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We propose networking superconducting quantum circuits by transducing their excitations (typically 4-8 GHz) to 100-500 MHz photons for transmission via cryogenic coaxial cables. Counter-intuitively, this frequency downconversion reduces noise and transmission losses. We introduce a multi-octave asymmetrically threaded SQUID circuit (MOATS) capable of the required efficient, high-rate transduction. For a 100-meter cable with $Q_i = 10^5$ at 10 mK, our approach achieves single-photon fidelities of 0.962 at 200 MHz versus 0.772 at 8 GHz, and triples the lower bound on quantum channel capacity. This method enables kilometer-scale quantum links while maintaining high fidelities, combining improved performance with the practical advantages of flexible, compact coaxial cables., Comment: 8 pages, 3 figures

Photonic addressing of superconducting circuits has been proposed to overcome wiring complexity and heat load challenges, but superconducting-photonic links suffer from an efficiency-noise trade-off that limits scalability. This trade-off arises because increasing power conversion efficiency requires reducing optical power, which makes the converted signal susceptible to shot noise. We analyze this trade-off and find the infidelity of qubit gates driven by photonic signals scales inversely with the number of photons used, and therefore the power efficiency of the converter. While methods like nonlinear detection or squeezed light could mitigate this effect, we consider generating higher frequency electrical signals, such as millimeter-waves (100 GHz), using laser light. At these higher frequencies, circuits have higher operating temperatures and cooling power budgets. We demonstrate an optically-driven cryogenic millimeter-wave source with a power efficiency of $10^{-4}$ that can generate ${1}~\mathrm{\mu W}$ of RF power at 80 GHz with 1500 thermal photons of added noise at 4 K. Using this source, we perform frequency-domain spectroscopy of superconducting NbTiN resonators at 80-90 GHz. Our results show a promising approach to alleviate the efficiency-noise constraints on optically-driven superconducting circuits while leveraging the benefits of photonic signal delivery. Further optimization of power efficiency and noise at high frequencies could make photonic control of superconducting qubits viable at temperatures exceeding 1 kelvin., Comment: 14 pages, 8 figures

Integrated optomechanical systems are one of the leading platforms for manipulating, sensing, and distributing quantum information. The temperature increase due to residual optical absorption sets the ultimate limit on performance for these applications. In this work, we demonstrate a two-dimensional optomechanical crystal geometry, named \textbf{b-dagger}, that alleviates this problem through increased thermal anchoring to the surrounding material. Our mechanical mode operates at 7.4 GHz, well within the operation range of standard cryogenic microwave hardware and piezoelectric transducers. The enhanced thermalization combined with the large optomechanical coupling rates, $g_0/2\pi \approx 880~\mathrm{kHz}$, and high optical quality factors, $Q_\text{opt} = 2.4 \times 10^5$, enables the ground-state cooling of the acoustic mode to phononic occupancies as low as $n_\text{m} = 0.35$ from an initial temperature of 3 kelvin, as well as entering the optomechanical strong-coupling regime. Finally, we perform pulsed sideband asymmetry of our devices at a temperature below 10 millikelvin and demonstrate ground-state operation ($n_\text{m} < 0.45$) for repetition rates as high as 3 MHz. Our results extend the boundaries of optomechanical system capabilities and establish a robust foundation for the next generation of microwave-to-optical transducers with entanglement rates overcoming the decoherence rates of state-of-the-art superconducting qubits., Comment: 13 pages, 4 main figures

Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within the calculation, avoiding the high computational cost issue in Vision Transformers. However, such methods usually obtain sparse tokens by hand-crafted or parallel-unfriendly designs, posing a challenge to reach a better balance between efficiency and performance. Different from them, this paper proposes to use learnable meta tokens to formulate sparse tokens, which effectively learn key information meanwhile improving the inference speed. Technically, the meta tokens are first initialized from image tokens via cross-attention. Then, we propose Dual Cross-Attention (DCA) to promote information exchange between image tokens and meta tokens, where they serve as query and key (value) tokens alternatively in a dual-branch structure, significantly reducing the computational complexity compared to self-attention. By employing DCA in the early stages with dense visual tokens, we obtain the hierarchical architecture LeMeViT with various sizes. Experimental results in classification and dense prediction tasks show that LeMeViT has a significant $1.7 \times$ speedup, fewer parameters, and competitive performance compared to the baseline models, and achieves a better trade-off between efficiency and performance., Comment: Accepted by IJCAI'2024. The code is available at https://github.com/ViTAE-Transformer/LeMeViT

This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks, a first of its kind in the field. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection, addressing the significant challenges posed by overfitting and limited training data in these domains. Our work categorizes data augmentation methods into two main types: data generation and data perturbation. Data generation covers techniques like graphic engine-based generation, generative model-based generation, and data recombination, while data perturbation is divided into image-level and human-level perturbations. Each method is tailored to the unique requirements of human-centric tasks, with some applicable across multiple areas. Our contributions include an extensive literature review, providing deep insights into the influence of these augmentation techniques in human-centric vision and highlighting the nuances of each method. We also discuss open issues and future directions, such as the integration of advanced generative models like Latent Diffusion Models, for creating more realistic and diverse training data. This survey not only encapsulates the current state of data augmentation in human-centric vision but also charts a course for future research, aiming to develop more robust, accurate, and efficient human-centric vision systems.

Employing data augmentation methods to enhance perception performance in adverse weather has attracted considerable attention recently. Most of the LiDAR augmentation methods post-process the existing dataset by physics-based models or machine-learning methods. However, due to the limited environmental annotations and the fixed vehicle trajectories in the existing dataset, it is challenging to edit the scene and expand the diversity of traffic flow and scenario. To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather in order to improve the perception performance of LiDAR in this scenario. We complete the modeling task of the rainy weather in the CARLA simulator and establish a pipeline for LiDAR data collection. In particular, we pay special attention to the spray and splash rolled up by the wheels of surrounding vehicles in rain and complete the simulation of this special scenario through the Spray Emitter method we developed. In addition, we examine the influence of different weather conditions on the intensity of the LiDAR echo, develop a prediction network for the intensity of the LiDAR echo, and complete the simulation of 4-feat LiDAR point cloud data. In the experiment, we observe that the model augmented by the synthetic data improves the object detection task's performance in the rainy sequence of the Waymo Open Dataset. Both the code and the dataset will be made publicly available at https://github.com/PJLab-ADG/PCSim#rainypcsim.

Vehicle-to-Everything (V2X) collaborative perception is crucial for autonomous driving. However, achieving high-precision V2X perception requires a significant amount of annotated real-world data, which can always be expensive and hard to acquire. Simulated data have raised much attention since they can be massively produced at an extremely low cost. Nevertheless, the significant domain gap between simulated and real-world data, including differences in sensor type, reflectance patterns, and road surroundings, often leads to poor performance of models trained on simulated data when evaluated on real-world data. In addition, there remains a domain gap between real-world collaborative agents, e.g. different types of sensors may be installed on autonomous vehicles and roadside infrastructures with different extrinsics, further increasing the difficulty of sim2real generalization. To take full advantage of simulated data, we present a new unsupervised sim2real domain adaptation method for V2X collaborative detection named Decoupled Unsupervised Sim2Real Adaptation (DUSA). Our new method decouples the V2X collaborative sim2real domain adaptation problem into two sub-problems: sim2real adaptation and inter-agent adaptation. For sim2real adaptation, we design a Location-adaptive Sim2Real Adapter (LSA) module to adaptively aggregate features from critical locations of the feature map and align the features between simulated data and real-world data via a sim/real discriminator on the aggregated global feature. For inter-agent adaptation, we further devise a Confidence-aware Inter-agent Adapter (CIA) module to align the fine-grained features from heterogeneous agents under the guidance of agent-wise confidence maps. Experiments demonstrate the effectiveness of the proposed DUSA approach on unsupervised sim2real adaptation from the simulated V2XSet dataset to the real-world DAIR-V2X-C dataset., Comment: ACM MM 2023

Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the placement of roadside LiDARs is a crucial but often overlooked problem. This paper proposes an approach to optimize the placement of roadside LiDARs by selecting optimized positions within the scene for better perception performance. To efficiently obtain the best combination of locations, a greedy algorithm based on perceptual gain is proposed, which selects the location that can maximize the perceptual gain sequentially. We define perceptual gain as the increased perceptual capability when a new LiDAR is placed. To obtain the perception capability, we propose a perception predictor that learns to evaluate LiDAR placement using only a single point cloud frame. A dataset named Roadside-Opt is created using the CARLA simulator to facilitate research on the roadside LiDAR placement problem.

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception., Comment: 19 pages

We demonstrate strong coupling of gigahertz-frequency nanomechanical resonators to a frequency-tunable superconducting microwave resonator via a galvanically bonded flip-chip method. By tuning the microwave resonator with an external magnetic field, we observe a series of hybridized microwave-mechanical modes and report coupling strengths of $\sim {15}~\text{MHz}$ at cryogenic temperatures. The demonstrated multi-chip approach provides flexible rapid characterization and simplified fabrication, and could potentially enable coupling between a variety of quantum systems. Our work represents a step towards a plug-and-play architecture for building more complex hybrid quantum systems., Comment: 10 pages, 8 figures. First three authors contributed equally to this work

We demonstrate strong dispersive coupling between a fluxonium superconducting qubit and a 690 megahertz mechanical oscillator, extending the reach of circuit quantum acousto-dynamics (cQAD) experiments into a new range of frequencies. We have engineered a qubit-phonon coupling rate of $g\approx2\pi\times14~\text{MHz}$, and achieved a dispersive interaction that exceeds the decoherence rates of both systems while the qubit and mechanics are highly nonresonant ($\Delta/g\gtrsim10$). Leveraging this strong coupling, we perform phonon number-resolved measurements of the mechanical resonator and investigate its dissipation and dephasing properties. Our results demonstrate the potential for fluxonium-based hybrid quantum systems, and a path for developing new quantum sensing and information processing schemes with phonons at frequencies below 700 MHz to significantly expand the toolbox of cQAD., Comment: 22 pages, 12 figures

Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to find the optimal placement of infrastructure sensors is rarely studied. In this paper, we investigate the problem of infrastructure sensor placement and propose a pipeline that can efficiently and effectively find optimal installation positions for infrastructure sensors in a realistic simulated environment. To better simulate and evaluate LiDAR placement, we establish a Realistic LiDAR Simulation library that can simulate the unique characteristics of different popular LiDARs and produce high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models. Then, we analyze the correlation between the point cloud distribution and perception accuracy by calculating the density and uniformity of regions of interest. Experiments show that when using the same number and type of LiDAR, the placement scheme optimized by our proposed method improves the average precision by 15%, compared with the conventional placement scheme in the standard lane scene. We also analyze the correlation between perception performance in the region of interest and LiDAR point cloud distribution and validate that density and uniformity can be indicators of performance. Both the RLS Library and related code will be released at https://github.com/PJLab-ADG/LiDARSimLib-and-Placement-Evaluation., Comment: 7 pages, 6 figures, accepted to the IEEE International Conference on Robotics and Automation (ICRA'23)

A quantum network that distributes and processes entanglement would enable powerful new computers and sensors. Optical photons with a frequency of a few hundred terahertz are perhaps the only way to distribute quantum information over long distances. Superconducting qubits on the other hand, which are one of the most promising approaches for realizing large-scale quantum machines, operate naturally on microwave photons that have roughly $40,000$ times less energy. To network these quantum machines across appreciable distances, we must bridge this frequency gap and learn how to generate entanglement across widely disparate parts of the electromagnetic spectrum. Here we implement and demonstrate a transducer device that can generate entanglement between optical and microwave photons, and use it to show that by detecting an optical photon we add a single photon to the microwave field. We achieve this by using a gigahertz nanomechanical resonance as an intermediary, and efficiently coupling it to optical and microwave channels through strong optomechanical and piezoelectric interactions. We show continuous operation of the transducer with $5\%$ frequency conversion efficiency, and pulsed microwave photon generation at a heralding rate of $15$ hertz. Optical absorption in the device generates thermal noise of less than two microwave photons. Joint measurements on optical photons from a pair of transducers would realize entanglement generation between distant microwave-frequency quantum nodes. Improvements of the system efficiency and device performance, necessary to realize a high rate of entanglement generation in such networks are within reach., Comment: 18 pages, 12 figures, 1 table

Many attractive photonics platforms still lack integrated photodetectors due to inherent material incompatibilities and lack of process scalability, preventing their widespread deployment. Here we address the problem of scalably integrating photodetectors in a photonic platform-independent manner. Using a thermal evaporation and deposition technique developed for nanoelectronics, we show that tellurium (Te), a quasi-2D semi-conductive element, can be evaporated at low temperature directly onto photonic chips to form air-stable, high-responsivity, high-speed, ultrawide-band photodetectors. We demonstrate detection at visible, telecom, and mid-infrared wavelengths, a bandwidth of more than 40 GHz, and platform-independent scalable integration with photonic structures in silicon, silicon nitride and lithium niobate.

Human pose estimation aims to accurately estimate a wide variety of human poses. However, existing datasets often follow a long-tailed distribution that unusual poses only occupy a small portion, which further leads to the lack of diversity of rare poses. These issues result in the inferior generalization ability of current pose estimators. In this paper, we present a simple yet effective data augmentation method, termed Pose Transformation (PoseTrans), to alleviate the aforementioned problems. Specifically, we propose Pose Transformation Module (PTM) to create new training samples that have diverse poses and adopt a pose discriminator to ensure the plausibility of the augmented poses. Besides, we propose Pose Clustering Module (PCM) to measure the pose rarity and select the "rarest" poses to help balance the long-tailed distribution. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method, especially on rare poses. Also, our method is efficient and simple to implement, which can be easily integrated into the training pipeline of existing pose estimation models., Comment: Accepted by ECCV 2022

Integrated photonics operating at visible-near-infrared (VNIR) wavelengths offer scalable platforms for advancing optical systems for addressing atomic clocks, sensors, and quantum computers. The complexity of free-space control optics causes limited addressability of atoms and ions, and this remains an impediment on scalability and cost. Networks of Mach-Zehnder interferometers can overcome challenges in addressing atoms by providing high-bandwidth electro-optic control of multiple output beams. Here, we demonstrate a VNIR Mach-Zehnder interferometer on lithium niobate on sapphire with a CMOS voltage-level compatible full-swing voltage of 4.2 V and an electro-optic bandwidth of 2.7 GHz occupying only 0.35 mm$^2$. Our waveguides exhibit 1.6 dB/cm propagation loss and our microring resonators have intrinsic quality factors of 4.4 $\times$ 10$^5$. This specialized platform for VNIR integrated photonics can open new avenues for addressing large arrays of qubits with high precision and negligible cross-talk., Comment: 10 pages, 4 figures

In this paper, we address the makeup transfer and removal tasks simultaneously, which aim to transfer the makeup from a reference image to a source image and remove the makeup from the with-makeup image respectively. Existing methods have achieved much advancement in constrained scenarios, but it is still very challenging for them to transfer makeup between images with large pose and expression differences, or handle makeup details like blush on cheeks or highlight on the nose. In addition, they are hardly able to control the degree of makeup during transferring or to transfer a specified part in the input face. In this work, we propose the PSGAN++, which is capable of performing both detail-preserving makeup transfer and effective makeup removal. For makeup transfer, PSGAN++ uses a Makeup Distill Network to extract makeup information, which is embedded into spatial-aware makeup matrices. We also devise an Attentive Makeup Morphing module that specifies how the makeup in the source image is morphed from the reference image, and a makeup detail loss to supervise the model within the selected makeup detail area. On the other hand, for makeup removal, PSGAN++ applies an Identity Distill Network to embed the identity information from with-makeup images into identity matrices. Finally, the obtained makeup/identity matrices are fed to a Style Transfer Network that is able to edit the feature maps to achieve makeup transfer or removal. To evaluate the effectiveness of our PSGAN++, we collect a Makeup Transfer In the Wild dataset that contains images with diverse poses and expressions and a Makeup Transfer High-Resolution dataset that contains high-resolution images. Experiments demonstrate that PSGAN++ not only achieves state-of-the-art results with fine makeup details even in cases of large pose/expression differences but also can perform partial or degree-controllable makeup transfer., Comment: Accepted by TPAMI 2021

In this paper, we address the problem of makeup transfer, which aims at transplanting the makeup from the reference face to the source face while preserving the identity of the source. Existing makeup transfer methods have made notable progress in generating realistic makeup faces, but do not perform well in terms of color fidelity and spatial transformation. To tackle these issues, we propose a novel Facial Attribute Transformer (FAT) and its variant Spatial FAT for high-quality makeup transfer. Drawing inspirations from the Transformer in NLP, FAT is able to model the semantic correspondences and interactions between the source face and reference face, and then precisely estimate and transfer the facial attributes. To further facilitate shape deformation and transformation of facial parts, we also integrate thin plate splines (TPS) into FAT, thus creating Spatial FAT, which is the first method that can transfer geometric attributes in addition to color and texture. Extensive qualitative and quantitative experiments demonstrate the effectiveness and superiority of our proposed FATs in the following aspects: (1) ensuring high-fidelity color transfer; (2) allowing for geometric transformation of facial parts; (3) handling facial variations (such as poses and shadows) and (4) supporting high-resolution face generation.

A room-temperature mechanical oscillator undergoes thermal Brownian motion with an amplitude much larger than the amplitude associated with a single phonon of excitation. This motion can be read out and manipulated using laser light using a cavity-optomechanical approach. By performing a strong quantum measurement, i.e., counting single photons in the sidebands imparted on a laser, we herald the addition and subtraction of single phonons on the 300K thermal motional state of a 4GHz mechanical oscillator. To understand the resulting mechanical state, we implement a tomography scheme and observe highly non-Gaussian phase-space distributions. Using a maximum likelihood method, we infer the density matrix of the oscillator and confirm the counter-intuitive doubling of the mean phonon number resulting from phonon addition and subtraction., Comment: 13 pages, 9 figures