DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

Transferable Time-Series Forecasting under Causal Conditional Shift

This paper focuses on the problem of semi-supervised domain adaptation for time-series forecasting, which is underexplored in literatures, despite being often encountered in practice. Existing methods on time-series domain adaptation mainly follow the paradigm designed for the static data, which cannot handle domain-specific complex conditional dependencies raised by data offset, time lags, and variant data distributions. In order to address these challenges, we analyze variational conditional dependencies in time-series data and find that the causal structures are usually stable among domains, and further raise the causal conditional shift assumption. Enlightened by this assumption, we consider the causal generation process for time-series data and propose an end-to-end model for the semi-supervised domain adaptation problem on time-series forecasting. Our method can not only discover the Granger-Causal structures among cross-domain data but also address the cross-domain time-series forecasting problem with accurate and interpretable predicted results. We further theoretically analyze the superiority of the proposed method, where the generalization error on the target domain is bounded by the empirical risks and by the discrepancy between the causal structures from different domains. Experimental results on both synthetic and real data demonstrate the effectiveness of our method for the semi-supervised domain adaptation method on time-series forecasting

A Cabled Acoustic Telemetry System for Detecting and Tracking Juvenile Salmon: Part 2. Three-Dimensional Tracking and Passage Outcomes

In Part 1 of this paper, we presented the engineering design and instrumentation of the Juvenile Salmon Acoustic Telemetry System (JSATS) cabled system, a nonproprietary sensing technology developed by the U.S. Army Corps of Engineers, Portland District (Oregon, USA) to meet the needs for monitoring the survival of juvenile salmonids through the hydroelectric facilities within the Federal Columbia River Power System. Here in Part 2, we describe how the JSATS cabled system was employed as a reference sensor network for detecting and tracking juvenile salmon. Time-of-arrival data for valid detections on four hydrophones were used to solve for the three-dimensional (3D) position of fish surgically implanted with JSATS acoustic transmitters. Validation tests demonstrated high accuracy of 3D tracking up to 100 m upstream from the John Day Dam spillway. The along-dam component, used for assigning the route of fish passage, had the highest accuracy; the median errors ranged from 0.02 to 0.22 m, and root mean square errors ranged from 0.07 to 0.56 m at distances up to 100 m. For the 2008 case study at John Day Dam, the range for 3D tracking was more than 100 m upstream of the dam face where hydrophones were deployed, and detection and tracking probabilities of fish tagged with JSATS acoustic transmitters were higher than 98%. JSATS cabled systems have been successfully deployed on several major dams to acquire information for salmon protection and for development of more “fish-friendly” hydroelectric facilities

A Cabled Acoustic Telemetry System for Detecting and Tracking Juvenile Salmon: Part 1. Engineering Design and Instrumentation

In 2001 the U.S. Army Corps of Engineers, Portland District (OR, USA), started developing the Juvenile Salmon Acoustic Telemetry System, a nonproprietary sensing technology, to meet the needs for monitoring the survival of juvenile salmonids through eight large hydroelectric facilities within the Federal Columbia River Power System (FCRPS). Initial development focused on coded acoustic microtransmitters and autonomous receivers that could be deployed in open reaches of the river for detection of the juvenile salmonids implanted with microtransmitters as they passed the autonomous receiver arrays. In 2006, the Pacific Northwest National Laboratory began the development of an acoustic receiver system for deployment at hydropower facilities (cabled receiver) for detecting fish tagged with microtransmitters as well as tracking them in two or three dimensions for determining route of passage and behavior as the fish passed at the facility. The additional information on route of passage, combined with survival estimates, is used by the dam operators and managers to make structural and operational changes at the hydropower facilities to improve survival of fish as they pass the facilities through the FCRPS

Vaccinia Virus G8R Protein: A Structural Ortholog of Proliferating Cell Nuclear Antigen (PCNA)

BACKGROUND: Eukaryotic DNA replication involves the synthesis of both a DNA leading and lagging strand, the latter requiring several additional proteins including flap endonuclease (FEN-1) and proliferating cell nuclear antigen (PCNA) in order to remove RNA primers used in the synthesis of Okazaki fragments. Poxviruses are complex viruses (dsDNA genomes) that infect eukaryotes, but surprisingly little is known about the process of DNA replication. Given our previous results that the vaccinia virus (VACV) G5R protein may be structurally similar to a FEN-1-like protein and a recent finding that poxviruses encode a primase function, we undertook a series of in silico analyses to identify whether VACV also encodes a PCNA-like protein. RESULTS: An InterProScan of all VACV proteins using the JIPS software package was used to identify any PCNA-like proteins. The VACV G8R protein was identified as the only vaccinia protein that contained a PCNA-like sliding clamp motif. The VACV G8R protein plays a role in poxvirus late transcription and is known to interact with several other poxvirus proteins including itself. The secondary and tertiary structure of the VACV G8R protein was predicted and compared to the secondary and tertiary structure of both human and yeast PCNA proteins, and a high degree of similarity between all three proteins was noted. CONCLUSIONS: The structure of the VACV G8R protein is predicted to closely resemble the eukaryotic PCNA protein; it possesses several other features including a conserved ubiquitylation and SUMOylation site that suggest that, like its counterpart in T4 bacteriophage (gp45), it may function as a sliding clamp ushering transcription factors to RNA polymerase during late transcription

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

The chromatin remodelling enzymes SNF2H and SNF2L position nucleosomes adjacent to CTCF and other transcription

Within the genomes of metazoans, nucleosomes are highly organised adjacent to the binding sites for a subset of transcription factors. Here we have sought to investigate which chromatin remodelling enzymes are responsible for this. We find that the ATP-dependent chromatin remodelling enzyme SNF2H plays a major role organising arrays of nucleosomes adjacent to the binding sites for the architectural transcription factor CTCF sites and acts to promote CTCF binding. At many other factor binding sites SNF2H and the related enzyme SNF2L contribute to nucleosome organisation. The action of SNF2H at CTCF sites is functionally important as depletion of CTCF or SNF2H affects transcription of a common group of genes. This suggests that chromatin remodelling ATPase's most closely related to the Drosophila ISWI protein contribute to the function of many human gene regulatory elements

Identification of Mammalian Protein Quality Control Factors by High-Throughput Cellular Imaging

Protein Quality Control (PQC) pathways are essential to maintain the equilibrium between protein folding and the clearance of misfolded proteins. In order to discover novel human PQC factors, we developed a high-content, high-throughput cell-based assay to assess PQC activity. The assay is based on a fluorescently tagged, temperature sensitive PQC substrate and measures its degradation relative to a temperature insensitive internal control. In a targeted screen of 1591 siRNA genes involved in the Ubiquitin-Proteasome System (UPS) we identified 25 of the 33 genes encoding for 26S proteasome subunits and discovered several novel PQC factors. An unbiased genome-wide siRNA screen revealed the protein translation machinery, and in particular the EIF3 translation initiation complex, as a novel key modulator of misfolded protein stability. These results represent a comprehensive unbiased survey of human PQC components and establish an experimental tool for the discovery of genes that are required for the degradation of misfolded proteins under conditions of proteotoxic stress