Miracle-dz/UniFAR-FTUs huggingface dataset:
ZVS modelling of the LLC converter operating as unity power factor front end

The series resonant, parallel loaded LLC with voltage in- and output has widely been studied and used for DC-DC power conversion applications. In contrast, this paper considers the standard LLC in the AC-DC case, which means a far wider input voltage range from the peak mains down to approximately zero. Beside the question whether and how the standard LLC converter can handle the resulting large gain ratios e.g., in the context of a power factor correction (PFC) application, it needs to be explored whether zero-voltage switching (ZVS) can always be achieved, especially regarding the highly nonlinear output capacitances of superjunction MOSFETs. To comprehensively deal with the variety of operation conditions, the converter design choices and the inverter devices, an analytical description is preferred over approaches merely based on circuit simulations. Thereto, a system of equations firstly describes the ideally switching steady state. This model is linked to a linearized inverter transition model for ZVS assessment is used to refine the solution space for the converter design. Three ways to extract that inverter model from the MOSFETs' output CV-characteristic are compared that all can predict ZVS but differ in complexity.
Audio-Visual Conversation Analysis by Smart Posterboard and Humanoid Robot

This paper addresses audio-visual signal processing for conversation analysis, which involves multi-modal behavior detection and mental-state recognition. We have investigated prediction of turn-taking by the audience in a poster session from their multi-modal behaviors, and found out that the eye-gaze provides an important cue compared with head nodding and verbal backchannels. This finding has been applied to audio-visual speaker diarization by combining eye-gaze information. We are now investigating engagement recognition in human-robot interaction based on the same scheme. Robust and realtime detection of laughing, backchannels and nodding is realized based on LSTM-CTC. We introduce a latent “character” model to cope with the subjectivity and variations of engagement annotations. Experimental evaluations demonstrate that (1) the latent character model is effective, (2) automatic behavior detection is robust and does not degrade the engagement recognition accuracy, and (3) the eye-gaze is the most important feature among others.
Study of Fiber Bragg Grating Samples Exposed to High Fast Neutron Fluences

Fiber Bragg grating (FBG) sensors are expected to provide valuable data in extreme radiation environments associated with nuclear research reactors. However, when the fast neutron fluence reaches 10<sup>18</sup>–10<sup>19</sup> n/cm<sup>2</sup>, the radiation-induced changes in the material density and refractive index may drastically bias the measurements. This paper evaluates the radiation effect on the FBG performances by comparing their properties before and after their exposure to fast neutron fluences exceeding 10<sup>19</sup> n/cm<sup>2</sup> (<inline-formula> <tex-math notation="LaTeX">$E > 1$ </tex-math></inline-formula> MeV). We studied the responses of FBGs manufactured by three different laboratories in the same single-mode optical fiber but using different inscription conditions. The Bragg wavelength and the reflectivity were measured before and after irradiation thanks to a dedicated mounting. For nearly all FBGs, the Bragg peak remains visible after the irradiation while the radiation-induced Bragg wavelength shifts (RI-BWSs) vary from a few picometers (equivalent temperature error <1 °C) to nearly 1 nm (~100 °C error) depending of the FBG inscription conditions. Such high RI-BWSs can be explained by the huge refractive-index variation and compaction observed for bare fiber samples through other experimental techniques. Our results show that by using specific hardening techniques, the FBG-based temperature measurements in a nuclear research reactor experiment may become feasible.
Improving Robot Navigation and Obstacle Avoidance using Kinect 2.0

Kinect is a low-cost controller-free device, originally designed for gaming and entertainment. This work presents a solution using the most recent version of Kinect sensor, which has got significant improvement in several ways in comparison with earlier versions, to cope with one important aspect of autonomous mobile robotics, obstacle avoidance. Modeling the environment based on the point cloud extracted from the depth image data as well as an obstacle avoidance method using the straight line segments and circle arcs, were the main focus of the paper. The environment is represented by a set of polygons and the obstacle avoidance algorithm attempts to find a collision-free path from the current position of the robot to the target pose subject to the shortest possible path considering a safeguard. The whole algorithm was implemented and simulated in V-rep software. A mobile robotic platform based on Pioneer This motivated using the Kinect 2 sensor to extend the ability to detect obstacles and hence to improve the local path planner and obstacle avoidance components.
On the Sample Complexity of Graphical Model Selection from Non-Stationary Samples

We characterize the sample size required for accurate graphical model selection from non-stationary samples. The observed samples are modeled as a zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This includes the case where observations form stationary or underspread processes. We derive a sufficient condition on the required sample size by analyzing a simple sparse neighborhood regression method.
Online Scheduling to Maximize Resource Utilization of Deadline-Constrained Workflows on the Cloud

In this paper, we assume workflows under deadline constraints are submitted to the cloud from time to time. Every time a workflow is submitted, the cloud needs to determine whether it can agree with the specific constraint set by the user. If the cloud agrees to admit the workflow, cloud resources can be allocated for its execution in a way the deadline constraint can be met, while the existing load in the underlying resources is considered. The focus of this paper is how to schedule the tasks of each admitted workflow so that the resource utilization can be maximized. A variety of online scheduling algorithms have been proposed and evaluated using a simulator that manages to generate a stream of workflows for which an optimal schedule, with 100% resource utilization and without deadline violation, is guaranteed to exist.
Quantum Statistics-Inspired Neural Attention

Sequence-to-sequence (encoder-decoder) models with attention constitute a cornerstone of deep learning research, as they have enabled unprecedented sequential data modeling capabilities. This effectiveness largely stems from the capacity of these models to infer salient temporal dynamics over long horizons; these are encoded into the obtained neural attention (NA) distributions. However, existing NA formulations essentially constitute point-wise selection mechanisms over the observed source sequences; that is, attention weights computation relies on the assumption that each source sequence element is independent of the rest. Unfortunately, although convenient, this assumption fails to account for higher-order dependencies which might be prevalent in real-world data. This paper addresses these limitations by leveraging Quantum-Statistical modeling arguments. Specifically, our work broadens the notion of NA, by attempting to account for the case that the NA model becomes inherently incapable of discerning between individual source elements; this is assumed to be the case due to higher-order temporal dynamics. On the contrary, we postulate that in some cases selection may be feasible only at the level of pairs of source sequence elements. To this end, we cast NA into inference of an attention density matrix (ADM) approximation. We derive effective training and inference algorithms, and evaluate our approach in the context of a machine translation (MT) application. We perform experiments with challenging benchmark datasets. As we show, our approach yields favorable outcomes in terms of several evaluation metrics.
Live streaming performing arts within a social virtual environment

This paper aims to explore the experience of live performing arts, why they are so enduringly popular, and yet why they have not become widespread as online live streaming video content. This paper explores this issue by first providing a literature review which compares the social experience of live performing arts to new forms of live online entertainment. The implementation of a proof-of-concept system for live streaming performing arts into a social virtual environment is then introduced. This paper concludes that social virtual environments have the potential to provide a greater degree of social awareness within a virtual audience. However, these environments still struggle to transmit the full extent of a live experience due to current limitations in the technology. Finally, the paper discusses directions for future research.
EGF ligand fused to truncated Pseudomonas aeruginosa exotoxin A specifically targets and inhibits EGFR‑positive cancer cells.

Cancer cells have been known to overexpress the epidermal growth factor receptor (EGFR) and hence relevant multiple‑targeted therapies have been developed, with a recent clinical application of the antibody‑mediated inhibition of the EGFR. However, this strategy is not useful in cancer cells with mutations in KRAS; a GTPase downstream of EGFR which constitutively activates the pathway without EGF stimulation. Furthermore, mutations in EGFR also reduce the binding of monoclonal antibodies and thereby render them ineffective. In the present study, we designed a chimeric EGF protein fused to the truncated N‑terminal domain fragment of Pseudomonas aeruginosa exotoxin A (EGF‑ETA), which has ADP‑ribosylation activity and induces apoptosis. The EGF‑ETA protein was expressed in E. coli as a His‑tagged fusion. Our results showed that EGF‑ETA significantly inhibited the proliferation of EGFR‑positive A431 epidermoid carcinoma (IC50 27 ng/ml) and HN5 head and neck squamous cell carcinoma (IC50 36 ng/ml) cells. However, its effect on cancer cells with little or no EGFR expression was limited (A549‑IC50 1,000 ng/ml; MCF‑7‑IC50 >10,000 ng/ml). Compared to cetuximab, EGF‑ETA was highly potent in its killing capacity of HN5 cancer cells at 1,000 ng/ml, while cetuximab had little effect at 1,000 ng/ml. Furthermore, EGF‑ETA was just as potent in HCT116 (KRAS G13D) and SW480 (KRAS G12V) colon cancer cell lines harbouring KRAS hyperactivating mutations when compared to KRAS wild‑type HT29 colon cancer cells. Finally, co‑incubation of EGF‑ETA with an anti‑EGF antibody abrogated its effect on the EGFR‑positive A431 cells. Our results show that the chimeric EGF‑ETA toxin is extremely effective against EGFR‑positive cancers and raises the potential to further develop this chimera for use in targeting EGFR‑positive tumours resistant to monoclonal antibodies.
Comparative Efficiency Study of Two Proposed Designs Tested in Water and Air Cooling Conditions for a High Power Humanoid Robot Hollow Joint

When a robot is designed, usually the weight of the same motors limits the maximum payload of the robot. While the designer tries to increase the power of the motors to give the robot better payload capacity, by doing so, it also makes the robot itself heavier and then the improvement in payload capacity is lost in lifting the robot itself. Many approaches to improve payload capacity consist in using an existing commercial electric motor and modifying it by adding external accessories to avoid non-safe motor internal heat. They usually use a mechanic reduction to increase payload capacity, but they lose maximum speed at the same time. Currently, there are no proper solutions for a motor in a humanoid robot application that manages to lift very heavy objects at high speed and that at the same time the motors are small and lightweight. In order to obtained more power from the robot joint actuator, in this paper we propose and compared a new thermal channel and thermal sealed jacket design for a permanent magnet electric motor to solve this problem. The design takes advantage of the fractional-slot concentrated-winding motor configuration and its fill factor to force liquid and air directly to the winding separately, in order to examine their head extradition to increase its performance in a specify scenario. The propose design shows an improvement in relation to the head extraction of the sealed jacket design with respect to the thermal channels when forcing liquid through them. Both designs are simulated assuming an extreme scenario where the winding reaches 100 °C for validation purposes. The experiments were carried out in the Ansys CFD Software to evaluate their heat transfer behavior.
Generalised Sidelobe Canceller for Noise Reduction in Hearing Devices Using an External Microphone

The use of an external microphone in conjunction with an existing local microphone array can be particularly beneficial for noise reduction tasks that are critical for hearing devices, such as cochlear implants and hearing aids. Recent work has already demonstrated how an external microphone signal can be effectively incorporated into the common noise reduction technique of using a Minimum Variance Distortionless Response (MVDR) beamformer. In this paper, we provide a further extension, whereby an external microphone signal can be incorporated into an existing framework of a Generalised Sidelobe Canceller (GSC) that has been designed for a local microphone signal array. It will be shown that the resulting GSC with an external microphone results in an easily implementable addition to the existing GSC framework for a local microphone array, and can exhibit an improved noise reduction performance.
Learning-Based Design of Measurement Matrix with Inter-Column Correlation for Compressive Sensing

In this paper, a new approach for the design of measurement matrix, $\Phi$, for compressive sensing (CS) in a generic context is proposed. In accordance with well-known classical CS theory, we take the elements of $\Phi$ to be random, yet, we include correlations within the elements of the individual columns of $\Phi$. To this end, a new structure for $\Phi$ is proposed where the correlations of interest are controlled by a selectable parameter. We aim at optimizing the proposed $\Phi$ with respect to the latter correlation parameter by leveraging an appropriate criterion in a learning-based framework. We evaluate the performance of the proposed $\Phi$ and compare it with the state-of-the-art literature including random $\Phi$ with independent and identically distributed (i.i.d.) elements. Performance advantage of the proposed approach is validated in different CS scenarios.
Concurrent Target Following with Active Directional Sensors

We propose a collision-avoidance tracker for agents with a directional sensor that aim to maintain a moving target in their field of view. The proposed tracker addresses the view maintenance issue within an Optimal Reciprocal Collision Avoidance (ORCA) framework. Our tracking agents adaptively share the responsibility of avoiding each other and minimise with a smooth actuation the deviation angle from their heading direction to their target. Experimental results with real people trajectories from public datasets show that the proposed method improves view maintenance.
Prescribed Performance Observer-Based Adaptive Fuzzy Control for Nonstrict-Feedback Stochastic Nonlinear Systems

This paper focuses on an observer-based adaptive fuzzy control problem for nonstrict-feedback stochastic nonlinear systems with input saturation and prescribed performance. By introducing a mean-value theorem, the obstacle arising from input saturation is overcome. The transient performance of system output is also achieved by means of designing performance function. On the other hand, a variable separation method is used to deal with the difficulty caused by nonstrict-feedback structure. Additionally, fuzzy logic systems are introduced to approximate unknown nonlinear functions. Moreover, an observer is constructed to estimate unknown state variables in the systems. By combining adaptive backstepping control method, an observer-based adaptive fuzzy controller is designed to ensure all signals of the resulting closed-loop system to be bounded, and the output of the system converge to a prescribed area. Finally, two examples are given to show the effectiveness of the proposed control scheme.
Persistent Coverage Control for Teams of Heterogeneous Agents

A distributed cooperative control law for persistent coverage tasks is proposed, capable of coordinating a team of heterogeneous agents in a structured environment. Team heterogeneity is considered both at vehicles' dynamics and at coverage capabilities levels. More specifically, the general dynamics of nonholonomic vehicles are considered. Agent heterogeneous sensing capabilities are addressed by means of the descriptor function framework, a set of analytical tools for controlling agents involved in generic coverage tasks. By means of formal arguments, we prove that the team performs the task and no collision occurs between agents nor with obstacles. A numerical simulation validates the proposed strategy.
Cognitive Reserve Factors in a Developing Country: Education and Occupational Attainment Lower the Risk of Dementia in a Sample of Lebanese Older Adults

Background: Dementia secondary to neurodegenerative diseases is prevalent among older adults and leads to social, psychological and economic burden on patients, caregivers and the community as a whole. Cognitive reserve factors such as education, and mental stimulation among others were hypothesized to contribute to the resilience against age-related cognitive impairment. Educational attainment, occupation complexity, physical activity, and leisure activity are explored in the context of protecting the older adults' cognitive function. We investigated the cognitive reserve effect on dementia, cognitive decline and impairment, and global cognitive function. Methods: This study is a secondary analysis of data from a cross-sectional, community-based cohort study that aimed at investigating factors associated with dementia and their prevalence. The sample was of 508 community based older adults in Lebanon, aged 65 years and above in addition to 502 informants designated by these older adults. Older adults and informants answered structured questionnaires administered by interviewers, as well as a physical assessment and a neurological examination. Older adults were diagnosed for dementia. Global cognitive function, depression, and cognitive decline were assessed. Results: Older adults with dementia had lower levels of education, and attained lower occupational complexity. Factors such as high education, complex occupation attainment, and leisure activity, significantly predicted better global cognitive function. An older adult who attained high education levels or high complexity level occupation was 7.1 or 4.6 times more likely to have better global cognitive function than another who attained lower education or complexity level occupation respectively. Conclusion: These results suggest that cognitive reserve factors ought to be taken into consideration clinically during the course of dementia diagnosis and when initiating community-based preventive strategies.
Doppler Spectrum Analysis of a Roadside Scatterer Model for Vehicle-to-Vehicle Channels: An Indirect Method

In vehicle-to-vehicle (V2V) channels, roadside scatterers (RSSs), such as houses, buildings, trees, and many more, play a crucial role in the determination of the Doppler power spectral density (DPSD) characteristics. However, the relevant research results are scarce due to the lack of computationally tractable analytic DPSD solutions. To fill this gap, we investigate an indirect method for the DPSD analysis of a generic 2-D RSS model for V2V channels. The indirect method, based on Hoeher’s theorem, employs successive transformations of random variables to obtain the DPSD. Compared with the conventional methods, leading to impractical multiple integral solutions, our method yields a single integral form, more useful for analytic studies, model validation/parameter estimation, and fading simulator design. Using the new DPSD solution, the impact of different RSS layouts on the DPSD characteristics is further investigated, and several new insights are provided. The joint probability density function (PDF) of the angle-of-departure and the angle-of-arrival (AoA) and the joint Doppler-AoA PDF are newly presented in closed forms and analyzed with respect to the DPSD shape. Comparisons with the DPSDs measured in highway and urban canyon environments demonstrate not only the validity of the generic 2-D RSS model but also the significant contribution of RSSs to V2V channels.
Spatial Audio Feature Discovery with Convolutional Neural Networks

The advent of mixed reality consumer products brings about a pressing need to develop and improve spatial sound rendering techniques for a broad user base. Despite a large body of prior work, the precise nature and importance of various sound localization cues and how they should be personalized for an individual user to improve localization performance is still an open research problem. Here we propose training a convolutional neural network (CNN) to classify the elevation angle of spatially rendered sounds and employing Layer-wise Relevance Propagation (LRP) on the trained CNN model. LRP provides saliency maps that can be used to identify spectral features used by the network for classification. These maps, in addition to the convolution filters learned by the CNN, are discussed in the context of listening tests reported in the literature. The proposed approach could potentially provide an avenue for future studies on modeling and personalization of head-related transfer functions (HRTFs).
Learning a Code-Space Predictor by Exploiting Intra-Image-Dependencies

We introduce a neural network based image coding model that utilizes a code-space predictor to reduce code length by modelling dependencies within the code. Inspired by the prediction mechanism of inpainting, we learn a spatial predictor in the code space to efﬁciently deal with spatial dependencies. It is jointly trained with the codec to estimate a probability distribution from adjacent code symbols. The resulting code stores information related to the image and the prediction of neighbours. To improve its optimization, we adapt the Generalized Divisive Normalization into a sparse variant. The resulting model outperforms other prediction based methods. We show that when integrated with the recently proposed hyperprior model, our approach obtains state-of-the-art performance for CNN-based image codecs on the MS-SSIM scale.