Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact CNN with Dynamic Window for SSVEP-based BCIs

Conventional recognition methods for the detection of steady-state visual evoked potential (SSVEP) usually use the strategy of fixed window. These methods would cost a lot of time finding an optimal window length by iterating various window lengths. Additionally, due to the instability and complexity of the electroencephalogram (EEG), the optimal window length is not static and is interfered by surroundings. Dynamic window methods were proposed to solve above problems, which could dynamically choose an optimal window length and output results at an appropriate time. Filter bank canonical correlation analysis with dynamic window (FBCCA-DW) is a dynamical training-free method which combines FBCCA method and a designed dynamic window strategy. FBCCA-DW achieves better performance than other similar methods. However, FBCCA method only extracts the components of SSVEP at the fundamental and harmonic frequencies, while some other small but essential information would be lost. Compact convolutional neural network (EEGNet) is a subject-independent training method which can extract more features from raw SSVEP data and outperforms FBCCA. In this study, EEGNet with dynamic window (EEGNet-DW) is proposed to make use of EEGNet and the dynamic window strategy. We evaluate EEGNet-DW across subjects on a public SSVEP dataset. We show that the EEGNet-DW, with an information transfer rate (ITR) of 147.6 bits/min, performed better than EEGNet with fixed window (EEGNet-FW) and FBCCA-DW. Our proposed method shows promise for real-time SSVEP-based BCI applications.
Two-step Method Grid Map Merging Based on Corner Points

Multi-robot SLAM (Simultaneous Localization and Mapping) can be used in many collaboration-based operations, such as military rescue tasks. Map merging is the most obvious solution to the multi-robot SLAM. However, traditional corner-based map fusion algorithms failed in complex environments. This paper proposes a corner-based two-step method for map merging in complex environments. First, the map is preprocessed, including orthogonal rotation, filtering, and edge reconstruction. The corners are identified by Harris Corner detection algorithm. By constructing a convex quadrilateral based on the corner points, the overlapping area can be selected for the first step. Then, the intersection of the obtained quadrilateral diagonals is used as a new feature point, and a triangle is constructed for matching to obtain the final overlapping area. Finally, the rotation angle and displacement can be obtained by calculating the transformation matrix. The results show that this method can solve the complex grid map merging problem effectively.
Analysis of Multivariate Chaotic Time Series using Neural Networks

With the advent of time series prediction in multidisciplinary domains, Multivariate Chaotic Time Series (MCTS) prediction has become a popular topic of re-search. Manifold applications like weather forecasting, stocks prediction, medical support, etc., deploy such kind prediction approach to predict the future of the time series based on past observations. In literature, various solutions have been explored and proposed to forecast future values in time series data. Significant efforts have been made to utilize various Neural Networks for time series prediction considering their applicability for future data prediction. However, a comprehensive evaluation of such existing methods is missing which demands attention for accurate and efficient prediction of time series data. In this paper, we have applied and evaluated various deep learning techniques on different dynamically generated data sets. Further, a comprehensive comparison of different techniques have been presented referencing loss observed with performance matrix Mean Absolute Error.
Analysis of Filtration Processes in Porous Environments Taking Into Aaccount the Movement of Capillaries.

The influence of the order of the fractional derivatives on the process of gas filtration in complex porous media has been investigated in the presented paper. A model of gas filtration based on the capillary approach is proposed. The last one allows studying the effect of capillary topology on the pressure distribution in porous media layers. The obtained theoretical results were approbated in the course of numerical experiments.
Application of the Multifactor Quantitative Method for Determination of Customs Value of Goods

The article proposes a method for determining the customs value of imported goods using the method of multi-subject multifactor evaluation through group expertise. The idea is to minimize subjectivity in evaluation. The characteristic of the proposed approach is that the influence of the assessment of an expert depends on the level of his competence. For quantitative ranking of individual competence, an anonymous assessment is performed through comparisons of experts in pairs. The method guarantees higher adequacy of the final assessment, which guarantees a better decision.
Swift/XRT view of S5 0716+714 during a flare

The blazar S5 0716+714 has been monitored in its flaring state between 2015 January 19 and February 22 with Swift/XRT. During this period an exceptional flux level was observed in the X-ray range as well as in the other wavelengths, e.g. optical, near infrared and very high energy $\gamma$ rays. Here, we report X-ray observations of S5 0716+714 carried out during the outburst. The observed X-ray spectra, well described with broken power-law model, disentangle both synchrotron and inverse Compton components. The break energy shifts towards higher energies with increasing flux, revealing the dominance of synchrotron radiation in the X-ray spectrum observed. We also report spectrum softening with increasing flux. During the recent flare, significant temporal intranight variability is observed in the X-ray range.
Observational Constraints on Phantom Crossing DGP Gravity

We study the observational constraints on the Phantom Crossing DGP model. We demonstrate that the crossing of the phantom divide does not occur within the framework of the original Dvali-Gabadadze-Porrati (DGP) model or the DGP model developed by Dvali and Turner. By extending their model in the framework of an extra dimension scenario, we study a model that realizes crossing of the phantom divide. We investigate the cosmological constraints obtained from the recent observational data of Type Ia Supernovae, Cosmic Microwave Background anisotropies, and Baryon Acoustic Oscillations. The best fit values of the parameters with 1$\sigma$ (68%) errors for the Phantom Crossing DGP model are $\Omega_{m,0}=0.27^{+0.02}_{-0.02}$, $\beta=0.54^{+0.24}_{-0.30}$. We find that the Phantom Crossing DGP model is more compatible with the observations than the original DGP model or the DGP model developed by Dvali and Turner. Our model can realize late-time acceleration of the universe, similar to that of $\Lambda$CDM model, without dark energy due to the effect of DGP gravity. In our model, crossing of the phantom divide occurs at a redshift of $z \sim 0.2$.
Forecasting Medium and Large Datasets with Vector Autoregressive Moving Average (VARMA) Models

We address the issue of forecasting key macroeconomics variables using medium and large datasets (from 10 to 40 variables). As an alternative to the standard VAR and AR models, we propose the use of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that usually arises in high dimensional VARMA models by adopting the Iterative Least Squares (IOLS) methodology as a feasible estimation procedure. We derive theoretical results which show that IOLS estimator is consistent even for large datasets. We report results from Monte Carlo simulations considering estimator performance and forecast accuracy. In the former one, we report results that show IOLS estimator is consistent and feasible for large systems and it performs better than Maximum Likelihood estimator (MLE), whereas in the latter one, we show that VARMA models outperform both VAR and AR(1) models under different sample and system size. On empirical application, we show that different specifications of VARMA models estimated using IOLS framework provide more accurate forecasts than VAR and AR(1) models considering different model dimensions. We conclude that the IOLS framework allows VARMA models to become a valid and competitive alternative for forecasting key macroeconomic variables using medium and large datasets.
A Faster Local Algorithm for Detecting Bounded-Size Cuts with Applications to Higher-Connectivity Problems

Consider the following "local" cut-detection problem in a directed graph: We are given a starting vertex $s$ and need to detect whether there is a cut with at most $k$ edges crossing the cut such that the side of the cut containing $s$ has at most $\Delta$ interior edges. If we are given query access to the input graph, then this problem can in principle be solved in sublinear time without reading the whole graph and with query complexity depending on $k$ and $\Delta$. We design an elegant randomized procedure that solves a slack variant of this problem with $O(k^2 \Delta)$ queries, improving in particular a previous bound of $O((2(k+1))^{k+2} \Delta)$ by Chechik et al. [SODA 2017]. In this slack variant, the procedure must successfully detect a component containing $s$ with at most $k$ outgoing edges and $\Delta$ interior edges if such a component exists, but the component it actually detects may have up to $O(k \Delta)$ interior edges. Besides being of interest on its own, such cut-detection procedures have been used in many algorithmic applications for higher-connectivity problems. Our new cut-detection procedure therefore almost readily implies (1) a faster vertex connectivity algorithm which in particular has nearly linear running time for polylogarithmic value of the vertex connectivity, (2) a faster algorithm for computing the maximal $k$-edge connected subgraphs, and (3) faster property testing algorithms for higher edge and vertex connectivity, which resolves two open problems, one by Goldreich and Ron [STOC '97] and one by Orenstein and Ron [TCS 2011].
DTLNet: Deep Transfer Learning-based Hybrid Model for Skin Lesion Detection and Classification

Skin cancer is the one of the most common cancer due to the increment of radiation in environment. Different types of skin cancer evolving every year, and it is challenging task for radiologists to classify each type. However, the conventional methods are resulted in poor subjective and objective performance. Therefore, this article is focused on segmentation of skin lesion using Deep transfer learning Network (DTLNet). Initially, hybrid gaussian-wiener filter (HGWF) applied on skin lesions for removal of different types of noises and enhancement operation. Then, AlexNet is used to localize the disease effected region from preprocessed skin lesion. Further, deep learning convolutional neural network (DLCNN) is also used for extraction disease specific deep features from segmented region. Finally, SoftMax Classifier is used to perform the multi-class classification with eight dissimilar types of cancers. The simulation results shows that the proposed DTLNet resulted in superior classification performance as compared to state of art methods.
RNN learning controller trained by ADRC

In this paper, the problem of training a recurrent neural network (RNN) controller to approximate an active disturbance rejection control (ADRC) is studied. Two learning methods, online learning and offline learning, are proposed. For the online setting, the RNN learning controller is trained by the error of the control quantity compared to that of ADRC in real time, while for the offline setting, the RNN controller is trained by the error of the whole state trajectory compared to that of ADRC obtained in advance. With randomized weight initialization, success rates of trained online and offline RNN learning controllers are compared by simulation. Furthermore, the robustness property of the trained RNN controller is analyzed. A metric of robustness, which can be calculated from the weights of the trained RNN controller is also proposed.
Morse decomposition for D-module categories on stacks

Let Y be a smooth algebraic stack exhausted by quotient stacks. Given a Kirwan-Ness stratification of the cotangent stack T^*Y, we establish a recollement package for twisted D-modules on Y, gluing the category from subquotients described via modules microsupported on the Kirwan-Ness strata of T^*Y. The package includes unusual existence and "preservation-of-finiteness'' properties for functors of the full category of twisted D-modules, extending the standard functorialities for holonomic modules. In the case that Y = X/G is a quotient stack, our results provide a higher categorical analogue of the Atiyah-Bott--Kirwan--Ness "equivariant perfection of Morse theory'' for the norm-squared of a real moment map. As a consequence, we deduce a modified form of Kirwan surjectivity for the cohomology of hyperkaehler/algebraic symplectic quotients of cotangent bundles.
Using ML Models to Predict Points in Fantasy Premier League

Fantasy Premier League is an ever-growing game, with millions of people playing the game. To outperform the rest, it is imperative for the players to accurately predict the expected points the footballer would earn over the course of the match. However, doing so is not easy as there are several aspects to consider as well as the human bias towards the players’ favourite footballers and teams. This paper attempts to build and compare three machine learning models to accurately predict the number of points that each footballer would earn over the course of the season. For doing so, the Linear Regression, Decision Tree, and Random Forest algorithms have been leveraged. Features such as fixture difficulty, form of the two teams, creativity, and threat of the footballer have been considered. This would help the players of this game to make more informed decisions while making their respective teams.
Interference Mitigation Approach using Massive MIMO towards 5G networks

There is a drastic increase in the number of users who subscribe to the mobile broadband every year. On the other hand, 4G networks have reached the theoretical limits on the data rate and therefore it is not sufficient to accommodate the above increasing traffic. To overcome this problem, new Generation of mobile communication known as fifth generation (5G) comes into the picture. Large network capacity, ultra-low latency and heterogeneous device support are the important features in 5G Technology. Massive MIMO in 5G Technology is built on multi-tier architecture using several low power Base Stations (BSs) inside small cell. Simultaneous usage of the same spectrum causes interference which further reduces the system throughput and network capacity. Thus resource management is an integral part of 5G Heterogeneous Networks (HetNets) so that interference between several base stations and different devices can be minimized. Proposed scheme introduces feedback on the existing cell association and antenna allocation algorithms and also introduces the evolutionary game theory for interference mitigation in HetNets as Game theory can be efficiently modelled for a competitive and compatible environment. Impact of feedback and game theory into RATs on data rate experienced by users and revenue generated by base station from users respectively are observed. Feedback mechanism along with Game theory approach enables to make efficient and effective resource allocation decisions. This facilitates the existing Cell Association algorithms to maximize the data rate of users in different classes and the antenna allocation algorithm to maximize the total profit of the Base station. Both users and base stations are self-interested to maximize their own benefits in terms of data rate and revenue.
A Deep Learning Based Surface Defect Detection Method with Coarse Granularity for Corrugated Fuel Tubes

Surface defect detection is a crucial task to achieve the automation goal in industrial production process. Nowadays, owing to the rapid development of computer vision, lots of detection methods based on deep learning have been proposed and applied in industrial manufacturing process. However, different from other deep learning tasks, the rarity of labeled defect training samples makes it a great challenge to attain expected performance for the surface defect detection of corrugated fuel tubes. To address this issue, this paper proposes a network with rougher detection granularity to obtain higher accuracy and recall rate. The boundary of detected defects will be less precise, which, however, is much less important than the gain of detection accuracy and is a tolerable cost. Experiments on two different datasets are carried out to demonstrate the effectiveness of this proposed method.
Event-Triggered Model Predictive Control for Linear Systems with Bounded Disturbance

The event-triggered model predictive control problem for linear systems with bounded disturbance is investigated in this paper. A new event-triggered mechanism is proposed to save the computational resources during solving the MPC optimization problem. The iterative feasibility and stability of closed-loop system are rigorously derived, which is related to the disturbance. Finally, numerical simulation results are shown to illustrate the effectiveness of proposed algorithm.
Modeling and Passivity-based Control of a Dual-tilt rotor UAV

This paper studies the modeling and control of an under-actuated dual-tilt rotor UAV based on energy for the reference tracking problem. According to the actual dual-tilt rotor UAV, instead of treating it as one rigid body, we treat the dual-tilt rotor UAV as three parts constructed by the center of mass of two rotors and the center of mass of the UAV main-body. By refining the energy of the UAV, a 8 degree of freedom under-actuated Euler-Lagrangian equation is given. Further, a controller is designed based on the passivity-based control methodology. It's proven that the tracking errors of the dual-tilt rotor UAV system asymptotically converge to zero. Finally, simulation results of tracking performance are provided to demonstrate the effectiveness of the proposed strategy.
Evidential Sensory Fusion of 2D Feature and 3D Shape Information for 3D Occluded Object Recognition in Robotics Applications

An evidential sensory fusion using 2D feature and 3D shape information method is proposed to recognize the occluded object. For the applications of robotic object fetching, the conventional object recognition methods usually applied the algorithms separately from 2D texture matching or 3D shape fitting. It often causes the wrong recognition results when the objects are occluded. The motivation in this study is to enhance the occluded object recognition via the estimate fusion method from the RGB-D sensor, which provides both 2D image and 3D depth information. To associate the 3D shape with the 2D texture, the region of interest (ROI) is firstly captured in 3D coordinate system, and mapped onto the 2D image. The Dempster-Shafer (DS) evidence theory is applied to fuse the confidences from the recognitions of both 2D texture and 3D shape to increase the recognition rate of occluded objects. The experimental results successfully demonstrate that the proposed evidence fusion recognizes the sample object correctly where it usually has the lower confidences from 2D and 3D recognition algorithms alone, when it operates in a separate fashion.
Shot noise probing of magnetic ordering in zigzag graphene nanoribbons

The nonequilibrium time-dependent fluctuations of charge current have recently emerged as a sensitive experimental tool to probe ballistic transport through evanescent wave functions introduced into clean wide and short graphene strips by the attached metallic electrodes. We demonstrate that such "pseudo-diffusive" shot noise can be substantially modified in zigzag graphene nanoribbon (ZGNR) due to the topology of its edges responsible for localized states that facilitate ferromagnetic ordering along the edge when Coulomb interaction is taken into account. Thus, the shot noise enhancement of unpolarized, and even more sensitively of spin-polarized, charge currents injected into ZGNR will act as an all-electrical and edge-sensitive probe of such low-dimensional magnetism.
SEARCH FOR BLAZAR FLUX-CORRELATED TEV NEUTRINOS IN ICECUBE 40-STRING DATA

We present a targeted search for blazar flux-correlated high-energy ( ≳ 1 TeV) neutrinos from six bright northern blazars, using the public database of northern hemisphere neutrinos detected during “IC40” 40-string operations of the IceCube neutrino observatory (2008 April to 2009 May). Our six targeted blazars are subjects of long-term monitoring campaigns by the VERITAS TeV γ-ray observatory. We use the publicly available VERITAS light curves to identify periods of excess and flaring emission. These predefined intervals serve as our “active temporal windows” in a search for an excess of neutrinos, relative to Poisson fluctuations of the near-isotropic atmospheric neutrino background, which dominates at these energies. After defining the parameters of an optimized search, we confirm the expected Poisson behavior with Monte Carlo simulations prior to testing for excess neutrinos in the actual data. We make two searches: one for excess neutrinos associated with the bright flares of Mrk 421 that occurred during the IC40 run, and one for excess neutrinos associated with the brightest emission periods of five other blazars (Mrk 501, 1ES 0806+524, 1ES 1218+304, 3C 66A, and W Comae), all significantly fainter than the Mrk 421 flares. We find no significant excess of neutrinos from the preselected blazar directions during the selected temporal windows. We derive 90% confidence upper limits on the number of expected flux-associated neutrinos from each search. These limits are consistent with previous point-source searches and Fermi GeV flux-correlated searches. Our upper limits are sufficiently close to the physically interesting regime that we anticipate that future analyses using already-collected data will either constrain models or yield discovery of the first blazar-associated high-energy neutrinos.
A Unified Approach for Safety Critical Control Problem via Output Regulation Theory and Barrier Function

This paper considers the safety critical control problem by a unified approach based on output regulation theory and barrier function. Different from the stabilization with guaranteed safety problem, we consider an arbitrary dynamic reference, which is even allowed to be outside the safe zone. Safety critical control is proposed to mediate the potential conflict between the multiple tasks, i.e., the tracking and the safety property, in the sense that safety is preliminarily guarantee and the tracking control is assured when the the reference signal is within the safe set. A reciprocal Lyapunov barrier function is constructed and the sufficient condition is provided to prove the multi-task control can be achieved.
The pump–probe coupling of matter wave packets to remote lattice states

The coherent manipulation of wave packets is an important tool in many areas of physics. We demonstrate the experimental realization of quasi-free wave packets of ultra-cold atoms bound by an external harmonic trap. The wave packets are produced by modulating the intensity of an optical lattice containing a Bose–Einstein condensate. The evolution of these wave packets is monitored in situ and their six-photon reflection at a band gap is observed. In direct analogy with pump–probe spectroscopy, a probe pulse allows for the resonant de-excitation of the wave packet into states localized around selected lattice sites at a long, controllable distance of more than 100 lattice sites from the main component. This precise control mechanism for ultra-cold atoms thus enables controlled quantum state preparation and splitting for quantum dynamics, metrology and simulation.