Robust Recovery of Subspace Structures by Low-Rank Representation

In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Rank Representation (LRR), which seeks the lowest-rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that LRR well solves the subspace recovery problem: when the data is clean, we prove that LRR exactly captures the true subspace structures; for the data contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for the data corrupted by arbitrary errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace segmentation and error correction, in an efficient way.Comment: IEEE Trans. Pattern Analysis and Machine Intelligenc

Modeling of Time with Metamaterials

Metamaterials have been already used to model various exotic "optical spaces". Here we demonstrate that mapping of monochromatic extraordinary light distribution in a hyperbolic metamaterial along some spatial direction may model the "flow of time". This idea is illustrated in experiments performed with plasmonic hyperbolic metamaterials. Appearance of the "statistical arrow of time" is examined in an experimental scenario which emulates a Big Bang-like event.Comment: 15 pages, 4 figures, this version is accepted for publication in JOSA

Gas kinematics and star formation in the filamentary molecular cloud G47.06+0.26

We performed a multi-wavelength study toward the filamentary cloud G47.06+0.26 to investigate the gas kinematics and star formation. We present the 12CO (J=1-0), 13CO (J=1-0) and C18O (J=1-0) observations of G47.06+0.26 obtained with the Purple Mountain Observation (PMO) 13.7 m radio telescope to investigate the detailed kinematics of the filament. The 12CO (J=1-0) and 13CO (J=1-0) emission of G47.06+0.26 appear to show a filamentary structure. The filament extends about 45 arcmin (58.1 pc) along the east-west direction. The mean width is about 6.8 pc, as traced by the 13CO (J=1-0) emission. G47.06+0.26 has a linear mass density of about 361.5 Msun/pc. The external pressure (due to neighboring bubbles and H II regions) may help preventing the filament from dispersing under the effects of turbulence. From the velocity-field map, we discern a velocity gradient perpendicular to G47.06+0.26. From the Bolocam Galactic Plane Survey (BGPS) catalog, we found nine BGPS sources in G47.06+0.26, that appear to these sources have sufficient mass to form massive stars. We obtained that the clump formation efficiency (CFE) is about 18% in the filament. Four infrared bubbles were found to be located in, and adjacent to, G47.06+0.26. Particularly, infrared bubble N98 shows a cometary structure. CO molecular gas adjacent to N98 also shows a very intense emission. H II regions associated with infrared bubbles can inject the energy to surrounding gas. We calculated the kinetic energy, ionization energy, and thermal energy of two H II regions in G47.06+0.26. From the GLIMPSE I catalog, we selected some Class I sources with an age of about 100000 yr, which are clustered along the filament. The feedback from the H II regions may cause the formation of a new generation of stars in filament G47.06+0.26.Comment: 10 pages, 11 figures, accepted for publication in A&

Isolation and characterization of multidrug-resistant side population cells in prostate carcinoma

Purpose: To isolate and characterize cancer stem-like side population (SP) cells from prostate cancer tissues using Hoechst 33342 dye exclusion.Methods: The presence of SP cells was analyzed in tumor samples by fluorescence activated cell sorting. The cell survival rate and ability for cell self-renewal using the sphere formation assay were evaluated after treatment with multiple drugs.Results: SP cells in the prostate cancer samples constituted 2.8 %, but fell to 0.6 % after treatment with verapamil. The SP cells showed high resistance to drugs such as 5-fluorouracil, cisplatin, paclitaxel (2 μmol/L) and oxaliplatin. The survival rate of SP cells after treatment with these drugs was significantly higher (p < 0.01) than that of non-SP cells. Furthermore, the number of spheres generated in serumfree medium was significantly higher in prostate cancer SP cells than in non-SP cells.Conclusion: The presence of SP cells is responsible for prostate treatment failure and tumor recurrence. Therefore, isolation and characterization of SP cells may provide new insights into the development of novel therapeutic agents targeting cancer stem cells for complete eradication of the tumor.Keywords: Side population cells, ABC transporters, Cancer stem cells, Chemotherapy, Prostate treatment failure, Tumor recurrence, Drug resistanc

Dual Associated Encoder for Face Restoration

Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild. The existing codebook prior mitigates the ill-posedness by leveraging an autoencoder and learned codebook of high-quality (HQ) features, achieving remarkable quality. However, existing approaches in this paradigm frequently depend on a single encoder pre-trained on HQ data for restoring HQ images, disregarding the domain gap between LQ and HQ images. As a result, the encoding of LQ inputs may be insufficient, resulting in suboptimal performance. To tackle this problem, we propose a novel dual-branch framework named DAEFR. Our method introduces an auxiliary LQ branch that extracts crucial information from the LQ inputs. Additionally, we incorporate association training to promote effective synergy between the two branches, enhancing code prediction and output quality. We evaluate the effectiveness of DAEFR on both synthetic and real-world datasets, demonstrating its superior performance in restoring facial details.Comment: Technical Repor