A Unified Method for First and Third Person Action Recognition

In this paper, a new video classification methodology is proposed which can be applied in both first and third person videos. The main idea behind the proposed strategy is to capture complementary information of appearance and motion efficiently by performing two independent streams on the videos. The first stream is aimed to capture long-term motions from shorter ones by keeping track of how elements in optical flow images have changed over time. Optical flow images are described by pre-trained networks that have been trained on large scale image datasets. A set of multi-channel time series are obtained by aligning descriptions beside each other. For extracting motion features from these time series, PoT representation method plus a novel pooling operator is followed due to several advantages. The second stream is accomplished to extract appearance features which are vital in the case of video classification. The proposed method has been evaluated on both first and third-person datasets and results present that the proposed methodology reaches the state of the art successfully.Comment: 5 page

Some properties of algebras of real-valued measurable functions

summary:Let $M(X, \mathscr{A})$ ($M^{*}(X, \mathscr{A})$) be the $f$-ring of all (bounded) real-measurable functions on a $T$-measurable space $(X, \mathscr{A})$, let $M_{K}(X, \mathscr{A})$ be the family of all $f\in M(X, \mathscr{A})$ such that ${{\,\mathrm{coz}}}(f)$ is compact, and let $M_{\infty }(X, \mathscr{A})$ be all $f\in M(X, \mathscr{A})$ that $\lbrace x\in X: |f(x)|\ge \frac{1}{n}\rbrace$ is compact for any $n\in \mathbb{N}$. We introduce realcompact subrings of $M(X, \mathscr{A})$, we show that $M^{*}(X, \mathscr{A})$ is a realcompact subring of $M(X, \mathscr{A})$, and also $M(X, \mathscr{A})$ is a realcompact if and only if $(X, \mathscr{A})$ is a compact measurable space. For every nonzero real Riesz map $\varphi : M(X, \mathscr{A})\rightarrow \mathbb{R}$, we prove that there is an element $x_0\in X$ such that $\varphi (f) =f(x_0)$ for every $f\in M(X, \mathscr{A})$ if $(X, \mathscr{A})$ is a compact measurable space. We confirm that $M_{\infty }(X, \mathscr{A})$ is equal to the intersection of all free maximal ideals of $M^{*}(X, \mathscr{A})$, and also $M_{K}(X, \mathscr{A})$ is equal to the intersection of all free ideals of $M(X, \mathscr{A})$ (or $M^{*}(X, \mathscr{A})$). We show that $M_{\infty }(X, \mathscr{A})$ and $M_{K}(X, \mathscr{A})$ do not have free ideal

A Family of Loss Distributions with an Application to the Vehicle Insurance Loss Data

Actuaries are often in search of finding an adequate loss model in the scenario of actuarial and financial risk management problems. In this work, we propose a new approach to obtain a new class of loss distributions. A special sub-model of the proposed family, called the Weibull-loss model is considered in detail. Some mathematical properties are derived and maximum likelihood estimates of the model parameters are obtained. Certain characterizations of the proposed family are also provided. A simulation study is done to evaluate the performance of the maximum likelihood estimators. Finally, an application of the proposed model to the vehicle insurance loss data set is presented

A Double-Blind Randomized Trial Comparing the Effectiveness and Safety of Nifedipine and Isosorbide Dinitrate in Chronic Anal Fissure

Background: Chronic anal fissure is a common disease that is accompanied with pain and bleeding during defecation. Various surgical and non-surgical methods have been offered for the treatment of this condition. The aim of this randomised clinical study was to compare the effectiveness and safety of nifedipine and isosorbide dinitrate (ISDN) in the treatment of chronic anal fissure. Methods: This double-blind clinical trial study was performed on patients aged 20 to 60 years old in 2012 to 2013. The samples with a primary diagnosis of chronic anal fissure were enrolled from the patients admitted to public treatment at the educational Imam Ali Clinic, Shahrekord, Iran by researchers and general surgery specialists. The patients were randomised into two groups: nifedipine 0.3% (n = 35) or ISDN 0.2% (n = 35) applied three times a day for three weeks. The patients were examined on the 7th, 14th, and 21st days of treatment, and the symptoms including bleeding, pain, and healing status, as well as the side effects of the drugs, were assessed. Pain was evaluated using a visual analogue scale (VAS). Results: After 21 days of follow-up, complete healing was achieved in 77.1% (n = 27) of patients in the nifedipine group and 51.4% (n = 18) in the ISDN group (P = 0.05). The mean VAS of the pain on day 21 was 0.91 (SD 0.01) in the ISDN group and 0.45 +/- 0.78 in the nifedipine group, with a statistically significant difference (P = 0.038). The bleeding was similar in the two groups (P = 0.498). Conclusion: In view of the findings on healing status and pain in the patients, nifedipine may be significantly more effective in the treatment of chronic anal fissure than ISDN

A New Flexible Bathtub-Shaped Modification of the Weibull Model: Properties and Applications

Many studies have suggested the modifications and generalizations of the Weibull distribution to model the nonmonotone hazards. In this paper, we combine the logarithms of two cumulative hazard rate functions and propose a new modified form of the Weibull distribution. The newly proposed distribution may be called a new flexible extended Weibull distribution. Corresponding hazard rate function of the proposed distribution shows flexible (monotone and nonmonotone) shapes. Three different characterizations along with some mathematical properties are provided. We also consider the maximum likelihood estimation procedure to estimate the model parameters. For the illustrative purposes, two real applications from reliability engineering with bathtub-shaped hazard functions are analyzed. The practical applications show that the proposed model provides better fits than the other nonnested models

Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks

In this study, we address the problem of classification of carrot fruit in order to manage and control their waste using improved deep neural networks. In this work, we perform a deep study of the problem of carrot classification and show that convolutional neural networks are a straightforward approach to solve the problem. Additionally, we improve the convolutional neural network (CNN) based on learning a pooling function by combining average pooling and max pooling. We experimentally show that the merging operation used increases the accuracy of the carrot classification compared to other merging methods. For this purpose, images of 878 carrot samples in various shapes (regular and irregular) were taken and after the preprocessing operation, they were classified by the improved deep CNN. To compare this method with the other methods, image features were extracted using Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) methods and they were classified by Multi-Layer Perceptron (MLP), Gradient Boosting Tree (GBT), and K-Nearest Neighbors (KNN) algorithms. Finally, the method proposed based on the improved CNN algorithm, was compared with other classification algorithms. The results showed 99.43% of accuracy for grading carrot through the CNN by configuring the proposed Batch Normalization (BN)-CNN method based on mixed pooling. Therefore, CNN can be effective in increasing marketability, controlling waste and improving traditional methods used for grading carrot fruit

Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.Comment: 44 pages, 11 figures, 5 table