Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular mechanisms linking peri-implantitis and type 2 diabetes mellitus revealed by transcriptomic analysis

Aims To explore molecular mechanisms that link peri-implantitis and type 2 diabetes mellitus (T2DM) by bioinformatic analysis of publicly available experimental transcriptomic data. Materials and methods Gene expression data from peri-implantitis were downloaded from the Gene Expression Omnibus database, integrated and differentially expressed genes (DEGs) in peri-implantitis were identified. Next, experimentally validated and computationally predicted genes related to T2DM were downloaded from the DisGeNET database. Protein–protein interaction network (PPI) pairs of DEGs related to peri-implantitis and T2DM related genes were constructed, “hub” genes and overlapping DEG were determined. Functional enrichment analysis was used to identify significant shared biological processes and signaling pathways. The PPI networks were subjected to cluster and specific class analysis for identifying “leader” genes. Module network analysis of the merged PPI network identified common or cross-talk genes connecting the two networks. Results A total of 92 DEGs overlapped between peri-implantitis and T2DM datasets. Three hub genes (IL-6, NFKB1, and PIK3CG) had the highest degree in PPI networks of both peri-implantitis and T2DM. Three leader genes (PSMD10, SOS1, WASF3), eight cross-talk genes (PSMD10, PSMD6, EIF2S1, GSTP1, DNAJC3, SEC61A1, MAPT, and NME1), and one signaling pathway (IL-17 signaling) emerged as peri-implantitis and T2DM linkage mechanisms. Conclusions Exploration of available transcriptomic datasets revealed IL-6, NFKB1, and PIK3CG expression along with the IL-17 signaling pathway as top candidate molecular linkage mechanisms between peri-implantitis and T2DM.
A Robust Spectral Algorithm for Overcomplete Tensor Decomposition

We give a spectral algorithm for decomposing overcomplete order-4 tensors, so long as their components satisfy an algebraic non-degeneracy condition that holds for nearly all (all but an algebraic set of measure $0$) tensors over $(\mathbb{R}^d)^{\otimes 4}$ with rank $n \le d^2$. Our algorithm is robust to adversarial perturbations of bounded spectral norm. Our algorithm is inspired by one which uses the sum-of-squares semidefinite programming hierarchy (Ma, Shi, and Steurer STOC'16, arXiv:1610.01980), and we achieve comparable robustness and overcompleteness guarantees under similar algebraic assumptions. However, our algorithm avoids semidefinite programming and may be implemented as a series of basic linear-algebraic operations. We consequently obtain a much faster running time than semidefinite programming methods: our algorithm runs in time $\tilde O(n^2d^3) \le \tilde O(d^7)$, which is subquadratic in the input size $d^4$ (where we have suppressed factors related to the condition number of the input tensor).
Debugging of Answer Set Programs Using Paracoherent Reasoning

Answer Set Programming is a well-known declarative programming paradigm proposed in the area of logic programming and non-monotonic reasoning. Although ASP features a simple syntax and an intuitive semantics, errors are common during the development of ASP programs. In this paper we propose a novel debugging approach based on paracoherent reasoning, which allows a user to identify bugs when they are related to wrong constraints. The approach has been implemented in a tool called paradebug, that is made freely available.
Ensemble Learning Approach for Clickbait Detection Using Article Headline Features

Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.
Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information

This joint extended abstract introduces and compares the results of (Auer et al., 2019) and (Chen et al., 2019), both of which resolve the problem of achieving optimal dynamic regret for nonstationary bandits without prior information on the non-stationarity. Specifically, Auer et al. (2019) resolve the problem for the traditional multi-armed bandits setting, while Chen et al. (2019) give a solution for the more general contextual bandits setting. Both works extend the key idea of (Auer et al., 2018) developed for a simpler two-armed setting.
Machine Learning-Based Predictions of Customers’ Decisions in Car Insurance

ABSTRACT Predicting customer decisions allows companies to obtain higher profits due to better resource management. The accuracy of those predictions can be currently boosted by the application of machine learning algorithms. We propose a new method to predict a car driver’s decision about taking a replacement car after a vehicle accident happens. We use feature engineering to create attributes of high significance. The generated attributes are related to time (e.g., school holidays), place of collision (e.g., distance from home), time and conditions (e.g., weather), vehicles (e.g., vehicle value), addresses of both the victim and the perpetrator. Feature engineering involves external sources of data. Five machine learning methods of classification are considered: decision trees, multi-layer perceptrons, AdaBoost, logistic regression and gradient boosting. Algorithms are tested on real data from a Polish insurance company. Over 80% accuracy of prediction is achieved. Significance of the attributes is calculated using the linear vector quantization method. Presented work shows the applicability of machine learning in the car insurance market.
The Relative Complexity of Maximum Likelihood Estimation, MAP Estimation, and Sampling

We prove that, for a broad range of problems, maximum-a-posteriori (MAP) estimation and approximate sampling of the posterior are at least as computationally difficult as maximum-likelihood (ML) estimation. By way of illustration, we show how hardness results for ML estimation of mixtures of Gaussians and topic models carry over to MAP estimation and approximate sampling under commonly used priors.
Combinatorial Algorithms for Optimal Design

In an optimal design problem, we are given a set of linear experiments v 1 , . , v n ∈ R d and k ≥ d , and our goal is to select a set or a multiset S ⊆ [ n ] of size k such that Φ(( P i ∈ S v i v > i ) − 1 ) is minimized. When Φ( M ) = Determinant ( M ) 1 /d , the problem is known as the D-optimal design problem, and when Φ( M ) = Trace ( M ) , it is known as the A-optimal design problem. One of the most common heuristics used in practice to solve these problems is the local search heuristic, also known as the Fedorov’s exchange method (Fedorov, 1972). This is due to its simplicity and its empirical performance (Cook and Nachtrheim, 1980; Miller and Nguyen, 1994; Atkinson et al., 2007). However, despite its wide usage no theoretical bound has been proven for this algorithm. In this paper, we bridge this gap and prove approximation guarantees for the local search algorithms for D-optimal design and A-optimal design problems. We show that the local search algorithms are asymptotically optimal when kd is large. In addition to this, we also prove similar approximation guarantees for the greedy algorithms for D-optimal design and A-optimal design problems when kd is large.
KRaider: a Crawler for Linked Data

The aim of the Semantic Web and Linked Data principles is to create a web of data that can be processed by machines. The web of data is seen as a single globally distributed dataset. During the years, an increasing amount of data was published on the Web. In particular, large knowledge bases such as Wikidata, DBPedia, LinkedGeoData, and others are freely available as Linked Data and SPARQL endpoints. Exploring and performing reasoning tasks on such huge knowledge graphs is practically impossible. Moreover, triples involving an entity can be distributed among different datasets hosted by different SPARQL endpoints. Given an entity of interest and a task, we are interested into extracting a fragment of knowledge relevant to that entity, such that the results of the given task performed on the fragment are the same as if the task was performed on the whole web of data. Here we propose a system, called KRaider (“Knowledge Raider”), for extracting the relevant fragment from different SPARQL endpoints, without the user knowing their location. The extracted triples are then converted into an OWL ontology, in order to allow inference tasks. The system is part of a still under development framework called SRL-Frame (“Statistical Relational Learning Framework”).
Evolution of Algorithm Portfolio for Solving Strategies

The last decades have witnessed tremendous growth in the performance of solvers dealing with NP-hard problem instances. Commonly, powerful solvers outperform the other candidates for the majority of cases but obtain poor results for particular sub-domains. For this reason, Algorithm Portfolio approaches have been developed, allowing to exploit a set of solvers with complementary strengths, instead of running a single best one for all instances. Recent successful implementations combine these approaches with automatic Algorithm Conﬁguration methods, which consider a single parameterized solver and identify the best putative values of its hyper-parameters for the analyzed instances distribution. This paper aims to illustrate the development of the principal approaches that leverage machine learning to accelerate the solution search of modern solvers. Furthermore, it presents the basic notions necessary to understand the process that enabled these techniques to achieve their amazing results with some of the many performing implementations for SAT, Answer Set Programming , and Constraint Programming .
A Provably Secure Biometrics-Based Authentication Scheme for Multiserver Environment

With the rapid development of mobile services, multiserver authentication protocol with its high efficiency has emerged as an indispensable security mechanism for mobile services. Recently, Ali et al. introduced a biometric-based multiserver authentication scheme and claimed the scheme is resistant to various attacks. However, after a careful examination, we find that Ali et al.’s scheme is vulnerable to various security attacks, such as user impersonation attack, server impersonation attack, privileged insider attack, denial of service attack, fails to provide forward secrecy and three-factor secrecy. To overcome these weaknesses, we propose an improved biometric-based multiserver authentication scheme using elliptic curve cryptosystem. Formal security analysis under the random oracle model proves that our scheme is provably secure. Furthermore, BAN (Burrows-Abadi-Needham) logic analysis demonstrates our scheme achieves mutual authentication and session key agreement. In addition, the informal analysis proves that our scheme is secure against all current known attacks and achieves desirable features. Besides, the performance and security comparison shows that our scheme is superior to related schemes.
Effects of Challenging Weather and Illumination on Learning-Based License Plate Detection in Noncontrolled Environments

License Plate Detection (LPD) is one of the most important steps of an Automatic License Plate Recognition (ALPR) system because it is the seed of the entire recognition process. In indoor controlled environments, there are many effective methods for detecting license plates. However, outdoors LPD is still a challenge due to the large number of factors that may affect the process and the results obtained. It is an evidence that a complete training set of images including as many as possible license plates angles and sizes improves the performance of every classifier. On this line of work, numerous training sets contain images taken under different weather conditions. However, no studies tested the differences in the effectiveness of different descriptors for these different conditions. In this paper, various classifiers were trained with features extracted from a set of rainfall images using different kinds of texture-based descriptors. The accuracy of these specific trained classifiers over a test set of rainfall images was compared with the accuracy of the same descriptor-classifier pair trained with features extracted from an ideal conditions images set. In the same way, we repeat the experiment with images affected by challenging illumination. The research concludes, on one hand, that including images affected by rain, snow, or fog in the training sets does not improve the accuracy of the classifier detecting license plates over images affected by these weather conditions. Classifiers trained with ideal conditions images improve the accuracy of license plate detection in images affected by rainfalls up to 19% depending on the kind of extracted features. However, on the other hand, results evidence that including images affected by low illumination regardless of the kind of the selected feature increases the accuracy of the classifier up to 29%.
Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes

We consider the variant of the stochastic multi-armed bandit problem where the stochastic reward distributions may change abruptly several times. In contrast to previous work, we are able to achieve (nearly) optimal mini-max regret bounds without knowing the number of changes. For this setting, we propose an algorithm called ADSWITCH and provide performance guarantees for the regret evaluated against the optimal non-stationary policy. Our regret bound is the first optimal bound for an algorithm that is not tuned with respect to the number of changes.
Adaptive Moving Object Detection Algorithm Based on Background Subtraction and Motion Estimation

Moving object detection plays an important role in video surveillance and object tracking. With a stationary camera it is still a challenging problem to precisely extract moving object from real-time video with dynamic backgrounds. In this work, we develop an efficient moving object detection framework to tackle this problem. First, we utilize adaptive Gaussian mixture model to cope with steadily varying components of backgrounds. Second, inspired by the motion estimation method used in video coding, we propose a motion estimation technique to deal with unsteadily varying components of backgrounds. Exhaustive experimental evaluations on complex scenes show that the proposed method is fast and stable, achieving more than 15% improvement in accuracy compared over existing state-of-the-art algorithms. Keyword: Dynamic Background, Adaptive Gaussian Mixture Model, Background Subtraction, Motion Estimation
Assessing the dimensions of mandibular incisive canal and its relationship to adjacent anatomical landmarks using cone beam computed tomography in Indian sub-population – A retrospective study

Objective: The purpose of the study is to explore the capability of the CBCT to provide information concerning the position and dimensions of the mandibular incisive canal in relation to adjacent anatomical landmarks. Methodology: CBCT scans of 150 subjects were retrospectively studied to analyze the dimensions of the mandibular incisive canal and to assess the relationship of the mandibular incisive canal to the adjacent anatomical structures of the mandible. Results: 150 CBCT scans were evaluated for the position and dimensions of mandibular incisive canal to adjacent structures. In 87% of cases, the MIC was identified and had a mean length of 13.4mm (range, 5.6 to 24.7 mm) on the right side and 12.4 mm (range, 4 to 22.5 mm) on the left side. From the mental foramen, the incisive canal runs forward toward the incisors, in a slight downward direction, with its diameter decreasing as it progresses towards the midline. Discussion: The data from this study indicates surgical anatomic relationships should be considered in pre-surgical planning to avoid neurosensory disturbances and other potential complications. With the increased interest in performing a thorough pre-surgical examination in the inter-foraminal region, crosssectional images should be utilized to obtain information on the appearance, location, and course of the foramina and canals and their relation to other anatomical structures of the jaw.
Overview of dentures' demand to support the improvement of life quality of the elderly at high-level life expectancy in Indonesia

Aims: The missing teeth should ideally be replaced to anticipate some serious consequences. The most obvious effect of that is esthetics, but something like migration and rotation of teeth, chewing inefficiencies, temporomandibular joint disorders, and speech disorders can also affect function and health. Based on preliminary research, almost all elders who live in nursing homes in Surabaya lose their teeth, partially or completely, and they leave without replacing them with dentures for several reasons. This research intended to study the demands of patients for dentures in the elderly group so that future trends in health and management strategies can be improved. Materials and Methods: A cross-sectional study was conducted among 80 elderly individuals with multistage random sampling. The design was a descriptive analytical study. The instrument of this study was a validated questionnaire about the demand of dentures. Data analysis method used in this study was descriptive method, and data calculation was presented in percentage form by using software SPSS 16.0. Results: Most of the participants (45 people) presented low level of demand toward dentures (56.2%), with the rest of 35 people having high demand on dentures (43.8%). Conclusions: The demand for low dentures can result in poor oral health, so it is highly recommended that public health programs cover the oral health population, especially the elderly, so that future trends in health and management strategies can be improved.
In vitro activation of ovary

Primary ovarian insufficiency (POI) affecting 1% of women leads to infertility due to a lack of follicle growth and ovulation, and oocyte donation is the only treatment option. Studies have reported that ovarian cryopreservation, fragmentation, and in vitro activation with drug treatment followed by autotransplantation can result in the successful pregnancy outcomes. Herein, we report a case of POI, in which we performed the procedure of in vitro ovarian activation (OA) and autotransplantation without using drugs to see if it would induce the follicular growth. This is the first case report from India, and it shows that the procedure of OA works and can be tried as an experimental procedure for women with POI who are very keen on trying pregnancy using their own oocytes.
A Distributed High-rate and Low-detection Complexity Cooperative Relay Transmission Scheme ⋆

Since the existing full-rate cooperative transmission schemes have a serious defect in low Bit Error Rate (BER) performance while the existing high-rate cooperative transmission schemes have a serious defect in low spectral efficiency, a distributed high-rate cooperative relay transmission scheme based on full-rate cooperative communication model is proposed in this paper, in which Cyclic Delay Diversity (CDD) technology and Linear Constellation Precoding (LCP) technology are employed. Moreover, the proposed scheme addresses the issue of obtaining maximum spatial and multipath diversity with low decoding complexity and high transmission reliability. The BER performance of the proposed scheme is contrasted to that of the full-rate transmission scheme where better performance is achieved, and is improved when the number of the multipath or the relay nodes increases.
Role of Calbindin-D28k in Diabetes-Associated Advanced Glycation End-Products-Induced Renal Proximal Tubule Cell Injury

Diabetes-associated advanced glycation end-products (AGEs) can increase extracellular matrix (ECM) expression and induce renal fibrosis. Calbindin-D28k, which plays a role in calcium reabsorption in renal distal convoluted tubules, is increased in a diabetic kidney. The role of calbindin-D28k in diabetic nephropathy still remains unclear. Here, calbindin-D28k protein expression was unexpectedly induced in the renal tubules of db/db diabetic mice. AGEs induced the calbindin-D28k expression in human renal proximal tubule cells (HK2), but not in mesangial cells. AGEs induced the expression of fibrotic molecules, ECM proteins, epithelial-mesenchymal transition (EMT) markers, and endoplasmic reticulum (ER) stress-related molecules in HK2 cells, which could be inhibited by a receptor for AGE (RAGE) neutralizing antibody. Calbindin-D28k knockdown by siRNA transfection reduced the cell viability and obviously enhanced the protein expressions of fibrotic factors, EMT markers, and ER stress-related molecules in AGEs-treated HK2 cells. Chemical chaperone 4-Phenylbutyric acid counteracted the AGEs-induced ER stress and ECM and EMT markers expressions. Calbindin-D28k siRNA in vivo delivery could enhance renal fibrosis in db/db diabetic mice. These findings suggest that inducible calbindin-D28k protects against AGEs/RAGE axis-induced ER stress-activated ECM induction and cell injury in renal proximal tubule cells.