On the finiteness of local homology modules

Let $R$ be a commutative Noetherian ring and $\mathfrak a$ an ideal of $R$. Let $M$ be a finitely generated $R$-module and $N$ an Artinian $R$-module. The concept of filter coregular sequence is introduced to determine the infimum of the integers $i$ such that the generalized local homology $\textrm{H}^{\mathfrak{a}}_i(M, N)$ is not finitely generated as an $\widehat{R}^{\mathfrak{a}}$-module, where $\widehat{R}^{\mathfrak{a}}$ denotes the $\mathfrak{a}$-adic completion of $R$. In particular, it is shown that $\textrm{H}^{\mathfrak{a}}_i(M, N)$ is a finitely generated $\widehat{R}^{\mathfrak{a}}$-module for all $i\in\mathbb{N}_0$ if and only if $(0:_N\mathfrak{a}+\textrm{Ann}_R(M))$ has finite length whenever $R$ is a complete semi-local ring

Improving Image Recognition by Retrieving from Web-Scale Image-Text Data

Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We also thoroughly study various ways of constructing the memory dataset. Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, and show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.Comment: Accepted to CVPR 202

Integration of Graph Theory and Matrix Approach with Fuzzy AHP for Equipment Selection

Purpose: The purpose of this paper is applying a new integrated method to equipment selection. Design/methodology/approach: In this paper, we proposed the new integrated approach. Proposed approach is based on fuzzy Analytic Hierarchy Process (FAHP) and GTMA (graph theory and matrix approach) methods. FAHP method is used in determining the weights of the criteria by decision makers and then rankings of equipments are determined by GTMA method. Proposed approach is applied to a problem of selecting CNC machines to be purchased in a company. Findings and Originality/value: The outcome of this research is ranking and selecting equipment using of Fuzzy AHP and GTMA techniques. Originality/value: This paper offers a new integrated method for equipment selection.Peer Reviewe

The Study of the Relation of Positive and Negative Emotions and Self-Control with Personality Types in Terms of Enneagram Model

The research aims to study the relation of positive and negative emotions and self-control with personality types in terms of Enneagram model. The method is descriptive and correlative. Abhar University which have been tested based on clustering sampling from humanities and technical faculties in the fields of commercial management (marketing and financial management), nursing, consulting and master guidance, industry engineering and electrical engineering as 100 and in terms of Kochran formula as sample group. In present research, Pearson correlation method and regression analysis were used and the results showed that there is a negative relation between personality reformist, helper, compliment, challenger, peaceful and instinct Triad and emotional Triad with self-control. Also, there is a positive relation between eager, faithful and cognitive personality types. Also, there is a negative significant relation between reformist, helper, compliment and challenger. A positive significant relation was observed between reformist, helper, individual compliment, eager, peaceful and challenger with cognitive and there is a negative significant relation between faithful, eager, peaceful and emotional personality. Self-control variable can determine 10% of variances for emotional personality and negative emotions can determine 7% of cognitive trait and 12% of instinct trait are determined by positive emotions

The Devil is in the Decoder: Classification, Regression and GANs

Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image. Models for such problems usually consist of encoders which decrease spatial resolution while learning a high-dimensional representation, followed by decoders who recover the original input resolution and result in low-dimensional predictions. While encoders have been studied rigorously, relatively few studies address the decoder side. This paper presents an extensive comparison of a variety of decoders for a variety of pixel-wise tasks ranging from classification, regression to synthesis. Our contributions are: (1) Decoders matter: we observe significant variance in results between different types of decoders on various problems. (2) We introduce new residual-like connections for decoders. (3) We introduce a novel decoder: bilinear additive upsampling. (4) We explore prediction artifacts