Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling Machine Learning Algorithms for Credit Scoring - Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models

Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in this case, the price to pay is to loose interpretability of a model while gaining on its accuracy and precision. For managers to control and effectively manage credit risk and for regulators to be convinced with model quality the price to pay is too high. In this paper, we show how to take credit scoring analytics in to the next level, namely we present comparison of various predictive models (logistic regression, logistic regression with weight of evidence transformations and modern artificial intelligence algorithms) and show that advanced tree based models give best results in prediction of client default. What is even more important and valuable we also show how to boost advanced models using techniques which allow to interpret them and made them more accessible for credit risk practitioners, resolving the crucial obstacle in widespread deployment of more complex, 'black box' models like random forests, gradient boosted or extreme gradient boosted trees. All this will be shown on the large dataset obtained from the Polish Credit Bureau to which all the banks and most of the lending companies in the country do report the credit files. In this paper the data from lending companies were used. The paper then compares state of the art best practices in credit risk modelling with new advanced modern statistical tools boosted by the latest developments in the field of interpretability and explainability of artificial intelligence algorithms. We believe that this is a valuable contribution when it comes to presentation of different modelling tools but what is even more important it is showing which methods might be used to get insight and understanding of AI methods in credit risk context.
VOLDOR+SLAM: For the times when feature-based or direct methods are not good enough

We present a dense-indirect SLAM system using external dense optical flows as input. We extend the recent probabilistic visual odometry model VOLDOR [1], by incorporating the use of geometric priors to 1) robustly bootstrap estimation from monocular capture, while 2) seamlessly supporting stereo and/or RGB-D input imagery. Our customized back-end tightly couples our intermediate geometric estimates with an adaptive priority scheme managing the connectivity of an incremental pose graph. We leverage recent advances in dense optical flow methods to achieve accurate and robust camera pose estimates, while constructing fine-grain globally-consistent dense environmental maps. Our open source implementation [https://github.com/htkseason/VOLDOR] operates online at around 15 FPS on a single GTX1080Ti GPU.
A Co-Operative Game Theoretic Approach for the Budgeted Influence Maximization Problem

Given a social network of users with selection cost, the \textsc{Budgeted Influence Maximization Problem} (\emph{BIM Problem} in short) asks for selecting a subset of the nodes (known as \emph{seed nodes}) within an allocated budget for initial activation to maximize the influence in the network. In this paper, we study this problem under the \emph{co\mbox{-}operative game theoretic} framework. We model this problem as a co\mbox{-}operative game where the users of the network are the players and for a group of users, the expected influence by them under the \emph{Maximum Influence Arborences} diffusion model is its utility. We call this game as \emph{BIM Game} and show this is `non-convex' and `sub-additive'. Based on the proposed game\mbox{-}theoretic model and using the solution concept called `Shapley Value', we propose an iterative algorithm for finding seed nodes. The proposed methodology is divided into mainly two broad steps: the first one is computing the approximate marginal gain in \emph{Shapley Value} for all the nodes of the network, and the second one is selecting seed nodes from the sorted list until the budget is exhausted. We also show that the proposed methodology can even be more effective when the community structure of the network is exploited. The proposed methodologies have been implemented, and an extensive set of experiments have been conducted with three publicly available social network datasets. From the experiments, we observe that the seed set selected by the proposed methodologies lead to more number of influence nodes compared to many standard and baseline methods from the literature with a reasonable computational overhead. In particular, if the community structure of the network is exploited then there is an increase upto $2 \%$ in number of influenced nodes.
Reward function shape exploration in adversarial imitation learning: an empirical study

For adversarial imitation learning algorithms (AILs), no true rewards are obtained from the environment for learning the strategy. However, the pseudo rewards based on the output of the discriminator are still required. Given the implicit reward bias problem in AILs, we design several representative reward function shapes and compare their performances by large-scale experiments. To ensure our results’ reliability, we conduct the experiments on a series of Mujoco and Box2D continuous control tasks based on four different AILs. Besides, we also compare the performance of various reward function shapes using varying numbers of expert trajectories. The empirical results reveal that the positive logarithmic reward function works well in typical continuous control tasks. In contrast, the so-called unbiased reward function is limited to specific kinds of tasks. Furthermore, several designed reward functions perform excellently in these environments as well.
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation

A conventional approach to improving the performance of end-to-end speech translation (E2E-ST) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition (ASR) and neural machine translation (NMT) tasks. However, since the input modalities are different, it is difficult to leverage source language text successfully. In this work, we focus on sequence-level knowledge distillation (SeqKD) from external text-based NMT models. To leverage the full potential of the source language information, we propose backward SeqKD, SeqKD from a target-to-source backward NMT model. To this end, we train a bilingual E2E-ST model to predict paraphrased transcriptions as an auxiliary task with a single decoder. The paraphrases are generated from the translations in bitext via back-translation. We further propose bidirectional SeqKD in which SeqKD from both forward and backward NMT models is combined. Experimental evaluations on both autoregressive and non-autoregressive models show that SeqKD in each direction consistently improves the translation performance, and the effectiveness is complementary regardless of the model capacity.
Weakly But Deeply Supervised Occlusion-Reasoned Parametric Layouts

We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a top-view parametric space. In contrast to prior works that require dense supervision such as semantic labels in perspective view, the only human annotations required by our method are for parametric attributes that are cheaper and less ambiguous to obtain. To solve this challenging task, our design is comprised of modules that incorporate inductive biases to learn occlusion-reasoning, geometric transformation and semantic abstraction, where each module may be supervised by appropriately transforming the parametric annotations. We demonstrate how our design choices and proposed deep supervision help achieve accurate predictions and meaningful representations. We validate our approach on two public datasets, KITTI and NuScenes, to achieve state-of-the-art results with considerably lower human supervision.
ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks

A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users. The evolving nature of the topological structure of these networks makes it difficult to locate and identify these influencers. In this paper, we propose an adaptive agent-based evolutionary approach to address this problem in the context of both static and dynamic networks. This approach is shown to be able to adapt the solution as the network evolves. It is also applicable to large-scale networks due to its distributed framework. Evaluation of our approach is performed by using both synthetic networks and real-world datasets. Experimental results demonstrate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence.
Glutamine Administration Attenuates Kidney Inflammation in Obese Mice Complicated with Polymicrobial Sepsis

Obesity is a well-known public health issue around the world. Sepsis is a lethal clinical syndrome that causes multiorgan failure. Obesity may aggravate inflammation in septic patients. Glutamine (GLN) is a nutrient with immune regulatory and anti-inflammatory properties. Since sepsis is a common contributing factor for acute kidney injury (AKI), this study investigated the effects of GLN administration on sepsis-induced inflammation and AKI in obese mice. A high-fat diet which consists of 60% of calories from fat was provided for 10 weeks to induce obesity in the mice. Then, the obese mice were subdivided into sepsis with saline (SS) or GLN (SG) groups. Cecal ligation and puncture (CLP) was performed to produce sepsis. The SS group was intravenously injected with saline while the SG group was administered GLN one or two doses after CLP. Obese mice with sepsis were sacrificed at 12, 24, or 48 h post-CLP. Results revealed that sepsis resulted in upregulated high-mobility group box protein-1 pathway-associated gene expression in obese mice. Also, expressions of macrophage/neutrophil infiltration markers and inflammatory cytokines in kidneys were elevated. Obese mice treated with GLN after sepsis reversed the depletion of plasma GLN, reduced production of lipid peroxides, and downregulated macrophage/neutrophil infiltration and the inflammatory-associated pathway whereas tight junction gene expression increased in the kidneys. These findings suggest that intravenously administered GLN to obese mice after sepsis alleviated inflammation and attenuated AKI. This model may have clinical application to obese patients with a risk for infection in abdominal surgery.
Occurrence and Antimicrobial Susceptibility Profile of Salmonella Isolates from Animal Origin Food Items in Selected Areas of Arsi Zone, Southeastern Ethiopia, 2018/19

The status of Salmonella and its antimicrobial susceptibility profile in animal origin food items from different catering establishments in Ethiopia is scarce. Hence, this study aimed to investigate the occurrence and antimicrobial susceptibility profile of Salmonella isolates from animal origin food items in the selected areas of Arsi Zone. One hundred ninety-two animal origin food samples were collected and processed for Salmonella isolation. Isolates were tested for their susceptibility to 13 antimicrobials using Kirby–Bauer disk diffusion assay. An overall prevalence of 9.4% (18/192) Salmonella spp. isolates were recovered from animal origin food samples collected from different catering establishments. Seven (21.9%) of “Dulet,” 4 (12.5%) of “Kitfo,” 3 (9.4%) of “Kurt,” 2 (6.3%) of raw milk, 1 (3.1%) of egg sandwich and 1 (3.1%) of cream cake samples were positive for Salmonella. Catering establishments, protective clothing, source of contamination, manner of hand washing, and money handling were among the putative risk factors that were significantly associated (p < 0.05) with Salmonella spp. occurrence. Ampicillin, nitrofurans, and sulphonamide resistance were significantly associated (p < 0.05) with Salmonella spp. occurrence in the selected food items. Three (16.7%), 5 (27.8%), 5 (27.8%), and 4 (22.2%) of the isolates were resistant to 3, 4, 5, and 6 antibiotics, respectively, whereas only a sole isolate was resistant to two antibiotics (viz. ampicillin and kanamycin). In conclusion, the general sanitary condition of the catering establishments, utensils used, and personnel hygienic practices were not to the recommended standards in the current study. Besides, detection of multidrug-resistant strains of Salmonella in animal origin food items from different catering establishments suggests the need for detailed epidemiological and molecular characterization of the pathogen so as to establish the sources of acquisition of resistant Salmonella strains. Hence, implementation of Salmonella prevention and control strategies from farm production to consumption of animal origin food items are crucial.
Differential Expression Profiles and Function Prediction of Transfer RNA-Derived Fragments in High-Grade Serous Ovarian Cancer

Background The present study is aimed at providing systematic insight into the composition and expression of transfer RNA (tRNA) derivative transcription in high-grade serous ovarian cancer (HGSOC). Methods tRNA derivative expression profiles in three pairs of HGSOC and adjacent normal ovarian tissues were conducted by tRNA-derived small RNA fragment (tRF) and tRNA half (tiRNA) sequencing. The differentially expressed tRFs and tiRNAs between HGSOC and paired adjacent normal samples were screened. The targeted genes of differentially expressed tRFs and tiRNAs were screened. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) of target genes of tRFs and tiRNAs were analyzed. Results There are a total of 20 significantly upregulated and 15 significantly downregulated tRFs and tiRNAs between the cancer group and the paracarcinoma group. The upregulated tRFs and tiRNAs are mucin-type O-glycan biosynthesis, glycosphingolipid biosynthesis, the glucagon signaling pathway, the AMPK signaling pathway, maturity-onset diabetes of the young, glycosphingolipid biosynthesis, the insulin signaling pathway, insulin resistance, leukocyte transendothelial migration, starch, and sucrose metabolism. The downregulated tRFs and tiRNAs are other glycan degradation, vitamin digestion and absorption, fatty acid elongation, and biosynthesis of unsaturated fatty acids. Conclusions There are significantly expressed tRFs and tiRNAs in HGSOC tissues, and these may provide potential diagnostic biomarkers and therapeutic targets for HGSOC.
Demarcated redness associated with increased vascular density/size: a useful marker of flat-type dysplasia in patients with ulcerative colitis

Abstract Background and study aims Recent advances in endoscopic equipment and diagnostic techniques have improved the detection of dysplasia in the inflamed mucosa of patients with ulcerative colitis (UC). However, it remains difficult to endoscopically identify flat-type dysplasia which has been formerly recognized as invisible dysplasia. Patients and methods In this retrospective, single-center study, we endoscopically identified 10 cases of flat-type-predominant dysplasia by targeted biopsy among 38 intramucosal dysplasia lesions from patients with UC who underwent surgical or endoscopic resection from 2007 to 2017. Their endoscopic and histological features were examined, including color changes, intramucosal vascular density/size, and vascular endothelial growth factor (VEGF) expression. Results All flat-type-predominant dysplasias were endoscopically recognized as demarcated red-colored areas and histologically diagnosed as low- (LGDs) or high-grade dysplasias (HGDs). Immunohistochemical examination using resected specimens revealed that flat-type dysplasia was characterized by significantly increased CD34-positive vascular density (LGDs, 1.7-fold, P < 0.01; HGDs, 2.2-fold, P < 0.01) and size (LGDs, 1.03-fold, P < 0.01; HGDs, 1.11-fold, P < 0.01) in the mucosa, compared to adjacent non-neoplastic areas. Increased numbers of vessels were observed at the base of the mucosa in LGDs, whereas HGDs contained increased/enlarged vessels throughout the mucosa. Moreover, VEGF expression was elevated in all dysplastic epithelia. Conclusions Demarcated red-colored areas, histologically characterized by an increased vascular density/size in the mucosa, are an endoscopic sign of formerly invisible flat-type dysplasia in patients with UC and should be considered for targeted biopsy. Prospective studies focusing on the mucosal color change for their early detection would be desirable in the future.
Increase in Blood-Brain Barrier (BBB) Permeability Is Regulated by MMP3 via the ERK Signaling Pathway

Background The blood-brain barrier (BBB) regulates the exchange of molecules between the brain and peripheral blood and is composed primarily of microvascular endothelial cells (BMVECs), which form the lining of cerebral blood vessels and are linked via tight junctions (TJs). The BBB is regulated by components of the extracellular matrix (ECM), and matrix metalloproteinase 3 (MMP3) remodels the ECM's basal lamina, which forms part of the BBB. Oxidative stress is implicated in activation of MMPs and impaired BBB. Thus, we investigated whether MMP3 modulates BBB permeability. Methods Experiments included in vivo assessments of isoflurane anesthesia and dye extravasation from brain in wild-type (WT) and MMP3-deficient (MMP3-KO) mice, as well as in vitro assessments of the integrity of monolayers of WT and MMP3-KO BMVECs and the expression of junction proteins. Results Compared to WT mice, measurements of isoflurane usage and anesthesia induction time were higher in MMP3-KO mice and lower in WT that had been treated with MMP3 (WT+MMP3), while anesthesia emergence times were shorter in MMP3-KO mice and longer in WT+MMP3 mice than in WT. Extravasation of systemically administered dyes was also lower in MMP3-KO mouse brains and higher in WT+MMP3 mouse brains, than in the brains of WT mice. The results from both TEER and Transwell assays indicated that MMP3 deficiency (or inhibition) increased, while MMP3 upregulation reduced barrier integrity in either BMVEC or the coculture. MMP3 deficiency also increased the abundance of TJs and VE-cadherin proteins in BMVECs, and the protein abundance declined when MMP3 activity was upregulated in BMVECs, but not when the cells were treated with an inhibitor of extracellular signal related-kinase (ERK). Conclusion MMP3 increases BBB permeability following the administration of isoflurane by upregulating the ERK signaling pathway, which subsequently reduces TJ and VE-cadherin proteins in BMVECs.
Toxicity Evaluation by Single and Repeated Administration of Yeast Hydrolysate DNF-10

This study aimed at investigating the acute and subacute oral toxicity of yeast hydrolysate (DNF-10) in Sprague-Dawley rats. To determine the acute toxicity of DNF-10, it was orally administered to rats at a dose of 5,000 mg/kg. Furthermore, to determine subacute toxicity of DNF-10, it was orally administered to female and male rats at a dose of 1,000 mg/kg for 90 days. In the acute toxicity test, the experimental group did not show changes in body and organ weights compared to the corresponding weights in the control group. When compared to female controls, the DNF-10-treated female rats showed decreased red blood cell counts, haemoglobin, and total bilirubin levels; however, the parameters remained within the normal range. In the subacute toxicity test, DNF-10 administration significantly reduced body weight among male rats, which appears attributable to the anti-obesity effect of the compound. In the experimental groups, significant differences in some haematological parameters were considered as non-toxic because the results were within the normal range. These results suggest that DNF-10 is safe and non-toxic at single doses (5,000 mg/kg) and repeated doses (1,000 mg/kg).
A novel 3D histotypic cartilage construct engineered by supercritical carbon dioxide decellularized porcine nasal cartilage graft and chondrocytes exhibited chondrogenic capability in vitro

Augmentative and reconstructive rhinoplasty surgical procedures use autologous tissue grafts or synthetic grafts to repair the nasal defect and aesthetic reconstruction. Donor site trauma and morbidity are common in autologous grafts. The desperate need for the production of grafted 3D cartilage tissues as rhinoplasty grafts without the adverse effect is the need of the hour. In the present study, we developed a bioactive 3D histotypic construct engineered with the various ratio of adipose-derived stem cells (ADSC) and chondrocytes together with decellularized porcine nasal cartilage graft (dPNCG). We decellularized porcine nasal cartilage using supercritical carbon dioxide (SCCO2) extraction technology. dPNCG was characterized by H&E, DAPI, alcian blue staining, scanning electron microscopy and residual DNA content, which demonstrated complete decellularization. 3D histotypic constructs were engineered using dPNCG, rat ADSC and chondrocytes with different percentage of cells and cultured for 21 days. dPNCG together with 100% chondrocytes produced a solid mass of 3D histotypic cartilage with significant production of glycosaminoglycans. H&E and alcian blue staining showed an intact mass, with cartilage granules bound to one another by extracellular matrix and proteoglycan, to form a 3D structure. Besides, the expression of chondrogenic markers, type II collagen, aggrecan and SOX-9 were elevated indicating chondrocytes cultured on dPNCG substrate facilitates the synthesis of type II collagen along with extracellular matrix to produce 3D histotypic cartilage. To conclude, dPNCG is an excellent substrate scaffold that might offer a suitable environment for chondrocytes to produce 3D histotypic cartilage. This engineered 3D construct might serve as a promising future candidate for cartilage tissue engineering in rhinoplasty.
First report on the seroprevalence of avian encephalomyelitis virus antibody in Sonali (cross-bred) chickens in Bogura, Bangladesh

Objectives: The study intended to detect the presence and distribution of avian encephalomyelitis virus (AEV)-specific antibodies in Sonali (cross-bred) parent chickens regarding farm location, flock size, and age in Bogura district of Bangladesh, a Sonali chicken belt. Materials and methods: A total of 275 Sonali parent chickens’ blood samples were collected randomly from 39 flocks during laying age with a healthy and non-vaccination history against AEV. Blood samples were collected aseptically from the wing veins of chickens using 3-ml syringes and sera were separated. Then, the sera were transferred to the laboratory by maintaining a cool chain. Indirect enzyme-linked immunosorbent assay was used to detect the specific antibodies against AEV present in the sera samples. Results: Overall, 70.18% of the chickens were found seropositive for AEV antibodies. Based on the location, the highest seropositivity was recorded in Bogura Sadar [91.30%, confidence intervals (CI) 79.21%–97.58%], and the lowest was in the Adomdighi sub-district (45.45%, CI 29.49%–63.08%). For flock size, AEV seropositivity was significantly (p < 0.05) higher in the large flock (82.22%, CI 72.74%–89.48%). Regarding age groups, the seropositivity of AEV was significantly (p < 0.05) increased with chickens’ age. Higher seropositivity was noted in chickens aged >51 weeks (89.32%, CI 81.69%–94.55%). Conclusion: The results indicate that AEV is circulating in the environment, and chickens were exposed to the field strain of AEV. To the best of our knowledge, this is the first report on AEV in chickens in Bangladesh. Proper vaccination and standard farm biosecurity practice could minimize AEV infection in chickens. A detailed epidemiology study, detection, and characterization of the AEV would be essential for effective AEV infection control.