Sparse Bayesian Learning with Diagonal Quasi-Newton Method for Large Scale Classification

Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic model with very competitive generalization. However, SBL needs to invert a big covariance matrix with complexity O(M^3 ) (M: feature size) for updating the regularization priors, making it difficult for practical use. There are three issues in SBL: 1) Inverting the covariance matrix may obtain singular solutions in some cases, which hinders SBL from convergence; 2) Poor scalability to problems with high dimensional feature space or large data size; 3) SBL easily suffers from memory overflow for large-scale data. This paper addresses these issues with a newly proposed diagonal Quasi-Newton (DQN) method for SBL called DQN-SBL where the inversion of big covariance matrix is ignored so that the complexity and memory storage are reduced to O(M). The DQN-SBL is thoroughly evaluated on non-linear classifiers and linear feature selection using various benchmark datasets of different sizes. Experimental results verify that DQN-SBL receives competitive generalization with a very sparse model and scales well to large-scale problems.Comment: 11 pages,5 figure

Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

Objective quality assessment of stereoscopic panoramic images becomes a challenging problem owing to the rapid growth of 360-degree contents. Different from traditional 2D image quality assessment (IQA), more complex aspects are involved in 3D omnidirectional IQA, especially unlimited field of view (FoV) and extra depth perception, which brings difficulty to evaluate the quality of experience (QoE) of 3D omnidirectional images. In this paper, we propose a multi-viewport based fullreference stereo 360 IQA model. Due to the freely changeable viewports when browsing in the head-mounted display (HMD), our proposed approach processes the image inside FoV rather than the projected one such as equirectangular projection (ERP). In addition, since overall QoE depends on both image quality and depth perception, we utilize the features estimated by the difference map between left and right views which can reflect disparity. The depth perception features along with binocular image qualities are employed to further predict the overall QoE of 3D 360 images. The experimental results on our public Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) show that the proposed method achieves a significant improvement over some well-known IQA metrics and can accurately reflect the overall QoE of perceived images

Interionic Energy Transfer in Y\u3csub\u3e3\u3c/sub\u3eAl\u3csub\u3e5\u3c/sub\u3eO\u3csub\u3e12\u3c/sub\u3e: Ce\u3csup\u3e3+\u3c/sup\u3e, Pr\u3csup\u3e3+\u3c/sup\u3e Phosphor

We present an investigation of dynamical processes of nonradiative energy transfer (ET)between Ce3+ and Pr3+ , and between Pr3+ ions in Y3Al5O12:Ce3+ , Pr3+ phosphor.Photoluminescence spectroscopy and fluorescence decay patterns are studied as a function ofPr3+ and Ce3+ concentrations. The analysis based on Inokuti–Hirayama model indicates that the ET from the lowest 5d state of Ce3+ to the D12 state of Pr3+ , and the quenching of theD12 state through a cross relaxation involving Pr3+ ions in the ground state are both governed by electric dipole–dipole interaction. An increase in the Ce3+–Pr3+ ET rate followed by the enhanced red emission line of Pr3+ relative to the yellow emission band of Ce3+ on only increasing Ce3+ concentration is observed. This behavior is attributed to the increase in thespectral overlap integrals between Ce3+ emission and Pr3+ excitation due to the fact that the yellow band shifts to the red spectral side with increasing Ce3+ concentration while the red line dose not move. For Ce3+ concentration of 0.01 in YAG:Ce3+ , Pr3+ , the rate constant and critical distance are evaluated to be 4.5×10−36 cm6 s−1 , 0.81 nm for Ce3+–Pr3+ ET and2.4×10−38 cm6 s−1 , 1.30 nm for Pr3+–Pr3+ ET. Spectroscopic study also demonstrates a pronounced ET from the lowest 4f5d of Pr3+ to the 5d of Ce3+ . A proportional dependence of the initial transfer rate on acceptor concentration is observed in each of these ET pathways. The proportional coefficient as the averaged ET parameters for initial decay are determined, meaning the ET efficiency for the same concentration of acceptors follows the order ofPr3+–Pr3+\u3ePr3+–Ce3+\u3eCe3+–Pr3+

Color Control and White Light Generation of Upconversion Luminescence by Operating Dopant Concentrations and Pump Densities in Yb\u3csup\u3e3+\u3c/sup\u3e, Er\u3csup\u3e3+\u3c/sup\u3e, and Tm\u3csup\u3e3+\u3c/sup\u3e Tri-Doped Lu\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e3\u3c/sub\u3e Nanocrystals

We synthesized a series of Yb3+, Er3+ and Tm3+ tri-doped Lu2O3 nanocrystals with various dopant concentrations by the hydrothermal approach. Due to a unique electronic state at the top of the valence band, Lu2O3 based materials exhibit intense upconversion luminescence involving 1G4 → 3H6 of Tm3+ in blue, (2H11/2, 4S3/2) → 4I15/2 in green and 4F9/2 → 4I15/2 in red of Er3+ upon near infrared excitation at 980 nm. The variation of upconversion spectra and color points with dopant concentrations and pump densities are studied in detail on the basis of energy transfer processes. An ideal white upconversion light with color coordinates of (0.327, 0.339) is obtained by controlling the intensity of red, green, and blue emission in Lu1.906Yb0.08Er0.008Tm0.006O3nanocrystals under a pump density of 8 W cm−2. Based on the present experimental data, we may predict the dopant concentrations and pump densities for any color point within or around the white light region in the tri-doped Lu2O3 nanocrystals

Long-Lasting Phosphorescence in BaSi\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e2\u3c/sub\u3eN\u3csub\u3e2\u3c/sub\u3e:Eu\u3csup\u3e2+\u3c/sup\u3e and Ba\u3csub\u3e2\u3c/sub\u3eSiO\u3csub\u3e4\u3c/sub\u3e:Eu\u3csup\u3e2+\u3c/sup\u3e Phases for X-Ray and Cathode Ray Tubes

We report the long-lasting bluish-green phosphorescence for X-ray or cathode ray tubes in the phosphors with compositions of either Ba2SiO4:0.01Eu2+–xSi3N4 (x=0–1) or 2BaCO3–ySi3N4:0.01Eu2+(y=1/6–1) synthesized by a solid-state reaction. By tuning the Si3N4content, the phosphorescence may originate from Eu2+ in BaSi2O2N2(peaking at 490 nm), Ba2SiO4 (505 nm), and Ba3SiO5 (590 nm) phases. The strong phosphorescence of the Ba2SiO4:Eu2+ phase in 2BaCO3–ySi3N4:0.01Eu2+ is attributed to N substitution for O to generate a shallow trap. In Ba2SiO4:0.01Eu2+–xSi3N4 , however, N prefers reacting with Ba2SiO4 to form BaSi2O2N2 , thereby exhibiting a strong phosphorescence of the BaSi2O2N2:Eu2+ phase but a weak phosphorescence of the Ba2SiO4:Eu2+ phase

Diesel degradation capability and environmental robustness of strain Pseudomonas aeruginosa WS02

Petroleum hydrocarbon (PHC) degrading bacteria have been frequently discovered. However, in practical application, a single species of PHC degrading bacterium with weak competitiveness may face environmental pressure and competitive exclusion due to the interspecific competition between petroleum-degrading bacteria as well as indigenous microbiota in soil, leading to a reduced efficacy or even malfunction. In this study, the diesel degradation ability and environmental robustness of an endophytic strain Pseudomonas aeruginosa WS02, were investigated. The results show that the cell membrane surface of WS02 was highly hydrophobic, and the strain secreted glycolipid surfactants. Genetic analysis results revealed that WS02 contained multiple metabolic systems and PHC degradation-related genes, indicating that this strain theoretically possesses the capability of oxidizing both alkanes and aromatic hydrocarbons. Gene annotation also showed many targets which coded for heavy metal resistant and metal transporter proteins. The gene annotation-based inference was confirmed by the experimental results: GC-MS analysis revealed that short chain PHCs (C10–C14) were completely degraded, and the degradation of PHCs ranging from C15–C22 were above 90% after 14 d in diesel-exposed culture; Heavy metal (Mn2+, Pb2+ and Zn2+) exposure was found to affect the growth of WS02 to some extent, but not its ability to degrade diesel, and the degradation efficiency was still maintained at 39–59%. WS02 also showed a environmental robustness along with PHC-degradation performance in the co-culture system with other bacterial strains as well as in the co-cultured system with the indigenous microbiota in soil fluid extracted from a PHC-contaminated site. It can be concluded that the broad-spectrum diesel degradation efficacy and great environmental robustness give P. aeruginosa WS02 great potential for application in the remediation of PHC-contaminated soil.<br/

Energy-efficient mobile node localization using CVA technology and SAI algorithm

In the evolving landscape of the Internet of Things (IoT), Mobile Wireless Sensor Networks (MWSN) play a pivotal role, particularly in dynamic environments requiring mobile sensing capabilities. A primary challenge in MWSNs is achieving accurate node positioning with minimal energy consumption, as these networks often consist of battery-powered, mobile sensors where energy replenishment is difficult. This paper addresses the critical problem of energy-efficient node localization in MWSNs. We propose a novel positioning approach leveraging Cooperative Virtual Array (CVA) technology, which strategically utilizes the mobility of nodes to enhance positioning accuracy while conservatively using energy resources. The methodology revolves around optimizing the number of transceiver nodes, considering factors such as node moving speed, total energy consumption, and positioning errors. Central to our approach is the Signal Arrival and Interaction (SAI) algorithm, an innovative technique devised for efficient and precise mobile node localization, replacing traditional Time of Arrival (ToA) methods. Our simulations, conducted under various scenarios, demonstrate the significant advantages of the CVA-based positioning algorithm. Results show a marked reduction in energy consumption and robust performance in mobile node scenarios. Key findings include substantial improvements in localization accuracy and energy efficiency, highlighting the potential of our approach in enhancing the operational sustainability of MWSNs. The implications of this research are far-reaching for IoT applications, particularly those involving mobile sensors, such as in smart cities, industrial monitoring, and disaster management. By introducing a novel, energy-efficient positioning method, our work contributes to the advancement of MWSN technology, offering a sustainable solution to the challenge of mobile node localization

YUAN 2.0: A Large Language Model with Localized Filtering-based Attention

In this work, we develop and release Yuan 2.0, a series of large language models with parameters ranging from 2.1 billion to 102.6 billion. The Localized Filtering-based Attention (LFA) is introduced to incorporate prior knowledge of local dependencies of natural language into Attention. A data filtering and generating system is presented to build pre-training and fine-tuning dataset in high quality. A distributed training method with non-uniform pipeline parallel, data parallel, and optimizer parallel is proposed, which greatly reduces the bandwidth requirements of intra-node communication, and achieves good performance in large-scale distributed training. Yuan 2.0 models display impressive ability in code generation, math problem-solving, and chatting compared with existing models. The latest version of YUAN 2.0, including model weights and source code, is accessible at Github