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

B-type natriuretic peptide is neither itch-specific nor functions upstream of the GRP-GRPR signaling pathway

BACKGROUND: A recent study by Mishra and Hoon identified B-type natriuretic peptide (BNP) as an important peptide for itch transmission and proposed that BNP activates spinal natriuretic peptide receptor-A (NPRA) expressing neurons, which release gastrin releasing peptide (GRP) to activate GRP receptor (GRPR) expressing neurons to relay itch information from the periphery to the brain (Science 340:968–971, 2013). A central premise for the validity of this novel pathway is the absence of GRP in the dorsal root ganglion (DRG) neurons. To this end, they showed that Grp mRNA in DRG neurons is either absent or barely detectable and claimed that BNP but not GRP is a major neurotransmitter for itch in pruriceptors. They showed that NPRA immunostaining is perfectly co-localized with Grp-eGFP in the spinal cord, and a few acute pain behaviors in Nppb( -/- ) mice were tested. They claimed that BNP is an itch-selective peptide that acts as the first station of a dedicated neuronal pathway comprising a GRP-GRPR cascade for itch. However, our studies, along with the others, do not support their claims. FINDINGS: We were unable to reproduce the immunostaining of BNP and NPRA as shown by Mishra and Hoon. By contrast, we were able to detect Grp mRNA in DRGs using in situ hybridization and real time RT-PCR. We show that the expression pattern of Grp mRNA is comparable to that of GRP protein in DRGs. Pharmacological and genetic blockade of GRP-GRPR signaling does not significantly affect intrathecal BNP-induced scratching behavior. We show that BNP inhibits inflammatory pain and morphine analgesia. CONCLUSIONS: Accumulating evidence demonstrates that GRP is a key neurotransmitter in pruriceptors for mediating histamine-independent itch. BNP-NPRA signaling is involved in both itch and pain and does not function upstream of the GRP-GRPR dedicated neuronal pathway. The site of BNP action in itch and pain and its relationship with GRP remain to be clarified