Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallelized rotation and flipping INvariant Kohonen maps (PINK ) on GPUs

Morphological classification is one of the most demanding challenges in astronomy. With the advent of all-sky surveys, an enormous amount of imaging data is publicly available. These data are typically analyzed by experts or encouraged amateur volunteers. For upcoming surveys with billions of objects, however, such an approach is not feasible anymore. In this work, we present a simple yet effective variant of a rotation-invariant self-organizing map that is suitable for many analysis tasks in astronomy. We show how to reduce the computational complexity via modern GPUs and apply the resulting framework to galaxy data for morphological analysis.
Language Technology CenterDepartment of Electrical and Electronic EngineeringUniversity of Science and Technology , HKUSTClear Water Bay

We propose a mixed language query disam-biguation approach by using co-occurrence information from monolingual data only. A mixed language query consists of words in a primary language and a secondary language. Our method translates the query into mono-lingual queries in either language. Two novel features for disambiguation, namely contextual word voting and 1-best contextual word, are introduced and compared to a baseline feature, the nearest neighbor. Average query translation accuracy for the two features are 81.37% and 83.72%, compared to the baseline accuracy of 75.50%.
the IIMA

An enterprise architecture (EA) plan is a long-term view or blueprint for an organization. It is a very important blueprint for balancing business and Information Technology (IT) and for adding value to an organization. Security is also nowadays an essential dimension for enterprises. It can prevent confidential information from being leaked, and/or stolen, lost succumbing to other serious disasters. There are many studies focusing on EA or on specific aspects of security. However, there are very few studies focusing on enterprise security architecture. This paper focuses on integrating the security dimension into the Zachman EA framework (Zachman, 2007) and is intended to serve as an enterprise security framework (ESA) to assist an organization in successfully and effectively implementing security. The efficacy of the ESA implementation is illustrated through an application in an organization.
Near-Infrared Illumination Add-On for Mobile Hand-Vein Acquisition

There is a growing need for mobile authentication solutions. Biometric recognition systems provide several advantages over conventional knowledge and token based solutions. Especially the use of vascular patterns as a biometric trait gains more and more attention. We present a near-infrared illumination add-on for smartphone devices which allows to capture the vascular pattern of the hands (hand-veins). This device is connected and controlled via Bluetooth and customised for the Nexus 5 smartphone but can be easily adopted to fit other models too. Due to the inherent risk of fraudulent authentication attempts on a nontrusted platform like a smartphone, we propose a challenge-response approach to ensure the authenticity of the captured hand-vein images. A hand-vein data set comprising of 31 subjects and 920 images in total is acquired with the presented device. A performance evaluation utilising different hand-vein recognition schemes is conducted to show the applicability of our device and the proposed challenge-response approach.
Efficient Weighted Kernel Sharing Convolutional Neural Networks

To lessen the redundancy of convolutional kernels, this paper proposes a new convolutional structure, i.e., weighted kernel sharing convolution (WKSC), which gathers the inputs with the same kernel, so the inputs in each group can share the same convolutional kernel. Also, an extra weighting is imposed for each input channel before the sharing process to manifest its diversity. As a consequence, the number of kernels can be greatly reduced, leading to a reduction of model parameters and the speedup of inference. Moreover, WKSC can be combined with other existing compression models such as depthwise separable convolutions, resulting in a more compressed architecture. Extensive experiments on CIFAR-100 and ImageNet classification demonstrate the effectiveness of the new approach in both computation cost and the parameters required compared with the state-of-the-art works.
Callus, Dedifferentiation, Totipotency, Somatic Embryogenesis: What These Terms Mean in the Era of Molecular Plant Biology?

Recent findings call for the critical overview of some incorrectly used plant cell and tissue culture terminology such as dedifferentiation, callus, totipotency, and somatic embryogenesis. Plant cell and tissue culture methods are efficient means to preserve and propagate genotypes with superior germplasm as well as to increase genetic variability for breading. Besides, they are useful research tools and objects of plant developmental biology. The history of plant cell and tissue culture dates back to more than a century. Its basic methodology and terminology were formulated preceding modern plant biology. Recent progress in molecular and cell biology techniques allowed unprecedented insights into the underlying processes of plant cell/tissue culture and regeneration. The main aim of this review is to provide a theoretical framework supported by recent experimental findings to reconsider certain historical, even dogmatic, statements widely used by plant scientists and teachers such as “plant cells are totipotent” or “callus is a mass of dedifferentiated cells,” or “somatic embryos have a single cell origin. ” These statements are based on a confused terminology. Clarification of it might help to avoid further misunderstanding and to overcome potential “terminology-raised” barriers in plant research.
Design of Multi-tooth Permanent Magnet Flux Switching Machine for Electric Bicycle Application

Permanent magnet flux-switching machine (PMFSM) with a single-phase multi-tooth design is analyzed in this paper. This paper presents a design comparison between conventional design single-tooth and novel multi-tooth design for a single-phase (PMFSM) with 8S-16P and 4S-8P designed specifically for an electric bicycle applications. The result analysis of this motors has been certified using the 2D-FEA analyses. While, matched with a conventional (FSM), a multi-tooth design motor involves a considerably lesser magnet volume material than a conventional PMFSM machine. Meanwhile, as only half of the number of slot pole and volume of permanent magnets is requisite, and it reveals a high torque performance. However, it shows that the torque capability and performance issues can be increase by using "deterministic optimization method" (DOM).
Topic Modeling of Short Texts: A Pseudo-Document View With Word Embedding Enhancement

Recent years have witnessed the unprecedented growth of online social media, resulting in short texts being the prevalent format of information on the Internet. Given the sparsity of data, however, short-text topic modeling remains a critical yet much-watched challenge in both academia and industry. Research has been devoted to building different types of probabilistic topic models for short texts, among which self-aggregation methods emerged recently to provide informative cross-text word co-occurrences. However, models along this line are still in their infancy and typically yield overfit results and exhibit high computational costs. In this paper, we propose a novel model called Pseudo-document-based Topic Model (PTM), which introduces the concept of pseudo-document to implicitly aggregate short texts against data sparsity. By modeling the topic distributions of latent pseudo-documents rather than short texts, PTM yields excellent performance in accuracy and efficiency. A word embedding-enhanced PTM (WE-PTM) is also proposed to leverage pre-trained word embeddings, which is essential to further alleviating data sparsity. Extensive experiments with self-aggregation or word embedding-based baselines on four real-world datasets including two online media short texts, demonstrate the high-quality topics learned by our models. Robustness to limited training samples and the explainable semantics of topics are also investigated.
Applying the Technology Acceptance Model to a Systems Development Methodology

Davis' (1989) Technology Acceptance Model (TAM) is a well-known, validated model originally intended to predict and explain the acceptance of end-user technological products such as PC systems and software packages. This research represents an initial exploration into the ability of the TAM to predict and explain the acceptance of a radically new technological process, object-oriented systems development (OOSD). The results obtained from a sample of 160 experienced developers indicates that, with some slight modification, the scales used for the TAM are reliable and valid for measuring the usefulness and ease of use of OOSD methods. Introduction A “software crisis” has plagued systems development for the past several decades (Pressman, 1992). A radically new methodology, object-oriented systems development (OOSD) has emerged to address this crisis (Fichman and Kemerer, 1992). While many advocates of OOSD claim that it is vastly superior to conventional methods (Booch 1994, Coleman et al. 1994; Jacobson et al. 1995; Rumbaugh et al., 1991), others are concerned that OOSD is not achieving widespread acceptance, especially in business circles (Pancake, 1995). They warn that OOSD may be so difficult to learn that it may never become a mainstream approach (Fichman and Kemerer, 1993). This study represents an initial investigation into the acceptance of a specific systems development methodology (OOSD) using Davis' (1989) Technology Acceptance Model, or TAM (Figure 1).
Fusing Distinguish Degree Neural Networks for Relational Classification

Relational classification is an important natural language processing (NLP) task. Conventional relational classification models predominantly rely on the shortest dependency paths (SDPs) obtained by NLP tools to obtain keywords information or introduce lexical-level extra information. Bidirectional long short-term memory neural network (Bi-LSTM) is a generally accepted and effective method for obtaining complete sentence information. This model uses the forward neural network and backward neural network encoding cascade on step as output results. However, for relational classification task that considers the direction of entities, direction is critical for good experimental results. In fact, the traditional Bi-LSTM cannot clearly distinguish between two relationships with only different entities directions, such as Cause-Effect (e1,e2) and Cause-Effect (e2,e1). Thus, we propose a model called Di-LCNN, which introduces a concept of distinguish degree to distinguish between two types of relationships that differ only in the direction of the entities. In the proposed model, the distinguish degree is defined as the vectorized representation of the relationship between two entities. After the distinguish degree is obtained, it is integrated into the model to enhance the ability of the model to distinguish the categories of different entity directions in the same relationship. The results of experiments conducted on the SemEval-2010 Task 8 dataset, which is designed for nominal relational classification tasks, indicate that our model is an improvement over the state-of-the-art on this dataset, achieving an F1 score of 85.6% without using any manual features or NLP tools. In addition, our experimental results show that (1) distinguish degree is a very important information for relational classification tasks; (2) distinguish degree can greatly improve the results of the basic model.
On the Performance of Master-Slave Parallelization Methods for Multi-Objective Evolutionary Algorithms ?

In multi-task learning [2], a variant of transfer learning [5], different but related tasks in the same feature space — for each of which (labeled) data is available — are learned simultaneously, with the goal of improving the performance of each task by incorporating knowledge from all other tasks. In our research, our goal is to learn regression models for related tasks which are similar in structure, i.e., they predominantly share the same features which represent the commonalities between the tasks, with only a small number of task-specific features that represent the differences between tasks. We present a method to unify models of different but related tasks using a multitask feature learning approach [1] based on symbolic regression [6]. The symbolic regression community usually relies on genetic programming [3] when implementing symbolic regression frameworks, but a novel implementation of symbolic regression called Fast Function Extraction (FFX) [4] uses regularized linear regression with pathwise learning to provide a scalable and deterministic symbolic regression framework. We utilize the FFX framework as a basis for an iterative multi-task feature learning approach. Each task ist learned separately using FFX, the selected features of the resulting task models are then weighted according to their frequency of ocurrence over all tasks. These weights are then used in the next iteration to promote features which are of importance for most of the tasks. Thus with each iteration, information from all tasks is used to consolidate the task models until the weights do not change anymore. Our experimental results on both real world and synthetic datasets show, that our resulting task models have higher similarity and lower complexity while yielding the same prediction performance when compared to a single symbolic regression run.
Innate Lymphoid Cells: Expression of PD-1 and Other Checkpoints in Normal and Pathological Conditions

Innate lymphoid cells (ILCs) belong to a family of immune cells. Recently, ILCs have been classified into five different groups that mirror the function of adaptive T cell subsets counterparts. In particular, NK cells mirror CD8+ cytotoxic T cells while ILC1, ILC2, ILC3, and Lymphoid tissue inducer (LTi)-like cells reflect the function of CD4+T helper (Th) cells (Th1, Th2, and Th17 respectively). ILCs are involved in innate host defenses against pathogens and tumors, in lymphoid organogenesis, and in tissue remodeling/repair. In recent years, important molecular inducible checkpoints (PD-1, TIM3, and TIGIT) were shown to control/inactivate different immune cell types. The expression of many of these receptors has been detected on NK cells and subsets of tissue-resident ILCs in both physiological and pathological conditions, including cancer. In particular, it has been demonstrated that the interaction between PD-1+ immune cells and PD-L1/PD-L2+ tumor cells may compromise the anti-tumor effector function leading to tumor immune escape. However, while the effector function of NK cells in tumor is well-established, limited information exists on the other ILC subsets. We will summarize what is known to date on the expression and function of these checkpoint receptors on NK cells and ILCs, with a particular focus on the recent data that reveal an essential contribution of the blockade of PD-1 and TIGIT on NK cells to the immunotherapy of cancer. A better information regarding the presence and the function of different ILCs and of the inhibitory checkpoints in pathological conditions may offer important clues for the development of new immune therapeutic strategies.
Enhanced phosphorylation of AMPK by lutein and oxidised lutein that lead to mitochondrial biogenesis in hyperglycemic HepG2 cells

The stimulation of adenosine monophosphate‐activated protein kinase (AMPK) is a prime target to decrease the hyperglycemic condition, hence it is a lutein (L) and oxidised lutein (OXL) is a target molecule for the treatment of type II diabetes. In the current study, a plausible interaction of L and OXL with AMPK was investigated by molecular docking. In addition, the effect of L and OXL for the activation of AMPK that triggers the downstream regulator peroxisome proliferator‐activated receptor γ coactivator 1α (PGC‐1α), TFAM expression, mitochondrial DNA (mtDNA), mitochondrial biogenesis and superoxide dismutase 2 (SOD2) in high glucose treated HepG2 cells were investigated by quantitative polymerase chain reaction and Western blot analysis. Molecular docking reveals higher binding affinity of L (ΔG = −6.3 kcal/mol) and OXL (ΔG = −15.5 kcal/mol) with AMPK, compared with metformin (ΔG = −5.0 kcal/mol). The phosphorylation of AMPK increased by 1.3‐ and 1.5‐fold with L and OXL treatment, respectively, in high glucose induced HepG2 cells. The activation of PGC‐1α is significant (P < 0.05) in OXL group than L. Similarly, TFAM expression is increased with L and OXL compared with the high glucose group. Further increase in SOD2 and mtDNA, confirms the efficacy of L and OXL in restoring the mitochondrial biogenesis in high glucose induced cells through AMPK, PGC‐1α, and TFAM.
A Method for Strategic Migration from Simulation to Real Manipulator System

The breakthrough of Reinforcement Learning (RL) in strategy games such as Go highlights its potential in the field of control. RL requires a large amount of data for training so it often relies on simulation. It is still an unresolved problem for the application of RL in the real physical system. In this paper, we propose a method which controls the joint angular velocities of the multi-degree-of-freedom manipulator in the kinematics level. By rationally designing the task environment, the strategy learned from the simulation platform is successfully and directly transferred to the real manipulator system. (The related codes and experimental videos are available on https://github.com/inksci/env-ur3).
Optimizing Computer Worm Detection Using Ensembles

The scope of this research is computer worm detection. Computer worm has been defined as a process that can cause a possibly evolved copy of it to execute on a remote computer. It does not require human intervention to propagate neither does it attach itself to an existing computer file. It spreads very rapidly. Modern computer worm authors obfuscate the code to make it difficult to detect the computer worm. This research proposes to use machine learning methodology for the detection of computer worms. More specifically, ensembles are used. The research deviates from existing detection approaches by using dark space network traffic attributed to an actual worm attack to train and validate the machine learning algorithms. It is also obtained that the various ensembles perform comparatively well. Each of them is therefore a candidate for the final model. The algorithms also perform just as well as similar studies reported in the literature.
Posture Detection for Elderly using Infrared Array Sensor and Fine Tuning

The risk of solitary death is rising because there is an increasing number of elderly living alone in Japan. Therefore, attempts are made to watch elderly remotely from his/her family. However, these systems have problems such as difficulty in confirming the status of the elderly in real time and privacy issues. In this paper, we propose a method to detect abnormal condition using infrared array sensor.
Digitally Predicting Protein Localization and Manipulating Protein Activity in Fluorescence Images Using Four-dimensional Reslicing GAN

Motivation While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One feasible solution is using deep neural networks to model the localization relationship between two proteins so that the localization of a protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflects the modeled relationship. Accordingly, observing the predictions via repeatedly manipulating input localizations is an explainable and feasible way to analyze the modeled relationships between the input and the predicted proteins. Results We propose a Protein Localization Prediction (PLP) method using a cGAN named Four-dimensional Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of imaged and target proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, with accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein and observe the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on four groups of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins and DA and DI provide guidance to study localization-based protein functions. Availability and Implementation The open-source code is at https://github.com/YangJiaoUSA/4DR-GAN.
Enumeration for FO Queries over Nowhere Dense Graphs

We consider the evaluation of first-order queries over classes of databases that are nowhere dense. The notion of nowhere dense classes was introduced by Nesetril and Ossona de Mendez as a formalization of classes of "sparse" graphs and generalizes many well-known classes of graphs, such as classes of bounded degree, bounded tree-width, or bounded expansion. It has recently been shown by Grohe, Kreutzer, and Siebertz that over nowhere dense classes of databases, first-order sentences can be evaluated in pseudo-linear time (pseudo-linear time means that for all ε there exists an algorithm working in time O(n1+ε), where n is the size of the database). For first-order queries of higher arities, we show that over any nowhere dense class of databases, the set of their solutions can be enumerated with constant delay after a pseudo-linear time preprocessing. In the same context, we also show that after a pseudo-linear time preprocessing we can, on input of a tuple, test in constant time whether it is a solution to the query.
Investigation of a Fuel Oil Viscosity and Temperature Control System Based on State Observation

To address the contradiction between simultaneous fixed-value control of heavy oil temperature and viscosity in current fuel viscosity and temperature control systems, this study analyzes the operating process and principle of heavy oil heating used in ocean vessels, and derives a differential equation for a fuel oil viscosity and temperature control system based on state observation. Using inlet and outlet temperatures as the state variables and heavy oil viscosity as the output variable, the system can be treated as a kind of affine nonlinear system, with coordinate transformation function vectors determined so as to derive the sliding mode variable structure form of the state observer. Finally, some programs were compiled for calculation and simulation using MATLAB. The results showed that, by using a sliding mode variable structure control, state variables could be tracked well during the control of output variables. This confirmed the superiority of output feedback control based on a model of the state observer.