Search arXiv⌕ Search

arXiv subjects

Yu Liu

Publications and source records attributed to Yu Liu.

At least 901 records · Page 50Linked to original sources
arXiv2020

Air temperature and humidity during the solar eclipses of 26 December 2019 and of 21 June 2020 in Saudi Arabia and in other eclipses with similar environments

We report air temperature and humidity changes during the two solar eclipses of 26 December 2019, and of 21 June 2020, respectively, in the cities of Al-Hofuf and Riyadh in Saudi Arabia. During the December eclipse the Sun rose already eclipsed (91.53% of the area covered) while the June eclipse, although also annular in other places of the Arabian Peninsula, was just partial at Riyadh (area covered 72.80%). This difference apparently affected the observed response on the recorded variables of temperature, relative humidity (RH) and vapor pressure (VP) in the two events. Change in these variables went unnoticed for the first eclipse since it was within the natural variability of the day; yet for the other, they showed clearly some trend alterations, which we analyze and discuss. A decrease in temperature of 3.2 °C was detected in Riyadh; however, RH and VP showed an oscillation that we explain in the light of a similar effect reported in other eclipses. We found a time lag of about 15 min measured from the eclipse central phase in this city. We made an inspection of related fluctuations and dynamics from the computed rates of the temporal variation of temperature and RH. Trying to identify the influence of solar eclipses in similar environments we have made a broad inter-comparison with other observations of these variables in the Near East, northern Africa and in the United States. We compare our results with results obtained by other authors working with the December eclipse but in the United Arab Emirates and Oman, which showed dissimilar results. These inter-comparisons show how effectively the lower atmosphere can respond to a solar eclipse within a desert environment and others similar. As a preamble, a historical revision of temperature and humidity in the context of eclipse meteorology is also included.

physics.ao-ph↗
arXiv2020

State-to-state control of ultracold molecular reactions

Quantum control of reactive systems has enabled microscopic probes of underlying interaction potentials, the opening of novel reaction pathways, and the alteration of reaction rates using quantum statistics. However, extending such control to the quantum states of reaction outcomes remains challenging. In this work, we realize this goal through the nuclear spin degree of freedom, a result which relies on the conservation of nuclear spins throughout the reaction. Using resonance-enhanced multiphoton ionization spectroscopy to investigate the products formed in bimolecular reactions between ultracold KRb molecules, we find that the system retains a near-perfect memory of the reactants' nuclear spins, manifested as a strong parity preference for the rotational states of the products. We leverage this effect to alter the occupation of these product states by changing the coherent superposition of initial nuclear spin states with an external magnetic field. In this way, we are able to control both the inputs and outputs of a bimolecular reaction with quantum state resolution. The techniques demonstrated here open up the possibilities to study quantum interference between reaction pathways, quantum entanglement between reaction products, and ultracold reaction dynamics at the state-to-state level.

physics.atom-ph↗
arXiv2020

Desires and Motivation: The Computational Rule, the Underlying Neural Circuitry, and the Relevant Clinical Disorders

As organism is a dissipative system. The process from multi desires to exclusive motivation is of great importance among all sensory-action loops. In this paper we argued that a proper Desire-Motivation model should be a continuous dynamic mapping from the dynamic desire vector to the sparse motivation vector. Meanwhile, it should at least have specific stability and adjustability of motivation intensity. Besides, the neuroscience evidences suggest that the Desire-Motivation model should have dynamic information acquisition and should be a recurrent neural network. A five-equation model is built based on the above arguments, namely the Recurrent Gating Desire-Motivation (RGDM) model. Additionally, a heuristic speculation based on the RGDM model about corresponding brain regions is carried out. It believes that the tonic and phasic firing of ventral tegmental area dopamine neurons should execute the respective and collective feedback functions of recurrent processing. The analysis about the RGMD model shows the expectations about individual personality from three dimensions, namely stability, intensity, and motivation decision speed. These three dimensions can be combined and create eight different personalities, which is correspondent to Jung's personality structure theorem. Furthermore, the RGDM model can be used to predict three different brand-new types of depressive disorder with different phenotypes. Moreover, it can also explain several other psychiatry disorders from new perspectives.

q-bio.NC↗
arXiv2020

DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images

Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance of current methods to pseudo-changes, in this paper, we propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet) for change detection in high-resolution images. Through the dual-attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e. unchanged samples are much more than changed samples, which is one of the main reasons resulting in pseudo-changes. We put forward the weighted double margin contrastive loss to address this problem by punishing the attention to unchanged feature pairs and increase attention to changed feature pairs. The experimental results of our method on the change detection dataset (CDD) and the building change detection dataset (BCDD) demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.1\% and 3.6\%, respectively, in the F1 score. Our Pytorch implementation is available at https://github.com/lehaifeng/DASNet.

cs.CV↗
arXiv2020

Visual Localization Under Appearance Change: Filtering Approaches

A major focus of current research on place recognition is visual localization for autonomous driving. In this scenario, as cameras will be operating continuously, it is realistic to expect videos as an input to visual localization algorithms, as opposed to the single-image querying approach used in other visual localization works. In this paper, we show that exploiting temporal continuity in the testing sequence significantly improves visual localization - qualitatively and quantitatively. Although intuitive, this idea has not been fully explored in recent works. To this end, we propose two filtering approaches to exploit the temporal smoothness of image sequences: i) filtering on discrete domain with Hidden Markov Model, and ii) filtering on continuous domain with Monte Carlo-based visual localization. Our approaches rely on local features with an encoding technique to represent an image as a single vector. The experimental results on synthetic and real datasets show that our proposed methods achieve better results than state of the art (i.e., deep learning-based pose regression approaches) for the task on visual localization under significant appearance change. Our synthetic dataset and source code are made publicly available at https://sites.google.com/view/g2d-software/home and https://github.com/dadung/Visual-Localization-Filtering.

cs.CV↗
arXiv2020

A gridded establishment dataset as a proxy for economic activity in China

Measuring the geographical distribution of economic activity plays a key role in scientific research and policymaking. However, previous studies and data on economic activity either have a coarse spatial resolution or cover a limited time span, and the high-resolution characteristics of socioeconomic dynamics are largely unknown. Here, we construct a dataset on the economic activity of mainland China, the gridded establishment dataset (GED), which measures the volume of establishments at a 0.01$^{\circ}$ latitude by 0.01$^{\circ}$ longitude scale. Specifically, our dataset captures the geographically based opening and closing of approximately 25.5 million firms that registered in mainland China over the period 2005-2015. The characteristics of fine granularity and long-term observability give the GED a high application value. The dataset not only allows us to quantify the spatiotemporal patterns of the establishments, urban vibrancy and socioeconomic activity, but also helps us uncover the fundamental principles underlying the dynamics of industrial and economic development.

cs.SI↗
arXiv2020

$τ$-tilting theory in abelian categories

Let $\mathcal A$ be a Hom-finite abelian category with enough projectives. In this note, we show that any covariantly finite $τ$-rigid subcategory is contained in a support $τ$-tilting subcategory. We also show that support $τ$-tilting subcategories are in bijection with certain finitely generated torsion classes. Some applications of our main results are also given.

math.RT↗
arXiv2020

Characterizing and Comparing COVID-19 Misinformation Across Languages, Countries and Platforms

Misinformation/disinformation about COVID-19 has been rampant on social media around the world. In this study, we investigate COVID-19 misinformation/ disinformation on social media in multiple languages - Farsi (Persian), Chinese, and English, about multiple countries - Iran, China, and the United States (US), and on multiple platforms such as Twitter, Facebook, Instagram, Weibo, and WhatsApp. Misinformation, especially about a global pandemic, is a global problem yet it is common for studies of COVID-19 misinformation on social media to focus on a single language, like English, a single country, like the US, or a single platform, like Twitter. We utilized opportunistic sampling to compile 200 specific items of viral and yet debunked misinformation across these languages, countries and platforms emerged between January 1 and August 31. We then categorized this collection based both on the topics of the misinformation and the underlying roots of that misinformation. Our multi-cultural and multilingual team observed that the nature of COVID-19 misinformation on social media varied in substantial ways across different languages/countries depending on the cultures, beliefs/religions, popularity of social media, types of platforms, freedom of speech and the power of people versus governments. We observe that politics is at the root of most of the collected misinformation across all three languages in this dataset. We further observe the different impact of government restrictions on platforms and platform restrictions on content in Iran, China, and the US and their impact on a key question of our age: how do we control misinformation without silencing the voices we need to hold governments accountable?

cs.CY↗
arXiv2020

On the Exploration of Incremental Learning for Fine-grained Image Retrieval

In this paper, we consider the problem of fine-grained image retrieval in an incremental setting, when new categories are added over time. On the one hand, repeatedly training the representation on the extended dataset is time-consuming. On the other hand, fine-tuning the learned representation only with the new classes leads to catastrophic forgetting. To this end, we propose an incremental learning method to mitigate retrieval performance degradation caused by the forgetting issue. Without accessing any samples of the original classes, the classifier of the original network provides soft "labels" to transfer knowledge to train the adaptive network, so as to preserve the previous capability for classification. More importantly, a regularization function based on Maximum Mean Discrepancy is devised to minimize the discrepancy of new classes features from the original network and the adaptive network, respectively. Extensive experiments on two datasets show that our method effectively mitigates the catastrophic forgetting on the original classes while achieving high performance on the new classes.

cs.CV↗
arXiv2020

Quantum optimal control using phase-modulated driving fields

Quantum optimal control represents a powerful technique to enhance the performance of quantum experiments by engineering the controllable parameters of the Hamiltonian. However, the computational overhead for the necessary optimization of these control parameters drastically increases as their number grows. We devise a novel variant of a gradient-free optimal-control method by introducing the idea of phase-modulated driving fields, which allows us to find optimal control fields efficiently. We numerically evaluate its performance and demonstrate the advantages over standard Fourier-basis methods in controlling an ensemble of two-level systems showing an inhomogeneous broadening. The control fields optimized with the phase-modulated method provide an increased robustness against such ensemble inhomogeneities as well as control-field fluctuations and environmental noise, with one order of magnitude less of average search time. Robustness enhancement of single quantum gates is also achieved by the phase-modulated method. Under environmental noise, an XY-8 sequence constituted by optimized gates prolongs the coherence time by $50\%$ compared with standard rectangular pulses in our numerical simulations, showing the application potential of our phase-modulated method in improving the precision of signal detection in the field of quantum sensing.

quant-ph↗
arXiv2020

Magnetic mixed valent semimetal EuZnSb$_2$ with Dirac states in the band structure

We report discovery of new antiferromagnetic semimetal EuZnSb$_2$, obtained and studied in the form of single crystals. Electric resistivity, magnetic susceptibility and heat capacity indicate antiferromagnetic order of Eu with $T_N$ = 20 K. The effective moment of Eu$^{2+}$ inferred from the magnetization and specific heat measurement is 3.5 $μ_B$, smaller than the theoretical value of Eu$^{2+}$ due to presence of both Eu$^{3+}$ and Eu$^{2+}$. Magnetic field-dependent resistivity measurements suggest dominant quasi two dimensional Fermi surfaces whereas the first-principle calculations point to the presence of Dirac fermions. Therefore, EuZnSb$_2$ could represent the first platform to study the interplay of dynamical charge fluctuations, localized magnetic 4$f$ moments and Dirac states with Sb orbital character.

cond-mat.str-el↗
arXiv2020

Commands 4 Autonomous Vehicles (C4AV) Workshop Summary

The task of visual grounding requires locating the most relevant region or object in an image, given a natural language query. So far, progress on this task was mostly measured on curated datasets, which are not always representative of human spoken language. In this work, we deviate from recent, popular task settings and consider the problem under an autonomous vehicle scenario. In particular, we consider a situation where passengers can give free-form natural language commands to a vehicle which can be associated with an object in the street scene. To stimulate research on this topic, we have organized the \emph{Commands for Autonomous Vehicles} (C4AV) challenge based on the recent \emph{Talk2Car} dataset (URL: https://www.aicrowd.com/challenges/eccv-2020-commands-4-autonomous-vehicles). This paper presents the results of the challenge. First, we compare the used benchmark against existing datasets for visual grounding. Second, we identify the aspects that render top-performing models successful, and relate them to existing state-of-the-art models for visual grounding, in addition to detecting potential failure cases by evaluating on carefully selected subsets. Finally, we discuss several possibilities for future work.

cs.CV↗
arXiv2020

Image Retrieval for Structure-from-Motion via Graph Convolutional Network

Conventional image retrieval techniques for Structure-from-Motion (SfM) suffer from the limit of effectively recognizing repetitive patterns and cannot guarantee to create just enough match pairs with high precision and high recall. In this paper, we present a novel retrieval method based on Graph Convolutional Network (GCN) to generate accurate pairwise matches without costly redundancy. We formulate image retrieval task as a node binary classification problem in graph data: a node is marked as positive if it shares the scene overlaps with the query image. The key idea is that we find that the local context in feature space around a query image contains rich information about the matchable relation between this image and its neighbors. By constructing a subgraph surrounding the query image as input data, we adopt a learnable GCN to exploit whether nodes in the subgraph have overlapping regions with the query photograph. Experiments demonstrate that our method performs remarkably well on the challenging dataset of highly ambiguous and duplicated scenes. Besides, compared with state-of-the-art matchable retrieval methods, the proposed approach significantly reduces useless attempted matches without sacrificing the accuracy and completeness of reconstruction.

cs.CV↗
arXiv2020

Low-Temperature Thermopower in CoSbS

We report giant thermopower S = 2.5 mV/K in CoSbS single crystals, a material that shows strong high-temperature thermoelectric performance when doped with Ni or Se. Changes of low temperature thermopower induced by magnetic field point to mechanism of electronic diffusion of carriers in the heavy valence band. Intrinsic magnetic susceptibility is consistent with the Kondo- Insulator-like accumulation of electronic states around the gap edges. This suggests that giant thermopower stems from temperature-dependent renormalization of the non-interacting bands and buildup of the electronic correlations on cooling.

cond-mat.str-el↗
arXiv2020

Three-dimensional Fermi surface and small effective masses in Mo8Ga41

We report Fermi surface characteristics of Mo8Ga41, a two-gap superconductor with critical temperature Tc = 10 K, obtained from quantum oscillation measurements. Four major frequencies have been observed with relatively small quasiparticle masses. Angular dependence of major frequencies indicates three-dimensional Fermi surface sheets. This argues for a relatively isotropic superconducting state and, given its relatively high Tc, shows that a search for materials in this class could be of interest for superconducting wire applications.

cond-mat.supr-con↗
arXiv2020

Discriminability Distillation in Group Representation Learning

Learning group representation is a commonly concerned issue in tasks where the basic unit is a group, set, or sequence. Previously, the research community tries to tackle it by aggregating the elements in a group based on an indicator either defined by humans such as the quality and saliency, or generated by a black box such as the attention score. This article provides a more essential and explicable view. We claim the most significant indicator to show whether the group representation can be benefited from one of its element is not the quality or an inexplicable score, but the discriminability w.r.t. the model. We explicitly design the discrimiability using embedded class centroids on a proxy set. We show the discrimiability knowledge has good properties that can be distilled by a light-weight distillation network and can be generalized on the unseen target set. The whole procedure is denoted as discriminability distillation learning (DDL). The proposed DDL can be flexibly plugged into many group-based recognition tasks without influencing the original training procedures. Comprehensive experiments on various tasks have proven the effectiveness of DDL for both accuracy and efficiency. Moreover, it pushes forward the state-of-the-art results on these tasks by an impressive margin.

cs.CV↗
arXiv2020

Valence band electronic structure of the van der Waals ferromagnetic insulators: VI$_3$ and CrI$_3$

Ferromagnetic van der Waals (vdW) insulators are of great scientific interest for their promising applications in spintronics. It has been indicated that in the two materials within this class, CrI$_3$ and VI$_3$, the magnetic ground state, the band gap, and the Fermi level could be manipulated by varying the layer thickness, strain or doping. To understand how these factors impact the properties, a detailed understanding of the electronic structure would be required. However, the experimental studies of the electronic structure of these materials are still very sparse. Here, we present the detailed electronic structure of CrI$_3$ and VI$_3$ measured by angle-resolved photoemission spectroscopy (ARPES). Our results show a band-gap of the order of 1 eV, sharply contrasting some theoretical predictions such as Dirac half-metallicity and metallic phases, indicating that the intra-atomic interaction parameter (U) and spin-orbit coupling (SOC) were not properly accounted for in the calculations. We also find significant differences in the electronic properties of these two materials, in spite of similarities in their crystal structure. In CrI$_3$, the valence band maximum is dominated by the I 5{\it p}, whereas in VI$_3$ it is dominated by the V 3{\it d} derived states. Our results represent valuable input for further improvements in the theoretical modeling of these systems.

cond-mat.str-el↗