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Abstract It is essential to delve into the strategy of multimodal model pre-training, which is an obvious impact on downstream tasks. Currently, clustering learning has achieved noteworthy benefits in multiple methods. However, due to the availability of open image-text pairs, it is challenging for multimodal with clustering learning. In this paper, we propose an approach that utilizes clustering swap prediction strategy to learn image-text clustering embedding space by interaction prediction between image and text features. Unlike existing models with clustering learning, our method (Clus) allows for an open number of clusters for web-scale alt-text data. Furthermore, in order to train the image and text encoders efficiently, we introduce distillation learning approach and evaluate the performance of the image-encoder in downstream visual tasks. In addition, Clus is pre-trained end-to-end by using large-scale image-text pairs. Specifically, both text and image serve as ground truth for swap prediction, enabling effective representation learning. Concurrently, extensive experiments demonstrate that Clus achieves state-of-the-art performance on multiple downstream fine-tuning and zero-shot tasks (i.e., Image-Text Retrieval, VQA, NLVR2, Image Captioning, Object Detection, and Semantic Segmentation).

It is essential to delve into the strategy of multimodal model pre-training, which is an obvious impact on downstream tasks. Currently, clustering learning has achieved noteworthy benefits in multiple methods. However, due to the availability of open image-text pairs, it is challenging for multimodal with clustering learning. In this paper, we propose an approach that utilizes clustering swap prediction strategy to learn image-text clustering embedding space by interaction prediction between image and text features. Unlike existing models with clustering learning, our method (Clus) allows for an open number of clusters for web-scale alt-text data. Furthermore, in order to train the image and text encoders efficiently, we introduce distillation learning approach and evaluate the performance of the image-encoder in downstream visual tasks. In addition, Clus is pre-trained end-to-end by using large-scale image-text pairs. Specifically, both text and image serve as ground truth for swap prediction, enabling effective representation learning. Concurrently, extensive experiments demonstrate that Clus achieves state-of-the-art performance on multiple downstream fine-tuning and zero-shot tasks (i.e., Image-Text Retrieval, VQA, NLVR2, Image Captioning, Object Detection, and Semantic Segmentation). [ABSTRACT FROM AUTHOR]

The mechanical and dynamic properties of developing networks near the gel point are susceptible to the distribution of clusters coexisting with percolating networks. The distribution of cluster numbers follows a broad power law, wrapped by a cutoff function that rapidly decays at a characteristic size. The form of the cutoff function has been speculated based on known results from lattice percolation and, in certain cases, solved. We obtained this cutoff function from simulated dynamic clusters of polymeric precursor chains using a hybrid Monte Carlo algorithm. The results obtained from three different precursor chain lengths are consistent with each other and are consistent with the expectation from lattice percolation.

Copyright of Erzincan University Journal of Science & Technology is the property of Erzincan Binali Yildirim Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite marked improvements in tropical cyclone (TC) track ensemble forecasting, forecasters still have difficulty in making quick decisions when facing multiple potential predictions, so it is demanding to develop post-processing techniques reducing the uncertainty in TC track forecasts, and one of such techniques is the cluster-based methods. To improve the effect and efficiency of the previous cluster-based methods, this study adopts recombination clustering (RC) by optimizing the use of limited TC variables and constructing better features that can accurately capture the good TC track forecasts from the ensemble prediction system (EPS) of the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). The RC technique is further optimized by constraining the number of clusters using the absolute track bias between the ensemble mean (EM) and ensemble spread (ES). Finally, the RC-based deterministic and weighted probabilistic forecasts are compared with the TC track forecasts from traditional methods. It is found that (1) for deterministic TC track forecasts, the RC-based TC track forecasts outperform all other methods at 12–72-h lead times; compared with the skillful EM (118.6 km), the improvements introduced by the use of RC reach up to 10.8% (8.1 km), 10.2% (13.7 km), and 8.7% (20.5 km) at forecast times of 24, 48, and 72 h, respectively. (2) For probabilistic TC track forecasts, RC yields significantly more accurate and discriminative forecasts than traditional equal-weight track forecasts, by increasing the weight of the best cluster, with a decrease of 4.1% in brier score (BS) and an increase of 1.4% in area under the relative operating characteristic curve (AUC). (3) In particular, for cases with recurved tracks, such as typhoons Saudel (2017) and Bavi (2008), RC significantly reduces track errors relative to EM by 56.0% (125.5 km) and 77.7% (192.2 km), respectively. Our results demonstrate that the RC technique not only improves TC track forecasts but also helps to unravel skillful ensemble members, and is likely useful for feature construction in machine learning. [ABSTRACT FROM AUTHOR]

The aim of the current research is to study and compare graphs authorship by Iranian researchers in Persian scientific research journals in the field of educational sciences by using algebraic parameters of mathematics. In this research, the data related to the scientific research documents of 336 Iranian researchers from 6 Persian scientific research journals in the field of educational sciences from 2017 to 2021 were extracted by using the issues printed in these journals and the graphs related to the authors of these journals have been drawn by mathematical methods and software, and then compared and analyzed by using algebraic parameters such as the average degrees of vertices, independence, color and matching numbers. In the main results, it was checked that the average grade of the Journal of the Educational Measurement and Evaluation Studies is 4.4 and higher than other journals. Also, the largest research group (cluster number) is 6 people, and the most research diversity (independence number) is 29 and it is related to of the Journal of the teaching research. Among the discussed journals in the field of educational sciences, according to the average grades, the amount of research cooperation of the Journal of Educational Measurement and Evaluation Studies is at a higher level than the other five journals.

Shale reservoirs have very low original permeability and n o economic productivity under natural conditions. Practice shows that large hydraulic fracturing is the most effective means to develop shale reservoirs efficiently. However, there are few reports on the optimization of fracture cluster number in fractured horizontal w ells in shale reservoirs. In view of this problem, the multi-well numerical model of multi-stage fractured horizontal wells is established b y u sing the unstable seepage theory, and a set of optimization method of fracture cluster number of multi-stage fractured horizontal wells in shale reservoir is put forward on the principle of full utilization of reserves. The results show that when the number of fracture clusters in multi-stage fractured horizontal wells in Changji oilfield is more than 80,the cumulative increase trend of oil production tends to be flat. It can be determined that the reasonable cluster spacing range is about 15~20 m, and the number of fracture clusters is 70~ 80. According to the theoretical model with an optimal number of fracture clusters of 75,the cumulative oil production of fractured horizontal wells in shale reservoirs in Changji oilfield was calculated and found to be 25 150 m3 • This work can provide theoretical reference for the design of fracture clusters in fractured horizontal wells in shale reservoirs in Changji oilfield, Xinjiang. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The designation of the cluster number K and the initial centroids is essential for K-modes clustering algorithm. However, most of the improved methods based on K-modes specify the K value manually and generate the initial centroids randomly, which makes the clustering algorithm significantly dependent on human-based decisions and unstable on the iteration time. To overcome this limitation, we propose a cohesive K-modes (CK-modes) algorithm to generate the cluster number K and the initial centroids automatically. Explicitly, we construct a labeled property graph based on index-free adjacency to capture both global and local cohesion of the node in the sample of the input datasets. The cohesive node calculated based on the property similarity is exploited to split the graph to a K-node tree that determines the K value, and then the initial centroids are selected from the split subtrees. Since the property graph construction and the cohesion calculation are only performed once, they account for a small amount of execution time of the clustering operation with multiple iterations, but significantly accelerate the clustering convergence. Experimental validation in both real-world and synthetic datasets shows that the CK-modes algorithm outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance (DED) measure. In the first phase, the fast global K-means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms.

In this paper we present a suggested automatic clustering procedure with the main aim to predict the number of clusters of unknown, heterogeneous images. We used the state-of-the-art Fisher-vector for mathematical representation of the images and these vectors were considered as input data points for the clustering algorithm. We implemented a novel variant of K-means, the kernel K-means++, furthermore the min-max kernel K-means plusplus (MMKK++) as clustering method. The proposed approach examines some candidate cluster numbers and uses the law of large numbers in order to choose the optimal cluster size. We conducted experiments on four image sets to demonstrate the efficiency of our solution. The first two image sets are subsets of different popular collections; the third is their union; the fourth is the complete Caltech101 image set.

In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring. [ABSTRACT FROM AUTHOR]

To overcome the main problem of the cluster number in many clustering applications, a new clustering approach with improved morphology similarity distance and the novel cluster validity index is proposed in this paper. An optimized morphology similarity distance based on the Standard Euclidean distance and ReliefF algorithm is used to create a new validity index, which can balance the intra-cluster consistency and inter-cluster consistency. The proposed validity index is combined with fuzzy C-means to produce a creative algorithm simply named the OMS-OSC algorithm. Experimental results obtained using different artificial data sets and real-world data sets show that the new algorithm can not only yield good performance but also detect the correct cluster number. [ABSTRACT FROM AUTHOR]

The role of hydrogen bond interaction on the structural orientation of pure alkanolamines, monoethanolamine (MEA), diethanolamine (DEA), and methyl diethanolamine (MDEA), was under consideration by computer simulation, molecular dynamics simulation as well as density functional theory. Molecular dynamics simulations were carried out at different pressures and temperatures to discover structural stability. A comprehensive force field validation was performed to obtain accurate density. It was also observed that independent of pressure and temperature, MEA molecules prefer head-tail parallel orientation, DEA molecules show both vertical and parallel orientation, and MDEA molecules favor parallel arrangement in liquid state. The probability of hydrogen bond as a substantial intermolecular interaction was also investigated and substantial sites were evaluated. Intermolecular and intramolecular energy, hydrogen bond strength, and transport properties, as well as cluster formation, were investigated to offer an irrefutable proof on structural orientation. Ab initio computation by analyzing frequency, stabilization energy, and electron location function confirmed similar orientation, proposed by molecular dynamics simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring.