Recently, object detection in remote sensing images has achieved rapid advancement. However, due to critical issues, such as low spatial resolution and complex background noises, it is still difficult to achieve satisfactory object detection performance for remote sensing images. For current widely used object detection methods, the feature resolution of the backbone network is decreased gradually with successive pooling operations. In this way, object spatial details are largely lost for the deeper feature layers, resulting in the difficulty of accurate object detection, especially for tiny objects. However, current methods fail to eliminate the adverse effects due to the loss of object details. To this end, considering that high-frequency information is more likely to be overlooked and high-frequency object details may be beneficial for detecting tiny objects, we propose to improve the previous spatial feature modeling pipeline with the learned features in the frequency domain. Specifically, discrete cosine transform (DCT) is first used to transform the original image into the frequency domain, obtaining the corresponding frequency spectrum features. We then utilize a dual-domain feature extraction (DFE) network based on a lightweight attention mechanism to align the features in two different domains. Finally, a domain synergy fusion (DSF) module is further employed to match and fuse the features in the spatial domain and the obtained features in the frequency domain. Extensive experimental results are obtained on the challenging remote sensing datasets, DIOR and DOTA. Experimental results show that our method can increase at least 2.9%${\mathbf {AP}}_{\mathbf {s}}^{\mathbf {50}}$in DIOR and 3.5%${\mathbf {AP}}_{\mathbf {s}}^{\mathbf {50}}$in DOTA compared to the new state-of-the-art methods, which effectively demonstrates the superiority of our proposed method.

Unsupervised Domain Adaptation (UDA) is critical for remote sensing object detection in real applications, aiming to address the significant performance degradation issue caused by the domain gap between source and target domain. This method achieves cross-domain alignment by leveraging the unlabeled target domain data, thus avoiding the expensive annotation cost. However, existing works mainly cope with CNN-based object detectors, which are characterized by complex adversarial learning architecture and fail to accurately align the features in remote sensing images with sparsely allocated objects and inevitable background noise. Compared to CNN-based methods, the DEtection TRansformer (DETR) largely simplifies the object detection pipeline and demonstrates the great potential by its intrinsic characteristics of global relation modeling between any pixels. On this basis, we propose the first strong DETR-based baseline, Remote Sensing Teacher, for unsupervised domain adaptation in remote sensing object detection. Specifically, the Remote Sensing Teacher introduces an innovative Learnable Frequency-enhanced feature Alignment (LFA) module. Within this module, we initially transform the features into frequency space to simplify the attention solver and effectively capture domain-specific information. Subsequently, the module significantly enhances the global feature representations of sparsely allocated objects by using a lightweight attention mechanism. Following this, the module incorporates learnable filters with a gated mechanism, enabling selective alignment of features in noisy backgrounds. Additionally, the Remote Sensing Teacher employs a Self-adaptive Pseudo label Assigner (SPA) that can automatically adjust the class-wise confidence threshold according to the model’s learning status, thereby enabling the generation of high-quality pseudo-labels in scenarios with a long-tailed distribution. Leveraging these pseudo-labels further mitigates the domain bias of the detector by establishing alignment at the label level. Extensive experimental results demonstrate superior performance and generalization capabilities of our proposed Remote Sensing Teacher in multiple remote sensing adaptation scenarios. Code is released at https://github.com/h751410234/RemoteSensingTeacher.

Call graphs have many applications in software engineering, including bug-finding, security analysis, and code navigation in IDEs. However, the construction of call graphs requires significant investment in program analysis infrastructure. An increasing number of programming languages compile to the Java Virtual Machine (JVM), and program analysis frameworks such as WALA and SOOT support a broad range of program analysis algorithms by analyzing JVM bytecode. This approach has been shown to work well when applied to bytecode produced from Java code. In this paper, we show that it also works well for diverse other JVM-hosted languages: dynamically-typed functional Scheme, statically-typed object-oriented Scala, and polymorphic functional OCaml. Effectively, we get call graph construction for these languages for free, using existing analysis infrastructure for Java, with only minor challenges to soundness. This, in turn, suggests that bytecode-based analysis could serve as an implementation vehicle for bug-finding, security analysis, and IDE features for these languages. We present qualitative and quantitative analyses of the soundness and precision of call graphs constructed from JVM bytecodes for these languages, and also for Groovy, Clojure, Python, and Ruby. However, we also show that implementation details matter greatly. In particular, the JVM-hosted implementations of Groovy, Clojure, Python, and Ruby produce very unsound call graphs, due to the pervasive use of reflection,invokedynamicinstructions, and run-time code generation. Interestingly, the dynamic translation schemes employed by these languages, which result in unsound static call graphs, tend to be correlated with poor performance at run time.

In computing clouds, burstiness of a virtual machine (VM) workload widely exists in real applications, where spikes usually occur aperiodically with low frequency and short duration. This could be effectively handled through dynamically scaling up/down in a virtualization-based computing cloud; however, to minimize energy consumption, VMs are often highly consolidated with the minimum number of physical machines (PMs) used. In this case, to meet the dynamic runtime resource demands of VMs in a PM, some VMs have to be migrated to some other PMs, which may cause potential performance degradation. In this paper, we investigate the burstiness-aware server consolidation problem from the perspective of resource reservation, i.e., reserving a certain amount of extra resources on each PM to avoid live migrations, and propose a novel server consolidation algorithm, QUEUE. We first model the resource requirement pattern of each VM as a two-state Markov chain to capture burstiness, then we design a resource reservation strategy for each PM based on the stationary distribution of a Markov chain. Finally, we present QUEUE, a complete server consolidation algorithm with a reasonable time complexity. We also show how to cope with heterogenous spikes and provide remarks on several extensions. Simulation and testbed results show that, QUEUE improves the consolidation ratio by up to 45 percent with large spike size and around 30 percent with normal spike size compared with the strategy that provisions for peak workload, and achieves a better balance between performance and energy consumption in comparison with other commonly-used consolidation algorithms.

Burstiness is a common pattern of virtual machines (VMs)'s workload in production data centers, where spikes usually occur aperiodically with low frequency and last shortly. Since virtualization technology enables elastic resource provisioning in a computing cloud, the bursty workloads could be handled effectively through dynamically scaling up/down. However, to cut back energy consumption, VMs are usually highly consolidated with the minimum number of physical machines (PMs) used. In this case, to meet the runtime expanding demands of the resources (spikes), some VMs have to be migrated to other idle PMs, which is costly and causes performance degradation potentially. In this paper, we investigate the elastic resource provisioning problem and propose a novel VM consolidation mechanism with resource reservation which takes burstiness into consideration as well as energy consumption. We model the resource requirement pattern as the popular ON-OFF Markov chain to represent burstiness, based on which a reservation strategy via queuing theory approach is given for each PM. Next we present a complete VM consolidation scheme with resource reservation within reasonable time complexity. The experiment result show that our algorithms improve the consolidation ratio by up to 45% with large spike size and around 30% with normal spike size compared to those provisioning for peak workload, and a better balance of performance and energy consumption is achieved in comparison with other commonly used consolidation algorithms.