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Nuclear de-excitations associated with neutrino-nucleus interactions and nucleon decays are playing an increasingly significant role in neutrino experiments. We explore the GEMINI++ code and estimate its ability to account for the de-excitation processes of highly excited $^{11}$B$^*$ and $^{15}$N$^*$, which can be created in the liquid scintillator and water Cherenkov detectors respectively. It is found that GEMINI++ can not describe the nuclear experimental data of $^{11}$B$^*$ and $^{15}$N$^*$ well. To improve its performance for de-excitations of light nuclei, we modify GEMINI++ and then develop a code of GEMINI++4$\nu$, which can give the best predictions compared with experimental measurements among some widely used statistical model codes., Comment: 7 pages, 4 figures, 2 tables

Indoor photovoltaics (IPVs) have attracted increasing attention for sustainably powering Internet of Things (IoT) electronics. Sb$_2$S$_3$ is a promising IPV candidate material with a bandgap of ~1.75 eV, which is near the optimal value for indoor energy harvesting. However, the performance of Sb$_2$S$_3$ solar cells is limited by nonradiative recombination, closely associated with the poor-quality absorber films. Additive engineering is an effective strategy to improved the properties of solution-processed films. This work shows that the addition of monoethanolamine (MEA) into the precursor solution allows the nucleation and growth of Sb$_2$S$_3$ films to be controlled, enabling the deposition of high-quality Sb$_2$S$_3$ absorbers with reduced grain boundary density, optimized band positions and increased carrier concentration. Complemented with computations, it is revealed that the incorporation of MEA leads to a more efficient and energetically favorable deposition for enhanced heterogeneous nucleation on the substrate, which increases the grain size and accelerates the deposition rate of Sb$_2$S$_3$ films. Due to suppressed carrier recombination and improved charge-carrier transport in Sb$_2$S$_3$ absorber films, the MEA-modulated Sb$_2$S$_3$ solar cell yields a power conversion efficiency (PCE) of 7.22% under AM1.5G illumination, and an IPV PCE of 17.55% under 1000 lux white light emitting diode (WLED) illumination, which is the highest yet reported for Sb$_2$S$_3$ IPVs. Furthermore, we construct high performance large-area Sb$_2$S$_3$ IPV modules to power IoT wireless sensors, and realize the long-term continuous recording of environmental parameters under WLED illumination in an office. This work highlights the great prospect of Sb$_2$S$_3$ photovoltaics for indoor energy harvesting., Comment: 28 pages, 6 figures

Highly efficient acousto-optic modulation plays a vital role in the microwave-to-optical conversion. Herein, we demonstrate a hybrid thin-film lithium niobate (TFLN) racetrack micro-ring acousto-optic modulator (AOM) implemented with low-loss chalcogenide (ChG) waveguide. By engineering the electrode configuration of the interdigital transducer, the double-arm micro-ring acousto-optic modulation is experimentally confirmed in nonsuspended ChG loaded TFLN waveguide platform. Varying the position of blue-detuned bias point, the half-wave-voltage-length product VpaiL of the hybrid TFLN micro-ring AOM is as small as 9 mVcm. Accordingly, the acousto-optic coupling strength is estimated to be 0.48 Hz s1/2 at acoustic frequency of 0.84 GHz. By analyzing the generation of phonon number from the piezoelectric transducer, the microwave-to-optical conversion efficiency is calculated to be 0.05%, approximately one order of magnitude larger than that of the state-of-the-art suspended counterpart. Efficient microwave-to-optical conversion thus provides new opportunities for low-power-consumption quantum information transduction using the TFLN-ChG hybrid piezo-optomechanical devices.

This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fine-tune Weaver for creative and professional writing purposes and align it to the preference of professional writers using a suit of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost. Evaluation on a carefully curated benchmark for assessing the writing capabilities of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage). We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance. Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs.

The detection of 3D objects through a single perspective camera is a challenging issue. The anchor-free and keypoint-based models receive increasing attention recently due to their effectiveness and simplicity. However, most of these methods are vulnerable to occluded and truncated objects. In this paper, a single-stage monocular 3D object detection model is proposed. An instance-segmentation head is integrated into the model training, which allows the model to be aware of the visible shape of a target object. The detection largely avoids interference from irrelevant regions surrounding the target objects. In addition, we also reveal that the popular IoU-based evaluation metrics, which were originally designed for evaluating stereo or LiDAR-based detection methods, are insensitive to the improvement of monocular 3D object detection algorithms. A novel evaluation metric, namely average depth similarity (ADS) is proposed for the monocular 3D object detection models. Our method outperforms the baseline on both the popular and the proposed evaluation metrics while maintaining real-time efficiency., Comment: 8 pages; 6 figures. typo fixed; reference changed

Nearest neighbor search plays a fundamental role in many disciplines such as multimedia information retrieval, data-mining, and machine learning. The graph-based search approaches show superior performance over other types of approaches in recent studies. In this paper, the graph-based NN search is revisited. We optimize two key components in the approach, namely the search procedure and the graph that supports the search. For the graph construction, a two-stage graph diversification scheme is proposed, which makes a good trade-off between the efficiency and reachability for the search procedure that builds upon it. Moreover, the proposed diversification scheme allows the search procedure to decide dynamically how many nodes should be visited in one node's neighborhood. By this way, the computing power of the devices is fully utilized when the search is carried out under different circumstances. Furthermore, two NN search procedures are designed respectively for small and large batch queries on the GPU. The optimized NN search, when being supported by the two-stage diversified graph, outperforms all the state-of-the-art approaches on both the CPU and the GPU across all the considered large-scale datasets.

Deep metric learning aims to transform input data into an embedding space, where similar samples are close while dissimilar samples are far apart from each other. In practice, samples of new categories arrive incrementally, which requires the periodical augmentation of the learned model. The fine-tuning on the new categories usually leads to poor performance on the old, which is known as "catastrophic forgetting". Existing solutions either retrain the model from scratch or require the replay of old samples during the training. In this paper, a complete online deep metric learning framework is proposed based on mutual distillation for both one-task and multi-task scenarios. Different from the teacher-student framework, the proposed approach treats the old and new learning tasks with equal importance. No preference over the old or new knowledge is caused. In addition, a novel virtual feature estimation approach is proposed to recover the features assumed to be extracted by the old models. It allows the distillation between the new and the old models without the replay of old training samples or the holding of old models during the training. A comprehensive study shows the superior performance of our approach with the support of different backbones.

We explore the neutrino signals from proton decays catalyzed by GUT monopoles in the Sun. Three typical proton decay modes, $p \rightarrow e^+ + (\rho^0, \eta, \omega...) \rightarrow \pi^+$, $p \rightarrow \mu^+ K^0$ and $p \rightarrow \bar{\nu}_e \pi^+$, have been analyzed for the Super-Kamiokande experiment. The monopole-induced neutrinos arise from interactions and subsequent decays of the proton decay products. To obtain the neutrino energy spectra, we use the Geant4 software to simulate propagations of daughter particles in the highly-dense solar center. It is found that $K^0$ can produce a large amount of 236 MeV monoenergetic $\nu_\mu$ neutrinos through the charge exchange process $K^0 + p \rightarrow K^+ + n$ and the subsequent decay $K^+ \rightarrow \mu^+ \nu_\mu$ at rest. Based on this interesting feature, $p \rightarrow \mu^+ K^0$ can give the best discovery potential among three decay modes for most of the parameter space. In addition, we present the Super-Kamiokande sensitivities to the monopole flux for three proton decay modes., Comment: 15 pages, 5 figures, 3 tables; Some comments and 3 references added; the version accepted for publication in JCAP

A highly efficient on-chip acousto-optic modulator, as a key component, occupies an exceptional position in microwave-to-optical conversion. Homogeneous thin-film lithium niobate is preferentially employed to build the suspended configuration forming the acoustic resonant cavity to improve the modulation efficiency of the device. However, the limited cavity length and complex fabrication recipe of the suspended prototype restrain further breakthrough in the modulation efficiency and impose challenges for waveguide fabrication. In this work, based on a nonsuspended thin-film lithium niobate-chalcogenide glass hybrid Mach-Zehnder interferometer waveguide platform, we propose and demonstrate a built-in push-pull acousto-optic modulator with a half-wave-voltage-length product as low as 0.03 V cm, presenting a modulation efficiency comparable to that of the state-of-the-art suspended counterpart. Based on the advantage of low power consumption, a microwave modulation link is demonstrated using our developed built-in push-pull acousto-optic modulator. The nontrivial acousto-optic modulation performance benefits from the superior photoelastic property of the chalcogenide membrane and the completely bidirectional participation of the antisymmetric Rayleigh surface acoustic wave mode excited by the impedance-matched interdigital transducer, overcoming the issue of amplitude differences of surface acoustic waves applied to the Mach-Zehnder interferometer two arms in traditional push-pull acousto-optic modulators.

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. Here we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a 2-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3 % with proper tuning of network parameters in 15 x 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware., Comment: 22 pages plus 8 pages supplemental material; 7 figures plus 7 supplemental figures