Miracle-dz/UniFAR-FTUs huggingface dataset:
DMiner: Dashboard Design Mining and Recommendation

Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: arrangement, which describes the position, size, and layout of each view in the display space; and coordination, which indicates the interaction between pairwise views. We build a new dataset containing 854 dashboards crawled online, and develop feature engineering methods for describing the single views and view-wise relationships in terms of data, encoding, layout, and interactions. Further, we identify design rules among those features and develop a recommender for dashboard design. We demonstrate the usefulness of DMiner through an expert study and a user study. The expert study shows that our extracted design rules are reasonable and conform to the design practice of experts. Moreover, a comparative user study shows that our recommender could help automate dashboard organization and reach human-level performance. In summary, our work offers a promising starting point for design mining visualizations to build recommenders.
SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization

Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) since it exhibits high intra-class and low inter-class variances due to occlusions, deformation, illuminations, etc. Thus, an expressive feature representation describing global structural information is a key to characterize an object/scene. To this end, we propose a method that effectively captures subtle changes by aggregating context-aware features from most relevant image-regions and their importance in discriminating fine-grained categories avoiding the bounding-box and/or distinguishable part annotations. Our approach is inspired by the recent advancement in self-attention and graph neural networks (GNNs) approaches to include a simple yet effective relation-aware feature transformation and its refinement using a context-aware attention mechanism to boost the discriminability of the transformed feature in an end-to-end learning process. Our model is evaluated on eight benchmark datasets consisting of fine-grained objects and human-object interactions. It outperforms the state-of-the-art approaches by a significant margin in recognition accuracy.
Bayesian nonparametric estimation of coverage probabilities and distinct counts from sketched data

The estimation of coverage probabilities, and in particular of the missing mass, is a classical statistical problem with applications in numerous scientific fields. In this paper, we study this problem in relation to randomized data compression, or sketching. This is a novel but practically relevant perspective, and it refers to situations in which coverage probabilities must be estimated based on a compressed and imperfect summary, or sketch, of the true data, because neither the full data nor the empirical frequencies of distinct symbols can be observed directly. Our contribution is a Bayesian nonparametric methodology to estimate coverage probabilities from data sketched through random hashing, which also solves the challenging problems of recovering the numbers of distinct counts in the true data and of distinct counts with a specified empirical frequency of interest. The proposed Bayesian estimators are shown to be easily applicable to large-scale analyses in combination with a Dirichlet process prior, although they involve some open computational challenges under the more general Pitman-Yor process prior. The empirical effectiveness of our methodology is demonstrated through numerical experiments and applications to real data sets of Covid DNA sequences, classic English literature, and IP addresses.
Semi-Supervised Domain Adaptation by Similarity based Pseudo-label Injection

One of the primary challenges in Semi-supervised Domain Adaptation (SSDA) is the skewed ratio between the number of labeled source and target samples, causing the model to be biased towards the source domain. Recent works in SSDA show that aligning only the labeled target samples with the source samples potentially leads to incomplete domain alignment of the target domain to the source domain. In our approach, to align the two domains, we leverage contrastive losses to learn a semantically meaningful and a domain agnostic feature space using the supervised samples from both domains. To mitigate challenges caused by the skewed label ratio, we pseudo-label the unlabeled target samples by comparing their feature representation to those of the labeled samples from both the source and target domains. Furthermore, to increase the support of the target domain, these potentially noisy pseudo-labels are gradually injected into the labeled target dataset over the course of training. Specifically, we use a temperature scaled cosine similarity measure to assign a soft pseudo-label to the unlabeled target samples. Additionally, we compute an exponential moving average of the soft pseudo-labels for each unlabeled sample. These pseudo-labels are progressively injected or removed) into the (from) the labeled target dataset based on a confidence threshold to supplement the alignment of the source and target distributions. Finally, we use a supervised contrastive loss on the labeled and pseudo-labeled datasets to align the source and target distributions. Using our proposed approach, we showcase state-of-the-art performance on SSDA benchmarks - Office-Home, DomainNet and Office-31.
SDSS J1058+5443: A Blue Quasar without Optical/NUV Broad Emission Lines

In this paper, the blue quasar SDSS J105816.19+544310.2 (=SDSS J1058+5443) at redshift 0.479 has been reported as the best true type 2 quasar candidate with the disappearance of central broad-line regions. There are no definite conclusions on the very existence of true type 2 active galactic nuclei (AGN), mainly due to detected optical broad emission lines in high-quality spectra of some previously classified true type 2 AGN candidates. Here, unlike previously reported true type 2 AGN candidates among narrow emission-line galaxies with weak AGN activities but strong stellar lights, the definitely blue quasar SDSS J1058+5443 can be well confirmed as a true type 2 quasar due to apparent quasar-shape blue continuum emissions but an apparent loss of both the optical broad Balmer emission lines and the near-UV (NUV) broad Mg ii emission line. Based on different model functions and the F-test statistical technique, after considering blueshifted optical and UV Fe ii emissions, there are no apparent broad optical Balmer emission lines and/or broad NUV Mg ii lines, and the confidence level is smaller than 1σ in support of broad optical and NUV emission lines. Moreover, assuming the virialization assumption to broad-line emission clouds, the reconstructed broad emission lines strongly indicate that the probable intrinsic broad emission lines, if they exist, cannot be hidden or overwhelmed in the noise of the Sloan Digital Sky Survey spectrum of SDSS J1058+5443. Therefore, SDSS J1058+5443 is so far the best and most robust true type 2 quasar candidate, leading to the clear conclusion of the very existence of true type 2 AGN.
Gender-Related Differences in Problem Solving at the 6th and the 8th Grade Levels.

Potential gender-related differences in the process of mathematical word problem solving were investigated. Sixth and eighth graders (n=302) solved 2-step mathematical word problems using a 9-step problem solving plan. The nine steps were: (1) identify the facts; (2) identify the question; (3) draw a diagram; (4) choose the operations; (5) write an open sentence; (6) estimate the answer; (7) compute the answer; (8) state the answer; and (9) validate the answer. A 2 x 2 between-subjects multivariate analysis of variance was computed to determine the effects of gender and grade level on the subjects' scores on each of the nine steps of the problem solving plan. There was a significant gender-related difference in the overall process of problem solving in favor of females. Univariate "F"'s for each of the nine steps were not significant. Although there were no significant differences at any of the individual steps, the combined use of the same steps produced a gender-related difference It is concluded that the use of a step-by-step problem solving plan might be gender biased. (Author/SLD) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ********************************************************************** O U.S. DEPARTMENT r'ILICATION Office of Educattonai Reseaivn and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER IERICi a? Cs document has been reproduced as received from the person or ofganqaPon originating a Cs Minor changes have been made to improve reproduction quality Points of view or opinions slated in this docu ment do not neCessartiy represent official OERI pesaion or policy "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY
Muon Collider Forum Report

A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique potential to provide both precision measurements and the highest energy reach in one machine that cannot be paralleled by any currently available technology. The topic generated a lot of excitement in Snowmass meetings and continues to attract a large number of supporters, including many from the early career community. In light of this very strong interest within the US particle physics community, Snowmass Energy, Theory and Accelerator Frontiers created a cross-frontier Muon Collider Forum in November of 2020. The Forum has been meeting on a monthly basis and organized several topical workshops dedicated to physics, accelerator technology, and detector R&D. Findings of the Forum are summarized in this report.
Do Large Language Models know what humans know?

Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages. In pre-registered analyses, we present a linguistic version of the False Belief Task to both human participants and a large language model, GPT-3. Both are sensitive to others' beliefs, but while the language model significantly exceeds chance behavior, it does not perform as well as the humans nor does it explain the full extent of their behavior-despite being exposed to more language than a human would in a lifetime. This suggests that while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible.
Consistent mass formulas for higher even-dimensional Taub-NUT spacetimes and their AdS counterparts

Currently, there is a great deal of interest in the seeking of consistent thermodynamics of the Lorentzian Taub-NUT spacetimes. Despite a lot of ``satisfactory'' efforts have been made, all of these activities have been restricted to the four-dimensional cases, with the higher even-dimensional cases remaining unexplored. The aim of this article is to fill the gap, for the first time. To the end of this subject, we first adopt our own idea that ``The NUT charge is a thermodynamical multi-hair"to investigate the consistent thermodynamics of $D = 6, 8, 10$ Lorentzian Taub-NUT spacetimes without a cosmological constant. Similarly to the $D = 4$ cases as did in our previous works, we find that the first law and Bekenstein-Smarr mass formulas are perfectly satisfied if we still assign the secondary hair: $J_n = Mn$ as a conserved charge in both mass formulae. Turning to the cases with a nonzero cosmological constant, our treatment continues to work very well and all the results can be fairly generalized to the corresponding Taub-NUT AdS spacetimes in higher even-dimensions, although we do not know how to define and introduce a similar higher-dimensional version of the dual (magnetic) mass that is well known in four dimensions. Based upon the preceding results, we will also derive the reduced version of the mass formulae when the secondary hair $J_n$ is viewed as a redundant thermodynamic variable.
Semi-supervised Training for Knowledge Base Graph Self-attention Networks on Link Prediction

The task of link prediction aims to solve the problem of incomplete knowledge caused by the difficulty of collecting facts from the real world. GCNs-based models are widely applied to solve link prediction problems due to their sophistication, but GCNs-based models are suffering from two problems in the structure and training process. 1) The transformation methods of GCN layers become increasingly complex in GCN-based knowledge representation models; 2) Due to the incompleteness of the knowledge graph collection process, there are many uncollected true facts in the labeled negative samples. Therefore, this paper investigates the characteristic of the information aggregation coefficient (self-attention) of adjacent nodes and redesigns the self-attention mechanism of the GAT structure. Meanwhile, inspired by human thinking habits, we designed a semi-supervised self-training method over pre-trained models. Experimental results on the benchmark datasets FB15k-237 and WN18RR show that our proposed self-attention mechanism and semi-supervised self-training method can effectively improve the performance of the link prediction task. If you look at FB15k-237, for example, the proposed method improves Hits@1 by about 30%.
Shocks in the Outflow of the RS Oph 2021 Eruption Observed with X-Ray Gratings

The 2021 outburst of the symbiotic recurrent nova RS Oph was observed with the Chandra High Energy Transmission Gratings (HETG) on day 18 after optical maximum and with XMM-Newton and its Reflection Grating Spectrographs (RGS) on day 21, before the supersoft X-ray source emerged and when the emission was due to shocked ejecta. The absorbed flux in the HETG 1.3–31 Å range was 2.6 × 10−10 erg cm−2 s−1, three orders of magnitude lower than the γ-ray flux measured on the same date. The spectra are well fitted with two components of thermal plasma in collisional ionization equilibrium, one at a temperature ≃ 0.75 keV and the other at a temperature in the 2.5–3.4 keV range. With the RGS we measured an average flux of 1.53 × 10−10 erg cm−2 s−1 in the 5–35 Å range, but the flux in the continuum and especially in the lines in the 23–35 Å range decreased during the 50 ks RGS exposure by almost 10%, indicating short-term variability on a timescale of hours. The RGS spectrum can be fitted with three thermal components, respectively at plasma temperatures between 70 and 150 eV, 0.64 keV, and 2.4 keV. The post-maximum epochs of the exposures fall between those of two grating spectra observed in the 2006 eruption on days 14 and 26: they are consistent with a similar spectral evolution, but in 2021 cooling seems to have been more rapid. Iron is depleted in the ejecta with respect to solar values, while nitrogen is enhanced.
Coexistence of Pulsed Radar and Communications: Interference Suppression and Multi-path Combining

—The focus of this study is on the spectrum sharing between multiple-input multiple-output (MIMO) communications and co-located pulsed MIMO radar systems in multi-path environments. The major challenge is to suppress the mutual interference between the two systems while combining the useful multi-path components received at each system. We tackle this challenge by jointly designing the communication precoder, radar transmit waveform and receive ﬁlter. Speciﬁcally, the signal-to-interference-plus-noise ratio (SINR) at the radar receiver is maximized subject to constraints on the radar wave-form, communication rate and transmit power. The multi-path propagation complicates the expressions of the radar SINR and communication rate, leading to a non-convex problem. To solve it, a sub-optimal algorithm based on the alternating maximization is used to optimize the precoder, radar transmit waveform and receive ﬁlter iteratively. The radar receive ﬁlter can be updated by a closed-form solution. The communication precoder and radar transmit waveform can be obtained by the successive convex approximation and alternating direction method of multipliers. Simulation results are provided to demonstrate the effectiveness of the proposed design.
Optimal empirical Bayes estimation for the Poisson model via minimum-distance methods

The Robbins estimator is the most iconic and widely used procedure in the empirical Bayes literature for the Poisson model. On one hand, this method has been recently shown to be minimax optimal in terms of the regret (excess risk over the Bayesian oracle that knows the true prior) for various nonparametric classes of priors. On the other hand, it has been long recognized in practice that Robbins estimator lacks the desired smoothness and monotonicity of Bayes estimators and can be easily derailed by those data points that were rarely observed before. Based on the minimum-distance distance method, we propose a suite of empirical Bayes estimators, including the classical nonparametric maximum likelihood, that outperform the Robbins method in a variety of synthetic and real data sets and retain its optimality in terms of minimax regret.
A Model of Non-Belief in the Law of Large Numbers

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this "non-belief in the Law of Large Numbers" by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a non-believer expects the distribution of signals will have fat tails, more so for larger samples. In inference, a non-believer remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.
Comparative Study of AR Versus Image and Video for Exercise Learning

There is inadequate attention to using mobile Augmented Reality (AR) in fitness, despite mobile AR being easy to use, requiring no extra cost, and can be a powerful learning tool. In this work, we present a mobile AR application that can help users learn exercises with a virtual personal trainer. We conduct a user study with 10 participants to investigate the learning quality of the ARFit (i.e., the proposed mobile AR application) in comparison to traditional methods such as Image-based learning and Video-based learning. Our results indicate that participants have a higher learning quality of exercise with mobile AR than (1) Image-based learning among all exercises selected and (2) video-based learning with exercise that requires greater spatial knowledge, with the performance evaluated by a qualified personal trainer. In addition, ARFit has an excellent rating in usability, is deemed to be highly acceptable, and is the preferred exercise learning method by most participants (N=9)
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.
Using Anisotropies as a Forensic Tool for Decoding Supernova Remnants

We present a method for analyzing supernova remnants (SNRs) by diagnosing the drivers responsible for structure at different angular scales. First, we perform a suite of hydrodynamic models of the Rayleigh–Taylor instability (RTI) as a supernova (SN) collides with its surrounding medium. Using these models we demonstrate how power spectral analysis can be used to attribute which scales in an SNR are driven by RTI and which must be caused by intrinsic asymmetries in the initial explosion. We predict the power spectrum of turbulence driven by RTI and identify a dominant angular mode that represents the largest scale that efficiently grows via RTI. We find that this dominant mode relates to the density scale height in the ejecta, and therefore reveals the density profile of the SN ejecta. If there is significant structure in an SNR on angular scales larger than this mode, then it is likely caused by anisotropies in the explosion. Structure on angular scales smaller than the dominant mode exhibits a steep scaling with wavenumber, possibly too steep to be consistent with a turbulent cascade, and therefore might be determined by the saturation of RTI at different length scales (although systematic 3D studies are needed to investigate this). We also demonstrate, consistent with previous studies, that this power spectrum is independent of the magnitude and length scales of perturbations in the surrounding medium and therefore this diagnostic is unaffected by “clumpiness” in the circumstellar medium.