Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-programming for cross-domain tensor optimizations

Many modern application domains crucially rely on tensor operations. The optimization of programs that operate on tensors poses difficulties that are not adequately addressed by existing languages and tools. Frameworks such as TensorFlow offer good abstractions for tensor operations, but target a specific domain, i.e. machine learning, and their optimization strategies cannot easily be adjusted to other domains. General-purpose optimization tools such as Pluto and existing meta-languages offer more flexibility in applying optimizations but lack abstractions for tensors. This work closes the gap between domain-specific tensor languages and general-purpose optimization tools by proposing the Tensor optimizations Meta-Language (TeML). TeML offers high-level abstractions for both tensor operations and loop transformations, and enables flexible composition of transformations into effective optimization paths. This compositionality is built into TeML's design, as our formal language specification will reveal. We also show that TeML can express tensor computations as comfortably as TensorFlow and that it can reproduce Pluto's optimization paths. Thus, optimized programs generated by TeML execute at least as fast as the corresponding Pluto programs. In addition, TeML enables optimization paths that often allow outperforming Pluto.
Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion

Semantic segmentation partitions a given image or 3D model of a scene into semantically meaning parts and assigns predetermined labels to the parts. With well‐established datasets, deep networks have been successfully used for semantic segmentation of RGB and RGB‐D images. On the other hand, due to the lack of annotated large‐scale 3D datasets, semantic segmentation for 3D scenes has not yet been much addressed with deep learning. In this paper, we present a novel framework for generating semantically segmented triangular meshes of reconstructed 3D indoor scenes using volumetric semantic fusion in the reconstruction process. Our method integrates the results of CNN‐based 2D semantic segmentation that is applied to the RGB‐D stream used for dense surface reconstruction. To reduce the artifacts from noise and uncertainty of single‐view semantic segmentation, we introduce adaptive integration for the volumetric semantic fusion and CRF‐based semantic label regularization. With these methods, our framework can easily generate a high‐quality triangular mesh of the reconstructed 3D scene with dense (i.e., per‐vertex) semantic labels. Extensive experiments demonstrate that our semantic segmentation results of 3D scenes achieves the state‐of‐the‐art performance compared to the previous voxel‐based and point cloud‐based methods.
Efficacy and Safety of BCG Vaccine for Control of Tuberculosis in Domestic Livestock and Wildlife

Bovine tuberculosis (TB) continues to be an intractable problem in many countries, particularly where “test and slaughter” policies cannot be implemented or where wildlife reservoirs of Mycobacterium bovis infection serve as a recurrent source of infection for domestic livestock. Alternative control measures are urgently required and vaccination is a promising option. Although the M. bovis bacille Calmette-Guérin (BCG) vaccine has been used in humans for nearly a century, its use in animals has been limited, principally as protection against TB has been incomplete and vaccination may result in animals reacting in the tuberculin skin test. Valuable insights have been gained over the past 25 years to optimise protection induced by BCG vaccine in animals and in the development of tests to differentiate infected from vaccinated animals (DIVA). This review examines factors affecting the efficacy of BCG vaccine in cattle, recent field trials, use of DIVA tests and the effectiveness of BCG vaccine in other domestic livestock as well as in wildlife. Oral delivery of BCG vaccine to wildlife reservoirs of infection such as European badgers, brushtail possums, wild boar, and deer has been shown to induce protection against TB and could prove to be a practical means to vaccinate these species at scale. Testing of BCG vaccine in a wide range of animal species has indicated that it is safe and vaccination has the potential to be a valuable tool to assist in the control of TB in both domestic livestock and wildlife.
Decomposing Images into Layers with Advanced Color Blending

Digital paintings are often created by compositing semi‐transparent layers using various advanced color‐blend modes, such as “color‐burn,” “multiply,” and “screen,” which can produce interesting non‐linear color effects. We propose a method of decomposing an input image into layers with such advanced color blending. Unlike previous layer‐decomposition methods, which typically support only linear color‐blend modes, ours can handle any user‐specified color‐blend modes. To enable this, we generalize a previous color‐unblending formulation, in which only a specific layering model was considered. We also introduce several techniques for adapting our generalized formulation to practical use, such as the post‐processing for refining smoothness. Our method lets users explore possible decompositions to find the one that matches for their purposes by manipulating the target color‐blend mode and desired color distribution for each layer, as well as the number of layers. Thus, the output of our method is a layered, easily editable image composition organized in a way that digital artists are familiar with. Our method is useful for remixing existing illustrations, flexibly editing single‐layer paintings, and bringing physically painted media (e.g., oil paintings) into a digital workflow.
A Systematic Review of Finger Vein Recognition Techniques

Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.
Représentations Gaussiennes pour le Filtrage Collaboratif

La plupart des systemes de filtrage collaboratifs, comme par exemple la factorisation matricielle, utilisent des representations vectorielles pour les articles et les utilisateurs. Ces representations sont deterministes, et ne permettent pas de modeliser l'incertitude des representations apprises, ce qui peut etre utile quand un utilisateur a evalue un petit nombre d'articles (probleme du demarrage a froid), ou quand le modele est confronte a des informations contradictoires concernant le comportement d'un utilisateur ou les evaluations d'un utilisateur. Dans cet article, nous nous appuyons sur des representations gaussiennes qui modelisent cette incertitude, et definissons un cadre d'apprentissage et d'inference approprie a ce type de representations. Nous montrons en outre que ce modele fonctionne bien sur trois collections representatives, et analysons les representations apprises par le modele.
Construction de patrons lexico-syntaxiques d’extraction pour l’acquisition de connaissances à partir du web (Relation pattern extraction and information extraction from the web)

Cet article présente une méthode permettant de collecter sur le web des informations complémentaires à une information prédéfinie, afin de remplir une base de connaissances. Notre méthode utilise des patrons lexico-syntaxiques, servant à la fois de requêtes de recherche et de patrons d’extraction permettant l’analyse de documents non structurés. Pour ce faire, il nous a fallu définir au préalable les critères pertinents issus des analyses dans l’objectif de faciliter la découverte de nouvelles valeurs.
Tolerating and managing extreme speech on social media

Purpose This study seeks to understand the opinions of internet users toward extreme speech on social media platforms and their willingness to censor such speech. The purpose of this paper is to examine how norms of freedom of expression are changing in an online communication environment dominated by these platforms. Design/methodology/approach Four focus groups were conducted in this study. Participants needed to use at least one social media platform daily. Groups were homogeneous in terms of race and gender: African-American females, African-American males, white females and white males. Findings Participants in general did not report a strong willingness to censor extreme speech on social media platforms. Rather, they expressed apathy and cynicism toward both their own and social media companies’ ability to combat extreme speech and make online discourse more positive. Female participants tended to value the overall health of public discourse and protection of more vulnerable social media users on social media platforms. African-American female participants called for platforms to recognize a special duty to protect minority users, whom they saw as responsible for the platforms’ success. Research limitations/implications Focus groups are useful for providing exploratory rather than generalizable data. However, by increasing the understanding of how individuals define extreme speech on social media, these data can reveal how individuals rhetorically shape the social media platforms and interpret their role in democratic discourse. Originality/value This research takes the rich field of studying tolerance toward extreme speech to new territory: the online realm where public discourse (and especially extreme discourse) is hosted more and more.
Integrated Design of Mechanical Structure and Control Algorithm for Closed-Chain Five-Bar Mechanism

In the traditional mechatronic systems, the controller designs are usually separated from the mechanical structure designs. Consequently, the controller algorithms become more and more complex, i.e., from the PD controller, to the computed-torque controller, and to all sorts of the adaptive controller. These complex control algorithms encounter many difficulties in implementation of the control algorithms to closed-chain mechanisms. The main reason for these difficulties is the so-called computational overhead with the dynamic model of the closed-chain mechanisms. The computational overhead is even experienced in a parallel manipulator system (one of the closed-chain types). One of the solutions to this problem is to seek a smart design of mechanical structures based on the methodology called Design For Control (DFC). DFC specifically proposes the procedure, i.e., design of a mechanical structure, simplification of the dynamic model of the mechanical structure, and design of a simple control algorithm (such as PD controller). In this paper, a 2-dof five-bar closed-chain mechanism (called Delta mechanism for short) is studied. It is shown that the smart design of the mechanical structure of the Delta mechanism can lead to some configuration invariants in the dynamic model. Therefore, the design of PD controllers, incorporating these invariants, can achieve excellent system performance. Keyword: DFC, five-bar mechanism, control algorithm, tracking performance
Robot Online Learning to Lift Weights: A Way to Expose Students to Robotics and Intelligent Technologies

During interaction with learning robots the students are often faced with the challenge of understanding the robot intent and its practical realization. To answer this challenge, we propose a connected environment which integrates the robot, its digital twin and virtual sensors. We implemented a reinforcement learning scenario in which a humanoid robot learns to lift a weight of unknown mass through autonomous trial-and-error search. To expedite the process, trials of the physical robot are substituted by simulations with its digital twin. The optimal parameters of the robot posture for executing the weightlifting task, found by analysis of the virtual trials, are transmitted to the robot through internet communication. The approach exposes students to the concepts and technologies of machine learning, parametric design, digital prototyping and simulation, connectivity and internet of things. Pilot implementation of the approach indicates its potential for teaching freshman and HS students, and for teacher education.
An Enhanced Hybrid 3D Localization Algorithm Based on APIT and DV-Hop

Wireless sensor networks (WSN), as a new method of information collection and processing, has a wide range of applications. Since the acquired data must be bound with the location information of sensor nodes, the sensor localization is one of the supporting technologies of wireless sensor networks. However, the common localization algorithms, such as APIT algorithm and DV-Hop algorithm, have the following problems: 1) the localization accuracy of beacon nodes is not high; 2) low coverage rate in sparse environment. In this paper, an enhanced hybrid 3D localization algorithm is designed with combining the advantages of APIT algorithm and DV-Hop algorithm. The proposed hybrid algorithm can improve the localization accuracy of the beacon nodes in dense environments by reducing the triangles in the triangle interior point test (PIT) and selecting good triangles. In addition, the algorithm can combine the advantages of APIT algorithm and DV-Hop algorithm localization algorithm to calculate the unknown node coordinates, and also improve the location coverage of the beacon nodes in sparse environment. Simulation results show that the proposed hybrid algorithm can effectively improve the localization accuracy of beacon nodes in the dense environment and the location coverage of beacon nodes in sparse environment.
A case study of gesture-based games in enhancing the fine motor skills and recognition of children with autism

ABSTRACT Children with Autism Spectrum Disorder (ASD) perform poorly in complex fine motor skills and recognition, and they have difficulties in learning complex and multistep motor skills. Gestures-based games, which enable users to get timely feedback, to learn from failure, to engage in games at every moment, and to interact with computers intuitively have become promising tools. However, relevant empirical research on using gesture-based games to assist the learning of children with ASD is limited. With a case study method, we aim to examine the effects of using gesture-based games on the learning of children with ASD. Two gesture-based matching games were developed to improve the performance of children with ASD with respect to fine motor skills and recognition, and three young children with ASD participated in a three-week experiment. By comparing the participants’ performance before and after the treatment, we found that the participants’ performance in playing the gesture-based games improved greatly during the intervention, and intervention helped improve their performance regarding fine motor skills and recognition. Also, they were able to transfer the rules and skills they had learnt from the first game to completing the tasks in the second game. The findings above suggest the usefulness of using gesture-based games in assisting the learning of children with ASD. Some suggestions and implications for the development of the gesture-based games and future work are also discussed.
The mechanotransduction of blood pressure

Two stretch-activated channels in arteries could be targets for some types of heart failure The arterial baroreceptor reflex is the most important mechanism for minimizing short-term arterial blood pressure fluctuations (1). In situations of sudden blood pressure drops, the baroreceptor reflex accelerates heart rate, increases cardiac contractility, and induces vasoconstriction. Conversely, sudden increases in blood pressure trigger the opposite response. The autonomic nervous system mediates these physiological reactions. Patients with baroreceptor reflex malfunctions typically suffer from orthostatic hypotension, a severe decrease in blood pressure that occurs when standing up, which leads to dizziness or even fainting (2). However, how blood pressure changes are converted into electrical signals for neurotransmission has remained a puzzle. On page 464 of this issue, Zeng et al. (3) show that the mechanosensitive ion channels PIEZO1 and PIEZO2 are transducers of blood pressure in the sensory neurons of the autonomic nervous system that trigger the baroreceptor reflex. Identifying the molecular players of this response may help clarify the role of arterial baroreceptors in maintaining normotension and help develop new drugs for the treatment of heart failure.
Automatic Mechanism Modeling from a Single Image with CNNs

This paper presents a novel system that enables a fully automatic modeling of both 3D geometry and functionality of a mechanism assembly from a single RGB image. The resulting 3D mechanism model highly resembles the one in the input image with the geometry, mechanical attributes, connectivity, and functionality of all the mechanical parts prescribed in a physically valid way. This challenging task is realized by combining various deep convolutional neural networks to provide high‐quality and automatic part detection, segmentation, camera pose estimation and mechanical attributes retrieval for each individual part component. On the top of this, we use a local/global optimization algorithm to establish geometric interdependencies among all the parts while retaining their desired spatial arrangement. We use an interaction graph to abstract the inter‐part connection in the resulting mechanism system. If an isolated component is identified in the graph, our system enumerates all the possible solutions to restore the graph connectivity, and outputs the one with the smallest residual error. We have extensively tested our system with a wide range of classic mechanism photos, and experimental results show that the proposed system is able to build high‐quality 3D mechanism models without user guidance.
Fuzzy Controller Based Unequal Clustering Algorithm with Fault Tolerance

In order to solve the hot spots problem in equal wireless sensor networks, a distributed Fuzzy controller based Unequal Clustering algorithm with Fault Tolerance (FUCFT) is proposed. Fuzzy controller in FUCFT uses the residual energy, node centrality, node degree and distance to base station as input variables and outputs chance and size based on the IF-THEN rules, so as to make the optimal nodes be elected as cluster heads with appropriate cluster size. Moreover, TDMA scheme is adopted to transmit data among the clusters and tolerate the faults for the cluster heads and member nodes. Reclustering on demand is used to reduce the energy dissipation of the consecutive clustering in rounds. The simulation results indicate that FUCFT ensures load balance among clusters, and reduces the energy consumption as well as improves the network lifetime compared with its counterparts.
The Endocannabinoid System and Oligodendrocytes in Health and Disease

Cannabinoid-based interventions are being explored for central nervous system (CNS) pathologies such as neurodegeneration, demyelination, epilepsy, stroke, and trauma. As these disease states involve dysregulation of myelin integrity and/or remyelination, it is important to consider effects of the endocannabinoid system on oligodendrocytes and their precursors. In this review, we examine research reports on the effects of the endocannabinoid system (ECS) components on oligodendrocytes and their precursors, with a focus on therapeutic implications. Cannabinoid ligands and modulators of the endocannabinoid system promote cell signaling in oligodendrocyte precursor survival, proliferation, migration and differentiation, and mature oligodendrocyte survival and myelination. Agonist stimulation of oligodendrocyte precursor cells (OPCs) at both CB1 and CB2 receptors counter apoptotic processes via Akt/PI3K, and promote proliferation via Akt/mTOR and ERK pathways. CB1 receptors in radial glia promote proliferation and conversion to progenitors fated to become oligodendroglia, whereas CB2 receptors promote OPC migration in neonatal development. OPCs produce 2-arachidonoylglycerol (2-AG), stimulating cannabinoid receptor-mediated ERK pathways responsible for differentiation to arborized, myelin basic protein (MBP)-producing oligodendrocytes. In cell culture models of excitotoxicity, increased reactive oxygen species, and depolarization-dependent calcium influx, CB1 agonists improved viability of oligodendrocytes. In transient and permanent middle cerebral artery occlusion models of anoxic stroke, WIN55212-2 increased OPC proliferation and maturation to oligodendroglia, thereby reducing cerebral tissue damage. In several models of rodent encephalomyelitis, chronic treatment with cannabinoid agonists ameliorated the damage by promoting OPC survival and oligodendrocyte function. Pharmacotherapeutic strategies based upon ECS and oligodendrocyte production and survival should be considered.
Inferring sun direction to improve visual odometry: A deep learning approach

We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, in which the sun is typically not visible. We leverage recent advances in Bayesian convolutional neural networks (BCNNs) to train and implement a sun detection model (dubbed Sun-BCNN) that infers a 3D sun direction vector from a single RGB image. Crucially, our method also computes a principled uncertainty associated with each prediction, using a Monte Carlo dropout scheme. We incorporate this uncertainty into a sliding window stereo visual odometry pipeline where accurate uncertainty estimates are critical for optimal data fusion. We evaluate our method on 21.6 km of urban driving data from the KITTI odometry benchmark where it achieves a median error of approximately 12° and yields improvements of up to 42% in translational average root mean squared error (ARMSE) and 32% in rotational ARMSE compared with standard visual odometry. We further evaluate our method on an additional 10 km of visual navigation data from the Devon Island Rover Navigation dataset, achieving a median error of less than 8° and yielding similar improvements in estimation error. In addition to reporting on the accuracy of Sun-BCNN and its impact on visual odometry, we analyze the sensitivity of our model to cloud cover, investigate the possibility of model transfer between urban and planetary analogue environments, and examine the impact of different methods for computing the mean and covariance of a norm-constrained vector on the accuracy and consistency of the estimated sun directions. Finally, we release Sun-BCNN as open-source software.
Correction: Pregnancy-Related Thromboembolism

In a recent article on pregnancy-related thromboembolism [1], the frequencies of deep venous thrombosis in the second paragraph were incorrect. The following are the correct values: 0.0018 rather than 1.8%, 0.013 rather than 1.3%, 0.07 rather than 7%, 0.061 rather than 6.1%, and 0.23 rather than 23%.