Miracle-dz/UniFAR-FTUs huggingface dataset:
Three short-period Jupiters from TESS

We report the confirmation and mass determination of three hot Jupiters discovered by the Transiting Exoplanet Survey Satellite (TESS) mission: HIP 65Ab (TOI-129, TIC-201248411) is an ultra-short-period Jupiter orbiting a bright (V=11.1 mag) K4-dwarf every 0.98 days. It is a massive 3.213 +/- 0.078 Mjup planet in a grazing transit configuration with an impact parameter of b = 1.17 +0.10/-0.08. As a result the radius is poorly constrained, 2.03 +0.61/-0.49 Rjup. The planet's distance to its host star is less than twice the separation at which it would be destroyed by Roche lobe overflow. It is expected to spiral into HIP 65A on a timescale ranging from 80 Myr to a few gigayears, assuming a reduced tidal dissipation quality factor of Qs' = 10^7 - 10^9. We performed a full phase-curve analysis of the TESS data and detected both illumination- and ellipsoidal variations as well as Doppler boosting. HIP 65A is part of a binary stellar system, with HIP 65B separated by 269 AU (3.95 arcsec on sky). TOI-157b (TIC 140691463) is a typical hot Jupiter with a mass of 1.18 +/- 0.13 Mjup and a radius of 1.29 +/- 0.02 Rjup. It has a period of 2.08 days, which corresponds to a separation of just 0.03 AU. This makes TOI-157 an interesting system, as the host star is an evolved G9 sub-giant star (V=12.7). TOI-169b (TIC 183120439) is a bloated Jupiter orbiting a V=12.4 G-type star. It has a mass of 0.79 +/- 0.06 Mjup and a radius of 1.09 +0.08/-0.05 Rjup. Despite having the longest orbital period (P = 2.26 days) of the three planets, TOI-169b receives the most irradiation and is situated on the edge of the Neptune desert. All three host stars are metal rich with [Fe/H] ranging from 0.18 - 0.24.
CPS: Class-level 6D Pose and Shape Estimation From Monocular Images

Contemporary monocular 6D pose estimation methods can only cope with a handful of object instances. This naturally limits possible applications as, for instance, robots need to work with hundreds of different objects in a real environment. In this paper, we propose the first deep learning approach for class-wise monocular 6D pose estimation, coupled with metric shape retrieval. We propose a new loss formulation which directly optimizes over all parameters, i.e. 3D orientation, translation, scale and shape at the same time. Instead of decoupling each parameter, we transform the regressed shape, in the form of a point cloud, to 3D and directly measure its metric misalignment. We experimentally demonstrate that we can retrieve precise metric point clouds from a single image, which can also be further processed for e.g. subsequent rendering. Moreover, we show that our new 3D point cloud loss outperforms all baselines and gives overall good results despite the inherent ambiguity due to monocular data.
Frequency-Tuned Universal Adversarial Attacks

Researchers have shown that the predictions of a convolutional neural network (CNN) for an image set can be severely distorted by one single image-agnostic perturbation, or universal perturbation, usually with an empirically fixed threshold in the spatial domain to restrict its perceivability. However, by considering the human perception, we propose to adopt JND thresholds to guide the perceivability of universal adversarial perturbations. Based on this, we propose a frequency-tuned universal attack method to compute universal perturbations and show that our method can realize a good balance between perceivability and effectiveness in terms of fooling rate by adapting the perturbations to the local frequency content. Compared with existing universal adversarial attack techniques, our frequency-tuned attack method can achieve cutting-edge quantitative results. We demonstrate that our approach can significantly improve the performance of the baseline on both white-box and black-box attacks.
Line Bundle Hidden Sectors for Strongly Coupled Heterotic Standard Models

We review the compactification to five‐dimensional heterotic M‐theory on a Schoen Calabi–Yau threefold and the specific SU(4) vector bundle leading to the “heterotic standard model” in the observable sector. Within strongly coupled heterotic M‐theory, a formalism for consistent hidden‐sector bundles associated with a single line bundle is presented, and a specific line bundle is introduced as a concrete example. Anomaly cancellation and the associated bulk space five‐branes are discussed in this context, as is slope‐stability of both the observable and hidden sector bundles. The further compactification to a four‐dimensional effective theory on a linearized BPS double domain wall is then presented to order κ114/3 . Specifically, the generic constraints required for anomaly cancellation and the restrictions imposed by positive squared gauge couplings to order κ114/3 are presented in detail. Three additional constraints are imposed, one guaranteeing that the S1/Z2 orbifold length is sufficiently larger than the average Calabi–Yau radius, and two enforcing that the hidden sector be compatible with both the unification mass scale and unified gauge coupling of the SO(10) group in the observable sector. Finally, the expression for the Fayet–Iliopoulos term associated with an anomalous U(1) symmetry is presented and its role in N=1 supersymmetry in the low‐energy effective theory is discussed. It is shown that N=1 supersymmetry can be preserved by cancelling the tree‐level and genus‐one contributions against each another. As a check on our results, we calculate several quantities to order κ116/3 and show that they remain physically acceptable, and even improve, when computed to higher order.
The effect of optimism bias and governmental action on siltation management within Japanese reservoirs surveyed via artificial neural network

ABSTRACT Reservoirs are installed as long-term assets to guarantee water and energy security for decades, if not centuries. However, the effect of siltation undermines reservoirs’ sustainability because it significantly reduces the reservoirs’ original capacity. The present paper attempts to evaluate the global reservoir siltation problem with the optimism bias theorem introduced by Kahneman and Tversky and applied to infrastructural mega-projects by Flyvbjerg and Ansar using artificial neural networks (ANNs) algorithms for large Japanese reservoirs. Japan possesses suitable long-term data and a legal directive concerning the sediment capacity siltation duration, which serves as a valid guide to check whether, over the past 100 years, engineers, planners and managers were capable of judging the sediment input correctly. Various ANN models were established to emulate Japanese reservoir siltation behavior. The networks demonstrate that reservoirs in Japan suffer from optimism bias. In contrast to the law, the dead storage volume of an average dam is supposed to reach capacity after 52 years. This finding joins the overall observation that mega-projects generally and globally suffer from optimism bias. The emulations were subsequently screened for a presumed influence of governance actions, namely, indicating plus monitoring and the change in the market competition situation. While reservoir siltation appears to continue regardless of the level of competition in public procurement, monitoring directives appear to have a considerable impact on improved siltation management, which demonstrates that dedicated governance action can significantly strengthen the sustainable behavior of key infrastructure elements such as reservoirs.
Optimization of Transmitter-Side Signal Rotations in the Presence of Laser Phase Noise

The effects of transmitter-side multidimensional signal rotations on the performance of multichannel optical transmission are studied in the presence of laser phase noise. In particular, the laser phase noise is assumed to be uncorrelated between channels. To carry out this study, a simple multichannel laser-phase-noise model that has been experimentally validated for weakly-coupled multicore-fiber transmission is considered. As the considered rotation scheme is intended to work in conjunction with receiver-side carrier phase estimation (CPE), the model is modified to further assume that imperfect CPE has taken place, leaving residual phase noise in the processed signal. Based on this model, two receiver structures are derived and used to numerically optimize transmitter-side signal rotations through Monte Carlo simulations. For reasonable amounts of residual phase noise, rotations based on Hadamard matrices are found to be near-optimal for transmission of 4-D signals. Furthermore, Hadamard rotations can be performed for any dimension that is a power of two. By exploiting this property, an increase of up to 0.25 bit per complex symbol in an achievable information rate is observed for transmission of higher-order constellations.
CC2Vec: Distributed Representations of Code Changes

Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and uses multiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.
Deformation Flow Based Two-Stream Network for Lip Reading

Lip reading is the task of recognizing speech content by analyzing movements in the lip region when people are speaking. Based on the continuity in adjacent frames in the speaking process, and the consistency in motion patterns among different people when they pronounce the same phoneme, we model lip movements as a sequence of apparent deformations in the lip region during the speaking process. Specifically, we introduce a Deformation Flow Network (DFN) to learn the deformation flow between adjacent frames, which directly captures the motion information within the lip region. The learned deformation flow is then combined with the original grayscale frames with a two-stream network to perform lip reading. To make the two streams learn from each other in the learning process, we introduce a bidirectional knowledge distillation loss to train the two branches jointly. Owing to the complementary cues provided by different branches, the two-stream network shows substantial improvement over using either single branch. A thorough experimental evaluation on two large-scale lip reading benchmarks is presented with detailed analysis. The results accord with our motivation, and show that our method achieves state-of-the-art or comparable performance on these two challenging datasets.
An Inverted-U Impact of Environmental Regulations on Carbon Emissions in China’s Iron and Steel Industry: Mechanisms of Synergy and Innovation Effects

Based on a panel data of China’s iron and steel (IS) industry from 2000 to 2014, this paper explores the impact of environmental regulations on CO2 emissions in the industry. The results show that there is a clear inverted-U relationship between environmental regulations and CO2 emissions in the IS industry. Additionally, there are regional heterogeneity and regulatory intensity on the impact of environmental regulations on CO2 emissions. The results in the eastern region are consistent with the whole sample results, while the upward trend in the central region and the downward trend in the western region together lay the basis for the inverted-U shape of the whole sample. High environmental regulations affect CO2 emissions in an inverted-U shape, while low environmental regulations present a U shape. The mechanisms of environmental regulations affecting CO2 emissions are synergy effect and technological innovation effect. Finally, this paper proposes some policy recommendations according to the above findings.
Impacts of Excise Taxation on Non-Alcoholic Beverage Consumption in Vietnam

Vietnam is faced with the issue of increasing overweight and obesity, particularly among children and adolescents in urban areas. As a result, the government of Vietnam recently introduced a proposal to impose a special consumption tax on sugar-sweetened beverages (SSB) in Vietnam, as the drinks are causing negative health consequences for Vietnamese people. This research is aimed at evaluating the impacts of a 10% special consumption tax on SSB in Vietnam using the Almost Ideal Demand System (AIDS) model. We found that a 10% SSB tax will reduce SSB consumption by 11.4%. Consumers will switch to substitutes, leading to an increase in the consumption of milk by 2.3%, beer by 2.5%, dried tea by 2.2%, and wine by 1.7%. However, in the short run, the tax could lead to a decrease in consumer welfare due to higher SSB prices. In addition to people from better-off households, people from the ethnic majority group, most of which live in urban areas with a large number of children, have a relatively high welfare reduction.
A Precisely Xtreme-Multi Channel Hybrid Approach for Roman Urdu Sentiment Analysis

In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this article for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove. The integrity of generated neural word embeddings is evaluated using intrinsic and extrinsic evaluation approaches. Considering the lack of publicly available benchmark datasets, it provides a first-ever Roman Urdu public dataset which consists of 3241 sentiments annotated against positive, negative, and neutral classes. To provide benchmark baseline performance over the presented dataset for Roman Urdu sentiment analysis, we adapt diverse machine learning (Support Vector Machine, Logistic Regression, Naive Bayes), deep learning (convolutional neural network, recurrent neural network), and hybrid deep learning approaches. Performance impact of generated neural word embeddings based representation is compared with other most widely used bag of words based feature representation approaches using diverse machine and deep learning classifiers. In order to improve the performance of Roman Urdu sentiment analysis, it proposes a novel precisely extreme multi-channel hybrid methodology which makes use of convolutional and recurrent neural networks along with pre-trained neural word embeddings. The proposed hybrid approach outperforms adapted machine learning approaches by the significant figure of 9% and deep learning approaches by the figure of 4% in terms of F1-score.
Approximation of random diffusion equations by nonlocal diffusion equations in free boundary problems of one space dimension

We show how the Stefan type free boundary problem with random diffusion in one space dimension can be approximated by the corresponding free boundary problem with nonlocal diffusion. The approximation problem is a slightly modified version of the nonlocal diffusion problem with free boundaries considered in [J. Cao, Y. Du, F. Li and W. -T. Li, The dynamics of a Fisher–KPP nonlocal diffusion model with free boundaries, J. Functional Anal. 277 (2019) 2772–2814; C. Cortazar, F. Quiros and N. Wolanski, A nonlocal diffusion problem with a sharp free boundary, Interfaces Free Bound. 21 (2019) 441–462]. The proof relies on the introduction of several auxiliary free boundary problems and constructions of delicate upper and lower solutions for these problems. As usual, the approximation is achieved by choosing the kernel function in the nonlocal diffusion term of the form [Formula: see text] for small [Formula: see text], where [Formula: see text] has compact support. We also give an estimate of the error term of the approximation by some positive power of [Formula: see text].
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information

Recently, with the availability of cost-effective depth cameras coupled with real-time skeleton estimation, the interest in skeleton-based human action recognition is renewed. Most of the existing skeletal representation approaches use either the joint location or the dynamics model. Differing from the previous studies, we propose a new method called Denoising Autoencoder with Temporal and Categorical Constraints (DAE_CTC)} to study the skeletal representation in a view of skeleton reconstruction. Based on the concept of learning under privileged information, we integrate action categories and temporal coordinates into a stacked denoising autoencoder in the training phase, to preserve category and temporal feature, while learning the hidden representation from a skeleton. Thus, we are able to improve the discriminative validity of the hidden representation. In order to mitigate the variation resulting from temporary misalignment, a new method of temporal registration, called Locally-Warped Sequence Registration (LWSR), is proposed for registering the sequences of inter- and intra-class actions. We finally represent the sequences using a Fourier Temporal Pyramid (FTP) representation and perform classification using a combination of LWSR registration, FTP representation, and a linear Support Vector Machine (SVM). The experimental results on three action data sets, namely MSR-Action3D, UTKinect-Action, and Florence3D-Action, show that our proposal performs better than many existing methods and comparably to the state of the art.
The Usefulness of Search Results: A Systematization of Types and Predictors

In evaluating search systems there is a growing trend to complement the established effectiveness indicator topical relevance by the usefulness of search results. Usefulness refers to the contribution of search results to a larger task generating information search. This study analyses articles on interactive information retrieval which either predict the usefulness of documents retrieved or evaluate search systems by the usefulness of search results. The aim is to systematize the findings of these studies by categorizing the types of usefulness and their predictor types. Significant empirical associations between predictor types and usefulness are systematized, too. The data consists of articles in journals or conference proceedings focusing on the usefulness of search results either in web or database environment. The results indicate that there is a growing trend to complement topical relevance by the usefulness of search results in evaluating search systems. Search tasks typically instruct participants to search information for a writing task. Perceived usefulness of search results is the established measure in studies, although there are alternative measures in use. Significant associations between predictors and usefulness do not accumulate much but vary notably. Growth of knowledge on the usefulness of search results is based on the increasing number and variety of proposition supported empirically.
Development, Validation and Comparison of Artificial Neural Network Models and Logistic Regression Models Predicting Survival of Unresectable Pancreatic Cancer

Background: Prediction models for the overall survival of pancreatic cancer remain unsatisfactory. We aimed to explore artificial neural networks (ANNs) modeling to predict the survival of unresectable pancreatic cancer patients. Methods: Thirty-two clinical parameters were collected from 221 unresectable pancreatic cancer patients, and their prognostic ability was evaluated using univariate and multivariate logistic regression. ANN and logistic regression (LR) models were developed on a training group (168 patients), and the area under the ROC curve (AUC) was used for comparison of the ANN and LR models. The models were further tested on the testing group (53 patients), and k-statistics were used for accuracy comparison. Results: We built three ANN models, based on 3, 7, and 32 basic features, to predict 8 month survival. All 3 ANN models showed better performance, with AUCs significantly higher than those from the respective LR models (0.811 vs. 0.680, 0.844 vs. 0.722, 0.921 vs. 0.849, all p < 0.05). The ability of the ANN models to discriminate 8 month survival with higher accuracy than the respective LR models was further confirmed in 53 consecutive patients. Conclusion: We developed ANN models predicting the 8 month survival of unresectable pancreatic cancer patients. These models may help to optimize personalized patient management.
Fracturing and Near-Surface Diagenesis of a Silicified Miocene Deltaic Sequence: The Montjuïc Hill (Barcelona)

Near-surface diagenesis has been studied in the Langhian siliciclastic rocks of the Montjuic Hill (Barcelona Plain) by means of petrographical (optical and cathodoluminescence) and geochemical (electron microprobe, δ18O, δ13C, δ34S and 87Sr/86Sr) analyses. In the hill, these rocks are affected by strong silicification, but the same unit remains non-silicified at depth. The results reveal that fracturing took place after lithification and during uplift. Fracture cementation is clearly controlled by the previous diagenesis of the host rock. In non-silicified areas, cementation is dominated by calcite, which precipitated from meteoric waters. In silicified areas, fractures show multiepisodic cementation produced firstly by barite and secondly by silica, following the sequence opal, lussatite, chalcedony, and quartz. Barite precipitated only in fractures from the mixing of upflowing seawater and percolating meteoric fluids. The presence of silica stalactites, illuviation, and geopetal structures, and δ18O values indicate that silica precipitation occurred in the vadose regime from low-temperature percolating meteoric fluids, probably during a glacial period. Moreover, the presence of alunite suggests that silica cement formed under acidic conditions. Karst features (vugs and caverns), formed by arenisation, reveal that silica was derived from the dissolution of surrounding silicified host rocks.