Miracle-dz/UniFAR-FTUs huggingface dataset:
Securitization Without Adverse Selection: The Case of Clos

In this paper, we investigate whether securitization was associated with risky lending in the corporate loan market by examining the performance of individual loans held by collateralized loan obligations. We employ two different data sets that identify loan holdings for a large set of CLOs and find that adverse selection problems in corporate loan securitizations are less severe than commonly believed. Using a battery of performance tests, we find that loans securitized before 2005 performed no worse than comparable unsecuritized loans originated by the same bank. Even loans originated by the bank that acts as the CLO underwriter do not show under-performance relative to the rest of the CLO portfolio. While some evidence exists of under-performance for securitized loans originated between 2005 and 2007, it is not consistent across samples, performance measures, and horizons. Overall, we argue that the securitization of corporate loans is fundamentally different from securitization of other assets classes because securitized loans are fractions of syndicated loans. Therefore, mechanisms used to align incentives in a lending syndicate are likely to reduce adverse selection in the choice of CLO collateral.
Study on the Logistics Center Location Problem Based on CURE

According to the cluster analysis of the logistics demand points, it can help logistics conveniently select reasonable logistics distribution center, then to reduce their transportation costs, and improve transport efficiency. This thesis firstly introduces the CURE algorithm and UFLP model, then, proposes an improved algorithm based on CURE, and describes the specific process. Finally, it also proves the algorithm's effectiveness and superiority for choosing Logistics Centre based on the numerical experiments.
Rapid Biodistribution of Fluorescent Outer-Membrane Vesicles from the Intestine to Distant Organs via the Blood in Mice

A cell’s ability to secrete extracellular vesicles (EVs) for communication is present in all three domains of life. Notably, Gram-negative bacteria produce a specific type of EVs called outer membrane vesicles (OMVs). We previously observed the presence of OMVs in human blood, which could represent a means of communication from the microbiota to the host. Here, in order to investigate the possible translocation of OMVs from the intestine to other organs, the mouse was used as an animal model after OMVs administration. To achieve this, we first optimized the signal of OMVs containing the fluorescent protein miRFP713 associated with the outer membrane anchoring peptide OmpA by adding biliverdin, a fluorescence cofactor, to the cultures. The miRFP713-expressing OMVs produced in E. coli REL606 strain were then characterized according to their diameter and protein composition. Native- and miRFP713-expressing OMVs were found to produce homogenous populations of vesicles. Finally, in vivo and ex vivo fluorescence imaging was used to monitor the distribution of miRFP713-OMVs in mice in various organs whether by intravenous injection or oral gavage. The relative stability of the fluorescence signals up to 3 days post-injection/gavage paves the way to future studies investigating the OMV-based communication established between the different microbiotas and their host.
Transaction Conflict Control in Hyperledger Fabric: A Taxonomy, Gaps, and Design for Conflict Prevention

The execute-order-validate approach to blockchain consensus, most notably implemented by Hyperledger Fabric, facilitates highly scalable execution of smart contract – in Fabric terminology, “chaincode” – invocations in cross-organizational blockchains; at the expense of requiring multi-version concurrency control conflict handling during block validation. Consequently, the system-level goodput can be significantly lower than throughput. Although several solutions have been proposed for handling and avoiding conflicts in Hyperledger Fabric, a systematic and holistic approach is missing. We introduce the notion of conflict-controlled operation, propose a novel taxonomy of its means based on the codified principles of dependable computing, and categorize the known approaches. Based on this taxonomy, we identified the critical gaps in the state-of-the-art. Design-time conflict prevention is one such gap, and we propose the application of a model-driven engineering process for this purpose. For the last storage mapping stage of the process, we propose entity attribute partitioning for conflict prevention, describe a data mapper-style chaincode layer, and empirically evaluate our solution.
RELIGIOUSNESS OF MEMBERS OF POLITICAL PARTIES IN SERBIA – POLITICAL MANIPULATION OR GENUINE BELIEF?

Transitional changes in economic, political and social contexts have also contributed to the changes within the value framework and religious affiliation. The paper analyzes the results of a survey carried out on the members of political parties concerning their attitudes towards religion and values important in the current social moment in Serbia. For forty years Serbia had been into socialistic system, in which it was not desirable to be religious, so the changes and period of transition that took place largely changed attitudes towards religion. The results indicate an increase in the number of religious people in relation to previous studies, and existence of differences in value judgment among the respondents who identify themselves as religious and those who identify themselves as unbiased and non-religious. Most respondents bind their attitudes to traditional values, not strictly religious, and in behavior, attitudes manifest themselves through religious customs more than beliefs.
CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System

In the present scenario, the transportation Cyber Physical System (CPS) improves the reliability and efficiency of the transportation systems by enhancing the interactions between the physical and cyber systems. With the provision of better storage ability and enhanced computing, cloud computing extends transportation CPS in Mobile Edge Computing (MEC). By inspecting the existing literatures, the cloud computing cannot fulfill the requirements in transportation CPS like lower context-awareness and latency. For enhancing the context-awareness and reducing the latency in a realistic MEC environment, an efficient portable deep learning model: Convolutional Neural Network (CNN) with Chaotic Lévy Flight based Firefly Algorithm (CLFFA) is implemented in this article. In the CNN model, the CLFFA selects the appropriate hyper-parameters or reduces the redundant parameters that results in minimal model size and inference latency than the traditional CNN models. Additionally, the CNN-CLFFA model significantly outperformed the existing models by means of recall, accuracy, F1-score, and precision on the benchmark datasets like German Traffic Sign Recognition Benchmark (GTSRB), MIOvision Traffic Camera Dataset (MIO-TCD) classification, and VCifar-100 datasets. The numerical analysis demonstrates that the CNN-CLFFA model obtained maximum accuracy of 99.02%, 99.11%, and 99.03% on the VCifar-100, MIO-TCD, and GTSRB-T datasets, which are superior to the traditional models.
Compaction of C-band synthetic aperture radar based sea ice information for navigation in the Baltic Sea

In this work operational sea ice synthetic aperture radar (SAR) data products were improved and developed. A SAR instrument is transmitting electromagnetic radiation at certain wavelengths and measures the radiation which is scattered back towards the instrument from the target, in our case sea and sea ice. The measured backscattering is converted to an image describing the target area through complex signal processing. The images, however, di er from optical images, i.e. photographs, and their visual interpretation is not straightforward. The main idea in this work has been to deliver the essential SAR-based sea ice information to end-users (typically on ships) in a compact and user-friendly format. The operational systems at Finnish Institute of Marine Research (FIMR) are currently based on the data received from a Canadian SAR-satellite, Radarsat-1. The operational sea ice classi cation, developed by the author with colleagues, has been further developed. One problem with the SAR data is typically that the backscattering varies depending on the incidence angle. The incidence angle is the angle in which the transmitted electromagnetic wave meets the target surface and it varies within each SAR image and between di erent SAR images depending on the measuring geometry. To improve this situation, an incidence angle correction algorithm to normalize the backscattering over the SAR incidence angle range for Baltic Sea ice has been developed as part of this work. The algorithm is based on SAR backscattering statistics over the Baltic Sea. To locate di erent sea ice areas in SAR images, a SAR segmentation algorithm based on pulse-coupled neural networks has been developed and tested. The parameters have been tuned suitable for the operational data in use at FIMR. The sea ice classi cation is based on this segmentation and the classi cation is segmentwise rather than pixel-wise. To improve SAR-based distinguishing between sea ice and open water an open water detection algorithm based on segmentation and local autocorrelation has been developed. Also ice type classi cation based on higher-order statistics and independent component analysis have been studied to get an improved SAR-based ice type classi cation. A compression algorithm for compressing sea ice SAR data for visual use has
Mating Pheromone (Gamone 1) in Blepharisma: A Glycoprotein Responsible for Species Diversity in Unicellular Organisms (Alveolata, Ciliophora)

The genus Blepharisma (Alveolata, Ciliophora) is a unicellular organism distributed worldwide, even in extreme environments, and comprises numerous species. While usually proliferating through cell division, Blepharisma undergoes sexual reproduction (conjugation) when cells are moderately starved. Conjugation is initiated by mating pheromones (gamone 1 and gamone 2) secreted by complementary mating-type cells. Gamone 1, a glycoprotein, functions in a species-specific manner, while gamone 2, an amino acid derivative, is a common molecule across species. The specific function of gamone 1 suggests the possibility that mutations in gamone 1 might have led to reproductive isolation during the evolutionary process, triggering species diversification. In this study, by comparing the amino acid sequences of gamone 1 homologs from 15 strains (representing five species), we found that mutations resulting in distinct amino acid properties occur across species boundaries and are mainly concentrated at two specific regions within gamone 1. These mutations potentially alter the binding affinity of gamone 1 to its receptors, suggesting their effect in causing reproductive isolation. The interspecies artificial conjugation conducted previously and the molecular phylogenetic tree constructed using the gamone 1 homolog sequences in this study provide insights into the speciation process within the genus Blepharisma.
In Situ Monitoring of Non-Thermal Plasma Cleaning of Surfactant Encapsulated Nanoparticles

Surfactants are widely used in the synthesis of nanoparticles, as they have a remarkable ability to direct their growth to obtain well-defined shapes and sizes. However, their post-synthesis removal is a challenge, and the methods used often result in morphological changes that defeat the purpose of the initial controlled growth. Moreover, after the removal of surfactants, the highly active surfaces of nanomaterials may undergo structural reconstruction by exposure to a different environment. Thus, ex situ characterization after air exposure may not reflect the effect of the cleaning methods. Here, combining X-ray photoelectron spectroscopy, in situ infrared reflection absorption spectroscopy, and environmental transmission electron microscopy measurements with CO probe experiments, we investigated different surfactant-removal methods to produce clean metallic Pt nanoparticles from surfactant-encapsulated ones. It was demonstrated that both ultraviolet-ozone (UV-ozone) treatment and room temperature O2 plasma treatment led to the formation of Pt oxides on the surface after the removal of the surfactant. On the other hand, when H2 was used for plasma treatment, both the Pt0 oxidation state and nanoparticle size distribution were preserved. In addition, H2 plasma treatment can reduce Pt oxides after O2-based treatments, resulting in metallic nanoparticles with clean surfaces. These findings provide a better understanding of the various options for surfactant removal from metal nanoparticles and point toward non-thermal plasmas as the best route if the integrity of the nanoparticle needs to be preserved.
Relative gravity measurements at the 3RD international comparison of absolute gravimeters

Relative gravimeter measurements at the 3rd International Comparison of Absolute Gravimeters in Paris 1989 were needed for the connection of the different sites and for the determination of vertical gradients at each site. 12 LaCoste gravimeters, 10 of which equiped with electrostatic feedback were employed in the campaign. Vertical gradients and gravity differences are estimated with an accuracy of about 1 μgal
Trie-Hashimoto: State Trie-Based Proof-of-Work Mining for Optimizing Blockchain Storage

Blockchain makes heavy use of cryptographic hashing to achieve integrity and consensus in a peer-to-peer network, but hashing causes some inefficiencies. For example, blockchain stores data with their hash digest as a key in the database, so the blockchain always reads and writes data in a random order. This can affect blockchain performance, especially for account-based blockchains such as Ethereum, which must maintain a huge, hash-based data structure for accounts, called the state trie. Also, Proof-of-Work (PoW) consensus algorithm requires the miners to find a nonce that makes the block hash lower than a difficulty threshold, but ASICs with parallel hashing have made PoW use a large dataset such as the Ethash DAG for memory-hardness and ASIC-resistance. Unfortunately, verification of the nonce is not easy for many light clients, which cannot deal with the overhead caused by the dataset. This paper proposes a novel PoW mining algorithm named Trie-Hashimoto to address these issues. Trie-Hashimoto adds a nonce field in a state trie node. It then makes the miners find a nonce of every newly-created trie node for a new block such that each node has a hash digest whose prefix is equal to the block number. This can accelerate the database performance by storing the trie nodes in a sequential order. The way for Trie-Hashimoto to achieve memory-hardness is also different. It uses the block headers that any client must maintain, obviating a separate dataset. Furthermore, it allows partial verification using a few Merkle proofs of accounts, so that a client with minimal resources or even a smart contract in another interoperable blockchain can verify a block with a high probability. Finally, Trie-Hashimoto discourages big mining pools by increasing the network overhead among the miners. Our experiment on the Geth client with 500K blocks and 100M accounts shows that Trie-Hashimoto improves the transaction execution time tangibly, reducing the full synchronization time by half. It also shows that Trie-Hashimoto has enough memory-hardness as Ethash. Lastly, a Trie-Hashimoto mining pool should exchange messages highly frequently, proportional to the total number of miners.
System of Agricultural Land Monitoring Using UAV

The purpose of this study is to develop a system configuration for gathering data and building a database for agriculture. Some foreign agriculture-related companies have already constructed such a database for scientific agriculture. The hardware of this system is composed of automatic capturing equipment based on aerial photography using a UAV. The software is composed of parts for stitching images, matching GPS data with captured images
Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

In the information age, people are increasingly being exposed to stress as societies are experiencing sudden changes fueled by advancements in cutting-edge scientific technology and the Information Technology (IT) industry. Consequently, research has been actively conducted on stress classification to mitigate psychological and physical diseases caused by constantly feeling stressed. Specifically, the number of studies examining electrocardiograms (ECGs), which record biosignals that provide insight into the response level of the body’s autonomic nervous system, has increased. However, previous studies on stress classification based on ECG used only one-dimensional feature data, thus entailing difficulties in analyzing the data more closely and comprehensively owing to bias toward a specific aspect. Therefore, to overcome the limitations of conventional stress classification based on ECGs, this paper developed a stress classification method based on multi-dimensional feature fusion of LSTM and Xception using ECGs from which outliers have been removed. Experimental results showed that applying multi-dimensional feature fusion of the weighted average method using ECG data with outlier signals removed resulted in a stress classification of 99.51%, a 1.25% improvement from previous studies which used only one-dimensional feature data of ECGs, thus highlighting the excellent performance of the proposed stress classification method using ECGs based on multi-dimensional feature fusion of LSTM and Xception.
PARC: a phase I/II study evaluating the safety and activity of pegylated recombinant human arginase BCT-100 in relapsed/refractory cancers of children and young adults

Background The survival for many children with relapsed/refractory cancers remains poor despite advances in therapies. Arginine metabolism plays a key role in the pathophysiology of a number of pediatric cancers. We report the first in child study of a recombinant human arginase, BCT-100, in children with relapsed/refractory hematological, solid or CNS cancers. Procedure PARC was a single arm, Phase I/II, international, open label study. BCT-100 was given intravenously over one hour at weekly intervals. The Phase I section utilized a modified 3 + 3 design where escalation/de-escalation was based on both the safety profile and the complete depletion of arginine (defined as adequate arginine depletion; AAD <8μM arginine in the blood after 4 doses of BCT-100). The Phase II section was designed to further evaluate the clinical activity of BCT-100 at the pediatric RP2D determined in the Phase I section, by recruitment of patients with pediatric cancers into 4 individual groups. A primary evaluation of response was conducted at eight weeks with patients continuing to receive treatment until disease progression or unacceptable toxicity. Results 49 children were recruited globally. The Phase I cohort of the trial established the Recommended Phase II Dose of 1600U/kg iv weekly in children, matching that of adults. BCT-100 was very well tolerated. No responses defined as a CR, CRi or PR were seen in any cohort within the defined 8 week primary evaluation period. However a number of these relapsed/refractory patients experienced prolonged radiological SD. Conclusion Arginine depletion is a clinically safe and achievable strategy in children with cancer. The RP2D of BCT-100 in children with relapsed/refractory cancers is established at 1600U/kg intravenously weekly and can lead to sustained disease stability in this hard to treat population. Clinical trial registration EudraCT, 2017-002762-44; ISRCTN, 21727048; and ClinicalTrials.gov, NCT03455140.
Inter-annual variability of rainfall in Central America: Connection with global and regional climate modulators

Maldonado, T. 2016. Inter-annual variability of rainfall in Central America. Connection with global and regional climate modulators. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1438. 67 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9716-3. Central America is a region regularly affected by natural disasters, with most of them having a hydro-meteorological origin. Therefore, the understanding of annual changes of precipitation upon the region is relevant for planning and mitigation of natural disasters. This thesis focuses on studying the precipitation variability at annual scales in Central America within the framework of the Swedish Centre for Natural Disaster Science. The aims of this thesis are: i) to establish the main climate variability sources during the boreal winter, spring and summer by using different statistical techniques, and ii) to study the connection of sea surface temperature anomalies of the neighbouring oceans with extreme precipitation events in the region. Composites analysis is used to establish the variability sources during winter. Canonical correlation analysis is employed to explore the connection between the SST anomalies and extreme rainfall events during May-June and August-October. In addition, a global circulation model is used to replicate the results found with canonical correlation analysis, but also to study the relationship between the Caribbean Sea surface temperature and the Caribbean low-level jet. The results show that during winter both El Niño Southern Oscillation and the Pacific Decadal Oscillation, are associated with changes of the sea level pressure near the North Atlantic Subtropical High and the Aleutian low. In addition, the El Niño Southern Oscillation signal is intensified (destroyed) when El Niño and the Pacific Decadal Oscillation have the same (opposite) sign. Sea surface temperature anomalies have been related to changes in both the amount and temporal distribution of rainfall. Precipitation anomalies during May-June are associated with sea surface temperature anomalies over the Tropical North Atlantic region. Whereas, precipitation anomalies during August-September-October are associated with the sea surface temperature anomalies contrast between the Pacific Ocean and the Tropical North Atlantic region. Model outputs show no association between sea surface temperature gradients and the Caribbean low-level jet intensification. Canonical correlation analysis shows potential for prediction of extreme precipitation events, however, forecast validation shows that socioeconomic variables must be included for more comprehensive natural disaster assessments.
Design andImplementation of 3D Terrain Data Self-adaption Pyramid Technology

Traditional pyramid model which concentrates on solving the problem with storing and dispatching of singlesource data,couldn't contented 3D GIS demands of dynamic expansibility of multi-source heterogeneous data and fast updating data.In view of this,we put emphasis on self-adaption pyramid model,which constructs the adapter of multisource heterogeneous data that converts 3D terrain data to terrain data logic unit.In this way of Self-adaption pyramid model,we could schedule and show integrated multi-source heterogeneous data as a unit.