Statistical Properties of Exciton Fine Structure Splittings and Polarization Angles in Quantum Dot Ensembles

We propose an effective model to describe the statistical properties of exciton fine structure splitting (FSS) and polarization angle of quantum dot ensembles (QDEs). We derive the distributions of FSS and polarization angle for QDEs and show that their statistical features can be fully characterized using at most three independent measurable parameters. The effective model is confirmed using atomistic pseudopotential calculations as well as experimental measurements for several rather different QDEs. The model naturally addresses three fundamental questions that are frequently encountered in theories and experiments: (I) Why the probability of finding QDs with vanishing FSS is generally very small? (II) Why FSS and polarization angle differ dramatically from QD to QD? and (III) Is there any direct connection between FSS, optical polarization and the morphology of QDs? The answers to these fundamental questions yield a completely new physical picture for understanding optical properties of QDEs.Comment: 6 pages, 3 figures, 1 tabl

Observation of Ultrahigh Mobility Surface States in a Topological Crystalline Insulator by Infrared Spectroscopy

Topological crystalline insulators (TCIs) possess metallic surface states protected by crystalline symmetry, which are a versatile platform for exploring topological phenomena and potential applications. However, progress in this field has been hindered by the challenge to probe optical and transport properties of the surface states owing to the presence of bulk carriers. Here we report infrared (IR) reflectance measurements of a TCI, (001) oriented $Pb_{1-x}Sn_{x}Se$ in zero and high magnetic fields. We demonstrate that the far-IR conductivity is unexpectedly dominated by the surface states as a result of their unique band structure and the consequent small IR penetration depth. Moreover, our experiments yield a surface mobility of 40000 $cm^{2}/(Vs)$, which is one of the highest reported values in topological materials, suggesting the viability of surface-dominated conduction in thin TCI crystals. These findings pave the way for exploring many exotic transport and optical phenomena and applications predicted for TCIs

A study on the adoption intention of cold chain prepared Dishes based on consumer orientation mentality

Background:  Cold chain prepared dishes, as a new type of food with low temperature and dual attributes, have attracted more and more attention from consumers. From the perspective of consumers' brand positioning mentality, this study integrated the brand, quality and other attributes of cold chain prepared vegetables, and studied the positioning mentality and adoption behavior of Chinese consumers   In this study, three cold-chain prefabricated dishes, "pickled cabbage fish", "tomato Braised beef brisket" and "Huangpi fishball", were selected as investigation objects. Contribution:  The contribution of this study is that different food value attributes explain the law of consumers' willingness to adopt cold chain prepared dishes through brand positioning mentality, and find out how different degrees of vulnerability and quality warranty Method:  Based on ZMET survey method, product effect data were used to analyze and verify structural equation model. Results:  Different value evaluation directly affects consumers' brand positioning mentality, brand positioning mind as an intermediate variable has a significant correlation with adoption intention. The degree of vulnerability moderates the relationship between value evaluation and brand positioning mentality, and the degree of warranty period regulates the relationship between quality value, brand positioning mentality and adoption intention respectively. Conclusion:  It also provides new marketing tools and theories for entrepreneurs and marketers in the cold chain food industry, which contributes to the promotion and diffusion of cold chain prepared food for consumer

VST++: Efficient and Stronger Visual Saliency Transformer

While previous CNN-based models have exhibited promising results for salient object detection (SOD), their ability to explore global long-range dependencies is restricted. Our previous work, the Visual Saliency Transformer (VST), addressed this constraint from a transformer-based sequence-to-sequence perspective, to unify RGB and RGB-D SOD. In VST, we developed a multi-task transformer decoder that concurrently predicts saliency and boundary outcomes in a pure transformer architecture. Moreover, we introduced a novel token upsampling method called reverse T2T for predicting a high-resolution saliency map effortlessly within transformer-based structures. Building upon the VST model, we further propose an efficient and stronger VST version in this work, i.e. VST++. To mitigate the computational costs of the VST model, we propose a Select-Integrate Attention (SIA) module, partitioning foreground into fine-grained segments and aggregating background information into a single coarse-grained token. To incorporate 3D depth information with low cost, we design a novel depth position encoding method tailored for depth maps. Furthermore, we introduce a token-supervised prediction loss to provide straightforward guidance for the task-related tokens. We evaluate our VST++ model across various transformer-based backbones on RGB, RGB-D, and RGB-T SOD benchmark datasets. Experimental results show that our model outperforms existing methods while achieving a 25% reduction in computational costs without significant performance compromise. The demonstrated strong ability for generalization, enhanced performance, and heightened efficiency of our VST++ model highlight its potential

SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation

Recently, the contrastive language-image pre-training, e.g., CLIP, has demonstrated promising results on various downstream tasks. The pre-trained model can capture enriched visual concepts for images by learning from a large scale of text-image data. However, transferring the learned visual knowledge to open-vocabulary semantic segmentation is still under-explored. In this paper, we propose a CLIP-based model named SegCLIP for the topic of open-vocabulary segmentation in an annotation-free manner. The SegCLIP achieves segmentation based on ViT and the main idea is to gather patches with learnable centers to semantic regions through training on text-image pairs. The gathering operation can dynamically capture the semantic groups, which can be used to generate the final segmentation results. We further propose a reconstruction loss on masked patches and a superpixel-based KL loss with pseudo-labels to enhance the visual representation. Experimental results show that our model achieves comparable or superior segmentation accuracy on the PASCAL VOC 2012 (+1.4% mIoU), PASCAL Context (+2.4% mIoU), and COCO (+5.6% mIoU) compared with baselines. We release the code at https://github.com/ArrowLuo/SegCLIP

Two-Beam Multiplexing with Inter-Subarray Coding for Arbitrary Directions Based on Interleaved Subarray Architectures

A new method is proposed to achieve millimeter-wave two-beam multiplexing with arbitrary directions based on the interleaved subarray architecture. Beam interference can be mitigated and beam gain augmented by multiplexing multiple beams. Previous techniques can only multiplex two beams whose directions satisfy a specific relationship. By the proposed design and the associated inter-coding technique, two-beam multiplexing for arbitrary directions to serve two users is achieved. Design examples are provided to demonstrate the effectiveness of the proposed method

Deep Generative Fixed-filter Active Noise Control

Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises. Selective fixed-filter active noise control (SFANC) can significantly reduce response time by selecting appropriate pre-trained control filters for different noises. Nonetheless, the limited number of pre-trained control filters may affect noise reduction performance, especially when the incoming noise differs much from the initial noises during pre-training. Therefore, a generative fixed-filter active noise control (GFANC) method is proposed in this paper to overcome the limitation. Based on deep learning and a perfect-reconstruction filter bank, the GFANC method only requires a few prior data (one pre-trained broadband control filter) to automatically generate suitable control filters for various noises. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.Comment: Accepted by ICASSP 2023. Code will be available after publicatio

A Survey of Integrating Wireless Technology into Active Noise Control

Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC systems. We discuss the main wireless technique integrated into the ANC system, equipped with some innovative algorithms, in diverse environments. Instead of using microphone arrays, which increase the computation complexity of the ANC system, to isolate multiple noise sources to improve noise reduction performance, the application of the wireless technique avoids extra computation demand. Wireless transmissions of reference, error, and control signals are also applied to improve the convergence performance of the ANC system. Furthermore, this paper lists some wireless ANC applications, such as earbuds, headphones, windows, and headrests, underscoring their adaptability and efficiency in various settings