Miracle-dz/UniFAR-FTUs huggingface dataset:
The skate spiracular organ develops from a unique neurogenic placode, distinct from lateral line placodes

The spiracular organ is an epithelial pouch or tube lined with mechanosensory hair cells, found embedded in the wall of the spiracle/spiracular chamber (remnant of the first pharyngeal cleft) in many non-teleost jawed fishes. It is innervated via a branch of the anterior lateral line nerve and usually considered a specialised lateral line organ, although it most likely functions as a proprioceptor for jaw movement. It is homologous to the paratympanic organ: a hair cell-lined epithelial pouch embedded in the wall of the middle ear (which evolved from the spiracular chamber) of birds, alligators and Sphenodon. A previous fate-mapping study showed that the chicken paratympanic organ and its afferent neurons originate from a molecularly distinct placode immediately dorsal to the geniculate (first epibranchial) placode. Here, DiI fate-mapping in a cartilaginous fish (little skate, Leucoraja erinacea) shows that the spiracular organ derives from a previously unrecognised neurogenic placode immediately dorsal to the geniculate placode and molecularly distinct from lateral line placodes. These findings further support the developmental and evolutionary independence of this unique jawed-vertebrate mechanosensory organ from the lateral line system. Summary statement Fate-mapping in a cartilaginous fish supports the developmental independence from the lateral line system of the mechanosensory spiracular organ, consistent with its homology with the amniote paratympanic organ.
Propositional bases for the physics of the Bernoulli oscillators (A theory of the hidden degree of freedom): III - Mechanical-Statistical framework

In a few previous papers, we developed a so-called classical fluctuation model providing (generalized) symmetrization rules - these make the classical expressions for energy transfer probabilities compliant with the detailed balance principle. For various physical processes, the symmetrized expressions of the transfer probabilities were shown to be remarkably improved - with respect to the performances of standard semiclassical models - in view of approaching quantum-mechanical results. Therefore, the possibility that a still undiscovered classical physics potential to describe quantum effects may be revealed by the model must be investigated. In this and a few next papers, we introduce some conceptual developments of the model and discuss the fundamental properties of the so-called Bernoulli oscillators (in the present paper I, the thermodynamic properties), whose behavior is analyzed in the light of the assumed existence of a hidden degree of freedom. The displayed properties are taken as a basis for a (proposed) classical interpretation of some quantum effects. As a final result, to be deployed in a last paper, a Newtonian-like equation of motion for quantum particles (uni-dimensional case) will be introduced, seeming to us the good candidate to set a bridge between classical and quantum physics. By these means - although we remain sometimes within the boundary of a conjectural framework, and limited to the case of translational motion - the possibility to approach a solution to the old problem of inconsistency between classical and quantum mechanics is displayed, and discussed as a proposal.
Calculation of eigenpair derivatives for asymmetric damped systems with distinct and repeated eigenvalues

An algorithm is derived for the computation of eigenpair derivatives of asymmetric quadratic eigenvalue problem with distinct and repeated eigenvalues. In the proposed method, the eigenvector derivatives of the damped systems are divided into a particular solution and a homogeneous solution. By introducing an additional normalization condition, we construct two extended systems of linear equations with nonsingular coefficient matrices to calculate the particular solution. The method is numerically stable, and the homogeneous solutions are computed by the second‐order derivatives of the eigenequations. Two numerical examples are used to illustrate the validity of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.
Different Vestibular Rehabilitation Modalities in Unilateral Vestibular Hypofunction: A Prospective Study

Objective To assess the effectiveness of three vestibular rehabilitation protocols in patients with chronic unilateral vestibular hypofunction. Study Design Prospective randomized trial. Setting Audiology-neurootology laboratory in a general public hospital. Patients Eighty-one patients were randomly allocated into three groups: adaptation exercises (AEs), habituation exercises (HEs), and combined exercises (AE-HEs). Intervention(s) Each patient completed an 8-week vestibular rehabilitation program with exercise, depending on their allocation group. Main Outcome Measure(s) Evaluations performed at baseline, 4 weeks, and 8 weeks with (a) Functional Gait Assessment (FGA), (b) Mini-BESTest, (c) Vestibular Rehabilitation Benefit Questionnaire, and (d) Dizziness Handicap Inventory. Results FGA and Mini-BESTest scores showed significant improvement between the baseline and 8-week scores in all groups (p < 0.001), except for the FGA score in the HE group. The AE-HE group showed better scores for all measurements at 4 weeks and had significantly better FGA and Mini-BESTest scores than the AE group and better FGA scores than the HE group. The Vestibular Rehabilitation Benefit Questionnaire and Dizziness Handicap Inventory scores in the AE-HE group were significantly better (p ≤ 0.001) than those in the HE group at 8 weeks. Conclusions The AE-HE group showed faster improvement and significantly better outcomes for static balance, dynamic postural stability, and self-perceived disability than the single-exercise protocols in chronic unilateral vestibular hypofunction.
Intraoperative Electrically Evoked Compound Action Potential Growth and Maximum Amplitudes in Hearing Preservation Cochlear Implant Recipients

Background Electrically evoked compound action potentials (eCAPs) obtained from cochlear implant (CI) recipients reflect responsiveness of the auditory nerve to electrical stimulation. The recent use of atraumatic electrode arrays and expansion of CI candidacy to listeners with greater residual hearing may lead to increased clinical utility of intraoperative eCAP recordings. Objectives To examine the effect of electrode array (slim modiolar versus slim straight) on suprathreshold intraoperative eCAP recordings in hearing preservation CI recipients. A secondary goal was to examine potential clinical applications of intraoperative eCAPs for predicting immediate hearing preservation and speech perception outcomes. Methods Retrospective study of 113 adult hearing preservation CI candidates implanted from 2015 to 2019 with either a slim modiolar or slim straight electrode array. Intraoperative eCAP growth functions and maximum amplitudes were obtained at several intracochlear electrodes and examined as a function of implanted array and hearing preservation status, while controlling for electrode impedance. Results From basal to apical electrodes, progressively larger eCAP amplitudes and steeper slopes were recorded. Steeper eCAP slopes at apical electrodes were also seen for recipients of the slim modiolar array (versus slim straight). Suprathreshold eCAP responses did not differ as a function of hearing preservation and were not associated with speech recognition. Conclusions More robust eCAP responses were obtained from apical electrodes, which is consistent with better low-frequency thresholds in hearing preservation recipients. This effect was compounded by type of electrode array. Results also suggest that intraoperative, suprathreshold eCAPs cannot be used to predict the success of hearing preservation surgery or performance with the CI.
pmmeter: A Microbenchmark for Understanding Synchronization Cost on Persistent Memory

Persistent memory is an emerging memory device, which offers durable, relatively large memory space cost-effectively. This technology potentially enables a new horizon for a broad spectrum of data-intensive applications. Historically, memory space has been deemed to be volatile for application programs. Only very recently the processor industry has introduced a variety of memory synchronization instructions to ensure the durability of persistent memory. Yet, no known programming practice yields how application programs can fully exploit those instructions. This paper proposes pmmeter, a new microbenchmarking tool that we have developed to measure the primary performance of persistent memory. pmmeter allows for clarifying the performance impact that the choice of synchronization instructions incurs on a given sequence of memory access. This paper presents our experiment to demonstrate that pmmeter unveils the synchronization cost of Intel Optane DCPMM.
Pothole Detection Using YOLOv3 Model

When it comes to moving people and goods around a country, road transports are like the heart and arteries. It is essential that roads need be kept in good shape at all times. Most of the times, potholes on the road surface causes major damages and they must be repaired for maintaining the roads in usable condition. Countries like India, where roads span millions of kilometers and require constant upkeep, it may be difficult to locate potholes. Since this is the case, rapid, accurate, and real-time automated pothole detection is essential. The primary motivation of this proposed work is to explain the pothole prediction models that are created using object detection algorithm called YOLOv3. The YOLOv3 model is trained on a dataset of potholes, and the results are analyzed and compared with variants of YOLO by computing the model's precision, recall, and mAP. When compared to other models, the mAP of this one is improved while the computing cost is reduced significantly.
A Localization Algorithm Based on DV-Hop and Distance Geometry Constraints

The error in the DV-Hop localization algorithm is primarily in computing the distance between unknown and anchor nodes.Using distance geometry constraints and the Cayley-Menger determinant in two-dimensional space,the distance between unknown and anchor nodes were optimized and corrected.The conditions of distance geometry constraints successfully reduced the error of distance measurements between unknown and anchor nodes,and node localization was determined using trilateration.Simulation results show that the measurement data needing correction increases with the number of anchor nodes,and that this localization algorithm accurately measures the distance between unknown and anchor nodes,thereby improving positioning accuracy of network nodes and greatly increasing the coverage rate of nodes.
Named Entity Recognition in Chinese Electronic Medical Records Based on ALBERT-IDCNN-CRF

The task of electronic medical record named entity recognition is an integral part of the medical informatics field, which lays a strong data foundation for medical research and medical practice. The main task of electronic medical records named entity recognition is to transform unstructured text in clinical electronic medical records into structured data, which is still facing challenges such as semantic deficiencies and inefficiencies. In this paper, we focus on Chinese medical electronic medical record text and propose a Chinese medical electronic medical record named entity recognition model based on fusion learning of the ALBert model, in which the ALBert pre-trained language model can better represent the contextual semantics in electronic medical record sentences, and the Iterated Dilated Convolutional Neural Network (IDCNN) has better recognition in convolutional coding of local entities and finally the sequence tagging of named entities based on the Conditional Random Fields(CRF). Experimental results on the standard dataset show that the method improves the F1 value by 7.03 % compared to the baseline model and significantly reduces the time overhead.
A variable-collimation display system

Two important human depth cues are accommodation and vergence. Normally, the eyes accommodate and converge or diverge in tandem; changes in viewing distance cause the eyes to simultaneously adjust both focus and orientation. However, ambiguity between accommodation and vergence cues is a well-known limitation in many stereoscopic display technologies. This limitation also arises in state-of-the-art full-flight simulator displays. In current full-flight simulators, the out-the-window (OTW) display (i.e., the front cockpit window display) employs a fixed collimated display technology which allows the pilot and copilot to perceive the OTW training scene without angular errors or distortions; however, accommodation and vergence cues are limited to fixed ranges (e.g., ~ 20 m). While this approach works well for long-range, the ambiguity of depth cues at shorter range hinders the pilot’s ability to gauge distances in critical maneuvers such as vertical take-off and landing (VTOL). This is the first in a series of papers on a novel, variable-collimation display (VCD) technology that is being developed under NAVY SBIR Topic N121-041 funding. The proposed VCD will integrate with rotary-wing and vertical take-off and landing simulators and provide accurate accommodation and vergence cues for distances ranging from approximately 3 m outside the chin window to ~ 20 m. A display that offers dynamic accommodation and vergence could improve pilot safety and training, and impact other applications presently limited by lack of these depth cues.
Wide-field corrector with ADC compensator and image stabilizer for f/1.7 Gemini telescope prime focus

KAOS (Kilo-Aperture Optical Spectrograph) is a multi object spectrograph fiber fed from the f/1.7 prime focus of Gemini north telescope. A wide field corrector has been designed for the focus to provide a sub arc-second image over 1.5° FOV. The corrector includes four lenses and ADC (atmospheric dispersion compensator) prisms, which also act as an imager stabilizer. This paper describes the corrector design and presents the optical performance. In the final section, some other design options are discussed.
Hierarchical Classification of Android Malware Traffic

In the last few years, Android mobile devices have encountered a large spread and nowadays a huge part of the traffic traversing the Internet is related to them. In parallel, the number of possible threats and attacks has also increased, thus emphasizing the need for accurate automatic malware detection systems. In this paper, we design and evaluate a system to detect whether a traffic object (biflow) is benign or malicious, possibly understanding its specific nature in the latter case. The proposal leverages machine learning in a hierarchical fashion, in order to capitalize on the structure of the traffic data and reap both design and performance benefits. The comparative evaluation—performed considering the public CICAndMal2017 dataset—assesses the performance of several machine-learning algorithms and witnesses that the hierarchical approach leads to improved performance w.r. t. the flat approach (up to +0.18 F1-score, depending on the granularity of the analysis and the machine learning algorithm considered). In addition, we evaluate the impact of a reject-option mechanism, showing the trade-off between classification accuracy and ratio of classified biflows.
Cross-Sectional Association of Dietary Patterns and Supplement Intake with Presence and Gray-Scale Median of Carotid Plaques—A Comparison between Women and Men in the Population-Based Hamburg City Health Study

This population-based cross-sectional cohort study investigated the association of the Mediterranean and DASH (Dietary Approach to Stop Hypertension) diet as well as supplement intake with gray-scale median (GSM) and the presence of carotid plaques comparing women and men. Low GSM is associated with plaque vulnerability. Ten thousand participants of the Hamburg City Health Study aged 45–74 underwent carotid ultrasound examination. We analyzed plaque presence in all participants plus GSM in those having plaques (n = 2163). Dietary patterns and supplement intake were assessed via a food frequency questionnaire. Multiple linear and logistic regression models were used to assess associations between dietary patterns, supplement intake and GSM plus plaque presence. Linear regressions showed an association between higher GSM and folate intake only in men (+9.12, 95% CI (1.37, 16.86), p = 0.021). High compared to intermediate adherence to the DASH diet was associated with higher odds for carotid plaques (OR = 1.18, 95% CI (1.02, 1.36), p = 0.027, adjusted). Odds for plaque presence were higher for men, older age, low education, hypertension, hyperlipidemia and smoking. In this study, the intake of most supplements, as well as DASH or Mediterranean diet, was not significantly associated with GSM for women or men. Future research is needed to clarify the influence, especially of the folate intake and DASH diet, on the presence and vulnerability of plaques.
Security Threats and Research Challenges of IoT - A Review

The Internet of Things (IoT) refers to a telecommunication network that allows various objects to connect and share information via the Internet. It allows for machine-to-machine interaction and collaboration that does not necessitate human involvement. Its use has ushered in a new era of everything smart, including smart residences, smart cities, smart buildings, smart agriculture, and more. However, the security and privacy considerations associated with IoT pose significant hurdles. This paper provides a descriptive examination of the layered architecture of the IoT, as well as ways for overcoming security vulnerabilities using already established methods. Furthermore, based on the literature analysis, a more safe layered design that can be readily modified to improve cybersecurity problems has been proposed.
Zero Trust-NIDS: Extended Multi-View Approach for Network Trace Anonymization and Auto-Encoder CNN for Network Intrusion Detection

As the enterprise networks are being constantly targeted by sophisticated cyber threats, Zero Trust Security has been suggested to address existing threats. Zero Trust Security models have been recently proposed for outsourcing network security monitoring to third-party analysts. Therefore, the current trends of security monitoring needs to shift to "Never Trust, Always Verify". There are no concerns about analysis accuracy, if a zero trust model is resistant against security attacks. In this paper, a modified multi-view approach is proposed to preserve privacy in network traces, emphasizing the challenges needed to be tackled. We then extend the multi-view approach for the features that are not in the known list of the analyzer and extend the partitioning methods to a more balanced approach. In addition, in order to send any data to the analyzer, we propose to use an Auto-Encoder Convolutional Neural Network, which has the ability to receive any type of input attributes for detecting intrusive behavior. Our proposed multi-view approach outperforms existing works and improves efficiency by improving indistinguishability and preserving privacy for any attributes. The proposed Intrusion Detection System also outperforms existing works by up to 1% higher accuracy without any need for feature engineering.
A discrete time variable structure observer with overlapping boundary layers

This paper further develops the Discrete-Time Sliding Mode Observer (DSMO) of Thein and Misawa (2001). The state estimation technique allows for bounded matched and unmatched uncertainties/disturbances and has an overlapping set of boundary layers. In this presentation of the Double Boundary Layer DSMO (DBL-DSMO), the DSMO has an attractive outer boundary layer to which state estimate errors converge. In addition, it is guaranteed that the state estimates do not chatter along the outer boundary layer and, in fact, converge to within the outer boundary layer to the smallest existing inner boundary layer which satisfies given stability requirements. Within this inner boundary layer, state estimate errors axe proven to have a finite bound. This inherent effect facilitates a convenient and efficient way of choosing observer parameters. The DSMO is implemented on a second order resonance model with unmatched uncertainties and with process noise. Here, the behavior and effects of the outer boundary layer, and resulting inner boundary layer, are seen.
An image segmentation method for quantitative measurement of infrared imaging

External field infrared measurement is often affected by environmental radiation, scattering, atmospheric transmission, and other factors, which cause the poor spatial correlation, low signal-to-noise ratio of the infrared images, and it is difficult to segment the target and background for quantitative calculation. Therefore, in this paper, a method of infrared image enhancement and segmentation for external field measurement is described; based on Butterworth high pass filter, an enhancement factor is proposed to improve the transfer function. An infrared image of a cargo ship on the sea is processed by this method. The results indicated that, this method can effectively segment the target and background, and provide support for the quantitative calculation of infrared characteristics.
Simulation and analysis of repeater deceptive jamming to SAR based on shift-frequency

With the change of battlefield form, the pressure jamming requires the jammer to have large power, and the jamming efficiency is low, so the spoofing jamming comes into being. In this paper, the linear frequency modulation signal and fuzzy function are analyzed to verify the feasibility of using frequency shift forwarding to generate virtual images, and the two-dimensional frequency shift method is used to generate virtual images at specific locations to deceive local radar.
Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.
Ermittlung von Spiegelbildern an gekrümmten Oberflächen mit Rotationsssymetrie durch ein graphisches Verfahren

On decrit la visualisation de la forme tridimensionnelle des objets avec des surfaces brillantes eclairees avec une structure type grille. On etudie les images reflechies et la structure geometrique correspondante des modeles de surfaces. La methode optique est concue pour la detection des defauts sur des objets tests dans le controle de qualite