TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization

Inter-process communication (IPC) is one of the core functions of modern robotics middleware. We propose an efficient IPC technique called TZC (Towards Zero-Copy). As a core component of TZC, we design a novel algorithm called partial serialization. Our formulation can generate messages that can be divided into two parts. During message transmission, one part is transmitted through a socket and the other part uses shared memory. The part within shared memory is never copied or serialized during its lifetime. We have integrated TZC with ROS and ROS2 and find that TZC can be easily combined with current open-source platforms. By using TZC, the overhead of IPC remains constant when the message size grows. In particular, when the message size is 4MB (less than the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2 IPC from hundreds of milliseconds to less than 1 millisecond. We also demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in autonomous driving scenarios. We show that by using TZC, the braking distance can be shortened by 16% than ROS

Faithful completion of images of scenic landmarks using internet images

Abstract—Previous works on image completion typically aim to produce visually plausible results rather than factually correct ones. In this paper, we propose an approach to faithfully complete the missing regions of an image. We assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. We first download thousands of images from the Internet using a text label provided by the user. Next, we apply two-step filtering to reduce them to a small set of candidate images for use as source images for completion. For each candidate image, a co-matching algorithm is used to find correspondences of both points and lines between the candidate image and the input image. These are used to find an optimal warp relating the two images. A completion result is obtained by blending the warped candidate image into the missing region of the input image. The completion results are ranked according to combination score, which considers both warping and blending energy, and the highest ranked ones are shown to the user. Experiments and results demonstrate that our method can faithfully complete images

DCNS: Automated Detection of Conservative Non-Sleep Defects in the Linux Kernel

International audienceFor waiting, the Linux kernel offers both sleep-able and non-sleep operations. However, only non-sleep operations can be used in atomic context. Detecting the possibility of execution in atomic context requires a complete inter-procedural flow analysis, often involving function pointers. Developers may thus conservatively use non-sleep operations even outside of atomic context, which may damage system performance, as such operations unproductively monopolize the CPU. Until now, no systematic approach has been proposed to detect such conservative non-sleep (CNS) defects. In this paper, we propose a practical static approach, named DCNS, to automatically detect conservative non-sleep defects in the Linux kernel. DCNS uses a summary-based analysis to effectively identify the code in atomic context and a novel file-connection-based alias analysis to correctly identify the set of functions referenced by a function pointer. We evaluate DCNS on Linux 4.16, and in total find 1629 defects. We manually check 943 defects whose call paths are not so difficult to follow, and find that 890 are real. We have randomly selected 300 of the real defects and sent them to kernel developers, and 251 have been confirmed

EGR1, EGFR and IGF1R protein expressions in non-small cell lung cancer and their clinical significances

The purpose of this study was to investigate the incidence and clinical significance of alterations in EGFR, IGF1R and the cell signaling pathway activities induced by them, as well as EGR1 expression in resected non-small cell lung cancer (NSCLC). The protein expressions of  biomarker were evaluated by Western blotting in tissues from 19 surgically resected NSCLCs. High expressions of EGR1, EGFR and  IGF1R were detected in more than 30% tumor tissues. High expressions of pErk and pAkt were detected in more than 50% paracancer tissues. There were significant correlations between the NSCLC target factors detected (p<0.05). Alterations of protein expressions of target factor detected in NSCLC were significantly associated with alterations in pathological subtype, differentiation, pathological stage, and smoking history. Positive EGR1 might be  associated with good survival, while positive pErk might be associated with poor prognosis.

A Novel Web Attack Detection System for Internet of Things via Ensemble Classification

Internet of things (IoT) has become one of the fastestgrowing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information generated or collected from an abundance of devices in real time. However, the diversity of IoT devices makes the IoT networks environments more complex and more vulnerable to various web attacks compared to traditional computer networks. In this paper, we propose a novel Ensemble Deep Learning based Web Attack Detection System (EDL-WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have designed three deep learning models to first detect web attacks separately. We then use an ensemble classifier to make the final decision according to the results obtained from the three deep learning models. In order to evaluate the proposed WADS, we have performed experiments on a public dataset as well as a realword dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates

Electroplating lithium transition metal oxides.

Materials synthesis often provides opportunities for innovation. We demonstrate a general low-temperature (260°C) molten salt electrodeposition approach to directly electroplate the important lithium-ion (Li-ion) battery cathode materials LiCoO2, LiMn2O4, and Al-doped LiCoO2. The crystallinities and electrochemical capacities of the electroplated oxides are comparable to those of the powders synthesized at much higher temperatures (700° to 1000°C). This new growth method significantly broadens the scope of battery form factors and functionalities, enabling a variety of highly desirable battery properties, including high energy, high power, and unprecedented electrode flexibility

Aphid Endosymbiont Facilitates Virus Transmission by Modulating the Volatile Profile of Host Plants

BACKGROUND: Most plant viruses rely on vectors for their transmission and spread. One of the outstanding biological questions concerning the vector-pathogen-symbiont multi-trophic interactions is the potential involvement of vector symbionts in the virus transmission process. Here, we used a multi-factorial system containing a non-persistent plant virus, cucumber mosaic virus (CMV), its primary vector, green peach aphid, Myzus persicae, and the obligate endosymbiont, Buchnera aphidicola to explore this uncharted territory. RESULTS: Based on our preliminary research, we hypothesized that aphid endosymbiont B. aphidicola can facilitate CMV transmission by modulating plant volatile profiles. Gene expression analyses demonstrated that CMV infection reduced B. aphidicola abundance in M. persicae, in which lower abundance of B. aphidicola was associated with a preference shift in aphids from infected to healthy plants. Volatile profile analyses confirmed that feeding by aphids with lower B. aphidicola titers reduced the production of attractants, while increased the emission of deterrents. As a result, M. persicae changed their feeding preference from infected to healthy plants. CONCLUSIONS: We conclude that CMV infection reduces the B. aphidicola abundance in M. persicae. When viruliferous aphids feed on host plants, dynamic changes in obligate symbionts lead to a shift in plant volatiles from attraction to avoidance, thereby switching insect vector’s feeding preference from infected to healthy plants