Miracle-dz/UniFAR-FTUs huggingface dataset:
An Obstacle Avoidance Approach Based on System Outlined Rectangle for Cooperative Transportation of Multiple Mobile Manipulators

Cooperative transportation by multiple mobile manipulators in unknown environments has been addressed in this paper. An obstacle avoidance approach based on system outlined rectangle (SOR) is proposed, which regards the cooperative transportation system as a virtual rectangle in the environment and focuses on the adjustment of moving direction. The width of system outlined rectangle can be adjusted via the pose changes of the mobile manipulators, which makes the decision making in obstacle avoidance more flexible and environmentally adaptable. Simulation results validate the effectiveness of the proposed approach. Moreover, comparisons between the experiments using the proposed approach and the experiments without considering the width changes of SOR indicates that the proposed approach is more time-saving and energy-economic.
System of National Accounts and Business Cycle

Current economic crisis poses a challenge on professional economists, analysts and academic researchers to re-consider the macroeconomic management as regards economic-policy tools and quality of information on which the macroeconomic management is based. Why official statistics did not indicate coming financial crisis? Why did GDP grow even after the beginning of the crisis? An ambition of this book is to provide a modest contribution to the important discussion on the ability of widely used macroeconomic aggregates to be a true reflection of reality. The book provides a detailed discussion of two key indicators in business cycle analysis, GDP and savings rate.
Öğretmen Failliği Ölçeği’nin (ÖFÖ) Geçerlik ve Güvenirlik Çalışması

Ogretmen failligi kavrami gectigimiz 30 yil icerisinde uluslararasi literaturde siklikla irdelenmis ve konuya iliskin calismalar son yillarda artis gostermistir. Ancak, konu Turkiye baglaminda incelenmemis ve ogretmen failligini olcmede kullanilabilecek herhangi bir olcek gelistirilmemis veya uyarlanmamistir. Bu calismanin amaci, Liu vd. (2016) tarafindan gelistirilen Ogretmen Failligi Olceginin Turkceye uyarlanmasidir. Arastirmanin orneklemini Konya, Adiyaman ve Yozgat illerindeki ilkokul, ortaokul ve lise turlerindeki okullarda gorev yapmakta olan 376 ogretmen olusturmaktadir. Olcegin yapi gecerligini tespit etmek amaciyla Dogrulayici Faktor Analizi (DFA) yapilmistir. Yapilan DFA sonucunda olcegin orijinal formu ile tutarlilik gosteren dort boyuttan (Ogrenme Etkililigi, Ogretme Etkililigi, Iyimserlik ve Yapici Katilim) olusan bir yapi elde edilmistir. Orijinal formda oldugu gibi Turkce formda da 24 maddeden olusan olcegin alt boyutlarindan elde edilen puanlarin ic tutarlilik degerleri . 72 ile . 83 arasinda degismektedir. Arastirma sonuclari Turkceye uyarlanan Ogretmen Failligi Olceginin (OFO) ilkokul, ortaokul ve liselerde gorev yapan ogretmenlerin faillik duzeylerini belirlemede kullanilabilecek gecerli ve guvenilir bir olcme araci oldugunu gostermektedir.
Propensity Score Stratification Using Support Vector Machine in HIV AIDS Case

Many observational studies applied in the field of health, but Randomized Controlled Trials (RCT) is not always can be applied because it is directly related to human life. Therefore, a method is needed to solve the problem of bias as the effect of non-random observation and unbalanced covariates using propensity score (PS), it is Propensity Score Stratification (PSS). The purpose of PSS is to obtain a strata group that balance on each covariate. The PSS estimation of this research is using support vector machine (SVM). The case used in this research is opportunistic infection of HIV AIDS at Grati Health Center in Pasuruan district with the number of respondents are 150 patients. In the case of opportunistic infections HIV AIDS found that giving ARV therapy becomes confounding variable.The highest accuracy of PSS SVM on strata is 4, that is 64%. Estimation of treatment effects (ATE) gave results that the variable of ARV therapy is a variable that influence the opportunistic infections (Y) in HIV AIDS patients. The number of strata that reduce the largest bias is in the strata of 4 with the percent bias reduction (PBR) is 37.168% with the smallest standard error value is 0.075 and ATE value is 0.516.
Phenotypic and functional profile of IFN-α-differentiated dendritic cells (IFN-DCs) from HIV-infected individuals

ABSTRACT Dendritic cell (DC)-based immunotherapy is a promising strategy for the treatment of HIV-infected individuals. Different from the conventional protocol for DC differentiation based on the cytokine IL-4 (IL4-DCs), several studies have suggested obtaining DCs by culturing monocytes with type I IFN (IFN-α) to yield IFN-DCs, as performed in cancer therapy. To evaluate the phenotypic and functional characteristics, monocytes from HIV-infected subjects were differentiated into IFN-DCs or IL4-DCs, pulsed with chemically inactivated HIV and stimulated with pro-inflammatory cytokines. A comparative analysis between both types of monocyte-derived DCs (MoDCs) showed that immature IFN-DCs were phenotypically distinct from immature IL4-DCs at the baseline of differentiation, presenting a pre-activated profile. From the functional profile, we determined that IFN-DCs were capable of producing the cytokine IL-12 p70 and of inducing the production of IFN-γ by CD4 + T lymphocytes but not by TCD8+ lymphocytes. Our results suggest that IFN-DCs derived from HIV-infected individuals are able to recognize and present viral antigens to induce TCD4+ cellular immunity to HIV.
A Riemannian trust‐region method for low‐rank tensor completion

The goal of tensor completion is to fill in missing entries of a partially known tensor (possibly including some noise) under a low‐rank constraint. This may be formulated as a least‐squares problem. The set of tensors of a given multilinear rank is known to admit a Riemannian manifold structure; thus, methods of Riemannian optimization are applicable. In our work, we derive the Riemannian Hessian of an objective function on the low‐rank tensor manifolds using the Weingarten map, a concept from differential geometry. We discuss the convergence properties of Riemannian trust‐region methods based on the exact Hessian and standard approximations, both theoretically and numerically. We compare our approach with Riemannian tensor completion methods from recent literature, both in terms of convergence behavior and computational complexity. Our examples include the completion of randomly generated data with and without noise and the recovery of multilinear data from survey statistics.
Adversarial Examples from Cryptographic Pseudo-Random Generators

In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption.
Transcriptional profiling identifies critical steps of cell cycle reprogramming necessary for Plasmodiophora brassicae‐driven gall formation in Arabidopsis

Summary Plasmodiophora brassicae is a soil‐borne biotroph whose life cycle involves reprogramming host developmental processes leading to the formation of galls on its underground parts. Formation of such structures involves modification of the host cell cycle leading initially to hyperplasia, increasing the number of cells to be invaded, followed by overgrowth of cells colonised by the pathogen. Here we show that P. brassicae infection stimulates formation of the E2Fa/RBR1 complex and upregulation of MYB3R1,MYB3R4 and A‐ and B‐type cyclin expression. These factors were previously described as important regulators of the G2−M cell cycle checkpoint. As a consequence of this manipulation, a large population of host hypocotyl cells are delayed in cell cycle exit and maintained in the proliferative state. We also report that, during further maturation of galls, enlargement of host cells invaded by the pathogen involves endoreduplication leading to increased ploidy levels. This study characterises two aspects of the cell cycle reprogramming efforts of P. brassicae: systemic, related to the disturbance of host hypocotyl developmental programs by preventing cell cycle exit; and local, related to the stimulation of cell enlargement via increased endocycle activity.
Overview and Categorization of Recent Approaches to Microblog Classification

A growing popularity of microblogs requires scientists to develop methods for effective and efficient analysis of short texts in order to understand opinions of microblog users and the trustworthiness of their messages. In this review, we examine most common approaches to sentiment analysis in microblogs and explain their advantages and weaknesses. We also review approaches to assessing credibility in news headlines and political statements and discuss their applicability in microblogs. Distinct characteristics of microblog posts, such as slang, emoticons, poor grammar and spelling are in the focus of the evaluation. Such properties of microblog messages deteriorate the performance of conventional classifiers and limit their use in practical applications.
Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes

In industrial Internet of Things applications with sensors sending dynamic process data at high speed, producing actionable insights at the right time is challenging. A key problem concerns processing a large amount of data, while the underlying dynamic phenomena related to the machine is possibly evolving over time due to factors, such as degradation. This makes any actionable model become obsolete and necessary to be updated. To cope with this problem, in this paper we propose a new unsupervised learning algorithm based on Gaussian mixture models called Gaussian-based dynamic probabilistic clustering (GDPC) mainly based on integrating and adapting three well known algorithms for use in dynamic scenarios: the expectation-maximization (EM) algorithm to estimate the model parameters and the Page-Hinkley test and Chernoff bound to detect concept drifts. Unlike other unsupervised methods, the model induced by the GDPC provides the membership probabilities of each instance to each cluster. This allows to determine, through a Brier score analysis, the robustness of the instance assignment and its evolution each time a concept drift is detected. Also, the algorithm works with very little data and significantly less computing power being able to decide whether (and when) to change the model. The algorithm is tested using synthetic data and data streams from an industrial testbed, where different operational states are automatically identified, giving good results in terms of classification accuracy, sensitivity, and specificity.
Metamaterial-based Sensor for Measurements of physical Quantities and Parameters of technological Processes

Metamaterials have become an important part of microwave technology. Metamaterials are artificial periodic structures with exotic properties which cannot be found in Nature. One of the many possible applications of the metamaterials is microwave measurement. In this paper, a sensor based on planar mushroom-shaped metamaterial is proposed as well as its S21 parameter which depends on certain measured characteristics of an investigated object.
Therapeutic effects of aquatic exercises on a boy with Duchenne muscular dystrophy

Participation in physical activities benefits individuals with disabilities in terms of addressing their social and psychological needs in order for the purposes of quality living. This study reports the findings from a case study conducted with a boy who has Duchenne muscular dystrophy (DMD) in a swimming pool located in the campus of a private university in North Cyprus. The current study reports the adjustments made in the swimming pool in accordance with the needs of the participant, how service quality was increased to cater for these needs and in what ways the participant benefited from the aquatic program. This qualitative investigation aims to shed light onto how aquatic therapy has affected the boy with DMD physically, psychologically, and socially from the parents’ perspectives. The results revealed that aquatic therapy had beneficial effects on the participant with DMD in terms of socialization, relaxation, quality of life and self-perception.
The Genetic Makeup and Expression of the Glycolytic and Fermentative Pathways Are Highly Conserved Within the Saccharomyces Genus

The ability of the yeast Saccharomyces cerevisiae to convert glucose, even in the presence of oxygen, via glycolysis and the fermentative pathway to ethanol has played an important role in its domestication. Despite the extensive knowledge on these pathways in S. cerevisiae, relatively little is known about their genetic makeup in other industrially relevant Saccharomyces yeast species. In this study we explore the diversity of the glycolytic and fermentative pathways within the Saccharomyces genus using S. cerevisiae, Saccharomyces kudriavzevii, and Saccharomyces eubayanus as paradigms. Sequencing data revealed a highly conserved genetic makeup of the glycolytic and fermentative pathways in the three species in terms of number of paralogous genes. Although promoter regions were less conserved between the three species as compared to coding sequences, binding sites for Rap1, Gcr1 and Abf1, main transcriptional regulators of glycolytic and fermentative genes, were highly conserved. Transcriptome profiling of these three strains grown in aerobic batch cultivation in chemically defined medium with glucose as carbon source, revealed a remarkably similar expression of the glycolytic and fermentative genes across species, and the conserved classification of genes into major and minor paralogs. Furthermore, transplantation of the promoters of major paralogs of S. kudriavzevii and S. eubayanus into S. cerevisiae demonstrated not only the transferability of these promoters, but also the similarity of their strength and response to various environmental stimuli. The relatively low homology of S. kudriavzevii and S. eubayanus promoters to their S. cerevisiae relatives makes them very attractive alternatives for strain construction in S. cerevisiae, thereby expanding the S. cerevisiae molecular toolbox.
The Impact of Gamification on Socio-technical Communities: A Case for Network Analysis

Designing for motivating and engaging experiences is at the core of gamification. The results of gamification are often evaluated with user experience testing involving recordings, surveys, and interviews. However, in multi-user socio-technical environments the benefits of gamification are often realized in interactions between users. We propose that social network analysis should be used more to analyze the impact of gamification at community level. To demonstrate the approach, we present a study where a gamified computer-supported collaborative learning system was introduced to a course, and compare the course to a previous instance. Furthermore, we present several examples of how social network analysis can be used with hypothesis testing and discuss the benefits of the approach. ACM Classification
A Study of User Capacity for Massive MIMO in Urban Street Canyon Environment

The purpose of this study is to indicate a user capacity for massive MIMO using block diagonalization (BD) method in an urban street canyon environment of a 3D channel model which is proposed by 3rd generation partnership project (3GPP). To clarify the user capacity, we distributed users randomly in one of three sectors in a cell the shape of which is a hexagon, and obtained the propagation channels by the channel model, and calculated total channel capacities in the sector as the number of users increase. As a result, the limit number of the user capacity $\pmb{K}_{\pmb{M}}$ became 0.76S/U ($\pmb{S}$: the number of BS elements and $\pmb{U}:$: the number of MS elements) in LOS environment.
“Small Data” Anomaly Detection for Unmanned Systems

This paper presents an approach for a low cost, platform-agnostic, unmanned systems anomaly detection system that learns normal operating conditions from limited data and computing resources to track deviations from those conditions over time. Machine learning and automatic anomaly detection use has exploded in the Big Data arena with the availability of large volumes of historical data and extensive computing resources. However, in the case of unmanned systems, there is typically limited historical data available and computational resources are often restricted to embedded devices similar to cell phones. We discuss the application of two algorithms for anomaly detection in this “small data” context: 1) sparse modeling and 2) T-Digest. These algorithms are also designed and chosen to perform generically across a number of target application domains with a standalone health monitoring sensor box coupled with noninvasive sensors. Acoustic and inertial sensors have been initially selected to illustrate and validate the system capability and performance.
TROAP regulates prostate cancer progression via the WNT3/survivin signalling pathways.

Prostate cancer (PCa) is one of the most commonly diagnosed malignancies, and 90% of advanced prostate cancer patients relapse after therapy. Trophinin associated protein (TROAP) is essential for centrosome integrity and proper bipolar organisation of spindle assembly during mitosis and plays an essential role in proliferation. We found that TROAP expression correlates with patient survival and speculated that it may be involved in PCa progression. The Oncomine database tool (http://www.oncomine.org) was used to analyse TROAP mRNA expression from microarray data, and patient survival analysis for target genes was performed using the PROGgeneV2 Database (http://watson.compbio.iupui.edu). Gene interference with lentivirus was used to silence TROAP expression in PCa cells and knockdown efficiency was detected by qRT-PCR and western blot analysis. Cell viability, colony formation, cell cycle and apoptosis were then assessed to determine the function of TROAP in PCa cells. Markers of cell cycle and apoptosis were tested by western blotting. The correlation between WNT3 or survivin expression and TROAP transcripts in prostate cancer tissues was analysed using GEPIA (http://gepia.cancer-pku.cn) and validated by western blotting. The in vivo role of TROAP was investigated using xenografts. This protein was overexpressed in PCa, and exhibited relatively higher expression in PCa cell lines, DU145 and 22Rv1. Importantly, analysing human cancer databases available from PROGgeneV2 showed that higher expression of TROAP is associated with shorter overall survival in prostate cancer patients. TROAP knockdown inhibited cell proliferation and led to cell cycle arrest at S phase in 22Rv1 and DU145 cells. Cell cycle arrest resulted in apoptosis in both cell lines via the cyclin A2-cyclin B1-caspase pathway. WNT3 and survivin expression levels were found to correlate with TROAP in PCa, and in vivo xenograft assays revealed that silencing of TROAP inhibited PCa tumour growth. Therefore, TROAP might represent a novel predictive marker to guide therapeutic intervention.
Toward Trustworthy Deep Learning in Security

In the security area, there has been an increasing tendency to apply deep learning, which is perceived as a black box method because of the lack of understanding of its internal functioning. Can we trust deep learning models when they achieve high test accuracy? Using a visual explanation method, we find that deep learning models used in security tasks can easily focus on semantically non-discriminative parts of input data even though they produce the right answers. Furthermore, when a model is re-trained without any change in the learning procedure (i.e., no change in training/validation data, initialization/optimization methods and hyperparameters), it can focus on significantly different parts of many samples while producing the same answers. For trustworthy deep learning in security, therefore, we argue that it is necessary to verify the classification criteria of deep learning models before deploying them, even though they successfully achieve high test accuracy.
SE(2)-Constrained Visual Inertial Fusion for Ground Vehicles

Visual inertial fusion is the problem to estimate vehicle states using visual and inertial measurements, and has recently been an active research topic. Existing visual inertial fusion approaches formulate their estimation algorithms for mobile robots in general 3-D space, and cannot satisfactorily model the nearly planar motion of ground vehicles. This paper proposed a new visual inertial fusion framework for ground vehicles, based on an SE(2)-constrained pose parameterization, which maximally conforms to the realistic situations of the ground vehicle motion. The estimation system is formulated as a graph optimization problem, and the SE(2)-constrained parameterization is implemented by introducing specially designed edges on the vehicle poses in the graph. Keyframe-based optimization and marginalization help to ensure the real-time performance. Experimental tests were conducted on challenging data sets with illumination changes and sharp-turn motion to validate the better accuracy of the proposed system compared with the state-of-the-art visual inertial estimation approaches.
Class-Constrained Transfer LDA for Cross-View Action Recognition in Internet of Things

Internet of Things (IoT) is a fast-growing field based on different techniques and applications. In this paper, we focus on IoT in monitoring mission which is widely used in everyday life, such as security surveillance, health-care, independent living, etc. In order to overcome the challenges of various viewpoints and heterogenous sensors in IoT, we propose a novel method named class-constrained transfer linear discriminant analysis (CTLDA), which learns two projection matrices with different dimensionality for mapping the original features into a common subspace. The target of learning two projection matrices is to maximize the interclass data and minimize the intraclass data. Meanwhile, we propose the class-constrained regularization which enforces the neighboring samples with the same class to be still close to each other so as to further improve the discrimination of CTLDA. The class-constrained regularization possesses two strategies, i.e., hard constraint and soft constraint. The experimental results demonstrate that our method achieves better performance than the state-of-the-art methods.