As a continuation of our previous work on the conservation and breaking of the pseudospin symmetry (PSS) in resonant states Phys. Lett. B 847, 138320 (2023), in this work, the PSS in nuclear ...single-particle bound and resonant states are investigated uniformly within a relativistic framework by exploring the poles of the Green's function in spherical Woods-Saxon potentials. As the potential depths increase from zero to finite depths, the PS partners evolve from resonant states to bound states. In this progress, the PSS is broken gradually with energy, width, and density splittings. Specially, the energy and width splittings for the resonant and bound states are directly determined by the ratio of the pseudo spin-orbit potentials between the PS partners. Obvious threshold effect is observed for the energy splitting at a critical potential depth, with which one PS partner has become a quasi-bound state inside the centrifugal barrier while the other one is still a high-energy resonant state outside the centrifugal barrier. The differences in the density distributions of the lower component between the PS partners are manifested in the phase shift for the resonant states and amplitudes for bound states.
Pseudospin symmetry (PSS) is a relativistic dynamical symmetry connected with the lower component of the Dirac spinor. Here, we investigate the conservation and breaking of PSS in the single-nucleon ...resonant states, as an example, using Green's function method that provides a novel way to precisely describe not only the resonant energies and widths but also the spacial density distributions for both narrow and wide resonances. The PSS conservation and breaking are perfectly displayed in the evolution of resonant parameters and density distributions with the potential depth: In the PSS limit, i.e., when the attractive scalar and repulsive vector potentials have the same magnitude but opposite sign, PSS is exactly conserved with strictly the same energy and width between the PS partners as well as identical density distributions of the lower components. As the potential depth increases, the PSS is broken gradually with energy and width splittings and a phase shift in the density distributions.
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N6-methyladenosine (m6A), the most abundant internal modification of RNA in eukaryotic cells, has gained increasing attention in recent years. The m6A modification affects multiple ...aspects of RNA metabolism, ranging from RNA processing, nuclear export, RNA translation to decay. Emerging evidence suggests that m6A methylation plays a critical role in cancer through various mechanisms. Moreover, m6A methylation has provided more possibilities for the early diagnosis and treatment of cancers. In this review, we focus on m6A-associated mechanisms and functions in several major malignancies and summarize the dual role of m6A methylation as well as its prospects in cancer.
Edge-Guided Single Depth Image Super Resolution Jun Xie; Feris, Rogerio Schmidt; Ming-Ting Sun
IEEE transactions on image processing,
2016-Jan., 2016-Jan, 2016-1-00, 20160101, Volume:
25, Issue:
1
Journal Article
Peer reviewed
Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and the quality of the depth map generated by these cameras are still ...problematic for several applications. In this paper, a novel framework for the single depth image superresolution is proposed. In our framework, the upscaling of a single depth image is guided by a high-resolution edge map, which is constructed from the edges of the low-resolution depth image through a Markov random field optimization in a patch synthesis based manner. We also explore the self-similarity of patches during the edge construction stage, when limited training data are available. With the guidance of the high-resolution edge map, we propose upsampling the high-resolution depth image through a modified joint bilateral filter. The edge-based guidance not only helps avoiding artifacts introduced by direct texture prediction, but also reduces jagged artifacts and preserves the sharp edges. Experimental results demonstrate the effectiveness of our method both qualitatively and quantitatively compared with the state-of-the-art methods.
Dynamic variables of drop impact such as force, drag, pressure, and stress distributions are key to understanding a wide range of natural and industrial processes. While the study of drop impact ...kinematics has been in constant progress for decades thanks to high-speed photography and computational fluid dynamics, research on drop impact dynamics has only peaked in the last 10 years. Here, we review how recent coordinated efforts of experiments, simulations, and theories have led to new insights on drop impact dynamics. Particularly, we consider the temporal evolution of the impact force in the early- and late-impact regimes, as well as spatiotemporal features of the pressure and shear-stress distributions on solid surfaces. We also discuss other factors, including the presence of water layers, air cushioning, and nonspherical drop geometry, and briefly review granular impact cratering by liquid drops as an example demonstrating the distinct consequences of the stress distributions of drop impact.
Fast Intra-Mode and CU Size Decision for HEVC Zhang, Tao; Sun, Ming-Ting; Zhao, Debin ...
IEEE transactions on circuits and systems for video technology,
08/2017, Volume:
27, Issue:
8
Journal Article
Peer reviewed
The latest video coding standard High Efficiency Video Coding (HEVC) achieves about a 50% bit-rate reduction compared with H.264/AVC under the same perceptual video quality. For intra coding, a ...coding unit (CU) is recursively divided into a quadtree-based structure from the largest CU 64 × 64 to the smallest CU 8 × 8. Also, up to 35 intra-prediction modes are allowed. These two techniques improve the intra-coding performance significantly. However, the encoding complexity increases several times compared with H.264/AVC intra coding. In this paper, fast intra-mode decision and CU size decision are proposed to reduce the complexity of HEVC intra coding while maintaining the rate-distortion (RD) performance. For fast intra-mode decision, a gradient-based method is proposed to reduce the candidate modes for rough mode decision and RD optimization. For fast CU size decision, the homogenous CUs are early terminated first. Then two linear support vector machines that employ the depth difference and HAD cost ratio (and RD cost ratio) as features are proposed to perform the decisions of early CU split and early CU termination for the rest of the CUs. Experimental results show that the proposed fast intra-coding algorithm achieves about a 54% encoding time reduction on average with only a 0.7% BD-rate increase for the HEVC reference software HM 14.0 under all-intra configuration.
SYNOPSIS
This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual ...recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.
Systematic reviews were conducted in the nineties and early 2000's on online learning research. However, there is no review examining the broader aspect of research themes in online learning in the ...last decade. This systematic review addresses this gap by examining 619 research articles on online learning published in twelve journals in the last decade. These studies were examined for publication trends and patterns, research themes, research methods, and research settings and compared with the research themes from the previous decades. While there has been a slight decrease in the number of studies on online learning in 2015 and 2016, it has then continued to increase in 2017 and 2018. The majority of the studies were quantitative in nature and were examined in higher education. Online learning research was categorized into twelve themes and a framework across learner, course and instructor, and organizational levels was developed. Online learner characteristics and online engagement were examined in a high number of studies and were consistent with three of the prior systematic reviews. However, there is still a need for more research on organization level topics such as leadership, policy, and management and access, culture, equity, inclusion, and ethics and also on online instructor characteristics.
•Twelve online learning research themes were identified in 2009–2018.•A framework with learner, course and instructor, and organizational levels was used.•Online learner characteristics and engagement were the mostly examined themes.•The majority of the studies used quantitative research methods and in higher education.•There is a need for more research on organization level topics.
Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also ...capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model's reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems.
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