Miracle-dz/UniFAR-FTUs huggingface dataset:
A PARAMETRIC MODELING APPROACH TO MEASURING THE GAS MASSES OF CIRCUMSTELLAR DISKS

The disks that surround young stars are mostly composed of molecular gas, which is harder to detect and interpret than the accompanying dust. Disk mass measurements have therefore relied on large and uncertain extrapolations from the dust to the gas. We have developed a grid of models to study the dependencies of isotopologue CO line strengths on disk structure and temperature parameters and find that a combination of 13CO and C18O observations provides a robust measure of the gas mass. We apply this technique to Submillimeter Array observations of nine circumstellar disks and published measurements of six well studied disks. We find evidence for selective photodissociation of C18O and determine masses to within a factor of about three. The inferred masses for the nine disks in our survey range from 0.7 to 6 MJup, and all are well below the extrapolation from the interstellar medium gas-to-dust ratio of 100. This is consistent with the low masses of planets found around such stars, and may be due to accretion or photoevaporation of a dust-poor upper atmosphere. However, the masses may be underestimated if there are more efficient CO depletion pathways than those known in molecular clouds and cold cores.
UAV-Assisted HetNets: Association Probability With Instantaneous Received Power

As user applications become more diverse and data rate-hungry, networks emerge into heterogeneous and flexible forms. Unmanned aerial vehicles (UAV) play a prominent role in achieving a next level of flexibility thanks to their rapid deployment, low cost and favorable propagation conditions. One of the most important aspects of UAV-assisted heterogeneous networks (HetNets) is the user association strategy. In this regard, we propose an instantaneous-sense association strategy, as opposed to the widely adopted average-sense one. We study the association performance in a two tiers mmWave HetNet, where a terrestrial network is supported by a tier of UAV BSs (UBS). In specific, using tools from stochastic geometry we provide tractable expressions for the association probability with each tier in addition to expression for coverage hole probability taking into account the users’ reception sensitivity. Monte Carlo simulations are provided to validate the accuracy of analytical formulas, and it is shown that the average-sense association strategy is a special case of the proposed method.
A Comparative Analysis of VirusTotal and Desktop Antivirus Detection Capabilities

VirusTotal has been widely used and being adopted by researchers mainly for the classification of files as malicious or not. Unfortunately, it is not well understood how reliable the results from the antivirus engines on VirusTotal are, especially compared to their desktop counterparts. In this paper, we shed light on the blackbox testing functionality of VirusTotal by evaluating the detection results of VirusTotal antivirus engines and their equivalent desktop versions. Based on our results, we arrive to the conclusion that there are discrepancies between the engines on VirusTotal and the desktop engines. In general, the malware detection rate of the engines on VirusTotal is lower compared to desktop products. This is mainly attributed to the fact that VirusTotal engines do not take advantage of cloud-based detection deteriorating their performance.
Person Re-identification with a Cloth-Changing Aware Transformer*

Cloth-Changing person re-identification is a challenging problem for its huge intra-class variance caused by changing clothes. In this paper, we focus on learning cloth-insensitive features without any cues from external models. Specifically, we introduce a novel Cloth-Changing Aware Transformer(CCAT) to learn identity-relevant cues. The transformer encoder utilizes Cloth Information Embedding(CIE) to encode outfits information and automatically focuses on the identity-discriminative feature. The tokens from the transformer encoder are then fed to the Cloth-Insensitive Consistency Learning(CICL) decoder to learn cloth-irrelevant features from pairwise pedestrian images. Besides, we impose Intra-class Constraint Learning(ICL) to pull the global tokens from the encoder closer. In this way, the model learns the identity-relevant and cloth-insensitive features. Extensive experimental results on three cloth-changing person ReID datasets demonstrate that our proposed algorithm can extract highly robust feature representations of cloth-changing persons, and it outperforms the state-of-the-art cloth-changing person ReID approaches. Our method achieves 87.4% and 69.7% on R-1 accuracy on the latest released LTCC [28] and PRCC [40], which outperforms previous methods with a large margin. The code is available at CCAT.
CoVal-SGAN: A Complex-Valued Spectral GAN architecture for the effective audio data augmentation in construction sites

Generative audio data augmentation in a construction site is one of challenging research areas due to the high dissimilarity between work sounds of involved machines and equipment. However, it becomes necessary since the availability of audio data of critical work classes is often rare. Motivated by these considerations and demands, in this paper, we propose a complex-valued GAN architecture working with the audio spectrogram, named CoVal-SGAN, for an effective augmentation of audio data. Specifically, the proposed CoVal-SGAN exploits both the magnitude and phase information to improve the quality of the artificially generated audio signals and increase the overall performance of the underlying classifier. Numerical results, performed on the data recorded in real-world construction sites, along with the comparisons with available state-of-the-art approaches, show the effectiveness of the proposed idea by obtaining an improved accuracy.
On Combining Robustness and Regularization in Training Multilayer Perceptrons over Small Data

Multilayer perceptrons (MLPs) continue to be commonly used for nonlinear regression modeling in numerous applications. Available robust approaches to training MLPs, which allow to yield reliable results also for data contaminated by outliers, have not much penetrated to real applications so far. Besides, there remains a lack of systematic comparisons of the performance of robust MLPs, if their training uses one of regularization techniques, which are available for standard MLPs to prevent overfitting. This paper is interested in comparing the performance of MLPs trained with various combinations of robust loss functions and regularization types on small datasets. The experiments start with MLPs trained on individual datasets, which allow graphical visualizations, and proceed to a study on a set of 163251 MLPs trained on well known benchmarks using various combinations of robustness and regularization types. Huber loss combined with $L_{2}$ - regularization turns out to outperform other choices; this combination is recommendable whenever the data do not contain a large proportion of outliers.
Design-Time Analysis of Real-Time Traffic for Networks-on-Chip using Constraint Models

Real-time networks-on-chips (RT-NoCs) were proposed to suit the needs of communication-intensive systems with real-time requirements. However, most of the current implementations found in the literature are based on custom routers, thus requiring a complete redesign of the interconnect architecture. This work presents a novel approach to tackle the analysis and scheduling of real-time traffic that requires no special mechanism to be implemented within the NoC design. Our solution relies on an auxiliary hardware module to synchronize the injection of packets into the network instead of custom routers, benefiting existing non-RT NoC designs. Our approach guarantees scheduled traffic to be congestion-free by using a design-time optimization process. Results present a didactic proof-of-concept using a synthetic application mapped onto a small NoC design.
Scanning tunneling spectroscopy reveals a silicon dangling bond charge state transition

We report the study of single dangling bonds (DBs) on a hydrogen-terminated silicon (100) surface using a low-temperature scanning tunneling microscope. By investigating samples prepared with different annealing temperatures, we establish the critical role of subsurface arsenic dopants on the DB electronic properties. We show that when the near-surface concentration of dopants is depleted as a result of 1250 °C flash anneals, a single DB exhibits a sharp conduction step in its I(V) spectroscopy that is not due to a density of states effect but rather corresponds to a DB charge state transition. The voltage position of this transition is perfectly correlated with bias-dependent changes in the STM images of the DB at different charge states. Density functional theory calculations further highlight the role of subsurface dopants on DB properties by showing the influence of the DB-dopant distance on the DB state. We discuss possible theoretical models of electronic transport through the DB that could account for our experimental observations.
Factors Impacting the Intention to use Role-playing Games in Learning Communicative English of University Students in Da Nang City

English could be described as a language used to achieve across borders and bridge the gaps. In regard to the development of globalization, which argues that economic globalization has motivated the global spread of English, while the global spread of English has also motivated globalization. Being an influent English communicator is one of the principals demands to enhance the scale of forceful globalization and technological advancements. In the integrated section on technological progress and language training programs, the grouping of learning is based on game-based and gamification has been come to be the choice of technology. The pool of numerous games, including digital Role-playing games (RPGs), has been augmented based on the combination and used for educational and recreational purposes concurrently. In the report, the researchers decided to be engaged do research, evaluate factors that impact on intention to use RPG games to reach a high level of achievement in communicative English of university students in Da Nang city, relying on six variables (five independent variables and one mediating variable) including Innovation, Popularity, Price, Content Quality, Enjoyment, and Attitude. We already gained roughly 275 responses based on simple random sampling; then, these samples would be analyzed using SPSS and AMOS for evaluating and concluding the variables' influences. Afterward, the research group provides a set of solutions that helps to increase learners' intention to use RPGs as a powerful supporter in becoming proficient at English communication.
THE CLUSTER OF BLUE STARS SURROUNDING THE M31 NUCLEAR BLACK HOLE

We obtained U330- and B-band images of the M31 nucleus using the High Resolution Camera of the Advanced Camera for Surveys on board the Hubble Space Telescope (HST). The spatial resolution in the U330 band, 0. ″03 FWHM, or 0.1 pc at M31, is sufficient to resolve the outskirts of the compact cluster (P3) of UV-bright stars surrounding the M31 black hole. The center of the cluster is marked by an extended source that is both brighter and redder than the other point sources within P3; it is likely to be a blend of several bright stars. We hypothesize that it marks the location of the M31 black hole. Both stellar photometry and a surface brightness fluctuation analysis show that the P3 stellar population is consistent with early-type main-sequence stars formed in a ∼100–200 Myr old starburst population. Evolutionary tracks of post early asymptotic giant branch (PEAGB) stars, associated with late-stage evolution of an old population, also traverse the U and U − B domain occupied by the P3 stars; but we argue that only a few stars could be accounted for that way. PEAGB evolution is very rapid, and there is no progenitor population of red giants associated with P3. The result that P3 comprises young stars is consistent with inferences from earlier HST observations of the integrated light of the cluster. Like the Milky Way, M31 harbors a black hole closely surrounded by apparently young stars.
SPECTRA OF TYPE IA SUPERNOVAE FROM DOUBLE DEGENERATE MERGERS

The merger of two white dwarfs (aka double-degenerate merger) has often been cited as a potential progenitor of Type Ia supernovae. Here we combine population synthesis, merger, and explosion models with radiation-hydrodynamics light-curve models to study the implications of such a progenitor scenario on the observed Type Ia supernova population. Our standard model, assuming double-degenerate mergers do produce thermonuclear explosions, produces supernova light curves that are broader than the observed type Ia sample. In addition, we discuss how the shock breakout and spectral features of these double-degenerate progenitors will differ from the canonical bare Chandrasekhar-massed explosion models. We conclude with a discussion of how one might reconcile these differences with current observations.
Towards Explainable Deep Learning for Image Captioning through Representation Space Perturbation

Most vision-language tasks, such as image captioning, require complex deep learning (DL) models/architectures. However, the high complexity of such models often leads their inner functioning to be regarded as black-boxes, preventing humans from understanding their decision process. Representation space is a key element in DL models, understanding this space and how information is encoded constitute an interesting approach for interpretable DL. Indeed, as far as we are aware, this paper is the first one presenting a novel method based on perturbation principle of representation space, to study the components that influence DL architectures used in image captioning. The core idea is to isolate and identify the importance of each component (element of the architecture) involved in the captioning pipeline by perturbing, by means of Gaussian functions, the representation space rather than the original space of inputs. We experimentally demonstrate that those components differ in their influence and relevance. The results show that the visual modality would constitute a critical explanation target in captioning models in contrast to language modality, thus leading to more fine-grained explanations. We also propose MSICE, an automatic evaluation metric for image captioning which addresses two important yet overlooked linguistic aspects, morphology and semantic. Our code will be publicly accessible to support future research in this field.
Association of Weight Change with Risk of Primary Liver Cancer in Middle-aged Men with Chronic Liver Disease

Obesity is considered an important risk factor for the development of primary liver cancer. However, the association of weight change with risk of primary liver cancer is not well-defined, especially among middle-aged men with chronic liver disease. This population-based longitudinal study used data obtained by the Korean National Health Insurance Service-National Sample Cohort. The study population comprised 33,260 middle-aged men aged between 40 and 64 with chronic liver disease. The association of weight change with risk of primary liver cancer was evaluated using multivariable-adjusted Cox proportional hazards regression. All participants were followed up until primary liver cancer, death, or December 31, 2013, whichever came earliest. During 226,619 person-years of follow-up, 536 cases (1.6 %) of primary liver cancer were identified. Weight gain (change in body mass index greater than 2.0 kg/m2) was associated with higher risk of primary liver cancer (adjusted hazard ratio [aHR], 1.57; 95% confidence interval [CI] 1.04-2.36), whereas no significant association was found for weight loss (aHR, 0.90; 95% CI, 0.55-1.46) compared to weight stable group. Stratified analyses revealed that weight gain-associated higher risk of primary liver cancer was more notable among non-obese and non-drinking subgroups. Weight gain is associated with higher risk of primary liver cancer in among middle-aged men with chronic liver disease. Future studies are warranted to confirm whether weight management is beneficial in populations at high risk of primary liver cancer.
The Effects of Teaching Efficacy and Self-esteem on Job Performance

The purpose of this study is to analyze the effects of teaching efficacy and self-esteem of teachers, who are persons in charge of actual educational quality at early childhood educational service institutions, on job performance in the rapidly changing society, and present strategic orientations and implications for long-term growth and development of the organizations. To that end, a self-report questionnaire survey was conducted with 280 teachers working in Seoul, Incheon, and Gyeonggi areas, and the collected data were analyzed by performing descriptive statistical analysis, Pearson product-moment correlation analysis, and stepwise regression analysis. The results of this study are summarized as follows. First, there were significant positive correlations between teaching efficacy, self-esteem, and job performance. Second, teaching efficacy and self-esteem had very significant positive (+) effects on job performance despite that education experience was set as a control variable. Third, the most influential sub-variable of teaching efficacy was belief in teaching efficacy. The study findings as such suggest that in order to create job performance through the improvement of educational service quality, it is necessary to prepare policies and present strategic orientation to improve the qualitative level of internal factors such as teachers’ belief in teaching efficacy.
An improved probabilistic approach for linking progenitor and descendant galaxy populations using comoving number density

Galaxy populations at different cosmic epochs are often linked together by comoving cumulative number density in observational studies. Many theoretical works, however, have shown that the number densities of tracked galaxy populations evolve in bulk and spread out over time. We present a number density method for linking progenitor and descendant galaxy populations which takes both of these effects into account. We define probability distribution functions that capture the evolution and dispersion of galaxy populations in comoving number density space, and use these functions to assign galaxies at one redshift $z_f$ probabilities of being progenitors or descendants of a galaxy population at another redshift $z_0$. These probabilities are then used as weights for calculating distributions of physical properties such as stellar mass, star formation rate, or velocity dispersion within the progenitor/descendant population. We demonstrate that this probabilistic method provides more accurate predictions for the evolution of physical properties then either the assumption of a constant number density or the assumption of an evolving number density in a bin of fixed width by comparing the predictions against galaxy populations directly tracked through a cosmological simulation. We find that the constant number density method performs most poorly at recovering galaxy properties, the evolving number method density slightly better, and the probabilistic number density method best of all. The improvement is present for predictions of both stellar mass as well as inferred quantities such as star formation rate and velocity dispersion which were not included in the number density fits. We demonstrate that this method can also be applied robustly and easily to observational data, and provide a code package for doing so.
Harnessing Growth-Based Morphological Development to Facilitate Learning ANN-Controlled Bipedal Walking

In human beings, the natural development of the body has been shown to facilitate learning. This approach has been applied in robotic learning with different results, being an advantage under some conditions and tasks. While it is still not well understood under what conditions morphological development helps to learn, several authors have proposed some high-level notions about when it could be interesting to apply it. In our previous work, we have used these notions with the objective of designing a morphological development strategy that facilitates learning in a bipedal locomotion task with an Artificial Neural Network (ANN) controlled robot. In this paper, we aim to go beyond the qualitative design principles previously used and support such considerations with an empirical quantitative study. An analysis of the learning results and how they are related to the design conditions that were established is carried out based on the evolution of the fitness landscape for each developmental stage. The long-term objective is to develop morphology-agnostic optimization strategies for morphological development, which would reduce the number of samples required and, thus, the computational cost, of learning in ANN-controlled robots.
Vehicle Trajectory Prediction for Automated Driving Based on Temporal Convolution Networks

For automated driving, trajectory prediction of other surrounding vehicles is crucial to ego vehicle’s driving decision. This is especially important when automated vehicles, e.g. SAE level 3 vehicles or fully-automated robotaxis, share the open road with human-driven vehicles. One of the main research directions in this field is to adopt the methods of deep learning. We propose a TCN-MLP encoder-decoder framework that considers both the trajectory data of predicted vehicle and surrounding vehicles in the input. To handle more complex trajectory prediction, a driving intention recognition module is added to the model to identify the intentions in longitudinal and lateral directions. Based on the HighD dataset, we have tested the proposed model and its two variations for ablation experiment. The results show that our model can achieve more accurate trajectory prediction than the state-of-the-art approaches, and the prediction RMSE is reduced by about 33.3% on average. Our model may serve as a promising solution to vehicle trajectory prediction problems in highway scenes.
Spatial Separable Multi-scale Feature Net: A 3D Reconstruction Method with More Features but Fewer Parameters

In recent years, 3D reconstruction methods based on implicit functions have shown advantages, but there are still two major problems: one is that the networks of these methods have high complexity with enormous parameters and huge memory consumption; another is that some methods' reconstructed surfaces are overly smooth and lack details, and some methods suffer from poor robustness after improving the surface details. To address these problems, we propose Spatial Separable Multi-scale Feature Net (SSMF-Net), which separately extracts the features of X-Y plane domain and Z depth domain on multiple feature scales, and generates a spatial separable multi-scale feature vector with sufficient local and global features. SSMF-Net obtains a powerful implicit function that is more faithful to the ground truth surfaces, and greatly reduces the network parameters and memory consumption at the same time. Experiments performed on ShapeNet and BUFF datasets show that our SSMF-Net outperforms PSGN, DMC, OccNet, and IF-Net. SSMF-Net preserves more local features while ensuring the global shape integrity, and reduces network parameters and memory consumption by 29%~84%. Moreover, SSMF-Net has stronger robustness and generalization.