Emerging Vectors of Narratology: Toward Consolidation or Diversification? (A Response)

This is a response to some of the questions asked by Franco Passalacqua and Federico Pianzola as a follow-up of the 2013 ENN conference. The discussions that originated at the conference  were rich and thought-provoking and so the editors of this special section of «Enthymema» decided to continue the dialogue about the state of the art and the future of narratology

Remote Sensing Scene Classification with Masked Image Modeling (MIM)

Remote sensing scene classification has been extensively studied for its critical roles in geological survey, oil exploration, traffic management, earthquake prediction, wildfire monitoring, and intelligence monitoring. In the past, the Machine Learning (ML) methods for performing the task mainly used the backbones pretrained in the manner of supervised learning (SL). As Masked Image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classification task. This research aims to explore the potential of MIM pretrained backbones on four well-known classification datasets: Merced, AID, NWPU-RESISC45, and Optimal-31. Compared to the published benchmarks, we show that the MIM pretrained Vision Transformer (ViTs) backbones outperform other alternatives (up to 18% on top 1 accuracy) and that the MIM technique can learn better feature representation than the supervised learning counterparts (up to 5% on top 1 accuracy). Moreover, we show that the general-purpose MIM-pretrained ViTs can achieve competitive performance as the specially designed yet complicated Transformer for Remote Sensing (TRS) framework. Our experiment results also provide a performance baseline for future studies.Comment: arXiv admin note: text overlap with arXiv:2301.1205

Mutational analyses of human thymidine kinase 2 reveal key residues in ATP-Mg2+ binding and catalysis

Mitochondrial thymidine kinase 2 (TK2) is an essential enzyme for mitochondrial dNTP synthesis in many tissues. Deficiency in TK2 activity causes devastating mitochondrial diseases. Here we investigated several residues involved in substrate binding and catalysis. We showed that mutations of Gln-110 and Glu-133 affected Mg2+ and ATP binding, and thus are crucial for TK2 function. Furthermore, mutations of Gln-110 and Tyr-141 altered the kinetic behavior, suggesting their involvement in substrate binding through conformational changes. Since the 3 D structure of TK2 is still unknown, and thus, the identification of key amino acids for TK2 function may help to explain how TK2 mutations cause mitochondrial diseases

Basic biochemical characterization of cytosolic enzymes in thymidine nucleotide synthesis in adult rat tissues: implications for tissue specific mitochondrial DNA depletion and deoxynucleoside-based therapy for TK2-deficiency

Background: Deficiency in thymidine kinase 2 (TK2) or p53 inducible ribonucleotide reductase small subunit (p53R2) is associated with tissue specific mitochondrial DNA (mtDNA) depletion. To understand the mechanisms of the tissue specific mtDNA depletion we systematically studied key enzymes in dTMP synthesis in mitochondrial and cytosolic extracts prepared from adult rat tissues. Results: In addition to mitochondrial TK2 a cytosolic isoform of TK2 was characterized, which showed similar substrate specificity to the mitochondrial TK2. Total TK activity was highest in spleen and lowest in skeletal muscle. Thymidylate synthase (TS) was detected in cytosols and its activity was high in spleen but low in other tissues. TS protein levels were high in heart, brain and skeletal muscle, which deviated from TS activity levels. The p53R2 proteins were at similar levels in all tissues except liver where it was similar to 6-fold lower. Our results strongly indicate that mitochondria in most tissues are capable of producing enough dTTP for mtDNA replication via mitochondrial TK2, but skeletal muscle mitochondria do not and are most likely dependent on both the salvage and de novo synthesis pathways. Conclusion: These results provide important information concerning mechanisms for the tissue dependent variation of dTTP synthesis and explained why deficiency in TK2 or p53R2 leads to skeletal muscle dysfunctions. Furthermore, the presence of a putative cytosolic TK2-like enzyme may provide basic knowledge for the understanding of deoxynucleoside-based therapy for mitochondrial disorders

Damped Newton Method - an Ann Learning Algorithm

This paper presents a new learning algorithm for training fully-connected, feedforward artificial neural networks. The proposed learning algorithm will be suitable for training neural networks to solve approximation problems. The framework of the new ANN learning algorithm is based on Newton's method for solving non-linear least squares problems. To improve the stability of the new learning algorithm, the Levenberg-Marquardt technique for safe-guarding the Gauss-Newton method is incorporated into the Newton method. This damped version of Newton's method has been implemented using FORTRAN 77, along with some other well-known ANN learning algorithms in order to evaluate the performance of the new learning algorithm. Satisfactory numerical results have been obtained. It is shown that the proposed new learning algorithm has a better performance than the other algorithms in dealing with function approximation problems and problems which may require a high precision of training accuracy

Robust approach for variable selection with high dimensional Logitudinal data analysis

This paper proposes a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates p increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth-threshold estimating equations under "large n, diverging p" are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that our proposed method is competitive with existing robust variable selection procedures.Comment: 32 pages, 7 tables, 5 figure