Miracle-dz/UniFAR-FTUs huggingface dataset:
Liquidity risk theory and coherent measures of risk

We discuss liquidity risk from a pure risk-theoretical point of view in the axiomatic context of coherent measures of risk. We propose a formalism for liquidity risk that is compatible with the axioms of coherency. We emphasize the difference between ‘coherent risk measures’ (CRM) ρ(X ) defined on portfolio values X as opposed to ‘coherent portfolio risk measures’ (CPRM) ρ(p) defined on the vector space of portfolios p, and we observe that in the presence of liquidity risk the value function on the space of portfolios is no longer necessarily linear. We propose a new nonlinear ‘Value’ function V ℒ(p) that depends on a new notion of ‘liquidity policy’ ℒ. The function V ℒ(p) naturally arises from a general description of the impact that the microstructure of illiquid markets has when marking a portfolio to market. We discuss the consequences of the introduction of the function V ℒ(p) in the coherency axioms and we study the properties induced on CPRMs. We show in particular that CPRMs are convex, finding a result that was proposed as a new axiom in the literature of so called ‘convex measures of risk’. The framework we propose is not a model but rather a new formalism, in the sense that it is completely free from hypotheses on the dynamics of the market. We provide interpretation and characterization of the formalism as well as some stylized examples.
Estimating P(Y < X) when X and Y are paired exponential variables

Inference procedures for γ = P(T 2 < T 1) are considered when T 1 and T 2 are correlated and distributed as bivariate exponential. Under this model, asymptotic inference procedures based on maximum likelihood, the method of moments, and generalized estimating equations are developed. Exact confidence limits and the bootstrap BCa confidence on γ are also given. Monte Carlo methods are used to estimate the coverage probabilities with the aim of evaluating the alternative strategies. Two examples are given to illustrate these techniques.
Analysis of a global energy confinement database for JET ohmic plasmas

A database containing global energy confinement data for JET ohmic plasmas in the campaigns from 1984 to 1992 has been established. An analysis is presented of this database and the results are compared with data from other tokamaks, such as the Axially Symmetric Divertor Experiment (ASDEX), Frascati Tokamak Upgrade (FTU) and Tore Supra. The trends of JET ohmic confinement appear to be similar to those observed on other tokamaks: a linear dependence of the global energy confinement time on density is observed up to a density value where a saturation is attained; this density value defines the border between the linear and the saturated ohmic confinement regimes; this border is shifted towards higher density values if the q value of the discharge is decreased; the global confinement time in the saturated ohmic regime increases less than linearly with the value of the magnetic field
A polarization flare in OJ 287.

On a effectue des observations simultanees de l'objet de type BL Lac OJ 287 aux longueurs d'onde infrarouges JHK et dans l'optique et l'IR proche a UBVRI en Janvier 1983. On observe un pic puis une decroissance du flux total ainsi que des changements complexes de la polarisation. On propose un modele comportant un jet emanant d'un disque d'accretion
Radiation Reaction in Nonrelativistic Quantum Electrodynamics

We derive the Heisenberg operator equation of motion for a nonrelativistic point electron coupled to the quantized electromagnetic field, including radiation reaction. The derivation proceeds in close analogy with the classical theory of extended charges (with the Compton wavelength formally playing the role of a size parameter), and we give a systematic treatment of the classical problem, showing explicitly from the equation of motion that the classical theory shows no runaway solutions or preacceleration when the electron size exceeds the classical electron radius. In the quantum-mechanical case, we show that the electrostatic self-energy of a point electron is zero and that, for values of the fine-structure constant α ≲ 1, the equation of motion admits neither runaway solutions nor noncausal motion. Furthermore, the correspondence limit of the solutions to the quantum-mechanical equation of motion agrees with that of the Lorentz-Dirac theory in the classical regime, but without the imposition of additional conditions and with no possibility of observable noncausality. Thus, a consistent picture of a classical point electron emerges in the correspondence limit of the quantum-mechanical theory.
A simple PD controller for robots with elastic joints

The point-to-point control of manipulators having elastic joints is considered. It is shown that a simple PD (proportional plus derivative) controller, similar to that used for rigid robots, suffices to globally stabilize the elastic joint robots about a reference position. A robustness analysis is also given with respect to uncertainties on the robot parameters. The results of numerical simulation tests of a manipulator with three revolute elastic joints are presented. >
High Brightness Temperatures in IDV Sources

High brightness temperatures are a characteristic feature of IntraDay Variability (IDV) of extragalactic radio sources. Recent studies of the polarization properties of some IDV sources (e.g., 1150+812, PKS 0405-385 and 0716+714) have shown that these sources harbor several compact IDV components with angular sizes of ∼10-30µas and very high polarizations (of up to ∼50%-70%). These results indicate the possibility of the existence of uniform magnetic fields in the IDV components. We investigate the incoherent synchrotron and self- Compton radiation of an anisotropic distribution of relativistic electrons which spin around the magnetic field lines at small pitch angles. The brightness temperature limit caused by second-order Compton losses is discussed and compared to the brightness temperatures derived from energyequipartitionarguments.It is found that anisotropic distributions of elec- trons moving in ordered magnetic fields can raise the equipartition and Compton brightness temperatures by a factor of up to ∼3-5. This would remove some of the difficulties in the interpretation of extremely high intrinsic brightness temperatures of > 10 12 K (or apparent brightness temperatures of ∼ 10 14 K with a Doppler factor of ∼30).
Liver Abscess Caused by Enterobius vermicularis as a Differential Diagnosis for Liver Metastasis in Colorectal Cancer, Case Report

Introduction: colorectal cancer is the fourth leading cause of cancer-related mortality worldwide. The identification of the metastases of this tumor in the preoperative stage is increasingly frequent due to the imaging studies currently available. We present the case of a patient with an infection caused by Enterobius vermicularis that simulates the presence of liver metastases. Case presentation: a female patient from a rural area showing a one-year abdominal pain evolution associated with lower gastrointestinal tract bleeding and weight loss. Endoscopic imaging and studies displayed a tumor lesion in the sigmoid colon, with biopsies reporting sigmoid colon adenocarcinoma and liver lesions suggesting malignancy. Anterior resection of the rectum and sigmoid was performed with high anastomosis and liver biopsies, which ruled out malignancy and reported the presence of liver infection by E. vermicularis. Discussion: in this case, the hepatic E. vermicularis infection was rare. This infection can simulate the presence of liver metastases; therefore, it should be considered a differential diagnosis of metastatic colorectal cancer.
Decentralized Annular Domain Finite-Time Platoon Control of Linear Heterogeneous Vehicles

This paper investigates a decentralized control issue of heterogeneous vehicular systems. The platoon model is regarded as an interconnected system instead of overlapping form, which makes the controller design simpler. Furthermore, the decentralized feedback control laws are designed to guarantee the system is annular domain finite-time stable (ADFTS). Further, a sufficient condition is proposed to achieve ADFTS. In the following, the corresponding stability conditions are given based on linear matrix inequality (LMI). Finally, simulation experiments are given to illustrate the effectiveness of the proposed method.
Recommending Restaurants based on Classification of Photos from the Gallery of Mobile Device

The restaurant recommender systems are important for travelers, but may suffer from the “cold start” problem for new users. In this paper, it is assumed that photos of food in a gallery of mobile device of a user reflect his or her gastronomic preferences. The novel pipeline for modeling of the user's food preferences is introduced. At first, scene recognition neural network is used to extract photos of the restaurants from a gallery. It is proposed to train the multi-task convolutional network to predict the presence of food in an input image and the attributes of the restaurant. The recognized types of cuisine are summarized in a profile of gastronomic preferences of the user. The restaurants in a given city are recommended based on these preferences and additional attributes of a restaurant such as cumulative rating. Experimental study for the Yelp datasets is provided. Several neural network architectures are compared, among which EfficientNet models demonstrated the best performance.
ROS-based Collaborative Algorithm Verification Framework for Multi-agent Systems

In recent years, the multi-agent system (MAS) has developed rapidly due to its broad application prospects. However, most of the current research results are proved to be effective through numerical simulation or indoor ideal conditions. There are few outdoor experiments, and the hardware is not uniform, which makes it difficult to reproduce. A ROS-based MAS collaborative algorithm verification framework is proposed in the paper. Using the UAV model provided by RotorS, it can be quickly deployed to verify the effectiveness of the algorithm. The verification framework not only has the convenience of numerical simulation, but also can simulate various adverse factors in the outdoor environment, so as to simulate the performance of the algorithm in the real world, and its visualization effect is pretty and the construction is simple. In addition, the paper also illustrates the feasibility and effectiveness of the framework through an example.
Rational and Trigonometric Models for Ultrasonic Transducers

Resonance properties of the transducers of ultrasound non-destructive testing system are investigated. Experimental system for investigating frequency characteristics of ultrasonic transducers is described. A sinusoidal model for input and output signals is used. Versions of the Prony method for frequency finding robust to noise and low-frequency interference are proposed. Several versions of the method of selective rational interpolation in the frequency domain are compared.
Quantized Average Consensus: Discontinuities and Hysteresis

Abstract It is well known that the consensus problem can be formulated in terms of a stabilization problem. In this note we consider continuous-time average consensus dynamics: We discuss how quantization can be included in the system, and focus on one model in which the agents’ states are communicated through uniform quantizers. Solutions to the resulting system are defined in the Krasowskii sense, and results are given proving their convergence to conditions of practical consensus. To cope with potential chattering phenomena, which may be undesired in the applications, we propose the use of a hysteretic quantizer, and study the convergence properties of the resulting dynamics by a hybrid system approach.
Automated Nutrition Doser for Hydroponic System Based on IoT

Along with population growth and the shrinking amount of arable land, agricultural technology is developing so quickly. Instead of using soil as a growing medium, hydroponic farming solely employs water that is enriched with the nutrients that plants require. Precision is a crucial factor in hydroponic gardening since it determines how accurately plants receive nutrients. This research involved the development of an automated nutrient dosing system for hydroponic plants. The Internet of Things (IoT)-based automation system can be utilized so that hydroponic plants can still be looked after and monitored remotely using the NodeMCU microcontroller, which is integrated with various sensors. A TDS meter sensor for monitoring nutrients in ppm (parts per million) and an ultrasonic sensor for assessing water levels are among the sensors utilized in this hydroponic research automation system. Peristaltic pumps, micro pumps, and venturi pumps are the actuators that are employed. The plant being researched in this study is pakcoy, and the dosage is based on mix AB's molarity dose to satisfy the plant's ppm range of 1050–1400 ppm. With reference to the weekly range, the doser is modified. The results of the nutritional solution discharge test using a peristaltic pump have an average of 0.9345 ml/s, the results of the calibration test for the TDS sensor have a percent error of 0.00563925%, and the results of the water discharge test using a small pump have an average of 17.989 ml/s The result of the automated system that is used is successful because every week it provides the appropriate dose of nutrients.
Quantized consensus of multi-agent systems under switching topology

This paper investigates the upper bound on the acceptable time duration of broken communication links of a multi-agent system, whose consensus is expected. For the concerned multi-agent system, the state of each agent is quantized before transmissions. In order to save network resources, we propose a simplified control scheme named conservative input. When the state of an agent is higher(or lower) than the average of states of multiple agents of this system and it has negative(or positive) input, then its input will not be updated. Simulations confirm that the proposed conservative input well reduces the updating frequency of the control law while the system performance is only slightly degraded. Considering practical applications, we also analyze the multi-agent systems with bounded noise and show through simulations our conservative control can still maintain the desired consensus even in the presence of bounded noise.
Monocular Feed-forward based 3D Target Aiming Using a 6-DOF Robotic Arm

With the overall development of artificial intelligence technology, the autonomous application of robotic arms has been significantly improved, which places extremely superior requirements on the reliability of algorithms such as target detection and target positioning. In this paper, based on a 6-DOF collaborative manipulator, we propose a 3D target aiming algorithm with high real-time performance through monocular vision feed-forward control. First, we present a coordinate system calibration algorithm to obtain the relative pose relationship between the monocular camera and the laser transmitter. Second, a modified YOLO-v4 algorithm is designed to further improve the speed and accuracy of target detection. Third, we propose a monocular aiming algorithm, which can directly aim at a 3D target based on the target’s 2D pixel deviation. Through experimental verification, the single-frame detection time of our algorithm can be reduced to 14.12 ms, and the response time of monocular aiming algorithm is only 0.61 s.
Defect Reconstruction for Class-imbalanced Power System Defect Recognition

Performing fault diagnosis is an important routine to keep power systems functioning properly. Since most facilities of power systems are located in the wild, unmanned aerial vehicles (UAV) are used to collect potentially damaged components of power systems by taking pictures. Those pictures are categorized into a certain type to take corresponding actions to repair the damaged components. It is vital to classify collected images accurately. However, the collected images distribute in a class-imbalanced style, which degrades the performance of the classifier if directly used for training. In this paper, we make use of the generative adversarial network (GAN) to generate extra images for classes that have fewer images. Our method achieves decent improvements on 4 different scenes, showing the effectiveness of GAN-generated images on the class-imbalanced power system defect classification task.
A Support Third-Order Tensor Machine Model for Tensor Recognition

Recognition of data sets composed fully of tensor modes is one of the challenging problems in the field of machine learning. In this paper, a dual support third-order tensor machine (STTM) optimization model ensuring the accurate recognition of tensor data sets is designed. The main idea is to exploit the unique classification property of a STTM model to avoid the overfitting and dimension disaster caused by the tensor vectorization. Then, by using Lagrangian multiplier method and tensor-Tucker decomposition method, a computable STTM recognizer with Tucker decomposition for tensor recognition problems is developed in the framework of tensor analysis. Based on established results, a learning algorithm is proposed to retain most of the core information of original tensors. Finally, an image recognition example is given to demonstrate the effectiveness of our STTM recognizer.
New Method for Determining the Lower Ionosphere Plasma Density by Tweek-Atmospherics: the Study of Applicability Limits

Tweek-atmospherics (tweeks), along with radio transmission by VLF radio stations, are used to study the lower ionosphere. The limits for using the new single-position method for determining of electron density in the lower ionosphere by results of analysis of multimodal tweek-atmospherics is studied upon the experimental records. The database accumulated at the Ukrainian Antarctic Station “Akademik Vernadsky” in 2019 has been used by clustering sources of tweeks. It is shown that the propagation West - East non-reciprocity results in the least attenuation for tweeks arriving from the geomagnetic east. In non-equatorial regions the method discussed can be used up to 10 Mm from receiving point for eastern tweeks and no more than 5 Mm for tweeks from western sector, and for tweeks with other propagation directions these limits lay at roughly 7 Mm from receiving station.
Linear active disturbance rejection tracking control for industrial manipulator with disturbance and time delay

In this paper, a linear active disturbance rejection controller (LADRC) is designed for the tracking control problem of industrial manipulator with uncertain factors, such as disturbance and time delay. Firstly, the discrete system model of industrial manipulator with disturbance and network induced delay is established by discretization analysis method, and then the disturbance and network induced delay uncertainties in the model are defined as a new extended state. Secondly, a linear extended state observer (LESO) is designed to estimate the system uncertain factors in time. Then, by using the estimated values, a linear state error feedback control law with feedforward compensation for uncertainties is designed to suppress the effect of disturbance and time delay for control performance, and to ensure that the system tracking errors can converge to zero stably. Finally, Comparative simulation results show the effectiveness of the proposed controller.
An Algorithmic Approach to Audio Processing and Emotion Mapping

Voice and speech pattern analysis can help improve conversational and persuading abilities, especially in situations or applications where direct face-to-face human engagement is not possible or preferable. This paper proposes methods for accurately predicting emotions. in a voice sample. Additive white Gaussian noise is added to avoid overfitting and create real-environment sound. This paper uses the Mel spectrogram to extract features. Multilayer Perceptron, 1D-CNN, 2D-CNN, and transformer-based parallel CNN have been used to appropriately classify human emotions from speech samples. Previously unseen, transformer-based architectures have had a breakthrough in recent times and using them in audio processing and emotion mapping is rather novel which we intend to do.
DRL-based Joint Beamforming Design for RIS-assisted mmWave MU-MISO system

Reconfigurable intelligence surface (RIS) is an emerging technology based on digital metamaterials, which can improve the transmission performance of wireless communication systems by controlling the incident signal beam by changing the electromagnetic properties of its reflecting elements. In this paper, a RIS-assisted millimeter-wave (mmWave) multi-user multiple input single output (MU-MISO) communication system is first designed, then the active hybrid precoding matrix at the base station (BS) and the shift matrix at the RIS are jointly optimized to maximize the user sum-rate. Finally, the deep deterministic policy gradient with prioritized experience replay (PER-DDPG) algorithm is proposed to solve the nonconvex problem arising from the joint optimization. Experimental results show that the proposed joint beamforming algorithm based on PER-DDPG has a higher average reward, faster convergence, and more stable training process than the traditional alternating iteration algorithm and the classical deep reinforcement learning (DRL) algorithm.
A first-principle assessment at atomistic scale of interface phenomena in down-scaling hafnium-based metal-insulator-metal diodes

In this paper, we present first-principle calculations to study the electrical properties of hafnium oxide (HfO2)-based metal-insulator-metal (MIM) diodes. These devices have been simulated by interposing 3 nm of HfO2 between drain and source contacts made of gold and platinum, respectively. The monoclinic and orthorhombic polymorphs of HfO2 have been considered to model different MIM diodes, and the interface geometries have been optimized to compute the I-V characteristics. The simulation results demonstrate the influence of the HfO2 polymorphs on the MIM properties and the importance to understand the interface phenomena that are related to the measurable properties of the proposed devices.
A Review on Methods for Managing the Risk of Android Ransomware

Ransomware is a noteworthy form of attack that is well-known for the threat it poses to users. This danger invariably leads to huge conflicts, which cybersecurity seeks to prevent. In most cases, ransomware will encrypt or lock files on the computer or mobile device that it is targeting, and then demand money to decrypt those files. It is necessary to implement some of the proposed solutions to prevent or mitigate this problem. Many research papers have developed certain tools and ways to prevent ransomware attacks before they occur or identify them when it occurs, however they are frequently compromised within a short period. The purpose of this survey is to provide an analysis of the most recent developments in ransomware research as well as detection and protection methods. This paper will, for the most part, revolve around three significant questions, each of which will be examined through the lens of risk management in turn: How can an android operating system be ready for and protected from a ransomware attack before it happens? What actions may a user take in response to a ransomware attack to stop it or lessen the damage it causes during the attack? And how exactly can a person whose life has been disrupted by ransomware get their life back on track after the attack? Answering these questions will put us one step closer to developing a unique methodology for managing the risk of Android ransomware. As a consequence of this, properly managing the risk will make it nearly impossible for an android ransomware attack to be successful, and in the worst-case scenario, the user will be able to recover the infected data by referring to the backup copy.