Miracle-dz/UniFAR-FTUs huggingface dataset:
User Position-Based Wireless Sensor Network Deployment Algorithm*

With the rapid development of Internet of Things (IoT), a huge number of IoT devices are connected to the network, such as sensors, actuators, etc. Generally, IoT adopts various communication techniques, such as 4G, 5G, and WiFi to support communications for all IoT devices, which requires public communication facilities for support. However, how to connect IoT devices in less communication support situation becomes a problem. The Wireless Sensor Networks (WSN) are similar to wireless ad hoc networks, it relies on sensors connectivity to create a wireless connection and provide communication service to the covered area. Therefore, WSN as a possible solution can provide flexible wireless networks in areas that have less public communication support. There are many studies that focus on improving the coverage rate of WSN to provide better communication services. Nonetheless, because the users of WSN are highly dynamic and are not evenly distributed in the area, it is difficult to leverage existing algorithms to solve the WSN deployment problem. To this end, in this paper, we propose a user position-based WSN deployment algorithm to address the WSN coverage problem. In detail, we design a coverage model to describe the coverage rate of WSN for dynamic users. Based on the coverage model, we propose an improved Particle Swarm Optimization (PSO) algorithm to obtain the optimal solution for dynamic users. The experimental results confirm that the proposed algorithm can improve the coverage rate for dynamic users, as compared to other representative algorithms.
Image Recognition of Wild Birds in Yunnan Based on Improved Mobile Deep Learning

The bird resources in Yunnan Province have great ornamental value and economic value. These variable and colorful features bring many challenges to their intelligent recognition and efficient management. The primary requirement of bird image intelligent recognition is convenience, portability and rapidity. This paper proposes MobileNet to recognize the bird images because MobileNet has smaller scale, fewer parameters and faster speed. The experiment results show MobileNet also have higher accuracy and can be applied in the bird watching and wild bird recognition. Therefore, this method has good practicability and can be generalized to the identification of other wild animals.
Privacy-aware Data Fusion and Prediction for Smart City Services in Edge Computing Environment

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain−LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods.
Mapping of Sri Lankan Road Signs by Using Google Street View Images

The development of autonomous vehicle driving systems and Intelligent Transportation System (ITS) have drawn massive attention since the 1980s. For the development of ITS, road sign detection and identification are considered to be very important due to the vital information provided by road signs. Generally, real-time video-based methods are used as the source of images for the operation of ITS. But they are inefficient and costly due to certain limitations like weather conditions, lighting conditions, and limited range in obtaining quality images. To overcome the limitations of the video-based approach, this research aims to develop techniques for detecting and identifying road signs by using Google Street View (GSV) as the image source, OpenCV for image processing and CNN for road sign identification. EdleNet, LeNet-5, and DenseNet were identified as accurate CNN models. Using images from GSV, generating a database of road signs with the relevant coordinates was possible, which is currently unavailable in Sri Lanka. In addition, this process leads to the generation of a valuable image dataset of Sri Lankan road sign images, and a web interface with mapped road signs. Consequently, this research would yield useful findings that may be applied to future research and provide the means to develop ITS, accident-avoidance systems, and driver assistance systems.
Bayesian Optimization with Fixed Constraints using Acceptance Functions

Bayesian optimization (BO) is a statistical approach that allows efficient modeling and optimization of black-box models. Most approaches using BO are focused on unconstrained context. In the case of BO with fixed constraints, the default solution is to include them during the optimization of the acquisition function. However, several alternatives could be used for the same purpose. In this work, three schemes for BO with constraints are compared using two synthetic objective functions combined with linear and nonlinear constraints. The results suggest that an approach based on including an acceptance function into the acquisition function formulation obtains the best results.
An accelerated direct demodulation method for image reconstruction using spherical data from the hard X-ray modulation telescope

The hard X-ray modulation telescope (HXMT) mission is mainly devoted to performing an all-sky survey at 1 – 250 keV with both high sensitivity and high spatial resolution. The observed data reduction as well as the image reconstruction for HXMT can be achieved by using the direct demodulation method (DDM). However the original DDM is too computationally expensive for multi-dimensional data with high resolution to be employed for HXMT data. We propose an accelerated direct demodulation method especially adapted for data from HXMT. Simulations are also presented to demonstrate this method.
Non-intrusive Load Monitoring for Consistent Shape Loads Based on Convolutional Neural Network

As the key method for demand-side management in power grid, non-intrusive load monitoring (NILM) keep up to the power consumption of various users in real time and provides data to support the formulation of relevant power policies. In order to achieve accurate resident load monitoring, this paper proposes a NILM architecture focus on consistent shape loads (CSL). Loads in CSL meet the following conditions: 1) current waveform images of different load individuals in the same type are highly similar. 2) different types of load waveform images are different in shapes which are distinguishable. Besides, a non-intrusive load monitoring method based on convolutional neural network (CNN) to identify CSL load is proposed and carried out on actual users. Power consumption data of CSL with different operating environments is taken as training samples. The outcome of our experiment shows the effectiveness of the method in accurately distinguishing CSL and high-precision identification which reaches 97.06%. The method ensures the real-time performance and accuracy of load monitoring.
MCC-Vis: Visual Analysis for City Regional Co-occurrence Pattern Based on Traffic Trajectory Data

Co-occurrence between urban regions is a kind of mobile mode that focuses on inter-regional flow relations. Co-occurrence refers to the phenomenon that people from multiple regions visit the same urban area in a specific period, which is of high research value for business strategy and urban planning. Existing research on inter-regional co-occurrence mainly focuses on pattern recognition and event extraction, but there is few research on establishing inter-regional association through co-occurrence. Extracting co-occurrence can help users understand the relationship between human movement patterns in spatial and temporal views. This paper proposes a vectorized region representation method based on region co-occurrence by using graph embedding technology. A novel visual analysis system is designed based on vectorized representation, enabling users to analyze the relationship between urban areas. Finally, results of case study shows that the analyst can effectively analyze information implied by the co-occurrence phenomenon in urban areas and explore similar areas between cities by interacting with the system.
Assessment of Exposure to Magnetic Field from Pulse Width Modulated Currents

This paper describes how it can be investigated whether fields caused by pulse width modulated (PWM) currents comply with exposure safety guidelines such as the ICNIRP (International Commission on Non-Ionizing Radiation Protection) guidelines, using weighting filters and coupling factors in such a way that the time and geometrical dependencies can be treated separately. With that separation, results can be obtained for different currents without the need to repeat computationally demanding field computations for each current and it can be investigated how for example the rise time of the current affects the field exposure. Such investigations can help determine how PWM currents can be modified to ensure that the ICNIRP exposure safety guidelines are complied with.
Deep Learning Based Constellation Rearrangement Design in Hybrid ARQ System

This paper proposes a constellation rearrangement autoencoder (CoRe-AE) for hybrid automatic repeat request (HARQ) system through a learning-based neural network. Two identical autoencoder networks are used in the CoRe-AE scheme respectively corresponding to the first data transmission and the retransmission. It can both optimize the bit mapping and the geometric shaping of the constellation points during the two transmissions. It is shown that the bit error rate (BER) performances of our constellations learned by CoRe-AE are noticeably better than those of the regular square quadrature amplitude modulation (QAM) and some well-known geometric shaped constellations with conventional constellation rearrangement (CoRe) scheme in HARQ systems.
Spitzer Observations of Tidal Dwarf Galaxies

Abstract We present Spitzer observations of Tidal Dwarf Galaxies (TDGs) in three interacting systems: NGC 5291, Arp 105 & Stephan's Quintet. The spectra show bright emission from polyaromatic hydrocarbons (PAHs), nebular lines and warm molecular hydrogen, characteristic of recent episodes of star formation. The PAH emission that falls in the IRAC 8.0 μm band leads to the TDGs having an extremely red IRAC color, with [4.5] − [8.0] > 3. The emission from PAHs is characterized by a model with mainly neutral 100-C PAH atoms.
Transmission and conversion of magnetoacoustic waves on the magnetic canopy in a quiet Sun region

We present evidence for the conversion and transmission of wave modes on the magnetic flux tubes that constitute mottles and form the magnetic canopy in a quiet Sun region, highlighting the details and key parameters of the mechanism that produces power halos and magnetic shadows at the magnetic network observed in H{\alpha}. We use calculations of the magnetic field vector and the height of the magnetic canopy and simple assumptions to determine the turning height, i.e., the height at which the fast magneto-acoustic waves reflect. We compare the variation of acoustic power in the magnetic shadow and the power halo with the results of a two-dimensional model on mode conversion and transmission. The key parameter of the model is the attack angle, which is related to the inclination of the magnetic field vector at the canopy height. Our analysis takes also into account that 1) there are projection effects on the propagation of waves, 2) the magnetic canopy and the turning height are curved layers, 3) waves with periods longer than 3 min reach the chromosphere in the presence of inclined magnetic fields (ramp effect), 4) mottles are canopy structures, and 5) the wings of H{\alpha} contain mixed signal from low- and high-{\beta} plasma. The dependence of power on the attack angle follows the anticipated by the two-dimensional model very well. Long-period slow waves are channeled to the upper chromospheric layers following the magnetic field lines of mottles, while short-period fast waves penetrate the magnetic canopy and reflect at the turning height. Although both magnetoacoustic modes contribute to velocity signals, making the interpretation of observations a challenging task, we conclude that conversion and transmission of the acoustic waves into fast and slow magnetoacoustic waves are responsible for forming power halos and magnetic shadows in the quiet Sun region.
Comparison of Parabolized Vorticity and Navier-Stokes Solutions in a Mildly Nonorthogonal Coordinate System.

Abstract : Two-dimensional, steady, incompressible, laminar flow in a diffuser and nozzle is considered as a model problem. A sheared mapping is used to provide a rectangular, wall-fitted, computational domain. This transformation produces a mildly nonorthogonal coordinate system in which the Navier-Stokes equations and parabolized vorticity approximation are solved. The discretization uses fourth-order accurate polynomial splines to resolve the wall boundary layer with a relatively sparse grid and standard finite differences in the main stream direction. The spline-finite difference equations are solved by line relaxation and Newton-Raphson iteration. Comparison of Navier-Stokes and parabolized vorticity results are presented for two diffusers (both with separation and reattachment of the boundary layer) and one nozzle flow. The parabolized and Navier-Stokes solutions are found to be in excellent agreement. Comparisons with a published parabolized result are also given. (Author)
THE FORNAX DWARF GALAXY AS A REMNANT OF RECENT DWARF–DWARF MERGING IN THE LOCAL GROUP

We present results from the first numerical analysis to support the hypothesis, first proposed in Coleman et al., that the Fornax dwarf galaxy was formed from the minor merging of two dwarfs about 2 Gyr ago. Using orbits for the Fornax dwarf that are consistent with the latest proper motion measurements, our dynamical evolution models show that the observed asymmetric shell-like substructures can be formed from the remnant of a smaller dwarf during minor merging. These models also predict the formation of diffuse stellar streams. We discuss how these stellar substructures depend on model parameters of dwarf–dwarf merging, and how the intermediate-age subpopulations found in the vicinity of these substructures may be formed from gas accretion in past merger events. We also suggest that one of Fornax's globular clusters originates from a merged dwarf companion, and demonstrate where as yet undetected tidal streams or H i gas formed from the dwarf merging may be found in the outer halo of the Galaxy.
Gentzen decision procedures for Lewis's systems S2 and S3

In [5] K. Matsumoto and the author gave a Gentzen decision procedure for Lewis's system S2. But the solution was an indirect one : we formulated a sequential calculus Q2 (indentical with E2*, see below), and proving a cut-elimination theorem for it we gave a Gentzen decision procedure for Q2, then we established the reduction of the decision problem of S2 to that of Q2. In [4] Matsumoto gave a Gentzen decision procedure for S3 the solution was indirect in just the same sense as mentioned above. In this paper we shall first present two sequential calculi S2* and S3* equivalent to S2 and S3 respectively, then we shall prove cutelimination theorems for them. Therefore direct solutions of Gentzen type will be given for both systems S2 and S3. The author expresses his hearty thanks to Mr. Matsumoto for many helpful comments on an earlier draft of this paper.
Famous Puzzles of Great Mathematicians

This entertaining book presents a collection of 180 famous mathematical puzzles and intriguing elementary problems that great mathematicians have posed, discussed, and/or solved. The selected problems do not require advanced mathematics, making this book accessible to a variety of readers. Mathematical recreations offer a rich playground for both amateur and professional mathematicians. Believing that creative stimuli and aesthetic considerations are closely related, great mathematicians from ancient times to the present have always taken an interest in puzzles and diversions. The goal of this book is to show that famous mathematicians have all communicated brilliant ideas, methodological approaches, and absolute genius in mathematical thoughts by using recreational mathematics as a framework. Concise biographies of many mathematicians mentioned in the text are also included. The majority of the mathematical problems presented in this book originated in number theory, graph theory, optimization, and probability. Others are based on combinatorial and chess problems, while still others are geometrical and arithmetical puzzles. This book is intended to be both entertaining as well as an introduction to various intriguing mathematical topics and ideas. Certainly, many stories and famous puzzles can be very useful to prepare classroom lectures, to inspire and amuse students, and to instill affection for mathematics.
Supersymmetry, Nonthermal Dark Matter and Precision Cosmology

Within the minimal supersymmetric Standard Model (MSSM), LHC bounds suggest that scalar superpartner masses are far above the electroweak scale. Given a high superpartner mass, nonthermal dark matter is a viable alternative to WIMP dark matter generated via freezeout. In the presence of moduli fields, nonthermal dark matter production is associated with a long matter-dominated phase, modifying the spectral index and primordial tensor amplitude relative to those in a thermalized primordial universe. Nonthermal dark matter can have a higher self-interaction cross section than its thermal counterpart, enhancing astrophysical bounds on its annihilation signals. We constrain the contributions to the neutralino mass from the bino, wino and Higgsino using existing astrophysical bounds and direct detection experiments for models with nonthermal neutralino dark matter. Using these constraints we quantify the expected change to inflationary observables resulting from the nonthermal phase.
Tau/Charm Factory Accelerator Report

The present Report concerns the current status of the Italian Tau/Charm accelerator project and in particular discusses the issues related to the lattice design, to the accelerators systems and to the associated conventional facilities. The project aims at realizing a variable energy Flavor Factory between 1 and 4.6 GeV in the center of mass, and succeeds to the SuperB project from which it inherits most of the solutions proposed in this document. The work comes from a cooperation involving the INFN Frascati National Laboratories accelerator experts, the young newcomers, mostly engineers, of the Cabibbo Lab consortium and key collaborators from external laboratories.
Use of a Bipedicled Pericranial Flap and a Split Thickness Skin Graft for Reconstruction of a Traumatic Scalp Injury: A Case Report

Reconstruction of a scalp defect should ensure the skull’s protection, soft-tissue bulk, and contour maintenance. When calvaria is exposed, each reconstruction option has its own advantages and disadvantages. We report a 2-year-old Saudi boy, a road traffic accident (RTA) victim, otherwise medically stable who sustained partial to full-thickness defects of the scalp involving the left temporoparietal region, measuring 20 × 10 cm2 in size. After optimal debridement of the wound, a bipedicled pericranial flap with a split-thickness skin graft (STSG) was done. This case reports the satisfactory outcomes of using a bipedicled pericranial flap with STSG in traumatic scalp injuries, specifically in the pediatric age population without creating any secondary scalp skin defect and its associated morbidities. Being bipedicled the vascularity of the flap is more reliable and robust.
Optimizing Hartree-Fock orbitals by the density-matrix renormalization group

We have proposed a density-matrix renormalization group (DMRG) scheme to optimize the one-electron basis states of molecules. It improves significantly the accuracy and efficiency of the DMRG in the study of quantum chemistry or other many-fermion system with nonlocal interactions. For a water molecule, we find that the ground state energy obtained by the DMRG with only 61 optimized orbitals already reaches the accuracy of best quantum Monte Carlo calculation with 92 orbitals.
1-Cycloalkanecarbonyl-substituted thioureas and thiosemicarbazides as effective dihydrofolate reductase inhibitors with antibacterial activity

Aim. Search for new antibacterial agents with dihydrofolate reductase-inhibitory activity among N -(R-carbamothiol)cycloalkylcarboxamides using in silico and in vitro methodology, SAR analysis to optimize the synthesis of new potential antinicrobials. Methods. Molecular docking, in vitro DHFR inhibition assay, antimicrobial evaluation, SAR analysis, statistical methods. Results. According to the results of molecular docking to the active center of dihydrofolate reductase (DHFR), namely affinity, the main types of interactions and arrangement in the active center of the enzyme, several N -(R-carbamothioyl)cycloalkylcarboxamides were selected for their inhibitory effect. Based on in vitro screening, few promising compounds with high ability to inhibit DHFR were identified. It was found, that diacylsemicarbazides are more effective inhibitors of DHFR compared to acylthioureas. The studies on antibacterial activity have revealed several promising compounds, namely N -(2-R-hydrazine-1-carbonothioyl)cy-cloalkanecarboxamides, as highly active antimicrobial agents against E. сoli and St. aureus (MIC 3.125–25.0 μg/ml) with high DHFR-inhibitory effect, the activity of which competes with the comparison drug “Nitrofurazone”. This justifies the continuation of systematic research in this direction. Conclusions. A well-founded search among N -(R-carbamothiol)cycloalkyl-carboxamides for new antibacterial agents with dihydrofolate reductase-inhibitory activity, using in silico and in vitro methodology, established relationship between the chemical structure and activity aimed at further design of new potential drug agents.
Hydrogen infrared recombination lines as a diagnostic tool for the geometry of the circumstellar material of hot stars

We have analysed the infrared hydrogen recombination lines of a sample of well studied hot massive stars observed with the Infrared Space Observatory. Our sample contains stars from several classes of objects, whose circumstellar environment is believed to be dominated by an ionized stellar wind (the Luminous Blue Variables) or by a dense disk-like geometry (Be stars and B(e) stars). We show that hydrogen infrared recombination lines can be used as a diagnostic tool to constrain the geometry of the ionized circumstellar material. The line strengths are sensitive to the density of the emitting gas. High densities result in optically thick lines for which line strengths are only dependent on the emitting surface. Low density gas produces optically thin lines which may be characterized by Menzel case B recombination. The ISO observations show that stellar winds are dominated by optically thin H i recombination lines, while disks are dominated by optically thick lines. Disks and winds are well separated in a diagnostic diagram using the Hu(14{6)/Br and the Hu(14{6)/Pf line flux ratios. This diagnostic tool is useful to constrain the nature of hot star environments in case they are highly obscured, for instance while they are still embedded in their natal molecular cloud.