10,000 publications from this institution
Astrophysical transients have been observed for millennia and have shaped our most basic assumptions about the Universe. In the last century, systematic searches have grown from detecting handfuls of transients per year to over 7000 in 2018 alone. As these searches have matured, we have discovered both large samples of "normal" classes and new, rare classes. Recently, a transient was the first object observed in both gravitational waves and light. Ground-based observatories, including LSST, will discover thousands of transients in the optical, but these facilities will not provide the high-fidelity near-infrared (NIR) photometry and high-resolution imaging of a space-based observatory. WFIRST can fill this gap. With its survey designed to measure the expansion history of the Universe with Type Ia supernovae, WFIRST will also discover and monitor thousands of other transients in the NIR, revealing the physics for these high-energy events. Small-scale GO programs, either as a supplement to the planned survey or as specific target-of-opportunity observations, would significantly expand the scope of transient science that can be studied with WFIRST.
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We have examined ROSAT soft X-ray observations of a complete, distance-limited sample of Seyfert and LINER galaxies. X-ray data are available for 46 out of 60 such objects which lie within a hemisphere of radius 18 Mpc. We have constructed radial profiles of the nuclear sources in order to characterize their spatial extent and, in some cases, to help constrain the amount of flux associated with a nuclear point source. PSPC data from ROSAT have been used to explore the spectral characteristics of the objects with sufficient numbers of detected counts. Based on the typical spectral parameters of these sources, we have estimated the luminosities of the weaker sources in the sample. We then explore the relationship between the soft X-ray and H alpha luminosities of the observed objects; these quantities are correlated for higher-luminosity AGNs. We find a weak correlation at low luminosities as well, and have used this relationship to predict L_X for the 14 objects in our sample that lack X-ray data. Using the results of the spatial and spectral analyses, we have compared the X-ray properties of Seyferts and LINERs, finding no striking differences between the two classes of objects. However, both types of objects often exhibit significant amounts of extended emission, which could minimize the appearance of differences in their nuclear properties. The soft X-ray characteristics of the type 1 and type 2 active galaxies in the sample are also discussed. We then compute the local X-ray volume emissivity of low-luminosity Seyferts and LINERs and investigate their contribution to the cosmic X-ray background. The 0.5-2.0 keV volume emissivity of 2.2e38 ergs/s/Mpc^3 we obtain for our sample suggests that low-luminosity AGNs produce at least 9% of the soft X-ray background.
Today, big and small organizations alike collect huge amounts of data, and they do so with one goal in mind: extract "value" through sophisticated exploratory analysis, and use it as the basis to make decisions as varied as personalized treatment and ad targeting. Unfortunately, existing data analytics tools are slow in answering queries, as they typically require to sift through huge amounts of data stored on disk, and are even less suitable for complex computations, such as machine learning algorithms. These limitations leave the potential of extracting value of big data unfulfilled. To address this challenge, we are developing Berkeley Data Analytics Stack (BDAS), an open source data analytics stack that provides interactive response times for complex computations on massive data. To achieve this goal, BDAS supports efficient, large-scale in-memory data processing, and allows users and applications to trade between query accuracy, time, and cost. In this talk, I'll present the architecture, challenges, results, and our experience with developing BDAS, with a focus on Apache Spark, an in-memory cluster computing engine that provides support for a variety of workloads, including batch, streaming, and iterative computations. In a relatively short time, Spark has become the most active big data project in the open source community, and is already being used by over one hundred of companies and research institutions.
This article examines the workings and effects of the penalization of poverty in urban Brazil at century's turn to uncover the deep logic of punitive containment as state strategy for the management of dispossessed and dishonored populations in the polarizing city in the age of triumphant neoliberalism. It shows how ramifying criminal violence (fed by extreme inequality and mass poverty), class and color discrimination in judicial processing, unchecked police brutality, and the catastrophic condition and chaotic operation of the carceral system combine to make the aggressive deployment of the penal apparatus in Brazil a surefire recipe for further disorder and disrespect for the law at the bottom of the urban hierarchy and steers the country into an institutional impasse. The policy of punitive containment pursued by political elites as a complement to the deregulation of the economy in the 1990s leads from the penalization to the militarization of urban marginality, under which residents of the declining favelas are treated as virtual enemies of the nation, tenuous trust in public institutions is undermined, and the spiral of violence accelerated. Brazil thus serves as a historical revelator of the full consequences of the penal disposal of the human detritus of a society swamped by social and physical insecurity. Drawing parallels between penal activity in the Brazilian and the U.S. metropolis further reveals that the neighborhoods of urban relegation wherein the marginal and stigmatized fractions of the postindustrial working class concentrate are the prime targets and proving ground upon which the neoliberal penal state is concretely being assembled, tried, and tested. Their study is therefore of urgent interest to analysts of international politics and state power at the dawn of the twenty-first century.
Excessive loading of fine sediments into western rivers has degraded spawning and rearing habitat for salmonids, and contributed substantially to their declines. Impacts on salmon redds have been studied extensively, but effects on juvenile rearing are less well documented. In a field experiment in the South Fork Eel River, we investigated the impacts of deposited fine sediment on juvenile steelhead. Our experimental design allowed us to isolate the effects of fine bed sediments from other covarying factors and to reveal the mechanisms of their effects. Increasing levels of embeddedness with deposited fine sediment (from zero to 100%) decreased growth and survival of juvenile steelhead trout. The nearly linear decreases in growth resulted from decreased food availability and metabolic costs of increased activity and intraspecific aggression. The invertebrate community changed from one of more available prey to one of unavailable burrowing taxa with higher levels of deposited fine sediment. Steelhead in more heavily embedded channels showed more continuous movement and aggression and higher incidence of injury. This study shows a direct impact of riverbed composition on salmonid rearing success, which has been identified as a life history bottleneckby models informing efforts to recover these populations.
