arXiv subjects

Bo Fu

Publications and source records attributed to Bo Fu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology

Histopathology Whole-Slide Images (WSIs) provide an important tool to assess cancer prognosis in computational pathology (CPATH). While existing survival analysis (SA) approaches have made exciting progress, they are generally limited to adopting highly-expressive network architectures and only coarse-grained patient-level labels to learn visual prognostic representations from gigapixel WSIs. Such learning paradigm suffers from critical performance bottlenecks, when facing present scarce training data and standard multi-instance learning (MIL) framework in CPATH. To overcome it, this paper, for the first time, proposes a new Vision-Language-based SA (VLSA) paradigm. Concretely, (1) VLSA is driven by pathology VL foundation models. It no longer relies on high-capability networks and shows the advantage of data efficiency. (2) In vision-end, VLSA encodes textual prognostic prior and then employs it as auxiliary signals to guide the aggregating of visual prognostic features at instance level, thereby compensating for the weak supervision in MIL. Moreover, given the characteristics of SA, we propose i) ordinal survival prompt learning to transform continuous survival labels into textual prompts; and ii) ordinal incidence function as prediction target to make SA compatible with VL-based prediction. Notably, VLSA's predictions can be interpreted intuitively by our Shapley values-based method. The extensive experiments on five datasets confirm the effectiveness of our scheme. Our VLSA could pave a new way for SA in CPATH by offering weakly-supervised MIL an effective means to learn valuable prognostic clues from gigapixel WSIs. Our source code is available at https://github.com/liupei101/VLSA.

cs.CV↗

arXiv2024

AdaptLIL: A Gaze-Adaptive Visualization for Ontology Mapping

This paper showcases AdaptLIL, a real-time adaptive link-indented list ontology mapping visualization that uses eye gaze as the primary input source. Through a multimodal combination of real-time systems, deep learning, and web development applications, this system uniquely curtails graphical overlays (adaptations) to pairwise mappings of link-indented list ontology visualizations for individual users based solely on their eye gaze.

cs.HC↗

arXiv2024

Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates

Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates. However, the backward induction algorithm used in Q-learning can introduce challenges, as nonignorable missing covariates at later stages can result in nonignorable missing pseudo-outcomes at earlier stages, leading to suboptimal DTRs, even if the longitudinal outcome variables are fully observed. To address this unique missing data problem in DTR settings, we propose two weighted Q-learning approaches where inverse probability weights for missingness of the pseudo-outcomes are obtained through estimating equations with valid nonresponse instrumental variables or sensitivity analysis. The asymptotic properties of the weighted Q-learning estimators are derived, and the finite-sample performance of the proposed methods is evaluated and compared with alternative methods through extensive simulation studies. Using EMR data from the Medical Information Mart for Intensive Care database, we apply the proposed methods to investigate the optimal fluid strategy for sepsis patients in intensive care units.

stat.ME↗

arXiv2024

Wavelet-based Mamba with Fourier Adjustment for Low-light Image Enhancement

Frequency information (e.g., Discrete Wavelet Transform and Fast Fourier Transform) has been widely applied to solve the issue of Low-Light Image Enhancement (LLIE). However, existing frequency-based models primarily operate in the simple wavelet or Fourier space of images, which lacks utilization of valid global and local information in each space. We found that wavelet frequency information is more sensitive to global brightness due to its low-frequency component while Fourier frequency information is more sensitive to local details due to its phase component. In order to achieve superior preliminary brightness enhancement by optimally integrating spatial channel information with low-frequency components in the wavelet transform, we introduce channel-wise Mamba, which compensates for the long-range dependencies of CNNs and has lower complexity compared to Diffusion and Transformer models. So in this work, we propose a novel Wavelet-based Mamba with Fourier Adjustment model called WalMaFa, consisting of a Wavelet-based Mamba Block (WMB) and a Fast Fourier Adjustment Block (FFAB). We employ an Encoder-Latent-Decoder structure to accomplish the end-to-end transformation. Specifically, WMB is adopted in the Encoder and Decoder to enhance global brightness while FFAB is adopted in the Latent to fine-tune local texture details and alleviate ambiguity. Extensive experiments demonstrate that our proposed WalMaFa achieves state-of-the-art performance with fewer computational resources and faster speed. Code is now available at: https://github.com/mcpaulgeorge/WalMaFa.

cs.CV↗

arXiv2024

1/2 Z Topological Invariants and the Half Quantized Hall Effect

The half-quantized Hall phase represents a unique metallic or semi-metallic state of matter characterized by a fractional quantum Hall conductance, precisely half of an integer $ν$ multiple of $e^{2}/h$. Here we demonstrate the existence of a novel $\frac{1}{2}\mathbb{Z}$ topological invariant that sets the half-quantized Hall phase apart from two-dimensional ordinary metallic ferromagnets. The $\frac{1}{2}\mathbb{Z}$ classification is determined by the line integral of the intrinsic anomalous Hall conductance, which is safeguarded by two distinct categories of local unitary and anti-unitary symmetries in proximity to the Fermi surface of electron states. We further validate the $\frac{1}{2}\mathbb{Z}$ topological order in the context of the quantized Hall phase by examining semi-magnetic topological insulator $\mathrm{Bi}_{2}\mathrm{Te}_{3}$ and $\mathrm{Bi}_{2}\mathrm{Se}_{3}$ film for $ν=1$ and topological crystalline insulator SnTe films for $ν=2$ or $4$. Our findings pave the way for future exploration and understanding of topological metals and their unique properties.

cond-mat.mes-hall↗

arXiv2024

OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging and joint modeling of multiple discrete and continuous clinical scores presents a promising new paradigm for multi-task problems in Ophthalmology. The bi-channel framework that arises from the Ophthalmic phenomenon of ``interocular asymmetries'' of both eyes (OU) calls for new employment on the SOTA transformer-based models. However, the application of copula models for multiple mixed discrete-continuous labels on deep learning (DL) is challenging. Moreover, the application of advanced large transformer-based models to small medical datasets is challenging due to overfitting and computational resource constraints. To resolve these challenges, we propose OU-CoViT: a novel Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF images, which can i) incorporate conditional correlation information across multiple discrete and continuous labels within a deep learning framework (by deriving the closed form of a novel Copula Loss); ii) take OU inputs subject to both high correlation and interocular asymmetries using a bi-channel model with dual adaptation; and iii) enable the adaptation of large vision transformer (ViT) models to small medical datasets. Solid experiments demonstrate that OU-CoViT significantly improves prediction performance compared to single-channel baseline models with empirical loss. Furthermore, the novel architecture of OU-CoViT allows generalizability and extensions of our dual adaptation and Copula Loss to various ViT variants and large DL models on small medical datasets. Our approach opens up new possibilities for joint modeling of heterogeneous multi-channel input and mixed discrete-continuous clinical scores in medical practices and has the potential to advance AI-assisted clinical decision-making in various medical domains beyond Ophthalmology.

cs.CV↗

arXiv2024

CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images

The ultra-widefield (UWF) fundus image is an attractive 3D biomarker in AI-aided myopia screening because it provides much richer myopia-related information. Though axial length (AL) has been acknowledged to be highly related to the two key targets of myopia screening, Spherical Equivalence (SE) measurement and high myopia diagnosis, its prediction based on the UWF fundus image is rarely considered. To save the high expense and time costs of measuring SE and AL, we propose the Copula-enhanced Convolutional Neural Network (CeCNN), a one-stop UWF-based ophthalmic AI framework to jointly predict SE, AL, and myopia status. The CeCNN formulates a multiresponse regression that relates multiple dependent discrete-continuous responses and the image covariate, where the nonlinearity of the association is modeled by a backbone CNN. To thoroughly describe the dependence structure among the responses, we model and incorporate the conditional dependence among responses in a CNN through a new copula-likelihood loss. We provide statistical interpretations of the conditional dependence among responses, and reveal that such dependence is beyond the dependence explained by the image covariate. We heuristically justify that the proposed loss can enhance the estimation efficiency of the CNN weights. We apply the CeCNN to the UWF dataset collected by us and demonstrate that the CeCNN sharply enhances the predictive capability of various backbone CNNs. Our study evidences the ophthalmology view that besides SE, AL is also an important measure to myopia.

cs.CV↗

arXiv2024

Why Misinformation is Created? Detecting them by Integrating Intent Features

Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinformation, namely Misinformation Detection (MD), has become an active research topic receiving widespread attention. As a complex phenomenon, the veracity of an article is influenced by various aspects. In this paper, we are inspired by the opposition of intents between misinformation and real information. Accordingly, we propose to reason the intent of articles and form the corresponding intent features to promote the veracity discrimination of article features. To achieve this, we build a hierarchy of a set of intents for both misinformation and real information by referring to the existing psychological theories, and we apply it to reason the intent of articles by progressively generating binary answers with an encoder-decoder structure. We form the corresponding intent features and integrate it with the token features to achieve more discriminative article features for MD. Upon these ideas, we suggest a novel MD method, namely Detecting Misinformation by Integrating Intent featuRes (DM-INTER). To evaluate the performance of DM-INTER, we conduct extensive experiments on benchmark MD datasets. The experimental results validate that DM-INTER can outperform the existing baseline MD methods.

cs.CL↗

arXiv2024

OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging is potentially significant for ophthalmic outcomes. Current multidisciplinary research between ophthalmology and deep learning (DL) concentrates primarily on disease classification and diagnosis using single-eye images, largely ignoring joint modeling and prediction for Oculus Uterque (OU, both eyes). Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores. We design a novel bi-channel multi-label CNN that can (1) take bi-channel image inputs subject to both high correlation and heterogeneity (by sharing the same backbone network and employing adapters to parameterize the channel-wise discrepancy), and (2) incorporate correlation information between continuous output labels (using a copula). Solid experiments show that OUCopula achieves satisfactory performance in myopia score prediction compared to backbone models. Moreover, OUCopula can far exceed the performance of models constructed for single-eye inputs. Importantly, our study also hints at the potential extension of the bi-channel model to a multi-channel paradigm and the generalizability of OUCopula across various backbone CNNs.

eess.IV↗

arXiv2024

Half Quantum Mirror Hall Effect

We report the discovery of the half-quantized mirror Hall effect, a novel quantum-anomaly induced by mirror symmetry in a strong topological insulator (TI) film. These films are known to host a pair of gapless Dirac cones associated with surface electrons. Our findings reveal that mirror symmetry assigns a unique mirror parity to each Dirac cone, resulting in a half-quantized Hall conductance of $\pm\frac{e^{2}}{2h}$ for each cone. Despite the total electric Hall conductance being null due to time-reversal invariance, the difference in the Hall conductance between the two cones yields a quantized Hall conductance of $\frac{e^{2}}{h}$ for the difference in mirror currents. The effect of helical edge mirror current, a crucial feature of this quantum effect, can be determined by means of electrical measurements. Overall, the half-quantum mirror Hall effect reveals a new type of mirror-symmetry induced quantum anomaly in a time-reversal invariant lattice system, giving rise to a topological metallic state of matter with time-reversal invariance.

cond-mat.mes-hall↗

arXiv2023

Identification and Estimation of Causal Effects with Confounders Missing Not at Random

Making causal inferences from observational studies can be challenging when confounders are missing not at random. In such cases, identifying causal effects is often not guaranteed. Motivated by a real example, we consider a treatment-independent missingness assumption under which we establish the identification of causal effects when confounders are missing not at random. We propose a weighted estimating equation (WEE) approach for estimating model parameters and introduce three estimators for the average causal effect, based on regression, propensity score weighting, and doubly robust estimation. We evaluate the performance of these estimators through simulations, and provide a real data analysis to illustrate our proposed method.

stat.ME↗

arXiv2023

Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. A simultaneous matching and routing problem (SMRP) is formulated to match the humans with robot guides according to their requested places of interest (POIs) and generate the routes for the robots according to uncertain time estimation. A large neighborhood search algorithm is developed to efficiently find sub-optimal low-cost solutions for the SMRP. The scalability and optimality of the multi-robot planner are evaluated computationally. The largest case tested involves 50 robots, 250 humans, and 50 POIs. A photo-realistic multi-robot simulation was developed to verify the tour guiding performance in an uncertain indoor environment.

cs.RO↗

arXiv2023

Metallic Quantized Anomalous Hall Effect without Chiral Edge States

The quantum anomalous Hall effect (QAHE) is a topological state of matter with a quantized Hall resistance. It has been observed in some two-dimensional insulating materials such as magnetic topological insulator films and twisted bilayer graphene. These materials are insulating in the bulk, but possess chiral edge states carrying the edge current around the systems. Here we discover a metallic QAHE in a topological insulator film with magnetic sandwich heterostructure, in which the Hall conductance is quantized to $e^{2}/h$, but the longitudinal conductance remains finite. This effect is attributed to the existence of a pair of massless Dirac cones of surface fermions, with each contributing half of the Hall conductance due to quantum anomaly. It is not characterized by a Chern number and not associated to any chiral edge states. Our study offers novel insights into topological transport phenomena and topological metallic states of matter.

cond-mat.mes-hall↗

arXiv2023

Signature of Parity Anomaly: Crossover from One Half to Integer Quantized Hall Conductance in a Finite Magnetic Field

The pursuit of understanding parity anomaly in condensed matter systems has led to significant advancements in both theoretical and experimental research in recent years. In this study, we explore the parity anomaly of massless Dirac fermions in a semimagnetic topological insulator (TI) thin film subjected to a finite magnetic field. Our findings reveal an anomalous half-quantized Hall conductance arising from the occupied electronic states far below the Fermi level, which is directly associated with the parity anomaly. This observation demonstrates a crossover from one-half quantized Hall conductance in a metallic phase at zero field to one or zero quantized Hall conductance in the insulating phase at a strong field in the presence of disorders, serving as a key indicator for confirming parity anomaly. Our work provides valuable insights into the intricate relationship between band topology in condensed matter systems and quantum anomaly in quantum field theory.

cond-mat.mes-hall↗

arXiv2023

Quasiparticle and Transport Properties of Disordered Bilayer Graphene

In recent experimental and theoretical studies of graphene, disorder scattering processes have been suggested to play an important role in its electronic and transport properties. In the preceding paper, it has been shown that the nonperturbative momentum-space Lanczos method is able to accurately describe all the multiple impurity scattering events and account for the quasiparticle and transport properties of disordered monolayer graphene. In the present study, we expand the range of applicability of this recursive method by numerically investigating the quasiparticle and transport properties of Bernal-stacked bilayer graphene in the presence of scalar Anderson disorder. The results are further compared with the findings of the same system using a self-consistent Born approximation, as well as the central findings in the preceding paper for monolayer graphene. It is found that in both systems, proper inclusions of all the scattering events are needed in order to reliably capture the role of disorder via multiple impurity scattering. In particular, the quasiparticle residue is shown to decrease sharply near the charge neutrality point, suggesting that the system is either a marginal Fermi liquid or a non-Fermi liquid. Furthermore, we reveal the dependences of the transport properties of disordered bilayer graphene on the carrier density and temperature, and explore the role of interlayer scattering at varying strengths. Our findings help to provide some new angles into the quasiparticle and transport properties of disordered bilayer graphene.

cond-mat.dis-nn↗

arXiv2023

Disorder Effects on the Quasiparticle and Transport Properties of Two-Dimensional Dirac Fermionic Systems

Despite extensive existing studies, a complete understanding of the role of disorder in affecting the physical properties of two-dimensional Dirac fermionic systems remains a standing challenge, largely due to obstacles encountered in treating multiple scattering events for such inherently strong scattering systems. Using graphene as an example and a nonperturbative numerical technique, here we reveal that the low energy quasiparticle properties are considerably modified by multiple scattering processes even in the presence of weak scalar potentials. We extract unified power-law energy dependences of the self-energy with fractional exponents from the weak scattering limit to the strong scattering limit from our numerical analysis, leading to sharp reductions of the quasiparticle residues near the Dirac point, eventually vanishing at the Dirac point. The central findings stay valid when the Anderson-type impurities are replaced by correlated Gaussian- or Yukawa-type disorder with varying correlation lengths. The improved understanding gained here also enables us to provide better interpretations of the experimental observations surrounding the temperature and carrier density dependences of the conductivity in ultra-high mobility graphene samples. The approach demonstrated here is expected to find broad applicability in understanding the role of various other types of impurities in two-dimensional Dirac systems.

cond-mat.mes-hall↗

arXiv2023

Theory of anomalous Hall effect in transition-metal pentatelluride $\mathrm{ZrTe}_{5}$ and $\mathrm{HfTe}_{5}$

The anomalous Hall effect has considerable impact on the progress of condensed matter physics and occurs in systems with time-reversal symmetry breaking. Here we theoretically investigate the anomalous Hall effect in nonmagnetic transition-metal pentatelluride $\mathrm{ZrTe_{5}}$ and $\mathrm{HfTe}_{5}$. In the presence of Zeeman splitting and Dirac mass, there is an intrinsic anomalous Hall conductivity induced by the Berry curvature in the semiclassical treatment. In a finite magnetic field, the anomalous Hall conductivity rapidly decays to zero for constant spin-splitting and vanishes for the magnetic-field-dependent Zeeman energy. A semiclassical formula is derived to depict the magnetic field dependence of the Hall conductivity, which is beneficial for experimental data analysis. Lastly, when the chemical potential is fixed in the magnetic field, a Hall conductivity plateau arises, which may account for the observed anomalous Hall effect in experiments.

cond-mat.mes-hall↗

arXiv2023

A Solvable Model for Discrete Time Crystal Enforced by Nonsymmorphic Dynamical Symmetry

Discrete time crystal is a class of nonequilibrium quantum systems exhibiting subharmonic responses to external periodic driving. Here we propose a class of discrete time crystals enforced by nonsymmorphic dynamical symmetry. We start with a system with nonsymmorphic dynamical symmetry, in which the instantaneous eigenstates become Möbius twisted, hence doubling the period of the instantaneous state. The exact solution of the time-dependent Schrödinger equation shows that the system spontaneously exhibits a period extension without undergoing quantum superposition states for a series of specifc evolution frequencies or in the limit of long evolution period. Moreover, in such case the system gains a π Berry phase after two periods' evolution. Finally, we show that the subharmonic response is stable even when many-body interactions are introduced, indicating a DTC phase in the thermodynamic limit.

quant-ph↗