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We investigate a continuous-time investment-consumption problem with model uncertainty in a general diffusion-based market with random model coefficients. We assume that a power utility investor is ambiguity-averse, with the preference to robustness captured by the homothetic multiplier robust specification, and the investor's investment and consumption strategies are constrained to closed convex sets. To solve this constrained robust control problem, we employ the stochastic Hamilton-Jacobi-Bellman-Isaacs equations, backward stochastic differential equations, and bounded mean oscillation martingale theory. Furthermore, we show the investor incurs (non-negative) utility loss, i.e. the loss in welfare, if model uncertainty is ignored. When the model coefficients are deterministic, we establish formally the relationship between the investor's robustness preference and the robust optimal investment-consumption strategy and the value function, and the impact of investment and consumption constraints on the investor's robust optimal investment-consumption strategy and value function. Extensive numerical experiments highlight the significant impact of ambiguity aversion, consumption and investment constraints, on the investor's robust optimal investment-consumption strategy, utility loss, and value function. Key findings include: 1) short-selling restriction always reduces the investor's utility loss when model uncertainty is ignored; 2) the effect of consumption constraints on utility loss is more delicate and relies on the investor's risk aversion level., Comment: 30 pages, 6 figures (including sub-figures), 4 tables

State-of-the-art techniques like dual-frequency Time-Domain Thermoreflectance (TDTR) and Frequency-Domain Thermoreflectance (FDTR) offer superb capability for simultaneous measurements of thermal conductivity and heat capacity with a spatial resolution on the order of 10 {\mu}m. However, their applicability is limited to highly conductive materials with an in-plane thermal conductivity exceeding 10 W/(m*K). In this paper, we introduce the Square-Pulsed Source (SPS) technique, offering a novel approach to concurrently measure thermal conductivity and heat capacity with a 10 {\mu}m spatial resolution, while significantly extending the measurable thermal conductivity range to an unprecedented low of 0.1 W/(m*K), offering enhanced versatility. To demonstrate and validate its efficacy, we conducted measurements on various standard materials--PMMA, silica, sapphire, silicon, and diamond--spanning a wide thermal conductivity range from 0.1 to 2000 W/(m*K). The obtained results exhibit remarkable agreement with literature values, with a typical measurement uncertainty of less than 10% across the entire thermal conductivity range. By providing a unique capability to characterize both highly and lowly conductive materials with micron-scale spatial resolution, the SPS method opens new avenues for understanding and engineering thermal properties across diverse applications.

In this paper we prove a conjecture of Griffiths about vanishing of the zeroth cohomology groups of locally homogeneous vector bundles on compact quotients of non-classical period domains, and construct a new $G_\R$-invariant complex structure on any non-classical period domain $D=G_\R/V$ with $G_\R$ of Hermitian type. Various geometric and algebraic characterizations of non-classical period domains and several geometric applications on their compact quotients are deduced as consequences of our results.

Sub-1-nm gate length $MoS_2$ transistors have been experimentally fabricated, but their device performance limit remains elusive. Herein, we explore the performance limits of the sub-1-nm gate length monolayer (ML) $MoS_2$ transistors through ab initio quantum transport simulations. Our simulation results demonstrate that, through appropriate doping and dielectric engineering, the sub-1-nm devices can meet the requirement of extended 'ITRS'(International Technology Roadmap for Semiconductors) $L_g$=0.34 nm. Following device optimization, we achieve impressive maximum on-state current densities of 409 $\mu A / \mu m$ for n-type and 800 $\mu A / \mu m$ for p-type high-performance (HP) devices, while n-type and p-type low-power (LP) devices exhibit maximum on-state current densities of 75 $\mu A / \mu m$ and 187 $\mu A / \mu m$, respectively. We employed the Wentzel-Kramer-Brillouin (WKB) approximation to explain the physical mechanisms of underlap and spacer region optimization on transistor performance. The underlap and spacer regions primarily influence the transport properties of sub-1-nm transistors by respectively altering the width and body factor of the potential barriers. Compared to ML $MoS_2$ transistors with a 1 nm gate length, our sub-1-nm gate length HP and LP ML $MoS_2$ transistors exhibit lower energy-delay products. Hence the sub-1-nm gate length transistors have immense potential for driving the next generation of electronics.

We perform a comprehensive study of the electron-doped $t-t'-J$ model on cylinders with Density Matrix Renormalization Group (DMRG). We adopt both periodic and anti-periodic boundary conditions along the circumference direction to explore the finite size effect. We study doping levels of $1/6$, $1/8$, and $1/12$ which represent the most interesting region in the phase diagram of electron-doped cuprates. We find that for width-4 and 6 systems, the ground state for fixed doping switches between anti-ferromagnetic Neel state and stripe state under different boundary conditions and with system widths, indicating the presence of large finite size effect in the $t-t'-J$ model. We also have a careful analysis of the $d$-wave pairing correlations which also changes quantitatively with boundary conditions and widths of the system. However, the pairing correlations are enhanced when the system becomes wider for all dopings, suggesting the existence of possible long-ranged superconducting order in the thermodynamic limit. The width-8 results are found to be dependent on the starting state in the DMRG calculation for the kept states we can reach. For width-8 system only Neel (stripe) state can be stabilized in DMRG calculation for $1/12$ ($1/6$) doping, while both stripe and Neel states are stable in the DMRG sweep for $1/8$ doping, regardless of the boundary conditions. These results indicate that $1/8$ doping is likely to lie in the boundary of a phase transition between the Neel phase with lower doping and the stripe phase with higher doping, consistent with the previous study. The sensitivity of ground state on boundary conditions and size observed in this work is similar to that in the $t'$- Hubbard model., Comment: 8 pages, 5 figures and 1 table

Accurate modeling of system dynamics holds intriguing potential in broad scientific fields including cytodynamics and fluid mechanics. This task often presents significant challenges when (i) observations are limited to cross-sectional samples (where individual trajectories are inaccessible for learning), and moreover, (ii) the behaviors of individual particles are heterogeneous (especially in biological systems due to biodiversity). To address them, we introduce a novel framework dubbed correlational Lagrangian Schr\"odinger bridge (CLSB), aiming to seek for the evolution "bridging" among cross-sectional observations, while regularized for the minimal population "cost". In contrast to prior methods relying on \textit{individual}-level regularizers for all particles \textit{homogeneously} (e.g. restraining individual motions), CLSB operates at the population level admitting the heterogeneity nature, resulting in a more generalizable modeling in practice. To this end, our contributions include (1) a new class of population regularizers capturing the temporal variations in multivariate relations, with the tractable formulation derived, (2) three domain-informed instantiations based on genetic co-expression stability, and (3) an integration of population regularizers into data-driven generative models as constrained optimization, and a numerical solution, with further extension to conditional generative models. Empirically, we demonstrate the superiority of CLSB in single-cell sequencing data analyses such as simulating cell development over time and predicting cellular responses to drugs of varied doses.

Roadside perception can greatly increase the safety of autonomous vehicles by extending their perception ability beyond the visual range and addressing blind spots. However, current state-of-the-art vision-based roadside detection methods possess high accuracy on labeled scenes but have inferior performance on new scenes. This is because roadside cameras remain stationary after installation and can only collect data from a single scene, resulting in the algorithm overfitting these roadside backgrounds and camera poses. To address this issue, in this paper, we propose an innovative Scenario Generalization Framework for Vision-based Roadside 3D Object Detection, dubbed SGV3D. Specifically, we employ a Background-suppressed Module (BSM) to mitigate background overfitting in vision-centric pipelines by attenuating background features during the 2D to bird's-eye-view projection. Furthermore, by introducing the Semi-supervised Data Generation Pipeline (SSDG) using unlabeled images from new scenes, diverse instance foregrounds with varying camera poses are generated, addressing the risk of overfitting specific camera poses. We evaluate our method on two large-scale roadside benchmarks. Our method surpasses all previous methods by a significant margin in new scenes, including +42.57% for vehicle, +5.87% for pedestrian, and +14.89% for cyclist compared to BEVHeight on the DAIR-V2X-I heterologous benchmark. On the larger-scale Rope3D heterologous benchmark, we achieve notable gains of 14.48% for car and 12.41% for large vehicle. We aspire to contribute insights on the exploration of roadside perception techniques, emphasizing their capability for scenario generalization. The code will be available at https://github.com/yanglei18/SGV3D, Comment: 13 pages, 8 figures

This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of ``offline" data examples. While recent years have seen a flurry of work on applying various machine learning techniques to the offline optimization problem, the majority of these work focused on learning a surrogate of the unknown objective function and then applying existing optimization algorithms. While the idea of modeling the unknown objective function is intuitive and appealing, from the learning point of view it also makes it very difficult to tune the objective of the learner according to the objective of optimization. Instead of learning and then optimizing the unknown objective function, in this paper we take on a less intuitive but more direct view that optimization can be thought of as a process of sampling from a generative model. To learn an effective generative model from the offline data examples, we consider the standard technique of ``re-weighting", and our main technical contribution is a probably approximately correct (PAC) lower bound on the natural optimization objective, which allows us to jointly learn a weight function and a score-based generative model. The robustly competitive performance of the proposed approach is demonstrated via empirical studies using the standard offline optimization benchmarks.

Optical ranging is a key technology in metrology. Optical frequency combs are shown to provide several advantages in light ranging, offering high precision with high acquisition rate. However, performance of traditional ranging systems based on microcombs is limited by the short ambiguity distance and non-real-time processing. Here, we show that dual-comb ranging system using coherent Kerr soliton microcombs and optical switch realizes extended ambiguity distance and provides a route to real-time processing. The ambguity distance is extended to 3.28 m from about 1.5 mm and the uncertainty reaches about 1.05 times 10^-7, while the system is compatible with low-bandwidth detectors. Combining coherent microcomb ranging systems with special FPGA could enable comb-based real-time ranging systems for several applications such as industrial process monitoring., Comment: 9 pages, 5 figures

Advancement of technologies relies on discovery of new materials with emerging physical properties that are determined by their crystal structures. Ruddlesden-Popper (R-P) phases with formula of $A_{n+1}$$B_n$$X_{3n+1}$ (n=1,2,3...) are among one of the most widely studied class of materials due to their electrical, optical, magnetic, thermal properties and their combined multifunctional properties(Ref.1-6). In R-P phases, intergrowth is well-known in the short range(Ref.7-9); however, no existing compounds have been reported to have different n mixed in bulk single crystals. Here we design a hybrid R-P nickelate $La_2NiO_4$$La_3Ni_2O_7$ by alternatively stacking bilayers, which is the active structural motif in the newly discovery high-$T_c$ superconductor $La_3Ni_2O_7$ and single layers of the antiferromagnetic insulator $La_2NiO_4$. We report the successful synthesis of $La_2NiO_4$$La_3Ni_2O_7$ single crystals, and X-ray diffraction and real-space imaging vis STEM show that the crystal structure consists of single layers and bilayers of $NiO_6$ octahedral stacking alternatively perpendicular to the ab plane, characterized by the orthorhombic Immm (No.71) space group. Resistivity measurements indicate a peculiar insulator-to-metal transition around 140 K on cooling. Correlated density functional theory calculations corroborate this finding, and reveal that the single layer becomes paramagnetic metallic due to charge transfer via LaO layers. The discovery of $La_2NiO_4$$La_3Ni_2O_7$ opens a door to access a completely new family of 3D hybrid R-P phases with the formula of $A_{n+1}$$B_n$$X_{3n+1}$$A'_{m+1}$$B'_m$$X'_{3m+1}$ which potentially host a plethora of emerging physical properties for various applications., Comment: 27 pages, 4 figures

Optic disc and cup segmentation plays a crucial role in automating the screening and diagnosis of optic glaucoma. While data-driven convolutional neural networks (CNNs) show promise in this area, the inherent ambiguity of segmenting objects and background boundaries in the task of optic disc and cup segmentation leads to noisy annotations that impact model performance. To address this, we propose an innovative label-denoising method of Multiple Pseudo-labels Noise-aware Network (MPNN) for accurate optic disc and cup segmentation. Specifically, the Multiple Pseudo-labels Generation and Guided Denoising (MPGGD) module generates pseudo-labels by multiple different initialization networks trained on true labels, and the pixel-level consensus information extracted from these pseudo-labels guides to differentiate clean pixels from noisy pixels. The training framework of the MPNN is constructed by a teacher-student architecture to learn segmentation from clean pixels and noisy pixels. Particularly, such a framework adeptly leverages (i) reliable and fundamental insight from clean pixels and (ii) the supplementary knowledge within noisy pixels via multiple perturbation-based unsupervised consistency. Compared to other label-denoising methods, comprehensive experimental results on the RIGA dataset demonstrate our method's excellent performance. The code is available at https://github.com/wwwtttjjj/MPNN, Comment: CSCWD 2024

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods., Comment: Accepted by IEEE TPAMI

Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways, (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97.75\% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 4\%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries.

The generation and control of optical frequency combs in integrated photonic systems enables complex, high-controllable, and large-scale devices. In parallel, harnessing topological physics in multipartite systems has allowed them with compelling features such as robustness against fabrication imperfections. Here we experimentally demonstrate on-chip topological transport for optical frequency combs at telecommunication wavelengths, both in classical and nonclassical domains. We access both the quantum frequency combs and dissipative Kerr soliton combs with a micro-resonator. The quantum frequency comb, that is, a coherent superposition of multiple frequency modes, is proven to be a frequency-entangled qudit state. We also show that dissipative Kerr soliton combs are highly coherent and mode-locked due to the collective coherence or self-organization of solitons. Moreover, the valley kink states allow both quantum frequency combs and dissipative Kerr soliton combs with robustness against sharp bends. Our topologically protected optical frequency combs could enable the inherent robustness in integrated complex photonic systems., Comment: 20 pages,12 figures

Local Feature Matching, an essential component of several computer vision tasks (e.g., structure from motion and visual localization), has been effectively settled by Transformer-based methods. However, these methods only integrate long-range context information among keypoints with a fixed receptive field, which constrains the network from reconciling the importance of features with different receptive fields to realize complete image perception, hence limiting the matching accuracy. In addition, these methods utilize a conventional handcrafted encoding approach to integrate the positional information of keypoints into the visual descriptors, which limits the capability of the network to extract reliable positional encoding message. In this study, we propose Feature Matching with Reconciliatory Transformer (FMRT), a novel Transformer-based detector-free method that reconciles different features with multiple receptive fields adaptively and utilizes parallel networks to realize reliable positional encoding. Specifically, FMRT proposes a dedicated Reconciliatory Transformer (RecFormer) that consists of a Global Perception Attention Layer (GPAL) to extract visual descriptors with different receptive fields and integrate global context information under various scales, Perception Weight Layer (PWL) to measure the importance of various receptive fields adaptively, and Local Perception Feed-forward Network (LPFFN) to extract deep aggregated multi-scale local feature representation. Extensive experiments demonstrate that FMRT yields extraordinary performance on multiple benchmarks, including pose estimation, visual localization, homography estimation, and image matching.

Fine-Grained Image Recognition (FGIR) is a fundamental and challenging task in computer vision and multimedia that plays a crucial role in Intellectual Economy and Industrial Internet applications. However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field. To address this gap, we present Hawkeye, a PyTorch-based library for FGIR with deep learning. Hawkeye is designed with a modular architecture, emphasizing high-quality code and human-readable configuration, providing a comprehensive solution for FGIR tasks. In Hawkeye, we have implemented 16 state-of-the-art fine-grained methods, covering 6 different paradigms, enabling users to explore various approaches for FGIR. To the best of our knowledge, Hawkeye represents the first open-source PyTorch-based library dedicated to FGIR. It is publicly available at https://github.com/Hawkeye-FineGrained/Hawkeye/, providing researchers and practitioners with a powerful tool to advance their research and development in the field of FGIR., Comment: ACM Multimedia 2023 Open Source Software Competition Winner Entry. X.-S. Wei is the corresponding author

Metal halide perovskites, a class of cost-effective semiconductor materials, are of great interest for modern and upcoming display technologies that prioritize the light-emitting diodes (LEDs) with high efficiency and excellent color purity. The prevailing approach to achieving efficient luminescence from pervoskites is enhancing exciton binding effect and confining carriers by reducing their dimensionality or grain size. However, splitting pervoskite lattice into smaller ones generates abundant boundaries in solid films and results in more surface trap states, needing exact passivation to suppress trap-assisted nonradiative losses. Here, an ions-induced heteroepitaxial growth method is employed to assembe perovskite lattices with different structures into large-sized grains to produce lattice-anchored nanocomposites for efficient LEDs with high color purity. This approach enables the nanocomposite thin films, composed of three-dimensional (3D) CsPbBr3 and its variant of zero-dimensional (0D) Cs4PbBr6, to feature significant low trap-assisted nonradiative recombination, enhanced light out-coupling with a corrugated surface, and well-balanced charge carrier transport. Based on the resultant 3D/0D perovskite nanocomposites, we demonstrate the perovskite LEDs achieving an remarkable external quantum efficiency of 31.0% at the emission peak of 521 nm with a narrow full width at half-maximum of only 18 nm. This research introduces a novel approach to the development of well-assembled nanocomposites for perovskite LEDs, demonstrating high efficiency comparable to that of state-of-the-art organic LEDs.

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology., Comment: 52 pages, 6 figures, 1 table

In this paper, we investigate the asymptotic behavior of the non-simple systole, which is the length of a shortest non-simple closed geodesic, on a random closed hyperbolic surface on the moduli space $\mathcal{M}_g$ of Riemann surfaces of genus $g$ endowed with the Weil-Petersson measure. We show that as the genus $g$ goes to infinity, the non-simple systole of a generic hyperbolic surface in $\mathcal{M}_g$ behaves exactly like $\log g$., Comment: 47 pages, 11 figures. Comments welcome

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

Long-lived ionic quantum states known as metastable electronic states in highly-charged ions (HCIs) are of great interest in fundamental physics. Especially, it generates transitions with very narrow natural linewidth which is a promising candidate for use in the next generation HCI atomic clocks to reach an accuracy below $10^{-19}$. A recent experiment reported in [Nature,581(7806) 2020], used Penning trap mass spectrometry to measure the energy of an extremely long-lived metastable electronic state, thus opening doors to search for HCI clock transitions. Building upon prior research, this study introduces an experimental proposal with the goal of measuring lifetimes of the metastable states beyond seconds. Our approach employs a sequential pulse-and-phase measurement scheme, allowing for direct observations of the decay processes from metastable electronic states through single-ion mass spectrometry in a Penning trap. This measurement poses a significant challenge to conventional techniques like fluorescence detection. To demonstrate the effectiveness of this method, we conducted a comprehensive simulation under real experimental conditions, yielding promising results in a specific scenario. Two suitable candidates are proposed for testing this method, and the state-of-the-art MCDHF theory are employed for accurate energy levels and transition rate calculations. Some future prospects in the experimental determinations of a wide range of energy and lifetimes of long-lived metastable electronic states, probing hyperfine and magnetic quenching effects on high-order forbidden transitions and search for highly quality HCI clock transitions are discussed.

Graph Pooling technology plays an important role in graph node classification tasks. Sorting pooling technologies maintain large-value units for pooling graphs of varying sizes. However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain useful information and can contribute considerably to the final decision. To maintain more useful information, a novel pooling technology, called Geometric Pooling (GP), was proposed to contain the unique node features with negative values by measuring the similarity of all node features. We reveal the effectiveness of GP from the entropy reduction view. The experiments were conducted on TUdatasets to show the effectiveness of GP. The results showed that the proposed GP outperforms the SOTA graph pooling technologies by 1%\sim5% with fewer parameters., Comment: 6 pages, 4 figures

We study the ground state of the doped Hubbard model on honeycomb lattice with both nearest ($t$) and next-nearest neighboring hoppings ($t'$) in the small doping and strongly interacting region. Previous study on the model without $t'$ showed the ground state is a half-filled stripe. We employ density matrix renormalization group and extrapolate the results with truncation errors in the converged region. In the $t' < 0$ side, we find the half-filled stripe phase at $t' = 0$ is stable against the frustration of $t'$ until a critical point $-0.4 < t'_c < -0.3$, beyond which the ground state switches to anti-ferromagnetic N$\acute e$el phase with charge modulation. With further increase of $t'$ to $-0.7$, the ground state becomes paramagnetic. In the $t' > 0$ side, the half-filled stripe stretches to $t' \approx 0.7$. We don't find obvious enhancement of pairing for the range of $t'$ studied. We study width-4 cylinders in this work but the results for spin, charge, and pairing correlation agree qualitatively for periodic and anti-periodic boundary conditions in the half-filled stripe and anti-ferromagnetic N$\acute e$el phases, suggesting the results are likely to be representative for true two-dimensional systems. The half-filled stripe to anti-ferromagnetic N$\acute e$el phase transition can be realized on real materials or ultra-cold atom platform., Comment: 5 pages, 4 figures

We present a comprehensive investigation of self-heating in gallium nitride (GaN) high-electron-mobility transistors (HEMTs) through technology computer-aided design (TCAD) simulations and phonon Monte Carlo (MC) simulations. With microscopic phonon-based electrothermal simulations, we scrutinize both the temperature profiles and electrothermal coupling effect within GaN HEMTs. Two metrics, maximum channel temperature ($T_\text{max}$) and equivalent channel temperature ($T_\text{eq}$), are introduced to measure the reliability and electrical performance degradation of the device, respectively. The influence of bias-dependent heat generation and phonon ballistic transport on the two indicators is thoroughly examined.

High-Tc superconductivity with possible $T_{c}\approx 80K$ has been reported in the single crystal of $\text{La}_{3}\text{Ni}_{2}\text{O}_{7}$ under high pressure. Based on the electronic structure given from the density functional theory calculations, we propose an effective bi-layer model Hamiltonian including both $3d_{z^{2}}$ and $3d_{x^{2}-y^{2}}$ orbital electrons of the nickel cations. The main feature of the model is that the $3d_{z^{2}}$ electrons form inter-layer $\sigma$-bonding and anti-bonding bands via the apical oxygen anions between the two layers, while the $3d_{x^{2}-y^{2}}$ electrons hybridize with the $3d_{z^{2}}$ electrons within each NiO$_2$ plane. The chemical potential difference of these two orbital electrons ensures that the $3d_{z^{2}}$ orbitals are close to half-filling and the $3d_{x^{2}-y^{2}}$ orbitals are near quarter-filling. The strong on-site Hubbard repulsion of the $3d_{z^{2}}$ orbital electrons gives rise to an effective inter-layer antiferromagnetic spin super-exchange $J$. Applying pressure can self-dope holes on the $3d_{z^{2}}$ orbitals with the same amount of electrons doped on the $3d_{x^{2}-y^{2}}$ orbitals. By performing numerical density-matrix renormalization group calculations on a minimum setup and focusing on the limit of large $J$ and small doping of $3d_{z^{2}}$ orbitals, we find the superconducting instability on both the $3d_{z^{2}}$ and $3d_{x^{2}-y^{2}}$ orbitals by calculating the equal-time spin singlet pair-pair correlation function. Our numerical results have provided useful insights in the high-Tc superconductivity in single crystal La$_3$Ni$_2$O$_7$ under high pressure., Comment: 6 pages, 4 figures; published version

Automatic segmentation of fluid in Optical Coherence Tomography (OCT) images is beneficial for ophthalmologists to make an accurate diagnosis. Although semi-supervised OCT fluid segmentation networks enhance their performance by introducing additional unlabeled data, the performance enhancement is limited. To address this, we propose Superpixel and Confident Learning Guide Point Annotations Network (SCLGPA-Net) based on the teacher-student architecture, which can learn OCT fluid segmentation from limited fully-annotated data and abundant point-annotated data. Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations. The label trust maps provide an indication of the reliability of the pseudo-labels. Furthermore, we propose the Confident Learning Guided Label Refinement (CLGLR) module identifies error information in the pseudo-labels and leads to further refinement. Experiments on the RETOUCH dataset show that we are able to reduce the need for fully-annotated data by 94.22\%, closing the gap with the best fully supervised baselines to a mean IoU of only 2\%. Furthermore, We constructed a private 2D OCT fluid segmentation dataset for evaluation. Compared with other methods, comprehensive experimental results demonstrate that the proposed method can achieve excellent performance in OCT fluid segmentation., Comment: Submission to MIA

The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material comprised of synthesized molecules with large dipole moments that are crystallized into a spherulite structure. The molecules align in an azimuthal direction, resulting in a vortex polarity with spontaneously broken symmetry leading to strong optical anisotropy and nonlinear optical responses. These unique polarization properties of the judiciously designed organic spherulite combined with the symmetry of structured optical beams enable a plethora of new linear and nonlinear light-matter interactions, including the generation of optical vortex beams with complex spin states and on-demand topological charges at the fundamental, doubled, and tripled frequencies. The results of this work are likely to enable numerous applications in areas such as high-dimensional quantum information processing, with large capacity and high security. The demonstrated spherulite crystals facilitate stand-alone micro-scale devices that rely on the unique micro-scale spontaneous vortex polarity that is likely to enable future applications for high-dimensional quantum information processing, spatiotemporal optical vortices, and a novel platform for optical manipulation and trapping.