The Supersonic Project: Shining Light on SIGOs - a New Formation Channel for Globular Clusters

Supersonically induced gas objects (SIGOs) with little to no dark matter component are predicted to exist in patches of the Universe with non-negligible relative velocity between baryons and the dark matter at the time of recombination. Using {\sc arepo} hydrodynamic simulations we find that the gas densities inside these objects are high enough to allow stars to form. An estimate of the luminosity of the first star clusters formed within these SIGOs suggests that they may be observed at high redshift using future HST and JWST observations. Furthermore, our simulations indicate that SIGOs lie in a distinct place in the luminosity-radius parameter space, which can be used observationally to distinguish SIGOs from dark-matter hosting gas systems. Finally, as a proof-of-concept, we model star formation before reionization and evolve these systems to current times. We find that SIGOs occupy a similar part of the magnitude-radius parameter space as globular clusters. These results suggest that SIGOs may be linked with present-day metal-poor local globular clusters. Since the relative velocity between the baryons and dark matter is coherent over a few Mpc scales, we predict that if this is the dominant mechanism for the formation of globular clusters, their abundance should vary significantly over these scales.Comment: 9 pages, 5 figures, submitted to ApJ

Executive orders are not a viable route around political gridlock

In his recent State of the Union address, President Obama told Congress that if they continued to obstruct rather than act, he would move forward wherever he could without them. One avenue for such initiatives could be the increased use of executive orders; however, it is unclear if the president can actually wield true power in this way. Using data on executive orders from 1947-2003, Fang-Yi Chiou and Lawrence S. Rothenberg examine whether and how the president’s supposedly independent actions are constrained by outside forces. They conclude the president cannot achieve true additional power through unilateral action and executive orders are unlikely to provide a means to work around a gridlocked political process

A Positioning Scheme Combining Location Tracking with Vision Assisting for Wireless Sensor Networks

This paper presents the performance of an adaptive location-estimation technique combining Kalman filtering (KF)with vision assisting for wireless sensor networks. For improving the accuracy of a location estimator, a KF procedureis employed at a mobile terminal to filter variations of the location estimate. Furthermore, using a vision-assistedcalibration technique, the proposed approach based on the normalized cross-correlation scheme is an accuracyenhancement procedure that effectively removes system errors causing uncertainty in real dynamic environments.Namely, according to the vision-assisted approach to extract the locations of the reference nodes as landmarks, a KFbasedapproach with the landmark information can calibrate the location estimation and reduce the corner effect of alocation-estimation system. In terms of the location accuracy estimated from the proposed approach, the experimentalresults demonstrate that more than 60 percent of the location estimates have error distances less than 1.4 meters in aZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, theproposed algorithm can achieve reasonably good performance

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR)

Decoherent Scattering of Light Particles in a D-Brane Background

We discuss the scattering of two light particles in a D-brane background. It is known that, if one light particle strikes the D brane at small impact parameter, quantum recoil effects induce entanglement entropy in both the excited D brane and the scattered particle. In this paper we compute the asymptotic `out' state of a second light particle scattering off the D brane at large impact parameter, showing that it also becomes mixed as a consequence of quantum D-brane recoil effects. We interpret this as a non-factorizing contribution to the superscattering operator S-dollar for the two light particles in a Liouville D-brane background, that appears when quantum D-brane excitations are taken into account.Comment: 18 pages LATEX, one figure (incorporated

On the unitarity of higher-dervative and nonlocal theories

We consider two simple models of higher-derivative and nonlocal quantu systems.It is shown that, contrary to some claims found in literature, they can be made unitary.Comment: 8 pages, no figure

Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation

Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics. Secondly, we introduce a pseudo-labelling loss, where we translate target data to source, label them by a source-domain network, and use the generated pseudo-labels to supervise the target-domain network. Our results show that this allows us to extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approaches, our approach yields substantial improvements, that consistently carry over to the semi-supervised and supervised learning settings

Protein and Carbohydrate Fractions of Feedstuffs in Taiwan

ABSTRACT: Various local and imported feedstuffs were analyzed for protein and carbohydrate using digestive kinetics. Protein was fractionated into non-protein nitrogen, fast degradable, intermediate degradable protein, slow degradable protein, and nondegradable and non-usable protein components. The carbohydrate was fractionated into non-fibrous carbohydrate (NFC) and fibrous carbohydrate (FC). NFC included the fast degradable and intermediate degradable components. FC included the slow degradable, the non-degradable and non-usable fractions. The local feedstuffs, i.e., corn showed a tendency to contain higher FC, lower NFC and higher cell wall protein with lower storage protein compared with similar material available from the United States.. The carbohydrate content and cell wall protein were higher in the locally manufactured soybean meal in comparison with that imported from the United States and this may be attributed to the lower crude protein (44 vs. 49%) and higher hull content in the local feedstuffs. The imported forage showed a trend towards a higher FC content than those available from locally. Alfalfa cub or alfalfa pellets however contained low FC. The differences observed in the protein fractions between the present data and those from the NRC and the previous reports can probably be explained by differences in cultivation, harvest season and maturity of the crop. It is concluded that the tabulated values of protein and carbohydrate fractions of the feedstuff therefore can only provided guideline

Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation

The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures