Pixelated Semantic Colorization

While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed convolutional neural network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art

Pixelated semantic colorization

While many image colorization algorithms have recently shown the capability of producing plausible color versions from grayscale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on Pascal VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art

Unconstrained Face Recognition Using A Set-to-Set Distance Measure on Deep Learned Features

Recently considerable efforts have been dedicated to unconstrained face recognition, which requires to identify faces "in the wild" for a set of images and/or video frames captured without human intervention. Unlike traditional face recognition that compares one-to-one medium (either a single image or a video frame) only, we consider a problem of matching sets with heterogeneous contents of both images and videos. In this paper, we propose a novel Set-to-Set (S2S) distance measure to calculate the similarity between two sets with the aim to improve the accuracy of face recognition in real-world situations such as extreme poses or severe illumination conditions. Our S2S distance adopts the kNN-average pooling for the similarity scores computed on all the media in two sets, making the identification far less susceptible to the poor representations (outliers) than traditional feature-average pooling and score-average pooling. Furthermore, we show that various metrics can be embedded into our S2S distance framework, including both predefined and learned ones. This allows to choose the appropriate metric depending on the recognition task in order to achieve the best results. To evaluate the proposed S2S distance, we conduct extensive experiments on the challenging set-based IJB-A face dataset, which demonstrate that our algorithm achieves the stateof- the-art results and is clearly superior to the baselines including several deep learning based face recognition algorithms

Virtual Water Flows Embodied in International and Interprovincial Trade of Yellow River Basin: A Multiregional Input-Output Analysis

With the imminent need of regional environmental protection and sustainable economic development, the concept of virtual water is widely used to solve the problem of regional water shortage. In this paper, nine provinces, namely Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong in the Yellow River Basin (YRB), are taken as the research objects. Through the analysis of input-output tables of 30 provinces in China in 2012, the characteristics of virtual water trade in this region are estimated by using a multi-regional input-output (MRIO) model. The results show that: (1) The YRB had a net inflow of 17.387 billion m³ of virtual water in 2012. In interprovincial trade, other provinces outside the basin export 21.721 billion m³ of virtual water into the basin. In international trade, the basin exports 4334 million m³ of virtual water to the international market. (2) There are different virtual flow paths in the basin. Shanxi net inputs virtual water by interprovincial trade and international trade, while Gansu and Ningxia net output virtual water by interprovincial trade and international trade. The other six provinces all net output virtual water through international trade, and obtain the net input of virtual water from other provinces outside the basin. (3) From the industrial structure of the provinces in the basin, the provinces with a relatively developed economy, such as Shandong and Shanxi, mostly import virtual water in the agricultural sector, while relatively developing provinces, such as Gansu and Ningxia, mostly import virtual water in the industrial sector. In order to sustain the overall high-quality development of the YRB, we propose the virtual water trade method to quantify the net flow of virtual water in each province and suggest the compensation responsibility of the virtual water net inflow area, and the compensation need of the virtual water net outflow area, in order to achieve efficient water resources utilization

P-gram: positional N-gram for the clustering of machine-generated messages

An IT system generates messages for other systems or users to consume, through direct interaction or as system logs. Automatically identifying the types of these machine-generated messages has many applications, such as intrusion detection and system behavior discovery. Among various heuristic methods for automatically identifying message types, the clustering methods based on keyword extraction have been quite effective. However, these methods still suffer from keyword misidentification problems, i.e., some keyword occurrences are wrongly identified as payload and some strings in the payload are wrongly identified as keyword occurrences, leading to the misidentification of the message types. In this paper, we propose a new machine language processing (MLP) approach, called P-gram, specifically designed for identifying keywords in, and subsequently clustering, machine-generated messages. First, we introduce a novel concept and technique, positional n-gram, for message keywords extraction. By associating the position as meta-data with each n-gram, we can more accurately discern which n-grams are keywords of a message and which n-grams are parts of the payload information. Then, the positional keywords are used as features to cluster the messages, and an entropy-based positional weighting method is devised to measure the importance or weight of the positional keywords to each message. Finally, a general centroid clustering method, K-Medoids, is used to leverage the importance of the keywords and cluster messages into groups reflecting their types. We evaluate our method on a range of machine-generated (text and binary) messages from the real-world systems and show that our method achieves higher accuracy than the current state-of-the-art tools

Whole Brain Mapping of Long-Range Direct Input to Glutamatergic and GABAergic Neurons in Motor Cortex

Long-range neuronal circuits play an important role in motor and sensory information processing. Determining direct synaptic inputs of excited and inhibited neurons is important for understanding the circuit mechanisms involved in regulating movement. Here, we used the monosynaptic rabies tracing technique, combined with fluorescent micro-optical sectional tomography, to characterize the brain-wide input to the motor cortex (MC). The whole brain dataset showed that the main excited and inhibited neurons in the MC received inputs from similar brain regions with a quantitative difference. With 3D reconstruction we found that the distribution of input neurons, that target the primary and secondary MC, had different patterns. In the cortex, the neurons projecting to the primary MC mainly distributed in the lateral and anterior portion, while those to the secondary MC distributed in the medial and posterior portion. The input neurons in the subcortical areas also showed the topographic shift model, as in the thalamus, the neurons distributed as outer and inner shells while the neurons in the claustrum and amygdala were in the ventral and dorsal part, respectively. These results lay the anatomical foundation to understanding the organized pattern of motor circuits and the functional differences between the primary and secondary MC

Evaluation of Lethal Giant Larvae as a Schistosomiasis Vaccine Candidate

Schistosomiasis is a neglected tropical disease of humans, and it is considered to be the second most devastating parasitic disease after malaria. Eggs produced by normally developed female worms are important in the transmission of the parasite, and they responsible for the pathogenesis of schistosomiasis. The tumor suppressor gene lethal giant larvae (lgl) has an essential function in establishing apical-basal cell polarity, cell proliferation, differentiation, and tissue organization. In our earlier study, downregulation of the lgl gene induced a significant reduction in the egg hatching rate of Schistosoma japonicum (Sj) eggs. In this study, the Sjlgl gene was used as a vaccine candidate against schistosomiasis, and vaccination achieved and maintained a stable reduction of the egg hatching rate, which is consistent with previous studies, in addition to reducing the worm burden and liver egg burden in some trials