Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (...Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.展开更多
The argument given by strong representationalists about phenomenal consciousness usually has two steps. The first is to identify all phenomenal consciousness with representation. The second is to identify all phenomen...The argument given by strong representationalists about phenomenal consciousness usually has two steps. The first is to identify all phenomenal consciousness with representation. The second is to identify all phenomenal aspects of phenomenal consciousness with certain representational content. Pain is often thought to be a counterexample torepresentationalism. However, current objections from this perspective mostly focus on the second step and try to show that pains have some special qualities that representational content cannot explain. This paper objects to representationalism with regard to pain （that pain is not representation） by way of a focus on the first step. First, it shows that by borrowing the notion of ＂representation＂ from the causal co-variation theory of representation, representationalists are not able to demonstrate that pain is representation. Second, by laying out some well-accepted criteria for what counts as representation, it argues that pains do not satisfy them. Thus, pain is not representation.展开更多
Transcription factors （TFs） are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between k...Transcription factors （TFs） are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between known TFs sequence and specificity, specificity prediction frameworks are highly desired. Key inputs to such frameworks are protein residues that modulate the specificity of TF under consideration. Simple measures like mutual information （MI） to delineate specificity influencing residues （SIRs） from alignment fail due to structural constraints imposed by the three-dimensional structure of protein. Structural restraints on the evolution of the amino-acid sequence lead to identification of false SIRs. In this manuscript we extended three methods （direct information, PSICOV and adjusted mutual information） that have been used to disentangle spurious indirect protein residue-residue contacts from direct contacts, to identify SIRs from joint alignments of amino-acids and specificity. We predicted SIRs for homeodomain （HI））, helix-loop-helix, LacI and GntR families of TFs using these methods and compared to MI. Using various measures, we show that the performance of these three methods is comparable but better than MI. Implication of these methods in specificity prediction framework is discussed. The methods are implemented as an R package and available along with the alignments at http：//stormo.wustl.edu/SpecPred.展开更多
文摘Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.
文摘The argument given by strong representationalists about phenomenal consciousness usually has two steps. The first is to identify all phenomenal consciousness with representation. The second is to identify all phenomenal aspects of phenomenal consciousness with certain representational content. Pain is often thought to be a counterexample torepresentationalism. However, current objections from this perspective mostly focus on the second step and try to show that pains have some special qualities that representational content cannot explain. This paper objects to representationalism with regard to pain （that pain is not representation） by way of a focus on the first step. First, it shows that by borrowing the notion of ＂representation＂ from the causal co-variation theory of representation, representationalists are not able to demonstrate that pain is representation. Second, by laying out some well-accepted criteria for what counts as representation, it argues that pains do not satisfy them. Thus, pain is not representation.
文摘Transcription factors （TFs） are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between known TFs sequence and specificity, specificity prediction frameworks are highly desired. Key inputs to such frameworks are protein residues that modulate the specificity of TF under consideration. Simple measures like mutual information （MI） to delineate specificity influencing residues （SIRs） from alignment fail due to structural constraints imposed by the three-dimensional structure of protein. Structural restraints on the evolution of the amino-acid sequence lead to identification of false SIRs. In this manuscript we extended three methods （direct information, PSICOV and adjusted mutual information） that have been used to disentangle spurious indirect protein residue-residue contacts from direct contacts, to identify SIRs from joint alignments of amino-acids and specificity. We predicted SIRs for homeodomain （HI））, helix-loop-helix, LacI and GntR families of TFs using these methods and compared to MI. Using various measures, we show that the performance of these three methods is comparable but better than MI. Implication of these methods in specificity prediction framework is discussed. The methods are implemented as an R package and available along with the alignments at http：//stormo.wustl.edu/SpecPred.
