arXiv subjects

Jing Hu

Publications and source records attributed to Jing Hu.

70 records · Page 4Linked to original sources

arXiv2022

Topological classification for intersection singularities of exceptional surfaces in pseudo-Hermitian systems

Exceptional points play a pivotal role in the topology of non-Hermitian systems, and significant advances have been made in classifying exceptional points and exploring the associated phenomena. Exceptional surfaces, which are hypersurfaces of exceptional degeneracies in parameter space, can support hypersurface singularities, such as cusps, intersections and swallowtail catastrophes. Here we topologically classify the intersection singularity of exceptional surfaces for a generic pseudo-Hermitian system with parity-time symmetry. By constructing the quotient space under equivalence relations of eigenstates, we reveal that the topology of such gapless structures can be described by a non-Abelian free group on three generators. Importantly, the classification predicts a new kind of non-Hermitian gapless topological phase and can systematically explain how the exceptional surfaces and their intersections evolve under perturbations with symmetries preserved. Our work opens a new pathway for designing systems with robust topological phases, and provides inspiration for applications such as sensing and lasing which can utilize the special properties inherent in exceptional surfaces and intersections.

cond-mat.mtrl-sci↗

arXiv2022

TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response

Predicting clinical outcomes to anti-cancer drugs on a personalized basis is challenging in cancer treatment due to the heterogeneity of tumors. Traditional computational efforts have been made to model the effect of drug response on individual samples depicted by their molecular profile, yet overfitting occurs because of the high dimension for omics data, hindering models from clinical application. Recent research shows that deep learning is a promising approach to build drug response models by learning alignment patterns between drugs and samples. However, existing studies employed the simple feature fusion strategy and only considered the drug features as a whole representation while ignoring the substructure information that may play a vital role when aligning drugs and genes. Hereby in this paper, we propose TCR (Transformer based network for Cancer drug Response) to predict anti-cancer drug response. By utilizing an attention mechanism, TCR is able to learn the interactions between drug atom/sub-structure and molecular signatures efficiently in our study. Furthermore, a dual loss function and cross sampling strategy were designed to improve the prediction power of TCR. We show that TCR outperformed all other methods under various data splitting strategies on all evaluation matrices (some with significant improvement). Extensive experiments demonstrate that TCR shows significantly improved generalization ability on independent in-vitro experiments and in-vivo real patient data. Our study highlights the prediction power of TCR and its potential value for cancer drug repurpose and precision oncology treatment.

cs.LG↗

arXiv2022

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering

We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial applications. We first group time series at each aggregated level, while simultaneously leveraging local and global information. The proposed method can cluster hierarchical time series (HTS) with different lengths and structures. For common two-level hierarchies, we employ a combined objective for local and global clustering over spaces of discrete probability measures, using Wasserstein distance coupled with Soft-DTW divergence. For multi-level hierarchies, we present a bottom-up procedure that progressively leverages lower-level information for higher-level clustering. Our final goal is to improve both the accuracy and speed of forecasts for a larger number of HTS needed for a real-world application. To attain this goal, each time series is first assigned the forecast for its cluster representative, which can be considered as a "shrinkage prior" for the set of time series it represents. Then this base forecast can be quickly fine-tuned to adjust to the specifics of that time series. We empirically show that our method substantially improves performance in terms of both speed and accuracy for large-scale forecasting tasks involving much HTS.

cs.LG↗

arXiv2022

Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration

Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have limited capabilities in registering images with large deformations. While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (SPAC), a novel reinforcement learning-based framework that performs step-wise registration. The key notion is warping a moving image successively by each time step to finally align to a fixed image. Considering that it is challenging to handle high dimensional continuous action and state spaces in the conventional reinforcement learning (RL) framework, we introduce a new concept `Plan' to the standard Actor-Critic model, which is of low dimension and can facilitate the actor to generate a tractable high dimensional action. The entire framework is based on unsupervised training and operates in an end-to-end manner. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations. Our empirical results highlight that our work achieves consistent, significant gains and outperforms state-of-the-art methods.

eess.IV↗

arXiv2021

Stochastic Actor-Executor-Critic for Image-to-Image Translation

Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces. In this paper, we draw inspiration from the recent success of the maximum entropy reinforcement learning framework designed for challenging continuous control problems to develop stochastic policies over high dimensional continuous spaces including image representation, generation, and control simultaneously. Central to this method is the Stochastic Actor-Executor-Critic (SAEC) which is an off-policy actor-critic model with an additional executor to generate realistic images. Specifically, the actor focuses on the high-level representation and control policy by a stochastic latent action, as well as explicitly directs the executor to generate low-level actions to manipulate the state. Experiments on several image-to-image translation tasks have demonstrated the effectiveness and robustness of the proposed SAEC when facing high-dimensional continuous space problems.

cs.CV↗

arXiv2021

Feature-based Recognition Framework for Super-resolution Images

In practical application, the performance of recognition network usually decreases when being applied on super-resolution images. In this paper, we propose a feature-based recognition network combined with GAN (FGAN). Our network improves the recognition accuracy by extracting more features that benefit recognition from SR images. In the experiment, we build three datasets using three different super-resolution algorithm, and our network increases the recognition accuracy by more than 6% comparing with ReaNet50 and DenseNet121.

cs.CV↗

arXiv2020

Koopman analysis in oscillator synchronization

Synchronization is an important dynamical phenomenon in coupled nonlinear systems, which has been studied extensively in recent years. However, analysis focused on individual orbits seems hard to extend to complex systems while a global statistical approach is overly cursory. Koopman operator technique seems to well balance the two approaches. In this paper, we extend Koopman analysis to the study of synchronization of coupled oscillators by extracting important eigenvalues and eigenfunctions from the observed time series. A renormalization group analysis is designed to derive an analytic approximation of the eigenfunction in case of weak coupling that dominates the oscillation. For moderate or strong couplings, numerical computation further confirms the importance of the average frequencies and the associated eigenfunctions. The synchronization transition points could be located with quite high accuracy by checking the correlation of neighbouring eigenfunctions at different coupling strengths, which is readily applied to other nonlinear systems.

nlin.CD↗

arXiv2020

Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning

The crucial components of a conventional image registration method are the choice of the right feature representations and similarity measures. These two components, although elaborately designed, are somewhat handcrafted using human knowledge. To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work. Specifically, an artificial agent, which is composed of a combined policy and value network, is trained to adjust the moving image toward the right direction. We train this network using an asynchronous reinforcement learning algorithm, where a customized reward function is also leveraged to encourage robust image registration. This trained network is further incorporated with a lookahead inference to improve the registration capability. The advantage of this algorithm is fully demonstrated by our superior performance on clinical MR and CT image pairs to other state-of-the-art medical image registration methods.

cs.CV↗

arXiv2019

Spatio-temporal variation of temperature for the recent 40 years in Lhasa

It was all known that Lhasa went through a high temperature of 30.8$^{\circ}$C in late June 2019, which hit record highs. To better understand the reasons, based on observations recorded at automatic weather stations in Lhasa, we studied the characteristics of temperature variation at multiple time scales using the linear trend method, Mann-Kendall mutation test, morlet wavelet analysis, R/S analysis and so on. The results showed that: (a) The annual mean temperature (AMT) is rising at a rate of 0.5$^{\circ}$C/10yr, and the average temperature for different seasons also increased significantly, especially in winter. (b) Although there was an intersection in 1995, we found that AMT, did not pass the reliability test of significance level $α$ =0.05, this means there are no abrupt changes for AMT, the values are 7.97$^{\circ}$C and 9.15$^{\circ}$C respectively before and after the intersection point. (c) AMT has a periodic oscillation for 18~25yr and 25~32yr based on a mass of data and the wavelet variance diagrams in Lhasa. AMT has a main cycle of 28yr, cyclic Patterns of temperature changes in spring, summer and autumn is similar to AMT, but it is relatively complex in winter. (d) The Hurst index of AMT and different seasons demonstrates that the temperature are likely to continue to rise in the future in Lhasa.

physics.ao-ph↗

arXiv2019

Stochastic thermodynamics of an electron-spin-resonance quantum dot system

We present a stochastic thermodynamics analysis of an electron-spin-resonance pumped quantum dot device in the Coulomb-blocked regime, where a pure spin current is generated without an accompanying net charge current. Based on a generalized quantum master equation beyond secular approximation, quantum coherences are accounted for in terms of an effective average spin in the Floquet basis. Elegantly, this effective spin undergoes a precession about an effective magnetic field, which originates from the non-secular treatment and energy renormalization. It is shown that the interaction between effective spin and effective magnetic field may have the dominant roles to play in both energy transport and irreversible entropy production. In the stationary limit, the energy and entropy balance relations are also established based on the theory of counting statistics.

cond-mat.stat-mech↗

arXiv2015

Connectedness properties of the space of complete nonnegatively curved planes

We study the space of complete Riemannian metrics of nonnegative curvature on the plane equipped with the C^k topology. If k is infinite, we show that the space is homeomorphic to the separable Hilbert space. For any k we prove that the space cannot be made disconnected by removing a finite dimensional subset. A similar result holds for the associated moduli space. The proof combines properties of subharmonic functions with results of infinite dimensional topology and dimension theory. A key step is a characterization of the conformal factors that make the standard Euclidean metric on the plane into a complete metric of nonnegative sectional curvature.

math.DG↗

arXiv2015

Unraveling of a generalized quantum Markovian master equation and its application in feedback control of a charge qubit

In the context of a charge qubit under continuous monitoring by a single electron transistor, we propose an unraveling of the generalized quantum Markovian master equation into an ensemble of individual quantum trajectories for stochastic point process. A suboptimal feedback algorism is implemented into individual quantum trajectories to protect a desired pure state. Coherent oscillations of the charge qubit could be maintained in principle for an arbitrarily long time in case of sufficient feedback strength. The effectiveness of the feedback control is also reflected in the detector's noise spectrum. The signal-to-noise ratio rises significantly with increasing feedback strength such that it could even exceed the Korotkov-Averin bound in quantum measurement, manifesting almost ideal quantum coherent oscillations of the qubit. The proposed unraveling and feedback protocol may open up the prospect to sustain ideal coherent oscillations of a charge qubit in quantum computation algorithms.

cond-mat.mes-hall↗

arXiv2015

Qubit detection with a T-shaped double quantum dot detector

We propose to continuously monitor a charge qubit by utilizing a T-shaped double quantum dot detector, in which the qubit and double dot are arranged in such a unique way that the detector turns out to be particularly susceptible to the charge states of the qubit. Special attention is paid to the regime where acquisition of qubit information and backaction upon the measured system exhibit nontrivial correlation. The intrinsic dynamics of the qubit gives rise to dynamical blockade of tunneling events through the detector, resulting in a super-Poissonian noise. However, such a pronounced enhancement of detector's shot noise does not necessarily produce a rising dephasing rate. In contrast, an inhibition of dephasing is entailed by the reduction of information acquisition in the dynamically blockaded regimes. We further reveal the important impact of the charge fluctuations on the measurement characteristics. Noticeably, under the condition of symmetric junction capacitances the noise pedestal of circuit current is completely suppressed, leading to a divergent signal-to-noise ratio, and eventually to a violation of the Korotkov-Averin bound in quantum measurement. Our study offers the possibility for a double dot detector to reach the quantum limited effectiveness in a transparent manner.

cond-mat.mes-hall↗

arXiv2014

Conditional spin counting statistics as a probe of Coulomb interaction and spin-resolved bunching

Full counting statistics is a powerful tool to characterize the noise and correlations in transport through mesoscopic systems. In this work, we propose the theory of conditional spin counting statistics, i.e., the statistical fluctuations of spin-up (down) current given the observation of the spin-down (up) current. In the context of transport through a single quantum dot, it is demonstrated that a strong Coulomb interaction leads to a conditional spin counting statistics that exhibits a substantial change in comparison to that without Coulomb repulsion. It thus can be served as an effective way to probe the Coulomb interactions in mesoscopic transport systems. In case of spin polarized transport, it is further shown that the conditional spin counting statistics offers a transparent tool to reveal the spin-resolved bunching behavior.

cond-mat.mes-hall↗

arXiv2012

Culturomics meets random fractal theory: Insights into long-range correlations of social and natural phenomena over the past two centuries

Culturomics was recently introduced as the application of high-throughput data collection and analysis to the study of human culture. Here we make use of this data by investigating fluctuations in yearly usage frequencies of specific words that describe social and natural phenomena, as derived from books that were published over the course of the past two centuries. We show that the determination of the Hurst parameter by means of fractal analysis provides fundamental insights into the nature of long-range correlations contained in the culturomic trajectories, and by doing so, offers new interpretations as to what might be the main driving forces behind the examined phenomena. Quite remarkably, we find that social and natural phenomena are governed by fundamentally different processes. While natural phenomena have properties that are typical for processes with persistent long-range correlations, social phenomena are better described as nonstationary, on-off intermittent, or Levy walk processes.

physics.soc-ph↗

arXiv2009

Automated Epilepsy Diagnosis Using Interictal Scalp EEG

Approximately over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy, which is very close to reported human recognition accuracy by experienced medical professionals.

cs.AI↗