Hasil untuk "Instruments and machines"

Yu Zhu, Kang Li, Zheng Li et al.

To continuously enhance model adaptability in surgical video scene parsing, recent studies incrementally update it to progressively learn to segment an increasing number of surgical instruments over time. However, prior works constantly overlooked the potential of positive forward knowledge transfer, i.e., how past knowledge could help learn new classes, and positive backward knowledge transfer, i.e., how learning new classes could help refine past knowledge. In this paper, we propose a self-reflection hierarchical prompt framework that unlocks the power of positive forward and backward knowledge transfer in class incremental segmentation, aiming to proficiently learn new instruments, improve existing skills of regular instruments, and avoid catastrophic forgetting of old instruments. Our framework is built on a frozen, pre-trained model that adaptively appends instrument-aware prompts for new classes throughout training episodes. To enable positive forward knowledge transfer, we organize instrument prompts into a hierarchical prompt parsing tree with the instrument-shared prompt partition as the root node, n-part-shared prompt partitions as intermediate nodes and instrument-distinct prompt partitions as leaf nodes, to expose the reusable historical knowledge for new classes to simplify their learning. Conversely, to encourage positive backward knowledge transfer, we conduct self-reflection refining on existing knowledge by directed-weighted graph propagation, examining the knowledge associations recorded in the tree to improve its representativeness without causing catastrophic forgetting. Our framework is applicable to both CNN-based models and advanced transformer-based foundation models, yielding more than 5% and 11% improvements over the competing methods on two public benchmarks respectively.

Guochu Deng, Garry J. McIntyre

Experimental data collected from a triple-axis spectrometer (TAS) are typically analysed by considering the instrument resolution, as the resolution of a TAS instrument is often complex and significantly influences the measured results. Two Python packages, TasVisAn and InsPy, have been developed to visualize and analyse data from TAS instruments - particularly from the cold-neutron TAS Sika and the thermal-neutron TAS Taipan at the Australian Centre for Neutron Scattering. TasVisAn offers a range of functions, including data importing, reduction, plotting, contour mapping, convolution fitting, and more, for data collected on TAS instruments, especially on Sika and Taipan. It also supports data reduction of the current trendy multi-analyser and multiplexing TAS instruments, including the multiplexing mode of Sika. Besides, it includes scan simulation and batch file validation tools for both Taipan and Sika, assisting users in designing and planning experiments in advance. InsPy is a general-purpose Python package designed to calculate the four-dimensional (4D) instrument resolution in momentum-energy space for any TAS instrument. Combined with InsPy, TasVisAn supports both instrument resolution calculation and resolution-convoluted data fitting. Its flexible external data import feature further allows TasVisAn to be adapted for the visualization and convolution analysis of inelastic neutron scattering data across various TAS instruments.

Huan Wu

This paper develops a general framework for identifying causal effects in settings with spillovers, where both outcomes and endogenous treatment decisions are influenced by peers within a known group. It introduces the generalized local average controlled spillover and direct effects (LACSEs and LACDEs), which extend the local average treatment effect framework to settings with spillovers and establish sufficient conditions for their point identification without restricting the cardinality of the support of instrumental variables. These conditions clarify the necessity of commonly imposed restrictions to achieve point identification with binary instruments in related studies. The paper then defines the marginal controlled spillover and direct effects (MCSEs and MCDEs), which naturally extend the marginal treatment effect framework to settings with spillovers and are nonparametrically point identified from continuous variation in instruments. These marginal effects serve as building blocks for a broad class of policy-relevant treatment effects, including some causal spillover parameters in the related literature. Semiparametric and parametric estimators are developed, and an application using Add Health data reveals heterogeneity in education spillovers within best-friend networks.

Jiseung Yoo, Curran Mahowald, Meiyu Li et al.

Large Language Models (LLMs) are transforming information extraction from academic literature, offering new possibilities for knowledge management. This study presents an LLM-based system designed to extract detailed information about research instruments used in the education field, including their names, types, target respondents, measured constructs, and outcomes. Using multi-step prompting and a domain-specific data schema, it generates structured outputs optimized for educational research. Our evaluation shows that this system significantly outperforms other approaches, particularly in identifying instrument names and detailed information. This demonstrates the potential of LLM-powered information extraction in educational contexts, offering a systematic way to organize research instrument information. The ability to aggregate such information at scale enhances accessibility for researchers and education leaders, facilitating informed decision-making in educational research and policy.

SUN Haoran, WANG Zhihao, WU Yifan et al.

Computer education is one of the requirements of modern information society education. With the development of large language models, there has been increasing attention on applying of large language models to the computer education process. However, the hallucination problem associated with large language models poses significant challenges to their application. To solve the challenges, RAG techniques by incorporating external knowledge bases can effectively enhance the quality of responses generated by large language models. However, the traditional RAG techniques lack a fine screening mechanism for the retrieved information, which leads to the retention of a large amount of low-correlation knowledge, and the interference of irrelevant information makes the model hallucination problem not effectively solved. We collected computer-related textbooks and knowledge documents, dividing them into knowledge document blocks according to the content structure to construct an external knowledge database. On this base, we introduced the large educational models question-answering enhancement method based on re-ranking and post-retrieval reflection, which utilized a high-performance multilingual re-ranking model based on a cross-encoder to capture deep semantic information, filter the retrieval information, filter out irrelevant information to improve the retrieval quality. The proposed method applied RAG techniques for model reflection so that the model can further enhance the quality of the model's answers through self-examination, and effectively improve the accuracy of the large language model in computer question-answering. This approach significantly improves the accuracy of large language models in computer question-answering tasks. The proposed method has been tested on several popular current generative models, achieving promising results on CS-Bench, with an approximate 5% increase in accuracy for computer question-answering tasks.

Stanton Heister, Felix Kin Peng Hui, David Ian Wilson et al.

The 2015 Paris Agreement paved the way for the carbon trade economy, which has since evolved but has not attained a substantial magnitude. While carbon credit exchange is a critical mechanism for achieving global climate targets, it faces persistent challenges related to transparency, double-counting, and verification. This paper examines how Distributed Ledger Technology (DLT) can address these limitations by providing immutable transaction records, automated verification through digitally encoded smart contracts, and increased market efficiency. To assess DLT’s strategic potential for leveraging the carbon markets and, more explicitly, whether its implementation can reduce transaction costs and enhance market integrity, three alternative approaches that apply DLT for carbon trading were taken as case studies. By comparing key elements in these DLT-based carbon credit platforms, it is elucidated that these proposed frameworks may be developed for a scalable global platform. The integration of existing compliance markets in the EU (case study 1), Australia (case study 2), and China (case study 3) can act as a standard for a global carbon trade establishment. The findings from these case studies suggest that while DLT offers a promising path toward more sustainable carbon markets, regulatory harmonization, standardization, and data transfer across platforms remain significant challenges.

Malte Londschien, Peter Bühlmann

We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition or as the number of instruments grows to infinity. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used non-weak-instrument-robust Wald test. Additionally, we provide a closed-form solution for subvector confidence sets obtained by inverting the subvector Anderson-Rubin test. We show that they are centered around a k-class estimator. We show that the subvector confidence sets for single coefficients of the causal parameter are jointly bounded if and only if Anderson's likelihood-ratio test rejects the null hypothesis that the first-stage regression parameter is of reduced rank, that is, that the causal parameter is not identified. Finally, we show that if a confidence set obtained by inverting the Anderson-Rubin test is bounded and nonempty, it is equal to a Wald-based confidence set with a data-dependent confidence level. We explicitly compute this Wald-based confidence set and its confidence level.

Sergii Degtyar, Oleh Kopiika, Yurii Shusharin

Branching processes as a mathematical concept has applications in various fields, including information technology. In information technology, branching processes can be used to model and analyze various scenarios, such as the propagation of data or information in a network, the growth of computer viruses, the spread of software bugs, and more. Branching processes are particularly useful for understanding the dynamics of systems where events can lead to multiple new events in a probabilistic manner. Overall, branching processes provide a valuable mathematical framework for modeling and analyzing various aspects of information technology, helping to make informed decisions and optimize IT systems and networks. We have studied transient phenomena for branching processes with an infinite number of types close to critical. The analytical apparatus for this study is Markov renewal theorems. Branched processes were used to evaluate the performance of IT systems and predict their behavior under different conditions. This is important for capacity planning and resource allocation.

Václav Diviš, Bastian Spatz, Marek Hrúz

Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out-of-Distribution recognition. Although the original problem remains unsolved, the term “Out-of-Model Scope” detection offers a clearer perspective. The ability to detect Out-of-Model Scope inputs is particularly beneficial in safety-critical applications such as autonomous driving or medicine. By detecting Out-of-Model Scope situations, the system’s robustness is enhanced and it is prevented from operating in unknown and unsafe scenarios. In this paper, we propose a novel approach for Out-of-Model Scope detection that integrates three sources of information: (1) the original input, (2) its latent feature representation extracted by an encoder, and (3) a synthesized version of the input generated from its latent representation. We demonstrate the effectiveness of combining original and synthetically generated inputs to defend against adversarial attacks in the computer vision domain. Our method, TRust Your GENerator (TRYGEN), achieves results comparable to those of other state-of-the-art methods and allows any encoder to be integrated into our pipeline in a plug-and-train fashion. Through our experiments, we evaluate which combinations of the encoder’s features are most effective for discovering Out-of-Model Scope samples and highlight the importance of a compact feature space for training the generator.

LIU Yao, QIN Xun, LIU Tianji

To address the issue of re-analyzing and repeating development of natural language processing tools and resource ana-lysis plugins when new requirements arise during project development,this paper proposes a business-oriented on-demand resource analysis solution.Firstly,a demand-driven resource analysis method from requirement to code is proposed,focusing on the construction of a demand concept indexing model for the requirement text itself.The constructed demand concept indexing model outperforms other classification models in terms of accuracy,recall,and F1 score.Secondly,this paper establishes a mapping mechanism from requirement text to code library categories based on the correlation between requirement text and code.For the mapping results,the precison@K is used as an evaluation metric,with an ultimate accuracy rate of 60%,demonstrating a certain practical value.In summary,this paper explores a set of key technologies for on-demand resource analysis with demand parsing capabilities and implements the correlation between requirements and code,covering the entire process from requirement text classification,code library classification,code library retrieval to plugin generation.The proposed method forms a complete business loop of “requirement-code-plugin-analysis” and experimentally verifies to be effective for on-demand resource analysis.Compared to existing large language models for business requirement analysis and code generation,this method focuses on the implementation of the full process of plugin code reuse within specific business domains,containing business characteristics.

张钊(ZHANG Zhao), 毛义华(MAO Yihua), 王凯(WANG Kai) et al.

With the impact of the epidemic and the deepening of population aging in China, the distribution and quality of medical resources have become important factors affecting housing prices, gradually generating the capitalization effects of medical resources. In this study, the differences in the resident's preference for the infectious hospital and general 3A hospitals in Harbin were explored in depth through a questionnaire survey and a comparative analysis of their capitalization effects. Furthermore, the social heterogeneity of the capitalization effects of medical resources and the homogeneity of the two kinds of medical resources were analyzed based on quantile regression models and interaction effects tests. The results show that (1) the infectious hospital depresses the prices of nearby housings, and general 3A hospitals increase the prices of nearby housings. The capitalization effect of both medical resources gradually decreases with increasing distance. (2) Medium-priced housings are more sensitive to the proximity of the infectious hospital, and the capitalization effect of general 3A hospitals gradually increases as the price of housings increases. (3) There is an interaction between the capitalization effects of the two kinds of medical resources, and the proximity of general 3A hospitals enhances the NIMBY (not in my backyard) effect of infectious hospitals.(受新型冠状病毒感染冲击以及我国人口老龄化程度加深的影响，医疗资源的分布与质量成为影响住宅价格的重要因素，从而产生了医疗资源资本化效应。通过问卷调查，分析了城市居民对哈尔滨市传染病医院与全科三甲医院两种医疗资源的偏好差异。基于分位数回归模型与交互效应检验，深入探讨了医疗资源资本化效应的空间异质性、社会异质性以及两种医疗资源的同质性。结果表明：（1）传染病医院抑制了附近的住宅价格，全科三甲医院提升了附近的住宅价格，且随着距离的增加，两种医疗资源的资本化效应均逐渐减弱；（2）中等价位住宅对与传染病医院的距离更敏感，随着住宅价格的提高，全科三甲医院的资本化效应逐渐增强；（3）两种医疗资源具有交互资本化效应，住宅与全科三甲医院临近增强了传染病医院的邻避效应。)

Viktor Nazymko, Liudmila Zakharova, Denis Boulik

Parametric and structural uncertainties complicate the project management processes. The critical path is one of the pivotal parameters, which helps to control the project schedule and is used to determine the criticality of the tasks and activities that are the most decisive and should be treated during a project expediting or controlling. There may be a set of the critical paths in uncertain environment. Therefore, the main question is which of the critical paths to select. The aim of this paper is to answer to this question. We used Monte Carlo simulation to investigate the multiple critical paths. We revealed and explained several paradoxes that emerged as results of the multiple critical paths occurrence. They are inevitable late bias of the project duration under uncertainty, the tasks probability and their correlation effects, the impact of concurrent chains of the tasks on their criticality, multiplicity of the critical paths and especially multi-chain critical paths. We demonstrated that multiple critical paths are not negative effect. On the contrary, they play extraordinary useful role and are the reliable criterion of the project robustness and stability.

Arya Fernandi, Sofia Sa'idah, Rita Magdalena

Hiragana is one of the primary alphabets used in Japanese. Hiragana is a phonetic symbol; each letter represents one syllable. Hiragana letters are formed from curved lines and strokes. However, detecting Hiragana letters causes many errors because people still rely on their vision to detect the letters, especially people familiar with them for the first time. It will be difficult and not very clear to read the letters. Therefore, a Convolutional Neural Network (CNN) method is used to detect handwritten Hiragana letters and help people who first get to know Hiragana letters when the letters are too complicated for human eyes to detect. This research uses the YOLOv8 model as a handwritten Hiragana letter detection algorithm. The Hiragana letters to be detected are basic letters with 46 characters. This research uses the YOLOv8 model run on Google Collaboratory with the Ultralytics library version 8.0.20 using the Python programming language. The dataset is collected from the internet and annotated using the Roboflow framework and dataset 4600 Hiragana letters. From the test results, the best model is YOLOv8l using SGD optimizer and learning rate 0.01 with a precision value of 98.5%, recall value of 95.7%, f1-score value of 97.1%, and mAP value of 95.5%. In the future, we aim to expand the number of datasets and employ a broader range of hyperparameter values to optimize the classification precision and accuracy of the Hiragana Letter Detection system.

Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter et al.

Surgical action triplets describe instrument-tissue interactions as (instrument, verb, target) combinations, thereby supporting a detailed analysis of surgical scene activities and workflow. This work focuses on surgical action triplet detection, which is challenging but more precise than the traditional triplet recognition task as it consists of joint (1) localization of surgical instruments and (2) recognition of the surgical action triplet associated with every localized instrument. Triplet detection is highly complex due to the lack of spatial triplet annotation. We analyze how the amount of instrument spatial annotations affects triplet detection and observe that accurate instrument localization does not guarantee better triplet detection due to the risk of erroneous associations with the verbs and targets. To solve the two tasks, we propose MCIT-IG, a two-stage network, that stands for Multi-Class Instrument-aware Transformer-Interaction Graph. The MCIT stage of our network models per class embedding of the targets as additional features to reduce the risk of misassociating triplets. Furthermore, the IG stage constructs a bipartite dynamic graph to model the interaction between the instruments and targets, cast as the verbs. We utilize a mixed-supervised learning strategy that combines weak target presence labels for MCIT and pseudo triplet labels for IG to train our network. We observed that complementing minimal instrument spatial annotations with target embeddings results in better triplet detection. We evaluate our model on the CholecT50 dataset and show improved performance on both instrument localization and triplet detection, topping the leaderboard of the CholecTriplet challenge in MICCAI 2022.

Yan Qian Lim, Yim Ling Loo

This paper presents a detailed analysis of the linguistic characteristics connected to specific levels of suicide risks, providing insight into the impact of the feature extraction techniques on the effectiveness of the predictive models of suicide ideation. Prevalent initiatives of research works had been observed in the detection of suicide ideation from social media posts through feature extraction and machine learning techniques but scarcely on the multiclass classification of suicide risks and analysis of linguistic characteristics' impact on predictability. To address this issue, this paper proposes the implementation of a machine learning framework that is capable of analyzing multiclass classification of suicide risks from social media posts with extended analysis of linguistic characteristics that contribute to suicide risk detection. A total of 552 samples of a supervised dataset of Twitter posts were manually annotated for suicide risk modeling. Feature extraction was done through a combination of feature extraction techniques of term frequency-inverse document frequency (TF-IDF), Part-of-Speech (PoS) tagging, and valence-aware dictionary for sentiment reasoning (VADER). Data training and modeling were conducted through the Random Forest technique. Testing of 138 samples with scenarios of detections in real-time data for the performance evaluation yielded 86.23% accuracy, 86.71% precision, and 86.23% recall, an improved result with a combination of feature extraction techniques rather than data modeling techniques. An extended analysis of linguistic characteristics showed that a sentence's context is the main contributor to suicide risk classification accuracy, while grammatical tags and strong conclusive terms were not.

ZANG Jie, ZHOU Wanlin, WANG Yan

Considering the matching problem of enterprise resources and customer requirements,the existing methods have the problems that the resource and requirement encapsulation is not accurate enough and the matching effect can't satisfy uses' requirement.In order to solve the problem of diversity and ambiguity of enterprise resource and requirement description,this paper proposes the dynamic user-defined template encapsulation.According to the feature that most of the encapsulated requirements and resources are Chinese short texts,an interactive text matching model which integrates multi-head attention mechanism and sia-mese network is proposed.The semantic differences and similarities between sentences are considered in this model.It uses word mixing vectors as input to enhance the semantic information of the text,combines the Siamese network with the multi-head attention mechanism,and extractes the semantic features of the context as an independent unit to fully interact with the semantic features.In order to verify the effectiveness of the model,the classical data set LCQMC and the self-constructed CSMD data set are used to conduct experiments on the model.The results show that the accuracy and performance of the model are improved in different degrees,which provides a more accurate matching method for enterprise resources and requirements.

Sergii Grybniak, Yevhen Leonchyk, Igor Mazurok et al.

Abstract A consensus protocol is a crucial mechanism of distributed networks by which nodes can coordinate their actions and the current state of data. This article describes a BlockDAG consensus algorithm based on the Proof of Stake approach. The protocol provides network participants with cross‐voting for the order of blocks, which, in the case of a fair vote, guarantees a quick consensus. Under conditions of dishonest behavior, cross‐voting ensures that violations will be quickly detected. In addition, the protocol assumes the existence of a Coordinating network containing information about the approved ordering, which qualitatively increases security and also serves to improve network synchronization.

Venelin Todorov, Slavi Georgiev, Ivan Dimov et al.

Oliver Schlenczek, Birte Thiede, Laura Turco et al.

Activities such as singing or playing a wind instrument release respiratory particles into the air that may contain pathogens and thus pose a risk for infection transmission. Here we report measurements of the size distribution, number, and volume concentration of exhaled particles from 31 healthy musicians playing 20 types of wind instruments using aerosol spectrometry and in-line holography in a strictly controlled cleanroom environment. We find that playing wind instruments carries a lower risk of airborne disease transmission than speaking or singing. We attribute this to the fact that the resonators of wind instruments act as filters for particles >10 $μ$m in diameter. We have also measured the size-dependent filtering properties of different types of filters that can be used as instrument masks. Based on these measurements, we calculated the risk of airborne transmission of SARS-CoV-2 in different near- and far-field scenarios with and without masking and/or distancing. We conclude that in all cases where there is a possibility that the musician is infectious, the only safe measure to prevent airborne transmission of the disease is the use of well-fitting and well-filtering masks for the instrument and the susceptible person.

Ibrahim Alameri, Jitka Komarkova, Tawfik Al-Hadhrami et al.

Mobile ad-hoc networks (MANETs) and wireless mesh networks (WMNs) are used in a variety of research areas, including the military, industry, healthcare, agriculture, the Internet of Things (IoT), transportation, and smart cities. The swift advancement in MANET technology is the driving force behind this rising adoption rate. Routing over MANET is a critical problem due to the dynamic nature of the link qualities, even when nodes are static. A key challenge in MANETs is the need for an efficient routing protocol that establishes a route according to certain performance metrics related to the link quality. The routing protocols utilised by the nodes in WMNs and MANETs are distinct. Nodes in both types of networks exchange data packets through the routing protocols. For this highly mobile network, the ad-hoc On-Demand Distance Vector (AODV) routing protocol has been suggested as a possible solution. Recent years have attracted researchers’ attention to AODV since it is a routing technique for ad-hoc networks that prevents looping. The architecture of this routing protocol considers several factors, including the mobility of nodes, the failure of connection links, and the loss of packets. In this systematic review, one of the key focuses is bringing attention to the classic AODV, which was developed after discussing the recent development of several versions of AODV. The AODV routing protocol performs a path strength check to generate a more reliable and secure route between the source and destination nodes. In AODV, investigations demonstrate advances in both the format protocol approach and the network simulation-2 (NS-2), and these improvements were made in the same scenario used to revitalise AODV. It has been discovered that the AODV is more effective in several aspects, such as throughput, end-to-end delay, packet delivery ratio (PDR), energy consumption, jitter, packet loss ratio, and network overhead. Furthermore, this paper presents this systematic review based on AODV modifications in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It also provides a methodological framework for the papers’ selection.