Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data Mining Study for Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Carpal Tunnel Syndrome

Background Carpal tunnel syndrome (CTS) is the most prevalent upper limb compressive neuropathy. A considerable number of clinical trials and meta-analyses have provided evidence supporting the effectiveness of acupuncture in treating CTS. Nevertheless, the ideal choice of acupoints remains ambiguous. Objective A data mining analysis was conducted with the objective of determining the most effective acupoint combinations and selection for CTS. Methods A search was conducted across seven Chinese and English electronic bibliographic databases spanning from their inception to March 2023. Selected were clinical trials that evaluated the efficacy of acupuncture therapy for CTS, with or without randomised controlled methods. Data extraction mainly included acupoint prescriptions. Information such as first author, study design and study setting were also extracted. The principal outcomes comprised the clinical manifestations linked to CTS. Statistical descriptions were generated using Excel 2019. The analysis of association rules was conducted using SPSS Modeler 18.0. Using SPSS Statistics 26.0, exploratory factor analysis and cluster analysis were conducted. Results 142 trials (including 86 RCTs and 56 non RCTs) were identified, and 193 groups of effective prescriptions involving 68 acupoints were extracted. The most frequently used acupoints were Da-ling (PC7), Nei-guan (PC6), He-gu (LI4), Wai-guan (TE5), and Yang-xi (LI5). The most frequently used meridians were the pericardial meridian and the large intestine meridian. The majority of special acupoints used were Five-shu points and Yuan-source points, with acupoints on the upper limbs being the most frequently used. The core acupoint groups were analyzed and 11 groups of association rules, 8 factors, and 5 effective cluster groups were obtained. Conclusion The evidence-based acupoint selection and combinations of acupuncture therapy for carpal tunnel syndrome were provided by the findings of this study.
A sort of fast matching algorithm of Auto Rack Girder Detection System

Auto Rack Girder Detection System based on image mosaicing technique for aerial images were studied. Phase correlation method is used to complete Rack Girder image mosaicing. With Fast Fourier Transform of images, the phase information in frequency domain is used to realize image mosaicing. Considering a lot of Sequence Images having features need to be real-time stitched, improved Fourier field stitching algorithm is put forward in the paper. At first large region is restricted within overlapped areas of image, and then two-dimension pulsing signal is gotten after cross-power spectrum is counted. Registration parameters are gained through maximum of pulsing signal. To bundle image, a new weighting image smoothing algorithm based on Canny is put forward. To the extent, the algorithm has general adaptability to some factors such as lamination variation and noise and so on. The algorithm suits a lot Sequence Images fast stitching as the fast speed of stitching because of fast speed of stitching. The analytic conclusions indicate that the algorithm is adopted to fast stitch a lot of sequence images, and it has general adaptability to some factors such as lamination variation, noise and so on.
When in Doubt Ask the Crowd: Employing Crowdsourcing for Active Learning

Crowdsourcing has become ubiquitous in machine learning as a cost effective method to gather training labels. In this paper we examine the challenges that appear when employing crowdsourcing for active learning, in an integrated environment where an automatic method and human labelers work together towards improving their performance at a certain task. By using Active Learning techniques on crowd-labeled data, we optimize the performance of the automatic method towards better accuracy, while keeping the costs low by gathering data on demand. In order to verify our proposed methods, we apply them to the task of deduplication of publications in a digital library by examining metadata. We investigate the problems created by noisy labels produced by the crowd and explore methods to aggregate them. We analyze how different automatic methods are affected by the quantity and quality of the allocated resources as well as the instance selection strategies for each active learning round, aiming towards attaining a balance between cost and performance.
Assessing the Validity of the Ergotex IMU in Joint Angle Measurement: A Comparative Study with Optical Tracking Systems

An observational, repeated measures design was used in this study to assess the validity of the Ergotex Inertial Measurement Unit (IMU) against a 3D motion capture system for measuring trunk, hip, and shoulder angles in ten healthy adult males (38.8 ± 7.3 y, bodyweight 79.2 ± 115.9 kg, body height 179.1 ± 8.1 cm). There were minimal systematic differences between the devices, with the most significant discrepancy being 1.4 degrees for the 80-degree target angle, denoting Ergotex’s precision in joint angle measurements. These results were statistically significant (p < 0.001), with predominantly trivial to small effect sizes, indicating high accuracy for clinical and biomechanical applications. Bland–Altman analysis showed Limits of Agreement (LoA) approximately ±2.5 degrees across all angles and positions, with overall LoA ranging from 3.6 to −2.4 degrees, reflecting Ergotex’s consistent performance. Regression analysis indicated uniform variance across measurements, with minor heteroscedastic errors producing a negligible underestimation trend of around 0.5 degrees in some instances. In conclusion, the Ergotex IMU is a reliable tool for accurate joint angle measurements. It offers a practical and cost-effective alternative to more complex systems, particularly in settings where precise measurement is essential.
Young people ’ s support and perceptions of political parties in 38 countries : An international comparison based on ICCS 2009

Over the past decades it has been claimed that young people have become less engaged in political parties and that youth sections of political parties are declining. However, there is little comparative international evidence about the extent to which students in junior secondary school form preferences for political parties and intend to align with parties as adults. The International Civic and Citizenship Education Study (ICCS 2009) focused on the civic knowledge and attitudes to aspects of citizenship (attitudes, values, behaviours and intended behaviours) in 38 countries. It generated information from large representative samples of students in their eighth year of schooling. The ICCS survey gathered data from students about their attachment to a particular party and its strength to measure their support for political parties. In addition the survey measured students’ views about the importance of joining a political party as an aspect of citizenship, their trust in political parties; and their expectation to join a political party in the future. This paper reports on the variation among countries in their support and perceptions of political parties as well as the extent to which students expect to join a political party. It reviews to what extent these results correspond to indicators at the national level and also investigates which background and affective-behavioural factors explain students’ commitment to a political party.
Principal interactions analysis for repeated measures data: application to gene–gene and gene–environment interactions

Many existing cohorts with longitudinal data on environmental exposures, occupational history, lifestyle/ behavioral characteristics, and health outcomes have collected genetic data in recent years. In this paper, we consider the problem of modeling gene–gene and gene–environment interactions with repeated measures data on a quantitative trait. We review possibilities of using classical models proposed by Tukey (1949) and Mandel (1961) using the cell means of a two‐way classification array for such data. Although these models are effective for detecting interactions in the presence of main effects, they fail miserably if the interaction structure is misspecified. We explore a more robust class of interaction models that are based on a singular value decomposition of the cell‐means residual matrix after fitting the additive main effect terms. This class of additive main effects and multiplicative interaction models (Gollob, 1968) provide useful summaries for subject‐specific and time‐varying effects as represented in terms of their contribution to the leading eigenvalues of the interaction matrix. It also makes the interaction structure more amenable to geometric representation. We call this analysis ‘principal interactions analysis’. While the paper primarily focuses on a cell‐mean‐based analysis of repeated measures outcome, we also introduce resampling‐based methods that appropriately recognize the unbalanced and longitudinal nature of the data instead of reducing the response to cell means. We illustrate the proposed methods by using data from the Normative Aging Study, a longitudinal cohort study of Boston area veterans since 1963. We carry out simulation studies under an array of classical interaction models and common epistasis models to illustrate the properties of the principal interactions analysis procedure in comparison with the classical alternatives. Copyright © 2012 John Wiley & Sons, Ltd.
Data streams: algorithms and applications

Data stream algorithms as an active research agenda emerged only over the past few years, even though the concept of making few passes over the data for performing computations has been around since the early days of Automata Theory. The data stream agenda now pervades many branches of Computer Science including databases, networking, knowledge discovery and data mining, and hardware systems. Industry is in synch too, with Data Stream Management Systems (DSMSs) and special hardware to deal with data speeds. Even beyond Computer Science, data stream concerns are emerging in physics, atmospheric science and statistics. Data Streams: Algorithms and Applications focuses on the algorithmic foundations of data streaming. In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Data Streams: Algorithms and Applications surveys the emerging area of algorithms for processing data streams and associated applications. An extensive bibliography with over 200 entries points the reader to further resources for exploration.
Satellite Navigation and Geospatial Awareness: Long-Term Effects of Using Navigation Tools on Wayfinding and Spatial Orientation

This article examined long-term effects of using navigation tools on wayfinding and spatial orientation, through a survey analysis of the experience of using navigation tools and spatial aptitudes, and a behavioral experiment of real-world navigation. Experience of tool use was measured in terms of regular use (time length and frequency) and accumulated experience (time length multiplied by frequency). The survey analysis showed that frequent users of pedestrian navigation systems tended to be low on sense of direction and mental rotation. In contrast, longtime users of maps tended to be high on sense of direction and favor survey navigation strategies. The behavioral experiment showed that people who had more accumulated experience of using in-car navigation systems traveled less efficiently and learned the configurations of traveled routes less accurately with a mobile tool and a paper map. The analysis of long-term effects through structural equation modeling showed that spatial aptitudes and accumulated experience of tool use independently affect wayfinding and spatial orientation and that the negative effects of accumulated experience were larger than the positive effects of spatial aptitudes. The results and implications are discussed in relation to existing studies of short-term effects and spatial thinking.
Streaming Partitioning of Sequences and Trees

We study streaming algorithms for partitioning integer sequences and trees. In the case of trees, we suppose that the input tree is provided by a stream consisting of a depth-first-traversal of the input tree. This captures the problem of partitioning XML streams, among other problems. We show that both problems admit deterministic (1+epsilon)-approximation streaming algorithms, where a single pass is sufficient for integer sequences and two passes are required for trees. The space complexity for partitioning integer sequences is O((1/epsilon) * p * log(nm)) and for partitioning trees is O((1/epsilon) * p^2 * log(nm)), where n is the length of the input stream, m is the maximal weight of an element in the stream, and p is the number of partitions to be created. Furthermore, for the problem of partitioning integer sequences, we show that computing an optimal solution in one pass requires Omega(n) space, and computing a (1+epsilon)-approximation in one pass requires Omega((1/epsilon) * log(n)) space, rendering our algorithm tight for instances with p,m in O(1).
The Dominant Folding Route Minimizes Backbone Distortion in SH3

Energetic frustration in protein folding is minimized by evolution to create a smooth and robust energy landscape. As a result the geometry of the native structure provides key constraints that shape protein folding mechanisms. Chain connectivity in particular has been identified as an essential component for realistic behavior of protein folding models. We study the quantitative balance of energetic and geometrical influences on the folding of SH3 in a structure-based model with minimal energetic frustration. A decomposition of the two-dimensional free energy landscape for the folding reaction into relevant energy and entropy contributions reveals that the entropy of the chain is not responsible for the folding mechanism. Instead the preferred folding route through the transition state arises from a cooperative energetic effect. Off-pathway structures are penalized by excess distortion in local backbone configurations and contact pair distances. This energy cost is a new ingredient in the malleable balance of interactions that controls the choice of routes during protein folding.
Procedural generation of flood-sensitive urban layouts

Aside from modeling geometric shape, three-dimensional (3D) urban procedural modeling has shown its value in understanding, predicting and/or controlling effects of shape on design and urban planning. In this paper, instead of the construction of flood resistant measures, we create a procedural generation system for designing urban layouts that passively reduce water depth during a flooding scenario. Our tool enables exploring designs that passively lower flood depth everywhere or mostly in chosen key areas. Our approach tightly integrates a hydraulic model and a parameterized urban generation system with an optimization engine so as to find the least cost modification to an initial urban layout design. Further, due to the computational cost of a fluid simulation, we train neural networks to assist with accelerating the design process. We have applied our system to several real-world locations and have obtained improved 3D urban models in just a few seconds.
Fast algorithms for recovering a corrupted low-rank matrix

This paper studies algorithms for solving the problem of recovering a low-rank matrix with a fraction of its entries arbitrarily corrupted. This problem can be viewed as a robust version of classical PCA, and arises in a number of application domains, including image processing, web data ranking, and bioinformatic data analysis. It was recently shown that under surprisingly broad conditions, it can be exactly solved via a convex programming surrogate that combines nuclear norm minimization and ℓ1-norm minimization. This paper develops and compares two complementary approaches for solving this convex program. The first is an accelerated proximal gradient algorithm directly applied to the primal; while the second is a gradient algorithm applied to the dual problem. Both are several orders of magnitude faster than the previous state-of-the-art algorithm for this problem, which was based on iterative thresholding. Simulations demonstrate the performance improvement that can be obtained via these two algorithms, and clarify their relative merits.
Using Lovász Local Lemma in the Space of Random Injections

The Lovasz Local Lemma is known to have an extension for cases where independence is missing but negative dependencies are under control. We show that this is often the case for random injections, and we provide easy-to-check conditions for the non-trivial task of verifying a negative dependency graph for random injections. As an application, we prove existence results for hypergraph packing and Turan type extremal problems. A more surprising application is that tight asymptotic lower bounds can be obtained for asymptotic enumeration problems using the Lovasz Local Lemma.
A Theory of Arrays with set and copy Operations

The theory of arrays is widely used in order to model main memory in program analysis, software verification, bounded model checking, symbolic execution, etc. Nonetheless, the basic theory as introduced by McCarthy is not expressive enough for important practical cases, since it only supports array updates at single locations. In programs, memory is often modified using functions such as memset or memcpy/memmove, which modify a userspecified range of locations whose size might not be known statically. In this paper we present an extension of the theory of arrays with set and copy operations which make it possible to reason about such functions. We also discuss further applications of the theory.
Scalable Multicast in Highly-Directional 60 GHz WLANs

60 GHz bands target multi-gigabit rate applications such as high definition video streaming. Unfortunately, to provide multicast service, the strong directionality required at 60 GHz precludes serving all clients in a multicast group with a single transmission. Instead, a multicast transmission is comprised of a sequence of beam-formed transmissions (a beam group) that together cover all multicast group members. In this paper, we design, implement, and experimentally evaluate Scalable Directional Multicast (SDM) as a technique to (i) train the access point with per-beam per client RSSI measurements via partially traversing a codebook tree. The training balances the objectives of limiting overhead with collecting sufficient data to form efficient beam groups. (ii) Using the available training information, we design a scalable beam grouping algorithm that approximates the minimum multicast group data transmission time. We implement the key components of SDM and evaluate with a combination of over the-air experiments and trace-driven simulations. Our results show that the gains provided by SDM increase with group size and provide near-optimal group selection with significantly reduced training time, yielding up to 1.8x throughput gains over exhaustive-search training and grouping.
Transposable Elements in Medaka Fish

Abstract DNA-based transposable elements appear to have been nearly or completely inactivated in vertebrates. Therefore the elements of the medaka fish Oryzias latipes that still have transposition activity provide precious materials for studying transposition mechanisms, as well as the evolution, of transposable elements in vertebrates. Fortunately, the medaka fish has a strong background for genetic and evolutionary studies. The advantages of this host species and their elements, together with results so far obtained, are here described.
[Study on the protective effect of green tea on gastric, liver and esophageal cancers].

OBJECTIVE To assess the protective effect of drinking green tea on the development of gastric, liver and esophageal cancers. METHODS A population based study was conducted in Taixing, Jiangsu province, including 206, 204, 218 cases, respectively, and 415 population controls. RESULTS Green tea decreased the development of gastric cancer risk by 40%. Dose-response relationships were observed between the length of time, concentration and quantity of green tea drinking and its protective effects on gastric cancer. For individuals who drink green tea for more than 250 g per month, the risk of gastric cancer reduced about 60%. Green tea might have protective effect on liver cancer. However, no protective effect of green tea was observed on esophageal cancer. CONCLUSION Green tea drinking might be a protective factor for gastric cancer. However, the protective effects of green tea on liver and esophageal cancer were not obvious.
SMAD3 Deficiency Promotes Inflammatory Aortic Aneurysms in Angiotensin II–Infused Mice Via Activation of iNOS

Background Ninety percent of the patients carrying distinct SMAD3 mutations develop aortic aneurysms and dissections, called aneurysms‐osteoarthritis syndrome (AOS). However, the etiology and molecular events downstream of SMAD3 leading to the pathogenesis of aortic aneurysms in these patients still remain elusive. Therefore, we aimed to investigate the vascular phenotypes of SMAD3‐knockout mice. Methods and Results We have shown that angiotensin II–induced vascular inflammation, but not hypertension, leads to aortic aneurysms and dissections, ultimately causing aortic rupture and death in mice. Lipopolysaccharide‐triggered inflammation confirmed that enhanced aortic macrophage recruitment was essential for aneurysm formation in angiotensin II–infused SMAD3‐knockout mice. In contrast, phenylephrine‐triggered hypertension alone was insufficient to induce aortic aneurysms in mice. Using uniaxial tensile and contractility tests, we showed that SMAD3 deficiency resulted in defective aortic biomechanics and physiological functions, which caused weakening of the aortic wall and predisposed the mice to aortic aneurysms. Chromatin immunoprecipitation (ChIP) and re‐ChIP assays revealed that the underlying mechanism involved aberrant upregulation of inducible nitric oxide synthase (iNOS)–derived nitric oxide production and activation of elastolytic matrix metalloproteinases 2 and 9. Administration of clodronate‐liposomes and iNOS inhibitor completely abrogated these aortic conditions, thereby identifying iNOS‐mediated nitric oxide secretion from macrophages as the downstream event of SMAD3 that drives this severe pathology. Conclusions Macrophage depletion and iNOS antagonism represent 2 promising approaches for preventing aortic aneurysms related to SMAD3 mutations and merit further investigation as adjunctive strategies for the life‐threatening manifestations of AOS.