Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordinating the SDN and TDMA Planes in Software-Defined Tactical MANETs with Adaptive Coding and Modulation

In this paper, we investigate a tactical mobile ad hoc network (MANET) enhanced with software defined networking (SDN) functionality. Radio transmissions of network links are orchestrated using time division multiple access (TDMA) and are subject to adaptive modulation and coding (AMC) to ad-dress the dynamic variations in the quality of MANET links. A significant challenge within these networks is meeting the quality of service (QoS) demands of end-user data traffic while maintaining the SDN control plane's responsiveness to MANET topology alterations. To address this challenge, we propose a novel approach that concurrently determines the optimal placement of the SDN controller and the transmission scheduling of links in the MANET. The management of residual resources has a direct impact on both performance metrics. Building upon previous research, we present a more comprehensive model of the quality variations of tactical MANET links, elucidating how these variations dictate transmission modes over each link through software-defined radio (SDR) technology and influence the slot allocation process. We contrast our joint optimization algorithm with alternative techniques that manage these two network control mechanisms independently. Numerical evaluations suggest that our proposed algorithm achieves superior performance in comparison with the techniques considered.
Going Beyond Convergence in Bayesian Estimation: Why Precision Matters Too and How to Assess It

Most of the software that is available to implement Bayesian approaches uses Markov chain Monte Carlo (MCMC) methods. It is our impression that many researchers are primarily concerned with convergence as assessed by the Potential Scale Reduction (PSR) and that other aspects of MCMC are largely ignored. In this article, we argue that the precision with which the Bayesian estimates are approximated by summary statistics for the MCMC chain is essential to ensure good statistical properties. We discuss the Effective Sample Size (ESS), which indicates how well an estimate is approximated, and present evidence from two simulation studies and an example from organizational research to support our claim that researchers should be concerned not only with convergence but also with precision, particularly when a multilevel model is estimated. In addition, we demonstrate how Mplus can be modified so that users can control the ESS, and we conclude with recommendations.
CONSEQUENCES OF SPATIAL AND TEMPORAL CLIMATE VARIABILITY FOR SPECIES DISTRIBUTION MODELING

Author(s): Fernandez, Miguel | Advisor(s): Kueppers, Lara | Abstract: Our understanding of how species will respond to global change is still limited. Species distribution models (SDMs) are used to generate hypotheses regarding the potential distributions of species under different environmental conditions. However, that species observations and climatic variables are not measured at the same spatial and temporal resolution still hinders our ability to forecast species range shifts and expansions in response to global change. One of the possible consequences of this data mismatch is the observed discrepancy between realized climate niches in species' native and invasive ranges. In the first chapter of my dissertation, I address this issue by evaluating niche similarity between native and invaded ranges for 10 species using a combination of monthly and inter-annual climatic variability data. My results suggest that some species' ranges may be constrained by one aspect of climatic variability in the native range but a different one in the invaded range. A second issue, also a consequence of the spatio-temporal mismatch, is that weather station data are often spatially interpolated to match the species observations without any uncertainty assessment. The second chapter evaluates and quantifies the effects of three complementary aspects of uncertainty present in weather station data interpolations. I examine the influence of topographic heterogeneity, interannual climatic variability, and distance to weather station on SDM performance for 20 well observed North American breeding birds, and show that topographic heterogeneity has the highest contribution to omission errors, or false absences. A third consequence of the spatio-temporal data mismatch is the inability of global simulations to capture local manifestations of climate change. This inability can limit the capacity of SDMs to produce accurate simulations for species whose distributions depend on small-scale climate phenomena. While changes in global climatic patterns are projected using global climate model (GCM) simulations, local climatic trends are not always well represented by GCMs, or by simple downscaled projections derived from GCMs. In the final chapter of my dissertation, I use interpolated, fine-scale historic climate records in a novel approach to estimate the sensitivity of SDM's to locally coherent changes in temperature and precipitation at larger scales, using coast redwood (Sequoia sempervirens) as an example. Overall, the results of this thesis confirm the importance of establishing an appropriate relational basis in time and space between species and climatic observations. Historical collection records should be thoroughly analyzed and integrated with historical climatic time series to gain a better understanding of species' response to climate variability in the past, thereby informing the selection of appropriate spatio-temporal scales of climate variability for projections under present and future conditions.
Etude des transferts d'eau et de nitrate dans les sols ferrallitiques de Maré (Nouvelle-Calédonie) : risques de pollution des lentilles d'eau douce

Le but principal de cette these est la determination des risques de pollution de l'eau souterraine dus a des pratiques agricoles intensives sur l'ile de Mare (Nouvelle-Caledonie). Mare est un atoll corallien sureleve ou la principale ressource en eau douce provient de lentilles situees a l'interieur du soubassement corallien. Le sol est un sol ferrallitique allitique tres permeable, et possedant une capacite importante d'echange anionique. Le climat est caracterise par une saison humide, ou des evenements pluvieux de fortes intensites peuvent survenir. Pour calculer le drainage et la lixiviation du nitrate en-dessous de la zone racinaire, une metrologie non destructive a ete mise en oeuvre sur plusieurs parcelles soumises a differentes pratiques culturales. Les caracteristiques hydrauliques du sol ont ete obtenues par infiltrometrie et les parametres de transfert de solutes grâce a des experiences realisees au laboratoire, sur colonnes de sol. Environ 100 kg N ha-1 ont ete apportes sur une parcelle sol nu, de mais et de graminees. Bien que la saison 1996 fut plus humide qu'en 1995, la quantite de nitrate livixie sous mais a diminue de 130 % (1995) a 48 % (1996) du nitrate apporte. En 1996, l'engrais a ete apporte en deux fois alors qu'en 1995, il a ete apporte en totalite le jour du semis. Malgre sa capacite d'echange anionique, la permeabilite du sol ainsi que les fortes intensites de pluie concourent a rendre le nitrate facilement lixiviable. Cependant, le risque de pollution peut etre fortement reduit avec des applications fractionnees d'engrais de maniere a ce que la consommation du nitrate par les plantes soit amelioree. Les parametres du sol determines sur le terrain et au laboratoire nous ont permis d'utiliser le modele mecaniste WAVE et d'evaluer sa capacite de reproduction et de prediction des donnees. Les mesures lors des trois saisons aux conditions climatiques differentes ont ete reproduites avec satisfaction.
Height of walks with resets, the Moran model, and the discrete Gumbel distribution

In this article, we consider several models of random walks in one or several dimensions, additionally allowing, at any unit of time, a reset (or"catastrophe") of the walk with probability $q$. We establish the distribution of the final altitude. We prove algebraicity of the generating functions of walks of bounded height $h$ (showing in passing the equivalence between Lagrange interpolation and the kernel method). To get these generating functions, our approach offers an algorithm of cost $O(1)$, instead of cost $O(h^3)$ if a Markov chain approach would be used. The simplest nontrivial model corresponds to famous dynamics in population genetics: the Moran model. We prove that the height of these Moran walks asymptotically follows a discrete Gumbel distribution. For $q=1/2$, this generalizes a model of carry propagation over binary numbers considered e.g. by von Neumann and Knuth. For generic $q$, using a Mellin transform approach, we show that the asymptotic height exhibits fluctuations for which we get an explicit description (and, in passing, new bounds for the digamma function). We end by showing how to solve multidimensional generalizations of these walks (where any subset of particles is attributed a different probability of dying) and we give an application to the soliton wave model.
RThreatDroid: A Ransomware Detection Approach to Secure IoT Based Healthcare Systems

The use of smartphone devices in healthcare has increased manifold due to their widespread use and ease of integration with Internet of Things (IoT) based medical devices. In healthcare, either in-home observation or in a hospital scenario, medical sensors use certain local communication devices to share measured vital signs with a fog/cloud-based medical system. The large user community of Android devices has also brought some serious challenges, such as potential malicious attacks. For the past few years, ransomware attacks on healthcare have been increasing dramatically, posing several challenges. Therefore, an effective ransomware detection mechanism is needed to protect critical assets such as healthcare data, patients’ private data, etc. In this work, a novel hybrid ransomware detection method is proposed that analyzes image data, text, and application code to extract plain or encrypted threat text. Threatening text is a potential tool and could be one of the most effective features for ransomware detection. Our proposed hybrid approach utilizes both static and dynamic techniques and uses multi-machine learning classifier models. The proposed approach also provides a family classification of ransomware. Experimental results show that the proposed approach achieves up to 94% accuracy and fewer false negatives.
Stability of Impulsive Time-Varying Delay Positive Switched Systems with Unstable Subsystems

In this paper, the exponential stability of continuous time positive impulse switched linear systems (PISLSs) with time-varying delays and unstable subsystems is studied. Firstly, by controlling the impulse within a reasonable range, a new linear copositive Lyapunov-Krasovskii functional is constructed, and the sufficient conditions for the stability of the considered system are given. Secondly, compared with the existing research results, when there exists impulse phenomenon, the state divergence generated by unstable subsystems can also be effectively compensated by dwell time switching, so that the improved stability criterion can be applied to the unstable situation of all subsystems. Finally, a simple example is given to verify the validity of the theoretical results.
construction and generation of transgenic

Modified nucleosides in tRNAs are critical for protein translation. N 1 -methylguanosine- 37 and N 1 -methylinosine-37 in tRNAs, both located at the 3’-adjacent to the anticodon, are formed by Trm5 and here we describe Arabidopsis thaliana AtTrm5 (At3g56120) as a Trm5 ortholog. We show that AtTrm5 complements the yeast trm5 mutant, and in vitro methylates tRNA guanosine-37 to produce N 1 -methylguanosine (m 1 G). We also show in vitro that AtTRM5 methylates tRNA inosine-37 to produce N 1 - methylinosine (m 1 I) and in Attrm5 mutant plants, we show a reduction of both N 1 - methylguanosine and N 1 -methylinosine. We also show that AtTRM5 is localized to the nucleus in plant cells. Attrm5 mutant plants have overall slower growth as observed by slower leaf initiation rate, delayed flowering and reduced primary root length. In Attrm5 mutants, mRNAs of flowering time genes are less abundant and correlated with delayed flowering. Finally, proteomics data show that photosynthetic protein abundance is affected in mutant plants. Our findings highlight the bifunctionality of AtTRM5 and the importance of the post-transcriptional tRNA modifications m 1 G and m 1 I at tRNA position 37 in general plant growth and development.
Fixed-Time Sliding Mode Control Based on Adaptive Disturbance Observer for a Class of Nonlinear Systems

In this paper, a fixed-time sliding control method is proposed for a class of nonlinear systems with unknown external perturbations to improve the disturbance rejection ability and achieve fixed-time convergence. An adaptive sliding mode distur-bance observer is incorporated to estimate the disturbance in fixed-time. Based on the proposed disturbance observer, the time taken to reach the desired setpoint from any initial states under mismatched disturbance is guaranteed to be fixed-time and the chattering in the control input is attenuated. Finally, the effectiveness of the proposed method is verified by simulations.
Game-Based Control of Systems Under Network Constraints via a High-Order Fully Actuated Predictive Control

This paper considers a game-based control problem of networked control systems (NCSs) under network constraints that contain the network-induced delays and the data dropouts in the forward and feedback channels. A high-order fully actuated (HOFA) system model is used to establish the dynamics of NCSs. In the view of game theories, a cost function is designed for each player to construct an optimal scheme for the implementation of Nash equilibrium. A HOFA predictive control approach is given to address this problem. In this approach, a Diophantine equation is utilized to develop an incremental prediction model in a HOFA form, such that the multi-step ahead output predictions are completed to obtain the optimal scheme for the balance of inconsistent targets and the compensation of network constraints when reaching Nash equilibrium. Furthermore, a simple criterion is presented to discuss the stability and output convergence of closed-loop systems. The capability of HOFA predictive control approach is demonstrated via an experiment of tracking control of air-bearing spacecraft (ABS) simulator.
AGRICULTURE, LIVESTOCK and FISHERIES GENETIC AND MOLECULAR CONTROL OF THE FLOWERING TIME IN SOYBEAN ( Glycine max (L.) Merrill)

Photoperiod response to flowering is one of the most vital factors that affect in regional adaptation and yield in soybean. Soybean adaption at high latitude areas (long-day) requires early flowering and low photoperiod sensitive cultivars; adaptation to low latitudes (short-day) areas needs delayed flowering cultivars, which maximize vegetative growth and seed yield. This paper represents a genetic and molecular regulation of flowering time in soybean, which will help broad adaptability across latitudes. It is revealed that one to eleven main genes control the flowering time in soybean. The FT family of flowering integrators plays a central role in controlling the flowering time. The juvenile growth phase (JGP) determines the development rate for flowering; a long JGP results in the lengthening of the vegetative period and increases the soybean production in low latitude areas. This review outlines the JGP-related gene in soybean. We emphasize the interaction between major genes and QTLs for flowering in soybean. Several major genes and quantitative trait loci (QTLs) for flowering interact with one another including the environment
Slice-Level Performance Metric Forecasting in Intelligent Transportation Systems and the Internet of Vehicles

The intricate web of vehicles connected in the fifth-generation (5G) wireless infrastructure forms the Internet of Vehicles (IoV) and enabling technologies, such as Multi-Access Edge Computing (MEC) and network slicing, are employed in guaranteeing application requirements in the IoV and optimizing network resource allocation. In particular, network slicing allows mobile network operators to support virtualized end-to-end networks with diverse slice requirements that are typically grouped into use case classes such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (uRLLC), and Massive Machine-Type Communication (mMTC). These enabling technologies rely on Performance Metrics, monitored and gathered within the network, in order to evaluate and suggest improvement for slice configuration. As such, this article considers a forecasting model for Performance Metrics at the network slice level by leveraging the use of the Network Data Analytics Function (NWDAF) and its edge placements. The results and analysis, including the scalability of the forecasting model, are assessed as a step towards total automation of network slice management within the 5G network. This evaluation is later illustrated using an end-to-end IoV use case incorporating the edge NWDAF placements to guide decision-making regarding management and orchestration for future networks.
A Dense Network Based Framework for the Fusion of Infrared and Visible Images with Edge Extraction

Image fusion of the infrared (IR) and visible images has become one of the essential pre-processing steps for image analysis in medical, defense, remote sensing, etc. Till now, the spatial and transform domain methods have produced fused images that are not of good quality. Also, deep learning-based techniques are mostly concerned with feature extraction from the source images before fusion, but the distinct features of the source images have been overlooked. In the proposed model an attempt has been made to extract the edges of the IR image followed by the feature extraction of the IR and visible images using a dense-network based convolutional neural network. The feature maps from the two pipelines are concatenated along with the edge-extracted IR image and the enhanced visible image to obtain the final fused image. The proposed model has been tested on the TNO image dataset. The quantitative and qualitative results show that the proposed method outperformed when compared with the state-of-the-art method in the entropy content and also produces fused images with better human perception quality.
Distributed Generalized Nash Equilibria Seeking for Aggregative Games with Community Structures

This paper investigates the problem of seeking distributed generalized Nash equilibria (GNE) for aggregative games with community structures. The considered systems comprise agents that are interconnected through a two-scale hierarchical structure. At the lower level, agents are organized into group structures and are governed by upper-level agents, which collectively form the upper-level regulatory network. The decision variables of the two-level agents are interdependent. To address this game, we propose a distributed algorithm based on the Lagrange perspective that seeks the GNE. In addition, we provide a sufficient and necessary condition for the GNE of the considered problem.
Efficient Machine Learning on Encrypted Data Using Hyperdimensional Computing

Fully Homomorphic Encryption (FHE) enables arbitrary computations on encrypted data without decryption, thus protecting data in cloud computing scenarios. However, FHE adoption has been slow due to the significant computation and memory overhead it introduces. This becomes particularly challenging for end-to-end processes, including training and inference, for conventional neural networks on FHE-encrypted data. Additionally, machine learning tasks require a high throughput system due to data-level parallelism. However, existing FHE accelerators only utilize a single SoC, disregarding the importance of scalability. In this work, we address these challenges through two key innovations. First, at an algorithmic level, we combine hyperdimensional Computing (HDC) with FHE. The machine learning formulation based on HDC, a brain-inspired model, provides lightweight operations that are inherently well-suited for FHE computation. Consequently, FHE-HD has significantly lower complexity while maintaining comparable accuracy to the state-of-the-art. Second, we propose an efficient and scalable FHE system for FHE-based machine learning. The proposed system adopts a novel interconnect network between multiple FHE accelerators, along with an automated scheduling and data allocation framework to optimize throughput and hardware utilization. We evaluate the value of the proposed FHE-HD system on the MNIST dataset and demonstrate that the expected training time is 4.7 times faster compared to state-of-the-art MLP training. Furthermore, our system framework exhibits up to 38.2 times speedup and 13.8 times energy efficiency improvement over the baseline scalable FHE systems that use the conventional data-parallel processing flow.
Deriving Snow Accumulation Rates of Greenland and the Antarctic Ice Sheet from Microwave Remote Sensing Data

In order to estimate the impact of climate change it is necessary to monitor the surface mass balance of the ice sheets. Much effort has gone into developing accurate methods for surface mass balance estimation over the past years. Mass balance is defined as the sum of mass gain and mass loss. Still, it remains difficult to quantify surface accumulation, which is the net gain term of the balance equation. This is due to the fact that the polar ice sheets are difficult to access, so in-situ measurements are sparse. Therefore, it is important to employ remote sensing techniques to obtain accumulation data with a better spatial and temporal resolution. However, the resulting datasets still exhibit large amounts of uncertainty. This thesis seeks to improve the currently available methods to derive snow accumulation rates from microwave remote sensing data. For this purpose, different types of microwave data are systematically evaluated with respect to their suitability for accumulation rate retrieval. The approach taken here makes use of the fact that microwaves interact with a volume of dry polar firn and are sensitive to accumulation rate-dependent firn characteristics. For this reason, firn microstructure is examined in more detail. On the basis of this analysis, an improved parameterization of firn properties (grain size and density) is developed, which is valid for the layers of the firn column that interact with the microwave radiation. The improved microstructure model is used in conjunction with a simple radiative transfer model to simulate firn-microwave interaction, resulting in a synthetic microwave signal. Accumulation rates can subsequently be inverted by matching the signal from the model to microwave data measured by satellite sensors. A number of assumptions made in the radiative transfer model have proven to be invalid. In this work, the radiative transfer model is improved by including Mie scattering instead of the widely-used Rayleigh approximation for the combination of scatterer sizes and microwave frequencies investigated. The results from the accumulation retrieval algorithm developed in this work are validated with in-situ data, and limits in its applicability are evaluated. Accumulation rates inverted from the method introduced in this work are found to agree with field data as well as with accumulation maps from external sources within the range of the model's validity.