Miracle-dz/UniFAR-FTUs huggingface dataset:
Surveying the Giant H ii Regions of the Milky Way with SOFIA. IV. Sgr D, W42, and a Reassessment of the Giant H ii Region Census

This is the fourth paper exploring the infrared properties of giant H ii regions with the FORCAST instrument on the Stratospheric Observatory For Infrared Astronomy (SOFIA). Our survey utilizes the census of 56 Milky Way giant H ii regions identified by Conti & Crowther, and in this paper we present the 20 and 37 μm imaging data we obtained from SOFIA for sources Sgr D and W42. Based upon the SOFIA data and other multiwavelength data, we derive and discuss the detailed physical properties of the individual compact sources and subregions as well as the large-scale properties of Sgr D and W42. However, improved measurements have revealed much closer distances to both regions than previously believed, and consequently, both sources are not powerful enough to be considered giant H ii regions any longer. Motivated by this, we revisit the census of giant H ii regions, performing a search of the last two decades of literature to update each source with the most recent and/or most accurate distance measurements. Based on these new distance estimates, we determine that 14 sources in total (25%) are at sufficiently reliable and closer distances that they are not powerful enough to be considered giant H ii regions. We briefly discuss the observational and physical characteristics specific to Sgr D and W42 and show that they have properties distinct from the giant H ii regions previously studied as a part of this survey.
Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing

Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. However, AM produced parts can be subject to undesirable porosity, negatively influencing the properties of printed components. Thus, controlling porosity is integral for creating effective parts. A precise understanding of the porosity distribution is crucial for accurately simulating potential fatigue and failure zones. Previous research on generating synthetic porous microstructures have succeeded in generating parts with high density, isotropic porosity distributions but are often inapplicable to cases with sparser, boundary-dependent pore distributions. Our work bridges this gap by providing a method that considers these constraints by deconstructing the generation problem into its constitutive parts. A framework is introduced that combines Generative Adversarial Networks with Mallat Scattering Transform-based autocorrelation methods to construct novel realizations of the individual pore geometries and surface roughness, then stochastically reconstruct them to form realizations of a porous printed part. The generated parts are compared to the existing experimental porosity distributions based on statistical and dimensional metrics, such as nearest neighbor distances, pore volumes, pore anisotropies and scattering transform based auto-correlations.
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation System

The recommendation system plays a vital role in many areas, especially academic ﬁelds, to support researchers in submitting and increasing the acceptance of their work through the conference or journal selection process. This study proposes a transformer-based model using transfer learning as an eﬃcient approach for the paper submission recommendation system. By combining essential information (such as the title, the abstract, and the list of keywords) with the aims & scopes of journals, the model can recommend the Top K journals that maximize the acceptance of the paper. Our model had developed through two states: (i) Fine-tuning the pre-trained language model (LM) with a simple contrastive learning framework. We utilized a simple supervised contrastive objective to ﬁne-tune all parameters, encouraging the LM to learn the document representation eﬀectively. (ii) The ﬁne-tuned LM was then trained on diﬀerent combinations of the features for the downstream task. This study suggests a more advanced method for enhancing the eﬃciency of the paper submission recommendation system compared to previous approaches when we respectively achieve 0.5173, 0.8097, 0.8862, 0.9496 for Top 1, 3, 5, 10 accuracies on the test set for combining the title, abstract, and keywords as input features. Incorpo-rating the journals’ aims and scopes, our model shows an exciting result by getting 0.5194, 0.8112, 0.8866, 0.9496 respective to Top 1, 3, 5, and 10. We provide the implementation and datasets for further reference at https://github.com/hduc-le/SimCPSR .
Social Distancing Alert with Smartwatches

Social distancing is an efficient public health practice during the COVID-19 pandemic. However, people would violate the social distancing practice unconsciously when they conduct some social activities such as handshaking, hugging, kissing on the face or forehead, etc. In this paper, we present SoDA, a social distancing practice violation alert system based on smartwatches, for preventing COVID-19 virus transmission. SoDA utilizes recordings of accelerometers and gyroscopes to recognize activities that may violate social distancing practice with simple yet effective Vision Transformer models. Extensive experiments over 10 volunteers and 1800+ samples demonstrate that SoDA achieves social activity recognition with the accuracy of 94.7%, 1.8% negative alert, and 2.2% missing alert.
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning

In this work, we investigate the properties of data that cause popular representation learning approaches to fail. In particular, we find that in environments where states do not significantly overlap, variational autoencoders (VAEs) fail to learn useful features. We demonstrate this failure in a simple gridworld domain, and then provide a solution in the form of metric learning. However, metric learning requires supervision in the form of a distance function, which is absent in reinforcement learning. To overcome this, we leverage the sequential nature of states in a replay buffer to approximate a distance metric and provide a weak supervision signal, under the assumption that temporally close states are also semantically similar. We modify a VAE with triplet loss and demonstrate that this approach is able to learn useful features for downstream tasks, without additional supervision, in environments where standard VAEs fail.
Some Grammatical Errors are Frequent, Others are Important

In Grammatical Error Correction, systems are evaluated by the number of errors they correct. However, no one has assessed whether all error types are equally important. We pro-vide and apply a method to quantify the importance of different grammatical error types to humans. We show that some rare errors are considered disturbing while other common ones are not. This affects possible directions to improve both systems and their evaluation. 1
Problems involving the fractional g-Laplacian with lack of compactness

In this paper, we prove compact embedding of a subspace of the fractional Orlicz–Sobolev space [Formula: see text] consisting of radial functions; our target embedding spaces are of Orlicz type. In addition, we prove a Lions and Lieb type results for [Formula: see text] that works together in a particular way to get a sequence whose weak limit is non-trivial. As an application, we study the existence of solutions to quasilinear elliptic problems in the whole space [Formula: see text] involving the fractional g-Laplacian operator, where the conjugated function [Formula: see text] of G does not satisfy the Δ2-condition.
Target Aware Network Architecture Search and Compression for Efficient Knowledge Transfer

Transfer Learning enables Convolutional Neural Networks (CNN) to acquire knowledge from a source domain and transfer it to a target domain, where collecting large-scale annotated examples is time-consuming and expensive. Conventionally, while transferring the knowledge learned from one task to another task, the deeper layers of a pre-trained CNN are finetuned over the target dataset. However, these layers are originally designed for the source task which may be over-parameterized for the target task. Thus, finetuning these layers over the target dataset may affect the generalization ability of the CNN due to high network complexity. To tackle this problem, we propose a two-stage framework called TASCNet which enables efficient knowledge transfer. In the first stage, the configuration of the deeper layers is learned automatically and finetuned over the target dataset. Later, in the second stage, the redundant filters are pruned from the fine-tuned CNN to decrease the network's complexity for the target task while preserving the performance. This two-stage mechanism finds a compact version of the pre-trained CNN with optimal structure (number of filters in a convolutional layer, number of neurons in a dense layer, and so on) from the hypothesis space. The efficacy of the proposed method is evaluated using VGG-16, ResNet-50, and DenseNet-121 on CalTech-101, CalTech-256, and Stanford Dogs datasets. Similar to computer vision tasks, we have also conducted experiments on Movie Review Sentiment Analysis task. The proposed TASCNet reduces the computational complexity of pre-trained CNNs over the target task by reducing both trainable parameters and FLOPs which enables resource-efficient knowledge transfer. The source code is available at: https://github.com/Debapriya-Tula/TASCNet.
Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs

We propose a generative model for text generation, which exhibits disentangled latent representations of syntax and semantics. Contrary to previous work, this model does not need syntactic information such as constituency parses, or semantic information such as paraphrase pairs. Our model relies solely on the inductive bias found in attention-based architectures such as Transformers. In the attention of Transformers, keys handle information selection while values specify what information is conveyed. Our model, dubbed QKVAE, uses Attention in its decoder to read latent variables where one latent variable infers keys while another infers values. We run experiments on latent representations and experiments on syntax/semantics transfer which show that QKVAE displays clear signs of disentangled syntax and semantics. We also show that our model displays competitive syntax transfer capabilities when compared to supervised models and that comparable supervised models need a fairly large amount of data (more than 50K samples) to outperform it on both syntactic and semantic transfer. The code for our experiments is publicly available.
AppTek’s Submission to the IWSLT 2022 Isometric Spoken Language Translation Task

To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding. We further increased translation length compliance by sentence-level selection of length-compliant hypotheses from different system variants, as well as rescoring of N-best candidates from a single system. Length-compliant back-translated and forward-translated synthetic data, as well as other parallel data variants derived from the original MuST-C training corpus were important for a good quality/desired length trade-off. Our experimental results show that length compliance levels above 90% can be reached while minimizing losses in MT quality as measured in BERT and BLEU scores.
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference

As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private information (e.g., home location), preventing many users from sharing their private information with a third party. Hence, identifying travel modes while protecting users' privacy is a significant issue. To address this challenge, we use federated learning (FL), a privacy-preserving machine learning technique that aims at collaboratively training a robust global model by accessing users' locally trained models but not their raw data. Specifically, we designed a novel ensemble-based Federated Deep Neural Network (eFedDNN). The ensemble method combines the outputs of the different models learned via FL by the users and shows an accuracy that surpasses comparable models reported in the literature. Extensive experimental studies on a real-world open-access dataset from Montréal demonstrate that the proposed inference model can achieve accurate identification of users' mode of travel without compromising privacy.
Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference

Aiming at recognizing and localizing the object of novel categories by a few reference samples, few-shot object detection (FSOD) is a quite challenging task. Previous works often depend on the fine-tuning process to transfer their model to the novel category and rarely consider the defect of fine-tuning, resulting in many application drawbacks. For example, these methods are far from satisfying in the episode-changeable scenarios due to excessive fine-tuning times, and their performance on low-quality (e.g., low-shot and class-incomplete) support sets degrades severely. To this end, this paper proposes an instant response few-shot object detector (IR-FSOD) that can accurately and directly detect the objects of novel categories without the fine-tuning process. To accomplish the objective, we carefully analyze the defects of individual modules in the Faster R-CNN framework under the FSOD setting and then extend it to IR-FSOD by improving these defects. Specifically, we first propose two simple but effective meta-strategies for the box classifier and RPN module to enable the object detection of novel categories with instant response. Then, we introduce two explicit inferences into the localization module to alleviate its over-fitting to the base categories, including explicit localization score and semi-explicit box regression. Extensive experiments show that the IR-FSOD framework not only achieves few-shot object detection with the instant response but also reaches state-of-the-art performance in precision and recall under various FSOD settings.
Approximated quantum-state preparation with entanglement dependent complexity

Ubiquitous in quantum computing is the step to encode data into a quantum state. This process is called quantum state preparation, and its complexity for non-structured data is exponential on the number of qubits. Several works address this problem, for instance, by using variational methods that train a ﬁxed depth circuit with manageable complexity. These methods have their limitations as the lack of a back-propagation technique and barren plateaus. This work proposes an algorithm to reduce state preparation circuit depth by oﬄoading computational complexity to a classical computer. The initialized quantum state can be exact or an approximation, and we show that the approximation is better on today’s quantum processors than the initialization of the original state. We veriﬁed through experimental evaluation that the proposed method allows the initialization of distributions in a quantum state and approximate loading images for quantum machine learning tasks. Indeed, our experiments on IBMQ devices show that they are likely to be aiding experimenters of quantum computing throughout the NISQ era.
Molecular Detection Methods in HPV-Related Cancers

Human papillomavirus (HPV) is responsible for most cervical cancers and some head and neck cancers, including oropharyngeal squamous cell carcinoma and sinonasal carcinoma. Cervical cancer is commonly diagnosed by liquid-based cytology, followed by HPV testing using commercially available DNA polymerase chain reaction (PCR), p16 immunohistochemistry (IHC), or DNA/RNA in situ hybridization. HPV in head and neck cancers is commonly diagnosed by p16 IHC or by RT-qPCR of HPV-16 E6 and E7 oncoproteins. Droplet digital PCR has been reported as an ultrasensitive and highly precise method of nucleic acid quantification for biomarker analysis and has been used to detect oncogenic HPV in oropharyngeal and cervical cancers.
A systematic review and meta-analysis of the effects of Hibiscus sabdariffa on blood pressure and cardiometabolic markers

Abstract Context Hibiscus sabdariffa (hibiscus) has been proposed to affect cardiovascular risk factors. Objective To review the evidence for the effectiveness of hibiscus in modulating cardiovascular disease risk markers, compared with pharmacologic, nutritional, or placebo treatments. Data Sources A systematic search of the Web of Science, Cochrane, Ovid (MEDLINE, Embase, AMED), and Scopus databases identified reports published up to June 2021 on randomized controlled trials using hibiscus as an intervention for lipid profiles, blood pressure (BP), and fasting plasma glucose levels in adult populations. Data Extraction Seventeen chronic trials were included. Quantitative data were examined using a random effects meta-analysis and meta-regression with trial sequential analysis to account for type I and type II errors. Data Analysis Hibiscus exerted stronger effects on systolic BP (−7.10 mmHg [95%CI, −13.00, −1.20]; I2 = 95%; P = 0.02) than placebo, with the magnitude of reduction greatest in those with elevated BP at baseline. Hibiscus induced reductions to BP similar to that resulting from medication (systolic BP reduction, 2.13 mmHg [95%CI, −2.81, 7.06], I2 = 91%, P = 0.40; diastolic BP reduction, 1.10 mmHg [95%CI, −1.55, 3.74], I2 = 91%, P = 0.42). Hibiscus also significantly lowered levels of low-density lipoprotein compared with other teas and placebo (−6.76 mg/dL [95%CI, −13.45, −0.07]; I2 = 64%; P = 0.05). Conclusions Regular consumption of hibiscus could confer reduced cardiovascular disease risk. More studies are warranted to establish an effective dose response and treatment duration. Systematic Review Registration PROSPERO registration no. CRD42020167295
Supplier Selection Process-Towards a Multi-Criteria Decision Making Model for Manned Guarding Services

This study seeks to identify the most important criteria for selecting private security service providers, specifically manned guarding. Previous research on provider selection has been generic focusing on multi-criteria model decision development, but not on the previous stage where industry related criteria should be identified. Given that manned guarding is a service that affects our daily lives, it is important to focus attention on this area of activity. Selecting a private security service provider should therefore be a process in researchers’ agenda. We contribute to the literature by identifying relevant criteria in manned guarding provider selection and by using a methodology than can be applied in other industries to identify sensitive criteria besides traditional factors (eg. price, quality, delivery). We gathered a panel of 15 experts in this field to apply Delphi method by gathering their opinion in ordering the most important criteria to select manned guarding service providers. Results shows price as the most important criterion, but it was closely related to the professional experience of the operations managers (supervisors and directors), the geographic location and the financial stability of the companies.
Persian mobile health applications for COVID-19: A use case-based study

BACKGROUND: Since the beginning of the COVID-19 outbreak, a significant number of mobile health apps have been created around the world and in Iran to help consequence reduction of this emerging pandemic. OBJECTIVES: This study aimed to review the characteristics of Persian Android and iOS apps related to COVID-19 and determine their use-cases based on a reference model. METHODS: This was a cross-sectional descriptive study conducted in three main steps. First, a systematic search was conducted via Iranian mobile apps’ markets using the keywords related to COVID-19 in January 2021. Then, the retrieved apps were analyzed according to their characteristics. Finally, the use-cases of the given apps were determined and categorized based on a reference model. RESULTS: Based on our inclusion criteria, 122 apps were selected and evaluated. Most of these apps (87.7%) was free. Small proportions (5%) of reviewed apps have been developed with participation of clinical expert and half of the apps mentioned the references they used. Furthermore, about half of the apps (50.8%) were provided contact information of the developers. The studied apps were classified into four use-case major categories, including educational (98%), fulfilling a contextual need (18%), communicating, and/or sharing the information (0.83%), and health-related management (2%). CONCLUSION: The results showed that the Persian mobile apps for COVID-19 are not in a satisfying situation. Furthermore, although these apps are significant in quantity but in terms of use-cases, they are not widespread.
Attention deficit hyperactivity disorder and associated learning difficulties among primary school children in district Dehradun, Uttarakhand, India

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a considerably common childhood-onset neurodevelopmental disorder, often associated with socio-behavioral and academic difficulties. There is an increased risk for development of a range of future problems such as psychiatric disorders, difficulties in employment, and relationships. The aim of this study was to know the prevalence and determinants of ADHD among primary school children in district Dehradun and to assess the learning difficulties and classroom behavior in these students. MATERIALS AND METHODS: In this cross-sectional study, overall, 228 students (aged 6–12 years) studying in a primary school were assessed for ADHD after seeking the written informed consent from their parents. The data were analyzed using Excel sheet and SPSS software (22.0 version). For all qualitative measures, frequency and percentages were calculated, and for quantitative measures, mean and standard deviation were calculated. For analysis of categorical values, Chi-square test was used. P < 0.05 was considered statistically significant. RESULTS: The prevalence of ADHD was found to be 11.8% based on the teacher tool only and 1.75% based on the parent and teacher tools combined. ADHD was found to be significantly more in males. Inattention was the most prevalent subtype of ADHD, and children screened positive for ADHD had significant learning difficulties at various levels. CONCLUSION: The possibility of ADHD in students with academic difficulties should not be ignored as children with ADHD usually face significant problems at school such as learning difficulties and have been shown to be at increased risk for a broad range of negative outcomes. Early identification and treatment of ADHD can significantly reduce the rates of some of these poor outcomes. Screening these children in the early years will help the parents, children, teachers, and community at large.