Miracle-dz/UniFAR-FTUs huggingface dataset:
A NICER View of a Highly Absorbed Flare in GRS 1915+105

After 26 yr in outburst, the black hole X-ray binary GRS 1915+105 dimmed considerably in early 2018; its flux dropped sharply in mid-2019, and it has remained faint ever since. This faint period, the “obscured state,” is punctuated by occasional X-ray flares, many of which have been observed by NICER as part of our regular monitoring program. Here we present detailed time-resolved spectroscopy of one bright flare, whose spectrum shows evidence of high column density partial covering absorption and extremely deep absorption lines (equivalent widths over 100 eV in some cases). We study the time-dependent ionization of the obscuring gas with xstar, ultimately attributing the absorption to a radially stratified absorber of density ∼1012–1013 cm−3 at ∼few ×1011 cm from the black hole. We argue that a vertically extended outer disk could explain this obscuration. We discuss several scenarios to explain the obscured state, including massive outflows, an increase in the mass accretion rate, and changes in the outer disk that herald the approach of quiescence, but none are entirely satisfactory. Alternative explanations, such as obscuration by the accretion stream impact point, may be testable with current or future data.
A multiwavelength study of the dual nuclei in Mrk 212

We present radio observations of the galaxy merger remnant Mrk 212 with the Karl G. Jansky Very Large Array (VLA) and the upgraded Giant Meter Radio Telescope (uGMRT). Mrk 212 has two previously known radio sources associated with the two optical nuclei, S1 and S2, with a projected separation of ~6 kpc, making it a dual active galactic nuclei (AGN) candidate. Our new 15 GHz VLA observations reveal that S1 is a double radio source centred around the optical nucleus; its total extent is ~750 parsec and its average 1.4-8.5 GHz spectral index is -0.81 +/- 0.06. S1 therefore, resembles a compact symmetric object (CSO). The 15 GHz VLA image identifies the radio source at S2 to be a compact core. Our radio observations therefore strongly support the presence of a dual AGN in Mrk 212. The optical emission line flux ratios obtained from the Himalayan Chandra Telescope (HCT) observations however, show that S1 and S2 both fall in the AGN + SF (star formation) region of the BPT diagram. Weak AGN lying in the SF or AGN + SF intermediate regions in the BPT diagram have indeed been reported in the literature; our sources clearly fall in the same category. We find an extended radio structure in our newly reduced 8.5 GHz VLA data, that is offset by ~1" from the optical nucleus S2. New deep FUV and NUV observations with the Ultraviolet Imaging Telescope (UVIT) aboard AstroSat reveal SF knots around S2 as well as kpc-scale tidal tails; the SF knots around S2 coincide with the extended radio structure detected at 8.5 GHz. The radio spectral indices are consistent with SF. Any possible association with the AGN in S2 is unclear at this stage.
CompRess: Self-Supervised Learning by Compressing Representations

Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller models. As a result, the gap between supervised and self-supervised learning has been greatly reduced for larger models. In this work, instead of designing a new pseudo task for self-supervised learning, we develop a model compression method to compress an already learned, deep self-supervised model (teacher) to a smaller one (student). We train the student model so that it mimics the relative similarity between the data points in the teacher's embedding space. For AlexNet, our method outperforms all previous methods including the fully supervised model on ImageNet linear evaluation (59.0% compared to 56.5%) and on nearest neighbor evaluation (50.7% compared to 41.4%). To the best of our knowledge, this is the first time a self-supervised AlexNet has outperformed supervised one on ImageNet classification. Our code is available here: this https URL
Recovery of mammal diversity in tropical forests: a functional approach to measuring restoration

Ecological restoration is increasingly applied in tropical forests to mitigate biodiversity loss and recover ecosystem functions. In restoration ecology, functional richness, rather than species richness, often determines community assembly, and measures of functional diversity provide a mechanistic link between diversity and ecological functioning of restored habitat. Vertebrate animals are important for ecosystem functioning. Here, we examine the functional diversity of small‐to‐medium sized mammals to evaluate the diversity and functional recovery of tropical rainforest. We assess how mammal species diversity and composition and functional diversity and composition, vary along a restoration chronosequence from degraded pasture to “old‐growth” tropical rainforest in the Wet Tropics of Australia. Species richness, diversity, evenness, and abundance did not vary, but total mammal biomass and mean species body mass increased with restoration age. Species composition in restoration forests converged on the composition of old‐growth rainforest and diverged from pasture with increasing restoration age. Functional metrics provided a clearer pattern of recovery than traditional species metrics, with most functional metrics significantly increasing with restoration age when taxonomic‐based metrics did not. Functional evenness and dispersion increased significantly with restoration age, suggesting that niche complementarity enhances species' abundances in restored sites. The change in community composition represented a functional shift from invasive, herbivorous, terrestrial habitat generalists and open environment specialists in pasture and young restoration sites, to predominantly endemic, folivorous, arboreal, and fossorial forest species in older restoration sites. This shift has positive implications for conservation and demonstrates the potential of tropical forest restoration to recover rainforest‐like, diverse faunal communities.
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets

To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth and width provides us a Rubik's cube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions. This paper aims to explore the twisting rules for obtaining deep neural networks with minimum model sizes and computational costs. Different from the network enlarging, we observe that resolution and depth are more important than width for tiny networks. Therefore, the original method, i.e., the compound scaling in EfficientNet is no longer suitable. To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint. Experimental results on the ImageNet benchmark illustrate that our TinyNet performs much better than the smaller version of EfficientNets using the inversed giant formula. For instance, our TinyNet-E achieves a 59.9% Top-1 accuracy with only 24M FLOPs, which is about 1.9% higher than that of the previous best MobileNetV3 with similar computational cost. Code will be available at this https URL, and this https URL.
Soil Nitrogen Storage, Distribution, and Associated Controlling Factors in the Northeast Tibetan Plateau Shrublands

Although the soils in the Tibetan Plateau shrublands store large amounts of total nitrogen (N), the estimated values remain uncertain because of spatial heterogeneity and a lack of field observations. In this study, we quantified the regional soil N storage, spatial and vertical density distributions, and related climatic controls using 183 soil profiles sampled from 61 sites across the Northeast Tibetan Plateau shrublands during the period of 2011–2013. Our analysis revealed a soil N storage value of 132.40 Tg at a depth of 100 cm, with an average density of 1.21 kg m−2. Soil N density was distributed at greater levels in alpine shrublands, compared with desert shrublands. Spatially, soil N densities decreased from south to north and from east to west, and, vertically, the soil N in the upper 30 and 50 cm accounted for 42% and 64% of the total soil N stocks in the Tibetan Plateau. However, compared with desert shrublands, the surface layers in alpine shrublands exhibited a larger distribution of soil N stocks. Overall, the soil N density in the top 30 cm increased significantly with the mean annual precipitation (MAP) and tended to decrease with the mean annual temperature (MAT), although the dominant climatic controls differed among shrubland types. Specifically, MAP in alpine shrublands, and MAT in desert shrubland, had a weak effect on N density. Soil pH can significant affect soil N density in the Tibetan Plateau shrublands. In conclusion, changes in soil N density should be monitored over the long term to provide accurate information about the effects of climatic factors.
The JAGWAR Prowls LIGO/Virgo O3 Paper I: Radio Search of a Possible Multimessenger Counterpart of the Binary Black Hole Merger Candidate S191216ap

We present a sensitive search with the Karl G. Jansky Very Large Array for the radio counterpart of the gravitational wave candidate S191216ap, which is classified as a binary black hole merger and suggested to be a possible multimessenger event, based on the detection of a high-energy neutrino and a TeV photon. We carried out a blind search at C band (4–8 GHz) over 0.3 deg2 of the gamma-ray counterpart of S191216ap reported by the High-Altitude Water Cerenkov Observatory (HAWC). Our search, spanning three epochs over 130 days of postmerger and having a mean source-detection threshold of 75 μJy beam−1 (4σ), yielded five variable sources associated with active galactic nucleus activity and no definitive counterpart of S191216ap. We find <2% (3.0% ± 1.3%) of the persistent radio sources at 6 GHz to be variable on a timescale of <1 week (week–months), consistent with previous radio variability studies. Our 4σ radio luminosity upper limit of ∼1.2 × 1028 erg s−1 Hz−1 on the afterglow of S191216ap, within the HAWC error region, is 5–10 times deeper than previous binary black hole (BBH) radio afterglow searches. Comparing this upper limit with theoretical expectations given by Perna et al. for putative jets launched by BBH mergers, for on-axis jets with energy ≃1049 erg, we can rule out jet opening angles ≲ 20° (assuming that the counterpart lies within the 1σ HAWC region that we observed).
The UCSC Xena platform for public and private cancer genomics data visualization and interpretation

UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.
Fighting Copycat Agents in Behavioral Cloning from Observation Histories

Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect of misattributing expert actions to nuisance correlates among the observed variables. We observe that a common instance of this causal confusion occurs in partially observed settings when expert actions are strongly correlated over time: the imitator learns to cheat by predicting the expert's previous action, rather than the next action. To combat this "copycat problem", we propose an adversarial approach to learn a feature representation that removes excess information about the previous expert action nuisance correlate, while retaining the information necessary to predict the next action. In our experiments, our approach improves performance significantly across a variety of partially observed imitation learning tasks.
Semi-inclusive charged-current neutrino-nucleus cross sections in the relativistic plane-wave impulse approximation

Neutrino-nucleus quasielastic scattering is studied in the plane wave impulse approximation for three nuclear models: the relativistic Fermi gas (RFG), the independent-particle shell model (IPSM) and the natural orbitals (NO) model with Lorentzian dependence of the excitation energy. A complete study of the kinematics of the semi-inclusive process and the associated cross sections are presented and discussed for 40 Ar and 12 C. Inclusive cross sections are also obtained by integrating the semi-inclusive expressions over the outgoing hadron. Results are consistent with previous studies restricted to the inclusive channel. In particular, a comparison with the analytical results for the RFG model is performed. Explicit expressions for the hadronic tensor and the 10 semi-inclusive nuclear responses are given. Theoretical predictions are compared with semi-inclusive experimental data from T2K experiment.
Dependency Smells in JavaScript Projects

Dependency management in modern software development poses many challenges for developers who wish to stay up to date with the latest features and fixes whilst ensuring backwards compatibility. Project maintainers have opted for varied, and sometimes conflicting, approaches for maintaining their dependencies. Opting for unsuitable approaches can introduce bugs and vulnerabilities into the project, introduce breaking changes, cause extraneous installations, and reduce dependency understandability, making it harder for others to contribute effectively. In this paper, we empirically examine evidence of recurring dependency management issues (dependency smells). We look at the commit data for a dataset of 1,146 active JavaScript repositories to catalog, quantify and understand dependency smells. Through a series of surveys with practitioners, we identify and quantify seven dependency smells with varying degrees of popularity and investigate why they are introduced throughout project history. Our findings indicate that dependency smells are prevalent in JavaScript projects with two or more distinct smells appearing in 80 percent of the projects, but they generally infect a minority of a project’s dependencies. Our observations show that the number of dependency smells tend to increase over time. Practitioners agree that dependency smells bring about many problems including security threats, bugs, dependency breakage, runtime errors, and other maintenance issues. These smells are generally introduced as developers react to dependency misbehaviour and the shortcomings of the npm ecosystem.
On an Induced Distribution and its Statistical Properties.

In this study an attempt has been made to propose a way to develop new distribution. For this purpose, we need only idea about distribution function. Some important statistical properties of the new distribution like moments, cumulants, hazard and survival function has been derived. The renyi entropy, shannon entropy has been obtained. Also ML estimate of parameter of the distribution is obtained, that is not closed form. Therefore, numerical technique is used to estimate the parameter. Some real data sets are used to check the suitability of this distribution over some other existing distributions such as Lindley, Garima, Shanker and many more. AIC, BIC, -2loglikihood, K-S test suggest the proposed distribution works better than others distributions considered in this study.
DisenE: Disentangling Knowledge Graph Embeddings

Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently. However, the existing research is mainly based on the black-box neural models, which makes it difficult to interpret the learned representation. In this paper, we introduce DisenE, an end-to-end framework to learn disentangled knowledge graph embeddings. Specially, we introduce an attention-based mechanism that enables the model to explicitly focus on relevant components of entity embeddings according to a given relation. Furthermore, we introduce two novel regularizers to encourage each component of the entity representation to independently reflect an isolated semantic aspect. Experimental results demonstrate that our proposed DisenE investigates a perspective to address the interpretability of KGE and is proved to be an effective way to improve the performance of link prediction tasks.
Exogenous Proline and Glycinebetaine Mitigate the Detrimental Effect of Salt Stress on Rice Plants

Exogenous application of proline and glycinebetaine (hereafter betaine) is often regarded as a shotgun approach for the protection and survival of plants under abiotic stresses including salinity. Here, we investigated effects of proline and betaine on hydrogen peroxide (H 2 O 2 ), malondialdehyde (MDA) and transpiration rate in salt-stressed rice plants. Generally, salt stress increased H 2 O 2 and lipid peroxidation as indicated by MDA content and decreased transpiration rate in rice plants. The exogenous application of proline and betaine decrease H 2 O 2 and MDA contents and increase transpiration rate in salt-stressed rice plants. It is suggested that exogenous proline and betaine mitigate the detrimental effects of salt stress by reducing H 2 O 2 and lipid peroxidation levels and by increasing transpiration rate in rice plants.
Generative Adversarial Networks in Human Emotion Synthesis: A Review

Deep generative models have become an emerging topic in various research areas like computer vision and signal processing. These models allow synthesizing realistic data samples that are of great value for both academic and industrial communities. Affective computing, a topic of a broad interest in computer vision society, has been no exception and has benefited from this powerful approach. In fact, affective computing observed a rapid derivation of generative models during the last two decades. Applications of such models include but are not limited to emotion recognition and classification, unimodal emotion synthesis, and cross-modal emotion synthesis. As a result, we conducted a comprehensive survey of recent advances in human emotion synthesis by studying available databases, advantages, and disadvantages of the generative models along with the related training strategies considering two principal human communication modalities, namely audio and video. In this context, facial expression synthesis, speech emotion synthesis, and the audio-visual (cross-modal) emotion synthesis are reviewed extensively under different application scenarios. Gradually, we discuss open research problems to push the boundaries of this research area for future works. As conclusions, we indicate common problems that can be explored from the Generative Adversarial Networks (GAN) topologies and applications in emotion synthesis.
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations and update shared state that represents a jointly learned (global) model. We consider big data analysis where training data is distributed among local data sets -- and we wish to move SGD computations to local compute nodes where local data resides. The results of these local SGD computations are aggregated by a central "aggregator" which mimics Hogwild! We show how local compute nodes can start choosing small mini-batch sizes which increase to larger ones in order to reduce communication cost (round interaction with the aggregator). We prove a tight and novel non-trivial convergence analysis for strongly convex problems which does not use the bounded gradient assumption as seen in many existing publications. The tightness is a consequence of our proofs for lower and upper bounds of the convergence rate, which show a constant factor difference. We show experimental results for plain convex and non-convex problems for biased and unbiased local data sets.
Development and survivorship of Scirtothrips dorsalis hood (Thysanoptera: Thripidae) in different growth stages of mango and selected weeds.

The research objective was to quantify the development and survivorship rate of S. dorsalis in different phenological stages of mango and selected weeds. The research was conducted in the laboratory of PT. Trigatra Rajasa, Mango plantation in Ketowan, Arjasa, Situbondo, East Java, Indonesia from February to September 2015. The development and survivorship rate were done through observation of life span of S. dorsalis from egg to pupa. Analysis of Variance and Duncan Multiple Range Test (p = 0.05) with 5 replications were applied to ensure the significant differences among the treatments. The result showed that development and survivorship of Scirtothrips dorsalis were supported by mango flushes and flower as well as some weeds such as Leucania leucochepala , Ipomoea triloba , Achalypha indica , Desmanthus leptophyllus and Azadirachta indica as source of food. Achalypha indica was the most suitable host with development time (12.82 ± 0.21 days) and survivorship (33 %). Weed Tridax procumbent , Momordica charantia and Mimosa pudica were unable to provide the living requirement for immature developmental stage of S. dorsalis.
Effects of Salicylicacid on the Ideotype and Activities of Antioxidant Enzymes of Narcissus Ttazetta L.Var.Chinensis Roem.

以不同浓度的水杨酸溶液对水仙叶片进行喷雾处理,发现不同浓度的水杨酸均能起到矮化植株和提高叶、根及花瓣中超氧化物歧化酶(SOD)和过氧化氢酶(CAT)的活力,降低过氧化物酶(POD)的活性和丙二醛(MDA)含量,质膜透性(PMP)降低,花期有不同程度的延长,其中以3 mmol/L处理效果较佳.