Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach

The algorithm selection problem aims at selecting the best algorithm for a given computational problem instance according to some characteristics of the instance. In this dissertation, we first introduce some results from theoretical investigation of the algorithm selection problem. We show, by Rice's theorem, the nonexistence of an automatic algorithm selection program based only on the description of the input instance and the competing hardness and algorithm performance based on Kolmogorov complexity to show that algorithm selection for search is also incomputable. Driven by the theoretical results, we propose a machine learning-based inductive approach using experimental algorithmic methods and machine learning techniques to solve the algorithm selection problem. Experimentally, we have applied the proposed methodology to algorithm selection for sorting and the MPE problem. In sorting, instances with an existing order are easier for some algorithms. We have studied different presortedness measures, designed algorithms to generate permutations with a specified existing order uniformly at random, and applied various learning algorithms to induce sorting algorithm selection models from runtime experimental results. In the MPE problem, the instance characteristics we have studied include size and topological type of the network, network connectedness, skewness of the distributions in Conditional Probability Tables (CPTs), and the proportion and distribution of evidence variables. The MPE algorithms considered include an exact algorithm (clique-tree propagation), two stochastic sampling algorithms (MCMC Gibbs sampling and importance forward sampling), two search-based algorithms (multi-restart hill-climbing and tabu search), and one hybrid algorithm combining both sampling and search (ant colony optimization). Another major contribution of this dissertation is the discovery of multifractal properties of the joint probability distributions of Bayesian networks. With sufficient asymmetry in individual prior and conditional probability distributions, the joint distribution is not only highly skewed, but it also has clusters of high-probability instantiations at all scales. We present a two phase hybrid random sampling and search algorithm to solve the MPE problem exploiting this clustering property. Since the MPE problem (decision version) is NP-complete, the multifractal meta-heuristic can be applied to solve other NP-hard combinatorial optimization problems as well.
CAMP: a Synthetic Micro-Benchmark for Assessing Deep Memory Hierarchies

This paper presents CAMP, a Configurable App for Memory Probing that facilitates assessment of intra-node deep memory hierarchies through performance measurements of synthetic kernels. The open-source tool provides several different access patterns and allows to vary kernels’ operational intensities. We introduce the supported features, their design and implementation, and analyse measurements for a deep memory hierarchy on a compute node of ARCHER2, the UK national supercomputer and compare it to measurements on a compute node on NEXTGenIO. We report results of a strong scaling study of contiguous, strided and stencil access patterns for various operational intensities and explore several thread placement options and data sizes. The results confirm that bandwidth saturation can be achieved with a relatively small number of threads on AMD Rome and that underpopulation may be beneficial as performance drops when the node is fully populated for configurations with lower operational intensity, whilst the effect is less pronounced on the less hierarchical Intel Cascade Lake. Finally we discuss potential benefits of sub-NUMA-node awareness and mention several suggestions for extending CAMP.
Parental and Adolescent Educational Expectations and Adolescent Problem Behaviors: The Role of Deviant Peer Affiliations

The comprehensive theory model of problem behaviors proposed that expectations are important factors affecting adolescent problem behaviors. The purpose of this study is to explore the association between educational expectations (in this study, this includes parental educational expectations and adolescent selfeducational expectations) and problem behaviors based on the framework of the CTMPB to provide empirical support for the prevention and intervention of adolescent problem behaviors. This study used cross-sectional data from the 2014–2015 academic year of the China Education Panel Survey (CEPS) conducted by the China survey and data center at the Renmin University of China. A nationwide representative sample of 9936 junior high school students was selected. Among them, 4870 (52.2%) were female, and the average age was 14.52 years (SD = 0.67 years). The results revealed that adolescent selfeducational expectations and deviant peer affiliations played a contributory mediating role in the association between parental educational expectations and adolescent problem behaviors. Both parental educational expectations and adolescent selfeducational expectations are protective factors against adolescent problem behaviors, and enhancing the two factors can decrease the likelihood of adolescent engagement in problem behaviors. In addition, deviant peer affiliations are risk factors for adolescent problem behaviors and represent a mediating factor between educational expectations and adolescent problem behaviors. However, this study was only based on cross-sectional data, requiring further support by longitudinal or experimental studies.
Kinematic cycle slip detection and correction for carrier phase based navigation applications in urban environment in case of ultra high rate GNSS observations

Abstract In recent years, GNSS has been more and more widely used for all kinds of applications, including those in urban environment. Carrier phase observations from GNSS are necessary for precise navigation applications. However, before that, cycle slip has to be detected and corrected. For static GPS observations, as the distance between the receiver and the GPS satellites is smooth, the testing quantities formed with carrier phase observations between satellites can be used for cycle slip detection and correction. These testing quantities have two advantages. First, as only carrier phase observations are used, they are not affected by big noise of code observations. Second, the wavelength of the testing quantities is about 20 cm, long enough to be insensitive to carrier phase noise and multipath. However, generally these testing quantities cannot be used for kinematic observations with a sample interval of 1 s as the moving of the receiver and the distance between the receiver and the satellites is not smooth. Kinematic cycle slip detection and correction has been a challenge for many years. Currently, two methods are popularly used: geometry-free and time relative. Both of these two methods are sensitive to observation noise and multipath of carrier phase and code, especially the latter one. For carrier phase based applications in urban environment, this weakness will become more outstanding. For kinematic ultra high rate observations, the changes of the speed and acceleration of the receiver can be neglected in very short time such as 1 s or less if there is no abrupt movement. In this case, the distance between the receiver and the satellites can be regarded as smooth and the testing quantities formed between satellites can be used for cycle slip detection and correction. Based on this, in this paper, a new kinematic cycle slip detection and correction method is proposed, aiming at navigation applications in urban environment with ultra high rate GPS observations. The new method has three features: first, it is based on the use of ultra high rate observations (20 Hz); second, the speed and acceleration change of the vehicle is neglected; third, code measurement is not involved. The new method is tested with practical ultra high rate GPS observations in urban environment and compared with ionospheric residual method and time relative method. The numerical results show that the new method performs obviously better than the others with all cycle slips detected and determined reliably.
Electro-osmotic slip and electroconvective instability

Electric conduction from an electrolyte solution into a charge selective solid, such as ion exchange membrane or electrode, becomes unstable when the electrolyte concentration near the interface approaches zero owing to diffusion limitation. The sequence of events leading to instability is as follows: upon the decrease of the interface concentration, the electric double layer at the interface transforms from its common quasi-equilibrium structure to a different, non-equilibrium one. The key feature of this new structure is an extended space charge added to the usual one of the quasi-equilibrium electric double layer. The non-equilibrium electro-osmotic slip related to this extended space charge renders the quiescent conductance unstable. A unified asymptotic picture of the electric double-layer undercurrent, encompassing all regimes from quasi-equilibrium to the extreme non-equilibrium one, is developed and employed for derivation of a universal electro-osmotic slip formula. This formula is used for a linear stability study of quiescent electric conduction, yielding the precise parameter range of instability, compared with that in the full electroconvective formulation. The physical mechanism of instability is traced both kinematically, in terms of non-equilibrium electro-osmotic slip, and dynamically, in terms of forces acting in the electric double layer.
Salient Preprocessing: Robotic ICP Pose Estimation Based on SIFT Features

The pose estimation can be effectively solved according to the feature point matching relationship in RGB-D. However, the extraction and matching process based on the whole image’s feature point is very computationally intensive and lacks robustness, which is the bottleneck of the traditional ICP algorithm. This paper proposes representing the whole image’s feature points by the salient objects’ robustness SIFT feature points through the salient preprocessing, and further solving the pose estimation. The steps are as follows: (1) salient preprocessing; (2) salient object’s SIFT feature extraction and matching; (3) RANSAC removes mismatching salient feature points; (4) ICP pose estimation. This paper proposes salient preprocessing aided by RANSAC processing based on the SIFT feature for pose estimation for the first time, which is a coarse-to-fine method. The experimental results show that our salient preprocessing algorithm can coarsely reduce the feature points’ extractable range and interfere. Furthermore, the results are processed by RANSAC good optimization, reducing the calculation amount in the feature points’ extraction process and improving the matching quality of the point pairs. Finally, the calculation amount of solving R, t based on all the matching feature points is reduced and provides a new idea for related research.
Origin of the Summertime Synoptic-Scale Wave Train in the Western North Pacific*

Abstract The origin of the summertime synoptic wave train in the western North Pacific is investigated with a multilevel, nonlinear baroclinic model. A realistic three-dimensional summer mean state is specified and eigenvectors are calculated by introducing small perturbation initially to the model. Numerical experiments indicate that the origin of the synoptic wave train may arise from instability of the summer mean flow in the presence of a convection–frictional convergence (CFC) feedback. In the lack of the CFC feedback, the summer mean flow supports only a least damped mode, characterized by a northwest–southeast-oriented wave train pattern with a zonal wavelength of 2500 km. In the presence of both the realistic summer mean flow and the CFC feedback, the model reproduces a fast growing mode, whose structure and propagation characters are similar to the observed. Sensitivity experiments with different initial perturbation patterns indicate that the model solution is not sensitive to initial conditions.
Identifying the Correlation between Alzheimer and type 2 Diabetes

In recent years it has been assessed that in people with type 2 diabetes the likelihood of developing Alzheimer's disease increases by more than 50%. The purpose of the analysis proposed in this paper is to identify visually the correlation between Alzheimer's disease and type 2 diabetes and determine whether Alzheimer's disease is a form of brain diabetes mellitus. A dataset containing genomic microarray data relating to the two diseases is used for the analysis. First, we conduct an exploratory analysis using clustering techniques to perform a first screening of the samples and divide them into two different clusters. Then, we propose a predictive model for the classification and identify the genes equally expressed in the two types of samples. This makes it possible to select genes with significant values for the research in progress, on which pathway analysis must be performed to identify the classes they belong to. We also study the gene expression alterations of genes belonging to a specific pathway to determine if the differential expression is statistically significant. We provide a visual representation of connections in the pathways of both the diseases. Results indicate that there is a set of genes of significant importance for both type 2 diabetes and Alzheimer's disease, but that there is also a significant correlation with other neurodegenerative diseases. Consequently, it is possible to define the Alzheimer's disease as a form of cerebral diabetes mellitus.
Extremely high performance, high density 20nm self-selecting cross-point memory for Compute Express Link

We demonstrate an array operation of 20 nm self-selecting memory (SSM) for the first time. SSM shows extremely high cell performance, great reliability, and promising scalability below technodes of 1z nm. The innovation of material engineering enables memory-selector duality, which can reduce the aspect ratio (AR) and consequently lead to better scalability. Read window margin (RWM), the main hurdle for high density array operation, was successfully obtained by cell stack materials engineering with the help of bipolar write operations. Sufficient RWM can be obtained even below a write pulse of 20 ns for both Set and Reset, and at a much lower write current than the conventional 3D cross-point memory (3DXP), which guarantees extremely low write latency and power consumption. Owing to the low write current and short write pulse, SSM also displayed superior write cycle endurance, compared with conventional 3DXP. Due to the elimination of phase change material (PCM), SSM showed no write disturbance (thermal disturbance), and this can improve the total system power consumption and performance as well.
Characterizing Graphene, Graphite, and Carbon Nanotubes by Raman Spectroscopy

Recent advances in Raman spectroscopy for characterizing graphene, graphite, and carbon nanotubes are reviewed comparatively. We first discuss the first-order and the double-resonance (DR) second-order Raman scattering mechanisms in graphene, which give rise to the most prominent Raman features. Then, we review phonon-softening phenomena in Raman spectra as a function of gate voltage, which is known as the Kohn anomaly. Finally, we review exciton-specific phenomena in the resonance Raman spectra of single-wall carbon nanotubes (SWNTs). Raman spectroscopy of SWNTs has been especially useful for understanding many fundamental properties of all sp2 carbons, given SWNTs can be either semiconducting or metallic depending on their geometric structure, which is denoted by two integers (n,m).
Mesoscale dynamics of cold fronts: Structures described by dropsoundings in FRONTS 87

The FRonts 87 project was a European experiment to make mesoscale observations to deduce the dynamics of active cold fronts approaching north-west Europe, with a minimum of orographic influences. In this paper data from dropsoundings made by the Meteorological Office's C-130 research aircraft are analysed. The aircraft flew a pattern with 4 or 5 runs oriented approximately at right angles to the front, but with each run displaced in the along-front direction by about 100 km. The dropsondes gave soundings to a height of approximately 7 km with a cross-frontal spacing of 20 km at best and 60 km on average. Between 30 and 50 soundings were made in each event, over an area of 500 × 500 km2. These data are unique in their mesoscale resolution over a synoptic-scale region; this was made possible by the ability to track up to 5 sondes in the air at any time. Several new aspects of cold-front dynamics have been identified from these data; the approach here is to treat each run as an instantaneous cross-frontal section. The discussion is centred on the hypotheses postulated before the experiment; these were intended to allow verification or refutation of aspects of models of frontal dynamics such as those embodied in semi-geostrophic theory. A minimal and purely objective analysis has been performed on the data presented here, but the high resolution of the dropsonde observations permits evaluation of differentiated quantities, such as potential vorticity (PV), with some confidence. Issues raised here include the degree of thermal-wind balance, the structure of conserved variables such as equivalent potential temperature and absolute momentum, the cross-frontal circulation and the role of diabatic processes such as the evaporation, of snow, and the potential vorticity structure on the mesoscale and its interpretation. The intention is to present an integrated view of the dynamical structure of fronts in the light of theoretical rather than conceptual or airflow models.
Aspergillus oryzae Fermentation Extract Alleviates Inflammation in Mycoplasma pneumoniae Pneumonia

The filamentous fungus Aspergillus oryzae, also known as koji mold, has been used for centuries in the production of fermented foods in East Asia. A. oryzae fermentation can produce enzymes and metabolites with various bioactivities. In this study, we investigated whether A. oryzae fermentation extract (AOFE) has any effect on Mycoplasma pneumoniae (Mp) pneumonia. We performed solid-state fermentation of A. oryzae and obtained the ethanol extract. AOFE was analyzed by HPLC, and the major component was identified to be kojic acid. In vitro, AOFE suppressed Mp growth and invasion into A549 lung epithelial cells as determined by the gentamicin protection assay. AOFE treatment also suppressed Mp-stimulated production of tumor necrosis factor (TNF)-α and interleukin (IL)-6 at mRNA and protein levels in murine MH-S alveolar macrophages. In a mouse model of Mp pneumonia, Mp infection induced a marked pulmonary infiltration of neutrophils, which was significantly reduced in mice pre-treated orally with AOFE. AOFE administration also suppressed the production of proinflammatory cytokines and chemokines in the lungs. Collectively, our results show that AOFE has the potential to be developed into a preventive/therapeutic agent for Mp pneumonia.
Nanorods of Co/Pd multilayers fabricated by glancing angle deposition for advanced media

Perpendicular anisotropy magnetic nanorods composed of Co/Pd multilayers have been successfully fabricated by glancing angle deposition (GLAD) in a planetary sputtering system. Co and Pd layer thickness, ratio, and bilayer number were optimized for both normal and GLAD depositions. Scanning electron micrographs estimated the nanorods to be about 12 nm in diameter. M-H loops showed that the coercivity for the GLAD nanorods increased from 1.3 kOe for the normally deposited continuous films to 2.9 kOe for the GLAD nanorod array, a 123% increase.
Generalized Synchroextracting-Based Stepwise Demodulation Transform and Its Application to Fault Diagnosis of Rotating Machinery

The nonstationary characteristics of time-varying signals can be characterized by time–frequency analysis (TFA) accurately, which has been widely used in fault diagnosis of rotating machinery. However, some practical signals contain complex multicomponent modes and noise interference, which will pose challenges to traditional TFA methods. In this article, a novel technique called generalized synchroextracting-based stepwise demodulation (GSET-SD) transform is proposed. GSET-SD introduces a strategy that combines synchroextracting transform (SET) with a stepwise demodulation procedure, thereby solving the problem of detecting and retrieving the amplitude- and frequency-modulated (AM-FM) components of a multicomponent signal from the time–frequency representation (TFR), while allowing the signal to be reconstructed from the TFR. Furthermore, to increase the estimation accuracy of the initial instantaneous frequency (IF) in processing the strong modulation signals, SET is extended to the second-order or higher order domain, and the IF is accurately estimated by the higher order polynomial method, which can obtain higher resolution TFR. The proposed approach has been applied to numerical simulations and application research. The experimental results verify the effectiveness and superiority of GSET-SD in processing strong modulation signals and demonstrate its promise in the field of fault diagnosis of rotating machinery under time-varying speeds.
Environmental Filtering Drives Fungal Phyllosphere Community in Regional Agricultural Landscapes

To adapt to climate change, several agricultural strategies are currently being explored, including a shift in land use areas. Regional differences in microbiome composition and associated phytopathogens need to be considered. However, most empirical studies on differences in the crop microbiome focused on soil communities, with insufficient attention to the phyllosphere. In this study, we focused on wheat ears in three regions in northeastern Germany (Magdeburger Börde (MBB), Müncheberger Sander (MSA), Uckermärkisches Hügelland (UKH)) with different yield potentials, soil, and climatic conditions. To gain insight into the fungal community at different sites, we used a metabarcoding approach (ITS-NGS). Further, we examined the diversity and abundance of Fusarium and Alternaria using culture-dependent and culture-independent techniques. For each region, the prevalence of different orders rich in phytopathogenic fungi was determined: Sporidiobolales in MBB, Capnodiales and Pleosporales in MSA, and Hypocreales in UKH were identified as taxonomic biomarkers. Additionally, F. graminearum was found predominantly in UKH, whereas F. poae was more abundant in the other two regions. Environmental filters seem to be strong drivers of these differences, but we also discuss the possible effects of dispersal and interaction filters. Our results can guide shifting cultivation regions to be selected in the future concerning their phytopathogenic infection potential.
Scheme for Multi-Chiplet Integration With Low Thermal Budget by Asymmetric Cu-Cu Bonding With Au Passivation Bonding Structure

This study proposed a multi-chiplet integration scheme using Cu–Cu bonding with a low thermal budget by utilizing an asymmetric passivation bonding structure. The single-sided passivation structure was achieved through one-sided heating thermal compression bonding at $150~^{\circ }\text{C}$ ; the bonding quality was enhanced by the passivation layer with small grain size. The bonding interface of the single-sided passivation structure was analyzed through transmission electron microscope, and the corresponding mechanism was inferred. Compared with original passivation structures, the single-sided passivation structure not only enhanced bonding quality but also provided higher bonding strength, and the contact resistance was lower than Cu-Cu direct bonding. Therefore, the single-sided passivation structure with low thermal budget, high bonding strength and high throughput can be applied to multi-chiplet integration.