Miracle-dz/UniFAR-FTUs huggingface dataset:
A Reduced τ-adic Naf (RTNAF) Representation for an Efficient Scalar Multiplication on Anomalous Binary Curves (ABC)

Elliptic curve cryptosystems (ECC) provides better security for each bit key utilized compared to the RSA cryptosystem. For this reason, it is projected to have more practical usage than the RSA. In ECC, scalar multiplication (or point multiplication) is the dominant operation, namely, computing nP from a point P on an elliptic curve, where n is an integer defined as the point resulting from adding P + P + . + P , n times. However, for practical uses, it is very important to improve the efficiency of the scalar multiplication. Solinas (1997) proposes that the τ-adic Non-Adjacent Form (τ-NAF) is one of the most efficient algorithms used to compute scalar multiplications on Anomalous Binary curves. In this paper, we give a new property (i.e., Theorem 1.2) of τ-NAF(n) representation for every length, l. This is useful for evaluating the maximum and minimum norms occurring among all length-l elements of Z(τ). We also propose a new cryptographic method by using randomization of a multiplier n to n an element of Z(τ). It is based on τ-NAF. We focused on estimating the length of RTNAF(n) expansion by using a new method.
Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems

Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.
Experimental and numerical study of an offset jet with different velocity and offset ratios

The present study examines the configuration of an offset jet issuing into either a quiescent medium or a moving stream (co-flowing). The mean velocity and turbulence characteristics of the turbulent offset jet have been investigated using a particle image velocimetry technique at three velocity ratios and for two offset ratios. A numerical simulation of a three-dimensional offset jet has also been carried out by comparing the corresponding results with previous experimental data and our measurements. The numerical investigation was performed by means of the finite volume method together with a second-order turbulent closure model – the Reynolds stress model (RSM) – to examine the behavior of the flow for different velocity ratio and offset ratios. Results give a satisfactory agreement between the experimental data and the calculations. Data from the early flow region clearly show a significant influence of the velocity ratio and the offset ratio on the mean flow and turbulence characteristics.
Moving object detection based on Markov Random Field model

Accurate detection of moving objects is an important precursor to stable tracking or recognition.This paper presents an object detection scheme using Markov Random Field and pixel-wise Multi Gaussian Background Model in stationary camera situation.The relation of each pixel and the other neighbor pixel is constituted by Markov Random Field.And the whole restriction is established.This fetches up the shortage of single pixel information with Multi Gaussian Background Model.So the foreground segmentation is more exact.This paper also presents a novel energy function based on Multi Gaussian Background Model and establishes solution scheme using simulated annealing method.Experiments demonstrate its effectiveness.
Co-designing an Integrated Health and Social Care Hub With and for Families Experiencing Adversity

Introduction: Integrated care research often fails to adequately describe co-design methods. This article outlines the process, principles and tools to co-design an integrated health and social care Hub for families experiencing adversity. Research methods: The Child and Family Hub was co-designed in four stages: (1) partnership building and stakeholder engagement, (2) formative research, (3) persona development and (4) co-design workshops and consultations. Local families, community members and intersectoral practitioners were engaged at each stage. The co-design workshops employed a human-centred design process and were evaluated using the Public and Patient Engagement Evaluation Tool (PEET). Results: 121 family participants and 80 practitioners were engaged in the Hub’s co-design. The PEET highlighted the co-design team’s satisfaction achieved by community members working alongside practitioners to generate mutual learning. Resourcing was a key challenge. Discussion: Human-centred design offered a systematic process and tools for integrating formative evidence with lived and professional experience in the Hub’s co-design. Applying community engagement principles meant that a diverse range of stakeholders were engaged across all stages of the project which built trust in and local ownership of the Hub model. Conclusion: Co-design research with families experiencing adversity should attend to language, engagement methods, team composition and resourcing decisions.
Winged Fruits of Friisifructus aligeri gen. et sp. nov. from the Late Cretaceous of Western North America

Premise of research. Fossil flowers and fruits with unresolved affinities among extant angiosperms may potentially represent extinct families or orders. The characterization and systematic assessments of such fossils will undoubtedly further our understanding of extinct angiosperm diversity, which is crucial for reconstructing the evolution of angiosperms in deep time. Here, we report two three-dimensionally preserved winged fruits from Campanian (∼82–80 Ma) deposits on Sucia Island, Washington State, that represent a new genus. We explore potential phylogenetic relationships of this genus relative to higher taxa across the angiosperm phylogeny. Methodology. The fossils were studied using microcomputed tomography (μCT) scanning and light microscopy. A three-dimensional reconstruction of one winged fruit was created from the μCT scans. The phyloscan method was used to assess the systematic positions of the winged fruits. Using the reconstruction, the fossil fruits were compared to extant angiosperms and extinct winged fruits to assess potential affinities. Pivotal results. The μCT scans and reconstruction of the winged fruit reveal a pentamerous perianth, five pairs of fin-wings, two whorls of persistent stamens, five semicircular nectary pads, an inferior unilocular ovary, and a style with three stylar branches. Phyloscan analyses recover the winged fruits within the rosid clade, but they cannot be assigned to any extant family or order within the group because of differing floral and fruit morphologies. Conclusions. The winged fruits are assigned to Friisifructus aligeri gen. et sp. nov. This new genus and species add to the diversity of winged fruits from the Cretaceous, which are relatively uncommon. Moreover, the unique morphology of Friisifructus indicates that this genus may belong to an extinct family of rosids.
Unravelling the effects of multiple cross-ecosystem subsidies on food webs.

4 Preface . 6 Table of Contents . 8 List of tables.10 List of Figures . 12 Chapter One: Subsidy trophic level affects bottom-­up and top-­down food web interactions: a meta-­analysis.15 Introduction . 15 Methods . 18 Statistical Analyses . 20 Results. 24 Bottom-­‐up and top-­‐down food-­‐web interactions . 25 Trophic cascade strength. 27 Discussion. 28 Bottom-­‐up and top-­‐down food-­‐web interactions . 29 Study duration . 29 Unknown experimental factors altering TC. 30 Conclusions . 31 Chapter Two: Subsidy trophic level alters food-­web structure . 34 Introduction . 34 Methods . 37 Experimental set-­‐up. 37 Food-­‐web sampling . 38 Statistical analysis . 39 Results. 42 Biomass. 42 Invertebrate isotopic niche . 45 Invertebrate community composition . 47 Discussion. 48 Implications of resources to top-­‐predator biomass . 48 Bottom-­‐up effects on intermediate predators . 49 Altered community structure. 50 Conclusions . 52 Chapter Three: Causes of variability and strength of trophic cascades revealed by resource subsidies to multiple trophic levels . 54 Introduction . 54 Methods . 58 Experimental set-­‐up. 58 Food-­‐web sampling . 59 Statistical analysis . 60 Results. 61 Trophic cascade strength. 61
A Hybrid Reinforcement Learning Approach With a Spiking Actor Network for Efficient Robotic Arm Target Reaching

The increasing demand for applications in competitive fields, such as assisted living and aerial robots, drives contemporary research into the development, implementation and integration of power-constrained solutions. Although, deep neural networks (DNNs) have achieved remarkable performances in many robotics applications, energy consumption remains a major limitation. The letter at hand proposes a hybrid variation of the well-established deep deterministic policy gradient (DDPG) reinforcement learning approach to train a 6 $^{\circ }$ of freedom robotic arm in the target-reach task available at: In particular, we introduce a spiking neural network (SNN) for the actor model and a DNN for the critic one, aiming to find an optimal set of actions for the robot. The deep critic network is employed only during training and discarded afterwards, allowing the deployment of the SNN in neuromorphic hardware for inference. The agent is supported by a combination of RGB and laser scan data exploited for collision avoidance and object detection. We compare the hybrid-DDPG model against a classic DDPG one, demonstrating the superiority of our approach.
ACTIVE DRAG CHANGES BETWEEN TRAINING SEASONS IN YOUNG SWIMMERS

The purpose of this study was to verify the changes in the hydrodynamic profile of young swimmers from one training year to the next. Twelve male and eight female swimmers (initial age: 13.87 ± 0.82 and 12.45 ± 0.34 years) were evaluated in front crawl using the velocity perturbation method, for determination of maximal velocity (Vmax), active drag (D.), drag coefficient (CD.) and power output. Interval between 1st and 2nd evaluations was 9 months. Female swimmers showed lower CD. at the 2nd: evaluation, probably caused by stronger technical improvement. In spite of dramatic increases in body mass, competitive performance, and Vmax , D. and CD. remained unchanged and the Froud number for a fixed velocity of . 5 m.s· 1 decreased, in both genders. Reduced Froud number, dependent on body height, may have counterbalanced the effect of increased Vmax and body mass, reducing the wave drag component.
An Image De-Noising Algorithm Based on K-SVD and BM3D

The existence of noise affects the quality of the image seriously. The image de-noising algorithm based on KSVD appears fuzzy, where weak texture smooth area also can appear false textures, at the same time, when the noise was very big, the de-noising effect would not always be ideal. This paper proposed an image de-noising method based on K-SVD dictionary and BM3D. The algorithm can solve image weak texture fuzzy problems and weak edges effectively. The experimental results show that, compare with K-SVD de-noising algorithm, this algorithm has a good de-noising ability, which keeping the detail and the edge character of the image better.
A Category-Contrastive Guided-Graph Convolutional Network Approach for the Semantic Segmentation of Point Clouds

The semantic segmentation of light detection and ranging (LiDAR) point clouds plays an important role in 3-D scene intelligent perception and semantic modeling. The unstructured, sparse and uneven characteristics of point clouds pose great challenges to the representation of the local geometric shapes, which degrades semantic segmentation performance. To address the challenges of describing local geometric shapes due to unstructured and sparse 3-D point clouds, this article proposes a category-contrastive-guided graph convolutional network (CGGC-Net) for the semantic segmentation of LiDAR point clouds. First, a detailed geometric structure of the raw point clouds is encoded to represent the inherent geometric pattern within the local neighborhood. At the same time, the geometric structures information is transmitted across multiple layers, so that the geometric structure encoding information containing different receptive fields and richer neighborhood spatial structure can be aggregated. Following this, the graph convolution neural network uses the edge convolution layer to adaptively describe the semantic correlation between the query point and its neighboring points, and combines the attention mechanism to gather the surrounding feature information to the query point. As a result, the graph convolution neural network and attention mechanism are iteratively stacked for the aggregation and fusion of spatial context semantic information, to generate highly discriminative semantic feature representation. Finally, the superparameters of the model are learned through a multitask optimization strategy guided by category-aware contrastive loss and cross-entropy loss. Experiments are conducted on the public SemanticKITTI dataset and the Stanford large-scale 3-D Indoor Spaces dataset to demonstrate the effectiveness and reliability of the proposed CGGC-Net from both quantitative and qualitative perspectives. The results indicate its capability of automatically classifying LiDAR point clouds, with a mean intersection-over-union of 58.4%. Moreover, multiple comparative experiments also demonstrate the superior performance of the proposed method, exceeding state-of-the-art methods.
Sensitivity Analysis of Assimilating Doppler Radar Radial Winds Within the Inner‐ and Outer‐Core Regions of Tropical Cyclones

The assimilation of Doppler radar radial wind observations is important for improving tropical cyclone (TC) prediction. However, the specific impacts and contributions of these observations over different areas are still unclear. In this study, the impact of assimilating radar radial winds within the inner‐ and outer‐cores of TCs is evaluated. For TC Mujigae (2015), analyses suggest that although the outer‐core observations better improve the upper‐level outflow of the TC, the inner‐core observations contribute more to enhancing tangential wind and updraft near the eyewall as well as correcting the TC size, warm core structure, and asymmetrical eyewall convection. The improvements conferred by the assimilating the total observations in the analysis and forecasting of TC tracks and intensities are mainly contributed by the inner‐core observations. This is probably because: (a) the model has a higher deficiency in simulating the TC inner‐core structure, which leads to a larger background deviation relative to the observations and the assimilation increment contributed by the inner‐core observations; (b) as determined by the stronger nonlinearity of the physical processes, the ensemble spread (i.e., model uncertainty) around the inner core is larger, resulting in a more distinct impact of data assimilation. The significant improvements in the inner‐core observations are further consolidated by analyses of two other TCs of different sizes and intensities, in which the number of inner‐core observations accounts for only 1/3 of the total on average. The TC inner‐core area should be a major consideration of future TC observation projects.
A Comprehensive Survey on the Use of Hypervisors in Safety-Critical Systems

Virtualization has become one of the main tools for making efficient use of the resources offered by multicore embedded platforms. In recent years, even sectors such as space, aviation, and automotive, traditionally wary of adopting this type of technology due to the impact it could have on the safety of their systems, have been forced to introduce it into their day-to-day work, as their applications are becoming increasingly complex and demanding. This article provides a comprehensive review of the research work that uses or considers the use of a hypervisor as the basis for building a virtualized safety-critical embedded system. Once the hypervisors developed or adapted for this type of system have been identified, an exhaustive qualitative comparison is made between them. an exhaustive qualitative comparison is made between them. To the best of our knowledge, this is the first time that all this information is collected in a single article. Therefore, the main contribution of this article is that it collects and categorizes the information of each hypervisor and compares them with each other, so that this article can be used as a starting point for future researchers in this area, who will be able to quickly check which hypervisor is best suited to their research needs.
Amplification/Reflection Attack Suppression Using Victim Separation

Amplification and reflection attacks represent flavors of DDoS attacks which use servers such as DNS, NTP, SNMP, SSDP and CoAP as amplifier to launch the attacks. In this letter, we describe a method to minimize the effects of reflection and amplification DDoS attacks by separating the victim service under the attack. We propose three approaches involving container level separation, packet limiting, and network hardware level separation. Our detailed experimental results on DNS based amplification attacks show a ‘near-no-attack’ performance with an improvement of up to 88% in the test completion time while using the proposed approaches.
Mathematical and numerical analysis of a pair of coupled Cahn-Hilliard equations with a logarithmic potential

Mathematical and numerical analysis has been undertaken for a pair of coupled Cahn-Hilliard equations with a logarithmic potential and with homogeneous Neumann boundary conditions. This pair of coupled equations arises in a phase separation model of thin film of binary liquid mixture. Global existence and uniqueness of a weak solution to the problem is proved using Faedo-Galerkin method. Higher regularity results of the weak solution are established under further regular requirements on the initial data. Further, continuous dependence on the initial data is presented. Numerically, semi-discrete and fully-discrete piecewise linear finite element approximations to the continuous problem are proposed for which existence, uniqueness and various stability estimates of the approximate solutions are proved. Semi-discrete and fully-discrete error bounds are derived where the time discretisation error is optimal. An iterative method for solving the resulting nonlinear algebraic system is introduced and linear stability analysis in one space dimension is studied. Finally, numerical experiments illustrating some of the theoretical results are performed in one and two space dimensions.
Evolution of multifractal cross-correlations between the Argentina MERVAL Index and international commodities prices

ABSTRACT We compute the auto-correlations and cross-correlations of the volatility time series of the Argentina MERVAL Index (the Buenos Aires Stock Exchange main index) and three agricultural commodities, in a multifractal context using the Detrended Cross-Correlation Analysis [12]. We observe a clear increase of the cross-correlations between the Merval series and the grain quotations which can be ascribed to a stronger coupling between the agricultural sector and the rest of the Argentinian economy. We connect this to fiscal decisions implemented since 2004 and reinforced after 2009.
Analysis of Turbulence Structure in the Stirred Tank with a Deep Hollow Blade Disc Turbine by Time-resolved PIV

在有深空的片(semi-ellispe ) 的搅动的坦克的骚乱结构磁盘汽轮机(HEDT ) 被使用解决时间的粒子图象 velocimetry (TRPIV ) 和传统的 PIV 调查。 在搅动的坦克，片经过产生的骚乱包括周期的部件和随机的狂暴的。 有解决角度的测量的传统的 PIV 和有小浪分析的 TRPIV 两个都被用来作为比较获得随机的狂暴的动能。 小浪分析方法成功地在这个工作被使用分开随机的狂暴的动能。 周期的动能和随机的狂暴的动能的分布被获得。 在 impeller 区域，平均随机的狂暴的动能是大约 2.6 次平均的周期的。 在到 d1 的从 a6 的不同小浪规模的动能也计算、比较。 TRPIV 被用来在 impeller 溪流记录即时速度的顺序。 impeller 溪流的进化清楚地被观察，涡度地的顺序也为旋涡的鉴定被获得。 精力光谱的斜坡是近似(在在搅动的坦克代表惯性的子域和一些各向同性的性质的存在的高频率的 5/3。 从力量光谱密度(PSD ) ，一座山峰显然存在，它在片经过产生的 f0 (片经过频率) 被定位。
FPPNet: A Fixed-Perspective-Perception Module for Small Object Detection Based on Background Difference

A roadside sensing unit can provide over-the-horizon perception information for autonomous vehicles due to its high perception perspective. However, numerous challenges need to be overcome such as the missing detection of small objects and occluded objects. To this end, this study proposed a fixed perspective perception (FPP) module, which considered background subtraction and a fixed camera for small object detection. The proposed FPP module was divided into two parts: a grayscale background subtraction (GBS) submodule and a background–current image fusion (BCF) submodule. Specifically, the GBS submodule introduces background spatial information into a current frame, and the BCF submodule combines feature maps of a current frame and background by using channel attention. Moreover, we designed an object detection network called FPPNet which uses the FPP module to facilitate small object detection. Experimental results demonstrate that the FPPNet achieved 39.8% average precision small ( ${\text {AP}}_{{S}}$ ) and 65.7% AP in a Dair-V2X-I dataset. Futhermore, we conducted an extension of the FPP module to mainstream object detection networks such as CenterNet, Faster-Rcnn, and RetinaNet. Experimental results show that the proposed module can effectively improve small object detection accuracy of the networks mentioned earlier.
Protein phosphatase 2 A promotes hepatocellular carcinogenesis in the diethylnitrosamine mouse model through inhibition of p 53

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Most HCCs develop in cirrhotic livers. Alcoholic liver disease, chronic hepatitis B and chronic hepatitis C are the most common underlying liver diseases. Hepatitis C virus (HCV)-specific mechanisms that contribute to HCC are presently unknown. Transgenic expression of HCV proteins in the mouse liver induces an overexpression of the protein phosphatase 2A catalytic subunit (PP2Ac). We have previously reported that HCVinduced PP2Ac overexpression modulates histone methylation and acetylation and inhibits DNA damage repair. In this study, we analyze tumor formation and gene expression using HCV transgenic mice that overexpress PP2Ac and liver tissues from patients with HCC. We demonstrate that PP2Ac overexpression interferes with p53-induced apoptosis. Injection of the carcinogen, diethylnitrosamine, induced significantly more and larger liver tumors in HCV transgenic mice that overexpress PP2Ac compared with control mice. In human liver biopsies from patients with HCC, PP2Ac expression was significantly higher in HCC tissue compared with non-tumorous liver tissue from the same patients. Our findings demonstrate an important role of PP2Ac overexpression in liver carcinogenesis and provide insights into the molecular pathogenesis of HCV-induced HCC.