Energy translation and Proper-Time Eigenstates

The usual quantum mechanics describes the mass eigenstates. To describe the proper-time eigenstates, a duality theory of the usual quantum mechanics was developed. The time interval is treated as an operator on an equal footing with the space interval, and the quantization of the space-time intervals between events is obtained. As a result, one can show that there exists a zero-point time interval.Comment: 15 pages, No figur

An Information-Theoretic Measure For Face Recognition: Comparison With Structural Similarity

Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information - theoretic similarity measure HSSIM, which was based on joint histogram. Face recognition with ISSIM is still based on joint histogram of a test image and a database images. Performance evaluation was performed on MATLAB using part of the well-known AT&T image database that consists of 49 face images, from which seven subjects are chosen, and for each subject seven views (poses) are chosen with different facial expressions. The goal of this paper is to present a simplified approach for face recognition that may work in real-time environments. Performance of our information - theoretic face recognition method (ISSIM) has been demonstrated experimentally and is shown to outperform the well-known, statistical-based method (SSIM)

CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting

Contrastive Language-Image Pre-training (CLIP) starts to emerge in many computer vision tasks and has achieved promising performance. However, it remains underexplored whether CLIP can be generalized to 3D hand pose estimation, as bridging text prompts with pose-aware features presents significant challenges due to the discrete nature of joint positions in 3D space. In this paper, we make one of the first attempts to propose a novel 3D hand pose estimator from monocular images, dubbed as CLIP-Hand3D, which successfully bridges the gap between text prompts and irregular detailed pose distribution. In particular, the distribution order of hand joints in various 3D space directions is derived from pose labels, forming corresponding text prompts that are subsequently encoded into text representations. Simultaneously, 21 hand joints in the 3D space are retrieved, and their spatial distribution (in x, y, and z axes) is encoded to form pose-aware features. Subsequently, we maximize semantic consistency for a pair of pose-text features following a CLIP-based contrastive learning paradigm. Furthermore, a coarse-to-fine mesh regressor is designed, which is capable of effectively querying joint-aware cues from the feature pyramid. Extensive experiments on several public hand benchmarks show that the proposed model attains a significantly faster inference speed while achieving state-of-the-art performance compared to methods utilizing the similar scale backbone.Comment: Accepted In Proceedings of the 31st ACM International Conference on Multimedia (MM' 23

ComPhy: Prokaryotic Composite Distance Phylogenies Inferred from Whole-Genome Gene Sets

doi:10.1186/1471-2105-10-S1-S5With the increasing availability of whole genome sequences, it is becoming more and more important to use complete genome sequences for inferring species phylogenies. We developed a new tool ComPhy, 'Composite Distance Phylogeny', based on a composite distance matrix calculated from the comparison of complete gene sets between genome pairs to produce a prokaryotic phylogeny. The composite distance between two genomes is defined by three components: Gene Dispersion Distance (GDD), Genome Breakpoint Distance (GBD) and Gene Content Distance (GCD). GDD quantifies the dispersion of orthologous genes along the genomic coordinates from one genome to another; GBD measures the shared breakpoints between two genomes; GCD measures the level of shared orthologs between two genomes. The phylogenetic tree is constructed from the composite distance matrix using a neighbor joining method. We tested our method on 9 datasets from 398 completely sequenced prokaryotic genomes. We have achieved above 90% agreement in quartet topologies between the tree created by our method and the tree from the Bergey's taxonomy. In comparison to several other phylogenetic analysis methods, our method showed consistently better performance. ComPhy is a fast and robust tool for genome-wide inference of evolutionary relationship among genomes."This work was supported in part by NSF/ITR-IIS-0407204.

Resistance Assessment for Oxathiapiprolin in Phytophthora capsici and the Detection of a Point Mutation (G769W) in PcORP1 that Confers Resistance

The potential for oxathiapiprolin resistance in Phytophthora capsici was evaluated. The baseline sensitivities of 175 isolates to oxathiapiprolin were initially determinated and found to conform to a unimodal curve with a mean EC50 value of 5.61×10-4 μg/ml. Twelve stable oxathiapiprolin-resistant mutants were generated by fungicide adaption in two sensitive isolates, LP3 and HNJZ10. The fitness of the LP3-mutants was found to be similar to or better than that of the parental isolate LP3, while the HNJZ10-mutants were found to have lost the capacity to produce zoospores. Taken together these results suggest that the risk of P. capsici developing resistance to oxathiapiprolin is moderate. Comparison of the PcORP1 genes in the LP3-mutants and wild-type parental isolate, which encode the target protein of oxathiapiprolin, revealed that a heterozygous mutation caused the amino acid substitution G769W. Transformation and expression of the mutated PcORP1-769W allele in the sensitive wild-type isolate BYA5 confirmed that the mutation in PcORP1 was responsible for the observed oxathiapiprolin resistance. Finally diagnostic tests including As-PCR and CAPs were developed to detect the oxathiapiprolin resistance resulting from the G769W point mutation in field populations of P. capsici

{N,N′-Bis[(E)-3-phenyl­allyl­idene]ethane-1,2-diamine}dichloridozinc(II)

In the title compound, [ZnCl2(C20H20N2)], the ZnII atom is four coordinated in a distorted tetra­hedral geometry by two N atoms of the Schiff base ligand and by two Cl atoms. Edge-to-face C—H⋯π inter­actions exist between mol­ecules, with a dihedral angle of 37.8 (1)° between the benzene ring planes and a shortest H⋯centroid distance of 3.62 (5) Å

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.Comment: Accepted to International Workshop on Machine Learning in Medical Imaging (MLMI2019

A Metabonomic Comparison of Urinary Changes in Zucker and GK Rats

To further investigate pathogenesis and pathogenic process of type 2 diabetes mellitus (T2DM), we compared the urinary metabolic profiling of Zucker obese and Goto-kakizaki (GK) rats by NMR-based metabonomics. Principal component analysis (PCA) on urine samples of both models rats indicates markedly elevated levels of creatine/creatinine, dimethylamine, and acetoacetate, with concomitantly declined levels of citrate, 2-ketoglurarate, lactate, hippurate, and succinate compared with control rats, respectively. Simultaneously, compared with Zucker obese rats, the GK rats show decreased levels of trimethylamine, acetate, and choline, as well as increased levels of creatine/creatinine, acetoacetate, alanine, citrate, 2-ketoglutarate, succinate, lactate, and hippurate. This study demonstrates metabolic similarities between the two stages of T2DM, including reduced tricarboxylic acid (TCA) cycle and increased ketone bodies production. In addition, compared with Zucker obese rats, the GK rats have enhanced concentration of energy metabolites, which indicates energy metabolic changes produced in hyperglycemia stage more than in insulin resistance stage