An Analysis of LED Light Distribution Based on Visual Spectral Characteristics

AbstractOn the analysis of the human visual structure characteristics and LED optical design principle, human visual color image model with background light was constructed in this paper, and the image sharpness function is defined. With high pressure sodium lamps, white light and green light LED as backlight, the model simulation of image sharpness is fulfilled. The results show that the green LED has better clarity and sensitivity with the same condition of radiation energy background light

Transcriptomic comparison of invasive bigheaded carps (\u3ci\u3eHypophthalmichthys nobilis\u3c/i\u3e and \u3ci\u3eHypophthalmichthys molitrix\u3c/i\u3e) and their hybrids

Bighead carp (Hypophthalmichthys nobilis) and silver carp (Hypophthalmichthys molitrix), collectively called bigheaded carps, are invasive species in the Mississippi River Basin (MRB). Interspecific hybridization between bigheaded carps has been considered rare within their native rivers in China; however, it is prevalent in the MRB. We conducted de novo transcriptome analysis of pure and hybrid bigheaded carps and obtained 40,759 to 51,706 transcripts for pure, F1 hybrid, and backcross bigheaded carps. The search against protein databases resulted in 20,336–28,133 annotated transcripts (over 50% of the transcriptome) with over 13,000 transcripts mapped to 23 Gene Ontology biological processes and 127 KEGG metabolic pathways. More transcripts were detected in silver carp than in bighead carp; however, comparable numbers of transcripts were annotated. Transcriptomic variation detected between two F1 hybrids may indicate a potential loss of fitness in hybrids. The neighbor-joining distance tree constructed using over 2,500 one-to-one orthologous sequences suggests transcriptomes could be used to infer the history of introgression and hybridization. Moreover, we detected 24,792 candidate SNPs that can be used to identify different species. The transcriptomes, orthologous sequences, and candidate SNPs obtained in this study should provide further knowledge of interspecific hybridization and introgression

Self-consistent simulations of beam-beam interaction in future e + e - circular colliders including beamstrahlung and longitudinal coupling impedance

For the past generation {e}^{+}{e}^{\ensuremath{-}} storage ring colliders, we usually used natural bunch length or its impedance lengthened value in beam-beam simulations instead of considering the impedance directly. In the future colliders, such as FCC-ee and CEPC, the beam-beam interaction becomes essentially three dimensional. In order to increase the luminosity, the future accelerators will collide very intense beams of high energy with low emittances and small beta functions at the collision points exploiting the crab waist collision scheme with a large Piwinski angle. For these extreme parameters several new effects become important for the collider performance such as beamstrahlung, coherent X-Z instability, 3D flip-flop so that the longitudinal beam dynamics should be also treated in a self-consistent manner. In this paper we describe the numerical code for the self-consistent 3D beam-beam simulations including beamstrahlung and the longitudinal beam coupling impedance and study interplay of different effects arising in beam-beam collisions of the future colliders

RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning

The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multi-task learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.Comment: 11 pages, 8 figure

Transcriptomic variation of hepatopancreas reveals the energy metabolism and biological processes associated with molting in Chinese mitten crab, Eriocheir sinensis

Molting is a critical developmental process for crustaceans, yet the underlying molecular mechanism is unknown. In this study, we used RNA-Seq to investigate transcriptomic profiles of the hepatopancreas and identified differentially expressed genes at four molting stages of Chinese mitten crab (Eriocheir sinensis). A total of 97,398 transcripts were assembled, with 31,900 transcripts annotated. Transcriptomic comparison revealed 1,189 genes differentially expressed amongst different molting stages. We observed a pattern associated with energy metabolism and physiological responses during a molting cycle. In specific, differentially expressed genes enriched in postmolt were linked to energy consumption whereas genes enriched in intermolt were related to carbohydrates, lipids metabolic and biosynthetic processes. In premolt, a preparation stage for upcoming molting and energy consumption, highly expressed genes were enriched in response to steroid hormone stimulus and immune system development. The expression profiles of twelve functional genes detected via RNA-Seq were corroborated through real-time RT-PCR assay. Together, our results, including assembled transcriptomes, annotated functional elements and enriched differentially expressed genes amongst different molting stages, provide novel insights into the functions of the hepatopancreas in energy metabolism and biological processes pertaining to molting in crustaceans

Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles

Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dynamic scenarios, and the excessive communication overheads possibly overwhelm the IoV system. Therefore, in this paper, we design a communication efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the communication overheads in a fully distributed architecture. In particular, RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by incorporating the idea of segment mixture and augmenting multiple model replicas from received neighboring policy segments. Afterwards, RSM-MAPPO adopts a theory-guided metric to regulate the selection of contributive replicas to guarantee the policy improvement. Finally, extensive simulations in a mixed-autonomy traffic control scenario verify the effectiveness of the RSM-MAPPO algorithm

Circulating tissue factor-positive procoagulant microparticles in patients with type 1 diabetes

Aim: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM). Methods: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF(+)PS(+)MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF(+)PS(+)MPs and PS(+)MPs (TF(+)PS(+)MPs/PS(+)MPs). Results: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS(+)MPs, TF(+)PS(+)MPs and TF(+)PS(+)MPs/PS(+)MPs were elevated in patients with T1DM (PS(+)MPs: 1078.5 +/- 158.08 vs 686.84 +/- 122.04/mu L, P &lt;0.001; TF(+)PS(+)MPs: 202.10 +/- 47.47 vs 108.33 +/- 29.42/mu L, P &lt;0.001; and TF(+)PS(+)MPs/PS(+)MPs: 0.16 +/- 0.04 vs 0.19 +/- 0.05, P = 0.004), mostly derived from platelet, lymphocytes and endothelial cells. In the subgroup analysis, the counts of total and platelet TF(+)PS(+)MPs were increased in patients with diabetic retinopathy (DR) and with higher HbA1c, respectively. Conclusion: Circulating TF(+)PS(+)MPs and those derived from platelet, lymphocytes and endothelial cells were elevated in patients with T1DM.De tre första författarna delar förstaförfattarskapet.</p

Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification

Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis. However, limited by the significant data imbalance and obvious extraneous artifacts, i.e., the hair and ruler markings, discriminative feature extraction from dermoscopic images is very challenging. In this study, we seek to resolve these problems respectively towards better representation learning for lesion features. Specifically, a GAN-based data augmentation (GDA) strategy is adapted to generate synthetic melanoma-positive images, in conjunction with the proposed implicit hair denoising (IHD) strategy. Wherein the hair-related representations are implicitly disentangled via an auxiliary classifier network and reversely sent to the melanoma-feature extraction backbone for better melanoma-specific representation learning. Furthermore, to train the IHD module, the hair noises are additionally labeled on the ISIC2020 dataset, making it the first large-scale dermoscopic dataset with annotation of hair-like artifacts. Extensive experiments demonstrate the superiority of the proposed framework as well as the effectiveness of each component. The improved dataset publicly avaliable at https://github.com/kirtsy/DermoscopicDataset.Comment: ICONIP 2021 conferenc