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Register a new userToward understanding rich superclusters
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We present a morphological study of the two richest superclusters from the 2dF Galaxy Redshift Survey (SCL126, the Sloan Great Wall, and SCL9, the Sculptor supercluster). We use Minkowski functionals, shapefinders, and galaxy group information to study the substructure of these superclusters as formed by different populations of galaxies. We compare the properties of grouped and isolated galaxies in the core region and in the outskirts of superclusters. The fourth Minkowski functional $V_3$ and the morphological signature $K_1$- $K_2$ show a crossover from low-density morphology (outskirts of supercluster) to high-density morphology (core of supercluster) at mass fraction $m_f approx 0.7$. The galaxy content and the morphology of the galaxy populations in supercluster cores and outskirts is different. The core regions contain a larger fraction of early type, red galaxies, and richer groups than the outskirts of superclusters. In the core and outskirt regions the fine structure of the two prominent superclusters as delineated by galaxies from different populations also differs. Our results suggest that both local (group/cluster) and global (supercluster) environments are important in forming galaxy morphologies and colors (and determining the star formation activity). The differences between the superclusters indicate that these superclusters have different evolutional histories (Abridged).
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The composite nature of baryons manifests itself in the existence of a rich spectrum of excited states, in particular in the important mass region 1-2 GeV for the light-flavoured baryons. The properties of these resonances can be identified by systematic investigations using electromagnetic and strong probes, primarily with beams of electrons, photons, and pions. After decades of research, the fundamental degrees of freedom underlying the baryon excitation spectrum are still poorly understood. The search for hitherto undiscovered but predicted resonances continues at many laboratories around the world. Recent results from photo- and electroproduction experiments provide intriguing indications for new states and shed light on the structure of some of the known nucleon excitations. The continuing study of available data sets with consideration of new observables and improved analysis tools have also called into question some of the earlier findings in baryon spectroscopy. Other breakthrough measurements have been performed in the heavy-baryon sector, which has seen a fruitful period in recent years, in particular at the B factories and the Tevatron. First results from the LHC indicate rapid progress in the field of bottom baryons. In this review, we discuss the recent experimental progress and give an overview of theoretical approaches.
We study the distribution of galaxies and galaxy clusters in a 10^deg x 6^deg field in the Aquarius region. In addition to 63 clusters in the literature, we have found 39 new candidate clusters using a matched-filter technique and a counts-in-cells analysis. From redshift measurements of galaxies in the direction of these cluster candidates, we present new mean redshifts for 31 previously unobserved clusters, while improved mean redshifts are presented for 35 other systems. About 45% of the projected density enhancements are due to the superposition of clusters and/or groups of galaxies along the line of sight, but we could confirm for 72% of the cases that the candidates are real physical associations similar to the ones classified as rich galaxy clusters. On the other hand, the contamination due to galaxies not belonging to any concentration or located only in small groups along the line of sight is ~ 10%. Using a percolation radius of 10 h^{-1} Mpc (spatial density contrast of about 10), we detect two superclusters of galaxies in Aquarius, at z = 0.086 and at z = 0.112, respectively with 5 and 14 clusters. The latter supercluster may represent a space overdensity of about 160 times the average cluster density as measured from the Abell et al. (1989) cluster catalog, and is possibly connected to a 40 h^{-1} Mpc filament from z ~ 0.11 to 0.14.
We present CO observations toward a sample of six HI-rich Ultra-diffuse galaxies (UDGs) as well as one UDG (VLSB-A) in the Virgo Cluster with the IRAM 30-m telescope. CO 1-0 is marginally detected at 4sigma level in AGC122966, as the first detection of CO emission in UDGs. We estimate upper limits of molecular mass in other galaxies from the non-detection of CO lines. These upper limits and the marginal CO detection in AGC122966 indicate low mass ratios between molecular and atomic gas masses. With the star formation efficiency derived from the molecular gas, we suggest that the inefficiency of star formation in such HI-rich UDGs is likely caused by the low efficiency in converting molecules from atomic gas, instead of low efficiency in forming stars from molecular gas.
Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. The human ability to understand and communicate about situations emerges gradually from experience and depends on domain-general principles of biological neural networks: connection-based learning, distributed representation, and context-sensitive, mutual constraint satisfaction-based processing. Current artificial language processing systems rely on the same domain general principles, embodied in artificial neural networks. Indeed, recent progress in this field depends on emph{query-based attention}, which extends the ability of these systems to exploit context and has contributed to remarkable breakthroughs. Nevertheless, most current models focus exclusively on language-internal tasks, limiting their ability to perform tasks that depend on understanding situations. These systems also lack memory for the contents of prior situations outside of a fixed contextual span. We describe the organization of the brains distributed understanding system, which includes a fast learning system that addresses the memory problem. We sketch a framework for future models of understanding drawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based attention. We highlight relevant current directions and consider further developments needed to fully capture human-level language understanding in a computational system.
In this paper, we describe novel components for extracting clinically relevant information from medical conversations which will be available as Google APIs. We describe a transformer-based Recurrent Neural Network Transducer (RNN-T) model tailored for long-form audio, which can produce rich transcriptions including speaker segmentation, speaker role labeling, punctuation and capitalization. On a representative test set, we compare performance of RNN-T models with different encoders, units and streaming constraints. Our transformer-based streaming model performs at about 20% WER on the ASR task, 6% WDER on the diarization task, 43% SER on periods, 52% SER on commas, 43% SER on question marks and 30% SER on capitalization. Our recognizer is paired with a confidence model that utilizes both acoustic and lexical features from the recognizer. The model performs at about 0.37 NCE. Finally, we describe a RNN-T based tagging model. The performance of the model depends on the ontologies, with F-scores of 0.90 for medications, 0.76 for symptoms, 0.75 for conditions, 0.76 for diagnosis, and 0.61 for treatments. While there is still room for improvement, our results suggest that these models are sufficiently accurate for practical applications.
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