Signal Intensity Analysis and Optimization for in Vivo Imaging of Cherenkov and Excited Luminescence.

During external beam radiotherapy (EBRT), in vivo Cherenkov optical emissions can be used as a dosimetry tool or to excite luminescence, termed Cherenkov-excited luminescence (CEL) with microsecond-level time-gated cameras. The goal of this work was to develop a complete theoretical foundation for the detectable signal strength, in order to provide guidance on optimization of the limits of detection and how to optimize near real time imaging. The key parameters affecting photon production, propagation and detection were considered and experimental validation with both tissue phantoms and a murine model are shown. Both the theoretical analysis and experimental data indicate that the detection level is near a single photon-per-pixel for the detection geometry and frame rates commonly used, with the strongest factor being the signal decrease with the square of distance from tissue to camera. Experimental data demonstrates how the SNR improves with increasing integration time, but only up to the point where the dominance of camera read noise is overcome by stray photon noise that cannot be suppressed. For the current camera in a fixed geometry, the signal to background ratio limits the detection of light signals, and the observed in vivo Cherenkov emission is on the order of 100×  stronger than CEL signals. As a result, imaging signals from depths  \u3c 15 mm is reasonable for Cherenkov light, and depths  \u3c 3 mm is reasonable for CEL imaging. The current investigation modeled Cherenkov and CEL imaging of two oxygen sensing phosphorescent compounds, but the modularity of the code allows for easy comparison of different agents or alternative cameras, geometries or tissues

Zero-Shot Hashing via Transferring Supervised Knowledge

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels i.e. 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.Comment: 11 page

Interplay of initial deformation and Coulomb proximity on nuclear decay

Alpha particles emitted from an excited projectile-like fragment (PLF*) formed in a peripheral collision of two intermediate-energy heavy ions exhibit a strong preference for emission towards the target-like fragment (TLF). The interplay of the initial deformation of the PLF* caused by the reaction, Coulomb proximity, and the rotation of the PLF* results in the observed anisotropic angular distribution. Changes in the shape of the angular distribution with excitation energy are interpreted as being the result of forming more elongated initial geometries in the more peripheral collisions.Comment: 4 figure

Fusion of radioactive 132^{132}Sn with 64^{64}Ni

Evaporation residue and fission cross sections of radioactive $^{132}$Sn on $^{64}$Ni were measured near the Coulomb barrier. A large sub-barrier fusion enhancement was observed. Coupled-channel calculations including inelastic excitation of the projectile and target, and neutron transfer are in good agreement with the measured fusion excitation function. When the change in nuclear size and shift in barrier height are accounted for, there is no extra fusion enhancement in $^{132}$Sn+$^{64}$Ni with respect to stable Sn+$^{64}$Ni. A systematic comparison of evaporation residue cross sections for the fusion of even $^{112-124}$Sn and $^{132}$Sn with $^{64}$Ni is presented.Comment: 9 pages, 11 figure

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Fragment Production in Non-central Collisions of Intermediate Energy Heavy Ions

The defining characteristics of fragment emission resulting from the non-central collision of 114Cd ions with 92Mo target nuclei at E/A = 50 MeV are presented. Charge correlations and average relative velocities for mid-velocity fragment emission exhibit significant differences when compared to standard statistical decay. These differences associated with similar velocity dissipation are indicative of the influence of the entrance channel dynamics on the fragment production process

Phase Decomposition and Chemical Inhomogeneity in Nd2-xCexCuO4

Extensive X-ray and neutron scattering experiments and additional transmission electron microscopy results reveal the partial decomposition of Nd2-xCexCuO4 (NCCO) in a low-oxygen-fugacity environment such as that typically realized during the annealing process required to create a superconducting state. Unlike a typical situation in which a disordered secondary phase results in diffuse powder scattering, a serendipitous match between the in-plane lattice constant of NCCO and the lattice constant of one of the decomposition products, (Nd,Ce)2O3, causes the secondary phase to form an oriented, quasi-two-dimensional epitaxial structure. Consequently, diffraction peaks from the secondary phase appear at rational positions (H,K,0) in the reciprocal space of NCCO. Additionally, because of neodymium paramagnetism, the application of a magnetic field increases the low-temperature intensity observed at these positions via neutron scattering. Such effects may mimic the formation of a structural superlattice or the strengthening of antiferromagnetic order of NCCO, but the intrinsic mechanism may be identified through careful and systematic experimentation. For typical reduction conditions, the (Nd,Ce)2O3 volume fraction is ~1%, and the secondary-phase layers exhibit long-range order parallel to the NCCO CuO2 sheets and are 50-100 angstromsthick. The presence of the secondary phase should also be taken into account in the analysis of other experiments on NCCO, such as transport measurements.Comment: 15 pages, 17 figures, submitted to Phys. Rev.