Radio Imaging of the NGC 1333 IRAS 4B Region

The NGC 1333 IRAS 4B region was observed in the 6.9 mm and 1.3 cm continuum with an angular resolution of about 0.4 arcseconds. IRAS 4BI was detected in both bands, and BII was detected in the 6.9 mm continuum only. The 1.3 cm source of BI seems to be a disk-like flattened structure with a size of about 50 AU. IRAS 4BI does not show any sign of multiplicity. Examinations of archival infrared images show that the dominating emission feature in this region is a bright peak in the southern outflow driven by BI, corresponding to the molecular hydrogen emission source HL 9a. Both BI and BII are undetectable in the mid-IR bands. The upper limit on the far-IR flux of IRAS 4BII suggests that it may be a very low luminosity young stellar object.Comment: To appear in the JKA

RADIAL HEAT OPERATORS ON JACOBI-LIKE FORMS

We consider a differential operator DX &#955; associated to an integer &#955; acting on the space of formal power series, which may be regarded as the heat operator with respect to the radial coordinate in the 2&#955;-dimensional space for &#955; &#62; 0. We show that DX &#955; carries Jacobilike forms of weight &#955; to ones of weight &#955;+2 and obtain the formula for the m-fold composite (DX &#955; )[m] of such operators. We then determine the corresponding operators on modular series and as well as on automorphic pseudodifferential operators.</p

Adversarial Lagrangian Integrated Contrastive Embedding for Limited Size Datasets

Certain datasets contain a limited number of samples with highly various styles and complex structures. This study presents a novel adversarial Lagrangian integrated contrastive embedding (ALICE) method for small-sized datasets. First, the accuracy improvement and training convergence of the proposed pre-trained adversarial transfer are shown on various subsets of datasets with few samples. Second, a novel adversarial integrated contrastive model using various augmentation techniques is investigated. The proposed structure considers the input samples with different appearances and generates a superior representation with adversarial transfer contrastive training. Finally, multi-objective augmented Lagrangian multipliers encourage the low-rank and sparsity of the presented adversarial contrastive embedding to adaptively estimate the coefficients of the regularizers automatically to the optimum weights. The sparsity constraint suppresses less representative elements in the feature space. The low-rank constraint eliminates trivial and redundant components and enables superior generalization. The performance of the proposed model is verified by conducting ablation studies by using benchmark datasets for scenarios with small data samples.Comment: Submitted to Neural Networks Journal: 36 pages, 6 figure