Miracle-dz/UniFAR-FTUs huggingface dataset:
The Group Velocity Picture of Metamaterial Systems

Metamaterial is a very fresh concept in modern photonics, which are referred to a new class of electromagnetic media whose permittivity or permeability is beyond traditional values. Fifty years ago, a kind of newmaterials whose permittivity ǫ and permeability μ are simultaneously negative was theoretically predicted to possess a negative refractive index n with many unusual properties[1]. In last decade, negative-n metallic resonating composites and two dimensional (2D) isotropic negative-n material have been constructed[2, 3], and negative light refraction was observed[4]. The unconventional properties of such materials, such as the evanescent wave could be amplified by negative-n so that the sub-wavelength resolution could be achieved[5], have drawn an increasing amount of attention in both science and engineering[6]. After negative-n material, more such unconventional materials are found, so that a new concept “metamaterial" is generated, which termed for the effective medium with very special permittivity ǫe f f , or permeability ǫe f f , or both, over a certain finite frequency band. Such physical media are composed of distinct elements (photonic atoms) which are generally made of sub-wavelength metallic structure and their size scale is much smaller than the wavelengths in the frequency range of interest. Thus, the effective composite media could be considered homogeneous at the wavelengths under consideration. Since their abnormal properties and related totally new phenomena can even go beyond the traditional physical limit, metamaterial becomes one of hottest topics in modern photonics.
Short-Time Noise Suppression Method with Multiple Signals for Received Time Estimation

The detection of the leading edge of a signal using a threshold value can be used as a method for estimating the received time, which is not easily affected by reverberation or the sensor characteristics of the speaker and microphone. When the noise level is relatively high compared with the signal, the threshold must be set high to prevent false alarms, causing a systematic error in the received time estimation. The noise can be suppressed by transmitting the signal multiple times and averaging the results; however, it is necessary to wait for the reverberation to disappear after each transmission. Therefore, this measurement is time consuming. In this study, we propose a noise suppression method that can be applied to cases in which signals are transmitted multiple times without waiting for the disappearance of reverberation. For the proposed method, an analysis of noise suppression performance was conducted, and the limits of performance were provided under the given conditions. In addition, numerical experiments on the performance of the received time estimation in a reverberant environment were conducted to compare the proposed method with the conventional method and confirm the effectiveness of the proposed method.
Software Solution of Secure Debug Based on RISC-V CPU

When using the CPU chip containing the debug module, the mechanism of secure debug is required to ensure the security of the internal data in the CPU chip. But, the specification of RISC-V instruction architecture set only describes the guideline of secure debug mechanism for hardware aspect. This paper presents a software solution for the RISC-V CPU secure debug mechanism, and the configuration program on the host computer first creates the black box area configuration data and secure authentication data, then passes the data through the debug channel to the firmware on the chip, then the firmware authenticates the security data and writes the configuration data of the black box area. The solution is combined with RISC-V CPU secure debug of hardware characteristics, and provides a comprehensive implementation reference for RISC-V CPU secure debug.
Transmission Line Information Extraction from Images Collected by UAV based on Generative Adversarial Networks

Based on PyTorch development platform, this paper builds the Generative Adversarial Networks (GAN) model. Through the preprocessing, label making, network training and algorithm improvement of UAV aerial images, this paper completes the deep-learning of transmission line feature information, solidifies the Generation Network parameters, and realizes the goal of automatic extraction of transmission line information from UAV images. Based on the Deep Convolution Neural Network, a multi generator GAN model is proposed. The cooperative working mechanism is introduced between the generation networks to speed up the model to obtain information and reduce the amount of parameters. The Wasserstein distance is introduced into the loss function of the model to avoid the problems of gradient disappearance and training instability in the process of GAN training. Through experimental analysis, it is proved that this method has a good reference for extracting transmission line information from high-resolution UAV images.
Generative adversarial networks for ensemble projections of future urban morphology

As city planners design and adapt cities for future resilience and intelligence, interactions among neighborhood morphological development with respect to changes in population and resultant built infrastructure's impact on the natural environment must be considered. For deep understanding of these interactions, explicit representation of future neighborhoods is necessary for future city modeling. Generative Adversarial Networks (GANs) have been shown to produce spatially accurate urban forms at scales representing entire cities to those at neighborhood and single building scale. Here we demonstrate a GAN method for generating an ensemble of possible new neighborhoods given land use characteristics and designated neighborhood type.
Research on classification method of business requirement text based on deep learning

The text of power grid business cost demand is complex and the description cannot be unified and standardized. As a single text description involves multiple business types, it is difficult to judge the business cost type. This paper presents a classification method of clustering specific cost types for business cost requirements text. Firstly, the business cost requirement text is transformed, and the key weight parameters in the Chinese word segmentation model are improved iteratively according to the cost representation report to obtain the global semantic vector. At the same time, the weights of recognition loss values of different samples were dynamically modified according to the difficulty of sample fitting. In this paper, the existing text clustering model is improved by k-means clustering algorithm model, and the cost types of 450 real business cost demand texts in the province are identified. The results show that the performance index value of the text classification method proposed in this paper is better than the commonly used text classification method, and the F1 value of the algorithm in this paper reaches more than 93%. The value of F1 is more than 3.5% higher than that of single BERT model.
Research on Neural Network Algorithm for Road Crack Detection

Aiming at the problem of road crack detection, experiments based on CFD data set and Unet model were carried out. The overall structure of the network and the contribution of each module to the final detection effect of Unet model were analyzed. Some modules in Unet model were cut and replaced based on that, and CCUnet model was obtained. Final experiment results show that detection rate of CCUnet model is greatly improved under the condition that model accuracy is maintained basically, and that has practical application value.
Gauge Quintessence

We discuss a new model of quintessence in which the quintessence field is identified with the extra-component of a gauge field in a compactified five-dimensional theory. We show that the extremely tiny energy scale $\sim (3\times 10^{-3} eV)^4$ needed to account for the present acceleration of the Universe can be naturally explained in terms of high energy scales such as the scale of Grand Unification.
Reinforcement learning of millimeter wave beamforming tracking over COSMOS platform

Communication over large-bandwidth millimeter wave (mmWave) spectrum bands can provide high data rate, through utilizing high-gain beamforming vectors (briefly, beams). Real-time tracking of such beams, which is needed for supporting mobile users, can be accomplished through developing machine learning (ML) models. While computer simulations were used to show the success of such ML models, experimental results are still limited. Consequently in this paper, we verify the effectiveness of mmWave beam tracking over the open-source COSMOS testbed. We particularly utilize a multi-armed bandit (MAB) scheme, which follows reinforcement learning (RL) approach. In our MAB-based beam tracking model, the beam selection is modeled as an action, while the reward of the algorithm is modeled through the link throughput. Experimental results, conducted over the 60-GHz COSMOS-based mobile platform, show that the MAB-based beam tracking learning model can achieve almost 92% throughput compared to the Genie-aided beams after a few learning samples.
Stock quantitative prediction analysis method based on deep learning transformer self-attention mechanism

In recent years, China's economy has developed rapidly, and accordingly, financial market has also developed rapidly in China, attracting the attention of investors domestic and foreign. Therefore, it is of vital significance to study the stock price trend in China's financial market for scholars, investors and regulators. With the rise of quantitative trading and other ideas, more and more scholars apply deep neural network (DNN) to the financial field. Although DNN has achieved great success in image, voice and text in recent years, it has encountered many challenges in financial time series prediction due to the highly dynamic and noisy feature of dataset. As a typical representative of DNN in time series data processing, LSTM, because this method does not consider the importance of data at different time points and different sources, the effect is still not ideal. Different from the introduction of the Attention mechanism on the traditional LSTM model, by improving the Self-Attention model, the daily data and the time-sharing data are encoded and fused separately, and the impact of changes in capital flow on changes in stock trends can be learned. The experimental results show that the proposed method improves the accuracy of trend judgment to 63.04%, and obtains a return of 6.562% in the two-month back testing experiment, which proves that the model has a certain effectiveness and practicability in stock price trend prediction.
Harnessing Collective Differences in Crowdsourcing Behaviour for Mass Photogrammetry of 3D Cultural Heritage

Disorganised and self-organised crowdsourcing activities that harness collective behaviours to achieve a specific level of performance and task completeness are not well understood. Such phenomena become indistinct when highly varied environments are present, particularly for crowdsourcing photogrammetry-based 3D models. Mass photogrammetry can democratise traditional close-range photogrammetry procedures by outsourcing image acquisition tasks to a crowd of non-experts to capture geographically scattered 3D objects. To improve public engagement, we need to understand how individual behaviour in collective efforts work in traditional disorganised crowdsourcing and how it can be organised for better performance. This research aims to investigate the effectiveness of disorganised and self-organised collaborative crowdsourcing. It examines the collaborative dynamics among participants and the trends we could leverage if team structures were incorporated. Two scenarios were proposed and constructed: asynchronous crowdsourcing, which implicitly aggregates isolated contributions from disorganised individuals; and synchronous collaborative crowdsourcing, which assigns participants into a crowd-based self-organised team. Our experiment demonstrated that a self-organised team working in synchrony can effectively improve crowdsourcing photogrammetric 3D models in terms of model completeness and user experience. Through our study, we demonstrated that this crowdsourcing mechanism can provide a social context where participants can exchange information via implicit communication, and collectively build a shared mental model that pertains to their responsibilities and task goals. It stimulates participants’ prosocial motivation and reinforces their commitment. With more time and effort invested, their positive sense of ownership increases, fostering higher dedication and better contribution. Our findings shed further light on the potentials of adopting team structures to encourage effective collaborations in conventionally individual-based voluntary crowdsourcing settings, especially in the digital heritage domain.
Quantitative Evaluation and Reevaluation of Security in Services

Services are software components or systems designed to support interoperable machine or application-oriented interaction over a network. The popularity of services grows because they are easily accessible, very flexible, provide reach functionality, and can constitute more complex services. During the service selection, the user considers not only functional requirements to a service but also security requirements. The user would like to be aware that security of the service satisfies security requirements before starting the exploitation of the service, i.e., before the service is granted to access assets of the user. Moreover, the user wants to be sure that security of the service satisfies security requirements during the exploitation which may last for a long period. Pursuing these two goals require security of the service to be evaluated before the exploitation and continuously reevaluated during the exploitation. This thesis aims at a framework consisting of several quantitative methods for evaluation and continuous reevaluation of security in services. The methods should help a user to select a service and to control the service security level during the exploitation. The thesis starts with the formal model for general quantitative security metrics and for risk that may be used for the evaluation of security in services. Next, we adjust the computation of security metrics with a refined model of an attacker. Then, the thesis proposes a general method for the evaluation of security of a complex service composed from several simple services using different security metrics. The method helps to select the most secure design of the complex service. In addition, the thesis describes an approach based on the Usage Control (UCON) model for continuous reevaluation of security in services. Finally, the thesis discusses several strategies for a cost-effective decision making in the UCON under uncertainties.
Classical diffusion of N interacting particles in one dimension: General results and asymptotic laws

I consider the coupled one-dimensional diffusion of a cluster of N classical particles with contact repulsion. General expressions are given for the probability distributions, allowing to obtain the transport coefficients. In the limit of large N, and within a Gaussian approximation, the diffusion constant is found to behave as N−1 for the central particle and as (ln N)−1 for the edge ones. Absolute correlations between the edge particles increase as (ln N)2. The asymptotic one-body distribution is obtained and discussed in relation of the statistics of extreme events.
CP violation in gamma gamma -> t(t)over-bar within the minimal supersymmetric standard model

The complete analysis of CP violation in the process gamma gamma --> t (t) over bar in the framework of the minimal supersymmetric model is presented. The CP-odd observables for describing the CP violating effects in polarized and unpolarized photon collisions, are calculated. We investigate the possible CP violation sources induced by the complex soft breaking parameters and study the CP violating effects contributed by gluino, neutralino and chargino sectors appearing in the loop diagrams. We find that it is possible to observe CP violating effects in top-quark pair production via polarized and unpolarized photon fusions by using optimal observables and favourable parameters.
Searches for Population III pair-instability supernovae: Impact of gravitational lensing magnification

Superluminous supernovae have been proposed to arise from Population III progenitors that explode as pair-instability supernovae. Pop III stars are the first generation of stars in the Universe, and are thought to form as late as $z \sim 6$. Future near-infrared imaging facilities such as ULTIMATE-Subaru can potentially detect and identify these PISNe with a dedicated survey. Gravitational lensing by intervening structure in the Universe can aid in the detection of these rare objects by magnifying the high-$z$ source population into detectability. We perform a mock survey with ULTIMATE-Subaru, taking into account lensing by line-of-sight structure to evaluate its impact on the predicted detection rate. We compare a LOS mass reconstruction using observational data from the Hyper Suprime Cam survey to results from cosmological simulations to test their consistency in calculating the magnification distribution in the Universe to high-$z$, but find that the data-based method is still limited by an inability to accurately characterize structure beyond $z \sim1.2$. We also evaluate a survey strategy of targeting massive galaxy clusters to take advantage of their large areas of high magnification. We find that targeting clusters can result in a gain of a factor of $\sim$two in the predicted number of detected PISNe at $z > 5$, and even higher gains with increasing redshift, given our assumed survey parameters. For the highest-redshift sources at $z \sim 7-9$, blank field surveys will not detect any sources, and lensing magnification by massive clusters will be necessary to observe this population.
Multicolor photometry of the GRB970508 optical remnant

We report results of follow-up multicolor photometry of the optical variable source that is a probable remnant of the gamma-ray burst GRB970508 discovered by the BeppoSAX satellite [3]. Observations were carried out in Johnson-Kron-Cousins BVRcIc system with the 1-m and 6-m telescopes of SAO RAS. Between the 2nd and the 5th day after the burst a fading of the remnant is well fitted with an exponential law in all four bands. During this period the ‘broadband spectrum’ of the object was unchanged and can be approximated by a power-law, Fν∝ν−1.1. After the 5th day the decline of brightness is slowed down. In the Rc band until the 84th day, the light curve can be described by a power-law relation, F∝t−1.179(±0.016).
UDOKS Project: Development of a Pedestrian Navigation System With Multiple Integrated Sensors

In both civilian and military areas, accurate and robust navigation systems have become a critical tool for positioning and tracking capabilities of pedestrians under Global Navigation Satellite Systems (GNSS)-denied conditions, e.g., underground workers, dismounted soldiers on battlefield, and first responders within a building. In order to navigate through these harsh environments, pedestrian navigation systems (PNSs) with multiple integrated sensors are indispensable. In the research project which is funded by ASELSAN and entitled "Positioning in GNSS Denied Environments (labeled as UDOKS)", we are working on the development of a PNS integrating inertial sensors (i.e., gyroscopes and accelerometers), Ultra-Wideband (UWB) ranging sensors, and aiding sensors such as magnetometers and a barometer. Furthermore, a simulation environment dedicated to PNSs with multiple integrated sensors is being implemented as a part of UDOKS project. The simulation environment will allow the user to compare several pedestrian navigation algorithms which are based on different types of sensor technologies and different levels of sensor errors. Moreover, the simulation environment will be used as a means to test and evaluate new strategies and approaches on pedestrian navigation during algorithm and configuration design phase. The simulation environment will have an open and modular architecture so that it will be capable of inserting and integrating complementary navigation algorithms and sensor technologies. This open and modular architecture of the simulation environment will provide a huge potential benefit to design PNSs especially for the needs in various GNSS-denied applications and usage scenarios. This Work-in-Progress (WiP) paper provides a high-level description of the PNS being developed and a general outline of the simulation environment being implemented in UDOKS project. Furthermore, preliminary indoor performance results of the pure inertial sensors-based PNS (i.e., utilizing only gyroscope and accelerometer measurements) developed as a single navigation system are given.
High power microwave beam steering based on gyromagnetic nonlinear transmission lines

We demonstrate electronically controlled beam steering by high power RF pulses produced by two gyromagnetic nonlinear transmission lines (NLTLs) connected to a one high voltage driver. Each NLTL is capable of producing several ns RF pulses with peak power from 50 to 700 MW (6% standard deviation) at frequencies from 0.5 to 1.7 GHz (1% standard deviation) with 100 Hz repetition rate. Using a helix antenna allows irradiating of RF pulses with almost circular polarization and 350 MW maximum peak power, which corresponds to 350 kV effective potential of radiation. At the installation of two identical channels, we demonstrate the possibility of beam steering within ±15° in the horizontal plane by coherent RF pulses with circular polarization at 1.0 GHz center frequency. Fourfold increase in the power flux density for in-phase irradiation of RF pulses is confirmed by comparison with one-channel operation.
Review of Image Intelligent Recognition Technology

In summary, as the computer improved, people need it to solve more complex problems. Thus, computer scientist invented artificial neural network. At first, ANN is very simple and only have single hidden layer. After decades development, ANN become more powerful and smart. With more algorithms have been invented and more hidden layers, Convolutional Neural Network have become a widely used nowadays: it could use for image classification, such as writings and photos. It could also use in autonomous vehicles. CNN could help doctor to diagnose disease and increase device security by facial recognition. It could also use on weather forecasting. One important usage of CNN is license plate recognition. There are four steps in this recognition: plate localization, plate inclination correction, character segmentation and character recognition. First, computers need to find vehicle plate in the photos since they are taken in various conditions. This requires a series of preprocessing. After locating plate in the photos, the computer would calculate the angle of inclination of the plate and correct it. Next, the computer would locate characters on the plate and size of them, using such information to segment them. Last, CNN analyzes these characters and gives out results. A CNN is consisted by input layer, hidden layer and output layer. CNN would adjust the weights in each layer based on difference between the output and the answer. More adjustment would lead to more accurate result. Finally, CNN would identify characters on plates. In conclusion, CNN has a bright prospect of application, and it will make things more convenient.