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This paper is a look at the valuable book "From Khayyam to Yalom" by Dr. Soroush Dabbagh, one of the important Iranian philosophers. This article discusses that book and the existential opinions of various philosophers, phycologists, and thinkers including Soroush Dabbagh, Irvin Yalom, Ludwig Wittgenstein, Baruch Spinoza, Arthur Schopenhauer, Jean-Paul Sartre, Albert Camus, Martin Seligman, Epicurus, Stoics, Sohrab Sepehri, Shams Tabrizi, and Mostafa Malekian. The paper discusses various existential and metaphysic topics such as meaning of life, death, solitude, freedom and responsibility, Stoicism, eudaimonic and hedonic happiness, and belief.

Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowledge, is the first work on magnification generalization for histopathology image embedding. We use an episodic trainable domain generalization technique for magnification generalization, namely Model Agnostic Learning of Semantic Features (MASF), which works based on the Model Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast cancer histopathology dataset with four different magnification levels show the proposed method's effectiveness for magnification generalization., Accepted for presentation at International Symposium on Biomedical Imaging (ISBI'2021)

This is a tutorial and survey paper on the attention mechanism, transformers, BERT, and GPT. We first explain attention mechanism, sequence-to-sequence model without and with attention, self-attention, and attention in different areas such as natural language processing and computer vision. Then, we explain transformers which do not use any recurrence. We explain all the parts of encoder and decoder in the transformer, including positional encoding, multihead self-attention and cross-attention, and masked multihead attention. Thereafter, we introduce the Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) as the stacks of encoders and decoders of transformer, respectively. We explain their characteristics and how they work.

Metric learning is one of the techniques in manifold learning with the goal of finding a projection subspace for increasing and decreasing the inter- and intra-class variances, respectively. Some of the metric learning methods are based on triplet learning with anchor-positive-negative triplets. Large margin metric learning for nearest neighbor classification is one of the fundamental methods to do this. Recently, Siamese networks have been introduced with the triplet loss. Many triplet mining methods have been developed for Siamese networks; however, these techniques have not been applied on the triplets of large margin metric learning for nearest neighbor classification. In this work, inspired by the mining methods for Siamese networks, we propose several triplet mining techniques for large margin metric learning. Moreover, a hierarchical approach is proposed, for acceleration and scalability of optimization, where triplets are selected by stratified sampling in hierarchical hyper-spheres. We analyze the proposed methods on three publicly available datasets, i.e., Fisher Iris, ORL faces, and MNIST datasets., The first two authors contributed equally to this work

In this paper, we introduce the Hinduism religion and philosophy. We start with introducing the holy books in Hinduism including Vedas and Upanishads. Then, we explain the simplistic Hinduism, Brahman, gods and their incarnations, stories of apocalypse, karma, reincarnation, heavens and hells, vegetarianism, and sanctity of cows. Then, we switch to the profound Hinduism which is the main core of Hinduism and is monotheistic. In profound Hinduism, we focus on the non-dualism or Advaita Vedanta approach in Hinduism. We discuss consciousness, causality, Brahman, psychology based on Hinduism, supportive scientific facts for Hinduism, the four levels of truth, and Maya. The four paths of knowledge, love, karma, and meditation are explained as well as the cosmic mind, the subtle body, and Aum. The risks for every path are also explained. Then, we introduce the orthodox and heterodox Indian schools including Yoga, Nyaya, Advaita Vedanta, Vishishtadvaita, and Dvaita. Connections to some other religions including Buddhism, Jainism, Sikhism, Judaism, Christianity, Islam, Islamic mysticism, and Zoroastrianism are analyzed. Finally, we explain the connection of Hindu philosophy with the Greek, western, and Islamic philosophies which include the philosophies of Plato, Aristotle, Plotinus, Spinoza, Descartes, Hegel, Schopenhauer, Avicenna, Suhrawardi, and Mulla Sadra.

We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and Mondrian forest which are existing methods for batch anomaly detection and online random forest, respectively. iMondrian forest takes the idea of isolation, using the depth of a node in a tree, and implements it in the Mondrian forest structure. The result is a new data structure which can accept streaming data in an online manner while being used for anomaly detection. Our experiments show that iMondrian forest mostly performs better than isolation forest in batch settings and has better or comparable performance against other batch and online anomaly detection methods., Accepted for presentation at the IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020. The first three authors contributed equally to this work

We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, both in online and offline mining. While many works focus solely on selecting the triplets online (batch-wise), we also study the effect of extreme distances and neighbor patches before training in an offline fashion. We analyze extreme cases’ impacts in terms of embedding distance for offline versus online mining, including easy positive, batch semi-hard, batch hard triplet mining, neighborhood component analysis loss, its proxy version, and distance weighted sampling. We also investigate online approaches based on extreme distance and comprehensively compare offline, and online mining performance based on the data patterns and explain offline mining as a tractable generalization of the online mining with large mini-batch size. As well, we discuss the relations of different colorectal tissue types in terms of extreme distances. We found that offline and online mining approaches have comparable performances for a specific architecture, such as ResNet-18 in this study. Moreover, we found the assorted case, including different extreme distances, is promising, especially in the online approach.

We present a new method which generalizes subspace learning based on eigenvalue and generalized eigenvalue problems. This method, Roweis Discriminant Analysis (RDA) named after Sam Roweis, is a family of infinite number of algorithms where Principal Component Analysis (PCA), Supervised PCA (SPCA), and Fisher Discriminant Analysis (FDA) are special cases. One of the extreme special cases, named Double Supervised Discriminant Analysis (DSDA), uses the labels twice and it is novel. We propose a dual for RDA for some special cases. We also propose kernel RDA, generalizing kernel PCA, kernel SPCA, and kernel FDA, using both dual RDA and representation theory. Our theoretical analysis explains previously known facts such as why SPCA can use regression but FDA cannot, why PCA and SPCA have duals but FDA does not, why kernel PCA and kernel SPCA use kernel trick but kernel FDA does not, and why PCA is the best linear method for reconstruction. Roweisfaces and kernel Roweisfaces are also proposed generalizing eigenfaces, Fisherfaces, supervised eigenfaces, and their kernel variants. We also report experiments showing the effectiveness of RDA and kernel RDA on some benchmark datasets.