Miracle-dz/UniFAR-FTUs huggingface dataset:
C FINITE ELEMENT APPROXIMATIONS OF LINEAR ELLIPTIC EQUATIONS IN NON-DIVERGENCE FORM AND HAMILTON-JACOBI-BELLMAN EQUATIONS WITH CORDES

Abstract. This paper is concerned with C0 finite element approximations of the linear elliptic equations in non-divergence form and the Hamilton-Jacobi-Bellman (HJB) equations with Cordes coefficients. Motivated by the Miranda-Talenti estimate, a discrete analog is proved once the finite element space is C0 on the (n − 1)-dimensional subsimplex (face) and C1 on (n − 2)-dimensional subsimplex. The main novelty of the non-standard finite element methods is to introduce an interior penalty term to argument the PDE-induced variational form of the linear elliptic equations in non-divergence form or the HJB equations. As a distinctive feature of the proposed methods, no penalization or stabilization parameter is involved in the variational forms. As a consequence, the coercivity constant (resp. monotonicity constant) for the linear elliptic equations in non-divergence form (resp. the HJB equations) at discrete level is exactly the same as that from PDE theory. Numerical experiments are provided to validate the convergence theory and to illustrate the accuracy and computational efficiency of the proposed methods.
EVALUATION OF IN VIVO AND IN VITRO ANTI-INFLAMMATORY ACTIVITY OF NOVEL ISOXAZOLE SERIES

Many Isoxazole derivatives are stated to have good anti-inflammatory activity. Chalcones are prepared by the reaction of aromatic aldehydes with aromatic ketones in aqueous alcoholic alkaline medium. Then these are made to react with hydroxylamine hydrochloride and sodium acetate to prepare isoxazole derivatives. The prepared isoxazole compounds are subjected to inflammatory activity by in-vitro and in-vivo methods. All compounds exhibited anti-inflammatory activity among tested 25 isoxazole derivatives. out of these 25 isoxazole derivatives 7 of them shows significant anti inflammatory activity.
Can Optimal and Stable Portfolios of US Industries Reduce Exposure to Global Currency Risk?

We investigate the extent to which optimal portfolio strategies of US equity assets can reduce or eliminate exposure to global currency risk in an International Asset Pricing Model (IAPM) framework. This is done by constructing global minimum portfolios, with various degrees of stability, diversified across 48 US industries. Our empirical tests are based on a conditional IAPM, augmented with the size and book-to-market factors of Fama and French (1993) and two real trade-weighted currency factors representing industrialized countries and emerging markets currency risks. Consistent with the predictions of IAPMs, we find that the price of currency risk is significant and time-varying for our cross-sections of global minimum portfolios (GMPs). However exposures of the stable GMPs to the currency risk factor are reduced by up to 50 percent compared to the average exposure estimated across individual US industry portfolios. Furthermore, we find that more stable optimal strategies are associated with more pronounced reductions in currency risk exposure and currency premium in absolute terms. The global minimum weights considered here are an alternative to market capitalization weights used in many empirical studies as well as in practice. Therefore, our findings complement the well-established results that currency risk is significantly priced and economically meaningful in equity markets, but offer new evidence on the potential for reducing the level of exposure to global currency risk factors through optimal and stable portfolio strategies which in turn can reduce foreign exchange risk hedging costs.
Analysing concatenation approaches to document-level NMT in two different domains

In this paper, we investigate how different aspects of discourse context affect the performance of recent neural MT systems. We describe two popular datasets covering news and movie subtitles and we provide a thorough analysis of the distribution of various document-level features in their domains. Furthermore, we train a set of context-aware MT models on both datasets and propose a comparative evaluation scheme that contrasts coherent context with artificially scrambled documents and absent context, arguing that the impact of discourse-aware MT models will become visible in this way. Our results show that the models are indeed affected by the manipulation of the test data, providing a different view on document-level translation quality than absolute sentence-level scores.
Improving cloud based encrypted databases.

Databases are a cornerstone for the operation of many services, such as banking, web stores and even health care. The cost of maintaining such a large collection of data on-premise is high, and the cloud can be used to share computational resources and mitigate this problem. Unfortunately, a great amount of data may be private or conﬁdential, thus requiring to be protected from both internal and external agents. Moreover, this data needs to be manipulated to provide useful information to its owner. In this dissertation, we propose modiﬁcations to CryptDB, a state-of-the-art encrypted cloud database, aiming to enhance its e ﬃ ciency, ﬂexibility and security; this is accomplished by improving or changing its underlying cryptographic primitives.
Kidney Tumor Segmentation Using Dual Fully Convolutional Networks

Kidney tumor segmentation is a hot topic in medical image analysis. In this work, we propose a concise but tricky method, dual Fully Convolutional Networks(FCN) to segment kidney and its surrounding tumor. First FCN is utilized to coarsely detect location of kidney and tumor, while second FCN is employed for reﬁning results of ﬁrst FCN. In the 2019 Kidney Tumor Segmentation Challenge(KiTS19), 160 patients’ CT scans were used for training and our best result were obtained by this two-step FCN models trained with Focal loss.
Natural Language Generation Challenges for Explainable AI

Good quality explanations of artificial intelligence (XAI) reasoning must be written (and evaluated) for an explanatory purpose, targeted towards their readers, have a good narrative and causal structure, and highlight where uncertainty and data quality affect the AI output. I discuss these challenges from a Natural Language Generation (NLG) perspective, and highlight four specific NLG for XAI research challenges.
Using reinforcement learning in solving exam timetabling problems

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Point Cloud Registration Method for Pipeline Workpieces Based on 4PCS and Improved ICP Algorithms

A new point cloud registration method based on 4PCS and improved ICP algorithm is proposed to solve the problem of point cloud registration of laser scanning workpiece position and pose data on industrial pipeline. Firstly, feature points set of point cloud is extracted from normal vector. Then, the rough registration of point cloud is realized by using the registration algorithm based on feature points (4PCS), and the initial recognition of workpiece position and pose is completed. Finally, based on the traditional ICP algorithm, the k-d tree is used to accelerate the search speed of the corresponding point pairs and complete the accurate registration of the point clouds. The proposed algorithm and the 4PCS+ICP algorithm are tested respectively, and the results show that the algorithm is faster, with lower error and fewer iterations.
A PURE CUMULANT-BASED METHOD WITH LOW COMPUTATIONAL COMPLEXITY FOR CLASSIFICATION AND LOCALIZATION OF MULTIPLE NEAR AND FAR FIELD SOURCES USING A SYMMETRIC ARRAY

The authors propose a new method based on spatial cumulants for estimating the parameters of multiple near-field and far-field sources. The Toeplitz property used in some studies is not applicable to fourth-order statistics to separate sources components. Therefore, in this paper, a method is proposed to compute output cumulants of specified sensors in special arrangements, by which the components of the near-field and the far-field sources are effectively separated using differencing. The angle and range estimations, as well as the classification of the sources, are obtained based on the data from two spatial cumulant matrices. One of them contains the angle information of all sources, and the other only contains the information of near-field sources. The parameters extraction algorithm is based on the ESPRIT technique; therefore, the proposed method does not require any spectral search. This leads to a significant reduction in computational complexity. Unlike some approaches, the proposed method does not suffer from array aperture loss. Also, the parameters pairing procedure is done automatically. Analysis and simulation results confirm the good performance of the proposed method in terms of computational complexity, estimation accuracy, correct classification of signals, and aperture loss.
Optimising the FIT: risk adjusted colorectal cancer screening using routine data

This thesis explores the value of risk-adjusted colorectal cancer screening using the faecal immunochemical test (FIT). Following the FIT pilot study in the English Bowel Cancer Screening Programme (BCSP), there was opportunity to investigate a risk-adjusted approach to screening. This thesis was informed by several evolving areas of research including risk prediction modelling, test accuracy, as well as the use of electronic health records (EHRs) and the statistical methods best applied to utilise these data. The emphasis of the research was on routine data and used the Bowel Cancer Screening System (BCSS) as well as anonymised GP records for model development (THIN – The Health Improvement Network). Three statistical modelling techniques were investigated to build a risk prediction model for use in screening referral based decisions. A conventional approach using logistic regression was investigated first, which showed an improvement in both model performance and test accuracy for the risk-adjusted model over the FIT alone. This model was then extended further by investigating a machine learning algorithm in the form of an artificial neural network. An advantage of this approach is the flexibility to model complex nonlinear associations. The performance of this model (discrimination), as well as the sensitivity when applied as a test, was significantly better than the logistic regression model. Next an anonymised GP record was investigated for additional predictors to add to a risk-adjusted model using survival analysis, which exploits the longitudinal nature of the data. Two models were produced; one which combined the faecal occult blood test (FOBT) with lab test results, symptoms and other predictors and another which was developed for those with negative FOBT results only, to determine whether additional predictors could be used for referral decisions. In order to utilise EHRs for research, the methods need to be reproducible and transparent. An Acceptable Electronic BCSP (AEB) date was developed for quality assurance of the primary care data as well as to help determine a screening cohort for analysis. As a collective, these studies show evidence for improved performance of risk-adjusted screening over using the FIT alone. Future research should focus on further BCSS predictors and external validation of a risk-adjusted model in the BCSP. Machine learning approaches may be better placed for more complex electronic data. Future risk prediction model studies should encompass the whole pathway from model development to external validation and model impact before being implemented in practice with the ultimate aim of improving
Hybrid2: Combining Caching and Migration in Hybrid Memory Systems

This paper considers a hybrid memory system composed of memory technologies with different characteristics; in particular a small, near memory exhibiting high bandwidth, i.e., 3D-stacked DRAM, and a larger, far memory offering capacity at lower bandwidth, i.e., off-chip DRAM. In the past, the near memory of such a system has been used either as a DRAM cache or as part of a flat address space combined with a migration mechanism. Caches and migration offer different tradeoffs (between performance, main memory capacity, data transfer costs, etc.) and share similar challenges related to data-transfer granularity and metadata management. This paper proposes Hybrid2, a new hybrid memory system architecture that combines a DRAM cache with a migration scheme. Hybrid2 does not deny valuable capacity from the memory system because it uses only a small fraction of the near memory as a DRAM cache; 64MB in our experiments. It further leverages the DRAM cache as a staging area to select the data most suitable for migration. Finally, Hybrid2 alleviates the metadata overheads of both DRAM caches and migration using a common mechanism. Using near to far memory ratios of 1:16, 1:8 and 1:4 in our experiments, Hybrid2 on average outperforms current state-of-the-art migration schemes by 7.9%, 9.1% and 6.4%, respectively. In the same system configurations, compared to DRAM caches Hybrid2 gives away on average only 0.3%, 1.2%, and 5.3% of performance offering 5.9%, 12.1%, and 24.6% more main memory capacity, respectively.
LHC probes of TeV-scale scalars in SO(10) grand unification

We investigate the possibility of TeV-scale scalars as low energy remnants arising in the nonsupersymmetric SO(10) grand unification framework where the field content is minimal. We consider a scenario where the SO(10) gauge symmetry is broken into the gauge symmetry of the Standard Model (SM) through multiple stages of symmetry breaking, and a colored and hypercharged scalar χ picks a TeV-scale mass in the process. The last stage of the symmetry breaking occurs at the TeV scale where the left-right symmetry, i.e. SU(2)L⊗ SU(2)R⊗U(1)B−L⊗ SU(3)C , is broken into that of the SM by a singlet scalar field S of mass MS ∼ 1 TeV, which is a component of an SU(2)Rtriplet scalar field, acquiring a TeV-scale vacuum expectation value. For the LHC phenomenology, we consider a scenario where S is produced via gluon-gluon fusion through loop interactions with χ and also decays to a pair of SM gauge bosons through χ in the loop. We find that the parameter space is heavily constrained from the latest LHC data. We use a multivariate analysis to estimate the LHC discovery reach of S into the diphoton channel.
Foodborne antimicrobial resistance as a biological hazard

The European Food Safety Authority (EFSA) asked its Panel on Biological Hazards to 15 identify, from a public health perspective, the extent to which food serves as a source for the 16 acquisition, by humans, of antimicrobial-resistant (AMR) bacteria or bacteria-borne 17 antimicrobial resistance genes, to rank the identified risks and to identify potential control 18 options for reducing exposure. 19
Comparing sat-based bounded model checking rtectl and ectl properties

We compare two SAT-based bounded model checking algorithms for the properties expressed in the existential fragment of a soft real-time computation tree logic (RTECTL) and in the existential fragment of computation tree logic (ECTL). To this end, we use the generic pipeline paradigm (GPP) and the train controller system (TC), the classic concurrency problems, which we formalise by means of a finite transition system. We consider several properties of the problems that can be expressed in both RTECTL and ECTL, and we present the performance evaluation of the mentioned bounded model checking methods by means of the running time and the memory used.
Story Analyzer – an Application using CoreNLP and D3 Visualizations

Story Analyzer is a software application that helps users visualize and understand a story through the use of natural language processing (NLP) and data visualization APIs. Specifically, Story Analyzer uses Stanford’s CoreNLP software library for performing information extraction on a story, and uses D3’s JavaScript visualization API to generate a dashboard of results, much which is shown in a set of interrelated and interactive word clouds. The term “story” refers to a narrative that involves people, groups, organizations, or other entities (subjects) performing actions that can affect other people, organizations, or entities (objects). These events occur in certain places and at certain times, and they may include other contextual features of interest. The term “story analysis” pertains to identifying these key elements of the story (subject, objects, actions, time, place, and other contexts), and moreover to represent the relationships between these elements for each event that takes place in the story. The software described in this paper attempts to visually and interactively answer this question: Who did what to whom, where and when did it happen, and what else was going on at the time?
Leveraging Reviews to Improve User Experience

In this paper, we will explore and present a method of finding characteristics of a restaurant using its reviews through machine learning algorithms. We begin by building models to predict the ratings of individual reviews using text and categorical features. This is to examine the efficacy of the algorithms to the task. Both XGBoost and logistic regression will be examined. With these models, our goal is then to identify key phrases in reviews that are correlated with positive and negative experience. Our analysis makes use of review data publicly made available by Yelp. Key bigrams extracted were non-specific to the restaurants examined, but key tri-grams were specific to restaurants, including menu items. While the models were successful in predicting high-rated reviews, they struggled to identify negative reviews with acceptable accuracy. The method outlined in this paper proved successful in extracting positive trigrams that are highly specific to the restaurants examined, and we propose these phrases be emphasized on Yelp pages to allow users to quickly learn the items of highest quality at a restaurant.
www.econstor.eu R&D Incentives and Market Structure: A Dynamic Analysis

We investigate dynamic R&D for process innovation in an oligopoly where firms invest in cost-reducing activities. We focus on the relationship between R&D intensity and market structure, proving that the industry R&D investment monotonically increases in the number of firms. This Arrowian result contradicts the established wisdom acquired from static games on the same topic. J.E. L. Classification: C73, D43, D92, L13, O31
Intelligent fault diagnosis system based on big data

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.
Computational Selection and Prioritization of Disease Candidate Genes

i Acknowledgements . iv Publications arising from this thesis . vi Table of contents . vii List of figures . x List of tables . xii Chapter 1. Overview. 1 1.1 Motivation. 1 1.2 Contributions of this Thesis . 2 Chapter 2. Systems Biology Based Integrative Approaches to Identify and Prioritize Novel Disease Candidate Genes . 4 2.1 Background . 4 2.1.1 Connecting phenotype with genotype: Disease gene discovery . 4 2.1.2 Traditional Candidate Gene Approach. 7 2.1. 3 Candidate disease gene prediction using protein-protein interactions. 8 2.1. 4 Candidate gene prioritization based on functional annotations . 10 2.2 Current work: improved functional-based and novel network-based prioritization methods. 14 2.3 Limitations of candidate gene prioritization approaches. 16 2.4 Summary . 18 Chapter 3. Discovery and Prioritization of Candidate Genes that Cause or Impact Disease Using an Integrative Genome-Transcriptome-Phenome-Bibliome Approach. 19 3.1 Background . 19 3.2 Materials and methods. 21 3.2.1 Data sources. 21 3.2.2 Pre-processing of annotation terms. 23 3.2.3 Processing of Training Set Genes . 24 3.2.4 Similarity measure. 24 3.2. 5 Processing of Test Set Genes . 26 3.3 Results. 27 3.3.1 Mouse Phenotype as a Feature for Candidate Gene Prioritization . 27
Software Implementations and Applications of Elliptic Curve Cryptography

KULTINOV, KIRILL. M.S.C.S. , Department of Computer Science and Engineering, Wright State University, 2019. SOFTWARE IMPLEMENTATIONS AND APPLICATIONS OF ELLIPTIC CURVE CRYPTOGRAPHY. Elliptic Curve Cryptography (ECC) is a public-key cryptography system. Elliptic Curve Cryptography (ECC) can achieve the same level of security as the public-key cryptography system, RSA, with a much smaller key size. It is a promising public key cryptography system with regard to time efficiency and resource utilization. This thesis focuses on the software implementations of ECC over finite field GF(p) with two distinct implementations of the Big Integer classes using character arrays, and bit sets in C++ programming language. Our implementation works on the ECC curves of the form y = x + ax + b (mod p). The point addition operation and the scalar multiplication are implemented on a real SEC (Standards for Efficient Cryptography) ECC curve over a prime field with two different implementations. The Elliptic Curve Diffie-Hellman key exchange, the ElGamal encryption/decryption system, and the Elliptic Curve Digital Signature Algorithm (ECDSA) on a real SEC ECC curve with two different implementations of the big integer classes are tested, and validated. The performances of the two different implementations are compared and analyzed.
Probabilities of False Alarm and Detection for the First-Order Cyclostationarity Test: Application to Modulation Classification

Automatic modulation classification is one of the most challenging problems of cognitive radio and has significant commercial and military application. This letter focuses on blind modulation classification of first-order cyclostationary signals under a Gaussian channel. A detailed theoretical analysis of the first-order cyclostationarity test has been performed. We present the theoretical expressions of the probability of false alarm <inline-formula> <tex-math notation="LaTeX">$P_{fa}$ </tex-math></inline-formula> and the probability of detection <inline-formula> <tex-math notation="LaTeX">$P_{d}$ </tex-math></inline-formula> as a function of the selected threshold for the first-order cyclostationarity test based constant false alarm rate detector. The <inline-formula> <tex-math notation="LaTeX">$P_{d}$ </tex-math></inline-formula> is presented for only AM and <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-FSK signals, which are the only signals that possess first-order cyclostationary features. Theoretical formulations presented in this letter are validated by extensive simulation results.