Miracle-dz/UniFAR-FTUs huggingface dataset:
Lead halide perovskite-based active hyperbolic metamaterials in the visible region

In this work we discuss the design, fabrication, and characterization of hyperbolic metamaterials (HMMs) based on plasmonic metals and solution processable perovskites. The optical properties of the HMMs are characterized using spectroscopic anisotropic ellipsometry, angle resolved reflectometry and compared with the theoretical/simulated results derived from the effective medium approximation (EMA) approach. Furthermore, the impact of replacing the outer layer on the homogenization of the composite is studied by monitoring the evolution of ψ and Δ of the HMMs in each fabrication step. The HMMs presented here exhibite epsilon near zero (ENZ) and epsilon near pole (ENP) near 550 nm. Finally, we show how this material can be used for effective light concentration in the field of perovskite solar cells exploiting the unusual optical constants near the ENZ/ENP regions.
Towards Vectorial Web Search

General similarity search of quantifiable resources is possible on the Web. For this a simple data structure called VRD ("Vectorial Resource Descriptor") is proposed, which contains a feature vector for representation of the object, and a VSI ("Vector Space Identifier") which uniquely identifies the kind of object which is represented by the feature vector. Feature vectors of VRDs with the same name are directly comparable using a given metric. At this similarities of the objects' data are mapped to spatial similarities of the feature vectors. So similarity search is possible by calculating distances: Objects are the more similar, the smaller the distance between the feature vectors of the representing VRDs is. This vectorial (numeric) similarity search could be efficiently combined with conventional word based search. We describe this here and give some examples.
Analysis of Twin with Mysterious Boundaries Using the Alternating Direction Scheme

: Recently, a normalized image prior was proposed so that theglobal minimum would not correspond to the blurred image . Multi-resolution approaches, which avoid some local minima, wererecently proposed. Good local minima can alsobe found by usingcontinuationschemes, where the regularizing parameter is gradually decreased . A recent come within reach of although not requiring previous in arrange on the blurring siftachieves high-tech recital for a wide range of real-worldSID tribulations.In this paper, we improve upon the method of . Wefully embrace the UBC, without an increase in computational cost, due to theway in which we use thealternating direction method of multipliersto solve the minimizations required by that method . We propose a new version of that technique in whichboth the optimization tribulations with respect to the unknown imageand with respect to the anonymous blur are solved by theirregulardirection technique of multipliers– an optimization tool thathas recently sparked much interest for solving inverse problems,namely owing to its modularity and state-of-the-art speed. Furthermore, the convolution operator is itself typically ill-conditioned, making the inverse problem extremely sensitive to inaccurate filter estimates and to the presence of noise. The results are shown in MATLB Platform effectively.
Strength for Today and Bright Hope for Tomorrow

The present work is based on an academic investigation conducted in the university community of Cape Coast, Ghana. The research target population comprised: students, lecturers and the non-academic staff. The study focused on the effect of inter-personal relationship on code choice. It also looked at the nature of the linguistic situation and the kinds of code choice in the university. A sociolinguistic approach was employed in the research and the investigation was conducted within the framework of ethnography of speaking and sociology of language. The study also adopted the ethnographic research design and the instruments used for data collection were: observation, questionnaire and interview. The results of the research revealed that the inter-personal relationship between interlocutors defined by age, sex, rank, status, religious affiliation, marital status, level of education and ethnicity affected code choice in discourse situations.
A U NIFIED FRAMEWORK FOR RANDOMIZED SMOOTHING BASED CERTIFIED DEFENSES

Randomized smoothing, which was recently proved to be a certified defensive technique, has received considerable attention due to its scalability to large datasets and neural networks. However, several important questions still remain unanswered in the existing frameworks, such as (i) whether Gaussian mechanism is an optimal choice for certifying `2-normed robustness, and (ii) whether randomized smoothing can certify `∞-normed robustness (on high-dimensional datasets like ImageNet). To answer these questions, we introduce a unified and selfcontained framework to study randomized smoothing-based certified defenses, where we mainly focus on the two most popular norms in adversarial machine learning, i.e., `2 and `∞ norm. We answer the above two questions by first demonstrating that Gaussian mechanism and Exponential mechanism are the (near) optimal options to certify the `2 and `∞-normed robustness. We further show that the largest `∞ radius certified by randomized smoothing is upper bounded by O(1/ √ d), where d is the dimensionality of the data. This theoretical finding suggests that certifying `∞-normed robustness by randomized smoothing may not be scalable to high-dimensional data. The veracity of our framework and analysis is verified by extensive evaluations on CIFAR10 and ImageNet.
Numerical Solution of Linear and Nonlinear Integral and Integro-Differential Equations using Biorthogonal Spline Wavelet Transform Method

Biorthogonal spline wavelet method is proposed for the numerical solution of Linear and nonlinear integral and integrodifferential equations. Biorthogonal spline wavelet filter coefficients have the prolongation and restriction operators. The performance of the proposed method is better than the existing ones in terms of super convergence with low computational time. Some of the test problems are demonstrated for the applicability and efficiency of the scheme. MSC: 65D30, 45B05.
Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming

Probabilistic logics, especially those based on logic programming (LP), are gaining popularity as modelling and reasoning tools, since they combine the power of logic to represent knowledge with the ability of probability theory to deal with uncertainty. In this paper, we propose a hybrid extension for probabilistic logic programming, which allows for exact inference for a much wider class of continuous distributions than existing extensions. At the same time, our extension allows one to compute approximations with bounded and arbitrarily small error. We propose a novel anytime algorithm exploiting the logical and continuous structure of distributions and experimentally show that our algorithm is, for typical relational problems, competitive with state-of-the-art sampling algorithms and outperforms them by far if rare events with deterministic structure are provided as evidence, despite the fact that it provides much stronger guarantees.
BRAIN TUMOUR AND CEREBRO SPINAL FLUID DYNAMICS

The project aims at understanding the flow dynamics of the cerebrospinal fluid flowing through the tumor affected region and sub-arachnoids space through computational fluid dynamics. This methodology is proposed by using MIMICS software in combinational analysis with ANSYS. This study will be useful to differentiate cancer affected brain from normal brain and can help for surgery planning and other possible chemotherapeutic drug delivery.
An HPLC method for estimation of volatile fatty acids in ruminal fluid

A simple liquid chromatographic method was developed for the estimation of volatile fatty acids in ruminal fluid. The procedure involved clarification of the fluid with stannous chloride and estimation of acetic, propionic and butyric acids by HPLC using a reversed-phase column, phosphate buffer pH 2.5 eluent and UV spectrophotometric detector. The overall recovery was found to be 84.55± 7.10% for acetic acid, 70.55±0.65 % tor propionic acid and 88.46±2.98% for butyric acid. A minimum of 1.4 mM acetic acid and 0.56 mM each of propionic and butyric acids could be estimated by the method.
Speechdat multilingual speech databases for teleservices: across the finish line

The goal of the SpeechDat project is to develop spoken language resources for speech recognisers suited to realise voice driven teleservices. SpeechDat created speech databases for all official languages of the European Union and some major dialectal varieties and minority languages. The size of the databases ranges between 500 and 5000 speakers. In total 20 databases are recorded over the fixed telephone network, 5 databases over the cellular network, and 3 databases are designed for speaker verification. To date the project has successfully reached its end. This paper briefly describes the project, addresses the validation of the databases, their availability to consortium members and third parties, publicity and awareness, and the spin-off of the project in speech recognition research.
Influence de stress oxydatifs sur la biosynthèse de mycotoxines de Fusarium spp. Contaminantes de l'épi de maïs

Fusarium est un champignon responsable de la fusariose, maladie necrosante observee chez de nombreuses cereales et le mais. Certaines especes fusariennes sont egalement productrices de mycotoxines, metabolites secondaires extremement stables dont les toxicites pour l'homme et les animaux sont largement decrites. Ces biosyntheses interviennent avant la recolte et entrainent la contamination des grains de cereales. A l'heure actuelle, il n'existe pas de procede permettant d'eliminer les mycotoxines ni meme de reduire leurs toxicites. Ainsi, limiter l'occurrence des mycotoxines sur les grains de cereales implique de limiter leurs productions au champ. Parmi les facteurs susceptibles de moduler les productions de toxines par Fusarium, la nature du substrat, dans notre cas les composes du grain de mais, pourrait etre determinante. De facon plus precise, les molecules pro ou anti-oxydantes impliquees dans les mecanismes de defense de la plante en reponse a l'attaque par un pathogene sont-elles susceptibles d'influencer la biosynthese de mycotoxines ? Le role central et ubiquitaire de H2O2 lors de la mise en place des mecanismes de defense de la plante nous a conduit a detailler les effets potentiels in vitro de H2O2 sur la production de toxines de la famille des trichothecenes de type B (TCTB) par Fusarium graminearum et Fusarium culmorum. Les effets d'autres composes pro- ou anti-oxydants impliques ou non dans les mecanismes de defense de la plante ont egalement ete testes. Apres une phase de mise au point methodologique, l'ensemble de nos resultats a permis de mettre en evidence l'existence d'un lien important entre la biosynthese de TCTB et le metabolisme oxydatif fongique. De plus, il semblerait que la nature de ce lien depende du type de TCTB considere. Une approche transcriptomique locale sur des genes Tri a permis de conclure a l'implication de regulations transcriptionnelles dans les modulations de la production de TCTB en conditions de stress oxydatifs. Une approche transcriptomique globale devrait, a terme, permettre d'identifier des voies metaboliques liees a celle de la production de toxines.
MySQL replication

This is the MySQL Replication extract from the MySQL 5.6 Reference Manual. For legal information, see the Legal Notices. For help with using MySQL, please visit the MySQL Forums, where you can discuss your issues with other MySQL users. Document generated on: 2019-10-21 (revision: 63901)
Balancing SoNaR: IPR versus Processing Issues in a 500-Million-Word Written Dutch Reference Corpus

In The Low Countries, a major reference corpus for written Dutch is beingbuilt. We discuss the interplay between data acquisition and data processingduring the creation of the SoNaR Corpus. Based on developments in traditionalcorpus compiling and new web harvesting approaches, SoNaR is designed tocontain 500 million words, balanced over 36 text types including bothtraditional and new media texts. Beside its balanced design, every text sampleincluded in SoNaR will have its IPR issues settled to the largest extentpossible. This data collection task presents many challenges because everydecision taken on the level of text acquisition has ramifications for the levelof processing and the general usability of the corpus. As far as thetraditional text types are concerned, each text brings its own processingrequirements and issues. For new media texts - SMS, chat - the problem is evenmore complex, issues such as anonimity, recognizability and citation right, allpresent problems that have to be tackled. The solutions actually lead to thecreation of two corpora: a gigaword SoNaR, IPR-cleared for research purposes,and the smaller - of commissioned size - more privacy compliant SoNaR,IPR-cleared for commercial purposes as well.
Sufficient Initial Conditions on a Boundary Value Problem for the Spherical Inverted Pendulum

This paper presents some preliminary results concerning a new foot placement algorithm for dynamically stable legged locomotion. For this, the nonlinear model of a particular spherical inverted pendulum is derived, using D’ Alembert's principle. A set of conditions is obtained, for the initial angles of the pendulum, such that a certain convenient trajectory of the center of mass is assured for a given initial linear velocity of the center of mass. Finally, a simple algorithm for finding an initial point is proposed.
The JHU Machine Translation Systems for WMT 2017

This paper describes the Johns Hop-kins University submissions to the shared translation task of EMNLP 2017 Second Conference on Machine Translation (WMT 2017). We set up phrase-based, syntax-based and/or neural machine translation systems for all 14 language pairs of this year’s evaluation campaign. We also performed neural rescoring of phrase-based systems for English-Turkish and English-Finnish.
End-to-End Active Learning for Computer Security Experts

Labelling a dataset for supervised learning is particularly expensive in computer security as expert knowledge is required for annotation. Some research works rely on active learning to reduce the labelling cost, but they often assimilate annotators to mere oracles providing ground-truth labels. Most of them completely overlook the user experience while active learning is an interactive procedure. In this paper, we introduce an end-to-end active learning system, ILAB, tailored to the needs of computer security experts. We have designed the active learning strategy and the user interface jointly to effectively reduce the annotation effort. Our user experiments show that ILAB is an efficient active learning system that computer security experts can deploy in real-world annotation projects.
Why do deep convolutional networks generalize so poorly to small image transformations?

Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to small image transformations: either because of the convolutional architecture or because they were trained using data augmentation. Recently, several authors have shown that this is not the case: small translations or rescalings of the input image can drastically change the network's prediction. In this paper, we quantify this phenomena and ask why neither the convolutional architecture nor data augmentation are sufficient to achieve the desired invariance. Specifically, we show that the convolutional architecture does not give invariance since architectures ignore the classical sampling theorem, and data augmentation does not give invariance because the CNNs learn to be invariant to transformations only for images that are very similar to typical images from the training set. We discuss two possible solutions to this problem: (1) antialiasing the intermediate representations and (2) increasing data augmentation and show that they provide only a partial solution at best. Taken together, our results indicate that the problem of insuring invariance to small image transformations in neural networks while preserving high accuracy remains unsolved.
Automatic Test Smell Detection Using Information Retrieval Techniques

Software testing is a key activity to control the reliability of production code. Unfortunately, the effectiveness of test cases can be threatened by the presence of faults. Recent work showed that static indicators can be exploited to identify test-related issues. In particular test smells, i.e., sub-optimal design choices applied by developers when implementing test cases, have been shown to be related to test case effectiveness. While some approaches for the automatic detection of test smells have been proposed so far, they generally suffer of poor performance: as a consequence, current detectors cannot properly provide support to developers when diagnosing the quality of test cases. In this paper, we aim at making a step ahead toward the automated detection of test smells by devising a novel textual-based detector, coined TASTE (Textual AnalySis for Test smEll detection), with the aim of evaluating the usefulness of textual analysis for detecting three test smell types, General Fixture, Eager Test, and Lack of Cohesion of Methods. We evaluate TASTE in an empirical study that involves a manually-built dataset composed of 494 test smell instances belonging to 12 software projects, comparing the capabilities of our detector with those of two code metrics-based techniques proposed by Van Rompaey et al. and Greiler et al. Our results show that the structural-based detection applied by existing approaches cannot identify most of the test smells in our dataset, while TASTE is up to 44% more effective. Finally, we find that textual and structural approaches can identify different sets of test smells, thereby indicating complementarity.
Capacity-aware Sequential Recommendations

Personalized recommendations are increasingly important to engage users and guide them through large systems, for example when recommending points of interest to tourists visiting a popular city. To maximize long-term user experience, the system should consider issuing recommendations sequentially, since by observing the user's response to a recommendation, the system can update its estimate of the user's (latent) interests. However, as traditional recommender systems target individuals, their effect on a collective of users can unintentionally overload capacity. Therefore, recommender systems should not only consider the users' interests, but also the effect of recommendations on the available capacity. The structure in such a constrained, multi-agent, partially observable decision problem can be exploited by a novel belief-space sampling algorithm which bounds the size of the state space by a limit on regret. By exploiting the stationary structure of the problem, our algorithm is significantly more scalable than existing approximate solvers. Moreover, by explicitly considering the information value of actions, this algorithm significantly improves the quality of recommendations over an extension of posterior sampling reinforcement learning to the constrained multi-agent case. We show how to decouple constraint satisfaction from sequential recommendation policies, resulting in algorithms which issue recommendations to thousands of agents while respecting constraints.
The Association Between Physical Violence Experience and 
Post-Traumatic Stress Disorder in Emergency Department Nurses: The Moderating Role of Isolation

The present study aimed to investigate the moderating role of isolation in the relationship between physical violence experience and post-traumatic stress disorder in emergency department nurses. Physical violence experience, post-traumatic stress disorder, and isolation of self-compassion were measured using a structured questionnaire among 96 nurses in the emergency departments located in D and G cities in South Korea from June to July of 2017. The collected data were analyzed using IBM SPSS Statistics 20.0 and SPSS PROCESS Macro. The results showed that isolation was significantly and positively correlated with post-traumatic stress disorder. The explanatory power for post-traumatic stress disorder in emergency department nurses increased when the interaction between isolation and physical violence experience was entered. The high-isolation group showed higher increase in post-traumatic stress disorder than the low-isolation group. Isolation was found to be the moderator that increased the negative influence of physical violence experience on post-traumatic stress disorder. The findings suggest that consideration should be given to the fact that the effect of physical violence experience on post-traumatic stress disorder varies depending on the level of isolation of emergency department nurses when developing intervention programs for post-traumatic stress disorder in emergency department nurses.
Identifying Passive Message Fingerprint Attacks via Honey Challenge in Collaborative Intrusion Detection Networks

To enhance the detection capability of a single intrusion detection system (IDS), collaborative intrusion detection networks (CIDNs) have been exploited and developed via enabling a set of IDS nodes to exchange information with each other. In CIDNs, challenge-based trust mechanism has been considered as one promising solution to identify malicious nodes by evaluating the satisfaction levels between challenges and responses. However, such mechanism is still vulnerable to some advanced insider attacks like passive message fingerprint attack (PMFA), which is deemed as an advanced attack on challenge-based CIDNs by collecting messages and identifying normal requests in a passive way. In this work, we focus on PMFA and design Honey Challenge, an improved challenge mechanism for challenge-based CIDNs characterized by sending challenges in a similar way of sending normal requests, in such a way malicious nodes cannot accurately identify the normal requests. In the evaluation, we investigate the attack performance under both simulated and real network environments. Experimental results demonstrate that our proposed mechanism can identify malicious nodes under PMFA and decrease their trust values in a quick manner.
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are extremely computationally demanding, presenting a large barrier to their deployment on resource-constrained devices. Since such systems are where some of their most useful applications lie (e.g. obstacle detection for mobile robots, vision-based medical assistive technology), significant bodies of work from both machine learning and systems communities have attempted to provide optimisations that will make CNNs available to edge devices. In this paper we unify the two viewpoints in a Deep Learning Inference Stack and take an across-stack approach by implementing and evaluating the most common neural network compression techniques (weight pruning, channel pruning, and quantisation) and optimising their parallel execution with a range of programming approaches (OpenMP, OpenCL) and hardware architectures (CPU, GPU). We provide comprehensive Pareto curves to instruct trade-offs under constraints of accuracy, execution time, and memory space.