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When a laser-cooled atomic sample is optically excited, the envelope of coherent forward scattering can often be decomposed into a few complex Gaussian profiles. The convenience of Gaussian propagation helps addressing key challenges in digital holography. In this work, we theoretically develop and experimentally demonstrate a Gaussian-decomposition-assisted approach to inline holography, for single-shot, simultaneous measurements of absorption and phase shift of small atomic samples sparsely distributed in 3D. Experimentally, we image a sparse lattice of $^{87}$Rb samples on the D2 line, to resolve their axial positions with micrometer precision, and to retrieve their complex-valued spectroscopic images. With the phase-angle readouts that are highly insensitive to atom-number and interaction-strength uncertainties, we achieve hundred-kHz-level single-shot-resolution to the transition frequency with merely hundreds of atoms. We further demonstrate 3D sensing of local light shift with micrometer spatial resolution., Comment: 20 pages, 7 figures; small corrections, with reference format updated

Under the data-driven research paradigm, research software has come to play crucial roles in nearly every stage of scientific inquiry. Scholars are advocating for the formal citation of software in academic publications, treating it on par with traditional research outputs. However, software is hardly consistently cited: one software entity can be cited as different objects, and the citations can change over time. These issues, however, are largely overlooked in existing empirical research on software citation. To fill the above gaps, the present study compares and analyzes a longitudinal dataset of citation formats of all R packages collected in 2021 and 2022, in order to understand the citation formats of R-language packages, important members in the open-source software family, and how the citations evolve over time. In particular, we investigate the different document types underlying the citations and what metadata elements in the citation formats changed over time. Furthermore, we offer an in-depth analysis of the disciplinarity of journal articles cited as software (software papers). By undertaking this research, we aim to contribute to a better understanding of the complexities associated with software citation, shedding light on future software citation policies and infrastructure.

We constructed a transportable 40Ca+ optical clock (with an estimated minimum systematic shift uncertainty of 1.3*10^(-17) and a stability of 5*10^(-15)/sqrt{tau} ) that can operate outside the laboratory. We transported it from the Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan to the National Institute of Metrology, Beijing. The absolute frequency of the 729 nm clock transition was measured for up to 35 days by tracing its frequency to the second of International System of Units. Some improvements were implemented in the measurement process, such as the increased effective up-time of 91.3 % of the 40Ca+ optical clock over a 35-day-period, the reduced statistical uncertainty of the comparison between the optical clock and hydrogen maser, and the use of longer measurement times to reduce the uncertainty of the frequency traceability link. The absolute frequency measurement of the 40Ca+ optical clock yielded a value of 411042129776400.26 (13) Hz with an uncertainty of 3.2*10^(-16), which is reduced by a factor of 1.7 compared with our previous results. As a result of the increase in the operating rate of the optical clock, the accuracy of 35 days of absolute frequency measurement can be comparable to the best results of different institutions in the world based on different optical frequency measurements., Comment: 15 pages, 5 figures

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.

In digital holography, the coherent scattered light fields can be reconstructed volumetrically. By refocusing the fields to the sample planes, absorption and phase-shift profiles of sparsely distributed samples can be simultaneously inferred in 3D. This holographic advantage is highly useful for spectroscopic imaging of cold atomic samples. However, unlike (e.g., biological samples or solid particles), the quasi-thermal atomic gases under laser-cooling are typically featureless without sharp boundaries, invalidating a class of standard numerical refocusing methods. Here, we extend the refocusing protocol based on the Gouy phase anomaly for small phase objects to free atomic samples. With a prior knowledge on a coherent spectral phase angle relation for cold atoms that is robust against probe condition variations, an ``out-of-phase'' response of the atomic sample can be reliably identified, which flips the sign during the numeric back-propagation across the sample plane to serve as the refocus criterion. Experimentally, we determine the sample plane of a laser-cooled $^{39}$K gas released from a microscopic dipole trap, with a $\delta z\approx 1~{\rm \mu m}$$\ll 2\lambda_p/{\rm NA}^2$ axial resolution, with a NA=0.3 holographic microscope at $\lambda_p=770~$nm probe wavelength., Comment: Accepted by Opt. Exp. A few typoes fixed

In scientific research, the method is an indispensable means to solve scientific problems and a critical research object. With the advancement of sciences, many scientific methods are being proposed, modified, and used in academic literature. The authors describe details of the method in the abstract and body text, and key entities in academic literature reflecting names of the method are called method entities. Exploring diverse method entities in a tremendous amount of academic literature helps scholars understand existing methods, select the appropriate method for research tasks, and propose new methods. Furthermore, the evolution of method entities can reveal the development of a discipline and facilitate knowledge discovery. Therefore, this article offers a systematic review of methodological and empirical works focusing on extracting method entities from full-text academic literature and efforts to build knowledge services using these extracted method entities. Definitions of key concepts involved in this review were first proposed. Based on these definitions, we systematically reviewed the approaches and indicators to extract and evaluate method entities, with a strong focus on the pros and cons of each approach. We also surveyed how extracted method entities are used to build new applications. Finally, limitations in existing works as well as potential next steps were discussed.

Nowadays, graph becomes an increasingly popular model in many real applications. The efficiency of graph storage is crucial for these applications. Generally speaking, the tune tasks of graph storage rely on the database administrators (DBAs) to find the best graph storage. However, DBAs make the tune decisions by mainly relying on their experiences and intuition. Due to the limitations of DBAs's experiences, the tunes may have an uncertain performance and conduct worse efficiency. In this paper, we observe that an estimator of graph workload has the potential to guarantee the performance of tune operations. Unfortunately, because of the complex characteristics of graph evaluation task, there exists no mature estimator for graph workload. We formulate the evaluation task of graph workload as a classification task and carefully design the feature engineering process, including graph data features, graph workload features and graph storage features. Considering the complex features of graph and the huge time consumption in graph workload execution, it is difficult for the graph workload estimator to obtain enough training set. So, we propose an active auto-estimator (AAE) for the graph workload evaluation by combining the active learning and deep learning. AAE could achieve good evaluation efficiency with limited training set. We test the time efficiency and evaluation accuracy of AAE with two open source graph data, LDBC and Freebase. Experimental results show that our estimator could efficiently complete the graph workload evaluation in milliseconds.

We demonstrate a simple method to obtain accurate optical waveforms with a GHz-level programmable modulation bandwidth and Watt-level output power for wideband optical control of free atoms and molecules. Arbitrary amplitude and phase modulations are transferred from microwave to light with a low-power fiber electro-optical modulator. The sub-milliWatt optical sideband is co-amplified with the optical carrier in a power-balanced fashion through a tapered semiconductor amplifier (TSA). By automatically keeping TSA near saturation in a quasi-continuous manner, typical noise channels associated with pulsed high-gain amplifications are efficiently suppressed. As an example application, we demonstrate interleaved cooling and trapping of two rubidium isotopes with coherent nanosecond pulses., Comment: 7 pages, 6 figures

Significant aspects of advanced quantum technology today rely on rapid control of atomic matterwaves with hyperfine Raman transitions. Unfortunately, efficient Raman excitations are usually accompanied by uncompensated dynamic phases and coherent spin-leakages, preventing accurate and repetitive transfer of recoil momentum to large samples. We provide systematic study to demonstrate that the limitations can be substantially overcame by dynamically programming an adiabatic pulse sequence. Experimentally, counter-propagating frequency-chirped pulses are programmed on an optical delay line to parallelly drive five $\Delta m=0$ hyperfine Raman transitions of $^{85}$Rb atoms for spin-dependent kick (SDK) within $\tau=40$~nanoseconds, with an $f_{\rm SDK}\approx 97.6\%$ inferred fidelity. Aided by numerical modeling, we demonstrate that by alternating the chirps of successive pulses in a balanced fashion, accumulation of non-adiabatic errors including the spin-leakages can be managed, while the dynamic phases can be robustly cancelled. Operating on a phase-stable delay line, the method supports precise, fast, and flexible control of spinor matterwave with efficient Raman excitations., Comment: 18 pages, 13 figures, improved clarity with substantial revisions

With the enrichment of literature resources, researchers are facing the growing problem of information explosion and knowledge overload. To help scholars retrieve literature and acquire knowledge successfully, clarifying the semantic structure of the content in academic literature has become the essential research question. In the research on identifying the structure function of chapters in academic articles, only a few studies used the deep learning model and explored the optimization for feature input. This limits the application, optimization potential of deep learning models for the research task. This paper took articles of the ACL conference as the corpus. We employ the traditional machine learning models and deep learning models to construct the classifiers based on various feature input. Experimental results show that (1) Compared with the chapter content, the chapter title is more conducive to identifying the structure function of academic articles. (2) Relative position is a valuable feature for building traditional models. (3) Inspired by (2), this paper further introduces contextual information into the deep learning models and achieved significant results. Meanwhile, our models show good migration ability in the open test containing 200 sampled non-training samples. We also annotated the ACL main conference papers in recent five years based on the best practice performing models and performed a time series analysis of the overall corpus. This work explores and summarizes the practical features and models for this task through multiple comparative experiments and provides a reference for related text classification tasks. Finally, we indicate the limitations and shortcomings of the current model and the direction of further optimization.

Recent years, the database committee has attempted to develop automatic database management systems. Although some researches show that the applying AI to data management is a significant and promising direction, there still exists many problems in implementing these techniques to real applications (long training time, various environments and unstable performance). In this paper, we discover that traditional rule based methods have the potential to solve the above problems. We propose three methodologies for improving learned methods, i.e. label collection for efficiently pre-training, knowledge base for model transfer and theoretical guarantee for stable performance. We implement our methodologies on two widely used learning approaches, deep learning and reinforcement learning. Firstly, the novel experience enhanced deep learning (EEDL) could achieve efficient training and stable performance. We evaluate EEDL with cardinality estimation, an essential database management. The experimental results on four real dataset [1] show that our EEDL could outperforms the general DL model [2]. Secondly, we design a novel experience-enhanced reinforcement learning (EERL), which could efficiently converge and has better performance than general RL models [3]. We test EERL with online index tuning task. The experiments on TPC-H shows that EERL could accelerate the convergence of agent and generate better solution that generalizes the reinforcement learning.

The propagation of light in moving media is dragged by atomic motion. The light-drag effect can be dramatically enhanced by reducing the group velocity with electro-magnetically induced transparency (EIT). We develop a systematic procedure to accurately reconstruct the complex wavefront of the slow light with single-shot measurements, enabling precise, photon shot-noise limited spectroscopic measurements of atomic response across EIT even in presence of generic atomic number fluctuations. Applying the technique to an expanding cloud of cold atoms, we demonstrate simultaneous inference of the atomic density distribution and the velocity field from the complex imaging data. This inline imaging technique may assist a wide range of cold atom experiments to access spectroscopic and phase space information with in situ and minimally destructive measurements., Comment: 11 pages, 9 figures, improved data, substantial revision

The interaction between light and cold atoms is a complex phenomenon potentially featuring many-body resonant dipole interactions. A major obstacle toward exploring these quantum resources of the system is macroscopic light propagation effects, which not only limit the available time for the microscopic correlations to locally build up, but also create a directional, superradiant emission background whose variations can overwhelm the microscopic effects. In this Letter, we demonstrate a method to perform ``background-free'' detection of the microscopic optical dynamics in a laser-cooled atomic ensemble. This is made possible by transiently suppressing the macroscopic optical propagation over a substantial time, before a recall of superradiance that imprints the effect of the accumulated microscopic dynamics into an efficiently detectable outgoing field. We apply this technique to unveil and precisely characterize a density-dependent, microscopic dipolar dephasing effect that generally limits the lifetime of optical spin-wave order in ensemble-based atom-light interfaces., Comment: 18 pages, 13 figures, improved data with substantial revision

Multidisciplinary cooperation is now common in research since social issues inevitably involve multiple disciplines. In research articles, reference information, especially citation content, is an important representation of communication among different disciplines. Analyzing the distribution characteristics of references from different disciplines in research articles is basic to detecting the sources of referred information and identifying contributions of different disciplines. This work takes articles in PLoS as the data and characterizes the references from different disciplines based on Citation Content Analysis (CCA). First, we download 210,334 full-text articles from PLoS and collect the information of the in-text citations. Then, we identify the discipline of each reference in these academic articles. To characterize the distribution of these references, we analyze three characteristics, namely, the number of citations, the average cited intensity and the average citation length. Finally, we conclude that the distributions of references from different disciplines are significantly different. Although most references come from Natural Science, Humanities and Social Sciences play important roles in the Introduction and Background sections of the articles. Basic disciplines, such as Mathematics, mainly provide research methods in the articles in PLoS. Citations mentioned in the Results and Discussion sections of articles are mainly in-discipline citations, such as citations from Nursing and Medicine in PLoS.

Big data management aims to establish data hubs that support data in multiple models and types in an all-around way. Thus, the multi-model database system is a promising architecture for building such a multi-model data store. For an integrated data hub, a unified and flexible query language is incredibly necessary. In this paper, an extensible and practical query language--Multi-SQL is proposed to realize the unified management of multi-model data considering the co-processing of multi-model data. To the best of our knowledge, Multi-SQL is the first query language based on various data models. Multi-SQL can also be expanded to suit more complicated scenarios as it is flexible to support other data models. Moreover, we provide a formal semantic definition of the core features of Multi-SQL, including the multi-model definition, multi-model filters, multi-model joins, etc. Furthermore, we propose a two-level query implementation method to totally exploit the existing query optimization capabilities of the underlying engines which could largely improve the query excution efficiency.

The absorption of traveling photons resonant with electric dipole transitions of an atomic gas naturally leads to electric dipole spin wave excitations. For a number of applications, it would be highly desirable to shape and coherently control the spatial waveform of the spin waves before spontaneous emission can occur. This paper details a recently developed optical control technique to achieve this goal, where counter-propagating, shaped sub-nanosecond pulses impart sub-wavelength geometric phases to the spin waves by cyclically driving an auxiliary transition. In particular, we apply this technique to reversibly shift the wave vector of a spin wave on the $D2$ line of laser-cooled $^{87}$Rb atoms, by driving an auxiliary $D1$ transition with shape-optimized pulses, so as to shut off and recall superradiance on demand. We investigate a spin-dependent momentum transfer during the spin-wave control process, which leads to a transient optical force as large as $\sim 1\hbar k$/ns, and study the limitations to the achieved $70\sim 75\%$ spin wave control efficiency by jointly characterizing the spin-wave control and matterwave acceleration. Aided by numerical modeling, we project potential future improvements of the control fidelity to the $99\%$ level when the atomic states are better prepared and by equipping a faster and more powerful pulse shaper. Our technique also enables a background-free measurement of the superradiant emission to unveil the precise scaling of the emission intensity and decay rate with optical depth., Comment: Jointly submitted with arXiv:1910.02289. Improved presentation with reduced overlapping contents

In the era of big data, the advancement, improvement, and application of algorithms in academic research have played an important role in promoting the development of different disciplines. Academic papers in various disciplines, especially computer science, contain a large number of algorithms. Identifying the algorithms from the full-text content of papers can determine popular or classical algorithms in a specific field and help scholars gain a comprehensive understanding of the algorithms and even the field. To this end, this article takes the field of natural language processing (NLP) as an example and identifies algorithms from academic papers in the field. A dictionary of algorithms is constructed by manually annotating the contents of papers, and sentences containing algorithms in the dictionary are extracted through dictionary-based matching. The number of articles mentioning an algorithm is used as an indicator to analyze the influence of that algorithm. Our results reveal the algorithm with the highest influence in NLP papers and show that classification algorithms represent the largest proportion among the high-impact algorithms. In addition, the evolution of the influence of algorithms reflects the changes in research tasks and topics in the field, and the changes in the influence of different algorithms show different trends. As a preliminary exploration, this paper conducts an analysis of the impact of algorithms mentioned in the academic text, and the results can be used as training data for the automatic extraction of large-scale algorithms in the future. The methodology in this paper is domain-independent and can be applied to other domains.