Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms

A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algorithms, it limits applications to very specific classes of systems. We introduce a novel nonparametric adaptive algorithm that learns an infinite-dimensional parameter density to cancel an unknown disturbance in a reproducing kernel Hilbert space. Surprisingly, the resulting control input admits an analytical expression that enables its implementation despite its underlying infinite-dimensional structure. While this adaptive input is rich and expressive – subsuming, for example, traditional linear parameterizations – its computational complexity grows linearly with time, making it comparatively more expensive than its parametric counterparts. Leveraging the theory of random Fourier features, we provide an efficient randomized implementation which recovers the computational complexity of classical parametric methods while provably retaining the expressiveness of the nonparametric input. In particular, our explicit bounds only depend polynomially on the underlying parameters of the system, allowing our proposed algorithms to efficiently scale to high-dimensional systems. As an illustration of the method, we demonstrate the ability of the algorithm to learn a predictive model for a 60-dimensional system consisting of ten point masses interacting through Newtonian gravitation.
Design optimisation of electric motors by genetic algorithms

A new approach to the design optimisation of three-phase electric motors is presented. The novelty lies in combining a motor analysis procedure with a genetic algorithm to optimise an objective function such as the motor torque, the efficiency, the material cost or some other motor figures or a combination of these. Two approaches to the motor analysis are presented: the first is based on a fully analytical procedure, while the second utilises the finite element method. Some optimised design examples, with reference to surface-mounted permanent-magnet three-phase motors, are included, and a comparison between the genetic algorithm optimisation technique and the classical hill-climbing direct-search method is made. A discussion on the limitations and benefits of the proposed design approach concludes the paper.
Using Word Embeddings Based on Psychological Studies of Fairness to Develop a Fairness Metric for Texts

Fairness is a principal social value that can be observed in civilisations around the world. A manifestation of this is in social agreements, often described in texts, such as contracts. Yet, despite the prevalence of such, a fairness metric for texts describing a social act remains wanting. To address this, we take a step back to consider the problem based on first principals. Instead of using rules or templates, we utilise social psychology literature to determine the principal factors that humans use when making a fairness assessment. We then attempt to digitise these using word embeddings into a multi-dimensioned sentence level fairness perceptions vector to serve as an approximation for these fairness perceptions. The method leverages a pro-social bias within word embeddings, for which we obtain an F1= 81.0. A second approach, using PCA and ML based on the said fairness approximation vector produces an F1 score of 86.2. We detail improvements that can be made in the methodology to incorporate the projection of sentence embedding on to a subspace representation of fairness.
Modeling of Iron Losses of Permanent-Magnet

Permanent-magnet (PM) motors offer potential en- ergy savings as compared with induction motors because of the vir- tual elimination of rotor loss and the reduction of stator loss from operation near unity power factor. In PM machines, iron losses form a significant fraction of the total loss partly due to the non- sinusoidal flux density distribution. Design optimization therefore requires good means of predicting these iron losses. Finite-element analysis can be employed but this approach is cumbersome and costly when used in the many iterations needed in optimizing the design. This paper presents a set of improved approximate models for the prediction of iron loss. They can be used in design optimiza- tion programs and, since they are directly related to machine di- mensions and material properties, they also provide quick insight into the effects of design changes. A time-stepped finite-element method is employed to evaluate the iron losses in a range of typ- ical PM machines and the results are used to evaluate the adequacy of the models. The predictions of overall iron losses are then com- pared with measurements made on two PM motors. tions. This simple analytical iron-loss model has been used by a few authors to determine the iron losses of PM synchronous motors (4)-(8). While the overall application of these approxi- mations produced an acceptable prediction of the total measured losses in an experimental machine, the validity of each approx- imation remained in doubt. In this paper, these approximations are examined in turn and their results are compared with those obtained by finite-element analysis.
Heroin addiction hijacks the Nucleus Accumbens: craving and reactivity to naturalistic stimuli

Drug-related cues hijack attention away from alternative reinforcers in drug addiction, inducing craving and motivating drug-seeking. However, the neural correlates underlying this biased processing, its expression in the real-world, and its relationship to cue-induced craving are not fully established, especially in opioid addiction. Here we tracked inter-brain synchronization in the Nucleus Accumbens (NAc), a hub of motivational salience, while heroin-addicted individuals and healthy control subjects watched the same engaging heroin-related movie. Strikingly, the left NAc was synchronized during drug scenes in the addicted individuals and non-drug scenes in controls, predicting scene- and movie-induced heroin craving in the former. Our results open a window into the neurobiology underlying shared drug-biased processing of naturalistic stimuli and cue-induced craving in opiate addiction as they unfold in the real world.
Social isolation and the aging brain. Social isolation is linked to declining grey matter structure and cognitive functions in the LIFE-Adult panel study

Background Social isolation is a risk factor for dementia, a devastating disease with a rapidly growing global prevalence. However, the link between social isolation and changes in brain structure and function is poorly understood, as studies are scarce in number, methodologically inconsistent and small in size. In this pre-registered analysis of a large population-based panel study, we aimed to determine the impact of social isolation on brain structures and cognitive functions central to age associated decline and dementia. Methods and findings We analysed data of 1992 cognitively healthy participants of the LIFE-Adult study at baseline (age range: 50-82 years) and of 1409 particpants at follow-up (average change in age: 5.89 years). We measured social isolation using the 30-point Lubben Social Network Scale (LSNS) and derived measures of grey matter structure from anatomical 3T MRIs. We employed covariate adjusted linear mixed models to test the associations of baseline social isolation and change in social isolation with hippocampal volume, cognitive functions (executive functions, memory, processing speed) and cortical thickness. We found stronger baseline social isolation to be significantly associated with smaller hippocampal volumes ({beta} = -5.5 mm3/LSNS point(pt), FDR q = 0.004, BF = 14.6) and lower cognitive functions (all {beta} < -0.014 SD/pt, FDR q < 0.003, BF > 49). Increases in social isolation over time were linked to hippocampal volume decline ({beta} = -4.9 mm3/pt, FDR q = 0.01, BF = 2.9) and worse memory performance ({beta} = -0.013 SD/pt, FDR q = 0.04, BF = 1.1). Furthermore, we detected a significant interaction of baseline social isolation with change in age on hippocampal volume ({beta} = -0.556 mm3/pt*a, q = 0.04, BF = 0.5), indicating accelerated brain aging in more isolated individuals. Moreover, social isolation cross-sectionally and longitudinally correlated with lower cortical thickness in multiple clusters in the orbitofrontal cortex, precuneus and other areas (FDR q < 0.05). Conclusions Here, we provide evidence that social isolation contributes to hippocampal and cortical atrophy and subtle cognitive decline in non-demented mid- to late-life adults. Importantly, within-subject effects of social isolation were similar to between-subject effects, indicating an opportunity for targeting social isolation to reduce dementia risk.
Minimax strategies for Bernoulli two-armed bandit on a moderate control horizon

ABSTRACT We consider a Bernoulli two-armed bandit problem on a moderate control horizon as applied to optimization of processing moderate amounts of data if there are two processing methods available with different a priori unknown efficiencies. One has to determine the most effective method and provide its predominant application. In contrast to big data processing for which several approaches have been developed, including batch processing, the optimization of moderate data processing is currently not well understood. We consider minimax approach and search for minimax strategy and minimax risk as Bayesian ones corresponding to the worst-case prior distribution for which Bayesian risk attains its maximal value. Close to the worst-case prior distribution and corresponding Bayesian risk are obtained by numerical methods. Calculations show that determined strategy provides the value of maximal regret close to determined Bayesian risk and, hence, is approximately minimax one. Results can be applied to big data processing if the data arises by batches of moderate size with approximately uniform properties.
An Adaptable and Modular Set of Laboratory Exercises Connecting Genotype to Phenotype in Sporulating Bacillus subtilis

Practical lab exercises that help students draw connections between genotype and phenotype, and make and test predictions about the identity of mutants, are invaluable in college-level cell biology, genetics, and microbiology courses. While many bacteria are easy to grow and manipulate within the time and resource constraints of a laboratory course, their phenotypes are not always observable or relevant-seeming to college students. ABSTRACT Practical lab exercises that help students draw connections between genotype and phenotype, and make and test predictions about the identity of mutants, are invaluable in college-level cell biology, genetics, and microbiology courses. While many bacteria are easy to grow and manipulate within the time and resource constraints of a laboratory course, their phenotypes are not always observable or relevant-seeming to college students. Here, we leverage sporulation by the bacterium Bacillus subtilis, a well-characterized and genetically tractable system, to create 5 adaptable lab exercises that can be implemented in different combinations to suit the needs of a variety of courses and instruction modes. Because phenotypic changes during sporulation are striking morphological changes to cells that are easily observable with basic light microscopy, and because spore-forming bacteria related to B. subtilis have clear applications for human and environmental health, these exercises have the potential to engage students’ interest while introducing and reinforcing key concepts in microbiology, cell biology, and genetics.
Bacteriophage-Mediated Modulation of Bacterial Competition during Selective Enrichment of Campylobacter

Phages are promising antimicrobial alternatives. In this study, we first demonstrated that phages can be used to facilitate selective isolation of fastidious bacteria that are prone to be outgrown by bacterial competitors during isolation. ABSTRACT Selective media using antimicrobial supplements generate unique microbial ecology to facilitate bacterial isolation. However, antibiotic-resistant bacteria indigenous to samples can interfere with the isolation process using selective media. Recent studies showed that extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli is highly prevalent on retail raw chicken and compromises the efficacy of Campylobacter isolation because ESBL-producing E. coli are resistant to antimicrobial supplements in Campylobacter-selective media and outgrows Campylobacter. The objective of this study was to improve Campylobacter isolation by inhibiting the growth of ESBL-producing E. coli using bacteriophages (phages). The supplementation of Campylobacter-selective media with E. coli phages reduced the level of ESBL-producing E. coli during the enrichment step. When E. coli phages were combined with the antimicrobial supplements of Campylobacter-selective media, antimicrobial synergy was observed, particularly with rifampicin, an antibiotic used in Preston medium. Although the same materials (i.e., phages and selective media) were used, the sequence of combining the materials markedly influenced the inhibition of ESBL-producing E. coli and the isolation of Campylobacter. These findings indicated that the modulation of microbial competition at the enrichment step was critical to the successful isolation of fastidious bacteria and that phages can be utilized to facilitate the selective enrichment of target bacteria by inhibiting their competitive bacteria. IMPORTANCE Phages are promising antimicrobial alternatives. In this study, we first demonstrated that phages can be used to facilitate selective isolation of fastidious bacteria that are prone to be outgrown by bacterial competitors during isolation. The effectiveness of a phage-based isolation method was primarily dependent on the antimicrobial synergy between phages and antibiotics used in selective media. The same approach could be applied to the development of isolation methods for other fastidious bacteria.
Calculation of the absorption efficiency of double‐clad fiber by using the modal analysis method

In this paper, we present a new method, based on the theory of mode analysis, to calculate the absorption efficiency of symmetric or offset double‐clad optical fibers. Both symmetric and offset conditions have been analyzed. In principle, our results are consistent with, but more accurate than, those of previous ray‐optics methods. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 111–113, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10839
Otolaryngology‐head and neck surgery patient information leaflets produced by the Royal Australasian College of Surgeons: Time for a re‐think?

Brooke Nickel: Conceptualization; data curation; formal analysis; methodology; project administration; writing original draft. Julie Miller: Conceptualization; methodology; project administration; resources; writing-review & editing. Erin Cvejic: Data curation; formal analysis; methodology; writing-review & editing. Matti Gild: Methodology; project administration; resources; writingreview & editing. Daron Cope: Data curation; methodology; writing-review & editing. Rachael Dodd: Data curation; formal analysis; methodology; writing-review & editing. Kirsten McCaffery: Conceptualization; supervision; writing-review & editing. Anthony Glover: Conceptualization; formal analysis; methodology; project administration; resources; supervision; writing-review & editing.
Unsupervised Domain Adaptation With Dense-Based Compaction for Hyperspectral Imagery

Enormously hard work of label obtaining leads to the lack of enough annotated samples in the hyperspectral imagery (HSI). The mentioned reality inferred the unsupervised classification performance barely satisfactorily. Unsupervised domain adaptation is exploited for knowledge delivery from a labeled source domain to boost the performance on an unlabeled target domain. In this article, we propose an unsupervised domain adaptation architecture with dense-based compaction (UDAD) for HSI classification (HSIC). The processes of spectral–spatial feature compaction, unsupervised domain adaptation, and classifier training are incorporated with an integrated framework to complete the HSI cross-scene classification. The core of the proposed framework is to utilize adversarial domain learning to reduce the domain discrepancy. To this end, the classifier trained in the source domain would accomplish well in the target domain for the unsupervised HSIC. Besides, to extract the discriminative spectral–spatial feature for the HSI domains, a dense-based compaction network is applied to complete the semisymmetric mapping. Our experiments illustrate that the UDAD model yields more effective classification performance than other state-of-the-art unsupervised HSIC methods.
A Manuscript Style Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Software Engineering

NAKANO, HOZUMI, “A methodology of software reuse”, Master of Software Engineering, May 2006, (Dr. Kasi Periyasamy and Dr. Mao Zheng). Software reuse has been practiced in industries with or without clear identification of reuse. In most cases, software reuse process uses design and code components, particularly the latter. From the literature survey, it was observed that there is no software reuse methodology that can be applied throughout the software life cycle. It was therefore decided to develop a reuse methodology that can be applied to the entire software life cycle. This research started by observing two pairs of industry software products one reusing the other. The reusability score was computed in each case and was confirmed to be satisfactory by the development team. Based on the results, a reuse methodology that can be applied to the entire software life cycle was proposed. The methodology gives a set of guidelines for a formal reuse process. This process assumes that the requirements for the new product to be developed are written first, and then the reusable software components can be searched in a reuse repository. This repository is assumed to include all components of existing products such as requirements, design, code, test cases and so on. The proposed methodology guides developers to identify reusable software artifacts and tailor them toward the new product.
Monte Carlo simulation of short channel heterostructure field-effect transistors

Self-consistent Monte Carlo simulation was used to study self-aligned, planar-doped AlGaAs-GaAs heterostructure field-effect transistors (HFETs) with gate lengths varying from 0.1 to 1.0 mu m and two different depths of the implanted contacts. The drain output conductance in saturation as well as the threshold voltage shift are found to be approximately inversely proportional to the square of the gate length for gate lengths smaller than 0.5 mu m. The predominant physical mechanism behind these short-channel effects at such gate lengths is the injection of electrons from the contacts into the GaAs buffer region beneath the two-dimensional channel. The critical parameter for the onset of large short-channel effects is the ratio between the source-region-drain-region separation and the contact depth. Hence, an optimum depth of the contacts should be found as a tradeoff between short-channel effects and parasitic series resistances. Simulated current-voltage characteristics exhibit pronounced negative differential resistance at large gate voltages because of real space transfer of channel elections into the AlGaAs layer and subsequent collection by the gate electrode. Simulated on and off transients have similar durations, but a trend toward shorter switch-off times exists. >
Proposals of European Citizens for Reviving the Future of Shrinking Areas

Abstract Shrinkage, depopulation and the related structural decline threaten development trajectories of more than a quarter of European territories from the present until 2050. In April 2021, the European Commission has launched the Conference on the Future of Europe to involve citizens and players beyond the traditional actors in shaping future policy agendas. The initiative consists of a wide-scale citizen engagement policy offering them a digital framework to actively contribute to the most relevant debates from April to December 2021. Given that shrinkage is a neglected theme in traditional policy arenas, this article examines the proposals of European citizens for reviving the future of shrinking areas. Through content analysis, the article highlights a limited relative presence of shrinkage in the Conference debate. Nevertheless, the results offer insights into the thematic concentration and the affinity of shrinkage with the most popular policy debates. The article also discusses the content of citizens’ ideas for the future of shrinking areas, thus offering concrete proposals that may fuel the definition of future policy agendas.
FULL-WAVE ANALYSIS OF THE EFFECTIVE RELATIVE PERMITTIVITY OF A COPLANAR WAVEGUIDE PRINTED INSIDE A CYLINDRICAL SUBSTRATE

The frequency-dependent dispersion characteristics of a coplanar wave-guide printed inside a cylindrical substrate are studied. A full-wave formulation incorporating the derived dyadic Green's function in a moment-method calculation is applied. The theoretical results are calculated and verified by the measurement, and the curvature effects on the dispersion characteristics of an inside cylindrical coplanar waveguide are discussed. © 1996 John Wiley & Sons, Inc.
Structural, antimicrobial, and molecular docking study of 3-(1-(4-hydroxyphenyl)amino) ethylidene)chroman-2,4-dione and its corresponding Pd complex

Synthetic coumarins are a class of compounds with promising biological functions. The structure of the 4-hydroxycoumarin derivative, 3-(1-(4-hydroxyphenyl)amino) ethylidene) chroman-2,4-dione, and its corresponding Pd complex, are described in this contribution. Inter- and intramolecular interactions governing stability were analyzed by the Natural Bond Orbital and Quantum Theory of Atoms in Molecules approaches. The antimicrobial activity towards several microorganisms was assessed and differences in activity towards Escherichia coli DNA Gyrase B enzyme were investigated by molecular docking.
Special Issue of the 1st International Applied Bioinformatics Conference (iABC'21)

Diseases can be tied to changes at the molecular level within affected cells. This can be concerning transcription, translation, or any other mechanism involved in gene expression, such as post-transcriptional regulation. Instrumentation for the measurement of such molecular changes is readily available and produces large amounts of data. For example, DNA and RNA sequencing, as well as protein quantitation, and sequencing can be achieved via next-generation sequencing and mass spectrometry, respectively. One current challenge is the analysis and integration of the resulting heterogeneous and large datasets. Bioinformatics is the field of study which produces algorithms and integrative approaches to attempt such data analyses. The primary aim in algorithmic bioinformatics is, however, the development of algorithms and not their application. Typically, novel algorithms are introduced with a proof of principle, and they are applied to some data for that purpose, but usually not comprehensively. Their data might slightly differ from the proof of principle, inducing further data analysis challenges. Additionally, applying such algorithms to their data may be involved for researchers from the biomedical domain.