayleigh--Taylor Instability by Horizontal Magnetic Field in an Inviscid MHD Fluid with Velocity Damping

It is still an open problem whether the inhibition phenomenon of Rayleigh–Taylor (RT) instability by horizontal magnetic field can be mathematically proved in a non-resistive magnetohydrodynamic (MHD) fluid in a two-dimensional (2D) horizontal slab domain, since it had been roughly verified by a 2D linearized motion equations in 2012 [43]. In this paper, we find that this inhibition phenomenon can be rigorously verified in the inhomogeneous, incompressible, inviscid case with velocity damping. More precisely, there exists a critical number mC such that if the strength |m| of horizontal magnetic field is bigger than mC, then the small perturbation solution around the magnetic RT equilibrium state is exponentially stable in time. Our result is also the first mathematical one based on the nonlinear motion equations for the proof of inhibition of flow instabilities by a horizontal magnetic field in a horizontal slab domain. In addition, we also provide a nonlinear instability result for the case |m| ∈ [0, mC). Our instability result presents that horizontal magnetic field can not inhibit the RT instability, if it’s strength is to small.
A Primer on Techtile: An R&D Testbed for Distributed Communication, Sensing and Positioning

The Techtile measurement infrastructure is a multi-functional, versatile testbed for new communication and sensing technologies relying on fine-grained distributed resources. The facility enables experimental research on hyper-connected interactive environments and validation of new wireless connectivity, sensing and positioning solutions. It consists of a data acquisition and processing equipment backbone and a fabric of dispersed edge computing devices, Software-Defined Radios, sensors, and LED sources. These bring intelligence close to the applications and can also collectively function as a massive, distributed resource. Furthermore, the infrastructure allows exploring more degrees and new types of diversity, i.e., scaling up the number of elements, introducing `3D directional diversity' by deploying the distributed elements with different orientations, and `interface diversity' by exploiting multiple technologies and hybrid signals (RF, acoustic, and visible light).
Tensor Expander Chernoff Bounds

The Chernoff bound is an important inequality relation in probability theory. The original version of the Chernoff bound is to give an exponential decreasing bound on the tail distribution of sums of independent random variables. Recent years, several works have been done by extending the original version of the Chernoff bound to high-dimensional random objects, e.g., random matrices, or/and to consider the relaxation that there is no requirement of independent assumptions among random objects. In this work, we generalize the matrix expander Chernoff bound studied by Garg et al. at work: A Matrix Expander Chernoff Bound, to tensor expander Chernoff bounds. Our main tool is to develop new tensor norm inequalities based on log-majorization techniques. These new tensor norm inequalities are used to bound the expectation of Ky Fan norm of the random tensor exponential function, then tensor expander Chernoff bounds can be established. Compared with the matrix expander Chernoff bound, the tensor expander Chernoff bounds proved at this work contributes following aspects: (1) the random objects dimensions are increased from matrices (two-dimensional data array) to tensors (multidimensional data array); (2) this bound generalizes the identity map of the random objects summation to any polynomial function of the random objects summation; (3) Ky Fan norm, instead only the maximum or the minimum eigenvalues, for the function of the random objects summation is considered; (4) we remove the restriction about the summation of all mapped random objects is zero, which is required in the matrix expander Chernoff bound derivation.
Active Metal Transfer Control by Monitoring Excited Droplet Oscillation

Controlled metal transfer implies controllable heat and mass in- puts and improved weld quality. Success of previous methods has been hindered by uncertainty in droplet detachment. In the proposed approach, an oscillation of the droplet is excited by switching the current from a peak level to a base level to initiate an active detachment period. The droplet oscillation is then monitored. When the droplet moves downward, the current is switched back to the peak level, which creates an ejection action and concludes the active detachment period. The droplet is detached by down- ward momentum and the increased cur- rent-induced electromagnetic force. Growth period of the succeeding cycle begins. The utilization of downward mo- mentum eliminates the need for a very high current to detach the droplet, which is conventionally used in pulsed gas metal arc welding (GMAW-P). This elim- ination guarantees that no unexpected detachment occurs during the growth period. Also, reliable high-frame rate vision monitoring of the oscillating drop- let guarantees that downward momen- tum will correspond with the ejection action. Hence, the metal transfer process can be precisely controlled. Experimen- tation has been conducted to demon- strate the effectiveness of the proposed approach in achieving the desired metal transfer process.
Integrating Cultural Humility Within the Health Belief Model: Application to A Diet and Exercise Intervention for Hispanics Low-Income Older Adults with Type 2 Diabetes

Introduction: Cultural humility is a patient-centered approach that involves reflective listening and awareness of personal cultural biases. The Health Belief Model was integrated with cultural humility to deliver a patientcentered diabetes and exercise intervention. Purpose: The purpose of this manuscript is to discuss how cultural humility was implemented into a diet and exercise intervention aimed at assisting low-income Hispanic older adults with type 2 diabetes formulate, reach, and maintain diet and exercise goals. Methods: Thirty-nine older Hispanics with type 2 diabetes (78.5±6.6 years) participated in the study at three congregate meal sites serving primarily Spanish speaking older adults. We modified evidence-based diabetes education materials and translated/back-translated into Spanish. We piloted these materials and made adjustment to the intervention based on process evaluations from participants and feedback given by the staff. Results: We became aware of our own ethnocentrism regarding diet and exercise and made modifications of the intervention based on cultural humility: listening to participants through process evaluations. We actualized our cultural humility by re-teaching using client-centered strategies. We made modification in the program based on what participants needed to become successful. We discovered that older Hispanic adults wanted more individualized instructions based on their specific diet and exercise preferences as compared to group instruction. Summary and Conclusions: To teach, reach, and retain participants, we recommend that diabetes educators incorporate cultural humility when planning community-based lifestyle intervention programs targeting older Hispanic adults. Most importantly, team meetings to review clients’ responses became important in updating and evaluating the program. *Correspondence: Trudy Gaillard, PhD, CDCES, Nicole Wertheim College of Nursing and Health Science, Department of Nursing, Florida International University, 11200 SW 8th Street, MMC AHC3 Room 240, Miami, FL 33199, USA, Tel. 305-348-7818. Received: 20 August 2020; Accepted: 16 September 2020
DaLAJ – a dataset for linguistic acceptability judgments for Swedish

We present DaLAJ 1.0, a Dataset for Linguistic Acceptability Judgments for Swedish, comprising 9 596 sentences in its first version; and the initial experiment using it for the binary classification task. DaLAJ is based on the SweLL second language learner data, consisting of essays at different levels of proficiency. To make sure the dataset can be freely available despite the GDPR regulations, we have sentence-scrambled learner essays and removed part of the metadata about learners, keeping for each sentence only information about the mother tongue and the level of the course where the essay has been written. We use the normalized version of learner language as the basis for the DaLAJ sentences, and keep only one error per sentence. We repeat the same sentence for each individual correction tag used in the sentence. For DaLAJ 1.0 we have used four error categories (out of 35 available in SweLL), all connected to lexical or word-building choices. Our baseline results for the binary classification show an accuracy of 58% for DaLAJ 1.0 using BERT embeddings. The dataset is included in the SwedishGlue (Swe. SuperLim) benchmark. Below, we describe the format of the dataset, first experiments, our insights and the motivation for the chosen approach to data sharing.
Review of carbon nanotubes production by thermal chemical vapor deposition technique

ABSTRACT We have reviewed carbon nanotubes (CNTs) production on a silicon wafer by thermal chemical vapor deposition (TCVD) using acetylene as a carbon source, cobalt as a catalyst and ammonia as a reactive gas. The DC-sputtering system was used to prepare cobalt thin films on Si substrates. Energy Dispersive X-ray (EDX) measurements were used to investigate the elemental composition of the Co nanocatalyst deposited on Si substrates. Atomic Force Microscopy (AFM) was used to characterize the surface topography of the Co nanocatalyst deposited on Si substrates. The as-grown CNTs were characterized under Field Emission Scanning Electron Microscopy (FESEM) to study the morphological properties of CNTs.
Sistem Rekomendasi Produk Pena Eksklusif Menggunakan Metode Content-Based Filtering dan TF-IDF

Sistem rekomendasi saat ini sedang menjadi tren. Kebiasaan masyarakat yang saat ini lebih mengandalkan transaksi secara online dengan berbagai alasan pribadi. Sistem rekomendasi menawarkan cara yang lebih mudah dan cepat sehingga pengguna tidak perlu meluangkan waktu terlalu banyak untuk menemukan barang yang diinginkan. Persaingan antar pelaku bisnis pun berubah sehingga harus mengubah pendekatan agar bisa menjangkau calon pelanggan. Oleh karena itu dibutuhkan sebuah sistem yang dapat menunjang hal tersebut. Maka dalam penelitian ini, penulis membangun sistem rekomendasi produk menggunakan metode Content-Based Filtering dan Term Frequency Inverse Document Frequency (TF-IDF) dari model Information Retrieval (IR). Untuk memperoleh hasil yang efisien dan sesuai dengan kebutuhan solusi dalam meningkatkan Customer Relationship Management (CRM). Sistem rekomendasi dibangun dan diterapkan sebagai solusi agar dapat meningkatkan brand awareness pelanggan dan meminimalisir terjadinya gagal transaksi di karenakan kurang nya informasi yang dapat disampaikan secara langsung atau offline. Data yang digunakan terdiri dari 258 kode produk produk yang yang masing-masing memiliki delapan kategori dan 33 kata kunci pembentuk sesuai dengan product knowledge perusahaan. Hasil perhitungan TF-IDF menunjukkan nilai bobot 13,854 saat menampilkan rekomendasi produk terbaik pertama, dan memiliki keakuratan sebesar 96,5% dalam memberikan rekomendasi pena.
THE EFFECT OF BLOOD TRANSFUSION AND TRANEXAMIC ACID ON LENGTH OF HOSPITAL STAY AND MORTALITY AFTER HIP FRACTURE SURGERY IN ELDERLY PATIENTS

Received: Accepted: Introduction: The timely and appropriate administration of the transfusion of blood and/or blood components may considerably improve patient outcomes of geriatric hip fractures. However, the transfusion requirement is not free of complications. The purpose of this study was to evaluate the outcomes of geriatric hip fracture patients who required transfusion and antifibrinolytic treatment.