Mean Shift Research Articles

Elbow kinematics with increased lengthening of a radial head arthroplasty evaluated with dynamic radiostereometric analysis.

The aim of this study was to evaluate the kinematics of the elbow following increasing length of the radius with implantation of radial head arthroplasties (RHAs) using dynamic radiostereometry (dRSA). Eight human donor arms were examined by dRSA during motor-controlled flexion and extension of the elbow with the forearm in an unloaded neutral position, and in pronation and supination with and without a 10 N valgus or varus load, respectively. The elbows were examined before and after RHA with stem lengths of anatomical size, + 2 mm, and + 4 mm. The ligaments were maintained intact by using a step-cut lateral humeral epicondylar osteotomy, allowing the RHAs to be repeatedly exchanged. Bone models were obtained from CT scans, and specialized software was used to match these models with the dRSA recordings. The flexion kinematics of the elbow were described using anatomical coordinate systems to define translations and rotations with six degrees of freedom. The greatest kinematic changes in the elbows were seen with the longest, + 4 mm, implant, which imposed a mean joint distraction of 2.8 mm in the radiohumeral joint and of 1.1 mm in the ulnohumeral joint, an increased mean varus angle of up to 2.4° for both the radius and the ulna, a mean shift of the radius of 2.0 mm in the ulnar direction, and a mean shift of the ulna of 1.0 mm posteriorly. The kinematics of the elbow deviated increasingly from those of the native joint with a 2 mm to a 4 mm lengthening of the radius. This confirms the importance of restoring the natural length of the radius when undertaking RHA.

A weighted dual cumulative sum chart for monitoring the process mean

AbstractThe cumulative sum (CUSUM) charts are well‐structured and widely used for detecting small‐to‐moderate changes in the process parameters. However, their performance can be limited when the shift size is unknown or varies over time. Various methods have been developed to address this issue, including dual CUSUM (DCUSUM), adaptive CUSUM, adaptive EWMA, and weighted CUSUM charts, all of which offer better sensitivity compared to traditional CUSUM charts. In this study, we propose a new weighted DCUSUM chart designed to effectively detect the mean shifts in a normally distributed process. Monte Carlo simulations are used to estimate the zero‐state and steady‐state average run‐length (ARL) profiles of the control charts. The ARL performances of the control charts are evaluated in terms of expected relative ARL (ERARL) and expected weighted run‐length (EWRL). Our results show that the proposed chart may outperform existing counterparts in terms of the ARL, ERARL, and EWRL measures, indicating superior performance in detecting a range of the mean shift sizes. Finally, we apply the existing and proposed charts to a real dataset, showcasing their practical utility in process monitoring.

Enhancing Recommendation Diversity and Novelty with Bi-LSTM and Mean Shift Clustering

In the digital age, personalized recommendation systems have become crucial for information dissemination and user experience. While traditional systems focus on accuracy, they often overlook diversity, novelty, and serendipity. This study introduces an innovative recommendation system model, Time-based Outlier Aware Recommender (TOAR), designed to address the challenges of content homogenization and information bubbles in personalized recommendations. TOAR integrates Neural Matrix Factorization (NeuMF), Bidirectional Long Short-Term Memory Networks (Bi-LSTM), and Mean Shift clustering to enhance recommendation accuracy, novelty, and diversity. The model analyzes temporal dynamics of user behavior and facilitates cross-domain knowledge exchange through feature sharing and transfer learning mechanisms. By incorporating an attention mechanism and unsupervised clustering, TOAR effectively captures important time-series information and ensures recommendation diversity. Experimental results on a news recommendation dataset demonstrate TOAR’s superior performance across multiple metrics, including AUC, precision, NDCG, and novelty, compared to traditional and deep learning-based recommendation models. This research provides a foundation for developing more intelligent and personalized recommendation services that balance accuracy with content diversity.

Meaning Shifts of Culture-Specific Expressions: A Case Study of Facebook

This research aimed to examine the effects of meaning shifts in culture-specific content on Facebook. Specifically, it sought to explore the linguistic, cultural, and grammatical similarities between English and Kiswahili, while highlighting the significant mismatches between these two languages. The study used the theory of Principle of Cultural Translation to provide the theoretical relevance. The target population comprised Facebook pages with a significant following and a focus on CSIs posted within the context of conversations on those pages. The study aimed to examine CSIs posted on Facebook pages based in Kenya on foods, fashion and cultural architecture where English-Kiswahili translations were assessed for errors. The study employed purposive sampling to select a sample of 11 Facebook pages for content analysis. Primary data was gathered through the content analysis method, which involved the systematic preparation, organization, and reporting of the data collected. The study found that lexical, rhetorical, and grammatical mis-matches contributed to misunderstandings and misrepresentations in a variety of domains, including food, fashion, and cultural architecture. Based on the analysis, 80% of the Facebook sources revealed food items like "ugali" and "Nyama Choma" were often reduced to overly simplified translations like "cornmeal" and "grilled meat," failing to capture their rich cultural significance. With regards of fashion, the study found that 70% of the Facebook pages mistranslated the garments such as "kanga" led to a loss of their deeper meanings, contributing to cultural appropriation. Similarly, architectural terms suffered from meaning shifts; 55% of the sources depicted that terms describing unique Kenyan architectural styles were reduced to words like "huts," ignoring the cultural and historical narratives they encapsulate. The study concludes that the meaning shifts of culture-specific expressions related to food, fashion, and architecture in Kenya on Facebook have various negative implications as it leads to misunderstandings that go beyond mere words. The study thus recommends that stickers that contain only the most basic information should be made more useful by scaling them down or making them smaller while figurative language, such as idioms, should be used to describe complex ideas. Keywords: Culture-specific expressions, Meaning shifts, Mistranslation, Lexical mismatches, Facebook content

An examination and analysis of the clustering of healthcare centers and their spatial accessibility in Tehran metropolis: Insights from Google POI data

Metropolises, as centers of diverse activities, encounter numerous challenges, including equitable access to healthcare services. This study examines the inequality in healthcare center accessibility in Tehran, a critical factor for sustainable urban development. Utilizing Google Point of Interest data, healthcare center clusters in Tehran were identified, and various clustering algorithms were tested. The Mean Shift Cluster algorithm was the most effective method. The study also mapped accessibility by analyzing the radius of neighborhoods around urban blocks. The findings reveal a significant imbalance in the distribution of healthcare centers in the city, with a concentration in the city center that does not align with population density—a key indicator of demand. Notably, District 7, despite being centrally located, relatively has a higher clusters' concentration of lower-quality healthcare centers. The findings reveal that four sub-districts situated in the peripheral regions of Tehran city, which collectively account for approximately 6.3% of the city's total area, exhibit significantly lower accessibility to the healthcare centers compared to other sub-districts. Overall, the areas with superior accessibility are predominantly found in the city center and extend towards the north, with accessibility decreasing as one moves towards the outskirts of the city. These results underscore the need for targeted urban planning to address the uneven distribution of healthcare resources and promote equitable access across Tehran.

Performance evaluation of extended EWMA control chart in the presence of measurement error

In any manufacturing or service industry, substantial effort is made to keep a process under control. However, factors such as measurement error are bound to create distortions. Many researchers have studied the effect of measurement error on various types of control charts including the EWMA control chart. In this paper, we study the effect of measurement error on the competency of the extended EWMA control chart. We examine the performance of the chart in the case of a shift in mean taking into account the model with linear covariates. The consequence of making multiple measurements on each sampled unit is also investigated. Last but not the least, we examine the case of linearly increasing variance. Through a simulation study, we demonstrate that the performance of the chart for detecting a shift in the mean is substantially reduced in the presence of measurement error. The important finding, however, is that, in spite of the existence of measurement error, the EEWMA control chart performs better than the EWMA control chart. A practical example is provided to elucidate this finding.

A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding

How to acquire the exact center of a circular sample is an essential task in object recognition. Present algorithms suffer from the high time consumption and low precision. To tackle these issues, we propose a novel circle center location algorithm based on sample density and adaptive thresholding. After obtaining circular contours through image pre-processing, these contours were segmented using a grid method to obtain the required coordinates. Based on the principle of three points forming a circle, a data set containing a large number of samples with circle center coordinates was constructed. It was highly probable that these circle center samples would fall within the near neighborhood of the actual circle center coordinates. Subsequently, an adaptive bandwidth fast Gaussian kernel was introduced to address the issue of sample point weighting. The mean shift clustering algorithm was employed to compute the optimal solution for the density of candidate circle center sample data. The final optimal center location was obtained by an iteration algorithm. Experimental results demonstrate that in the presence of interference, the average positioning error of this circle center localization algorithm is 0.051 pixels. Its localization accuracy is 64.1% higher than the Hough transform and 86.4% higher than the circle fitting algorithm.

A mean shift algorithm incorporating reachable distance for spatial clustering

Spatial clustering is a widely used technique in spatial analysis that groups similar objects together based on their proximity in space. However, traditional clustering algorithms often fail to ensure the accessibility of cluster centers, which limits their validity in practical applications such as facility location problems. To address this issue, this article introduces a novel Mean Shift algorithm that incorporates reachable distance and an iterative mechanism to accurately locate cluster centers. The proposed algorithm initially labels clustering elements with road network coordinates to facilitate the calculation of reachable distance and the cluster center iterative mechanism. Subsequently, the mean shift vector function is modified to employ reachable distance as the measure of geographic reachable similarity. Unlike existing algorithms, our approach allows for cluster centers to be positioned independently of the clustering elements, guaranteeing geographical accessibility. Through simulation experiments, we demonstrate that our proposed algorithm not only outperforms existing methods in terms of solution quality, but also effectively addresses the limitations of disregarding geographical obstacles and unreachable cluster centers.

Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the MK-LDA (MeanShift Kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-K-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

MNN: Mixed nearest-neighbors for self-supervised learning

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the issue of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is a significant yet often overlooked challenge. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors (MNN) for Self-Supervised Learning. The primary objective of the MNN is to enhance the robustness and accuracy of the model by strategically incorporating synthesized neighboring samples. Specifically, our proposed method utilizes image mixture to mitigate the effects of noise introduced by false neighbors, while adopting an intuitive weighting strategy that effectively integrates these synthesized neighbors into the model. The linear evaluation of MNN on CIFAR-10, CIFAR,100, STL-10, and Tiny ImageNet shows an improvement of 0.41% to 1.96% over the previous state-of-the-art (SOTA) methods and 1.82% to 8.8% over the Mean Shift (MSF) method. Furthermore, the proposed components can be seamlessly integrated into other self-supervised learning algorithms to enhance their performance. Code is available at https://github.com/pc-cp/MNN.

Cultural Perspectives on Financial Accountability in a Balinese Traditional Village

Abstract This study aims to develop accountability practices that harmonize with the local spiritual culture in the Jimbaran traditional village. The current accountability practices are viewed not just as formal procedures but also as informal practices involving moral and ethical relationships with God. An ethnomethodological study integrated with Catur Marga culture as a critical perspective revealed the problem of delays in submitting accountability reports due to the shift in the cultural meaning of “ngayah” and the shift in the implementation of religious ceremonies from spiritual practices to more formal activities. These problems can be addressed by synergizing Catur Marga principles with accountability practices. First, involve community representatives, religious leaders, and traditional elders in the village's financial management, as in regular meetings to align the vision and mission to strengthen the “ngayah” motivation in governance and financial accountability. Second, the traditional village governments must consistently provide Dharmawacana and management accounting training to balance scientific knowledge with traditional spiritual–cultural knowledge. Last is the development of the Catur Marga-based Pararem, which regulates the technical aspects of financial management accountability and should be synchronized with the spirit of devotion to God to preserve the local spiritual culture in the Jimbaran traditional village.

Studying word meaning evolution through incremental semantic shift detection

AbstractThe study of semantic shift, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computational semantics have accelerated the development of computational approaches to detecting such shift. In this paper, we introduce a novel approach to tracing the evolution of word meaning over time. Our analysis focuses on gradual changes in word semantics and relies on an incremental approach to semantic shift detection (SSD) called What is Done is Done (WiDiD). WiDiD leverages scalable and evolutionary clustering of contextualised word embeddings to detect semantic shift and capture temporal transactions in word meanings. Existing approaches to SSD: (a) significantly simplify the semantic shift problem to cover change between two (or a few) time points, and (b) consider the existing corpora as static. We instead treat SSD as an organic process in which word meanings evolve across tens or even hundreds of time periods as the corpus is progressively made available. This results in an extremely demanding task that entails a multitude of intricate decisions. We demonstrate the applicability of this incremental approach on a diachronic corpus of Italian parliamentary speeches spanning eighteen distinct time periods. We also evaluate its performance on seven popular labelled benchmarks for SSD across multiple languages. Empirical results show that our results are comparable to state-of-the-art approaches, while outperforming the state-of-the-art for certain languages.

Exchange Rates and Prices in the Netherlands and Britain over the Past Four Centuries

AbstractThe paper studies the long‐term stability of the purchasing‐power‐parity (PPP) relation using data for the Netherlands and Great Britain (earlier the Dutch Republic and England) from 1590 until 2020. We begin by investigating the behavior of the real exchange rate in rate‐of‐growth form finding strong evidence supporting long‐run relative PPP. Turning to price levels, we find evidence of mean‐reverting behavior over the whole period with one long‐lived shift in mean between 1788 and 1931. The myriad other factors that varied over this long period that might have affected the stability of the PPP relation do not appear to have mattered.

Analysis of Early-Stage Behavior and Multi-Parameter Early Warning Algorithm Research for Overcharge Thermal Runaway of Energy Storage LiFePO4 Battery Packs

Overcharging of lithium-ion batteries may lead to severe thermal runaway (TR) incidents, resulting in significant economic losses and safety hazards. Therefore, it is crucial to research early warning methods for TR behavior in overcharged lithium batteries. This study initially conducted overcharging experiments on LiFePO4 battery packs under different initial charging states and charging rates, analyzing variations in temperature, voltage, and inter-group pressure during overcharging. The TR process was divided into three stages: non-overcharged, early, and middle. Based on this, temperature change rate, pressure change rate, and voltage were extracted as input feature parameters, and the Mean Shift algorithm was employed for stage identification and classification of overcharging experiments on LiFePO4 battery packs. According to experimental results, the algorithm achieved an accuracy of over 96% in stage identification and classification of TR in overcharged lithium batteries, accurately determining the current stage of TR and providing a reliable and effective solution for preventing TR in overcharged lithium batteries.

Advances in breeding phenology outpace latitudinal and elevational shifts for North American birds tracking temperature.

Terrestrial species can respond to a warming climate in multiple ways, including shifting in space (via latitude or elevation) and time (via phenology). Evidence for such shifts is often assessed independent of other temperature-tracking mechanisms; critically, no study has compared shifts across all three spatiotemporal dimensions. Here we used two continental-scale monitoring databases to estimate trends in the breeding latitude (311 species), elevation (251 species) and phenology (111 species) of North American landbirds over 27 years, with a shared pool of 102 species. We measured the magnitude of shifts and compared them relative to average regional warming (that is, shift ratios). Species shifted poleward (1.1 km per year, mean shift ratio 11%) and to higher elevations (1.2 m per year, mean shift ratio 17%), while also shifting their breeding phenology earlier (0.08 days per year, mean shift ratio 28%). These general trends belied substantial variation among species, with some species shifting faster than climate, whereas others shifted more slowly or in the opposite direction. Across the three dimensions (n = 102), birds cumulatively tracked temperature at 33% of current warming rates, 64% of which was driven by advances in breeding phenology as opposed to geographical shifts. A narrow focus on spatial dimensions of climate tracking may underestimate the responses of birds to climate change; phenological shifts may offer an alternative for birds-and probably other organisms-to conserve their thermal niche in a warming world.

Abstract P115: Hypothetical Blood Pressure Lowering Interventions and Risk of Mortality in Middle-Aged and Older Chinese Adults: A Population-Based Modelling Study

Background: Improving hypertension control is an important global health priority. We aimed to study the effects of hypothetical interventions on systolic blood pressure (SBP) on mortality risk at the population level. Methods: We estimated the relationship between SBP and all-cause mortality in adults aged 45 years and older from the China Health and Retirement Longitudinal Study and compared the mortality reductions associated with lowering SBP to different hypothetical targets (120, 130, 140, 150 mm Hg). Using the parametric g-computation modeling technique, we estimated the mean blood pressure reduction required to achieve each target, the share of the population in need of management, and the number needed to treat (NNT) to avert one death under different hypothetical population-wide scale-up scenarios. Results: Among 12355 participants, 1203 died over 7-year follow-up. We found a weak, non-linear relationship between SBP and mortality, with larger incremental mortality benefits at higher SBP values: reducing SBP from 160 mm Hg to 120 mm Hg was associated with a relative risk of mortality of 0.75 (95% CI 0.65 to 0.86). At the population level, reducing SBP to 120 mm Hg among all those with a starting SBP of more than 120 mm Hg (120 mm Hg scenario) was associated with 13.7 deaths averted per 1000 population (95% CI -19.3 to -8.1), compared with 3.8 deaths averted per 1000 population (95% CI -6.0 to -1.6) associated with reducing SBP to 150 mm Hg among all those with a starting SBP of more than 150 mm Hg (150 mm Hg scenario). However, there were large differences between scenarios in the proportion of the population that would require treatment, ranging from 64.3% (95% CI 63.4 to 65.1) in the 120 mm Hg scenario to 16.7% (6.0 to 17.3) in the 150 mm Hg scenario. To achieve the target, the 120 mm Hg scenario required a mean shift of -14.4 mm Hg in SBP (95% CI -14.7 to -14.1) compared with a -2.9 mm Hg mean change (-3.0 to -2.7) for the 150 mm Hg scenario. Based on the ratio of number of deaths averted to the proportion requiring treatment, the 150 mm Hg scenario had the smallest NNT (n=44), followed by the 120 mm Hg scenario (n=47), the 140 mm Hg scenario (n=49), and the 130 mm Hg scenario (n=51). Conclusions: These findings highlight the possible substantial benefits of reducing blood pressure. We find that scaling up management based on a 150 mm Hg target is more efficient in terms of the NNT compared with strategies to reduce SBP to lower levels.

The language and motivations of expertise in political discourse

The study aims to explore how language elements of specialised and political communication are represented in parliamentary discourse. Within the ParlaMint corpus, we examine Hungarian parliamentary speeches between 2020 and 2022 on the ‘KATA’ (specific tax of small taxpayer businesses). The specialised terms, terms used in standard language, sentiment and attitude values are analysed. Results show that there is a significant difference in term use and the sentence sentiments between the different discourse participants (policy actors, opposition, governing party). There are only a few emotionless speeches, and no large differences in the proportion of terms and emotions are observed. The results can be relevant for studies on populist communication, the relationship between technocracy and democracy, and the methodological aspects of computer-assisted discourse analysis. The paper also outlines future orientations for research, including extended thematic analysis and investigation of contextual shifts in the actual meaning of terms within political discourse.

Metafiction, intertextuality, and consumerism in transmedia resemiotization: a postmodern analysis of a comic book adaptation of the Chinese classic Journey to the West

Abstract Classic Chinese novels have frequently been adapted into other media formats. Such transmedia adaptations involve shifts in meaning, which are often shaped by contemporary postmodern culture. This article explores the textual transformations in a comic book adaptation of the classic Chinese novel, Journey to the West, and the postmodern culture behind these transformations. Viewing transmedia adaptation as resemiotization, we analyse the metafictional strategies, intertextuality, and representation of consumerism in the comic book. These three aspects, which subvert the narrative of the original novel, define the postmodern nature of the comic book adaptation. We conclude that the comic book adaptation is not only a nostalgic representation but also a critical rewriting of the Chinese classic, which is shaped by postmodern norms and values. This study enriches our understanding of the adaptation and reception of the classical novel Journey to the West in contemporary popular culture, thus shedding light on the postmodern rewriting of Chinese classics in general.

EMS: A Large-Scale Eye Movement Dataset, Benchmark, and New Model for Schizophrenia Recognition.

Schizophrenia (SZ) is a common and disabling mental illness, and most patients encounter cognitive deficits. The eye-tracking technology has been increasingly used to characterize cognitive deficits for its reasonable time and economic costs. However, there is no large-scale and publicly available eye movement dataset and benchmark for SZ recognition. To address these issues, we release a large-scale Eye Movement dataset for SZ recognition (EMS), which consists of eye movement data from 104 schizophrenics and 104 healthy controls (HCs) based on the free-viewing paradigm with 100 stimuli. We also conduct the first comprehensive benchmark, which has been absent for a long time in this field, to compare the related 13 psychosis recognition methods using six metrics. Besides, we propose a novel mean-shift-based network (MSNet) for eye movement-based SZ recognition, which elaborately combines the mean shift algorithm with convolution to extract the cluster center as the subject feature. In MSNet, first, a stimulus feature branch (SFB) is adopted to enhance each stimulus feature with similar information from all stimulus features, and then, the cluster center branch (CCB) is utilized to generate the cluster center as subject feature and update it by the mean shift vector. The performance of our MSNet is superior to prior contenders, thus, it can act as a powerful baseline to advance subsequent study. To pave the road in this research field, the EMS dataset, the benchmark results, and the code of MSNet are publicly available at https://github.com/YingjieSong1/EMS.

ANOVA (Benova) correction in relative homogenization: Why it is indispensable

AbstractThis paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name “Benova” to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend‐like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided.