Detecting Ca2+ sparks on stationary and varying baselines.

Studies concerning the physiological significance of Ca2+ sparks often depend on the detection and measurement of large populations of events in noisy microscopy images. Automated detection methods have been developed to quickly and objectively distinguish potential sparks from noise artifacts. However, previously described algorithms are not suited to the reliable detection of sparks in images where the local baseline fluorescence and noise properties can vary significantly, and risk introducing additional bias when applied to such data sets. Here, we describe a new, conceptually straightforward approach to spark detection in linescans that addresses this issue by combining variance stabilization with local baseline subtraction. We also show that in addition to greatly increasing the range of images in which sparks can be automatically detected, the use of a more accurate noise model enables our algorithm to achieve similar detection sensitivities with fewer false positives than previous approaches when applied both to synthetic and experimental data sets. We propose, therefore, that it might be a useful tool for improving the reliability and objectivity of spark analysis in general, and describe how it might be further optimized for specific applications.

Changes in muscle sympathetic nerve activity and vascular responses evoked in the spinotrapezius muscle of the rat by systemic hypoxia.

Responses evoked in muscle sympathetic nerve activity (MSNA) by systemic hypoxia have received relatively little attention. Moreover, MSNA is generally identified from firing characteristics in fibres supplying whole limbs: their actual destination is not determined. We aimed to address these limitations by using a novel preparation of spinotrapezius muscle in anaesthetised rats. By using focal recording electrodes, multi-unit and discriminated single unit activity were recorded from the surface of arterial vessels. This had cardiac- and respiratory-related activities expected of MSNA, and was increased by baroreceptor unloading, decreased by baroreceptor stimulation and abolished by autonomic ganglion blockade. Progressive, graded hypoxia (breathing sequentially 12, 10, 8% O2 for 2 min each) evoked graded increases in MSNA. In single units, mean firing frequency increased from 0.2 ± 0.04 in 21% O2 to 0.62 ± 0.14 Hz in 8% O2, while instantaneous frequencies ranged from 0.04–6 Hz in 21% O2 to 0.09–20 Hz in 8% O2. Concomitantly, arterial pressure (ABP), fell and heart rate (HR) and respiratory frequency (RF) increased progressively, while spinotrapezius vascular resistance (SVR) decreased (Spinotrapezius blood flow/ABP), indicating muscle vasodilatation. During 8% O2 for 10 min, the falls in ABP and SVR were maintained, but RF, HR and MSNA waned towards baselines from the second to the tenth minute. Thus, we directly show that MSNA increases during systemic hypoxia to an extent that is mainly determined by the increases in peripheral chemoreceptor stimulation and respiratory drive, but its vasoconstrictor effects on muscle vasculature are largely blunted by local dilator influences, despite high instantaneous frequencies in single fibres.

Detection of Voigt Spectral Line Profiles of Hydrogen Radio Recombination Lines toward Sagittarius B2(N)

We report the detection of Voigt spectral line profiles of radio recombination lines (RRLs) toward Sagittarius B2(N) with the 100 m Green Bank Telescope (GBT). At radio wavelengths, astronomical spectra are highly populated with RRLs, which serve as ideal probes of the physical conditions in molecular cloud complexes. An analysis of the Hn alpha lines presented herein shows that RRLs of higher principal quantum number (n > 90) are generally divergent from their expected Gaussian profiles and, moreover, are well described by their respective Voigt profiles. This is in agreement with the theory that spectral lines experience pressure broadening as a result of electron collisions at lower radio frequencies. Given the inherent technical difficulties regarding the detection and profiling of true RRL wing spans and shapes, it is crucial that the observing instrumentation produce flat baselines as well as high-sensitivity, high-resolution data. The GBT has demonstrated its capabilities regarding all of these aspects, and we believe that future observations of RRL emission via the GBT will be crucial toward advancing our knowledge of the larger-scale extended structures of ionized gas in the interstellar medium (ISM).

Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding

In this paper, we introduce an application of matrix factorization to produce corpus-derived, distributional
models of semantics that demonstrate cognitive plausibility. We find that word representations
learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse,
effective, and highly interpretable. To the best of our knowledge, this is the first approach which
yields semantic representation of words satisfying these three desirable properties. Though extensive
experimental evaluations on multiple real-world tasks and datasets, we demonstrate the superiority
of semantic models learned by NNSE over other state-of-the-art baselines.

ALMA hints at the presence of two companions in the disk around HD 100546

HD 100546 is a well-studied Herbig Be star-disk system that likely hosts a close-in companion with compelling observational evidence for an embedded protoplanet at 68 AU. We present ALMA observations of the HD 100546 disk which resolve, for the first time, the gas and dust structure at (sub)mm wavelengths. The CO emission (at 345.795 GHz) originates from an extensive molecular disk (390 AU in radius) whereas the continuum emission is more compact (230 AU in radius) suggesting radial drift of the mm-sized grains. The CO emission is similar in extent to scattered light images indicating well-mixed gas and um-sized grains in the disk atmosphere. Assuming an azimuthally-symmetric disk, the continuum visibilities at long baselines (> 100 klambda) are reproduced by a compact ring with a width of 21 AU centered at 26 AU. An outer component is required to fit the short baselines: assuming a flat brightness distribution, the best-fit model is a ring with a width of 75 AU centered at 190 AU. The influence of a companion and protoplanet on the dust evolution is investigated. The companion at 10 AU facilitates the accumulation of mm-sized grains within a compact ring, ~20-30 AU, by ~10 Myr. The injection of a protoplanet at 1 Myr hastens the ring formation (~1.2 Myr) and also triggers the development of an outer ring (~100-200 AU). These observations provide additional evidence for the presence of a close-in companion and hint at dynamical clearing by a protoplanet at 68 AU.

ALEA: Fine-Grain Energy Profiling with Basic Block Sampling

Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand how workloads affect it. Significant recent research effort has targeted direct power measurements on production computing systems using on-board sensors or external instruments. These direct methods have in turn guided studies of software techniques to reduce energy consumption via workload allocation and scaling. Unfortunately, direct energy measurements are hampered by the low power sampling frequency of power sensors. The coarse granularity of power sensing limits our understanding of how power is allocated in systems and our ability to optimize energy efficiency via workload allocation.
We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.

Temporal Spatial-Keyword Top-k publish/subscribe

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date tweets such that their locations are close to a user specified location and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo-textual objects (e.g., geo-tagged Tweets) for the query. The TaSK query takes into account text relevance, spatial proximity, and recency of geo-textual objects in evaluating its relevance with a geo-textual object. We propose a novel solution to efficiently process a large number of TaSK queries over a stream of geotextual objects. We evaluate the efficiency of our approach on two real-world datasets and the experimental results show that our solution is able to achieve a reduction of the processing time by 70-80% compared with two baselines.

Fast Mining of Interesting Phrases from Subsets of Text Corpora

We address the problem of mining interesting phrases from subsets of a text corpus where the subset is specified using a set of features such as keywords that form a query. Previous algorithms for the problem have proposed solutions that involve sifting through a phrase dictionary based index or a document-based index where the solution is linear in either the phrase dictionary size or the size of the document subset. We propose the usage of an independence assumption between query keywords given the top correlated phrases, wherein the pre-processing could be reduced to discovering phrases from among the top phrases per each feature in the query. We then outline an indexing mechanism where per-keyword phrase lists are stored either in disk or memory, so that popular aggregation algorithms such as No Random Access and Sort-merge Join may be adapted to do the scoring at real-time to identify the top interesting phrases. Though such an approach is expected to be approximate, we empirically illustrate that very high accuracies (of over 90%) are achieved against the results of exact algorithms. Due to the simplified list-aggregation, we are also able to provide response times that are orders of magnitude better than state-of-the-art algorithms. Interestingly, our disk-based approach outperforms the in-memory baselines by up to hundred times and sometimes more, confirming the superiority of the proposed method.