Mode competition in superradiant scattering of matter waves

Superradiant Rayleigh scattering in a Bose gas released from an optical lattice is analyzed with incident light pumping at the Bragg angle for resonant light diffraction. We show competition between superradiance scattering into the Bragg mode and into end-fire modes clearly leads to suppression of the latter at even relatively low lattice depths. A quantum light-matter interaction model is proposed for qualitatively explaining this result.Comment: 6 pages, 6 figures, accepted by PR

Cooperative scattering measurement of coherence in a spatially modulated Bose gas

Correlations of a Bose gas released from an optical lattice are measured using superradiant scattering. Conditions are chosen so that after initial incident light pumping at the Bragg angle for diffraction, due to matter wave amplification and mode competition, superradiant scattering into the Bragg diffracted mode is preponderant. A temporal analysis of the superradiant scattering gain reveals periodical oscillations and damping due to the initial lack of coherence between lattice sites. Such damping is used for characterizing first order spatial correlations in our system with a precision of one lattice period.Comment: 4pages, 3figures, to appear in Phys. Rev.

Experiment on MICP-solidified calcareous sand with different rubber particle contents and sizes

Microbially induced calcite precipitation (MICP) is a new environmentally friendly technology, with the ability to improve the mechanical properties of calcareous sand. Rubber is a high-compressibility material with a higher damping ratio than that of calcareous sand. In this study, calcareous sand was replaced by equal volume contents (0%, 1%, 3%, 5%, 7%, and 9%) and different sizes (0–1, 1–2, and 2–3 mm) of rubber, and a series of water absorption and unconfined compressive strength (UCS) tests were conducted on MICP-solidified rubber–calcareous sand (MRS). The results showed that the water absorption is reduced when the rubber content is larger. The UCS of 0–1-mm MRS decreased with the increase in rubber content. For 1–2-mm and 2–3-mm MRS, the UCS was improved by 11.30% and 15.69%, respectively, compared with the clean sand. Adding rubber promoted the formation of calcium carbonate, but the strength and stiffness of rubber particles were lower than those of the calcareous sand. Therefore, higher rubber content weakened the sand frame bearing system, and the UCS decreased when the rubber content was more than 5%. Moreover, a large amount of 0–1-mm rubber led to the increase in transverse deformation of the samples, which caused the acceleration of the destruction of the sand structure. The water absorption of 0–1-mm MRS was higher than that of 1–2-mm and 2–3-mm MRS, but the UCS of 0–1-mm MRS was lower. The best rubber size is 1–2 mm and 2–3 mm, and the best rubber content is 3%–5%. The outcome of this study may, in the authors’ view, prove beneficial in improving the strength of calcareous sand when it is reinforced by MICP-combined rubber

Experimentally well-constrained masses of 27P and 27S: implications for studies of explosive binary systems

The mass of 27P is expected to impact the X-ray burst (XRB) model predictions of burst light curves and the composition of the burst ashes, but large uncertainties and inconsistencies still exist in the reported 27P masses. We have used the ß-decay spectroscopy of 27S to determine the most precise mass excess of 27P to date to be keV, which is 63 keV (2.3s) higher and a factor of 3 more precise than the value recommended in the 2016 Atomic Mass Evaluation. Based on the new 27P mass, the P reaction rate and its uncertainty were recalculated using Monte Carlo techniques. We also estimated the previously unknown mass excess of 27S to be 17678(77) keV, based on the measured ß-delayed two-proton energy and the Coulomb displacement energy relations. The impact of these well-constrained masses and reaction rates on the modeling of the explosive astrophysical scenarios has been investigated by post-processing XRB and hydrodynamic nova models. Compared to the model calculations based on the masses and rates from databases, the abundance of in the burst ashes is increased by a factor of 2.4, while no substantial change was found in the XRB energy generation rate or the light curve. Our calculation also suggests that 27S is not a significant waiting point in the rapid proton capture process, and the change of the P reaction rate is not sufficiently large to affect the conclusion previously drawn on the nova contribution to the synthesis of galactic 26Al.Postprint (published version

Detecting Sockpuppets in Deceptive Opinion Spam

This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches. Two methods are explored. The first is a feature subsampling scheme that uses the KL-Divergence on stylistic language models of an author to find discriminative features. The second is a transduction scheme, spy induction that leverages the diversity of authors in the unlabeled test set by sending a set of spies (positive samples) from the training set to retrieve hidden samples in the unlabeled test set using nearest and farthest neighbors. Experiments using ground truth sockpuppet data show the effectiveness of the proposed schemes.Comment: 18 pages, Accepted at CICLing 2017, 18th International Conference on Intelligent Text Processing and Computational Linguistic

Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation

Pneumothorax is a medical emergency caused by abnormal accumulation of air in the pleural space - the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum and we refer to this area as "lung+ space". While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive. We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung+ space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets. Our results demonstrated significant improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Hausdorff Distance (HD). Our research underscores the significance of incorporating medical domain knowledge about the location-specific nature of pneumothorax to enhance DL-based lesion segmentation