The win ratio method, used to analyze composite endpoints in clinical trials, has gained substantial popularity in recent years because of its ability to prioritize components of the composite outcome. Despite gaining popularity and being extended to solve some of its issues, little work has been done to incorporate covariate information into the win ratio. In this article, we extend the win ratio method by incorporating weights to each win or loss based on the distance between the compared pair using their covariate values. This approach aims to improve the power of the original win ratio when the covariates used for computing the weights are associated with the components of the composite outcome. Through detailed simulation studies and real data analyses, we demonstrate the utility of our proposed method. In general, our simulation studies indicate that the proposed method is more powerful when covariates used to calculate the weights are associated with the outcomes, and it performs similarly to the original method when there is no such association.

Based on the need to address the uncertainty and fuzziness of evaluation information in multi-criteria decision-making while reflecting probability distributions, a novel Probabilistic Uncertain Linguistic T-Spherical Fuzzy Set (PULTSFS) is proposed by integrating the probabilistic expression advantages of PULTS with the three-dimensional evaluation characteristics of TSFS. Firstly, the related concepts and fundamental operational rules of PULTSFS are defined, including the score function, accuracy function, Hamming distance measure, and the Probabilistic Uncertain Linguistic T-Spherical Fuzzy Weighted Averaging (PULTSFWA) operator, with properties such as monotonicity, idempotency, and boundedness being analyzed. Subsequently, the ARAS method is extended to establish a multi-criteria decision-making model based on PULTSF-ARAS. In this model, the criterion weights are determined by integrating subjective weights and objective distance-based weights, and the relative closeness of alternatives is calculated using the positive ideal solution to rank the alternatives. Finally, a case study on green supplier selection for new energy vehicle enterprises is conducted to demonstrate the validity and feasibility of the proposed method

In public health research, survival data denoting different causes of death are often collected across geographical regions. The data may cause invalid inference, however, if employed in a general competing risk model, which assumes constant relationships between risk factors and competing risks across regions. In addition, some applications might require spatially varying cause-specific hazard ratios. To address these limitations, this study proposes a geographically weighted cause-specific hazard regression (GWCHR) model to estimate spatially varying coefficients with a common spatial scale across multiple covariates. In identifying spatial variations of coefficients, we assign distance-based weights for each location in likelihood construction. We choose the bandwidth in the weighting function according to suitable selection criteria. We analyze the asymptotic properties of the proposed GWCHR model in detail. Our simulation studies compare the finite sample performance of the proposed model with general competing risk models. We apply the proposed method to prostate cancer data from Korea’s National Health Information Service database to examine the spatially varying effects of environmental and social factors on second primary cancers for prostate cancer patients.

Accurate feature extraction and quality variable prediction are critical problems for time sequences in industrial processes. However, industrial samples often exhibit strong temporal correlations with each other that have different positional distances, making it challenging for conventional data-driven models like long short-term memory (LSTM) and Vanilla transformer to capture these underlying features. In this article, a difference metric attention with position distance-based weighting is proposed for transformer (DMA-trans) in industrial time series modeling. First, the DMA is established to calculate the difference of query-key vector pair in transformer to measure the spatial similarity. In this fashion, the difference can accurately represent the spatial similarity of vectors, compared with the original dot product directly on two vectors. Then, positional distance-based weights are designed to capture the sample relevance that has different positional distances. This may help to extract more potential features because the closer samples tend to have higher relevance while there may be weak correlations if two samples are far in positional distance. The effectiveness of the DMA-trans model is validated in industrial hydrocracking processes for C5 content of the light naphtha and the final boiling point of the jet fuel.

An enhanced RSS-distance-angle weighted geometric filter for device-free localization

This paper proposes an efficient federated distillation learning system (EFDLS) for multi-task time series classification (TSC). EFDLS consists of a central server and multiple mobile users, where different users may run different TSC tasks. EFDLS has two novel components: a feature-based student-teacher (FBST) framework and a distance-based weights matching (DBWM) scheme. For each user, the FBST framework transfers knowledge from its teacher’s hidden layers to its student’s hidden layers via knowledge distillation, where the teacher and student have identical network structures. For each connected user, its student model’s hidden layers’ weights are uploaded to the EFDLS server periodically. The DBWM scheme is deployed on the server, with the least square distance used to measure the similarity between the weights of two given models. This scheme finds a partner for each connected user such that the user’s and its partner’s weights are the closest among all the weights uploaded. The server exchanges and sends back the user’s and its partner’s weights to these two users which then load the received weights to their teachers’ hidden layers. Experimental results show that compared with a number of state-of-the-art federated learning algorithms, our proposed EFDLS wins 20 out of 44 standard UCR2018 datasets and achieves the highest mean accuracy (70.14%) on these datasets. In particular, compared with a single-task Baseline, EFDLS obtains 32/4/8 regarding ’win’/’tie’/’lose’ and results in an improvement of approximately 4% in terms of mean accuracy.

Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by a severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways. In this paper, we demonstrate that this problem is arisen by gradient erosion and dilation of the neighborhood voxels. During back-propagation, if the ratio of the foreground gradient to background gradient is small while the class imbalance is local, the foreground gradients can be eroded by their neighborhoods. This process cumulatively increases the noise information included in the gradient flow from top layers to the bottom ones, limiting the learning of small structures in CNNs. To alleviate this problem, we use group supervision and the corresponding WingsNet to provide complementary gradient flows to enhance the training of shallow layers. To further address the intra-class imbalance between large and small airways, we design a General Union loss function that obviates the impact of airway size by distance-based weights and adaptively tunes the gradient ratio based on the learning process. Extensive experiments on public datasets demonstrate that the proposed method can predict the airway structures with higher accuracy and better morphological completeness than the baselines.

Comparing walkability methods: Creation of street smart walk score and efficacy of a code-based 3D walkability index

The overpopulation in urban areas and the rapid increase in freight volume cause various social and environmental problems, and at the same time require more complex distribution networks and logistics facilities locating. In South Korea, demand for logistics facilities have also increased along with the explosive growth of the e-commerce industry since the mid-1990s, but there have not yet been many studies analyzing the demand so far, and in particular, only few studies focus on regional differences in demand for logistics facilities. Therefore, this study collected the national warehouse data registered until 2017, visualized the distribution pattern of the gross warehouse space, and investigated whether the warehouse demand really differs by region. As a result, warehouses were actually distributed differently by region and were concentrated in specific regions. Accordingly, this study explored socioeconomic, geographic, and industrial factors that explain the different warehouse demands for 225 administrative districts nationwide, measured the distance-based impact among 225 administrative districts, applied weights into the variables for regression analysis. The results showed that the coefficients and those signs are more statistically reliable in the model using the distance-based weights compared to the model with non-weighted variables, and 5 of the total 6 variables were found to be significant.

Computed Tomography (CT) images have a high dynamic range, which makes visualization challenging. Histogram equalization methods either use spatially invariant weights or limited kernel size due to the complexity of pairwise contribution calculation. We present a weighted histogram equalization-based tone mapping algorithm which utilizes Fast Fourier Transform for distance-dependent contribution calculation and distance-based weights. The weights follow power-law without distance-based cut-off. The resulting images have good local contrast without noticeable artefacts. The results are compared to eight popular tone mapping operators.

BackgroundWhen analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance.MethodsThe popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the spatial weights matrix are defined, which are classified into four classes: adjacency-based weights, and weights based on geographic distance, distance between covariate values, and a hybrid of geographic and covariate distances. These last two definitions embody the main novelty of this research. Three synthetic data sets are generated, each representing a different underlying spatial structure. These data sets together with a real spatial data set from the literature are analysed using the models. The models are evaluated using the deviance information criterion and Moran’s I statistic.ResultsThe deviance information criterion indicated that the model which uses binary, first-order adjacency weights to perform spatial smoothing is generally an optimal choice for achieving a good model fit. Distance-based weights also generally perform quite well and offer similar parameter interpretations. The less commonly explored options for performing spatial smoothing generally provided a worse model fit than models with more traditional approaches to smoothing, but usually outperformed the benchmark model which did not conduct spatial smoothing.ConclusionsThe specification of the spatial weights matrix can have a colossal impact on model fit and parameter estimation. The results provide some evidence that a smaller number of neighbours used in defining the spatial weights matrix yields a better model fit, and may provide a more accurate representation of the underlying spatial random field. The commonly used binary, first-order adjacency weights still appear to be a good choice for implementing spatial smoothing.

Many-core processors will have many processing cores with a network-on-chip (NoC) that provides access to shared resources such as main memory and on-chip caches. However, locally-fair arbitration in multi-stage NoC can lead to globally unfair access to shared resources and impact system-level performance depending on where each task is physically placed. In this work, we propose an arbitration to provide equality-of-service (EoS) in the network and provide support for location-oblivious task placement. We propose using probabilistic arbitration combined with distance-based weights to achieve EoS and overcome the limitation of round-robin arbiter. However, the complexity of probabilistic arbitration results in high area and long latency which negatively impacts performance. In order to reduce the hardware complexity, we propose an hybrid arbiter that switches between a simple arbiter at low load and a complex arbiter at high load. The hybrid arbiter is enabled by the observation that arbitration only impacts the overall performance and global fairness at a high load. We evaluate our arbitration scheme with synthetic traffic patterns and GPGPU benchmarks. Our results shows that hybrid arbiter that combines round-robin arbiter with probabilistic distance-based arbitration reduces performance variation as task placement is varied and also improves average IPC.

Errors in the scanning procedures lead to uncertainties when trying to segment the scanned images. Fuzzy c-means is a clustering method that can be applied to segment images with uncertainty estimates. Bias-corrected fuzzy c-means (BCFCM) clustering compensates for two sources of uncertainty by modeling noise and bias fields during the segmentation process. In this paper, we present an approach to improve BCFCM clustering and apply it to magnetic resonance imaging (MRI) data of the human brain. Our approach is based on two variants of BCFCM clustering, the classical one and the one with distance-based weights. We improve both variants by slightly modifying their main algorithms for better bias field estimation. To evaluate the improved algorithms, we apply the algorithms to synthetic data, simulated MRI brain data, and real MRI brain data with ground truth in form of manual segmentation. All experiment results show that our improved methods outperform the original methods in both the segmentation accuracy and efficiency (the number of iterations).

The economic growth effects of terrorism have generally been examined in a cross-country framework where socio-economic differences among the countries are ignored. This highly restrictive assumption may result in heterogeneity bias, which could be overcome by resorting to country studies rather than cross-country analysis. Moreover, the relationship between the terrorist incidents and various factors may not be stationary in space. The majority of terrorist incidents in Turkey are concentrated mainly in Eastern, and South Eastern Turkey and big cities. Thus, the geographical dispersion of terrorist incidents in Turkey may result in uneven regional impact, necessitating local parameter estimates. This study analyses the effects of terrorism on economic growth across provinces of Turkey for the time period 1987—2001. Following a traditional global regression analysis, spatial variations in the relationships are examined with geographically weighted regression (GWR) to obtain locally different parameter estimates. A GWR approach allows the modeling of relationships that vary over space by introducing distance-based weights to provide parameter estimates for each variable and each geographical location. Empirical evidence indicates that a GWR model significantly improves the model fitting over the traditional global model. Even though the traditional convergence analysis reveals that terrorism hinders economic growth, GWR results indicate that its provincial effects are more pronounced for the Eastern and South Eastern provinces compared to the Western provinces. Moreover, empirical findings suggest that there is a considerable variation in speeds of convergence of provinces, which cannot be captured by the traditional beta convergence analysis.

The average of household expenditure per capita, which is usually used as a main indicator of poverty, is commonly explained as a function of some variables in a global regression framework. In the global regression framework, the coefficients in the model are assumed to be equal for all national spatial units. Naturally, however, the average of household expenditure per capita is not equally distributed across the national territory. In fact, the expenditure covariates do not have the same influence on per capita expenditure all over a country or region. The geographically weighted regression (GWR) analysis could be a solution to capture spatial variations and to solve the spatial non-stationarity. Because in GWR models, the spatial relationships are modeled by introducing distance-based weights and the estimates are provided for each variable k and each geographical region i. We have explored the possibility of combining census and survey data in order to construct a GWR model for the average of household expenditure. In view of the predictions, GWR cannot be used to interpolate to other regions without estimating a local parameter, whereas the global regression model allows one to make prediction in other geographical region. To handle this limitation, we propose spatial kriging predictor for estimation of local parameter in other regions. By this approach, we classified all the villages in Jawa Tengah into poor or disadvantaged villages and non-poor villages. To classify villages into poor or disadvantaged villages and non-poor villages, we compare the estimates of the average of household expenditure with the poverty line, which have been defined in another survey by BPS Statistics Indonesia.

Using Linda to compute spatial autocorrelation in parallel