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We study the relevance of random flux on stability and emergence of topological phases of matter. A particularly interesting model in the presence of random flux is the anisotropic Wilson-Dirac model in two spatial dimensions. We show that this model exhibits an intriguing topological phase transition from a weak topological insulator to a Chern insulator driven by random flux. We numerically establish a global phase diagram of this model in presence of random flux. We uncover the underlying mechanism of topological phase transitions with an analytical effective medium theory, illustrating momentum-dependent renormalizations of model parameters by random flux. This analysis allows us to identify quasi-critical phase points at transitions between weak and strong topological phases, where eigen states are extended in one spatial direction but localized in the other one. Our results describe a qualitatively new effect of disorder on topological phases of matter., Comment: 6+10 pages, 4+7 figures

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., Comment: 18 pages, 8 figures, ACCV2024

Multimodal Large Language Models (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split Conformal Prediction (SCP) in language modeling to construct prediction sets with statistical guarantees. However, these methods typically rely on internal model logits or are restricted to multiple-choice settings, which hampers their generalizability and adaptability in dynamic, open-ended environments. In this paper, we introduce TRON, a two-step framework for risk control and assessment, applicable to any MLLM that supports sampling in both open-ended and closed-ended scenarios. TRON comprises two main components: (1) a novel conformal score to sample response sets of minimum size, and (2) a nonconformity score to identify high-quality responses based on self-consistency theory, controlling the error rates by two specific risk levels. Furthermore, we investigate semantic redundancy in prediction sets within open-ended contexts for the first time, leading to a promising evaluation metric for MLLMs based on average set size. Our comprehensive experiments across four Video Question-Answering (VideoQA) datasets utilizing eight MLLMs show that TRON achieves desired error rates bounded by two user-specified risk levels. Additionally, deduplicated prediction sets maintain adaptiveness while being more efficient and stable for risk assessment under different risk levels., Comment: 15 pages, 6 figures

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 $\nu$ 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 $\nu=1$ and topological crystalline insulator SnTe films for $\nu=2$ or $4$. Our findings pave the way for future exploration and understanding of topological metals and their unique properties., Comment: 34 pages, 5 figures and 2 tables. Comments are welcomed

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 architectures and only coarse-grained patient-level labels to learn prognostic visual representations from gigapixel WSIs. Such learning paradigm suffers from important 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 prognostic language prior and then employs it as auxiliary signals to guide the aggregating of prognostic visual 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., Comment: 24 pages, 11 tables, 6 figures

The parity anomalous semimetal is a topological state of matter characterized by its semi-metallic nature and a quantum Hall conductance of one-half $e^{2}/h$ ($e$ is the elementary charge and $h$ is the Planck constant). Here we investigate the topological phase transition driven by disorder in a semi-magnetic structure of topological insulator, and narrow-gap weak topological insulator film. We demonstrate that strong disorder not only leads to a topological transition from the parity anomalous semimetal to a diffusive metal with non-quantized anomalous Hall conductance, but also induces the topological phase in a trivial insulating phase. Our calculations of the local density of states provide clear picture for the formation of a single gapless Dirac fermion, which emerges as the disorder strength increases. The half quantized Hall effect is attributed to the existence of the gapless Dirac cone. Our findings of disorder-induced parity anomalous semimetal and diffusive metal significantly advance our understanding of the disorder-driven topological phase transition in magnetic topological insulators, opening up new avenues for further exploration in the field of quantum materials., Comment: 6 pages, 4 figures

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.

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., Comment: 11 pages, 3 figures. Accepted by CIKM 2024

We develop a Dirac fermion theory for topological phases in magnetic topological insulator films. The theory is based on exact solutions of the energies and the wave functions for an effective model of the three-dimensional topological insulator (TI) film. It is found that the TI film consists of a pair of massless or massive Dirac fermions for the surface states, and a series of massive Dirac fermions for the bulk states. The massive Dirac fermion always carries zero or integer quantum Hall conductance when the valence band is fully occupied while the massless Dirac fermion carries a one-half quantum Hall conductance when the chemical potential is located around the Dirac point for a finite range. The magnetic exchange interaction in the magnetic layers in the film can be used to manipulate either the masses or chirality of the Dirac fermions and gives rise to distinct topological phases, which cover the known topological insulating phases, such as quantum anomalous Hall effect, quantum spin Hall effect and axion effect, and also the novel topological metallic phases, such as half quantized Hall effect, half quantum mirror Hall effect, and metallic quantum anomalous Hall effect.

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.

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., Comment: 26 pages, 3 figures

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., Comment: This paper includes a supplementary material in the latter part of this arXiv version

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.

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., Comment: ICRA 2022 Workshop Paper (https://sites.google.com/view/icra22ws-cor-wotf/accepted-papers). arXiv admin note: substantial text overlap with arXiv:2201.10635

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., Comment: 6 pages, 4 figures

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., Comment: 7 pages, 3 figures

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., Comment: 17 pages, 10 figures

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., Comment: 15 pages, 11 figures

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., Comment: 11 pages, 6 figures, reference information is updated

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 M\"obius twisted, hence doubling the period of the instantaneous state. The exact solution of the time-dependent Schr\"odinger 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 {\pi} 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., Comment: 5 pages, 4 figures

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., Comment: arXiv admin note: substantial text overlap with arXiv:2211.15018

The coherence length of two Majorana zero energy modes in a p-wave topological superconductor is inversely proportional to the superconducting order parameter. We studied the finite size effect of the Majorana zero modes at vortices in a topological insulator/superconductor heterostructure in the presence of a vortex and found that the the coherence length of the two zero energy modes at the terminals of a vortex line is independent of superconducting order parameter, and determined by the intrinsic properties of the topological insulator. This anomalous property illustrates that the superconducting topological insulator is topologically distinct, contrary to a $p$-wave topological superconductor., Comment: 9 pages with 2 figures

The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm., Comment: 15 pages, 10 figures, 8 tables

Causal inference in observational studies can be challenging when confounders are subject to missingness. Generally, the identification of causal effects is not guaranteed even under restrictive parametric model assumptions when confounders are missing not at random. To address this, We propose a general framework to establish the identification of causal effects when confounders are subject to treatment-independent missingness, which means that the missing data mechanism is independent of the treatment, given the outcome and possibly missing confounders. We give special consideration to commonly-used models for continuous and binary outcomes and provide counterexamples when identification fails. For estimation, we provide a weighted estimation equation estimating method for model parameters and purpose three estimators for the average causal effect based on the estimated models. We evaluate the finite-sample performance of the estimators via simulations. We further illustrate the proposed method with real data sets from the National Health and Nutrition Examination Survey., Comment: This paper is updated and the new version is on arXiv:2303.05878

This paper presents a learning framework to estimate an agent capability and task requirement model for multi-agent task allocation. With a set of team configurations and the corresponding task performances as the training data, linear task constraints can be learned to be embedded in many existing optimization-based task allocation frameworks. Comprehensive computational evaluations are conducted to test the scalability and prediction accuracy of the learning framework with a limited number of team configurations and performance pairs. A ROS and Gazebo-based simulation environment is developed to validate the proposed requirements learning and task allocation framework in practical multi-agent exploration and manipulation tasks. Results show that the learning process for scenarios with 40 tasks and 6 types of agents uses around 12 seconds, ending up with prediction errors in the range of 0.5-2%., Comment: The video and open-source code are at https://brg.engin.umich.edu/publications/learn-multiagent-taskreq/

Let $B(n,p)$ denote a binomial random variable with parameters $n$ and $p$. Vas\v{e}k Chv\'{a}tal conjectured that for any fixed $n\geq 2$, as $m$ ranges over $\{0,\ldots,n\}$, the probability $q_m:=P(B(n,m/n)\leq m)$ is the smallest when $m$ is closest to $\frac{2n}{3}$. This conjecture has been solved recently. Motivated by this conjecture, in this paper, we consider the corresponding minimum value problem on the probability that a random variable is not more than its expectation, when its distribution is the Poisson distribution, the geometric distribution or the Pascal distribution., Comment: 9 pages

Condensed matter realization of a single Dirac cone of fermions in two dimensions is a long-standing issue. Here we report the discovery of a single gapless Dirac cone of half-quantized Hall conductance in a magnetically-doped topological insulator heterostructure. It demonstrates that the Hall conductance is half-quantized in the unit e^{2}/h when the parity symmetry is invariant near the Fermi surface. The gapless Dirac point is stable and protected by the local parity symmetry and the topologically nontrivial band structure of the topological insulator. The one-half Hall conductance observed in a recent experiment [Mogi et al, Nat. Phys. 18, 390 (2022)] is attributed to the existence of the gapless Dirac cone. The results suggest a condensed matter realization of a topological phase with a one-half topological invariant., Comment: 6 pages with 4 figures

Parity anomaly is a quantum mechanical effect that the parity symmetry in a two-dimensional classical action is failed to be restored in any regularization of the full quantum theory and is characterized by a half-quantized Hall conductivity. Here we propose a scheme to explore the experimental signature from parity anomaly in the measurement of optical Hall conductivity, in which the optical Hall conductivity is nearly half quantized for a proper range of frequency. The behaviors of optical Hall conductivity are studied for several models, which reveal the appearance of half-quantized Hall conductivity in low or high-frequency regimes. The optical Hall conductivity can be extracted from the measurement of Kerr and Faraday rotations and the absorption rate of the circularly polarized light. This proposal provides a practical method to explore the signature of parity anomaly in topological quantum materials., Comment: 9 pages, 5 figures

The cancer prognosis on gigapixel Whole-Slide Images (WSIs) has always been a challenging task. To further enhance WSI visual representations, existing methods have explored image pyramids, instead of single-resolution images, in WSIs. In spite of this, they still face two major problems: high computational cost and the unnoticed semantical gap in multi-resolution feature fusion. To tackle these problems, this paper proposes to efficiently exploit WSI pyramids from a new perspective, the dual-stream network with cross-attention (DSCA). Our key idea is to utilize two sub-streams to process the WSI patches with two resolutions, where a square pooling is devised in a high-resolution stream to significantly reduce computational costs, and a cross-attention-based method is proposed to properly handle the fusion of dual-stream features. We validate our DSCA on three publicly-available datasets with a total number of 3,101 WSIs from 1,911 patients. Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4.6%; (ii) our DSCA network is more efficient in computation -- it has more learnable parameters (6.31M vs. 860.18K) but less computational costs (2.51G vs. 4.94G), compared to a typical existing multi-resolution network. (iii) the key components of DSCA, dual-stream and cross-attention, indeed contribute to our model's performance, gaining an average C-Index rise of around 2.0% while maintaining a relatively-small computational load. Our DSCA could serve as an alternative and effective tool for WSI-based cancer prognosis., Comment: 12 pages, 6 figures, 7 tables