The JetCurry Code. I. Reconstructing Three-Dimensional Jet Geometry from Two-Dimensional images

We present a reconstruction of jet geometry models using numerical methods based on a Markov ChainMonte Carlo (MCMC) and limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimized algorithm. Our aim is to model the three-dimensional geometry of an AGN jet using observations, which are inherently two-dimensional. Many AGN jets display complex hotspots and bends over the kiloparsec scales. The structure of these bends in the jets frame may be quite different than what we see in the sky frame, transformed by our particular viewing geometry. The knowledge of the intrinsic structure will be helpful in understanding the appearance of the magnetic field and hence emission and particle acceleration processes over the length of the jet. We present the method used, as well as a case study based on a region of the M87 jet.Comment: Submitted to ApJ on Feb 01, 201

Static and Dynamic Analysis in Cryptographic-API Misuse Detection of Mobile Application

With Android devices becoming more advanced and gaining more popularity, the number of cryptographic-API misuses in mobile applications is escalating. Numerous snippets of code in Android are from Stack Overflow and over 90% of them contain several crypto-issues. Various crypto-misuse detectors come out aiming to report vulnerabilities of apps and better secure users’ privacy. These detectors can be broadly classified into two categories based on the analysis strategies employed to catch misuses – static analysis (i.e., by scanning the code base) and dynamic analysis (i.e., by executing the code). However, there are not enough research on comparing their underlying differences, making it difficult to explain the pervasiveness of static crypto-detectors in both academia and industry. The lack of studies potentially limits the improvement of crypto-detection efficiency. In this study, a holistic evaluation and comparison on static and dynamic analysis’ underlying mechanisms, robustness, and efficiency are carried out. A systematic empirical experiment is implemented on testing 1003 popular Android applications across 21 categories from Google Play. We find that 93.3% of the apps make at least one mistake using cryptographic APIs and closely analyze top four cryptographic rules reported to be violated most frequently by static crypto detector. Instead of merely comparing statistics such as false positives (i.e., false alarms), we focus on examining the crypto rules whose number of violations reported by static and dynamic crypto detectors diverge greatly. In addition, we firstly posit a new taxonomy schema that classifies cryptographic rules based on how they are inspected rather than their attack type or severity level. This schema will be useful to both researchers and practitioners to decide how to efficiently combine static and dynamic techniques to improve the reliability and accuracy of crypto-detection

The Efficacy of Transformer-based Adversarial Attacks in Security Domains

Today, the security of many domains rely on the use of Machine Learning to detect threats, identify vulnerabilities, and safeguard systems from attacks. Recently, transformer architectures have improved the state-of-the-art performance on a wide range of tasks such as malware detection and network intrusion detection. But, before abandoning current approaches to transformers, it is crucial to understand their properties and implications on cybersecurity applications. In this paper, we evaluate the robustness of transformers to adversarial samples for system defenders (i.e., resiliency to adversarial perturbations generated on different types of architectures) and their adversarial strength for system attackers (i.e., transferability of adversarial samples generated by transformers to other target models). To that effect, we first fine-tune a set of pre-trained transformer, Convolutional Neural Network (CNN), and hybrid (an ensemble of transformer and CNN) models to solve different downstream image-based tasks. Then, we use an attack algorithm to craft 19,367 adversarial examples on each model for each task. The transferability of these adversarial examples is measured by evaluating each set on other models to determine which models offer more adversarial strength, and consequently, more robustness against these attacks. We find that the adversarial examples crafted on transformers offer the highest transferability rate (i.e., 25.7% higher than the average) onto other models. Similarly, adversarial examples crafted on other models have the lowest rate of transferability (i.e., 56.7% lower than the average) onto transformers. Our work emphasizes the importance of studying transformer architectures for attacking and defending models in security domains, and suggests using them as the primary architecture in transfer attack settings.Comment: Accepted to IEEE Military Communications Conference (MILCOM), AI for Cyber Workshop, 202

Learning Vision-and-Language Navigation from YouTube Videos

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3D environments using natural language instructions. Existing VLN methods suffer from training on small-scale environments or unreasonable path-instruction datasets, limiting the generalization to unseen environments. There are massive house tour videos on YouTube, providing abundant real navigation experiences and layout information. However, these videos have not been explored for VLN before. In this paper, we propose to learn an agent from these videos by creating a large-scale dataset which comprises reasonable path-instruction pairs from house tour videos and pre-training the agent on it. To achieve this, we have to tackle the challenges of automatically constructing path-instruction pairs and exploiting real layout knowledge from raw and unlabeled videos. To address these, we first leverage an entropy-based method to construct the nodes of a path trajectory. Then, we propose an action-aware generator for generating instructions from unlabeled trajectories. Last, we devise a trajectory judgment pretext task to encourage the agent to mine the layout knowledge. Experimental results show that our method achieves state-of-the-art performance on two popular benchmarks (R2R and REVERIE). Code is available at https://github.com/JeremyLinky/YouTube-VLNComment: Accepted by ICCV 202

Plant-Morphing Strategies and Plant-Inspired Soft Actuators Fabricated by Biomimetic Four-Dimensional Printing: A Review

From Frontiers via Jisc Publications RouterHistory: collection 2021, received 2021-01-10, accepted 2021-03-09, epub 2021-05-04Publication status: PublishedFor prey, seeding, and protection, plants exhibit ingenious adaptive motions that respond autonomously to environmental stimuli by varying cellular organization, anisotropic orientation of cellulose fibers, mechanical instability design, etc. Notably, plants do not leverage muscle and nerves to produce and regulate their motions. In contrast, they harvest energy from the ambient environment and compute through embodied intelligence. These characteristics make them ideal candidates for application in self-morphing devices. Four-dimensional (4D) printing is a bottom-up additive manufacturing method that builds objects with the ability to change shape/properties in a predetermined manner. A versatile motion design catalog is required to predict the morphing processes and final states of the printed parts. This review summarizes the morphing and actuation mechanisms of plants and concludes with the recent development of 4D-printed smart materials inspired by the locomotion and structures of plant systems. We provide analyses of the challenges and our visions of biomimetic 4D printing, hoping to boost its application in soft robotics, smart medical devices, smart parts in aerospace, etc

Cobalt Nickel Boride Nanocomposite as High-Performance Anode Catalyst for Direct Borohydride Fuel Cell

Similar to MXene, MAB is a group of 2D ceramic/metallic boride materials which exhibits unique properties for various applications. However, these 2D sheets tend to stack and therefore lose their active surface area and functions. Herein, an amorphous cobalt nickel boride (Co–Ni–B) nanocomposite is prepared with a combination of 2D sheets and nanoparticles in the center to avoid agglomeration. This unique structure holds the 2D nano-sheets with massive surface area which contains numerous catalytic active sites. This nanocomposite is prepared as an electrocatalyst for borohydride electrooxidation reaction (BOR). It shows outstanding catalytic activity through improving the kinetic parameters of BH4− oxidation, owing to abundant ultrathin 2D structure on the surface, which provide free interspace and electroactive sites for charge/mass transport. The anode catalyst led to a 209 mW/cm2 maximum power density with high open circuit potential of 1.06 V at room temperature in a miniature direct borohydride fuel cell (DBFC). It also showed a great longevity of up to 45 h at an output power density of 64 mW/cm2, which is higher than the Co–B, Ni–B and PtRu/C. The cost reduction and prospective scale-up production of the Co–Ni–B catalyst are also addressed