Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of landslide detection method based on improved mask R-CNN

Aiming at the problems of difficult feature extraction, low detection accuracy and long detection time in traditional landslide detection methods, considering the diversity and complexity of landslide and its background environment, a landslide detection method based on mask region-based convolutional networks (Mask R-CNN) and Squeeze-andExcitation (SE) attention mechanism is proposed. The training set with 2050 samples and the test set with 600 samples were established for landslide detection, which verified the effectiveness of the improved method. The precision, recall and F1-score of the improved recognition are 89.2 %, 98.0 % and 93.4 %, respectively. Precision, recall, and F1-score all rose in comparison to the model before the improvement by 2.9%, 3.9%, and 3.4%, respectively. The findings demonstrate that the enhanced method can effectively increase the accuracy of landslide identification. , and the landslide boundary segmentation results are closer to the real landslide boundary, so as to realize automatic and efficient landslide detection.
Image calibration using ensemble of transformer and CNN-based frameworks

Image distortion is a problem due to wide field-of-view cameras, and camera calibration is a fundamental step in various applications such as image undistortion, 3D reconstruction, and camera motion estimation to overcome this problem. In image calibration, intrinsic camera parameters such as focal length and distortion are estimated. The quality of the undistorted/enhanced image depends on the correctness of focal length and distortion. However, existing methods consist of two approaches: checkerboard, which requires manual interaction, and others are deep learning approaches. Most Deep Learning approaches are based on the Convolution Neural Network (CNN) framework, and it fails to capture the long-term dependency in a distorted image. This paper proposes a fully automated EnsembleNet method to infer the focal length and distortion parameters to overcome this problem. The proposed model extracts various contexts (local patches) by exploiting ViT(Vision Transformer) and spatial features from various CNN-based models using a single input image. The proposed model uses the differential evolution (DE) approach to learn the ensemble weights. The experiments show that the proposed EnsembleNet outperforms the state-of-the-art deep learning-based models in terms of mean squared error.
FuseDet: detection and positioning based on multi-field fusion

With the application of a new generation of information technology in the field of industry and manufacturing, the era of big data-driven manufacturing is coming. However, as the most basic component of the digital manufacturing workshop, the distribution of manufacturing resources such as personnel, equipment and products in the workshop is highly uncertain, so the management of manufacturing resources has become a major difficulty in the safe production link of the digital manufacturing workshop. Compared with the traditional target detection algorithm, this paper greatly reduces the detection omission problem due to occlusion through multi-field fusion, and improves the positioning accuracy of workshop manufacturing resources through absolute coordinate mapping and data fusion. We train the model from scratch, achieved 46.5 box AP and the max positioning deviation is 0.81m on NJUST workshop dataset.
3D reconstruction of gastrointestinal regions using shape-from-focus

3D shape reconstruction from images is an active topic in computer vision. Shape-from-Focus (SFF) is an important approach which requires image stack in a focus controlled manner to infer the 3D shape. In this article, 3D reconstruction of synthetic gastrointestinal regions is done using SFF. Image stack is generated in Blender software with focus controlled camera. A color focus measure is applied for shape recovery followed by a weighted L2 regularizer to estimate for inaccurate depth values. A precise comparison is done between recovered shape and ground truth data by measuring the depth error and correlation between them. Results shows that SFF technique will be practical for 3D reconstruction of GI regions with focus and motion controlled pillcams which is technologically feasible to implement.
Theoretical evaluation of the performance of ambient LoRa backscatter communication

In the search of low power communication technology for future battery-free device and IoT, ambient backscatter communication is a promising solution. To this end, this work aims to analyze theoretically the performance of ambient backscatter system using LoRa transmission as an excitation signal. The tag data are encoded using FSK modulation, and at the receiver side, the quadrature demolator is used to generate the baseband signal and a correlator demodulator combined with a square law detection is used to decode the tag data. A theoretical BER calculation is provided using the numerical computation of the approximated union band equation and show a good performance of LoRa backscatter over the bi-static backscatter configuration for SNR values comprise between 2db and 9dB.
Time-gated-single-pixel-camera: a promising sensor for robust object detection in adverse weather conditions for autonomously driven vehicles

The degree of autonomy in vehicles depends directly on the performance of their sensor systems. The transition to even more autonomously driven cars therefore requires the development of robust sensor systems with different skills. Especially in adverse and changing weather conditions (rain, snow, fog, etc.), conventional sensor systems such as cameras perform unreliably. Moreover, data evaluation has to be performed in real-time, i.e. within a fraction of seconds, in order to safely guide the car through traffic and to avoid a crash with any obstacle. Therefore, we propose to use a so called time-gated-single-pixel-camera, which combines the principles of time gating and compressed sensing. In a single pixel camera, the amount of recorded data can be significantly reduced compared to a conventional camera by exploiting the inherent sparsity of scenes. The lateral information is gained with the help of binary masks in front of a simple photodiode. We optimize the pattern of the masks by including them as trainable parameters within our data evaluation neural network. Additionally, our camera is able to cope with adverse weather conditions due to the underlying time gating principle. The feasibility of our method is demonstrated by simulated and measured data as well.
Reconstructed intensity image-based loop detection method for event-based visual odometry

Existing methods related to event-based SLAM mostly realize front-end visual odometry function in visual navigation and it is difficult to handle loop closure task directly with accumulated events, in which case a complete SLAM framework is needed to be accomplished. This paper presents a solution to the problem of loop closure detection in real time event-based visual odometry with assistance of reconstructed intensity image. The proposed method selects keyframe in frontend and detects loop closure using calculated bag-of-words from intensity image. While loop closure is detected, the photometric bundle adjustment method is implemented to refine global camera pose and global 3D depth points constraints. The proposed method has been tested in MVSEC event-based stereo datasets with loop information, and successfully detects and corrects loop closure. Experiments have shown that the proposed method can successfully reduce about 50% error triggered by visual odometry in real-time trajectory estimation.
The judicialization of territorial politics in Italy

ABSTRACT This article focuses on Italy as an example of the judicialization of territorial politics, with the Constitutional Court adopting a growing role in centre–region relationships. It addresses the questions of why and how the role of the Italian Constitutional Court in terms of territorial governance has changed over the past two decades. It shows how the dynamics of decentralization, as pushed forward by the reform of the Title V of the Constitution, favoured the consolidation of the Court as a fundamental actor in intergovernmental relationships. Drawing on an extensive dataset of judicial decisions, the article provides empirical data demonstrating how the Court has become increasingly preoccupied with questions related to territorial politics over the last years. The article explores how the Constitutional Court shaped territorial politics through judicial review with the reform of Title V of the Constitutional Charter by focusing on the Court’s most significant decisions on the topic. The article emphasizes the fact that the limits of the constitutional reform have tended to produce a judicial redefinition of the centre–periphery arena and pushed the Constitutional Court to play a significant role in the new centre–region relationships.
Understanding Concurrent Transmissions: The Impact of Carrier Frequency Offset and RF Interference on Physical Layer Performance

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15. 4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15. 4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.
Video Decoding Energy Reduction Using Temporal-Domain Filtering

In this paper, we study decoding energy reduction opportunities using temporal-domain filtering and subsampling methods. In particular, we study spatiotemporal filtering using a contrast sensitivity function and temporal downscaling, i.e., frame rate reduction. We apply these concepts as a pre-filtering to the video before compression and evaluate the bitrate, the decoding energy, and the visual quality with a dedicated metric targeting temporally down-scaled sequences. We find that decoding energy savings yield 35% when halving the frame rate and that spatiotemporal filtering can lead to up to 5% of additional savings, depending on the content.
A gas‐kinetic scheme for the simulation of turbulent flows on unstructured meshes

This paper presents a numerical method for simulating turbulent flows via coupling the Boltzmann BGK equation with Spalart–Allmaras one equation turbulence model. Both the Boltzmann BGK equation and the turbulence model equation are carried out using the finite volume method on unstructured meshes, which is different from previous works on structured grid. The application of the gas‐kinetic scheme is extended to the simulation of turbulent flows with arbitrary geometries. The adaptive mesh refinement technique is also adopted to reduce the computational cost and improve the efficiency of meshes. To organize the unstructured mesh data structure efficiently, a non‐manifold hybrid mesh data structure is extended for polygonal cells. Numerical experiments are performed on incompressible flow over a smooth flat plate and compressible turbulent flows around a NACA 0012 airfoil using unstructured hybrid meshes. These numerical results are found to be in good agreement with experimental data and/or other numerical solutions, demonstrating the applicability of the proposed method to simulate both subsonic and transonic turbulent flows. Copyright © 2016 John Wiley & Sons, Ltd.
Detection of Fibrillatory Episodes in Atrial Fibrillation Rhythms via Topology-informed Machine Learning

Effective and efficient methods for diagnosing cardiac conditions remain of significant importance and relevance in clinical cardiology. As such, advances in machine- and deep-learning technologies pave the way to high throughput approaches to automated classification of cardiac abnormalities. While there is rich literature on ECG-based classification of cardiac conditions, particularly on diagnosing Atrial Fibrillation, there is a dearth on algorithms that can effectively measure the onset and offset of atrial fibrillation events within an ECG. In this work, we show that an off-the-shelf machine learning algorithm can be trained on mathematically-computable shape signatures embedded within the local topology of ECGs to identify fibrillatory episodes in ECGs of AF patients. More precisely, we show that a topology-informed machine learning algorithm can accurately classify segments within an ECG as either resembling an atrial fibrillation event or not. Furthermore, we show that based on the model-provided classification of segments, a simple criterion may be used to determine whether the AF rhythm is paroxysmal or persistent.
The Method of Moving Frames for Surface Global Parametrization

This article introduces a new representation of surface global parametrization based on Cartan’s method of moving frames. We show that a system of structure equations, characterizing the local coordinates changes with respect to a local frame system, completely characterizes the set of possible cone parametrizations. The discretization of this system provably provides necessary and sufficient conditions for the existence of a valid mapping. We are able to derive a versatile algorithm for surface parametrization, allowing feature constraints and singularities. As the first structure equation is independent of the global coordinate system, we do not require prior knowledge of cuts or cone positions. So, a single non-linear least-square problem is enough to place quantized cones while minimizing a given distortion energy. We are therefore able to take full advantage of the link between the parametrization geometry and the topology of its cone metric to solve challenging constrained parametrization problems.
Energy Efficiency Improvements in Software-Based Video Encoding

In this paper, we discuss the evolution in energy efficiency for software-based encoding on general-purpose processors, along with recent trends that enable increasingly power-efficient video encoding. We provide an overview of different contributing factors that have led to a massive increase in software-based performance, along with innovations that are leading to further efficiency improvements.
Quality metrics prediction in process manufacturing based on CNN-LSTM transfer learning algorithm

The prediction of production process quality indicators plays an important role in product quality and production scheduling in process industries. In order to exploit the effective information contained in the massive process data, improve the prediction accuracy of production process quality indicators and apply to the changes of processing conditions, a hybrid model quality indicator migration learning prediction method based on convolutional network (CNN) and long-short-term memory (LSTM) is proposed. Massive amounts of historical process data, operational data and date data were constructed into a continuous feature matrix with a time-sliding window. The feature vectors are first extracted using CNN, and the feature vectors are constructed in a time-series sequence and used as input data for the LSTM network. Then the LSTM network is used for quality index prediction. In this process, migration learning strategy is introduced, which reduced the training time while ensuring the training accuracy. Finally, the correctness and effectiveness of the proposed method is verified by using the process data of a tobacco factory microtobacco cutting test line as a case object.
Crystal structure of SUN1-KASH6 reveals an asymmetric LINC complex architecture compatible with nuclear membrane insertion

The LINC complex transmits cytoskeletal forces into the nucleus to control the structure and movement of nuclear contents. It is formed of nuclear SUN and cytoplasmic KASH proteins, which interact within the nuclear lumen, immediately below the outer nuclear membrane. However, the symmetrical location of KASH molecules within SUN-KASH complexes in previous crystal structures has been difficult to reconcile with the steric requirements for insertion of their immediately upstream transmembrane helices into the outer nuclear membrane. Here, we report the crystal structure of the SUN-KASH complex between SUN1 and JAW1/LRMP (KASH6) in an asymmetric 9:6 configuration. This intertwined assembly involves two distinct KASH conformations such that all six KASH molecules emerge on the same molecular surface. Hence, they are ideally positioned for insertion of upstream sequences into the outer nuclear membrane. Thus, we report the first structure of a SUN-KASH complex that is directly compatible with its biological role.
Subcutaneous and orally self-administered high-dose carprofen in male and female mice: pharmacokinetics, tolerability and impact on cage-side pain indicators

Surgical interventions in mice are prerequisite in various research fields and require appropriate pain relief, not only to ensure animal welfare but also to avoid influence of pain on research findings. Carprofen is a non-steroidal anti-inflammatory drug that is commonly used as an analgesic for interventions inducing mild to moderate pain in animals. Despite its frequent use also in laboratory rodents, data on pharmacokinetics and side effects, and on its potential impact on behavioral pain indicators are rare. This study aimed to determine pharmacokinetic and tolerability profiles of high dose carprofen in male and female C57Bl/6J mice, administered via single subcutaneous injection (s.c.) and oral self-administration per drinking water (d.w.). Plasma concentrations of carprofen were measured at various time points, and side effects were evaluated using a modified Irwin test protocol, hematology and histopathology. Additionally, potential effects on behavioral pain indicators commonly used to assess post-surgical pain, such as the mouse grimace scale, wheel running activity, burrowing, nesting and grooming behavior were investigated. Quantification of carprofen in plasma revealed maximum plasma concentrations of 133.4 ± 11.3 µg/ml after 1 hour and an elimination half-life of 8.52 hour after single s.c. injection of 20 mg/kg carprofen. Oral self-administration of carprofen (25 mg/kg/24 h) resulted in a steady-state < 24 hours over 5 days after treatment start with plasma levels of around 60 µg/ml. The carprofen-medicated water was highly accepted, and increased d.w. intake was observed in the first 24 hours after exposure for both sexes (p < 0.0001). Irwin test detected only minor side effects, and hematology and histopathology where without pathological findings that could be attributed to carprofen treatment. Except for a decrease of 49-70 % in wheel running activity in male mice, behavioral pain indicators were only very mildly affected. This study determined carprofen plasma levels in mice lying well above an estimated therapeutic concentration for both routes of administration. Carprofen was well tolerated at recommended high doses and may provide sufficient analgesia for minor interventions as well as be applied as a tolerable component in multimodal analgesic regimens.
Lamivudine (3TC), a Nucleoside Reverse Transcriptase Inhibitor, Prevents the Neuropathological Alterations Present in Mutant Tau Transgenic Mice

The dysregulation of transposable elements contributes to neurodegenerative disorders. Previous studies have reported an increase in retrotransposon transcription in Drosophila models, as well as in human tauopathies. In this context, here we tested the possible protective effects of a reverse transcriptase inhibitor, namely lamivudine (also known as 3TC), in P301S mice, an animal model of Alzheimeŕs disease based on FTDP-17-tau overexpression. Transgenic P301S mice administered lamivudine through drinking water showed a decrease in the following histopathological marks typical of tauopathies: tau phosphorylation; inflammation; neuronal death; and hippocampal atrophy. Lamivudine treatment attenuated motor deficits (Rotarod test) and improved short-term memory (Y-maze test). To evaluate the role of tau in retrotransposition, we cotransfected HeLa cells with a plasmid containing a complete LINE-1 sequence and a neomycin reporter cassette designed for retrotransposition assays, and a plasmid with the tau sequence. LINE-1 insertion increased considerably in the cotransfection compared to the transfection without tau. In addition, lamivudine inhibited the insertion of LINE-1. Our data suggest that the progression of the tauopathy can be attenuated by the administration of lamivudine upon the first symptoms of neuropathology. Highlights Lamivudine treatment in drinking water inhibited a tau-associated pathology in a mouse model of tauopathy. Tau promotes the insertion of transposable elements LINE-1 in vitro. LINE-1 insertion in vitro is inhibited by lamivudine. Lamivudine attenuates memory and motor deficits in tau transgenic mice.