Dynamics and Clustering in Locust Hopper Bands

In recent years, technological advances in animal tracking have renewed interests in collective animal behavior, and in particular, locust swarms. These swarms pose a major threat to agriculture in northern Africa, the Middle East, and other regions. In their early life stages, locusts move in hopper bands, which are huge aggregations traveling on the ground. Our main goal is to understand the underlying mechanisms for the emergence and organization of these bands. We construct an agent-based model that tracks individual locusts and a continuum model that tracks the evolution of locust density. Both these models are motivated by experimental observations of individuals’ behavior. The macroscopic emergent behavior of the group is studied through numerical simulation of these models

Interval Parsing Grammars for File Format Parsing

File formats specify how data is encoded for persistent storage. They cannot be formalized as context-free grammars since their specifications include context-sensitive patterns such as the random access pattern and the type-length-value pattern. We propose a new grammar mechanism called Interval Parsing Grammars IPGs) for file format specifications. An IPG attaches to every nonterminal/terminal an interval, which specifies the range of input the nonterminal/terminal consumes. By connecting intervals and attributes, the context-sensitive patterns in file formats can be well handled. In this paper, we formalize IPGs' syntax as well as its semantics, and its semantics naturally leads to a parser generator that generates a recursive-descent parser from an IPG. In general, IPGs are declarative, modular, and enable termination checking. We have used IPGs to specify a number of file formats including ZIP, ELF, GIF, PE, and part of PDF; we have also evaluated the performance of the generated parsers.Comment: To appear on PLDI'2

UFuzzer: Lightweight Detection of PHP-Based Unrestricted File Upload Vulnerabilities Via Static-Fuzzing Co-Analysis

Unrestricted file upload vulnerabilities enable attackers to upload malicious scripts to a web server for later execution. We have built a system, namely UFuzzer, to effectively and automatically detect such vulnerabilities in PHP-based server-side web programs. Different from existing detection methods that use either static program analysis or fuzzing, UFuzzer integrates both (i.e., static-fuzzing co-analysis). Specifically, it leverages static program analysis to generate executable code templates that compactly and effectively summarize the vulnerability-relevant semantics of a server-side web application. UFuzzer then “fuzzes” these templates in a local, native PHP runtime environment for vulnerability detection. Compared to static-analysis-based methods, UFuzzer preserves the semantics of an analyzed program more effectively, resulting in higher detection performance. Different from fuzzing-based methods, UFuzzer exercises each generated code template locally, thereby reducing the analysis overhead and meanwhile eliminating the need of operating web services. Experiments using real-world data have demonstrated that UFuzzer outperforms existing methods in either efficiency, or accuracy, or both. In addition, it has detected 31 unknown vulnerable PHP scripts including 5 CVEs

Towards Interactive Image Inpainting via Sketch Refinement

One tough problem of image inpainting is to restore complex structures in the corrupted regions. It motivates interactive image inpainting which leverages additional hints, e.g., sketches, to assist the inpainting process. Sketch is simple and intuitive to end users, but meanwhile has free forms with much randomness. Such randomness may confuse the inpainting models, and incur severe artifacts in completed images. To address this problem, we propose a two-stage image inpainting method termed SketchRefiner. In the first stage, we propose using a cross-correlation loss function to robustly calibrate and refine the user-provided sketches in a coarse-to-fine fashion. In the second stage, we learn to extract informative features from the abstracted sketches in the feature space and modulate the inpainting process. We also propose an algorithm to simulate real sketches automatically and build a test protocol with different applications. Experimental results on public datasets demonstrate that SketchRefiner effectively utilizes sketch information and eliminates the artifacts due to the free-form sketches. Our method consistently outperforms the state-of-the-art ones both qualitatively and quantitatively, meanwhile revealing great potential in real-world applications. Our code and dataset are available

Effects of Na +

Fibrotic remodeling, characterized by fibroblast phenotype switching, is often associated with atrial fibrillation and heart failure. This study aimed to investigate the effects on electrotonic myofibroblast-myocyte (Mfb-M) coupling on cardiac myocytes excitability and repolarization of the voltage-gated sodium channels (VGSCs) and single mechanogated channels (MGCs) in human atrial Mfbs. Mathematical modeling was developed from a combination of (1) models of the human atrial myocyte (including the stretch activated ion channel current, ISAC) and Mfb and (2) our formulation of currents through VGSCs (INa_Mfb) and MGCs (IMGC_Mfb) based upon experimental findings. The effects of changes in the intercellular coupling conductance, the number of coupled Mfbs, and the basic cycle length on the myocyte action potential were simulated. The results demonstrated that the integration of ISAC, INa_Mfb, and IMGC_Mfb reduced the amplitude of the myocyte membrane potential (Vmax) and the action potential duration (APD), increased the depolarization of the resting myocyte membrane potential (Vrest), and made it easy to trigger spontaneous excitement in myocytes. For Mfbs, significant electrotonic depolarizations were exhibited with the addition of INa_Mfb and IMGC_Mfb. Our results indicated that ISAC, INa_Mfb, and IMGC_Mfb significantly influenced myocytes and Mfbs properties and should be considered in future cardiac pathological mathematical modeling

SafeCrowdNav: safety evaluation of robot crowd navigation in complex scenes

Navigating safely and efficiently in dense crowds remains a challenging problem for mobile robots. The interaction mechanisms involved in collision avoidance require robots to exhibit active and foresighted behaviors while understanding the crowd dynamics. Deep reinforcement learning methods have shown superior performance compared to model-based approaches. However, existing methods lack an intuitive and quantitative safety evaluation for agents, and they may potentially trap agents in local optima during training, hindering their ability to learn optimal strategies. In addition, sparse reward problems further compound these limitations. To address these challenges, we propose SafeCrowdNav, a comprehensive crowd navigation algorithm that emphasizes obstacle avoidance in complex environments. Our approach incorporates a safety evaluation function to quantitatively assess the current safety score and an intrinsic exploration reward to balance exploration and exploitation based on scene constraints. By combining prioritized experience replay and hindsight experience replay techniques, our model effectively learns the optimal navigation policy in crowded environments. Experimental outcomes reveal that our approach enables robots to improve crowd comprehension during navigation, resulting in reduced collision probabilities and shorter navigation times compared to state-of-the-art algorithms. Our code is available at https://github.com/Janet-xujing-1216/SafeCrowdNav