An adaptive forecast combination approach based on meta intuitionistic fuzzy functions

Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although there are a few studies on determining the methods, there are numerous studies on determining the optimum weights of the forecasting methods. In this sense, the questions like “What methods should we choose in the combination?” and “What combination function or the weights should we choose for the methods” are handled in the proposed method. Thus, the first two contributions that the paper aims to propose are to obtain the optimum weights and the proper forecasting methods in combination functions by employing meta fuzzy functions (MFFs). MFFs are recently introduced for aggregating different methods on a specific topic. Although meta-analysis aims to combine the findings of different primary studies, MFFs aim to aggregate different methods based on their performances on a specific topic. Thus, forecasting is selected as the specific topic to propose a novel forecast combination approach inspired by MFFs in this study. Another contribution of the paper is to improve the performance of MFFs by employing intuitionistic fuzzy c-means. 14 meteorological datasets are used to evaluate the performance of the proposed method. Results showed that the proposed method can be a handy tool for dealing with forecasting problems. The outstanding performance of the proposed method is verified in terms of RMSE and MAPE.

Genome-wide pathway-based quantitative multiple phenotypes analysis

For complex diseases, genome-wide pathway association studies have become increasingly promising. Currently, however, pathway-based association analysis mainly focus on a single phenotype, which may insufficient to describe the complex diseases and physiological processes. This work proposes a combination model to evaluate the association between a pathway and multiple phenotypes and to reduce the run time based on asymptotic results. For a single phenotype, we propose a semi-supervised maximum kernel-based U-statistics (mSKU) method to assess the pathway-based association analysis. For multiple phenotypes, we propose the fisher combination function with dependent phenotypes (FC) to transform the p-values between the pathway and each marginal phenotype individually to achieve pathway-based multiple phenotypes analysis. With real data from the Alzheimer Disease Neuroimaging Initiative (ADNI) study and Human Liver Cohort (HLC) study, the FC-mSKU method allows us to specify which pathways are specific to a single phenotype or contribute to common genetic constructions of multiple phenotypes. If we only focus on single-phenotype tests, we may miss some findings for etiology studies. Through extensive simulation studies, the FC-mSKU method demonstrates its advantages compared with its counterparts.

Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

This study proposes a Self-evolving Takagi-Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) for solving identification and prediction problems. The proposed STFCMAC model uses the hypercube firing strength for generating external loops and internal feedback. A differentiable Gaussian function is used in the fuzzy hypercube cell of the proposed model, and a linear combination function of the model inputs is used as the output of the proposed model. The learning process of the STFCMAC is initiated using an empty hypercube base. Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm. The proposed STFCMAC network has some advantages that are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively.

Soft labeling with quasi-Gaussian structure for training samples of deep classification trackers

Deep classification tracking aims at classifying the candidate samples into target or background by a classifier generally trained with a binary label. However, the binary label merely distinguishes samples of different classes, while inadvertently ignoring the distinction among the samples belonging to the same class, which weakens the classification and locating ability. To cope with this problem, this article proposes a soft labeling with quasi-Gaussian structure instead of the binary labeling, which distinguishes the samples belonging to different classes and the same class simultaneously. Like as the binary label, the signs of labels for target and background samples are set to be plus and minus respectively to distinguish samples of different classes. Further, to exploit the difference among samples in the same class, the label values of samples in the same class are designed as a monotonically decreasing quasi-Gaussian function about Intersection over Union. Therefore, the corresponding response function is a two-piecewise monotonically increasing quasi-Gaussian combination function about Intersection over Union. Due to such response function, deep classification tracking trained with this proposed soft labeling achieves better classification and location performance. To validate this, the proposed soft labeling is integrated into the pipeline of the deep classification tracker SiamFC. Experimental results on OTB-2015 and VOT benchmark show that our variant achieves significant improvement to the baseline tracker while maintaining real-time tracking speed and acquires comparable accuracy as recent state-of-the-art trackers.

White Jews: An Intersectional Approach

“Intersectionality,” a concept coined and developed by legal scholar Kimberlé Crenshaw, examines how our various identities change in meaning and valence when placed in dynamic relation with one another. Instead of exploring identity traits like “race,” “gender,” “religion,” and so on in isolation, an intersectional approach asks what these various characteristics “do” to one another in combination. I suggest that an intersectional approach—asking “what does Whiteness do to Jewishness?”—can help illuminate elements of the Jewish experience that would otherwise remain obscure. The core claim is that Whiteness and Jewishness in combination function in ways that are not necessarily grasped if one atomizes the identities and holds them apart. What Whiteness “does” to Jewishness is act as an accelerant for certain forms of antisemitic marginalization even as it ratifies a racialized hierarchy within the Jewish community. Absent an intersectional vantage, many political projects and controversies surrounding Jewish equality will be systematically misunderstood.

Lump solutions and interaction behaviors to the (2+1)-dimensional extended shallow water wave equation

In this paper, the exact solutions to the (2[Formula: see text]+[Formula: see text]1)-dimensional extended shallow water wave (SWW) equation are investigated by using its bilinear form and ansatz techniques. Following the method given by Ma [Phys. Lett. A 379 (2015) 1975–1978], two classes of lump solutions are constructed by searching for positive quadratic function solutions to the associated bilinear equation. Furthermore, two kinds of interaction solutions between a lump and solitary waves are presented by taking the solution of the associated bilinear equation as a linear combination function of a quadratic function and the double exponential function, one of which is the interaction solution between a lump and an exponentially decayed soliton, and the other one is the interaction solution between a lump and an exponentially decayed twin soliton. Finally, some figures are given to illustrate the dynamic properties of these obtained solutions.

Multiple allogeneic progenitors in combination function as a unit to support early transient hematopoiesis in transplantation

Cord blood (CB) is a valuable donor source in hematopoietic cell transplantation. However, the initial time to engraftment in CB transplantation (CBT) is often delayed because of low graft cell numbers. This limits the use of CB. To overcome this cell dose barrier, we modeled an insufficient dose CBT setting in lethally irradiated mice and then added hematopoietic stem/progenitor cells (HSCs/HPCs; HSPCs) derived from four mouse allogeneic strains. The mixture of HSPCs rescued recipients and significantly accelerated hematopoietic recovery. Including T cells from one strain favored single-donor chimerism through graft versus graft reactions, with early hematopoietic recovery unaffected. Furthermore, using clinically relevant procedures, we successfully isolated a mixture of CD34+ cells from multiple frozen CB units at one time regardless of HLA-type disparities. These CD34+ cells in combination proved transplantable into immunodeficient mice. This work provides proof of concept that when circumstances require support of hematopoiesis, combined multiple units of allogeneic HSPCs are capable of early hematopoietic reconstitution while allowing single-donor hematopoiesis by a principal graft.