Cluster’s Competitiveness of Photoelectron Industry of Optics Valley of Wuhan Based on the GEM Model

Wuhan East Lake High-tech Zone was called Optics Valley of Wuhan ratified by the Ministry of Science and Technology of China in 2001, which became a national photoelectron industry base now. As time goes by, Optics Valley of Wuhan photoelectron industry cluster become more and more powerful, and it has become a major form of regional economy in gaining competitive advantages. This paper establishes a GEM model of optics Valley photoelectron industry Cluster, and creates its competitiveness evaluation system. At the same time, not only do we measure the cluster’s competitiveness by distributing questionnaires, but also preliminary analyze and evaluate the measurement results

Genome-wide identification and functional analysis of lincRNAs acting as miRNA targets or decoys in maize

LincRNA information derived from three articles. (XLS 20 kb

Part-per-trillion LC-MS/MS determination of neonicotinoids in small volumes of songbird plasma

NSERC Discovery (RGPIN-2016-05436) Mitacs Accelerate in partnership with Bird Studies Canada (IT09196)Peer ReviewedNeonicotinoids are the most widely used class of insecticides in the world, and there are increasing concerns about their effects on non-target organisms. Analytical methods to diagnose exposure to neonicotinoids in wildlife are still very limited, particularly for small animals such as songbirds. Blood can be used as a non-lethal sampling matrix, but the sample volume is limited by body size. Neonicotinoids have a low bioaccumulation potential and are rapidly metabolized, therefore, sensitive assays are critically needed to reliably detect their residues in blood samples. We developed an efficient LC-MS/MS method at a part-per-trillion (pg/ml) level to measure eight neonicotinoid related insecticides (acetamiprid, clothianidin, dinotefuran, flonicamid, imidacloprid, nitenpyram, thiacloprid and thiamethoxam) plus one metabolite (6-chloronicotinic acid) in small volumes (50 μL) of avian plasma. The average recovery of target compounds ranged from 95.7 to 101.3%, and relative standard deviations were between 0.82 and 2.13%. We applied the method to screen blood samples from 36 seed-eating songbirds (white-crowned sparrows; Zonotrichia leucophrys) at capture, and detected imidacloprid in 78% (28 of 36), thiamethoxam in 22% (8 of 36), thiacloprid in 11% (4 of 36), and acetamiprid in 11% (4 of 36) of wild-caught sparrows. 6 h after capture, birds were orally dosed with 0 (control), 1.2 or 3.9 mg of imidacloprid/kg bw, test results using this method indicated that plasma imidacloprid was significantly elevated (low 26-times, high 316-times) in exposed groups. This is the first study to confirm neonicotinoid exposure in small free-living songbirds through non-lethal blood sampling, and to demonstrate that environmentally realistic doses significantly elevate circulating imidacloprid concentrations. This sensitive method could be applied to characterize exposure to neonicotinoids in free-living wildlife and in toxicological studies

Keyword-Guided Neural Conversational Model

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.Comment: AAAI-202

Towards Persona-Based Empathetic Conversational Models

Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in empathetic conversations. To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for persona-based empathetic conversations. We then propose CoBERT, an efficient BERT-based response selection model that obtains the state-of-the-art performance on our dataset. Finally, we conduct extensive experiments to investigate the impact of persona on empathetic responding. Notably, our results show that persona improves empathetic responding more when CoBERT is trained on empathetic conversations than non-empathetic ones, establishing an empirical link between persona and empathy in human conversations.Comment: Accepted to EMNLP 2020 (A new dataset is proposed: https://github.com/zhongpeixiang/PEC

Boundary-Aware Proposal Generation Method for Temporal Action Localization

The goal of Temporal Action Localization (TAL) is to find the categories and temporal boundaries of actions in an untrimmed video. Most TAL methods rely heavily on action recognition models that are sensitive to action labels rather than temporal boundaries. More importantly, few works consider the background frames that are similar to action frames in pixels but dissimilar in semantics, which also leads to inaccurate temporal boundaries. To address the challenge above, we propose a Boundary-Aware Proposal Generation (BAPG) method with contrastive learning. Specifically, we define the above background frames as hard negative samples. Contrastive learning with hard negative mining is introduced to improve the discrimination of BAPG. BAPG is independent of the existing TAL network architecture, so it can be applied plug-and-play to mainstream TAL models. Extensive experimental results on THUMOS14 and ActivityNet-1.3 demonstrate that BAPG can significantly improve the performance of TAL

Structure-Aware Generation Network for Recipe Generation from Images

Sharing food has become very popular with the development of social media. For many real-world applications, people are keen to know the underlying recipes of a food item. In this paper, we are interested in automatically generating cooking instructions for food. We investigate an open research task of generating cooking instructions based on only food images and ingredients, which is similar to the image captioning task. However, compared with image captioning datasets, the target recipes are long-length paragraphs and do not have annotations on structure information. To address the above limitations, we propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task. Our approach brings together several novel ideas in a systematic framework: (1) exploiting an unsupervised learning approach to obtain the sentence-level tree structure labels before training; (2) generating trees of target recipes from images with the supervision of tree structure labels learned from (1); and (3) integrating the inferred tree structures with the recipe generation procedure. Our proposed model can produce high-quality and coherent recipes, and achieve the state-of-the-art performance on the benchmark Recipe1M dataset.Comment: Published at ECCV 202

Learning Structural Representations for Recipe Generation and Food Retrieval

Food is significant to human daily life. In this paper, we are interested in learning structural representations for lengthy recipes, that can benefit the recipe generation and food cross-modal retrieval tasks. Different from the common vision-language data, here the food images contain mixed ingredients and target recipes are lengthy paragraphs, where we do not have annotations on structure information. To address the above limitations, we propose a novel method to unsupervisedly learn the sentence-level tree structures for the cooking recipes. Our approach brings together several novel ideas in a systematic framework: (1) exploiting an unsupervised learning approach to obtain the sentence-level tree structure labels before training; (2) generating trees of target recipes from images with the supervision of tree structure labels learned from (1); and (3) integrating the learned tree structures into the recipe generation and food cross-modal retrieval procedure. Our proposed model can produce good-quality sentence-level tree structures and coherent recipes. We achieve the state-of-the-art recipe generation and food cross-modal retrieval performance on the benchmark Recipe1M dataset.Comment: Accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:2009.0094