Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection Lung Cancer Using Gray Level Co-Occurrence Matrix ( GLCM ) and Back Propagation Neural Network Classification

Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung tissue sample microscopic analysis has some weakness. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted with the Gray Level Co-Occurrence Matrix (GLCM) method and classified using back propagation neural network. This method is implemented to detection both normal and cancerous lung of biopsy samples. In the stage of training, 20 biopsy image samples were analyzed using back propagation neural network with 95% accuracy. On the other hand, 16 biopsy samples were analyzed during testing, with an accuracy of 81.25%. These results show that microscopic biopsy image processing can be implemented in a system of lung cancer detection.
The combination of preoperative serum C-reactive protein and carcinoembryonic antigen is a useful prognostic factor in patients with esophageal squamous cell carcinoma: a combined ROC analysis

Background The prognostic value of inflammatory index in esophageal cancer (EC) has not been established. In the present study, therefore, we initially evaluated a novel prognostic system, named the COCC (COmbination of C-reactive protein [CRP] and carcinoembryonic antigen [CEA]), for making a prognosis in patients with esophageal squamous cell carcinoma (ESCC). Methods A total of 327 patients with ESCC between January 2006 and December 2008 were included in this retrospective study. The COCC was calculated by combined CRP and CEA according to the logistic equation. The Kaplan–Meier method was used to calculate the cancer-specific survival (CSS), and the difference was assessed by the log-rank test. Cox regression analyses were performed to evaluate the prognostic factors. Results In our study, COCC was defined as CRP +0.71 CEA according to the logistic equation. Receiver operating characteristic curves for CSS prediction were plotted to verify the optimum cutoff points for CRP, CEA, and COCC, which were 9.8 mg/L, 4.2 ng/mL, and 8.0, respectively. Patients with COCC ≤8.0 had a significantly better CSS than patients with COCC >8.0 (53.1% vs 15.3%, P<0.001). Multivariate analysis revealed that COCC was an independent prognostic factor in patients with ESCC (P=0.006). In addition, the area under the curve (AUC) was 0.722 for COCC, 0.645 for CRP, and 0.618 for CEA, indicating that COCC was superior to CRP or CEA for CSS prediction. Conclusion The COCC is an independent prognostic factor in patients with ESCC. We conclude that COCC was superior to CRP or CEA as a more precise prognostic factor in patients with ESCC.
The structure at infinity of linear delay systems and the row-by-row decoupling problem

The row-by-row decoupling problem is studied for linear delay systems. The structural approach is used to design a decoupling precompensator. The realization of the given precompensator by static state feedback is studied. Using various structural and geometric tools, a detailed description of the feedback is given, in particular, derivative of the delayed new reference can be needed in the realization of the precompensator.
Jurnal Mitra Kesehatan (JMK)

Introduction: Adolescent is a nation’s future asset that is prone to nutritional problems. One of the nutritional problem in adolescent is anemia. Anemia is a condition when the hemoglobin level is below normal limits. Currenty, anemia among adolescent is still a health problem both globally and in Indonesia. Anemia has a negative impact on adolescents, including reducing concentration in learning, reducing work productivity, and disrupting the body’s defense system. Furthermore, anemia is one of the indirect factors that cause maternal death. The prevalence of anemia among adolescent in Karawang Regency, especially at SMK Pratama Mulya Karawang is still quite high (53,3%), so it is necessary to do further research on the factors that influence the incidence of anemia among adolescent girls. This study aims to determine the relationship between knowledge, nutritional adequacy level, tea drinking habits, and nutritional status with incidence of adolescent girls anemia in SMK Pratama Mulya Karawang. Method: This study use a quantitative observational research with a cross sectional design. The minimum number of samples in this study was 65 people. Anemia data was collected using biochemical measurements of hemoglobin levels, knowledge data using a knowledge questionnare, nutritional intake data and tea drinking habits were carried out by interviewing using the SQ-FFQ form, while nutritional status data was measured by anthropometric measurements. Results: The results showed that there was a significant relationship between energy adequacy levels, protein adequacy levels, iron adequacy levels, and vitamin C adequacy levels with the incidence of anemia and there was no significant relationship between knowledge, vitamin B12 adequacy levels, tea drinking habits and nutritional status with the incidence of anemia in adolescent girls. Conclusion: Nutritional adequacy level is related to the incident of anemia among adolescent gils in SMK Pratama Mulya Karawang
Regulation of Proton–α Differential Flow by Compressive Fluctuations and Ion-scale Instabilities in the Solar Wind

Large-scale compressive slow-mode-like fluctuations can cause variations in the density, temperature, and magnetic-field magnitude in the solar wind. In addition, they also lead to fluctuations in the differential flow U pα between α-particles and protons (p), which is a common source of free energy for the driving of ion-scale instabilities. If the amplitude of the compressive fluctuations is sufficiently large, the fluctuating U pα intermittently drives the plasma across the instability threshold, leading to the excitation of ion-scale instabilities and thus the growth of corresponding ion-scale waves. The unstable waves scatter particles and reduce the average value of U pα . We propose that this “fluctuating-drift effect” maintains the average value of U pα well below the marginal instability threshold. We model the large-scale compressive fluctuations in the solar wind as long-wavelength slow-mode waves using a multi-fluid model. We numerically quantify the fluctuating-drift effect for the Alfvén/ion-cyclotron and fast-magnetosonic/whistler instabilities. We show that measurements of the proton–α differential flow and compressive fluctuations from the Wind spacecraft are consistent with our predictions for the fluctuating-drift effect. This effect creates a new channel for a direct cross-scale energy transfer from large-scale compressions to ion-scale fluctuations.
Total outer-connected domination number of middle graphs

In this paper, we study the total outer-connected domination number of the middle graph of a simple graph and we obtain tight bounds for this number in terms of the order of the middle graph. We also compute the total outer-connected domination number of some families of graphs, explicitly. Moreover, some Nordhaus-Gaddum-like relations are presented for the total outer-connected domination number of middle graphs.
Gender Differences in Motor Skill Proficiency From Childhood to Adolescence

Students' proficiency in three object control and three locomotor skills were assessed in 2000 (M age = 10.06 years, SD = 0.63) in New South Wales, Australia and in 2006–07 (M age = 16.44 years, SD = 0.64). In 2006–07, 266 students, 138 girls (51.9%) and 128 boys (48.1%), had at least one skill reassessed. Boys were more object control proficient than girls. Childhood object control proficiency significantly predicted (p = .001) adolescent object control proficiency (r2 = .39), and, while gender was significant (p = .001), it did not affect the relationship between these variables (p = .53). Because childhood object control proficiency is predictive of subsequent object control proficiency, developing skills in childhood is important.
Dynamics of drop impact onto a solid sphere: spreading and retraction

In this paper, drop impact onto a sphere is numerically investigated at moderate Reynolds and Weber numbers. It is naturally expected that the aspect ratio of the sphere to the drop, $\unicode[STIX]{x1D706}_{r}$ , would make a big difference to drop spreading and retraction on the sphere, compared with drop impact onto a flat substrate. To quantitatively assess the effect of $\unicode[STIX]{x1D706}_{r}$ , a diffuse-interface immersed-boundary method is adopted after being validated against experiments. With the help of numerical simulations, we identify the key regimes in the spreading and retraction, analyse the results by scaling laws, and quantitatively evaluate the effect of $\unicode[STIX]{x1D706}_{r}$ on the impact dynamics. We find that the thickness of the liquid film spreading on the sphere can be well approximated by $h_{L,\infty }(1+3/4\unicode[STIX]{x1D706}_{r}^{-3/2})$ , where $h_{L,\infty }$ represents the film thickness of drop impact on a flat substrate. At the early stage of spreading, the temporal variation of the wetted area is independent of $\unicode[STIX]{x1D706}_{r}$ when the time is rescaled by the thickness of the liquid film. Drops are observed to retract on the sphere at a roughly constant speed, and the predictions of theoretical analysis are in good agreement with numerical results.
Intensity-free Integral-based Learning of Marked Temporal Point Processes

In the marked temporal point processes (MTPP), a core problem is to parameterize the conditional joint PDF (probability distribution function) $p^*(m,t)$ for inter-event time $t$ and mark $m$, conditioned on the history. The majority of existing studies predefine intensity functions. Their utility is challenged by specifying the intensity function's proper form, which is critical to balance expressiveness and processing efficiency. Recently, there are studies moving away from predefining the intensity function -- one models $p^*(t)$ and $p^*(m)$ separately, while the other focuses on temporal point processes (TPPs), which do not consider marks. This study aims to develop high-fidelity $p^*(m,t)$ for discrete events where the event marks are either categorical or numeric in a multi-dimensional continuous space. We propose a solution framework IFIB (\underline{I}ntensity-\underline{f}ree \underline{I}ntegral-\underline{b}ased process) that models conditional joint PDF $p^*(m,t)$ directly without intensity functions. It remarkably simplifies the process to compel the essential mathematical restrictions. We show the desired properties of IFIB and the superior experimental results of IFIB on real-world and synthetic datasets. The code is available at \url{https://github.com/StepinSilence/IFIB}.
Virtual Lexicality. The semantics of innovative prefixed verbal anglicisms in German

The increasing influence of the digital media affects not only the behaviour of Internet users, but also calls for constant lexical innovation. Since most of the IT-related vocabulary originated in English, it is a particular challenge to examine how individual lexical items from this domain are processed as loan words in a receptor language. In this article it will be shown that there is a tendency in present-day German to form verbs denoting the use of online services by means of analogy (e.g. googeln > facebooken, youtuben, whatsappen) and that these verbs already participate in native derivational processes. Significantly, web-based searches performed for this study revealed that they occur in the context of the German inseparable prefixes er-, be-, ent-, ver- and zer- at least in computer-mediated communication. Given this behaviour on the one hand and the restricted use on the other, it is assumed here that these hybrid formations are pragmatically motivated. In particular, they allow Internet users.
Well-posedness for the extended Schr\"odinger-Benjamin-Ono system

In this work we prove that the initial value problem associated to the Schr\"odinger-Benjamin-Ono type system \begin{equation*} \left\{ \begin{array}{ll} \mathrm{i}\partial_{t}u+ \partial_{x}^{2} u= uv+ \beta u|u|^{2}, \partial_{t}v-\mathcal{H}_{x}\partial_{x}^{2}v+ \rho v\partial_{x}v=\partial_{x}\left(|u|^{2}\right) u(x,0)=u_{0}(x), \quad v(x,0)=v_{0}(x), \end{array} \right. \end{equation*} with $\beta,\rho \in \mathbb{R}$ is locally well-posed for initial data $(u_{0},v_{0})\in H^{s+\frac12}(\mathbb{R})\times H^{s}(\mathbb{R})$ for $s>\frac54$. Our method of proof relies on energy methods and compactness arguments. However, due to the lack of symmetry of the nonlinearity, the usual energy has to be modified to cancel out some bad terms appearing in the estimates. Finally, in order to lower the regularity below the Sobolev threshold $s=\frac32$, we employ a refined Strichartz estimate introduced in the Benjamin-Ono setting by Koch and Tzvetkov, and further developed by Kenig and Koenig.
Model Provenance via Model DNA

Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e.g., understanding where the model comes from, how it is trained, and how it is used). This paper focuses on a novel problem within this field, namely Model Provenance (MP), which concerns the relationship between a target model and its pre-training model and aims to determine whether a source model serves as the provenance for a target model. This is an important problem that has significant implications for ensuring the security and intellectual property of machine learning models but has not received much attention in the literature. To fill in this gap, we introduce a novel concept of Model DNA which represents the unique characteristics of a machine learning model. We utilize a data-driven and model-driven representation learning method to encode the model's training data and input-output information as a compact and comprehensive representation (i.e., DNA) of the model. Using this model DNA, we develop an efficient framework for model provenance identification, which enables us to identify whether a source model is a pre-training model of a target model. We conduct evaluations on both computer vision and natural language processing tasks using various models, datasets, and scenarios to demonstrate the effectiveness of our approach in accurately identifying model provenance.
Open superclusters I: The most populated primordial groups of open clusters in the third quadrant of the Galactic disc

An open supercluster (OSC) is defined as a cluster of at least six open clusters (OCs) born from the same giant molecular cloud (GMC). We survey the recent catalogs of OCs based on Gaia data and relevant literature to find 17 OSCs of the third galactic quadrant, along with 190 likely members of them. OSCs are frequent enough to be considered an extra class of objects in the hierarchy of star formation. Some of these supersystems are new and most of them contain more members than previously thought. The detailed study of some OSCs has leaded to the discovery of four new young clusters that are members of them, named Casado-Hendy 2 to 5. In certain instances, subgroups with distinct PMs or 3D positions have been found within an OSC, suggesting the presence of multiple generations of stars formed from several bursts of star formation within the same GMC. OSCs are typically unbound and tend to disintegrate on timescales of 0.1 Gyr. The present results support the Primordial Group hypothesis (Casado 2022) and suggest that globular clusters are not formed from the accretion of open superclusters, at least in the local Universe at late times.
Evaluating the structure of cognitive tasks with transfer learning

Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, which is not the case in this setting. This study investigates the transferability of deep learning representations between different EEG decoding tasks. We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets, ERP CORE and M$^3$CV, containing over 140 subjects and 11 distinct cognitive tasks. We measure the transferability of learned representations by pre-training deep neural networks on one task and assessing their ability to decode subsequent tasks. Our experiments demonstrate that, even with linear probing transfer, significant improvements in decoding performance can be obtained, with gains of up to 28% compare with the pure supervised approach. Additionally, we discover evidence that certain decoding paradigms elicit specific and narrow brain activities, while others benefit from pre-training on a broad range of representations. By revealing which tasks transfer well and demonstrating the benefits of transfer learning for EEG decoding, our findings have practical implications for mitigating data scarcity in this setting. The transfer maps generated also provide insights into the hierarchical relations between cognitive tasks, hence enhancing our understanding of how these tasks are connected from a neuroscientific standpoint.
Bubble testing under deterministic trends

This paper develops the asymptotic theory of the ordinary least squares estimator of the autoregressive (AR) coefficient in various AR models, when data is generated from trend-stationary models in different forms. It is shown that, depending on how the autoregression is specified, the commonly used right-tailed unit root tests may tend to reject the null hypothesis of unit root in favor of the explosive alternative. A new procedure to implement the right-tailed unit root tests is proposed. It is shown that when the data generating process is trend-stationary, the test statistics based on the proposed procedure cannot find evidence of explosiveness. Whereas, when the data generating process is mildly explosive, the unit root tests find evidence of explosiveness. Hence, the proposed procedure enables robust bubble testing under deterministic trends. Empirical implementation of the proposed procedure using data from the stock and the real estate markets in the US reveals some interesting findings. While our proposed procedure flags the same number of bubbles episodes in the stock data as the method developed in Phillips, Shi and Yu (2015a, PSY), the estimated termination dates by the proposed procedure match better with the data. For real estate data, all negative bubble episodes flagged by PSY are no longer regarded as bubbles by the proposed procedure.
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization

Obtaining versions of deep neural networks that are both highly-accurate and highly-sparse is one of the main challenges in the area of model compression, and several high-performance pruning techniques have been investigated by the community. Yet, much less is known about the interaction between sparsity and the standard stochastic optimization techniques used for training sparse networks, and most existing work uses standard dense schedules and hyperparameters for training sparse networks. In this work, we examine the impact of high sparsity on model training using the standard computer vision and natural language processing sparsity benchmarks. We begin by showing that using standard dense training recipes for sparse training is suboptimal, and results in under-training. We provide new approaches for mitigating this issue for both sparse pre-training of vision models (e.g. ResNet50/ImageNet) and sparse fine-tuning of language models (e.g. BERT/GLUE), achieving state-of-the-art results in both settings in the high-sparsity regime, and providing detailed analyses for the difficulty of sparse training in both scenarios. Our work sets a new threshold in terms of the accuracies that can be achieved under high sparsity, and should inspire further research into improving sparse model training, to reach higher accuracies under high sparsity, but also to do so efficiently.
A Transformer-Based Prediction Method for Depth of Anesthesia During Target-Controlled Infusion of Propofol and Remifentanil

Accurately predicting anesthetic effects is essential for target-controlled infusion systems. The traditional (PK-PD) models for Bispectral index (BIS) prediction require manual selection of model parameters, which can be challenging in clinical settings. Recently proposed deep learning methods can only capture general trends and may not predict abrupt changes in BIS. To address these issues, we propose a transformer-based method for predicting the depth of anesthesia (DOA) using drug infusions of propofol and remifentanil. Our method employs long short-term memory (LSTM) and gate residual network (GRN) networks to improve the efficiency of feature fusion and applies an attention mechanism to discover the interactions between the drugs. We also use label distribution smoothing and reweighting losses to address data imbalance. Experimental results show that our proposed method outperforms traditional PK-PD models and previous deep learning methods, effectively predicting anesthetic depth under sudden and deep anesthesia conditions.
Lifshitz transition enabling superconducting dome around the quantum critical point in TiSe$_2$

Superconductivity often emerges as a dome around a quantum critical point (QCP) where long-range order is suppressed to zero temperature. So far, this has been mostly studied in magnetically ordered materials. By contrast, the interplay between charge order and superconductivity at a QCP is not fully understood. Here, we present resistance measurements proving that a dome of superconductivity surrounds the charge-density-wave (CDW) QCP in pristine samples of 1$T$-TiSe$_2$ tuned with hydrostatic pressure. Furthermore, we use quantum oscillation measurements to show that the superconductivity sets in at a Lifshitz transition in the electronic band structure. We use density functional theory to identify the Fermi pockets enabling superconductivity: large electron and hole pockets connected by the CDW wave vector $\vec{Q}$ which emerge upon partial suppression of the zero-pressure CDW gap. Hence, we conclude that superconductivity is of interband type enabled by the presence of hole and electron bands connected by the CDW $\vec{Q}$ vector. Earlier calculations show that interband interactions are repulsive, which suggests that unconventional s$_{\pm}$ superconductivity is realised in TiSe$_2$ - similar to the iron pnictides. These results highlight the importance of Lifshitz transitions in realising unconventional superconductivity and help understand its interaction with CDW order in numerous materials.