Note on a non-critical holographic model with a magnetic field

We consider a noncritical holographic model constructed from an intersecting brane configuration D4/$\bar{\rm{D4}}$-D4 with an external magnetic field. We investigate the influences of this magnetic field on strongly coupled dynamics by the gauge/gravity correspondence.Comment: 18 pages, references added and typos revise

Detecting variability in massive astronomical time-series data. II. Variable candidates in the Northern Sky Variability Survey

We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method, which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves having data points at more than 15 epochs as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight-dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated with IRAS, AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GALEX objects, as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database, which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10% of our entire NSVS sample. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSV

Detecting Variability in Massive Astronomical Time-series Data. II. Variable Candidates in the Northern Sky Variability Survey

We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method, which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves having data points at more than 15 epochs as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight-dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated with IRAS , AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GALEX objects, as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database, which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10% of our entire NSVS sample. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSVS.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98631/1/1538-3881_143_3_65.pd

Low Intensity Resistance Exercise Training with Blood Flow Restriction: Insight into Cardiovascular Function, and Skeletal Muscle Hypertrophy in Humans

Attenuated functional exercise capacity in elderly and diseased populations is a common problem, and stems primarily from physical inactivity. Decreased function and exercise capacity can be restored by maintaining muscular strength and mass, which are key factors in an independent and healthy life. Resistance exercise has been used to prevent muscle loss and improve muscular strength and mass. However, the intensities necessary for traditional resistance training to increase muscular strength and mass may be contraindicated for some at risk populations, such as diseased populations and the elderly. Therefore, an alternative exercise modality is required. Recently, blood flow restriction (BFR) with low intensity resistance exercise (LIRE) has been used for such special populations to improve their function and exercise capacity. Although BFR+LIRE has been intensively studied for a decade, a comprehensive review detailing the effects of BFR+LIRE on both skeletal muscle and vascular function is not available. Therefore, the purpose of this review is to discuss previous studies documenting the effects of BFR+LIRE on hormonal and transcriptional factors in muscle hypertrophy and vascular function, including changes in hemodynamics, and endothelial function

Demand Layering for Real-Time DNN Inference with Minimized Memory Usage

When executing a deep neural network (DNN), its model parameters are loaded into GPU memory before execution, incurring a significant GPU memory burden. There are studies that reduce GPU memory usage by exploiting CPU memory as a swap device. However, this approach is not applicable in most embedded systems with integrated GPUs where CPU and GPU share a common memory. In this regard, we present Demand Layering, which employs a fast solid-state drive (SSD) as a co-running partner of a GPU and exploits the layer-by-layer execution of DNNs. In our approach, a DNN is loaded and executed in a layer-by-layer manner, minimizing the memory usage to the order of a single layer. Also, we developed a pipeline architecture that hides most additional delays caused by the interleaved parameter loadings alongside layer executions. Our implementation shows a 96.5% memory reduction with just 14.8% delay overhead on average for representative DNNs. Furthermore, by exploiting the memory-delay tradeoff, near-zero delay overhead (under 1 ms) can be achieved with a slightly increased memory usage (still an 88.4% reduction), showing the great potential of Demand Layering.Comment: 14 pages, 16 figures. Accepted to the 43rd IEEE Real-Time Systems Symposium (RTSS), 202