Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency transfer of the ventriloquism aftereffect

The ventriloquism aftereffect (VAE) describes the persistent shift of perceived sound location after having been adapted to a ventriloquism condition, in which the sound was repeatedly paired with a displaced visual stimulus. In the latter case, participants consistently mislocalize the sound in the direction of the visual stimulus (ventriloquism effect, VE). Previous studies provide conflicting reports regarding the strength of the VAE, ranging from 0 to nearly 100%. Moreover, there is controversy about its generalization to different sounds than the one inducing the VE, ranging from no transfer at all, to full transfer across different sound spectra. Here, we imposed the VE for three different sounds: a low-frequency and a high-frequency narrow-band noise, and a broadband Gaussian white noise (GWN). In the adaptation phase, listeners generated fast goal-directed head movements to localize the sound, presented across a 70 deg range in the horizontal plane, while ignoring a visual distracter that was consistently displaced 10 deg to the right of the sound. In the post-adaptation phase, participants localized narrow-band sounds with center frequencies from 0.5 to 8 kHz, as well as GWN, without the visual distracter. Our results show that the VAE amounted to approximately 40% of the VE and generalized well across the entire frequency domain. We also found that the strength of the VAE correlated with the pre-adaptation sound-localization performance. We compare our results with previous reports and discuss different hypotheses regarding optimal audio-visual cue integration. Significance statement Where we believe sounds to emanate from is heavily influenced by what we see. We experience this illusion - the ventriloquism effect - regularly. For example, when watching movies, we perceive the voices of actors to originate from the actors’ lips rather than from the speakers. Long exposure to a spatial mismatch between what we hear and what we see, leads to a recalibration of our localization of sounds - the ventriloquism aftereffect. Unclear was whether exposure to an audiovisual mismatch for one sound type would also lead to adaptive changes for other sounds. The present study shows that the ventriloquism aftereffect generalizes, implying that it does not act at an early auditory stage, but at a late multisensory stage.
Swirls: A Platform for Enabling Multicluster and Multicloud Execution of Parallel Programs

Swirls is a general purpose application for interactive building, deploying, and execution of message-passing parallel programs that address multicluster and multicloud requirements. It is implemented on HPC Shelf, a cloud-based platform for providing HPC services. Swirls enables the communication between MPI programs written in C#, C, C++, and Python across one or more clusters, either on-premise or cloud-based ones. At the current implementation status, The users of Swirls may use clusters formed by virtual machines over Amazon Elastic Compute Cloud (EC2) and Google Cloud Platform (GCP).
The Molecular Biodiversity of Protein Targeting and Protein Transport Related to the Endoplasmic Reticulum

Looking at the variety of the thousands of different polypeptides that have been focused on in the research on the endoplasmic reticulum from the last five decades taught us one humble lesson: no one size fits all. Cells use an impressive array of components to enable the safe transport of protein cargo from the cytosolic ribosomes to the endoplasmic reticulum. Safety during the transit is warranted by the interplay of cytosolic chaperones, membrane receptors, and protein translocases that together form functional networks and serve as protein targeting and translocation routes. While two targeting routes to the endoplasmic reticulum, SRP (signal recognition particle) and GET (guided entry of tail-anchored proteins), prefer targeting determinants at the N- and C-terminus of the cargo polypeptide, respectively, the recently discovered SND (SRP-independent) route seems to preferentially cater for cargos with non-generic targeting signals that are less hydrophobic or more distant from the termini. With an emphasis on targeting routes and protein translocases, we will discuss those functional networks that drive efficient protein topogenesis and shed light on their redundant and dynamic nature in health and disease.
Non-analytic at the origin, behavioral models for active or passive non-linearity

Most non-linear behavioral models of amplifiers are based on functions that are analytic at the origin and thus can be replaced by their Taylor series development around this point, e.g. polynomials of the input signal. Chebyshev Transforms can be used to compute the harmonic response of the model to a sine input signal. These responses are polynomials of the input signal amplitude. A second application of the Chebyshev transform to the first harmonic response or radio frequency (RF) characteristic will lend the carriers and intermodulation (IM) products for a two-carrier input signal, again polynomials. An important class of non-analytic non-linear behavior encountered in practice, such as hard limiters and detectors are either empirically treated or only approximated by an analytic function such as the hyperbolic tangent. This work proposes to generalize the polynomial non-linearity theory by adding non-analytic at the origin functions that, like polynomials, are invariant elements of the Chebyshev Transform. Devices modeled with these non-analytic at the origin functions exhibit intermodulation behavior significantly different from that of classical polynomial models, giving theoretical foundation to a number of important unexplained practical measurement observations.
Using proton irradiation to probe the origins of low-frequency noise variations in SiGe HBTs

We use proton irradiation to probe the origins of the geometry-dependent variation of low-frequency noise in 120 GHz SiGe heterojunction bipolar transistors (HBTs). Before irradiation, small-sized transistors show a strong variation in noise magnitude across many samples, whereas the noise in larger devices is more statistically reproducible. Although the noise magnitude shows little degradation after 2/spl times/10/sup 13/ p/cm/sup 2/ irradiation, the observed noise variation decreases. Its dependence on both geometry and bias is quantified. This fundamental geometrical scaling effect is investigated using theoretical calculations based on the superposition of generation/recombination (G/R) noise sources.
Predictors of Poor Adherence to CART and Treatment Failure at Second-Line Regimens Among Adults in Public Hospitals of Amhara Region, North-Western Ethiopia: A Retrospective Cohort Study

Background Globally, HIV/AIDS has challenged the lives of 36.3 million people and resulted in 17 million orphans. The disease has neither a vaccine nor a cure and the only option currently is highly active antiretroviral therapy. This research was conducted to identify the predictors of poor adherence to CART and treatment failure at second-line regimen among adults living with HIV/AIDS in public hospitals of Amhara region, north-western Ethiopia. Methods A retrospective cohort study design was conducted on 700 HIV-positive people who were receiving a second-line CART regimen. Participants on second-line regimens who followed their treatment between 2016 and 2019 were considered. Data were extracted from participants’ clinical charts from June 18–July 7, 2020. Results A multivariate regression analysis indicates that age of patients (OR = 1.025, 95% CI: 1.001–1.321; p = 0.005), follow-up visits (OR = 0.979, 95% CI: 0.873–0.998; p < 0.001), CD4 cell count change (OR = 0.9860; 95% CI: 0.835–0.998; p < 0.01), marital status (OR = 0.973, 95% CI: 0.789– 0.997; p = 0.006), female HIV-infected patients (OR = 0.990; 95% CI: 0.789–0.999; p <0.001), rural patients (OR = 1.151; 95% CI 1.065–1.398; p = 0.004), non-educated adult patients (OR = 1.026, 95% CI: 1.002–1.198; p = 0.003), existence of social violence (OR = 0.012, 95% CI: 0.008–0.134; p< 0.01), patients with opportunistic diseases (OR = 1.0345, 95% CI 1.002–1.142; p = 0.001), CD4 cell count (OR = 0.901, 95% CI: 0.843–0.995; p = 0.025) and malnutrition (OR = 0.883, 95% CI; 0.762–0.954; p = 0.001) significantly affected the two response variables. Conclusion Several variables affected both poor adherence to HAART and treatment failure at second-line regimens in the current investigation. Due attention should be given to aged patients, rural residents, non-educated patients, and patients with other morbidities to be successful with second-line treatment regimens.
Broadband and high gain waveguide-fed slot antenna array in the Ku-band

A broadband and high-gain slot antenna array operating in the Ku-band is reported in this study. The antenna is with waveguide-fed cavity backed configuration and made entirely via simple milling process. The overall antenna structure consists of four layers. The bottom layer is a waveguide feed network to provide corporate power division. In turn, the waveguide feed network is excited by a conventional coaxial probe from the back. The waveguide network couples energy to an array of cavity via an aperture plate. These constitute the middle two layers. Each cavity then excites a subarray of 2 × 2 radiating slots in the top layer. The radiating slot elements and the feed network are designed to achieve wide bandwidth and high-gain performance. Finally, an 8 × 8 array antenna has been designed and fabricated with about 25 dBi gain over 14.8% bandwidth, and the cross-polarisation was around -30 dB in the boresight.
GaAs f.e.t. transimpedance front-end design for a wideband optical receiver

A GaAs f.e. t. transimpedance front-end amplifier was built and operated as part of a regenerator at 274 Mb/s. The noise characteristics were optimised for operation with a Ge photodiode at 1.32 μm, but were measured at 0.82 μm to compare Si and Ge photodiodes. The amplifier input capacitance was 3.2 pF with the Si p-i-n diode and 4.6 pF with the Ge diode. At 0.82 μm, we measured a sensitivity for 10−9 error rate of about −35 dBm with the Si diode and −32 dBm with the Ge diode. We predict a sensitivity of −34 dBm at 1.32 μm.
Traffic Sign Recognition Based on CNN and Twin Support Vector Machine Hybrid Model

With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly affect the performance of the entire network. Traditional processing methods include classification models such as fully connected network models and support vector machines. In order to solve the problem that the traditional convolutional neural network is prone to over-fitting for the classification of small samples, a CNN-TWSVM hybrid model was proposed by fusing the twin support vector machine (TWSVM) with higher computational efficiency as the CNN classifier, and it was applied to the traffic sign recognition task. In order to improve the generalization ability of the model, the wavelet kernel function is introduced to deal with the nonlinear classification task. The method uses the network initialized from the ImageNet dataset to fine-tune the specific domain and intercept the inner layer of the network to extract the high abstract features of the traffic sign image. Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. On GTSRB and BELGIUMTS datasets, the validity and generalization ability of the improved model is verified by comparing with different kernel functions and different SVM classifiers.
Building Boundary Extraction from LiDAR Point Cloud Data

Building boundary extraction from LiDAR point cloud data is important for urban planning and 3D modelling. Due to the uneven point distribution, missing data, and occlusion in LiDAR point cloud data, extraction of boundary points is challenging. Existing approaches have shortcomings either in detecting boundary points on concave shapes or separate identification of ‘hole’ boundary points inside the building roof. This paper, presents a method for detecting both inner and outer boundary points of the extracted building point cloud. Based on the properties of Delaunay Triangulation and distance from the mean point of the calculated neighbourhood for any point, we extract both inner and outer boundary points. Experimental results using some synthetic shapes as well as some real datasets show the competitive performance of the proposed method.
Wavelength shift of gallium nitride light emitting diode with p-down structure

Both the p-gallium nitride (GaN) on an n-light-emitting diode (LED) structure and the n-GaN on p-LED structure were fabricated by metal-organic vapor phase epitaxy on c axis sapphire substrate. The output spectra with different applied bias voltages were studied on these structures. The small amount of the wavelength shift under a forward bias condition in the proposed p-down LED structure was observed but not in the n-down LED structure. This phenomenon was characterized by the effective electric field in the well created from the built-in electric field and the applied electric field. This proposed p-down LED shows an opportunity in the application of wavelength shift-free optoelectronic devices.
A novel vector control scheme for transistor PWM inverter-fed induction motor drive

A novel vector control scheme for a transistor pulse-width-modulation (PWM)-inverter-fed induction motor drive is presented. The system is based on a current control loop that consists of two independent nonlinear controllers that regulate the DC (field-oriented) components of the stator current vector. Three-level hysteresis comparators are used as current controllers. The outputs of the comparators select the appropriate inverter output voltage vectors via a switching erasable programmable ROM (EPROM) table. The theoretical principle of this method is discussed. Simulation and experimental results that illustrate the operation of the proposed system and performance in comparison with the other known schemes based on two-level hysteresis comparators are presented. >
Taking stock of the past: A psychometric evaluation of the Autobiographical Interview

Autobiographical memory (AM) involves a rich phenomenological re-experiencing of a spatio-temporal event from the past, which is challenging to objectively quantify. The Autobiographical Interview (AI; Levine et al. Psychology and Aging, 17 (4), 677–689, 2002 ) is a manualized performance-based assessment designed to quantify episodic (internal) and semantic (external) features of recalled and verbally conveyed prior experiences. The AI has been widely adopted, yet has not undergone a comprehensive psychometric validation. We investigated the reliability, validity, association to individual differences measures, and factor structure in healthy younger and older adults ( N = 352). Evidence for the AI’s reliability was strong: the subjective scoring protocol showed high inter-rater reliability and previously identified age effects were replicated. Internal consistency across timepoints was robust, suggesting stability in recollection. Central to our validation, internal AI scores were positively correlated with standard, performance-based measures of episodic memory, demonstrating convergent validity. The two-factor structure for the AI was not well supported by confirmatory factor analysis. Adjusting internal and external detail scores for the number of words spoken (detail density) improved trait estimation of AM performance. Overall, the AI demonstrated sound psychometric properties for inquiry into the qualities of autobiographical remembering.
Frequency and Risk Indicators of Periodontal Diseases in a Sample of Adult Egyptian Patients: A Hospital-Based Cross-Sectional Study

This hospital-based cross-sectional study aimed at determining frequency and risk indicators/predictors of periodontitis in a sample of Egyptian adult population and to develop a prediction equation for classifying periodontal diseases. Seven hundred and fifty subjects were consecutively recruited from outpatient Diagnostic Center, Faculty of Dentistry, Cairo University. Validated oral health questionnaire for adults and oral health impact profile-14 (OHIP-14) questionnaire were filled by all patients. Diagnosis was made based on measurements of clinical periodontal parameters including plaque index, bleeding on probing, pocket depth, clinical attachment level and gingival recession. Radiographic examination was performed using digital periapical radiographs. Ordinal logistic regression analysis was used to determine significant predictors of periodontal diseases and discriminant analysis was performed to predict periodontal disease classification. Gingivitis was the most frequent periodontal disease (39.6%) followed by periodontitis stage I (38%), stage II (20.4%), stage III (1.6%) and stage IV (0.4%). The lowest OHIP-14 scores were in patients with periodontitis stages III and IV. Multivariate analysis showed that education (p < 0.001), OHIP-14 score (p = 0.003), non-smoking (p = 0.001) and non-alcohol drinking (p = 0.021) were significant negative predictors, while never to clean the teeth (p < 0.001) were significant positive predictors of periodontal disease. Periodontitis stages III and IV were the least frequent on a sample of Egyptian adult patients. Education, frequency of teeth cleaning, smoking, alcohol drinking and OHIP-14 scores were significant predictors of periodontal disease. Through discriminant analysis this study could classify patients into different periodontal diseases with an overall correct prediction of 99.2%.
Towards Magnetic Field Gradient-Based Imaging and Control of In-Body Devices

This papers reports a magnetic field gradient-based imaging system for in-body devices which takes inspiration from the localization principles of magnetic resonance imaging. By applying three orthogonal magnetic field gradients, the location of a device inside the body can be determined by measuring the magnetic fields in the device and transmitting this information to an external reader. The proposed system consists of one pair of Helmholtz coils and two pairs of saddle coils and is capable of generating the three orthogonal gradient fields. To emulate an implantable device, a miniature sensor module was designed using off-the-shelf components and semi-passive UHF RFID. The proposed localization system produces magnetic field gradients up to 187.4 G/m while consuming 1 A and achieves an average localization error of 80 µm.
Intradiscal injection for the management of low back pain

Low back pain (LBP) is a common clinical problem and a major cause of physical disability, imposing a prominent socioeconomic burden. Intervertebral disc degeneration (IDD) has been considered the main cause of LBP. The current treatments have limited efficacy because they cannot address the underlying degeneration. With an increased understanding of the complex pathological mechanism of IDD, various medications and biological reagents have been used for intradiscal injection for the treatment of LBP. There is increasing clinical evidence showing the benefits of these therapies on symptomatic relief and their potential for disc repair and regeneration by targeting the disrupted pathways underlying the cause of the disease. A brief overview of the potential and limitations for these therapies are provided in this review, based on the recent and available data from clinical trials and systematic reviews. Finally, future perspectives are discussed.
Linked Data: A Framework for Publishing FiveStar Open Government Data

With the increased adoption of open government initiatives around the world, a huge amount of governmental raw datasets was released. However, the data was published in heterogeneous formats and vocabularies and in many cases in bad quality due to inconsistency, messy, and maybe incorrectness as it has been collected by practicalities within the source organization, which makes it inefficient for reusing and integrating it for serving citizens and third-party apps. This research introduces the LDOG (Linked Data for Open Government) experimental framework, which aims to provide a modular architecture that can be integrated into the open government hierarchy, allowing huge amounts of data to be gathered in a fine-grained manner from source and directly publishing them as linked data based on Tim Berners lee’s five-star deployment scheme with a validation layer using SHACL, which results in high quality data. The general idea is to model the hierarchy of government and classify government organizations into two types, the modeling organizations at higher levels and data source organizations at lower levels. Modeling organization’s experts in linked data have the responsibility to design data templates, ontologies, SHACL shapes, and linkage specifications. whereas non-experts can be incorporated in data source organizations to utilize their knowledge in data to do mapping, reconciliation, and correcting data. This approach lowers the needed experts that represent a problem of linked data adoption. To test the functionality of our framework in action, we developed the LDOG platform which utilizes the different modules of the framework to power a set of user interfaces that can be used to publish government datasets. we used this platform to convert some of UAE's government datasets into linked data. Finally, on top of the converted data, we built a proof-of-concept app to show the power of five-star linked data for integrating datasets from disparate organizations and to promote the governments' adoption. Our work has defined a clear path to integrate the linked data into open governments and solid steps to publishing and enhancing it in a fine-grained and practical manner with a lower number of experts in linked data, It extends SHACL to define data shapes and convert CSV to RDF.
Popular Song Composition Based on Deep Learning and Neural Network

For the general public, composition appears to be professional and the threshold is relatively high. However, automatic composition can improve this problem, allowing more ordinary people to participate in the composition, especially popular music composition, so the music becomes more entertaining, and its randomness can also inspire professionals. This article combines deep learning to extract note features from the demonstration audio and builds a neural network model to complete the composition of popular music. The main work of this paper is as follows. First, we extract the characteristic notes, draw on the design process of mel-frequency cepstral coefficient extraction, and combine the characteristics of piano music signals to extract the note characteristics of the demonstration music. Then, the neural network model is constructed, using the memory function of the cyclic neural network and the characteristics of processing sequence data, the piano notes are combined into a sequence according to the musical theory rules, and the neural network model automatically learns this rule and then generates the note sequence. Finally, the ideal popular piano music scores are divided into online music lover scores and offline professional ratings. The score index is obtained, and each index is weighted by the entropy weight method.