Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cost of Quality of Service: SLA Aware VNF Placement and Routing Using Column Generation

In the Network Function Virtualization (NFV) paradigm, Internet Service Providers (ISP) provide network services to customers by routing and processing traffic through an ordered sequence of Virtual Network Functions (VNF). The Quality of the Service (QoS) depends on the quantity and relative placement of the VNFs, and is quantified by a set of Key Performance Indicators (KPIs) in a Service Level Agreement (SLA): a contract reached between the ISP and customer. In order to provide the service in line with the SLA, ISPs must consider the SLA constraints directly when placing VNFs and provisioning the network services in the physical network infrastructure. In this paper, we present a VNF placement and routing algorithm based on the column generation method which iterates between generating improving paths, and optimising the placement of the VNFs given the generated paths. SLA constraints are modelled as soft constraints for which violation incurs a cost, the sum of which is minimised. Unlike prior approaches, we consider the throughput, latency and availability SLA constraints. We validate our approach against a heuristic greedy algorithm, on a multi-tiered Radio Access Network (RAN) and show that the column generation method provides solutions with significantly lower SLA violation cost versus the greedy approach, while still being able to solve problems of a practical size. We also highlight that satisfying QoS can significantly increase the number of host nodes required, thus a trade-off exists between QoS and operational cost which should be explored further.
Spatial simulation of crop conditions - application for crop risk monitoring, crop management and water footprint estimation

Spatial mapping and monitoring of crop conditions (crop stress and risks, growing conditions, water use etc.) becomes more and more important for practical applications in crop management at the stakeholder level as well as for more reliable regional assessments. This study demonstrates a GIS based model system for agricultural land in Austria, designed for high spatial resolution, and focused on the estimation of spatial crop growing conditions for monitoring and mapping. It is demonstrated for soil born pest, driven by soil temperatures, and crop water balance parameter actual transpiration for water footprint estimation.
A Gaussian mixture model with multiple tangent planes

Second-order descriptors play an increasingly important role in many signal and image processing applications including for example remote sensing and medical imaging. In order to develop machine learning models adapted to this kind of descriptors, different probabilistic models based on the Gaussian assumption have been proposed. This includes intrinsic models on the manifold of symmetric positive definite (SPD) matrices such as the Riemannian Gaussian distribution, but also conventional Gaussian models defined on a tangent plane at a reference point. Even if the former is defined on the manifold, it suffers from a practical point of view (no close form expression for the normalization factor, scalar dispersion parameter). The Gaussian model on the tangent plane does not have these drawbacks but is limited by a fixed reference point which might lead to some distortions. To overcome these difficulties, we propose to define a Gaussian model on the tangent plane where the reference point is learned. We found that the maximum likelihood estimator of this reference point is the Karcher/Fréchet mean. Based on this, we introduce a Gaussian mixture model (GMM) with multiple reference points and derive the maximum likelihood estimator. Experimental results on synthetic dataset show that the proposed approach allows to limit the distortion while having an anisotropic dispersion matrix. Finally, an experiment on remote sensing image scene classification is performed to illustrate the potential of the proposed GMM model with a Fisher vector encoding of second-order descriptors computed on the feature maps of a convolutional neural network.
Power Allocation in 1-Bit Massive MIMO Downlink with Zero-Forcing Precoding

The use of 1-bit digital-to-analog converters (DACs) is a promising way to improve the energy efficiency of massive multiple-input-multiple-output (MIMO) systems. Many linear and nonlinear precoding techniques mitigating the severe quantization distortions have been introduced in order to enable the use of 1-bit DACs in the massive MIMO downlink. However, the vast majority of the proposed designs do not account for the channel strength imbalances between the users, which are highly likely to occur in the downlink scenarios. To achieve high sum rates in such scenarios, a user-specific power allocation scheme (USPA) is needed. In this paper, we propose a quantization aware USPA technique for the linear zero-forcing (ZF) precoder. Numerical results illustrate that higher achievable sum rates than the existing linear methods can be achieved with the proposed method, especially in the systems with low order modulated inputs. In the systems with higher modulation, the proposed method achieves similar performance to the existing methods, yet with a lower computational complexity.
The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis

Tomographic artifacts are unwanted distortions and/or structures not present in the scanned body that may appear in the reconstructed images. Recent deep learning-based methods for suppressing artifacts in tomographic images are currently not informed by the nature of these artifacts in the design of their network architectures. In this work, we present the Deep Steerable Convolutional Framelet Network (DSCFN), inspired by the theory of deep convolutional framelets, which exploits the regular pattern of ripple artifacts presented in sparse-view X-ray tomosynthesis images. Experiments with simulated data show that the DSCFN outperforms the regular U-net and its deep convolutional framelet, the tight U-net, in terms of PSNR.
Energy Efficient UAV-assisted Communications via Collaborative Beamforming

In this paper, we propose collaborative beamforming (CB) in unmanned aerial vehicle (UAV)-assisted communication networks to improve transmission data rate with minimum energy consumption. Specifically, CB allows a group of UAVs forming a virtual element antenna array (VEAA) and transmitting data collaboratively in a synchronous manner through a high-gain mainlobe (ML) beam. The goal is to optimize the deployment locations of UAVs in the VEAA and excitation current weights for performing CB transmissions considering the energy cost for UAV deployment. Accordingly, we formulate an Energy-Efficient Communication Multi-objective Optimization Problem (EECMOP) to jointly maximize the transmission rate and minimize the maximum sidelobe level (SLL) as well as UAV energy consumption. Then, we propose an Enhanced Multi Objective Ant Lion Optimizer (EMOALO) algorithm which incorporates a chaos theory to develop chaotic initialization and adjustable mode operators for solving the problem. Simulation results demonstrate the effectiveness of the EMOALO algorithm in improving energy efficiency for UAV-assisted communication networks.
Comparision of Planar Ferrite Cores in High Frequency GAN DC-DC Converter Application based on Performance Factor and Thermal Imaging

The paper analyses magnetic cores of a different ferrite materials for application in a high frequency DC-DC converter based on GaN transistors. In the paper it is shown a simple approach how to compare cores based on their datasheet data. Theoretical results are compared with experimental ones. In the experiments the temperature of the cores was measured in similar conditions thus comparing core losses of two materials. The experimental results agree with the theoretical ones.
Maximizing Mobile Communication Efficiency with Smart Antenna Systems using Beam forming and DOA Algorithms

This study aims to investigate the efficacy of advanced smart antenna systems utilizing beamforming and Direction of Arrival (DOA) algorithms. The primary objective of this research is to explore the potential of these innovative antenna technologies in enhancing the performance of mobile communication networks and optimizing spectrum utilization. By harnessing beamforming algorithms, smart antenna systems can precisely direct their signal emission toward specific users or locations, thereby mitigating interference and multipath effects. This, in turn, leads to improved communication efficiency through heightened signal quality, elevated signal-to-noise ratios (SNR), and expanded coverage areas. The study further seeks to align network architecture, cost considerations, and overall performance to effectively address the escalating requirements of future mobile broadband services. The significance of smart antennas extends beyond mere programmable phase adjustments within an antenna array, as they effectively capitalize on the spatial attributes of available radio resources. This investigation outlines two distinct smart antenna types, outlines the constituent components of an adaptive antenna, and delves into the associated theories, advantages, and limitations of smart antenna deployment. Additionally, the research incorporates a simulation and analysis segment aimed at evaluating the performance of a smart antenna. Through these efforts, the study endeavors to provide comprehensive insights into the potential contributions and novel aspects of employing smart antenna technology in modern communication networks.
Assessment of a Two-Step Integration Method as an Optimizer for Deep Learning

It is a known fact that accelerated (non-stochastic) optimization methods can be understood as multi-step integration ones: e.g. the heavy ball's Polyak and Nesterov accelerations can be derived as particular instances of a two-step integration method applied to the gradient flow. However, in the stochastic context, to the best of our knowledge, multi-step integration methods have not been exploited as such, only as some particular instances, i.e. SGD (stochastic gradient descent) with momentum or with the Nesterov acceleration. In this paper we propose to directly use a two-step (TS) integration method in the stochastic context. Furthermore, we assess the computational effectiveness of selecting the TS method's weights after considering its lattice representation. Our experiments includes several well-known multiclass classification architectures (AlexNet, VGG16 and EfficientNetV2) as well as several established stochastic optimizer e.g. SGD along with momentum/Nesterov acceleration and ADAM. The TS based method attains a better test accuracy than the first two, whereas it is competitive with to a well-tuned ($\epsilon /$ learning rate) ADAM.
Expectation-Propagation with Low-Rank Constraints for Linear Inverse Problems

In this work, we address the problem of scalable approximate inference and covariance estimation for linear inverse problems using Expectation-Propagation (EP). Traditional EP methods rely on Gaussian approximations with either diagonal or full covariance structures. Full covariance matrices can capture correlation but do not scale as the dimensions of the problem increases, while diagonal matrices scale better but omit potentially important correlations. For the first time to the best of our knowledge, we propose to investigate low-rank decompositions within EP for linear regression. The potential benefits of such covariance structures are illustrated thorough simulations results obtained on sparse linear regression problems and a more challenging spectral unmixing problem where the sparse mixing coefficients are, in addition, subject to positivity constraints.
Autism Spectrum Disorder Detection using Autistic Image Dataset

Autism spectrum disorders (ASD) are neurodevelopmental disorders that are marked by enduring difficulties with speech, nonverbal communication, and restricted or repetitive behaviors. Early detection and intervention can greatly improve outcomes for people with ASD. Recently, deep learning algorithms have been applied to aid in the early detection of ASD using facial images. In this work, modifications of the commonly used VGG16 and VGG19 models for image recognition tasks are proposed to improve the performance of detecting ASD from a child’s frontal face image. The proposed model is unique, as it alters the architecture of existing models, adds an attentional mechanism, and applys transfer learning. These changes are intended to decrease the chance of overfitting and enhance the model’s capacity to capture subtle face characteristics. The performance of the updated model is assessed through accuracy, which is 82.55% for VGG19 and 80% for VGG16 model, and contrasted the outcomes of the original model. Performance of the modified model is also compared with that of the original model. The obtained results show that the modified model outperforms in detecting ASD from facial images, suggesting that the proposed modification is non-invasive for early detection of ASD and has the potential to contribute to the development of efficient tools.
Field Investigations of Two Super-long Steel Pipe Piles in Offshore Areas

A research on super-long piles has been primarily based on cast-in-place bored piles. In this article, field tests associated with selected measuring technologies were conducted on two super-long steel pipe piles in offshore areas to investigate the behaviors and performance of super-long steel pipe piles. The strain along the pile shaft was monitored by adopting the Brillouin optical time domain reflection and fiber Bragg grating techniques. Static load tests were also conducted on two test piles to determine the bearing capacities. In addition, the axial forces, relative displacements between piles and soils and pile shaft resistances were calculated based on the measured strain. According to the results of the static load tests, the ultimate bearing capacities of the two test piles are greater than 15,000 and 15,500 kN. Both of these values meet the design requirements. In addition, the two test piles can be treated as pure friction piles, and the load transfer mechanism and relationships between the pile shafts and relative displacements are also discussed. Finally, recommendations for practical engineering and significant conclusions are presented.
REVIEW Shiga Toxin-Producing Escherichia coli (STEC) Ecology in Cattle and Management Based Options for Reducing Fecal Shedding

Cattle can be naturally colonized with foodborne pathogenic bacteria such as Shiga Toxin-producing E. coli (STEC) in their gastrointestinal tract. While these foodborne pathogens are a threat to food safety, they also cause human illnesses via cross contamination of other foods and the water supply, as well as via direct animal contact. In order to further curtail these human illnesses and ensure a safe and wholesome food supply, research into preharvest pathogen reduction controls and interventions has grown in recent years. This review addresses the ecology of STEC in cattle and potential controls and interventions that have been proposed or implemented to reduce STEC in cattle. We focus in this review on the use of management practices and the effects of diet and water management. Implementation of preharvest strategies will not eliminate the need for good sanitation procedures in the processing plant and during food preparation and consumer handling. Instead, live-animal management interventions must be implemented as part of a multiple-hurdle approach that complements the in-plant interventions, so that the reduction in pathogen entry to the food supply can be maximized.
R ains F Rom s uccessive H uRRicanes R educe n esting s uccess oF tHe m aRbled s alamandeR ( A mbystomA opAcum )

— Current models predict that the intensity of extreme weather will increase as climate change progresses. Indeed, 2020 brought a record number of tropical storms to the Southeastern U.S. The effect of these storms is a conservation concern for amphibians because they depend on aquatic habitats for reproduction. I studied the effect of three subsequent hurricanes on the nesting success of Marbled Salamanders ( Ambystoma opacum ) in Tuskegee National Forest, Alabama, USA. In autumn, females migrate to nesting grounds, lay eggs terrestrially and guard them until seasonal rains flood the nest causing eggs to hatch. Rains from Hurricane Sally brought the salamanders into the breeding grounds, but rains from Hurricane Delta drove them back out, leaving the nesting area flooded before eggs could be laid. Most nests were constructed on the periphery of the semi-flooded nesting grounds, and subsequent seasonal rains flooded nests before the eggs were ready to hatch. Rains from Hurricane Zeta maintained high water levels. Of 28 nests, three were successful. Two nests were never covered in water (i.e., 100% egg mortality), and the rest were flooded prematurely. Concomitant lab studies demonstrated that premature flooding of nests reduces hatching success, indicating that increased hurricane frequency can harm salamander populations even if they are relatively far from the coast. Few studies have focused on the effects of hurricanes on salamanders; thus, additional research is warranted because the Southeastern U.S. is a hotspot for both salamander diversity and hurricane activity.
Improved prediction of protein-protein binding sites using a support vector machines approach.

MOTIVATION Structural genomics projects are beginning to produce protein structures with unknown function, therefore, accurate, automated predictors of protein function are required if all these structures are to be properly annotated in reasonable time. Identifying the interface between two interacting proteins provides important clues to the function of a protein and can reduce the search space required by docking algorithms to predict the structures of complexes. RESULTS We have combined a support vector machine (SVM) approach with surface patch analysis to predict protein-protein binding sites. Using a leave-one-out cross-validation procedure, we were able to successfully predict the location of the binding site on 76% of our dataset made up of proteins with both transient and obligate interfaces. With heterogeneous cross-validation, where we trained the SVM on transient complexes to predict on obligate complexes (and vice versa), we still achieved comparable success rates to the leave-one-out cross-validation suggesting that sufficient properties are shared between transient and obligate interfaces. AVAILABILITY A web application based on the method can be found at http://www.bioinformatics. leeds.ac.uk/ppi_pred. The dataset of 180 proteins used in this study is also available via the same web site. CONTACT westhead@bmb.leeds.ac.uk SUPPLEMENTARY INFORMATION http://www.bioinformatics. leeds.ac.uk/ppi-pred/supp-material.
PREVALENCE OF LEFT/RIGHT CONFUSION AMONG MEDICAL STUDENTS IN MUTAH UNIVERSITY- JORDAN.

The ability to distinguish left from right has been shown to vary substantially within healthy individuals, yet its characteristics and mechanisms are poorly understood.The study was designed in an effort to spot the extents of left-right confusion (LRC) among medical students in Mutah university and to investigate the relationship between LRC and multiple health and socioeconomic variables.Analytic cross section study was performed. Database gathered by google sheet in order to introduce them to software (starting form 12th - October / 2021 ended in 15/1/2022). Before analysis, survey was checked for missing data, statistically analysed by SPSS version 25.The prevalence in our sample was slightly lower than the prevalence reported in prior research, which was 14.7%, and this may be attributed to medical students' greater level of education and overall understanding when compared to the general population.It was found that there is a statistically significant link between gender, communication issues, and ADHD. The challenge now is where we go from here; since the relationship we discovered between ADHD and left-right confusion is novel, we need to carry further research to establish this unique association more solidly.
APPROACHES TO TEST SET GENERATION USING BINARY DECISION DIAGRAMS

Approaches to Test Set Generation Using Binary Decision Diagrams. (December 2003) James Wingfield, B.S., Texas A&M University Chair of Advisory Committee: Dr. M. Ray Mercer This research pursues the use of powerful BDD-based functional circuit analysis to evaluate some approaches to test set generation. Functional representations of the circuit allow the measurement of information about faults that is not directly available through circuit simulation methods, such as probability of random detection and test-space overlap between faults. I have created a software tool that performs experiments to make such measurements and augments existing test generation strategies with this new information. Using this tool, I explored the relationship of fault model difficulty to test set length through fortuitous detection, and I experimented with the application of function-based methods to help reconcile the traditionally opposed goals of making test sets that are both smaller and more effective.
DYNAMICS OF PARTICLE CONTAMINATION IN 550 kV THREE PHASE GAS INSULATED BUSDUCT WITH VARIOUS INSULATING GAS MIXTURES

A conventional three phase gas protected substation comprises of every one of its segments encased in sulphur hexafluoride gas. The dielectric quality of SF6 is too high when contrasted with that of air and subsequently makes it electrically better than customary air protection. In any case, SF6 is thought to be a green house gas and subsequently its use is to be constrained. This can be accomplished by utilizing selective gases like Nitrogen (N2), Carbon dioxide (CO2). On the other hand , for better after effects of getting both high dielectric properties and decreasing green house impacts, blends of these gases alongside SF6 can be utilized for agreeable activity of GIS. The significant issue with the GIS is the presence of electric contaminants which for the most part begin from the inside parts of the enclosures. The movement of these metallic contaminants is to be limited to enhance the proficient activity of GIS. The present work manages investigation of the movement of generally experienced aluminium and copper metallic particle contaminants in the Gas insulated Substations. The blends of SF6+CO2, SF6+N2 and SF6+air have been utilized as options for unadulterated SF6 gas. The developments of metallic particles are investigated for various extents of gas mixtures and 550kV power frequency ac voltage, the details of which are discussed.