Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Denoising using Convolutional Neural Network

Image denoising is one of the fundamental aspects for removing noise from an image and enhancing its features containing visual information. Based on this, the Convolutional Neural Networks (CNNs) along with Autoencoders when applied with image denoising have been a hot topic of study, with a wide range of applications in fields as diverse as diagnostic image denoising and low-light image denoising. As compared to non-CNN image denoising approaches, a CNN based image denoising approach seems to be more noise-free. When applied on the Modified National Institute of Standards and Technology (MNIST) dataset, this work presents a research and practical method to image denoising utilizing deep CNN algorithms.
Aprus: An Airborne Altitude-Adaptive Purpose-Related UAV System for Object Detection

Unmanned aerial vehicle (UAV) system plays an important role in new edge scenarios such as disaster relief, situational awareness, and so on. In order to improve efficiency, computing tasks such as object detection are carried out on the drone-mounted computing unit. However, there is a trade-off between flight altitude and mission accuracy. Higher altitudes result in smaller images, which influence the accuracy. In comparison, shorter altitudes reduce the difficulty of detection while decreasing the recognition range, which affects user experience. Therefore, this paper proposes an airborne altitude adaptive UAV System, Aprus, which selects the most suitable object detection models based on a self-adaptation strategy at different altitudes. The selection strategy is based on a purpose-related evaluation indicator, PEI. It comprehensively considers the model's accuracy, recall, and inference speed at the current altitude according to the user's purpose. Moreover, Aprus sends the original image to a divider instead of scaling the image before pushing it to the model, thus ensuring that the original picture information will not be lost. To evaluate the system, we build a high-resolution UAV dataset with altitude, UDWA, which contains 46037 images. From the experiments, Aprus obtained 58.52% mAP, 94.17%mAP50, 66.17%mAR, 1.61FPS results on DJI Manifold 2G when the purpose is set to Balance mode, and the system can be adjusted by multiple preset purposes or according to the user customs.
Forecasting the Propagation of LF-MF-HF Radio Waves over Ice Paths

The field of a vertical electric dipole over a homogeneous impedance surface is considered. A method for calculating the attenuation function of the ground wave field over a flat surface is given. The effects associated with the presence of a surface wave at highly inductive impedances when a signal is received at different heights above the surface are considered. The electromagnetic field exhibits a clearly expressed surface character. It is shown that the conditions for the propagation of LF-MF-HF radio waves over the structure “ice-sea” with highly inductive impedance are more favorable than over the sea without ice due to the occurrence of surface electromagnetic waves (SEW). It has been established that the conditions for the propagation of radio waves over ice paths depend on the frequency, ice thickness and distance from the transmitter.
Interpreting Deep Learning Models for Multi-modal Neuroimaging

Accurately analyzing both structural and functional brain data from multimodal neuroimaging is a challenge for deep learning methods. Recent progress in explainable AI (XAI) has helped to gain insight into structural relationships across brain regions and complex dynamics of cognitive states, both in healthy and diseased. In this brief note, we will touch upon selected recent directions of our research where machine learning techniques help to analyze brain measurements from EEG, fNIRS, sMRI and fMRI. We owe these steps that we are summarizing mainly to activities of members of the BBCI team and their collaborators. Clearly, unavoidably and intentionally this abstract will have a high overlap to prior own contributions as it reports about and discusses these novel ideas and directions.
GSpTC: High-Performance Sparse Tensor Contraction on CPU-GPU Heterogeneous Systems

Many fields of scientific simulation, such as chem-istry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we centre around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address and alleviate the above difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained index partition based on element-wise tensor con-traction, which facilitates efficient use of the parallel computing power of GPUs. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merge phase, which greatly accelerates the computation phase in sparse tensor contraction. Through evaluation of five datasets, we find that the proposed GSpTC achieves an average performance improvement of 74.32% compared to the previous state-of-the-art Sparta framework.
Solar Powered Automatic Waste Management System using LoRaWAN

Solid waste segregation is essential in waste management. This project seeks to present a method of separating four kinds of wastes; metal waste, plastic waste, solid wet waste and paper waste. A short message is sent using LoRaWAN technology to the waste collector alerting him on the status of the waste in the bin. The project has a front ultrasonic sensor which detects the presence of a person 40cm to the bin and then automatically opens the bin for the person to drop the waste. Three sensors made up of inductive and capacitive proximity sensor and a moisture sensor are employed in the dump tray to detect whether the waste is metal, paper or a wet substance and subsequently direct the waste to the appropriate bin. Four other ultrasonic sensors are fitted on top of each of the individual bins to determine the level of the waste in the bins. These four sensors are linked to the front sensor to automatically shut the bin to prevent further addition of waste. A 12volt 60watts solar panel is used to power the system through a lithium rechargeable battery. This project will go a long way to solve the waste challenges wwe have in schools, hotels, hospitals and churches.
Existence and Uniqueness of the Weak Solution of the Space-Time Fractional Diffusion Equation and a Spectral Method Approximation

Abstract. In this paper, we investigate initial boundary value problems of the spacetime fractional diffusion equation and its numerical solutions. Two definitions, i.e., Riemann-Liouville definition and Caputo one, of the fractional derivative are considered in parallel. In both cases, we establish the well-posedness of the weak solution. Moveover, based on the proposed weak formulation, we construct an efficient spectral method for numerical approximations of the weak solution. The main contribution of this work are threefold: First, a theoretical framework for the variational solutions of the space-time fractional diffusion equation is developed. We find suitable functional spaces and norms in which the space-time fractional diffusion problem can be formulated into an elliptic weak problem, and the existence and uniqueness of the weak solution are then proved by using existing theory for elliptic problems. Secondly, we show that in the case of Riemann-Liouville definition, the well-posedness of the space-time fractional diffusion equation does not require any initial conditions. This contrasts with the case of Caputo definition, in which the initial condition has to be integrated into the weak formulation in order to establish the well-posedness. Finally, thanks to the weak formulation, we are able to construct an efficient numerical method for solving the space-time fractional diffusion problem. AMS subject classifications: 35S10, 35A05, 65M70, 65M12
Comparative Study of Ransomwares

Over the past few years cyberattacks have increased significantly as we have moved to a digital world. In 2021 this number rises drastically. Ransomware Attacks are not only limited to attacking an organization or a group of people to steal/encrypt their data, but it also refers to attacking networks or servers to slow down the services to such an extent thus creating the services unusable for the user, but at the same time, unlike other malware whose detection and analysis is not much complex, in ransomware, we find that they cannot be reverse engineered easily, due to high obfuscation and packing techniques involved. Skilled attackers are always a step ahead thus they find ways to evade the security systems or execute zero-day vulnerabilities.
Knowledge Graph creation on Windows malwares and completion using knowledge graph embedding

In current internet era, it is convenient to access information within few clicks. This ease of access to information is not only attracting more consumer markets but also attracts various computer threats. The Microsoft windows is the most popular desktop operating system used worldwide due to its simplicity, various hardware vendor’s support. Such massive user base attracts various hacker’s attention to exploit vulnerabilities present in windows. Windows Portable executable is file format used by windows to store executable files. Portable Executable file contain various sections, among those import section contain OS functionality required by executable file for it’s working. This work proposes Knowledge graph from real windows malware executables and tries to complete it using knowledge graph embedding models. Live Malware executables are gathered from Dike dataset and entity, relations are extracted accordingly. Later Knowledge graph embedding models are used for completion and various models are evaluated based on metrices like hit@k, MR, MRR.
KAM iteration with nearly infinitely small steps in dynamical systems of polynomial character

In this paper we present a new variant of the KAM theory, containing an artificial parameter $q$, $0 < q < 1$, which makes the steps of the KAM iteration infinitely small in the limit $q$↗$1$. This KAM procedure can be compared for $q<1$ with a Riemann sum which tends, for $q$↗$1$, to the corresponding Riemann integral. As a consequence this limit has all advantages of an integration process compared with its preliminary stages: Simplification of the conditions for the involved parameters and global linearization which therefore improves numerical results. But there is a difference from integrals: The KAM iteration itself works only for $q<1$, however, $q$ can be chosen as near to $1$ as we want and the limit $q$↗$1$ exists for all involved parameters. Hence, the mentioned advantages remain mainly preserved. The new technique of estimation differs completely from all what has appeared about KAM theory in the literature up to date. Only Kolmogorov's idea of local linearization and Moser's modifying terms are left. The basic idea is to use the polynomial structure in order to transfer, at least partially, the whole KAM procedure outside of the original domain of definition of the given dynamical system.
Diagnosis of invasive respiratory mycoses in the immunocompromised host

Purpose of review The burden of invasive fungal infection is increasing worldwide, largely due to a growing population at-risk. Most serious human fungal pathogens enter the host via the respiratory tract. Early identification and treatment of invasive fungal respiratory infections (IFRIs) in the immunocompromised host saves lives. However, their accurate diagnosis is a difficult challenge for clinicians and mortality remains high. Recent findings This article reviews IFRIs, focussing on host susceptibility factors, clinical presentation, and mycological diagnosis. Several new diagnostic tools are coming of age including molecular diagnostics and point-of-care antigen tests. As diagnosis of IFRI relies heavily on invasive procedures like bronchoalveolar lavage and lung biopsy, several novel noninvasive diagnostic techniques are in development, such as metagenomics, ‘volatilomics’ and advanced imaging technologies. Summary Where IFRI cannot be proven, clinicians must employ a ‘weights-of-evidence’ approach to evaluate host factors, clinical and mycological data. Implementation studies are needed to understand how new diagnostic tools can be best applied within clinical pathways. Differentiating invasive infection from colonization and identifying antifungal resistance remain key challenges. As our diagnostic arsenal expands, centralized clinical mycology laboratories and efforts to ensure access to new diagnostics in low-resource settings will become increasingly important.
Streaming vorticity flux from oscillating walls with finite amplitude

How to describe vorticity creation from a moving wall is a long standing problem. This paper discusses relevant issues at the fundamental level. First, it is shown that the concept of ‘‘vorticity flux due to wall acceleration’’ can be best understood by following fluid particles on the wall rather than observing the flow at fixed spatial points. This is of crucial importance when the time‐averaged flux is to be considered. The averaged flux has to be estimated in a wall‐fixed frame of reference (in which there is no flux due to wall acceleration at all); or, if an inertial frame of reference is used, the generalized Lagrangian mean (GLM) also gives the same result. Then, for some simple but typical configurations, the time‐averaged vorticity flux from a harmonically oscillating wall with finite amplitude is analyzed, without appealing to small perturbation. The main conclusion is that the wall oscillation will produce an additional mean vorticity flux (a fully nonlinear streaming effect), which is partial.
The Study on a Practical Method for Distributed Photovoltaic Capacity Forecasting

With the rapid development of distributed photovoltaic (PV), the prediction of the installed scale and generation power of distributed PV plays an important role in studying the impact of PV on the power grid. The paper proposes a method for forecasting the installed capacity of distributed PV which takes into account the impact of changes in the return on investment (ROI) of the distributed PV. Case Study takes Shanghai as an example and estimates its distributed PV installation potential and forecasts the installation scale in the next few years.
RLRBM: A Reinforcement Learning-based RAN Buffer Management Scheme

The development of 5G pushes the research community to concentrate on more innovative 5G beyond/6G networks to provide instantaneous connectivity to futuristic applications, such as e-health, autonomous vehicles, and entertainment services broadcasting, etc., which leads to a huge traffic data explosion. How to mitigate the traffic congestion and the bufferbloat problem is a formidable task. The interaction between transport congestion control protocol and the radio access network (RAN) buffer management scheme drastically impacts the congestion and bufferbloat problem. In this paper, we aim to study the RAN buffer management problem from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL). We propose a model-free approach, RLRBM, which enables the agent to learn the best buffer size tuning policy as human beings learn skills. We simulate several typical scenarios to evaluate RLRBM. Experimental results show that RLRBM achieves best trade-off between high throughput and low latency.
Design, modeling and optimization of gallium nitride-based photonic crystal structures

We present results of numerical modeling of photonic crystal (PhC) structures fabricated in gallium nitride (GaN). GaN is a wide band gap semiconductor material with large refractive index and very good thermal and mechanical properties, so it is considered a valuable candidate for photonic crystal application - in particular for devices exposed to the harsh environment. In this paper are considered the ideal 2D PhC with infinite high for a different lattice structures and calculated optical band gap maps for each. We also calculated air-bridge type slab and "sandwich-type" PhC slabs with finite height. The dependence of transmission and reflection spectra on holes size, width and profile of "sandwich-type" PhC slab structure are investigated. All calculations were performed using plane wave expansion method (PWE) and finite difference time domain method (FDTD).
Exciton binding energy in spherical quantum dots in a magnetic field

The binding energy of an exciton in spherical GaAs quantum dots in a magnetic field is calculated by a variational approach within the effective-mass approximation. The quantum dot is modeled by an isotropic parabolic potential. The magnetic effects on exciton binding energies and exciton energy levels in spherical GaAs quantum dots with different parabolic potentials are studied. The calculated results indicate that the interplay of the spatial confinement and the magnetic confinement on electrons and holes in the quantum dots leads to complicated exciton binding energies, and high magnetic fields obviously enhance exciton binding energies in the case of the weak spatial confinement.
On the Radiative Properties of Cirrus in the Window Region and Their Influence on Remote Sensing of the Atmosphere

Abstract Transmission, emission and reflection characteristics of cirrus clouds in the 800–1200 cm−1 window region are obtained by means of solving the transfer of infrared radiation in the realistic moist and dry model atmospheres. These model atmospheres include the water vapor within, above and below the cirrus clouds which are assumed to be composed of randomly oriented cylinders. Results of the radiation parameters are presented in terms of the geometrical thickness of circus as well as the model atmospheres. The discrepancies between the transmission and emission for flux and those for the vertically emergent intensity are pointed out and discussed. It is shown that the radiative properties of cirrus depend strongly upon the particle concentration. For a typical cirrus with a thickness of 1 km whose concentration is 0.05 cm−3, the intensity transmission is about 0.65 with an emissivity of about 0.35. Further, based upon the theoretical analyses, the reductions in the effective temperature when cirru.
Altered Cerebellar Volumes and Intrinsic Cerebellar Network in Juvenile Myoclonic Epilepsy

Objectives. This study is aimed at investigating the alterations in cerebellar volumes and intrinsic cerebellar network in patients with juvenile myoclonic epilepsy (JME) in comparison with healthy controls. Methods. Patients newly diagnosed with JME and healthy controls were enrolled. Three-dimensional T1-weighted imaging was conducted, and no structural lesions were found on brain magnetic resonance imaging. Cerebellar volumes were obtained using the ACAPULCO program, while the intrinsic cerebellar network was evaluated by applying graph theory using the BRAPH program. The nodes were defined as individual cerebellar volumes and edges as partial correlations, controlling for the effects of age and sex. Cerebellar volumes and intrinsic cerebellar networks were compared between the two groups. Results. Forty-five patients with JME and 45 healthy controls were enrolled. Compared with the healthy controls, the patients with JME had significantly lower volumes of the right and left cerebellar white matter (3.33 vs. 3.48%, p = 0.009 ; 3.35 vs. 3.49%, p = 0.009 ), corpus medullare (0.99 vs. 1.03%, p = 0.04 ), and left lobule V (0.19 vs. 0.22%, p = 0.002 ). The intrinsic cerebellar networks also showed significant differences between the two groups. The small-worldness index in the patients with JME was significantly lower than that in the healthy controls (0.771 vs. 0.919, p = 0.04 ). Conclusion. The cerebellar volumes and intrinsic cerebellar network demonstrated alterations in the patients with JME when compared with those of the healthy controls. Our study results provide evidence that the cerebellum may play a role in the pathogenesis of JME.
Experimental evidence of nonlocal hydrodynamic influence on the dynamic contact angle

The dynamic contact angle formed when a liquid curtain impinges onto a moving solid is measured for aqueous glycerol solutions in different flow regimes. It is usually assumed that the dynamic contact angle is simply a function of the contact-line speed and the material properties of the contacting media. The new results show that this is not the case. For a given gas/liquid/solid combination and a given contact-line speed, the dynamic contact angle can be varied by varying the flow rate of the liquid and/or the curtain height, that is by varying the flow field near the contact line. The possibility of attributing this effect merely to free-surface bending and interpreting the results in terms of the so-called “apparent” contact angle is discussed and ruled out on the basis of some general qualitative arguments and analysis of the characteristic length scales involved. A probable connection between the observed effect and the physical mechanism of interface disappearance and formation incorporated in a re.
Comparison of an analog direct detection and a micropulse aerosol lidar at 1.5-μm wavelength for wind field observations—with first results over the ocean

Abstract. The performance of two direct-detection atmospheric lidar systems with very different methods of generating and detecting laser radiation is compared as the result of a field experiment held in March 2015, in Chico, California. During the noncontinuous, 11-day test period, in which the systems operated side by side, the micropulse lidar was operated at its maximum pulse repetition frequency (15 kHz) and integrated elastic backscatter over the interpulse period of the analog direct-detection lidar (0.1 s). Operation at the high pulse repetition frequency resulted in second-trip echoes that contaminated portions of the data. The performance of the micropulse lidar varied with background brightness—as expected with a photon-counting receiver—yet showed equal or larger backscatter intensity signal-to-noise ratio throughout the experiment. Examples of wind fields and time series of wind vectors from both systems during the Chico experiment are presented. In addition, scans over the ocean that were collected by the micropulse lidar during a subsequent deployment on the northern California coast are presented. We conclude by reviewing the advantages and disadvantages of each system and make some suggestions to improve the design and performance of future systems.
Stabilization of two cycles of difference equations with stochastic perturbations

A map which experiences a period doubling route to chaos, under a stochastic perturbation with a positive mean, can have a stable blurred two-cycle for large enough values of the parameter. The limit dynamics of this cycle is described, and it is demonstrated that most well-known population dynamics models (e.g. Ricker, truncated logistic, Hassel and May, Bellows maps) have this stable blurred two-cycle. For a general type of maps, in addition, there may be a blurred stable area near the equilibrium.
ASSESSING AND OPTIMIZING THE ECONOMIC AND ENVIRONMENTAL IMPACTS OF DISTRIBUTED POWER GENERATION

In this paper, a general model of distributed power generation is established, according to the inter-temporal equilibrium of energy supplies and demands and the characteristics of the local electric network, an optimizing model of an office building distributed generation system is established. Some economic factors and environment impacts are investigated. That can assist decision-making for government, utilities and adopters at a competitive electric market structure.