Miracle-dz/UniFAR-FTUs huggingface dataset:
HST/NICMOS observations of a proto-brown dwarf candidate

We present deep HST/NICMOS observations peering through the outflow cavity of the protostellar candidate IRAS 04381+2540 in the Taurus Molecular Cloud-1. A young stellar object as central source, a jet and a very faint and close (0.6 �� ) companion are identified. The primary and the companion have similar colours, consistent with strong reddening. We argue that the companion is neither a shock-excited knot nor a background star. The colour/magnitude information predicts a substellar upper mass limit for the companion, but the final confirmation will require spectroscopic information. Because of its geometry, young age and its rare low-mass companion, this system is likely to provide a unique insight into the formation of brown dwarfs.
Mixing and B s → μ + μ − Decay in the NMSSM with the Flavour Expansion Theorem

In this paper, motivated by the observation that the Standard Model predictions are now above the experimental data for the mass difference ∆Ms(d), we perform a detailed study of Bs(d) − B̄s(d) mixing and Bs → μ+μ− decay in the Z3-invariant NMSSM with nonminimal flavour violation, using the recently developed procedure based on the Flavour Expansion Theorem, with which one can perform a purely algebraic mass-insertion expansion of an amplitude written in the mass eigenstate basis without performing diagrammatic calculations in the interaction/flavour basis. Specifically, we consider finite orders of mass insertions for neutralinos but general orders for squarks and charginos, under two sets of assumptions for the squark flavour structures (i.e., while the flavour-conserving off-diagonal element δLR 33 is kept in both cases, only the flavour-violating off-diagonal elements δLL 23 and δ RR i3 (i = 1, 2) are kept in cases I and II, respectively). Our analytic results are then expressed directly in terms of the initial Lagrangian parameters in the interaction/flavour basis, making it easy to impose experimental bounds on them. It is found numerically that the NMSSM effects in both cases can accommodate the observed deviation for ∆Ms(d), while complying with the experimental constraints from the branching ratios of Bs → μ+μ− and B → Xsγ decays. ∗huquanyi@mail.ccnu. edu.cn †xqli@mail.ccnu. edu.cn ‡yangyd@mail.ccnu. edu.cn §zhengmindi@mails.ccnu. edu.cn 1 ar X iv :1 90 3. 06 92 7v 1 [ he pph ] 1 6 M ar 2 01 9
A New Test for Market Efficiency and Uncovered Interest Parity

We suggest a new single-equation test for Uncovered Interest Parity (UIP) based on a dynamic regression approach. The method provides consistent and asymptotically efficient parameter estimates, and is not dependent on assumptions of strict exogeneity. This new approach is asymptotically more efficient than the common approach of using OLS with HAC robust standard errors in the static forward premium regression. The coefficient estimates when spot return changes are regressed on the forward premium are all positive and remarkably stable across currencies. These estimates are considerably larger than those of previous studies, which frequently find negative coefficients. The method also has the advantage of showing dynamic effects of risk premia, or other events that may lead to rejection of UIP or the efficient markets hypothesis.
CircleSnake: Instance Segmentation with Circle Representation

Circle representation has recently been introduced as a medical imaging optimized representation for more effective instance object detection on ball-shaped medical objects. With its superior performance on instance detection, it is appealing to extend the circle representation to instance medical object segmentation. In this work, we propose CircleSnake, a simple end-to-end circle contour deformation-based segmentation method for ball-shaped medical objects. Compared to the prevalent DeepSnake method, our contribution is three-fold: (1) We replace the complicated bounding box to octagon contour transformation with a computation-free and consistent bounding circle to circle contour adaption for segmenting ball-shaped medical objects; (2) Circle representation has fewer degrees of freedom (DoF=2) as compared with the octagon representation (DoF=8), thus yielding a more robust segmentation performance and better rotation consistency; (3) To the best of our knowledge, the proposed CircleSnake method is the first end-to-end circle representation deep segmentation pipeline method with consistent circle detection, circle contour proposal, and circular convolution. The key innovation is to integrate the circular graph convolution with circle detection into an end-to-end instance segmentation framework, enabled by the proposed simple and consistent circle contour representation. Glomeruli are used to evaluate the performance of the benchmarks. From the results, CircleSnake increases the average precision of glomerular detection from 0.559 to 0.614. The Dice score increased from 0.804 to 0.849. The code has been released: https://github.com/hrlblab/CircleSnake
Multipartite entanglement detection based on generalized state-dependent entropic uncertainty relation for multiple measurements

We present the generalized state-dependent entropic uncertainty relations in multiple measurements setting, and the optimal lower bound is obtained by considering different measurement sequences. We then apply this uncertainty relation to witness entanglement, and give the experimentally accessible lower bounds on both bipartite and tripartite entanglements. This method of detecting entanglement is applied to a physical system of two particles on a one-dimensional lattice, and GHZ-Werner state. It is shown that, for measurements that are not in mutually unbiased bases, this new entropic uncertainty relation is superior to the previous state-independent one in entanglement detection. Furthermore, we conduct an online experiment to detect multipartite entanglement of GHZ states up to 10 qubits on Quafu cloud quantum computation platform. Our results might play important roles in detecting multipartite entanglement experimentally.
A Transformer-based Framework for POI-level Social Post Geolocation

POI-level geo-information of social posts is critical to many location-based applications and services. However, the multi-modality, complexity and diverse nature of social media data and their platforms limit the performance of inferring such fine-grained locations and their subsequent applications. To address this issue, we present a transformer-based general framework, which builds upon pre-trained language models and considers non-textual data, for social post geolocation at the POI level. To this end, inputs are categorized to handle different social data, and an optimal combination strategy is provided for feature representations. Moreover, a uniform representation of hierarchy is proposed to learn temporal information, and a concatenated version of encodings is employed to capture feature-wise positions better. Experimental results on various social datasets demonstrate that three variants of our proposed framework outperform multiple state-of-art baselines by a large margin in terms of accuracy and distance error metrics.
Autoregressive GAN for Semantic Unconditional Head Motion Generation

In this work, we address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space from a single reference pose. Different from traditional audio-conditioned talking head generation that seldom puts emphasis on realistic head motions, we devise a GAN-based architecture that learns to synthesize rich head motion sequences over long duration while maintaining low error accumulation levels. In particular, the autoregressive generation of incremental outputs ensures smooth trajectories, while a multi-scale discriminator on input pairs drives generation toward better handling of high- and low-frequency signals and less mode collapse. We experimentally demonstrate the relevance of the proposed method and show its superiority compared to models that attained state-of-the-art performances on similar tasks.
Instance-Dependent Generalization Bounds via Optimal Transport

Existing generalization bounds fail to explain crucial factors that drive the generalization of modern neural networks. Since such bounds often hold uniformly over all parameters, they suffer from over-parametrization and fail to account for the strong inductive bias of initialization and stochastic gradient descent. As an alternative, we propose a novel optimal transport interpretation of the generalization problem. This allows us to derive instance-dependent generalization bounds that depend on the local Lipschitz regularity of the learned prediction function in the data space. Therefore, our bounds are agnostic to the parametrization of the model and work well when the number of training samples is much smaller than the number of parameters. With small modifications, our approach yields accelerated rates for data on low-dimensional manifolds and guarantees under distribution shifts. We empirically analyze our generalization bounds for neural networks, showing that the bound values are meaningful and capture the effect of popular regularization methods during training.
It's DONE: Direct ONE-shot learning with quantile weight imprinting

Learning a new concept from one example is a superior function of the human brain and it is drawing attention in the field of machine learning as a one-shot learning task. In this paper, we propose one of the simplest methods for this task with a nonparametric weight imprinting, named Direct ONE-shot learning (DONE). DONE adds new classes to a pretrained deep neural network (DNN) classifier with neither training optimization nor pretrained-DNN modification. DONE is inspired by Hebbian theory and directly uses the neural activity input of the final dense layer obtained from data that belongs to the new additional class as the synaptic weight with a newly-provided-output neuron for the new class, transforming all statistical properties of the neural activity into those of synaptic weight by quantile normalization. DONE requires just one inference for learning a new concept and its procedure is simple, deterministic, not requiring parameter tuning and hyperparameters. DONE overcomes a severe problem of existing weight imprinting methods that DNN-dependently interfere with the classification of original-class images. The performance of DONE depends entirely on the pretrained DNN model used as a backbone model, and we confirmed that DONE with current well-trained backbone models perform at a decent accuracy.
VLT+UVES Spectroscopy of the CaII LoBAL Quasar SDSS J030000.56+004828.0

We study high-resolution spectra of the ‘overlapping-trough’ low-ionization broad absorption line (LoBAL) quasar SDSS J030000.56+004828.0. The Ca II , Mg II and Mg I column densities in this object are the largest reported to date for any BAL outﬂow. The broad Ca II absorption is mildly blended, but the blending can be disentangled to measure the Ca II column density, which is large enough that the outﬂow must include a strong hydrogen ionization front. The outﬂow begins at a blueshift of ∼ 1650km s − 1 from the systemic redshift. The lowest velocity BAL region produces strong Ca II absorption but does not produce signiﬁcant excited Fe II absorption, while the higher velocity excited Fe II absorption region produces very little Ca II absorption. We have found that only a disk wind outﬂow can explain this segregation. Whether the outﬂow is smooth or clumpy, we conclude that the Ca II BAL region has a density high enough to populate excited levels of Fe II , but a temperature low enough to prevent them from being signiﬁcantly populated. This requirement means the Ca II BAL region has T . 1100 K, and perhaps even T . 550 K. This quasar also has an associated absorption line system (AAL) that exhibits partial covering, and therefore is likely located near the central engine. Its association with the BAL outﬂow is unclear. Blending of the AAL with the BAL trough shows that the spatial region covered by the BAL outﬂow can vary over velocity diﬀerences of ∼ 1700 km s − 1
Leadership for inclusive online learning in public primary schools during COVID-19: A multiple case study in Hong Kong

Despite the increasing number of studies on educational leadership during COVID-19, little attention has been paid to the intersections of different educational experiences and perspectives of school leaders, students, and their families that occur both inside and outside of schools. Drawing on eight case studies of public primary schools in Hong Kong, this article explores the challenges and strategies of online learning with a focus on effective leadership practices for supporting economically disadvantaged students during COVID-19. To incorporate the perspectives of multiple stakeholders, a series of questionnaires were distributed to principals (n = 8), teachers (n = 150), parents (n = 775), lower primary students (n = 855), and upper primary students (n = 850) and interviews were conducted with principals (n = 8), teachers (n = 37), parents (n = 32), and students (n = 62). Findings indicated that school leaders and teacher teams demonstrated strategic leadership practices prioritising and responding to the needs and constraints of economically disadvantaged students and their families. The present study draws attention to leadership encouraging collaboration among schools, families, and the wider community, which has become necessary during the shift to online learning during COVID-19.
Long-term inorganic nitrate administration protects against ovariectomy-induced osteoporosis in rats

The risk of osteoporotic fractures increases in women after menopause. This study aims at determining the effects of long-term inorganic nitrate administration against ovariectomy-induced osteoporosis in rats. Rats were divided into 4 groups (n=6/group): Control, control+nitrate, ovariectomized (OVX), and OVX+nitrate. Sodium nitrate (100 mg/L in drinking water) was administered for 9 months. Trabecular bone quality in the proximal tibia was measured using a Micro-Computed Tomography (micro-CT) scanner at months 0, 1, 3, and 9. Levels of nitric oxide (NO) metabolites (NOx) and oxidative stress indices, and mRNA expression of endothelial NO synthase (eNOS) were measured at month 9 in the proximal tibia. Compared to controls, OVX rats had lower NOx levels by 47 %, eNOS mRNA expression by 55 %, catalase activity (CAT) by 45 %, total antioxidant capacity (TAC) by 70 %, and higher malondialdehyde (MDA) levels by 327 % in the bone tissue at month 9. OVX rats, compared to controls, had lower bone volume/tissue volume (BV/TV), trabecular number (Tb.N.), and trabecular thickness (Tb.Th.) by 32 %, 58 %, and 17 %, respectively, and higher trabecular separation (Tb.Sp.) by 123 %, at month 9. Nitrate administration to control rats increased TAC by 46 % in the bone tissue at month 9 but did not significantly affect other parameters in serum and bone tissue. Nitrate in OVX rats significantly increased NOx levels by 86 %, eNOS expression by 2.14-fold, CAT activity by 75 %, TAC by 170 %, and decreased MDA levels by 36 % at month 9 in the bone tissue. Nitrate-treated OVX rats at month 9 had higher BV/TV (42 %) and Tb.N. (61 %) and lower Tb.Sp. (15 %). Long-term inorganic nitrate administration at a low dose has protective effects against OVX-induced osteoporosis in rats; this effect is associated with increasing eNOS-derived NO and decreasing oxidative stress in the bone tissue.
Serendipitous Discovery of RR Lyrae Stars in the Leo V Ultra-faint Galaxy

During the analysis of RR Lyrae stars (RRLs) discovered in the High Cadence Transient Survey (HiTS) taken with the Dark Energy Camera at the 4 m telescope at Cerro Tololo Inter-American Observatory, we found a group of three very distant, fundamental mode pulsator RR Lyrae (type ab). The location of these stars agrees with them belonging to the Leo V ultra-faint satellite galaxy, for which no variable stars have been reported to date. The heliocentric distance derived for Leo V based on these stars is 173 ± 5 kpc. The pulsational properties (amplitudes and periods) of these stars locate them within the locus of the Oosterhoff II group, similar to most other ultra-faint galaxies with known RRLs. This serendipitous discovery shows that distant RRLs may be used to search for unknown faint stellar systems in the outskirts of the Milky Way.
How has the COVID Crisis Impacted Local Governments’ Sustainability Efforts? An Examination of Initial Effects

Although many U.S. municipalities have adopted climate protection and sustainability as explicit objectives, they are not among their traditional responsibilities. As a result, compared to policies focused around core functions, those related to sustainability may be at greater risk of retrenchment or change in times of crisis. This research examines how the COVID-19 pandemic has impacted local governments’ sustainability efforts. Using data from a nation-wide survey, we examine the degree to which the pandemic has affected programmatic priorities, resources, and operations related to sustainability. Findings indicate that the pandemic hurt the implementation of sustainability initiatives in almost half of U.S. cities. At the same time, many cities increased the priority of economic and social sustainability initiatives in response to the pandemic. Cities which have formally included sustainability principles into a city plan appear more sensitive to COVID-induced challenges to their program operations.
Constraints on Extended Quintessence from High-Redshift Supernovae

We obtain constraints on quintessence models from magnitude-redshift measurements of 176 Type Ia supernovae. The considered quintessence models are ordinary quintessence with Ratra-Peebles or supergravity (SUGRA) potentials, and extended quintessence with a Ratra-Peebles potential. We compute confidence regions in the Ωm0-α plane and find that for SUGRA potentials it is not possible to obtain useful constraints on these parameters; for the Ratra-Peebles case, for both the extended and ordinary quintessence we find α ≲ 0.8 at the 1 σ level. We also consider simulated data sets for the Supernova/Acceleration Probe (SNAP) satellite for the same models. Again, for a SUGRA potential it is not possible to obtain constraints on α, while with a Ratra-Peebles potential, its value is determined, with an error ≲0.6. We evaluate the inaccuracy made by approximating the time evolution of the equation of state with a linear or constant w(z) instead of using its exact redshift evolution. Finally, we discuss the effects of different systematic errors in the determination of quintessence parameters.
Stellar winds and coronae of low-mass Population II/III stars

We investigated stellar winds from zero/low-metallicity low-mass stars by magnetohydrodynamical simulations for stellar winds driven by \Alfven waves from stars with mass $M_{\star}=(0.6-0.8)M_{\odot}$ and metallicity $Z=(0-1)Z_{\odot}$, where $M_{\odot}$ and $Z_{\odot}$ are the solar mass and metallicity, respectively. \Alfvenic waves, which are excited by the surface convection, travel upward from the photosphere and heat up the corona by their dissipation. For lower $Z$, denser gas can be heated up to the coronal temperature because of the inefficient radiation cooling. The coronal density of Pop.II/III stars with $Z\le 0.01Z_{\odot}$ is 1-2 orders of magnitude larger than that of the solar-metallicity star with the same mass, and as a result, the mass loss rate, $\dot{M}$, is $(4.5-20)$ times larger. This indicates that metal accretion on low-mass Pop.III stars is negligible. The soft X-ray flux of the Pop.II/III stars is also expected to be $\approx (1-30)$ times larger than that of the solar-metallicity counterpart owing to the larger coronal density, even though the radiation cooling efficiency is smaller. A larger fraction of the input \Alfvenic wave energy is transmitted to the corona in low $Z$ stars because they avoid severe reflection owing to the smaller density difference between the photosphere and the corona. Therefore, a larger fraction is converted to the thermal energy of the corona and the kinetic energy of the stellar wind. From this energetics argument, we finally derived a scaling of $\dot{M}$ as $\dot{M}\propto L R_{\star}^{11/9}M_{\star}^{-10/9}T_{\rm eff}^{11/2}\left[\max (Z/Z_{\odot},0.01)\right]^{-1/5}$, where $L$, $R_{\star}$, and $T_{\rm eff}$ are stellar luminosity, radius, and effective temperature, respectively.
Association between expression of ZBP1, AIM2, and MDA5 genes and severity of COVID-19

Antiviral and inflammatory responses following the detection of the virus genome by nucleic acid sensors play a vital role in the pathogenesis and outcome of diseases. In this study, we investigated the ZBP1, AIM2, and MDA5 expression levels in COVID-19 patients with different intensities of the disease. 75 quantitative Real-Time PCR (qRT-PCR)-confirmed COVID-19 patients were included consecutively and divided into 3 groups of mild, severe, and critical based on the severity of the disease. Also, 25 healthy volunteer subjects were included. PBMCs were collected from the whole blood, and RNA was extracted using commercial kit. The expression of ZBP1, AIM2, and MDA5 genes was investigated using qRT-PCR technique. The mean age of the patients and healthy volunteers was 52.73±13.78 and 49.120±12.490, respectively. In each group, 13 out of 25 participants were male. The expression levels of ZBP1 (P=0.001), AIM2 (P=0.001), and MDA5 (P= 0.003) transcript were significantly higher in COVID-19 patients than the control group. The results also revealed that the expression levels of ZBP1, AIM2, and MDA5 were significantly higher in the critical and severe COVID-19 patients compared to those with mild disease (P<0.05). Moreover, regarding the gender, the expression levels of AIM2 and MDA5 were significantly elevated in male severe (P=0.04 and P=0.003, respectively) and critical (P=0.005 and P=0.0004, respectively) patients than the female ones. The results indicated that ZBP1, AIM2, and MDA5 genes might have an important role in the severity of COVID-19 disease. Moreover, the severity of COVID-19 disease in male and female patients might be related to AIM2, and MDA5 expression levels. More studies are recommended to be conducted to clarify this issue.
Messaging Should Reflect the Nuanced Relationship between Land Change and Zoonotic Disease Risk

Abstract A hallmark of the media publicity surrounding COVID-19 has been the message that land change causes zoonotic diseases to spill over from wild animals to humans. The secondary peer-reviewed literature sends a similar message. However, as indicated in the primary peer-reviewed literature, the complexity of interacting variables involved in zoonotic disease spillover makes it unlikely for such a claim to be universally applicable. The secondary peer-reviewed literature and the mainstream media also differ markedly from the primary peer-reviewed literature in their lack of nuance in messaging about the relationship between land change and spillover risk. We advocate accurate, nuanced messaging for the sake of the local communities at greatest risk from zoonotic disease, for the sake of scientific credibility, and so that proportionate attention may be given to other possible drivers of spillover risk.