Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Invariants of Linear Multivariable Systems

This article identifies some of the structural properties of the matrix triple $(C,A,B)$ which remain invariant under various transformation groups. The paper begins with a brief account of a recent result which states that the controllable space of $(A,B)$ can be decomposed into a direct sum of singly-generated controllability subspaces, the dimension of each subspace being determined by one of the controllability indices of $(A,B)$. In certain instances the component subspaces of this decomposition can be chosen so that their C-images also decompose system output space in a special way; matrix triples $(C,A,B)$ for which such a decomposition is possible are called prime. If $\bar {\mathcal{C}}$ is an appropriately defined group of system-coordinate and state-feedback transformations acting on prime triples, then the controllability indices of $(A,B)$ determine a complete orbital invariant under $\bar {\mathcal{C}}$. By imbedding $\bar {\mathcal{C}}$ in a richer transformation group $\mathcal{C}^ * $, whi.
Control of inversion height by surface heating

Viscous dissipation is inadequate to account for the destruction of all the thermal turbulence generated by upward transfer of heat in a deep convection layer. It is suggested that the surplus turbulent kinetic energy is destroyed by buoyancy forces in a region of downward transfer of heat in the upper part of the convection layer. This process is associated with mass transfer downwards through the convection (or subsidence) inversion and also with an upward movement of the inversion, though the latter may be overcome by sufficiently strong subsidence. A simple theory of the process is developed and the results deduced thereby agree well with observations of the diurnal variation of inversion height in Central Australia.
A Course in Robust Control Theory: A Convex Approach

0 Introduction. - 1 Preliminaries in Finite Dimensional Space. - 2 State Space System Theory. - 3 Linear Analysis. - 4 Model Realizations and Reduction. - 5 Stabilizing Controllers. - 6 H2 Optimal Control. - 7 H? Synthesis. - 8 Uncertain Systems. - 9 Feedback Control of Uncertain Systems. - 10 Further Topics: Analysis. - 11 Further Topics: Synthesis. - A Some Basic Measure Theory. - A.1 Sets of zero measure. - A.2 Terminology. - Notes and references. - B Proofs of Strict Separation. - Notes and references. - Notes and references. - Notation. - References.
Rascbec: Raman Spectroscopy Calculation Via Born Effective Charge

We advance the algorithm for ab initio calculations of Raman spectra for large systems via applying external electric field, and complement it by a code implementation we name RASCBEC. With the RASCBEC code, we have successfully benchmark crystalline materials and compute Raman spectra of large molecules, and amorphous oxides. Our results demonstrate a remarkable level of agreement with the results from other commonly used codes as well as the experimental data. The electric field approach for Raman spectra calculation is designed to overcome the computational challenges associated with the conventional approach, which requires the calculation of the macroscopic dielectric tensor at numerous molecular geometries. The key innovation in our approach lies in obtaining the first-order derivatives with respect to the external electric field directly from VASP (the Vienna Ab Initio Simulation Package), as the Born Effective Charge (BEC). The RASCBEC code not only significantly reduces computational time, up to a factor of $N/8$, compared to the conventional approach, where $N$ is the total count of atoms within the simulation box. But also maintains the same level of accuracy, employing first-order numerical derivatives that avoid the numerical noise associated with algorithms requiring second-order derivatives, as seen in other electric field-based methods. This advantage makes RASCBEC particularly beneficial for large molecules and expansive amorphous systems.
Mesh-Splitting Impedance Transition Boundary Condition for Accurate Modeling of Thin Structures

Numerical modeling of thin structures can result in dense volumetric discretization and poor mesh quality, which can be computationally expensive. A few equivalent boundary conditions (BCs) are able to replace thin structures with zero-thickness surfaces and avoid volumetric discretization. However, their near-field solutions suffer from loss of accuracy as the thickness increases, because the reduction of thin structures to zero-thickness surfaces changes the original problem. This work presents a mesh-splitting technique for the impedance transition BC (ITBC), which effectively eliminates the aforementioned error without additional number of unknowns. Under certain assumptions, the technique can be easily applied and yield much more accurate solutions than several other equivalent BCs; otherwise, it may be unfeasible or lead to marginal improvements. Moreover, the mesh-splitting ITBC is incorporated into the spectral-element spectral-integral (SESI) method for the modeling of doubly periodic problems including metasurfaces. Numerical examples include error analysis for the proposed technique and demonstrate its potential in more complex structures.
Invertibility of a room impulse response

When a conversation takes place inside a room, the acoustic speech signal is distorted by wall reflections. The room’s effect on this signal can be characterized by a room impulse response. If the impulse response happens to be minimum phase, it can easily be inverted. Synthetic room impulse responses were generated using a point image method to solve for wall reflections. A Nyquist plot was used to determine whether a given impulse response was minimum phase. Certain synthetic room impulse responses were found to be minimum phase when the initial delay was removed. A minimum phase inverse filter was successfully used to remove the effect of a room impulse response on a speech signal.
The Entropy of K-Pop songs

We compared the entropy of K-Pop songs with the entropy of American Pop songs. The results show that K-Pop songs tend to have lower entropy in respect of chords, tones, and rhythms, but have higher entropy in phrase. This implies that the uncertainty of K-Pop song is lower than that of American Pop songs.
Advances in the study of aerobic glycolytic effects in resistance to radiotherapy in malignant tumors

Aerobic glycolysis is a metabolic mode of tumor cells different from normal cells that plays an important role in tumor proliferation and distant metastasis. Radiotherapy has now become a routine and effective treatment for many malignancies, however, resistance to radiotherapy remains a major challenge in the treatment of malignant tumors. Recent studies have found that the abnormal activity of the aerobic glycolysis process in tumor cells is most likely involved in regulating chemoresistance and radiation therapy resistance in malignant tumors. However, research on the functions and mechanisms of aerobic glycolysis in the molecular mechanisms of resistance to radiotherapy in malignant tumors is still in its early stages. This review collects recent studies on the effects of aerobic glycolysis and radiation therapy resistance in malignant tumors, to further understand the progress in this area. This research may more effectively guide the clinical development of more powerful treatment plans for radiation therapy resistant subtypes of cancer patients, and take an important step to improve the disease control rate of radiation therapy resistant subtypes of cancer patients.
SCALING LAWS IN GRANULAR FLOWS DOWN ROUGH INCLINED PLANES

In this paper, new scaling properties for granular flows down rough inclined planes are presented. In the dense steady uniform flow regime, we have systematically measured the mean velocity of the flow as a function of the inclination of the surface θ and of the thickness h of the layer. The results obtained for different systems of beads corresponding to different surface roughness conditions are shown to collapse into a single curve when properly scaled. The scaling is based on the measurement of the minimum thickness hstop(θ) necessary to observe a steady uniform flow at inclination θ. From this experimental observation an empirical description for granular flows down inclined planes is proposed in terms of a dynamic friction coefficient.
Impact of Geometric and Electronic Factors on Selective Hydro-Deoxygenation of Guaiacol by Surface-Rich Metal/Silica Catalysts

The selective production of hydrocarbons for the chemical industry from biogenic feedstock is a significant challenge when ensuring hydrocarbon and fuel supply, despite the heterogeneity of this feed. In this study, guaiacol, as a surrogate for complex lignin-based biomass resources, is converted by an inert silica carrier material with different d-metal impregnation (Mo, W, Re, Fe, Co, Ni, Cu, Pd, Ag) to reveal the reasons for different product selectivity to hydrogenated and deoxygenated hydrocarbon products. Hydrogen at 15 bar(gauge) and guaiacol are converted on metal/silica catalysts between 250 °C and 400 °C, while the physicochemical catalyst properties are characterized before and after catalytic tests. Volcano plots for the conversion, hydrogenation and deoxygenation products versus the d-band energy, surface atom distance and fouling properties reveal three groups of metals: (i) those that are less active and show high coking (Ag, α-Fe); (ii) those that show high activity for hydrogenation (β-Co, Ni, Pd) and, therefore, preferably yielded cyclohexane, cyclohexanol and 2-methoxycyclohexanol; (iii) those that preferably promote deoxygenation (Mo, W, Re, Cu) and, therefore, promoted the formation of phenol, benzene, anisole and catechol. The results are summarized in a pseudo van Krevelen diagram and interpreted as a complex interdependency from Sabatier’s principle of geometric correspondence of hexagonal metal surface for hydrogenation, electronic correspondence for the activation of hydrogen and electronic correspondence by oxophilicity for deoxygenation from the d-band center model.
Tumor Suppressor miR-613 Alleviates Non-Small Cell Lung Cancer Cell via Repressing M2 Macrophage Polarization

Background Non-small cell lung cancer (NSCLC) is a crucial crux of cancer-related death, and M2 macrophage polarization facilitates NSCLC development. MicroRNA-613 (miR-613) is a tumor suppressor. This research aimed to clarify the miR-613 function in NSCLC and its impact on M2 macrophage polarization. Methods. miR-613 expressions in NSCLC tissues and cells were evaluated using quantitative real-time PCR. For miR-613 function in NSCLC, cell proliferation analysis, cell counting kit-8, flow cytometry, western blot, transwell, and wound-healing were conducted. Meanwhile, the miR-613 impact on M2 macrophage polarization was assessed by the NSCLC models. Results. miR-613 was lessened in NSCLC cells and tissues. It was corroborated that miR-613 overexpression retrained NSCLC cell proliferation, invasion, and migration but facilitated cell apoptosis. Moreover, miR-613 overexpression restrained NSCLC development by repressing M2 macrophage polarization. Conclusion Tumor suppressor miR-613 ameliorated NSCLC by restraining M2 macrophage polarization.
Rapid and Efficient Regeneration of Rhododendron decorum from Flower Buds

Rhododendron decorum is a woody species with high ornamental and medical value. Herein, we introduce a novel in vitro regeneration method for R. decorum. We used flower buds to develop an efficient and rapid plant regeneration protocol. Sterile flower buds of R. decorum of a 2 cm size were used as explants to study the effects of the culture medium and plant growth regulators on the callus induction and adventitious shoot differentiation, proliferation, and rooting. According to the results, the optimal medium combination for callus induction was WPM + 1 mg/L TDZ + 0.2 mg/L NAA, and its induction rate reached 95.08%. The optimal medium combination for adventitious shoot differentiation from the callus was WPM + 0.5 mg/L TDZ + 0.1 mg/L NAA, and its differentiation rate reached 91.32%. The optimal medium combination for adventitious shoot proliferation was WPM + 2 mg/L ZT + 0.5 mg/L NAA, for which the proliferation rate reached 95.32% and the proliferation coefficient reached 9.45. The optimal medium combination for rooting from adventitious shoots was WPM + 0.1 mg/L NAA + 1 mg/L IBA, and its rooting rate reached 86.90%. The survival rates of the rooted regenerated plantlets exceeded 90% after acclimatization and transplantation. This regeneration system has the advantages of being simple and highly efficient, and it causes little damage to the shoots of the mother plants, laying a foundation for the plantlet propagation, genetic transformation, and new-variety breeding of R. decorum.
Upper bounds for nearly optimal diagonal scaling of matrices

Optimal diagonal scaling of an n×n matrix A consists in finding a diagonal matrix D that minimizes a condition number of AD. Often a nearly optimal scaling of A is achieved by taking a diagonal matrix D 1 such that all diagonal elements of D 1 AT AD 1 are equal to one. It is shown in this paper that the condition number of AD 1 can be at least (n/2)1/2 times the minimal one. Some questions for a further research are posed.
A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Prediction

Developing LSTM neural networks that can accurately predict the future trajectory of ongoing cases and their remaining runtime is an active area of research in predictive process monitoring. In this work a novel complete remaining trace prediction (CRTP) LSTM is proposed. This model is trained to directly predict the complete remaining trace and runtime of cases in contrast to single event prediction as is considered in previously published research on this topic. This makes the CRTP-LSTM robust in terms of utilizing all available attributes of previously observed events for prediction, consequently it can be considered natively data aware. In an extensive experimental assessment the authors show that CRTP-LSTMs consistently outperform other considered approaches for both remaining trace and runtime prediction. Furthermore, the authors show that including all available information contained in previously observed events has a positive impact on the performance of the CRTP-LSTM model. This indicates that valuable information can be extracted from attributes of events in order to make more accurate trace and runtime predictions. This opens up interesting avenues for future research including the incorporation of inter-case features into a modeling setup when predicting the remaining trace and runtime of cases.
Association between Exposure to Selected Heavy Metals and Blood Eosinophil Counts in Asthmatic Adults: Results from NHANES 2011–2018

(1) Background: Heavy metals are widely used and dispersed in the environment and people’s daily routines. Many studies have reported an association between heavy metal exposure and asthma. Blood eosinophils play a crucial role in the occurrence, progression, and treatment of asthma. However, there have thus far been few studies that aimed to explore the effects of heavy metal exposure on blood eosinophil counts in adults with asthma. Our study aims to discuss the association between metal exposure and blood eosinophil counts among asthmatic adults. (2) Methods: A total of 2026 asthmatic individuals were involved in our research from NHANES with metal exposure, blood eosinophils, and other covariates among the American population. A regression model, the XGBoost algorithm, and a generalized linear model (GAM) were used to explore the potential correlation. Furthermore, we conducted a stratified analysis to determine high-risk populations. (3) Results: The multivariate regression analysis indicated that concentrations of blood Pb (log per 1 mg/L; coefficient β, 25.39; p = 0.010) were positively associated with blood eosinophil counts. However, the associations between blood cadmium, mercury, selenium, manganese, and blood eosinophil counts were not statistically significant. We used stratified analysis to determine the high-risk group regarding Pb exposure. Pb was identified as the most vital variable influencing blood eosinophils through the XGBoost algorithm. We also used GAM to observe the linear relationship between the blood Pb concentrations and blood eosinophil counts. (4) Conclusions: The study demonstrated that blood Pb was positively correlated with blood eosinophil counts among asthmatic adults. We suggested that long-time Pb exposure as a risk factor might be correlated with the immune system disorder of asthmatic adults and affect the development, exacerbation, and treatment of asthma.
3D simulations of the fragmentation of photoevaporating clumps embedded in a stellar wind

We present 3D numerical simulations of the interaction of a dense, neutral clump with an impinging external wind+ionizing photon eld. We nd that models with very weak ionizing photon elds produce clumps that fragment as the time evolution proceeds, in agreement with previous clump/wind interaction models. Models with stronger ionizing photon elds present less fragmentation, and for strong enough photon elds only a single, dense condensation is produced.
Ice supersaturation in the tropopause region over Lindenberg, Germany

The occurrence of ice-supersaturation layers (in either clear air or in cirrus) over the Meteorological Observatory Lindenberg is investigated for the period February 2000 to April 2001 by means of the humidity, temperature, and pressure reports obtained from the Lindenberg corrected RS80A routine radiosonde. The RS80A routine sonde data are corrected on the basis of weekly comparison ascents with Lindenberg research radiosonde humidity data. This research sonde applies the Lindenberg measuring and evaluation technique of standardized frequencies. We study the frequency of occurrence of ice supersaturation in the tropopause region over Lindenberg, the vertical distribution of ice-supersaturation layers in the upper troposphere and lowermost stratosphere, their situation relative to the tropopause, their vertical dimensions, their temperatures and the statistical distribution of relative humidities. The mean frequency of occurrence of ice-supersaturation layers is about 28%. Most of them occur within a broad layer extending 200 hPa down from the tropopause. Most events occur in cold air with temperature below -40°C. Their vertical extensions can be fitted by a pair of Weibull distributions with mean 560 ± 610 m. The results are compared with findings from the MOZAIC project, from the SAGE II satellite instrument, and with various results from Lidar measurements.
Deep CCD survey - Galaxy luminosity and color evolution

Imaging and photometric observations of a statistically complete sample of galaxies in 12 high-latitude fields, obtained in the BJ (360-520 nm), R (580-720 nm) and I (780-1100 nm) bands using CCD detectors on the 4-m telescopes at CTIO and KPNO, are reported. The data are presented in extensive graphs and sample images and analyzed in detail with reference to theoretical models of galactic origin and evolution. The galaxy number-count slopes, d(log N)/dm, are found to be sub-Euclidean, varying from 0.34 in the I band to 0.45 in the BJ band, where the corrected counts appear to saturate at about 27 mag. The predictions of no-evolution models are shown to underpredict the counts at 25 mag (BJ) by a factor of 5-15 and the extragalactic background light from all galaxies (6.8 x 10 to the -6th erg/sq cm sec sr micron at 450 nm) by a factor greater than 2. 114 references.
Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory

How to generate road conditions from urban GPS trajectory is an important problem in transportation systems. However, this computation process usually suffers from serious missing value problem due to the observation uncertainty or limited reports from crowdsourcing systems. Conventional tensor factorization approaches learn the spatio-temporal dependencies in a collaborative filtering way, which ignores the complex road network structure information and temporal heterogeneity. In this study, we propose a multi-view model with multiple aspects of prior knowledge to impute traffic state computed from a real-world trajectory dataset. More specifically, in the spatial view, rather than focusing on a specific type of road segment, we take the heterogeneity of road network into consideration and model the multiple relations of adjacent road segments. Meanwhile, the temporal pattern is also viewed as a heterogeneous graphical structure that discriminates the weekly/hourly adjacency in the temporal view. Finally, we fuse the above spatio-temporal features to provide a robust estimation under different sparse conditions. Intensive experiments on two types of missing scenarios (i.e., random and non-random) demonstrate that the proposed imputation method outperforms all the other state-of-the-art approaches. In addition, our model represents interpretable patterns for spatio-temporal graph analysis.
Kernel Methods and Machine Learning

Part I. Machine Learning and Kernel Vector Spaces: 1. Fundamentals of machine learning 2. Kernel-induced vector spaces Part II. Dimension-Reduction: Feature Selection and PCA/KPCA: 3. Feature selection 4. PCA and Kernel-PCA Part III. Unsupervised Learning Models for Cluster Analysis: 5. Unsupervised learning for cluster discovery 6. Kernel methods for cluster discovery Part IV. Kernel Ridge Regressors and Variants: 7. Kernel-based regression and regularization analysis 8. Linear regression and discriminant analysis for supervised classification 9. Kernel ridge regression for supervised classification Part V. Support Vector Machines and Variants: 10. Support vector machines 11. Support vector learning models for outlier detection 12. Ridge-SVM learning models Part VI. Kernel Methods for Green Machine Learning Technologies: 13. Efficient kernel methods for learning and classifcation Part VII. Kernel Methods and Statistical Estimation Theory: 14. Statistical regression analysis and errors-in-variables models 15: Kernel methods for estimation, prediction, and system identification Part VIII. Appendices: Appendix A. Validation and test of learning models Appendix B. kNN, PNN, and Bayes classifiers References Index.
Cutaneous and Pulmonary Tuberculosis—Diagnostic and Therapeutic Difficulties in a Patient with Autoimmunity

Cutaneous tuberculosis (CTB) is a very rare disease and accounts for only 1–2% of cases of extrapulmonary tuberculosis (EPTB). Due to the variety of its clinical manifestations, the uncharacteristic appearance of its lesions, resembling other dermatoses in the early stages, and the limited experience of clinicians due to the rarity of CTB, diagnosis is very difficult. Particularly noteworthy is the fact that most cases of EPTB, including skin tuberculosis (TB), can be a manifestation of systemic involvement. In this paper, we present a case of an immunocompromised patient who was diagnosed with CTB almost a year after the first dermatological lesions were located on the lower extremities. At the same time, due to respiratory symptoms, a diagnosis of pulmonary TB (PTB) was made, and radiological and microbiological confirmations were obtained.