Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest

There is a general agreement in the brain computer interface community that the feature extracting method called the common spatial pattern (CSP) combined with nonlinear classifiers can provide excellent results in some cases. However, CSP is also known to be very sensitive to noise and prone to over fitting, and the performance of this spatial filter is closely related to the operational frequency band of electroencephalogram data. To address this issue, we propose a new method FB-TRCSP+RF based on CSP and random forest. The FB-TRCSP is combined by the 8th-order Butterworth bandpass-filters and the CSP with Tikhonov regularization, which is a more robust feature extraction method compared to the CSP. Then, the model is applied to an experimental data set collected from 14 subjects and is compared with the non-regularization method FB-CSP+RF. The results show that the method we proposed yields relatively higher median classification accuracies and shows a stronger ability in subject-to-subject learning compared to prevailing approaches.
Migratory, metabolic and functional alterations of fibrocytes in type 2 diabetes

Fibrocytes are bloodborne mesenchymal progenitor cells that are recruited to injured tissue sites and contribute to the repair process by acquiring a myofibroblast‐like phenotype and producing extracellular matrix components and growth factors. Treatment with normal fibrocytes or their exosomes restores the ability of genetically diabetic mice to heal skin wounds, suggesting the existence of dysfunctional alterations in diabetic fibrocytes. This study compared the migratory, metabolic and functional characteristics of fibrocytes from patients with type 2 diabetes (T2DPs) and healthy controls (HCs). It was found that the frequency of these cells was abnormally low in the peripheral blood of T2DPs. Diabetic fibrocytes showed reduced expression of the C‐X‐C motif and C‐C motif chemokine receptors (CXCR)4, (CCR)5, and CCR7, and demonstrated reduced migration in response to their ligands (CXCL)12, (CCL)5, and CCL21. They exhibited increased expression of the receptor for advanced glycation end product, suppression of the alternative AGE receptor 1, increased intracellular concentrations of AGEs, decreased expression of sirtuin‐1 and elevated oxidative stress. In short‐term cultures, fibrocytes from T2DPs released larger amounts of proinflammatory cytokines than those from HCs. Unlike normal fibrocytes, diabetic fibrocytes did not exhibit increased expression of type I collagen and α‐smooth muscle actin on stimulation with transforming growth factor (TGF)‐β1 and this abnormal response was associated with downregulation of TGF‐β1 type II receptor on the cell surface. Study findings uncover multiple migratory and functional alterations of diabetic fibrocytes that may contribute to explain why T2DPs experience impaired wound healing and chronic ulcers. © 2018 IUBMB Life, 70(11):1122–1132, 2018
A Multirotor Platform Employing a Three-Axis Vertical Articulated Robotic Arm for Aerial Manipulation Tasks

We describe the concept, design and implementation of an aerial robot that is able to achieve precise and complex manipulation tasks at elevated altitudes. The aerial robot in this study consists of a hexarotor platform, a top-mounted gripper module and a robotic manipulator mounted at the bottom of the air-frame. The top-mounted gripper is used to grasp a bar-like object located above the air-frame with the intention to bring the system to a stable resting position at high altitudes. During the process of grasping, the translational and rotational movements of the gripper module is autonomously controlled based on visual feedback obtained from the on-board camera. This, in combination with the manual control of the air-frame, helps the operator to easily achieve grasping a targeted object. After fixing the robot-frame to a suitable target, the bottom-mounted manipulator is used to perform diverse manipulation task. The end-effector of the bottom-mounted manipulator is also able to manipulate the work area above the air-frame by reaching through the space of the arms of the multirotor frame. The combination of a bottom-mounted 2 DOF manipulator and a 1 DOF rotation mechanism of the top mounted gripper results in a three-axis vertical articulated manipulator system able to operate at elevated altitudes.
Adaptive Path Planning for Unmanned Aircraft Using In-flight Wind Velocity Estimation

Small fixed-wing unmanned aerial vehicle’s path following performance is highly dependent to the local prevailing wind conditions because of their limited airspeed and flight envelope. In the proposed approach the path following performance is improved, not by optimized control algorithms, but by using wind adaptive path planning. We use a wind velocity estimation, which is capable of estimating steady and turbulent wind using a basic set of small unmanned aircraft on-board sensors. The path planning algorithm considers the aircraft’s kinematics, flight envelope and wind estimate. Simulation results show an improved path following performance and a better exploitation of the flight performance of an unmanned aircraft by the use of the wind adaptive path planning algorithm.
A Hierarchical LMB/PHD Filter for Multiple Groups of Targets with Coordinated Motions

In some multi-object tracking scenarios such as convoys or road constrained motions, it can be advantageous to track groups of targets sharing common motion characteristics, even if they are not necessarily close to each other. The objective is twofold: reducing the computational cost while increasing the accuracies of the individual trajectory estimates. In a previous communication, we introduced a generic model based on hierarchical random finite sets (RFSs) to represent these types of multigroup multi-target scenarios. A first RFS is used to represent the multi-group state: the number of groups, their common motion characteristics and their target compositions are assumed to be random variables. Then, for each group, a second layer of RFSs represents the multi-target state assuming that the number of targets inside a group and their trajectories are also random variables. The main contribution of this paper is to derive a filter dedicated to the state estimation of hierarchical RFSs from sequential sensor measurements. The proposed solution is based on the labeled multi-Bernoulli filter to estimate the group characteristics, which interacts with a bank of probability hypothesis density filters to address the multi-target layer.
Bendamustine alone or with rituximab modifies expression of apoptosis-regulating genes and proteins of CLL cells, depending on IGVH mutational status

Abstract We studied whether bendamustine (BENDA) alone or with rituximab (RIT) modifies in vitro expression of apoptosis-involved genes and proteins of chronic lymphocytic leukemia (CLL) cells depending on IGVH mutational status. Circulating lymphocytes from 34 untreated patients (18 IGVH-MUT and 16 IGVH-UNMUT) were incubated with above drugs to evaluate proteins expression. Microarray analysis of 93 genes was performed in 14 patients. BENDA and BENDA + RIT increased expression of BAX and BBC3 in IGVH-MUT and IGVH-UNMUT groups, and significant differences in expression of above genes after BENDA + RIT were observed between both groups. Additionally, BENDA + RIT decreased NFκB and BCL-2 genes in IGVH-UNMUT patients and increased expression of P53, BAX and PUMA proteins in IGVH-MUT and UNMUT subjects. However, no significant differences were found between these groups. In conclusion, BENDA + RIT modified gene expression profile in CLL cells and affected expression of some apoptosis-regulating proteins in vitro. Expression of BAX and BBC3 depends on action of drugs and IGVH mutational status.
SDLI: Static Detection of Leaks Across Intents

Intents are Android's intra and inter-application communication mechanism. They specify an action to perform, with extra data, and are sent to a receiver component or broadcast to many components. Components, in the same or in a distinct app, receive the intent if they are available to perform the desired action. Hence, a sound static analyzer must be aware of information flows through intents. That can be achieved by considering intents as both source (when reading) and sink (when writing) of confidential data. But this is overly conservative if the intent stays inside the same app or if the set of apps installed on the device is known in advance. In such cases, a sound approximation of the flow of intents leads to a more precise analysis. This work describes SDLI, a novel static analyzer that, for each app, creates an XML summary file reporting a description of the tainted information in outwards intents and of the intents the app is available to serve. SDLI discovers confidential information leaks when two apps communicate, by matching their XML summaries, looking for tainted outwards intents of the first app that can be inwards intents of the second app. The tool is implemented inside Julia, an industrial static analyzer. On the DroidBench testcases, its shows a precision higher than 75%. On some popular apps from the Google Play marketplace, it spots inter-apps leaks of confidential data, hence showing its practical effectiveness.
Constructing accurate Radio Environment Maps with Kriging Interpolation in Cognitive Radio Networks

The increased development of information industry and relevant radio communication services is making the spectrum management problem more challenging to solve. The Radio Environment Map (REM) can be a powerful tool for solving spectrum scarcity and spectrum access problems, making context-aware resource allocation more efficient. We can optimize the accuracy of the REM using a geostatistical tool named Kriging interpolation in cognitive radio networks. In this paper, the proposed REM construction method combines residual maximum likelihood-based radio propagation parameter estimation with Kriging-based transmission power prediction. Additionally, we compare the performance of the constructed REMs with the metric of root mean square error (RMSE) in three methods: the path loss-based method, the Kriging-based method without the fit of a path loss model beforehand, and the proposed method. With the Monte Carlo simulation, the result indicates that the path loss-based method provides the most unsatisfied performance and the interpolation accuracy can be improved by 2 dB with the proposed method, which proves that our approach can effectively determine sharing conditions of radio spectrum use both in time and space.
Improved Weighted Label Propagation Algorithm in Social Network Computing

Nowadays social networks play an important role in people's daily life, where community detection is essential for business as well as security applications. And weighted networks are gradually taking the dominance in social networks whose attributes of the connections can be recorded and accessed flexibly. As the classic community detection algorithm Label Propagation Algorithm(LPA) failed to handle weighted social networks, the Weighted Label Propagation Algorithm(WLPA) was proposed recently. However, the WLPA still exposes insufficient accuracy, and costly time complexity compared with the LPA. In this paper, we optimize the WLPA in accuracy and execution time by modifying the propagation intensity and order in the label propagation process. And the optimization methods are confirmed to be efficient both in theoretical analysis and experimental verification. Moreover, the specific functions of the optimization methods are discussed in detail during the experiment. By means of the optimized community detection algorithm, we manage to extract the useful information of the Twitter social network. We also make use of the network visualization tool to recognize the concrete network structure and validate the community detection result.
Performance Comparison of Ad-hoc Routing Protocols for Networks with Node Energy Constraints

A major issue with ad-hoc networks is energy consumption since nodes are sometimes mobile and battery-operated. In this paper we have compared the performance of ad-hoc routing protocols during a network wherever every taking part mobile node contains a given battery life and can shut-down once a threshold is reached. we have studied 2 routing protocols, Dynamic supply Routing (DSR), and Destination-Sequenced Distance-Vector Routing (DSDV), by examination the node termination rate further because the over-all goodput of the network. From the simulations we have a tendency to determine that in high node density, DSR performs higher than DSDV. The performance gap is attenuated by smaller range of nodes within the network or a lot of path loss and shadowing variance.
Quantifying the effects of pollen nutrition on honey bee queen egg laying with a new laboratory system

Honey bee populations have been declining precipitously over the past decade, and multiple causative factors have been identified. Recent research indicates that these frequently co-occurring stressors interact, often in unpredictable ways, therefore it has become important to develop robust methods to assess their effects both in isolation and in combination. Most such efforts focus on honey bee workers, but the state of a colony also depends on the health and productivity of its queen. However, it is much more difficult to quantify the performance of queens relative to workers in the field, and there are no laboratory assays for queen performance. Here, we present a new system to monitor honey bee queen egg laying under laboratory conditions and report the results of experiments showing the effects of pollen nutrition on egg laying. These findings suggest that queen egg laying and worker physiology can be manipulated in this system through pollen nutrition, which is consistent with findings from field colonies. The results generated using this controlled, laboratory-based system suggest that worker physiology controls queen egg laying behavior. Additionally, the quantitative data generated in these experiments highlight the utility of the system for further use as a risk assessment tool.
Low-Cost Energy-Efficient Side-Channel Attacks Resistant AES S-Box with Power-Aware Hiding Inverter in GF(2^4)

Power-Aware Hiding (PAH) has been proposed as a kind of energy-efficient Side Channel Attacks (SCA) countermeasure for S-boxes. However, it has large area overhead. In this paper, an improved SCA-resistant AES S-box implementation, which is based on PAH technique but has smaller area, is proposed. It implements the S-Box in the masked composited field approach but applies the PAH method to the inversion in GF(2^4). Evaluation result shows that its area is shrunken to 47.7% of the full PAH S-box, and that its power-delay product is about 81% lower than that of the traditional masking implementation. Simulation shows that the PAH inverter's power difference is smaller than the other candidates, including the PAH S-box. It is secure under moment-correlating power analysis with 70,000 noiseless power traces at least.
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation

We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tune its parameters for ST. We demonstrate that our approach is effective by pre-training on 300 hours of English ASR data to improve Spanish English ST from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data are available. Through an ablation study, we find that the pre-trained encoder (acoustic model) accounts for most of the improvement, despite the fact that the shared language in these tasks is the target language text, not the source language audio. Applying this insight, we show that pre-training on ASR helps ST even when the ASR language differs from both source and target ST languages: pre-training on French ASR also improves Spanish-English ST. Finally, we show that the approach improves performance on a true low-resource task: pre-training on a combination of English ASR and French ASR improves Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1 BLEU.
Filtering When Object Custody is Ambiguous

Filtering involves predicting the future state of a space object in orbit about the earth given observations (e.g. angles-only or radar measurements) about its current and past states. The task is simplest when the identity of the object is known. A recently developed “Adapted STructural (AST)” coordinate system enables the task to be carried out in a computationally efficient manner. Propagation for a single state (or a small number of sigma points) can be carried out using Keplerian dynamics or using a numerically more expensive propagator to accommodate perturbation effects. In either case, the uncertainty can be represented in AST coordinates as Gaussian to a high level of accuracy. An Unscented Kalman Filter (UKF) has been developed in this situation; in particular, there is no need to use particle filters. However, when object custody is uncertain, i.e. when the latest observation might correspond to two or more objects in a catalog, the filtering task is more complicated. In this case we propose a mixture of Gaussians in AST coordinates to represent the state. The paper will demonstrate the feasibility of this approach.
The Concept of Anti-Collision System for Underwater Vehicles Based on Forward Looking Sonar

In this paper authors analyze the possibilities of using forward looking sonar in the process of using it in an anti-collision system designed for underwater unmanned vehicles. Existing hardware solutions and data processing have been presented. The sonar image processing process has been analyzed. The concept of the model with the use of the decision support system with conclusions has been presented.
Doc2Img: A New Approach to Vectorization of Documents

Vector space representations of text have increased in popularity and are used in various text classification problems. We present Doc2Img, a new approach to create document vectors that improves upon existing approaches such as Word2Vec and Doc2Vec in capturing similarities between words within a document and the differences across documents. We apply this new vector space representation to the problem of deriving the sensor requirements of apps (for smartphones and IoT devices) by learning a classification model using document vectors. We show that this learned model outperforms existing vector space representations (Word2Vec and Doc2Vec) by more than 10%. Further, this model can predict with an average accuracy of 75% and greater than 85% on the top-20 sensor requirements for 300 different applications.
Reduction of Residual Vibration in Displacement-Amplified Micro-Electromagnetic Actuators with Non-linear Dynamics Using Input Shaping

Micro-electromagnetic actuators have been used in many fields and industries for systems such as microftuidic systems, positioning stages, and robotic manipulators. Small-scale electromagnetic actuators are able to provide rapid motion with high positioning accuracy. The actuator presented in this paper utilizes a displacement amplification mechanism to increase the maximum stroke length that can be achieved. The dynamics of this actuator are nonlinear due to the dependence of the applied force on gap distance between the coils and the amplification mechanism. This nonlinearity causes the performance of PID control to vary with respect to the displacement of the actuator. The control method proposed in this paper to limit the overshoot resulting from nonlinearity uses a combination of PID control and robust input shapers. Using robust input shapers to account for parameter variation across the workspace, the combined controller eliminates the overshoot while maintaining short settling times. Simulations are presented to demonstrate the performance of the proposed method.
State Estimation with a Heading Constraint

Ahstract- The state estimation problem with a heading constraint, in which only the fixed heading of the target is prior known without any other information about the constraint, is investigated. This situation can be discovered in some practical applications, such as the tracking of a vehicle moving on a straight road. Then the heading of the target is limited to the direction of the road segment which may be known a priori. But the specific information of the road may not be available. The partly known prior information of the road constraint can be utilized to enhance the tracking performance. Since the existing estimation methods cannot be applied to produce constrained estimates in this situation, a filtering algorithm is proposed to address this issue. The state augmentation approach, which straightforwardly augment the intercept of the constraint line into the state vector, is presented. Then the heading constraint can be formulated using the state components and the prior known heading. The pseudo-measurements can be constructed accordingly and incorporated into the estimator, which is called heading constraint Kalman filter(HCKF). The performance of the HCKF is evaluated by comparing against one unconstrained state estimation algorithm as well as two popularly constrained state estimation methods. Simulation results demonstrate the effectiveness and the superior performance of the proposed method.
Control of a PMSM With Quadruple Three-Phase Star-Connected Windings Under Inverter Short-Circuit Fault

This paper proposes a novel approach for the control of a permanent magnet synchronous machine with a quadruple three-phase star-connected winding configuration after a short-circuit (SC) fault in one voltage source inverter. The approach includes an adaption of the closed-loop current control that allows operation of the faulty unit to proceed without the need to adapt the reference currents. This allows an operation of the faulty drive with increased postfault mean torque. Additionally, a strategy for postfault torque error compensation is proposed, which can be used to reduce the torque ripple while the load distribution is better balanced. Results are respectively compared to the case when applying a symmetric SC to the faulty unit, which is the conventional strategy in case of a transistor SC fault. The current limit of the drive is considered in all cases. The benefits of the approach are validated through experimental tests.
Discovery of Interstellar Isocyanogen (CNCN): Further Evidence that Dicyanopolyynes Are Abundant in Space

It is thought that dicyanopolyynes could potentially be abundant interstellar molecules; though, their lack of a dipole moment makes it impossible to detect them through radioastronomical techniques. Recently, the simplest member of this chemical family, cyanogen (NCCN), was indirectly probed for the first time in interstellar space through the detection of its protonated form toward the dense clouds L483 and TMC-1. Here we present a second set of firm evidence indicating the presence of NCCN in interstellar space, namely the detection of the metastable and polar isomer isocyanogen (CNCN). This species has been identified in L483 and tentatively in TMC-1 by observing various rotational transitions in the λ 3 mm band with the IRAM 30 m telescope. We derive beam-averaged column densities for CNCN of 1.6 × 1012 cm−2 in L483 and 9 × 1011 cm−2 in TMC-1, which imply fractional abundances relative to H2 in the range of (5–9) × 10−11. While the presence of NCCN in interstellar clouds seems undoubtable due to the detection of NCCNH+ and CNCN, putting tight constraints on its abundance is still hampered by the poor knowledge of the chemistry that links NCCN with NCCNH+ and especially with CNCN. We estimate that NCCN could be fairly abundant, in the range of 10−9–10−7 relative to H2, such as other abundant nitriles like HCN and HC3N.