Miracle-dz/UniFAR-FTUs huggingface dataset:
Théorie algébrique des systèmes à évènements discrets

Considerons les systemes a evenements discrets qui sont modelisables par des reseaux de Petri du type "graphes d'evenements temporises", Ils ont un comportement optimal (fonctionnement au plus tot) qui peut-etre calcule sans simulation par un systeme dynamique qui est lineaire dans l'algebre des diodes (max,+) ou (min,+). Le comportement asymptotique d'un tel systeme a evenements discrets est cyclique et les caracteristiques de ce cycle (periode, delai, motif) sont analysables par un calcul de valeur propre sur la matrice de dynamique. A partir de cette formulation lineaire, une representation externe (fonction de transfert) peut-etre obtenue grâce a un calcul formel sur des series a coefficients dans les diodes, la fonction de transfert d'un tel systeme est rationnelle au sens des dioides et est factorisable en une expression finie de polynomes.
A Novel MAC Scheduling Approach for Mobility based 5G Millimeter Wave Networks

Recent days have seen an increase in network traffic caused by bandwidth-hungry apps like augmented reality (AR), virtual reality (VR), and vehicle-to-everything (V2X), which 4G standards and technologies are unable to handle. The millimeter-wave (mmWave) can be seen as a gift for 5G systems meeting extremely high traffic demands by guaranteeing high throughput and low latency. More performance metrics such as SINR, throughput, and RTT inspection are required while increasing the number of UEs in different challenging regions. In UMa situations, MAC scheduling, as well as admission control, are also confusing TCP performance issues. This article uses ns3 to demonstrate a mobility based novel Proportional-Fair (PF) MAC scheduler for 5G networks integrated with mmWave. In this paper, we investigated resource allocation and access to the nearest data center in a practical mobility model based on 5G heterogeneous networks (HetNets). The result spreads higher-yielding in simultaneously growing quantities in the future complex structure of 5G networks.
Aptian Oceanic Anoxic Event 1a in the shallow, carbonate‐dominated intrashelf Kazhdumi Basin, Zagros Mountains

The early Aptian Selli Oceanic Anoxic Event 1a (ca 120 Ma) is a major Mesozoic episode of biotic, climatic, environmental and geochemical perturbations associated with the extensive burial of organic matter. Based on the new sedimentological, biostratigraphic and carbon‐isotope data from the Kazhdumi Intrashelf Basin, this study tracks the Oceanic Anoxic Event 1a‐associated perturbations along the north‐eastern margin of the Arabian Plate. The studied succession has five complete third‐order sequences with three facies associations: (i) deep open‐marine and intrashelf basin; (ii) shallow open‐marine mid‐ramp; and (iii) inner ramp. The Oceanic Anoxic Event 1a‐equivalent deposits include the former two facies associations – (i) and (ii) – and are 25 to 31 m thick, which corresponds to the accumulation rates of ca 1.9 to 3.1 cm kyr−1 (uncorrected for compaction) based on the 1.0 to 1.3 Myr duration of the event. The interval retained a high‐resolution record of carbon‐isotope and sea‐level fluctuations, which enables correlation across the Arabian Plate and Tethys. The Oceanic Anoxic Event 1a onset is marked by a biotic change from ostreid‐dominated shallow‐marine limestone with abundant foraminifera (Palorbitolina lenticularis and Choffatella decipiens) to dark grey deep‐water planktonic foraminifera‐rich and radiolaria‐rich limestone with chert and organic‐rich shale (Radiolarian Flood Zone). The upper oceanic anoxic event part of 1a is characterized by the shallowing‐upward, backstepping facies marking the return to a shallow open‐marine setting, albeit without recovery of the Palorbitolina–Choffatella association. Deep open‐marine deposition (Kazhdumi Tongue) was restored early during the Oceanic Anoxic Event 1a recovery in the latest early Aptian. A return to shallow open‐marine environments with Mesorbitolina‐rich limestone marks the beginning of the late Aptian, followed by a major sea‐level fall and karstification. This study resolves some of the uncertainties related to the order and timing of the events influencing the environmental disturbances across the eastern Arabian Plate during Oceanic Anoxic Event 1a and stresses the importance of shallow carbonate‐dominated intrashelf basins as faithful recorders of carbon‐isotope and sea‐level oscillations and palaeoenvironmental changes during global oceanic perturbations.
Modeling of Perimeter Gated SPAD-based Direct Time-of-Flight Sensor for Low Light LiDAR Applications

This paper presents a novel model of perimeter gated single photon avalanche (SPAD)-based direct time-of-flight (TOF) sensor for low light detection and ranging (LiDAR) applications. A perimeter gated SPAD (PGSPAD) is an improved version of SPAD which incorporates an extra field modulating gate terminal to mitigate perimeter edge breakdown without unnecessarily increasing the device size. Additionally, the extra gate terminal of PGSPAD device provides controls for the device’s noise, sensitivity, and dynamic range. In order to predict and optimize the performance of the system, we have taken into account all the fundamental factors that affect a typical LiDAR acquisition system, including background noise and device noise in the developed model. Results show that the applied gate voltage of PGSPAD helps to improve the signal-to-noise ratios (SNR) by reducing the device noise in low light scenarios. Additionally, the model incorporates the coincidence detection to minimize background noise.
Innovative use of tea bags to investigate the interactive effect of nutrient status and climatic factors on litter decomposition

ABSTRACT Interactive effects of nutrient status and climatic factors on litter decomposition remain unclear, partly due to the difficulty of extracting these effects from multiple decomposition data obtained from geographically diverse locations. Conducting the tea bag method repeatedly across different seasons, using the same fertilization study site and a short 90-day period, can be a useful tool for examining the interactive effects, as this method allows for a high degree of control over site effects by using an identical study site. In this study, we explored the sensitivity of the tea bag method in detecting the impacts of nutrient addition on tea material decomposition, as well as the interactive effects of nutrient addition and climatic factors on decomposition. At a 38-year continuous nitrogen and phosphorus fertilization experimental Sakhalin fir stand, we demonstrated that the mass loss ratio of rooibos tea and decomposition constant k, both of which are indicators of early-stage decomposition, were elevated by fertilization in summer, but not in winter. This was probably because, in summer when decomposition rates are less constrained by climatic conditions, the impact of nutrient availability on decomposition rates was relatively stronger compared to other seasons. Thus, the tea bag method can serve as a suitable tool for examining the effect of nutrient addition on early-stage litter decomposition and their interaction with climatic effects. Meanwhile, we concluded that the effects of nutrient addition on late-stage litter decomposition are undetectable by the tea bag method, because this method does not take into account lignin degradation.
Distortion Distribution and Convergent Analysis of the Global Discrete Grid Based on QTM

The partition principle of QTM on sphere is introduced. In order to reduce the area or shape distortion of grids and to improve the conversion efficiency between grid data and geographic coordinates, the traditional methods of QTM partition is modified by using latitude-line instead of circle-line. Then the distortion distribution characters in areas and edges of grids are analyzed in detail based on experimental data. The result shows that with the increase of partition levels,1) distortion distribution character in areas is close to the normal distribution, and 2) the ratios of S_(max)/S_(min) and L_(max)/L_(min) become greater, but their increments are less and less, at last converging to 1.73 and 1.86 respectively.
Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis

Simple Summary Digital pathology (DP) and computer-aided diagnosis (CAD) are rapidly evolving fields that have great potential for improving the accuracy and efficiency of cancer diagnosis, including that of canine mammary tumors (CMTs), the most common neoplasm in female dogs. The work presents a study on the development of CAD systems for the automated classification of CMTs utilizing convolutional neural networks (CNNs) to extract features from histopathological images of CMTs and classify them into benign or malignant tumors. The study shows that the proposed framework can accurately distinguish between benign and malignant CMTs, with testing accuracies ranging from 0.63 to 0.85. The study emphasizes how digital pathology and CAD could help veterinarians and pathologists in accurately diagnosing the tumor type, which is crucial in determining the optimal course of treatment. Overall, digital pathology and CAD are promising tools that could improve the accuracy and efficiency of cancer diagnosis, including that of canine mammary tumors. Abstract Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset—namely CMTD—of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.
The Nature of the Meteorological Fluctuations in Clouds

Abstract As in-cloud measurements have accumulated it has become increasingly apparent that clouds are markedly non-uniform in structure. This is demonstrated by sizable and rapid variations in the horizontal for almost all meteorological variables. A systematic study was made of the fluctuations in measurements obtained in hurricane clouds to establish whether they could be ascribed to a random process or whether they exhibited some spatial order. Variance spectrum analysis of continuous measurements of temperature, liquid-water content and air-speed for 49 cloud areas indicated that, in most cases, the variations were not random and that, in addition to the cloud scale, there were non-random components of smaller size. With considerable consistency, particularly in turbulent convection, the spectra indicated the existence of two frequency “domains” within which one or more dominant modes of activity occurred. These domains extended a) from 1.8–4 cycle mile−1 with peaks most frequent around 2.5 cycle mil.
Deep zero-inflated negative binomial model and its application in scRNA-seq data integration

In the past decades, many single-cell RNA sequencing (scRNA-seq) datasets have been generated with wide applications such as cell type identification and differential gene expression analysis. In practice, it is important to integrate different scRNA-seq datasets, which is beneficial for the efficiency of downstream analysis. However, this integration task is challenging due to the unwanted technical effects within the integrated dataset that would blur the biological effect. To address this issue, we propose a novel deep learning based statistical model, using the Maximum Mean Discrepancy (MMD) loss and zero-inflated negative binomial (ZINB) regression model, to remove two technical effects, i.e., batch effect and drop out effect, which are commonly existed unwanted technical noises confounding with the biological signals. We demonstrate that our model can effectively remove the technical effects while preserve the significant biological effects through a real scRNA-seq data analysis.
Techniques de construction en géométrie analytique. IX. Quelques problèmes de modules

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New Construction of Multiwavelength

In this paper, we investigate multiwavelength optical orthogonal codes (MWOOCs) for optical code-division multiple access. Particularly, we present a new construction method for MWOOCs with the number of available wave- lengths , codeword length , and constant Hamming weight that have autocorrelation and cross-correlation values not exceeding . In the proposed scheme, there is no constraint on the relationship between the number of available wavelengths and the codeword length, and it is also possible to use an arbitrary . W e show that the constructed code is optimal, especially for . Finally, we analyze the bit error rate of the new code and compare it with that of other optical codes. Index Terms—Multiwavelength optical orthogonal codes (MWOOCs), optical code-division multiple access (OCDMA), optical networks, optical orthogonal codes (OOCs).
Generalizations of the Alternating Direction Method of Multipliers for Large-Scale and Distributed Optimization

Abstract : The alternating direction method of multipliers (ADMM) has been revived in recent years due to its effectiveness at solving many large-scale and distributed optimization problems, particularly arising from the areas of compressive sensing, signal and image processing, machine learning and statistics. This thesis makes important generalizations to ADMM as well as extending its convergence theory. We propose a generalized ADMM framework that allows more options of solving the subproblems, either exactly or approximately. Such generalization is of great practical importance because it brings more flexibility in solving the subproblems efficiently, thereby making the entire algorithm run faster and scale better for large problems. We establish its global convergence and further show its linear convergence under a variety of scenarios, which cover a wide range of applications. The derived rate of convergence also provides some theoretical guidance for optimizing the parameters of the algorithm. In addition, we introduce a simple technique to improve an existing convergence rate from O(1/k) to o(1/k).
Multi-hop Question Generation without Supporting Fact Information

Question generation is the parallel task of question answering, where given an input context and optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have experienced success by utilizing sequence-to-sequence models, there is a need for question generation models to handle increasingly complex input contexts with the goal of producing increasingly elaborate questions. Multi-hop question generation is a more challenging task that aims to generate questions by connecting multiple facts from multiple input contexts. In this work we apply a transformer model to the task of multi-hop question generation, without utilizing any sentence-level supporting fact information. We utilize concepts that have proven effective in single-hop question generation, including a copy mechanism and placeholder tokens. We evaluate our model's performance on the HotpotQA dataset using automated evaluation metrics and human evaluation, and show an improvement over the previous works.
Information Express of Soft Bionic Finger Based on Visual Recognition

Soft robots have developed rapidly because of their own structure, adaptability and safety, which can be applied in different fields. However, the human robot interaction of soft robots faces enormous challenges. In this paper, we take the soft bionic finger (SBF) as the research object, design a multi chamber SBF, and propose a visual recognition to drive SBF. The bending information of the finger is converted into the information of driving air pressure, and drive the SBF. The SBF can simulate the bending of human finger and replace human finger to express some information. Finally, in the visual bending experiment, it is verified that the error between the bending angle of the human finger and the bending angle of the SBF is within a certain allowable range. It shows the feasibility of driving SBF to express information based on visual recognition, and also reflects strong human robot interaction.
Revisiting Dirty Paper Coding to Hybrid Precoding for Massive MIMO Downlink Broadcast Channel

It is well-known that dirty paper coding (DPC)-based transceiver can achieve the sum capacity of a multi-input multi-output (MIMO) broadcast channel. In this paper, we study DPC-based hybrid precoding to maximize the sum-rate of a massive MIMO (mMIMO) downlink broadcast channel, subject to the constant modulus constraint of the analog phase shifter network (PSN). Using the duality between a massive MIMO broadcast channel (mMIMO-BC) and a massive MIMO multiple access channel (mMIMO-MAC), we propose to first optimize the transmitters and the hybrid receiver for the mMIMO-MAC, before obtaining the hybrid precoder of mMIMO-BC according to the MAC-BC duality. The algorithm is also extended for mMIMO orthogonal frequency division multiplexing (OFDM) systems over broadband frequency-selectivity channels. Numerical results show that the proposed DPC-based hybrid precoding can significantly outperform the existing linear counterpart, especially in the case of numerous users. In simulations based on the 5G new radio (5G-NR) standard, the relative gain of the proposed scheme appears prominent, which suggests that the DPC can be a worthy candidate for the future generation of wireless communications.
Mitigating Age Biases in Resume Screening AI Models

As populations age, an increasing number of workers beyond the traditional retirement age are opting to continue working. Nevertheless, discrimination against older job seekers seeking new employment opportunities remains widespread. To address this issue, we enlisted a pool of crowdworkers to assess the resumes of IT job candidates and guess each candidate's age, race, and gender. Using this crowdsourced data, we trained an AI model and applied bias correction techniques from IBM's AI 360 and Microsoft's Fairlearn toolkits to correct for biases based on race, gender, and age. We analyzed the effectiveness of these tools in mitigating different types of bias in job hiring algorithms, explored why age may be more challenging to eliminate than other forms of bias, and discussed additional approaches to enhance fairness. Our results indicate that implicit age bias, or ageism, is prevalent in hiring decisions and more pervasive than other well-documented forms of bias, such as race and gender biases.
Retinal analysis from OCT images to identify fluid filled abnormalities

Blindness, being one of the most common disabilities in humans, is often caused due to various infections in the anterior and posterior regions of the Human eye. One of the common reasons include a significant variation in the levels of fluid in the retina of the eye. This may be due to excessive fluids or due to reduction in the levels of fluids. This paper reviews various tools of assessment designed towards early detection and diagnosis of such fluid filled abnormalities. The proposed idea has evaluated the various methodologies that have been designed and implemented using Image processing techniques, beginning from the preprocessing to classification at different stages. Not only focusing on binary classification, say normal and abnormal, various other techniques to classify the input image as Choroidal NeoVascular Membrane (CNVM), Cystoid Macular Edema (CME), Macular Hole (MH) and normal images has also been validated. Validation of the implemented speckle noise removal preprocessing filters and classifiers for efficient classification has also been focused on the paper.
“That’s a great question!” instructors’ positive responses to students’ questions improve STEM-related outcomes

ABSTRACT How instructors respond to students’ questions may serve as an important cue that shapes students’ self-perceptions and motivation. Across five studies, when participants imagined asking questions in a STEM context and receiving a positive (vs. neutral or negative) response from instructors, they felt greater self-efficacy and belonging, which predicted greater intentions to join the lab and to recruit other students. Positive verbal responses were effective regardless of whether they were directed toward participants or other students, occurred in public or private, in STEM or non-STEM settings, and when they came from warm/friendly versus cold/critical professors. Women especially benefitted from receiving positive instructor responses. Instructors’ positive responses to students’ questions may thus be a powerful cue that boosts students’ academic-related outcomes.