arXiv subjects

Hui Wu

Publications and source records attributed to Hui Wu.

At least 55 records · Page 3Linked to original sources

arXiv2023

Strain Engineering for High-Performance Phase Change Memristors

A new mechanism for memristive switching in 2D materials is through electric-field controllable electronic/structural phase transitions, but these devices have not outperformed status quo 2D memristors. Here, we report a high-performance bipolar phase change memristor from strain engineered multilayer 1T'-MoTe$_{2}$ that now surpasses the performance metrics (on/off ratio, switching voltage, switching speed) of all 2D memristive devices, achieved without forming steps. Using process-induced strain engineering, we directly pattern stressed metallic contacts to induce a semimetallic to semiconducting phase transition in MoTe2 forming a self-aligned vertical transport memristor with semiconducting MoTe$_{2}$ as the active region. These devices utilize strain to bring them closer to the phase transition boundary and achieve ultra-low ~90 mV switching voltage, ultra-high ~10$^8$ on/off ratio, 5 ns switching, and retention of over 10$^5$ s. Engineered tunability of the device switching voltage and on/off ratio is also achieved by varying the single process parameter of contact metal film force (film stress $\times$ film thickness).

physics.app-ph↗

arXiv2023

Giant Thermomechanical Bandgap Engineering in Quasi-two-dimensional Tellurium

Mechanical straining-induced bandgap modulation in two-dimensional (2D) materials has been confined to volatile and narrow modulation due to substrate slippage and poor strain transfer. We report the thermomechanical modulation of the inherent bandgap in quasi-2D tellurium nanoflakes (TeNFs) via non-volatile strain induction during hot-press synthesis. We leveraged the coefficient of thermal expansion (CTE) mismatch between TeNFs and growth substrates by maintaining a high-pressure enforced non-slip condition during thermal relaxation (623 to 300K) to achieve the optimal biaxial compressive strain of -4.6 percent in TeNFs/sapphire. This resulted in an enormous bandgap modulation of 2.3 eV, at a rate of up to ~600 meV/%, which is two-fold larger than reported modulation rate. Strained TeNFs display robust band-to-band radiative excitonic blue photoemission with an intrinsic quantum efficiency (IQE) of c.a. 79.9%, making it promising for energy efficient blue LEDs and nanolasers. Computational studies reveal that biaxial compressive strain inhibits exciton-exciton annihilation by evading van-Hove singularities, hence promoting radiative-recombination. Bandgap modulation by such nonvolatile straining is scalable to other 2D semiconductors for on-demand nano(opto)-electronics.

physics.optics↗

arXiv2022

A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction

A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work. The Neuron-ADC leverages level-crossing sampling and a bio-inspired refractory circuit to compressively converts bio-signal to digital spikes and information-of-interest. The proposed design can not only avoid dissipating ADC energy on unnecessary data but also achieve reconfigurable sampling, making it appropriate for either low power operation or high accuracy conversion when dealing with various kinds of bio-signals. Moreover, the proposed dynamic comparator can reduce static power up to 41.1% when tested with a 10 kHz sinusoidal input. Simulation results of 40 nm CMOS process show that the Neuron-ADC achieves a maximum ENOB of 6.9 bits with a corresponding FoM of 97 fJ/conversion under 0.6 V supply voltage.

eess.SP↗

arXiv2022

Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief

Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed.

eess.SP↗

arXiv2022

SimVQA: Exploring Simulated Environments for Visual Question Answering

Existing work on VQA explores data augmentation to achieve better generalization by perturbing the images in the dataset or modifying the existing questions and answers. While these methods exhibit good performance, the diversity of the questions and answers are constrained by the available image set. In this work we explore using synthetic computer-generated data to fully control the visual and language space, allowing us to provide more diverse scenarios. We quantify the effect of synthetic data in real-world VQA benchmarks and to which extent it produces results that generalize to real data. By exploiting 3D and physics simulation platforms, we provide a pipeline to generate synthetic data to expand and replace type-specific questions and answers without risking the exposure of sensitive or personal data that might be present in real images. We offer a comprehensive analysis while expanding existing hyper-realistic datasets to be used for VQA. We also propose Feature Swapping (F-SWAP) -- where we randomly switch object-level features during training to make a VQA model more domain invariant. We show that F-SWAP is effective for enhancing a currently existing VQA dataset of real images without compromising on the accuracy to answer existing questions in the dataset.

cs.CV↗

arXiv2022

The m-core-EP inverse in Minkowski space

In this paper, we introduce the m-core-EP inverse in Minkowski space, consider its properties, and get several sufficient and necessary conditions for the existence of the m-core-EP inverse. We give the m-core-EP decomposition in Minkowski space, and note that not every square matrix has the decomposition. Furthermore, by applying the m-core-EP inverse and the m-core-EP decomposition, we introduce the m-core-EP order and give some characterizations of it.

math.FA↗

arXiv2021

Learning Token-based Representation for Image Retrieval

In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract deep local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.

eess.IV↗

arXiv2021

Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

Model-free deep reinforcement learning (RL) algorithms have been widely used for a range of complex control tasks. However, slow convergence and sample inefficiency remain challenging problems in RL, especially when handling continuous and high-dimensional state spaces. To tackle this problem, we propose a general acceleration method for model-free, off-policy deep RL algorithms by drawing the idea underlying regularized Anderson acceleration (RAA), which is an effective approach to accelerating the solving of fixed point problems with perturbations. Specifically, we first explain how policy iteration can be applied directly with Anderson acceleration. Then we extend RAA to the case of deep RL by introducing a regularization term to control the impact of perturbation induced by function approximation errors. We further propose two strategies, i.e., progressive update and adaptive restart, to enhance the performance. The effectiveness of our method is evaluated on a variety of benchmark tasks, including Atari 2600 and MuJoCo. Experimental results show that our approach substantially improves both the learning speed and final performance of state-of-the-art deep RL algorithms.

cs.LG↗

arXiv2021

Contextual Similarity Aggregation with Self-attention for Visual Re-ranking

In content-based image retrieval, the first-round retrieval result by simple visual feature comparison may be unsatisfactory, which can be refined by visual re-ranking techniques. In image retrieval, it is observed that the contextual similarity among the top-ranked images is an important clue to distinguish the semantic relevance. Inspired by this observation, in this paper, we propose a visual re-ranking method by contextual similarity aggregation with self-attention. In our approach, for each image in the top-K ranking list, we represent it into an affinity feature vector by comparing it with a set of anchor images. Then, the affinity features of the top-K images are refined by aggregating the contextual information with a transformer encoder. Finally, the affinity features are used to recalculate the similarity scores between the query and the top-K images for re-ranking of the latter. To further improve the robustness of our re-ranking model and enhance the performance of our method, a new data augmentation scheme is designed. Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms. We conduct comprehensive experiments on four benchmark datasets to demonstrate the generality and effectiveness of our proposed visual re-ranking method.

cs.CV↗

arXiv2021

Physical properties of a quasi-two-dimensional square lattice antiferromagnet Ba$_2$FeSi$_2$O$_7$

We report the magnetization ($χ$, $M$), specific heat ($C_{\text{P}}$), and neutron powder diffraction results on a quasi-two-dimensional $S$ = 2 square lattice antiferromagnet Ba$_2$FeSi$_2$O$_7$ consisting of FeO$_4$ tetragons with a large compressive distortion (27%). Despite of the quasi-two-dimensional lattice structure, both $χ$ and $C_{\text{P}}$ present three dimensional magnetic long-range order below the Néel temperature $T_{\text{N}}$ = 5.2 K. Neutron diffraction data shows a collinear $Q_{m}$ = (1,0,0.5) antiferromagnetic (AFM) structure with the in-plane ordered magnetic moment suppressed by 26% below $T_{\text{N}}$. Both the AFM structure and the suppressed moments are well explained by the Monte Carlo simulation with a large single-ion ab-plane anisotropy $D$ = 1.4 meV and a rather small in-plane Heisenberg exchange $J_{\text{intra}}$ = 0.15 meV. The characteristic two dimensional spin fluctuations can be recognized in the magnetic entropy release and diffuse scattering above $T_{\text{N}}$. This new quasi-2D magnetic system also displays unusual non-monotonic dependence of the $T_{\text{N}}$ as a function of magnetic field $H$.

cond-mat.str-el↗

arXiv2021

Separating Skills and Concepts for Novel Visual Question Answering

Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them into "skills" and "concepts". "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned in the question, such as objects and people. VQA methods should be able to compose skills and concepts in novel ways, regardless of whether the specific composition has been seen in training, yet we demonstrate that existing models have much to improve upon towards handling new compositions. We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts. We enforce these properties with a novel contrastive learning procedure that does not rely on external annotations and can be learned from unlabeled image-question pairs. Experiments demonstrate the effectiveness of our approach for improving compositional and grounding performance.

cs.CV↗

arXiv2021

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existing NAS methods makes it difficult to directly search the architectures on large-scale tasks. The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset. Despite a number of recent results that show the promise of transfer from proxy datasets, a comprehensive evaluation of different NAS methods studying the impact of different source datasets has not yet been addressed. In this work, we propose to analyze the architecture transferability of different NAS methods by performing a series of experiments on large scale benchmarks such as ImageNet1K and ImageNet22K. We find that: (i) The size and domain of the proxy set does not seem to influence architecture performance on the target dataset. On average, transfer performance of architectures searched using completely different small datasets (e.g., CIFAR10) perform similarly to the architectures searched directly on proxy target datasets. However, design of proxy sets has considerable impact on rankings of different NAS methods. (ii) While different NAS methods show similar performance on a source dataset (e.g., CIFAR10), they significantly differ on the transfer performance to a large dataset (e.g., ImageNet1K). (iii) Even on large datasets, random sampling baseline is very competitive, but the choice of the appropriate combination of proxy set and search strategy can provide significant improvement over it. We believe that our extensive empirical analysis will prove useful for future design of NAS algorithms.

cs.CV↗

arXiv2021

Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning

During recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems for its low-cost and intelligence. This paper proposes two smart train operation algorithms by integrating the expert knowledge with reinforcement learning algorithms. Compared with previous works, the proposed algorithms can realize the control of continuous action for the subway system and optimize multiple critical objectives without using an offline speed profile. Firstly, through learning historical data of experienced subway drivers, we extract the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality, and the safety of the subway system. Then we develop two algorithms for optimizing the energy efficiency of train operation. One is the smart train operation (STO) algorithm based on deep deterministic policy gradient named (STOD) and the other is the smart train operation algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data from the Yizhuang Line of the Beijing Subway and illustrate that the developed smart train operation algorithm are better than expert manual driving and existing ATO algorithms in terms of energy efficiency. Moreover, STOD and STON can adapt to different trip times and different resistance conditions.

cs.CE↗

arXiv2020

Learning from Lexical Perturbations for Consistent Visual Question Answering

Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. In this paper, we propose a novel approach to address this issue based on modular networks, which creates two questions related by linguistic perturbations and regularizes the visual reasoning process between them to be consistent during training. We show that our framework markedly improves consistency and generalization ability, demonstrating the value of controlled linguistic perturbations as a useful and currently underutilized training and regularization tool for VQA models. We also present VQA Perturbed Pairings (VQA P2), a new, low-cost benchmark and augmentation pipeline to create controllable linguistic variations of VQA questions. Our benchmark uniquely draws from large-scale linguistic resources, avoiding human annotation effort while maintaining data quality compared to generative approaches. We benchmark existing VQA models using VQA P2 and provide robustness analysis on each type of linguistic variation.

cs.CV↗

arXiv2020

Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images. We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialog-based image retrieval. We believe that our dataset will encourage further work on developing more natural and real-world applicable conversational shopping assistants.

cs.CV↗

arXiv2020

Large Scale Neural Architecture Search with Polyharmonic Splines

Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification. Due to the significant computational burden of the search phase, most NAS methods have focused so far on small, balanced datasets. All attempts at conducting NAS at large scale have employed small proxy sets, and then transferred the learned architectures to larger datasets by replicating or stacking the searched cells. We propose a NAS method based on polyharmonic splines that can perform search directly on large scale, imbalanced target datasets. We demonstrate the effectiveness of our method on the ImageNet22K benchmark[16], which contains 14 million images distributed in a highly imbalanced manner over 21,841 categories. By exploring the search space of the ResNet [23] and Big-Little Net ResNext [11] architectures directly on ImageNet22K, our polyharmonic splines NAS method designed a model which achieved a top-1 accuracy of 40.03% on ImageNet22K, an absolute improvement of 3.13% over the state of the art with similar global batch size [15].

cs.CV↗

arXiv2020

Confirmed width-Eiso and width-Liso relations in GRB: comparison with the Amati and Yonetoku relations

In this paper, we select a sample including 141 BEST time-integrated F spectra and 145 BEST peak flux P spectra observed by the Konus-Wind with known redshift to recheck the connection between the spectral width and $E_{iso}$ as well as $L_{iso}$. We define six types of absolute spectral widths. It is found that all of the rest-frame absolute spectral widths are strongly positive correlated with $E_{iso}$ as well as $L_{iso}$ for the long burst for both the F and P spectra. All of the short bursts are the outliers for width-$E_{iso}$ relation and most of the short bursts are consistent with the long bursts for the width-$L_{iso}$ relation for both F and P spectra. Moreover, all of the location energy, $E_{2}$ and $E_{1}$, corresponding to various spectral widths are also positive correlated with $E_{iso}$ as well as $L_{iso}$. We compare all of the relations with the Amati and Yonetoku relations and find the width-$E_{iso}$ and width-$L_{iso}$ relations when the widths are at about 90\% maximum of the $EF_{E}$ spectra almost overlap with Amati relation and Yonetoku relation, respectively. The correlations of $E_{2}-E_{iso}$, $E_{1}-E_{iso}$ and $E_{2}-L_{iso}$, $E_{1}-L_{iso}$ when the location energies are at 99\% maximum of the $EF_{E}$ spectra are very close to the Amati and Yonetoku relations, respectively. Therefore, we confirm the existence of tight width-$E_{iso}$ and width-$L_{iso}$ relations for long bursts. We further show that the spectral shape is indeed related to $E_{iso}$ and $L_{iso}$. The Amati and Yonetoku relations are not necessarily the best relationships to relate the energy to the $E_{iso}$ and $L_{iso}$. They may be the special cases of the width-$E_{iso}$ and width-$L_{iso}$ relations or the energy-$E_{iso}$ and energy-$L_{iso}$ relations.

astro-ph.HE↗

arXiv2019

A selected review on reinforcement learning based control for autonomous underwater vehicles

Recently, reinforcement learning (RL) has been extensively studied and achieved promising results in a wide range of control tasks. Meanwhile, autonomous underwater vehicle (AUV) is an important tool for executing complex and challenging underwater tasks. The advances in RL offers ample opportunities for developing intelligent AUVs. This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking. To this end, we first present a concise introduction to the RL based control framework. Then, we provide an overview of RL methods for AUVs control problems, where the main challenges and recent progresses are discussed. Finally, two representative cases of RL-based controllers are given in detail for the model-free RL methods on AUVs.

cs.RO↗