Miracle-dz/UniFAR-FTUs huggingface dataset:
A new instability domain of CNO-flashing low-mass He-core stars on their early white-dwarf cooling branches

Context. Before reaching their quiescent terminal white-dwarf cooling branch, some low-mass helium-core white dwarf stellar models experience a number of nuclear flashes which greatly reduce their hydrogen envelopes. Just before the occurrence of each flash, stable hydrogen burning may be able to drive global pulsations that could be relevant in shedding some light on the internal structure of these stars through asteroseismology, similarly to what occurs with other classes of pulsating white dwarfs. Aims. We present a pulsational stability analysis applied to low-mass helium-core stars on their early white-dwarf cooling branches going through CNO flashes in order to study the possibility that the ε mechanism is able to excite gravity-mode pulsations. We assess the ranges of unstable periods and the corresponding instability domain in the log g − Teff plane. Methods. We carried out a nonadiabatic pulsation analysis for low-mass helium-core white-dwarf models with stellar masses between 0.2025 and 0.3630 M⊙ going through CNO flashes during their early cooling phases. Results. We found that the ε mechanism due to stable hydrogen burning can excite low-order (ℓ = 1, 2) gravity modes with periods between ∼80 and 500 s for stars with 0.2025 ≲ M⋆/M⊙ ≲ 0.3630 located in an extended region of the log g − Teff diagram, with effective temperature and surface gravity in the ranges 15 000 ≲ Teff ≲ 38 000 K and 5.8 ≲ log g ≲ 7.1, respectively. For the sequences that experience multiple CNO flashes, we found that with every consecutive flash, the region of instability becomes wider and the modes are more strongly excited. The magnitudes of the rate of period change for these modes are in the range of ∼10−10–10−11 [s/s]. Conclusions. Since the timescales required for these modes to reach amplitudes large enough to be observable are shorter than their corresponding evolutionary timescales, the detection of pulsations in these stars is feasible. Given the current problems in distinguishing some stars that populate the same region of the log g − Teff plane, the eventual detection of short-period pulsations may help in the classification of such stars. Furthermore, if a low-mass white dwarf star were found to pulsate with low-order gravity modes in this region of instability, it would confirm our result that such pulsations can be driven by the ε mechanism. In addition, confirming a rapid rate of period change in these pulsations would support the idea that these stars actually experience CNO flashes, as has been predicted by evolutionary calculations.
Automated coordination corrected enthalpies with AFLOW-CCE

The computational design of materials with ionic bonds poses a critical challenge to thermodynamic modeling since density functional theory yields inaccurate predictions of their formation enthalpies. Progress requires leveraging physically insightful correction methods. The recently introduced coordination corrected enthalpies (CCE) method delivers accurate formation enthalpies with mean absolute errors close to room temperature thermal energy, i.e., 25meV/atom. The CCE scheme, depending on the number of cation-anion bonds and oxidation state of the cation, requires an automated analysis of the system to determine and apply the correction. Here, we present AFLOW-CCE -- our implementation of CCE into the AFLOW framework for computational materials design. It features a command line tool, a web interface and a Python environment. The workflow includes a structural analysis, automatically determines oxidation numbers, and accounts for temperature effects by parametrizing vibrational contributions to the formation enthalpy per bond.
A review for Tone-mapping Operators on Wide Dynamic Range Image

The dynamic range of our normal life can exceeds 120 dB, however, the smart-phone cameras and the conventional digital cameras can only capture a dynamic range of 90 dB, which sometimes leads to loss of details for the recorded image. Now, some professional hardware applications and image fusion algorithms have been devised to take wide dynamic range (WDR), but unfortunately existing devices cannot display WDR image. Tone mapping (TM) thus becomes an essential step for exhibiting WDR image on our ordinary screens, which convert the WDR image into low dynamic range (LDR) image. More and more researchers are focusing on this topic, and give their efforts to design an excellent tone mapping operator (TMO), showing detailed images as the same as the perception that human eyes could receive. Therefore, it is important for us to know the history, development, and trend of TM before proposing a practicable TMO. In this paper, we present a comprehensive study of the most well-known TMOs, which divides TMOs into traditional and machine learning-based category.
More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource

Complex network infrastructure systems for power supply, communication, and transportation support our economic and social activities; however, they are extremely vulnerable to frequently increasing large disasters or attacks. Thus, the reconstruction of a damaged network is more advisable than an empirically performed recovery of the original vulnerable one. To reconstruct a sustainable network, we focus on enhancing loops so that they are not trees, which is made possible by node removal. Although this optimization corresponds with an intractable combinatorial problem, we propose self-healing methods based on enhancing loops when applying an approximate calculation inspired by statistical physics. We show that both higher robustness and efficiency are obtained in our proposed methods by saving the resources of links and ports when compared to ones in conventional healing methods. Moreover, the reconstructed network can become more tolerant than the original when some damaged links are reusable or compensated for as an investment of resource. These results present the potential of network reconstruction using self-healing with adaptive capacity in terms of resilience.
The FIP and Inverse-FIP Effects in Solar Flares

The inverse first ionization potential (FIP) effect, the depletion in the coronal abundance of elements like Fe, Mg, and Si that are ionized in the solar chromosphere relative to those that are neutral, has been identified in several solar flares. We give a more detailed discussion of the mechanism of fractionation by the ponderomotive force associated with magnetohydrodynamic waves, paying special attention to the conditions in which inverse-FIP fractionation arises in order to better understand its relation to the usual FIP effect, i.e., the enhancement of the coronal abundance of Fe, Mg, Si, etc. The FIP effect is generated by parallel propagating Alfvén waves, with either photospheric, or more likely coronal, origins. The inverse-FIP effect arises as upward-propagating fast-mode waves with an origin in the photosphere or below refract back downwards in the chromosphere where the Alfvén speed is increasing with altitude. We give a more physically motivated picture of the FIP fractionation, based on the wave refraction around inhomogeneities in the solar atmosphere, and inspired by previous discussions of analogous phenomena in the optical trapping of particles by laser beams. We apply these insights to modeling the fractionation and find good agreement with the observations of Katsuda et al. and Dennis et al.
Approximate Query Processing for Group-By Queries based on Conditional Generative Models

The Group-By query is an important kind of query, which is common and widely used in data warehouses, data analytics, and data visualization. Approximate query processing is an effective way to increase the querying efficiency on big data. The answer to a group-by query involves multiple values, which makes it difficult to provide sufficiently accurate estimations for all the groups. Stratified sampling improves the accuracy compared with the uniform sampling, but the samples chosen for some special queries cannot work for other queries. Online sampling chooses samples for the given query at query time, but it requires a long latency. Thus, it is a challenge to achieve both accuracy and efficiency at the same time. Facing such challenge, in this work, we propose a sample generation framework based on a conditional generative model. The sample generation framework can generate any number of samples for the given query without accessing the data. The proposed framework based on the lightweight model can be combined with stratified sampling and online aggregation to improve the estimation accuracy for group-by queries. The experimental results show that our proposed methods are both efficient and accurate.
Observations on the Bias of Nonnegative Mechanisms for Differential Privacy

We study two methods for differentially private analysis of bounded data and extend these to nonnegative queries. We first recall that for the Laplace mechanism, boundary inflated truncation (BIT) applied to nonnegative queries and truncation both lead to strictly positive bias. We then consider a generalization of BIT using translated ramp functions. We explicitly characterise the optimal function in this class for worst case bias. We show that applying any square-integrable post-processing function to a Laplace mechanism leads to a strictly positive maximal absolute bias. A corresponding result is also shown for a generalisation of truncation, which we refer to as restriction. We also briefly consider an alternative approach based on multiplicative mechanisms for positive data and show that, without additional restrictions, these mechanisms can lead to infinite bias.
Example-based Real-time Clothing Synthesis for Virtual Agents

We present a real-time cloth animation method for dressing virtual humans of various shapes and poses. Our approach formulates the clothing deformation as a high-dimensional function of body shape parameters and pose parameters. In order to accelerate the computation, our formulation factorizes the clothing deformation into two independent components: the deformation introduced by body pose variation (Clothing Pose Model) and the deformation from body shape variation (Clothing Shape Model). Furthermore, we sample and cluster the poses spanning the entire pose space and use those clusters to efficiently calculate the anchoring points. We also introduce a sensitivity-based distance measurement to both find nearby anchoring points and evaluate their contributions to the final animation. Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points. Compared to previous methods, our approach is general and able to add the shape dimension to any clothing pose model. %and therefore it is more general. Furthermore, we can animate clothing represented with tens of thousands of vertices at 50+ FPS on a CPU. Moreover, our example database is more representative and can be generated in parallel, and thereby saves the training time. We also conduct a user evaluation and show that our method can improve a user's perception of dressed virtual agents in an immersive virtual environment compared to a conventional linear blend skinning method.
VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency

We introduce a new approach for audio-visual speech separation. Given a video, the goal is to extract the speech associated with a face in spite of simultaneous back-ground sounds and/or other human speakers. Whereas existing methods focus on learning the alignment between the speaker’s lip movements and the sounds they generate, we propose to leverage the speaker’s face appearance as an additional prior to isolate the corresponding vocal qualities they are likely to produce. Our approach jointly learns audio-visual speech separation and cross-modal speaker embeddings from unlabeled video. It yields state-of-the-art results on five benchmark datasets for audio-visual speech separation and enhancement, and generalizes well to challenging real-world videos of diverse scenarios. Our video results and code: http://vision.cs. utexas.edu/projects/VisualVoice/.
Action Word Prediction for Neural Source Code Summarization

Source code summarization is the task of creating short, natural language descriptions of source code. Code summarization is the backbone of much software documentation such as JavaDocs, in which very brief comments such as "adds the customer object" help programmers quickly understand a snippet of code. In recent years, automatic code summarization has become a high value target of research, with approaches based on neural networks making rapid progress. However, as we will show in this paper, the production of good summaries relies on the production of the action word in those summaries: the meaning of the example above would be completely changed if "removes" were substituted for "adds. " In this paper, we advocate for a special emphasis on action word prediction as an important stepping stone problem towards better code summarization – current techniques try to predict the action word along with the whole summary, and yet action word prediction on its own is quite difficult. We show the value of the problem for code summaries, explore the performance of current baselines, and provide recommendations for future research.
Unsupervised Domain Adaptation of Black-Box Source Models

Unsupervised domain adaptation (UDA) aims to learn a model for unlabeled data on a target domain by transferring knowledge from a labeled source domain. In the traditional UDA setting, labeled source data are assumed to be available for the use of model adaptation. Due to the increasing concerns for data privacy, source-free UDA is highly appreciated as a new UDA setting, where only a trained source model is assumed to be available, while the labeled source data remain private. However, exposing details of the trained source model for UDA use is prone to easily committed white-box attacks, which brings severe risks to the source tasks themselves. To address this issue, we advocate studying a subtly different setting, named Black-Box Unsupervised Domain Adaptation (B2UDA), where only the input-output interface of the source model is accessible in UDA; in other words, the source model itself is kept as a black-box one. To tackle the B2UDA task, we propose a simple yet effective method, termed Iterative Noisy Label Learning (IterNLL). IterNLL starts with getting noisy labels of the unlabeled target data from the black-box source model. It then alternates between learning improved target models from the target subset with more reliable labels and updating the noisy target labels. Experiments on benchmark datasets confirm the efficacy of our proposed method. Notably, IterNLL performs comparably with methods of the traditional UDA setting where the labeled source data are fully available.
Differentially private depth functions and their associated medians

In this paper, we investigate the differentially private estimation of data depth functions and their associated medians. We start with several methods for privatizing depth values at a fixed point, and show that for some depth functions, when the depth is computed at an out of sample point, privacy can be gained for free when $n\rightarrow \infty$. We also present a method for privately estimating the vector of sample depth values, and show that privacy is not gained for free asymptotically. We also introduce estimation methods for depth-based medians for both depth functions with low global sensitivity and depth functions with only highly probably, low local sensitivity. We provide a general Theorem (Lemma 1) which can be used to prove consistency of an estimator produced by the exponential mechanism, provided the asymptotic cost function is uniquely minimized and is sufficiently smooth. We introduce a general algorithm to privately estimate minimizers of a cost function which has low local sensitivity, but high global sensitivity. This algorithm combines propose-test-release with the exponential mechanism. An application of this algorithm to generate consistent estimates of the projection depth-based median is presented. For these private depth-based medians, we show that it is possible for privacy to be free when $n\rightarrow \infty$.
Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

Proximity preserving and structural role-based node embeddings became a prime workhorse of applied graph mining. Novel node embedding techniques are repetitively tested on the same benchmark datasets which led to a range of methods with questionable performance gains. In this paper, we propose Twitch Gamers a new social network dataset with multiple potential target attributes. Our descriptive analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity-preserving and structural role-based node embedding algorithms.
Dynamic Graph Collaborative Filtering

Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Based on this assumption, many previous works focus on interaction sequences and learn evolutionary embeddings of users and items. However, we argue that sequence-based models are not able to capture collaborative information among users and items directly. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. We propose three update mechanisms: zero-order ‘inheritance’, first-order ‘propagation’, and second-order ‘aggregation’, to represent the impact on a user or item when a new interaction occurs. Based on them, we update related user and item embeddings simultaneously when interactions occur in turn, and then use the latest embeddings to make recommendations. Extensive experiments conducted on three public datasets show that DGCF significantly outperforms the state-of-the-art dynamic recommendation methods up to 30%. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.
High Solar Flux Concentration Water Splitting with Hematite (α‐Fe2O3) Photoanodes

© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. The water splitting performance of hematite photoanodes is explored under a wide range of light intensities. The photocurrent scales linearly and the photovoltage scales logarithmically with the intensity, showing steeper rate than in photovoltaics, probably due to the enhanced ratio between charge transfer and recombination at high intensities. Hence, light concentration serves as another handle to enhance the efficiency of hematite photoanodes.
A comparison of several kinetic models for the adsorption of gold cyanide onto activated carbon

SYNOPSIS Several rate models for the adsorption of gold cyanide from aqueous solution onto activated carbon are given in the literature. The purpose of this study is to critically examine their ability to simulate the adsorption process over a wide range of conditions. An account is given of a comprehensive set of experimental work on the kinetics of gold adsorption onto a coconutshell-based activated carbon. Equilibrium isotherms were also determined. It is concluded that the homogeneous surface-diffusion model fits the experimental data reasonably well and provides a good balance between simplicity and descriptive accuracy. The model contains two parameters: the extra-particle mass-transfer coefficient and an effective intraparticle surface-diffusion coefficient. Both can be evaluated readily from simple adsorption experiments. SAMEVATTING Daar word verskeie tempomodelle vir die adsorpsie van goudsianied uit 'n waterige oplossing op geaktiveerde koolstof in die literatuur aangegee. Die doel van hierdie studie is om hul vermoe aan die adsorpsieproses in 'n groot verskeidenheid omstandighede te simuleer, krities te ondersoek. Daar word verslag gedoen oor'n omvattende stel eksperimente in verband met die kinetika van goudadsorpsie op geaktiveerde koolstof wat van klapperdoppe b~rei is. Daar is ook ewewigsisoterme vasgestel. Die gevolgtrekking word gemaak dat die homogene oppervlakdiffusiemodel die eksperimentele data redelik goed pas en 'n goeie balans tussen eenvoudige en beskrywende akkuraatheid gee. Die model het twee parameters: die ekstrapartikel-massaoordragkoeffisient en 'n effektiewe intrapartikel-oppervlakdiffusiekoeffisient. Albei kan maklik aan die hand van eenvoudige adsorpsie-eksperimente ge-evalueer word.
Fabrication and characterization of transparent conductive Sn-doped β-Ga2O3 single crystal

Transparent conductive Sn-doped β-Ga2O3 single crystal with high crystallinity was successfully fabricated as a substrate for the growth of GaN-based compounds. Sn-doped β-Ga2O3 single crystals were grown using a floating zone (FZ) method, and the properties of electrical conductivity, optical transmittance, and crystallinity were characterized to optimize the growth condition. It was found that these properties are controlled by the Sn doping concentration, and the specimen for 32 ppm Sn-doped β-Ga2O3 single crystal was optimized to satisfy these properties simultaneously, i.e. the substrate with the internal optical transmittance of above ∼85% in the visible region, the electrical resistivity of 4.27×10–2 Ωcm, the carrier density of 2.26×1018 cm–3, and the FWHM of X-ray rocking curve of 43 arcsec was obtained. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Highly Kinked Uranyl Chromate Nitrate Layers in the Crystal Structures of A[(UO2)(CrO4)(NO3)] (A = K, Rb)

Two first uranyl chromate nitrates, K[(UO2)(CrO4)(NO3)] (1) and Rb[(UO2)(CrO4)(NO3)] (2), were prepared by solid-state reactions and characterized by electron microprobe analysis and single-crystal X-ray diffraction. The compounds are isotypic [1: monoclinic, P21/c, a = 9.881(5), b = 7.215(4), c = 14.226(6) A, β = 124.85(3)°, V = 832.3(7) A3; 2: monoclinic, P21/c, a = 9.804(1), b = 7.359(1), c = 14.269(1) A, β = 122.048(4), V = 872.6(1) A3]. The structures of 1 and 2 are based upon the complex [(UO2)(CrO4)(NO3)]– layers with unprecedented structural topology, which consist of the UrO3NO3 units linked through CrO4 tetrahedra. The resulted kinked layer can be divided into chains arranged in the ladder fashion. The layers are parallel to (100) and are linked by A+ (A = K, Rb) cations located between the layers.