Miracle-dz/UniFAR-FTUs huggingface dataset:
15 seconds to alpha: CMBX risk pricing with limited information

In this paper we introduce and validate a method for daily and intraday estimation of reduced form risk decompositions for CMBX before and during the Covid pandemic. We estimate default, rates, liquidity, and excess liquidity risk partitions from monthly primitives found in Christopoulos (2017) and Christopoulos and Jarrow (2018). Within the Covid pandemic, we perform CMBX risk partition estimations in intervals of 15 seconds intraday for 240 trading days and disclose regular patterns of risk partition volatility in the cross section. We fuse these insights into CMBX risk to the related REIT market in 54 automated long/short day trading strategies. Using the ICAPM of Merton (1990), we find 96% of our strategies exhibited statistically significant alphas, with 65% of them producing abnormal positive returns between 0.73% and 48.74%. Our findings support the assessment of CMBX and REIT liquidity with our estimates of reduced form CMBX risk partitions.
Attack Model GCN GraphSage GCN GraphSage GCN GraphSage

Meta-Self [125] is a poisoning attack model for node classification by leveraging meta-learning to generate attacks, i.e., using meta-gradients to solve the bilevel optimization problem. It conducts adversarial attacks on global node classification of a single graph. It aims to solve a bilevel optimization problem: (1) training classification on graphs and (2) attacking graphs. It gradually improves attack performance by using meta learning to iteratively solve the above two problems. The GMA model utilizes meta learning to find good attack starting points in two graphs.
Aspects of reproductive indices and enteroparasitic infestation of Clarias gariepinus (Burchell, 1822) in a tropical reservoir

The study was carried out between September 2011 and August 2013, using 328 Clarias gariepinus in Oba reservoir, Oyo State, Nigeria. Standard methods were used to determine gonado-somatic index (GSI), fecundity values, and enteroparasitic infestation of the fish. Sex ratio was 1.3:1, male to female, and females on an average invested 20.54% of its body weight in egg production while male GSI was 6.05 ± 0.19%. Spawning was prominent in rainy season; the fish was highly fecund (31,169 eggs) with relative fecundity of 222 ± 10 eggs per gram weight of fish. Fecundity correlated linearly (positive) with gonad weight, body weight, total length and GSI. Over fishing, led the fish to invest its body weight and growth in egg production, to increase and enhance its chances of survival. Parasites recovered were Procamallanus laevionchus, Paracamallanus cyathopharynx, Anomotaenia species, Monobothrium species, Polyonchobothrium clariae, and Neoechinorhynchus rutili. Prevalence and intensity were higher in female than in male and more in dry than rainy season. Parasitic infestation reduces weight growth in the fish; this influenced the values of reproductive indices in which body weight was a numerator or denominator. Keywords: Clarias gariepinus, enteroparasites, fecundity, gonado-somatic.
Cultural Intelligence: A Review Perspective for Business Administration

This work aims to review the existing publications on cultural intelligence (CQ), presenting what has been discussed and discovered on the subject, the measurements as well as the methods to improve it. A business conceptual framework for the business field is suggested which provides a basis for theory development and debate. Finally, some insights will be highlighted, which can propel the readers into future research. A search was performed on papers selected from the Science Direct database, which houses the empirical studies in the business management field. Sixteen articles were studied in detail. The relationships between skills of using CQ with expatriation or international cultural adjustment were found to be the most investigated themes in the business arena. Most of them revealed the existence of a positive association between the expatriate performance and crosscultural adaptation. The most used instrument used to measure CQ was the CQ Scale, a scale with psychological origins. No scale was developed or found from the business area. From the literature review an initial framework is suggested for the business environment.
Path-Link Graph Neural Network for IP Network Performance Prediction

Dynamic resource provisioning and quality assurance for the plethora of end-to-end slices running over 5G and B5G networks require advanced modeling capabilities. Graph Neural Networks (GNN) have already proven their efficiency for network performance prediction. GNN architecture matches well the structures usually met in communications networks. In this paper, the focus is on the IP transport network as one of the end-to-end 5G architecture domains. The recently published RouteNet GNN is taken as a reference and starting point for our study. RouteNet performance is verified by a new implementation in the PyTorch ML library. Next, an alternative Path-Link neural network (PLNet) architecture is proposed and evaluated. After hyper-parameter tuning for both models, the results show that PLNet and RouteNet achieve a similar accuracy level. The advantage of PLNet is in parallel architecture. It is demonstrated that its inference speed is not sensitive to the length of the network’s paths.
GENERIC LIGHTLIKE SUBMANIFOLDS OF AN INDEFINITE KAEHLER MANIFOLD WITH A QUARTER-SYMMETRIC METRIC CONNECTION

Jin studied lightlike hypersurfaces of an indefinite Kaehler manifold [6, 8] or indefinite trans-Sasakian manifold [7] with a quartersymmetric metric connection. Jin also studied generic lightlike submanifolds of an indefinite trans-Sasakian manifold with a quarter-symmetric metric connection [10]. We study generic lightlike submanifolds of an indefinite Kaehler manifold with a quarter-symmetric metric connection.
Move-Step Structure of the Introduction Section of English Medical Science Research Articles Written by Chinese Researchers Published in International Journals

Recently, English has rapidly developed more complex relationships between communities of speakers around the world (Sharifian, 2009), and it has acquired the status of an international language. As a result, research articles (RAs) in English play the indispensable roles in advancing scientific knowledge among scholars worldwide. During the past two decades, many genre analysts have been aware that writing RAs Introduction section poses a big difficulty for academic writers, so they have tried to find an approach to figure out the move-step structure of this genre. Swales (1990) states that writing RAs Introduction section is troublesome to almost all academic writers since they have many difficulties in academic writing. Because of the importance of Introduction section to the whole RAs and the difficulties writers may have, it has gotten more attention than other sections. Many scholars
The Impact of Workplace Spirituality on Turnover Intentions in Malaysian Banking Sector

The purpose of this paper is to provide a hypothesized model which investigates the relationship of workplace spirituality and turnover intentions in the banking employees of Malaysia. This study was conducted on managerial employees of banking sector of Malaysia. Sample was drawn from different commercial banks operating in Malaysia for this study. A total of 381 usable returns were collected for the analysis. Structural equation model (SEM) through PLS were implemented to obtain the best fit model and to determine the role of Workplace spirituality to improve the turnover intentions in banking employees. As predicted, the hypothesized model best fits the data. The results of direct relationship analysis and SEM also contribute to the clarification of the causal relations between workplace spirituality and turnover intentions in banking employees. Moreover, the results revealed that the influence of workplace spirituality on turnover intention was significant in banking environment in Malaysia. Research limitations/implications – The results of this study might only be generalized to banks and similar contexts. Moreover, although the sample of the study was yielded from sample in Malaysian banking sector, the common methods bias might be a problem in generalizing the results. Workplace spirituality is the concept though which Banks can retain and their employees and workplace spirituality also helps the organizations to reduce the negative work attitude for employees. The results also helps the decisions makers to focus on building training programs in order to understand the benefits of enriching spiritual environment and be more supportive of spirituality movement. Furthermore, organizations can design some of activities that make work environment more meaningful. This study is unique as it is the first that examined theoretically and empirically the influence of workplace spirituality on turnover intentions especially in the banking environment.
Soluble Receptor for Advanced Glycation End-products regulates age-associated Cardiac Fibrosis

Myocardial aging increases the cardiovascular risk in the elderly. The Receptor for Advanced Glycation End-products (RAGE) is involved in age-related disorders. The soluble isoform (sRAGE) acts as a scavenger blocking the membrane-bound receptor activation. This study aims at investigating RAGE contribution to age-related cardiac remodeling. We analyzed the cardiac function of three different age groups of female Rage-/- and C57BL/6N (WT) mice: 2.5- (Young), 12- (Middle-age, MA) and 21-months (Old) old. While aging, Rage-/- mice displayed an increase in left ventricle (LV) dimensions compared to age-matched WT animals, with the main differences observed in the MA groups. Rage-/- mice showed higher fibrosis and a larger number of α-Smooth Muscle Actin (SMA)+ cells with age, along with increased expression of pro-fibrotic Transforming Growth Factor (TGF)-β1 pathway components. RAGE isoforms were undetectable in LV of WT mice, nevertheless, circulating sRAGE declined with aging and inversely associated with LV diastolic dimensions. Human cardiac fibroblasts stimulated with sRAGE exhibited a reduction in proliferation, pro-fibrotic proteins and TGF-beta Receptor 1 (TGFbR1) expression and Smad2-3 activation. Finally, sRAGE administration to MA WT animals reduced cardiac fibrosis. Hence, our work shows that RAGE associates with age-dependent myocardial changes and indicates sRAGE as an inhibitor of cardiac fibroblasts differentiation and age-dependent cardiac fibrosis.
Convolutional Extreme Learning Machines: A Systematic Review

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.
Adapting the LodView RDF Browser for Navigation over the Linguistic Linked Open Data Cloud in Russian and the Languages of Russia

This paper is dedicated to using of LodView RDF browser for navigation on the Linguistic Linked Open Data cloud in Russian and languages of Russia. We reveal several limitations of LodView, that prevents its using for this purpose. These limitations are: 1) resolution of Cyrillic URIs; 2) Cyrillic URIs in Turtle representations of resources; 3) support for Cyrillic literals; 4) support for URIs with IDs of fragments; 5) human-readable URLs for RDF representations of resources; 6) deployment of embedded resources. We updated the LodView for fix the recovered limitations.
MultilayerTuple: A General, Scalable and High-performance Packet Classification Algorithm for Software Defined Network System

Packet classification is one of the core components in Software Defined Network (SDN) systems, e.g., Open vSwitch. However, the current packet classification algorithm Tuple Space Search (TSS), which is implemented in SDN systems, has low lookup speed and can be attacked. Although some algorithms that support incremental updates are proposed to improve the lookup speed, e.g., TupleMerge and PartitionSort, but are not general and scalable to apply in SDN systems and replace TSS. In this paper, we propose a general, scalable, and high-performance packet classification algorithm MultilayerTuple. MultilayerTuple reduces the number of tuples by splitting the prefix lengths of rules into ranges in each layer, then creates the next layer to replace the long rule chain recursively. The experimental results demonstrate that compared to TSS, TupleMerge, and PartitionSort, MultilayerTuple achieves 21.8x, 2.1x, 2.2x lookup speed and 2.3x, 12.3x, 8.5x update speed. Furthermore, we have implemented MultilayerTuple in the OpenFlow table and MegaFlow cache of Open vSwitch, and it achieves 16.0x and 10.2x lookup speed than TSS. Especially when TSE attack happens, MultilayerTuple can effectively defend against it.
The Impact Of Government Policies On House Prices In Malaysia: An Autoregressive Distributed Lag (ARDL) Cointegration Approach

This paper examines the impact of government policies on house prices in Malaysia. In addition, this study also includes the role of gross domestic product, interest rate and total population as emphasised by the Life Cycle and Overlapping Generation Models. This study used an annual time series data from 1988 to 2017. By using autoregressive distributed lag (ARDL) framework, we develop a model of house price determination with a focus on government policy. This study revealed that PR1MA has a positive association with house prices. On the other hand, MM2H is not a primary contributor to the house prices in Malaysia.
A Wideband Triple-Mode Differentially Fed Microstrip Patch Antenna

A wideband differentially fed microstrip patch antenna (MPA) with triple-resonant modes is presented in this letter. The proposed triple-mode MPA is realized by combining two dual-mode MPAs (MPA-I and MPA-II) with different resonant frequency ratios. First, the TM<sub>0,1</sub> mode and TM<sub>0,1/2</sub> mode of dual-mode MPA-I can be concurrently excited by adding a pair of coupling shorted patches beside the strip MPA. The ratio of <italic>f</italic><sub>0,1/2</sub>/<italic>f</italic><sub>0,1</sub> can be easily adjusted by moving the shorting pins between the strip MPA and shorted patches. Second, by properly designing the dimensions of a conventional MPA, the TM<sub>0,1</sub> and TM<sub>2,1</sub> modes of dual-mode MPA-II are simultaneously excited. To further reduce the ratio of <italic>f</italic><sub>2,1</sub>/<italic>f</italic><sub>0,1</sub>, four slots are elaborately etched on the conventional MPA. Finally, by combining the two dual-mode MPAs, a triple-mode MPA with the frequency ratio of <italic>f</italic><sub>0,1/2</sub>:<italic>f</italic><sub>2,1</sub>:<italic>f</italic><sub>0,1</sub> = 1.2:1.1:1 is realized. To verify the design concept, a prototype of triple-mode MPA was fabricated and measured. Experimental results show that the proposed microstrip antenna achieves a wide bandwidth of 26.5%, a low cross polarization of –23 dB, and high harmonic suppression.
Knowledge Graph Lifecycle: Building and Maintaining Knowledge Graphs

Knowledge Graphs can integrate a large amount of data from heterogeneous sources. They can be an important resource for various applications; however, they are only useful if they satisfy the requirements of those applications in terms of quality. In this in use experience paper, we present our approach and tools for supporting the Knowledge Graph Lifecycle that starts with creation and hosting and continues with the curation and deployment. The curation process enables the mainteinance of a Knowledge Graph, especially in terms of correctness and completeness. We provide process models and evaluation of developed tools with Knowledge Graphs in tourism domain. We discuss the lessons learned from implementing such an approach in an open and commercial setting and conclude with a summary and a description of currently ongoing use cases.
[RE] ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

In this report, in-depth comparisons between BERT and ALBERT are made using various open-source interpretability 2 and testing tools to verify the claim that ALBERT achieves better performance and faster inference compared to BERT. 3 ALBERT achieves higher benchmarks across many different downstream tasks and demonstrates appropriate sentence 4 embeddings visualization. However, experiment results from behavioral testings and adversarial attacks suggest that 5 ALBERT has relatively worse capabilities, particularly in terms of Robustness and Fairness. 6
Book Review Deep Learning Approaches to Text Production

Text production (Reiter and Dale 2000; Gatt and Krahmer 2018) is also referred to as natural language generation (NLG). It is a subtask of natural language processing focusing on the generation of natural language text. Although as important as natural language understanding (NLU) for communication, NLG had received relatively less research attention over a long period of time. Recently, the rise of deep learning techniques has led to a surge of research interest in text production, both in general and for specific applications such as text summarization and dialogue systems. Deep learning allows NLG models to be constructed based on neural representations, thereby enabling endto-end NLG systems to replace traditional pipeline approaches, which frees us from tedious engineering efforts and improves the output quality. In particular, a neural encoder-decoder structure (Cho et al. 2014; Sutskever, Vinyals, and Le 2014) has been widely used as a basic framework, which computes input representations using a neural encoder, according to which a text sequence is generated token by token using a neural decoder. Very recently, pre-training techniques (Devlin et al. 2019; Radford 2018; Broscheit et al. 2010) have further allowed neural models to collect knowledge from large raw text data, further improving the quality of both encoding and decoding. This book introduces the fundamentals of neural text production, discussing both the mostly investigated tasks and the foundational neural methods. NLG tasks with different types of inputs are introduced, and benchmark datasets are discussed in detail. The encoder-decoder architecture is introduced together with basic neural network components such as convolutional neural network (CNN) (Kim 2014) and recurrent neural network (RNN) (Cho et al. 2014). Elaborations are given on the encoder, the decoder, and task-specific optimization techniques. A contrast is made between the neural solution and traditional solutions to the task. Towards the end of the book, more recent techniques such as self-attention networks (Vaswani et al. 2017) and pre-training are briefly discussed. Throughout the book, figures are given to facilitate understanding and references are provided to enable further reading. Chapter 1 introduces the task of text production, discussing three typical input settings, namely generation from meaning representations (MR; i.e., realization), generation from data (i.e., data-to-text), and generation from text (i.e., text-to-text). At the end of the chapter, a book outline is given, and the scope, coverage and notation convention are briefly discussed. I enjoyed the examples and figures demonstrating the typical NLG tasks such as AMR to text generation (May and Priyadarshi 2017), the E2E dialogue task
ZADÁNÍ DIPLOMOVÉ PRÁCE​

This thesis is focused on the area coverage problem divided into two phases by the fixed-wing Unmanned Aerial Vehicle (UAV). The classical approach to the area coverage problem is to capture the whole area at once. Therefore, the coverage is performed at roughly the same altitude with approximately the same resolution. The two-phase approach proposed in this thesis allows us to perform a fast scan of the whole area and detect its regions that require further inspection. The second phase of the proposed approach involves the high-resolution coverage of multiple disconnected regions. This thesis proposes a method solving the multiple-region coverage with the use of the re-entries into the regions and compares this approach with the classical approach not involving the re-entries.
Multiagent Reinforcement Learning for Hyperparameter Optimization of Convolutional Neural Networks

Nowadays, deep convolutional neural networks (DCNNs) play a significant role in many application domains, such as computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, challenging, and time-consuming task, due to the extremely large design space, as a consequence of a large number of layers and their corresponding hyperparameters. In this work, we address the challenge of performing hyperparameter optimization of DCNNs through a novel multiagent reinforcement learning (MARL)-based approach, eliminating the human effort. In particular, we adapt <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning and define learning agents per layer to split the design space into independent smaller design subspaces such that each agent fine tunes the hyperparameters of the assigned layer concerning a global reward. Moreover, we provide a novel formation of <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-tables along with a new update rule that facilitates agents’ communication. Our MARL-based approach is data driven and able to consider an arbitrary set of design objectives and constraints. We apply our MARL-based solution to different well-known DCNNs, including GoogLeNet, VGG, and U-Net, and various datasets for image classification and semantic segmentation. Our results have shown that compared to the original CNNs, the MARL-based approach can reduce the model size, training time, and inference time by up to, respectively, <inline-formula> <tex-math notation="LaTeX">$83\times $ </tex-math></inline-formula>, 52%, and 54% without any degradation in accuracy. Moreover, our approach is very competitive to state-of-the-art neural architecture search methods in terms of the designed CNN accuracy and its number of parameters while significantly reducing the optimization cost.
Recurrent Neural Networks as Dynamical Systems: Exploring Chaos and Order

Chaos is a property of many dynamical systems. The impact of this phenomenon of chaos on recurrent neural networks (RNNs) is studied based on prior work. Some problems with the most common RNN architectures due to chaos and instability are analyzed theoretically and empirically. These are related to having strange attractors and the consequent vanishing/exploding gradient problems that make it difﬁcult to learn long-term dependencies. Specialized architectures from the literature such as Chaos-Free RNN (CF-RNN) and Antisymmetric RNN (AS-RNN) claim to help mitigate these issues and ensure that RNNs learn good attractors. A novel variant of CF-RNN, called the Asymmetric Chaos-Free RNN (AS CF-RNN) is proposed and studied. Some empirical analysis is carried out to examine the advantages brought by each of these architectures. This is a learning attempt to view RNNs through the lens of dynamical systems and understand them in perspective.