Hyperbolic Interaction Model For Hierarchical Multi-Label Classification

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available

The 2010 spring drought reduced primary productivity in southwestern China

Many parts of the world experience frequent and severe droughts. Summer drought can significantly reduce primary productivity and carbon sequestration capacity. The impacts of spring droughts, however, have received much less attention. A severe and sustained spring drought occurred in southwestern China in 2010. Here we examine the influence of this spring drought on the primary productivity of terrestrial ecosystems using data on climate, vegetation greenness and productivity. We first assess the spatial extent, duration and severity of the drought using precipitation data and the Palmer drought severity index. We then examine the impacts of the drought on terrestrial ecosystems using satellite data for the period 2000–2010. Our results show that the spring drought substantially reduced the enhanced vegetation index (EVI) and gross primary productivity (GPP) during spring 2010 (March–May). Both EVI and GPP also substantially declined in the summer and did not fully recover from the drought stress until August. The drought reduced regional annual GPP and net primary productivity (NPP) in 2010 by 65 and 46 Tg C yr−1, respectively. Both annual GPP and NPP in 2010 were the lowest over the period 2000–2010. The negative effects of the drought on annual primary productivity were partly offset by the remarkably high productivity in August and September caused by the exceptionally wet conditions in late summer and early fall and the farming practices adopted to mitigate drought effects. Our results show that, like summer droughts, spring droughts can also have significant impacts on vegetation productivity and terrestrial carbon cycling

Designing superhard magnetic material in clathrate \b{eta}-C3N2 through atom embeddedness

Designing new compounds with the coexistence of diverse physical properties is of great significance for broad applications in multifunctional electronic devices. In this work, based on density functional theory, we predict the coexistence of mechanical superhardness and the controllable magnetism in the clathrate material \b{eta}-C3N2 through the implant of the external atom into the intrinsic cage structure. Taking hydrogen-doping (H@\b{eta}-C3N2) and fluorine-doping (F@\b{eta}-C3N2) as examples, our calculations indicate these two doped configurations are stable and discovered that they belong to antiferromagnetic semiconductor and ferromagnetic semi-metal, respectively. These intriguing magnetic phase transitions originate from their distinctive band structure around the Fermi level and can be well understood by the 3D Hubbard model with half-filling occupation and the Stoner model. Moreover, the high Vickers hardness of 49.0 GPa for H@\b{eta}-C3N2 and 48.2 GPa for F@\b{eta}-C3N2 are obtained, suggesting they are clathrate superhard materials as its host. Therefore, the incorporation of H and F in \b{eta}-C3N2 gives rise to a new type of superhard antiferromagnetic semiconductor and superhard ferromagnetic semimetal, respectively, which could have potential applications in harsh conditions. Our work provides an effective strategy to design a new class of highly desirable multifunctional materials with excellent mechanical properties and magnetic properties, which may arouse spintronic applications in superhard materials in the future.Comment: 14 pages, 5 figure

Attention, Please! Adversarial Defense via Attention Rectification and Preservation

This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change. In particular, we observed that: (1) images with incomplete attention regions are more vulnerable to adversarial attacks; and (2) successful adversarial attacks lead to deviated and scattered attention map. Accordingly, an attention-based adversarial defense framework is designed to simultaneously rectify the attention map for prediction and preserve the attention area between adversarial and original images. The problem of adding iteratively attacked samples is also discussed in the context of visual attention change. We hope the attention-related data analysis and defense solution in this study will shed some light on the mechanism behind the adversarial attack and also facilitate future adversarial defense/attack model design

Two-factor remote authentication protocol with user anonymity based on elliptic curve cryptography

In order to provide secure remote access control, a robust and efficient authentication protocol should realize mutual authentication and session key agreement between clients and the remote server over public channels. Recently, Chun-Ta Li proposed a password authentication and user anonymity protocol by using smart cards, and they claimed that their protocol has satisfied all criteria required by remote authentication. However, we have found that his protocol cannot provide mutual authentication between clients and the remote server. To realize ‘real’ mutual authentication, we propose a two-factor remote authentication protocol based on elliptic curve cryptography in this paper, which not only satisfies the criteria but also bears low computational cost. Detailed analysis shows our proposed protocol is secure and more suitable for practical application

Why Factory1: The Spatial Significance of Architectural Education Buildings

The educational space of the Architecture faculty is used to cultivate architects. At the same time, it becomes the carrier of architectural ideas and teaching methods. The type of architecture and its spatial organization reflect the architectural education philosophy and attitude. Back in history, as early as the Renaissance, there had study places for architects emerged. After the industrial revolution and the modernist process, the types of architectural education sites are more diverse, and their main features are the spatial form of hybrid and box-in-box. This article preliminarily analyzes the evolution of the outline of architectural education building and interprets the spatial ideas in each period. The study focuses on the famous Dutch architectural school–BKCity of the Delft University of Technology, analyzing the teaching space logic of its distinctive Why Factory and exploring how the related space could stimulate the vitality of architectural education. By the analogy of some architectural schools, it also tries to compare the differences and characteristics of Chinese and Western architectural academies, finding out the spatial significance in architecture discipline, education method as well as sustainable application

Contact characteristics of orthogonal face gear with spur involute pinion

The proposed mechanism of face gear shaping is applied to develop the mathematical model of face gears. Based on the developed mathematical model of the face gear, computer graph of the face gear set is created. Then, the transmission errors of the face gear pairs under various assembly errors are investigated based on the constraints theory of six-degree-of-freedom rigid body motion. The assembly errors are finally obtained by applying the TCA (tooth contact analysis) method. Also, the developed computer simulation programs quantitatively evaluate the influence of assembly errors including offset and angular position errors on the position of contact path and TE (transmission error) in a complete mesh cycle. Besides, the loaded tooth contact characteristics are investigated by using the FEM (finite element method) to study the stick-slip trajectories of the surfaces. The results are illustrated with numerical examples

Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification

Multi-label text classification (MLTC) is one of the key tasks in natural language processing. It aims to assign multiple target labels to one document. Due to the uneven popularity of labels, the number of documents per label follows a long-tailed distribution in most cases. It is much more challenging to learn classifiers for data-scarce tail labels than for data-rich head labels. The main reason is that head labels usually have sufficient information, e.g., a large intra-class diversity, while tail labels do not. In response, we propose a Pairwise Instance Relation Augmentation Network (PIRAN) to augment tailed-label documents for balancing tail labels and head labels. PIRAN consists of a relation collector and an instance generator. The former aims to extract the document pairwise relations from head labels. Taking these relations as perturbations, the latter tries to generate new document instances in high-level feature space around the limited given tailed-label instances. Meanwhile, two regularizers (diversity and consistency) are designed to constrain the generation process. The consistency-regularizer encourages the variance of tail labels to be close to head labels and further balances the whole datasets. And diversity-regularizer makes sure the generated instances have diversity and avoids generating redundant instances. Extensive experimental results on three benchmark datasets demonstrate that PIRAN consistently outperforms the SOTA methods, and dramatically improves the performance of tail labels