Miracle-dz/UniFAR-FTUs huggingface dataset:
Covariant density functional theory input for 
r
-process simulations in actinides and superheavy nuclei: The ground state and fission properties

The systematic investigation of the ground state and fission properties of even-even actinides and superheavy nuclei with $Z=90-120$ from the two-proton up to two-neutron drip lines with proper assessment of systematic theoretical uncertainties has been performed for the first time in the framework of covariant density functional theory (CDFT). These results provide a necessary theoretical input for the r-process modeling in heavy nuclei and, in particular, for the study of fission recycling. Four state-of-the-art globally tested covariant energy density functionals (CEDFs), namely, DD-PC1, DD-ME2, NL3* and PC-PK1, representing the major classes of the CDFT models are employed in the present study. Ground state deformations, binding energies, two neutron separation energies, $\alpha$-decay $Q_{\alpha}$ values and half-lives and the heights of fission barriers have been calculated for all these nuclei. Theoretical uncertainties in these physical observables and their evolution as a function of proton and neutron numbers have been quantified and their major sources have been identified. Spherical shell closures at $Z=120$, $N=184$ and $N=258$ and the structure of the single-particle (especially, high-$j$) states in their vicinities as well as nuclear matter properties of employed CEDFs are two major factors contributing into theoretical uncertainties. However, different physical observables are affected in a different way by these two factors. For example, theoretical uncertainties in calculated ground state deformations are affected mostly by former factor, while theoretical uncertainties in fission barriers depend on both of these factors.
SPICY: The Spitzer/IRAC Candidate YSO Catalog for the Inner Galactic Midplane

We present ∼120,000 Spitzer/IRAC candidate young stellar objects (YSOs) based on surveys of the Galactic midplane between ℓ ∼ 255° and 110°, including the GLIMPSE I, II, and 3D, Vela-Carina, Cygnus X, and SMOG surveys (613 square degrees), augmented by near-infrared catalogs. We employed a classification scheme that uses the flexibility of a tailored statistical learning method and curated YSO data sets to take full advantage of Spitzer’s spatial resolution and sensitivity in the mid-infrared ∼3–9 μm range. Multiwavelength color/magnitude distributions provide intuition about how the classifier separates YSOs from other red IRAC sources and validate that the sample is consistent with expectations for disk/envelope-bearing pre–main-sequence stars. We also identify areas of IRAC color space associated with objects with strong silicate absorption or polycyclic aromatic hydrocarbon emission. Spatial distributions and variability properties help corroborate the youthful nature of our sample. Most of the candidates are in regions with mid-IR nebulosity, associated with star-forming clouds, but others appear distributed in the field. Using Gaia DR2 distance estimates, we find groups of YSO candidates associated with the Local Arm, the Sagittarius–Carina Arm, and the Scutum–Centaurus Arm. Candidate YSOs visible to the Zwicky Transient Facility tend to exhibit higher variability amplitudes than randomly selected field stars of the same magnitude, with many high-amplitude variables having light-curve morphologies characteristic of YSOs. Given that no current or planned instruments will significantly exceed IRAC’s spatial resolution while possessing its wide-area mapping capabilities, Spitzer-based catalogs such as ours will remain the main resources for mid-infrared YSOs in the Galactic midplane for the near future.
SOME ANATOMICAL FEATURES OF AN ACACIA HYBRID , A . MANGIUM AND A . AURICULIFORMIS GROWN IN INDONESIA WITH REGARD TO PULP YIELD AND PAPER STRENGTH

YAHYA R, SUGIYAMA J, SILSIA D & GRIL J. 2010. Some anatomical features of an Acacia hybrid, A. mangium and A. auriculiformis grown in Indonesia with regard to pulp yield and paper strength. The objectives of this study were to compare anatomical wood properties, chemical composition and wood density between an Acacia hybrid and its parents, namely, Acacia mangium and Acacia auriculiformis. The strength of the relationship between these anatomical properties or wood density and chemical composition as a way to predict the pulp yield and paper strength was studied. Three seven-year-old trees per species were randomly selected from an experimental plantation. Compared with both parents, the Acacia hybrid had longer fibre, and higher slenderness ratio, fibre proportion and holocellulose content but smaller proportions of vessels, parenchyma cells and extractives. In addition, the hybrid tended to have thinner cell wall, and lower proportion of ray cells, rigidity and lignin content but higher flexibility coefficient and wood density than A. mangium. Holocellulose, α-cellulose and lignin contents were all reliably predicted by fibre length. Slenderness ratio was a better predictor of extractives content than fibre length. Both fibre length and slenderness ratio were better predictors of chemical composition than wood density. Therefore, fibre length and slenderness ratio could be good predictors of pulp yield and paper strength for acacias.
Randomized Transferable Machine

Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is truly domain-invariant, and thus directly train a transfer model $M$ on source domain. In this paper, we consider a more realistic scenario where the new feature representation is suboptimal and small divergence still exists across domains. We propose a new learning strategy with a transfer model called Randomized Transferable Machine (RTM). More specifically, we work on source data with the new feature representation learned from existing feature-based transfer methods. The key idea is to enlarge source training data populations by randomly corrupting source data using some noises, and then train a transfer model $\widetilde{\mathcal{M}}$ that performs well on all the corrupted source data populations. In principle, the more corruptions are made, the higher the probability of the target data can be covered by the constructed source populations. and thus better transfer performance can be achieved by $\widetilde{\mathcal{M}}$ An ideal case is with infinite corruptions, which however is infeasible in reality. We develop a marginalized solution with linear regression model and dropout noise. With a marginalization trick, we can train an RTM that is equivalently to training using infinite source noisy populations without truly conducting any corruption. More importantly, such an RTM has a closed-form solution, which enables very fast and efficient training. Extensive experiments on various real-world transfer tasks show that RTM is a promising transfer model.
Cooperative efficiency boost for quantum heat engines

The power and efficiency of many-body heat engines can be boosted by performing cooperative non-adiabatic operations in contrast to the commonly used adiabatic implementations. Here, the key property relies on the fact that non-adiabaticity is required in order to allow for cooperative effects, that can use the thermodynamic resources only present in the collective non-passive state of a many-body system. In particular, we consider the efficiency of an Otto cycle, which increases with the number of copies used and reaches a many-body bound, which we discuss analytically.
Cinnamic acid based thiazolidinediones inhibit human P 450 c 17 and 3-hydroxysteroid dehydrogenase and improve insulin sensitivity independent of PPAR agonist activity

Thiazolidinediones improve insulin sensitivity in type 2 diabetes mellitus by acting as peroxisome proliferator-associated receptor gamma (PPAR ) agonists, and decrease circulating androgen concentrations in polycystic ovary syndrome by unknown mechanisms. Some thiazolidinediones directly inhibit the steroidogenic enzymes P450c17 and 3 -hydroxysteroid dehydrogenase type II (3 HSDII) by distinct mechanisms. We synthesized five novel thiazolidinediones, CLX-M1 to -M5 by linking a 2,4-thiazolidinedione moiety to a substituted -phenyl cinnamic acid previously shown to have glucose-lowering effects. Using yeast microsomes expressing human P450c17 and 3 HSDII we found that cinnamic acid methyl esters with a double bond in the thiazolidinedione core structure (M3, M5) were stronger inhibitors of P450c17 than methyl esters with the conventional core (M1, M4). These four compounds inhibited 3 HSDII equally well, while the free cinnamic acid analog (M2) did not inhibit either enzyme. Thus, the inhibition of P450c17 and 3 HSDII by these novel thiazolidinediones reveals structure–activity relationships independent of PPAR transactivation. PPAR transactivation was moderate (M1), weak (M2, M3) or even absent (M4, M5). While the PPAR agonist activity of M1 was only 3% of that of rosiglitazone, both increased glucose uptake by 3T3-L1 adipocytes and reduced serum glucose levels in ob/ob and db/db mice to a similar extent. The similar glucose-lowering effects of M1 and rosiglitazone, despite their vast differences in PPAR agonist activity, suggests these two actions may occur by separate mechanisms. Journal of Molecular Endocrinology (2004) 32, 425–436
An Examination of the Differences in Flow between Individual and Team Athletes

Abstract The purpose of this exploratory study was to investigate differences between the flow experiences of NCAA Division I team athletes versus individual athletes. A volunteer sample of 104 collegiate athletes completed a 42-item flow questionnaire. Multiple linear regressions showed mean flow scores for team athletes were significantly higher than individual athletes (β = -1.66, p = 0.004), with an R2 value of 0.03. The type of sport was a significant predictor of three of the nine dimensions of flow, with the largest difference explaining 9% of the variance. The results of this study are unique and answer the repeated call in the literature for team flow research. In summary, team sport athletes experienced total flow at a higher overall rate than individual sport athletes, allowing for the conclusion that team sport athletes can and do experience flow.
Unsupervised part representation by Flow Capsules

Capsule networks are designed to parse an image into a hierarchy of objects, parts and relations. While promising, they remain limited by an inability to learn effective low level part descriptions. To address this issue we propose a novel self-supervised method for learning part descriptors of an image. During training, we exploit motion as a powerful perceptual cue for part definition, using an expressive decoder for part generation and layered image formation with occlusion. Experiments demonstrate robust part discovery in the presence of multiple objects, cluttered backgrounds, and significant occlusion. The resulting part descriptors, a.k. a. part capsules, are decoded into shape masks, filling in occluded pixels, along with relative depth on single images. We also report unsupervised object classification using our capsule parts in a stacked capsule autoencoder.
Remarks on a triangulated version of Auslander-Kleiner's Green correspondence.

For a finite group $G$ and an algebraically closed field $k$ of characteristic $p>0$ for any indecomposable finite dimensional $kG$-module $M$ with vertex $D$ and a subgroup $H$ of $G$ containing $N_G(D)$ there is a unique indecomposable $kH$-module $N$ of vertex $D$ being a direct summand of the restriction of $M$ to $H$. This correspondence, called Green correspondence, was generalised by Auslander-Kleiner to the situation of pairs of adjoint functors between additive categories. In the original situation of group rings Carlson-Peng-Wheeler proved that this correspondence is actually restriction of triangle functors between triangulated quotient categories of the corresponding module categories. We review this theory and show how we got a common generalisation of the approaches of Auslander-Kleiner and Carlson-Peng-Wheeler, using Verdier localisations.
Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations

Data augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmented samples, a torrent of methods regularizing the distance between embeddings/representations of the original samples and their augmented counterparts have been introduced. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. Our analysis suggests the ideal choices of regularization correspond to various assumptions. With an invariance test, we argue that regularization is important if the model is to be used in a broader context than the accuracy-driven setting because non-regularized approaches are limited in learning the concept of invariance, despite equally high accuracy. Finally, we also show that the generic approach we identified (squared $\ell_2$ norm regularized augmentation) outperforms several recent methods, which are each specially designed for one task and significantly more complicated than ours, over three different tasks.
United Nations Population Fund (UNFPA) Analytical Review of the Interaction between Urban Growth Trends and Environmental Changes Paper 1 URBAN DENSITY AND CLIMATE CHANGE 1

The paper therefore examines the implications of different urban densities for the emission of greenhouse gases (particularly, although not exclusively, in high-income countries), and the implications of this for global climate change. The paper explores the relationship between form, density, economy and society within cities to assess whether particular spatial patterns can have a positive or negative effect on the emission of greenhouse gases and consequently climate change.
A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field

Ground segmentation is an important preprocessing task for autonomous vehicles (AVs) with 3D LiDARs. However, the existing ground segmentation methods are very difficult to balance accuracy and computational complexity. This paper proposes a fast point cloud ground segmentation approach based on a coarse-to-fine Markov random field (MRF) method. The method uses the coarse segmentation result of an improved local feature extraction algorithm instead of prior knowledge to initialize an MRF model. It provides an initial value for the fine segmentation and dramatically reduces the computational complexity. The graph cut method is then used to minimize the proposed model to achieve fine segmentation. Experiments on two public datasets and field tests show that our approach is more accurate than both methods based on features and MRF and faster than graph-based methods. It can process Velodyne HDL-64E data frames in real-time (24.86 ms, on average) with only one thread of the I7-8700 CPU. Compared with methods based on deep learning, it has better environmental adaptability.
DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution

Previous top-performing approaches for point cloud in-stance segmentation involve a bottom-up strategy, which often includes inefficient operations or complex pipelines, such as grouping over-segmented components, introducing additional steps for refining, or designing complicated loss functions. The inevitable variation in the instance scales can lead bottom-up methods to become particularly sensitive to hyper-parameter values. To this end, we propose in-stead a dynamic, proposal-free, data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. To make the kernels discriminative, we explore a large context by gathering homogeneous points that share identical semantic categories and have close votes for the geometric centroids. Instances are then decoded by several simple convolutional layers. Due to the limited receptive field introduced by the sparse convolution, a small light-weight transformer is also devised to capture the long-range dependencies and high-level interactions among point samples. The proposed method achieves promising results on both ScanetNetV2 and S3DIS, and this performance is robust to the particular hyper-parameter values chosen. It also improves inference speed by more than 25% over the current state-of-the-art. Code is available at: https://git.io/DyCo3D
Running head: MEMORY FOR GAINS AND LOSSES I Owe You: Age-Related Similarities and Differences in Associative Memory for Gains and Losses

Older adults often experience associative memory impairments but can sometimes remember important information. The current experiments investigate potential age-related similarities and differences associate memory for gains and losses. Younger and older participants were presented with faces and associated dollar amounts, which indicated how much money the person “owed” the participant, and were later given a cued recall test for the dollar amount. Experiment 1 examined face-dollar amount pairs while Experiment 2 included negative dollar amounts to examine both gains and losses. While younger adults recalled more information relative to older adults, both groups were more accurate in recalling the correct value associated with high value faces compared to lower value faces and remembered gist-information about the values. However, negative values (losses) did not have a strong impact on recall among older adults versus younger adults, illustrating important associative memory differences between younger and older adults.
A Primer on Zeta Functions and Decomposition Spaces.

Many examples of zeta functions in number theory and combinatorics are special cases of a construction in homotopy theory known as a decomposition space. This article aims to introduce number theorists to the relevant concepts in homotopy theory and lays some foundations for future applications of decomposition spaces in the theory of zeta functions.
TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game

StarCraft, one of the most difficult esport games with long-standing history of professional tournaments, has attracted generations of players and fans, and also, intense attentions in artificial intelligence research. Recently, Google's DeepMind announced AlphaStar, a grandmaster level AI in StarCraft II. In this paper, we introduce a new AI agent, named TStarBot-X, that is trained under limited computation resources and can play competitively with expert human players. TStarBot-X takes advantage of important techniques introduced in AlphaStar, and also benefits from substantial innovations including new league training methods, novel multi-agent roles, rule-guided policy search, lightweight neural network architecture, and importance sampling in imitation learning, etc. We show that with limited computation resources, a faithful reimplementation of AlphaStar can not succeed and the proposed techniques are necessary to ensure TStarBot-X's competitive performance. We reveal all technical details that are complementary to those mentioned in AlphaStar, showing the most sensitive parts in league training, reinforcement learning and imitation learning that affect the performance of the agents. Most importantly, this is an open-sourced study that all codes and resources (including the trained model parameters) are publicly accessible via this https URL We expect this study could be beneficial for both academic and industrial future research in solving complex problems like StarCraft, and also, might provide a sparring partner for all StarCraft II players and other AI agents.