Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H]. "Linearly supporting" means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS)$_{bs}$; third, estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate $T_{eff}$, 62 features for log$~g$, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Sarameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log$~T_{eff}$ (83 K for $T_{eff}$), 0.2345 dex for log$~g$, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log$~T_{eff}$ (32 K for $T_{eff}$), 0.0337 dex for log$~g$, and 0.0268 dex for [Fe/H].Comment: 21 pages, 7 figures, 8 tables, The Astrophysical Journal Supplement Series (accepted for publication

Large Language Models for Robotics: A Survey

The human ability to learn, generalize, and control complex manipulation tasks through multi-modality feedback suggests a unique capability, which we refer to as dexterity intelligence. Understanding and assessing this intelligence is a complex task. Amidst the swift progress and extensive proliferation of large language models (LLMs), their applications in the field of robotics have garnered increasing attention. LLMs possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots. Researchers and engineers in the field of robotics have recognized the immense potential of LLMs in enhancing robot intelligence, human-robot interaction, and autonomy. Therefore, this comprehensive review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and path planning. We first provide an overview of the background and development of LLMs for robotics, followed by a description of the benefits of LLMs for robotics and recent advancements in robotics models based on LLMs. We then delve into the various techniques used in the model, including those employed in perception, decision-making, control, and interaction. Finally, we explore the applications of LLMs in robotics and some potential challenges they may face in the near future. Embodied intelligence is the future of intelligent science, and LLMs-based robotics is one of the promising but challenging paths to achieve this.Comment: Preprint. 4 figures, 3 table

Laboratory experimental study of water drag force exerted on ridge keel

With the diminishing Arctic sea ice, the dynamic energy-exchange process between sea ice and ocean gains in importance. Concerning how the ice bottom topography affects the drift of sea ice, it is unclear how the ridge–keel-drag force exerted by seawater changes the momentum balance of sea ice. We thus conducted laboratory experiments to investigate how the local drag coefficient of the ridge keel depends on keel shape and on the relative velocity of ice with respect to seawater. A dimensional analysis is used to obtain the relationship between the local drag coefficient Cr, the Reynolds number Re, the dimensionless keel depth h0, and the keel slope angle φ. The results indicate that the local drag coefficient Cr is only relevant to Re when Re < 4000 and the flow is in the laminar regime. With increasing Re, Cr depends on h0 and φ, which are independent variables, as the flow transitions to the turbulent regime. The parameterization formulas for Cr are also provided

ELM of ELM-WD: An extremely low mass hot donor star discovered in LAMOST survey

The Extremely Low Mass White Dwarfs (ELM WDs) and pre-ELM WDs are helium core white dwarfs with mass $<\sim 0.3M_{\odot}$. They are formed in close binaries and have lost over half of their initial masses via Common Envelope (CE) ejection or stable Roche Lobe Over Flow (RLOF). Both evolution simulations and observations show that a lower mass limit for ELM WDs exists at $\approx0.14M_{\odot}$. Here we report the discovery of an extremely low mass ELM WD, ID70904216 in LAMOST survey, that may be lower than the ELM WD mass limit. Based on LAMOST and P200 spectroscopic observations, ID70904216 shows orbital period $P_{orb} =$ 0.219658 days and radial velocity semi-amplitude $K1=317.33km/s$, which gives the mass function of 0.73$M_{\odot}$, indicating the companion is a compact star. The low resolution spectra shows a F type star with $T_{\rm eff} \sim 7361K$ without emission features. The temperature is consistent with that derived from SED fitting($7440K$) and multi-color light curve solution($7400K$). The optical light curves, in ZTF g, r and i bands and Catalina V band, show ellipsoidal variability with amplitudes $\approx30\%$, suggesting that the visible companion is heavily tidal distorted. Combining with the distance from Gaia survey, the WD code modeling estimates that the mass of the visible star is $M1=0.08^{+0.06}_{-0.03}M_{\odot}$, and the mass of the invisible star is $M2=0.94^{+0.45}_{-0.10}M_{\odot}$. The radius of the visible donor is $R=0.29\pm0.01R_{\odot}$. The inclination angle is constrained between 60$^{\circ}$ and 90$^{\circ}$. The observations indicate the system is a pre-ELM WD + WD/NS binary system with an extremely low mass hot donor below the $0.14M_{\odot}$ theoretical limit.Comment: 16 pages, 10 figure

Multiphoton graph states from a solid-state single-photon source

This work was supported by the National Natural Science Foundation of China (Grants No. 11575174, No. 11674308, No. 11704424, and No. 11774326), the Chinese Academy of Sciences, and the National Key Research and Development Program of China.Photonic graph states are underlying resources for one-way optical quantum computation, quantum error correction, fundamental testing of quantum mechanics, and quantum communication networks. Most existing works, however, are based on the spontaneous parametric down-conversion sources that intrinsically suffer from probabilistic generation and double pair components. Here, we create two important classes of graph states, a polarization-encoded four-photon Greenberger–Horne–Zeilinger (GHZ) state and a linear cluster state, by actively demultiplexing a deterministic single-photon source from a semiconductor quantum dot embedded in a micropillar. A state fidelity of 0.790 ± 0.009 (0.763 ± 0.004) and a count rate of ∼13 Hz are observed for the four-photon GHZ (cluster) state. The results constitute a new route toward the multiphoton entanglement with deterministic single-photon sources.PostprintPeer reviewe

Metabolomics analysis of stool in rats with type 2 diabetes mellitus after single-anastomosis duodenal–ileal bypass with sleeve gastrectomy

BackgroundSingle-anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S) is one of the most effective bariatric procedures in the treatment of type 2 diabetes mellitus (T2DM). However, the mechanisms by which SADI-S improves T2DM are not well-known.ObjectiveTo explore the effects of SADI-S on metabolites in the stool of rats with T2DM.MethodsTwenty rats were fed on high-fat diet and administered with a low-dose (30mg/kg) of streptozotocin to establish T2DM models. The rats were then randomly assigned to the SADI-S group (n=10) and sham operation group (n=9). Stool samples were collected from all rats at 8 weeks after surgery and stored at -80 °C. Metabolomics analysis was performed to identify differential metabolites through ultra- performance liquid chromatography-mass spectrometry.ResultsAt 8-week after surgery, rats of the SADI-S group showed significantly decreased fasting blood glucose, glucose tolerance test 2-hour, glycated haemoglobin, and body weight compared with those of the sham group. A total of 245 differential metabolites were identified between the two groups, among which 8 metabolites were detectable under both the positive ion model and negative ion model. Therefore, a total of 237 differential metabolites were identified in our study which were mainly involved in tryptophan metabolism; cysteine and methionine metabolism; phenylalanine metabolism; phenylalanine; tyrosine and tryptophan biosynthesis; arginine biosynthesis; alanine, aspartate and glutamate metabolism; Arginine and proline metabolism; glyoxylate and dicarboxylate metabolism; alpha-Linolenic acid metabolism; Linoleic acid metabolism; riboflavin metabolism; nicotinate and nicotinamide metabolism; pyrimidine metabolism; porphyrin and chlorophyll metabolism.ConclusionSADI-S significantly improved the glucose metabolism in T2DM rats. In addition, SADI-S significantly changed the composition of metabolites in T2DM rats which were involved in tryptophan metabolism pathway, linoleic acid metabolism pathway and so on. This may be the mechanism by which SADI-S improved T2DM