Miracle-dz/UniFAR-FTUs huggingface dataset:
First use of LHC Run 3 Conditions Database infrastructure for auxiliary data files in ATLAS

Processing of the large amount of data produced by the ATLAS experiment requires fast and reliable access to what we call Auxiliary Data Files (ADF). These files, produced by Combined Performance, Trigger and Physics groups, contain conditions, calibrations, and other derived data used by the ATLAS software. In ATLAS this data has, thus far for historical reasons, been collected and accessed outside the ATLAS Conditions Database infrastructure and related software. For this reason, along with the fact that ADF are effectively read by the software as binary objects, this class of data appears ideal for testing the proposed Run 3 conditions data infrastructure now in development. This paper describes this implementation as well as the lessons learned in exploring and refining the new infrastructure with the potential for deployment during Run 2.
Face detection in color images with complex background

This paper presents a novel method to detect human face automatically in color image with (complex) background. Firstly, YCrCb and HSVcolor spaces are used to extract the skin color regions. In CrCb space, the detected skin pixels are clustered into several centers. Then, the opening morphological operation is used to remove the small objects in the background area. Some rules are employed to select the candidate face regions according to the shape and size information. Finally, the face regions can be located by using BP to detect the appearance of two eyes in the candidate face regions. Experimental results show that the proposed approach is efficient.
the Economics Classroom

With the widespread availability of software and micro and mainframe hardware, teachers can assign real-world problems with real-world complexity and expect students to be able to understand those problems. In the process of solving the classroom problems, students gain insight into how these tools can be applied directly to problems they will face in business. The purpose of this brief note is to review an approach I have taken in which the student develops a computer simulation model in the classroom and then uses it to solve macroeconomic problems. The software used is discussed also. Two different approaches to simulation have been tried in my classes: "canned" simulation games and class-created simulation models. Basically, there are two problems with canned programs as learning devices. First, some time must be taken from other teaching activities in order to make room for them during the semester. In my case, it was necessary to speed up the first introductory chapters so that twenty minutes per class period could be made available during the last few weeks of instruction for the game. I suspect that there was some learning loss from the increased pace of instruction at the beginning of the semester, but this was not verified. More important, even though students enjoyed the game atmosphere of the simulation exercises, I did not observe that use of the simulation model resulted in any major improvement in understanding macroeconomics. No systematic
New synthesis of the 6H-pyrido[4,3-b]carbazoles ellipticine and olivacine via cycloaddition of 2-phenylsulfonyl 1,3-dienes to indoles

An efficient synthesis of the antitumor alkaloids ellipticine and olivacine, starting from indole, was developed. The cycloaddition of 3-(phenylsulfonyl)-2,4-hexadiene or 2-(phenylsulfonyl)-1,3-pentadiene to the magnesium salt of indole was followed by C-C-N chain addition via a Michael reaction. Subsequent Bischler-Napieralski cyclization and aromatization afforded ellipticine and olivacine, respectively
Distributed fault-tolerant tracking control for multi-agent system in the environments with obstacles

This paper investigates the problem of collision-free coordination tracking control for multi-agent system under modeling uncertainties, actuator faults and disturbances. By syncretizing the Null-Space-based Behavioral (NSB) control and finite-time control method, a novel reference velocity signal is predesigned to achieve finite-time obstacle avoidance and coordination tracking. Then a set of finite-time coordination control laws are presented to guarantee all the agents to track a dynamic target while avoiding obstacles/collisions. Finally, numerical simulation is presented to demonstrate the efficacy of the control strategy.
Structured prediction for feature selection and performance evaluation

Machine learning methods can be employed to discover the relationship between inputs and their desired outputs from a large collection of data points. The outputs of many real-world problems are naturally formed as structured objects in which elements are interdependent in terms of the given structure. Although the conventional methods may be directly applied to solve these problems by treating the elements in each output object independently, they tend to yield suboptimal performance because of the ignorance of interdependency information in the structured outputs. During the last few years, structured prediction provides a natural way to directly model the relationship between inputs and structured outputs. Structure prediction model consists of three components: feature engineering, learning the optimal hypothesis from data by minimizing specific loss, and prediction. In the thesis, the main focus is on the first two components which are interweaved in the following three pieces of my works, where the generalized linear model is used in the prediction. The first work of this thesis deals with the feature engineering problem which is critical in the structured prediction modeling. The success of structure prediction models is attributed to the fact that their discriminative models are able to account for overlapping features on the whole input observations. These features are usually generated from data by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate the difficulty of template selection in feature engineering phrase, a novel multiple template learning paradigm has been proposed to learn a structured prediction model and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution of degraded performance. This paradigm has been further extended for structured prediction using generalized p-block norm regularization.
A review of optimistic planning in Markov decision processes

We review a class of online planning algorithms for deterministic and stochastic optimal control problems, modeled as Markov decision processes. At each discrete time step, these algorithms maximize the predicted value of planning policies from the current state, and apply the first action of the best policy found. An overall receding-horizon algorithm results, which can also be seen as a type of model-predictive control. The space of planning policies is explored optimistically, focusing on areas with largest upper bounds on the value - or upper confidence bounds, in the stochastic case. The resulting optimistic planning framework integrates several types of optimism previously used in planning, optimization, and reinforcement learning, in order to obtain several intuitive algorithms with good performance guarantees. We describe in detail three recent such algorithms, outline the theoretical guarantees on their performance, and illustrate their behavior in a numerical example.
Effective distribution of codewords for Low Density Parity Check Cycle codes in the presence of disorder

We review the zeta-function representation of codewords allowed by a parity-check code based on a bipartite graph, and then investigate the effect of disorder on the effective distribution of codewords. The randomness (or disorder) is implemented by sampling the graph from an ensemble of random graphs, and computing the average zeta function of the ensemble. In the limit of arbitrarily large size for the vertex set of the graph, we find an exponential decay of the likelihood for nontrivial codewords corresponding to graph cycles. This result provides a quantitative estimate of the effect of randomization in cybersecurity applications.
The Effects of Spread-Spectrum Techniques in Mitigating Conducted EMI to LED Luminance

Rapid voltage and current changes in recently ubiquitous LED driver have a potency to interfere other devices. Solutions with special converter design, component design, EMI filter, and spread-spectrum techniques have been proposed. Due to cost-size-weight constraints, spread-spectrum technique seems a potential candidate in alleviating EMI problem in LED application. In this paper, the effectiveness of conducted EMI suppression performance of the spread-spectrum technique is evaluated. Spread-spectrum techniques applied by giving disturbance to the system LED driver with 3 profile signals, filtered square, triangular, and sine disturbance signal to the switching pattern of a buck LED driver. From the test results, 472.5 kHz triangular and 525 kHz sine signal can reduce EMI about 42 dBuV while the filtered square signal can reduce EMI 40.70 dBuV compare with fundamental constantfrequency reference 669 kHz. The average reduction in the power level of the third signal in the frequency range of 199 kHz to 925 kHz for 5.154281 dBuV and the filtered square signal can reduce the average power level better than other signal disturbance of 5.852618 dBuV. LED luminance decrease when the spread-spectrum technique is applied to the system about 2814 lux.
Pilot allocation for MIMO-ZP-OFDM systems in underwater acoustic channel based on structured compressive sensing

Recently, many key techniques which was proved effective in radio communication have been utilized in underwater acoustic (UWA) communication, such as multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM). Channel estimation for MIMO-OFDM systems has been improved observably based on compressive sensing (CS) techniques. In recent years, by exploiting the inherent common sparsity of channels between different transmitting and receiving antennas pairs, channel estimation for MIMO-OFDM systems can be further enhanced. To take advantage of this characteristic, some block-structured CS methods have been proposed for MIMO-OFDM systems. In this paper, we treat the UWA channel with same Doppler scaling factor on all propagation paths and adopt an approach to mitigating the Doppler effect. Then, we investigate the channel estimation property for MIMO-OFDM systems based on the block-structured CS techniques. Finally, we introduce the blocked orthogonal matching pursuit (BOMP) algorithm to obtain the channel estimation result. Simulation results demonstrate that the proposed pilot allocation scheme and the modified block-structured CS method perform very well in MIMO-OFDM systems.
Robust Lattice-based Motion Planning

This paper proposes a robust lattice-based motion-planning algorithm for nonlinear systems affected by a bounded disturbance. The proposed motion planner utilizes the nominal disturbance-free system model to generate motion primitives, which are associated with fixed-size tubes. These tubes are characterized through designing a feedback controller, that guarantees boundedness of the errors occurring due to mismatch between the disturbed nonlinear system and the nominal system. The motion planner then sequentially implements the tube-based motion primitives while solving an online graph-search problem. The objective of the graph-search problem is to connect the initial state to the final state, through sampled states in a suitably discretized state space, such that the tubes do not pass through any unsafe states (representing obstacles) appearing during runtime. The proposed strategy is implemented on an Euler-Lagrange based ship model which is affected by significant wind disturbance. It is shown that the uncertain system trajectories always stay within a suitably constructed tube around the nominal trajectory and terminate within a region around the final state, whose size is dictated by the size of the tube.
Introduction to Linguistic Steganography

Abstract The specialty of data covering up has gotten much consideration in the late years as security of data has turn into a major concern in this web time. As sharing of delicate data by means of a typical correspondence station has get to be unavoidable, Steganography – the workmanship and art of concealing data has increased much consideration. We are likewise encompassed by a universe of mystery correspondence, where individuals of numerous types are transmitting data as guiltless as an encoded Visa number to an online store than and as deceptive as a terrorist plot to robbers. Steganography is derived from two Greek words, steganos, meaning covered or secret, and graphia, meaning writing. In simple terms, steganography is the art and science of hiding information in plain sight. Steganography is an innovation where advanced information pressure, data hypothesis, spread range, and cryptography innovations are united to fulfill the requirement for security on the Internet. This paper is an endeavor to examine the different methods utilized as a part of steganography and to recognize zones in which this method can be connected, so that humanity can be profited massively.
Synthesis and biological evaluation of several 3‐(coumarin‐4‐yl)tetrahydroisoxazole and 3‐(coumarin‐4‐yl)dihydropyrazole derivatives

A series of novel 3-(coumarin-4-yl)tetrahydroisoxazoles 5a,b, 7, 9 and 3-(coumarin-4-yl)dihydropyra-zoles 13a-d, 14,15a,b were synthesized from coumarin-4-carboxaldehyde 1 via the intermediate N-methyl nitrone 3 and N-phenyl or N-methyl hydrazones 11a,b. These coumarin derivatives were isolated, characterized and evaluated in vitro for their ability to inhibit trypsin, β-glucuronidase, soybean lipoxygenase and to interact with the stable radical 1,1-diphenyl-2-picrylhydrazyl. The compounds were tested in vivo as anti-inflammatory agents in the rat carrageenin paw edema assay. Compound 15a seems to be a lead molecule to be modified in order to improve the lipoxygenase inhibition. The results are discussed in terms of structural characteristics.
Physical Human-Robot Interaction Control of an Upper Limb Exoskeleton with a Decentralized Neuro-Adaptive Control Scheme

Within the concept of physical human-robot interaction (pHRI), the most important criterion is the safety of the human operator interacting with a high degree of freedom (DoF) robot. Therefore, a robust control scheme is in high demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In this paper, an adaptive decentralized control strategy is designed to accomplish the abovementioned objectives. To do so, a human upper limb model and an exoskeleton model are decentralized and augmented at the subsystem level to enable a decentralized control action design. Moreover, human exogenous force (HEF) that can resist exoskeleton motion is estimated using radial basis function neural networks (RBFNNs). Estimating both human upper limb and robot rigid body parameters, along with HEF estimation, makes the controller adaptable to different operators, ensuring their physical safety. The barrier Lyapunov function (BLF) is employed to guarantee that the robot can operate in a safe workspace while ensuring stability by adjusting the control law. Unknown actuator uncertainty and constraints are also considered in this study to ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole system is established by means of the virtual stability concept and virtual power flows (VPFs) under the proposed robust controller. The experimental results are presented and compared to proportional-derivative (PD) and proportional-integral-derivative (PID) controllers. To show the robustness of the designed controller and its good performance, experiments are performed at different velocities, with different human users, and in the presence of unknown disturbances. The proposed controller showed perfect performance in controlling the robot, whereas PD and PID controllers could not even ensure stable motion in the wrist joints of the robot.
AD conversion with high speed based on FPGA

In the design of radar,the received signal is needed with AD conversion,the accuracy of after computing,like FFT,is determined by the speed and accuracy of AD conversion.This article,using the ADS7890,achieve a 14 bit Serial AD conversion with high speed based on FPGA.The hardware system is the composition of ADS7890's basic peripheral circuits and it's control connect with EP2C35F672C,The software system adopts the Quartus Ⅱ 8.0 software as development environment.
Speech Enhancement Algorithm Based on a Modified Threshold Function

Threshold de-noising technology for speech signal based wavelet analysis is an important topic in speech signal processing. Due to the nonstationarity of speech signals,traditional Wavelet Threshold De-noising Algorithm could attenuate part of the noise in speech signal. However,this algorithm would inevitably cause the loss of the useful speech signal. By using wavelet packet the speech signal could be decomposed into the low frequency part and the high frequency part,thus the speech information could be better,kept while the influence of noise be eliminated. And a new threshold function is proposed,which could de-noise the speech signal more completely. Simulation results show that,this proposed function,as compared to the hard or soft threshold function,is of obvious superiority.
Petri Net Model of Input and Output System

Petri net is a powerful tool for analying systems . In this paper, the basic concepts of Petri net and input output system are introduced and the methods of constructing the Petri net model of input output system are given, at the same time, the properties of the model are discussed. Based on the Petri net model of input output system, the abundant condition of the resolvability and the essential condition of the unresolvability of input output system are given. To clarify the problem, an example of a real input output system is introduced. The study of applying Petri net to input output system can reflect directly the structure and the behavior of input output system which is a very meaningful thing.
An Improved DBSCAN Clustering Algorithm

At first,a DBSCAN(density-based spatial clastering of applications with noise) clustering algorithm was discussed.Its characteristics,problems,and idea to improve it were analyzed.The identifying method of traffic accidents multiple-points based on the DBSCAN clustering algorithm and the idea to improve it were proposed.With the combination of J2EE technology,a web environment data mining system of traffic accidents was achieved.The architecture,design,and application of accidents data mining system based on J2EE were illustrated.
Musical Genre Classification Using SVM and Audio Features

The need of advance Music Information Retrieval increases as well as a huge amount of digital music files distribution on the internet. Musical genres are the main top-level descriptors used to organize digital music files. Most of work in labeling genre done manually. Thus, an automatic way for labeling a genre to digital music files is needed. The most standard approach to do automatic musical genre classification is feature extraction followed by supervised machine-learning. This research aims to find the best combination of audio features using several kernels of non-linear Support Vector Machines (SVM). The 31 different combinations of proposed audio features are dissimilar compared in any other related research. Furthermore, among the proposed audio features, Linear Predictive Coefficients (LPC) has not been used in another works related to musical genre classiffication. LPC was originally used for speech coding. An experimentation in classifying digital music file into a genre is carried out. The experiments are done by extracting feature sets related to timbre, rhythm, tonality and LPC from music files. All possible combination of the extracted features are classified using three different kernel of SVM classifier that are Radial Basis Function (RBF), polynomial and sigmoid. The result shows that the most appropriate kernel for automatic musical genre classification is polynomial kernel and the best combination of audio features is the combination of musical surface, Mel-Frequency Cepstrum Coefficients (MFFC), tonality and LPC. It achieves 76.6 % in classification accuracy.
Security Research on a SAML-based Single Sign-on implement mode

We introduce the SAML technology and two types of message transfer of this protocol. Two mode to implement SAML Sin-gle Sign-on are discussed in this article. We present analyses of the possible security leak existed in every steps of Browser/Artifact mode protocol flow especially. Then we bring forward the breach point of solving these problems and the feasible scheme at next stage.
Operant conditioning reflex learning control scheme based on SMC and Elman network

A bionic operant conditioning reflex(OCR) learning control scheme is proposed based on the thought of sliding model control(SMC) and Elman network for a class of SISO higher-order nonlinear control system.In this method,an Elman network is used as an evaluation function of sliding surface and action in the scheme.Reward signal is designed according to the change of sliding surface,and then the evaluation function is updated through the reward stimulation,while the behavior choice probability is changed.Through the definition of entropy for each round,a quantitative analysis about the knowledge change in the learning process is given.The results of the simulation experiment in the walking inverted pendulum system show that,bionic OCR learning control is used,which realizes the balancing control for the walking inverted pendulum system.
On the Neural Network Modeling with Support Rough Set Theory

A method is introduced to cooperate the decision support and classification, in which rough set theory and neural network are formed integrated into a model. A neural network modelling way based on rough set theory is proposed. The neural network is preprocessed by the intelligent data analysis capability of rough set theory, and the key components are extracted as the inputs of the neural network to determine original topology of the rough neural network. Furthermore, the realization steps of the model are analyzed, and the rough neural network is trained with original data. The model constructed is applied to extraction of classification rules. The experimental results show that the model can increase the classification correctness effectively.
States generated in the $K$-multi-$\rho$ interactions

In the present work we use three-body interaction formalism to investigate the $K$-multi-$\rho$ interactions. First, we reproduced the resonances $f_2(1270)$ and $K_1(1270)$ in the $\rho \rho$ and $\rho K$ two-body interactions respectively, as the clusters of the fixed center approximation. Then, we study the three-body $K -\rho \rho (f_2)$ and $\rho - \rho K (K_1)$ interactions with the fixed center approximation of the Faddeev equations. Furthermore, we extrapolate the formalism to study the four-body, five-body and six-body systems, containing one $K$ meson and multi-$\rho$ mesons. In our research, without introducing any free parameters, we generate the $K_2 (1770)$ state in the three-body interaction with the mass of 1707 MeV and a width about 113 MeV, which are consistent with the experiments. We also find a clear resonant structure in our results of the five-body interaction, with a mass 2505 MeV and a width about 32 MeV or more, which is associated to the $K_4 (2500)$ state, where we obtain consistent results with the experimental findings. Furthermore, we predict some new states in the other many body interactions, $K_3 (2080)$, $K_5 (2670)$ (isospin $I=1/2$), and $K_4 (2640)$ (isospin $I=3/2$), with uncertainties.