Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.Comment: Accepted as a full paper in IEEE Computer Vision and Pattern Recognition (CVPR) 201

Structural design using a parallel sequential approximate optimization

In this paper, a parallel sampling strategy dedicated to SAO is proposed to enhance the competence of exploration and convergence simultaneously in the optimization process. In the parallel sampling procedure, new sampling points are identified in the pareto front of the multi-objective optimization problem, which is solved with the Normal constraint (NC) method. The objectives of the optimization problem are the two indices to represent the competence of exploration and convergence, and the sampled points will be evaluated by the true model in a parallel way. Furthermore, the proposed methodology is evaluated on two benchmark tests. Compared to other optimization algorithms, the PSAO algorithm yields equivalent or better objective values while the number of optimization iterations required to find the same global optima is reduced by multiple orders of magnitude, which substantially reduce the computing costs in the tested structural design optimization tasks, highlighting the applicability of the PSAO structural design optimization problems

Effects of Shading on Carbohydrates of Syzygium samarangense

Wax apple (Syzygium samarangense) is an important tropical fruit tree cultivated in Southeast Asian. It produces red pear-like shape fruits. The fruit flesh is considered high in antioxidants, phenolics, and flavonoids that have a potential to contribute to the human healthy diet, and was proved to have anti-inflammatory and antimicrobial characteristics. To allow year-round marketing of high quality wax apple fruit, growers always perform shading to inhibit new flushes so as to repress vegetative growth and promote reproductive growth. To investigate the effect of shading on carbohydrates, wax apple trees were shaded with sun shade nets under field conditions. The effects of shading on shoot growth were studied and leaf carbohydrate levels of the trees were determined. The results showed that shading inhibit the the growth of the terminal shoots and promoted bud dormancy. Shading also reduced total soluble sugar, sucrose, glucose, fructose, and starch levels of leaves. The results suggested that shading reduced carbohydrate accumulation and repressed vegetative growth

Coverage Dependent H2_2 Desorption Energy: a Quantitative Explanation Based on Encounter Desorption Mechanism

Recent experiments show that the desorption energy of H$_2$ on a diamond-like carbon (DLC) surface depends on the H$_2$ coverage of the surface. We aim to quantitatively explain the coverage dependent H$_2$ desorption energy measured by the experiments. We derive a math formula to calculate an effective H$_2$ desorption energy based on the encounter desorption mechanism. The effective H$_2$ desorption energy depends on two key parameters, the desorption energy of H$_2$ on H$_2$ substrate and the ratio of H$_2$ diffusion barrier to its desorption energy. The calculated effective H$_2$ desorption energy qualitatively agrees with the coverage dependent H$_2$ desorption energy measured by the experiments if the values of these two parameters in literature are used in the calculations. We argue that the difference between the effective H$_2$ desorption energy and the experimental results is due to the lacking of knowledge about these two parameters. So, we recalculate these two parameters based on experimental data. Good agreement between theoretical and experimental results can be achieved if these two updated parameters are used in the calculations.Comment: 6 pages,6 figures,2 tables, accepted for publication in MNRA

Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography

Lineage tracing for multiple lung cancer by spatiotemporal heterogeneity using a multi-omics analysis method integrating genomic, transcriptomic, and immune-related features

IntroductionThe distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) holds clinical significance in staging, therapeutic intervention, and prognosis assessment for multiple lung cancer. Lineage tracing by clinicopathologic features alone remains a clinical challenge; thus, we aimed to develop a multi-omics analysis method delineating spatiotemporal heterogeneity based on tumor genomic profiling.MethodsBetween 2012 and 2022, 11 specimens were collected from two patients diagnosed with multiple lung cancer (LU1 and LU2) with synchronous/metachronous tumors. A novel multi-omics analysis method based on whole-exome sequencing, transcriptome sequencing (RNA-Seq), and tumor neoantigen prediction was developed to define the lineage. Traditional clinicopathologic reviews and an imaging-based algorithm were performed to verify the results.ResultsSeven tissue biopsies were collected from LU1. The multi-omics analysis method demonstrated that three synchronous tumors observed in 2018 (LU1B/C/D) had strong molecular heterogeneity, various RNA expression and immune microenvironment characteristics, and unique neoantigens. These results suggested that LU1B, LU1C, and LU1D were MPLC, consistent with traditional lineage tracing approaches. The high mutational landscape similarity score (75.1%), similar RNA expression features, and considerable shared neoantigens (n = 241) revealed the IPM relationship between LU1F and LU1G which were two samples detected simultaneously in 2021. Although the multi-omics analysis method aligned with the imaging-based algorithm, pathology and clinicopathologic approaches suggested MPLC owing to different histological types of LU1F/G. Moreover, controversial lineage or misclassification of LU2’s synchronous/metachronous samples (LU2B/D and LU2C/E) traced by traditional approaches might be corrected by the multi-omics analysis method. Spatiotemporal heterogeneity profiled by the multi-omics analysis method suggested that LU2D possibly had the same lineage as LU2B (similarity score, 12.9%; shared neoantigens, n = 71); gefitinib treatment and EGFR, TP53, and RB1 mutations suggested the possibility that LU2E might result from histology transformation of LU2C despite the lack of LU2C biopsy and its histology. By contrast, histological interpretation was indeterminate for LU2D, and LU2E was defined as a primary or progression lesion of LU2C by histological, clinicopathologic, or imaging-based approaches.ConclusionThis novel multi-omics analysis method improves the accuracy of lineage tracing by tracking the spatiotemporal heterogeneity of serial samples. Further validation is required for its clinical application in accurate diagnosis, disease management, and improving prognosis

Topological Electronic Structure Evolution with Symmetry Breaking Spin Reorientation in (Fe1−x_{1-x}Cox_{x})Sn

Topological materials hosting kagome lattices have drawn considerable attention due to the interplay between topology, magnetism, and electronic correlations. The (Fe$_{1-x}$Co$_x$)Sn system not only hosts a kagome lattice but has a tunable symmetry breaking magnetic moment with temperature and doping. In this study, angle resolved photoemission spectroscopy and first principles calculations are used to investigate the interplay between the topological electronic structure and varying magnetic moment from the planar to axial antiferromagnetic phases. A theoretically predicted gap at the Dirac point is revealed in the low temperature axial phase but no gap opening is observed across a temperature dependent magnetic phase transition. However, topological surface bands are observed to shift in energy as the surface magnetic moment is reduced or becomes disordered over time during experimental measurements. The shifting surface bands may preclude the determination of a temperature dependent bulk gap but highlights the intricate connections between magnetism and topology with a surface/bulk dichotomy that can affect material properties and their interrogation.Comment: 11 pages, 4 figure

Deep interest shifting network with meta embeddings for fresh item recommendation

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications