Miracle-dz/UniFAR-FTUs huggingface dataset:
A FUZZY CHARACTERISTIC SELECTION AND EXTRACTION FOR TEXT CATEGORIZATION

The dimensionality of the characteristic vector is generally enormous in text classification. Such high dimensionality can be a ruthless obstacle for classification algorithms. To reduce the dimensionality of feature vectors for text classification a powerful method called feature clustering is used. A fuzzy similarity-based self-constructing algorithm for feature clustering is proposed in this paper. Based on similarity test the words in the feature vector of a document set are grouped into clusters. Words that are related to each other are grouped into the same cluster. Each cluster is distinguished by a membership function with statistical mean and divergence. A desired number of clusters are formed automatically when all the words have been fed in and we have one extracted feature for each cluster. The extracted feature which is equivalent to a cluster is a weighted combination of the words contained in the cluster. By this algorithm, the resulting membership functions match closely with and describe properly the real sharing of the training data. Besides, for determining the appropriate number of extracted features which can then be avoided as the user need not specify the number of extracted features in advance, and trial-and-error. Our simulation results show that our technique can run faster and attain better extracted features than other methods.
Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

Background Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.
The effectiveness of immersive virtual reality in physical recovery of stroke patients: A systematic review

Background Over the past few years, technological innovations have been increasingly employed to augment the rehabilitation of stroke patients. Virtual reality (VR) has gained attention through its ability to deliver a customized training session and to increase patients’ engagement. Virtual reality rehabilitation programs allow the patient to perform a therapeutic program tailored to his/her needs while interacting with a computer-simulated environment. Purpose This study aims to investigate the effectiveness of a fully immersive rehabilitation program using a commercially available head-mounted display in stroke patients. Methods A systematic search was conducted in three databases, namely, PubMed, Google Scholar, and PEDro. Four hundred thirty-two references were identified. The keywords used for the literature search were in English, which are given as follows: immersive, virtual reality, neurorehabilitation, stroke, and head-mounted display. Additionally, applicable articles were identified through screening reference lists of relevant articles. Results Only 12 studies used head-mounted display for immersing the patient into the virtual world. Apart from the feasibility of this new technology, a range of benefits were identified, especially in terms of functional ability as measured by FIM or Barthel, the Action Research arm Test, Box and Block Test, Fugl-Meyer assessment of physical performance, strength, and balance outcomes. Conclusion The results from this review support the potential beneficial effect of fully immersive virtual reality in the rehabilitation of stroke patients, maximizing recovery through increased motivation and adherence.
Acoustic emission feature events during rock failure and their cumulative probability distribution: Case study of phosphate rock and granite

Rock mass failure is gradually becoming more common as the number of geotechnical engineering projects continues to increase. In this paper, the probability density distributions of initial and peak frequency events in the acoustic emission (AE) from two types of rock (phosphate rock and granite) undergoing failure are analyzed. Feature events (FEs) in this AE are proposed and obtained. The probabilities of events with an initial frequency of 1,000 kHz and peak frequency of 625 kHz are found to be higher than those with other frequencies. The evolutionary behavior of the cumulative probability distributions (CPDs) of the FEs as the rocks fail is subsequently investigated. The characteristic FEs of the AE and their CPD evolution behavior in the two rocks are then compared and contrasted. The CPD curves derived for both types of rock consist of four stages: slow rise—concave rise—rapid rise—slow rise. The differences related to the FEs for the two rocks are also found. The duration of the last stage (near rock failure) is quite different for phosphate rock and granite. The peak frequencies of the FEs are the highest and the smallest in the two rocks, respectively. Our method of analyzing the AE data and results provide a theoretical method for analyzing the stability of rock masses and predicting their failure.
Necrotizing periodontal disease in a nutritionally deficient patient: A case report

Necrotizing periodontal disease (NPD) is a microbial infectious inflammatory disease of the gingiva and/or periodontium that is characterized by a rapid onset of inflammation, pain, and “punched out” cratered interdental papillae. Although NPD is not very common, diagnosis is mostly based on its unique clinical presentation. Some predisposing factors for developing NPD include poor oral hygiene, smoking, malnutrition, immunosuppression (such as in HIV infection, uncontrolled diabetes, and cancer), and stress. This case report presented a 22-year-old Caucasian female, heavy smoker but otherwise no systemic disease, who suffered from severe inflammation of the gingiva, ulceration, and necrosis of the interdental papillae, and pseudomembrane formation. The patient had been hospitalized for a seizure episode and was diagnosed with malnutrition-induced seizure. NPD was diagnosed and the patient was treated successfully with nonsurgical scaling and root planing and behavioral modifications including smoking cessation and balanced nutrition intake. Disease remission was achieved after treatment. In addition, the present case report reviewed the effect of nutrients on the health of the periodontium.
Comparing the application of mNGS after combined pneumonia in hematologic patients receiving hematopoietic stem cell transplantation and chemotherapy: A retrospective analysis

Rapid and accurate pathogen identification is essential for timely and effective treatment of pneumonia. Here, we describe the use of metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage (BALF) fluid to identify pathogens in patients with hematologic comorbid respiratory symptoms in a retrospective study with 84 patients. In the transplantation group, 8 cases (19.5%) and 47 cases (97.9%) were positive for BALF by conventional method detection and mNGS detection, respectively, and 6 cases (14.0%) and 41 cases (91.1%) in chemotherapy group, respectively. The detection rate of mNGS in both groups was significantly higher than that of conventional detection methods (all P<0.05). Pseudomonas aeruginosa and Streptococcus pneumoniae were the most common bacterial infections in the transplantation and chemotherapy groups, respectively. Aspergillus was the most common fungal infection in both groups. Human betaherpesvirus 5 (HHV-5), torque teno virus and human betaherpesvirus 7 (HHV-7) were the most common pathogen species in both groups. The most common type of infection in patients in the transplantation and chemotherapy groups was the mixed infection of bacteria-virus. Most patients in the transplantation group had mixed infections based on multiple viruses, with 42 cases of viral infections in the transplantation group and 30 cases of viral infections in the chemotherapy group, which were significantly higher in the transplantation group than in the chemotherapy group (χ2 = 5.766, P=0.016). and the mixed infection of virus-virus in the transplantation group was significantly higher than that in the chemotherapy group (27.1% vs 4.4%, P=0.003). The proportion of death due to pulmonary infection was significantly higher in the transplantation group than in the chemotherapy group (76.9% vs 16.7%, χ2 = 9.077, P=0.003). This study demonstrated the value of mNGS of BALF in improving the diagnosis and prognosis of hematologic comorbid pneumonia, helping patients to obtain timely and effective treatment, and giving guidance on the overall treatment plan for patients, with particular benefit for patients with hematologic chemotherapy comorbid pneumonia.
Infinite time bubble towers in the fractional heat equation with critical exponent

In this paper, we consider the fractional heat equation with critical exponent in $\mathbb{R}^n$ for $n>6s,s\in(0,1),$ \begin{equation*} u_t=-(-\Delta)^su+|u|^{\frac{4s}{n-2s}}u,\quad (x,t)\in \mathbb{R}^n\times\mathbb{R}. \end{equation*} We construct a bubble tower type solution both for the forward and backward problem by establishing the existence of the sign-changing solution with multiple blow-up at a single point with the form \begin{equation*} u(x,t)=(1+o(1))\sum_{j=1}^{k}(-1)^{j-1}\mu_j(t)^{-\frac{n-2s}{2}}U\left(\frac{x}{\mu_j(t)}\right) \quad\mbox{as}\quad t\to+\infty, \end{equation*} and the positive solution with multiple blow-up at a single point with the form \begin{equation*} u(x,t)=(1+o(1))\sum_{j=1}^{k}\mu_j(t)^{-\frac{n-2s}{2}}U\left(\frac{x}{\mu_j(t)}\right) \quad\mbox{as}\quad t\to-\infty, \end{equation*} respectively. Here $k\ge2$ is a positive integer, $$U(y)=\alpha_{n,s}\left(\frac{1}{1+|y|^2}\right)^{\frac{n-2s}{2}},$$ and \begin{equation*} \mu_j(t)=\beta_j |t|^{-\alpha_j}(1+o(1))~\mathrm{as}~t\to\pm\infty, \quad \alpha_j=\frac{1}{2s}\left(\frac{n-2s}{n-6s}\right)^{j-1}-\frac{1}{2s}, \end{equation*} for some certain positive numbers $\beta_j,j=1,\cdots,k. $
LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation

Although recent gaze estimation methods lay great emphasis on attentively extracting gaze-relevant features from facial or eye images, how to define features that include gaze-relevant components has been ambiguous. This obscurity makes the model learn not only gaze-relevant features but also irrelevant ones. In particular, it is fatal for the cross-dataset performance. To overcome this challenging issue, we propose a gaze-aware analytic manipulation method, based on a data-driven approach with generative adversarial network inversion's disentanglement characteristics, to selectively utilize gaze-relevant features in a latent code. Furthermore, by utilizing GAN-based encoder-generator process, we shift the input image from the target domain to the source domain image, which a gaze estimator is sufficiently aware. In addition, we propose gaze distortion loss in the encoder that prevents the distortion of gaze information. The experimental results demonstrate that our method achieves state-of-the-art gaze estimation accuracy in a cross-domain gaze estimation tasks. This code is available at https://github.com/leeisack/LatentGaze/.
Takasaki’s rational fourth Painlevé-Calogero system and geometric regularisability of algebro-Painlevé equations

We propose a notion of regularisation which extends Okamoto’s construction of spaces of initial conditions for the Painlevé differential equations to the class of systems with globally finite branching about movable singularities in the sense of the algebro-Painlevé property. We illustrate this regularisation first in the case of a Hamiltonian system obtained by Takasaki as part of the Painlevé-Calogero correspondence, which is related by an algebraic transformation to the fourth Painlevé equation. Through a combination of compactification, blowups and removal of certain curves we obtain a space on which the system is everywhere either regular or regularisable by certain algebraic transformations. We provide an atlas for this space in which the system has a global Hamiltonian structure, with all Hamiltonian functions being polynomial in coordinates just as in the case of the Painlevé equations on Okamoto’s spaces. We also compare the surface associated with the Takasaki system with that of the fourth Painlevé equation, showing that they are related by a combination of blowdowns and a branched double cover. We provide more examples of algebro-Painlevé equations regularised in this way and also discuss applications of this generalised construction of the space of initial conditions to the identification and classification of algebro-Painlevé equations.
Corner-free sets via the torus

A corner is a triple of points in Z 2 of the form ( x, y ) , ( x + d, y ) , ( x, y + d ) where d 6 = 0. One can think of them as being 2D-analogues to 3-term arithmetic progressions.Inthisshort note, we extend ideas of Green-Wolf from this latter setting to the former, achieving slightly better constructions of corner-free sets.
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization

Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine learning or reinforcement learning tasks. In many practical scenarios, however, only the output series that result from the interactions of simulation agents are observable. Therefore, simulators need to be calibrated so that the simulated output series resemble historical – which amounts to solving a complex simulation optimization problem. In this paper, we propose a simple and efficient framework for calibrating simulator parameters from historical output series observations. First, we consider a novel concept of eligibility set to bypass the potential non-identifiability issue. Second, we generalize the two-sample Kolmogorov-Smirnov (K-S) test with Bonferroni correction to test the similarity between two high-dimensional distributions, which gives a simple yet effective distance metric between the output series sample sets. Third, we suggest using Bayesian optimization (BO) and trust-region BO (TuRBO) to minimize the aforementioned distance metric. Finally, we demonstrate the efficiency of our framework using numerical experiments both on a multi-agent financial market simulator.
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts

Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system's dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham's historical manuscript collections to obtain some really promising results in this direction.
Interactive Abstract Interpretation: Reanalyzing Whole Programs for Cheap

To put static program analysis at the ﬁn-gertips of the software developer, we propose a framework for interactive abstract interpretation. While pro-viding sound analysis results, abstract interpretation in general can be quite costly. To achieve quick response times, we incrementalize the analysis infrastructure, including postprocessing, without necessitating any modiﬁcations to the analysis speciﬁcations themselves. We rely on the local generic ﬁxpoint engine TD , which dynamically tracks dependencies, while exploring the unknowns contributing to answering an initial query. Lazy invalidation is employed for analysis results aﬀected by program change. Dedicated improvements support the incremental analysis of concurrency deﬁciencies such as data-races. The framework has been implemented for multithreaded C within the static analyzer Goblint , using MagpieBridge to relay ﬁndings to IDEs. We evaluate our implementation w.r. t. the yard sticks of response time and consistency: formerly proven invariants should be retained – when they are not aﬀected by the change. The results indicate that with our approach, a reanalysis after small changes only takes a fraction of from-scratch analysis time, while most of the precision is retained. We also provide examples of program development highlighting the usability of the overall approach.
Ensuring planetary survival: the centrality of organic carbon in balancing the multifunctional nature of soils

Abstract Not only do soils provide 98.7% of the calories consumed by humans, they also provide numerous other functions upon which planetary survivability closely depends. However, our continuously increasing focus on soils for biomass provision (food, fiber, and energy) through intensive agriculture is rapidly degrading soils and diminishing their capacity to deliver other vital functions. These tradeoffs in soil functionality – the increased provision of one function at the expense of other critical planetary functions – are the focus of this review. We examine how land-use change for biomass provision has decreased the ability of soils to regulate the carbon pool and thereby contribute profoundly to climate change, to cycle the nutrients that sustain plant growth and ecosystem health, to protect the soil biodiversity upon which many other functions depend, and to cycle the Earth’s freshwater supplies. We also examine how this decreasing ability of soil to provide these other functions can be halted and reversed. Despite the complexity and the interconnectedness of soil functions, we show that soil organic carbon plays a central role and is a master indicator for soil functioning and that we require a better understanding of the factors controlling the behavior and persistence of C in soils. Given the threats facing humanity and their economies, it is imperative that we recognize that Soil Security is itself an existential challenge and that we need to increase our focus on the multiple functions of soils for long-term human welfare and survivability of the planet. Graphical abstract
Constraints on variations of m$_\mathrm{p}$/m$_\mathrm{e}$ based on UVES observations of H$_2$

This article summarizes the latest results on the proton-to-electron mass ratio $\mu$ derived from H$_2$ observations at high redshift in the light of possible variations of fundamental physical constants. The focus lies on UVES observations of the past years as enormous progress was achieved since the first positive results on $\Delta\mu/\mu$ were published. With the better understanding of systematics, dedicated observation runs, and numerous approaches to improve wavelength calibration accuracy, all current findings are in reasonable good agreement with no variation and provide an upper limit of $\Delta\mu/\mu$ $< 1\times 10^{-5}$ for the redshift range of $2 <$ z $< 3$.
Monte Carlo simulations of ferroelectric crystal growth and molecular electronic structure of atoms and molecules

In this thesis, we explore two stochastic techniques to study properties of materials in realistic systems. Specifically, the kinetic Monte Carlo (KMC) method is utilized to study the crystal growth process of ferroelectric materials and the quantum Monte Carlo (QMC) approach is used to investigate the ground state properties of atoms and molecules. In the growth simulations, we study the growth rates and chemical ordering of ferroelectric alloys using an electrostatic model with long-range Coulomb interactions. Crystal growth is characterized by thermodynamic processes involving adsorption and evaporation, with solid-on-solid restrictions and excluding diffusion. A KMC algorithm is formulated to simulate this model efficiently in the presence of long-range interactions. The growth process is simulated as a function of temperature, chemical composition, and substrate orientation. We carried out the simulations on two heterovalent binaries, those of the NaCl and the Ba(Mgi/3 Nb2 /3 ) 0 3 (BMN) structures. Compared to the simple rocksalt ordered structures, ordered BMN grows only at very low temperatures and only under finely tuned conditions. For materials with tetravalent compositions, such as (1 x)Ba(Mgi/3 Nb2 /3 ) 0 3 + xBaZr0 3 (BMN-BZ), the model does not incorporate tetravalent ions at low-temperature, exhibiting a phase-separated ground state instead. At higher temperatures, tetravalent ions can be incorporated, but the resulting crystals show no chemical ordering in the absence of diffusive mechanisms. In the second part of the thesis, we present results from an auxiliary field quantum Monte Carlo (AFQMC) study of ground state properties, in particular dissociation and ionization energy, of second-row atoms and molecules. The method projects the many-body ground state from a trial wavefunction by random walks in the space of Slater determinants. The Hubbard-Stratonovich transformation is employed to decouple the Coulomb interaction between electrons. A trial wave function IT T) is used in the approximation to control the “phase problem”. We also carry out Hartree-Fock (HF) and Density Functional Theory (DFT) calculations for comparison to AFQMC results and to serve as starting wavefunctions for our AFQMC calculations. Results of dissociation energy are in excellent agreement with experimental values. Ionization energy errors are somewhat larger than those of other methods. We conclude with a discussion of several possible sources of error as well as a direction for the improvement.
A Generative Method for Antenna Optimization

The key to rapid antenna design is to improve the optimization of design parameters by simplifying the modeling of antenna geometric structures. We propose a flexible geometric modeling scheme that can be formed into any arbitrary shape by simply connecting nodes. We also propose a machine learning based generative method for high dimensional design parameter problems. It consists of discriminators and generators. The discriminators predict the performance of geometric models and the generators create new candidates that will pass the discrim-inators. Further, an evolutionary criterion approach is applied to update the discriminators and generators. The eﬃciency of the new design methodology is illustrated by a dual resonance antenna design example.