Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in China

ABSTRACT Recognizing the underlying relationship between e-learning practice and the institutional environments hosted in, the Chinese educational practice on branching high school students into science, technology, engineering, and mathematics (STEM) and non-STEM academic major groups before being admitted into universities or colleges is examined. By extending the well-established Technology Acceptance Model (TAM) with computer self-efficacy, this study aims to examine the difference in perceptions and behaviours on e-learning adoption from the STEM and non-STEM students. The results revealed that STEM’s score of computer self-efficacy, perceived ease of use and behavioural intention to use e-learning are all greater than non-STEM’s.
GN-Net: The Gauss-Newton Loss for Deep Direct SLAM

Direct methods for SLAM have shown exceptional performance on odometry tasks. However, they still suffer from dynamic lighting/weather changes and from a bad initialization on large baselines. To mitigate both of these effects, we propose an approach which feeds deep visual descriptors for each pixel as input to the SLAM system. In this work, we introduce GN-Net: a network optimized with the novel Gauss-Newton loss for training deep features. It is designed to maximize the probability of the correct pixel correspondence inside the Gauss-Newton algorithm. This results in features with a larger convergence basin when compared with single-channel grayscale images generally used in SLAM-based approaches. Our network can be trained with ground-truth pixel correspondences between different images, produced either from simulation data or by any state-of-the-art SLAM algorithm. We show that our approach is more robust against bad initialization, variations in day-time, and weather changes thereby outperforming state-of-the-art direct and indirect methods. Furthermore, we release an evaluation benchmark for what we refer to as relocalization tracking. It has been created using the CARLA simulator as well as sequences taken from the Oxford RobotCar Dataset.
Control of Neurotransmission by NaV1.7 in Human, Guinea Pig, and Mouse Airway Parasympathetic Nerves

Little is known about the neuronal voltage-gated sodium channels (NaVs) that control neurotransmission in the parasympathetic nervous system. We evaluated the expression of the α subunits of each of the nine NaVs in human, guinea pig, and mouse airway parasympathetic ganglia. We combined this information with a pharmacological analysis of selective NaV blockers on parasympathetic contractions of isolated airway smooth muscle. As would be expected from previous studies, tetrodotoxin potently blocked the parasympathetic responses in the airways of each species. Gene expression analysis showed that that NaV 1.7 was virtually the only tetrodotoxin-sensitive NaV1 gene expressed in guinea pig and human airway parasympathetic ganglia, where mouse ganglia expressed NaV1.1, 1.3, and 1.7. Using selective pharmacological blockers supported the gene expression results, showing that blocking NaV1.7 alone can abolish the responses in guinea pig and human bronchi, but not in mouse airways. To block the responses in mouse airways requires that NaV1.7 along with NaV1.1 and/or NaV1.3 is blocked. These results may suggest novel indications for NaV1.7-blocking drugs, in which there is an overactive parasympathetic drive, such as in asthma. The data also raise the potential concern of antiparasympathetic side effects for systemic NaV1.7 blockers.
Optimization of Masks for High Aspect Ration UV-Lithographic Patterning

In this paper we present an optimization method for mask layouts used in high aspect ratio UV-lithographic patterning. Different levels of microstructures and substrate cause diffraction effects during the exposure which lead to a distortion of the original mask pattern. A simulation tool for the estimation of diffraction effects during the exposure of complex three-dimensional structures was developed and coupled to an evolutionary optimization algorithm. The simulation tool itself enables a designer to calculate intensity profiles on the resist surface and to estimate the impact of diffraction effects on the resulting resist pattern. Together with the newly implemented optimization method an automatic generation of compensation elements could be achieved. The simulation method and the used algorithm is described in detail. Finally, an example is used to show how the tool can be used for the optimization of mask structures by generating compensation structures.
An object tracking method using extreme learning machine with online learning

Target tracking is a challenging task in computer vision. It aims to detect and track particular objects in sequences. Illumination variation, motion of target, occlusion and background clutter make target tracking extremely challenging. We propose an novel online target tracking method which based on extreme learning machine(ELM). This tracking method consists of three modules: training, tracking and classifier update. The training stage aims to train ELM by using the training set. Extracting histograms of oriented gradients (HOG) features in the first frame of each sequence for training ELM. Then the tracking stage will make predictions about the object position and detect the target in candidate regions. A simple object motion model is designed to predict the object position. Finally, according to tracking results, the classifier can be updated for online learning. A large number of experimental results have validated this proposed method.
Distinct Effects of Unfractionated Heparin versus Bivalirudin on Circulating Angiogenic Peptides

Background Human studies of therapeutic angiogenesis, stem-cell, and progenitor-cell therapy have failed to demonstrate consistent clinical benefit. Recent studies have shown that heparin increases circulating levels of anti-angiogenic peptides. Given the widely prevalent use of heparin in percutaneous and surgical procedures including those performed as part of studies examining the benefit of therapeutic angiogenesis and cell-based therapy, we compared the effects of unfractionated heparin (UFH) on angiogenic peptides with those of bivalirudin, a relatively newer anticoagulant whose effects on angiogenic peptides have not been studied. Methodology/Principal Findings We measured soluble fms-like tyrosine kinase-1 (sFLT1), placental growth factor (PlGF), vascular endothelial growth factor (VEGF), and soluble Endoglin (sEng) serum levels by enzyme linked immunosorbent assays (ELISA) in 16 patients undergoing elective percutaneous coronary intervention. Compared to baseline values, sFLT1 and PlGF levels increased by 2629±313% and 253±54%, respectively, within 30 minutes of UFH therapy (p<0.01 for both; n = 8). VEGF levels decreased by 93.2±5% in patients treated with UFH (p<0.01 versus baseline). No change in sEng levels were observed after UFH therapy. No changes in sFLT1, PlGF, VEGF, or sEng levels were observed in any patients receiving bivalirudin (n = 8). To further explore the direct effect of anticoagulation on circulating angiogenic peptides, adult, male wild-type mice received venous injections of clinically dosed UFH or bivalirudin. Compared to saline controls, sFLT1 and PlGF levels increased by >500% (p<0.01, for both) and VEGF levels increased by 221±101% (p<0.05) 30 minutes after UFH treatment. Bivalirudin had no effect on peptide levels. To study the cellular origin of peptides after anticoagulant therapy, human coronary endothelial cells were treated with UFH and demonstrated increased sFLT1 and PlGF levels (ANOVA p<0.01 for both) with reduced VEGF levels (ANOVA p<0.05). Bivalirudin had no effect on peptide levels in vitro. Conclusions/Significance Circulating levels of sFLT1, PlGF, and VEGF are significantly altered by UFH, while bivalirudin therapy has no effect. These findings may have significant implications for clinical studies of therapeutic angiogenesis, stem-cell and progenitor-cell therapy.
Expanding indications for transcatheter aortic valve replacement

Transcatheter aortic valve replacement (TAVR) has emerged as an accepted indication for non-operable patients with severe symptomatic native aortic valve stenosis (AS) and as a reasonable alternative for high-risk surgical AS patients. Nonetheless, the safety and efficacy of performing TAVR in several other potential indications are yet unclear. In the present manuscript the authors review the current evidence supporting TAVR for other potential indications than the typical high-risk/non-operable AS patients, providing updated results of the main clinical trials and registries exploring these particular indications. Finally, the authors provide practical recommendations for TAVR in each of these conditions.
Project plan: Approximation of NP-hard optimization problems European Research Council Advanced Grant

The goal of complexity theory is to study the amount of computational resources that are needed to solve a particular computational problem. The most studied resource is computer time and here “time” is used to mean the number of elementary operations getting a theory that is independent of technology. Clearly it is more difficult to solve large instances of a problem and thus one studies the running time as a function of the input length. A definition that has turned out to be useful is to say that a problem can be solved efficiently if the running time increases polynomially in the size of the input. This class of problems is denoted by P and another central complexity class is NP; problems where a found solution can be verified in polynomial time. The question whether these two complexity classes are equal is the most famous open problem in complexity theory. It is almost universally believed that the two classes are not equal but it seems like our understanding of computation is far from the point where this can be proved. The common approach, also adopted in this proposal, to get interesting results is to assume that NP = P and derive consequences of this assumption. A family of hard problems is given by the NP-complete problems defined originally by Cook [11]. Many basic problems fall into this class, some famous examples being graph colorability, the traveling salesman problem and satisfiability of Boolean formulas.
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called 'adversarial subspaces') in which adversarial examples lie. We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID). LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors. We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the distinction of adversarial examples generated using state-of-the-art attacks. As a proof-of-concept, we show that a potential application of LID is to distinguish adversarial examples, and the preliminary results show that it can outperform several state-of-the-art detection measures by large margins for five attack strategies considered in this paper across three benchmark datasets. Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs.
Intraosteal Behavior of Porous Scaffolds: The mCT Raw-Data Analysis as a Tool for Better Understanding

The aim of the study is to determine the existing correlation between high-resolution 3D imaging technique obtained through Micro Computed Tomography (mCT) and histological-histomorphometric images to determine in vivo bone osteogenic behavior of bioceramic scaffolds. A Ca-Si-P scaffold ceramic doped and non-doped (control) with a natural demineralized bone matrix (DBM) were implanted in rabbit tibias for 1, 3, and 5 months. A progressive disorganization and disintegration of scaffolds and bone neoformation occurs, from the periphery to the center of the implants, without any differences between histomorphometric and radiological analysis. However, significant differences (p < 0.05) between DMB-doped and non-doped materials where only detected through mathematical analysis of mCT. In this way, average attenuation coefficient for DMB-doped decreased from 0.99 ± 0.23 Hounsfield Unit (HU) (3 months) to 0.86 ± 0.32 HU (5 months). Average values for non-doped decreased from 0.86 ± 0.25 HU (3 months) to 0.66 ± 0.33 HU. Combination of radiological analysis and mathematical mCT seems to provide an adequate in vivo analysis of bone-implanted biomaterials after surgery, obtaining similar results to the one provided by histomorphometric analysis. Mathematical analysis of Computed Tomography (CT) would allow the conducting of long-term duration in vivo studies, without the need for animal sacrifice, and the subsequent reduction in variability.
Tuning Local Search for Satisfiability Testing

Local search algorithms, particularly GSAT and WSAT have attracted considerable recent attention primarily because they are the best known approaches to several hard classes of satisfiability problems. However, replicating reported results has been difficult because the setting of certain key parameters is something of an art, and because details of the algorithms, not discussed in the published papers, can have a large Impact on performance. In this paper we present an efficient probabilistic method for finding the optimal setting for a critical local search parameter, Maxflips, and discuss important details of two differing versions of WSAT. We then apply the optimization method to study performance of WSAT on satisfiable instances of Random 3SAT at the crossover point and present extensive experimental results over a wide range of problem sizes. We find that the results are well described by having the optimal value of Maxflips scale as a simple power of the number of variables, n, and the average run time scale sub-exponentially (basically as nlog(n)) over the range n = 25,. ,400.
Perfusion of the isolated mammary gland of the goat.

Improvements to a previously reported technique for the perfusion of isolated goat mammary glands are described. Secretion of milk of relatively normal composition in vitro has been maintained at approximately 50% of the in vivo rate for about 12 hr. Factors found to be important for successful perfusion are a normal rate of mammary blood flow, the provision of adequate glucose, acetate and amino acids, and the inclusion in the circuit of an efficient oxygenator and dialyser. Contrary to earlier results successful perfusions have been performed with glands from anaesthetized and from shot animals, although removal of glands from the tranquillized animal under spinal anaesthesia is still preferred. There was a marked dependence of milk secretion rate on the rate of blood flow below a critical value, but at supra-normal flows there was no correlation between these variables. Evidence has been obtained suggesting that kinins or similar substances may be formed during perfusion. The specific dependence of milk secretion on glucose is again stressed. Other carbohydrates sustained oxygen consumption similar to that shown with glucose, but by contrast did not support the secretion of water, a small amount of milk low in lactose but high in fat and protein being produced. Although milk fat secretion was dependent on the provision of acetate, the omission of β-hydroxybutyrate, lactate and neutral fat (all shown to be absorbed by the gland in vivo) from the substrate mixture did not seriously impair milk fat secretion in vitro. Amino acids were shown to be necessary for normal rates of milk protein secretion, but the alteration of the substrate mixture to correspond to the amino acid absorption observed in vivo produced no significant change in milk secretion from that observed when amino acids were infused at rates corresponding to their occurrence in casein. The infusion of B vitamins and various hormones had no effect on milk secretion. Perfusions carried out with a conscious goat in circuit were no more successful than the completely in vitro system, but glands grafted onto recipient goats (even males) following complete removal from the donors produced substantially more milk than in vitro preparations. In some experiments milk [Na] and [Cl] increased during perfusion while [K] and [lactose] decreased, these trends being apparent in milk secreted before perfusion was begun and being probably due to the oxytocin used to produce milk ejection. In other experiments milk [Na] and [Cl] showed little change but [K] and [lactose] tended to rise. Possible factors limiting the performance of perfused organs are discussed.
2 1 Ju n 20 17 Cluster Analysis is Convex Madhushini

In this paper, we show that the popular K-means clustering problem can equivalently be reformulated as a conic program of polynomial size. The arising convex optimization problem is NP-hard, but amenable to a tractable semidefinite programming (SDP) relaxation that is tighter than the current SDP relaxation schemes in the literature. In contrast to the existing schemes, our proposed SDP formulation gives rise to solutions that can be leveraged to identify the clusters. We devise a new approximation algorithm for K-means clustering that utilizes the improved formulation and empirically illustrate its superiority over the state-of-the-art solution schemes.
A model for describing communication between aggregate objects in the specification and design of embedded systems

The elevation of design description abstractions is a well accepted technique for handling the complexity and shortening the design time of modern embedded systems. It is shown that abstractions for communication are as important as for behaviour for specification and system level abstractions, and an extension on a novel higher level communication mechanism which has features for supporting the description of complex aggregate associations between objects in specifications such as UML is investigated. The communication primitives have been implemented as extensions to SystemC, and a comprehensive example from a UML specification through functional specification down to an executable SystemC description is included.
Extracting knowledge from text using SHELDON, a Semantic Holistic framEwork for LinkeD ONtology data

SHELDON is the first true hybridization of NLP machine reading and the Semantic Web. It extracts RDF data from text using a machine reader: the extracted RDF graphs are compliant to Semantic Web and Linked Data. It goes further and applies Semantic Web practices and technologies to extend the current human-readable web. The input is represented by a sentence in any language. SHELDON includes different capabilities in order to extend machine reading to Semantic Web data: frame detection, topic ex- traction, named entity recognition, resolution and coreference, terminology extraction, sense tagging and disambiguation, taxonomy induction, semantic role labeling, type induction, sentiment analysis, citation inference, relation and event extraction, nice visualization tools which make use of the JavaScript infoVis Toolkit and RelFinder. A demo of SHELDON can be seen and used at http://wit.istc. cnr.it/stlab-tools/sheldon.
DIETARY PHYTATE LOWERS IRON STATUS , SUPPRESSES ABERRANT CRYPT FOCI AND INHIBITS PROGRESSION TO ADENOMA IN AZOXYMETHANE-INDUCED COLON CANCER

Phytate is known for its anti-neoplastic activity in various cancers, but the underlying mechanisms are less understood. We therefore examined pure Inositol Hexaphosphate (IP6, phytate) as a dietary ingredient provided post-initiation, for its ability to inhibit dysplastic aberrant crypt foci (ACF) formation, and their progression to colon adenoma/adenocarcinomas viz. a viz. the role of altered iron homeostasis, in F344 rats using azoxymethane (AOM) as a carcinogen. Treatment with 1 or 2% IP6 was initiated after 20 weeks of AOM administration and continued for 16 weeks. IP6 suppressed ACF formation and inhibited their progression to tumors in a dose-dependent manner. Phytate significantly ameliorated the tumor and inflammatory markers such as proliferated cell nuclear antigen (PCNA), -catenin, cyclooxygenase-2 (COX-2), whose expression was upregulated with azoxymethane. Systemic iron status indicators including hemoglobin, serum iron, ferritin, and hepcidin levels suggested a clear depletion of body iron stores with phytate supplementation. Apoptotic indices, such as TUNEL staining and caspase-3 activity were promoted by IP6 in AOM-administered colon epithelium. Thus, dietary phytate consumption effectively reduced the tumor incidence and multiplicity even in a post-initiation model of azoxymethane, with a decrease in body iron stores.
Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition

MOTIVATION A key goal of genomics is to assign function to genes, especially for orphan sequences. RESULTS We compared the clustered functional domains in the SBASE database to each protein sequence using BLASTP. This representation for a protein is a vector, where each of the non-zero entries in the vector indicates a significant match between the sequence of interest and the SBASE domain. The machine learning methods nearest neighbour algorithm (NNA) and support vector machines are used for predicting protein functional classes from this information. We find that the best results are found using the SBASE-A database and the NNA, namely 72% accuracy for 79% coverage. We tested an assigning function based on searching for InterPro sequence motifs and by taking the most significant BLAST match within the dataset. We applied the functional domain composition method to predict the functional class of 2018 currently unclassified yeast open reading frames. AVAILABILITY A program for the prediction method, that uses NNA called Functional Class Prediction based on Functional Domains (FCPFD) is available and can be obtained by contacting Y.D. Cai at y.cai@umist.ac. uk
New Mechanism for the Integration of Organizational Requirements and Object Oriented Modeling

The success of computer applications depends on a good understanding of the organizational environment. Thus, requirement-modeling techniques may be used to help to understand a process in terms of goals, business rules, tasks, resources and the relationship between their actors. We have observed a growing influence of the object-orientation paradigm but the dominant technique of object oriented modeling UML (Unified Modeling Language) is still ill equipped to represent the organizational requirements. So, we have advocated the use of the i* technique to model requirements in terms of the relationships among the several organizational actors, as well as a means for understanding the rationale for the decision-making. In this paper we discuss some improved guidelines for the integration of early and late requirements specifications. We ext end the i * technique so that we establish an order in the execution of the i* task dependency. We are also proposing to extend the prototype tool (GOOD Goal Object Oriented Development) to support the new guidelines.