The critical binary star separation for a planetary system origin of white dwarf pollution

The atmospheres of between one quarter and one half of observed single white dwarfs in the Milky Way contain heavy element pollution from planetary debris. The pollution observed in white dwarfs in binary star systems is, however, less clear, because companion star winds can generate a stream of matter which is accreted by the white dwarf. Here we (i) discuss the necessity or lack thereof of a major planet in order to pollute a white dwarf with orbiting minor planets in both single and binary systems, and (ii) determine the critical binary separation beyond which the accretion source is from a planetary system. We hence obtain user-friendly functions relating this distance to the masses and radii of both stars, the companion wind, and the accretion rate onto the white dwarf, for a wide variety of published accretion prescriptions. We find that for the majority of white dwarfs in known binaries, if pollution is detected, then that pollution should originate from planetary material.Comment: Accepted for publication in MNRA

Texture Segmentation using LBP embedded Region Competition

In this paper, we modify the region competition method to segment textures. First, local Binary pattern (LBP) histogram is adopted to capture the texture information. Then, considering the specific goal of texture segmentation, we propose new assumption about region competition and rewrite the energy function based on LBP histograms. We also develop the two-stage iterative algorithm to make our energy converge to a local minimum. Because of the fast LBP operator and nonparametric histogram model, we can simplify the step of parameter estimating, which is always the most time-consuming. Besides, LBP' s high performance for texture characterization helps to make our method more suitable for texture segmentation problem. Experiments show that the performance of our proposed method is promising, and a robust and fast segmentation of texture images is obtained

Chemical composition of the outer halo globular cluster Palomar 15

Globular clusters (GCs) in the outer Milky Way halo are important tracers of the assembly history of our Galaxy. Only a few of these objects show spreads in heavier elements beyond the canonical light-element variations that have essentially been found throughout the entire Galactic GC system, suggesting a more complex origin and evolution of these objects. Here, we present the first abundance analysis of three red giants in the remote ($R_{\rm GC}=38$ kpc) outer halo GC Palomar 15, based on medium-resolution spectra obtained with the Keck/ESI instrument. Our results ascertain a low iron abundance of $-$1.94$\pm$0.06 dex with no evidence of any significant abundance spreads, although this is based on low number statistics. Overall, abundance ratios of 16 species were measured, including carbon, Na, Al, $\alpha$-peak (Mg,Si,Ca,Ti) and Fe-peak (Sc,V,Cr,Fe,Co,Ni) elements, and the three neutron-capture elements Sr, Ba, and Eu. The majority of abundances are compatible with those of halo field stars and those found in other GCs in the outer and inner halos at similar metallicity. Pal 15 is enhanced to [Mg/Fe]=0.45 dex, while other $\alpha$-elements, Ca and Ti, are lower by 0.3 dex. Taking Mg as a representative for [$\alpha$/Fe], and coupled with the lack of any significant spread in any of the studied elements we conclude that Pal 15 is typical of the outer halo, as is bolstered by its chemical similarity to the benchmark outer halo cluster NGC 7492. One star shows evidence of elevated Na and Al abundances, hinting at the presence of multiple stellar populations in this cluster.Comment: 9 pages, 8 figures, published in Astronomy & Astrophysics (July 2019

Perturbative Unitarity and NEC Violation in Genesis Cosmology

Explorations of the violation of null energy condition (NEC) in cosmology could enrich our understanding of the very early universe and the related gravity theories. Although a fully stable NEC violation can be realized in the “beyond Horndeski” theory, it remains an open question whether a violation of the NEC is allowed by some fundamental properties of UV-complete theories or the consistency requirements of effective field theory (EFT). We investigate the tree-level perturbative unitarity for stable NEC violations in the contexts of both Galileon and “beyond Horndeski” genesis cosmology, in which the universe is asymptotically Minkowskian in the past. We find that the constraints of perturbative unitarity imply that we may need some unknown new physics below the cut-off scale of the EFT other than that represented by the “beyond Horndeski” operators

Predicting pragmatic functions of Chinese echo questions using prosody: evidence from acoustic analysis and data modeling

Echo questions serve two pragmatic functions (recapitulatory and explicatory) and are subdivided into two types (yes-no echo question and wh-echo question) in verbal communication. Yet to date, most relevant studies have been conducted in European languages like English and Spanish. It remains unknown whether the different functions of echo questions can be conveyed via prosody in spoken Chinese. Additionally, no comparison was made on the diversified algorithmic models in predicting functions by the prosodity of Chinese echo questions, a novel linguistic cognition in nature. This motivated us to use different acoustic cues to predict different pragmatic functions of Chinese echo questions by virtue of acoustic experiment and data modeling. The results showed that for yes-no echo question, explicatory function exhibited higher pitch and intensity patterns than recapitulatory function whereas for wh-echo question, recapitulatory function demonstrated higher pitch and intensity patterns than explicatory function. With regard to data modeling, the algorithm Support Vector Machine (SVM) relative to Random Forest (RF) and Logistic Regression (LR) performed better when predicting different functions using prosodic cues in both yes-no and wh-echo questions. This study from a digitized perspective adds evidence to the cognition of echo questions’ functions on a prosodic basis

Efficient Offline Policy Optimization with a Learned Model

MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations, thus costing huge computing time. This paper investigates a few hypotheses where MuZero Unplugged may not work well under the offline RL settings, including 1) learning with limited data coverage; 2) learning from offline data of stochastic environments; 3) improperly parameterized models given the offline data; 4) with a low compute budget. We propose to use a regularized one-step look-ahead approach to tackle the above issues. Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout. Policy improvements are towards the direction that maximizes the estimated advantage with regularization of the dataset. We conduct extensive empirical studies with BSuite environments to verify the hypotheses and then run our algorithm on the RL Unplugged Atari benchmark. Experimental results show that our proposed approach achieves stable performance even with an inaccurate learned model. On the large-scale Atari benchmark, the proposed method outperforms MuZero Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e., 1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM normalized score with the same hardware and software stacks. Our implementation is open-sourced at https://github.com/sail-sg/rosmo.Comment: ICLR202

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

IntroductionIn contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.MethodsFor automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.ResultsThese models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.DiscussionThis research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species