The radiation emitted from axion dark matter in a homogeneous magnetic field, and possibilities for detection

We study the direct radiation excited by oscillating axion (or axion-like particle) dark matter in a homogenous magnetic field and its detection scheme. We concretely derive the analytical expression of the axion-induced radiated power for a cylindrical uniform magnetic field. In the long wave limit, the radiation power is proportional to the square of the B-field volume and the axion mass $m_a$, whereas it oscillate as approaching the short wave limit and the peak powers are proportional to the side area of the cylindrical magnetic field and $m_a^{-2}$. The maximum power locates at mass $m_a\sim\frac{3\pi}{4R}$ for fixed radius $R$. Based on this characteristic of the power, we discuss a scheme to detect the axions in the mass range $1-10^4$\,neV, where four detectors of different bandwidths surround the B-field. The expected sensitivity for $m_a\lesssim1\,\mu$eV under typical-parameter values can far exceed the existing constraints.Comment: 10 pages, 9 figures, comments welcome

Suppressed star formation in circumnuclear regions in Seyfert galaxies

Feedback from black hole activity is widely believed to play a key role in regulating star formation and black hole growth. A long-standing issue is the relation between the star formation and fueling the supermassive black holes in active galactic nuclei (AGNs). We compile a sample of 57 Seyfert galaxies to tackle this issue. We estimate the surface densities of gas and star formation rates in circumnuclear regions (CNRs). Comparing with the well-known Kennicutt-Schmidt (K-S) law, we find that the star formation rates in CNRs of most Seyfert galaxies are suppressed in this sample. Feedback is suggested to explain the suppressed star formation rates.Comment: 1 color figure and 1 table. ApJ Letters in pres

N-glycosylation in the Protease Domain of Trypsin-like Serine Proteases Mediates Calnexin-assisted Protein Folding

Trypsin-like serine proteases are essential in physiological processes. Studies have shown that N-glycans are important for serine protease expression and secretion, but the underlying mechanisms are poorly understood. Here, we report a common mechanism of N-glycosylation in the protease domains of corin, enteropeptidase and prothrombin in calnexin-mediated glycoprotein folding and extracellular expression. This mechanism, which is independent of calreticulin and operates in a domain-autonomous manner, involves two steps: direct calnexin binding to target proteins and subsequent calnexin binding to monoglucosylated N-glycans. Elimination of N-glycosylation sites in the protease domains of corin, enteropeptidase and prothrombin inhibits corin and enteropeptidase cell surface expression and prothrombin secretion in transfected HEK293 cells. Similarly, knocking down calnexin expression in cultured cardiomyocytes and hepatocytes reduced corin cell surface expression and prothrombin secretion, respectively. Our results suggest that this may be a general mechanism in the trypsin-like serine proteases with N-glycosylation sites in their protease domains

Strategic Optimization of Water Reuse in Wafer Fabs via Multi-Constraint Linear Programming Technique

The risk of water shortage has been posing as a threat to water demanding industries in Taiwan, including the high-tech industries where ultrapure water is needed for the production of microchips. Such risks are especially unpredictable in the age of climate change, where more frequent extreme climate events such as prolonged droughts have sent these industries scrambling for securing water supply at a very high cost. The national policy also mandates strict water recycling standards for these high-tech plants, while the risk of water supply shortage also forces the industry to be water-conscious. However, most plants set their water recycling strategies based on experience or ‘‘rules of thumb” practices, without implementing optimization tools that can help making decisions in a more scientific approach. In this study we applied linear programming technique to optimize the water recovery path for a microchip assembly plant. A water balance diagram was formulated and completed to determine the existing water recycling performance, and the data was converted to a water flow network. The water flow network was then derived with a mathematical model to formulate a linear optimization problem. The proposed linear programming model is composed of mass balance constraints, unit specification constraints, capacity constraints as well as water quality constraints (discharge limits). The linear programming method was effectively applied to improve the efficiency of water reuse. With the installation of the regeneration units, an increase of 40.1% in the volume of reused water was predicted. The results from water cost structure also indicated that, at higher water tariff, water reuses through reclaiming and generating spent effluents can alleviate the overall water consumption costs

Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. {2)} Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. {3)} Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained $97\%$ -- $99\%$ performance of its fully-supervised version with only ten annotated points per image.Comment: 9 pages, 8 figure

Broadly conserved roles of TMEM131 family proteins in intracellular collagen assembly and secretory cargo trafficking.

Collagen is the most abundant protein in animals. Its dysregulation contributes to aging and many human disorders, including pathological tissue fibrosis in major organs. How premature collagen proteins in the endoplasmic reticulum (ER) assemble and route for secretion remains molecularly undefined. From an RNA interference screen, we identified an uncharacterized Caenorhabditis elegans gene tmem-131, deficiency of which impairs collagen production and activates ER stress response. We find that amino termini of human TMEM131 contain bacterial PapD chaperone-like domains, which recruit premature collagen monomers for proper assembly and secretion. Carboxy termini of TMEM131 interact with TRAPPC8, a component of the TRAPP tethering complex, to drive collagen cargo trafficking from ER to the Golgi. We provide evidence that previously undescribed roles of TMEM131 in collagen recruitment and secretion are evolutionarily conserved in C. elegans, Drosophila, and humans

A Real-Time Feature Indexing System on Live Video Streams

Most of the existing video storage systems rely on offline processing to support the feature-based indexing on video streams. The feature-based indexing technique provides an effec- tive way for users to search video content through visual features, such as object categories (e.g., cars and persons). However, due to the reliance on offline processing, video streams along with their captured features cannot be searchable immediately after video streams are recorded. According to our investigation, buffering and storing live video steams are more time-consuming than the YOLO v3 object detector. Such observation motivates us to propose a real-time feature indexing (RTFI) system to enable instantaneous feature-based indexing on live video streams after video streams are captured and processed through object detectors. RTFI achieves its real-time goal via incorporating the novel design of metadata structure and data placement, the capability of modern object detector (i.e., YOLO v3), and the deduplication techniques to avoid storing repetitive video content. Notably, RTFI is the first system design for realizing real-time feature-based indexing on live video streams. RTFI is implemented on a Linux server and can improve the system throughput by upto 10.60x, compared with the base system without the proposed design. In addition, RTFI is able to make the video content searchable within 20 milliseconds for 10 live video streams after the video content is received by the proposed system, excluding the network transfer latency