This paper studies structure detection problems in high temperature ferromagnetic (positive interaction only) Ising models. The goal is to distinguish whether the underlying graph is empty, i.e., the model consists of...
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In article number 2005447, Donghee Son, Taeyoon Lee, and co‐workers develop self‐bondable and self‐weavable fibers as novel components for fiber‐based electronic devices. The fibers are both conductive and stretch...
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In article number 2005447, Donghee Son, Taeyoon Lee, and co‐workers develop self‐bondable and self‐weavable fibers as novel components for fiber‐based electronic devices. The fibers are both conductive and stretchable, which eliminates the trade‐off associated with the percolation theory. The integration with self‐bondable and self‐weavable interconnects represents a new integration strategy for fiber‐based devices.
We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gra...
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We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6
Robotic swarms are distributed systems that exhibit global behaviors arising from local interactions between individual robots. Each robot can be programmed with several local control laws that can be activated depend...
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Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene e...
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Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.
Bilinear models provide rich representations compared with linear models. They have been applied in various visual tasks, such as object recognition, segmentation, and visual question-answering, to get state-of-the-ar...
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This paper presents a Gaussian fuzzy set-based evolving modeling method, FBeM-G, to predict tropical cyclone tracks 6 hours in advance. FBeM-G summarizes similar data into Gaussian granules evolved from a sequence of ...
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This paper presents a Gaussian fuzzy set-based evolving modeling method, FBeM-G, to predict tropical cyclone tracks 6 hours in advance. FBeM-G summarizes similar data into Gaussian granules evolved from a sequence of data. It uses a recursive learning algorithm to update its parameters and structure over time and therefore is able to cope with nonstationarities. Past values of latitude, longitude, maximum sustained wind, pressure and wind radii in different quadrants of the Katrina, Sandy and Wilma tropical cyclones were obtained from the 'best track' analysis provided by the National Hurricane Center (NOAA). An ensemble of cloud-based and fuzzy models was considered to compare the estimated tracks. FBeM-G provided more accurate 6-hourly track estimates using a smaller number of local models and parameters. Although less accurate, longer-term estimates given by the ensemble approach became closer to those provided by FBeM-G. An outer approximation of the pointwise track prediction is a particular characteristic of the method that is useful to determine risk areas and actions to be taken.
Nonreciprocal devices such as isolators and circulators are key enabling technologies for communication systems, both at microwave and optical frequencies. While nonreciprocal devices based on magnetic effects are ava...
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A central goal of evolutionary biology is to explain the origins and distribution of diversity across life. Beyond species or genetic diversity, we also observe diversity in the circuits (genetic or otherwise) underly...
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Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients...
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