Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Optimal Transport to Improve Spherical Harmonic Quantification of Complex Biological Shapes

The knowledge of the anatomical shape of both gross and microscopic structures is the key to understanding the effects of disease processes on cellular structure. Geometric morphometric methods, such as Procrustes superimposition, and Spherical Harmonics (SPHARM), have been used to capture the biological shape variation and group differences in morphology. Previous SPHARM-MAT techniques use the CALD algorithm to parameterize the mesh surface. It starts from initial mapping and performs local and global smoothing methods alternately to control the area and length distortions simultaneously. However, this parameterization may not be sufficient in complex morphological cases. To bridge this gap, we propose SPHARM-OT, an enhanced SPHARM surface modeling method using optimal transport (OT) for spherical parameterization. First, the genus 0 3D objects are conformally mapped onto a sphere. Then the optimal transport theory via spherical power diagram is introduced to minimize the area distortion. This new algorithm can effectively reduce the area distortion and lead to a better reconstruction result. We demonstrate the effectiveness of the method by applying it to the human sphenoidal paranasal sinuses.
Economic Performance of a 250kW Solar PV System: Application to Lebanon

Climate change issues have negative impact on the environment and the economy. Renewable energy sources are then a need for any country in order to reduce the pollution and the economic cost of the electricity sector. Therefore, the renewable energy systems are considered as necessary components that must be implemented in any electric grid. In this paper the installation of a grid-connected PV system for the Lebanese Center for Educational Research and Development (CERD) is discussed. The aim of this study is to show the positive impact of the implementation of a grid-connected Photovoltaic (PV) and a battery storage system to meet the building load demand and minimize the dependency of the grid. In addition, a supervisory system is implemented in order to prevent faults and increase the PV system’s efficiency. The study is used in order to define the economic and environment benefits of the PV system.
Minimum Wage Impacts on Wages and Hours Worked of Low-Income Workers in Ecuador

The minimum wage policy in Ecuador aims to raise the real income of low-wage workers. We analyze the effects of the January 2012 increase in minimum wages on wages and hours worked of low-wage workers. Individuals may select themselves into the occupations of the groups of workers who are covered by the minimum wage legislation, or into those who are not. We apply a difference-in-differences estimation as an identification strategy to account for selection on unobservables. We construct individual panel data from a household panel. The main results suggest a significant and positive effect of the minimum wage increase on the wages of affected workers, increasing their wages by 0.41% to 0.48% for each 1% increase in minimum wages, relative to the earnings of unaffected workers. Results from hours worked highlight several variables that should be accounted for to find significant and sensible estimations that differentiate between full time work and other heterogeneous effects on the treated group.
Planetary Waves in Horizontal and Vertical Shear: The Generalized Eliassen-Palm Relation and the Mean Zonal Acceleration

Abstract Using a new generalization of the Eliassen-Palm relations, we discuss the zonal-mean-flow tendency ∂ū/∂t due to waves in a stratified, rotating atmosphere, with particular attention to equatorially trapped modes. Wave transience, forcing and dissipation are taken into account in a very general way. The theory makes it possible to discuss the latitudinal (y) and vertical (z) dependence of ∂ū/∂t qualitatively and calculate it directly from an approximate knowledge of the wave structure. For equatorial modes it reveals that the y profile of ∂ū/∂t is strongly dependent on the nature of the forcing or dissipation mechanism. A by-product of the theory is a far-reaching generalization of the theorems of Charney-Drazin, Dickinson and Holton on the forcing of ∂ū/∂t by conservative linear waves. Implications for the quasi-biennial oscillation in the equatorial stratosphere are discussed. Graphs of y profiles of ∂ū/∂t are given for the equatorial waves considered in the recent analysis of observational data.
Cyber-attack Group Representation based on Adversary Artifacts with Machine Learning

The problem with the Indicator of Compromise (IoC) repository or a hybrid analysis requires human intervention to contribute to the detection, whereby the dynamic analysis base on Malware Identification produces vast numbers of noise and false positives, which make detection inefficient. Most internet attacks include multiple techniques and tactics by the threat actor to gain access to the target. However, the threat detection techniques are signature-based, which makes sophisticated attacks undetected. Hence this paper proposed methods that can complement the existing detection style with multiple approaches that will increase attack detection precision. This research also introduces the Zero reputation Indicator (ZrI) consists of a honeypot to gain real-time attacks to use with Machine Learning. Data reZrI will employ a continuous learning flow to complement existing knowledge from a new attack pattern. The result shows the improvement in knowledge representation based on the data acquisition process that consists of Malware analysis and identification.
CELR: Cloud Enhanced Local Reconstruction from low-dose sparse Scanning Electron Microscopy images

Current Scanning Electron Microscopy (SEM) acquisition techniques are far too slow to capture large volumes in a feasible time. One solution is to use low-dose and sparse imaging. By computationally denoising and inpainting an image with acceptable quality can be approximated. This approach, however, requires significant compute resources. Therefore, this paper proposes CELR, a framework, that hides the computationally expensive workload of reconstructing low-dose sparse SEM images, such that (delayed) live reconstruction is possible. Live reconstruction is possible by using Convolutional Neural Networks (CNNs) that approximate a classical reconstruction algorithm like GOAL. The reconstruction by CNNs is done locally, while recurring training of CNNs is done in the cloud. Moreover, training labels are generated by GOAL in the cloud. Next to the framework, this paper evaluates and optimizes the CNN reconstruction throughput by employing Nvidia's TensorRT. This paper also touches upon open research questions about on-the-fly CNN training. The combination of CELR and TensorRT enables large volume acquisitions with a dwell-time of $\mathbf{1}\mu s$ and 10% pixel coverage to be reconstructed on a single GPU.
Fake News Detection on COVID 19 tweets via Supervised Learning Approach

In December 2019, the first cases of an infection caused by the virus called Covid19 were recorded in the Chinese city of Wuhan. As the months passed, this virus gave rise to a global pandemic that has not yet been eradicated. The COVID19 information disseminated on digital platforms has very different contents, which makes it difficult to recognize whether the published news is true or false, as well as the sentiments associated with it. Therefore, the hypothesis that feelings about COVID19 may differ between Fake news and Real news is considered. The aim of the present study is to support the identification of real tweets from fake ones and to compare the sentiments that users express in them. To achieve this goal, two different datasets obtained from the English version of the social network Twitter were used: the first dataset was downloaded from ’Kaggle’ and relates to the year 2021, while the second dataset is more recent and was obtained via ’Python’. Supervised Learning techniques were applied to the dataset downloaded from ’Kaggle’, also highlighting variables not recognizable at first glance (metadata) and associating the sensations manifested in the publications. From the analysis of the first d ataset, we derived an algorithm, which we subsequently applied to the second dataset for news recognition. The performances obtained are of interest and show the change and trend of emotions and feelings conveyed by the tweets.
Attention-based Skin Cancer Classification Through Hyperspectral Imaging

In recent years, hyperspectral imaging has been employed in several medical applications, targeting automatic diagnosis of different diseases. These images showed good performance in identifying different types of cancers. Among the methods used for classification, machine learning and deep learning techniques emerged as the most suitable algorithms to handle these data. In this paper, we propose a novel hyperspectral image classification architecture exploiting Vision Transformers. We validated the method on a real hyperspectral dataset containing 76 skin cancer images. Obtained results clearly highlight that the Vision Transforms are a suitable architecture for this task. Measured results outperform the state-of-the-art both in terms of false negative rates and of processing times. Finally, the attention mechanism is evaluated for the first time on medical hyperspectral images.
Polynomial Identities of Algebras of Small Dimension

It is well known that given an associative algebra or a Lie algebra A, its codimension sequence c n (A) is either polynomially bounded or grows at least as fast as 2 n . In [2] we proved that for a finite dimensional (in general nonassociative) algebra A, dim A = d, the sequence c n (A) is also polynomially bounded or c n (A) ≥ a n asymptotically, for some real number a > 1 which might be less than 2. Nevertheless, for d = 2, we may take a = 2. Here we prove that for d = 3 the same conclusion holds. We also construct a five-dimensional algebra A with c n (A) < 2 n .
ShapeShifter: Resolving the Hidden Latency Contention Problem in MEC

Mobile Edge Computing (MEC) creates new infrastructure at the edges of the mobile networks, thus providing transformative opportunities for applications seeking latency benefits by operating closer to end-users and devices. However, the reduced network distance between the application endpoints of the MEC flows causes pattern shifts in the packet bursts exchanged at the network edges. The longer and denser bursts create a new source of contention that is not considered by current solutions. As a result, naively collocating applications onto the MEC tier can negatively affect latency-critical workloads, resulting in up to 73% packets experiencing as much as 3.8x increased latency. This makes it impossible to support latency-centric SLOs in MEC, obviating its expected benefits from MEC. This paper is the first to describe this new contention point in mobile networks and its potentially crippling impact on the achievable latency benefit from MEC. We propose ShapeShifter, a new component in the MEC architecture which solves the MEC latency contention problem through adaptive latency-centric burst management of MEC flows. ShapeShifter is effective - it eliminates SLO violations for latency-critical applications and improves application performance in multi-tenant scenarios by up to 3.8 x – and practical – it can be deployed with minimal disruption to the current mobile network ecosystem.
Predicting LncRNA-Disease Associations Based on LncRNA-MiRNA-Disease Multilayer Association Network and Bipartite Network Recommendation

The pathogenesis of many human diseases is unclear, but many studies have shown that lncRNAs are deeply involved in the development of diseases. However, the exploration of lncRNA-disease associations in the laboratory requires a lot of time and financial resources, and computational-based methods have obvious advantages and become a promising research direction. But few experiments consider the relationship between other biological factors and lncRNAs and diseases. In this paper, a novel lncRNA-disease association prediction method, MANBNR, is proposed. MiRNAs are introduced by MANBNR to construct a lncRNA-miRNA-disease multilayer association network (MAN). The main innovation of MANBNR is to mine potential lncRNA-disease association information based on miRNA information. For any lncRNA-disease pair, lncRNA-associated miRNAs and disease-associated miRNAs are sorted into two sets, respectively. The association of this lncRNA-disease pair is judged by comparing the number of miRNAs shared in the two sets. This solves the problem that the known lncRNA-disease association matrices are too sparse. Finally, the bipartite network recommendation (BNR) algorithm was used to accurately predict potential lncRNA-disease association. The performance of MANBNR is better than that of many advanced methods at present. Case studies of breast cancer and lung cancer further demonstrate that MANBNR is an effective and reliable method for LDAs prediction.
Self-Consistent Model of Hydrogen Chemisorption

The chemisorption of a hydrogen atom on a transition-metal surface is treated theoretically on the basis of the Anderson Hamiltonian in Hartree-Fock approximation, which includes the interelectronic interaction within the $1s$ orbital. One-electron theory is shown to be inadequate for this problem. The localized states which may occur are discussed. A simple expression for the chemisorption energy $\ensuremath{\Delta}E$ is obtained, and a variational method is given for obtaining its self-consistent value. The metal eigenfunctions enter $\ensuremath{\Delta}E$ only through a function $\ensuremath{\Delta}(\ensuremath{\epsilon})$, and the foregoing results are exemplified and applied when this function is semielliptical. When the band is half-filled, a single analytic formula for the one-electron part of $\ensuremath{\Delta}E$ is obtained, in accord with the Kohn-Majumdar theorem. With some further assumptions, $\ensuremath{\Delta}E$ and the charge on the atom are calculated for adsorption on Ti, Cr, Ni, and Cu. The values of the hopping integral between the $1s$ orbital and a neighboring metal $d$ orbital required to fit the experimental $\ensuremath{\Delta}E$ are found to be similar and are reasonable. The correct prediction that ${|\ensuremath{\Delta}E|}_{\mathrm{Ni}}g{|\ensuremath{\Delta}E|}_{\mathrm{Cu}}$ is believed to be significant. A suggestive correlation is found between observations of catalytic ortho-para hydrogen interconversion on Pd-Au alloys and a rigidband calculation of $\ensuremath{\Delta}E$.
Human Tracking and Following using Machine Vision on a Mobile Service Robot

Service robot technology is rapidly improving to give rise to robust and reliable machines operating alongside humans. This paper presents a human-following system that can identify a target human in a crowded environment and track the person’s motion, simultaneously avoiding obstacles while navigating through the environment. We implement the system on a mobile service robot platform with light detection and ranging (LIDAR) and RGBD sensors. The system uses a Discriminative Generative network (DG-net) for human detection. After detection, the localization module will locate the target person’s position in the environment. The navigation module generates a cost map of the surroundings for path planning. It allows the robot to navigate the changing environment avoiding obstacles while tracking the target person. Experimental results showed that the robot could identify and follow the target person reliably. At the same time, the robot navigates the crowded environment safely, avoiding other people and obstacles in the environment. Despite all that, the recovery module could not recover reliably after losing the target person. The demonstration video is available at https://github.com/LeoYong95/human_following. git
Two‐photon wave‐packet interferometry

The excitation of a molecule by a short pulse creates a wave packet whose motion depends on the nuclear forces of the excited electronic state. The properties of such a packet can be studied interferometrically. This is done by exposing the molecule to two identical pulses delayed from each other. Two packets are thus created and the excited state population has a term which depends on the interference between them. This interference population is a quantum effect and its dependence on the delay time reflects the coherence properties of the packet and is very sensitive to the potential energy surface. In this paper, we examine the use of wave‐packet interferometry to study the dynamic properties of packets created by two‐photon absorption. We find several interference populations since the packets created by two‐photon absorption interfere with each other and with packets created by absorption of one photon from each pulse. The single interference terms contain different physical information and we discus.
A Pipeline for the Diagnosis and Classification of Lung Lesions for Patients with COVID-19

The current study considers the development of a 5-layer pipeline for identifying and classifying COVID-19-induced lung lesions. Such system is multilayer, built upon convolutional and fully connected neural networks and logistic self-organised forest built using the group method of data handling (GMDH) principles. This pipeline includes a mechanism for finding lesions regions in lungs computer tomography images and for calculating related lung damage volume. The layer for finding images with lesions reached a Matthews Correlation Coefficient of 0.98. The layer for lesions segmentation reached a Dice similarity coefficient of 0.74, while the layer for lesions classification reached Fl-scores of 1, 0.95, 0.93 respectively for the ground-glass, opacity, crazy-paving and consolidation lesion type. Results demonstrate the effectiveness of the implemented multi-layer system in solving tasks of lesions identification and classification while being composed into a single pipeline.
Second Order Sliding Mode Controller for a Bridgeless Boost Power Factor Correction

In this paper, a sliding mode controller is applied to a Bridgeless Boost PFC converter. This controller is replaced with the traditional PI controller to improve the dynamic performance, stability, and attenuate the output voltage ripple. Moreover, by using a bridgeless converter, lower power dissipation can be achieved from the elimination of the diode bridge. The simulation results in SIMULINK MATLAB show the performance of the sliding mode controller in a bridgeless boost PFC converter with fast dynamic response, low undershoot and stability of the controller.
EARL: An Efficient Approximate HaRdware Framework for AcceLerating Fault Tree Analysis

This paper proposes an efficient hardware framework that utilizes approximate computing units to mitigate the fault tree (FT) simulation time while considering accuracy and energy. To do so, first, we introduce two hybrid-precision computational units, which can operate in either accurate or approximate modes. Then, we utilize these computational units and an accuracy propagation technique to evaluate the desired accuracy level of each computational part. Finally, EARL, the proposed framework, by taking into account determined accuracy levels, in an offline stage through machine learning technique, predicts the best hardware platform (i.e., CPU, FPGA, or GPU) for executing each benchmark and provides a trade-off among simulation time, accuracy, and energy consumption. To demonstrate EARL's efficiency, we study the case of reliability calculation through fault tree analysis. However, EARL is useful in top-down structured computational problems. Our evaluations show that the proposed framework improves the simulation time and energy consumption of fault tree analysis on average by 78.1%, and 72.6%, respectively, with a negligible accuracy loss.
Pseudo-Siamese Neural Network Based Graph and Sequence Representation Learning for Molecular Property Prediction

Molecular property prediction has received great attention due to its wide application in biomedical field. Effective molecular representation learning is of substantial significance to facilitate molecular property prediction. In recent years, with the development of artificial intelligence technology, more and more computer scientists began to apply deep learning methods to molecular property prediction instead of traditional machine learning methods. However, these methods only utilize the SMILES sequences to learn sequence representation or use the molecular graphs to learn graph representation to predict molecular property, which fails to integrate the capabilities of both approaches in preserving molecular characteristics for further improvement. In this study, we propose a joint graph and sequence representation learning model for molecular property prediction, called PSGS. Specifically, PSGS utilizes a fusion layer to combine graph and sequence representation and capture the critical features of the molecular. In addition, PSGS is trained by a new self-supervised task, which maximizes the similarity between graph and sequence representations of the same molecular by using a pseudo-Siamese neural network. We conduct extensive experiments to compare our model with state-of-the-art models. Experimental results show that our model significantly outperforms the current state-of-the-art methods on four independent datasets.
Intelligent Configuration of PHY-Layer Parameters to Reduce Energy Consumption in LoRa

Communications over long distances and strong resilience to interference are vital aspects of LoRa. LoRa adjusts the modulation to allow higher data transmission rates, depending on the reception sensitivity threshold and the communication distance. The spreading factor and the transmission power, in turn, are directly related to energy consumption, influencing network performance. This paper proposes the use of supervised learning techniques to conFigure the spreading factor and the transmission power simultaneously. This approach differs from the literature as it configures two parameters instead of just one, the spreading factor. Different learning techniques are evaluated through simulations using a LoRa network. Our experiments compare the performance of our proposal with the traditional LoRaWAN and the state-of-the-art on intelligent configuration using only the spreading factor. The obtained results show that our proposal successfully reduces the energy consumption without affecting the packet delivery ratio.
A survey on co-optimization of sizing and energy management for fuel cell electric vehicles

The overall performance of fuel cell hybrid electric vehicles is largely dependent on the powertrain sizing configuration and energy management strategies (EMS). Due to their strong coupling relationship, it is thus instrumental to maximize vehicle's economic potential by coordinating both aspects simultaneously. This paper presents a comprehensive review on the existing co-optimization methods of EMS and sizing. Firstly, the necessity of co-optimization is illustrated. Secondly, approaches for EMS, sizing, and especially co-optimization are reviewed and classified. Thirdly, the advantages and disadvantages of different co-optimization approaches are further summarized. In general, this work presents not only a comprehensive literature review of existing co-optimization approaches for sizing and EMS, but, more importantly, the relevant analyses could act as a baseline guideline for prospective designers in the field of control and optimization for fuel cell electric vehicles (FCEV) powertrain.