Miracle-dz/UniFAR-FTUs huggingface dataset:
Analogies Without Commonalities? Evidence of Re-representation via Relational Category Activation

Analogies between cases with matching sets of connected relational structure is well-explained by existing theory. Re-representation is posited as an important mechanism to increase the flexibility of analogical processing by allowing the alignment of non-identical predicates across compared cases. It has been proposed that certain kind of categories can be characterized in terms of the relational structure that its exemplars tend to satisfy. Such relational categories have the property that all members of the category are analogous to one another. We ask whether a process of re-representation can alter the construal of a case and bring two evidently non-analogous cases into analogical alignment if they are both seen as members of the same relational category. We examine analogies between pairs of cases where the base is a canonical example of a relational category and the target would not be considered a member of the category on its own – critically, the cases themselves share no evident relational identities or similarities. In Experiment 1, we ask whether presenting a target case as part of an analogical pairing alters its construal. In Experiment 2, the pairs are presented for judgment as potential analogies. In both studies, participants interpret the target cases differently (consistent with the relational category) as a result of processing the analogy. There are two main implications: (1) a form of re-representation is at work in which the activation of a relational category triggers an alternate construal of the target case; and (2) this suggests a path to analogical status for cases that lack relational identities or similarities if the cases can both be fit to the same relational category.
Feature Matters: A Stage-by-Stage Approach for Knowledge Transfer

Convolutional Neural Networks (CNNs) become deeper and deeper in recent years, making the study of model acceleration imperative. It is a common practice to employ a shallow network, called student, to learn from a deep one, which is termed as teacher. Prior work made many attempts to transfer different types of knowledge from teacher to student, however, there are two problems remaining unsolved. Firstly, the knowledge used by existing methods is usually manually defined, which may not be consistent with the information learned by the original model. Secondly, there lacks an effective training scheme for the transfer process, leading to degradation of performance. In this work, we argue that feature is the most important knowledge from teacher. It is sufficient for student to achieve appealing performance by just learning similar features as teacher without any processing. Based on this discovery, we further present an efficient learning strategy, which is to make student mimic features of teacher stage by stage. Extensive experiments suggest that the proposed approach significantly narrows down the gap between student and teacher, and shows strong stability on various tasks, ie classification and detection, outperforming the state-of-the-art methods.
The Association between Deliberate Self-Harm and School Bullying Victimization and the Mediating Effect of Depressive Symptoms and Self-Stigma: A Systematic Review

Background Identifying deliberate self-harm in the young and its relationship with bullying victimization is an important public health issue. Methods A systematic review was performed to explore evidence of the association between deliberate self-harm and school bullying victimization in young people, as well as the mediating effect of depressive symptoms and self-stigma on this association. An advanced search in the following electronic databases was conducted in January 2018: PubMed/Medline; CINAHL; PsycINFO; PsycARTICLES; Science Direct; Scopus, and Cochrane Library. Studies that fulfilled the inclusion criteria were further assessed for their methodological integrity. The Norwegian Knowledge Centre for Health Services tool was applied for cross-sectional studies and the Critical Appraisal Skills Programme instrument for the cohort studies. Only empirical quantitative studies published in the English language in peer reviewed journals during the last decade (2007-2018) aimed at exploring the association between deliberate self-harm and school bullying victimization in community-based schoolchildren with a mean age of under 20 years were included. Results The reviewed cross-sectional and cohort studies (22) revealed a positive association between school bullying victimization and deliberate self-harm, including nonsuicidal self-injury, which remained statistically significant when controlled for the main confounders. The mediating role of depressive symptoms in the association between deliberate self-harm and school bullying victimization was confirmed. A dose-response effect was shown in the association between nonsuicidal self-injury and school bullying victimization, whilst the mediating effect of depressive symptoms needs to be further explored. No studies were found directly exploring the mediating effect of self-stigma in the association between deliberate self-harm and bullying victimization. Conclusion Targeted interventions aimed at eliminating victimization behaviours within the school context are therefore proposed, as well as interventions to promote healthy parenting styles for the parents of schoolchildren. Moreover, school healthcare professionals should screen students involved in bullying for self-injury, and vice versa.
Osteoimmunology of tumor necrosis factor-alpha, IL-6, and RANTES/CCL5: a review of known and poorly understood inflammatory patterns in osteonecrosis

Background The immune and bone systems are closely linked via cytokine cross-talk. This interdisciplinary field of research is referred to as osteoimmunology and pertains to inflammatory and osteoarticular diseases that feature the primary expression of tumor necrosis factor-alpha (TNF-α) and IL-6. Objective Are there bone resorptive processes wherein chronic inflammatory conditions are not linked to TNF-α and IL-6 expression, but rather to the expression of other cytokines? Materials and methods A comprehensive literature search was performed in PubMed Central. Discussion Although all diseases with cytokines involved in bone resorption (TNF-α and IL-6) are at the forefront of destructive inflammatory processes, there is one exception in the literature: fatty oxide osteoporosis/osteolysis in the jawbone (FDOJ), which is associated with significant bone softening. However, it should be noted that TNF-α and IL-6 fall below the levels found in a healthy jawbone in this condition. Another conspicuous finding is that there is a nearly 35-fold overexpression of the chemokine RANTES/CCL5 (R/C) in all FDOJ cases studied thus far in the literature. Conclusion FDOJ appears to represent a unique cytokine and inflammatory pattern from osteolysis in the body. R/C can be defined as the dominant carrier of a “maxillomandibular osteoimmunology”.
Ice ages and butterflyfishes: Phylogenomics elucidates the ecological and evolutionary history of reef fishes in an endemism hotspot

Abstract For tropical marine species, hotspots of endemism occur in peripheral areas furthest from the center of diversity, but the evolutionary processes that lead to their origin remain elusive. We test several hypotheses related to the evolution of peripheral endemics by sequencing ultraconserved element (UCE) loci to produce a genome‐scale phylogeny of 47 butterflyfish species (family Chaetodontidae) that includes all shallow water butterflyfish from the coastal waters of the Arabian Peninsula (i.e., Red Sea to Arabian Gulf) and their close relatives. Bayesian tree building methods produced a well‐resolved phylogeny that elucidated the origins of butterflyfishes in this hotspots of endemism. We show that UCEs, often used to resolve deep evolutionary relationships, represent an important tool to assess the mechanisms underlying recently diverged taxa. Our analyses indicate that unique environmental conditions in the coastal waters of the Arabian Peninsula probably contributed to the formation of endemic butterflyfishes. Older endemic species are also associated with narrow versus broad depth ranges, suggesting that adaptation to deeper coral reefs in this region occurred only recently (<1.75 Ma). Even though deep reef environments were drastically reduced during the extreme low sea level stands of glacial ages, shallow reefs persisted, and as such there was no evidence supporting mass extirpation of fauna in this region.
Inhibition of Human Dendritic Cell ER Stress Response Reduces T Cell Alloreactivity Yet Spares Donor Anti-tumor Immunity

Acute graft- vs. -host disease (GVHD) is an important cause of morbidity and death after allogeneic hematopoietic cell transplantation (HCT). We identify a new approach to prevent GVHD that impairs monocyte-derived dendritic cell (moDC) alloactivation of T cells, yet preserves graft- vs.-leukemia (GVL). Exceeding endoplasmic reticulum (ER) capacity results in a spliced form of X-box binding protein-1 (XBP-1s). XBP-1s mediates ER stress and inflammatory responses. We demonstrate that siRNA targeting XBP-1 in moDCs abrogates their stimulation of allogeneic T cells. B-I09, an inositol-requiring enzyme-1α (IRE1α) inhibitor that prevents XBP-1 splicing, reduces human moDC migration, allo-stimulatory potency, and curtails moDC IL-1β, TGFβ, and p40 cytokines, suppressing Th1 and Th17 cell priming. B-I09-treated moDCs reduce responder T cell activation via calcium flux without interfering with regulatory T cell (Treg) function or GVL effects by cytotoxic T lymphocytes (CTL) and NK cells. In a human T cell mediated xenogeneic GVHD model, B-I09 inhibition of XBP-1s reduced target-organ damage and pathogenic Th1 and Th17 cells without impacting donor Tregs or anti-tumor CTL. DC XBP-1s inhibition provides an innovative strategy to prevent GVHD and retain GVL.
Learning Attraction Field Representation for Robust Line Segment Detection

This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region coloring problem. The latter is then addressed by learning deep convolutional neural networks (ConvNets) for accuracy, robustness and efficiency. For a 2D line segment map, our dual representation consists of three components: (i) A region-partition map in which every pixel is assigned to one and only one line segment; (ii) An attraction field map in which every pixel in a partition region is encoded by its 2D projection vector w.r. t. the associated line segment; and (iii) A squeeze module which squashes the attraction field to a line segment map that almost perfectly recovers the input one. By leveraging the duality, we learn ConvNets to compute the attraction field maps for raw in-put images, followed by the squeeze module for LSD, in an end-to-end manner. Our method rigorously addresses several challenges in LSD such as local ambiguity and class imbalance. Our method also harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution. In experiments, our method is tested on the WireFrame dataset and the YorkUrban dataset with state-of-the-art performance obtained. Especially, we advance the performance by 4.5 percents on the WireFramedataset. Our method is also fast with 6.6∼10.4 FPS, outperforming most of existing line segment detectors.
Contextual Oblivious Similarity Searching for Encrypted Data on Cloud Storage Services

With the development of collaborative cloud storage services, files have been typically stored and secured through encryption making them hard to retrieve and search. Search over encrypted cloud approaches have consequently been utilizing cryptographic and indexing procedures. The vast majority use exact matching to fulfill their search criteria, which is then expanded by incorporating similarity ranking algorithms. However, this complex expansion does not always succeed due to its dependence on third parties to evaluate the search and the possible compromise on the privacy of the stored information. It also requires significant computational resources. This work demonstrates novel approach to similarity search, known as Contextual Oblivious Similarity based Search (COS2). In the proposed system, authorized users can categories searches resilient to typing errors. COS2 also introduces browsing caches to improve subscriber experience. Dual encryption mechanisms improve the relevance in searches without revealing confidential data on untrusted cloud service providers. Finally, this contextual search thrives to reduce the computational overhead of the overall search procedure, leading to a 86% improvement in terms of search efficiency.
A Parameter Sharing Method for Reinforcement Learning Model between AirSim and UAVs

In recent years, unmanned aerial vehicle aerial photography has developed rapidly. Unmanned aerial vehicle can get a different perspective and allow us to do more difficult tasks. Controlling unmanned aerial vehicle requires a lot of manpower, so there are a number of studies that use reinforcement learning to make the unmanned aerial vehicle fly autonomously. It is an expensive and time-consuming task to use reinforcement learning and training unmanned aerial vehicle to accomplish specific tasks in a realistic environment. Therefore this study in a virtual environment using the Q - learning training unmanned aerial vehicle landing, then transplanted model of virtual environment in which to train good into real environment, makes the realistic environment of unmanned aerial vehicle can use cheaper and quickly achieve the same task.
Microplastic Detection in Soil Amended With Municipal Solid Waste Composts as Revealed by Transmission Electronic Microscopy and Pyrolysis/GC/MS

Urban compost application in agroecosystems enhances soil fertility but can also be a source of (micro)plastics, which are not completely removed during the composting process. Knowledge of the fate of these plastics in regularly-amended soils is thus an issue for the environmental management of these soils. The aims of this study were 1) to develop a method combining soil fractionation, microscopic observation and chemical characterization to follow the fate of plastics in soils and 2) to apply this method on a long-term experimental field, where municipal solid waste composts were applied every other year during 10 years. The presence of plastics was investigated within compost and soil fractions using morphological and analytical characterization by transmission electronic microscopy (TEM-EDX) and pyrolysis coupled to gas chromatography and mass spectrometry (Py/GC/MS). Specific features of plastics allowed us to distinguish these polymers from soil organic matter even in the <200 µm soil fractions. Ti and Ba detection associated with these features, as they are initially added during the polymer production, also constituted plastic tracers within organo-mineral fractions. Plastic fragments as detected by TEM were less abundant in the fine soil fractions compared to the coarsest ones. The abundance of styrene produced upon pyrolysis, used as a molecular tracer of plastics, also decreased relative to produced toluene according to the same particle size gradient. Our results evidenced that plastics and microplastics were present in the soil that was amended for 10 years with compost, while not in the control soil. MPs were mostly observed as individualized particles, present in the coarsest fractions as well as some of the fine soil fractions, but they were little associated with the soil matrix. They mostly did not show any degradation features such as microbial lysis. We thus suggest that their evolution in soil was mainly due to fragmentation. Our methodological approach provides tools to monitor the fate of microplastics over time and specify the contribution of such contaminants in soil amended with bio-based products.
Test Diagnosis of Digital Microfluidic Biochips Using Image Segmentation

Digital microfluidic biochip has been developed as a promising alternative to the traditional approach of benchtop biochemical laboratory tests. Dependability is an important attribute for microfluidic biochips that are used for safety-critical applications, such as point-of-care health assessment, air-quality monitoring, and food-safety testing. Therefore, the robust offline and online test techniques are required after manufacturing and during bioassay operations. In this work, we are presenting an image segmentation based testing methodology to detect the catastrophic faults and to locate the faulty cells. The design-for diagnosability scheme is proposed, and it is shown that faults can be located and tolerated by providing alternative paths in biochips. Moreover this testing method also facilitates the testing of a biochip with other bioassay operations running concurrently.
Blockchain-Powered Parallel Healthcare Systems Based on the ACP Approach

To improve the accuracy of diagnosis and the effectiveness of treatment, a framework of parallel healthcare systems (PHSs) based on the artificial systems + computational experiments + parallel execution (ACP) approach is proposed in this paper. PHS uses artificial healthcare systems to model and represent patients’ conditions, diagnosis, and treatment process, then applies computational experiments to analyze and evaluate various therapeutic regimens, and implements parallel execution for decision-making support and real-time optimization in both actual and artificial healthcare processes. In addition, we combine the emerging blockchain technology with PHS, via constructing a consortium blockchain linking patients, hospitals, health bureaus, and healthcare communities for comprehensive healthcare data sharing, medical records review, and care auditability. Finally, a prototype named parallel gout diagnosis and treatment system is built and deployed to verify and demonstrate the effectiveness and efficiency of the blockchain-powered PHS framework.
Towards Modeling of Perception Errors in Autonomous Vehicles

Detection and tracking of dynamic traffic objects such as pedestrians, cyclists, and surrounding ground vehicles is an important part of the perception of Autonomous Vehicle (AV). In practice, the presence of noise corrupts sensors' ideal performance, causing detection and state estimation of moving objects to be erroneous. These detection errors propagate through the overall system and potentially compromise the reliability and safety of the AV. To get an assurance that the vehicle will operate safely, any simulation platform for an AV must include a realistic representation of the fallacies in vehicle's perception. In this study, the perception error for a vision based detection algorithm of the camera sensor is modeled by applying auto-regressive moving average (ARMA) and nonlinear autoregressive (NAR) method. It will enable statistical error values to be injected into ideal values obtained from simulation models. The proposed approach is evaluated based on several test case scenarios using various environmental and traffic information. A comparative analysis of the behavior of the AV with and without perception error model for the imperfection of camera sensor has been undertaken using the CarMaker platform. The investigation of the impact on the behavior of the AV by the variation of the state (distance, brake-torque) clearly depict the effectiveness of incorporating the error model at detection level in CarMaker.
Information-Theoretic Analysis and Performance Evaluation of Optimal Demappers for Multi-Layer Broadcast Systems

Multi-layer broadcast systems distribute services across time and frequency domain by means of power-division multiplexing. Successive interference cancelation is required, in general, in order to extract the content of all services. For a low-complexity implementation, the receiver can obtain the strongest (top-layer) signal assuming underlying signals to behave like thermal noise. The thermal noise assumption may not be valid under certain conditions and a more accurate characterization of the interference could bring improved performance. This paper analyzes the validity of the noise-like assumption considering the power ratio between signals and the required carrier-to-noise ratio for error-free reception. The main contribution of the paper is the proposal of a demapping algorithm that exploits the knowledge of the constellation of underlying signals. Generalized mutual information, performance evaluation, and complexity analysis are provided with the additive white Gaussian noise-like assumptions and with the proposed alternative in order to assess the potential performance improvements that can be achieved.
A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes

Traffic scene understanding involves the perception and accurate representation of a vehicles surroundings within a comprehensive environment model. Generally, automotive environment models incorporate multiple different and complementary representations each focusing on specific aspects of traffic scenes such as the drivable terrain, dynamic objects, the lane layout, etc. Considering camera based environment perception, multi-task models offer an efficient approach capable of generating complementary representations in an integrated manner. Towards this objective, we propose a convolutional neural network (CNN) comprised of a shared encoder stage and specific decoders for road segmentation and object detection. In comparison to exclusive models for each separate task, the redundant computation of common feature maps is avoided resulting in fast inference times even on low cost embedded systems. Furthermore, the detection stage is extended to predict the orientation of detected objects. The orientation estimate guides a consecutive 3D bounding box estimation based on analytic geometry. Thus, the CNN suffices to generate a basic environment model consisting of the spatial information of dynamic objects as well as the free space area obtained from road segmentation.
Energy Efficient SWIPT Systems in Multi-Cell MISO Networks

This paper studies beamforming design problems for multi-cell multi-user downlink networks with simultaneous wireless information and power transfer (SWIPT). In this system, multi-antenna base stations (BSs) concurrently transfer information and energy to multiple single-antenna information decoding (ID) and energy-harvesting (EH) users. We aim to maximize EH efficiency (EHE) that is defined as the ratio of the harvested energy at the EH users to the amount of energy consumption at the BSs while guaranteeing the quality-of-service constraint for each ID user. The EHE metric quantifies the efficiency of the power transfer capability of the SWIPT network. For the EH operation, both an ideal linear model and a practical non-linear model are individually investigated. We optimally solve this non-convex problem in two different scenarios according to the cooperation level among the BSs. First, for the centralized case, where global channel state information (CSI) is available at all BSs, we propose a centralized beamforming method based on the semi-definite relaxation and the successive convex approximation techniques. Next, in order to reduce the backhaul signaling overhead, decentralized algorithms are presented where each BS computes its beamforming vector by only using local CSI. The simulation results show that the proposed SWIPT beamforming algorithms offer a significant EHE performance gain over conventional schemes.