Miracle-dz/UniFAR-FTUs huggingface dataset:
Galaxy Mergers up to z < 2.5. II. AGN Incidence in Merging Galaxies at Separations of 3–15 kpc

We present a study of the incidence of active galactic nucleus (AGN) in a sample of major merging systems at 0.3 < z < 2.5. Galaxies in this merger sample have projected separations between 3 and 15 kpc and are selected from the CANDELS/3D-Hubble Space Telescope catalogs using a peak-finding algorithm. AGNs in mergers and non-mergers are identified on the basis of their X-ray emission, optical lines, mid-infrared colors, and radio emission. Among galaxies with adequate measurements to find potential AGNs, we find a similar fraction of AGNs in mergers (16.4 %) compared to the fraction found in non-merging galaxies (15.4% ± 0.6%). In mergers, this fraction is obtained by assuming that, in unresolved observations, only one of the merging galaxies is the AGN source (16 merging systems have at least one AGN galaxy). The similarity between the fractions is possibly due to the higher availability of cold gas at high redshifts, where the excess of nuclear activity as a result of merging is less important than at lower redshifts. Star-forming galaxies have a higher incidence of AGNs than quiescent galaxies. In particular, starbursts in mergers are the most common sites of AGN activity, since they present higher AGN fractions and black hole accretion rates. We find no clear correlation between the black hole accretion rate and the galaxy properties (i.e., star formation rate, stellar mass) in mergers and non-mergers. However, mergers seem to have a higher correlation with star formation than non-mergers, which possibly indicates that the merging process is starting to influence the star formation and AGN activity even at this pre-coalescence stage.
Doubling inequalities and nodal sets in periodic elliptic homogenization

Abstract We prove explicit doubling inequalities and obtain uniform upper bounds (under -dimensional Hausdorff measure) of nodal sets of weak solutions for a family of linear elliptic equations with rapidly oscillating periodic coefficients. The doubling inequalities, explicitly depending on the doubling index, are proved at different scales by a combination of convergence rates, a three-ball inequality from certain “analyticity,” and a monotonicity formula of a frequency function. The upper bounds of nodal sets are shown by using the doubling inequalities, approximations by harmonic functions and an iteration argument.
Big Self-Supervised Models Advance Medical Image Classification

Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis. This paper studies the effectiveness of self-supervised learning as a pre-training strategy for medical image classification. We conduct experiments on two distinct tasks: dermatology condition classification from digital camera images and multi-label chest X-ray classification, and demonstrate that self-supervised learning on ImageNet, followed by additional self-supervised learning on unlabeled domain-specific medical images significantly improves the accuracy of medical image classifiers. We introduce a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple images of the underlying pathology per patient case, when available, to construct more informative positive pairs for self-supervised learning. Combining our contributions, we achieve an improvement of 6.7% in top-1 accuracy and an improvement of 1.1% in mean AUC on dermatology and chest X-ray classification respectively, outperforming strong supervised baselines pretrained on ImageNet. In addition, we show that big self-supervised models are robust to distribution shift and can learn efficiently with a small number of labeled medical images.
ARC: Alignment-based Redirection Controller for Redirected Walking in Complex Environments

We present a novel redirected walking controller based on alignment that allows the user to explore large and complex virtual environments, while minimizing the number of collisions with obstacles in the physical environment. Our alignment-based redirection controller, ARC, steers the user such that their proximity to obstacles in the physical environment matches the proximity to obstacles in the virtual environment as closely as possible. To quantify a controller's performance in complex environments, we introduce a new metric, Complexity Ratio (CR), to measure the relative environment complexity and characterize the difference in navigational complexity between the physical and virtual environments. Through extensive simulation-based experiments, we show that ARC significantly outperforms current state-of-the-art controllers in its ability to steer the user on a collision-free path. We also show through quantitative and qualitative measures of performance that our controller is robust in complex environments with many obstacles. Our method is applicable to arbitrary environments and operates without any user input or parameter tweaking, aside from the layout of the environments. We have implemented our algorithm on the Oculus Quest head-mounted display and evaluated its performance in environments with varying complexity. Our project website is available at https://ganuna.umd. edu/arc/.
The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology

Recently, consumer-facing health technologies such as Artificial Intelligence (AI)-based symptom checkers (AISCs) have sprung up in everyday healthcare practice. AISCs solicit symptom information from users and provide medical suggestions and possible diagnoses, a responsibility that people usually entrust with real-person authorities such as physicians and expert patients. Thus, the advent of AISCs begs a question of whether and how they transform the notion of medical authority in people's everyday healthcare practice. To answer this question, we conducted an interview study with thirty AISC users. We found that users assess the medical authority of AISCs using various factors including AISCs’ automated decisions and interaction design patterns, associations with established medical authorities like hospitals, and comparisons with other health technologies. We reveal how AISCs are used in healthcare delivery, discuss how AI transforms conventional understandings of medical authority, and derive implications for designing AI-enabled health technology.
Restyling Images with the Bangladeshi Paintings Using Neural Style Transfer: A Comprehensive Experiment, Evaluation, and Human Perspective

In today’s world, Neural Style Transfer (NST) has become a trendsetting term. NST combines two pictures, a content picture and a reference image in style (such as the work of a renowned painter) in a way that makes the output image look like an image of the material, but rendered with the form of a reference picture. However, there is no study using the artwork or painting of Bangladeshi painters. Bangladeshi painting has a long history of more than two thousand years and is still being practiced by Bangladeshi painters. This study generates NST stylized image on Bangladeshi paintings and analyzes the human point of view regarding the aesthetic preference of NST on Bangladeshi paintings. To assure our study’s acceptance, we performed qualitative human evaluations on generated stylized images by 60 individual humans of different age and gender groups. We have explained how NST works for Bangladeshi paintings and assess NST algorithms, both qualitatively & quantitatively. Our study acts as a pre-requisite for the impact of NST stylized image using Bangladeshi paintings on mobile UI/GUI and material translation from the human perspective. We hope that this study will encourage new collaborations to create more NST related studies and expand the use of Bangladeshi artworks.
Memory-Augmented Reinforcement Learning for Image-Goal Navigation

In this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization performance, or are heavily-reliant on pose/depth sensors. Our method is based on an attention-based end-to-end model that leverages an episodic memory to learn to navigate. First, we train a state-embedding network in a self-supervised fashion, and then use it to embed previously-visited states into the agent's memory. Our navigation policy takes advantage of this information through an attention mechanism. We validate our approach with extensive evaluations, and show that our model establishes a new state of the art on the challenging Gibson dataset. Furthermore, we achieve this impressive performance from RGB input alone, without access to additional information such as position or depth, in stark contrast to related work.
UNSW‐NB15 computer security dataset: Analysis through visualization

Class imbalance refers to a major issue in data mining where data with unequal class distribution can deteriorate classification performance. Although it alone affects the performance of the classifiers, the joint‐effect of class imbalance and overlap is more damaging. Data overlap happens when multiple classes are assigned to a single data point causing the classifiers to misidentify the class boundaries. This study offers a deep insight into the intricacies of the UNSW‐NB15 dataset and two issues that may lead the data‐driven models to demonstrate poor performance. The most commonly used visualization methods such as bar chart, 3D and 2D scatter plots, intercluster distance map, and parallel coordinate diagram were employed to depict the data imbalanced and overlap. However, their limitations in capturing the overlapping issue led us to propose an accurate, easy‐to‐interpret, and scalable overlapping visualization method. The method clearly detects the data overlap and illustrates the effect of several data scalers in dealing with the data overlap. To verify the accuracy of the proposed method, a number of classifiers were implemented along with the scalers and the calculated AUC scores were compared to those calculated from the classifiers that were implemented on the original dataset.
A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles

Identifying anomalies in multi-dimensional datasets is an important task in many real-world applications. A special case arises when anomalies are occluded in a small set of attributes, typically referred to as a subspace, and not necessarily over the entire data space. In this paper, we propose a new subspace analysis approach named Agglomerative Attribute Grouping (AAG) that aims to address this challenge by searching for subspaces that are comprised of highly correlative attributes. Such correlations among attributes represent a systematic interaction among the attributes that can better reflect the behavior of normal observations and hence can be used to improve the identification of two particularly interesting types of abnormal data samples: anomalies that are occluded in relatively small subsets of the attributes and anomalies that represent a new data class. AAG relies on a novel multi-attribute measure, which is derived from information theory measures of partitions, for evaluating the"information distance"between groups of data attributes. To determine the set of subspaces to use, AAG applies a variation of the well-known agglomerative clustering algorithm with the proposed multi-attribute measure as the underlying distance function. Finally, the set of subspaces is used in an ensemble for anomaly detection. Extensive evaluation demonstrates that, in the vast majority of cases, the proposed AAG method (i) outperforms classical and state-of-the-art subspace analysis methods when used in anomaly detection ensembles, and (ii) generates fewer subspaces with a fewer number of attributes each (on average), thus resulting in a faster training time for the anomaly detection ensemble. Furthermore, in contrast to existing methods, the proposed AAG method does not require any tuning of parameters.
Monoterpene and sesquiterpene emissions from Sugi (Cryptomeria japonica) based on a branch enclosure measurements

Abstract Sugi ( Cryptomeria japonica ) is the dominant tree species in the forested area of Japan. The forested area is approximately 250 000 km 2 and covers almost 70% of total area of Japan. However, there is little information available on biogenic volatile organic compounds (BVOCs) emissions from Sugi trees. BVOCs are known to have an important role in atmospheric chemistry due to their high reactivity and aerosol formation potential. Emissions of monoterpenes and sesquiterpenes, which are groups of BVOCs, were measured from Sugi using a bag enclosure system over four seasons at the Tanashi Experimental Station, located in a suburban area of Tokyo, Japan. Six monoterpenes accounted for the major emissions, and total monoterpene emission ranged up to 0.4 µg g –1 (dw) h –1 . The emission rates of both β –farnesene; which is a sesquiterpene, and monoterpenes clearly showed temperature dependence. Only β –farnesene was detected in the sesquiterpene fraction and determined to have a larger emission rate compared to that of total monoterpenes. The coefficient β for total monoterpene and β –farnesene calculated by G93 algorithm were 0.10 and 0.22, respectively. However, β showed largely different values on August (monoterpenes) and April (β–farnesene). The emission rates of β – farnesene show almost the same values in measured individuals and ranged from the detection limit (approximately 0.1 ng g –1 (dw) h –1 ) to 1.5 µg g –1 (dw) h –1 . The basal emission factor of monoterpenes in summer and sesquiterpenes in spring were generally lower compared to other seasons. This study indicated that Sugi trees in suburban areas emit sesquiterpene at a higher rate than total monoterpenes.
Instanton Floer homology, sutures, and Euler characteristics

This is a companion paper to an earlier work of the authors. In this paper, we provide an axiomatic definition of Floer homology for balanced sutured manifolds and prove that the graded Euler characteristic $\chi_{\rm gr}$ of this homology is fully determined by the axioms we proposed. As a result, we conclude that $\chi_{\rm gr}(SHI(M,\gamma))=\chi_{\rm gr}(SFH(M,\gamma))$ for any balanced sutured manifold $(M,\gamma)$. In particular, for any link $L$ in $S^3$, the Euler characteristic $\chi_{\rm gr}(KHI(S^3,L))$ recovers the multi-variable Alexander polynomial of $L$, which generalizes the knot case. Combined with the authors' earlier work, we provide more examples of $(1,1)$-knots in lens spaces whose $KHI$ and $\widehat{HFK}$ have the same dimension. Moreover, for a rationally null-homologous knot in a closed oriented 3-manifold $Y$, we construct canonical $\mathbb{Z}_2$-gradings on $KHI(Y,K)$, the decomposition of $I^\sharp(Y)$ discussed in the previous paper, and the minus version of instanton knot homology $\underline{\rm KHI}^-(Y,K)$ introduced by the first author.
Discourse-Aware Unsupervised Summarization for Long Scientific Documents

We propose an unsupervised graph-based ranking model for extractive summarization of long scientific documents. Our method assumes a two-level hierarchical graph representation of the source document, and exploits asymmetrical positional cues to determine sentence importance. Results on the PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins in automatic metrics and human evaluation. In addition, it achieves performance comparable to many state-of-the-art supervised approaches which are trained on hundreds of thousands of examples. These results suggest that patterns in the discourse structure are a strong signal for determining importance in scientific articles.
Electrophysiological characterization of chronic stress-induced sensitization of noradrenergic neurons of the locus coeruleus

Chronic stress exposure can produce sensitization of norepinephrine release in the terminal fields of locus coeruleus (LC) neurons. The present studies explore the potential localization and mechanism underlying the sensitized response of LC neurons in rats following chronic exposure to cold (2 weeks; 5*C). Single unit recordings of LC neurons in halothane-anesthetized rats were used to compare the effect of intraventricular administration of corticotropin releasing hormone (CRH; 0.3-3.0µg) in control and previously cold-exposed rats. The CRH-evoked increase in LC neuron activity was enhanced following chronic cold exposure, without alteration in basal activity. The enhanced activation was only apparent at higher doses of CRH, resulting in an increased slope of the dose-response relationship for CRH in previously cold-exposed rats. It is concluded that the sensitization of CRH-evoked norepinephrine release in cold-exposed rats is accompanied by sensitization of LC neuron activity. We hypothesized that the response of LC neurons to multiple excitatory inputs is enhanced. Using in vitro intracellular recordings, we subsequently examined whether CRH exerts a direct effect on LC neurons, and which ionic currents and second messenger systems are likely affected by CRH. It was demonstrated that CRH dose-dependently increases the firing rate of LC neurons through a direct (TTX-insensitive) mechanism by decreasing a potassium conductance via adenylate cyclase and protein kinase A. The CRH-evoked activation of LC neurons is, at least in part, mediated by CRH1 receptors. In subsequent in vitro experiments using intracellular recordings, the electrophysiological properties of LC neurons were compared between control and cold-exposed rats. We observed that the excitability and input resistance of LC neurons was enhanced in slices from cold-exposed rats. In addition, the accommodation of spike firing was reduced and there was a strong trend toward a reduction of the post-activation inhibitory period. These data demonstrate that the stress-induced sensitization of LC neurons is, at least in part, maintained in vitro and suggest that alterations in electrophysiological properties of LC neurons contribute to the chronic stress-induced sensitization of central noradrenergic function observed in vivo. Furthermore, these data suggest that an alteration in auto-inhibitory control of LC activity is involved in chronic stress-induced alterations.
Management and Outcomes of Unilateral Group D Tumors in Retinoblastoma

Purpose Retinoblastoma presents most commonly as advanced unilateral disease, particularly in developing countries for which primary enucleation has been the preferred method of treatment. However, with the evolution of newer treatment modalities including intravitreal chemotherapy, intra-arterial chemotherapy and newer chemotherapeutic combinations, a trend towards more conservative approaches is being observed. Our aim is to evaluate outcomes of group D eyes following conservative and non-conservative treatment options. Patients and Methods The ocular oncology database was used to identify eyes with unilateral retinoblastoma that fulfilled the International Intraocular Retinoblastoma Classification (IIRC) group D criteria from August 2010 to August 2018 and these were retrospectively reviewed. Overall, 39 eyes were identified. Results Nineteen (49%) eyes underwent primary enucleation and 20 (51%) received eye-conserving treatment. Eye salvage was possible in 15 (75%) eyes in the attempted salvage group. None of the patient revealed signs of metastasis. All eyes received conventional chemotherapy (carboplatin, vincristine, etoposide) and focal laser therapy. Additional treatment modalities offered included intravitreal chemotherapy, intra-arterial chemotherapy and topotecan. Three (11%) eyes in the primary enucleation group showed high-risk features on histopathology and none developed metastasis. Conclusion The results of the study seem promising and conservative measures can be adopted in selected unilateral group D eyes.
Configuration Analysis of Influencing Factors of Technical Efficiency Based on DEA and fsQCA: Evidence from China’s Medical and Health Institutions

Purpose This paper aims to measure the technical efficiency of China’s medical and health institutions from 2012 to 2017 and outline the path to achieve high-quality development. Methods The DEA-Malmquist was used to evaluate the total factor productivity of medical and health institutions in 31 provinces. A fuzzy set Qualitative Comparative Analysis (fsQCA) was used for configuration analysis of determinants affecting technical efficiency. Results The average total factor productivity (TFP) of those institutions was 0.965, namely TFP declined averagely by 3.5% annually. The efficiency change and the technical change were 0.998 and 0.967, respectively. The realization paths of high technical efficiency are composed of high fatality rate and high financial allocation-led, high population density and high GDP-led. Low dependency ratio and low financial allocation-led, low fatality rate and low financial allocation-led are the main reasons for low technical efficiency. Conclusion Due to advanced medical technology and economic development, major cities like Beijing, Shanghai, and Guangdong have attracted a large number of high-level health personnel, achieving long-term and stable health business growth. Hubei, Anhui, and Sichuan also have made rapid development of health care through appropriate financial subsidies and policy supports. The technical changes in Qinghai, Yunnan, and Inner Mongolia are higher than the national average, but the operation and management level of the medical and health institutions is relatively weak. Henan, Jiangxi, and Heilongjiang have a prominent performance in the efficiency change, but the technical change is weaker than the national average.