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We report an experimental study of a one-dimensional quintuple-quantum-dot array integrated with two quantum dot charge sensors in an InAs nanowire. The device is studied by measuring double quantum dots formed consecutively in the array and corresponding charge stability diagrams are revealed with both direct current measurements and charge sensor signals. The one-dimensional quintuple-quantum-dot array are then tuned up and its charge configurations are fully mapped out with the two charge sensors. The energy level of each dot in the array can be controlled individually by using a compensated gate architecture (i.e., "virtual gate"). After that, four dots in the array are selected to form two double quantum dots and ultra strong inter-double-dot interaction is obtained. A theoretical simulation based on a 4-dimensional Hamiltonian confirms the strong coupling strength between the two double quantum dots. The highly controllable one-dimensional quantum dot array achieved in this work is expected to be valuable for employing InAs nanowires to construct advanced quantum hardware in the future.

The recent observation of the fractional quantum anomalous Hall effect in moir\'e fractional Chern insulators (FCI) provides opportunities for investigating zero magnetic field anyons. So far, both experimental and theoretical results suggest that filling > 1/3 FCI states in the first Chern band share features with those of the lowest Landau level (LL). To create the possibility of realizing non-Abelian anyons, one route is to engineer higher flat Chern bands that mimic higher LLs. Here, we investigate the interaction, topology, and ferromagnetism of the second moir\'e miniband in twisted MoTe2 bilayer (tMoTe2). Around filling factor v = -3, i.e., half-filling of the second miniband, we uncover spontaneous ferromagnetism and an incipient Chern insulator state. By measuring the anomalous Hall effect as a function of twist angle, we find that the Chern numbers (C) of the top two moir\'e flat bands have opposite sign (C = -+1) at twist angles above 3.1{\deg} but the same sign (C = -1) around 2.6{\deg}. This observation is consistent with the recently predicted twist-angle dependent band topology, resulting from the competition between moir\'e ferroelectricity and piezoelectricity. As we increase the magnetic field, only the small twist-angle device (2.6{\deg}) experiences a topological phase transition with an emergent C = -2 state. This is attributed to a Zeeman field-induced band crossing between opposite valleys, with the determined C = -1 for the top two bands. Our results lay a firm foundation for understanding the higher flat Chern bands, which is essential for the prediction or discovery of non-Abelian FCIs., Comment: 24 pages, 4 figures

Despite the remarkable progress in synthetic aperture radar automatic target recognition (SAR ATR), recent efforts have concentrated on the detection or classification of a specific and coarse category, e.g., vehicles, ships, airplanes, or buildings. One of the fundamental limitations of the top-performing SAR ATR methods is that the learning paradigm is supervised, task-specific, limited-category, closed-world learning, which depends on massive amounts of accurately annotated samples that are expensively labeled by expert SAR analysts and has limited generalization capability and scalability. In this work, we make the first attempt towards building a foundation model for SAR ATR, termed SARatrX. SARatrX learns generalizable representations via self-supervised learning (SSL) and provides a basis for label-efficient model adaptation to generic SAR target detection and classification tasks. Specifically, SARatrX is trained on 0.18 M unlabelled SAR target samples, which are curated by combining contemporary benchmarks and constitute the largest publicly available dataset till now. Considering the characteristics of SAR images, a backbone tailored for SAR ATR is carefully designed, and a two-step SSL method endowed with multi-scale gradient features was applied to ensure the feature diversity and model scalability of SARatrX. The capabilities of SARatrX are evaluated on classification under few-shot and robustness settings and detection across various categories and scenes, and impressive performance is achieved, often competitive with or even superior to prior fully supervised, semi-supervised, or self-supervised algorithms. Our SARatrX and the curated dataset are released at https://github.com/waterdisappear/SARatrX to foster research into foundation models for SAR ATR and SAR image interpretation.

Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising <2K images with only mono-category objects) and inaccessible source code. To tackle these challenges, we establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a large-scale and diverse dataset for research purposes. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created. With this high-quality dataset, we conducted comprehensive experiments and uncovered a crucial challenge in SAR object detection: the substantial disparities between the pretraining on RGB datasets and finetuning on SAR datasets in terms of both data domain and model structure. To bridge these gaps, we propose a novel Multi-Stage with Filter Augmentation (MSFA) pretraining framework that tackles the problems from the perspective of data input, domain transition, and model migration. The proposed MSFA method significantly enhances the performance of SAR object detection models while demonstrating exceptional generalizability and flexibility across diverse models. This work aims to pave the way for further advancements in SAR object detection. The dataset and code is available at https://github.com/zcablii/SARDet_100K., Comment: 22 Pages, 10 Figures, 9 Tables

Image-to-video (I2V) generation tasks always suffer from keeping high fidelity in the open domains. Traditional image animation techniques primarily focus on specific domains such as faces or human poses, making them difficult to generalize to open domains. Several recent I2V frameworks based on diffusion models can generate dynamic content for open domain images but fail to maintain fidelity. We found that two main factors of low fidelity are the loss of image details and the noise prediction biases during the denoising process. To this end, we propose an effective method that can be applied to mainstream video diffusion models. This method achieves high fidelity based on supplementing more precise image information and noise rectification. Specifically, given a specified image, our method first adds noise to the input image latent to keep more details, then denoises the noisy latent with proper rectification to alleviate the noise prediction biases. Our method is tuning-free and plug-and-play. The experimental results demonstrate the effectiveness of our approach in improving the fidelity of generated videos. For more image-to-video generated results, please refer to the project website: https://noise-rectification.github.io.

Recently, video generation has achieved significant rapid development based on superior text-to-image generation techniques. In this work, we propose a high fidelity framework for image-to-video generation, named AtomoVideo. Based on multi-granularity image injection, we achieve higher fidelity of the generated video to the given image. In addition, thanks to high quality datasets and training strategies, we achieve greater motion intensity while maintaining superior temporal consistency and stability. Our architecture extends flexibly to the video frame prediction task, enabling long sequence prediction through iterative generation. Furthermore, due to the design of adapter training, our approach can be well combined with existing personalized models and controllable modules. By quantitatively and qualitatively evaluation, AtomoVideo achieves superior results compared to popular methods, more examples can be found on our project website: https://atomo-video.github.io/., Comment: Technical report. Page: https://atomo-video.github.io/

The growing Synthetic Aperture Radar (SAR) data has the potential to build a foundation model through Self-Supervised Learning (SSL) methods, which can achieve various SAR Automatic Target Recognition (ATR) tasks with pre-training in large-scale unlabeled data and fine-tuning in small labeled samples. SSL aims to construct supervision signals directly from the data, which minimizes the need for expensive expert annotation and maximizes the use of the expanding data pool for a foundational model. This study investigates an effective SSL method for SAR ATR, which can pave the way for a foundation model in SAR ATR. The primary obstacles faced in SSL for SAR ATR are the small targets in remote sensing and speckle noise in SAR images, corresponding to the SSL approach and signals. To overcome these challenges, we present a novel Joint-Embedding Predictive Architecture for SAR ATR (SAR-JEPA), which leverages local masked patches to predict the multi-scale SAR gradient representations of unseen context. The key aspect of SAR-JEPA is integrating SAR domain features to ensure high-quality self-supervised signals as target features. Besides, we employ local masks and multi-scale features to accommodate the various small targets in remote sensing. By fine-tuning and evaluating our framework on three target recognition datasets (vehicle, ship, and aircraft) with four other datasets as pre-training, we demonstrate its outperformance over other SSL methods and its effectiveness with increasing SAR data. This study showcases the potential of SSL for SAR target recognition across diverse targets, scenes, and sensors.Our codes and weights are available in \url{https://github.com/waterdisappear/SAR-JEPA., Comment: 15 pages, 7 figures

With the increasing prevalence of smartphones and websites, Image Aesthetic Assessment (IAA) has become increasingly crucial. While the significance of attributes in IAA is widely recognized, many attribute-based methods lack consideration for the selection and utilization of aesthetic attributes. Our initial step involves the acquisition of aesthetic attributes from both intra- and inter-perspectives. Within the intra-perspective, we extract the direct visual attributes of images, constituting the absolute attribute. In the inter-perspective, our focus lies in modeling the relative score relationships between images within the same sequence, forming the relative attribute. Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images. For absolute attributes, we leverage multiple absolute-attribute perception modules and an absolute-attribute interacting network. The absolute-attribute perception modules are first pre-trained on several absolute-attribute learning tasks and then used to extract corresponding absolute attribute features. The absolute-attribute interacting network adaptively learns the weight of diverse absolute-attribute features, effectively integrating them with generic aesthetic features from various absolute-attribute perspectives and generating the aesthetic prediction. To model the relative attribute of images, we consider the relative ranking and relative distance relationships between images in a Relative-Relation Loss function, which boosts the robustness of the UMAAF. Furthermore, UMAAF achieves state-of-the-art performance on TAD66K and AVA datasets, and multiple experiments demonstrate the effectiveness of each module and the model's alignment with human preference.

High-quality free-standing InAs nanosheets are emerging layered semiconductor materials with potentials in designing planar Josephson junction devices for novel physics studies due to their unique properties including strong spin-orbit couplings, large Land\'e g-factors and the two dimensional nature. Here, we report an experimental study of proximity induced superconductivity in planar Josephson junction devices made from free-standing InAs nanosheets. The nanosheets are grown by molecular beam epitaxy and the Josephson junction devices are fabricated by directly contacting the nanosheets with superconductor Al electrodes. The fabricated devices are explored by low-temperature carrier transport measurements. The measurements show that the devices exhibit a gate-tunable supercurrent, multiple Andreev reflections, and a good quality superconductor-semiconductor interface. The superconducting characteristics of the Josephson junctions are investigated at different magnetic fields and temperatures, and are analyzed based on the Bardeen-Cooper-Schrieffer (BCS) theory. The measurements of ac Josephson effect are also conducted under microwave radiations with different radiation powers and frequencies, and integer Shapiro steps are observed. Our work demonstrates that InAs nanosheet based hybrid devices are desired systems for investigating forefront physics, such as the two-dimensional topological superconductivity.

Vehicle recognition is a fundamental problem in SAR image interpretation. However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations. Additionally, the lack of large datasets further complicates the task. Inspired by the analysis of target signature variations and deep learning explainability, this paper proposes a novel domain alignment framework named the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve robustness under various operating conditions. Concisely, HDANet integrates feature disentanglement and alignment into a unified framework with three modules: domain data generation, multitask-assisted mask disentanglement, and domain alignment of target features. The first module generates diverse data for alignment, and three simple but effective data augmentation methods are designed to simulate target signature variations. The second module disentangles the target features from background clutter using the multitask-assisted mask to prevent clutter from interfering with subsequent alignment. The third module employs a contrastive loss for domain alignment to extract robust target features from generated diverse data and disentangled features. Lastly, the proposed method demonstrates impressive robustness across nine operating conditions in the MSTAR dataset, and extensive qualitative and quantitative analyses validate the effectiveness of our framework.

In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning can overfit clutter to reduce training errors. Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In this paper, we quantify the contributions of different regions to target recognition based on the Shapley value. The Shapley value of clutter measures the degree of overfitting. Moreover, we explain how data bias and model bias contribute to non-causality. Concisely, data bias leads to comparable signal-to-clutter ratios and clutter textures in training and test sets. And various model structures have different degrees of overfitting for these biases. The experimental results of various models under standard operating conditions on the MSTAR dataset support our conclusions. Our code is available at https://github.com/waterdisappear/Data-Bias-in-MSTAR.

A serial triple quantum dot (TQD) integrated with a quantum dot (QD) charge sensor is realized from an InAs nanowire via a fine finger-gate technique. The complex charge states and intriguing properties of the device are studied in the few-electron regime by direct transport measurements and by charge-sensor detection measurements. The measurements of the charge stability diagram for a capacitively coupled, parallel double-QD formed from a QD in the TQD and the sensor QD show a visible capacitance coupling between the TQD and the sensor QD, indicating a good sensitivity of the charge sensor. The charge stability diagrams of the TQD are measured by the charge sensor and the global features seen in the measured charge stability diagrams are well reproduced by the simultaneous measurements of the direct transport current through the TQD and by the simulation made based on an effective capacitance network model. The complex charge stability diagrams of the TQD are measured in detail with the integrated charge sensor in an energetically degenerate region, where all the three QDs are on or nearly on resonance, and the formations of quadruple points and of all possible eight charge states are observed. In addition, the operation of the TQD as a quantum cellular automata is demonstrated and discussed., Comment: 18 pages, 4 figures, Supplementary Information included

In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural language (NL) utterance and table schema representations for text-to-SQL conversations. Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation. In addition, we construct a high-quality large-scale context-dependent text-to-SQL conversation corpus to pre-train STAR. Extensive experiments show that STAR achieves new state-of-the-art performance on two downstream benchmarks (SParC and CoSQL), significantly outperforming previous pre-training methods and ranking first on the leaderboard. We believe the release of the constructed corpus, codebase and pre-trained STAR checkpoints would push forward the research in this area. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/star., Comment: EMNLP 2022

Strongly bound excitons and many-body interactions between them determine light-matter interactions in van der Waals (vdW) heterostructures of 2D semiconductors. Unlike fundamental particles, quasiparticles in condensed matter, such as excitons, can be tailored to alter their interactions and realize emergent quantum phases. Here, using a WS$_2$/WSe$_2$/WS$_2$ heterotrilayer, we create a quantum superposition of oppositely oriented dipolar excitons - a quadrupolar exciton - wherein an electron is layer-hybridized in WS$_2$ layers while the hole localizes in WSe$_2$. In contrast to dipolar excitons, symmetric quadrupolar excitons only redshift in an out-of-plane electric field, consistent with ab initio calculations, regaining dipolar characteristics at higher fields. Electric field tunes the hybridization and allows for lifetime control through modification of the excitonic wavefunction. Lack of density-dependent blue shift of heterotrilayer excitons compared to dipolar excitons is consistent with quadrupolar interactions. Our results present vdW heterotrilayers as a field-tunable platform to engineer light-matter interactions and explore quantum phase transitions between spontaneously ordered many-exciton phases.

Reconfigurable Intelligent Surface (RIS) has become a popular technology to improve the capability of a THz multiuser Multi-input multi-output (MIMO) communication system. THz wave characteristics, on the other hand, restrict THz beam coverage on RIS when using a uniform planar array (UPA) antenna. In this study, we propose a dynamic RIS subarray structure to improve the performance of a THz MIMO communication system. In more details, an RIS is divided into several RIS subarrays according to the number of users. Each RIS subarray is paired with a user and only reflects beams to the corresponding user. Based on the structure of RIS, we first propose a weighted minimum mean square error - RIS local search (WMMSE-LS) scheme, which requires that each RIS element has limited phase shifts. To improve the joint beamforming performance, we further develop an adaptive Block Coordinate Descent(BCD)-aided algorithm, an iterative optimization method. Numerical results demonstrate the effectiveness of the dynamic RIS subarray structure and the adaptive BCD-aided joint beamforming scheme and also show the merit of our proposed system.

Moir\'e heterostructures are rapidly emerging as a tunable platform to study correlated electronic phenomena. Discovery of exotic quantum phases in moir\'e systems requires novel probes of charge and spin order. Unlike detection schemes which average over several moir\'e cells, local sensors can provide richer information with greater sensitivity. We study a WSe$_2$/MoSe$_2$/WSe$_2$ heterotrilayer which hosts excitons and electrons in distinct moir\'e lattices, and show that localized dipolar excitons are sensitive proximity charge sensors, uncovering numerous correlated electronic states at fractional fillings of the multi-orbital moir\'e lattice. In addition, the emission polarization can reveal the local electronic spin configuration at different fillings. Our results establish dipolar excitons as promising candidates to study emergent quantum matter and quantum magnetism in moir\'e crystals with higher spatial resolution.

The goal of object navigation is to reach the expected objects according to visual information in the unseen environments. Previous works usually implement deep models to train an agent to predict actions in real-time. However, in the unseen environment, when the target object is not in egocentric view, the agent may not be able to make wise decisions due to the lack of guidance. In this paper, we propose a hierarchical object-to-zone (HOZ) graph to guide the agent in a coarse-to-fine manner, and an online-learning mechanism is also proposed to update HOZ according to the real-time observation in new environments. In particular, the HOZ graph is composed of scene nodes, zone nodes and object nodes. With the pre-learned HOZ graph, the real-time observation and the target goal, the agent can constantly plan an optimal path from zone to zone. In the estimated path, the next potential zone is regarded as sub-goal, which is also fed into the deep reinforcement learning model for action prediction. Our methods are evaluated on the AI2-Thor simulator. In addition to widely used evaluation metrics SR and SPL, we also propose a new evaluation metric of SAE that focuses on the effective action rate. Experimental results demonstrate the effectiveness and efficiency of our proposed method., Comment: Accepted by ICCV21