Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling of Class-wise Training Losses for Post-hoc Calibration

The class-wise training losses often diverge as a result of the various levels of intra-class and inter-class appearance variation, and we find that the diverging class-wise training losses cause the uncalibrated prediction with its reliability. To resolve the issue, we propose a new calibration method to synchronize the class-wise training losses. We design a new training loss to alleviate the variance of class-wise training losses by using multiple class-wise scaling factors. Since our framework can compensate the training losses of overfitted classes with those of under-fitted classes, the integrated training loss is preserved, preventing the performance drop even after the model calibration. Furthermore, our method can be easily employed in the post-hoc calibration methods, allowing us to use the pre-trained model as an initial model and reduce the additional computation for model calibration. We validate the proposed framework by employing it in the various post-hoc calibration methods, which generally improves calibration performance while preserving accuracy, and discover through the investigation that our approach performs well with unbalanced datasets and untuned hyperparameters.
Mind the cap!—constrained portfolio optimisation in Heston's stochastic volatility model

We consider a portfolio optimisation problem for a utility-maximising investor who faces convex constraints on his portfolio allocation in Heston's stochastic volatility model. We apply existing duality methods to obtain a closed-form expression for the optimal portfolio allocation. In doing so, we observe that allocation constraints impact the optimal constrained portfolio allocation in a fundamentally different way in Heston's stochastic volatility model than in the Black Scholes model. In particular, the optimal constrained portfolio may be different from the naive ‘capped’ portfolio, which caps off the optimal unconstrained portfolio at the boundaries of the constraints. Despite this difference, we illustrate by way of a numerical analysis that in most realistic scenarios the capped portfolio leads to slim annual wealth equivalent losses compared to the optimal constrained portfolio. During a financial crisis, however, a capped solution might lead to compelling annual wealth equivalent losses.
VALIDATION OF 2005 HEAVY RAIN EVENTS OVER THE YANGTZE RIVER BASIN FORECAST BY GRAPES

GRAPES(Global/Regional Assimilation and Prediction System)is a new generation of mesoscale weather forecast model in China.By comparing model prediction with observation,the model ability in predicting heavy rain events,together with the governing weather systems,over the Yangtze River Basin in 2005,is validated in the paper.The results show a generally good performance of the model in the prediction.The work will be helpful in better utilizing the model results in conventional weather prediction,as well as in providing valuable suggestions to further improvement of the model.
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models

Model-based imitation learning (MBIL) is a popular reinforcement learning method that improves sample efficiency on high-dimension input sources, such as images and videos. Following the convention of MBIL research, existing algorithms are highly deceptive by task-irrelevant information, especially moving distractors in videos. To tackle this problem, we propose a new algorithm - named Separated Model-based Adversarial Imitation Learning (SeMAIL) - decoupling the environment dynamics into two parts by task-relevant dependency, which is determined by agent actions, and training separately. In this way, the agent can imagine its trajectories and imitate the expert behavior efficiently in task-relevant state space. Our method achieves near-expert performance on various visual control tasks with complex observations and the more challenging tasks with different backgrounds from expert observations.
Evolution of thermodynamic quantities on cosmological horizon in 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi mathvariant="normal">Λ</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math>


The horizon of a flat Friedmann--Robertson--Walker (FRW) universe is considered to be dynamic when the Hubble parameter $H$ and the Hubble radius $r_{H}$ vary with time, unlike for de Sitter universes. To clarify the thermodynamics on a dynamic horizon, the evolution of a dynamical Kodama--Hayward temperature and Bekenstein--Hawking entropy on the horizon of a flat FRW universe is examined in a $\Lambda(t)$ model similar to time-varying $\Lambda(t)$ cosmologies. The $\Lambda(t)$ model includes both a power-law term proportional to $H^{\alpha}$ (where $\alpha$ is a free variable) and the equation of state parameter $w$, extending a previous analysis [Phys. Rev. D 100, 123545 (2019) (arXiv:1911.08306)]. Using the present model, a matter-dominated universe ($w=0$) and a radiation-dominated universe ($w=1/3$) are examined, setting $\alpha<2$. Both universes tend to approach de Sitter universes and satisfy the maximization of entropy in the last stage. The evolution of several parameters (such as the Bekenstein--Hawking entropy) is similar for both $w=0$ and $w=1/3$, though the dynamical temperature $T_{H}$ is different. In particular, $T_{H}$ is found to be constant when $w=1/3$ with $\alpha=1$, although $H$ and $r_{H}$ vary with time. To discuss this case, the specific conditions required for constant $T_{H}$ are examined. Applying the specific condition to the present model gives a cosmological model that can describe a universe at constant $T_{H}$, as if the dynamic horizon is in contact with a heat bath. The relaxation processes for the universe are also discussed.
Chain-mapping methods for relativistic light-matter interactions

The interaction between localized emitters and quantum fields, both in relativistic settings and in the case of ultra-strong couplings, requires non-perturbative methods beyond the rotating-wave approximation. In this work we employ chain-mapping methods to achieve a numerically exact treatment of the interaction between a localized emitter and a scalar quantum field. We extend the application range of these methods beyond emitter observables and apply them to study field observables. We first provide an overview of chain-mapping methods and their physical interpretation, and discuss the thermal double construction for systems coupled to thermal field states. Modelling the emitter as an Unruh-DeWitt particle detector, we then calculate the energy density emitted by a detector coupling strongly to the field. As a stimulating demonstration of the approach's potential, we calculate the radiation emitted from an accelerated detector in the Unruh effect, which is closely related to the thermal double construction as we discuss. We comment on prospects and challenges of the method.
TOBY: A Tool for Exploring Data in Academic Survey Papers

This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper. The visualization consists of four components: a hierarchical view of taxonomic data in the survey, a document similarity view in the space of taxonomic classes, a network view of citations, and a new paper recommendation tool. In this paper, we will discuss these features in the context of three separate deployments of the tool.
Concept-Based Visual Analysis of Dynamic Textual Data

Analyzing how interrelated ideas flow within and between multiple social groups helps understand the propagation of information, ideas, and thoughts on social media. The existing dynamic text analysis work on idea flow analysis is mostly based on the topic model. Therefore, when analyzing the reasons behind the flow of ideas, people have to check the textual data of the ideas, which is annoying because of the huge amount and complex structures of these texts. To solve this problem, we propose a concept-based dynamic visual text analytics method, which illustrates how the content of the ideas change and helps users analyze the root cause of the idea flow. We use concepts to summarize the content of the ideas and show the flow of concepts with the flow lines. To ensure the stability of the flow lines, a constrained t-SNE projection algorithm is used to display the change of concepts over time and the correlation between them. In order to better convey the anomalous change of the concepts, we propose a method to detect the time periods with anomalous change of concepts based on anomaly detection and highlight them. A qualitative evaluation and a case study on real-world Twitter datasets demonstrate the correctness and effectiveness of our visual analytics method.
Quantum Advantage of Noisy Grover's Algorithm

Quantum advantage is the core of quantum computing. Grover's search algorithm is the only quantum algorithm with proven advantage to any possible classical search algorithm. However, realizing this quantum advantage in practice is quite challenging since Grover's algorithm is very sensitive to noise. Here we present a noise-tolerant method that exponentially improves the noise threshold of Grover's algorithm. We present a lower bound for average fidelity of any quantum circuit with O(log D log D) cost under time-independent noise, where D is the dimension of Hilbert space. According to this bound value, we determine the number of iterates which will be applied in Grover's algorithm. Numerical simulation shows that the noise threshold of quantum advantage of Grover's algorithm by our noise-tolerant method is improved by an exponential factor with qubit amount rise.
Earth's rotation forms the general circulation of the atmosphere

The general circulation of the atmosphere determines the long-term variability of weather processes. This circulation is driven by the temperature differences between the poles and the equator, causing air to move along the Earth's surface. However, this requires enhanced pressure at the poles, which is not observed. To sustain the circulation, an additional non-hydrostatic pressure gradient is required. In my research, I propose the emergence of an additional non-hydrostatic pressure gradient resulting from the centrifugal force generated by the Earth's rotation. This centrifugal force creates a non-hydrostatic vertical pressure gradient, which is essential for the closed circulation of unequally heated air in the meridional direction. The circulation is composed of three distinct streams flowing in opposite directions, with the polar and tropical tropopause acting as boundaries. The temperature in the atmosphere decreases from the surface to the polar tropopause and remains constant above it.
UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM

We present an uncertainty learning framework for dense neural simultaneous localization and mapping (SLAM). Estimating pixel-wise uncertainties for the depth input of dense SLAM methods allows re-weighing the tracking and mapping losses towards image regions that contain more suitable information that is more reliable for SLAM. To this end, we propose an online framework for sensor uncertainty estimation that can be trained in a self-supervised manner from only 2D input data. We further discuss the advantages of the uncertainty learning for the case of multi-sensor input. Extensive analysis, experimentation, and ablations show that our proposed modeling paradigm improves both mapping and tracking accuracy and often performs better than alternatives that require ground truth depth or 3D. Our experiments show that we achieve a 38% and 27% lower absolute trajectory tracking error (ATE) on the 7-Scenes and TUM-RGBD datasets respectively. On the popular Replica dataset using two types of depth sensors, we report an 11% F1-score improvement on RGBD SLAM compared to the recent state-of-the-art neural implicit approaches. Source code: https://github.com/kev-in-ta/UncLe-SLAM.
Dropout Regularization Versus $\ell_2$-Penalization in the Linear Model

We investigate the statistical behavior of gradient descent iterates with dropout in the linear regression model. In particular, non-asymptotic bounds for expectations and covariance matrices of the iterates are derived. In contrast with the widely cited connection between dropout and $\ell_2$-regularization in expectation, the results indicate a much more subtle relationship, owing to interactions between the gradient descent dynamics and the additional randomness induced by dropout. We also study a simplified variant of dropout which does not have a regularizing effect and converges to the least squares estimator.
Dynamic Perceiver for Efficient Visual Recognition

Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for "easy" samples can be generated at earlier exits, negating the need for executing deeper layers. Current multi-exit networks typically implement linear classifiers at intermediate layers, compelling low-level features to encapsulate high-level semantics. This sub-optimal design invariably undermines the performance of later exits. In this paper, we propose Dynamic Perceiver (Dyn-Perceiver) to decouple the feature extraction procedure and the early classification task with a novel dual-branch architecture. A feature branch serves to extract image features, while a classification branch processes a latent code assigned for classification tasks. Bi-directional cross-attention layers are established to progressively fuse the information of both branches. Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features. Dyn-Perceiver constitutes a versatile and adaptable framework that can be built upon various architectures. Experiments on image classification, action recognition, and object detection demonstrate that our method significantly improves the inference efficiency of different backbones, outperforming numerous competitive approaches across a broad range of computational budgets. Evaluation on both CPU and GPU platforms substantiate the superior practical efficiency of Dyn-Perceiver. Code is available at https://www.github. com/LeapLabTHU/Dynamic_Perceiver.
RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation

Large-scale road surface reconstruction is becoming important to autonomous driving systems, as it provides valuable training and testing data effectively. In this paper, we introduce a simple yet efficient method, RoMe, for large-scale Road surface reconstruction via Mesh representations. To simplify the problem, RoMe decomposes a 3D road surface into a triangle-mesh and a multilayer perception network to model the road elevation implicitly. To retain fine surface details, each mesh vertex has two extra attributes, namely color and semantics. To improve the efficiency of RoMe in large-scale environments, a novel waypoint sampling method is introduced. As such, RoMe can properly preserve road surface details, with only linear computational complexity to road areas. In addition, to improve the accuracy of RoMe, extrinsics optimization is proposed to mitigate inaccurate extrinsic calibrations. Experimental results on popular public datasets also demonstrate the high efficiency and accuracy of RoMe.
Towards Large-Scale Incremental Dense Mapping using Robot-centric Implicit Neural Representation

Large-scale dense mapping is vital in robotics, digital twins, and virtual reality. Recently, implicit neural mapping has shown remarkable reconstruction quality. However, incremental large-scale mapping with implicit neural representations remains problematic due to low efficiency, limited video memory, and the catastrophic forgetting phenomenon. To counter these challenges, we introduce the Robot-centric Implicit Mapping (RIM) technique for large-scale incremental dense mapping. This method employs a hybrid representation, encoding shapes with implicit features via a multi-resolution voxel map and decoding signed distance fields through a shallow MLP. We advocate for a robot-centric local map to boost model training efficiency and curb the catastrophic forgetting issue. A decoupled scalable global map is further developed to archive learned features for reuse and maintain constant video memory consumption. Validation experiments demonstrate our method's exceptional quality, efficiency, and adaptability across diverse scales and scenes over advanced dense mapping methods using range sensors. Our system's code will be accessible at \url{https://github.com/HITSZ-NRSL/RIM.git}.
A General Form of Covariate Adjustment in Randomized Clinical Trials

In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted (AIPW) estimator, which is a general form of covariate adjustment that uses linear, generalized linear, and non-parametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we establish general theorems that show a complete picture of the asymptotic normality, {efficiency gain, and applicability of AIPW estimators}. In particular, we provide for the first time a rigorous theoretical justification of using machine learning methods with cross-fitting for dependent data under covariate-adaptive randomization. Based on the general theorems, we offer insights on the conditions for guaranteed efficiency gain and universal applicability {under different randomization schemes}, which also motivate a joint calibration strategy using some constructed covariates after applying AIPW. Our methods are implemented in the R package RobinCar.
High-resolution simulations of LS 5039

Context. We present an analysis of our high-resolution relativistic-hydrodynamics model of the stellar- and pulsar-wind interaction in the LS-5039 system. Aims. With our high-resolution simulation covering three orbital periods, we analyse the impact of turbulence with a particular focus on short-term and orbit-to-orbit variations. Methods. Our model uses a relativistic hydrodynamics description of the wind interaction in the LS-5039 system assuming a pulsar-wind driven scenario. The corresponding system of equations is solved using the finite-volume code Cronos. We compute statistical quantities, also relevant for particle acceleration in this system, from results of multiple consecutive timesteps. Results. In our simulation we find the previously observed shock structures related to the wind-collision region (WCR), including the pulsar-wind termination, being dynamically influenced by orbital motion. In our high-resolution simulation we find high turbulence levels following from instabilities driven at the WCR. These instabilities lead to strong fluctuations of several dynamical quantities especially around and after apastron. These fluctuations are expected to impact the particle transport and also especially the related emission of non-thermal radiation. As an important example, the region from which gamma-ray emission has been found to be boosted due to relativistic beaming in previous studies shows strong variations in size both on short and on orbital timescales. Conclusions. Using a large computational domain together with high spatial resolution allowed a detailed study of fluctuations in the stellar- and pulsar-wind interaction. The results indicate a possible influence on the non-thermal emission from this system, which will be analysed with dedicated simulations in a forthcoming publication.
Catastrophic Forgetting in the Context of Model Updates

A large obstacle to deploying deep learning models in practice is the process of updating models post-deployment (ideally, frequently). Deep neural networks can cost many thousands of dollars to train. When new data comes in the pipeline, you can train a new model from scratch (randomly initialized weights) on all existing data. Instead, you can take an existing model and fine-tune (continue to train) it on new data. The former is costly and slow. The latter is cheap and fast, but catastrophic forgetting generally causes the new model to 'forget' how to classify older data well. There are a plethora of complicated techniques to keep models from forgetting their past learnings. Arguably the most basic is to mix in a small amount of past data into the new data during fine-tuning: also known as 'data rehearsal'. In this paper, we compare various methods of limiting catastrophic forgetting and conclude that if you can maintain access to a portion of your past data (or tasks), data rehearsal is ideal in terms of overall accuracy across all time periods, and performs even better when combined with methods like Elastic Weight Consolidation (EWC). Especially when the amount of past data (past 'tasks') is large compared to new data, the cost of updating an existing model is far cheaper and faster than training a new model from scratch.
KiDS-1000: Cosmology with improved cosmic shear measurements

We present refined cosmological parameter constraints derived from a cosmic shear analysis of the fourth data release of the Kilo-Degree Survey (KiDS-1000). Our main improvements include enhanced galaxy shape measurements made possible by an updated version of the lensfit code and improved shear calibration achieved with a newly developed suite of multi-band image simulations. Additionally, we incorporated recent advancements in cosmological inference from the joint Dark Energy Survey Year 3 and KiDS-1000 cosmic shear analysis. Assuming a spatially flat standard cosmological model, we constrain $S_8\equiv\sigma_8(\Omega_{\rm m}/0.3)^{0.5} = 0. 776_{-0.027-0.003}^{+0.029+0.002}$, where the second set of uncertainties accounts for the systematic uncertainties within the shear calibration. These systematic uncertainties stem from minor deviations from realism in the image simulations and the sensitivity of the shear measurement algorithm to the morphology of the galaxy sample. Despite these changes, our results align with previous KiDS studies and other weak lensing surveys, and we find a ${\sim}2.3\sigma$ level of tension with the Planck cosmic microwave background constraints on $S_8$.
Gradient-type subspace iteration methods for the symmetric eigenvalue problem

This paper explores variants of the subspace iteration algorithm for computing approximate invariant subspaces. The standard subspace iteration approach is revisited and new variants that exploit gradient-type techniques combined with a Grassmann manifold viewpoint are developed. A gradient method as well as a conjugate gradient technique are described. Convergence of the gradient-based algorithm is analyzed and a few numerical experiments are reported, indicating that the proposed algorithms are sometimes superior to a standard Chebyshev-based subspace iteration when compared in terms of number of matrix vector products, but do not require estimating optimal parameters. An important contribution of this paper to achieve this good performance is the accurate and efficient implementation of an exact line search. In addition, new convergence proofs are presented for the non-accelerated gradient method that includes a locally exponential convergence if started in a $\mathcal{O(\sqrt{\delta})}$ neighbourhood of the dominant subspace with spectral gap $\delta$.