Miracle-dz/UniFAR-FTUs huggingface dataset:
How Do Plants Cope with DNA Damage? A Concise Review on the DDR Pathway in Plants

DNA damage is induced by many factors, some of which naturally occur in the environment. Because of their sessile nature, plants are especially exposed to unfavorable conditions causing DNA damage. In response to this damage, the DDR (DNA damage response) pathway is activated. This pathway is highly conserved between eukaryotes; however, there are some plant-specific DDR elements, such as SOG1—a transcription factor that is a central DDR regulator in plants. In general, DDR signaling activates transcriptional and epigenetic regulators that orchestrate the cell cycle arrest and DNA repair mechanisms upon DNA damage. The cell cycle halts to give the cell time to repair damaged DNA before replication. If the repair is successful, the cell cycle is reactivated. However, if the DNA repair mechanisms fail and DNA lesions accumulate, the cell enters the apoptotic pathway. Thereby the proper maintenance of DDR is crucial for plants to survive. It is particularly important for agronomically important species because exposure to environmental stresses causing DNA damage leads to growth inhibition and yield reduction. Thereby, gaining knowledge regarding the DDR pathway in crops may have a huge agronomic impact—it may be useful in breeding new cultivars more tolerant to such stresses. In this review, we characterize different genotoxic agents and their mode of action, describe DDR activation and signaling and summarize DNA repair mechanisms in plants.
An Ensemble Learning Approach with Multi-depth Attention Mechanism for Road Damage Detection

Road damage detection is significant for road maintenance. Traditional manual visual inspection methods consume lots of time and labor. Developments in the field of computer vision create opportunities for automated and efficient image-based road damage detection. Through deep convolution neural networks, road damage localization and classification can be achieved simultaneously. This paper proposes an ensemble model with test time augmentation based on the You Only Look Once (YOLOv5) network and attention modules. The approach utilizes a state-of-the-art object detector known as YOLOv5. To focus more on the road in images, five improved YOLOv5 models with attention modules are proposed. Moreover, ensemble learning and test time augmentation are adopted to improve model generalization and detection performance. The proposed method was evaluated through the IEEE Big Data Crowdsensing-based Road Damage Detection Challenge 2022. Different ensemble models achieved an average F1-score of 0.65177 on the five test datasets.
Non-negative Matrix Factorization and DenseNet based Monophonic Singing Voice Separation

Most of traditional singing voice separation methods usually assume that the vocal model is the source-filter model thatextracts the transfering functions of the vibration source and the filters separately. In recent years, with the rapid development of deep learning, end-to-end methods have become increasingly popular and achieved better separation results. Deep neural networks are very useful for processing complex nonlinear data. However, such models usually have large parameter sizes and lack interpretability. In this paper, we propose a novel singing voice separation model which combine the advantages of both source-filter model and deep neural network. We use DenseNet to extract the fundamental frequency of singing voice by using its powerful feature extraction ability, and use human voice resonance model and non-negative matrix factorization (NMF) to obtain the soft mask to get the final separated voice. Experimental results show that our system can obtain better separation effect and generalization performance.
4 O ct 2 00 9 The Square Kilometre Array

The Square Kilometre Array (SKA) is intended as the next-gen eration radio telescope and will address fundamental questions in astrophysics, physics, a nd astrobiology. The international science community has developed a set of Key Science Programs: ( 1) Emerging from the Dark Ages and the Epoch of Reionization, (2) Galaxy Evolution, Cosmol ogy, and Dark Energy, (3) The Origin and Evolution of Cosmic Magnetism, (4) Strong Field T ests of Gravity Using Pulsars and Black Holes, and (5) The Cradle of Life/Astrobiology. In add ition, there is a design philosophy of “exploration of the unknown,” in which the objective is to ke ep the design as flexible as possible to allow for future discoveries. Both a significant challeng and opportunity for the SKA is to obtain a significantly wider field of view than has been obtain ed with radio telescopes traditionally. Given the breadth of coverage of cosmic magnetism and g laxy evolution in this conference, I highlight some of the opportunities that an expanded field o f view will present for other Key Science Programs.
A likelihood ratio-based forensic voice comparison using formant trajectories of Thai diphthongs

This study aims to quantify and compare acoustic parameters of the three Thai diphthongs /ia, īa, ua/ forensically. The likelihood-ratio approach is applied to the parameterized formant trajectories of each instance of each diphthong. The aim of this study is to assess whether such an approach can be used to effectively distinguish, in a probabilistic sense, two or more speech samples as belonging to the same speaker or as opposed to belonging to different speakers. Formant trajectories were fitted using polynomial interpolation. Likelihood ratio values were derived using the multivariate likelihood ratio (MVLR) estimation approach [1] and then calibrated by using the Log-Likelihood Ratio Cost function -Cllr [2]. Speech samples for this study were obtained from 15 male speakers of Standard Thai who performed various tasks aimed at eliciting speech such as giving directions from a map and reading from a script. Such speech data were recorded over two non-contemporaneous sessions separated by at least a week. The results show that the strength of evidence with calibrated Log10LRs > 4 were obtained with the smallest Cllr of 0.01.
On Link Concordance and Milnor's μ Invariants

The precise indeterminacy of Milnor's concordance μ¯ invariants is determined. It is shown that the (nilpotent quotient) Artin representation of string links is surjective. Orr's computation of the number of linearly independent μ¯ invariants of a fixed length is recovered. A structure theorem for the set of links up to concordance is proven. We define an action of 2l‐component string links on l‐component string links, which passes to concordance classes. It is shown that the set of links up to concordance is in bijection with the orbit space of the restriction of this action to the stabilizer of the identity. Via the Artin representation, the action passes to a unipotent action, defined purely algebraically and consequently algorithmically computable, on the corresponding automorphism groups. 1991 Mathematics Subject Classification 57M25.
Experiments on transition enhancement by distributed roughness

Distributed roughness with nominal height k in a boundary layer with displacement thickness δ* is known to lead to early non‐Tollmien–Schlichting–Schubauer (TS) transition when k/δ* exceeds unity in nonaccelerated flows. But could commonly occurring milder random roughness enhance the regular phase‐conditioned TS mechanisms and also lead to early transition? The new affirmative answer rests on extensive hot‐wire explorations and smoke‐wire visualization of flow over smooth and rough walls. The effects appear to be threefold. First, the low‐inertia fluid in the valleys between the elements evidently respond more readily to free‐stream disturbances. Second, once the TS fluctuations commence, they grow faster. Whether this is caused by continued input from free‐stream disturbances along the path or to an increased destabilization of the system or both remains unclear. No inflected mean boundary‐layer profiles were registered in careful measurements. Third, there is evidence of roughness‐induced three‐dimensi.
Comprehensive model for the nucleus of Periodic Comet Tempel 2 and its activity

Observational data obtained primarily during 1988 are analyzed and synthesized to develop a comprehensive physical model for the nucleus of Periodic Comet Tempel 2, one of the best studied members of Jupiter's family of short-period comets. It is confirmed that a previous investigation (Sekanina Z. , 1987, Diversity and Similarity of Comets, ESA SP-278) provided reliable information on the comet's spin-axis orientation, which implies an obliquity of 54° of the orbit plane to the equatorial plane and which appears to have varied little -if at all- with time. This conclusion is critical for fitting a triaxial ellipsoid to approximate the figure of the nucleus.
Stealthy Cyberattacks Detection Based on Control Performance Assessment Methods for the Air Conditioning Industrial Installation

This paper aims to study the workflow of the detection centre of stealthy attacks on industrial installations that generate an increase in energy consumption. Such long-lasting, undetected attacks on industrial facilities make production more expensive and less competitive or damage the installation in the long term. We present the concept of the remote detection system of cyberattacks directed at maliciously changing the controlled variable in an industrial process air conditioning system. The monitored signals are gathered at the PLC-controlled installation and sent to the remote detection system, where the discrepancies of signals are analysed based on the Control Performance Assessment indices. The results of performed tests prove the legitimacy of the adopted approach.
Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets

US corporations regularly spend millions of dollars reviewing electronically-stored documents in legal matters. Recently, attorneys apply text classification to efficiently cull massive volumes of data to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs of legal matters, it also faces a perception challenge: amongst lawyers, this technology is sometimes looked upon as a "black box. " Put simply, no extra information is provided for attorneys to understand why documents are classified as responsive. In recent years, explainable machine learning has emerged as an active research area. In an explainable machine learning system, predictions or decisions made by a machine learning model are human understandable. In legal ‘document review’ scenarios, a document is responsive, because one or more of its small text snippets are deemed responsive. In these scenarios, if these responsive snippets can be located, then attorneys could easily evaluate the model’s document classification decisions – this is especially important in the field of responsible AI. Our prior research identified that predictive models created using annotated training text snippets improved the precision of a model when compared to a model created using all of a set of documents’ text as training. While interesting, manually annotating training text snippets is not generally practical during a legal document review. However, small increases in precision can drastically decrease the cost of large document reviews. Automating the identification of training text snippets without human review could then make the application of training text snippet-based models a practical approach. This paper proposes two simple machine learning methods to locate responsive text snippets within responsive documents without using human annotated training text snippets. The two methods were evaluated and compared with a document classification method using three datasets from actual legal matters. The results show that the two proposed methods outperform the document-level training classification method in identifying responsive text snippets in responsive documents. Additionally, the results suggest that we can automate the successful identification of training text snippets to improve the precision of our predictive models in legal document review and thereby help reduce the overall cost of review.
Optimizing CNN‐LSTM hybrid classifier using HCA for biomedical image classification

In medical science, imaging is the most effective diagnostic and therapeutic tool. Almost all modalities have transitioned to direct digital capture devices, which have emerged as a major future healthcare option. Three diseases such as Alzheimer's (AD), Haemorrhage (HD), and COVID‐19 have been used in this manuscript for binary classification purposes. Three datasets (AD, HD, and COVID‐19) were used in this research out of which the first two, that is, AD and HD belong to brain Magnetic Resonance Imaging (MRI) and the last one, that is, COVID‐19 belongs to Chest X‐Ray (CXR) All of the diseases listed above cannot be eliminated, but they can be slowed down with early detection and effective medical treatment. This paper proposes an intelligent method for classifying brain (MRI) and CXR images into normal and abnormal classes for the early detection of AD, HD, and COVID‐19 based on an ensemble deep neural network (DNN). In the proposed method, the convolutional neural network (CNN) is used for automatic feature extraction from images and long‐short term memory (LSTM) is used for final classification. Moreover, the Hill‐Climbing Algorithm (HCA) is implemented for finding the best possible value for hyper parameters of CNN and LSTM, such as the filter size of CNN and the number of units of LSTM while fixing the other parameters. The data‐set is pre‐processed (resized, cropped, and noise removed) before feeding the train images to the proposed models for accurate and fast learning. Forty‐five MR images of AD, Sixty MR images of HD, and 600 CXR images of COVID‐19 were used for testing the proposed model ‘CNN‐LSTM‐HCA’. The performance of the proposed model is evaluated using six types of statistical assessment metrics such as; Accuracy, Sensitivity, Specificity, F‐measure, ROC, and AUC. The proposed model compared with the other three types of hybrid models such as CNN‐LSTM‐PSO, CNN‐LSTM‐Jaya, and CNN‐LSTM‐GWO and also with state‐of‐art techniques. The overall accuracy of the proposed model received was 98.87%, 85.75%, and 99.1% for COVID‐19, Haemorrhage, and Alzheimer's data sets, respectively.
Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and Exploitation

In recent years, vein-based biometric recognition has received ever-increasing attention from both academia and industry, due to the advantages it offers over traditional biometric traits such as fingerprint, iris, and face. Nonetheless, some issues related to the use of vein biometrics still need to be investigated and understood. Specifically, in this study, we speculate about the gender-related variations in vein patterns, and their effects on biometric verification performance. An analysis on the feasibility of recognizing male and female subjects depending on their hand-vein patterns, and on the level of similarity characterizing the biometric templates extracted from male and female populations, are here carried out considering three different databases. Specifically, the public VERA dataset, containing samples of palm-vein patterns, and two datasets containing images of finger-vein patterns, i.e., the UTFVP public database, and an in-house dataset collected with an on-the-move contactless modality, are here considered. The obtained experimental results show that the approach here proposed to perform gender recognition allows to reach an accuracy up to 95.83% on the public finger-vein UTFVP dataset, and to outperform the current state-of-the-art on the public palm-vein VERA dataset, with accuracy at 93.55%. It is also shown that vein-based biometric systems can benefit from the exploitation of information regarding the gender of the considered subjects, with achievable recognition rates that can be significantly improved by designing a biometric verification system relying on gender-specific models for extracting the employed discriminative templates.
Representation Theory of Artin Algebras I

This is the first of a series of papers dealing with the representation theory of artin algebras, where by an artin algebra we mean an artin ring having the property that its center is an artin ring and λ is a finitely generated module over its center. The over all purpose of this paper is to develop terminology and background material which will be used in the rest of the papers in the series. While it is undoubtedly true that much of this material can be found in the literature or easily deduced from results already in the literature, the particular development presented here appears to be new and is especially well suited as a foundation for the papers to come.
Particle Swarm Optimization of Multiple-Burn Rendezvous Trajectories

The particle swarm algorithm is a population-based heuristic method successfully applied in several fields of research, only recently to aerospace trajectories. It represents a very intuitivemethodology for optimization, inspired by the behavior of bird flocks while searching for food. In this work, the method is applied to (impulsive and finitethrust) multiple-burn rendezvous trajectories. First, the technique is employed to determine the globally optimal four-impulse rendezvous trajectories for two challenging test cases. Second, the same problems are solved under the assumption of using finite thrust. In this context, the control function is assumed to be a linear combination of B-splines. Themethod at hand is relatively straightforward to implement anddoes not require an initial guess, unlike gradient-based solvers. Despite its simplicity and intuitiveness, the particle swarm methodology proves to be quite effective in finding the optimal solution to orbital rendezvous optimization problems with considerable numerical accuracy.
On the structure of the low surface brightness dwarf galaxies in the M81 group

A detailed photometry of spheroidal dwarf galaxies in the M81 group has been carried out. The integral characteristics and the structural parameters of the spheroidal dwarfs has been determined. Their luminosity profiles are well fitted to a King law. The investigated spheroidal dwarfs together with the prototypes of the Local Group form a common sequence according to their main parameters. The observational data presented show that dSphs are not linked evolutionary with normal E and dE galaxies, but probably form a separate branch together with irregular low surface brightness dwarfs. Ergebnisse einer Photometrie von spheroidalen Zwerggalaxien in der M81-Gruppe werden mitgeleilt. Es wurden die integralen Eigenschaften und die Strukturparameter der Zwerggalaxien bestimmt. Ihre Helligkeitsprofile lassen sich gut durch ein Kingprofil darstellen. Die hier untersuchten spheroidalen Zwerggalaxien bilden zusammen mit den Prototypen der Lokalen Gruppe eine Gruppe. Die vorgelegten Ergebnisse zeigen, das die spheroidalen Zwerggalaxien moglicherweise zusammen mit den irregularen Zwerggalaxien geringer Flachenhelligkeit einen eigenen Entwicklungszweig bilden.
USO INTEGRADO DE IMAGEM DE ALTA RESOLUÇÃO ESPACIAL E ALTURA DERIVADA DO LASER SCANNER NA ESCOLHA DO CLASSIFICADOR ORIENTADO A REGIÃO

Neste artigo sao apresentados os resultados de um estudo que aborda o tema da integracao das alturas derivadas de um levantamento laser scanner com imagens de satelite de alta resolucao espacial, Quickbird II, para o mapeamento tematico de areas urbanas. Justifica-se este trabalho, devido o uso de imagens orbitais nao ser suficiente para distinguir os objetos associados ao mapeamento em areas urbanas, de forma semi-automatica, tornando-se necessaria a inclusao de outras informacoes auxiliares. Para isto, uma metodologia orientada a analise de regioes na imagem e proposta. O ganho obtido com a inclusao da altura derivada do laser scanner e comparado com o ganho resultante do uso de parâmetros espaciais derivados das imagens, mediante a utilizacao do algoritmo de classificacao da rede neural artificial (RNA) e classificadores convencionais (maxima verossimilhanca, distância minima). Os resultados mostram que, a contribuicao da informacao altimetrica e muito util na classificacao de edificacoes, vias e vegetacao. Dentre os algoritmos testados, o uso de RNA incluindo informacoes de elevacoes derivadas do laser scanner foi o que forneceu os melhores resultados. Integrated Use Of Spatial High Resolution Image And Laser Scanner Derived Hight Classificator Oriented To Region To Choose Abstract In this article, the results of a research about the integration of height derived from a laser scanner measurement with high resolution spatial sattelite image are presented, QUICKBIRD II, for the tematic mapping of urban areas. This research is justified, for so as not to suffice the use of orbital images, to distinguish the associated objects to the urban area mapping, in a semi-automatic way, one needs the inclusion of other auxiliary information. Therefore, it is proposed a metodology oriented to a regional analysis in the image. The gain obtained with the laser scanner derived height inclusion is compared to the resulting gain obtained from the use of spatial parameters derived from the images, through the use an artificial neural network classification algorithm (ANN) and usual classifiers (maximum likelihood and minimum distance). The results show that the altimetry information contribution is very useful in building, road and vegetation classification. From the tested algorithms, the use of an ANN including information of height derived from a laser scanner was the one which got the best results.
Commentary: Arbitrarily polarized long-range surface-plasmon-polariton waves

designs, a NAD can plot the field ratio that is determined by the interference effect in the multilayer. 5 A design method that is based on NAD is utilized to design a multilayered system for the propagation of an LRSPP wave with a predetermined angle of incidence θi (tangential componentofwavevector)inaprism-couplingconfiguration.WhentheLRSPPwaveisexcited, the locus of the metal film in NAD is characterized as a projected locus that traverses a huge distance to the terminal point and intersects the real axis far from the origin, as shown in Fig. 1. The projected locus is caused by a multilayer under the metal film that can provide a large imaginary part of normalized admittance η LRSPP(s) as the start of the locus. The analysis of LRSPP in a NAD supports the design of a new configuration for exciting either s -o rppolarized LRSPP waves. Furthermore, a configuration for exciting s- and p-polarized LRSPP waves simultaneously can be realized using multilayers on both sides of the metal film. 6 In the traditional Kretschmann prism/metal film/air configuration, the p-polarized light can be applied to excite the SPP wave with lower initial admittance η SPP ip compared with η LRSPP ip and the associated locus of the metal film is plotted in Fig. 1. However, the s-polarized light cannot excite the SPP wave because the s-polarized admittance of air η SPP is always negative imaginary when total reflection occurs. The locus of a metal film would never let the locus point go toward the characteristic admittance of a prism to reach a coupling effect. However, the restriction on an s-polarized LRSPP wave can be overcome using a symmetrical cell that is equivalent to a single layer with designed characteristic admittance and phase thickness. The initial admittance can be provided using a periodic multilayer composed of several symmetric cells of the ABA or ABCBA type with designable equivalent characteristic admittance. 5 The initial admittances for s- and p-polarization are functions of the angle of incidence θi, the refractive index, and the thickness of each layer in the unit cell. To excite an LRSPP wave on a metal film at a designated angle of incidence θi, both positive imaginary admittances with values larger than the absolute imaginary part of characteristic admittance of metal can be designed by tuning parameters in