Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Networks for Clobber

C lobber is a strategic board game played on a chequered board with black stones on all black squares and white stones on all white squares. The game is played with two players and each of them is assigned to a color. Players take turns into capturing stones from the opponent, but only in adjacent squares, either horizontally or vertically. The winner is the player who makes the last move. For this thesis we have developed different strategies to play C lobber with. These algorithms are R andom , M onte C arlo , M onte C arlo T ree S earch and a N eural N etwork . The focus lies on creating a N eural N etwork that can play better than any other algorithm and hopefully can play optimally. After training our N eural N etwork by playing against all other algorithms and even itself, M onte C arlo T ree S earch still manages to win most games against the N eural N etwork , especially when it has a large playout depth.
Impact of scaling laws on tokamak reactor dimensioning

A simple and comprehensive method is derived and used to quantify the impact of scaling laws on tokamak reactor dimensioning. Assuming prescribed geometrical coefficients, we find the ensemble of possible triplets R, B and normalized beta which allow one to reach target fusion gain Q and fusion power , at an arbitrary Greenwald fraction. The model is generic and derived for any scaling law of the energy confinement time. Using the IPB98(y,2) scaling law (ITER Physics Expert Group on Confinement and Transport and Confinement Modeling and Database and ITER Physics Basis Editors 1999 Nucl. Fusion 39 2175) leads to ITER specifications, as expected. The recently proposed new scaling law for H-mode plasmas (DS03, (Sips et al 2018 Nucl. Fusion 58 126010)) is shown to lead to modest changes to the dimensioning, except for B which could be significantly smaller for the same target performance. The impact on the dimensioning of critical exponents of the scaling law—both regarding engineer and dimensionless variables—is assessed, pushing for their determination with refined accuracy. Finally, the method is applied to a DEMO-like machine. The DS03 scaling law is found to have favorable consequences on the dimensioning as compared to IPB98(y,2), provided one is able to operate at larger , which can reveal challenging in a reactor aiming at zero disruption. Importantly, the opposite scaling of both scaling laws with respect to the aspect ratio are shown to have significant consequences on the optimal choice of this critical parameter.
Three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA

Purpose The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high. Design/methodology/approach A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points. Findings The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points. Originality/value A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.
Rigorous Evaluation of Losses in Uniform Leaky-Wave Antennas

General formulas are proposed for thoroughly evaluating the radiation efficiency of traveling-wave antennas operating in any radiating regime. Indeed, existing theoretical models only work in the scanned-beam region, where the radiating mode is considerably above cutoff and the perturbation method applies. The analysis provided here extends the application of the perturbation method close to the cutoff condition, thus providing a correct estimation of the losses even in the case of broadside radiation. This case is particularly important for Fabry–Perot cavity leaky-wave antennas (FPC-LWAs) based on partially reflecting screens (PRSs), which are commonly designed to generate a broadside pencil beam. However, the previous works on FPC-LWAs always assume either a lossless PRS, or a lossless dielectric cover layer thus neglecting metal and dielectric losses: a hypothesis that is no longer fulfilled at high frequencies, such as millimeter-wave and submillimeter-wave frequencies. An original theoretical framework is thus developed to derive analytical formulas for a rigorous evaluation of losses when either lossy PRS or lossy dielectric cover layers are employed. Interestingly, the equivalence between a lossy PRS and a lossy dielectric cover layer is shown from a theoretical viewpoint. All the findings derived here are validated through full-wave simulations to corroborate the proposed analysis.
Personalization across de-centralized domains with a modular user profile architecture

User profiling has been used to provide personalized e-services. In our previous work we have highlighted the advantages of a modular approach to developing user profiles. This approach separates the information to be stored into levels according to the dependency level on the customer or transaction. Therefore the customer’s personal information is stored in a separate layer while individual transaction information is stored in another layer. A middle layer stores information generalized from the individual transactions, which are used to reduce the need for obtrusive information extraction from a customer during a subsequent transaction. In this paper, we highlight the advantages of the modular user profiles when used in a de-centralized setting. The separate layers provide a flexible structure, which provide the possibility of locating in vendor, server or client sites. A decentralized profile architecture is presented in the paper, and the advantages are discussed using possible scenarios.
Particle charge fluctuation and its consequences on aerosol dynamics in dusty plasmas: from continuous to discrete statistical models

In this work we investigated different approaches and models used for the estimation of the charge fluctuation of solid particles in dusty plasmas. We compared the charge distributions inferred from these models and analyzed the consequences in terms of coagulation kinetics and particle size distributions. We especially showed that taking explicitly into account the discrete nature of the charging process along with the enhancement of ion flux due to ion-neutral elastic collisions result in significantly larger values for the fractions of neutral and positively charged particles, which affects the coagulation kinetics and the predicted values of particle density. We also showed, by comparing sectional and modal approaches, that the typical size distributions obtained in the studied plasmas may be well described using a bimodal distribution with a single size nucleation mode and a Gaussian large size mode. Such a possibility enables a great simplification of the aerosol dynamic description and open up an interesting perspective as far as multidimensionnal self consistent modeling of dusty plasmas is concerned.
Motor Imagery Classification based on RNNs with Spatiotemporal-Energy Feature Extraction

With the recent advances in artificial intelligence and robotics, Brain Computer Interface (BCI) has become a rapidly evolving research area. Motor imagery (MI) based BCIs have several applications in neuro-rehabilitation and the control of robotic prosthesis because they offer the potential to seamlessly translate human intentions to machine language. However, to achieve adequate performance, these systems require extensive training with high-density EEG systems even for two-class paradigms. Effectively extracting and translating EEG data features is a key challenge in Brain Computer Interface (BCI) development. This paper presents a method based on Recurrent Neural Networks (RNNs) with spatiotemporal-energy feature extraction that significantly improves the performance of existing methods. We present cross-validation results based on EEG data collected by a 16-channel, dry electrodes system to demonstrate the practical use of our algorithm.
An Efficient Approach To Civil Structures Health Monitoring Using Fog Computing As Clusters Through 5G Network Environment

Structural Health Monitoring (SHM) is a process to trace real time information about the physical health of a civil structures at any given point of time. SHM systems can also approximate the life expectancy of the structure. This constant monitoring can help during emergency situations such as earthquakes. The increasing need for SHM is the result of deterioration of several set of structures over time. A proper maintenance plan is beneficial to not just keep the structure in a favorable state throughout its lifecycle, but to additionally minimize the relative costs required by the structures through resource optimization. Various forms of SHM have been implemented over the past three decades like traditional wired systems. Recent development has resulted in Wireless Sensor Networks (WSN) being designed for the same purpose. Few publications have also included IoT framework along with WSNs. The introduction of WSN has led to easy deployment of SHM compared to the traditional wired network. But WSN's have led to significant challenges. The main challenge that has been focused in this paper is the constraint of power consumption. The power consumption is most critical factor for the sensors deployed in high-rise structures, high density traffic bridges etc., since it is difficult to reach the places to replace the battery drained sensors and also involved high installation cost. This paper proposes a concept of a framework for SHM with FOG computing which hasn't been explored in the field of SHM yet. In this model, the WSN is comprised of a piconet which transmits data through Bluetooth Low energy (LE) and the FOG transmits data to cloud by exploiting 5G network features in order to reduce the power consumption furthermore. Thus, saving power at every level possible in the SHM architecture. It also introduced a head sensor algorithm within clusters which maximizes the lifetime of the WSN. A mathematical model has been developed in support to evaluate how the power of the system can be minimized for the FOG node and cloud servers in accordance with the 5G network environment Experiments have been carried out to validate proposed framework and demonstrate the results.
Learning Domain Randomization Distributions for Training Robust Locomotion Policies

This paper considers the problem of learning behaviors in simulation without knowledge of the precise dynamical properties of the target robot platform(s). In this context, our learning goal is to mutually maximize task efficacy on each environment considered and generalization across the widest possible range of environmental conditions. The physical parameters of the simulator are modified by a component of our technique that learns the Domain Randomization (DR) that is appropriate at each learning epoch to maximally challenge the current behavior policy, without being overly challenging, which can hinder learning progress. This so-called sweet spot distribution is a selection of simulated domains with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution; and 2) The DR distribution made as wide as possible, to increase variability in the environments. These properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the DR distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment1.
Low-Complexity Loeffler DCT Approximations for Image and Video Coding

This paper introduced a matrix parametrization method based on the Loeffler discrete cosine transform (DCT) algorithm. As a result, a new class of 8-point DCT approximations was proposed, capable of unifying the mathematical formalism of several 8-point DCT approximations archived in the literature. Pareto-efficient DCT approximations are obtained through multicriteria optimization, where computational complexity, proximity, and coding performance are considered. Efficient approximations and their scaled 16- and 32-point versions are embedded into image and video encoders, including a JPEG-like codec and H.264/AVC and H.265/HEVC standards. Results are compared to the unmodified standard codecs. Efficient approximations are mapped and implemented on a Xilinx VLX240T FPGA and evaluated for area, speed, and power consumption.
Efficient Checksum Calculation using Reduction Trees

As network traffic throughput requirements continue to increase dramatically, network processors are adding specialized instructions that accelerate the more common arithmetic operations. An example is the checksum, which appears in several popular protocols to guard against using corrupted packets. This paper presents two approaches to checksum calculation that provide significant performance improvement over conventional methods. These approaches use reduction trees similar to those found in parallel multipliers to produce two operands for final summation. Reduction trees are appropriate when an array of words is available to be summed at one time, in contrast to serial summing of word pairs. The two methods vary in the design of the reduction tree. The first approach, leveled reduction based checksumming, uses predefined array sizes of partial sums, leading to a single circuit design. This approach has the benefit of fixed, design-once circuit parameters. The second approach, 3-to-2 reduction based checksumming, takes a greedy approach to reducing as many application words as possible at each stage. This approach has the benefit of parameterized circuit design amenable to automatic generation of reduction stages to fit application array size. Both approaches are suitable for a variety of circuit implementations.
Analysis of Crowd Flow Parameters Using Artificial Neural Network

Abstract Research scientists have been developing mathematical tools to detect objects, recognize objects and actions, and discover behaviours and events to human abilities. In all these efforts, the understanding of human actions is of a special interest for both application and research purposes. In this study, crowd flow parameters are analysed by considering linear and non linear relationships between stream flow parameters using conventional and soft computing approach. Deterministics models like Greenshield and Underwood were applied in the study to describe flow characteristics. A non-linear model based on Artificial Neural Network (ANN) approach is also used to build a relationship between different crowd flow parameters and compared with the other deterministic models. ANN model gave good results based on accuracy measurement to deterministic models because of their self-processing and intelligent behaviour. Mean absolute error (MAE) and root mean square error (RMSE) values for the best fitted ANN model are less than those for the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters.
Longitudinally Slotted SIW Leaky-Wave Antenna for Low Cross-Polarization Millimeter-Wave Applications

A longitudinally slotted TE20-mode-driven substrate integrated waveguide (SIW) leaky-wave antenna (LWA) with low side-lobe and low cross-polarization is proposed and presented for millimeter-wave applications. In this work, a pair of long slots is etched symmetrically on the top broadside of an SIW working in the TE20 mode. With the odd-symmetry of TE20 mode field distribution, the even-symmetrically etched long slots paired along the centerline of the SIW have aperture fields of the same direction, thereby resulting in a constructive superposition of radiation. Thanks to these long slots with a meandering shape that is designed according to a specific aperture amplitude illumination (e.g., −35 dB Taylor line-source distribution) and their even-symmetry with respect to the centerline of the SIW, both low side-lobe and low cross-polarization are simultaneously achieved. Two meandering schemes with respect to the centerline of the SIW are adopted and, thus, two types of the designed LWAs are developed. For experimental prototypes of the proposed LWAs, a 3 dB directional coupler and a 90° phase shifter are combined together to excite the desired TE20 mode of the SIW. The proposed two types of LWAs are modeled, simulated, and fabricated. Both the simulated and measured results are in a good agreement.
Microjoule Sub-Two-Cycle Mid-Infrared Intrapulse-DFG Driven by 3- $\mu$ m OPCPA

We report a microjoule, <inline-formula> <tex-math notation="LaTeX">$10.3~\mu \text{m}$ </tex-math></inline-formula>, mid-infrared (MIR) intrapulse difference frequency generation (IPDFG) in GaSe, driven by a <inline-formula> <tex-math notation="LaTeX">$3~\mu \text{m}$ </tex-math></inline-formula> optical parametric chirped-pulse amplifier. The generated IPDFG pulse spanning from 7 to 15 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> in spectrum is measured with a pulse width of 60 fs, corresponding to 1.8 optical cycles. We experimentally confirmed that the spectral broadening of the driving pulses caused by the self-phase modulation in GaSe plays a crucial role in the process of IPDFG. To the best of our knowledge, this is the first exploration of IPDFG driven at <inline-formula> <tex-math notation="LaTeX">$3~\mu \text{m}$ </tex-math></inline-formula> wavelength. It provides an approach to generate few-cycle long-wavelength MIR pulses, with the recent rapid development of the <inline-formula> <tex-math notation="LaTeX">$3~\mu \text{m}$ </tex-math></inline-formula> driving sources.
Re2EMA: Regularized and Reinitialized Exponential Moving Average for Target Model Update in Object Tracking

Target model update plays an important role in visual object tracking. However, performing optimal model update is challenging. In this work, we propose to achieve an optimal target model by learning a transformation matrix from the last target model to the newly generated one, which results into a minimization objective. In this objective, there exists two challenges. The first is that the newly generated target model is unreliable. To overcome this problem, we propose to impose a penalty to limit the distance between the learned target model and the last one. The second is that as time evolves, we can not decide whether the last target model has been corrupted or not. To get out of this dilemma, we propose a reinitialization term. Besides, to control the complexity of the transformation matrix, we also add a regularizer. We find that the optimization formula’s solution, with some simplifications, degenerates to EMA. Finally, despite the simplicity, extensive experiments conducted on several commonly used benchmarks demonstrate the effectiveness of our proposed approach in relatively long term scenarios.
Mechanical Recovery of Cracked Fiber-Reinforced Mortar Incorporating Crystalline Admixture, Expansive Agent, and Geomaterial

This research is sought to characterize the stimulated autogenous healing of fiber-reinforced mortars that incorporate healing agents such as crystalline admixtures, expansive agents, and geomaterials. The effects of the healing materials on mechanical performance and water permeability were evaluated experimentally. Furthermore, microscopic and microstructural observations were conducted to investigate the characteristics and physical appearance of healing products within healed cracks. Test results are presented herein regarding index of strength recovery (ISR), index of damage recovery (IDR) and index of dissipation energy gain (IDEG) in relation to crack healing, and reduction of water flow rate. The self-healing capability of the mortars was greater in terms of resisting water flow rather than recovering mechanical performance likely because water flow depends on surface crack sealing, whereas mechanical performance depends on bonding capacity as well as full-depth healing of cracks; thus, mechanical performance may further be improved after longer healing duration.
Variational formulation of the finite calculus equations in solid mechanics and diffusion-reaction problems

We present a variational formulation of the finite calculus (FIC) equations for problems in mechanics governed by differential equations with symmetric operators. Applications considered include solid mechanics, diffusion-transport and diffusion-reaction problems. The key of the variational formulation is the identification of the FIC governing equations with the classical differential equations of mechanics written in terms of modified non-local variables. A total potential energy (TPE) functional is found in terms of the modified variables. The FIC equations in the domain and the boundary are recovered as the Euler-Lagrange equations and the natural boundary condition of the TPE functional, respectively. Symmetric finite element equations are obtained after discretization of the TPE functional, therefore preserving the symmetry of the governing infinitesimal equations. The variational FIC expression is reinterpreted as a Petrov Galerkin weighted residual form of the original FIC equations with non-local weighting functions. The analogy of the variational FIC-FEM formulation with a discontinuous Galerkin method is recognized. Extensions to multidimensional linear elastostatics and diffusion-reaction problems are presented.
CBFA 2 T 2 is associated with a cancer stem cell state in renal cell carcinoma

Renal cell carcinoma (RCC) is the most common kidney cancer, accounting for approximately 80–90 % of all primary kidney cancer. Treatment for patients with advanced RCC remains unsatisfactory. Rare cancer stem cells (CSCs) are proposed to be responsible for failure of current treatment. Here, we investigate the effects of CBFA2T2 on CSCs regulation in RCC, as well as to elucidate the possible mechanisms. We showed that CBFA2T2 expression can significantly predict the survival of RCC patients. Functional assays showed that knocking-down of CBFA2T2 can inhibit cell migration and invasion in 786-O and A-498 cells in vitro, and reduce ALDHhigh CSCs populations. Furthermore, CBFA2T2 expression is necessary for sphere-forming ability and cancer stem cells marker expression in RCC cell lines. Collectively, our data suggest that CBFA2T2 expression correlates with aggressive characteristics of RCC and CBFA2T2 is required for maintenance of “stemness” through regulation of stem cells factors, thereby highlighting CBFA2T2 as a potential therapeutic target for RCC treatment.