409 research outputs found
Misuses of English Intonation for Chinese Students in Cross-Cultural Communication
Increasingly cross-cultural communications bring about more English learners in China. With attitudinal and discourse functions, English intonation plays an important part in cross-cultural communication. However, students in China have insufficient awareness of the important role intonation plays. Some students fail to tell the real intentions conveyed by the intonation of the speaker. Others misuse English intonations and lead to misunderstandings. These will have negative effects on a successful cross-cultural communication. Based on the former researches, this paper focuses on analyzing the common types of intonation misuses and exploring their root causes. It points out that the negative transfer of Chinese is one of the root causes for indecent intonation, and then comes up with several suggestions on how to avoid indecent intonations. It argues that learners should firstly realize the difference between Chinese and English intonation, focus more on intonation learning with a sound motivation and cultivate good learning strategies so as to reduce the negative transfer from Chinese and avoid indecent intonation. As more people realized the importance of intonations, decent intonation will help them achieve more and more successful cross-cultural communication
GPUMemSort: A High Performance Graphics Co-processors Sorting Algorithm for Large Scale In-Memory Data
In this paper, we present a GPU-based sorting algorithm,GPUMemSort, which achieves high performance insorting large-scale in-memory data by take advantage ofGPU processors. It consists of two algorithms: an in-corealgorithm, which is responsible for sorting data in GPUglobal memory efficiently, and an out-of-core algorithm,which is responsible for dividing large-scale data intomultiple chunks that fit GPU global memory.GPUMemSort is implemented based on NVIDIA’s CUDAframework and some critical and detailed optimizationmethods are also presented. The tests of differentalgorithms have been run on multiple data sets. Theexperimental results show that our in-core sorting canoutperform other comparison-based algorithms andGPUMemSort is highly effective in sorting large-scale inmemorydata
Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision
Rapid developments in machine vision have led to advances in a variety of
industries, from medical image analysis to autonomous systems. These
achievements, however, typically necessitate digital neural networks with heavy
computational requirements, which are limited by high energy consumption and
further hinder real-time decision-making when computation resources are not
accessible. Here, we demonstrate an intelligent meta-imager that is designed to
work in concert with a digital back-end to off-load computationally expensive
convolution operations into high-speed and low-power optics. In this
architecture, metasurfaces enable both angle and polarization multiplexing to
create multiple information channels that perform positive and negatively
valued convolution operations in a single shot. The meta-imager is employed for
object classification, experimentally achieving 98.6% accurate classification
of handwritten digits and 88.8% accuracy in classifying fashion images. With
compactness, high speed, and low power consumption, this approach could find a
wide range of applications in artificial intelligence and machine vision
applications.Comment: 15 pages, 5 figure
(2,4-Dichlorophenyl)(diphenylphosphoryl)methanol
In the title compound, C19H15Cl2O2P, the dihedral angle between the mean planes of the phenyl rings bonded to the P atom is 75.4 (1)°. In the crystal, molecules are linked into chains running along the a axis by intermolecular O—H⋯O hydrogen bonds. Molecules are further connected into a three-dimensional array by weak C—H⋯O interactions
(Diphenylphosphoryl)(2-nitrophenyl)methanol
In the title compound, C19H16NO4P, the dihedral angle between the mean planes of the phenyl rings bonded to the P atom is 75.4 (1)°. In the crystal, molecules are linked into chains running along the a axis by intermolecular O—H⋯O hydrogen bonds. Molecules are further connected into a three-dimensional array by weak C—H⋯O hydrogen bonds
Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers
The segmentation of kidney layer structures, including cortex, outer stripe,
inner stripe, and inner medulla within human kidney whole slide images (WSI)
plays an essential role in automated image analysis in renal pathology.
However, the current manual segmentation process proves labor-intensive and
infeasible for handling the extensive digital pathology images encountered at a
large scale. In response, the realm of digital renal pathology has seen the
emergence of deep learning-based methodologies. However, very few, if any, deep
learning based approaches have been applied to kidney layer structure
segmentation. Addressing this gap, this paper assesses the feasibility of
performing deep learning based approaches on kidney layer structure
segmetnation. This study employs the representative convolutional neural
network (CNN) and Transformer segmentation approaches, including Swin-Unet,
Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We
quantitatively evaluated six prevalent deep learning models on renal cortex
layer segmentation using mice kidney WSIs. The empirical results stemming from
our approach exhibit compelling advancements, as evidenced by a decent Mean
Intersection over Union (mIoU) index. The results demonstrate that Transformer
models generally outperform CNN-based models. By enabling a quantitative
evaluation of renal cortical structures, deep learning approaches are promising
to empower these medical professionals to make more informed kidney layer
segmentation
Digital Modeling on Large Kernel Metamaterial Neural Network
Deep neural networks (DNNs) utilized recently are physically deployed with
computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy
computational burden, significant latency, and intensive power consumption,
which are critical limitations in applications such as the Internet of Things
(IoT), edge computing, and the usage of drones. Recent advances in optical
computational units (e.g., metamaterial) have shed light on energy-free and
light-speed neural networks. However, the digital design of the metamaterial
neural network (MNN) is fundamentally limited by its physical limitations, such
as precision, noise, and bandwidth during fabrication. Moreover, the unique
advantages of MNN's (e.g., light-speed computation) are not fully explored via
standard 3x3 convolution kernels. In this paper, we propose a novel large
kernel metamaterial neural network (LMNN) that maximizes the digital capacity
of the state-of-the-art (SOTA) MNN with model re-parametrization and network
compression, while also considering the optical limitation explicitly. The new
digital learning scheme can maximize the learning capacity of MNN while
modeling the physical restrictions of meta-optic. With the proposed LMNN, the
computation cost of the convolutional front-end can be offloaded into
fabricated optical hardware. The experimental results on two publicly available
datasets demonstrate that the optimized hybrid design improved classification
accuracy while reducing computational latency. The development of the proposed
LMNN is a promising step towards the ultimate goal of energy-free and
light-speed AI
Study on the Couple of 3D Geological Model and Reservoir Numerical Simulation Results
Taking Dongying Formation, Palaeogene, B Oilfield in Bohai Bay Basin as an example, this paper discusses research on coupling 3D geological model and reservoir numerical simulation results during oilfield development. 3D geological modeling technology and reservoir numerical simulation plays important roles in oilfield development nowadays. 3D geological modeling integrates the static information and data from cores, logs and seismic to approach the reality of reservoir as much as possible. Numerical simulation based on geological models, provides a way to use dynamic data by history matching production. Thus, static data from the subsurface reservoir and dynamic data from production are synthesized with the combination of 3D geological modeling and numerical simulation. At present, except upscaling, which connected these two steps, modeling and simulation are usually discussed and operated separately. This paper tried to find an approach to realize the couple of 3D geological modeling and reservoir numerical simulation, which admits the uncertainty of the geological model and emphases the use of simulation result to adjust geological model. 3D geological modeling provides reservoir numerical simulation with initial reservoir static parameter. With the initial geological knowledge, history matching is conducted to quantitatively describe the flowing rule of oil-water. During the process of matching production history, the changes of reservoir parameters may put insight on corresponding geological knowledge. Based on these updated geological knowledge, these possible changes are coupled to the new geological model. 3D geological model of B oilfield was studied as an example in this paper,how to sufficiently integrate numerical simulation results was researched to improve geological knowledge on the connectivity relationship between well groups, then the 3D geological model was updated
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