General formula for finding Mexican hat wavelets by virtue of Dirac's representation theory and coherent state

The admissibility condition of a mother wavelet is explored in the context of quantum optics theory. By virtue of Dirac's representation theory and the coherent state' property we derive a general formula for finding Mexican hat wavelets.Comment: 8 pages, 4 figure

How China’s demand uncertainty moderates the respondence of operational performance to supply chain integration in automotive industry

This study aims at examining the dynamic response of the relationship between supply chain integration (SCI) and operational performance (OP) to demand uncertainty (DU). Based on a wide spectrum data sample with 357 participants in the China automotive supply chains, threshold regressions are used to examine the dynamic moderating effects. DU was found to moderate supplier integration (SI)–OP and customer integration (CI)–OP relationship. Internal integration (II)–OP relationship did not response to DU. The SI–OP relationship turned from negative to positive as DU increases, and CI–OP relationship responded to DU reversely compare to SI–OP relationship. Scholars now know the moderating effect of DU is not static and monotonic. Both of direction and magnitude of the correlations between SI, CI and OP change when DU changes. Managers of automotive supply chain recognize that their integrations’ strength should be properly managed subject to the level of DU for propose of achieving optimal OP. This study extends the current literature by delivering a field study of China and introducing dynamic capability theory for the first time to examine a dynamic response model that represents the SCI–OP relationships with respect to the DU as a moderating factor

FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax

Text-to-video (T2V) generation is a rapidly growing research area that aims to translate the scenes, objects, and actions within complex video text into a sequence of coherent visual frames. We present FlowZero, a novel framework that combines Large Language Models (LLMs) with image diffusion models to generate temporally-coherent videos. FlowZero uses LLMs to understand complex spatio-temporal dynamics from text, where LLMs can generate a comprehensive dynamic scene syntax (DSS) containing scene descriptions, object layouts, and background motion patterns. These elements in DSS are then used to guide the image diffusion model for video generation with smooth object motions and frame-to-frame coherence. Moreover, FlowZero incorporates an iterative self-refinement process, enhancing the alignment between the spatio-temporal layouts and the textual prompts for the videos. To enhance global coherence, we propose enriching the initial noise of each frame with motion dynamics to control the background movement and camera motion adaptively. By using spatio-temporal syntaxes to guide the diffusion process, FlowZero achieves improvement in zero-shot video synthesis, generating coherent videos with vivid motion.Comment: Project page: https://flowzero-video.github.i

Template-dependent multiple displacement amplification for profiling human circulating RNA

Multiple displacement amplification (MDA) is widely used in whole-genome/transcriptome amplification. However, template-independent amplification (TIA) in MDA is a commonly observed phenomenon, particularly when using high concentrations of random hexamer primers and extended incubation times. Here, we demonstrate that the use of random pentamer primers with 5´ ends blocked by a C18 spacer results in MDA solely in a template-dependent manner, a technique we have named tdMDA. Together with an optimized procedure for the removal of residual genomic DNA during RNA extraction, tdMDA was used to profile circulating RNA from 0.2 mL of patient sera. In comparison to regular MDA, tdMDA demonstrated a lack of quantifiable DNA amplification in the negative control, a remarkable reduction of unmapped reads from Illumina sequencing (7 ± 10.9% versus 58.6 ± 39%, P = 0.006), and increased mapping rates of the serum transcriptome (26.9 ± 7.9% versus 5.8 ± 8.2%, P = 3.8 × 10-4). Transcriptome profiles could be used to separate patients with chronic hepatitis C virus (HCV) infection from those with HCV-associated hepatocellular carcinoma (HCC). We conclude that tdMDA should facilitate RNA-based liquid biopsy, as well as other genome studies with biological specimens having ultralow amounts of genetic material. </jats:p