RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation

Automated 3D city generation, focusing on road networks and building layouts, is in high demand for applications in urban design, multimedia games and autonomous driving simulations. The surge of generative AI facilitates designing city layouts based on deep learning models. However, the lack of high-quality datasets and benchmarks hinders the progress of these data-driven methods in generating road networks and building layouts. Furthermore, few studies consider urban characteristics, which generally take graphics as analysis objects and are crucial for practical applications, to control the generative process. To alleviate these problems, we introduce a multimodal dataset with accompanying evaluation metrics for controllable generation of Road networks and Building layouts (RoBus), which is the first and largest open-source dataset in city generation so far. RoBus dataset is formatted as images, graphics and texts, with $72,400$ paired samples that cover around $80,000km^2$ globally. We analyze the RoBus dataset statistically and validate the effectiveness against existing road networks and building layouts generation methods. Additionally, we design new baselines that incorporate urban characteristics, such as road orientation and building density, in the process of generating road networks and building layouts using the RoBus dataset, enhancing the practicality of automated urban design. The RoBus dataset and related codes are published at https://github.com/tourlics/RoBus_Dataset.

Tracking and fast imaging of a translational object via Fourier modulation

The tracking and imaging of high-speed moving objects hold significant promise for application in various fields. Single-pixel imaging enables the progressive capture of a fast-moving translational object through motion compensation. However, achieving a balance between a short reconstruction time and a good image quality is challenging. In this study, we present a approach that simultaneously incorporates position encoding and spatial information encoding through the Fourier patterns. The utilization of Fourier patterns with specific spatial frequencies ensures robust and accurate object localization. By exploiting the properties of the Fourier transform, our method achieves a remarkable reduction in time complexity and memory consumption while significantly enhancing image quality. Furthermore, we introduce an optimized sampling strategy specifically tailored for small moving objects, significantly reducing the required dwell time for imaging. The proposed method provides a practical solution for the real-time tracking, imaging and edge detection of translational objects, underscoring its considerable potential for diverse applications.

Multi-Depth Branches Network for Efficient Image Super-Resolution

Significant progress has been made in the field of super-resolution (SR), yet many convolutional neural networks (CNNs) based SR models primarily focus on restoring high-frequency details, often overlooking crucial low-frequency contour information. Transformer-based SR methods, while incorporating global structural details, frequently come with an abundance of parameters, leading to high computational overhead. In this paper, we address these challenges by introducing a Multi-Depth Branches Network (MDBN). This framework extends the ResNet architecture by integrating an additional branch that captures vital structural characteristics of images. Our proposed multi-depth branches module (MDBM) involves the stacking of convolutional kernels of identical size at varying depths within distinct branches. By conducting a comprehensive analysis of the feature maps, we observe that branches with differing depths can extract contour and detail information respectively. By integrating these branches, the overall architecture can preserve essential low-frequency semantic structural information during the restoration of high-frequency visual elements, which is more closely with human visual cognition. Compared to GoogLeNet-like models, our basic multi-depth branches structure has fewer parameters, higher computational efficiency, and improved performance. Our model outperforms state-of-the-art (SOTA) lightweight SR methods with less inference time. Our code is available at https://github.com/thy960112/MDBN

A detail-enhanced sampling strategy in Hadamard single-pixel imaging

Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost. To improve imaging efficiency, many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples. In this study, we present an efficient HSI imaging method that employs an exponential probability function to sample Hadamard spectra along a direction with better energy concentration for obtaining Hadamard patterns. We also propose an XY order to further optimize the pattern-selection method with extremely fast Hadamard order generation while retaining the original performance. We used the compressed sensing algorithm for image reconstruction. The simulation and experimental results show that these pattern-selection method reliably reconstructs objects and preserves the edge and details of images.

Single-pixel tracking and imaging of a high-speed moving object

Image-free tracking methods based on single-pixel detection have been able to track a moving object at a very high frame rate, but these tracking methods can not achieve simultaneous imaging of the object. Here we report a method for simultaneously tracking and imaging a high-speed moving object. Four binary Fourier patterns and two differential Hadamard patterns are used to modulate one frame of the object, then the modulated light signals are obtained by single-pixel detection. The trajectory and the image of the moving object can be gradually obtained along with the detection. The proposed method does not need any prior knowledge of the object and its motion. It has been verified by simulations and experiments which achieves a frame rate of 3332$~\mathrm{Hz}$ at a spatial resolution of $128 \times 128$ pixels by using a 20000$~\mathrm{Hz}$ digital micromirror device. This proposed method can broaden the application of image-free tracking methods and realize the detection of spatial information of the moving object.

Thompson Sampling for Unimodal Bandits

In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal structure better, at each step, instead of exploration from the entire decision space, our algorithm makes decision according to posterior distribution only in the neighborhood of the arm that has the highest empirical mean estimate. We theoretically prove that, for Bernoulli rewards, the regret of our algorithm reaches the lower bound of unimodal bandits, thus it is asymptotically optimal. For Gaussian rewards, the regret of our algorithm is $\mathcal{O}(\log T)$, which is far better than standard Thompson Sampling algorithms. Extensive experiments demonstrate the effectiveness of the proposed algorithm on both synthetic data sets and the real-world applications.

Optimize Neural Fictitious Self-Play in Regret Minimization Thinking

Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e.g. StarCraft and poker. Neural Fictitious Self-Play (NFSP) has provided an effective way to learn approximate Nash Equilibrium without prior domain knowledge in imperfect information games. However, optimality gap was left as an optimization problem of NFSP and by solving the problem, the performance of NFSP could be improved. In this study, focusing on the optimality gap of NFSP, we have proposed a new method replacing NFSP's best response computation with regret matching method. The new algorithm can make the optimality gap converge to zero as it iterates, thus converge faster than original NFSP. We have conduct experiments on three typical environments of perfect-information games and imperfect information games in OpenSpiel and all showed that our new algorithm performances better than original NFSP.

Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning

Stochastic Gradient Descent (SGD) has become the de facto way to train deep neural networks in distributed clusters. A critical factor in determining the training throughput and model accuracy is the choice of the parameter synchronization protocol. For example, while Bulk Synchronous Parallel (BSP) often achieves better converged accuracy, the corresponding training throughput can be negatively impacted by stragglers. In contrast, Asynchronous Parallel (ASP) can have higher throughput, but its convergence and accuracy can be impacted by stale gradients. To improve the performance of synchronization protocol, recent work often focuses on designing new protocols with a heavy reliance on hard-to-tune hyper-parameters. In this paper, we design a hybrid synchronization approach that exploits the benefits of both BSP and ASP, i.e., reducing training time while simultaneously maintaining the converged accuracy. Based on extensive empirical profiling, we devise a collection of adaptive policies that determine how and when to switch between synchronization protocols. Our policies include both offline ones that target recurring jobs and online ones for handling transient stragglers. We implement the proposed policies in a prototype system, called Sync-Switch, on top of TensorFlow, and evaluate the training performance with popular deep learning models and datasets. Our experiments show that Sync-Switch achieves up to 5.13X throughput speedup and similar converged accuracy when comparing to BSP. Further, we observe that Sync-Switch achieves 3.8% higher converged accuracy with just 1.23X the training time compared to training with ASP. Moreover, Sync-Switch can be used in settings when training with ASP leads to divergence errors. Sync-Switch achieves all of these benefits with very low overhead, e.g., the framework overhead can be as low as 1.7% of the total training time.

Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers

Cloud GPU servers have become the de facto way for deep learning practitioners to train complex models on large-scale datasets. However, it is challenging to determine the appropriate cluster configuration---e.g., server type and number---for different training workloads while balancing the trade-offs in training time, cost, and model accuracy. Adding to the complexity is the potential to reduce the monetary cost by using cheaper, but revocable, transient GPU servers. In this work, we analyze distributed training performance under diverse cluster configurations using CM-DARE, a cloud-based measurement and training framework. Our empirical datasets include measurements from three GPU types, six geographic regions, twenty convolutional neural networks, and thousands of Google Cloud servers. We also demonstrate the feasibility of predicting training speed and overhead using regression-based models. Finally, we discuss potential use cases of our performance modeling such as detecting and mitigating performance bottlenecks.

Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models

Deep learning models are increasingly used for end-user applications, supporting both novel features, such as facial recognition, and traditional features, such as web search. To accommodate high inference throughput, it is common to host a single pre-trained Convolutional Neural Network (CNN) in dedicated cloud-based servers with hardware accelerators such as Graphics Processing Units (GPUs). However, GPUs can be orders of magnitude more expensive than traditional Central Processing Unit (CPU) servers. Under-utilized server resources brought about by dynamic workloads can influence provisioning decisions, which may result in inflated serving costs. One potential way to alleviate this problem is by allowing hosted models to share the underlying resources, which we refer to as multi-tenant inference serving. One of the key challenges is maximizing the resource efficiency for multi-tenant serving given hardware with diverse characteristics, models with unique response time Service Level Agreement (SLA), and dynamic inference workloads. In this paper, we present Perseus, a measurement framework that provides the basis for understanding the performance and cost trade-offs of multi-tenant model serving. We implemented Perseus in Python atop a popular cloud inference server called Nvidia TensorRT Inference Server. Leveraging Perseus, we evaluated the inference throughput and cost for various serving deployments and demonstrated that multi-tenant model serving can lead to up to 12% cost reduction.