Linxin Wang

Fine-grained traffic flow measurement, which provides useful information for network management tasks and security analysis, can be challenging to obtain due to monitoring resource constraints. The alternate approach of inferring flow statistics from partial measurement data has to be robust against dynamic temporal/spatial fluctuations of network traffic. In this paper, we propose an intelligent Traffic Aggregation and Measurement Paradigm, which partitions TCAM entries of switches/routers into two parts to: 1) optimally aggregate part of incoming flows for aggregate measurements, and 2) de-aggregate and directly measure the most informative flows for per-flow measurements. iSTAMP then processes these aggregate and per-flow measurements to effectively estimate network flows using a variety of optimization techniques. With the advent of Software-Defined-Networking, such real-time rule configuration can be achieved via OpenFlow or other similar SDN APIs. We first show how to design the optimal aggregation matrix for minimizing the flow-size estimation error. Moreover, we propose a method for designing an efficient-compressive flow aggregation matrix under hard resource constraints of limited TCAM sizes. In addition, we propose an intelligent Multi-Armed Bandit based algorithm to adaptively sample the most 'rewarding' flows, whose accurate measurements have the highest impact on the overall flow measurement and estimation performance. We evaluate the performance of iSTAMP using real traffic traces from a variety of network environments and by considering two applications: traffic matrix estimation and heavy hitter detection. Also, we have implemented a prototype of iSTAMP and demonstrated its feasibility and effectiveness in Mininet environment. © 2014 IEEE.

We present new results on the Hubble diagram of distant type Ia supernovae segregated according to the type of host galaxy. This makes it possible to check earlier evidence for a cosmological constant by explicitly comparing SNe residing in galaxies likely to contain negligible dust with the larger sample. The cosmological parameters derived from these SNe Ia hosted by presumed dust-free early-type galaxies support earlier claims for a cosmological constant, which we demonstrate at ≃5σ significance, and the internal extinction implied is small even for late-type systems. Thus, our data demonstrate that host galaxy extinction is unlikely to systematically dim distant SNe Ia in a manner that would produce a spurious cosmological constant. Our analysis is based on new Hubble Space Telescope STIS 'snapshot' images and Keck-II echellette spectroscopy at the locations of the SNe, spanning the redshift range 0 < z < 0.8. Selecting from the sample discovered by the Supernova Cosmology Project, we classify the host galaxies of 39 distant SNe using the combination of STIS imaging, Keck spectroscopy and ground-based broad-band photometry. The distant data are analysed in comparison with a low-redshift sample of 25 SNe Ia re-calibrated according to the precepts of the SCP. The scatter observed in the SNe Ia Hubble diagrams correlates closely with host galaxy morphology. We find this scatter is smallest for SNe Ia occurring in early-type hosts and largest for those occurring in late-type galaxies. Moreover, SNe residing in late-type hosts appear ≃0.14 ± 0.09 mag fainter in their light-curve-width-corrected luminosity than those in early-type hosts, as expected if a modest amount of dust extinction is a contributing factor. As in previous studies, these results are broadly independent of whether corrections based upon SN light-curve shapes are performed. We also use our high-redshift data set to search for morphological dependences in the SNe light curves, as are sometimes seen in lowredshift samples. No significant trends are found, possibly because the range of light-curve widths is too limited.

We present measurements of Ωm and Ω Λ from a blind analysis of 21 high-redshift supemovae using a new technique for fitting the multicolor light curves of Type la supernovae, first introduced by Wang and coworkers. CMAGIC takes advantage of the remarkably simple behavior of Type la supernovae on color-magnitude diagrams and has several advantages over current techniques based on maximum magnitudes. Among these are a reduced sensitivity to host galaxy dust extinction, a shallower luminosity-width relation, and the relative simplicity of the fitting procedure. This allows us to provide a cross-check of previous supernova cosmology results, despite the fact that current data sets were not observed in a manner optimized for CMAGIC. We describe the details of our novel blindness procedure, which is designed to prevent experimenter bias. The data are broadly consistent with the picture of an accelerating universe and agree with a flat universe within 1.7 σ, including systematics. We also compare the CMAGIC results directly with those of a maximum magnitude fit to the same supernovae, finding that CMAGIC favors more acceleration at the 1.6 σ level, including systematics and the correlation between the two measurements. A fit for w assuming a flat universe yields a value that is consistent with a cosmological constant within 1.2 σ. © 2006. The American Astronomical Society. All rights reserved.

Immune cells, including natural killer cells, recognize transformed cells and eliminate them in a process termed immunosurveillance. It is thought that tumor cells evade immunosurveillance by shedding membrane ligands that bind to the NKG2D-activating receptor on NK cells and/or Tcells, and desensitize these cells. In contrast, we show that in mice, a shed form of MULT1, a high-affinity NKG2D ligand, causes NK cell activation and tumor rejection. Recombinant soluble MULT1 stimulated tumor rejection in mice. Soluble MULT1 functions, at least in part, by competitively reversing a global desensitization of NK cells imposed by engagement of membrane NKG2D ligands on tumor-associated cells, such as myeloid cells. The results overturn conventional wisdom that soluble ligands are always inhibitory and suggest a new approach for cancer immunotherapy.

© 2015 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.We use observedUVthrough near-IR spectra to examine whether SN 2011fe can be understood in the framework of Branch-normal Type Ia supernovae and to examine its individual peculiarities. As a benchmark, we use a delayed-detonationmodel with a progenitormetallicity of Z⊙/20. We study the sensitivity of features to variations in progenitor metallicity, the outer density profile, and the distribution of radioactive nickel. The effect of metallicity variations in the progenitor have a relatively small effect on the synthetic spectra. We also find that the abundance stratification of SN 2011fe resembles closely that of a delayed-detonation model with a transition density that has been fit to other Branch-normal SNe Ia. At early times, the model photosphere is formed in material with velocities that are too high, indicating that the photosphere recedes too slowly or that SN 2011fe has a lower specific energy in the outer ≈0.1 M⊙ than does the model. We discuss several explanations for the discrepancies. Finally, we examine variations in both the spectral energy distribution and in the colours due to variations in the progenitor metallicity, which suggests that colours are only weak indicators for the progenitor metallicity, in the particular explosion model that we have studied. We do find that the flux in the U band is significantly higher at maximum light in the solar metallicity model than in the lower metallicity model and the lower metallicity model much better matches the observed spectrum.

In recent years, roles of the immune system in immune surveillance of cancer have been explored using a variety of approaches. The roles of the adaptive immune system have been a major emphasis, but increasing evidence supports a role for innate immune effector cells such as natural killer cells in tumor surveillance. Here, we discuss some of the evidence for roles in tumor surveillance of innate immune cells. In particular, we focus on NK cells and other immune cells that express germline-encoded receptors, often labeled NK receptors. The impact of these receptors and the cells that express them on tumor suppression is summarized. We discuss in detail some of the pathways and events in tumor cells that induce or upregulate cell-surface expression of the ligands for these receptors, and the logic of how those pathways serve to identify malignant, or potentially malignant cells. How tumors often evade tumor suppression mediated by innate killer cells is another major subject of the review. We end with a discussion on some of the implications of the various findings with respect to possible therapeutic approaches. © 2014 Elsevier Inc.

The NKG2D activating receptor has been implicated in numerous autoimmune diseases. We tested the role of NKG2D in models of autoimmunity and inflammation using NKG2D knockout mice and antibody blockade experiments. The severity of experimental autoimmune encephalitis was decreased in NKG2D-deficient mice when the disease was induced with a limiting antigen dose, but unchanged with an optimal antigen dose. Surprisingly, however, NKG2D deficiency had no detectable effect in several other models, including two models of type 1 diabetes, and a model of intestinal inflammation induced by poly. NKG2D antibody blockade in normal mice also failed to inhibit disease in the NOD diabetes model or the intestinal inflammation model. Published evidence using NKG2D knockout mice demonstrated a role for NKG2D in mouse models of atherosclerosis and liver inflammation, as well as in chronic obstructive pulmonary disease. Therefore, our results suggest that NKG2D plays selective roles in inflammatory diseases. © 2013 Elsevier Inc.

© 2015 Macmillan Publishers Limited All rights reserved. Genome-wide gene expression measurements have enabled comprehensive studies that integrate the changes of gene expression and phenotypic information to uncover their novel associations. Here we reported the association analysis between psychophysical phenotypes and genome-wide gene expression changes in human adaptation to one of the most extreme climates on Earth, the Antarctic Dome Argus. Dome A is the highest ice feature in Antarctica, and may be the coldest, driest and windiest location on earth. It is considered unapproachable due to its hostile environment. In 2007, a Chinese team of 17 male explorers made the expedition to Dome A for scientific investigation. Overall, 133 psychophysical phenotypes were recorded, and genome-wide gene expression profiles from the blood samples of the explorers were measured before their departure and upon their arrival at Dome A. We found that mood disturbances, including tension, depression, anger and fatigue, had a strong, positive, linear relationship with the level of a male sex hormone, testosterone, using the Pearson correlation coefficient analysis. We also demonstrated that significantly lowest-level Gene Ontology groups in changes of gene expression in blood cells with erythrocyte removal were consistent with the adaptation of the psychophysical characteristics. Interestingly, we discovered a list of genes that were strongly related to significant phenotypes using phenotype and gene expression PCC analysis. Importantly, among the 70 genes that were identified, most were significantly related to mood disturbances, where 42 genes have been reported in the literature mining, suggesting that the other 28 genes were likely novel genes involved in the mood disturbance mechanism. Taken together, our association analysis provides a reliable method to uncover novel genes and mechanisms related to phenotypes, although further studies are needed.