New Optimal Binary Sequences with Period 4p4p via Interleaving Ding-Helleseth-Lam Sequences

Binary sequences with optimal autocorrelation play important roles in radar, communication, and cryptography. Finding new binary sequences with optimal autocorrelation has been an interesting research topic in sequence design. Ding-Helleseth-Lam sequences are such a class of binary sequences of period $p$, where $p$ is an odd prime with $p\equiv 1(\bmod~4)$. The objective of this letter is to present a construction of binary sequences of period $4p$ via interleaving four suitable Ding-Helleseth-Lam sequences. This construction generates new binary sequences with optimal autocorrelation which can not be produced by earlier ones

Target-Tailored Source-Transformation for Scene Graph Generation

Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations,e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets. To achieve this goal, we propose a Target-TailoredSource-Transformation (TTST) method to efficiently propagate information among object proposals and relations. Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account. We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation. By doing so the target object is able to extract target-specific information from the source object and source relation accordingly to refine its representation. Our framework is validated on the Visual Genome bench-mark and demonstrated its state-of-the-art performance for the scene graph generation. The experimental results show that the performance of object detection and visual relation-ship detection are promoted mutually by our method

Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalization

Recognizing elementary underlying concepts from observations (disentanglement) and generating novel combinations of these concepts (compositional generalization) are fundamental abilities for humans to support rapid knowledge learning and generalize to new tasks, with which the deep learning models struggle. Towards human-like intelligence, various works on disentangled representation learning have been proposed, and recently some studies on compositional generalization have been presented. However, few works study the relationship between disentanglement and compositional generalization, and the observed results are inconsistent. In this paper, we study several typical disentangled representation learning works in terms of both disentanglement and compositional generalization abilities, and we provide an important insight: vector-based representation (using a vector instead of a scalar to represent a concept) is the key to empower both good disentanglement and strong compositional generalization. This insight also resonates the neuroscience research that the brain encodes information in neuron population activity rather than individual neurons. Motivated by this observation, we further propose a method to reform the scalar-based disentanglement works ($\beta$-TCVAE and FactorVAE) to be vector-based to increase both capabilities. We investigate the impact of the dimensions of vector-based representation and one important question: whether better disentanglement indicates higher compositional generalization. In summary, our study demonstrates that it is possible to achieve both good concept recognition and novel concept composition, contributing an important step towards human-like intelligence.Comment: Preprin

Exploring the Semantics for Visual Relationship Detection

Scene graph construction / visual relationship detection from an image aims to give a precise structural description of the objects (nodes) and their relationships (edges). The mutual promotion of object detection and relationship detection is important for enhancing their individual performance. In this work, we propose a new framework, called semantics guided graph relation neural network (SGRN), for effective visual relationship detection. First, to boost the object detection accuracy, we introduce a source-target class cognoscitive transformation that transforms the features of the co-occurent objects to the target object domain to refine the visual features. Similarly, source-target cognoscitive transformations are used to refine features of objects from features of relations, and vice versa. Second, to boost the relation detection accuracy, besides the visual features of the paired objects, we embed the class probability of the object and subject separately to provide high level semantic information. In addition, to reduce the search space of relationships, we design a semantics-aware relationship filter to exclude those object pairs that have no relation. We evaluate our approach on the Visual Genome dataset and it achieves the state-of-the-art performance for visual relationship detection. Additionally, Our approach also significantly improves the object detection performance (i.e. 4.2\% in mAP accuracy)

Impact of drug-eluting stents with different coating strategies on stent thrombosis: A meta-analysis of 19 randomized trials

Background: Whether drug-eluting stents with biodegradable polymers (BP-DES) improve safety, especially with respect to stent thrombosis (ST) compared with permanent polymers DES (PP-DES), remains uncertain. We aimed to compare the short- and long-term outcomes and the ST risk in patients treated with BP-DES vs. PP-DES.Methods: We searched Medline, Embase, Web of science, CENTRAL databases, and conferenceproceedings/abstracts for randomized controlled trials (RCTs) comparing BP-DES with PP-DES. The primary endpoint was to compare the risks of overall and different temporalc ategories of definite/probable ST. Other clinical outcomes were target lesion revascularization (TLR), myocardial infarction (MI), and all-cause death in short-term (≤ 1 year) and long-term follow-up. The meta-analyses were performed by computing odds ratios (ORs) with 95% confidence intervals (CIs) using a random-effects model.Results: Nineteen RCTs including 20,229 patients were analyzed. Overall, BP-DES significantly decreased the risks of very late definite/probable ST (OR 0.33; 95% CI 0.16–0.70), and TLR in long-term follow-up (OR 0.70; 95% CI 0.52–0.95) compared with PP-DES. There were no significant differences between the groups regarding MI incidence and mortality during both short and long follow-up periods. In stratified analyses, the long-term superiority of BP-DES was maintained only by using first-generation DES as the comparators.Conclusions: The present meta-analysis indicated that BP-DES were more efficacious than PP-DES at reducing the risks of very late ST and long-term TLR, but it could vary by heterogeneities in the use of PP-DES comparators. Additional rigorous RCTs with longer follow-up periods are warranted to verify these very promising long-term endpoints.

EST analysis of gene expression in the tentacle of Cyanea capillata

AbstractJellyfish, Cyanea capillata, has an important position in head patterning and ion channel evolution, in addition to containing a rich source of toxins. In the present study, 2153 expressed sequence tags (ESTs) from the tentacle cDNA library of C. capillata were analyzed. The initial ESTs consisted of 198 clusters and 818 singletons, which revealed approximately 1016 unique genes in the data set. Among these sequences, we identified several genes related to head and foot patterning, voltage-dependent anion channel gene and genes related to biological activities of venom. Five kinds of proteinase inhibitor genes were found in jellyfish for the first time, and some of them were highly expressed with unknown functions

Curcumin Reduces Cognitive Deficits by Inhibiting Neuroinflammation through the Endoplasmic Reticulum Stress Pathway in Apolipoprotein E4 Transgenic Mice.

Apolipoprotein E4 (ApoE4) is the main genetic risk factor for Alzheimer's disease (AD), but the exact way in which it causes AD remains unclear. Curcumin is considered to have good therapeutic potential for AD, but its mechanism has not been clarified. This study aims to observe the effect of curcumin on ApoE4 transgenic mice and explore its possible molecular mechanism. Eight-month-old ApoE4 transgenic mice were intraperitoneally injected with curcumin for 3 weeks, and the Morris water maze test was used to evaluate the cognitive ability of the mice. Immunofluorescence staining, immunohistochemistry, western blotting, and enzyme-linked immunosorbent assay (ELISA) were used to examine the brain tissues of the mice. Curcumin reduced the high expression of ApoE4 and the excessive release of inflammatory factors in ApoE4 mice. In particular, the expression of marker proteins of endoplasmic reticulum (ER) stress was significantly increased in ApoE4 mice, while curcumin significantly reduced the increase in the expression of these proteins. Collectively, curcumin alleviates neuroinflammation in the brains of ApoE4 mice by inhibiting ER stress, thus improving the learning and cognitive ability of transgenic mice

CD180 Ligation Inhibits TLR7- and TLR9-Mediated Activation of Macrophages and Dendritic Cells Through the Lyn-SHP-1/2 Axis in Murine Lupus

Activation of TLR7 and TLR9 by endogenous RNA- or DNA-containing ligands, respectively, can lead to hyper-activation of immune cells, including macrophages and DCs, subsequently contributes to the pathogenesis of SLE. CD180, a TLR-like protein, is specifically involved in the development and activation of immune cells. Our previous study and others have reported that CD180-negative B cells are dramatically increased in SLE patients and responsible for the production of auto-antibodies. However, the mode of CD180 expression on macrophages and DCs in SLE remains unclear and the role of CD180 on regulating TLR7- and TLR9-mediated activation of macrophages and DCs are largely unknown. In the present study, we found that the percentages of CD180-negative macrophages and DCs were both increased in SLE patients and lupus-prone MRL/lpr mice compared with healthy donors and wild-type mice, respectively. Notably, ligation of CD180 significantly inhibited the activation of TLR7 and TLR9 signaling pathways in macrophages and DCs through the Lyn-SHP-1/2 axis. What's more, injection of anti-CD180 Ab could markedly ameliorate the lupus-symptoms of imiquimod-treated mice and lupus-prone MRL/lpr mice through inhibiting the activation of macrophages and DCs. Collectively, our results highlight a critical role of CD180 in regulating TLR7- and TLR9-mediated activation of macrophages and DCs, hinting that CD180 can be regarded as a potential therapeutic target for SLE treatment