Asymmetric sequential Landau-Zener dynamics of Bose condensed atoms in a cavity

We explore the asymmetric sequential Landau-Zener (LZ) dynamics in an ensemble of interacting Bose condensed two-level atoms coupled with a cavity field. Assuming the couplings between all atoms and the cavity field are identical, the interplay between atom-atom interaction and detuning may lead to a series of LZ transitions. Unlike the conventional sequential LZ transitions, which are symmetric to the zero detuning, the LZ transitions of Bose condensed atoms in a cavity field are asymmetric and sensitively depend on the photon number distribution of the cavity. In LZ processes involving single excitation numbers, both the variance of the relative atom number and the step slope of the sequential population ladder are asymmetric, and the asymmetry become more significant for smaller excitation numbers. Furthermore, in LZ processes involving multiple excitation numbers, there may appear asymmetric population ladders with decreasing step heights. During a dynamical LZ process, due to the atom-cavity coupling, the cavity field shows dynamical collapse and revivals. In comparison with the symmetric LZ transitions in a classical field, the asymmetric LZ transitions in a cavity field originate from the photon-number-dependent Rabi frequency. The asymmetric sequential LZ dynamics of Bose condensed atoms in a cavity field may open up a new way to explore the fundamental many-body physics in coupled atom-photon systems.Comment: 14 pages, 6 figure

Active spheroids in viscosity gradients

In this paper, we explore the hydrodynamics of spheroidal active particles in viscosity gradients. This work provides a more accurate modeling approach, in comparison to spherical particles, for anisotropic organisms like Paramecium swimming through inhomogeneous environments, but more fundamentally examines the influence of particle shape on viscotaxis. We find that spheroidal squirmers generally exhibit dynamics consistent with their spherical analogs, irrespective of the classification of swimmers as pushers, pullers, or neutral swimmers. However, the slenderness of the spheroids tends to reduce the impact of viscosity gradients on their dynamics; when swimmers become more slender, the viscosity difference across their body is reduced, which leads to slower reorientation. We also derive the mobility tensor for passive spheroids in viscosity gradients generalizing previous results for spheres and slender bodies. This work enhances our understanding of how shape factors into the dynamics of passive and active particles in viscosity gradients, and offers new perspectives that could aid the control of both natural and synthetic swimmers in complex fluid environments.Comment: 19 pages, 5 figure

Facile synthesis of solid-state fluorescent organosilica nanoparticles with a photoluminescence quantum yield of 73.3% for fingerprint recognition and white-light-emitting diodes

Polymer-like coated OSiNPs with a solid-state PLQY of up to 73.3% for applications in WLEDs and fingerprint recognition are fabricated by a simple hydrothermal method

Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data including LiDAR data and satellite imagery data. We used an expanded definition of pedestrian infrastructure inventory which goes beyond the traditional transportation elements to include street furniture objects that are important for accessibility but are often omitted from the traditional definition. Our contributions lie in producing the necessary knowledge to answer the following two questions. First, which data representation can facilitate zero-shot segmentation of infrastructure objects with SAM? Second, how well does the SAM-based method perform on segmenting pedestrian infrastructure objects? Our findings indicate that street view images generated from mobile LiDAR point cloud data, when paired along with satellite imagery data, can work efficiently with SAM to create a scalable pedestrian infrastructure inventory approach with immediate benefits to GIS professionals, city managers, transportation owners, and walkers, especially those with travel-limiting disabilities, such as individuals who are blind, have low vision, or experience mobility disabilities

Improving Input-label Mapping with Demonstration Replay for In-context Learning

In-context learning (ICL) is an emerging capability of large autoregressive language models where a few input-label demonstrations are appended to the input to enhance the model's understanding of downstream NLP tasks, without directly adjusting the model parameters. The effectiveness of ICL can be attributed to the strong language modeling capabilities of large language models (LLMs), which enable them to learn the mapping between input and labels based on in-context demonstrations. Despite achieving promising results, the causal nature of language modeling in ICL restricts the attention to be backward only, i.e., a token only attends to its previous tokens, failing to capture the full input-label information and limiting the model's performance. In this paper, we propose a novel ICL method called Repeated Demonstration with Sliding Causal Attention, (RdSca). Specifically, we duplicate later demonstrations and concatenate them to the front, allowing the model to `observe' the later information even under the causal restriction. Besides, we introduce sliding causal attention, which customizes causal attention to avoid information leakage. Experimental results show that our method significantly improves the input-label mapping in ICL demonstrations. We also conduct an in-depth analysis of how to customize the causal attention without training, which has been an unexplored area in previous research

Controlling the polarization of nitrogen ion lasing

Air lasing provides a promising technique to remotely produce coherent radiation in the atmosphere and attracts continuous attention. However, the polarization properties of N2+ lasing with seeding has not been understood since it was discovered ten years ago, in which the behaviors appear disordered and confusing. Here, we performed an experimental and theoretical investigation on the polarization properties of N2+ lasing and successfully revealed its underlying physical mechanism. We found that the optical gain is anisotropic owing to the permanent alignment of N2+ induced by the preferential ionization of the pump light. As a result, the polarization of N2+ lasing tends to align with that of the pump light after amplification, which becomes more pronounced with increasing amplification factor. Based on the permanent alignment of N2+, we built a theoretical model that analytically interpreted and numerically reproduced the experimental observations, which points out the key factors for controlling the polarization of N2+ lasing.Comment: 12 pages, 4 figure

Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders

Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network with learnable graph structure to establish global pixel-wise combinations.In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect.Finally, to improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner.Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies.Comment: 14 pages, 9 figure

Amplification of light pulses with orbital angular momentum (OAM) in nitrogen ions lasing

Nitrogen ions pumped by intense femtosecond laser pulses give rise to optical amplification in the ultraviolet range. Here, we demonstrated that a seed light pulse carrying orbital angular momentum (OAM) can be significantly amplified in nitrogen plasma excited by a Gaussian femtosecond laser pulse. With the topological charge of +1 and -1, we observed an energy amplification of the seed light pulse by two orders of magnitude, while the amplified pulse carries the same OAM as the incident seed pulse. Moreover, we show that a spatial misalignment of the plasma amplifier with the OAM seed beam leads to an amplified emission of Gaussian mode without OAM, due to the special spatial profile of the OAM seed pulse that presents a donut-shaped intensity distribution. Utilizing this misalignment, we can implement an optical switch that toggles the output signal between Gaussian mode and OAM mode. This work not only certifies the phase transfer from the seed light to the amplified signal, but also highlights the important role of spatial overlap of the donut-shaped seed beam with the gain region of the nitrogen plasma for the achievement of OAM beam amplification.Comment: 10 pages, 7 figure

PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models

While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use. Therefore, effectively compressing large-scale PLMs becomes an increasingly important problem. Quantization, which represents high-precision tensors with low-bit fix-point format, is a viable solution. However, most existing quantization methods are task-specific, requiring customized training and quantization with a large number of trainable parameters on each individual task. Inspired by the observation that the over-parameterization nature of PLMs makes it possible to freeze most of the parameters during the fine-tuning stage, in this work, we propose a novel ``quantize before fine-tuning'' framework, PreQuant, that differs from both quantization-aware training and post-training quantization. PreQuant is compatible with various quantization strategies, with outlier-aware parameter-efficient fine-tuning incorporated to correct the induced quantization error. We demonstrate the effectiveness of PreQuant on the GLUE benchmark using BERT, RoBERTa, and T5. We also provide an empirical investigation into the workflow of PreQuant, which sheds light on its efficacy.Comment: Findings of ACL202