Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of artificial light at night on nocturnal insects: A review and synthesis

Abstract In recent decades, advances in lighting technology have precipitated exponential increases in night sky brightness worldwide, raising concerns in the scientific community about the impact of artificial light at night (ALAN) on crepuscular and nocturnal biodiversity. Long‐term records show that insect abundance has declined significantly over this time, with worrying implications for terrestrial ecosystems. The majority of investigations into the vulnerability of nocturnal insects to artificial light have focused on the flight‐to‐light behavior exhibited by select insect families. However, ALAN can affect insects in other ways as well. This review proposes five categories of ALAN impact on nocturnal insects, highlighting past research and identifying key knowledge gaps. We conclude with a summary of relevant literature on bioluminescent fireflies, which emphasizes the unique vulnerability of terrestrial light‐based communication systems to artificial illumination. Comprehensive understanding of the ecological impacts of ALAN on diverse nocturnal insect taxa will enable researchers to seek out methods whereby fireflies, moths, and other essential members of the nocturnal ecosystem can coexist with humans on an increasingly urbanized planet.
Computational model for tactile sensing system with wrinkle’s morphological change

ABSTRACT We previously developed Wrin’Tac, a tactile sensing system that can select sensing modalities by changing its morphology. In this paper, we present a computational model to estimate a wrinkle’s morphology and predict the output of embedded sensing elements in both static indentation and sliding action cases. We evaluated the wrinkle shape and the posture of the sensing element by calculating its height. The wrinkle’s mechanical change is assessed by ascertaining its stiffness under vertical indentation by a spherical indenter. The output voltages of the sensor under static indentation are calculated by the proposed model, and the experimental values have error less than 10%, which validates the accuracy of our proposed model. For dynamic sliding action, this proposed model clarified the capability of wrinkle morphology in an evaluation of such sliding action’s characteristics as the sliding direction and velocity. We also identified the role of the wrinkle’s morphology in the sensor’s sensitivity under different conditions of dynamic sliding motion, implying that this sensing system may select suitable sensitivity for specific sensation tasks. We expect this work to pave the way for assessing the role of morphological changes to tactile sensation and developing soft active tactile sensing systems. GRAPHICAL ABSTRACT
Improved Sliding Shapes for Instance Segmentation of Amodal 3D Object

State-of-art instance segmentation networks are successful at generating 2D segmentation mask for region proposals with highest classification score, yet 3D object segmentation task is limited to geocentric embedding or detector of Sliding Shapes. To this end, we propose an amodal 3D instance segmentation network called A3IS-CNN, which extends the detector of Deep Sliding Shapes to amodal 3D instance segmentation by adding a new branch of 3D ConvNet called A3IS-branch. The A3IS-branch which takes 3D amodal ROI as input and 3D semantic instances as output is a fully convolution network(FCN) sharing convolutional layers with existing 3d RPN which takes 3D scene as input and 3D amodal proposals as output. For two branches share computation with each other, our 3D instance segmentation network adds only a small overhead of 0.25 fps to Deep Sliding Shapes, trading off accurate detection and point-to-point segmentation of instances. Experiments show that our 3D instance segmentation network achieves at least 10% to 50% improvement over the state-of-art network in running time, and outperforms the state-of-art 3D detectors by at least 16.1 AP.
A Memory Configuration Method for Virtual Machine Based on User Preference in Distributed Cloud

It is well-known that virtualization technology can bring many benefits not only to users but also to service providers. From the view of system security and resource utility, higher resource sharing degree and higher system reliability can be obtained by the introduction of virtualization technology in distributed cloud. The small size time-sharing multiplexing technology which is based on virtual machine in distributed cloud platform can enhance the resource utilization effectively by server consolidation. In this paper, the concept of memory block and user satisfaction is redefined combined with user requirements. According to the unbalanced memory resource states and user preference requirements in multi-virtual machine environments, a model of proper memory resource allocation is proposed combined with memory block and user satisfaction, and at the same time a memory optimization allocation algorithm is proposed which is based on virtual memory block, makespan and user satisfaction under the premise of an orderly physical nodes states also. In the algorithm, a memory optimal problem can be transformed into a resource workload balance problem. All the virtual machine tasks are simulated in Cloudsim platform. And the experimental results show that the problem of virtual machine memory resource allocation can be solved flexibly and efficiently.
Interactive Full Image Segmentation

We address the task of interactive full image annotation, where the goal is to produce accurate segmentations for all object and stuff regions in an image. To this end we propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions. This enables the annotator to focus on the largest errors made by the machine across the whole image, and to share corrections across nearby regions. Furthermore, we adapt Mask-RCNN into a fast interactive segmentation framework and introduce a new instance-aware loss measured at the pixel-level in the full image canvas, which lets predictions for nearby regions properly compete. Finally, we compare to interactive single object segmentation on the on the COCO panoptic dataset. We demonstrate that, at a budget of four extreme clicks and four corrective scribbles per region, our interactive full image segmentation approach leads to a 5% IoU gain, reaching 90% IoU.
Towards Neural Network Patching: Evaluating Engagement-Layers and Patch-Architectures

In this report we investigate fundamental requirements for the application of classifier patching on neural networks. Neural network patching is an approach for adapting neural network models to handle concept drift in nonstationary environments. Instead of creating or updating the existing network to accommodate concept drift, neural network patching leverages the inner layers of the network as well as its output to learn a patch that enhances the classification and corrects errors caused by the drift. It learns (i) a predictor that estimates whether the original network will misclassify an instance, and (ii) a patching network that fixes the misclassification. Neural network patching is based on the idea that the original network can still classify a majority of instances well, and that the inner feature representations encoded in the deep network aid the classifier to cope with unseen or changed inputs. In order to apply this kind of patching, we evaluate different engagement layers and patch architectures in this report, and find a set of generally applicable heuristics, which aid in parametrizing the patching procedure.
Granulocyte-macrophage colony-stimulating factor receptor expression in clinical pain disorder tissues and role in neuronal sensitization

Introduction: Granulocyte-macrophage colony-stimulating factor receptor (GM-CSFR) is highly expressed in peripheral macrophages and microglia, and is involved in arthritis and cancer pain in animal models. However, there is limited information on GM-CSFR expression in human central nervous system (CNS), peripheral nerves, or dorsal root ganglia (DRG), particularly in chronic pain conditions. Objectives: Immunohistochemistry was used to quantify GM-CSFR expression levels in human tissues, and functional sensory effects of GM-CSF were studied in cultured DRG neurons. Results: Granulocyte-macrophage colony-stimulating factor receptor was markedly increased in microglia at lesional sites of multiple sclerosis spinal cords (P = 0.01), which co-localised with macrophage marker CD68 (P = 0.009). In human DRG, GM-CSFR was expressed in a subset of small/medium diameter cells (30%) and few large cells (10%), with no significant change in avulsion-injured DRG. In peripheral nerves, there was a marked decrease in axonal GM-CSFR after chronic painful nerve injury (P = 0.004) and in painful neuromas (P = 0.0043); CD-68–positive macrophages were increased (P = 0.017) but did not appear to express GM-CSFR. Although control synovium showed absent GM-CSFR immunostaining, this was markedly increased in macrophages of painful osteoarthritis knee synovium. Granulocyte-macrophage colony-stimulating factor receptor was expressed in 17 ± 1.7% of small-/medium-sized cultured adult rat DRG neurons, and in 27 ± 3.3% of TRPV1-positive neurons. Granulocyte-macrophage colony-stimulating factor treatment sensitized capsaicin responses in vitro, which were diminished by p38 MAPK or TrkA inhibitors. Conclusion: Our findings support GM-CSFR as a therapeutic target for pain and hypersensitivity in clinical CNS and peripheral inflammatory conditions. Although GM-CSFR was decreased in chronic painful injured peripheral nerves, it could mediate CNS neuroinflammatory effects, which deserves study.
Effect of Sublethal Doses of Imidacloprid on the Biological Performance of Aphid Endoparasitoid Aphidius gifuensis (Hymenoptera: Aphidiidae) and Influence on Its Related Gene Expression

The integrated pest management (IPM) strategy was developed and used in combination with pesticides and beneficial biological control agents. To further develop IPM efficiency, it is important to evaluate the side effects of pesticides on biological control agents. Aphidius gifuensis is one of the most important aphid natural enemies and has been successfully used to control Myzys persicae and other aphid species. Imidacloprid (IMD) is a popular pesticide used worldwide and is highly toxic to non-target arthropods. Here, we investigated the short-term sublethal toxicity of IMD in Aphidius gifuensis and its impact on the biological performance and gene expression of this parasitoid. We found that sublethal IMD doses had a significant negative effect on the life history traits of female A. gifuensis, including shortening the lifespan and lowering parasitic capacity. Moreover, exposure to sublethal IMD also adversely affected the response of A. gifuensis to aphid-infested plant volatiles. Based on the transcriptome analysis, we found that the exposure to sublethal IMD doses significantly affected expression of genes involved in the central nervous system, energy metabolism, olfactory, and detoxification system of A. gifuensis. RT-qPCR also revealed that short term expose to sublethal IMD doses significantly induced the gene expression of genes related to the central nervous system (nAChRa7, nAChRa9, TbH, OAR1, NFR, TYR, and DAR1), olfactory system (OR28 and IR8a1), and detoxification system (CYP49p3, CYP6a2, and POD), while it suppressed the expression of genes involved in the central nervous system (nAChRa4 and nAChRb1), olfactory system (Orco1, IR8a2, and GR1), and detoxification system (GST2). Furthermore, exposure to sublethal doses of IMD also significantly increased the activities of CarEs and POD, whereas we observed no influence on the activities of CAT, GST, and SOD. Our results indicate that sublethal IMD doses might adversely affect the biological performance of A. gifuensis by altering gene expression related to the function of olfactory, nervous, energy metabolism, and detoxification systems. Thus, how the use of pesticides directly affect insect population should be considered when used in conjunction with natural pest parasitoids in IPM strategies.
Estudio sobre la intuición artificial

En este trabajo se exponen los conceptos y las propiedades de la intuición artificial que se han generado por medio de un análisis de fuentes de información y experimentación propia. Asimismo, se plantea una clasificación para los algoritmos que integran el procesamiento de resolución de problemas a través de la intuición artificial. En este artículo se da a conocer un modelo innovador para que se desarrollen nuevos algoritmos en control de máquinas, donde la intuición artificial es el pilar para que el procesamiento de la información se realice con mayor rapidez y sus requerimientos de procesamiento sean mínimos.
A Low-Profile Differentially Feeding Dual-Polarized Antenna Using Wideband AMC Reflector

In this paper, we presented a novel low-profile differentially feeding dual-polarized antenna adopting wideband artificial magnetic conductor (AMC) reflector for the fifth generation (5G) base-station communications. There are some winding inductors arranged at both ends of the cross slots, which result in a small-size radiating patch as well as a wide bandwidth. Both impedance bandwidths of 47.3% (3.28–5.31 GHz) and 47.2% (3.29–5.32 GHz) can be achieved for Ports I and II, respectively. Due to differentially feeding scheme, a high port isolation of below -40 dB during the working frequency band is obtained. In addition, thanks to the AMC structure, the profile of proposed antenna can be reduced from 18 to 9.5 mm ($0.13{{\lambda }_{c}},{{\lambda }_{c}}$ is the free-space wavelength at the center frequency).
Discussion On Density-Based Clustering Methods Applied for Automated Identification of Airspace Flows

Air Traffic Management systems generate a huge amount of track data daily. Flight trajectories can be clustered to extract main air traffic flows by means of unsupervised machine learning techniques. A well-known methodology for unsupervised extraction of air traffic flows conducts a two-step process. The first step reduces the dimensionality of the track data, whereas the second step clusters the data based on a density-based algorithm, DBSCAN. This paper explores advancements in density-based clustering such as OPTICS or HDBSCAN*. This assessment is based on quantitative and qualitative evaluations of the clustering solutions offered by these algorithms. In addition, the paper proposes a hierarchical clustering algorithm for handling noise in this methodology. This algorithm is based on a recursive application of DBSCAN* (RDBSCAN*). The paper demonstrates the sensitivity of these algorithms to different hyper-parameters, recommending a specific setting for the main one, which is common for all methods. RDBSCAN* outperforms the other algorithms in terms of the density-based internal validity metric. Finally, the outcome of the clustering shows that the algorithm extracts main clusters of the dataset effectively, connecting outliers to these main clusters.
Automated Real-Time At-Scene Reporting System

This paper proposes a framework for real-time tracking of objects in real scene using computer vision technique implemented on a mobile application for news reporting. This automated reporting system is intended to complement news reporters in reporting real-time scenario especially in dangerous environment such as disaster situation, fire, explosion or war zones. The system comprises assembly of some functionalities and algorithms to successfully build and run function by converting scene images into paragraphing and voice output to describe the scenarios in real-time. A variety of functions such as object motion detection, scene recognition, emotional recognition and distance detection are presented so that paragraphing and voice generation can be more accurate and close in meaning to real-time human reporting situations. Since the proposed system can facilitate news reporting in real turbulent situations, the burden of news reporting can be reduced under such dangerous circumstances. This work briefly reviews the rudimentary concepts of image processing and computer vision that serve as the components of the proposed real-time automated reporting system and describes how these various compelling applications are coupled and work together. The work also outlines the choice of design for effectiveness and efficiency of such reporting systems.
Designing of Security Hardware for Wireless Network

Wireless network usage is continuously increasing all over the world. People connect to many different wireless networks in everyday life. In this work, it is aimed to create a hardware solution against the threats that may arise from wireless networks. IPS (Intrusion Preventation System) application was implemented on the Raspberry Pi development board using the open source Snort application. Instead of connecting the computer directly to the wireless network, it is connected via board and all traffic is controlled by Snort. The critical point of the work is to run these components efficiently on a low-end device. As a result of the tests; it has been shown that the system can reliably perform IPS functions up to 4.2Mb/s. For more than this traffic, processor performance is inadequate.
Neural Text Normalization for Turkish Social Media

Social media has become a rich data source for natural language processing tasks with its worldwide use; however, it is hard to process social media data due to its informal nature. Text normalization is the task of transforming the noisy text into its canonical form. It generally serves as a preprocessing task in other NLP tasks that are applied to noisy text. In this study, we apply two approaches for Turkish text normalization: Contextual Normalization approach using distributed representations of words and Sequence-to-Sequence Normalization approach using neural encoder-decoder models. As the approaches applied to Turkish and also other languages are mostly rule-based, additional rules are required to be added to the normalization model in order to detect new error patterns arising from the change of the language use in social media. In contrast to rule-based approaches, the proposed approaches provide the advantage of normalizing different error patterns that change over time by training with a new dataset and updating the normalization model. Therefore, the proposed methods provide a solution to language change dependency in social media by updating the normalization model without defining new rules.
Virtual Image Points Based Geometrical Parameters’ Calibration for Focused Light Field Camera

In the light field imaging, the geometrical parameters’ calibration of the light field camera is the foundation of computational imaging techniques, such as the calibration of the light field data, digital refocusing, depth reconstruction, all-in-focus imaging, and so on. In this paper, we propose a virtual image points based geometrical parameters’ calibration for focused light field camera. In the forward model, the mapping from the object points to the virtual image points with respect to the main lens as well as the mapping from the virtual image points to the image points on the detector with respect to the microlens array are established. While calibrating geometrical parameters, the inverse problem of calculating the virtual image points from the corner on the detector is established and the model of calculating the geometrical parameters of the focused light field camera is established. Levenberg–Marquardt algorithm is used to solve the geometrical parameters iteratively. We use the checkerboard for the calibration experiment and validate the calculation via the reprojection of the checkerboard corners.
Modeling Mixed Traffic in Shared Space Using LSTM with Probability Density Mapping

In shared spaces, vulnerable road users (i.e., pedestrians) are encouraged to directly interact with other road users (i.e., vehicles). They self-organize to give or take right-of-way without being regulated by explicit traffic rules. The safety based on their behavior patterns in such areas is, hence, critical and needs to be fully investigated. In this paper, we first carry out a collision probability method based on safety distance to quantitatively study behavior patterns in mixed traffic. Then we propose a Long Short-Term Memories recurrent neural networks model that takes 2D trajectory data at discrete time steps and incorporates collision probability that captures user behavior patterns as a density mapping function for trajectory prediction. The model handles collisions explicitly based on the relative positions of the neighboring users in an ego user's vicinity with considering the impact of personal space and vehicle geometry. It also provides a friend flock detection mechanism to allow close interactions between friends. After training by real-world trajectories, the model outputs comparative results to the state-of-the-art methods for predicting three-second trajectories in complicated situations in a shared space. It can be applied for intent detection and on-board alarming system for autonomous driving when interacting with multimodal road users in shared spaces.
Corridor Selection Under Semantic Uncertainty for Autonomous Road Vehicles

Automated driving systems are likely to encounter situations in which their understanding of the road is uncertain. State-of-the-art systems only act on a single road hypothesis. If that hypothesis is incorrect, the likely consequence is an undesired behavior of the automated vehicle. This paper shows that the consideration of multiple road hypotheses in the selection of a driving corridor can improve the performance of an automated driving system and relax the requirements on its perception system. Two algorithms are presented that infer a corridor from a multi-hypothesis road representation with respect to different navigational goals.
The Effectiveness of Privacy Enhancing Technologies against Fingerprinting

We measure how effective Privacy Enhancing Technologies (PETs) are at protecting users from website fingerprinting. Our measurements use both experimental and observational methods. Experimental methods allow control, precision, and use on new PETs that currently lack a user base. Observational methods enable scale and drawing from the browsers currently in real-world use. By applying experimentally created models of a PET's behavior to an observational data set, our novel hybrid method offers the best of both worlds. We find the Tor Browser Bundle to be the most effective PET amongst the set we tested. We find that some PETs have inconsistent behaviors, which can do more harm than good.
Long-Term Relief of Painful Bladder Syndrome by High-Intensity, Low-Frequency Repetitive Transcranial Magnetic Stimulation of the Right and Left Dorsolateral Prefrontal Cortices

Aim: To show the value of low-frequency repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) to treat bladder pain syndrome (BPS), characterized by suprapubic pain, urgency and increased micturition frequency. Methods: A 68-year-old woman with BPS underwent 16 sessions of high-intensity, low-frequency (1 Hz) rTMS of the DLPFC, first on the right hemisphere (one daily session for 5 days, followed by one weekly session for 5 weeks), and then on the left hemisphere (one monthly session for 6 months). Results: At the end of the rTMS protocol, suprapubic pain completely vanished, micturition frequency dramatically decreased (by 60–80%), while fatigue and sleep quality improved (by 57–60%). The patient reported an overall satisfaction rate of 80% and her activities of daily living tending to normalize. Conclusion: This is the first report showing that high-intensity, low-frequency rTMS delivered on the DLPFC region of both hemispheres can relieve most symptoms of BPS (pain, urinary symptoms, and interference with physical functioning) in clinical practice.