Towards Optimal Discrete Online Hashing with Balanced Similarity

When facing large-scale image datasets, online hashing serves as a promising solution for online retrieval and prediction tasks. It encodes the online streaming data into compact binary codes, and simultaneously updates the hash functions to renew codes of the existing dataset. To this end, the existing methods update hash functions solely based on the new data batch, without investigating the correlation between such new data and the existing dataset. In addition, existing works update the hash functions using a relaxation process in its corresponding approximated continuous space. And it remains as an open problem to directly apply discrete optimizations in online hashing. In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework. BSODH employs a well-designed hashing algorithm to preserve the similarity between the streaming data and the existing dataset via an asymmetric graph regularization. We further identify the "data-imbalance" problem brought by the constructed asymmetric graph, which restricts the application of discrete optimization in our problem. Therefore, a novel balanced similarity is further proposed, which uses two equilibrium factors to balance the similar and dissimilar weights and eventually enables the usage of discrete optimizations. Extensive experiments conducted on three widely-used benchmarks demonstrate the advantages of the proposed method over the state-of-the-art methods.Comment: 8 pages, 11 figures, conferenc

Facial Action Unit Detection Using Attention and Relation Learning

Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefine fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation. Extensive experiments show that our framework (i) soundly outperforms the state-of-the-art methods for both AU detection and AU intensity estimation on the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can adaptively capture the correlated regions of each AU, and (iii) also works well under severe occlusions and large poses.Comment: This paper is accepted by IEEE Transactions on Affective Computin

Superalloys for Advanced Ultra-Super-Critical Fossil Power Plant Application

Superalloys are world-wildly used not only for aerospace but also for chemistry, oil & gas and power engineering application. In recent years the 700 °C level Advanced Ultra-Super-Critical (A-USC) technology with high thermal efficiency is developing in the world to reduce the coal consumption and pollution emissions. Any kind of advanced ferritic and austenitic heat-resisting steels can not meet 700 °C A-USC technology requirement. In this case high quality Ni-base superalloys must be adopted for 700 °C A-USC technology. The research and development of Ni-Fe and Ni-base superalloys such as HR6W, GH2984, Haynes 230, Inconel 617/617B, Nimonic 263, Haynes 282, Inconel 740 and 740H are reviewed in this chapter

A Joint Replication-Migration-based Routing in Delay Tolerant Networks

Abstract—Delay tolerant networks (DTNs) use mobility-assisted routing, where nodes carry, store, and forward data to each other in order to overcome the intermittent connectivity and limited network capacity of this type of network. In this paper, we propose a routing protocol that includes two mechanisms: message replication and message migration. Each mechanism has two steps: message selection and node selection. In message repli-cation, we choose the smallest hop-count message to replicate. The hop-count threshold is used to control the replication speed. We propose a metric called 2-hop activity level to measure the relay node’s transmission capacity, which is used in node selection. Our protocol includes a novel message migration policy that is used to overcome the limited buffer space and bandwidth of DTN nodes. We validate our protocol via extensive simulation experiments; we use a combination of synthetic and real mobility traces. Index Terms—Buffer management, delay tolerant networks (DTNs), message migration, message replication, routing. I

Spatio-Temporal Relation and Attention Learning for Facial Action Unit Detection

Spatio-temporal relations among facial action units (AUs) convey significant information for AU detection yet have not been thoroughly exploited. The main reasons are the limited capability of current AU detection works in simultaneously learning spatial and temporal relations, and the lack of precise localization information for AU feature learning. To tackle these limitations, we propose a novel spatio-temporal relation and attention learning framework for AU detection. Specifically, we introduce a spatio-temporal graph convolutional network to capture both spatial and temporal relations from dynamic AUs, in which the AU relations are formulated as a spatio-temporal graph with adaptively learned instead of predefined edge weights. Moreover, the learning of spatio-temporal relations among AUs requires individual AU features. Considering the dynamism and shape irregularity of AUs, we propose an attention regularization method to adaptively learn regional attentions that capture highly relevant regions and suppress irrelevant regions so as to extract a complete feature for each AU. Extensive experiments show that our approach achieves substantial improvements over the state-of-the-art AU detection methods on BP4D and especially DISFA benchmarks

Dynamic Distribution Pruning for Efficient Network Architecture Search

Network architectures obtained by Neural Architecture Search (NAS) have shown state-of-the-art performance in various computer vision tasks. Despite the exciting progress, the computational complexity of the forward-backward propagation and the search process makes it difficult to apply NAS in practice. In particular, most previous methods require thousands of GPU days for the search process to converge. In this paper, we propose a dynamic distribution pruning method towards extremely efficient NAS, which samples architectures from a joint categorical distribution. The search space is dynamically pruned every a few epochs to update this distribution, and the optimal neural architecture is obtained when there is only one structure remained. We conduct experiments on two widely-used datasets in NAS. On CIFAR-10, the optimal structure obtained by our method achieves the state-of-the-art $1.9$\% test error, while the search process is more than $1,000$ times faster (only $1.5$ GPU hours on a Tesla V100) than the state-of-the-art NAS algorithms. On ImageNet, our model achieves 75.2\% top-1 accuracy under the MobileNet settings, with a time cost of only $2$ GPU days that is $100\%$ acceleration over the fastest NAS algorithm. The code is available at \url{ https://github.com/tanglang96/DDPNAS

Hypomethylation of IL10 and IL13 Promoters in CD4+ T Cells of Patients with Systemic Lupus Erythematosus

Interleukin- (IL-)10 and IL-13 play important roles in Th2 cell differentiation and production of autoantibodies in patients with (SLE). However, the mechanisms leading to IL10 and IL13 overexpression in SLE patients are not well understood. In this study, we confirm that the levels of both IL10 and IL13 mRNA in CD4+ T cells and of serum IL10 and IL13 proteins are increased in SLE patients. We show that the DNA methylation levels within IL10 and IL13 gene regulatory domains are reduced in SLE CD4+ T cells relative to healthy controls and negatively correlate with IL10 and IL13 mRNA expression. Moreover, treating healthy CD4+ T cells with the demethylating agent 5-azacytidine (5-azaC) increased IL10 and IL13 mRNA transcription. Together, our results show that promoter methylation is a determinant of IL10 and IL13 expression in CD4+ T cells, and we propose that DNA hypomethylation leads to IL10 and IL13 overexpression in SLE patients

Study on stress performance and free brickwork height limit of traditional chinese cavity wall

Tradicionalni kineski zid sa šupljinom često je izložen in-plane i out-of-plane oštećenjima tijekom prirodnih nepogoda poput oluja i zemljotresa. Međutim, otpor potresu i vjetru zida sa šupljinom, zatvorene konstrukcije, rijetko se proučava. Umjesto toga, sprječavanje najgorega i napori za stvaranje sigurnosti koncentrirani su na konstrukcijsku analizu i štete od potresa glavne konstrukcije. Usmjerivši se na tehnike zidanja kod 2 uobičajena tipa zida sa šupljinom i 1 vrste punog zida, u ovom se radu konstruira specijalni uređaj za opterećenje i koristi za ispitivanje in-plane i out-of-plane naprezanja zida sa šupljinom i punog zida pod horizontalnim opterećenjem. Rezultati pokazuju da su sva out-of-plane oštećenja rezultat nedovoljne izdržljivosti na savijanje; zid sa šupljinom ima daleko nižu out-of-plane nosivost nego puni zid. Uz to, postoje znatna ograničenja visine kod zidanja zida sa šupljinom zbog potresa i snažnih vjetrova, s obzirom na shematski dijagram interne sile konzolnog nosača i na osnovu izmjerene savojno-vlačne i smične čvrstoće. Ustanovljeno je da out-of-plane ponašanje određuje granice zidanja opekom. Autori predlažu da se na svakom katu postave vezne konstrukcije ako zid sa šupljinom treba biti povezan s glavnom konstrukcijom.Traditional Chinese cavity wall often suffers in-plane and out-of-plane damages in natural disasters like gales and earthquakes. However, the seismic and wind resistance of the cavity wall, an enclosure structure, are seldom studied in the engineering field. Instead, the disaster prevention and relief efforts are concentrated on the structural analysis and seismic damage of the main structure. Focusing on the bricklaying methods for 2 common types of cavity walls and 1 kind of solid wall, this paper designs a special loading device and uses it to examine the in-plane and out-of-plane stress performance of cavity wall and solid wall under the horizontal load. The results show that all out-of-plane damages have resulted from the flexural-bending failure of the bend; the cavity wall has far lower out-of-plane bearing capacity than the solid wall. Moreover, the free brickwork height limits of the cavity wall under the action of earthquake and wind load are deducted respectively, in reference to the schematic diagram of the internal force of the cantilever beam and on the basis of the measured flexural-tensile strength and shear strength. It is found that the out-of-plane performance controls the brickwork limits. The authors suggest that connecting structures should be installed on each floor if the cavity wall is to be connected with the main structure

MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation

We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations. We demonstrate a long-range open-domain conversation through iterative "memorization-retrieval-response" cycles. This requires us to carefully design tailored tuning instructions for each distinct stage. The instructions are reconstructed from a collection of public datasets to teach the LLMs to memorize and retrieve past dialogues with structured memos, leading to enhanced consistency when participating in future conversations. We invite experts to manually annotate a test set designed to evaluate the consistency of long-range conversations questions. Experiments on three testing scenarios involving both open-source and API-accessible chatbots at scale verify the efficacy of MemoChat, which outperforms strong baselines.Comment: Codes, data and models will be available soo