Miracle-dz/UniFAR-FTUs huggingface dataset:
The mechanisms of the impact of high-tech product export trade on regional carbon performance

Export trade is a driving force of economic development, but it is also an important source of increasing carbon dioxide emissions. Against the background of suggestions of conflicts between economic development and environmental health, the proportion of high-tech products in the export trade of various countries has been rising, leading governments and scholars to focus on identifying the appropriate export structure for countries. While there is some knowledge on how high-tech product exports affect carbon dioxide emissions, the mechanisms involved in that link have not been adequately addressed in previous studies. This study is based on China’s inter-provincial panel data from 2006 to 2017, and uses the fixed effects model stepwise regression method and the bootstrap method to systematically investigate the mediating effects of industrial structure supererogation, low-carbon technological innovation and human capital accumulation, operating in the impact of high-tech product export trade on regional carbon performance. The research results show that high-tech product exports can help improve regional carbon performance. The mediating variables, industrial structure supererogation, low-carbon technological innovation and human capital accumulation, separately have positive and partially mediating effects on the link between high-tech products export trade and regional carbon performance. After considering the alternative indicator of regional carbon performance and the replacement of test method, the results of this study are still robust. The research results are instructive for an understanding of the relationship between high-tech product export trade and regional carbon performance. They highlight the important role of high-tech product exports in improving regional carbon performance, and have significance in promoting China’s green and low-carbon transition.
Formula for the contrast sensitivity of the human eye

For design criteria of displayed images and for the judgment of image quality, it is very important to dispose of a trustful formula for the contrast sensitivity of the human eye. The contrast sensitivity function or CSF depends on a number of conditions. Most important are the luminance and the viewing angle of the object, but surround illumination can also play a role. In the paper a practical formula is given for a standard observer. This formula is derived from a more general physical formula for the contrast sensitivity. In this paper also the effects of orientation angle and surround luminance will be treated. The orientation angle will be incorporated in the formula and a correction factor will be given for the dependence on surround luminance.
The Features

The article considers the features of the development and implementation mechanism of financial and economic security strategy that provides enterprises with competitive advantages in achieving strategic goals of their economic development. It is justified that it would be expedient to implement the process of development and implementation of strategies to ensure financial and economic security of enterprises according to the main stages proposed by the author of the article: determination of the total period of formation and action of the strategy; monitoring and analysis of internal and external factors; determining the presence of processes that are threats for the development of the company and its financial and economic security; conduct a comprehensive assessment of the actual level of financial and economic security; the formation of a list of indicators that point to the existing threats; formation of a project scenarios of financial and economic security strategy; evaluation and selection of the most priority strategy scenario; providing the implementation of the chosen strategy; organization of control over the implementation of the financial and economic security of the enterprise strategy.
Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.
SLBQT-DSR: SOURCE-BASED LOAD BALANCING ROUTING ALGORITHM UNDER CONSTRAINTS OF QUALITY OF TRANSMISION FOR MANET

The routing technique under the constraints of the quality of transmision (QoT) in mobile ad hoc networks (MANET) has been studied widely recently. For this routing technique, QoT of the data transmission routes is improved. However, for the network models with mesh topologies such as MANET, The routing technique under the constraints of QoT can increase the bottlenecks due to the unbalanced traffic load. In this paper, we improve the DSR protocol by using the source-based load balancing in combined with the QoT constraint. The simulation results have shown that, the proposed algorithm outperforms the original algorithms in terms of the signal to noise ratio, bit error rate, blocking probability of the data packets and throughput.The routing technique under the constraints of the quality of transmision (QoT) inmobile ad hoc networks (MANET) has been studied widely recently. For this routing technique, QoTof the data transmission routes is improved. However, for the network models with mesh topologiessuch as MANET, The routing technique under the constraints of QoT can increase the bottlenecksdue to the unbalanced trac load. In this paper, we improve the DSR protocol by using the source-based load balancing in combined with the QoT constraint. The simulation results have shown that,the proposed algorithm outperforms the original algorithms in terms of the signal to noise ratio, biterror rate, blocking probability of the data packets and throughput.
Geographical interdependent robustness measures in transportation networks

Most of transportation networks interacts with other to support our modern society way of life. The proper performance of interdependent networks depends on the normal operation of the networks that are interconnected. The aim of this thesis is to measure and analyze the robustness of different interdependent networks models under large-scale failures and, in particular, to consider interdependent networks where at least one of the networks is a telecommunication network. Both, the effects of different network models and the dynamic process of failure propagation between networks are considered. New interconnection strategies are proposed to improve the robustness of the interconnected networks by analyzing the vulnerability of networks to failures and targeted attacks. Moreover, an enhanced region-based interconnection model is proposed by considering a limit to the number of interlinks between the interconnected nodes.
Association of Immature Platelet Fraction and Trombopoietin Levels Based on the Grades of Sepsis Severity

Sepsis is one of the major causes of morbidity and mortality around the world. Organ dysfunction in sepsis can be identified using Sepsis-related Organ Failure Assessment . The dysfunction of the coagulation cascade, the number of pro-inflammatory cytokines, and endothelial dysfunction promote the consumptive thrombocytopenia in sepsis patients, which triggers a compensatory response in the body by increasing thrombopoietin release. As a result, it increases platelet production which can be assessed by immature platelet fraction (IPF). This study is a preliminary research to see the relationship between IPF values and serum thrombopoietin (TPO) levels in terms of the clinical severity of patients with sepsis based on SOFA scores. A cross-sectional design was performed in this study with 49 samples of patients with sepsis, collected during June-July 2019, at Dr. Wahidin Sudirohusodo Hospital in Makassar. The study was conducted in August 2019. IPF values were measured by the fluorescent flow cytometry method. Meanwhile, serum thrombopoietin levels were calculated by the enzyme-linked immunosorbent assay (ELISA) method using human Thrombopoietin ELISA (RayBioTech, Australia). The results showed that the IPF value increased along with the SOFA score, followed by a decrease in platelet (p = 0.014). TPO levels presented an upward along with SOFA scores with a decrease in platelet (p = 0.008). There was a significant correlation between IPF values and TPO levels (r = 0.606, p <0.001) where the Immature Platelet Fraction score and thrombopoietin levels increased with SOFA scores. In addition, there was also a positive correlation between IPF values and TPO levels in sepsis patients.
Multi-Agent Dual Learning

Dual learning has attracted much attention in machine learning, computer vision and natural language processing communities. The core idea of dual learning is to leverage the duality between the primal task (mapping from domain X to domain Y) and dual task (mapping from domain Y to X ) to boost the performances of both tasks. Existing dual learning framework forms a system with two agents (one primal model and one dual model) to utilize such duality. In this paper, we extend this framework by introducing multiple primal and dual models, and propose the multi-agent dual learning framework. Experiments on neural machine translation and image translation tasks demonstrate the effectiveness of the new framework. In particular, we set a new record on IWSLT 2014 German-to-English translation with a 35.44 BLEU score, achieve a 31.03 BLEU score on WMT 2014 English-toGerman translation with over 2.6 BLEU improvement over the strong Transformer baseline, and set a new record of 49.61 BLEU score on the recent WMT 2018 English-to-German translation.
A Mean Field Games approach for multi-lane traffic management

In this work we discuss an Mean Field Games approach to traffic management on multi-lane roads. Such approach is particularly indicated to model self driven vehicles with perfect information of the domain. The mathematical interest of the problem is related to the fact that the system of partial differential equations obtained in this case is not in the classic form, but it consists of two continuity equations (one for each lane) and a variational inequality, coming from the Hamilton-Jacobi theory of the hybrid control.
University-industry technology transfer within the context of RIS3 North of Portugal

ABSTRACT The purpose of this paper is to assess stakeholders’ perceptions on the implementation of the smart specialization strategy (RIS3) in the North of Portugal, with a particular focus on technology transfer (TT) activities between 2014 and 2017. A qualitative methodology was followed through semi-structured interviews applied to the actors in the region based on the eight priority domains of RIS3 North of Portugal. Data analysis was performed using Iramuteq software. Our results suggest that the stakeholders’ perception of the fitness of the RIS3 defined for their region does not match with the RIS3 defined by the policy makers. Our results contribute to a better understanding of the real perception of universities and industry on TT in the context of RIS3 and the need for ongoing evaluation and adjustments of RIS3 priority domains by policy makers to address them to the real necessities of the industry/market.
Prediction and integration of semantics during L2 and L1 listening

ABSTRACT Using the visual world paradigm, we tested whether Dutch-English bilinguals predict upcoming semantic information in auditory sentence comprehension to the same extent in their native (L1) and second language (L2). Participants listened to sentences in L1 and L2 while their eye-movements were measured. A display containing a picture of either a target word or a semantic competitor, and three unrelated objects was shown before the onset of the auditory target word in the sentence. There were more fixations on the target and competitor pictures relative to the unrelated pictures in both languages, before hearing the target word could affect fixations. Also, semantically stronger related competitors attracted more fixations. This relatedness effect was stronger, and it started earlier in the L1 than in the L2. These results suggest that bilinguals predict semantics in the L2, but the spread of semantic activation during prediction is slower and weaker than in the L1.
Sexual orientation and wage differentials using anthropometric and health measures

Abstract This paper analyzes salary differentials based on sexual orientation. Hitherto, the incipient studies addressing the theme in Brazil used the 2010 demographic census conducted by the Brazilian Institute of Geography and Statistics (IBGE). However, the implementation of the Integrated Household Survey System (SIPD) in IBGE’s sample surveys started to include the distinction between same-sex and different-sex spouses for the person responsible for the domicile in its questionnaire. In this context, this research seeks to advance the scant literature on the subject using the unprecedented 2013 National Health Survey (PNS), which like the 2010 census presents a nationwide comprehensive set of socioeconomic characteristics. Also, the PNS contains information on the health of the Brazilian population and anthropometric measures, which allows the expansion of the controls associated with human capital variables. Part of the results corroborate findings in the international literature where wage differentials in favor of lesbians were observed when compared to their sexual counterparts.
Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

Purpose To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry. Methods We trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics. Results Dice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]). Conclusions Utilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.
Secure Cloud File Distribution System for Untrusted Network Environment

- Cloud computing provides reliable and efficient services for sharing data among cloud users at low maintenance cost. Providing privacy, confidentiality, and integrity of data among users in untrusted network environment is a major issue in cloud computing systems, especially where members frequently change their groups. In this paper, we proposed secure file distribution system for untrusted network environment. Our proposed scheme allows users anonymously share their files among members by using group signature and broadcast encryption techniques. The scheme also provides low storage overheads and computational cost independent of the number of revoked users, and support handheld devices with little computational power, low memory and storage capacity. We subjected our scheme to rigorous proofs and security analysis through stimulation and compare the results with other existing protocols thus, MONA and ODBE by considering computational cost for file generation and access.
Tissue Characterization by Deep Learning in Medical Hyperspectral Images

Hyperspectral imaging (HSI) is a promising imaging modality in medical applications, especially for non-invasive and non-contact disease diagnosis and image-guided surgery. Encoding both spatial and spectral information, it can detect subtle changes in the biochemical and morphological properties of a tissue, revealing the early progression of a pathological condition like cancer. Previous medical hyperspectral image analysis approaches depended on handcrafted features or feature extraction principle, requiring considerable domain expertise. To overcome this, automatic feature learning approaches like convolutional neural networks (CNN), previously used in tasks like classification, detection and segmentation in medical images were applied to hyperspectral data, although in a limited number of research studies. This thesis was proposed to review the state-of-the-art in medical hyperspectral image analysis, identify the limitations in current methods, and present a proof-of-concept for using limited hyperspectral image data in CNN-driven tissue characterization. The goal of this thesis is to characterize, using CNNs, ex vivo head and neck (tongue) tissue of patients affected by tumors. While previous work in this field implemented patch-based classification of tissue, in this thesis, a pixel-wise classification approach was proposed to obtain a smooth and continuous segmentation of hyperspectral images. To this end, two types of CNN models were trained from scratch using limited labelled training data, one to automatically learn the spectral features present in the hyperspectral data and the other to learn the combined spectral-spatial features from the hyperspectral data. Performance of four different trained models was evaluated by using a leave-one-out testing scheme, with the spectral-spatial learning approach with larger input spatial dimensions outperforming the other considered approaches.
An Efficient Steganography Technique for Images using Chaotic Bitstream

Steganography is a science of security technique through invisible communication; hiding secret message into cover objects without any suspicion. Protection of the hidden information from an adversary is the main purpose of any steganography system even if the embedding method is obvious. In this paper, an effective image steganography system based on a least significant bit (LSB) algorithm and chaos is proposed. The proposed method contains a spatial domain technique in which the Logistic map is used for generating chaotic bitstream and bitwise XOR operation which is used to create control bit. Control bit determines whether the LSB of a specific pixel is changed or not according to the secret message. This provides direct manipulation over the pixels of the image with a very low precision hence enhances the system security. In this study, gray image is used as secret message and a larger scale image as cover image. Experimental results demonstrate that the proposed method is very efficient to detect LSB replacement in the algorithm. Moreover, the proposed algorithm is highly sensitive to the stego key parameters due to complex structure of chaos which provides high level of security in the whole system and effectively hides and detects the image information.
Agent-based modelling for epidemiological applications

Anna Shcherbacheva Agent-based modelling for epidemiological applications Lappeenranta 2019 112 pages Acta Universitatis Lappeenrantaensis 844 Diss. Lappeenranta University of Technology ISBN 978-952-335-344-2, ISBN 978-952-335-345-9 (PDF), ISSN-L 1456-4491, ISSN 1456-4491 Malaria is an infectious disease induced by protozoan parasite called Plasmodium and transmitted by Anopheles mosquitoes which still imposes a health threat to virtually half of the human populace and causes severe mortality, especially in sub-Saharan Africa. The characteristics of malaria transmission depend on many relevant factors, such as socio-economic conditions, availability of control measures and local composition of mosquito species. Mathematical modelling of disease propagation is conducted to evaluate the impact of these factors and to ameliorate the lack of experimental data in this field. In this thesis an agent-based model of short-range mosquito host-seeking behaviour in the presence of Long-Lasting Insecticidal Nets (LLINs) is introduced and further calibrated by the means of experimental data. The model is extended to quantify the overall impact of intervention strategies in community-level situations. As demonstrated in the present study, other optional factors can be included into the simulations, such as parasite ecology and socio-economic conditions. Model simulations are conducted for a short-term period of one day. Additionally, when coupled with regression models based on the simulation results under various conditions, the proposed approach allows transition from the in situ mechanisms of mosquito behaviour to commonly estimated characteristics of malaria transmission.