arXiv subjects

He Huang

Publications and source records attributed to He Huang.

At least 91 records · Page 5Linked to original sources

arXiv2022

Emergent scenario in mimetic gravity

The emergent scenario provides a possible way to avoid the big bang singularity by assuming that the universe originates from an Einstein static state. Therefore, an Einstein static universe stable under perturbations is crucial to a successful implementation of the emergent mechanism. In this paper, we analyze the stability of the Einstein static universe against the scalar perturbations in the mimetic theory and find that stable Einstein static solutions exist under certain conditions in this theory. In the original mimetic gravity, the Einstein static universe is unstable. Then, we find that the universe can naturally exit from the initial static state, evolve into an inflationary era and then exit from the inflationary era. Thus, the emergent scenario can be used to resolve the big bang singularity in the mimetic theory.

gr-qc↗

arXiv2022

Stability analysis of the Tsallis holographic dark energy model

Using the generalized Tsallis entropy, the Tsallis holographic dark energy(THDE) was proposed recently. In this paper we analyze the cosmological consequences of the THDE model with an interaction between dark energy and dark matter $Q=H(αρ_{m}+βρ_{D})$. We find that the THDE model can explain the current accelerated cosmic expansion, and it is stable under certain conditions. Furthermore, through investigating the dynamical analysis, we find that there exists an attractor which represents an accelerated expansion phase of the universe. When $β=0$, this attractor corresponds to a dark energy dominated de Sitter solution and the universe can evolve into an era which is depicted by the $Λ$CDM model. The age of universe in this model is also explored.

gr-qc↗

arXiv2022

Dynamical analysis and statefinder of Barrow holographic dark energy

Based on the holographic principle and the Barrow entropy, Barrow holographic dark energy had been proposed. In order to analyze the stability and the evolution of Barrow holographic dark energy, we, in this paper, apply the dynamical analysis and statefinder methods to Barrow holographic dark energy with different IR cutoff and interacting terms. In the case of using Hubble horizon as IR cutoff with the interacting term $Q=\fracλ{H}ρ_{m}ρ_{D}$, we find this model is stable and can be used to describe the whole evolution of the universe when the energy transfers from the pressureless matter to the Barrow holographic dark energy. When the dynamical analysis method is applied to this stable model, an attractor corresponding to an accelerated expansion epoch exists and this attractor can behave as the cosmological constant. Furthermore, the coincidence problem can be solved in this case. Then, after using the statefinder analysis method to this model, we find this model can be discriminated from the standard $Λ$CDM model. Finally, we have discussed the turning point of Hubble diagram in Barrow holographic dark energy and find the turning point does not exist in this model.

gr-qc↗

arXiv2021

Translational Concept Embedding for Generalized Compositional Zero-shot Learning

Generalized compositional zero-shot learning means to learn composed concepts of attribute-object pairs in a zero-shot fashion, where a model is trained on a set of seen concepts and tested on a combined set of seen and unseen concepts. This task is very challenging because of not only the gap between seen and unseen concepts but also the contextual dependency between attributes and objects. This paper introduces a new approach, termed translational concept embedding, to solve these two difficulties in a unified framework. It models the effect of applying an attribute to an object as adding a translational attribute feature to an object prototype. We explicitly take into account of the contextual dependency between attributes and objects by generating translational attribute features conditionally dependent on the object prototypes. Furthermore, we design a ratio variance constraint loss to promote the model's generalization ability on unseen concepts. It regularizes the distances between concepts by utilizing knowledge from their pretrained word embeddings. We evaluate the performance of our model under both the unbiased and biased concept classification tasks, and show that our model is able to achieve good balance in predicting unseen and seen concepts.

cs.CV↗

arXiv2021

High-performance Ba1-xKxFe2As2 superconducting joints for persistent current operation

Superconducting joints are one of the key technologies to make Ba1-xKxFe2As2 (Ba-122) superconducting wires or tapes for high-field applications. Herein, superconducting joints were fabricated by a simple cold-pressing method, and the joint resistance of the iron-based superconducting joint was estimated for the first time. The superconducting properties, microstructures, and elements distribution in the joint regions were investigated. At 4.2 K and 10 T, a transport critical current Ic of 105 A for the joint was obtained, and the critical current ratio (CCR= Ic-joint/Ic-tape) of the joint was 94.6%. On the other hand, the joint show very low joint resistance of 2.7x10^-13 ohm in self-field at 4.2 K. Among iron-based superconductors (IBS), this work is the first to successfully realize a superconducting joint with such high CCR and low joint resistance. This work shows great potential to apply Ba-122 in a range of practical applications, where superconducting joints are essential.

cond-mat.supr-con↗

arXiv2021

Giant and bidirectionally tunable thermopower in non-aqueous ionogels enabled by selective ion doping

Ionic thermoelectrics show great potential in low-grade heat harvesting and thermal sensing owing to their ultrahigh thermopower, low cost and ease in production. However, the lack of effective n-type ionic thermoelectric materials seriously hinders their applications. Here, we report giant and bidirectionally tunable thermopowers within an ultrawide range from -23 to +32 mV K-1 at 90% RH in solid ionic-liquid-based ionogels, rendering it among the best n- and p-type ionic thermoelectric materials. A novel thermopower regulation strategy through ion doping to selectively induce ion aggregates via strong ion-ion interactions is proposed. These charged aggregates are found decisive in modulating the sign and enlarging the magnitude of the thermopower in the ionogels. A prototype wearable device integrated with 12 p-n pairs is demonstrated with a total thermopower of 0.358 V K-1 in general indoor conditions, showing promise for ultrasensitive body heat detection.

physics.app-ph↗

arXiv2021

The Multi-Modal Video Reasoning and Analyzing Competition

In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021. This competition is composed of four different tracks, namely, video question answering, skeleton-based action recognition, fisheye video-based action recognition, and person re-identification, which are based on two datasets: SUTD-TrafficQA and UAV-Human. We summarize the top-performing methods submitted by the participants in this competition and show their results achieved in the competition.

cs.CV↗

arXiv2021

SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

Traffic event cognition and reasoning in videos is an important task that has a wide range of applications in intelligent transportation, assisted driving, and autonomous vehicles. In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, we propose 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events. Moreover, we propose Eclipse, a novel Efficient glimpse network via dynamic inference, in order to achieve computation-efficient and reliable video reasoning. The experiments show that our method achieves superior performance while reducing the computation cost significantly. The project page: https://github.com/SUTDCV/SUTD-TrafficQA.

cs.CV↗

arXiv2021

The Power of Randomization: Efficient and Effective Algorithms for Constrained Submodular Maximization

Submodular optimization has numerous applications such as crowdsourcing and viral marketing. In this paper, we study the fundamental problem of non-negative submodular function maximization subject to a $k$-system constraint, which generalizes many other important constraints in submodular optimization such as cardinality constraint, matroid constraint, and $k$-extendible system constraint. The existing approaches for this problem achieve the best-known approximation ratio of $k+2\sqrt{k+2}+3$ (for a general submodular function) based on deterministic algorithmic frameworks. We propose several randomized algorithms that improve upon the state-of-the-art algorithms in terms of approximation ratio and time complexity, both under the non-adaptive setting and the adaptive setting. The empirical performance of our algorithms is extensively evaluated in several applications related to data mining and social computing, and the experimental results demonstrate the superiorities of our algorithms in terms of both utility and efficiency.

cs.DS↗

arXiv2021

Hash Adaptive Bloom Filter

Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set. However, as Bloom filter maps each element with uniformly random hash functions, few flexibilities are provided even if the information of negative keys (elements are not in the set) are available. The problem gets worse when the misidentification of negative keys brings different costs. To address the above problems, we propose a new Hash Adaptive Bloom Filter (HABF) that supports the customization of hash functions for keys. The key idea of HABF is to customize the hash functions for positive keys (elements are in the set) to avoid negative keys with high cost, and pack customized hash functions into a lightweight data structure named HashExpressor. Then, given an element at query time, HABF follows a two-round pattern to check whether the element is in the set. Further, we theoretically analyze the performance of HABF and bound the expected false positive rate. We conduct extensive experiments on representative datasets, and the results show that HABF outperforms the standard Bloom filter and its cutting-edge variants on the whole in terms of accuracy, construction time, query time, and memory space consumption (Note that source codes are available in [1]).

cs.DB↗

arXiv2021

2D MXenes: Visible Black but Infrared White Materials

Black materials with low infrared absorption/emission (or IR white) are rare in nature but highly desired in numerous areas, such as solar-thermal energy harvesting, multispectral camouflage, thermal insulation, and anti-counterfeiting. Due to the lack of spectral selectivity in intrinsic materials, such counter-intuitive properties are generally realized by constructing complicated subwavelength metamaterials with costly nanofabrication techniques. Here we report the low mid-IR emissivity (down to 10%) of 2D Ti3C2Tx MXenes. Associated with a high solar absorptance (up to 90%), they embrace the best spectral selectivity among the reported intrinsic black solar absorbing materials. Their appealing potentials in several aforementioned areas are experimentally demonstrated. First-principles calculations reveal that the IR emissivity of MXenes relies on both the nanoflake orientations and terminal groups, indicating great tunability. The calculations also suggest that more MXenes including Ti2CTx, Nb2CTx, and V2CTx are also potential low-emissivity materials. This work opens the avenue to further exploration of a family of intrinsically low-emissivity materials with over 70 members.

cond-mat.mtrl-sci↗

arXiv2021

High-performance Ba1-xKxFe2As2 superconducting tapes with grain texture engineered via a scalable and cost-effective fabrication

Nowadays the development of high-field magnets strongly relies on the performance of superconducting materials. Iron-based superconductors exhibit high upper critical fields and low electromagnetic anisotropy, making them particularly attractive for high-field applications, especially in particle accelerator magnets, nuclear magnetic resonance spectrometers, medical magnetic resonance imaging systems and nuclear fusion reactors. Herein, through an industrially scalable and cost-effective manufacturing strategy, a practical level critical current density up to 1.1x10^5 A/cm2 at 4.2 K in an external magnetic field of 10 T was achieved in Cu/Ag composite sheathed Ba0.6K0.4Fe2As2 superconducting tapes. The preparation strategy combines flat rolling to induce grain texture and a subsequent hot-isostatic-pressing densification. By varying the parameters of rolling, the degree of grain texture was engineered. It is found that the transport properties of the Ba1-xKxFe2As2 tapes can be enhanced by applying a large amount of deformation during rolling, which can be attributed to the improved degree of c-axis texture. Microstructure characterizations on the highest-performance tape demonstrate that the Ba1-xKxFe2As2 phase has a uniform element distribution and small grains with good connectivity. Grain boundary pinning is consequently enhanced as proved by large currents circulating through the sample even at 25 K. Our work proves that Cu/Ag composite sheathed Ba1-xKxFe2As2 superconducting tapes can be a promising competitor for practical high-field applications in terms of the viable, scalable and cost-effective fabrication strategy applied and the high transport properties achieved in this work.

cond-mat.supr-con↗

arXiv2020

Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score

Scene graph parsing aims to detect objects in an image scene and recognize their relations. Recent approaches have achieved high average scores on some popular benchmarks, but fail in detecting rare relations, as the highly long-tailed distribution of data biases the learning towards frequent labels. Motivated by the fact that detecting these rare relations can be critical in real-world applications, this paper introduces a novel integrated framework of classification and ranking to resolve the class imbalance problem in scene graph parsing. Specifically, we design a new Contrasting Cross-Entropy loss, which promotes the detection of rare relations by suppressing incorrect frequent ones. Furthermore, we propose a novel scoring module, termed as Scorer, which learns to rank the relations based on the image features and relation features to improve the recall of predictions. Our framework is simple and effective, and can be incorporated into current scene graph models. Experimental results show that the proposed approach improves the current state-of-the-art methods, with a clear advantage of detecting rare relations.

cs.CV↗

arXiv2020

Generalized Optimal Two-way Relays Subsets Pairings in Cloud-based Region Cognitive Networks

Communication reliability improving is one of most important research requirements in cognitive networks, as 5G communications technology rapidly develop nowadays. In this paper, we propose generalized optimal cloud-based region relays subsets paring model in underlay dual-hop cognitive networks, this unified model reveals three relays nodes characteristics of cloud-based cooperative networks in the nearby area- A subset, only needs receiving for first hop, B subset, just receive and forward, C subset, only forwarding. In addition, this generalized model can be converted into various classical relay selection algorithms when A, B and C subsets are taken as special selection values [Table II]. Furthermore, we put forward optimal relays subsets pairings and replacement algorithm flowchart to improve minimum outage probability (OP) for communication reliability, and prove that optimal relays subsets pairings (A, B, C) will better guarantee reliability of communication, comparing other popular relays selection schemes. Simulation results show that optimal relays paired subsets are exist and this generalized algorithm enormously reduces OP, comparing other selection algorithms.

cs.IT↗

arXiv2020

FDMA with Layers-based Optimized Mobile Relays Subsets Algorithm in B5G/6G Cognitive IoT Networks

In view of noteworthy communications performance improvements for future B5G/6G (such as cognitive Internet of Things, space-ground integration network and so on), cooperative communications (CC) diversity with relays selection algorithms have been extensively studied to significantly improve communications quality, but so far there is still a lot of potential optimization work with CC schemes. In this paper, in the light of NP-hard problem of subsets relays selection, further studies for theorems of relays subsets with K-layers power allocation standard have been put forward to explore better performance in B5G/6G cognitive IoT (Internet of Things) networks, we propose unified layers-based optimized mobile relays subsets algorithms for full-duplex (FD) non-orthogonal multiple access (NOMA) to greatly improve transmission rate. After revealing and taking into account fundamental properties of relays, such as mobile relays nodes state, relays locations, fading characteristics and so on, optimized FD-NOMA algorithm based on these relays features has been presented to improve transmission validity, and a related series of relays subsets theorems have been derived and proved, then minimum upper bound of maximum transmission rates has been estimated to reveal two-way balanced optimal transmission conclusion for FD-NOMA. In general, proposed general and optimized algorithm can be used in multiple future cooperative communications scenarios in B5G/6G networks such as cognitive IoT. Simulations results show that proposed scheme has several times transmission rates than other classical relays selection algorithms

cs.IT↗

arXiv2020

Cooperative Communications for Internet of Everything in B5G/6G Hybrid and Ubiquitous Networks: Foundation, Further Optimization and Solutions

Cooperative Communications (CC) has been one of most critical communication technologies which plays a founding role on Internet of Everything in B5G/6G networks. As 5G communications standard is gradually established recently, core communications technologies with CC are further studied to significantly improve communication quality and develop new communications scenarios for B5G/6G ubiquitous networks. Considering that CC has been regarded as foundation theory which widely exists in future multiple B5G/6G hybrid scenarios, such as, Cognitive Internet of Things (CIOT) networks, UAVs communications, air-space-ground of integrated networks, underwater acoustic communication and so on, besides it is closely combined with other key technologies, for examples, Massive MIMO, NOMA, Full-duplex transmission, Polar code and so on. Hence, in this paper we review foundation of CC for Internet of Everything in B5G/6G multiple heterogeneous CC networks, and compare fundamental CC algorithms to reveal key of performance improvement. Furthermore we propose that collective communications ideology is theory of foundation to realize communications for arbitrary two points as source/destination devices, sensors, relays, IOT nodes and so on in future.

eess.SP↗

arXiv2020

Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge

Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. On one hand, the unconstrained number of labels assigned to each image makes the model more easily overfit to those seen classes. On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets. To address these difficult issues, this paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge. We observe that ImageNet is commonly used to pretrain the feature extractor and has a large and fine-grained label space. This motivates us to exploit it as external knowledge to bridge the seen and unseen classes and promote generalization. Specifically, we construct a knowledge graph including not only classes from the target dataset but also those from ImageNet. Since ImageNet labels are not available in the target dataset, we propose a novel PosVAE module to infer their initial states in the extended knowledge graph. Then we design a relational graph convolutional network (RGCN) to propagate information among classes and achieve knowledge transfer. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach.

cs.CV↗

arXiv2020

Instance Scale Normalization for image understanding

Scale variation remains a challenging problem for object detection. Common paradigms usually adopt multiscale training & testing (image pyramid) or FPN (feature pyramid network) to process objects in a wide scale range. However, multi-scale methods aggravate more variations of scale that even deep convolution neural networks with FPN cannot handle well. In this work, we propose an innovative paradigm called Instance Scale Normalization (ISN) to resolve the above problem. ISN compresses the scale space of objects into a consistent range (ISN range), in both training and testing phases. This reassures the problem of scale variation fundamentally and reduces the difficulty of network optimization. Experiments show that ISN surpasses multi-scale counterpart significantly for object detection, instance segmentation, and multi-task human pose estimation, on several architectures. On COCO test-dev, our single model based on ISN achieves 46.5 mAP with a ResNet-101 backbone, which is among the state-of-the-art (SOTA) candidates for object detection.

cs.CV↗