arXiv subjects

Jie Ren

Publications and source records attributed to Jie Ren.

At least 145 records · Page 8Linked to original sources

arXiv2023

Geometric Thermoelectric Pump: Energy Harvesting beyond Seebeck and Pyroelectric Effects

Thermal-electric conversion is crucial for smart energy control and harvesting, such as thermal sensing and waste heat recovering. So far, people are aware of two main ways of direct thermal-electric conversion, Seebeck and pyroelectric effects, each with different working mechanisms, conditions and limitations. Here, we report the concept of "Geometric Thermoelectric Pump", as the third way of thermal-electric conversion beyond Seebeck and pyroelectric effects. In contrast to Seebeck effect that requires spatial temperature difference, Geometric Thermoelectric Pump converts the time-dependent ambient temperature fluctuation into electricity. Moreover, Geometric Thermoelectric Pump does not require polar materials but applies to general conducting systems, thus is also distinct from pyroelectric effect. We demonstrate that Geometric Thermoelectric Pump results from the temperature-fluctuation-induced charge redistribution, which has a deep connection to the topological geometric phase in non-Hermitian dynamics, as a consequence of the fundamental nonequilibrium thermodynamic geometry. The findings advance our understanding of geometric phase induced multiple-physics-coupled pump effect and provide new means of thermal-electric energy harvesting.

cond-mat.mes-hall↗

arXiv2023

Hybrid Spin and Anomalous Spin-Momentum Locking in Surface Elastic Waves

Transverse spin of surface waves is a universal phenomenon which has recently attracted significant attention in optics and acoustics. It appears in gravity water waves, surface plasmon-polaritons, surface acoustic waves, and exhibits remarkable intrinsic spin-momentum locking, which has found useful applications for efficient spin-direction couplers. Here we demonstrate, both theoretically and experimentally, that the transverse spin of surface elastic (Rayleigh) waves has an anomalous sign near the surface, opposite to that in the case of electromagnetic, sound, or water surface waves. This anomalous sign appears due to the hybrid (neither transverse nor longitudinal) nature of elastic surface waves. Furthermore, we show that this sign anomaly can be employed for the selective spin-controlled excitation of symmetric and antisymmetric Lamb modes propagating in opposite directions in an elastic plate. Our results pave the way for spin-controlled manipulation of elastic waves and can be important for a variety of areas, from phononic spin-based devices to seismic waves.

physics.app-ph↗

arXiv2023

Gate-Tunable Critical Current of the Three-Dimensional Niobium Nano-Bridge Josephson Junction

Recent studies have shown that the critical currents of several metallic superconducting nanowires and Dayem bridges can be locally tuned using a gate voltage {V_g}. Here, we report a gate-tunable Josephson junction structure constructed from a three-dimensional (3D) niobium nano-bridge junction (NBJ) with a voltage gate on top. Measurements up to 6 K showed that the critical current of this structure can be tuned to zero by increasing {V_g}. The critical gate voltage Vgc was reduced to 16 V and may possibly be reduced further by reducing the thickness of the insulation layer between the gate and the NBJ. Furthermore, the flux modulation generated by Josephson interference of two parallel 3D NBJs can also be tuned using {V_g} in a similar manner. Therefore, we believe that this gate-tunable Josephson junction structure is promising for superconducting circuit fabrication at high integration levels.

cond-mat.supr-con↗

arXiv2023

Geometric Scaling of the Current-Phase Relation of Niobium Nano-Bridge Junctions

The nano-bridge junction (NBJ) is a type of Josephson junction that is advantageous for the miniaturization of superconducting circuits. However, the current-phase relation (CPR) of the NBJ usually deviates from a sinusoidal function which has been explained by a simplified model with correlation only to its effective length. Here, we investigated both measured and calculated CPRs of niobium NBJs of a cuboidal shape with a three-dimensional bank structure. From a sine-wave to a saw-tooth-like form, we showed that deviated CPRs of NBJs can be described quantitatively by its skewness Δθ. Furthermore, the measured dependency of Δθ on the critical current {I_0} from 108 NBJs turned out to be consistent with the calculated ones derived from the change in geometric dimensions. It suggested that the CPRs of NBJs can be tuned by their geometric dimensions. In addition, the calculated scaling behavior of Δθ versus {I_0} in three-dimensional space was provided for the future design of superconducting circuits of a high integration level by using niobium NBJs.

cond-mat.mes-hall↗

arXiv2023

A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models

Contrastively trained text-image models have the remarkable ability to perform zero-shot classification, that is, classifying previously unseen images into categories that the model has never been explicitly trained to identify. However, these zero-shot classifiers need prompt engineering to achieve high accuracy. Prompt engineering typically requires hand-crafting a set of prompts for individual downstream tasks. In this work, we aim to automate this prompt engineering and improve zero-shot accuracy through prompt ensembling. In particular, we ask "Given a large pool of prompts, can we automatically score the prompts and ensemble those that are most suitable for a particular downstream dataset, without needing access to labeled validation data?". We demonstrate that this is possible. In doing so, we identify several pathologies in a naive prompt scoring method where the score can be easily overconfident due to biases in pre-training and test data, and we propose a novel prompt scoring method that corrects for the biases. Using our proposed scoring method to create a weighted average prompt ensemble, our method outperforms equal average ensemble, as well as hand-crafted prompts, on ImageNet, 4 of its variants, and 11 fine-grained classification benchmarks, all while being fully automatic, optimization-free, and not requiring access to labeled validation data.

cs.LG↗

arXiv2023

The Bayesian Regularized Quantile Varying Coefficient Model

The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable, and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. Although extensive studies have been conducted to examine variable selection for the high-dimensional quantile varying coefficient models, the Bayesian analysis has been rarely developed. The Bayesian regularized quantile varying coefficient model has been proposed to incorporate robustness against data heterogeneity while accommodating the non-linear interactions between the effect modifier and predictors. Selecting important varying coefficients can be achieved through Bayesian variable selection. Incorporating the multivariate spike-and-slab priors further improves performance by inducing exact sparsity. The Gibbs sampler has been derived to conduct efficient posterior inference of the sparse Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit of the proposed model in selection and estimation accuracy over the alternatives has been systematically investigated in simulation under specific quantile levels and multiple heavy-tailed model errors. In the case study, the proposed model leads to identification of biologically sensible markers in a non-linear gene-environment interaction study using the NHS data.

stat.ME↗

arXiv2023

Morse Neural Networks for Uncertainty Quantification

We introduce a new deep generative model useful for uncertainty quantification: the Morse neural network, which generalizes the unnormalized Gaussian densities to have modes of high-dimensional submanifolds instead of just discrete points. Fitting the Morse neural network via a KL-divergence loss yields 1) a (unnormalized) generative density, 2) an OOD detector, 3) a calibration temperature, 4) a generative sampler, along with in the supervised case 5) a distance aware-classifier. The Morse network can be used on top of a pre-trained network to bring distance-aware calibration w.r.t the training data. Because of its versatility, the Morse neural networks unifies many techniques: e.g., the Entropic Out-of-Distribution Detector of (Macêdo et al., 2021) in OOD detection, the one class Deep Support Vector Description method of (Ruff et al., 2018) in anomaly detection, or the Contrastive One Class classifier in continuous learning (Sun et al., 2021). The Morse neural network has connections to support vector machines, kernel methods, and Morse theory in topology.

stat.ML↗

arXiv2023

Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos

Localization of the narrowest position of the vessel and corresponding vessel and remnant vessel delineation in carotid ultrasound (US) are essential for carotid stenosis grading (CSG) in clinical practice. However, the pipeline is time-consuming and tough due to the ambiguous boundaries of plaque and temporal variation. To automatize this procedure, a large number of manual delineations are usually required, which is not only laborious but also not reliable given the annotation difficulty. In this study, we present the first video classification framework for automatic CSG. Our contribution is three-fold. First, to avoid the requirement of laborious and unreliable annotation, we propose a novel and effective video classification network for weakly-supervised CSG. Second, to ease the model training, we adopt an inflation strategy for the network, where pre-trained 2D convolution weights can be adapted into the 3D counterpart in our network for an effective warm start. Third, to enhance the feature discrimination of the video, we propose a novel attention-guided multi-dimension fusion (AMDF) transformer encoder to model and integrate global dependencies within and across spatial and temporal dimensions, where two lightweight cross-dimensional attention mechanisms are designed. Our approach is extensively validated on a large clinically collected carotid US video dataset, demonstrating state-of-the-art performance compared with strong competitors.

cs.CV↗

arXiv2023

The role of high-order anharmonicity and off-diagonal terms in thermal conductivity: a case study of multi-phase CsPbBr3

We investigate the influence of three- and four-phonon scattering, perturbative anharmonic phonon renormalization, and off-diagonal terms of coherent phonons on the thermal conductivity of CsPbBr3 phase change perovskite, by using advanced implementations and first-principles simulations. Our study spans a wide temperature range covering the entire structural spectrum. Notably, we demonstrate that the interactions between acoustic and optical phonons result in contrasting trends of phonon frequency shifts for the high-lying optical phonons in orthorhombic and cubic CsPbBr3 as temperature varies. Our findings highlight the significance of wave-like tunneling of coherent phonons in ultralow and glass-like thermal conductivity in halide perovskites.

cond-mat.mtrl-sci↗

arXiv2023

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models

We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector that leverages text-image pre-trained models in a zero-shot fashion and incorporates various descriptions of in-domain and OOD. Our approach is designed to detect anything not in-domain and offers the flexibility to detect a wide variety of OOD, defined via fine- or coarse-grained labels, or even in natural language. We evaluate our approach on challenging benchmarks including large-scale datasets containing fine-grained, semantically similar classes, distributionally shifted images, and multi-object images containing a mixture of in-domain and OOD objects. Our method shows superior performance over previous methods on all benchmarks. Code is available at https://github.com/gyhandy/One-Class-Anything

cs.CV↗

arXiv2023

Improving Zero-shot Generalization and Robustness of Multi-modal Models

Multi-modal image-text models such as CLIP and LiT have demonstrated impressive performance on image classification benchmarks and their zero-shot generalization ability is particularly exciting. While the top-5 zero-shot accuracies of these models are very high, the top-1 accuracies are much lower (over 25% gap in some cases). We investigate the reasons for this performance gap and find that many of the failure cases are caused by ambiguity in the text prompts. First, we develop a simple and efficient zero-shot post-hoc method to identify images whose top-1 prediction is likely to be incorrect, by measuring consistency of the predictions w.r.t. multiple prompts and image transformations. We show that our procedure better predicts mistakes, outperforming the popular max logit baseline on selective prediction tasks. Next, we propose a simple and efficient way to improve accuracy on such uncertain images by making use of the WordNet hierarchy; specifically we augment the original class by incorporating its parent and children from the semantic label hierarchy, and plug the augmentation into text prompts. We conduct experiments on both CLIP and LiT models with five different ImageNet-based datasets. For CLIP, our method improves the top-1 accuracy by 17.13% on the uncertain subset and 3.6% on the entire ImageNet validation set. We also show that our method improves across ImageNet shifted datasets, four other datasets, and other model architectures such as LiT. The proposed method is hyperparameter-free, requires no additional model training and can be easily scaled to other large multi-modal architectures. Code is available at https://github.com/gyhandy/Hierarchy-CLIP.

cs.CV↗

arXiv2023

Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN?

Masking some input variables of a deep neural network (DNN) and computing output changes on the masked input sample represent a typical way to compute attributions of input variables in the sample. People usually mask an input variable using its baseline value. However, there is no theory to examine whether baseline value faithfully represents the absence of an input variable, \emph{i.e.,} removing all signals from the input variable. Fortunately, recent studies show that the inference score of a DNN can be strictly disentangled into a set of causal patterns (or concepts) encoded by the DNN. Therefore, we propose to use causal patterns to examine the faithfulness of baseline values. More crucially, it is proven that causal patterns can be explained as the elementary rationale of the Shapley value. Furthermore, we propose a method to learn optimal baseline values, and experimental results have demonstrated its effectiveness.

cs.LG↗

arXiv2023

Full velocities and propagation directions of coronal mass ejections inferred from simultaneous full-disk imaging and Sun-as-a-star spectroscopic observations

Coronal mass ejections (CMEs) are violent ejections of magnetized plasma from the Sun, which can trigger geomagnetic storms, endanger satellite operations and destroy electrical infrastructures on the Earth. After systematically searching Sun-as-a-star spectra observed by the Extreme-ultraviolet Variability Experiment (EVE) onboard the Solar Dynamics Observatory (SDO) from May 2010 to May 2022, we identified eight CMEs associated with flares and filament eruptions by analyzing the blue-wing asymmetry of the O III 52.58 nm line profiles. Combined with images simultaneously taken by the 30.4 nm channel of the Atmospheric Imaging Assembly onboard SDO, the full velocity and propagation direction for each of the eight CMEs are derived. We find a strong correlation between geomagnetic indices (Kp and Dst) and the angle between the CME propagation direction and the Sun-Earth line, suggesting that Sun-as-a-star spectroscopic observations at EUV wavelengths can potentially help to improve the prediction accuracy of the geoeffectiveness of CMEs. Moreover, an analysis of synthesized long-exposure Sun-as-a-star spectra implies that it is possible to detect CMEs from other stars through blue-wing asymmetries or blueshifts of spectral lines.

astro-ph.SR↗

arXiv2023

On the Origin of Acoustic Spin and Elastic Spin: Uncovering Hidden Wave Spin of Scalar Fields with Higher-Order Derivative Lagrangian

Scalar field should have no spin angular momentum according to conventional understandings in classical field theory. Yet, recent studies demonstrate the undoubted existence of wave spin endowed by acoustic and elastic longitudinal waves, which are of irrotational curl-free nature without vorticity and can be described by scalar fields. Here, to solve this seeming discrepancy, we uncover the origin of wave spin in scalar fields beyond traditional formalism by clarifying that the presence of higher order derivatives in scalar field Lagrangians can give rise to non-vanishing spin. For scalar fields with only first order derivative, we can make the hidden wave spin emerge, by constructing a latent field that leads to the original field through a time derivative so that is of second order. We exemplify the wave spin for elastic and acoustic fields, as well as for dissipative media, following Noether's theorem in higher-order derivative Lagrangian. The results would prompt people to build more comprehensive and fundamental understandings of structural wave spin in classical fields.

physics.class-ph↗

arXiv2023

Holographic superconductors at zero density

We construct holographic superconductors at zero density. The model enjoys a luxury property that the background geometry dual to the ground state is analytically available. It has a hyperscaling-violating geometry in the IR and is asymptotically AdS in the UV. Classification by IR geometries gives new insights on supergravity solutions. We numerically construct the finite temperature solution of hairy black holes and verify the phase transition by tuning a double-trace deformation parameter. For a holographic superconductor from M-theory, we obtain an analytic solution of the AC conductivity, which explicitly shows a superconducting delta function and a hard gap.

hep-th↗

arXiv2023

Rethinking Memory Profiling and Migration for Multi-Tiered Large Memory Systems

Multi-tiered large memory systems call for rethinking of memory profiling and migration because of the unique problems unseen in the traditional memory systems with smaller capacity and fewer tiers. We develop MTM, an application-transparent page management system based on three principles: (1) connecting the control of profiling overhead with the profiling mechanism for high-quality profiling; (2) building a universal page migration policy on the complex multi-tiered memory for high performance; and (3) introducing huge page awareness. We evaluate MTM using common big-data applications with realistic working sets (hundreds of GB to 1 TB). MTM outperforms seven state-of-the-art solutions by up to 42% (17% on average)

cs.PF↗

arXiv2023

Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection

Interest point detection methods have received increasing attention and are widely used in computer vision tasks such as image retrieval and 3D reconstruction. In this work, second-order anisotropic Gaussian directional derivative filters with multiple scales are used to smooth the input image and a novel blob detection method is proposed. Extensive experiments demonstrate the superiority of our proposed method over state-of-the-art benchmarks in terms of detection performance and robustness to affine transformations.

cs.CV↗

arXiv2023

Anomalous impact of thermal fluctuations on spintransfer torque induced ferrimagnetic switching

The dynamics of a spin torque driven ferrimagnetic (FiM) system is investigated using the two-sublattice macrospin model. We demonstrate an ultrafast switching in the picosecond range. However, we find that the excessive current leads to the magnetic oscillation. Therefore, faster switching cannot be achieved by unlimitedly increasing the current. By systematically studying the impact of thermal fluctuations, we find the dynamics of FiMs can also be distinguished into the precessional region, the thermally activated region, and the cross-over region. However, in the precessional region, there is a significant deviation between FiM and ferromagnet (FM), i.e., the FM is insensitive to thermal fluctuations since its switching is only determined by the amount of net charge. In contrast, we find that the thermal effect is pronounced even a very short current pulse is applied to the FiM. We attribute this anomalous effect to the complex relation between the anisotropy and overdrive current. By controlling the magnetic anisotropy, we demonstrate that the FiM can also be configured to be insensitive to thermal fluctuations. This controllable thermal property makes the FiM promising in many emerging applications such as the implementation of tunable activation functions in the neuromorphic computing.

cond-mat.mes-hall↗