Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of propagation of internal gravity waves in gases

Propagation of small-amplitude internal gravity waves (IGWs) in a compressible fluid is considered. The problem is characterized by the parameter β defined as the ratio of the vertical and horizontal scales. In the dimensionless equations, the vertical acceleration term is multiplied by β 2 , so that the limit case of β= 0 corresponds to the quasi-static approximation. It is argued that only methods that are convergent uniformly in β can be used in numerical simulations of IGWs. Some uniformly convergent methods are proposed and analyzed. The convergence is proved in such a way that the existence of a solution to a fluid-dynamics problem and the limit process β → 0 are simultaneously investigated. The quasi-static problem (with β = 0) is analyzed. This problem admits solutions in which density, pressure, and horizontal velocity are discontinuous in the horizontal direction x. However, they do not have any physical meaning and cannot be obtained in the framework of the complete fluid-dynamical problem in the limit β → 0. Some results of model computations are presented to illustrate the high quality of the proposed methods.
Trajectory Clustering Based on Trajectory Structure and Longest Common Subsequence

Trajectory clustering is an important method for mining valuable information from spatio-temporal data. Longest common subsequence clustering algorithm has advantages in distinguishing the overall trend of the trajectory, but ignores the trajectory structure details. At the same time, trajectory structural similarity clustering algorithm performs recognizing track structural details better than overall trends . Therefore we come up with a trajectory clustering algorithm based on trajectory structure and longest common subsequence in order to combine the advantages of them. The point distance calculation in LCSS is changed to trajectory structural distance which reflects the trajectory details better. The partition algorithm in trajectory structure clustering is improved to reduce the influence of noise points. The simulation results shows that TS-LCSS performs better than TC-SS when we need to recognize trajectories’ over all trends.
On Some Geometry of Graphs

On Some Geometry of Graphs by Zachary S. McGuirk Adviser: Professor Dr. Melvyn Nathanson In this thesis we study the intrinsic geometry of graphs via the constants that appear in discretized partial differential equations associated to those graphs. By studying the behavior of a discretized version of Bochner’s inequality for smooth manifolds at the cone point for a cone over the set of vertices of a graph, a lower bound for the internal energy of the underlying graph is obtained. This gives a new lower bound for the size of the first non-trivial eigenvalue of the graph Laplacian in terms of the curvature constant that appears at the cone point and the size of the vertex set for the underlying graph. For the sake of completeness, the main analysis for cones is actually done for cones over subsets of the vertex set. We follow this analysis up by studying which types of functions can achieve equality in the discrete Bochner inequality, in particular functions which yield the largest possible curvature bound at the cone point come with a dynamical definition. We are then able to classify the space of all such functions via spectral graph theory and recast the regularity of a graph in terms of the dimension of this space of functions.
A geometric proof of the polarization property

We analyze one version of the successive cancellation (SC) decoding that uses two random functions of the transmitted symbols: their likelihoods and variations of posterior probabilities. The first function increases its expected value on the upgrading channels, while the second does so on the degrading channels. We show that both quantities can be bounded by $\sin \theta$ and $\cos\theta$ of another random variable $\theta$ that ranges from $0$ to $\pi/2. $ We then present a simple proof that shows that the expected value of $\sin\theta\cos\theta$ tends to 0 in the consecutive iterations of the SC algorithm. This proves the polarization property of the SC decoding.
A Proxy Approach to Family Involvement and Neurocognitive Function in First Episode of Non-Affective Psychosis: Sex-Related Differences

Schizophrenia spectrum disorders (SSD) often show cognitive deficits (CD) impacting daily life. Family support has been shown to be protective against CD, yet the relationship between these in psychotic patients remains complex and not fully understood. This study investigated the association between a subdomain of family support, namely, family involvement (estimated through a proxy measure), cognitive functioning, and sex in first-episode psychosis (FEP) patients. The sample included 308 patients enrolled in the Program for Early Phases of Psychosis (PAFIP), divided into 4 groups based on their estimated family involvement (eFI) level and sex, and compared on various variables. Women presented lower rates of eFI than men (37.1% and 48.8%). Higher eFI was associated with better cognitive functioning, particularly in verbal memory. This association was stronger in women. The findings suggest that eFI may be an important factor in FEP patients’ cognitive functioning. This highlights the importance of including families in treatment plans for psychotic patients to prevent CD. Further research is needed to better understand the complex interplay between family support, sex, and cognitive functioning in psychotic patients and develop effective interventions that target these factors.
State of Cell Unjamming Correlates with Distant Metastasis in Cancer Patients

Pathological morphological changes in tumor tissue enable collective cancer cell unjamming, a cellular motility transition. However, fundamental questions remain: Is unjamming essential for tumor progression? Which different unjamming states can be found in patients? Here, vital cell tracking in patient-derived solid tumor explants ( N ¼ 16 ) reveals that states of cell unjamming can be recognized by elongated cell and nucleus shape (CeNuS) and low nucleus number density. These static variables serve as a morphodynamic link to map the broad range of morphologies and associated motility states found in histological slides of 1380 breast cancer patients to generate a comprehensive state diagram of cancer cell unjamming. An increase in predicted cell motility in primary tumors through unjamming significantly correlates with distant metastases that may even occur a decade later. Patient risk groups are quantified via a decision boundary in the state space found by machine learning. The resulting clinical prognostic potential is evaluated using a range of quantifiers, including Harrel ’ s concordance index. Using multivariable Cox models, we find that cell unjamming as a prognostic parameter adds a 26% information gain in the concordance index when combined with the established prognostic criteria (tumor diameter, tumor grade, lymph node status) used in the Nottingham index. Unjamming complements the information on affected lymph nodes in patients regarding metastatic risk. The derived state diagram of cancer cell unjamming reconciles conflicting observations regarding shape- or density-induced unjamming and stresses the nuclei ’ s mechanical importance, which is not considered in current theories of cell unjamming. We conclude that cancer cell unjamming is part of the metastatic cascade; thus, an emergent physical phenomenon contributes to tumor progression.
One Model Is Enough: Toward Multiclass Weakly Supervised Remote Sensing Image Semantic Segmentation

Semantic segmentation of remote sensing images (RSIs) is effective for large-scale land cover mapping, which heavily relies on a large amount of training data with laborious pixel-level labeling. Due to the easy availability of image-level labels, weakly supervised semantic segmentation (WSSS) based on them has attracted intensive attention. However, existing image-level WSSS methods for RSIs mainly focus on binary segmentation, which are difficult to apply to multiclass scenarios. This study proposes a comprehensive framework for image-level multiclass WSSS of RSIs, consisting of appropriate image-level label generation, high-quality pixel-level pseudo mask generation, and segmentation network iterative training. Specifically, a training sample filtering method, as well as a dataset co-occurrence evaluation metric, is proposed to demonstrate proper image-level training samples. Leveraging multiclass class activation maps (CAMs), an uncertainty-driven pixel-level weighted mask is proposed to relieve the overfitting of labeling noise in pseudo masks when training the segmentation network. Extensive experiments demonstrate that the proposed framework can achieve high-quality multiclass WSSS performance with image-level labels, which can attain 94.23% and 90.77% of the mean intersection over union (mIoU) from pixel-level labels for the ISPRS Potsdam and Vaihingen datasets, respectively. Beyond that, for the DeepGlobe dataset with more complex landscapes, the WSSS framework can achieve an accuracy close to 99% of the fully supervised case. In addition, we further demonstrate that compared to adopting multiple binary WSSS models, directly training a multiclass WSSS model can achieve better results, which can provide new thoughts to achieve WSSS of RSIs for multiclass application scenarios. Our code is publically available at https://github.com/NJU-LHRS/OME.
SELECTION OF Coffea arabica L. HYBRIDS USING MIXED MODELS WITH DIFFERENT STRUCTURES OF VARIANCE-COVARIANCE MATRICES

This study aimed to evaluate different structures of variance-covariance matrices in modeling of productive performance of coffee genotypes over the years, and select hybrids of Coffea arabica using mixed models. A mixed linear model was used to estimate variance components, heritability coefficients, and prediction of genetic values of hybrids and cultivars. Three commercial cultivars and eight hybrids of C. arabica L. were evaluated. The field production after acclimatization of seedlings was conducted in March 2006. The yield averages from 2009, 2010, 2011, 2013, and 2014 agricultural years were evaluated. The selection criteria of models were used to test 10 structures of variance-covariance matrices, and later a model was chosen to estimate the components of variance, heritability coefficients, and prediction of genetic values. According to Bayesian information criterion (BIC), the best structure was ARMA (Autoregressive Moving Average); however, considering the Akaike Information Criterion (AIC) and corrected Akaike Information Criterion (AICC), the CSH (Heterogeneous Composite Symmetric) was indicated. The Spearman correlation between the genotypic values obtained in the models with ARMA and CSH type R matrix was 0.84. The high and positive correlation indicates that the best model could involve the R matrix with ARMA or CSH structure. The heritability of individual genotypes differed from heritability in broad sense, which considers the independence among agricultural years. Hybrids with higher performance were identified by ordering the genotypic effects, among them, H 2.2, H 4.2, and H 6.1 hybrids were highlighted.
A New Method for Heart Disease Detection: Long Short-Term Feature Extraction from Heart Sound Data

Heart sounds have been extensively studied for heart disease diagnosis for several decades. Traditional machine learning algorithms applied in the literature have typically partitioned heart sounds into small windows and employed feature extraction methods to classify samples. However, as there is no optimal window length that can effectively represent the entire signal, windows may not provide a sufficient representation of the underlying data. To address this issue, this study proposes a novel approach that integrates window-based features with features extracted from the entire signal, thereby improving the overall accuracy of traditional machine learning algorithms. Specifically, feature extraction is carried out using two different time scales. Short-term features are computed from five-second fragments of heart sound instances, whereas long-term features are extracted from the entire signal. The long-term features are combined with the short-term features to create a feature pool known as long short-term features, which is then employed for classification. To evaluate the performance of the proposed method, various traditional machine learning algorithms with various models are applied to the PhysioNet/CinC Challenge 2016 dataset, which is a collection of diverse heart sound data. The experimental results demonstrate that the proposed feature extraction approach increases the accuracy of heart disease diagnosis by nearly 10%.
Investigating elements influencing cultural communication behavior of kindergarten students from ethnic minorities in Vietnam's educational system

The development of communication skills is crucial, particularly for directing the cultural communication practices of kindergarten students and ethnic minority students. The goal of the study was to examine the reality of children's engagement in cultural communication behavior and the reality of the elements that influenced the educational process of cultural communication behavior. There were 130 participants, including 30 administrators and 100 kindergarten teachers from ethnic minorities. The study found out that students were engaged in intercultural activities at an average rate and less frequently. In addition, it was evident that elements such as focus, the direction of management, and the expertise of educators had the biggest influence on the educational process. The study recommends that educators implement a policy of fair support for teachers and students, provide instructors with capacity-building training, and collaborate closely with students' families to increase the learning quality of student work. For kindergarteners to learn how to act in a way that is culturally communicative they need a lot of attention and direction from all levels on how to make policies that will improve the skills of both teachers and students. This is because the goal of education is to help children develop behaviors that are culturally communicative. At the same time, teachers work with the kids' families to teach them about cultural communication patterns in the best way possible.
An Optimized Filter Design with Cuckoo Search Algorithm for Industrial Microgrids

Most of the IT industries with microgrids are facing the impacts of harmonics on lighting loads, UPS, Elevators and Air Handling Units with Variable Frequency Drives, thus reduces the equipment performance and its life cycle. Many passive filters like single tuned filter, double tuned filter or combination of both single and double tuned filters et are implemented practically in the industries. It requires a careful tuning, otherwise, the resonance problems will arouse in the entire microgrid and leads to instability between the generation and loads, thus the design of an optimized passive filter is still a challenging problem for mitigating the harmonics. The Cuckoo Search Algorithm (CSA) is proposed in this paper to design the passive harmonic filter to reduce the Total Harmonic Distortion and size of the filter, which provides better tuning than the manual tuning. The advantages of this algorithm are: simple and provides less latency in processing in the iterations. This CSA is implemented on real time data from an IT industry with Fluke analyzer and the filter design is carried out using MATLAB simulation software and desired waveforms are presented.
An algorithm to adaptive determination of density threshold for density-based clustering

Density-based clustering is a good solution for classifying enormous data without any early knowledge. In this paper, the focus is distance measure of dense regions, which can be set adaptively. By translating a density threshold selection problem into a determination problem of the spherality radius, a novel density-based clustering algorithm based on determination of density threshold is proposed. First, we propose the definition of the partial cluster which is a radius threshold composed of a single object. Second, based on the analysis of radius threshold range from dense transitive closure via Hopkins statistic techniques, dense regions are formed by several partial cluster. Then we realize clustering according to the fact whether these regions can be recognized as clusters. Finally, the clustering results on various datasets are presented and its future direction is also discussed.
Progress on non-linear SFG use for high resolution imaging

For more than ten years we intend to promote the use of nonlinear optics in the frame of high angular resolution imaging for astronomy. The shift of the optical spectrum by nonlinear effects allows to overcome many difficulties resulting from the processing and detection of an infrared light. This paper reports on the experimental implementation of a sum frequency generation process (SFG) powered by a pump spectral doublet. The aim of this configuration is to allow the use of the SFG process over an enlarged spectral domain. By analyzing the converted signal, we numerically and experimentally demonstrate a frequency spectral compression effect from the infrared input signal to the visible one converted through the SFG process. The experimental setup, based on an up-conversion interferometer, permits to demonstrate an experimental frequency spectral compression factor of 3.56. This study takes place in the general field of coherence analysis through second order non-linear processes.
PAF-Net: A Progressive and Adaptive Fusion Network for Pavement Crack Segmentation

Automatic crack detection remains challenging due to factors such as irregular crack shapes and sizes, uneven illumination, complex backgrounds, and image noise. Deep learning has shown promise in computer vision for pixel-wise crack detection, but existing methods still suffer from limitations such as information loss, insufficient feature fusion, and semantic gap issues. To address these challenges, a novel pavement crack segmentation network, called PAF-Net, is proposed, which incorporates progressive and adaptive feature fusion. To mitigate information loss caused by feature downsampling, a progressive context fusion (PCF) block is introduced to capture context information from adjacent scales. To better capture strong features from local regions, a dual attention (DA) block is proposed that leverages both global and local context information, reducing the semantic gap issue. Furthermore, to achieve effective multi-scale feature fusion, a dynamic weight learning (DWL) block is proposed that enables efficient fusion of feature maps from different network layers. Additionally, a multi-scale input unit is incorporated to provide the proposed segmentation network with more contextual information. To evaluate the performance of PAF-Net, we conduct experiments using four common evaluation metrics and compare it with multiple mainstream segmentation models on three public datasets. The proposed PAF-Net demonstrates superior segmentation accuracy for pixel-level crack detection compared to other segmentation models, as evident from qualitative and quantitative experimental results.
Capability of Workers of the Ant Myrmica sabuleti to Categorize Numbers of Elements into Even and Odd

Categorizing numbers into even and odd is an ability held by humans that has recently been found to be also held by honeybees. We examined whether ants could also make such parity discrimination. Working on the species Myrmica sabuleti, we learned the ants of two colonies to associate 2 black circles with a reward and 5 of these circles with the absence of a reward, as well as learned the ants of two other colonies to associate 3 of these same cues with a reward and 4 of these cues with the absence of a reward. By collectively testing foragers of each colony in a separate tray in front of these cues, it was first verified during three days if they dully learned the ‘correct’ cue. Thereafter, while the ants continued to be trained, foragers of each colony were collectively subjected to nine successive choice tests, each day in front of a pair of cues different from the one used during the learning process. The cues used during these tests differed from those used to train the ants by the number and size of the dots, the cumulative surface of the dots, the perimeter of their area (surfaces and perimeters being maintained equal between the cues) and their layout. A ‘correct’ cue had the same parity (even or odd) as the cue learned during training. The tested ants each time consistently responded to the number that had the same parity as that of the number they learned during conditioning. This sensitivity to the number parity occurred from the number 1 until the number 7. Discrimination between 7 and 8 dots was beyond the ant capability.
Externally Applied Electromagnetic Fields and Hyperthermia Irreversibly Damage Cancer Cells

Simple Summary Moderate loco-regional hyperthermia (40–45 °C) is a therapeutic modality that can improve the effects of chemotherapy and radiotherapy and, in addition, improve our immune response against cancer. However, the effects of these combinations on many different cancers are modest. The use of much higher temperatures (>60 °C, thermal ablation) can cause severe damage to healthy tissues. In fact, our results show that cancer cells show extraordinary resistance to moderate hyperthermia. However, we found that the combination of hyperthermia (not higher than 52 °C) with low-strength electromagnetic fields acts synergistically, causing irreversible damage to different cancer cells. Moreover, these externally applied energies can be combined with chemotherapy and/or targeted therapies to achieve complete cancer eradication. In vivo, the energy causing focal hyperthermia can be distributed in multiple beams that can be concentrated in the tumor, thus avoiding damaging the healthy tissues that it passes through. Abstract At present, the applications and efficacy of non-ionizing radiations (NIR) in oncotherapy are limited. In terms of potential combinations, the use of biocompatible magnetic nanoparticles as heat mediators has been extensively investigated. Nevertheless, developing more efficient heat nanomediators that may exhibit high specific absorption rates is still an unsolved problem. Our aim was to investigate if externally applied magnetic fields and a heat-inducing NIR affect tumor cell viability. To this end, under in vitro conditions, different human cancer cells (A2058 melanoma, AsPC1 pancreas carcinoma, MDA-MB-231 breast carcinoma) were treated with the combination of electromagnetic fields (EMFs, using solenoids) and hyperthermia (HT, using a thermostated bath). The effect of NIR was also studied in combination with standard chemotherapy and targeted therapy. An experimental device combining EMFs and high-intensity focused ultrasounds (HIFU)-induced HT was tested in vivo. EMFs (25 µT, 4 h) or HT (52 °C, 40 min) showed a limited effect on cancer cell viability in vitro. However, their combination decreased viability to approximately 16%, 50%, and 21% of control values in A2058, AsPC1, and MDA-MB-231 cells, respectively. Increased lysosomal permeability, release of cathepsins into the cytosol, and mitochondria-dependent activation of cell death are the underlying mechanisms. Cancer cells could be completely eliminated by combining EMFs, HT, and standard chemotherapy or EMFs, HT, and anti-Hsp70-targeted therapy. As a proof of concept, in vivo experiments performed in AsPC1 xenografts showed that a combination of EMFs, HIFU-induced HT, standard chemotherapy, and a lysosomal permeabilizer induces a complete cancer regression.