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Excellent high-temperature mechanical properties of Ni-based single crystal superalloys (NSCSs) are attributed to the yield strength anomaly of Ni$_{3}$Al that is intimately related to generalized stacking fault energies (GSFEs). Therefore, clarifying the effects of alloying elements on the GSFEs is of great significance for alloys design. Here, by means of ab initio density functional theory calculations, we systematically calculated the GSFEs of different slip systems of Ni and Ni$_{3}$Al without and with alloying elements using the alias shear method. We obtained that for Ni, except for magnetic elements Mn, Fe, and Co, most of alloying elements decrease the unstable stacking fault energy ($\gamma_{usf}$) of the $[01\bar{1}](111)$ and $[11\bar{2}](111)$ slip systems and also decrease the stable stacking fault energy ($\gamma_{sf}$) of the $[11\bar{2}](111)$ slip system. For Ni$_{3}$Al, most of alloying elements in groups IIIB-VIIB show a strong Al site preference. Except for Mn and Fe, the elements in groups VB-VIIB and the first column of group VIII increase the values of $\gamma_{usf}$ of different slip systems of Ni$_{3}$Al. On the other hand, the elements in groups IIIB-VIIB also increase the value of $\gamma_{sf}$. We found that Re is an excellent strengthening alloying element that significantly increases the slip barrier of the tailing slip process for Ni, and also enhances the slip barrier of the leading slip process of three slip systems for Ni$_{3}$Al. W and Mo exhibit similar effects as Re. We predicted that Os, Ru, and Ir are good strengthening alloying elements as well, since they show the strengthening effects on both the leading and tailing slip process for Ni and Ni$_{3}$Al.

Quantum lock-in amplifier aims to extract an alternating signal within strong noise background by using quantum strategy. However, as the target signal usually has an unknown initial phase, we can't obtain the complete information of its amplitude, frequency and phase in a single lock-in measurement. Here, to overcome this challenge, we give a general protocol for achieving a quantum double lock-in amplifier and illustrate its realization. In analog to a classical double lock-in amplifier, our protocol is accomplished via two quantum mixers under orthogonal pulse sequences. The two orthogonal pulse sequences act the roles of two orthogonal reference signals in a classical double lock-in amplifier. Combining the output signals, the complete characteristics of the target signal can be obtained. As an example, we illustrate the realization of our quantum double lock-in amplifier via a five-level double-$\Lambda$ coherent population trapping system with $^{87}$Rb atoms, in which each $\Lambda$ structure acts as a quantum mixer and the two applied dynamical decoupling sequences take the roles of two orthogonal reference signals. Our numerical calculations show that the quantum double lock-in amplifier is robust against experimental imperfections, such as finite pulse length and stochastic noise. Our study opens an avenue for extracting complete characteristics of an alternating signal within strong noise background, which is beneficial for developing practical quantum sensing technologies.

The improvement of the multi-mode capability of quantum memory can further improve the utilization efficiency of the quantum memory and reduce the requirement of quantum communication for storage units. In this letter, we experimentally investigate the multi-mode light multiplexing storage of orbital angular momentum (OAM) mode based on rubidium vapor, and demultiplexing by a photonic OAM mode splitter which combines a Sagnac loop with two dove prisms. Our results show a mode extinction ratio higher than 80$\%$ at 1 $\mu$s of storage time. Meanwhile, two OAM modes have been multiplexing stored and demultiplexed in our experimental configuration. We believe the experimental scheme may provide a possibility for high channel capacity and multi-mode quantum multiplexed quantum storage based on atomic ensembles.

In this paper, we have creatively employed the shadow radius to study the thermodynamics of a charged AdS black hole with a nonlinear electrodynamics(NLED) term. First, the connection between the shadow radius and event horizon is constructed with the aid of the geodesic analysis. It turns out that the black hole shadow radius shows a positive correlation as a function of the event horizon radius. Then in the shadow context, we found that the black hole temperature and heat capacity can be presented by the shadow radius. And further analysis shows that the shadow radius can do as well as the event horizon in revealing black hole phase transition process. In this sense, we constructed the thermal profile of the charged AdS black hole with inclusion of the NLED effect. In P < Pc case, it is found that the N-type trend of the temperature given by the shadow radius is always coincide with that obtained by using the event horizon. So, we can concluded for the charged AdS black hole that the phase transition process can be intuitively presented as the thermal profile in the shadow context. Finally, the effects of NLED have been carefully analysed through out the paper., Comment: 17 pages, 21 figures

Topology plays a crucial role in many physical systems, leading to interesting states at the surface. The paradigmatic example is the Chern number defined in the Brillouin zone that leads to the robust gapless edge states. Here we introduce the reduced Chern number, defined in subregions of the Brillouin zone (BZ), and construct a family of Chern dartboard insulators (CDIs) with quantized reduced Chern numbers in the sub-BZ (sBZ) but with trivial bulk topology. CDIs are protected by mirror symmetries and exhibit distinct pseudospin textures, including (anti)skyrmions, inside the sBZ. These CDIs host exotic gapless edge states, such as M\"{o}bius fermions and midgap corner states, and can be realized in photonic crystals. Our work opens up new possibilities for exploring sBZ topology and nontrivial surface responses in topological systems.

Free-positioning and multi-user supportive wireless power transfer systems represent the next-generation technology for wireless charging under the Qi standard. Traditional approaches employ multiple transmitting coils and multi-channel driving circuits with active control algorithms to achieve these goals. However, these traditional approaches are significantly limited by cost, weight, and heating due to their relatively low efficiency. Here, we demonstrate an innovative approach by using a metasurface to achieve free-positioning and multi-user compatibility. The metasurface works as a passive device to reform the magnetic field and enables high-efficiency free-positioning wireless power transfer with only a single transmitting coil. It shows up to 4.6 times improvement in efficiency. The metasurface also increases the coverage area from around 5 cm by 5 cm with over 40% efficiency to around 10 cm by 10 cm with over 70% efficiency. We further show that the system can support multiple receivers. Besides increasing the overall efficiency, we demonstrate tuning the power division between the multiple receivers, enabling compensation of receivers of different sizes to achieve their desired power.

Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. In this paper, we investigate whether multilingual sentence Transformer LaBSE is a strong multilingual word aligner. This idea is non-trivial as LaBSE is trained to learn language-agnostic sentence-level embeddings, while the alignment extraction task requires the more fine-grained word-level embeddings to be language-agnostic. We demonstrate that the vanilla LaBSE outperforms other mPLMs currently used in the alignment task, and then propose to finetune LaBSE on parallel corpus for further improvement. Experiment results on seven language pairs show that our best aligner outperforms previous state-of-the-art models of all varieties. In addition, our aligner supports different language pairs in a single model, and even achieves new state-of-the-art on zero-shot language pairs that does not appear in the finetuning process., Comment: Published at Findings of EMNLP 2022

Within the effective Lagrangian approach, we carry out a unified study of the $J/\psi (\psi')\to VP$, $J/\psi \to P\gamma$ and relevant radiative decays of light-flavor hadrons. Large amount of experimental data, including the various decay widths and electromagnetic form factors, are fitted to constrain the numerous hadron couplings. Relative strengths between the strong and electromagnetic interactions are revealed in the $J/\psi \to VP$ and $\psi' \to VP$ processes. The effect from the strong interaction is found to dominate in the $J/\psi \to \rho\pi$ decay, while the electromagnetic interaction turns out to be the dominant effect in $\psi' \to \rho\pi$ decay, which provides an explanation to the $\rho\pi$ puzzle. For the $J/\psi\to K^{*}\bar{K}+\bar{K}^{*}K$ and $\psi' \to K^{*}\bar{K}+\bar{K}^{*}K$, the former process is dominated by the strong interactions, and the effects from the electromagnetic parts are found to be comparable with those of strong interactions in the latter process. Different $SU(3)$ breaking effects from the electromagnetic parts appear in the charged and neutral channels for the $\psi' \to K^{*}\bar{K}+\bar{K}^{*}K$ processes explain the rather different ratios between $B(\psi'\to K^{*+}K^{-}+K^{*-}K^{+})/B(J/\psi\to K^{*+}K^{-}+K^{*-}K^{+})$ and $B(\psi'\to K^{*0}\bar{K}^{0}+\bar{K}^{*0}K^{0})/B(J/\psi\to K^{*0}\bar{K}^{0}+\bar{K}^{*0}K^{0})$., Comment: 20 pages, 5 figures

Text generation rarely considers the control of lexical complexity, which limits its more comprehensive practical application. We introduce a novel task of lexical complexity controlled sentence generation, which aims at keywords to sentence generation with desired complexity levels. It has enormous potential in domains such as grade reading, language teaching and acquisition. The challenge of this task is to generate fluent sentences only using the words of given complexity levels. We propose a simple but effective approach for this task based on complexity embedding. Compared with potential solutions, our approach fuses the representations of the word complexity levels into the model to get better control of lexical complexity. And we demonstrate the feasibility of the approach for both training models from scratch and fine-tuning the pre-trained models. To facilitate the research, we develop two datasets in English and Chinese respectively, on which extensive experiments are conducted. Results show that our approach better controls lexical complexity and generates higher quality sentences than baseline methods.

Open-domain conversational question answering can be viewed as two tasks: passage retrieval and conversational question answering, where the former relies on selecting candidate passages from a large corpus and the latter requires better understanding of a question with contexts to predict the answers. This paper proposes ConvADR-QA that leverages historical answers to boost retrieval performance and further achieves better answering performance. In our proposed framework, the retrievers use a teacher-student framework to reduce noises from previous turns. Our experiments on the benchmark dataset, OR-QuAC, demonstrate that our model outperforms existing baselines in both extractive and generative reader settings, well justifying the effectiveness of historical answers for open-domain conversational question answering., Comment: AACL-IJCNLP 2022

We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fracture simulation is powered by a recent physically based algorithm that efficiently generates a variety of fracture modes of an object. Existing shape assembly datasets decompose objects according to semantically meaningful parts, effectively modeling the construction process. In contrast, Breaking Bad models the destruction process of how a geometric object naturally breaks into fragments. Our dataset serves as a benchmark that enables the study of fractured object reassembly and presents new challenges for geometric shape understanding. We analyze our dataset with several geometry measurements and benchmark three state-of-the-art shape assembly deep learning methods under various settings. Extensive experimental results demonstrate the difficulty of our dataset, calling on future research in model designs specifically for the geometric shape assembly task. We host our dataset at https://breaking-bad-dataset.github.io/., Comment: NeurIPS 2022 Track on Datasets and Benchmarks. The first three authors contributed equally to this work. Project page: https://breaking-bad-dataset.github.io/ Code: https://github.com/Wuziyi616/multi_part_assembly Dataset: https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/LZNPKB

We present an overview of the history of the heat kernel and eigenfunctions on Riemannian manifolds and how the theory has lead to modern methods of analyzing high dimensional data via eigenmaps and other spectral embeddings. We begin with Varadhan's Theorem relating the heat kernel to the distance function on a Riemannian manifold. We then review various theorems which bound the heat kernel on classes of Riemannian manifolds. Next we turn to eigenfunctions, the Sturm-Liouville Decomposition of the heat kernel using eigenfunctions, and various theorems which bound eigenfunctions on classes of Riemannian manifolds. We review various notions of convergence of Riemannian manifolds and which classes of Riemannian manifolds are compact with respect to which notions of convergence. We then present B\'erard-Besson-Gallot's heat kernel embeddings of Riemannian manifolds and the truncation of those embeddings. Finally we turn to Applications of Spectral embeddings to the Dimension Reduction of data sets lying in high dimensional spaces reviewing, in particular, the work of Belkin-Niyogi and Coifman-Lafon. We also review the Spectral Theory of Graphs and the work of Dodziuk and Chung and others. We close with recent theorems of Portegies and of the first author controlling truncated spectral embeddings uniformly on key classes of Riemannian manifolds. Throughout we provide many explicitly computed examples and graphics and attempt to provide as complete a set of references as possible. We hope that this article is accessible to both pure and applied mathematicians working in Geometric Analysis and their doctoral students., Comment: v2 minor typos corrected and reformatted for the journal

Intelligent virtual assistants are currently designed to perform tasks or services explicitly mentioned by users, so multiple related domains or tasks need to be performed one by one through a long conversation with many explicit intents. Instead, human assistants are capable of reasoning (multiple) implicit intents based on user utterances via commonsense knowledge, reducing complex interactions and improving practicality. Therefore, this paper proposes a framework of multi-domain dialogue systems, which can automatically infer implicit intents based on user utterances and then perform zero-shot prompting using a large pre-trained language model to trigger suitable single task-oriented bots. The proposed framework is demonstrated effective to realize implicit intents and recommend associated bots in a zero-shot manner.