Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards End-to-end Semi-supervised Learning for One-stage Object Detection

Semi-supervised object detection (SSOD) is a research hot spot in computer vision, which can greatly reduce the requirement for expensive bounding-box annotations. Despite great success, existing progress mainly focuses on two-stage detection networks like FasterRCNN, while the research on one-stage detectors is often ignored. In this paper, we focus on the semi-supervised learning for the advanced and popular one-stage detection network YOLOv5. Compared with Faster-RCNN, the implementation of YOLOv5 is much more complex, and the various training techniques used in YOLOv5 can also reduce the benefit of SSOD. In addition to this challenge, we also reveal two key issues in one-stage SSOD, which are low-quality pseudo-labeling and multi-task optimization conflict, respectively. To address these issues, we propose a novel teacher-student learning recipe called OneTeacher with two innovative designs, namely Multi-view Pseudo-label Refinement (MPR) and Decoupled Semi-supervised Optimization (DSO). In particular, MPR improves the quality of pseudo-labels via augmented-view refinement and global-view filtering, and DSO handles the joint optimization conflicts via structure tweaks and task-specific pseudo-labeling. In addition, we also carefully revise the implementation of YOLOv5 to maximize the benefits of SSOD, which is also shared with the existing SSOD methods for fair comparison. To validate OneTeacher, we conduct extensive experiments on COCO and Pascal VOC. The extensive experiments show that OneTeacher can not only achieve superior performance than the compared methods, e.g., 15.0% relative AP gains over Unbiased Teacher, but also well handle the key issues in one-stage SSOD. Our source code is available at: https://github.com/luogen1996/OneTeacher.
Universal Morphology Control via Contextual Modulation

Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. However, it poses a challenging multi-task reinforcement learning problem, as the optimal policy may be quite different across robots and critically depend on the morphology. Existing methods utilize graph neural networks or transformers to handle heterogeneous state and action spaces across different morphologies, but pay little attention to the dependency of a robot's control policy on its morphology context. In this paper, we propose a hierarchical architecture to better model this dependency via contextual modulation, which includes two key submodules: (1) Instead of enforcing hard parameter sharing across robots, we use hypernetworks to generate morphology-dependent control parameters; (2) We propose a fixed attention mechanism that solely depends on the morphology to modulate the interactions between different limbs in a robot. Experimental results show that our method not only improves learning performance on a diverse set of training robots, but also generalizes better to unseen morphologies in a zero-shot fashion.
Robustness of normal theory methods in the analysis of linear latent variate models.

The structure of the covariance matrix of sample covariances under the class of linear latent variate models is derived using properties of cumulants. This is employed to provide a general framework for robustness of statistical inference in the analysis of covariance structures arising from linear latent variate models. Conditions for normal theory estimators and test statistics to retain each of their usual asymptotic properties under non-normality of latent variates are given. Factor analysis, LISREL and other models are discussed as examples.
The Evryscope Fast Transient Engine: Real-time Detection for Rapidly Evolving Transients

Astrophysical transients with rapid developments on subhour timescales are intrinsically rare. Due to their short durations, events like stellar superflares, optical flashes from gamma-ray bursts, and shock breakouts from young supernovae are difficult to identify on timescales that enable spectroscopic follow-up. This paper presents the Evryscope Fast Transient Engine (EFTE), a new data reduction pipeline that is designed to provide low-latency transient alerts from the Evryscopes—a north–south pair of ultra-wide-field telescopes with an instantaneous footprint covering 38% of the entire sky—and tools for building long-term light curves from Evryscope data. EFTE leverages the optical stability of the Evryscopes by using a simple direct image subtraction routine that is suited to continuously monitoring the transient sky at a cadence of a minute. Candidates are produced within the base Evryscope 2 minute cadence for 98.5% of images, and internally filtered using vetnet, a convolutional neural network real–bogus classifier. EFTE provides an extensible and robust architecture for transient surveys probing similar timescales, and serves as the software test bed for the real-time analysis pipelines and public data distribution systems for the Argus Array, a next-generation all-sky observatory with a data rate 62 times higher than that of Evryscope.
Variable fluid properties and variable heat flux effects on the flow and heat transfer in a non-Newtonian Maxwell fluid over an unsteady stretching sheet with slip velocity

The effects of variable fluid properties and variable heat flux on the flow and heat transfer of a non-Newtonian Maxwell fluid over an unsteady stretching sheet in the presence of slip velocity have been studied. The governing differential equations are transformed into a set of coupled non-linear ordinary differential equations and then solved with a numerical technique using appropriate boundary conditions for various physical parameters. The numerical solution for the governing non-linear boundary value problem is based on applying the fourth-order Runge—Kutta method coupled with the shooting technique over the entire range of physical parameters. The effects of various parameters like the viscosity parameter, thermal conductivity parameter, unsteadiness parameter, slip velocity parameter, the Deborah number, and the Prandtl number on the flow and temperature profiles as well as on the local skin-friction coefficient and the local Nusselt number are presented and discussed. Comparison of numerical results is made with the earlier published results under limiting cases.
Guiding Large Language Models via Directional Stimulus Prompting

We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model (e.g., T5) to generate an auxiliary directional stimulus prompt for each input instance. These directional stimulus prompts act as nuanced, instance-specific hints and clues to guide LLMs in generating desired outcomes, such as including specific keywords in the generated summary. Our approach sidesteps the challenges of direct LLM tuning by optimizing the policy model to explore directional stimulus prompts that align LLMs with desired behaviors. The policy model can be optimized through 1) supervised fine-tuning using labeled data and 2) reinforcement learning from offline or online rewards based on the LLM's output. We assess our method across summarization, dialogue response generation, and chain-of-thought reasoning tasks. Our experiments demonstrate that the framework consistently improves LLMs' (e.g., ChatGPT, Codex, InstructGPT) performance on these supervised tasks using minimal labeled data. Notably, using just 80 dialogues on the MultiWOZ dataset, our approach enhances ChatGPT's performance by an impressive 41.4%, matching or surpassing some fully supervised start-of-the-art models. Additionally, the instance-specific chain-of-thought prompt generated by our approach improves InstructGPT's reasoning accuracy compared to human-crafted or automatically generated prompts. The code and data are publicly available at \url{https://github.com/Leezekun/Directional-Stimulus-Prompting}.
A 4D‐Var re‐analysis of FASTEX

A complete re‐analysis of the Fronts and Atlantic Storm‐Track EXperiment (5 January–27 February 1997) is performed. It provides a reference description of the life cycle of an ensemble of cyclogeneses over the north Atlantic. Many aspects make this re‐analysis noteworthy. In particular, the FASTEX period is characterized by an exceptional increase in the number of dropsoundings or radiosoundings concentrated on the storm track. Another striking point of the re‐analysis is the use of a four‐dimensional variational (4D‐Var) assimilation scheme on such a long period associated with the French Arpège (Action des Recherche Petite Echelle et Grand Echelle) global stretched model, that allows a locally high resolution in the area where dense networks of dropsondes were deployed. A twin re‐analysis with the same characteristics but without any FASTEX data is also performed. It shows that the impact of the special FASTEX data is quite noticeable in the resulting analyses and is lower, but still perceptible, in the associated short‐range forecasts (6 h–36 h). The impact of FASTEX observations on a particular FASTEX case is also shown. Copyright © 2003 Royal Meteorological Society
Prediction-Correction Pedestrian Flow by Means of Minimum Flow Problem

We study a new variant of mathematical prediction-correction model for crowd motion. The prediction phase is handled by a transport equation where the vector field is computed via an eikonal equation $\Vert \nabla\varphi\Vert=f$, with a positive continuous function $f$ connected to the speed of the spontaneous travel. The correction phase is handled by a new version of the minimum flow problem. This model is flexible and can take into account different types of interactions between the agents, from gradient flow in Wassersetin space to granular type dynamics like in sandpile. Furthermore, different boundary conditions can be used, such as non-homogeneous Dirichlet (e.g., outings with different exit-cost penalty) and Neumann boundary conditions (e.g., entrances with different rates). Combining finite volume method for the transport equation and Chambolle-Pock's primal dual algorithm for the eikonal equation and minimum flow problem, we present numerical simulations to demonstrate the behavior in different scenarios.
Galactic Model Parameters and Spatial Density of Cataclysmic Variables in the Gaia Era: New Constraints on Population Models

The spatial distribution, Galactic model parameters, and luminosity function of cataclysmic variables (CVs) are established using reestimated trigonometric parallaxes from Gaia DR3. The data sample of 1587 CVs in this study is claimed to be suitable for Galactic model parameter estimation as the distances are based on trigonometric parallaxes, and the Gaia DR3 photometric completeness limits were taken into account when the sample was created. According to the analysis, the scale height of all CVs increases from 248 ± 2 to 430 ± 4 pc toward shorter periods near the lower limit of the period gap and suddenly drops to 300 ± 2 pc for the shortest orbital period CVs. The exponential scale heights of all CVs and the magnetic systems are found to be 375 ± 2 and 281 ± 3 pc, respectively, considerably larger than those suggested in previous observational studies. The local spatial density of all CVs and the magnetic systems in the sample are 6.8−1.1+1.3× 10−6 and 2.1−0.4+0.5×10−6 pc−3, respectively. Our measurements strengthen the 1–2 order of magnitude discrepancy between the CV spatial densities predicted by population synthesis models and observations. It is likely that this discrepancy is due to objects undetected by CV surveys, such as systems with very low Ṁ and ones in the period gap. A comparison of the luminosity function of white dwarfs with the luminosity function of all CVs in this study show that 500 times the luminosity function of CVs fits very well to the luminosity function of white dwarfs. We conclude that the estimations and data sample in this study can be confidently used for further analyses of CVs.
Adversarial Model for Offline Reinforcement Learning

We propose a novel model-based offline Reinforcement Learning (RL) framework, called Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary reference policy regardless of data coverage. ARMOR is designed to optimize policies for the worst-case performance relative to the reference policy through adversarially training a Markov decision process model. In theory, we prove that ARMOR, with a well-tuned hyperparameter, can compete with the best policy within data coverage when the reference policy is supported by the data. At the same time, ARMOR is robust to hyperparameter choices: the policy learned by ARMOR, with"any"admissible hyperparameter, would never degrade the performance of the reference policy, even when the reference policy is not covered by the dataset. To validate these properties in practice, we design a scalable implementation of ARMOR, which by adversarial training, can optimize policies without using model ensembles in contrast to typical model-based methods. We show that ARMOR achieves competent performance with both state-of-the-art offline model-free and model-based RL algorithms and can robustly improve the reference policy over various hyperparameter choices.
Reinforcement Learning for Block Decomposition of CAD Models

We present a novel AI-assisted method for decomposing (segmenting) planar CAD (computer-aided design) models into well shaped rectangular blocks as a proof-of-principle of a general decomposition method applicable to complex 2D and 3D CAD models. The decomposed blocks are required for generating good quality meshes (tilings of quadrilaterals or hexahedra) suitable for numerical simulations of physical systems governed by conservation laws. The problem of hexahedral mesh generation of general CAD models has vexed researchers for over 3 decades and analysts often spend more than 50% of the design-analysis cycle time decomposing complex models into simpler parts meshable by existing techniques. Our method uses reinforcement learning to train an agent to perform a series of optimal cuts on the CAD model that result in a good quality block decomposition. We show that the agent quickly learns an effective strategy for picking the location and direction of the cuts and maximizing its rewards as opposed to making random cuts. This paper is the first successful demonstration of an agent autonomously learning how to perform this block decomposition task effectively thereby holding the promise of a viable method to automate this challenging process.
Magnetic nanowire based high resolution magnetic force microscope probes

We report an efficient process for controlled fabrication of high-resolution magnetic force microscope probes using preformed magnetic nanowires. Nickel and cobalt nanowires produced by electrodeposition were directly assembled onto the tip of a commercial atomic force microscope cantilever with controlled orientation and length by dielectrophoresis. The properties of these nanowire-based probes are characterized.
Holomorphic curves in the 6-pseudosphere and cyclic surfaces

The space $\mathbf{H}^{4,2}$ of vectors of norm -1 in $\mathbb{R}^{4,3}$ has a natural pseudo-Riemannian metric and a compatible almost complex structure. The group of automorphisms of both of these structures is the split real form $G_2'$. In this paper we consider a class of holomorphic curves in $\mathbf{H}^{4,2}$ which we call alternating. We show that such curves admit a so called Frenet framing. Using this framing, we show that the space of alternating holomorphic curves which are equivariant with respect to a surface group are naturally parameterized by certain $G_2'$-Higgs bundles. This leads to a holomorphic description of the moduli space as a fibration over Teichm\"uller space with a holomorphic action of the mapping class group. Using a generalization of Labourie's cyclic surfaces, we then show that equivariant alternating holomorphic curves are infinitesimally rigid.
Chow-Witt rings and topology of flag varieties

The paper computes the Witt-sheaf cohomology rings of partial flag varieties in type A in terms of the Pontryagin classes of the subquotient bundles. The proof is based on a Leray-Hirsch-type theorem for Witt-sheaf cohomology for the maximal rank cases, and a detailed study of cohomology ring presentations and annihilators of characteristic classes for the general case. The computations have consequences for the topology of real flag manifolds: we show that all torsion in the integral cohomology is 2-torsion, which was not known in full generality previously. This allows for example to compute the Poincar\'e polynomials of complete flag varieties for cohomology with twisted integer coefficients. The computations also allow to describe the Chow-Witt rings of flag varieties, and we sketch an enumerative application to counting flags satisfying multiple incidence conditions to given hypersurfaces.
Delving into Identify-Emphasize Paradigm for Combating Unknown Bias

Dataset biases are notoriously detrimental to model robustness and generalization. The identify-emphasize paradigm appears to be effective in dealing with unknown biases. However, we discover that it is still plagued by two challenges: A, the quality of the identified bias-conflicting samples is far from satisfactory; B, the emphasizing strategies only produce suboptimal performance. In this paper, for challenge A, we propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy, along with two practical strategies -- peer-picking and epoch-ensemble. For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples. Then, we propose gradient alignment (GA), which employs gradient statistics to balance the contributions of the mined bias-aligned and bias-conflicting samples dynamically throughout the learning process, forcing models to leverage intrinsic features to make fair decisions. Furthermore, we incorporate self-supervised (SS) pretext tasks into training, which enable models to exploit richer features rather than the simple shortcuts, resulting in more robust models. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases and achieve state-of-the-art performance.
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data

In this paper, we propose a feature affinity (FA) assisted knowledge distillation (KD) method to improve quantization-aware training of deep neural networks (DNN). The FA loss on intermediate feature maps of DNNs plays the role of teaching middle steps of a solution to a student instead of only giving final answers in the conventional KD where the loss acts on the network logits at the output level. Combining logit loss and FA loss, we found via convolutional network experiments on CIFAR-10/100, and Tiny ImageNet data sets that the quantized student network receives stronger supervision than from the labeled ground-truth data. The resulting FA quantization-distillation (FAQD), trained to convergence with a cosine annealing scheduler for 200 epochs, is capable of compressing models on label-free data up to or exceeding the accuracy levels of their full precision counterparts, which brings immediate practical benefits as pre-trained teacher models are readily available and unlabeled data are abundant. In contrast, data labeling is often laborious and expensive. Finally, we propose and prove error estimates for a fast feature affinity (FFA) loss function that accurately approximates FA loss at a lower order of computational complexity, which helps speed up training for high resolution image input. Source codes are available at: https://github.com/lzj994/FAQD
Precede-and-command revisited

The relation of c-command (Reinhart 1976, 1983) is widely believed to be the fundamental relation in syntax, underlying such diverse phenomena as coreference (the binding principles ), scope and variable binding, syntactic movement, and so on. Precedence is generally held to be irrelevant. This article argues that this view is mistaken. Syntax does not involve c-command at all, but rather a much coarser notion of command, phase-command , where only phasal nodes matter, not every node in the tree. Precedence also plays an important role. The article argues this point in detail for the binding principles, and shows that the relation that is required is precede-and-command (Langacker 1969, Jackendoff 1972, Lasnik 1976), where command is phase-command. It revisits Reinhart’s arguments for c-command and against precedence, and shows that those arguments do not go through. Finally, precede-and-command does not need to be stipulated, but follows from a view of grammar and processing where sentences are built in a left-to-right fashion.
Thermal Effects in Binary Neutron Star Mergers

We study the impact of finite-temperature effects in numerical-relativity simulations of binary neutron star mergers with microphysical equations of state and neutrino transport in which we vary the effective nucleon masses in a controlled way. We find that, as the specific heat is increased, the merger remnants become colder and more compact due to the reduced thermal pressure support. Using a full Bayesian analysis, we demonstrate that this effect will be measurable in the postmerger gravitational wave signal with next-generation observatories at signal-to-noise ratios of 15.
Identification of a Heterogeneous Generalized Regression Model with Group Effects

We consider identification in a "generalized regression model" (Han, 1987) for panel settings in which each observation can be associated with a "group" whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully nonparametric and allows for the endogeneity of group-specific observables, which might include prices, policies, and/or treatments. The model features heterogeneous responses to observables and unobservables, and arbitrary heteroskedasticity. We provide sufficient conditions for full identification of the model, as well as weaker conditions sufficient for identification of the latent group effects and the distribution of outcomes conditional on covariates and the group effect.
Dynamic contact angle effects onto the maximum drop impact spreading on solid surfaces

This paper reports experimental investigations of drop impacts onto chemically treated surfaces with wettability from 5° to 160°. To follow in time the drop spreading, a high speed video camera was used, and it allows us to determine precisely the expansion of the drop and the profile of the free surface at the contact line. By changing the impact velocity, between less than 0.5 and 5 m/s, and the viscosity, from 1 to 100 mPa s, at constant surface tension, a broad range of Reynolds and Weber numbers is explored. This paper is divided into two parts. In the first part, the experimental drop evolution during spreading is directly reported and compared with previous works. Secondly, the emphasis is on the importance of the apparent dynamic contact angle for the prediction of the maximum spreading diameter. This achievement is manifested at low Reynolds numbers at which the matching between the experiment and the model is improved greatly.