arXiv subjects

Ying Wu

Publications and source records attributed to Ying Wu.

At least 73 records · Page 4Linked to original sources

arXiv2023

Nonlinear Topological Mechanics in Elliptically Geared Isostatic Metamaterials

Despite the extensive studies of topological systems, the experimental characterizations of strongly nonlinear topological phases have been lagging. To address this shortcoming, we design and build elliptically geared isostatic metamaterials. Their nonlinear topological transitions can be realized by collective soliton motions, which stem from the transition of nonlinear Berry phase. Endowed by the intrinsic nonlinear topological mechanics, surface polar elasticity and dislocation-bound zero modes can be created or annihilated as the topological polarization reverses orientation. Our approach integrates topological physics with strongly nonlinear mechanics and promises multi-phase structures at the micro and macro scales.

cond-mat.mtrl-sci↗

arXiv2023

A Survey on Approximate Multiplier Designs for Energy Efficiency: From Algorithms to Circuits

Given the stringent requirements of energy efficiency for Internet-of-Things edge devices, approximate multipliers, as a basic component of many processors and accelerators, have been constantly proposed and studied for decades, especially in error-resilient applications. The computation error and energy efficiency largely depend on how and where the approximation is introduced into a design. Thus, this article aims to provide a comprehensive review of the approximation techniques in multiplier designs ranging from algorithms and architectures to circuits. We have implemented representative approximate multiplier designs in each category to understand the impact of the design techniques on accuracy and efficiency. The designs can then be effectively deployed in high-level applications, such as machine learning, to gain energy efficiency at the cost of slight accuracy loss.

cs.AR↗

arXiv2023

An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models

Nowadays, the interpretation of why a machine learning (ML) model makes certain inferences is as crucial as the accuracy of such inferences. Some ML models like the decision tree possess inherent interpretability that can be directly comprehended by humans. Others like artificial neural networks (ANN), however, rely on external methods to uncover the deduction mechanism. SHapley Additive exPlanations (SHAP) is one of such external methods, which requires a background dataset when interpreting ANNs. Generally, a background dataset consists of instances randomly sampled from the training dataset. However, the sampling size and its effect on SHAP remain to be unexplored. In our empirical study on the MIMIC-III dataset, we show that the two core explanations - SHAP values and variable rankings fluctuate when using different background datasets acquired from random sampling, indicating that users cannot unquestioningly trust the one-shot interpretation from SHAP. Luckily, such fluctuation decreases with the increase of the background dataset size. Also, we notice an U-shape in the stability assessment of SHAP variable rankings, demonstrating that SHAP is more reliable in ranking the most and least important variables compared to moderately important ones. Overall, our results suggest that users should take into account how background data affects SHAP results, with improved SHAP stability as the background sample size increases.

cs.LG↗

arXiv2023

Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness

It is broadly known that deep neural networks are susceptible to being fooled by adversarial examples with perturbations imperceptible by humans. Various defenses have been proposed to improve adversarial robustness, among which adversarial training methods are most effective. However, most of these methods treat the training samples independently and demand a tremendous amount of samples to train a robust network, while ignoring the latent structural information among these samples. In this work, we propose a novel Local Structure Preserving (LSP) regularization, which aims to preserve the local structure of the input space in the learned embedding space. In this manner, the attacking effect of adversarial samples lying in the vicinity of clean samples can be alleviated. We show strong empirical evidence that with or without adversarial training, our method consistently improves the performance of adversarial robustness on several image classification datasets compared to the baselines and some state-of-the-art approaches, thus providing promising direction for future research.

cs.LG↗

arXiv2023

ALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation

It is critical that the models pay attention not only to accuracy but also to the certainty of prediction. Uncertain predictions of deep models caused by noisy data raise significant concerns in trustworthy AI areas. To explore and handle uncertainty due to intrinsic data noise, we propose a novel method called ALUM to simultaneously handle the model uncertainty and data uncertainty in a unified scheme. Rather than solely modeling data uncertainty in the ultimate layer of a deep model based on randomly selected training data, we propose to explore mined adversarial triplets to facilitate data uncertainty modeling and non-parametric uncertainty estimations to compensate for the insufficiently trained latent model layers. Thus, the critical data uncertainty and model uncertainty caused by noisy data can be readily quantified for improving model robustness. Our proposed ALUM is model-agnostic which can be easily implemented into any existing deep model with little extra computation overhead. Extensive experiments on various noisy learning tasks validate the superior robustness and generalization ability of our method. The code is released at https://github.com/wwzjer/ALUM.

cs.LG↗

arXiv2023

Adaptive Modeling of Uncertainties for Traffic Forecasting

Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic forecasting models. These models are typically trained to minimize error on averaged test cases and produce a single-point prediction, such as a scalar value for traffic speed or travel time. However, single-point predictions fail to account for prediction uncertainty that is critical for many transportation management scenarios, such as determining the best- or worst-case arrival time. We present QuanTraffic, a generic framework to enhance the capability of an arbitrary DNN model for uncertainty modeling. QuanTraffic requires little human involvement and does not change the base DNN architecture during deployment. Instead, it automatically learns a standard quantile function during the DNN model training to produce a prediction interval for the single-point prediction. The prediction interval defines a range where the true value of the traffic prediction is likely to fall. Furthermore, QuanTraffic develops an adaptive scheme that dynamically adjusts the prediction interval based on the location and prediction window of the test input. We evaluated QuanTraffic by applying it to five representative DNN models for traffic forecasting across seven public datasets. We then compared QuanTraffic against five uncertainty quantification methods. Compared to the baseline uncertainty modeling techniques, QuanTraffic with base DNN architectures delivers consistently better and more robust performance than the existing ones on the reported datasets.

cs.LG↗

arXiv2023

Superradiant phase transition induced by the indirect Rabi interaction

We theoretically study the superradiant phase transition (SPT) in an indirect Rabi model, where both a two-level system and a single mode bosonic field couple to an auxiliary bosonic field. We find that the indirect spin-field coupling induced by the virtual excitation of the auxiliary field can allow the occurrence of a SPT at a critical point, and the influence of the so-called $A^2$ term in the normal Rabi model is naturally avoided. In the large detuning regime, we present the analytical expression of quantum critical point in terms of the original system parameters. The critical atom-field coupling strength is tunable, which will loosen the conditions on realizing the SPT. Considering a hybrid magnon-cavity-qubit system, we predict the squeezed cat state of magnon generated with feasible experimental parameters, which has potential applications in quantum metrology and quantum information processing.

quant-ph↗

arXiv2023

Out-of-Time-Order Correlation as a Witness for Topological Phase Transitions

We propose a physical witness for dynamically detecting topological phase transitions (TPTs) via an experimentally observable out-of-time-order correlation (OTOC). The distinguishable OTOC dynamics appears in the topological trivial and non-trivial phases due to the topological locality. In the long-time limit, the OTOC undergoes a {\it zero-to-finite-value transition} at the critical point of the TPTs. This transition is robust to the choices of the initial state of the system and the used operators in OTOC. The proposed OTOC witness can be applied into the systems with and without chiral symmetry, e.g., the lattices described by the SSH model, Creutz model, and Haldane model. Moreover, our proposal, as a physical witness in real space, is still valid even in the presence of disorder. Our work fundamentally offers a new prospect of exploring topological physics with quantum correlations.

quant-ph↗

arXiv2023

Collective Radiance of Giant Atoms in Non-Markovian Regime

We investigate the non-Markovian dynamics of two giant artificial atoms interacting with a continuum of bosonic modes in a one-dimensional (1D) waveguide. Based on the diagrammatic method, we present the exact analytical solutions, which predict the rich phenomena of collective radiance. For the certain collective states, the decay rates are found to be far beyond that predicted in the the Dicke model and standard Markovian framework, which indicates the occurrence of super-superradiance. The superadiance-to-subradiance transition could be realized by adjusting the exchange symmetry of giant atoms. Moreover, there exists multiple bound states in continuum (BICs), with photons/phonons bouncing back and forth in the cavity-like geometries formed by the coupling points. The trapped photons/phonons in the BICs can also be re-released conveniently by changing the energy level splitting of giant atoms. The mechanism relies on the joint effects of the coherent time-delayed feedback and the interference between the coupling points of giant atoms. This work fundamentally broadens the fields of giant atom collective radiance by introducing non-Markovianity. It also paves the way for a clean analytical description of nonlinear open quantum system with more complex retardation.

quant-ph↗

arXiv2022

Toward a Fairness-Aware Scoring System for Algorithmic Decision-Making

Scoring systems, as a type of predictive model, have significant advantages in interpretability and transparency and facilitate quick decision-making. As such, scoring systems have been extensively used in a wide variety of industries such as healthcare and criminal justice. However, the fairness issues in these models have long been criticized, and the use of big data and machine learning algorithms in the construction of scoring systems heightens this concern. In this paper, we propose a general framework to create fairness-aware, data-driven scoring systems. First, we develop a social welfare function that incorporates both efficiency and group fairness. Then, we transform the social welfare maximization problem into the risk minimization task in machine learning, and derive a fairness-aware scoring system with the help of mixed integer programming. Lastly, several theoretical bounds are derived for providing parameter selection suggestions. Our proposed framework provides a suitable solution to address group fairness concerns in the development of scoring systems. It enables policymakers to set and customize their desired fairness requirements as well as other application-specific constraints. We test the proposed algorithm with several empirical data sets. Experimental evidence supports the effectiveness of the proposed scoring system in achieving the optimal welfare of stakeholders and in balancing the needs for interpretability, fairness, and efficiency.

cs.LG↗

arXiv2022

Transition behavior of the seizure dynamics modulated by the astrocyte inositol triphosphate noise

Epilepsy is a neurological disorder with recurrent seizures of complexity and randomness. Until now, the mechanism of epileptic randomness has not been fully elucidated. Inspired by the recent finding that astrocyte GTPase-activating protein (G-protein)-coupled receptors could be involved in stochastic epileptic seizures, we proposed a neuron-astrocyte network model, incorporating the noise of the astrocytic second messager, inositol triphosphate (IP3) which is modulated by the G-protein)-coupled receptor activation. Based on this model, we have statistically analysed the transitions of epileptic seizures by performing tens of simulation trials. Our simulation results show that the increase of the IP3 noise intensity induces the depolarization-block epileptic seizures together with an increase in neuronal firing frequency. Meanwhile, a bistable state of neuronal firing emerges under certain noise intensity, during which the neuronal firing pattern switches between regular sparse spiking and epileptic seizure states. This random presence of epileptic seizures is absent when the noise intensity continues to increase, accompanying with an increase in the epileptic depolarization block duration. The simulation results also shed light on the fact that calcium signals in astrocytes play significant roles in the pattern formations of the epileptic seizure. Our results provide a potential pathway for understanding the epileptic randomness.

q-bio.NC↗

arXiv2022

Sound Waveguiding by Spinning: An Avenue towards Unidirectional Acoustic Spinning Fibers

Waveguiding in general and acoustic waveguiding in particular are possible at the condition of having a transverse "discontinuity" or modulation of the refractive index. We propose here a radically different approach that relies on imposing spinning on a column of air, leading to high modified acoustic refractive indices for specific azimuthal modes. Such discovery may be leveraged to realize not only the airborne acoustic counterpart of the optical fiber, i.e., the acoustic spinning fiber (ASF), but also nonreciprocal unidirectional waveguiding mechanism, reminiscent of the ''acoustic Zeeman effect''. The concept is demonstrated in the realm of acoustics, yet it can be applicable to other wave systems, e.g., photonics or elastodynamics.

physics.class-ph↗

arXiv2022

Quantum Magnetometer with Dual-Coupling Optomechanics

An experimentally feasible magnetometer based on a dual-coupling optomechanical system is proposed, where the radiation-pressure coupling transduces the magnetic signal to the optical phase, and the quadratic optomechanical interaction induces a periodic squeezing effect. The latter not only amplifies the signal to be measured, but also accelerates the signal transducing rate characterized by an experimentally observable phase accumulation efficiency. In the vicinity of opto-mechanical decoupled time, the ultimate bound to the estimability of magnetic signal is proportional to $\exp(-6r)$, and then the optimized accuracy of estimation can be enhanced nearly 3 orders with a controllable squeezing parameter $r<1$. Moreover, our proposal is robust against the mechanical thermal noise, and the sensitivity of a specific measurement can reach to the order of $10^{-17}{\rm T/\sqrt{Hz}}$ in the presence of dissipations and without ground state cooling of mechanical oscillator. Our proposal fundamentally broadens the fields of quantum metrology and cavity optomechanics, with potential application for on-chip magnetic detection with high precision.

quant-ph↗

arXiv2022

Observation of Single Pair of Type-III Weyl Points in Sonic Crystals

In electronics systems, the Weyl points can be classified into three types based on the geometry of the Fermi surface, and each type exhibits various unique and intriguing phenomena. While the type-I and type-II Weyl points have been achieved in both spinful and spinless systems, the realization of type-III Weyl points remains challenging, and has not been reported in artificial periodic systems. Here, we for the first time report the experimental observation of the type-III Weyl points in a sonic crystal. Remarkably, a single pair of type-III Weyl points are observed as the only band crossings in a frequency range, experimentally disproving a common belief in the field, namely, the minimal number of Weyl points in nonmagnetic systems is four. The consistency between experimental results and theoretical predictions confirms the existence of type-III Weyl points, noncontractible Fermi arc surface states, and chiral edge states. Our work not only fill the gap of the type-III Weyl point in sonic crystal but also stimulate related researches in other systems, such as photonic, mechanical and cold atom systems.

cond-mat.mes-hall↗

arXiv2022

Lifespan associations of resting-state brain functional networks with ADHD symptoms

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition (NSP) approach to study the resting-state brain functional networks of ADHD patients (n=97) and healthy controls (HCs, n=97) across the lifespan (7-50 years). Compared to the linear lifespan associations of brain functional segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD symptoms and provide potential shared neural bases of distinct ADHD symptoms in children and adults.

q-bio.NC↗

arXiv2022

SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering

Visual Question Answering (VQA) attracts much attention from both industry and academia. As a multi-modality task, it is challenging since it requires not only visual and textual understanding, but also the ability to align cross-modality representations. Previous approaches extensively employ entity-level alignments, such as the correlations between the visual regions and their semantic labels, or the interactions across question words and object features. These attempts aim to improve the cross-modality representations, while ignoring their internal relations. Instead, we propose to apply structured alignments, which work with graph representation of visual and textual content, aiming to capture the deep connections between the visual and textual modalities. Nevertheless, it is nontrivial to represent and integrate graphs for structured alignments. In this work, we attempt to solve this issue by first converting different modality entities into sequential nodes and the adjacency graph, then incorporating them for structured alignments. As demonstrated in our experimental results, such a structured alignment improves reasoning performance. In addition, our model also exhibits better interpretability for each generated answer. The proposed model, without any pretraining, outperforms the state-of-the-art methods on GQA dataset, and beats the non-pretrained state-of-the-art methods on VQA-v2 dataset.

cs.CV↗

arXiv2021

Topological materials for full-vector elastic waves

Elastic wave manipulation is important in a wide variety of scales in applications including information processing in tiny elastic devices and noise control in big solid structures. The recent emergence of topological materials opens a new avenue toward modulating elastic waves in solids. However, because of the full-vector feature, and the complicated couplings of the longitudinal and transverse components of elastic waves, manipulating elastic waves is generally difficult, compared with manipulating acoustic waves (scalar waves) and electromagnetic waves (vectorial waves but transverse only). Up to date, topological materials, including insulators and semimetals, have been realized for acoustic and electromagnetic waves. Although topological materials of elastic waves have also been reported, the topological edge modes observed all lie on the domain wall. A natural question can be asked: whether there exists an elastic metamaterial with the topological edge modes on its own boundary only? Here, we report a 3D metal-printed bilayer metamaterial, insulating topologically the elastic waves. By introducing the chiral interlayer couplings, the spin-orbit couplings for elastic waves are induced, which give rise to nontrivial topological properties. The helical edge states with the vortex feature are demonstrated on the boundary of the single topological phase. We further show a heterostructure of the metamaterial, which exhibits tunable edge transport. Our work may have potential in devices based on elastic waves in solids.

cond-mat.mtrl-sci↗

arXiv2021

A Novel Load Balancing Scheme for Mobile Edge Computing

To overcome long propagation delays for data exchange between the remote cloud data center and end devices in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) is merging to push mobile computing, network control and storage to the network edges. A cloudlet in MEC is a mobility-enhanced small-scale cloud, which contains several MEC servers located in close proximity to mobile devices. The main purpose of a cloudlet is to stably provide services to mobile devices with low latency. When a cloudlet offers hundreds kinds of services to millions of mobile devices, it is critical to balance the loads so as to improve performance. In this paper, we propose a three-layer mobile hybrid hierarchical P2P (MHP2P) model as a cloudlet. MHP2P performs satisfactory service lookup efficiency and system scalability as well as high stability. More importantly, a load balance scheme is provided so that the loads of MHP2P can be well balanced with the increasing of MEC servers and query load. A large number of simulation experiments indicate that the proposed scheme is efficient for enhancing the load balancing performance in MHP2P based MEC systems.

cs.DC↗