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While Large language models (LLMs) have become excellent writing assistants, they still struggle with quotation generation. This is because they either hallucinate when providing factual quotations or fail to provide quotes that exceed human expectations. To bridge the gap, we systematically study how to evaluate and improve LLMs' performance in quotation generation tasks. We first establish a holistic and automatic evaluation system for quotation generation task, which consists of five criteria each with corresponding automatic metric. To improve the LLMs' quotation generation abilities, we construct a bilingual knowledge base that is broad in scope and rich in dimensions, containing up to 32,022 quotes. Moreover, guided by our critiria, we further design a quotation-specific metric to rerank the retrieved quotations from the knowledge base. Extensive experiments show that our metrics strongly correlate with human preferences. Existing LLMs struggle to generate desired quotes, but our quotation knowledge base and reranking metric help narrow this gap. Our dataset and code are publicly available at https://github.com/GraceXiaoo/QUILL., Comment: 17 pages, 6 figures

Highly r-process-enhanced stars are rare and usually metal-poor ([Fe/H] < - 1.0), and mainly populate the Milky Way halo and dwarf galaxies. This study presents the discovery of a relatively bright (V = 12.72), highly r-process-enhanced (r-II) star ([Eu/Fe] = +1.32, [Ba/Eu] = - 0.95), LAMOST J020632.21 + 494127.9. This star was selected from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution (R ~ 7500) spectroscopic survey; follow-up high-resolution (R ~ 25,000) observations were conducted with the High Optical Resolution Spectrograph (HORuS) installed on the Gran Telescopio Canarias (GTC). The stellar parameters (${T_{\rm eff}}$ = 4130 K, $\rm log\,g $ = 1.52, $ \rm[Fe/H] $ = $ - $0.54, $\xi$ = 1.80 $ \rm{km\,{s^{-1}}} $) have been inferred taking into account non-local thermodynamic equilibrium (NLTE) effects. The abundances of [Ce/Fe], [Pr/Fe], and [Nd/Fe] are +0.19, +0.65 and +0.64, respectively, relatively low compared to the Solar r-process pattern normalized to Eu. This star has a high metallicity ([Fe/H] = - 0.54) compared to most other highly r-process-enhanced stars, and has the highest measured abundance ratio of Eu to H ([Eu/H] = +0.78). It is classified as a thin-disk star based on its kinematics, and does not appear to belong to any known stream or dwarf galaxy., Comment: 5 figures, 3 tables

Lithium is a fragile but crucial chemical element in the universe, exhibits interesting and complex behaviors. Thanks to the massive spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution survey (MRS), we can investigate the lithium abundances in a large and diverse sample of stars, which could bring vital help to study the origin and evolution of lithium. In this work, we use the Li 6,707.8 {\AA} line to derive the lithium abundance through a template-matching method. A catalog of precise lithium abundance is presented for 795,384 spectra corresponding to 455,752 stars from the LAMOST MRS Data Release (DR) 9. Comparing our results with those of external high-resolution references we find a good consistency with a typical deviation of {\sigma} A(Li) ~ 0.2 dex. We also analyze the internal errors using stars that have multiple LAMOST MRS observations, which will reach as low as 0.1 dex when the signal-to-noise ratio (S/N) of the spectra > 20. Besides, our result indicates that a small fraction of giant stars still exhibit surprisingly high amount of lithium contents, and 967 stars are identified as Li-rich giants with A(Li) > 1.5 dex, accounting for ~ 2.6% of our samples. If one takes into account the fact that nearly all stars deplete lithium during the main sequence, then the fraction of Li-rich stars may exceed 2.6% much. This new catalog covers a wide range of stellar evolutionary stages from pre-main sequence to giants, and will provide help to the further study of the chemical evolution of lithium., Comment: 16 pages, 9 figures, 2 tables. Accepted for publication in ApJS

We derive the initial spin period distribution of neutron stars by studying the population of young pulsars associated with supernova remnants. Our hierarchical Bayesian approach accounts for the measurement uncertainties of individual observations and selection effects. Without correcting for selection effects, as done in previous studies, we find that pulsar initial spin periods follow a Weibull distribution, peaking at 40~ms, which is favoured against the log-normal distribution with a Bayes factor of 200. The known selection effects in radio pulsar surveys, including pulse broadening and a period-dependent beaming fraction, have been quantitatively investigated. We show that, based on measurements of pulsar luminosity and spin period from the ATNF Pulsar Catalogue, the impact of pulse broadening on the inference of pulsar period distribution is likely to be insignificant. Correcting for the beaming selection effect, a Weibull distribution remains to be the preferred model, while its peak slightly shifts to longer periods at 50~ms. Our method will prove useful in constraining the birth properties of neutron stars in the Square Kilometre Array era., Comment: Accepted for publication in The Astrophysical Journal

Spin period distribution provides important clues to understand the formation of millisecond pulsars (MSPs). To uncover the intrinsic period distribution, we analyze three samples of radio MSPs in the Galactic field and in globular clusters. The selection bias due to pulse broadening has been corrected but turns out to be negligible. We find that all the samples can be well described by a Weibull distribution of spin frequencies. Considering MSPs in the Galactic field or in globular clusters, and in isolation or in binary systems, we find no significant difference in the spin distribution among these subpopulations. Based on the current known population of MSPs, we find that sub-millisecond pulsars are unlikely to be discovered by the Square Kilometer Array, although up to $\sim10$ discoveries of pulsars that spin faster than the current record holder of $P=1.4$~ms are expected., Comment: 14 pages, 7 figures. Accepted by the ApJ for publication

Recently, multiple pulsar timing array collaborations have presented compelling evidence for a stochastic signal at nanohertz frequencies, potentially originating from cosmic strings. Cosmic strings are linear topological defects that can arise during phase transitions in the early Universe or as fundamental strings in superstring theory. This paper focuses on investigating the detection capabilities of Taiji, a planned space-based gravitational wave detector, for the gravitational wave background generated by cosmic strings. By analyzing simulated Taiji data and utilizing comprehensive Bayesian parameter estimation techniques, we demonstrate a significant improvement in precision compared to the NANOGrav 15-year data, surpassing it by an order of magnitude. This highlights the enhanced measurement capabilities of Taiji. Consequently, Taiji can serve as a valuable complementary tool to pulsar timing arrays in validating and exploring the physics of cosmic strings in the early Universe., Comment: 24 pages, 5 figures

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO.

Two-dimensional metal-organic porous networks (2D-MOPNs) are highly ordered quantum boxes for exploring surface confinements. In this context, the electron confinement from occupied Shockley-type surface states (SS) has been vigorously studied in 2D-MOPNs. In contrast, the confinement of excited surface states, such as image potential states (IPSs), remains elusive. In this work, we apply two-photon photoemission to investigate the confinement exemplarily for the first image state in a Cu-coordinated T4PT porous network (Cu-T4PT). Due to the lateral potential confinement in the Cu-T4PT, periodic replicas of the IPS as well as the SS are present in a momentum map. Surprisingly, the first IPS transforms into a nearly flat band with a substantial increase of the effective mass (> 150 %), while the band dispersion of the SS is almost unchanged. The giant confinement effect of the excited electrons can be attributed to the wavefunction location of the first IPS perpendicular to the surface, where the majority probability density mainly resides at the same height as repulsive potentials formed by the Cu-T4PT network. This coincidence leads to a more effective scattering barrier to the IPS electrons, which is not observed in the SS. Our findings demonstrate that the vertical potential landscape in a porous architecture also plays a crucial role in surface electron confinement.

Two-dimensional organic porous networks (2DOPNs) have opened new vistas for tailoring the physicochemical characteristics of metallic surfaces. These typically chemically bound nanoporous structures act as periodical quantum wells leading to the 2D confinements of surface electron gases, adatoms and molecular guests. Here we propose a new type of porous network with weakly interacting 2,4,6-triphenyl-1,3,5-triazine (TPT) molecules on a Cu(111) surface, in which a temperature-driven (T-driven) phase transition can reversibly alter the supramolecular structures from a close-packed (CP-TPT) phase to a porous-network (PN-TPT) phase. Crucially, only the low-temperature PN-TPT exhibits subnano-scale cavities that can confine the surface state electrons and metal adatoms. The confined surface electrons undergo a significant electronic band renormalization. To activate the spin degree of freedom, the T-driven PN-TPT structure can additionally trap Co atoms within the cavities, forming highly ordered quantum dots. Our theoretical simulation reveals a complex spin carrier transfer from the confined Co cluster to the neighbouring TPT molecules via the underlying substrate. Our results demonstrate that weakly interacting 2DOPN offers a unique quantum switch capable of steering and controlling electrons and spin at surfaces via tailored quantum confinements.

Peculiar velocity encodes rich information about the formation, dynamics, evolution, and merging history of binary black holes. In this work, we employ a hierarchical Bayesian model to infer the peculiar velocity distribution of binary black holes. We use the data from GWTC-3 and assume a Maxwell-Boltzmann distribution for the peculiar velocities, but do not consider the dependence of peculiar velocity on the masses of black hole binaries. The constraint on the peculiar velocity distribution parameter, $v_0$, is weak and uninformative. However, the determination of peculiar velocity distribution can be significantly improved with next-generation ground-based gravitational wave detectors. For the Einstein Telescope, the relative uncertainty of $v_0$ will reduce to $\sim$ 10\% using $10^3$ golden binary black hole events. Our statistical approach thus provides a robust and prospective inference for determining the peculiar velocity distribution., Comment: 18 pages, 3 figures; Accepted by JCAP

Camouflaged objects are typically assimilated into their backgrounds and exhibit fuzzy boundaries. The complex environmental conditions and the high intrinsic similarity between camouflaged targets and their surroundings pose significant challenges in accurately locating and segmenting these objects in their entirety. While existing methods have demonstrated remarkable performance in various real-world scenarios, they still face limitations when confronted with difficult cases, such as small targets, thin structures, and indistinct boundaries. Drawing inspiration from human visual perception when observing images containing camouflaged objects, we propose a three-stage model that enables coarse-to-fine segmentation in a single iteration. Specifically, our model employs three decoders to sequentially process subsampled features, cropped features, and high-resolution original features. This proposed approach not only reduces computational overhead but also mitigates interference caused by background noise. Furthermore, considering the significance of multi-scale information, we have designed a multi-scale feature enhancement module that enlarges the receptive field while preserving detailed structural cues. Additionally, a boundary enhancement module has been developed to enhance performance by leveraging boundary information. Subsequently, a mask-guided fusion module is proposed to generate fine-grained results by integrating coarse prediction maps with high-resolution feature maps. Our network surpasses state-of-the-art CNN-based counterparts without unnecessary complexities. Upon acceptance of the paper, the source code will be made publicly available at https://github.com/clelouch/BTSNet.

Chaotic semiconductor lasers have been widely investigated for high-speed random bit generation, which is applied for the generation of cryptographic keys for classical and quantum cryptography systems. Here, we propose and demonstrate a self-chaotic microlaser with enhanced chaotic bandwidth for high-speed random bit generation. By designing tri-mode interaction in a deformed square microcavity laser, we realize a self-chaotic laser caused by two-mode internal interaction, and achieve an enhanced chaotic standard bandwidth due to the photon-photon resonance effect by introducing the third mode. Moreover, 500 Gb/s random bit generation is realized and the randomness is verified by the NIST SP 800-22 statistics test. Our demonstration promises the applications of microlasers in secure communication, chaos radar, and optical reservoir computing, and also provides a platform for the investigations of multimode nonlinear laser dynamics., Comment: 10 pages,6 figures

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures challenge users in computational biology for utilizing the structural information and protein surface property representation. Recently, AlphaFold2 released the comprehensive proteome of various species, and protein surface property representation plays a crucial role in protein-molecule interaction prediction such as protein-protein interaction, protein-nucleic acid interaction, and protein-compound interaction. Here, we proposed the first comprehensive database, namely ProNet DB, which incorporates multiple protein surface representations and RNA-binding landscape for more than 326,175 protein structures covering 16 model organism proteomes from AlphaFold Protein Structure Database (AlphaFold DB) and experimentally validated protein structures deposited in Protein Data Bank (PDB). For each protein, we provided the original protein structure, surface property representation including hydrophobicity, charge distribution, hydrogen bond, interacting face, and RNA-binding landscape such as RNA binding sites and RNA binding preference. To interpret protein surface property representation and RNA binding landscape intuitively, we also integrate Mol* and Online 3D Viewer to visualize the representation on the protein surface. The pre-computed features are available for the users instantaneously and boost computational biology development including molecular mechanism exploration, geometry-based drug discovery and novel therapeutics development. The server is now available on https://proj.cse.cuhk.edu.hk/aihlab/pronet/., Comment: 12 pages, 6 figures

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low Resolution Spectroscopic Survey (LRS) provides massive spectroscopic data of M-type stars, and the derived stellar parameters could bring vital help to various studies. We adopt the ULySS package to perform $\chi^2$ minimization with model spectra generated from the MILES interpolator, and determine the stellar atmospheric parameters for the M-type stars from LAMOST LRS Data Release (DR) 8. Comparison with the stellar parameters from APOGEE Stellar Parameter and Chemical Abundance Pipeline (ASPCAP) suggests that most of our results have good consistency. For M dwarfs, we achieve dispersions better than 74 K, 0.19 dex and 0.16 dex for $T_{\rm eff}$, $\log{g}$ and [Fe/H], while for M giants, the internal uncertainties are 58 K, 0.32 dex and 0.26 dex, respectively. Compared to ASPCAP we also find a systematic underestimation of $\Delta {T_{\rm eff}} =$ $-$176 K for M dwarfs, and a systematic overestimation of $\Delta {\log{g}} =$ 0.30 dex for M giants. However, such differences are less significant when we make comparison with common stars from other literature, which indicates that systematic biases exist in the difference of ASPCAP and other measurements. A catalog of 763,136 spectra corresponding to 616,314 M-type stars with derived stellar parameters is presented. We determine the stellar parameters for stars with $T_{\rm eff}$ higher than 2,900 K, with $\log{g}$ from -0.24 dex to 5.9 dex. The typical precisions are 45 K, 0.25 dex and 0.22 dex, for $T_{\rm eff}$, $\log{g}$ and [Fe/H], respectively, which are estimated from the duplicate observations of the same stars., Comment: 21 pages, 20 figures, 3 tables, accepted for publication in The Astrophysical Journal Supplement Series

It is believed that millisecond pulsars attain their fast spins by accreting matter and angular momentum from companion stars. Theoretical modelling of the accretion process suggests a spin-up line in the period-period derivative ($P$-$\dot{P}$) diagram of millisecond pulsars, which plays an important role in population studies of radio millisecond pulsars and accreting neutron stars in X-ray binaries. Here we present observational evidence for such a spin-up line using a sample of 143 radio pulsars with $P$ < 30 ms. We also find that PSRs~J1823$-$3021A and J1824$-$2452A, located near the classic spin-up line, are consistent with the broad population of millisecond pulsars. Finally, we show that our approach of Bayesian inference can probe accretion physics, allowing constraints to be placed on the accretion rate and the disk-magnetosphere interaction., Comment: 10 pages, 4 figures, 2 tables. Accepted for publication by ApJL

Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design computational methods for predicting their structures and functions. The lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and evolution prediction, protein-RNA binding preference modeling, and gene expression regulation modeling. The comprehensive experiments show that the proposed method improves the RNA structural and functional modelling results significantly and consistently. Despite only being trained with unlabelled data, RNA-FM can serve as the foundational model for the field.

Cloud-based software has many advantages. When services are divided into many independent components, they are easier to update. Also, during peak demand, it is easier to scale cloud services (just hire more CPUs). Hence, many organizations are partitioning their monolithic enterprise applications into cloud-based microservices. Recently there has been much work using machine learning to simplify this partitioning task. Despite much research, no single partitioning method can be recommended as generally useful. More specifically, those prior solutions are "brittle"; i.e. if they work well for one kind of goal in one dataset, then they can be sub-optimal if applied to many datasets and multiple goals. In order to find a generally useful partitioning method, we propose DEEPLY. This new algorithm extends the CO-GCN deep learning partition generator with (a) a novel loss function and (b) some hyper-parameter optimization. As shown by our experiments, DEEPLY generally outperforms prior work (including CO-GCN, and others) across multiple datasets and goals. To the best of our knowledge, this is the first report in SE of such stable hyper-parameter optimization. To aid reuse of this work, DEEPLY is available on-line at https://bit.ly/2WhfFlB., Comment: version 3

Objectives: To assess self-reported population oral health conditions amid COVID-19 pandemic using user reports on Twitter. Method and Material: We collected oral health-related tweets during the COVID-19 pandemic from 9,104 Twitter users across 26 states (with sufficient samples) in the United States between November 12, 2020 and June 14, 2021. We inferred user demographics by leveraging the visual information from the user profile images. Other characteristics including income, population density, poverty rate, health insurance coverage rate, community water fluoridation rate, and relative change in the number of daily confirmed COVID-19 cases were acquired or inferred based on retrieved information from user profiles. We performed logistic regression to examine whether discussions vary across user characteristics. Results: Overall, 26.70% of the Twitter users discuss wisdom tooth pain/jaw hurt, 23.86% tweet about dental service/cavity, 18.97% discuss chipped tooth/tooth break, 16.23% talk about dental pain, and the rest are about tooth decay/gum bleeding. Women and younger adults (19-29) are more likely to talk about oral health problems. Health insurance coverage rate is the most significant predictor in logistic regression for topic prediction. Conclusion: Tweets inform social disparities in oral health during the pandemic. For instance, people from counties at a higher risk of COVID-19 talk more about tooth decay/gum bleeding and chipped tooth/tooth break. Older adults, who are vulnerable to COVID-19, are more likely to discuss dental pain. Topics of interest vary across user characteristics. Through the lens of social media, our findings may provide insights for oral health practitioners and policy makers., Comment: Accepted for publication in Quintessence International

In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results. How well does Mono2Micro perform against other decomposition techniques, and how do practitioners perceive the tool? This paper describes the technical foundations of Mono2Micro and presents results to answer these two questions. To answer the first question, we evaluated Mono2Micro against four existing techniques on a set of open-source and proprietary Java applications and using different metrics to assess the quality of decomposition and tool's efficiency. Our results show that Mono2Micro significantly outperforms state-of-the-art baselines in specific metrics well-defined for the problem domain. To answer the second question, we conducted a survey of twenty-one practitioners in various industry roles who have used Mono2Micro. This study highlights several benefits of the tool, interesting practitioner perceptions, and scope for further improvements. Overall, these results show that Mono2Micro can provide a valuable aid to practitioners in creating functionally cohesive and explainable microservice decompositions.

When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller *microservices*. While many tools have been proposed for this task, we warn that the evaluation of those approaches has been incomplete; e.g. minimal prior exploration of hyperparameter optimization. Using a set of open source Java EE applications, we show here that (a) such optimization can significantly improve microservice partitioning; and that (b) an open issue for future work is how to find which optimizer works best for different problems. To facilitate that future work, see [https://github.com/yrahul3910/ase-tuned-mono2micro](https://github.com/yrahul3910/ase-tuned-mono2micro) for a reproduction package for this research., Comment: Accepted to ASE 2021 (industry track, short paper)

Standard stellar evolution model predicts a severe depletion of lithium (Li) abundance during the first dredge-up process (FDU). Yet a small fraction of giant stars are still found to preserve a considerable amount of Li in their atmospheres after FDU. Those giants are usually identified as Li-rich by a widely used criterion, A(Li) $ > 1.5$\,{\it dex}. A large number of works dedicated to search for and investigate this minority of the giant family, and the amount of Li-rich giants has been largely expanded, especially in the era of big data. In this paper, we present a catalog of Li-rich giants found from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey with Li abundances derived from a template matching method developed for LAMOST low-resolution spectra. The catalog contains $10,535$ Li-rich giants with Li abundances from $\sim 1.5$\,{\it dex} to $\sim 4.9$\,{\it dex}. We also confirm that the ratio of Li-rich phenomenon among giant stars is about one percent, or for a more expression, $1.29\%$ from our statistically important sample. This is the largest Li-rich giant sample ever reported to date, which significantly exceeds amount of all the reported Li-rich giants combined. The catalog will help the community to better understand the Li-rich phenomenon in giant stars., Comment: 16 pages, 11 figures

Early childhood caries (ECC) is the most common, yet preventable chronic disease in children under the age of 6. Treatments on severe ECC are extremely expensive and unaffordable for socioeconomically disadvantaged families. The identification of ECC in an early stage usually requires expertise in the field, and hence is often ignored by parents. Therefore, early prevention strategies and easy-to-adopt diagnosis techniques are desired. In this study, we propose a multistage deep learning-based system for cavity detection. We create a dataset containing RGB oral images labeled manually by dental practitioners. We then investigate the effectiveness of different deep learning models on the dataset. Furthermore, we integrate the deep learning system into an easy-to-use mobile application that can diagnose ECC from an early stage and provide real-time results to untrained users., Comment: MICCAI 2020 Workshop

Suppose $w$ is a sense-preserving harmonic mapping of the unit disk $\mathbb{D}$ such that $w(\mathbb{D})\subseteq\mathbb{D}$ and $w$ has a zero of order $p\geq1$ at $z=0$. In this paper, we first improve the Schwarz lemma for $w$, and then, we establish its boundary Schwarz lemma. Moreover, by using the automorphism of $\mathbb{D}$, we further generalize this result., Comment: 10 pages; To appear in Bulletin of the Malaysian Mathematical Sciences Society

Chaotic semiconductor lasers have been widely investigated for generating true random numbers with the merit of unpredictability relative to pseudorandom numbers generated by a computing process, especially for the lasers with external optical feedback. Here, we report the first spontaneous chaotic microlaser due to the interaction of nearly degenerate modes. The circular-side hexagonal microlaser is designed and demonstrated with adjusted mode intervals in the order of GHz for the fundamental and the first order transverse modes. Four modes including degenerate modes with strong mode beating will result in chaos. True random numbers at 10Gb/s is obtained from the laser output power with a high dimension of 5.46. Our finding provides a novel and easy way to create controllable and robust optical chaos for high speed random number generation, which also paves the way for the nonlinear dynamics in a solitary laser.

First-principles calculations of charged defects have become a cornerstone of research in semiconductors and insulators by providing insights into their fundamental physical properties. But current standard approach using the so-called jellium model has encountered both conceptual ambiguity and computational difficulty, especially for low-dimensional semiconducting materials. In this Communication, we propose a physical, straightforward, and dimension-independent universal model to calculate the formation energies of charged defects in both three-dimensional (3D) bulk and low-dimensional semiconductors. Within this model, the ionized electrons or holes are placed on the realistic host band-edge states instead of the virtual jellium state, therefore, rendering it not only naturally keeps the supercell charge neutral, but also has clear physical meaning. This realistic model reproduces the same accuracy as the traditional jellium model for most of the 3D semiconducting materials, and remarkably, for the low-dimensional structures, it is able to cure the divergence caused by the artificial long-range electrostatic energy introduced in the jellium model, and hence gives meaningful formation energies of defects in charged state and transition energy levels of the corresponding defects. Our realistic method, therefore, will have significant impact for the study of defect physics in all low-dimensional systems including quantum dots, nanowires, surfaces, interfaces, and 2D materials.

Negative Poisson's ratio (NPR) materials have attracted tremendous interest due to their unusual physical properties and potential applications. Certain two-dimensional (2D) monolayer materials have also been found to exhibit NPR and the corresponding deformation mechanism varies. In this study, we found, based on first-principles calculations, that the Poisson's ratio (PR) sign of monolayer Blue Phosphorus Oxide (BPO) can be tuned by strain: the PR is positive under uniaxial strain <= -1% but becomes negative under > 0. The deformation mechanism for BPO under strain depends on the mutual competition between the P-P attraction and P-O repulsion effect, and these two factors induce two different deformation pathways (one with positive PR, and the other with NPR). Moreover, with increasing of strain, both the decreased strength of P-P attraction and the increased strength of P-O repulsion effect modulate the PR of BPO from positive to negative., Comment: 16 Pages, 4 figures

Dopability in semiconductors plays a crucial role in device performance. Using the first-principles density-functional theory calculations, we investigate systematically the doping properties of layered MX2 (M= Mo, W; X=S, Te) by replacing M or X with the groups III, V and VII elements. It is found that the defect BM is hard to form in MX2 due to the large formation energy originating from the crystal distortion, while AlM is easy to realize compared to the former. In MoS2, WS2 and MoTe2, Al is the most desirable p-type dopant under anion-rich conditions among the group III components, since AlM has relatively low transition and formation energies. With respect to the doping of the group V elements, it is found that the substitutions on the cation sites have deeper defect levels than those on the anion sites due to the strong electronegativity. AsTe and SbTe in MoTe2 and WTe2 are trend to form shallow acceptors under cation-rich conditions, indicating high hole-concentrations for p-type doping, whereas SbS in MoS2 and PTe in WTe2 are shown to be good p-type candidates under cation-rich conditions. In despite of that the substitutions of group VII on X site have low formation energies, the transition energies are too high to achieve n-type MoS2 and WS2. Nevertheless, for MoTe2, the substitutions with the group VII elements on the anion sites are suitable for n-type doping on account of the shallow donor levels and low formation energies under Mo-rich condition. As to WTe2, F is the only potential donor due to the shallow transition energy of FTe. Our findings of filtering out unfavorable and identifying favorable dopants in MX2 are very valuable for experimental implementations., Comment: Some of the calculations are problematic. We have to withdraw the paper in arXiv

Deformed square resonators with the flat sides replaced by circular sides are proposed and demonstrated to enhance mode Q factors and adjust transverse mode intervals using the regular ray dynamic analysis and numerical simulations. Dual-transverse-mode emissions due to the ultrahigh-Q factors with different wavelength intervals are realized experimentally for AlGaInAs/InP circular-side square microlasers, and the stationary condition of the dual-mode emission is satisfied because the high-Q confined modes have totally different mode numbers. Furthermore, optical frequency combs are generated using the dual-mode lasing microlaser as a seeding light source by cascaded four-wave mixing in a highly nonlinear optical fiber.

Relic gravitational waves (RGWs) generated in the early Universe form a stochastic GW background, which can be directly probed by measuring the timing residuals of millisecond pulsars. In this paper, we investigate the constraints on the RGWs and on the inflationary parameters by the observations of current and potential future pulsar timing arrays. In particular, we focus on effects of various cosmic phase transitions (e.g. $e^{+}e^{-}$ annihilation, QCD transition and SUSY breaking) and relativistic free-streaming gases (neutrinos and dark fluids) in the general scenario of the early Universe, which have been neglected in the previous works. We find that the phase transitions can significantly damp the RGWs in the sensitive frequency range of pulsar timing arrays, and the upper limits of tensor-to-scalar ratio $r$ increase by a factor $\sim 2$ for both current and future observations. However, the effects of free-steaming neutrinos and dark fluids are all too small to be detected. Meanwhile, we find that, if the effective equation of state $w$ in the early Universe is larger than $1/3$, i.e. deviating from the standard hot big bang scenario, the detection of RGWs by pulsar timing arrays becomes much more promising., Comment: 19 pages, 17 figures, 6 tables, Phys.Rev.D accepted

A transfer model was applied to data from a survey of workers in four Chinese electronics firms. Results showed that transfer of training depends on organizational factors that facilitate use of knowledge, skills, and attitudes acquired. Supervision and other human factors were most influential. (SK)