Time series databases play a critical role in large-scale performance monitoring systems. Metrics are required to be observable immediately after being generated to support real-time analysis. However, the commonly used Log-Structured Merge-Tree structure suffers from periodically visible delay spikes when a new segment is created due to the instantaneous index construction pressure.

Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of history, it is still limited to recognizing 2D images. Recognizing objects in the physical world (i.e., 3D environment) poses a unique challenge – discriminative information is not only present in visible local regions but also in other unseen views. Therefore, in addition to finding the distinguishable part from the current view, efficient and accurate recognition requires inferring the critical perspective with minimal glances. E.g., a person might recognize a "Ford sedan" with a glance at its side and then know that looking at the front can help tell which model it is. In this paper, towards FGVC in the real physical world, we put forward the problem of multi-view active fine-grained visual recognition (MAFR) and complete this study in three steps: (i) a multi-view, fine-grained vehicle dataset is collected as the testbed, (ii) a pilot experiment is designed to validate the need and research value of MAFR, (iii) a policy-gradient-based framework along with a dynamic exiting strategy is proposed to achieve efficient recognition with active view selection. Our comprehensive experiments demonstrate that the proposed method outperforms previous multi-view recognition works and can extend existing state-of-the-art FGVC methods and advanced neural networks to become "FGVC experts" in the 3D environment. Our code is available at https://github.com/PRIS-CV/MAFR.

In recent years, fine-grained visual classification (FGVC) algorithms have achieved excellent performance across a variety of datasets. However, it is still rare to see these algorithms applied in daily life. The main reasons for this are i) the algorithms are developed based on different design guidelines and cannot be deployed in the same environment; ii) there is not a simple and efficient platform to present the algorithm's results to the user - the accuracy is meaningless to the users. To address the above problem, we built a complex scenario-oriented fine-grained visual classification platform. The platform consists of a PyTorch-based fine-grained visual recognition algorithm library (FGL) and a WeChat applet-based user interaction module (WEM). We can quickly develop new algorithms or readily apply existing algorithms in the same environment through FGL. Driven by FGL, the WEM enables users to achieve fine-grained recognition of complex scenes interactively. In addition to showing the user the fine-grained labels of objects, we will also show how the model makes decisions to help the user master the ability to recognise the fine-grained object so that everyone can become a domain expert. A video demo shows an example of the proposed platform in a real-world scenario: https://reurl.cc/rRZE7O.

The state explosion problem faced by concolic execution is significantly serious when detecting program bugs especially in large scale software systems. To mitigate the issue, we propose a practical concolic execution approach to detect vulnerabilities in this paper. First, we identify the correlation between symbolic memory and control flow by statically analyzing the software under test, and distinguish the critical symbolic memory and ordinary symbolic memory according to the above correlation. Then, we design different strategies for two kinds of symbolic memory in order to generate state when performing concolic execution of the target software. We present these ideas in a prototype system, Pracolic, and evaluate it with four file systems in Linux, i.e., ext4, XFS, Btrfs, and ReiserFS. Experimental results show that Pracolic can effectively mitigate the problem of state explosion in concolic execution, and outperform S2E, a state-of-the-art analysis system, on state reduction and code coverage for large scale software systems.

There are two inherent obstacles to effectively using Graphics Processing Units (GPUs) for query processing in search engines: (a) the highly restricted GPU memory space, and (b) the CPU-GPU transfer latency. Previously, Ao et al. presented a GPU method for lists intersection, an essential component in AND-based query processing. However, this work assumes the whole inverted index can be stored in GPU memory and does not address document ranking. In this paper, we describe and analyze a GPU query processing method which incorporates both lists intersection and top-k ranking. We introduce a parameterized pruned posting list GPU caching method where the parameter determines how much GPU memory is used for caching. This method allows list caching for large inverted indexes using the limited GPU memory, thereby making a qualitative improvement over previous work. We also give a mathematical model which can identify an approximately optimal choice of the parameter. Experimental results indicate that this GPU approach under the pruned list caching policy achieves better query throughput than its CPU counterpart, even when the inverted index size is much larger than the GPU memory space.