Star 5-edge-colorings of subcubic multigraphs

The star chromatic index of a multigraph $G$, denoted $\chi'_{s}(G)$, is the minimum number of colors needed to properly color the edges of $G$ such that no path or cycle of length four is bi-colored. A multigraph $G$ is star $k$-edge-colorable if $\chi'_{s}(G)\le k$. Dvo\v{r}\'ak, Mohar and \v{S}\'amal [Star chromatic index, J Graph Theory 72 (2013), 313--326] proved that every subcubic multigraph is star $7$-edge-colorable, and conjectured that every subcubic multigraph should be star $6$-edge-colorable. Kerdjoudj, Kostochka and Raspaud considered the list version of this problem for simple graphs and proved that every subcubic graph with maximum average degree less than $7/3$ is star list-$5$-edge-colorable. It is known that a graph with maximum average degree $14/5$ is not necessarily star $5$-edge-colorable. In this paper, we prove that every subcubic multigraph with maximum average degree less than $12/5$ is star $5$-edge-colorable.Comment: to appear in Discrete Mathematics. arXiv admin note: text overlap with arXiv:1701.0410

Describing Images using a Multilayer Framework based on Qualitative Spatial Models

To date most research in image processing has been based on quantitative representations of image features using pixel values, however, humans often use abstract and semantic knowledge to describe and analyze images. To enhance cognitive adequacy and tractability, we here present a multilayer framework based on qualitative spatial models. The layout features of segmented images are defined by qualitative spatial models which we introduce, and represented as a set of qualitative spatial constraints. Assigned different semantic and context knowledge, the image segments and the qualitative spatial constraints are interpreted from different perspectives. Finally, the knowledge layer of the framework enables us to describe the image in a natural way by integrating the domain-specified semantic constraints and the spatial constraints

Critical size of ego communication networks

With the help of information and communication technologies, studies on the overall social networks have been extensively reported recently. However, investigations on the directed Ego Communication Networks (ECNs) remain insufficient, where an ECN stands for a sub network composed of a centralized individual and his/her direct contacts. In this paper, the directed ECNs are built on the Call Detail Records (CDRs), which cover more than 7 million people of a provincial capital city in China for half a year. Results show that there is a critical size for ECN at about 150, above which the average emotional closeness between ego and alters drops, the balanced relationship between ego and network collapses, and the proportion of strong ties decreases. This paper not only demonstrate the significance of ECN size in affecting its properties, but also shows accordance with the "Dunbar's Number". These results can be viewed as a cross-culture supportive evidence to the well-known Social Brain Hypothesis (SBH).Comment: 6 pages, 4 figures, 1 tabl

Simple Recurrent Units for Highly Parallelizable Recurrence

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on translation by incorporating SRU into the architecture.Comment: EMNL

A New Calculation Model of Detection Time for Heat Detector in Long and Narrow Space

AbstractFire detector plays an important role in ship fire safety system. Usually, there are two types of fire detectors including fire smoke detector and heat detector, which are widely used in exit passageway, corridor, ladderway, and other long and narrow spaces in ship. Due to the smoke plume characteristics in these limited spaces are different to that of free plume in open place, the detection time calculating model of heat detector for these two different conditions are consequently different. This work is to develop a new detection time calculating model based on the fire plume rules including characteristics of temperature and velocity of fire smoke distributing in long and narrow spaces. Numeric method for calculating detection time is also presented. Finally, some calculations and analysis for a given fire scenario are performed. The numeric results are compared with that of the existing detection time calculating model based on free plume theory, which demonstrate the applicability of the model proposed in this article

Different Oxygen Levels of Dimethyl Ether Combustion Influence Numerical Simulation

AbstractAiming at the dimethyl ether itself with oxygen, this paper simulate that how much less oxygen quantity Dimethyl ether combustion required than liquefied petroleum gas in the same fuel quantity, and obtain optimum normoxia of dimethyl ether desired. This paper simulated dimethyl ether, liquefied petroleum gas (LPG) combustion with the air for 10%, 20%, 30%, 40%, 50%, 90% Oxygen levels. Under the different oxygen levels ,the study found that the best oxygen levels dimethyl ether combustion needed is around 30%, and the best oxygen levels liquefied petroleum gas (LPG) combustion needed is around 50%, so that Oxygen levels dimethyl ether needed is less than liquefied petroleum gas (LPG) needed in the same amount of fuel