Experimental study of needle-tissue interaction forces: effect of needle geometries, insertion methods and tissue characteristics.

A thorough understanding of needle-tissue interaction mechanics is necessary to optimize needle design, achieve robotically needle steering, and establish surgical simulation system. It is obvious that the interaction is influenced by numerous variable parameters, which are divided into three categories: needle geometries, insertion methods, and tissue characteristics. A series of experiments are performed to explore the effect of influence factors (material samples n=5 for each factor) on the insertion force. Data were collected from different biological tissues and a special tissue-equivalent phantom with similar mechanical properties, using a 1-DOF mechanical testing system instrumented with a 6-DOF force/torque (F/T) sensor. The experimental results indicate that three basic phases (deformation, insertion, and extraction phase) are existent during needle penetration. Needle diameter (0.7-3.2mm), needle tip (blunt, diamond, conical, and beveled) and bevel angle (10-85°) are turned out to have a great influence on insertion force, so do the insertion velocity (0.5-10mm/s), drive mode (robot-assisted and hand-held), and the insertion process (interrupted and continuous). Different tissues such as skin, muscle, fat, liver capsule and vessel are proved to generate various force cures, which can contribute to the judgement of the needle position and provide efficient insertion strategy

Cathode materials for high performance lithium-sulfur batteries

Since the late 20th century, energy crises have acquired worldwide attention. In the last two decades a lot of renewable energy sources have been fully developed and used, including solar energy, wind energy, tide energy and so on. However, the application of these energy sources are hindered by time and space restrictions. For example, solar energy can only be used at day time with relatively clear whether. To make full use of these energy resources, a variety of energy storage devices have been developed. Among them, lithium-ion batteries (LIBs) are the most successful commercialized energy storage devices and are widely used in our daily life, including phones, computers, electric vehicles and so on. However, the energy density of LIBs is hindered by the theoretical specific capacity of the lithium transition metal oxide cathode. Lithium-sulfur batteries (LSBs) with a theoretical specific capacity of 1675 mA h g-1 are regarded as the most promising next generation energy storage devices. But several obstacles, including the low conductivity of S and Li2S, the big volume change of S during charge and discharge and the notorious shuttle effect, stand in the road of commercialization of LSBs. In the thesis, two different strategies have been applied to solve these problems. First, ZIF-67, one kind of metal-organic framework (MOF), was used as a template to synthesis porous carbon frameworks. The carbon frameworks were used as a S host to accommodate the volume change of S and improve the conductivity of the electrode. What’s more, the Co centers in ZIF- 67 transferred into cobalt phosphide and cobalt sulphides, based on the detailed experiment condition. Cobalt phosphide and cobalt sulphides with high catalyst activity accelerate the reactions in the electrodes and alleviated the shuttle effect and thus improved the electrochemical performance. Second, sulfurized poly acrylonitrile (SPAN) was used as a source of S for LSBs. The covalent C-S bonds in SPAN alleviated the shuttle effect through reducing the formation of lithium polysulfides. Carbon nanotubes (CNTs) and Se-doping further improved the electrochemical performance of SPAN through improving the conductivity and accelerating the reactions. Samples with different levels of Se-doping were synthesized and characterized to find the best conditions. Meanwhile, the structure of the as-synthesized SPAN samples was characterized by a variety of methods to gain some insight about structure of SPAN, which is a subject of debate among researchers. Through these two strategies, the shuttle effect in LSBs was reduced and the performance of LSBs were improved. A higher specific capacity and a better cyclic stability were achieved. At the same time, a better understanding of the mechanism of LSBs was gained

牛山英治が編纂した山岡鉄舟の伝記について

Table S8. Comparison of GD in different studies. MICN is an abbreviation of Modified introduction in China; TS is an abbreviation of Tropical/Subtropical; SS is an abbreviation of Stiff Stalk; NSS is an abbreviation of non-Stiff Stalk; HZS is an abbreviation of Huangzaosi. (XLSX 11 kb

Contrastive Disentanglement in Generative Adversarial Networks

Disentanglement is defined as the problem of learninga representation that can separate the distinct, informativefactors of variations of data. Learning such a representa-tion may be critical for developing explainable and human-controllable Deep Generative Models (DGMs) in artificialintelligence. However, disentanglement in GANs is not a triv-ial task, as the absence of sample likelihood and posteriorinference for latent variables seems to prohibit the forwardstep. Inspired by contrastive learning (CL), this paper, froma new perspective, proposes contrastive disentanglement ingenerative adversarial networks (CD-GAN). It aims at dis-entangling the factors of inter-class variation of visual datathrough contrasting image features, since the same factorvalues produce images in the same class. More importantly,we probe a novel way to make use of limited amount ofsupervision to the largest extent, to promote inter-class dis-entanglement performance. Extensive experimental resultson many well-known datasets demonstrate the efficacy ofCD-GAN for disentangling inter-class variation

What you don't know... can't hurt you? A natural field experiment on relative performance feedback in higher education

This paper studies the effect of providing feedback to college students on their position in the grade distribution by using a natural field experiment. This information was updated every six months during a three-year period. We find that greater grades transparency decreases educational performance, as measured by the number of examinations passed and grade point average (GPA). However, self-reported satisfaction, as measured by surveys conducted after feedback is provided but before students take their examinations, increases. We provide a theoretical framework to understand these results, focusing on the role of prior beliefs and using out-of-trial surveys to test the model. In the absence of treatment, a majority of students underestimate their position in the grade distribution, suggesting that the updated information is “good news” for many students. Moreover, the negative effect on performance is driven by those students who underestimate their position in the absence of feedback. Students who overestimate initially their position, if anything, respond positively. The performance effects are short lived—by the time students graduate, they have similar accumulated GPA and graduation rates

Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code and dataset will be publicly available

LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech

Recent advances in neural text-to-speech (TTS) models bring thousands of TTS applications into daily life, where models are deployed in cloud to provide services for customs. Among these models are diffusion probabilistic models (DPMs), which can be stably trained and are more parameter-efficient compared with other generative models. As transmitting data between customs and the cloud introduces high latency and the risk of exposing private data, deploying TTS models on edge devices is preferred. When implementing DPMs onto edge devices, there are two practical problems. First, current DPMs are not lightweight enough for resource-constrained devices. Second, DPMs require many denoising steps in inference, which increases latency. In this work, we present LightGrad, a lightweight DPM for TTS. LightGrad is equipped with a lightweight U-Net diffusion decoder and a training-free fast sampling technique, reducing both model parameters and inference latency. Streaming inference is also implemented in LightGrad to reduce latency further. Compared with Grad-TTS, LightGrad achieves 62.2% reduction in paramters, 65.7% reduction in latency, while preserving comparable speech quality on both Chinese Mandarin and English in 4 denoising steps.Comment: Accepted by ICASSP 202

Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation

The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image. This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM), which introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Technically, we design a fine-grained cross-modal alignment module to well explore the sequence similarity between the two modalities for learning robust feature representations. To improve the generalization ability, we propose a contrast-augmented diffusion model to explicitly explore positive and negative samples by maximizing a novel contrastive variational objective, which is mathematically inferred to provide a tighter bound for the model's optimization. Moreover, the context-aware cross attention module is developed to capture the contextual information within markup language during the denoising process, yielding better noise prediction results. Extensive experiments are conducted on four benchmark datasets from different domains, and the experimental results demonstrate the effectiveness of the proposed components in FSA-CDM, significantly exceeding state-of-the-art performance by about 2%-12% DTW improvements. The code will be released at https://github.com/zgj77/FSACDM.Comment: Accepted to ACM MM 2023. The code will be released at https://github.com/zgj77/FSACD

Don't Chase Your Tail! Missing Key Aspects Augmentation in Textual Vulnerability Descriptions of Long-tail Software through Feature Inference

Augmenting missing key aspects in Textual Vulnerability Descriptions (TVDs) for software with a large user base (referred to as non-long-tail software) has greatly advanced vulnerability analysis and software security research. However, these methods often overlook software instances that have a limited user base (referred to as long-tail software) due to limited TVDs, variations in software features, and domain-specific jargon, which hinders vulnerability analysis and software repairs. In this paper, we introduce a novel software feature inference framework designed to augment the missing key aspects of TVDs for long-tail software. Firstly, we tackle the issue of non-standard software names found in community-maintained vulnerability databases by cross-referencing government databases with Common Vulnerabilities and Exposures (CVEs). Next, we employ Large Language Models (LLMs) to generate the missing key aspects. However, the limited availability of historical TVDs restricts the variety of examples. To overcome this limitation, we utilize the Common Weakness Enumeration (CWE) to classify all TVDs and select cluster centers as representative examples. To ensure accuracy, we present Natural Language Inference (NLI) models specifically designed for long-tail software. These models identify and eliminate incorrect responses. Additionally, we use a wiki repository to provide explanations for proprietary terms. Our evaluations demonstrate that our approach significantly improves the accuracy of augmenting missing key aspects of TVDs for log-tail software from 0.27 to 0.56 (+107%). Interestingly, the accuracy of non-long-tail software also increases from 64% to 71%. As a result, our approach can be useful in various downstream tasks that require complete TVD information