Miracle-dz/UniFAR-FTUs huggingface dataset:
A deep-based compound model for lung cancer detection

$X$-ray image analysis is primarily performed by medical specialists. Patients expect a correct interpretation of these images regardless of cost. Despite various advantages of chest radiography, the interpretation of Magnetic Resonance Imaging (MRI) has always been a major issue for the physician and the radiologist due to misdiagnosis. According to the World Health Organization, Lung cancer cost around 1.8 million deaths in 2020, which makes it the leading cause of cancer death worldwide. Late diagnosis and lack of means of screening are the main problems. The algorithm can help radiologists accurately estimate the malignancy risk of lung nodules. This paper aims to detect and classify lung cancer using deep learning. We used the Convolutional Neural Network (CNN) algorithm combined with the Faster Regions with CNN (Fast R-CNN). Our model provides very encouraging results compared to those obtained by the work of the literature, which provides a model with a high accuracy rate for medical assistance.
Towards a wireless micropackaged implant with hermeticity monitoring

The development of reliable hermetic chip-scale micropackaging is one of the major challenges in the miniaturization of implantable medical devices. Protecting the patient from the implanted foreign body and the implant itself from the biological environment is crucial. This paper presents an implantable micropackaging concept to protect a microelectronic system-on-chip. A hermetic chamber is formed by bonding the active CMOS chip to a silicon cover using a gold-tin eutectic sealant. The cover’s fabrication method and the die’s post-processing steps are presented. A humidity sensor inside the chamber monitors the humidity to assess permeability. To power the sensor and read its data, interconnections in the CMOS chip have been designed; these metal tracks pass underneath the cover and thus create a connection between the inside and the outside of the cavity. As an alternative to these connections, an on-chip wireless power management and data communication system is presented with simulated results.
TOPOLOGY OF AMORPHOUS TETRAHEDRAL SEMICONDUCTORS ON INTERMEDIATE LENGTH SCALES

Using the recently-proposed ``activation-relaxation technique'' for optimizing complex structures, we develop a structural model appropriate to a-GaAs which is almost free of odd-membered rings, i.e., wrong bonds, and possesses an almost perfect coordination of four. The model is found to be superior to structures obtained from much more computer-intensive tight-binding or quantum molecular-dynamics simulations. For the elemental system a-Si, where wrong bonds do not exist, the cost in elastic energy for removing odd-membered rings is such that the traditional continuous-random network is appropriate. Our study thus provides, for the first time, direct information on the nature of intermediate-range topology in amorphous tetrahedral semiconductors.
Assessment method of balance ability of older adults using an in-shoe motion sensor

Falls rank as the second main cause of accidental death and injury in older adults, so technologies are urgently needed that are accurate, inexpensive, and easy to use to administer fall risk assessments in daily living. In this study, as the first stage of assessing fall risk in daily living, by using in-shoe motion sensor (IMS), we aim to develop an assessment method for dynamic balance ability that is an important factor for fall risk assessment. Sixty-four elderly participants were recruited for constructing the assessment model, and 20 other participants were recruited for model validation. We used statistical parametric mapping to analyze the correlation between the IMS signals and the outcome of FRT, the clinical score for evaluating dynamic balance ability, and then constructed predictors from highly correlated parts. Intra-class correlation coefficients (ICCs) were used for evaluating and limits of agreement (LoA) of Bland-Altman plots was used for validating the constructed model. In the results for both men and women, the estimation model achieved "excellent" ICC inter-rater agreement between true and estimated values, and in the validation, 90% of participants were effectively estimated inside the LoA.
Slow manifold analysis of accelerated gradient methods

Accelerated gradient descent iterations are widely used in optimization. It is known that, in the continuous-time limit, these iterations converge to a second-order differential equation which we refer to as the accelerated gradient flow. Using geometric singular perturbation theory, we show that, under certain conditions, the accelerated gradient flow possesses an attracting invariant slow manifold to which the trajectories of the flow converge asymptotically. We obtain a general explicit expression in the form of functional series expansions that approximates the slow manifold to any arbitrary order of accuracy. To the leading order, the accelerated gradient flow reduced to this slow manifold coincides with the usual gradient descent. We illustrate the implications of our results on three examples.
Design and Experiment on a Novel SMA Driven Micro-catheter for Active Navigation in Vascular Interventions

This paper presents a shape memory alloy (SMA) driven micro-catheter for active navigation in vascular interventions. SMA wires with microscale diameters and silicone rubber are employed for developing prototypes of such catheters. Selectively processed U shape SMA wires are biasedly embedded into silicone rubber to form the distal actuators with submillimeter diameters, which are characterized by infrared and CCD cameras. The transformation temperatures of SMA wires used for creation of distal actuator is proven to be approximately 35°C by the infrared imaging. The deflection angles of distal actuators are determined by the residual stress resulting from biased SMA wires and their shape memory effect. As a result, distal deflection within acute angles range is accomplished with actuators in submillimeter diameters. A micro-catheter (0.8 mm in diameter) with a distal actuator (0.5 mm in diameter) is mounted inside a vascular introducing sheath fixed on a 3D vascular model to experimentally validate the ability of proposed catheter for navigation.
Probability distributions for holdover time of lightning-caused wildfires

The holdover phase (i.e., the time between lightning-induced ignition and fire detection) is a phenomenon characteristic of lightning-caused wildfires. In this paper, we analyzed multiple holdover time datasets to determine the best statistical distribution for this phenomenon. We found that the gamma distribution seems a suitable candidate to model holdover times. We propose applications of the gamma distribution to improve the investigation of lightning-caused wildfires.
A Review and Strategic Approach for the Transition towards Third-Wave Trustworthy and Explainable AI in Connected, Cooperative and Automated Mobility (CCAM)

Traditional "2nd Wave (2WAI)" AI algorithms are highly specialized in narrowly-defined tasks in the transportation and mobility domain, however, quite hardly explainable and unable to cover the needs of unbiased and trusted decision-making in the CCAM domain. There is a strong need to accelerate the shift towards human-like "3rd Wave AI (3WAI)" to overcome the challenges of road transport to make autonomous vehicles and driving safe, cleaner and more efficient. This paper aims to shed light on ambitious pathways toward more trustworthy and explainable AI by presenting a strategic approach envisioning the requirements of 5 main audience profiles in the CCAM context (developers, decision-makers, regulators, end-users, and CCAM Service providers). The presented approach envisages a 6-dimensional concept that brings features of i) Transparent and explainable; ii) Fair and impartial; iii) Responsible and accountable; iv) Robust and Reliable; v) Respectful of Privacy; vi) Safe and Secure, transportation. To achieve the transition towards 3WAI a model-based n-sprint V methodology is introduced that is enriched with continuous validation by putting ethics at the centre of all potential innovations. The paper presents a review of the recently funded European projects within the concept of AI-powered CCAM.
Validation of NOMA System-Level Abstraction

Non-orthogonal multiple access (NOMA) transmission techniques promise reduced latency, massive user deployments, and higher data rates. To investigate how these per-formance enhancements translate to practical deployments in a network, a reliable system-level abstraction for downlink two-user power domain NOMA with successive interference cancellation at the receiver is needed. A simple and efficient abstraction is described and then validated through comparison with link-level simulation results. With the developed abstraction, the effects of the deployment of NOMA techniques in a wireless network with a large number of machine type communication users are evaluated in terms of cell throughput, number of users served simultaneously, and fairness.
Fast High-Resolution Disparity Estimation for Laparoscopic Surgery

An intraoperative Image Guidance System (IGS), facilitating the localisation of pathological tissue or vasculature, could greatly support medical decisions during minimally invasive interventions. In our IGS for laparoscopic surgery, the 3D reconstruction of abdominal organs requires fast and accurate depth information from stereo images. To this end, we employ a state-of-the-art algorithm for dense disparity estimation. To cope with low processing performance, previous solutions used only downscaled images, and hence produced disparities of low quality. In this work, we present methods and implementations which improve and accelerate disparity estimation such that it runs with FullHD resolution images at full camera framerate.
Green Supply Chain Quality Management and Corporate Green Performance: Structural Equation Modeling based on the PLS Method Axe: Green and Sustainable Supply Chain

This research aimed to examine the impact of Green Supply Chain Quality Management (GSCQM) on Corporate Green Performance (CGP). A questionnaire was distributed to a sample of (113) practitioners of large multinational companies in the industrial sector and which are established in Morocco. Data were analyzed using PLS-Smart software which is specialized in structural equation modeling. The software was used to determine whether the tin dimensions of Green Supply Chain Quality Management (i.e. independent variable); Green Human Resource Management, Green Leadership Behavior, Green Supplier Quality Management, Customer Focus, Process Management, Information and Analysis, Eco-Design, Green Purchasing, Environmental Cooperation, and Reverse Logistics have a significant impact on Corporate Green Performance(i.e. dependent variable). The software was also used to test the two mediation variables, namely: Green Organizational Culture and Green Innovation. The findings revealed that the practices of « Green Supply Chain Management » are having a significant impact on green innovation; and the implementation of the « Green Supply Chain Management » has a positive effect on Corporate Green Performance. However, Green Organizational Culture and Green Innovation did not have a significant impact on Corporate Green Performance according to the respondents' ratings. Therefore, this study is based on two main theories, namely: cost theory, institutional theory, green theory, stakeholder theory and resource-based theory.
Acquisition of Time and Doppler Shift in WFRFT Encrypted Chaotic Direct Sequence Spread Spectrum System

Weighted fractional Fourier transform encrypted chaotic direct sequence spread spectrum (WFRFT-CD3S) signal is aperiodic and complex Gaussian distributed. The aperiodicity and complex Gaussian distribution are conducive to the covert transmission of information but also make it difficult to acquire the WFRFT-CD3S signal. An acquisition method implemented in two steps is proposed to acquire the Doppler-shifted WFRFT-CD3S signal. First, a bidirectional correlation search method is proposed to align the received signal with the local spreading sequence in the time domain. The correlation peak attenuation caused by the Doppler frequency shift is eliminated by differential. Second, the accumulated phase angle caused by the Doppler frequency shift is obtained by the differential correlation value between the aligned received signal and the local spreading sequence. Then, the Doppler frequency shift is estimated by the accumulated phase angle. The detection probability, the false alarm probability, and the root mean square error (RMSE) of the Doppler shift estimation are analyzed theoretically and simulated. The theoretical results are verified by simulation. Theoretical results and simulations show that the detection probability is higher than 0.999, the false alarm probability is less than 0.001, and the RMSE of Doppler shift estimation is lower than 63 Hz when the signal-to-noise ratio is higher than −7.3 dB.
A Low-Noise Ex-Vivo CMOS MEA with 4k Recording Sites, 4k Recording Channels, and 1k Stimulation Sites

A Multi-Electrode-Array (MEA) in a 180 nm CMOS technology is presented with 4225 recording sides, 4225 recording channels, and 1024 stimulation sites. The two versions of the chip provide active areas of 2.3 mm x 2.3 mm and 4.1 mm x 4.1 mm with a spacial recording site resolution of 36 µm and 64 µm, respectively. The devices are capable of full imaging at a frame rate of 25 kHz at an input-referred noise of 13.1 µVrms in the action potential (AP) band from 300 Hz to 10 kHz, 10 µVrms in the AP band from 300 Hz to 6 kHz, and 15.1 µVrms in the local field potential (LFP) band from 1 Hz to 300 Hz. To reach these noise levels the signals from the recording sites are routed out of the active array to dedicated low noise amplifiers in the periphery. Crosstalk between the related pixels is significantly reduced by individual actively shielded traces.
CU-DRL: A Novel Deep Reinforcement Learning-assisted Offloading Scheme for Supporting Vehicular Edge Computing

In recent years, data-driven and AI-based intelligent transportation systems have been greatly developed to alleviate the public's concern about the increasingly severe traffic congestion and traffic safety issues. For supporting various safety-related ITS applications, vehicular edge computing (VEC) has been proposed as a promising technology that can effectively provide computing power and storage capacity support for vehicles in close proximity. However, in the face of the instability of communication between vehicles and other devices caused by the high-speed motion of vehicles and the complex relative motion between vehicles, how to effectively realize the relatively stable arithmetic power sharing between vehicles and edge computing devices is a critical problem that must be solved to realize VEC. Therefore, in this paper, we propose a distributed online offloading method, called Candidate Utilization-based Deep Reinforcement Learning (CU-DRL) algorithm, by exploiting the deep reinforcement learning technique. We further evaluate and demonstrate the effectiveness and correctness of the proposed CU-DRL model through simulations.
Network Intrusion Detection based on Improved CNN

This paper aims to improve the defects of traditional convolutional neural networks in intrusion detection systems and provide an analysis of dynamic data flow. The system first preprocesses the off-line network traffic through numeration, normalization, and grey value mapping. Secondly, the construction method of data visualization processing after the above transformation is explored. Two representative methods are proposed: right-shift filling of a single data cycle and right-shift filling of multiple data cycles. The network traffic captured online is input to obtain the real-time detection results through THE CNN engine interface. Finally, we evaluate the training model. Through comparison with relevant literature and real-time flow detection test, it is found that the optimized visual construction scheme explored has an excellent effect on accuracy and can detect basic flow types in real-time.
Towards a high-performance threat-aware system for software-defined networks

With the rapid development of intelligent devices and high-speed networks, the popularity of Internet services and the Internet of Things (IoT) has been increasing significantly in the last decade. This leads to the explosion of data exchanged over the Internet, also known as the Big Data era, which has posed several challenges in preventing security threats, especially for intrusion detection systems (IDS) due to high data velocity. In this paper, we propose a Distributed Network Intrusion Detection System (DisIDS) that accurately detects security threats by gathering statistical information about flows from software-defined network (SDN) switches in real-time and identifying abnormal traffic patterns using a distributed machine learning model. Evaluation results on a simulated system show that our proposal could identify several security threats with high accuracy (94.7% f1-score) and a relatively low false alarm rate. Moreover, DisIDS architecture is designed using highly scalable components to accelerate the detection rate.
Variational Bayesian Inference Based Channel Estimation for OTFS System with LSM Prior

Orthogonal time frequency space (OTFS) is a new emerging modulation scheme that performs better than orthogonal frequency division multiplexing (OFDM) in high mobility scenarios. In this paper, we consider the delay-Doppler (DD) channel estimation problem in an OTFS system. By exploiting the inherent sparse nature of the DD channel, the channel estimation problem is modeled as a sparse signal recovery problem. Next, we build a two-layer graphical model with the Laplacian scale mixture (LSM) prior utilized to model the sparse channel. Then, a variational Bayesian inference (VBI) based algorithm is proposed to solve this problem. Simulation results are presented to show that the proposed algorithm can achieve better performance than other existing channel estimation algorithms.
Scattering of elastic waves by periodic arrays of spherical bodies

We develop a formalism for the calculation of the frequency band structure of a phononic crystal consisting of nonoverlapping elastic spheres, characterized by Lam\'e coefficients which may be complex and frequency dependent, arranged periodically in a host medium with different mass density and Lam\'e coefficients. We view the crystal as a sequence of planes of spheres, parallel to and having the two-dimensional periodicity of a given crystallographic plane, and obtain the complex band structure of the infinite crystal associated with this plane. The method allows one to calculate, also, the transmission, reflection, and absorption coefficients for an elastic wave (longitudinal or transverse) incident, at any angle, on a slab of the crystal of finite thickness. We demonstrate the efficiency of the method by applying it to a specific example.
Nucleon axial form factor from generalized parton distributions

It is well established that the nucleon form factors can be related to Generalized Parton Distributions (GPDs) through sum-rules. On the other hand, GPDs can be expressed in terms of Parton Distribution Functions (PDFs) according to Diehl's model. In this work, we use this model to calculate polarized GPDs for quarks ($\widetilde{H}_q$) using the available polarized PDFs obtained from the experimental data, and then study the axial form factor of nucleon. We determine parameters of the model using standard $\chi^2$ analysis of experimental data. It is shown that some parameters should be readjusted, as compared to some previously reported values, to obtain better consistency between the theoretical predictions and experimental data. Moreover, we study in details the uncertainty of nucleon axial form factor due to various sources.
Non-Markovian memory in IBMQX4.

We measure and quantify non-Markovian effects in IBM's Quantum Experience. Specifically, we analyze the temporal correlations in a sequence of gates by characterizing the performance of a gate conditioned on the gate that preceded it. With this method, we estimate (i) the size of fluctuations in the performance of a gate, i.e., errors due to non-Markovianity; (ii) the length of the memory; and (iii) the total size of the memory. Our results strongly indicate the presence of non-trivial non-Markovian effects in almost all gates in the universal set. However, based on our findings, we discuss the potential for cleaner computation by adequately accounting the non-Markovian nature of the machine.
Weyl Anomaly and Vacuum Magnetization Current of M5‐brane in Background Flux

It was recently discovered that for a boundary system in the presence of a background magnetic field, the quantum fluctuation of the vacuum would create a non‐uniform magnetization density for the vacuum and a magnetization current is induced in the vacuum. It was also shown that this ‘magnetic Casimir effect’ of the vacuum is closely related to another quantum effect of the vacuum, the Weyl anomaly. Furthermore, the phenomena can be understood in terms of the holography of the boundary system. In this article, we review the derivation of this phenomena from QFT as well as the derivation of it using AdS/BCFT. We then generalize this four dimensional effect to six‐dimensions. We use the AdS/BCFT holography to show that in the presence of a 3‐form magnetic field strength H, a string current is induced in a six‐dimensional boundary conformal field theory. This allows us to determine the gauge field contribution to the Weyl anomaly in six‐dimensional conformal field theory in a H‐flux background. For the (2,0) superconformal field theory of N M5‐branes, the current has a magnitude proportional to N3 for large N. This suggests that the degree of freedoms scales as N3 in the (2,0) superconformal theory of N multiple M5‐branes. Our result for the Weyl anomaly is a new prediction for the (2,0) theory.
Constraining URCA cooling of neutron stars from the neutron radius of Pb-208

Recent observations by the Chandra observatory suggest that some neutron stars may cool rapidly, perhaps by the direct URCA process which requires a high proton fraction. The proton fraction is determined by the nuclear symmetry energy whose density dependence may be constrained by measuring the neutron radius of a heavy nucleus, such as 208Pb. Such a measurement is necessary for a reliable extrapolation of the proton fraction to the higher densities present in the neutron star. A large neutron radius in 208Pb implies a stiff symmetry energy that grows rapidly with density, thereby favoring a high proton fraction and allowing direct URCA cooling. Predictions for the neutron radius in 208Pb are correlated to the proton fraction in dense matter by using a variety of relativistic effective field-theory models. Models that predict a neutron (Rn) minus proton (Rp) root-mean-square radius in 208Pb to be Rn-Rp 0.25 fm, the direct URCA process is allowed (by all models) to cool down a 1.4 solar-mass neutron star. The Parity Radius Experiment at Jefferson Laboratory aims to measure the neutron radius in 208Pb accurately and model independently via parity-violating electron scattering. Such a measurement would greatly enhance our ability to either confirm or dismiss the direct URCA cooling of neutron stars.
Random walk hitting times and effective resistance in sparsely connected Erdős‐Rényi random graphs

We prove a bound on the effective resistance R ( x , y ) between two vertices x , y of a connected graph which contains a suitably well‐connected subgraph. We apply this bound to the Erdős‐Rényi random graph G ( n , p ) with n p = Ω ( log n ) , proving that R ( x , y ) concentrates around 1 / d ( x ) + 1 / d ( y ) , that is, the sum of reciprocal degrees. We also prove expectation and concentration results for the random walk hitting times, Kirchoff index, cover cost, and the random target time (Kemeny's constant) on G ( n , p ) in the sparsely connected regime log n + log log log n ≤ n p < n 1 / 10 .