Miracle-dz/UniFAR-FTUs huggingface dataset:
Lower limb sagittal kinematic and kinetic modeling of very slow walking for gait trajectory scaling

Lower extremity powered exoskeletons (LEPE) are an emerging technology that assists people with lower-limb paralysis. LEPE for people with complete spinal cord injury walk at very slow speeds, below 0.5m/s. For the able-bodied population, very slow walking uses different neuromuscular, locomotor, postural, and dynamic balance control. Speed dependent kinetic and kinematic regression equations in the literature could be used for very slow walking LEPE trajectory scaling; however, kinematic and kinetic information at walking speeds below 0.5 m/s is lacking. Scaling LEPE trajectories using current reference equations may be inaccurate because these equations were produced from faster than real-world LEPE walking speeds. An improved understanding of how able-bodied people biomechanically adapt to very slow walking will provide LEPE developers with more accurate models to predict and scale LEPE gait trajectories. Full body motion capture data were collected from 30 healthy adults while walking on an instrumented self-paced treadmill, within a CAREN-Extended virtual reality environment. Kinematic and kinetic data were collected for 0.2 m/s—0.8 m/s, and self-selected walking speed. Thirty-three common sagittal kinematic and kinetic gait parameters were identified from motion capture data and inverse dynamics. Gait parameter relationships to walking speed, cadence, and stride length were determined with linear and quadratic (second and third order) regression. For parameters with a non-linear relationship with speed, cadence, or stride-length, linear regressions were used to determine if a consistent inflection occurred for faster and slower walking speeds. Group mean equations were applied to each participant’s data to determine the best performing equations for calculating important peak sagittal kinematic and kinetic gait parameters. Quadratic models based on walking speed had the strongest correlations with sagittal kinematic and kinetic gait parameters, with kinetic parameters having the better results. The lack of a consistent inflection point indicated that the kinematic and kinetic gait strategies did not change at very slow gait speeds. This research showed stronger associations with speed and gait parameters then previous studies, and provided more accurate regression equations for gait parameters at very slow walking speeds that can be used for LEPE joint trajectory development.
Using a robotic fish to investigate individual differences in social responsiveness in the guppy

Responding towards the actions of others is one of the most important behavioral traits whenever animals of the same species interact. Mutual influences among interacting individuals may modulate the social responsiveness seen and thus makes it often difficult to study the level and variation of individuality in responsiveness. Here, biomimetic robots (BRs) that are accepted as conspecifics but controlled by the experimenter can be a useful tool. Studying the interactions of live animals with BRs allows pinpointing the live animal’s level of responsiveness by removing confounding mutuality. In this paper, we show that live guppies (Poecilia reticulata) exhibit consistent differences among each other in their responsiveness when interacting with a biomimetic fish robot - ‘Robofish’ - and a live companion. It has been repeatedly suggested that social responsiveness correlates with other individual behavioral traits like risk-taking behavior (‘boldness’) or activity level. We tested this assumption in a second experiment. Interestingly, our detailed analysis of individual differences in social responsiveness using the Robofish, suggests that responsiveness is an independent trait, not part of a larger behavioral syndrome formed by boldness and activity.
Fault Detection Using Attention Models Based on Visual Saliency

In this paper, we present an approach for detecting faults within seismic volumes using a saliency detection framework that employs a 3D-FFT local spectra and multi-dimensional plane projections. The projection scheme divides a 3D-FFT local spectrum into three distinct components, each depicting variations along different dimensions of the data. To detect seismic structures oriented at different angles and to capture directional features within 3D volume, we modify the center-surround model to incorporate directional comparisons around each voxel. The weighted combination of the obtained features then yields a saliency map. Experimental results on a real seismic dataset from the Great South Basin in New Zealand show the effectiveness of the proposed algorithm in the detection of complex fault networks, which are hardly conspicuous within original seismic volume. The subjective evaluation of the results show that the proposed method outperforms the state-of-the-art saliency algorithms and seismic attributes in detecting complex structures and holds a promising future in computer-aided extraction of other geologic features as well.
/ Modeling and Characterization of in-Body Antennas

Emerging wireless in-body devices pave the way to many breakthroughs in healthcare and clinical research. This technology enables monitoring of physiological parameters while maintaining mobility and freedom of movement of its user. However, establishing reliable communication between an in-body device and external equipment is still a major challenge. The radiation efficiency is constrained by attenuation and reflection losses in tissues. Furthermore, the antennas suffer from impedance detuning issues caused by uncertain electromagnetic properties of body tissues. First, we show that choosing an optimal operating frequency depends on application scenarios and can reduce the losses. Specific designs are then discussed to mitigate the antenna detuning effects due to surrounding biological tissue. Modeling approaches are proposed to lessen the design and optimization complexity. Finally, we present an accurate characterization method of in-body antennas in canonical phantoms using analog fiber optic links.
Using Vehicle GPS Data to Infer Offloading Times of Waste Collection Vehicles at Transfer Stations

Residential waste collection is an expensive but essential service. Since such a large portion of the cost associated with residential waste collection is attributable to transportation it has become the focus of numerous studies on vehicle routing and optimisation. While vehicle routing models have great potential for reducing waste collection costs, a key challenge remains the input data used in these models. Literature focuses predominantly on problem formulation and solution methods as opposed to developing methods to estimate input parameters. Big data, and in particular GPS devices fitted on waste collection vehicles, offer a great opportunity to address this shortcoming. This is demonstrated, in this paper, by analysing GPS records of waste collection vehicles in and around a waste transfer station. Through the analysis we show that the duration of waste drop-offs follow a power-law distribution, whereas vehicle routing models treat it as deterministic. The fitted distribution can be used in future studies to evaluate the operational impact of stochastic drop-off times on routes developed using traditional routing models. We further analyse whether congestion in the transfer station results in an increase in drop-off duration and found that it does not have a major impact.
Greedy Algorithm with Approximation Ratio for Sampling Noisy Graph Signals

We study the optimal sampling set selection problem in sampling a noisy $k$ -bandlimited graph signal. To minimize the effect of noise when trying to reconstruct a $k$ -bandlimited graph signal from $m$ samples, the optimal sampling set selection problem has been shown to be equivalent to finding a $m\times k$ submatrix with the maximum smallest singular value, $\sigma_{\min}$ [3]. As the problem is NP-hard, we present a greedy algorithm inspired by a similar submatrix selection problem known in computer science and to which we add a local search refinement. We show that 1) in experiments, our algorithm finds a submatrix with larger $\sigma_{\min}$ than prior greedy algorithm [3], and 2) has a proven worst-case approximation ratio of $1/(1+\epsilon)k$, where $\epsilon$ is a constant.
Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data

Multiple countries have recently experienced extreme political polarization, which, in some cases, led to escalation of hate crime, violence and political instability. Besides the much discussed presidential elections in the USA and France, Britain's Brexit vote and Turkish constitutional referendum showed signs of extreme polarization. Among the countries affected, Ukraine faced some of the gravest consequences. In an attempt to understand the mechanisms of these phenomena, we here combine social media analysis with agent-based modelling of opinion dynamics, targeting Ukraine's crisis of 2014. We use Twitter data to quantify changes in the opinion divide and parametrize an extended bounded confidence XY model, which provides a spatio-temporal description of the polarization dynamics. We demonstrate that the level of emotional intensity is a major driving force for polarization that can lead to a spontaneous onset of collective behaviour at a certain degree of homophily and conformity. We find that the critical level of emotional intensity corresponds to a polarization transition, marked by a sudden increase in the degree of involvement and in the opinion bimodality.
Industrial Case Studies of SoC Test Scheduling Optimization by Selecting Appropriate EDT Architectures

Modern large system-on-chip (SoC) designs typically have hundreds of cores. Each core requires a certain number of input/output test channels. At the chip level, however, the total number of test pins is limited such that all core-level test channels cannot be accessed at the same time. Therefore, hierarchical pattern retargeting is required for SoC test. Test scheduling algorithms can be applied to reduce the total test time. In this paper, we add the EDT architectures as one additional dimension of parameter into the prior test scheduling algorithm. Experimental results based on real case studies show that with the proposed flow, the test time can be further reduced up to approximately 24%.
Automatic Optimized 3D Path Planner for Steerable Catheters with Heuristic Search and Uncertainty Tolerance

In this paper, an automatic planner for minimally invasive neurosurgery is presented. The solution provides the neurosurgeon with the best path to connect a user-defined entry point with a target in accordance with a specific cost function. The approach guarantees the avoidance of obstacles which can be found along the insertion pathway. The method is tailored to the EDEN2020* programmable bevel-tip needle, a multisegment steerable probe intended to be used to perform drug delivery for the treatment of glioblastomas. A sample-based heuristic search inspired by the BIT* algorithm is used to define the asymptotically-optimal solution in terms of path length, followed by a smoothing phase to meet the required kinematic constraints of the needle. To account for inaccuracies in catheter modeling, which could determine unexpected control errors over the insertion procedure, an uncertainty margin is defined in order to increase the algorithm's safety. The feasibility of the proposed solution was demonstrated by testing the method in simulated neurosurgical scenarios with different degrees of obstacle occupancy and against other sample-based algorithms present in literature: RRT, RRT* and an enhanced version of the RRT-Connect.
Finite-Alphabet Noma for Two-User Uplink Channel

We consider the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to the majority of existing NOMA schemes using continuous Gaussian distributed inputs, we consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, whose sizes are not necessarily the same. By adjusting the scaling factors (i.e., instantaneous transmitting powers) of both users, we aim to maximize the minimum Euclidean distance of the received sum-constellation for a maximum likelihood (ML) receiver. The formulated problem is a mixed continuous-discrete optimization problem and in general it is nontrivial to resolve. By carefully examining the structure of the objective function, we discover that Farey sequence can be employed to tackle the formulated problem. However, the existing Farey sequence is not applicable when the constellation sizes of the two users are different. To address this challenge, we define a new type of Farey sequence, termed punched Farey sequence. Based on this new definition and its properties, we manage to attain a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all the subintervals. Finally, computer simulations are carried out to verify our theoretical analysis, and to demonstrate the advantages of the proposed NOMA over known orthogonal and non-orthogonal designs.
Parallel Arousal Pathways in the Lateral Hypothalamus

Visual Abstract Until recently, hypocretin (Hcrt) neurons were the only known wake-promoting neuronal population in the lateral hypothalamus (LH), but subpopulations of inhibitory neurons in this area and glutamatergic neurons in the nearby supramammillary nucleus (SuM) have recently been found that also promote wakefulness. We performed chemogenetic excitation of LH neurons in mice and observed increased wakefulness that lasted more than 4 h without unusual behavior or EEG anomalies. The increased wakefulness was similar in the presence or absence of the dual orexin receptor blocker almorexant (ALM). Analysis of hM3Dq transfection and c-FOS expression in LH inhibitory neurons and in the SuM failed to confirm that the increased wakefulness was due to these wake-promoting populations, although this possibility cannot be completely excluded. To evaluate the relationship to the Hcrt system, we repeated the study in Orexin-tTA mice in the presence or absence of dietary doxycycline (DOX), which enabled us to manipulate the percentage of Hcrt neurons that expressed hM3Dq. In DOX-fed mice, 18% of Hcrt neurons as well as many other LH neurons expressed hM3Dq; these mice showed a profound increase in wake after hM3Dq activation even in the presence of ALM. In mice switched to normal chow, 62% of Hcrt neurons expressed hM3Dq along with other LH cells; chemogenetic activation produced even more sustained arousal which could be reduced to previous levels by ALM treatment. Together, these results indicate an LH neuron population that promotes wakefulness through an Hcrt-independent pathway that can act synergistically with the Hcrt system to prolong arousal.
Perceived Brand Loyalty Toward Taiwanese Advanced Electronics Industry

In today's modern life. , when people are getting busier and busier; laptop computers. , notepads. , and mobile phones with their merit functions are seen as the indispensable fellow-traveller to many people. In light of this trend. , laptop makers. , especially in Taiwan have concentrated on information technology (IT) to build a solid foundation of research. , development and branding. , as well as a comprehensive supply chain covering design. , manufacturing. , marketing and branding functions. This study's objective is to report on Taiwanese laptops brand and the influences among country of origin (COO). , perceived brand awareness (PBA). , brand image/association (BI/A). , perceived brand quality (PBQ) and perceived brand loyalty (PBL). In doing so. , seven maj or hypotheses were tested through structured questionnaire and statistical analyses. A total sample of 402 respondents. , from 19 different countries is used to test the hypotheses via statistical tools (Cronbach‘s alpha., factor analysis and regression model) and with the aid of SPSS software. As a result. , all the predetermined hypotheses are accepted which means that there are significant influences among country of origin. , brand awareness. , brand image/association. , perceived brand quality and perceived brand loyalty toward Taiwanese brand. Implications of the study may be used as references for the planning of marketing strategies and as the basis for future researches in the consumer behaviour with regard to Taiwanese laptop brands
Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Software Defined Networking (SDN) has emerged as a key enabler for future agile Internet architecture. Nevertheless, the flexibility provided by SDN architecture manifests several new design issues in terms of network security. These issues must be addressed in a unified way to strengthen overall network security for future SDN deployments. Consequently, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection systems for SDNs. The proposed approach is tested using the NSL-KDD dataset, and we achieve an accuracy of 89% with only six raw features. Our experiment results also show that the proposed GRU-RNN does not deteriorate the network performance. Through extensive experiments, we conclude that the proposed approach exhibits a strong potential for intrusion detection in the SDN environments.
Training Deep Neural Networks for Visual Servoing

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF positioning tasks by visual servoing. A convolutional neural network is fine-tuned to estimate the relative pose between the current and desired images and a pose-based visual servoing control law is considered to reach the desired pose. The paper describes how to efficiently and automatically create a dataset used to train the network. We show that this enables the robust handling of various perturbations (occlusions and lighting variations). We then propose the training of a scene-agnostic network by feeding in both the desired and current images into a deep network. The method is validated on a 6 DOF robot.
A Single-Channel Noise Reduction Filtering/Smoothing Technique in the Time Domain

In this paper, we present a single-channel smoothing-and-filtering technique for noise reduction in the time domain. Unlike traditional noise reduction methods, which directly apply a noise reduction filter to the noisy signal, the developed technique achieves noise reduction in two steps. It first applies a time smoothing window to the noisy signal, which, on the one hand, can help reduce high frequency noise and, on the other hand, can help leverage the correlation between successive signal samples. A noise reduction filter is then applied to the smoothed noisy signal to estimate the speech signal of interest. Three optimal and suboptimal noise reduction filters are derived, including the Wiener, maximum signal-to-noise-ratio (SNR), and tradeoff filters. Simulation results reveal that the developed method can produce better noise reduction performance, i.e., higher gains in the perceptual-evaluation-of-speech-quality (PESQ) score, than the traditional methods without smoothing.
Out-of-Vocabulary Word Recovery using FST-Based Subword Unit Clustering in a Hybrid ASR System

The paper presents a new approach to extracting useful information from out-of-vocabulary (OOV) speech regions in ASR system output. The system makes use of a hybrid decoding network with both words and sub-word units. In the decoded lattices, candidates for OOV regions are identified as sub-graphs of sub-word units. To facilitate OOV word recovery, we search for recurring OOV s by clustering the detected candidate OOV s. The metrics for clustering is based on a comparison of the sub-graphs corresponding to the OOV candidates. The proposed method discovers repeating out-of-vocabulary words and finds their graphemic representation more robustly than more conventional techniques taking into account only one best sub-word string hypotheses.
Speedup and Power Scaling Models for Heterogeneous Many-Core Systems

Traditional speedup models, such as Amdahl’s law, Gustafson’s, and Sun and Ni’s, have helped the research community and industry better understand system performance capabilities and application parallelizability. As they mostly target homogeneous hardware platforms or limited forms of processor heterogeneity, these models do not cover newly emerging multi-core heterogeneous architectures. This paper reports on novel speedup and energy consumption models based on a more general representation of heterogeneity, referred to as the normal form heterogeneity, that supports a wide range of heterogeneous many-core architectures. The modelling method aims to predict system power efficiency and performance ranges, and facilitates research and development at the hardware and system software levels. The models were validated through extensive experimentation on the off-the-shelf big.LITTLE heterogeneous platform and a dual-GPU laptop, with an average error of 1 percent for speedup and of less than 6.5 percent for power dissipation. A quantitative efficiency analysis targeting the system load balancer on the Odroid XU3 platform was used to demonstrate the practical use of the method.
Aerial Grasping Based on Shape Adaptive Transformation by HALO: Horizontal Plane Transformable Aerial Robot with Closed-Loop Multilinks Structure

In this paper, we present the achievement of aerial grasping by shape adaptive transformation to the object shape, using a novel transformable aerial robot called HALO: Horizontal Plane Transformable Aerial Robot with Closed-loop Multilinks Structure. Aerial manipulation is an active research area and using multiple aerial robots is an effective solution for the large size object. However the cooperation is considered that there are some difficulties such as the synchronized flight control and collision with each other. Then, we focus on the transformable aerial robot with two-dimensional multilinks proposed in our previous works, which can transform to the suitable form for the target object and grasp it. However the transformable aerial robot with the serial-link structure could not achieve stable flight in terms of horizontal position and yaw control due to the low rigidity and large inertia in the case of more than 4 links. Thus, first we construct a novel type of multilinks with closed-loop structure to avoid the deformation and a new link module with a tilted propeller for fully-actuated control. Second, we describe transformation method with closed-loop multilinks. Third, we present the optimization planning method for the multilinks form to be adaptive to the two-dimensional shape of the target object. Finally, we present experimental results to demonstrate the feasibility of closed-loop aerial transformation and aerial grasping for the large size object.