Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometry-based Partitioning for Predictive Video Coding with Transform Adaptation

Rectangular block partitioning as it is used in state of the art video codecs such as HEVC can produce visually displeasing artifacts at low bitrates. This effect is particularly noticeable at moving object boundaries. This contribution presents a comprehensive geometry-based block partitioning framework in a post-HEVC codec for motion compensated prediction, intra-prediction and transform coding as a solution. The method is evaluated on the set of sequences defined by the Joint Call for Proposals on Video Compression with Capabilities beyond HEVC [1]. Our contribution aims at visually improving the quality of object boundaries and provides an objective BD-rate gain of 0.82% on average compared to the reference Joint Video Exploration Team (JVET) test model (JEM 7.0).
The Role of Impulsivity and Expectancies in Predicting Marijuana Use: An Application of the Acquired Preparedness Model

ABSTRACT Impulsivity and substance use covary. Smith and Anderson’s acquired preparedness model proposes that impulsivity predicts substance use through a mediational model such that substance use expectancies mediate the relation between impulsivity and drug use. The present study seeks to examine the relation between positive urgency, an important component of impulsivity with specific relations to substance use behavior, marijuana expectancies, and marijuana use patterns. The study focused on a sample of frequent marijuana users (n = 3,616) and assessed positive urgency using the UPPS-P, expectancies using the Biphasic Marijuana Effects Scale, an adapted form of the Biphasic Alcohol Effects Scale to measure the sedative and stimulant properties of marijuana, and also assessed use patterns. Findings suggest that stimulant expectancies predict heavier, more frequent marijuana use than sedative expectancies and that marijuana expectancies vary based on the limb of marijuana intoxication. Examination of the acquired preparedness model revealed that positive urgency’s link to marijuana use was fully mediated by expectancies.
A Novel Approach for Edge Detection for Blurry Images by using Digital Image Processing

This paper proposed a algorithm to find edges of blurry images effected by noise and compares the different edge detection method in blurry condition with available techniques. This included going from a basic gray scale bitmap image all the way to a fully encoded file readable by standard image readers. Edges are boundaries between different texture and edge detection is a technique for finding the boundaries of object with in image. Edges are very important part of image to recognize objects, feature extraction, pattern recognition etc. For blurry and noisy images have to apply some pre-processing techniques to restore the images. This paper proposed an algorithm to restore images those are effected by noise and blueness. We also shows that are result is comparatively better as compare to existing techniques.
Chunking to improve verbal forward spans in Korsakoff’s syndrome

Abstract Chunking is a mnemonic strategy that involves organizing information into appropriate units. Our article examined the use of this strategy on forward and backward span performance in Korsakoff’s syndrome. Fifteen patients with Korsakoff’s syndrome and 17 age-and-education matched healthy controls participated to the study. Digit span performance (both forward and backward) was tested before and after chunking training. Results demonstrated an increased performance on the forward spans after chunking training in the patients with Korsakoff’s syndrome, but no beneficial effect was observed on the backward spans in these participants. Controls demonstrated a chunking effect on both forward and backward span performance. Our findings suggest that a simple training in chunking may be useful as part of a cognitive strategy training for improving working memory performance in patients with Korsakoff’s syndrome.
Automatic Photography Device FocusSphere for Micro Close Range Photogrammetry

The paper presents basic construction principles of a new, original device called FocusSphere. The device is used to acquire photographic data and their geometrical properties for the purpose of close range photogrammetry. First of all, the paper shows how micro close range photogrammetry is successfully used in many areas. Subsequently, the construction of the device is briefly presented. The most important features and their influence for obtained results are discussed. The paper portrays also the process of determining the orientation of images for any FocusSphere program. For this purpose total station and photogrammetric measurements are made. The results of the measurements are analysed due to determine movements characteristics of the machine. Finally, the paper depicts main similarities and differences between FocusSphere and existing, conterminous technologies. The advantages and disadvantages of each solution are discussed.
Status, Challenges, and Perspectives of Mainstream Nitritation–Anammox for Wastewater Treatment

The nitritation–anammox process is an efficient and cost‐effective approach for biological nitrogen removal, but its application in treating mainstream wastewater remains a great challenge. Mainstream nitritation–anammox processes could create opportunities for achieving energy self‐sufficient, or energy‐generating water resource recovery facilities. Significant advancements have been achieved via pilot‐ and full‐scale trials to overcome the major obstacles under mainstream conditions, such as repression of nitrite‐oxidizing bacteria, limiting the overgrowth of denitrifiers, and effective selection and retention of ammonia‐oxidizing bacteria and anammox bacteria. This review paper intends to provide a detailed update of research progress on mainstream nitritation–anammox processes, discuss metabolic interactions, and examine major challenges and possible solutions towards the future development.
Capacitive Probe Fed Broadband Circularly Polarized Omnidirectional Antenna

A broadband circularly polarized (CP) omnidirectional antenna fed by a capacitive probe is presented in this paper. To attain wideband vertical polarization, a circular monopole patch connecting to the ground by a set of shorting pins is applied. Two groups of curved branches are introduced to create wideband horizontal polarization. The CP radiation can be generated with a proper phase difference between the vertical polarization and the horizontal polarization. The antenna is fabricated and measured. It exhibits an impedance bandwidth (VSWR<2) of 34.6% (ranges from 4.50 to 6.38 GHz) and an axial ratio bandwidth (AR<3dB) of 33.5% (ranges from 4.60 to 6.45 GHz). The profile of the antenna is merely 0. 06λL (λL is the wavelength corresponding to the lowest operating frequency).
Learning User Preferences and Understanding Calendar Contexts for Event Scheduling

With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior. However, event scheduling is still time-consuming even with the development of online calendars. Although machine learning based event scheduling models have automated scheduling processes to some extent, they often fail to understand subtle user preferences and complex calendar contexts with event titles written in natural language. In this paper, we propose Neural Event Scheduling Assistant (NESA) which learns user preferences and understands calendar contexts, directly from raw online calendars for fully automated and highly effective event scheduling. We leverage over 593K calendar events for NESA to learn scheduling personal events, and we further utilize NESA for multi-attendee event scheduling. NESA successfully incorporates deep neural networks such as Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Highway Network for learning the preferences of each user and understanding calendar context based on natural languages. The experimental results show that NESA significantly outperforms previous baseline models in terms of various evaluation metrics on both personal and multi-attendee event scheduling tasks. Our qualitative analysis demonstrates the effectiveness of each layer in NESA and learned user preferences.
New advances in the molecular classification of pediatric mesenchymal tumors

Pediatric soft tissue tumors are relatively rare and show significant overlap in morphology and immunoprofile, often posing diagnostic and management challenges. Thus, their classification remains often subjective or lumped under “unclassified categories,” as a number of lesions lack objective and reproducible criteria in diagnosis. Although in a subset of cases immunohistochemistry has been proved useful to identify a specific line of differentiation, most tumors lack a readily defined histogenesis, being characterized by a rather non‐specific immunoprofile. Furthermore, tumors with an ambiguous diagnosis are difficult to grade and their risk of malignancy or clinical management remains uncertain. Advances in molecular genetics, including the more wide application of next generation sequencing in routine clinical practice, have improved diagnosis and refined classification based on objective molecular markers. Importantly, some soft tissue tumors in children are characterized by recurrent gene fusions involving either growth factors (eg, PDGFB) or protein kinases (eg, ALK, ROS, NTRK, BRAF), which have paved the way for new targeted treatments that block the respective upregulated downstream pathways. However, the majority of gene fusions or mutations detected in soft tissue tumors result in an abnormal function of transcription factors or chromatin remodeling. The present review focuses on the latest genetic discoveries in the spectrum of both benign and malignant pediatric soft tissue neoplasia. These genetic abnormalities promise to provide relevant insight for their proper classification, prognosis, and treatment. The entities discussed herein are grouped either based on their shared genetic mechanism or based on their presumed line of differentiation.
Comparative Performance of Simultaneous Localization and Mapping Algorithms for Unmanned Aircraft Based Navigation Systems

Simultaneous Localization and Mapping (SLAM) is an essential navigation tool for unmanned aircraft systems (UAS) operating in “GNSS denied” locations. In order to assess the performance of various sensing and SLAM algorithm combinations, clear and well-defined metrics are required. Currently, only one metric has been proposed, based on SLAM simulations, the Santos Metric. This metric has limitation in terms of low map noise sensitivity, and the use of insignificant data points. As a result, two new metrics have been proposed, and compared to the Santos Metric. All three metrics were found to identify poor performing sensors; however, the three metrics disagreed in terms of the best sensor.
A 2.5-GHz CMOS Full-Duplex Front-End for Asymmetric Data Networks

Electrical balance-based full-duplex front-end allows high power operation but has strong tradeoff between Tx and Rx insertion loss. In this paper, we present a capacitive bridge-based duplexer for full-duplex operation with tunable Tx/Rx insertion loss to improve link budget in an asymmetric data network. Theoretical analysis is done to show that capacitive bridge-based duplexer can be better than hybrid transformer in CMOS process. Capacitive bridge architecture is suitable for insertion loss tunability and this tunability gives an additional advantage of increasing the range of allowed antenna impedance for the given balance network. The fully integrated duplexer with receiver is implemented in a 130-nm CMOS process, and is capable of handling Tx power of upto +16dBm at antenna. The prototype chip demonstrates tunable Tx/Rx insertion loss achieving an overall receiver noise figure of 5.7–7.5dB and a Tx insertion loss of 3.9–5.6dB. Self-interference cancellation of >50dB is measured for 20-MHz RF bandwidth in 2.4–2.6-GHz frequency range.
Multisource learning for skeleton-based action recognition using deep LSTM and CNN

Abstract. Human action recognition based on information concerning the skeleton of the human body has been widely studied because such information can simply and clearly express features related to actions, and is unaffected by physical features of the body. Therefore, this paper discusses action recognition based on three-dimensional skeletal information obtained from RGB-D videos. We propose a multisource action recognition model that combines features of the temporal and spatial domains. Our model focuses on action features from three levels: global-level, local-level, and detail-level, since different actions concern different parts of the body. For temporal features, we adopt long short-term memory to create a model that analyzes skeleton sequence. For features of the spatial domain, we analyze the effects of three features on action recognition: skeleton joint coordinate, pairwise relative position, and speed of movement. Finally, the temporal and spatial domain models are combined into a multisource model for improving the accuracy of action recognition. Experiments show that our model brings about considerable improvement in the recognition of a variety of general actions and interactive activities.
Interoception in Psychiatric Disorders: A Review of Randomized, Controlled Trials with Interoception-Based Interventions

Abstract Interoception, or the process of sensing, interpreting, and integrating internal bodily signals, has increasingly been the subject of scientific research over the past decade but is still not well known in clinical practice. The aim of this article is to review clinical treatment interventions that use interoception, to synthesize the current research knowledge, and to identify the gaps where future research is needed. We conducted a comprehensive literature search on randomized, controlled trials that both include interoception in treatment interventions for individuals with psychiatric disorders and measure aspects of interoception using self-report measures. Out of 14 randomized, controlled trials identified, 7 found that interventions with interoception were effective in ameliorating symptoms. These studies included individuals with anxiety disorders, eating disorders, psychosomatic disorders, and addictive disorders. All of the intervention studies with positive clinical outcomes also demonstrated changes on interoceptive measures; however, these measures were often related to specific illness symptoms. Interoception may be a mechanism of action in improving clinical symptomatology, though studies incorporating general, symptom-independent interoceptive measures remain scarce. To further our understanding of the role interoception has in psychiatric disorders and their treatment, more studies integrating interoceptive measures are needed, along with a clearer definition of interoceptive terms used.
On-Body Transmission Performance of a Novel Dual-Mode Wearable Microstrip Antenna

The path loss established between several wearable antennas placed on a tissue-equivalent phantom is investigated through numerical simulations and measurements. The on-body performance of a previously proposed dual-mode wearable textile antenna, designed to operate in the 2.45 GHz industrial, scientific and medical band, is evaluated and compared with a wearable textile patch antenna. Simulation results of a $\lambda $ /4 resonant wire monopole are also presented to serve as a reference. Five different scenarios for the location of the transmitting and receiving antennas are analyzed: one with line of sight (LOS), two with non-LOS (NLOS), and the remaining two in the transition between LOS and NLOS. The transmission results are evaluated with the antennas mounted on the surface of a homogeneous phantom with 2/3 muscle properties mimicking the shape and physical macroscopic properties of a human torso. A general good agreement is obtained between numerical simulation and experimental results. It is shown that in the NLOS scenario, the proposed antenna has an equivalent path loss only about 3.4 dB higher than the monopole and about 10 dB lower than the patch.
Measuring the absolute distance of a front vehicle from an in-car camera based on monocular vision and instance segmentation

Abstract. The requirements of distance measurement have increased with the development of auto vehicle driving. Traditional methods for distance estimation require the complex calibration from intrinsic and external parameters of the camera. Recent methods based on the neural network structure mainly measure the relative depth of whole images. We adopt monocular vision with instance segmentation and camera focal length to detect the absolute distance of front cars from in-car cameras. First, we extract the location of the cars from the object detection network. Second, the location of cars is sent to the vehicle classification network and instance segmentation network to obtain the type of the cars and their mask value. Here, we use a model trained by the CompCars dataset to classify car types, and we train a new instance segmentation model using the Cityscapes dataset to obtain each car’s mask. Third, in accordance with the camera imaging principle, the absolute distance of cars in the images is calculated based on the relationship between the size information of different car types and their mask values. The proposed method is examined with the KITTI dataset, and the experiment shows that its results can be close to the ground truth. Moreover, the proposed method uses the instance segmentation network to reduce complexity of the depth estimation process and it can still generate satisfactory results even when cars are partly occluded.
Adaptive Finite-Time Dynamic Output-Feedback FTC Design for MIMO Nonlinear Systems With Actuator and Sensor Faults

This paper investigates the problem of the finite-time fault-tolerant control for multiple-inputs multiple-outputs nonlinear systems. The controlled systems contain the unmeasurable states, unknown parameters and possible actuator, and sensor faults. First, a new dynamic output-feedback control strategy (high-gain K-filter observer with new dynamic gain update rule) is designed, which can simultaneously estimate the unmeasurable states and unknown parameters. Then, by employing a cubic absolute-value Lyapunov function and our designed dynamic high-gain K-filter observer, both actuator and sensor faults can be compensated concurrently. The controller design utilizes the adaptive backstepping approach and the stability analysis employs the Lyapunov function stability theory and the finite-time stability criterion. It is proved that by using the proposed method, all the signals in the closed-loop systems are semi-global practical finite-time stable, and also the tracking errors can converge to a sufficiently small neighborhood of zero in finite time. Final, simulation results verify the effectiveness of the proposed method.
Constrained Estimation-based Nonlinear Model Predictive Control for UAV-Elastic Tool Interaction

Interaction between an unmanned aerial vehicle (UAV) and an object or environment is a promising field for real-time applications. However, this state of the art brings challenging control problems. In order to handle the problem called stiff equation as well as bouncing, a tailored elastic tool based on a passive spring is proposed which can provide a safe interaction with the environment. Due to having constraints, e.g., the touching sensor's upper force bound and limited space for the operation environment, a constrained optimization-based algorithm is considered. The full system state, including an unmeasured vertical force, is estimated by nonlinear moving horizon estimation (NMHE) after each new measurement becomes available. The estimated values are then fed into the nonlinear model predictive control (NMPC) which provides the total force and three angular positions. Thanks to the vertical force information given by the NMHE, the presented interaction controller is able to interact with the ceiling in a predefined range. As opposed to multi-model approaches, a modeling, estimation, and control of the whole system are presented with a centralized approach. However, the loop consisting of an NMPC and NMHE is closed in an average of 5 ms.
A Volumetric Fusing Method for TLS and SFM Point Clouds

A terrestrial laser scanning (TLS) point cloud acquired from a given ground view is incomplete because of severe occlusion and self-occlusion. The models reconstructed by aligning the cross-source point clouds [TLS and structure-from-motion (SFM) point clouds] provide a more complete large-scale outdoor scene. However, because of differences in nonrigid deformation, stratified redundancy of alignment is inevitable and ubiquitous. Therefore, this paper presents a volumetric fusing method for cross-source three-dimensional reconstructions. To eliminate the stratification of aligned cross-source point clouds, we propose a graph-cuts method with boundary constraints for blending the two cross-source point clouds. Then, to reduce the gaps that exist in the blending results, we develop a progressive migration method combined with the local average direction of normal vectors to smooth the unconnected boundary. Finally, experimental results demonstrate the effectiveness of eliminating stratification with the proposed blending algorithm, and the progressive migration method achieves a smooth connection in the boundary of the blended point clouds.
One Secure Access Scheme Based on Trusted Execution Environment

The wide adoption of mobile devices poses an imminent threat to preserving privacy. In order to prevent the leakage of sensitive information (such as the contact list, or private pictures) raised by inappropriate access, we propose a secure access scheme based on Trusted Execution Environment (TEE) specifically designed for mobile devices. The proposed scheme has two purposes: secure access and minimizing losses. For secure access, we use TEE to run access authorization and verification; for minimizing losses, we use the strategy of file slice to divide the sensitive file into multiple parts and then encrypt the file parts respectively in case of large-scale leakage. Practically, our scheme's "minimizing losses" can be easily expanded to cloud computing by mapping one file part in our scheme to a whole file in cloud computing scenario. The theoretical analysis and experimental evaluation indicate that our scheme meets the requirement in respects of efficiency and security.