Detecting interactions between dark matter and photons at high energy e+e−e^+e^- colliders

We investigate the sensitivity to the effective operators describing interactions between dark matter particles and photons at future high energy $e^+e^-$ colliders via the \gamma+ \slashed{E} channel. Such operators could be useful to interpret the potential gamma-ray line signature observed by the Fermi-LAT. We find that these operators can be further tested at $e^+ e^-$ colliders by using either unpolarized or polarized beams. We also derive a general unitarity condition for $2 \to n$ processes and apply it to the dark matter production process $e^+e^-\to\chi\chi\gamma$.Comment: 13 pages, 8 figure

4-(4-Nitro­benzene­sulfonamido)pyridinium bromide

In the title compound, C11H10N3O4S+·Br−, the benzene ring makes an angle of 88.4 (2)° with the pyridinium ring. The dihedral angle between the nitro group and the benzene ring is 16.5 (2)°. The ions in the crystal structure are linked by a combination of inter­molecular N—H⋯Br and non-conventional C—H⋯Br and C—H⋯O hydrogen bonds, forming a three-dimensional network

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAMComment: 7 pages, accepted at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLA

4-(2-Nitro­benzene­sulfonamido)pyridinium nitrate

There are two mol­ecules in the asymmetric unit of the title compound, C11H10N3O4S+·NO3 −. All bond distances have normal values. The C—N bond distances in the sulfonamide group [1.389 (3) and 1.382 (3) Å] may indicate slight conjugation of the sulfonamide N-atom π-electrons with those of the pyridinium ring. The crystal structure is stabilized by N—H⋯O hydrogen bonds

MOfinder: A Novel Algorithm for Detecting Overlapping Modules from Protein-Protein Interaction Network

Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease