Miracle-dz/UniFAR-FTUs huggingface dataset:
SARGDV: Efficient identification of groundwater-dependent vegetation using synthetic aperture radar

Groundwater depletion impacts the sustainability of numerous groundwater-dependent vegetation (GDV) globally, placing significant stress on their capacity to provide environmental and ecological support for flora, fauna, and anthropic benefits. Industries such as mining, agriculture, and plantations are heavily reliant on groundwater, the over-exploitation of which risks impacting groundwater regimes, quality, and accessibility for nearby GDVs. Cost effective methods of GDV identification will enable strategic protection of these critical ecological systems, through improved and sustainable groundwater management by communities and industry. Recent application of synthetic aperture radar (SAR) earth observation data in Australia has demonstrated the utility of radar for identifying terrestrial groundwater-dependent ecosystems at scale. We propose a robust classification method to advance identification of GDVs at scale using processed SAR data products adapted from a recent previous method. The method includes the development of SARGDV, a binary classification model, which uses the extreme gradient boosting (XGBoost) algorithm in conjunction with three data cubes composed of Sentinel-1 SAR interferometric wide images. The images were collected as a one-year time series over Mount Gambier, a region in South Australia, known to support GDVs. The SARGDV model demonstrated high performance for classifying GDVs with 77% precision, 76% true positive rate and 96% accuracy. This method may be used to support the protection of GDV communities globally by providing a long term, cost-effective solution to identify GDVs over variable regions and climates, via the use of freely available, high-resolution, globally available Sentinel-1 SAR data sets.
The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations

We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By employing an artificial neural network to learn these SC points, we can perform Monte Carlo simulations with large time steps. Basic error analysis indicates that this data-driven scheme results in accurate SDE solutions in the sense of strong convergence, provided the learning methodology is robust and accurate. With a method variant called the compression–decompression collocation and interpolation technique, we can drastically reduce the number of neural network functions that have to be learned, so that computational speed is enhanced. As a proof of concept, 1D numerical experiments confirm a high-quality strong convergence error when using large time steps, and the novel scheme outperforms some classical numerical SDE discretizations. Some applications, here in financial option valuation, are also presented.
Channel-wise Alignment for Adaptive Object Detection

Generic object detection has been immensely promoted by the development of deep convolutional neural networks in the past decade. However, in the domain shift circumstance, the changes in weather, illumination, etc., often cause domain gap, and thus performance drops substantially when detecting objects from one domain to another. Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest, which naturally, cannot fully utilize the fine-grained channel information. In this paper, we realize adaptation from a thoroughly different perspective, i.e., channel-wise alignment. Motivated by the finding that each channel focuses on a specific pattern (e.g., on special semantic regions, such as car), we aim to align the distribution of source and target domain on the channel level, which is finer for integration between discrepant domains. Our method mainly consists of self channel-wise and cross channel-wise alignment. These two parts explore the inner-relation and cross-relation of attention regions implicitly from the view of channels. Further more, we also propose a RPN domain classifier module to obtain a domain-invariant RPN network. Extensive experiments show that the proposed method performs notably better than existing methods with about 5% improvement under various domain-shift settings. Experiments on different task (e.g. instance segmentation) also demonstrate its good scalability.
Anomalous U(1) to Z$_q$ cross-over in quantum and classical $q$-state clock models

We consider two-dimensional $q$-state quantum clock models with quantum fluctuations connecting states with all-to-all clock transitions with different matrix elements, including the case of transitions restricted to only the nearest clock states. We study the quantum phase transitions in these models with the aim of characterizing the cross-over from emergent U(1) symmetry at the transition (for $q \ge 4$) to $Z_q$ symmetry of the ordered state. As in classical three-dimensional clock models, the cross-over is governed by a dangerously irrelevant operator with scaling dimension $\Delta_q>3$. We specifically study $q=5,6$ models with different forms of the quantum fluctuations and different anisotropies in the classical models. In all cases studied, we find consistency with the expected classical XY critical exponents related to the conventional order parameter as well as the scaling dimensions $\Delta_q$. However, the initial weak violation of the U(1) symmetry in the ordered phase, characterized by an angular $Z_q$ symmetric order parameter $\phi_q$, scales with the system size in an unexpected way. As a function of the system size (length) $L$ in the close neighborhood of the critical temperature, $\phi_q \propto L^p$, where the known value of the exponent is $p=2$ in the classical isotropic XY model. In contrast, for strongly anisotropic XY models and all the quantum models studied, we observe $p=3$. For weakly anisotropic models we observe a cross-over from $p=2$ to $p=3$ scaling. The exponent $p$ also directly impacts the exponent $\nu'$ characterizing the divergence of the U(1)length scale $\xi'$ in the thermodynamic limit, according to the general relationship $\nu'=\nu(1+|y_q|/p)$, where $y_q=3-\Delta_q$ is the scaling dimension of the clock field. We present a phenomenological argument for the $p=3$.
Predicting Requests in Large-Scale Online P2P Ridesharing

Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic), as we showed in a recent publication where we proposed an online approximate solution algorithm for large-scale P2P-RS. In this paper we tackle the fundamental question of assessing the benefit of predicting ridesharing requests in the context of P2P-RS optimisation. Results on a public real-world show that, by employing a perfect predictor, the total reward can be improved by 5.27% with a forecast horizon of 1 minute. On the other hand, a vanilla long short-term memory neural network cannot improve upon a baseline predictor that simply replicates the previous day's requests, whilst achieving an almost-double accuracy.
PySAD: A Streaming Anomaly Detection Framework in Python

PySAD is an open-source python framework for anomaly detection on streaming data. PySAD serves various state-of-the-art methods for streaming anomaly detection. The framework provides a complete set of tools to design anomaly detection experiments ranging from projectors to probability calibrators. PySAD builds upon popular open-source frameworks such as PyOD and scikit-learn. We enforce software quality by enforcing compliance with PEP8 guidelines, functional testing and using continuous integration. The source code is publicly available on this https URL.
Stabilizing invertible neural networks using mixture models

In this paper, we analyze the properties of invertible neural networks, which provide a way of solving inverse problems. Our main focus lies on investigating and controlling the Lipschitz constants of the corresponding inverse networks. Without such a control, numerical simulations are prone to errors and not much is gained against traditional approaches. Fortunately, our analysis indicates that changing the latent distribution from a standard normal one to a Gaussian mixture model resolves the issue of exploding Lipschitz constants. Indeed, numerical simulations confirm that this modification leads to significantly improved sampling quality in multimodal applications.
Measuring mixed state entanglement through single-photon interference

Entanglement is a fundamental feature of quantum mechanics, considered a key resource in quantum information processing. Measuring entanglement is an essential step in a wide range of applied and foundational quantum experiments. When a two-particle quantum state is not pure, standard methods to measure the entanglement require detection of both particles. We introduce a method in which detection of only one of the particles is required to characterize the entanglement of a two-particle mixed state. Our method is based on the principle of quantum interference. We use two identical sources of a two-photon mixed state and generate a set of single-photon interference patterns. The entanglement of the two-photon quantum state is characterized by the visibility of the interference patterns. Our experiment thus opens up a distinct avenue for verifying and measuring entanglement, and can allow for mixed state entanglement characterization even when one particle in the pair cannot be detected.
Quantitative weighted estimates for rough singular integrals on homogeneous groups.

In this paper, we study weighted $L^{p}(w)$ boundedness ($1<p<\infty$ and $w$ a Muckenhoupt $A_{p}$ weight) of singular integrals with homogeneous convolution kernel $K(x)$ on an arbitrary homogeneous group $\mathbb H$ of dimension $\mathbb{Q}$, {under the assumption that $K_0$, the restriction of $K$ to the unit annulus, is mean zero and $L^{q}$ integrable for some $q_{0}<q\leq \infty$,} where $q_{0}$ is a fixed constant depending on $w$. We obtain a quantitative weighted bound, which is consistent with the one obtained by Hytonen--Roncal--Tapiola in the Euclidean setting, for this operator on $L^{p}(w)$. Comparing to the previous results in the Euclidean setting, our assumptions on the kernel and on the underlying space are weaker. Moreover, we investigate the quantitative weighted bound for the bi-parameter rough singular integrals on product homogeneous Lie groups.
Visual Object Tracking by Segmentation with Graph Convolutional Network

Segmentation-based tracking has been actively studied in computer vision and multimedia. Superpixel based object segmentation and tracking methods are usually developed for this task. However, they independently perform feature representation and learning of superpixels which may lead to sub-optimal results. In this paper, we propose to utilize graph convolutional network (GCN) model for superpixel based object tracking. The proposed model provides a general end-to-end framework which integrates i) label linear prediction, and ii) structure-aware feature information of each superpixel together to obtain object segmentation and further improves the performance of tracking. The main benefits of the proposed GCN method have two main aspects. First, it provides an effective end-to-end way to exploit both spatial and temporal consistency constraint for target object segmentation. Second, it utilizes a mixed graph convolution module to learn a context-aware and discriminative feature for superpixel representation and labeling. An effective algorithm has been developed to optimize the proposed model. Extensive experiments on five datasets demonstrate that our method obtains better performance against existing alternative methods.
Nonlinear spectral geometry processing via the TV transform

We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such an operator admits a generalized notion of spectral decomposition, yielding a convenient multiscale representation akin to Laplacian-based methods, while at the same time avoiding undesirable over-smoothing effects typical of such techniques. Our approach entails accurate, detail-preserving decomposition and manipulation of 3D shape geometry while taking an especially intuitive form: non-local semantic details are well separated into different bands, which can then be filtered and re-synthesized with a straightforward linear step. Our computational framework is flexible, can be applied to a variety of signals, and is easily adapted to different geometry representations, including triangle meshes and point clouds. We showcase our method through multiple applications in graphics, ranging from surface and signal denoising to enhancement, detail transfer, and cubic stylization.
Multilinear Common Component Analysis via Kronecker Product Representation

Abstract We consider the problem of extracting a common structure from multiple tensor data sets. For this purpose, we propose multilinear common component analysis (MCCA) based on Kronecker products of mode-wise covariance matrices. MCCA constructs a common basis represented by linear combinations of the original variables that lose little information of the multiple tensor data sets. We also develop an estimation algorithm for MCCA that guarantees mode-wise global convergence. Numerical studies are conducted to show the effectiveness of MCCA.
Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles

In this paper, we propose POTATOES (Partitioning OverfiTting AuTOencoder EnSemble) a new type of autoencoder ensembles for unsupervised outlier detection. Autoencoders are a popular method for this type of problem, especially if the data is located near a submanifold of smaller dimension than that of the ambient space. The standard approach is to approximate the data with the decoder submanifold of the autoencoder and to use the reconstruction error as anomaly score. However, one of the main problems is often to find the right amount of regularization. If the regularization is too strong, the data is underfitted and we obtain many false positives. If the regularization is too weak, the data is overfitted which results in false negatives. The remedy we propose is to not regularize at all, but to rather randomly partition the data into sufficiently many equally sized parts, overfit each part with its own autoencoder, and to use the maximum over all autoencoder reconstruction errors as the anomaly score. We apply our model to realistic data and show that it often outperforms current outlier detection methods.
KoSpeech: Open-Source Toolkit for End-to-End Korean Speech Recognition

We present KoSpeech, an open-source software, which is modular and extensible end-to-end Korean automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch. Several automatic speech recognition open-source toolkits have been released, but all of them deal with non-Korean languages, such as English (e.g. ESPnet, Espresso). Although AI Hub opened 1,000 hours of Korean speech corpus known as KsponSpeech, there is no established preprocessing method and baseline model to compare model performances. Therefore, we propose preprocessing methods for KsponSpeech corpus and a baseline model for benchmarks. Our baseline model is based on Listen, Attend and Spell (LAS) architecture and ables to customize various training hyperparameters conveniently. By KoSpeech, we hope this could be a guideline for those who research Korean speech recognition. Our baseline model achieved 10.31% character error rate (CER) at KsponSpeech corpus only with the acoustic model. Our source code is available here.
Hadronic interaction model sibyll 2.3d and extensive air showers

We present a new version of the hadron interaction event generator sibyll. While the core ideas of the model have been preserved, the new version handles the production of baryon pairs and leading particles in a new way. In addition, production of charmed hadrons is included. Updates to the model are informed by high-precision measurements of the total and inelastic cross sections with the forward detectors at the LHC that constrain the extrapolation to ultrahigh energy. Minimum-bias measurements of particle spectra and multiplicities support the tuning of fragmentation parameters. This paper demonstrates the impact of these changes on air-shower observables such as ${X}_{\mathrm{max}}$ and ${N}_{\ensuremath{\mu}}$, drawing comparisons with other contemporary cosmic-ray interaction models.
A Survey of the Insect Pests and Farmers' Practices in the Cropping of Tomato in Nigeria

Summary A survey of some tomato producing areas of Nigeria indicated that the major insects attacking tomato included the fruit borer Helicoverpa armigera Hubner, the grasshopper Zonocerus variegatus L. , the whitefly Bemisia tabacci Gennadius, and various species of aphids, mostly Aphis gossypii Glover. Interviews conducted to assess farmers’ practices which contribute to insect damage showed that inappropriate application of insecticides and the use of wrong dosages may have contributed to insect control failures. Intercropping tomato with crops such as cereals tubers and other vegetables reduced infestation in some areas. However, most farmers’ practices did not affect insect pest abundance. Insect populations and percentages of infestation were, in most cases, found to be significantly higher in Oyo state (in the rain forest agro-ecological zone) than in other surveyed states located in the savannah agro-ecological zones. Resume
Autophagy in Cerebral Ischemia: Progress and Challenge

Cerebral ischemia is a high-mortality neurological disease with limited therapy. The involvement of autophagy in this disease has drawn increasing attention recently. Here we review the recent research in this field, including the cell signaling and key factors participating in autophagy regulation in the context of cerebral ischemia. On the basis of the studies in our laboratory, we further discuss the contributions of autophagy, especially mitochondria autophagy (mitophagy), to ischemic brains. Finally, some challenging and cutting-edge questions in this field are raised. We hope to provide new ideas for the related research in pathology, pharmacology and therapeutics.
Phylogeny of pentatomomorphan bugs (Hemiptera-Heteroptera: Pentatomomorpha) based on six Hox gene fragments

Pentatomomorpha is one of the most diversified infraorders of the true bugs (Insecta: Hemiptera: Heteroptera). The phylogenetic relationships among superfamilies within this infraorder are still in dispute. In this study, 31 species representing 26 pentatomomorphan and four cimicomorphan putative families were chosen, and six Hox gene fragments with as many as 4 kilobases for each representative were analyzed to reconstruct the phylogeny of the Pentatomomorpha. The (Homeotic) Hox gene family is a group of nuclear genes, which is considered to determine animal segmentation. The combined nucleotide and amino acid sequences were used separately as two data matrices, and analyzed by employing maximum likelihood and Bayesian methods. Results strongly support the monophyly of Trichophora and the superfamilies Pentatomoidea, Lygaeoidea, Coreoidea, and Pyrrhocoroidea. The relationship of (Aradoidea + (Pentatomoidea + (Lygaeoidea + (Coreoidea + Pyrrhocoroidea)))) was mostly congruent with previous results based on the morphological data. Our results suggested that the Hox genes could be used as novel molecular markers for phylogenetic research on the Pentatomomorpha and other insects.
A rapid and simple extraction method for plant pathogenic fungi

Urea extraction has been recently reported as a method only for extraction of Arabidopsis genomic DNA, which has not applied in extraction of plant pathogenic fungal DNA. In this paper, four kinds of extraction methods (modified urea, CTAB, benzyl chloride and SDS-CTAB) were applied to extract the genomic DNA of five different plant pathogenic fungi (Phytophthora fragariae,Didymella bryoniae,Colletotrichum fragariae,Fusarium oxysporum f.sp.cucumerirum,Rhizoctonia solani) and they are assessed on simplicity, rapidity and efficacy. Results showed that the urea method had better efficacy, simpler, more convenient procedure and broader applicability.
Comprehensive Semantic Segmentation on High Resolution Aerial Imagery for Natural Disaster Assessment

In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation. The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation. We discuss the challenges of the dataset and train the state-of-the-art methods on this dataset to evaluate how well these methods can recognize the disaster situations. Finally, we discuss challenges for future research.
STUDY ON DNA DAMAGE INDUCED BY INDIRECT MUTAGENS USING COMET ASSAY IN TESTICULAR CELLS IN VITRO

Purpose:To stuty the metabolic role of primary tissue cells in detecting indirect mutagen. Methods: the DNA damage effects of cyclophosphamide, dimethylbenz(a)anthracene and 2-aminofluorene were studied using the single cell gel electrophoresis technique(Comet assay) in isolated mice testicular cells. Results: In certain concentration, the three tested substances could induce the breakage of DNA single strand and resulted in comet cells with tail. Both the number of comet cells and the length of DNA migration increased with increasing of concentration of tested substances. Conclusion: The primary mice testicular cells had certain metabolic role and could be used to detect the effects of DNA damage induced by indirect mutagen in comet assay.
Spacing Pattern of the Crested Serpent-Eagle (Spilornis Cheela Hoya) in Southern Taiwan

We studied the home ranges and core areas of a native population of Crested Serpent-eagles in southern Taiwan by radio-tracking from November 2005 to May 2007. Mean home range areas for eight male and six female individuals were 16.65 km^2 and 6.93 km^2 based on the estimation by the minimum convex polygon (MCP) method and 4.52 km^2 and 1.46 km^2 based on the 95% fixed kernel (FK) method, respectively. Mean core areas for males and females were 1.16 km^2 and 0.36 km^2 based on the estimation by the Area Independent Method (AIM) and 0.64 km^2 and 0.20 km^2 based on the 50% FK method, respectively. Mean fixed kernel home ranges of males were 3.1 times greater than those of females, and AIM core areas of males were 3.2 times greater than those of females. All of the individuals exhibited core area overlap, especially in the case of male to male core areas. Male to male, male to female and female to female average AIM core area overlap was 37.0%, 14.9% and 1.9%, respectively with these differences all being statistically significant. Therefore, at the level of AIM core area overlap, core areas of females were smaller, and female to female relationships showed considerably more range exclusivity than those of male to female and male to male. In contrast, male core areas were more regularly shared with other male or female individuals. Since radio-tracked females were 18% heavier than radio-tracked males, we discuss how the differences in spacing pattern may be caused by female dominance.