Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional hierarchical formation control method for multi-agent systems with time delay

In this paper, a novel hierarchical formation control strategy is proposed for multi-agent systems with time delay, which can not only guarantee the stability of the formation but also expand the scale of the system. The proposed strategy combines the concept of generalized graph Laplacian and affine transformation so that the generalized graph based method can be extended to large-scale systems. In order to realize the hierarchical formation, the design of the distributed control protocol of low order and high order multi-agent systems with time delay are studied respectively. The stabilizing sets of the distributed control parameters are obtained, which guarantees the stability of the whole system. The simulation results demonstrate the effectiveness of the proposed method. The main result is the hierarchical formation in the three-dimensional space is guaranteed for multi-agent systems.
VENUS: Verifiable range query in data streaming

Internet of Things (IoT) devices have gained the popularity in our daily life. As IoT devices are usually resource-constrained, the sensed data cannot be kept in the local memory and need to be outsourced to the cloud in a streaming manner. However, the untrusted cloud might return the falsified answer to the query made by the user. Verifiable data outsourcing addresses the similar problem, but the design challenge here mainly comes with the fact that the user can only have the vision on a single data at any time slot. In this paper, we propose VENUS to achieve verifiable range query in data streaming. In particular, VENUS ensures data confidentiality and query answer integrity of the range query in the data streaming, by taking advantage of our proposed flexible and authenticated data structure HPBTree. The simulation and analytical results demonstrate the efficiency and effectiveness of VENUS.
Optimization on parametric model

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with large number of weights consume considerable storage and memory bandwidth. To address this limitation, prun­ing is an effective way to compress neural networks with high accuracy. To address this limitation, we proposed a method for optimization on parametric model. This method contains three steps. First, we train the network like conventional training. Next, we prune the unimportant connections and retrain the network to get the sparse weight matrix. Finally, we use Singularly Valuable Decomposition (SVD) to do further compression on the sparse weight matrix. Our experiments on MNIST dataset show that our method has the ability on reducing the model size by 4 times and the accuracy could still be kept over 90%.
Adaptive arbitrary-order finite time sliding mode differentiator

An adaptive arbitrary-order finite time differentiator is proposed in this paper. High-order derivatives can be estimated in finite time. In order to deal with the difficulty in providing suitable parameters, especially when the order of the differentiator becomes very high, an adaptive mechanism is adopted to tune the parameters in a more efficient way. In addition, taking advantage of the robustness of sliding-mode method, the derivatives can be estimated in the presence of external disturbance. Numerical simulation results are presented to illustrate the effectiveness of the proposed method.
Expert knowledge based multi-agent reinforcement learning and its application in multi-robot hunting problem

We propose a novel reinforcement learning algorithm based on expert knowledge to further solve curse of dimensionality and accelerate the convergence when solving multi-robot hunting problem. Two kinds of expert knowledge are designed: one is the multi-agent joint state abstraction method based on dynamic ID, which dramatically reduces the number of state space; the other is the multi-agent Q-learning algorithm based on artificial potential field (APF). In this method, the artificial potential field is utilized to initialize the Q value according to the environmental prior knowledge, hence the robot can acquire a better learning foundation and accelerate convergence. Finally, the proposed algorithm is validated by the multi-robot hunting problem. The results show that the method can reduce the number of state space and accelerate convergence, thus improve the performance of the algorithm.
Active contour model based on partition entropy and local fitting energy

In order to overcome the problem of initialization sensitivity in segmenting images with inhomogeneous intensity, an active contour model based on global and local fitting information is proposed. According to the theory of entropy can measure the degree of heterogeneity of the segmentation regions, the global fitting energy based on partition entropy is established. The introduction of variance makes the global information of the image more fully described. Also, the energy functional is constructed by combining the established global partition entropy energy and the local binary fitting energy. In the energy functional, the contribution of global information and local information is controlled by a dynamic weight parameter, which can improve the convergence speed. At last, the segmentation experiments are carried out on synthetic images, medical images and natural scene images with intensity inhomogeneity. The experimental results show that the proposed algorithm has good performance in terms of sensitivity, accuracy and efficiency.
Price decisions for reverse supply chain considering IERs and UIC

In reverse supply chain under our study, IERs and UIC are the inspection-error-rates and the unit inspection cost respectively. This reverse supply chain consists of two members: remanufacturer and collector. The collector is responsible for collecting the used-products from the used-product market and inspecting the collected used-products. In order to reduce the IERs, the collector increases investment, namely, the unit inspecting cost increases. In this paper, considering IERs and UIC, we focus on the price decisions for reverse supply chain by game theory. We present the optimal price decisions, and analyze the impacts of IERs and UIC on the optimal results by numerical example. The research results can help the remanufacturer and the collector make their optimal price decisions for reverse supply chain considering both IERs and UIC.
Singularity-conquering Zhang-gradient controller groups for tracking control of Brockett integrator

Dynamic tracking control problems are important issues in nonlinear dynamic systems. So far, many methods have been proposed to deal with tracking control problems in different control fields. However, almost no effective method can solve the tracking control problem when tracking trajectories encounter singularities. Thus, a Zhang-gradient (ZG) method proposed recently by Zhang et al, which is combined by Zhang dynamics (ZD) and gradient dynamics (GD) methods, is pretty effective for conquering the singularity problem in tracking control process, and has been applied in single-input-single-output (SISO) nonlinear systems. In this study, we offer the extended application of the ZG method in a multiple-input-multiple-output (MIMO) nonlinear system (i.e., Brockett integrator) to solve the singularity problem in tracking control. By the ZG method, ZG controller groups are designed for the tracking control of Brockett integrator. Theoretical analyses and simulative verifications substantiate that the tracking errors are bounded and exponentially convergent. More importantly, comparative simulation results illustrate that the ZG controller groups designed by ZG method are superior to the ZG controller groups designed only by ZD method in conquering the singularity problem.
Monoaminergic Neuromodulation of Sensory Processing

All neuronal circuits are subject to neuromodulation. Modulatory effects on neuronal processing and resulting behavioral changes are most commonly reported for higher order cognitive brain functions. Comparatively little is known about how neuromodulators shape processing in sensory brain areas that provide the signals for downstream regions to operate on. In this article, we review the current knowledge about how the monoamine neuromodulators serotonin, dopamine and noradrenaline influence the representation of sensory stimuli in the mammalian sensory system. We review the functional organization of the monoaminergic brainstem neuromodulatory systems in relation to their role for sensory processing and summarize recent neurophysiological evidence showing that monoamines have diverse effects on early sensory processing, including changes in gain and in the precision of neuronal responses to sensory inputs. We also highlight the substantial evidence for complementarity between these neuromodulatory systems with different patterns of innervation across brain areas and cortical layers as well as distinct neuromodulatory actions. Studying the effects of neuromodulators at various target sites is a crucial step in the development of a mechanistic understanding of neuronal information processing in the healthy brain and in the generation and maintenance of mental diseases.
Leaky-wave antenna designed in SIW technique with SRR slots

In this paper the design of a leaky-wave antenna realized in SIW technique has been presented. The developed antenna is composed of unit cells in which pairs of slots in a shape of split ring resonators (SRR) have been introduced. It is shown that the application of such slots in upper metallization layer allows for achieving similar bandwidths of right and left handed regions of the designed CRLH unit cell. Moreover, leaky-wave antenna developed with the proposed unit cell features high beam-scanning range to bandwidth ratio.
One-to-many stable logistics matching in the fourth party logistics

In this paper we propose an approach that can efficiently address one-to-many multi-attributes logistics matching problem in the fourth party logistics. During the whole matching process, the fourth party logistics as the mediator collects the value and finds the final solution. To deal with the proposed problem, we adopt bilateral negotiation approach to evaluate the degree of satisfaction and get the preference order of both sides. Then, according to the preference order obtained, stable matching is performed to find the stable solution. The stable matching model's objective is minimizing the overall preference under the stability constraint, and the CPLEX is used to solve the problem. Finally, a numerical experiment is performed to compare the performance in the stable matching and the traditional matching. The result demonstrates that compared with the traditional matching, the stable matching has a better performance in aspect of stability and overall fairness.
An image haze removal algorithm based on blockwise processing using LAB color space and bilateral filtering

In order to solve the problem in image haze removal algorithms based on dark channel prior like color distortion and blurred image edge information, an improved image haze removal algorithm based on blockwise processing is proposed. Firstly, we choose LAB color space instead of RGB color space. Then, bilateral filtering is used to preserve edge smoothly and refine the transmission map. Finally, we set the tolerance for blockwise processing to remove color distortion problem. The results demonstrate that, the new algorithm we proposed can successfully solve the problems like color distortion and blurred image edge information in image removal based on dark channel prior, the algorithm's running time is greatly reduced simultaneously.
External Patch-Based Image Restoration Using Importance Sampling

This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling. The minimum mean squared error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new method, which can be interpreted as a generalization of the external non-local means, uses self-normalized importance sampling to efficiently approximate the MMSE estimates. The use of self-normalized importance sampling endows the proposed method with great flexibility, namely regarding the statistical properties of the measurement noise. The effectiveness of the proposed method is shown in a series of experiments using both generic large-scale and class-specific external datasets.
LightMANO: Converging NFV and SDN at the edges of the network

Network Function Virtualization (NFV) has raised tremendous attention in the academic and industrial commu­nities alike. The former have been attracted by the service centric orchestration challenges. The latter have found that, by decoupling network functions from the underling hardware, significant savings can be obtained by means of infrastructure homogenization and service automation. As opposed to the initial NFV solutions, which heavily relied on a centralized cloud model, the emerging Multi-access Edge Computing trend is calling for a distribution of computational capacity at the customers' sites. This change of paradigm could result in the deployment of tens of thousands or even millions of Points-of-Presence. In this article we discuss the fundamental challenges of deploying NFV in scattered environments, then we introduce the Light-MANO framework, a Multi-access Network Operating System converging SDN and NFV into a single lightweight platform for management and orchestration of network services over distributed NFV infrastructure. Finally, we report on a proof- of-concept implementation of LightMANO and on its evaluation.
A case study on auditory brain activations relative to musical experience by means of eeg complexity levels

In this pre-work, entropy estimation methods have been firstly applied to single trial auditory oscillations mediated by tonal (non-target) and atonal (target) chords in order to show functional superior performance of musicians in auditory tasks. For this purpose, both embedding and probabilistic as well as wavelet entropy methods are compared to each other with respect to classification performance criteria in tests where two groups (5 musicians and 5 non-musicians) are classified by using 10-fold cross validated Support Vector Machine Classifiers for both target and non-target records. An embedding entropy so called Permutation Entropy provided the highest classification accuracies (99.96% for target records, 99.29% for non-target records) with respect to specified features extracted from 22 electrode locations near to auditory cortex (P7, TP7, T7, FT7, F7, AF7, AF8, F8, FT8, T8, TP8, P8, P5, CP5, C5, FC5, F5, F6, FC6, C6, CP6, P6). In conclusion, musical training enhances the neural encoding of sound and this performance can be quantified in terms of entropy values. In detail, the degree of EEG complexity increases depending on the existing of musical experience in auditory tasks.
Tone Recognition Using Lifters and CTC

In this paper, we present a new method for recognizing tones in continuous speech for tonal languages. The method works by converting the speech signal to a cepstrogram, extracting a sequence of cepstral features using a convolutional neural network, and predicting the underlying sequence of tones using a connectionist temporal classification (CTC) network. The performance of the proposed method is evaluated on a freely available Mandarin Chinese speech corpus, AISHELL-1, and is shown to outperform the existing techniques in the literature in terms of tone error rate (TER).
Natural Language Processing for Information Extraction

With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and labor intensive. This explosion of information and need for more sophisticated and efficient information handling tools gives rise to Information Extraction(IE) and Information Retrieval(IR) technology. Information Extraction systems takes natural language text as input and produces structured information specified by certain criteria, that is relevant to a particular application. Various sub-tasks of IE such as Named Entity Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction, Knowledge Base reasoning forms the building blocks of various high end Natural Language Processing (NLP) tasks such as Machine Translation, Question-Answering System, Natural Language Understanding, Text Summarization and Digital Assistants like Siri, Cortana and Google Now. This paper introduces Information Extraction technology, its various sub-tasks, highlights state-of-the-art research in various IE subtasks, current challenges and future research directions.
Resveratrol Inhibits Key Steps of Steroid Metabolism in a Human Estrogen-Receptor Positive Breast Cancer Model: Impact on Cellular Proliferation

The role of resveratrol (RES) in preventing breast cancer is controversial, as low concentrations may stimulate the proliferation of estrogen-receptor alpha positive (ERα+) breast cancer cells. As metabolism is the key factor in altering cellular estrogens, thereby influencing breast tumor growth, we investigated the effects of RES on the formation of estrogen metabolites, namely 4-androstene-3,17-dione (AD), dehydroepiandrosterone (DHEA), dehydroepiandrosterone-3-O-sulfate (DHEA-S), estrone (E1), estrone-3-sulfate (E1-S), 17β-estradiol (E2), 17β-estradiol-3-O-(β-D-glucuronide) (E2-G), 17β-estradiol-3-O-sulfate (E2-S), 16α-hydroxy-17β-estradiol (estriol, E3), and testosterone (T) in ERα- MDA-MB-231 and ERα+ MCF-7 cells. Incubation of both of the cell lines with the hormone precursors DHEA and E1 revealed that sulfation and glucuronidation were preferred metabolic pathways for DHEA, E1 and E2 in MCF-7 cells, compared with in MDA-MB-231 cells, as the Vmax values were significantly higher (DHEA-S: 2873.0 ± 327.4 fmol/106 cells/h, E1-S: 30.4 ± 2.5 fmol/106 cells/h, E2-S: 24.7 ± 4.9 fmol/106 cells/h, E2-G: 7.29 ± 1.36 fmol/106 cells/h). RES therefore significantly inhibited DHEA-S, E1-S, E2-S and E2-G formation in MCF-7, but not in MDA-MB-231 cells (Kis: E2-S, 0.73 ± 0.07 μM < E1-S, 0.94 ± 0.03 μM < E2-G, 7.92 ± 0.24 μM < DHEA-S, 13.2 ± 0.2 μM). Suppression of these metabolites subsequently revealed twofold higher levels of active E2, concomitant with an almost twofold increase in MCF-7 cell proliferation, which was the most pronounced upon the addition of 5 μM RES. As the content of RES in food is relatively low, an increased risk of breast cancer progression in women is likely to only be observed following the continuous consumption of high-dose RES supplements. Further long-term human studies simultaneously monitoring free estrogens and their conjugates are therefore highly warranted to evaluate the efficacy and safety of RES supplementation, particularly in patients diagnosed with ERα+ breast cancer.
Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain.
Continuous passive TDOA location method based on multi-flight-vehicle cooperation

In order to detect passively targets fixed on the ground or targets on the sea surface with multi-flight-vehicle cooperation, a continuous passive TDOA (Time difference of arrival) location method is presented which can be applied to multi-flight-vehicle platform. Measurement information used in the method includes time difference, angle of target, and distance between receiving stations. Nonlinear system model is established in the method. In order to continuously estimate the target's accurate position, Unscented Kalman Filter (UKF) is used to solve the nonlinear model. The method solves the problem that mobile platforms in the air cannot give accurate passive TDOA location of targets, when the receiving stations' positioning is inaccurate and the configuration of the stations is unfixed. Simulation results show that the continuous passive TDOA location method has high location accuracy and strong convergence, and change of spatial configuration of multi-flight-vehicle cooperative flight has little influence on the positioning accuracy.
Pseudo-random noise radar for short-range applications in SiGe technologies

This paper describes alternative scalable radar architectures based on pseudo-random noise (PRN) sensing technique. These architectures can be implemented in standard Silicon-Germanium technology and are very suitable for highresolution automotive applications that require a high degree of reliability. The bandwidth required for the baseband blocks of the PRN radar systems can be reduced significantly using several methods introduced in this paper. Several single-channel transceivers are cascaded together to build a MIMO radar system with a flexible number of channels. The transceivers are equipped with binary phase-shift keying modulators and I/Q receiver. A large set of suitable pseudo-random binary sequences with different desirable properties is available.