Miracle-dz/UniFAR-FTUs huggingface dataset:
Public smog knowledge, risk perception, and intention to reduce car use: Evidence from China

ABSTRACT Smog pollution has received widely attention in recent years due to its negative effects. There is consensus that the motor vehicle exhaust emission is one of the sources of smog pollution and reduced private car use would significantly improve the air quality and alleviate the smog pollution problem. In this research, we aim to investigate how public smog knowledge and risk perception (physical health risk perception and mental health risk perception) affect public attitude and intention to reduce car use. A questionnaire survey of 334 randomly sampled respondents was designed to test these relationships. The results show that public smog knowledge is positively and significantly related to physical health risk perception, mental health risk perception, attitude and intention to reduce car use. Public smog knowledge has the largest impact on intention to reduce car use. Furthermore, physical health risk perception and mental health risk perception are positively and significantly associated with attitude and intention to reduce car use. In addition, the results also indicated that public smog knowledge is at a low level. Based on the results, implications and suggestions for future research were discussed.
Automatic text summarization: What has been done and what has to be done

Summaries are important when it comes to process huge amounts of information. Their most important benefit is saving time, which we do not have much nowadays. Therefore, a summary must be short, representative and readable. Generating summaries automatically can be beneficial for humans, since it can save time and help selecting relevant documents. Automatic summarization and, in particular, Automatic text summarization (ATS) is not a new research field; It was known since the 50s. Since then, researchers have been active to find the perfect summarization method. In this article, we will discuss different works in automatic summarization, especially the recent ones. We will present some problems and limits which prevent works to move forward. Most of these challenges are much more related to the nature of processed languages. These challenges are interesting for academics and developers, as a path to follow in this field.
Phloeotribus pruni (Coleoptera: Scolytidae) Associated with Peach (Prunus persica) Collapse in Coatepec de Harinas, State of Mexico, Mexico

Summary Phloeotribus pruni Wood (Coleoptera: Scolytidae) is reported for the first time associated with the collapse of peach (Prunus persica) trees in Coatepec de Harinas, State of Mexico, Mexico. One hundred percent incidence of collapsed trees was found in a 2 ha peach orchard. In 10 trees sampled, a mean number of 3.3 adults and 4 larvae per 25 cm2 of bark were found. Trees died 12 to 18 months after the onset of symptoms. The 100% incidence of attacked trees, and the tree death 12 to 18 months after the first detection of the insect, point out the importance of this pest.
Scene Graph Generation With External Knowledge and Image Reconstruction

Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction, etc. However, existing datasets are biased in terms of object and relationship labels, or often come with noisy and missing annotations, which makes the development of a reliable scene graph prediction model very challenging. In this paper, we propose a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome these dataset issues. In particular, we extract commonsense knowledge from the external knowledge base to refine object and phrase features for improving generalizability in scene graph generation. To address the bias of noisy object annotations, we introduce an auxiliary image reconstruction path to regularize the scene graph generation network. Extensive experiments show that our framework can generate better scene graphs, achieving the state-of-the-art performance on two benchmark datasets: Visual Relationship Detection and Visual Genome datasets.
Adaptive stabilisation of ODE systems via distributed effect of uncertain diffusion-dominated actuator dynamics

ABSTRACT Adaptive stabilisation is investigated for ordinary differential equation systems via distributed effect of uncertain diffusion-dominated actuator dynamics. The presence of the unknowns/uncertainties and the distributed effect makes the system under investigation more general and representative than those of the related literature, and moreover yields the ineffectiveness of the existing methods on this topic. By constructing an invertible spatial and time-varying state transformation, the original closed-loop system is first changed into a target system, from which the design of parameter updating law and the analysis of the closed-loop system performance become more convenient. Then, by Lyapunov method and adaptive compensation technique, an adaptive stabilising controller is successfully constructed, which guarantees that the original system states converge to zero while other closed-loop system states are bounded. A simulation example is provided to validate the proposed method.
Rossby and Magnetic Prandtl Number Scaling of Stellar Dynamos

Rotational scaling relationships are examined for the degree of equipartition between magnetic and kinetic energies in stellar convection zones. These scaling relationships are approached from two paradigms, with first a glance at scaling relationship built on an energy-balance argument and second a look at a force-based scaling. The latter implies a transition between a nearly constant inertial scaling when in the asymptotic limit of minimal diffusion and magnetostrophy, whereas the former implies a weaker scaling with convective Rossby number. Both scaling relationships are then compared to a suite of 3D convective dynamo simulations with a wide variety of domain geometries, stratifications, and range of convective Rossby numbers.
Transit least-squares survey

We apply for the first time the transit least-squares (TLS) algorithm to search for new transiting exoplanets. TLS has been developed as a successor to the box least-squares (BLS) algorithm, which has served as a standard tool for the detection of periodic transits. In this proof-of-concept paper, we demonstrate that TLS finds small planets that have previously been missed. We show the capabilities of TLS using the K2 EVEREST-detrended light curve of the star K2-32 (EPIC 205071984), which has been known to have three transiting planets. TLS detects these known Neptune-sized planets K2-32 b, d, and c in an iterative search and finds an additional transit signal with a high signal detection efficiency (SDETLS) of 26.1 at a period of 4.34882−0.00075+0.00069 d. We show that this additional signal remains detectable (SDETLS = 13.2) with TLS in the K2SFF light curve of K2-32, which includes a less optimal detrending of the systematic trends. The signal is below common detection thresholds if searched with BLS in the K2SFF light curve (SDEBLS = 8.9), however, as in previous searches. Markov chain Monte Carlo sampling with the emcee software shows that the radius of this candidate is 1.01−0.09+0.10 R⊕. We analyzed its phase-folded transit light curve using the vespa software and calculated a false-positive probability FPP = 3.1 × 10−3. Taking into account the multiplicity boost of the system, we estimate an FPP < 3.1 × 10−4, which formally validates K2-32 e as a planet. K2-32 now hosts at least four planets that are very close to a 1:2:5:7 mean motion resonance chain. The offset of the orbital periods of K2-32 e and b from a 1:2 mean motion resonance agrees very well with the sample of transiting multiplanet systems from Kepler, lending further credence to the planetary nature of K2-32 e. We expect that TLS can find many more transits of Earth-sized and even smaller planets in the Kepler and K2 data that have so far remained undetected with algorithms that search for box-like signals.
RAPID: Early Classification of Explosive Transients Using Deep Learning

We present Real-time Automated Photometric IDentification (RAPID), a novel time series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with gated recurrent units (GRUs), we present the first method specifically designed to provide early classifications of astronomical timeseries data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPID well suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of observed transients from the ZTF data stream. We have made RAPID available as an open-source software package for machine-learning-based alert brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds.
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements

Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems. Although state-of-the-art methods can obtain decent results in certain situations, performance declines significantly when tackling more general real-world cases. These failures stem from the intrinsic difficulty of single image reflection removal -- the fundamental ill-posedness of the problem, and the insufficiency of densely-labeled training data needed for resolving this ambiguity within learning-based neural network pipelines. In this paper, we address these issues by exploiting targeted network enhancements and the novel use of misaligned data. For the former, we augment a baseline network architecture by embedding context encoding modules that are capable of leveraging high-level contextual clues to reduce indeterminacy within areas containing strong reflections. For the latter, we introduce an alignment-invariant loss function that facilitates exploiting misaligned real-world training data that is much easier to collect. Experimental results collectively show that our method outperforms the state-of-the-art with aligned data, and that significant improvements are possible when using additional misaligned data.
High-Reynolds number Taylor-Couette turbulence.

Taylor-Couette flow, the flow between two coaxial co- or counter-rotating cylinders, is one of the paradigmatic systems in the physics of fluids. The (dimensionless) control parameters are the Reynolds numbers of the inner and outer cylinders, the ratio of the cylinder radii, and the aspect ratio. One key response of the system is the torque required to retain constant angular velocities, which can be connected to the angular velocity transport through the gap. Whereas the low–Reynolds number regime was well explored in the 1980s and 1990s of the past century, in the fully turbulent regime major research activity developed only in the past decade. In this article, we review this recent progress in our understanding of fully developed Taylor-Couette turbulence from the experimental, numerical, and theoretical points of view. We focus on the parameter dependence of the global torque and on the local flow organization, including velocity profiles and boundary layers. Next, we discuss transitions between diff.
M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis

Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M2FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition. It contains 397,544 images of 229 subjects with yaw, pitch, attribute, illumination and accessory. M2FPA is the most comprehensive multi-view face database for facial pose analysis. Further, we provide an effective benchmark for face frontalization and pose-invariant face recognition on M2FPA with several state-of-the-art methods, including DR-GAN, TP-GAN and CAPG-GAN. We believe that the new database and benchmark can significantly push forward the advance of facial pose analysis in real-world applications. Moreover, a simple yet effective parsing guided discriminator is introduced to capture the local consistency during GAN optimization. Extensive quantitative and qualitative results on M2FPA and Multi-PIE demonstrate the superiority of our face frontalization method. Baseline results for both face synthesis and face recognition from state-of-theart methods demonstrate the challenge offered by this new database.
Parabolic PDE-based multi-agent formation control on a cylindrical surface

ABSTRACT This paper considers the modelling and control design of the multi-agent systems in the 3-D space. The communication graph of the agents is a mesh-grid 2-D cylindrical surface. Different from most existing literatures, where the agents are modelled by ordinary differential equations (ODEs), we treat the agents as a continuum in this paper. More specifically, we model the collective dynamics of the agents by two reaction–advection–diffusion 2-D partial differential equations (PDEs). The PDE states represent the agent positions, and the equilibria correspond to possible formation manifolds. These PDEs can be open-loop unstable, and the boundary stabilisation problem of the PDEs on the cylindrical surface is solved using the backstepping method. An all-explicit observer-based output control scheme is constructed, which is distributed in the sense that each agent only needs local information. Closed-loop exponential stability in the L 2, H 1, and H 2 spaces is proved for the controller designs. Numerical simulations illustrate the effectiveness of our proposed approach.
KloakDB: A Data Federation for Analyzing Sensitive Data with K -anonymous Query Processing

A private data federation enables data owners to pool their information for querying without disclosing their secret tuples to one another. Here, a client queries the union of the records of all data owners. The data owners work together to answer the query using privacy-preserving algorithms that prevent them from learning unauthorized information about the inputs of their peers. Only the client, and a federation coordinator, learn the query’s output. KloakDB is a private data federation that uses trusted hardware to process SQL queries over the inputs of two or more parties. Currently private data federations compute their queries fully-obliviously, guaranteeing that no information is revealed about the sensitive inputs of a data owner to their peers by observing the query’s instruction traces and memory access patterns. Oblivious querying almost always exacts multiple orders of magnitude slowdown in query runtimes compared to plaintext execution, making it impractical for many applications. KloakDB oﬀers a semi-oblivious computing framework, k -anonymous query processing . We make the query’s observable transcript k - anonymous because it is a popular standard for data release in many domains including medicine, educational research, and government data. KloakDB’s queries run such that each data owner may deduce information about no fewer than k individuals in the data of their peers. In addition, stake-holders set k , creating a novel trade-oﬀ between privacy and performance. Our results show that KloakDB enjoys speedups of up to 117X using k -anonymous query processing over full-oblivious evaluation.
Transmon qubit in a magnetic field: Evolution of coherence and transition frequency

We report on spectroscopic and time-domain measurements on a fixed-frequency concentric transmon qubit in an applied in-plane magnetic field to explore its limits of magnetic field compatibility. We demonstrate quantum coherence of the qubit up to field values of $B={40}\,\mathrm{mT}$, even without an optimized chip design or material combination of the qubit. The dephasing rate $\Gamma_\varphi$ is shown to be not affected by the magnetic field in a broad range of the qubit transition frequency. For the evolution of the qubit transition frequency, we find the unintended second junction created in the shadow angle evaporation process to be non-negligible and deduce an analytic formula for the field-dependent qubit energies. We discuss the relevant field-dependent loss channels, which can not be distinguished by our measurements, inviting further theoretical and experimental investigation. Using well-known and well-studied standard components of the superconducting quantum architecture, we are able to reach a field regime relevant for quantum sensing and hybrid applications of magnetic spins and spin systems.
Self Supervised Occupancy Grid Learning from Sparse Radar for Autonomous Driving

Occupancy grid mapping is an important component of autonomous vehicle perception. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. To this end, radars are becoming widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches. In this work, we propose a data driven approach for learning an inverse sensor model used for occupancy grid mapping from clustered radar data. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned in a self-supervised manner using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data.
Human Values and Attitudes towards Vaccination in Social Media

Psychological, political, cultural, and even societal factors are entangled in the reasoning and decision-making process towards vaccination, rendering vaccine hesitancy a complex issue. Here, administering a series of surveys via a Facebook-hosted application, we study the worldviews of people that “Liked” supportive or vaccine resilient Facebook Pages. In particular, we assess differences in political viewpoints, moral values, personality traits, and general interests, finding that those sceptical about vaccination, appear to trust less the government, are less agreeable, while they are emphasising more on anti-authoritarian values. Exploring the differences in moral narratives as expressed in the linguistic descriptions of the Facebook Pages, we see that pages that defend vaccines prioritise the value of the family while the vaccine hesitancy pages are focusing on the value of freedom. Finally, creating embeddings based on the health-related likes on Facebook Pages, we explore common, latent interests of vaccine-hesitant people, showing a strong preference for natural cures. This exploratory analysis aims at exploring the potentials of a social media platform to act as a sensing tool, providing researchers and policymakers with insights drawn from the digital traces, that can help design communication campaigns that build confidence, based on the values that also appeal to the socio-moral criteria of people.
State-dependent effective interactions in oscillator networks through coupling functions with dead zones

The dynamics of networks of interacting dynamical systems depend on the nature of the coupling between individual units. We explore networks of oscillatory units with coupling functions that have ‘dead zones’, that is the coupling functions are zero on sets with interior. For such networks, it is convenient to look at the effective interactions between units rather than the (fixed) structural connectivity to understand the network dynamics. For example, oscillators may effectively decouple in particular phase configurations. Along trajectories, the effective interactions are not necessarily static, but the effective coupling may evolve in time. Here, we formalize the concepts of dead zones and effective interactions. We elucidate how the coupling function shapes the possible effective interaction schemes and how they evolve in time. This article is part of the theme issue ‘Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences’.
Quantum transport in high-quality shallow InSb quantum wells

InSb is one of the promising candidates to realize a topological state through proximity induced superconductivity in a material with strong spin-orbit interactions. In two-dimensional systems, thin barriers are needed to allow strong coupling between superconductors and semiconductors. However, it is still challenging to obtain a high-quality InSb two-dimensional electron gas in quantum wells close to the surface. Here we report on a molecular beam epitaxy grown heterostructure of InSb quantum wells with substrate-side Si-doping and ultra-thin InAlSb (5 nm, 25 nm, and 50 nm) barriers to the surface. We demonstrate that the carrier densities in these quantum wells are gate-tunable and electron mobilities up to 350,000 $\rm{cm^2(Vs)^{-1}}$ are extracted from magneto-transport measurements. Furthermore, from temperature-dependent magneto-resistance measurements, we extract an effective mass of 0.02 $m_0$ and find a Zeeman splitting compatible with the expected g-factor.
Room Geometry Estimation from Room Impulse Responses using Convolutional Neural Networks.

We describe a new method to estimate the geometry of a room given room impulse responses. The method utilises convolutional neural networks to estimate the room geometry and uses the mean square error as the loss function. In contrast to existing methods, we do not require the position or distance of sources or receivers in the room. The method can be used with only a single room impulse response between one source and one receiver for room geometry estimation. The proposed estimation method can achieve ten centimetre accuracy and is shown to be computationally efficient comparing to state-of-the-art methods.
GAN You Do the GAN GAN?

Generative Adversarial Networks (GANs) have become a dominant class of generative models. In recent years, GAN variants have yielded especially impressive results in the synthesis of a variety of forms of data. Examples include compelling natural and artistic images, textures, musical sequences, and 3D object files. However, one obvious synthesis candidate is missing. In this work, we answer one of deep learning's most pressing questions: GAN you do the GAN GAN? That is, is it possible to train a GAN to model a distribution of GANs? We release the full source code for this project under the MIT license.