Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiently Imaging Accreting Protoplanets from Space: Reference Star Differential Imaging of the PDS 70 Planetary System Using the HST/WFC3 Archival PSF Library

Accreting protoplanets provide key insights into how planets assemble from their natal protoplanetary disks. Recently, Zhou et al. (2021) used angular differential imaging (ADI) with Hubble Space Telescope’s Wide Field Camera 3 (HST/WFC3) to recover the young accreting planet PDS 70 b in F656N (Hα) at a signal-to-noise ratio (S/N) of 7.9. In this paper, we demonstrate a promising approach to efficiently imaging accreting planets by applying reference star differential imaging (RDI) to the same data set. We compile a reference library from the database of WFC3 point-spread functions (PSFs) provided by the Space Telescope Science Institute and develop a set of morphology-significance criteria for preselection of reference frames to improve RDI subtraction. RDI with this PSF library results in a detection of PDS 70 b at an S/N of 5.3. Astrometry and photometry of PDS 70 b are calibrated using a forward-modeling method and injection-recovery tests, resulting in a separation of 186 ± 13 mas, a position angle of 142° ± 5°, and a Hα flux of (1.7 ± 0.3) × 10−15 erg s−1 cm−2. The lower detection significance with RDI can be attributed to the ∼100 times lower peak-to-background ratios of the reference PSFs compared to the ADI PSFs. Building a high-quality reference library with WFC3 will provide unique opportunities to study accretion variability on short timescales not limited by roll angle scheduling constraints and efficiently search for actively accreting protoplanets in Hα around targets inaccessible to ground-based adaptive optics systems, such as faint transition disk hosts.
Discovery of 16 New Members of the Solar Neighborhood Using Proper Motions from CatWISE2020

In an effort to identify nearby and unusual cold objects in the solar neighborhood, we searched for previously unidentified moving objects using CatWISE2020 proper motion data combined with machine learning methods. We paired the motion candidates with their counterparts in 2MASS, UHS, and VHS. Then we searched for white dwarf, brown dwarf, and subdwarf outliers on the resulting color–color diagrams. This resulted in the discovery of 16 new dwarfs, including 2 nearby M dwarfs (<30 pc), a possible young L dwarf, a high-motion early-T dwarf, and 3 later-T dwarfs. This research represents a step forward in completing the census of the Sun’s neighbors.
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition

This work focuses on the task of elderly activity recognition, which is a challenging task due to the existence of individual actions and human-object interactions in elderly activities. Thus, we attempt to effectively aggregate the discriminative information of actions and interactions from both RGB videos and skeleton sequences by attentively fusing multi-modal features. Recently, some nonlinear multi-modal fusion approaches are proposed by utilizing nonlinear attention mechanism that is extended from Squeeze-and-Excitation Networks (SENet). Inspired by this, we propose a novel Expansion-Squeeze-Excitation Fusion Network (ESE-FN) to effectively address the problem of elderly activity recognition, which learns modal and channel-wise Expansion-Squeeze-Excitation (ESE) attentions for attentively fusing the multi-modal features in the modal and channel-wise ways. Specifically, ESE-FN firstly implements the modal-wise fusion with the Modal-wise ESE Attention (M-ESEA) to aggregate discriminative information in modal-wise way, and then implements the channel-wise fusion with the Channel-wise ESE Attention (C-ESEA) to aggregate the multi-channel discriminative information in channel-wise way (referring to Figure 1). Furthermore, we design a new Multi-modal Loss (ML) to keep the consistency between the single-modal features and the fused multi-modal features by adding the penalty of difference between the minimum prediction losses on single modalities and the prediction loss on the fused modality. Finally, we conduct experiments on a largest-scale elderly activity dataset, i.e., ETRI-Activity3D (including 110,000+ videos, and 50+ categories), to demonstrate that the proposed ESE-FN achieves the best accuracy compared with the state-of-the-art methods. In addition, more extensive experimental results show that the proposed ESE-FN is also comparable to the other methods in terms of normal action recognition task.
Risk bounds for aggregated shallow neural networks using Gaussian priors

Analysing statistical properties of neural networks is a central topic in statistics and machine learning. However, most results in the literature focus on the properties of the neural network minimizing the training error. The goal of this paper is to consider aggregated neural networks using a Gaussian prior. The departure point of our approach is an arbitrary aggregate satisfying the PAC-Bayesian inequality. The main contribution is a precise nonasymptotic assessment of the estimation error appearing in the PAC-Bayes bound. We also review available bounds on the error of approximating a function by a neural network. Combining bounds on estimation and approximation errors, we establish risk bounds that are sharp enough to lead to minimax rates of estimation over Sobolev smoothness classes.
Feasibility Analysis of Cracking RSA with Improved Quantum Circuits of the Shor’s Algorithm

Since the RSA public key cryptosystem was proposed, it has been widely used because of its strong security. Although the proposal of the Shor’s algorithm offers hope for cracking RSA, it is debatable whether the algorithm can actually pose a threat in practice. From the perspective of the quantum circuit of the Shor’s algorithm, we analyse the feasibility of cracking RSA with improved quantum circuits using an ion-trap quantum computer. We present an improved quantum circuit for the modular exponentiation of a constant, which is the most expensive operation in Shor’s algorithm for integer factorization. Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit, we minimize the number of CNOTs, which greatly affects the time to run the algorithm on an ion-trap quantum computer. First, we give the implementation of the basic arithmetic with the lowest known number of CNOTs and the construction of an improved modular exponentiation of a constant by accumulating intermediate data and using a windowing technique. Then, we precisely estimate the number of improved quantum circuits needed to perform the Shor’s algorithm for factoring an n -bit integer, which is 217 n 3 / log 2 n + 4 n 2 + n . We analyse the running time and feasibility of the Shor’s algorithm on an ion-trap quantum computer according to the number of CNOTs. Finally, we discussed the lower bound of the number of CNOTs needed to implement the Shor’s algorithm.
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements

Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in today's software ecosystem, where vulnerable code can flow easily and unwittingly within and across software repositories like GitHub. Across such millions of lines of code, traditional static and dynamic approaches struggle to scale. Although existing machine-learning-based approaches look promising in such a setting, most work detects vulnerable code at a higher granularity – at the method or file level. Thus, developers still need to inspect a significant amount of code to locate the vulnerable statement(s) that need to be fixed. This paper presents Velvet, a novel ensemble learning approach to locate vulnerable statements. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph and effectively understand code semantics and vulnerable patterns. To study Velvet's effectiveness, we use an off-the-shelf synthetic dataset and a recently published real-world dataset. In the static analysis setting, where vulnerable functions are not detected in advance, Velvet achieves 4.5× better performance than the baseline static analyzers on the real-world data. For the isolated vulnerability localization task, where we assume the vulnerability of a function is known while the specific vulnerable statement is unknown, we compare Velvet with several neural networks that also attend to local and global context of code. Velvet achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively, outperforming the baseline deep learning models by 5.3-29.0%.
Imagery agnosia and its phenomenology

Lack of vivid sensory imagery has recently become an active subject of research, under the name of aphantasia. Extremely vivid imagery, or hyperphantasia, is at the other end of the spectrum of individual differences. While most research has focused on visual imagery in this paper I argue that from a neuropsychological perspective this phenomenon is much more widespread, and should be categorized as imagery sensory agnosia. After over twenty years of learning to play music phenomenology of auditory imagery agnosia is described from the first-person perspective. Reflections on other forms of imagery agnosia and deficits of autobiographical memories are presented and a hypothesis about putative brain processes that can account for such phenomena is discussed. Extreme individual differences in imagery and in autobiographical memory have implications for many fields of study, from consciousness research to education.
New Method for Computing zeros of monotone maps in Lebesgue spaces with applications to integral equations, fixed points, optimization, and variational inequality problems

Let E = Lp, 1 < p ≤ 2, and A : E → E be a bounded monotone map such that 0 ∈ R(A). In this paper, we introduce and study an algorithm for approximating zeros of A. Furthermore, we study the application of this algorithm to the approximation of Hammerstein integral equations, fixed points, convex optimization, and variational inequality problems. Finally, we present numerical and illustrative examples of our results and their applications.
Exponential Decay of Intersection Volume With Applications on List-Decodability and Gilbert-Varshamov Type Bound

We give some natural sufficient conditions for balls in a metric space to have small intersection. Roughly speaking, this happens when the metric space is (i) expanding and (ii) well-spread, and (iii) a certain random variable on the boundary of a ball has a small tail. As applications, we show that the volume of intersection of balls in Hamming, Johnson spaces and symmetric groups decay exponentially as their centers drift apart. To verify condition (iii), we prove some large deviation inequalities ‘on a slice’ for functions with Lipschitz conditions. We then use these estimates on intersection volumes to 1) obtain a sharp lower bound on list-decodability of random q-ary codes, confirming a conjecture of Li and Wootters, and 2) improve the classical bound of Levenshtein from 1971 on constant weight codes by a factor linear in dimension, resolving a problem raised by Jiang and Vardy. Our probabilistic point of view also offers a unified framework to obtain improvements on other Gilbert-Varshamov type bounds, giving conceptually simple and calculation-free proofs for $q$ -ary codes, permutation codes, and spherical codes. Another consequence is a counting result on the number of codes, showing ampleness of large codes.
Deep Learning and Earth Observation to Support the Sustainable Development Goals

The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the living planet challenges. This paper reviews current deep learning approaches for Earth observation data, along with their application towards monitoring and achieving the SDGs most impacted by the rapid development of deep learning in Earth observation. We systematically review case studies to 1) achieve zero hunger, 2) sustainable cities, 3) deliver tenure security, 4) mitigate and adapt to climate change, and 5) preserve biodiversity. Important societal, economic and environmental implications are concerned. Exciting times ahead are coming where algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.
Physics-informed neural network method for modelling beam-wall interactions

A mesh-free approach for modelling beam-wall interactions in particle accelerators is proposed. The key idea of our method is to use a deep neural network as a surrogate for the solution to a set of partial differential equations involving the particle beam, and the surface impedance concept. The proposed approach is applied to the coupling impedance of an accelerator vacuum chamber with thin conductive coating, and also verified in comparison with the existing analytical formula.
The effect of age and sex on glycated hemoglobin in dogs

We investigated the effect of age and sex on canine glycated hemoglobin (HbA1c) using a validated capillary electrophoresis assay. Aliquots of EDTA blood samples collected for routine health checks were used. HbA1c was measured using the Capillarys 2 flex-piercing system (Sebia). We included 58 clinically and hematologically healthy, normoglycemic dogs (29 males, 29 females), allocated to 3 age groups: young (14 dogs <1-y-old), adult (31 dogs 1–7.9-y-old), and senior (13 dogs ≥8-y-old). The mean (± SD) HbA1c was not significantly different (p = 0.428) between the age groups (young: 1.68 ± 0.54%; adult: 1.59 ± 0.41%; senior: 1.80 ± 0.57%). The HbA1c was not significantly correlated with age (rho = 0.144, p = 0.280). The median (range) HbA1c was not significantly different (p = 0.391) between male [1.7% (0.5–2.5%)] and female [1.5% (1.0–2.7%)] dogs. Age and sex do not appear to affect canine HbA1c; however, a study of geriatric dogs would be needed to fully exclude an effect of age on HbA1c.
Super resolution microscopy reveals how elongating RNA polymerase II and nascent RNA interact with nucleosome clutches

Abstract Transcription and genome architecture are interdependent, but it is still unclear how nucleosomes in the chromatin fiber interact with nascent RNA, and which is the relative nuclear distribution of these RNAs and elongating RNA polymerase II (RNAP II). Using super-resolution (SR) microscopy, we visualized the nascent transcriptome, in both nucleoplasm and nucleolus, with nanoscale resolution. We found that nascent RNAs organize in structures we termed RNA nanodomains, whose characteristics are independent of the number of transcripts produced over time. Dual-color SR imaging of nascent RNAs, together with elongating RNAP II and H2B, shows the physical relation between nucleosome clutches, RNAP II, and RNA nanodomains. The distance between nucleosome clutches and RNA nanodomains is larger than the distance measured between elongating RNAP II and RNA nanodomains. Elongating RNAP II stands between nascent RNAs and the small, transcriptionally active, nucleosome clutches. Moreover, RNA factories are small and largely formed by few RNAP II. Finally, we describe a novel approach to quantify the transcriptional activity at an individual gene locus. By measuring local nascent RNA accumulation upon transcriptional activation at single alleles, we confirm the measurements made at the global nuclear level.
Similar Risk of Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Similar Nucleocapsid Antibody Levels in People With Well-Controlled Human Immunodeficiency Virus (HIV) and a Comparable Cohort of People Without HIV

Abstract Background Within the ongoing AGEhIV Cohort Study in Amsterdam, we prospectively compared the incidence of and risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection between human immunodeficiency virus (HIV)–positive and HIV-negative participants. Moreover, we compared SARS-CoV-2 nucleocapsid antibody levels between participants with incident infection from both groups. Methods Starting in September 2020, consenting HIV-positive and HIV-negative participants were assessed every 6 months for incident SARS-CoV-2 infection, using combined immunoglobulin (Ig) A/IgM/IgG SARS-CoV-2 nucleocapsid antibody assay. Cumulative incidence of SARS-CoV-2 infection and associated risk factors were assessed from 27 February 2020 through 30 April 2021, using complementary log-log regression. In those with incident SARS-CoV-2 infection, nucleocapsid (N) antibody levels were compared between groups using linear regression. Results The study included 241 HIV-positive (99.2% virally suppressed) and 326 HIV-negative AGEhIV participants. The cumulative SARS-CoV-2 incidence by April 2021 was 13.4% and 11.6% in HIV-positive and HIV-negative participants, respectively (P = .61). Younger age and African origin were independently associated with incident infection. In those with incident infection, only self-reported fever, but not HIV status, was associated with higher N antibody levels. Conclusions HIV-positive individuals with suppressed viremia and adequate CD4 cell counts had similar risk of SARS-CoV-2 acquisition and similar SARS-CoV-2 N antibody levels after infection compared with a comparable HIV-negative cohort. Clinical Trial Registration NCT01466582.
Frequency of middle mesial canal in mandibular first molars and its association with various anatomic variables.

This study aimed to determine the frequency of the middle mesial canal in the mandibular first molars and its association with various anatomical features. Two experienced endodontists evaluated 149 CBCT images, containing n = 298 mandibular first molars and n = 298 mandibular second molars. Binary logistic regression was used to explore the relationship between the presence of middle mesial canal and other independent variables. The middle mesial canal was found in 7.7% (n = 23) of mandibular first molars and 11.4% of which exhibited adjacent C-shaped mandibular second molars. The presence of an adjacent C-shaped mandibular second molar was associated with a 3 times increased likelihood of finding a middle mesial canal (P = 0.048, OR: 3.108) in mandibular first molars. The middle mesial canal is an uncommon finding with a rare occurrence of bilateral presence in mandibular molars; however, an adjacent C-molar is an important predictor of middle mesial canal presence.
Large contribution of biomass burning emissions to ozone throughout the global remote troposphere

Significance Understanding the sources of tropospheric ozone is important for effective air quality management and accurate radiative forcing attribution. Biomass burning emits large quantities of ozone precursors to the lower atmosphere. This source can drive regional-scale ozone production, but its impact on global tropospheric ozone is poorly constrained. Here, we present unique global in situ aircraft observations of ozone and continental pollution tracers. Ozone enhancements attributable to biomass burning equal or exceed those from urban emissions, a result that is not predicted by current chemical transport models. These findings represent a potentially major shift in the understanding of the sources of ozone in the lower atmosphere and indicate the need for model developments to improve the representation of global tropospheric ozone. Ozone is the third most important anthropogenic greenhouse gas after carbon dioxide and methane but has a larger uncertainty in its radiative forcing, in part because of uncertainty in the source characteristics of ozone precursors, nitrogen oxides, and volatile organic carbon that directly affect ozone formation chemistry. Tropospheric ozone also negatively affects human and ecosystem health. Biomass burning (BB) and urban emissions are significant but uncertain sources of ozone precursors. Here, we report global-scale, in situ airborne measurements of ozone and precursor source tracers from the NASA Atmospheric Tomography mission. Measurements from the remote troposphere showed that tropospheric ozone is regularly enhanced above background in polluted air masses in all regions of the globe. Ozone enhancements in air with high BB and urban emission tracers (2.1 to 23.8 ppbv [parts per billion by volume]) were generally similar to those in BB-influenced air (2.2 to 21.0 ppbv) but larger than those in urban-influenced air (−7.7 to 6.9 ppbv). Ozone attributed to BB was 2 to 10 times higher than that from urban sources in the Southern Hemisphere and the tropical Atlantic and roughly equal to that from urban sources in the Northern Hemisphere and the tropical Pacific. Three independent global chemical transport models systematically underpredict the observed influence of BB on tropospheric ozone. Potential reasons include uncertainties in modeled BB injection heights and emission inventories, export efficiency of BB emissions to the free troposphere, and chemical mechanisms of ozone production in smoke. Accurately accounting for intermittent but large and widespread BB emissions is required to understand the global tropospheric ozone burden.
Can increased load carriage affect lower limbs kinematics during military gait?

Abstract The aim of this study was to investigate if increased load carriage, in male military personnel, can affect the lower limbs kinematics. Twelve male military volunteers from the Portuguese Army were recruited and evaluated in an unloaded and loaded gait condition. Linear kinematics and lower limbs joint angle at heel strike, midstance and toe off were calculated. The stance, swing and double support times were found to be different between load conditions (p < 0.05). There was an interaction between load and limb (p < 0.05) for joint angles, during midstance, with limbs performing different movements in the frontal plane during loaded gait. Load increase had a different effect on the right knee, with a reduction in the abduction (valgus). This study may be beneficial in offering suggestion to improve the performance of gait with load and in an attempt to help prevent possible injuries. Practitioner summary: Increased load can affect lower limbs of male soldiers at the pelvic, hip and knee angles on the frontal plane, which can alter the joint force distribution. While these alterations may indicate protective mechanics, load management procedures should be implemented along with gait monitoring to avoid negative effects in performance.