Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Thresholding Algorithms for Signal Recovery With Sparsity

The algorithms based on the technique of optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods. However, the computational cost for OT-based algorithms remains high at the current stage of their development. This stimulates the development of the so-called natural thresholding (NT) algorithm and its variants in this paper. The family of NT algorithms is developed through the first-order approximation of the so-called regularized optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding model, and thus the computational cost for this family of algorithms is significantly lower than that of the OT-based algorithms. The guaranteed performance of NT-type algorithms for signal recovery from noisy measurements is shown under the restricted isometry property and concavity of the objective function of regularized optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding model. Empirical results indicate that the NT-type algorithms are robust and very comparable to several mainstream algorithms for sparse signal recovery.
Robust Object Detection With Inaccurate Bounding Boxes

Learning accurate object detectors often requires large-scale training data with precise object bounding boxes. However, labeling such data is expensive and time-consuming. As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data. In this work, we aim to address the challenge of learning robust object detectors with inaccurate bounding boxes. Inspired by the fact that localization precision suffers significantly from inaccurate bounding boxes while classification accuracy is less affected, we propose leveraging classification as a guidance signal for refining localization results. Specifically, by treating an object as a bag of instances, we introduce an Object-Aware Multiple Instance Learning approach (OA-MIL), featured with object-aware instance selection and object-aware instance extension. The former aims to select accurate instances for training, instead of directly using inaccurate box annotations. The latter focuses on generating high-quality instances for selection. Extensive experiments on synthetic noisy datasets ( i.e. , noisy PASCAL VOC and MS-COCO) and a real noisy wheat head dataset demonstrate the effectiveness of our OA-MIL. Code is available at https://github.com/cxliu0/OA-MIL .
Scattered Light in the Hinode/EIS and SDO/AIA Instruments Measured from the 2012 Venus Transit

Observations from the 2012 transit of Venus are used to derive empirical formulae for long- and short-range scattered light at locations on the solar disk observed by the Hinode Extreme ultraviolet Imaging Spectrometer (EIS) and the Solar Dynamics Observatory Atmospheric Imaging Assembly (AIA) instruments. Long-range scattered light comes from the entire solar disk, while short-range scattered light is considered to come from a region within 50″ of the region of interest. The formulae were derived from the Fe xii 195.12 Å emission line observed by EIS and the AIA 193 Å channel. A study of the weaker Fe xiv 274.20 Å line during the transit and a comparison of scattering in the AIA 193 Å and 304 Å channels suggests the EIS scattering formula applies to other emission lines in the EIS wavebands. Both formulae should be valid in regions of fairly uniform emission such as coronal holes and quiet Sun, but may be less accurate for faint areas close (around 100″) to bright active regions. The formula for EIS is used to estimate the scattered light component of Fe xii λ195.12 for seven on-disk coronal holes observed between 2010 and 2018. Scattered light contributions of 56%–100% are found, suggesting that these features are dominated by scattered light, consistent with earlier work of Wendeln & Landi. Emission lines from the S x and Si x ions—formed at the same temperature as Fe xii and often used to derive the first ionization potential bias from EIS data—are also expected to be dominated by scattered light in coronal holes.
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State-of-the-Art and Future Directions

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community’s interest in this emerging field.
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation

3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when handling dynamic scenes. This can significantly hinder the navigation capabilities of self-driving vehicles. This paper advances the state of the art in this research field. Our first contribution consists in analysing a new unexplored scenario in point cloud segmentation, namely Source-Free Online Unsupervised Domain Adaptation (SF-OUDA). We experimentally show that state-of-the-art methods have a rather limited ability to adapt pre-trained deep network models to unseen domains in an online manner. Our second contribution is an approach that relies on adaptive self-training and geometric-feature propagation to adapt a pre-trained source model online without requiring either source data or target labels. Our third contribution is to study SF-OUDA in a challenging setup where source data is synthetic and target data is point clouds captured in the real world. We use the recent SynLiDAR dataset as a synthetic source and introduce two new synthetic (source) datasets, which can stimulate fu-ture synthetic-to-real autonomous driving research. Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds. Code and synthetic datasets are available at https://github.com/saltoricristiano/gipso-sfouda . point cloud segmentation, geometric propagation.
Temporal and cross-modal attention for audio-visual zero-shot learning

Audio-visual generalised zero-shot learning for video classification requires understanding the relations between the audio and visual information in order to be able to recognise samples from novel, previously unseen classes at test time. The natural semantic and temporal alignment between audio and visual data in video data can be exploited to learn powerful representations that generalise to unseen classes at test time. We propose a multi-modal and Temporal Cross-attention Framework (\modelName) for audio-visual generalised zero-shot learning. Its inputs are temporally aligned audio and visual features that are obtained from pre-trained networks. Encouraging the framework to focus on cross-modal correspondence across time instead of self-attention within the modalities boosts the performance significantly. We show that our proposed framework that ingests temporal features yields state-of-the-art performance on the \ucf, \vgg, and \activity benchmarks for (generalised) zero-shot learning. Code for reproducing all results is available at \url{https://github.com/ExplainableML/TCAF-GZSL}.
SLAMER: Simultaneous Localization and Map-Assisted Environment Recognition

This paper presents a simultaneous localization and map-assisted environment recognition (SLAMER) method. Mobile robots usually have an environment map and environment information can be assigned to the map. Important information such as no entry zone can be predicted from the map if localization has succeeded. However, this prediction is failed when localization does not work. Uncertainty of pose estimate must be considered for robust-map-based environ-mental object prediction. Robots also have external sensors and can recognize environmental object; however, sensor-based recognition of course contain uncertainty. SLAMER fuses map-based prediction and sensor-based recognition while coping with these uncertainties and achieves accurate localization and environment recognition. In this paper, we demonstrate LiDAR-based implementation of SLAMER in two cases. In the first case, we use the SemanticKITTI dataset and show that SLAMER achieves accurate estimate more than traditional methods. In the second case, we use an indoor mobile robot and show that unmeasurable environmental objects such as open doors and no entry lines can be recognized.
Understanding and Minimizing VOC Losses in All‐Perovskite Tandem Photovoltaics

Understanding performance losses in all‐perovskite tandem photovoltaics is crucial to accelerate advancements toward commercialization, especially since these tandem devices generally underperform in comparison to what is expected from isolated layers and single junction devices. Here, the individual sub‐cells in all‐perovskite tandem stacks are selectively characterized to disentangle the various losses. It is found that non‐radiative losses in the high‐gap subcell dominate the overall recombination in the baseline system, as well as in the majority of literature reports. Through a multi‐faceted approach, the open‐circuit voltage (VOC) of the high‐gap perovskite subcell is enhanced by 120 mV. Employing a novel (quasi) lossless indium oxide interconnect, this enables all‐perovskite tandem solar cells with 2.00 V VOC and 23.7% stabilized efficiency. Reducing transport losses as well as imperfect energy‐alignments boosts efficiencies to 25.2% and 27.0% as identified via subcell selective electro‐ and photo‐luminescence. Finally, it is shown how, having improved the VOC, improving the current density of the low‐gap absorber pushes efficiencies even further, reaching 25.9% efficiency stabilized, with an ultimate potential of 30.0% considering the bulk quality of both absorbers measured using photo‐luminescence. These insights not only show an optimization example but also a generalizable evidence‐based optimization strategy utilizing optoelectronic sub‐cell characterization.
Unsupervised Deep Multi-Shape Matching

3D shape matching is a long-standing problem in computer vision and computer graphics. While deep neural networks were shown to lead to state-of-the-art results in shape matching, existing learning-based approaches are limited in the context of multi-shape matching: (i) either they focus on matching pairs of shapes only and thus suffer from cycle-inconsistent multi-matchings, or (ii) they require an explicit template shape to address the matching of a collection of shapes. In this paper, we present a novel approach for deep multi-shape matching that ensures cycle-consistent multi-matchings while not depending on an explicit template shape. To this end, we utilise a shape-to-universe multi-matching representation that we combine with powerful functional map regularisation, so that our multi-shape matching neural network can be trained in a fully unsupervised manner. While the functional map regularisation is only considered during training time, functional maps are not computed for predicting correspondences, thereby allowing for fast inference. We demonstrate that our method achieves state-of-the-art results on several challenging benchmark datasets, and, most remarkably, that our unsupervised method even outperforms recent supervised methods.
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup

Image classiﬁcation with deep neural networks has seen a surge of technological breakthroughs with promising ap-plications in areas such as face recognition, medical imaging, autonomous driving. In engineering problems however, such as high speed imaging of engine fuel injector sprays or body paint sprays, deep neural networks face a fundamental challenge related to the availability of adequate and diverse data. Typically, only thousands or sometimes even hundreds of samples are available for training. In addition, the transition between different spray classes is a “continuum” and requires a high level of domain expertise to label the images accurately. In this work, we used Mixup as an approach to systematically deal with the data scarcity and ambiguous class boundaries found in industrial spray applications. We show that data augmentation can mitigate the over-ﬁtting problem of large neural networks on small data-sets, to a certain level, but cannot fundamentally resolve the issue. We discuss how a convex linear interpolation of different classes naturally aligns with the continuous transition between different classes in our application. Our experiments demonstrate Mixup as a simple yet effective method to train an accurate and robust deep neural network classiﬁer with only a few hundred samples.
Object-Compositional Neural Implicit Surfaces

The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on holistic scene representation yet ignore individual objects inside it, thus limiting potential downstream applications. In order to learn object-compositional representation, a few works incorporate the 2D semantic map as a cue in training to grasp the difference between objects. But they neglect the strong connections between object geometry and instance semantic information, which leads to inaccurate modeling of individual instance. This paper proposes a novel framework, ObjectSDF, to build an object-compositional neural implicit representation with high fidelity in 3D reconstruction and object representation. Observing the ambiguity of conventional volume rendering pipelines, we model the scene by combining the Signed Distance Functions (SDF) of individual object to exert explicit surface constraint. The key in distinguishing different instances is to revisit the strong association between an individual object's SDF and semantic label. Particularly, we convert the semantic information to a function of object SDF and develop a unified and compact representation for scene and objects. Experimental results show the superiority of ObjectSDF framework in representing both the holistic object-compositional scene and the individual instances. Code can be found at https://qianyiwu.github. io/objectsdf/
Self-powered broadband photodetector based on MoS2/Sb2Te3 heterojunctions: a promising approach for highly sensitive detection

Abstract Topological insulators have shown great potential for future optoelectronic technology due to their extraordinary optical and electrical properties. Photodetectors, as one of the most widely used optoelectronic devices, are crucial for sensing, imaging, communication, and optical computing systems to convert optical signals to electrical signals. Here we experimentally show a novel combination of topological insulators (TIs) and transition metal chalcogenides (TMDs) based self-powered photodetectors with ultra-low dark current and high sensitivity. The photodetector formed by a MoS2/Sb2Te3 heterogeneous junction exhibits a low dark current of 2.4 pA at zero bias and 1.2 nA at 1V. It shows a high photoresponsivity of >150 mA W−1 at zero bias and rectification of 3 times at an externally applied bias voltage of 1V. The excellent performance of the proposed photodetector with its innovative material combination of TMDs and TIs paves the way for the development of novel high-performance optoelectronic devices. The TIs/TMDs transfer used to form the heterojunction is simple to incorporate into on-chip waveguide systems, enabling future applications on highly integrated photonic circuits.
HoloLens 2 Technical Evaluation as Mixed Reality Guide

Mixed Reality (MR) is an evolving technology lying in the continuum spanned by related technologies such as Virtual Reality (VR) and Augmented Reality (AR), and creates an exciting way of interact-ing with people and the environment. This technology is fast becoming a tool used by many people, potentially improving living environments and work eﬃciency. Microsoft HoloLens [1] has played an important role in the progress of MR, from the ﬁrst generation to the second generation. In this paper, we systematically evaluate the functions of applicable functions in HoloLens 2 [2]. These evaluations can serve as a performance benchmark that can help people who need to use this in-strument for research or applications in the future. The detailed tests and the performance evaluation of the diﬀerent functionalities show the usability and possible limitations of each function. We mainly divide the experiment into the existing functions of the HoloLens 1, the new functions of the HoloLens 2, and the use of research mode. This research results will be useful for MR researchers who want to use HoloLens 2 as a research tool to design their own MR applications.
Thermodynamic duality symmetry and uncertainty relation between conjugate variables in superstatistics

Superstatistics generalizes Boltzmann statistics by assuming spatio-temporal fluctuations of the intensive variables. It has many applications in the analysis of experimental and simulated data, but the mathematical foundation of superstatistical theory from the perspective of statistics is still lacking. In the framework of large deviation theory, we show that the fluctuation of intensive variable origins from the spatio-temporal heterogeneity of the superstatistical dataset, and the superstatistical distribution emerges naturally in the infinitely large data limit. We demonstrate the conditional probability distribution of the intensity variable also follows the Boltzmann statistics and the conjugate variable of the intensive variable is the extensive variable, indicating a thermodynamic duality symmetry between conjugate variables in the superstatistical systems. A new thermodynamic relation between the entropy functions of conjugate variables is obtained. Moreover, due to the heterogeneity of the superstatistical dataset, uncertainty relations between the conjugate variables arise naturally. Our work provides a large deviation approach for studying the statistical thermodynamics of superstatistical systems and reveals the dual symmetry between conjugate variables. This symmetry will be broken in the homogeneous situation.
Predicting Word Learning in Children from the Performance of Computer Vision Systems

For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the concreteness of words, which are in turn a predictor of children's word learning, suggesting that these models are capturing the relationship between words and visual phenomena.
Downwash-aware Control Allocation for Over-actuated UAV Platforms

Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing undesired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexity of modeling aerodynamic airflow challenges control algorithms from properly compensating for such a side effect. Leveraging the input redundancies in over-actuated UAVs, we tackle this issue with a novel control allocation framework that considers downwash effects and explores the entire allocation space for an optimal solution. This optimal solution avoids downwash effects while providing high thrust efficiency within the hardware constraints. To the best of our knowledge, ours is the first formal derivation to investigate the downwash effects on over-actuated UAVs. We verify our framework on different hardware configurations in both simulation and experiment.
Cluster analysis of acoustic emission signals and tensile properties of carbon/glass fiber–reinforced hybrid composites

Understanding the damage and failure of carbon/glass epoxy hybrid woven composites under tensile loading based on acoustic emission signals is a challenging task in their practical uses. In this study, an approach based on fuzzy c-means algorithm is proposed to process the acoustic emission signals from tensile loading of composites monitored by combining acoustic emission technology and digital image correlation method. The results show that the acoustic emission signals from tensile loading can be divided into three clusters. The three clusters correspond to three kinds of damage modes including matrix cracking, fiber/matrix debonding, delamination, and fiber breakage. By comparing the acoustic characteristics of these classes, a correlation procedure between the clusters and the damage mechanisms observed is proposed. Meanwhile, it can be found that debonding and fiber break signals for glass fiber correspond to a lower frequency range than that for carbon fiber. Moreover, the method combining acoustic emission and digital image correlation can effectively monitor the damage process of the specimen both on the inside and outside, which can provide a reference for the health monitoring of composite structure.
Influence of Fuel Injection, Turbocharging and EGR Systems Control on Combustion Parameters in an Automotive Diesel Engine

Indicated pressure diagrams were measured during experimental campaigns on the control of fuel injection, turbocharging and hybrid exhaust gas recirculation systems in an automotive downsized diesel engine. Three-part load operating conditions were selected for four test sets, where strategies aimed at the reduction of NOX emissions and fuel consumption, limiting penalties in soot emissions and combustion noise were applied to the selected systems. Processing of in-cylinder pressure signal, its first derivative and curves of the rate of heat release allowed us to evaluate seven parameters related to the combustion centre and duration, maximum values of pressure, heat release and its first derivative, heat released in the premixed phase and a combustion noise indicator. Relationships between these quantities and engine operating, energy and environmental parameters were then obtained by referring to the four test sets. In the paper, the most significant links are presented and discussed, aiming at a better understanding of the influence of control variables on the combustion process and the effects on engine behaviour. The proposed methodology proved to be a consistent tool for this analysis, useful for supporting the application of alternative fuels or advanced combustion modes.