Miracle-dz/UniFAR-FTUs huggingface dataset:
Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration

Dissertation submitted in partial fulfillment of the requirements for the academic degree doctor rerum naturalium Report The main contribution of this thesis is the concept of Fenchel duality with a focus on its application in the field of machine learning problems and image restoration tasks. We formulate a general optimization problem for modeling support vector machine tasks and assign a Fenchel dual problem to it, prove weak and strong duality statements as well as necessary and sufficient optimality conditions for that primal-dual pair. In addition, several special instances of the general optimization problem are derived for different choices of loss functions for both the regression and the classification task. The convenience of these approaches is demonstrated by numerically solving several problems. We formulate a general nonsmooth optimization problem and assign a Fenchel dual problem to it. It is shown that the optimal objective values of the primal and the dual one coincide and that the primal problem has an optimal solution under certain assumptions. The dual problem turns out to be nonsmooth in general and therefore a regularization is performed twice to obtain an approximate dual problem that can be solved efficiently via a fast gradient algorithm. We show how an approximate optimal and feasible primal solution can be constructed by means of some sequences of proximal points closely related to the dual iterates. Furthermore, we show that the solution will indeed converge to the optimal solution of the primal for arbitrarily small accuracy. Finally, the support vector regression task is obtained to arise as a particular case of the general optimization problem and the theory is specialized to this problem. We calculate several proximal points occurring when using different loss functions as well as for some regularization problems applied in image restoration tasks. Numerical experiments illustrate the applicability of our approach for these types of problems. Acknowledgments First of all, I am much obliged to my adviser Prof. Dr. Gert Wanka for supporting me during the last years and giving me the opportunity to work on this thesis. I would like to thank him for many useful hints and input on research issues as well as help on all matters. I am strongly indebted to Dr. Radu Ioan Bot¸for his unremitting commitment in providing suggestions and being available for countless enlightening discussions to make this thesis possible. Thank you, Radu, you were the one who got the ball rolling …
Study of phenolic composition of olive fruits: validation of a simple and fast HPLC-UV method

A high-performance liquid chromatography (HPLC) method endowed with a gradient elution and a UV detection system was established and validated for the determination of phenolic acids, phenolic alcohols, hydroxycinnamic acid derivatives, flavonoids, secoiridoids and lignans during olive (Olea europaea L.) fruit development (green, purple and black olives). Within the test range, the calibration curves exposed a good linear regression (R 2>0.9995). Detection limits ranged between 0.63 and 13.43 mg/L for the detected phenolic compounds. The presented method yielded satisfactory repeatability in terms of retention times and average concentrations of phenolic compounds (RSD < 0.3%). Verbascoside was established as the major phenolic compound in black olives. Oleuropein was established as the dominating phenolic compound in green olives, and its level decreased during maturation. Additionally, this research is the first to experimentally evidence that the flavone luteolin-7-rutinoside is the predominant flavonoid glucoside in black olives, showing the most significant variation with fruit development. The above results validate the method for an easy and fast determination of different classes of phenolic compounds present in olive fruits.
A Sino-German λ6 cm polarization survey of the Galactic plane - VII. Small supernova remnants

We study the spectral and polarization properties of supernova remnants (SNRs) based on our 6cm survey data. The observations were taken from the Sino-German 6cm polarization survey of the Galactic plane. By using the integrated flux densities at 6cm together with measurements at other wavelengths from the literature we derive the global spectra of 50 SNRs. In addition, we use the observations at 6cm to present the polarization images of 24 SNRs. We derived integrated flux densities at 6cm for 51 small SNRs with angular sizes less than 1 degree. Global radio spectral indices were obtained in all the cases except for Cas A. For SNRs G15.1-1.6, G16.2-2.7, G16.4-0.5, G17.4-2.3, G17.8-2.6, G20.4+0.1, G36.6+2.6, G43.9+1.6, G53.6-2.2, G55.7+3.4, G59.8+1.2, G68.6-1.2, and G113.0+0.2, the spectra have been significantly improved. From our analysis we argue that the object G16.8-1.1 is probably an HII region instead of a SNR. Cas A shows a secular decrease in total intensity, and we measured a flux density of 688+/-35 Jy at 6cm between 2004 and 2008. Polarized emission from 25 SNRs were detected. For G16.2-2.7, G69.7+1.0, G84.2-0.8 and G85.9-0.6, the polarized emission is detected for the first time confirming them as SNRs. High frequency observations of SNRs are rare but important to establish their spectra and trace them in polarization in particular towards the inner Galaxy where Faraday effects are important.
Photospheric modeling through spectral line inversion. Temperature and radial velocity stratificatio

We aim to determine average radial stratifications of various physical parameters throughout the solar photosphere at high angular resolution for non-magnetic and magnetic areas and to compare these with standard semiempirical 1D modeling and with 3D hydrodynamics (HD) and magnetohydrodynamics (MHD) simulations. Methods. We analyse a 15-min sequence of adaptive-optics spectrograms of very high angular resolution taken at solar disk centre. We split the data between a quiet area and a magnetic one and derive mean temperature and velocity stratifications and fluctuations for these separately by applying LTE inversion based on response functions. Results. The mean temperature stratifications in the non-magnetic region agree well with the classical 1D models and the 3D simulations at all heights. However, the observed rms temperature is much lower than in the simulations, the observed mean velocities indicate more upflows, and the observed velocity fluctuations are smaller except in upper layers. Some of the discrepancies are likely to result from remaining smearing by atmospheric seeing and instrumental limitations. The magnetic area shows conspicuous behaviour at large height. We also find evidence of fast low-photosphere downflows in the magnetic area and of enhanced temperature above a small pore.
In-Depth Metaproteomics Analysis of Oral Microbiome for Lung Cancer

The human oral microbiome correlates with numerous diseases, including lung cancer. Identifying the functional changes by metaproteomics helps understand the disease-related dysbiosis, yet characterizing low-abundant bacteria is challenging. Here, we developed a free-flow isoelectric focusing electrophoresis-mass spectrometry- (FFIEF-MS-) based metaproteomics strategy to reduce host interferences and enrich low-abundant bacteria for in-depth interpretation of the oral microbiome. With our method, the number of interfering peptides decreased by 52.87%, whereas the bacterial peptides and species increased by 94.97% and 44.90%, respectively, compared to the conventional metaproteomics approach. We identified 3647 bacterial proteins, which is the most comprehensive oral metaproteomics study to date. Lung cancer-associated bacteria were validated among an independent cohort. The imbalanced Fusobacterium nucleatum and Prevotella histicola and their dysregulated functions in inhibiting immune response and maintaining cell redox homeostasis were revealed. The FFIEF-MS may serve as a valuable strategy to study the mechanisms between human diseases and microbiomes with broader applications.
Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types

Simple Summary The DL model predictions in automated breast density assessment were independent of the imaging technologies, moderately or substantially agreed with the clinical reader density values, and had improved performance as compared to inclusion of commercial software values. Abstract Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss’ Kappa score between-observers ranged from 0.31–0.50 and 0.55–0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61–0.66 and 0.70–0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.
CONSTRUCTION AND CHARACTERIZATION OF STEE DELETION MUTANT OF SALMONELLA PULLORUM

Salmonella Pullorum (S. Pullorum) is one of the host-restricted serotypes causing systemic infection in poultry. After S. Pullorum infection, chicks and turkeys usually have acute systemic infection. The main clinical symptoms are white dysentery and dyspnea, and the mortality can be as high as 100%. In adult chickens, local and chronic infections are the most common without obvious clinical symptoms, and can be transmitted vertically to offspring through ovary. Although the use of antibiotics reduces the death of sick chickens, it can not completely eliminate the pathogenic microorganisms in hosts, and is prone to public health problems such as drug resistance and drug residues. No study has ever reported the role of steE in HD-11 cells infected by S. Pullorum. The growth and biochemical characteristics of S. Pullorum ΔsteE were similar to that of S. Pullorum. Furthermore, we also observed the effects of steE on cell proliferation and apoptosis in S. Pulloruminfected HD-11 cells.In order to define the pathogenicity of steE gene of S. Pullorum, the steE deletion strain of S. Pullorum and its complemented strain were successfully constructed, and then its characterization were analyzed. S. Pullorum was preserved by the microbiology laboratory of the college of animal science and veterinary medicine, Henan Institute of Science and Technology. The pKD4, pKD46 and pCP20 or pBR322 plasmids were used for the λ-Red recombination system or complementary strain. The biological characteristics of S. Pullorum ΔsteE were consistent with those of its parent strain S. Pullorum and complementary strain S. Pullorum ΔsteE (pBR322-steE). Construction and confirmation of the ΔsteE strain. To identify the roles of steE in S. Pullorum, the steE deletion mutant of S. Pullorum was correctly constructed.The virulence test showed S. Pullorum ΔsteE decreased the proliferation and apoptosis of HD-11 cells compared to that of S. Pullorum and S. Pullorum ΔsteE (pBR322-steE). Taken together, our data demonstrate that the deletion of steE in S. Pullorum had no effect the growth and biochemical characteristics, but its proliferation ability decreased significantly in HD-11 cells, which decreased cell apoptosis, indicating that steE was closely related to virulence of S. Pullorum. Altogether, our research suggest that the steE gene was required for S. Pullorum virulence, which laid a foundation for further related research in S. Pullorum vaccine strains.
Lack of benefit sharing undermines support for nature conservation in an Eastern Afromontane biodiversity hotspot

Successful forest conservation in the tropics depends on various biophysical, socioeconomic, cultural, and political factors. Researchers, environmental practitioners, and local people recognize the need to resolve longstanding systemic weaknesses in environmental governance institutions, to make mainstream environmental policy and action, and to find locally informed and adaptive conservation measures. This also applies to the preservation of cloud-forest fragments of the Taita Hills in southern Kenya, a section of the Afromontane biodiversity hotspot. These forest remnants host many endemic and endangered plant and animal species, and suffer under deforestation and forest degradation. We conducted structured surveys with 300 smallholder farmers living around three forest fragments in the Taita Hills. Our results indicate a lack of knowledge about biodiversity and ecosystem functions among local people. We found an inverse relationship between the level of formal education and practical environmental knowledge, and a bias toward the protection of plant species, because of their provisional ecosystem services, as opposed to the protection of wild animals, because they are mainly associated with human-wildlife conflicts and large-scale tourism. Unresolved human-wildlife conflicts and missing benefit sharing from tourism has created an anti-conservation attitude. Our study underlines that nature conservation is only feasible if the local people benefit from it in the medium and long terms, and if the added value of conservation for high humanlivelihood quality is clearly communicated.
Black hole masses and starbursts in X-shaped radio sources

It has been suggested that the X-shaped morphology observed in some radio sources can reflect either a recent merger of two supermassive black holes (SMBHs) or the presence of a second active black hole in the galactic nucleus. These scenarios are tested by studying the relationship between the black hole mass, radio and optical luminosity, starburst history, and dynamic age of radio lobes in a sample of 29 X-shaped radio galaxies drawn from a list of 100 X-shaped radio source candidates identified from the FIRST survey. The same relationships are also studied in a control sample consisting of 36 radio-loud active nuclei with similar redshifts and optical and radio luminosities. The X-shaped objects are found to have statistically higher black hole masses and older starburst activity compared to the objects from the control sample. Implications of these findings are discussed for the black hole merger scenario and for the potential presence of active secondary black holes in post-merger galaxies.
Fixed-Time Fuzzy Adaptive Control of Manipulator Systems Under Multiple Constraints: A Modified Dynamic Surface Control Approach

In this article, the fixed-time fuzzy adaptive tracking control problem is studied for a class of one-link manipulator systems with stochastic disturbances and multiple constraints. First, a modified dynamic surface control technique is proposed for the stochastic system, which provides a useful filtering solution to the fixed-time control of the stochastic nonlinear systems. Then, an extended stochastic fixed-time stability criterion is introduced to simplify the complex discussion process of stability analysis instead of existing fixed-time stability criteria. By using the unified barrier function, the constrained nonlinear system can be transformed into the nonconstrained nonlinear system. Moreover, a fuzzy adaptive controller is constructed with the funnel error transformation function, which improves the transient response of the system. The control objective of this article is that the steady-state error can be driven to the prespecified funnel in a fixed time based on the Lyapunov stability theorem and the extended stochastic fixed-time stability criterion. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.
Artificial Black Holes

Contents: Introduction and Survey (M Visser) Acoustic Black Holes in Dilute Bose-Einstein Condensates (L Garay) Slow Light (U Leonhardt) Black Hole and Baby Universe in a Thin Film of 3He-A (T Jacobson & T Koike) Measurability of Dumb Hole Radiation? (W Unruh) Effective Gravity and Quantum Vacuum in Superfluids (G Volovik) Emergent Relativity and the Physics of Black Hole Horizons (G Chapline et al.) Quasi-Gravity in Branes (B Carter) Towards a Collective Treatment of Quantum Gravitational Interactions (R Parentani) Role of Sonic Metric in Relativistic Superfluid (B Carter) Effective Geometry in Nonlinear Field Theory (Electrodynamics and Gravity) (M Novello) Non-Inertial Quantum Mechanical Fluctuations (H Rosu) Phonons and Forces: Momentum versus Pseudomomentum (M Stone) Coda (M Visser) Appendix: Elements of General Relativity (M Visser).
Ganking the Ranking: The self-reported Learning Potential from a Selection of game Genres to Develop self-directed Learning

Game-based learning (GBL) is said to have encouraging potential for varying educational contexts and scenarios, but how do practitioners select suitable edifying gaming content for their own unique environments? Moreover, what are the correct strategies, recommendations, procedures and/or parameters for choosing appropriate gaming media for learning? There are countless options to choose from, varying in genre, play style, medium, difficulty, aim(s), etc. This paper presents the results of an interpretive study seeking to discern a set of requirements and qualities of informed GBL selection. Online surveys completed by history-for-education students at a South African Higher Education institution hope to bring us closer to guidelines for more effective GBL selection and application in tertiary education contexts across the globe. The following paper begins with remarks on the significance of self-direction in contemporary Higher Education and the potential for GBL to not only spur this tendency on, but to frame and support it. The conceptual framework used in the project is then unpacked as it relates to self-directed learning, game-based learning, video games and supplementary theoretical structures. The proceeding section is divided into three sections related to central study concepts, including: meta-behaviour, metacognition, and meta-emotion, with trial and error, observation and modelling, as well as reinforcement learning as subcategories of meta-behaviour that follow. Additional subcategories surrounding metacognition are then explored, namely: connected learning, reflect and improvise, logical and analytical reasoning, inquiry-based learning, and synthesis. The methodology then describes the hybrid video/survey techniques utilised to gather data relating to participant impression(s), motivational factors, challenge and educational value of GBL selection for Higher Education contexts. Results obtained provide a suitable starting point to construct a viable applied framework for such an environment.
Dual Collaborative Constraints Regularized Low-Rank and Sparse Representation via Robust Dictionaries Construction for Hyperspectral Anomaly Detection

The low rank and sparse representation (LRSR) technique has attracted increasing attention for hyperspectral anomaly detection (HAD). Although a large quantity of research based on LRSR for HAD is proposed, the detection performance is still limited, due to the unsatisfactory dictionary construction and insufficient consideration of global and local characteristics. To tackle the above-mentioned concern, a novel HAD method, termed dual collaborative constraints regularized low-rank and sparse representation via robust dictionaries construction, is proposed in this article. Concretely, a robust dictionary construction strategy, which thoroughly excavates the potential of the density estimation model and local outlier factor, is proposed to yield pure and representative dictionary atoms. To fully exploit the global and local characteristics of hyperspectral images, dual collaborative constraints corresponding to the background and anomaly components are imposed on the LRSR model. Notably, two weighted matrices are further exerted on the representation coefficients to improve the effect of collaborative constraints, considering the fact that the surrounding pixels similar to the testing pixel should be given a large weight, otherwise the weight is expected to be small. In this way, the background and anomaly components can be well modeled. Additionally, a nonlinear transformation operation, which combines the output of the density estimation model and local outlier factor with the detection result derived from the LRSR model, is developed to suppress the background. The experiments conducted on one simulated dataset and three real datasets demonstrate the superiority of the proposed method compared with the four typical methods and four state-of-the-art methods.
Left Atrial Appendage Thrombosis and Oral Anticoagulants: A Meta-Analysis of Risk and Treatment Response

Abstract: Background Left atrial appendage thrombus (LAAT) is the main cause of cardioembolism in patients with nonvalvular atrial fibrillation (AF). Emerging evidence indicates that direct oral anticoagulants (DOACs) may be a preferred, safer choice for patients with LAAT. However, current guidelines indicate vitamin K antagonist (VKA) as the preferred treatment for LAAT. We conducted a meta-analysis to compare the efficacy of VKA and DOAC for the treatment of LAAT. Methods: The search was conducted in the PubMed, Embase, Google Scholar, and Cochrane Library databases from inception to July 2022, with the language restricted to English. A first analysis was conducted to evaluate the risk of LAAT under VKA or DOAC treatment. A second analysis was conducted to compare the resolution of LAAT under VKA and DOAC treatment. Results: In 13 studies comparing LAAT incidence rates under VKA and DOAC treatment, significant superiority of DOAC was detected (pooled RR = 0.65, 95% CI = 0.47–0.90, p = 0.009) with moderate heterogeneity being identified in the pooled studies. In 13 studies comparing LAAT resolution under VKA and DOAC use, treatment with DOAC exhibited a significantly increased probability of LAAT resolution compared with VKA (pooled odds ratio = 1.52, 95% CI = 1.02–2.26, p = 0.040). Conclusions: This meta-analysis suggests a superiority of DOAC over VKA with respect to LAAT incidence in people with AF and the likelihood of LAAT resolution. Due to their established safety profile, DOAC is a preferable choice for anticoagulation, although further randomized controlled studies are warranted to provide further evidence of their suitability as a new recommended treatment.