Miracle-dz/UniFAR-FTUs huggingface dataset:
KEEC: Embed to Control on An Equivariant Geometry

This paper investigates how representation learning can enable optimal control in unknown and complex dynamics, such as chaotic and non-linear systems, without relying on prior domain knowledge of the dynamics. The core idea is to establish an equivariant geometry that is diffeomorphic to the manifold defined by a dynamical system and to perform optimal control within this corresponding geometry, which is a non-trivial task. To address this challenge, Koopman Embed to Equivariant Control (KEEC) is proposed for model learning and control. Inspired by Lie theory, KEEC begins by learning a non-linear dynamical system defined on a manifold and embedding trajectories into a Lie group. Subsequently, KEEC formulates an equivariant value function equation in reinforcement learning on the equivariant geometry, ensuring an invariant effect as the value function on the original manifold. By deriving analytical-form optimal actions on the equivariant value function, KEEC theoretically achieves quadratic convergence for the optimal equivariant value function by leveraging the differential information on the equivariant geometry. The effectiveness of KEEC is demonstrated in challenging dynamical systems, including chaotic ones like Lorenz-63. Notably, our results show that isometric functions, which maintain the compactness and completeness of geometry while preserving metric and differential information, consistently outperform loss functions lacking these characteristics.
Can a star be smaller than a black hole of the same mass?

It is commonly believed that black holes are the smallest self-gravitating objects of the same mass in the Universe. Here, we demonstrate, in a subclass of higher-order pure gravities known as quasi-topological gravity, that by modifying general relativity (GR) to reduce the strength of gravity in strong-field regimes while keeping GR unchanged in weak-field regimes, it is possible for stars to collapse to radii less than $2M$ while still maintaining equilibrium between gravity and pressure gradients, leading to physically-reasonable neutron stars smaller in size than a black hole of the same mass. We present concrete solutions for such objects and discuss some of their observational consequences. These objects may furnish new avenues for understanding the nature of gravity in strong-field regimes and leave imprints on gravitational wave echoes from compact binary mergers. An observation of these imprints may constitute evidence for new physics beyond GR when effects of gravity in strong-field regimes are concerned.
A Review of Communicating Robot Learning during Human-Robot Interaction

For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot's internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this review we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot's learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multi-modal feedback systems and measures of human-robot interaction. We find that -- when learning and communication are developed together -- the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multi-modal interfaces, and measuring the resulting changes in human and robot behavior. See videos of our case study here: https://youtu.be/EXfQctqFzWs
GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians

We introduce GaussianAvatars, a new method to create photorealistic head avatars that are fully controllable in terms of expression, pose, and viewpoint. The core idea is a dynamic 3D representation based on 3D Gaussian splats that are rigged to a parametric morphable face model. This combination facilitates photorealistic rendering while allowing for precise animation control via the underlying parametric model, e.g., through expression transfer from a driving sequence or by manually changing the morphable model parameters. We parameterize each splat by a local coordinate frame of a triangle and optimize for explicit displacement offset to obtain a more accurate geometric representation. During avatar reconstruction, we jointly optimize for the morphable model parameters and Gaussian splat parameters in an end-to-end fashion. We demonstrate the animation capabilities of our photorealistic avatar in several challenging scenarios. For instance, we show reenactments from a driving video, where our method outperforms existing works by a significant margin.
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning

Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision. To reduce the labeling cost, pre-training on self-supervised pretext tasks has become a popular paradigm, but there is often a gap between the pre-trained model and downstream tasks, stemming from the divergence in their objectives. To bridge the gap, prompt learning has risen as a promising direction especially in few-shot settings, without the need to fully fine-tune the pre-trained model. While there has been some early exploration of prompt-based learning on graphs, they primarily deal with homogeneous graphs, ignoring the heterogeneous graphs that are prevalent in downstream applications. In this paper, we propose HGPROMPT, a novel pre-training and prompting framework to unify not only pre-training and downstream tasks but also homogeneous and heterogeneous graphs via a dual-template design. Moreover, we propose dual-prompt in HGPROMPT to assist a downstream task in locating the most relevant prior to bridge the gaps caused by not only feature variations but also heterogeneity differences across tasks. Finally, we thoroughly evaluate and analyze HGPROMPT through extensive experiments on three public datasets.
Level Set Learning for Poincaré Plots of Symplectic Maps

Many important qualities of plasma confinement devices can be determined via the Poincar\'e plot of a symplectic return map. These qualities include the locations of periodic orbits, magnetic islands, and chaotic regions of phase space. However, every evaluation of the magnetic return map requires solving an ODE, meaning a detailed Poincar\'e plot can be expensive to create. Here, we propose a kernel-based method of learning a single labeling function that is approximately invariant under the symplectic map. From the labeling function, we can recover the locations of invariant circles, islands, and chaos with few evaluations of the underlying symplectic map. Additionally, the labeling function comes with a residual, which serves as a measure of how invariant the label function is, and therefore as an indirect measure of chaos and map complexity.
Self-consistent Conditions for 26Al Injection into a Protosolar Disk from a Nearby Supernova

The early solar system contained a short-lived radionuclide, 26Al (its half-life time t 1/2 = 0.7 Myr). The decay energy of 26Al is thought to have controlled the thermal evolution of planetesimals and, possibly, the water contents of planets. Many hypotheses have been proposed for the origin of 26Al in the solar system. One of the possible hypotheses is the “disk injection scenario”: when the protoplanetary disk of the solar system had already formed, a nearby (<1 pc) supernova injected radioactive material directly into the disk. Such a 26Al injection hypothesis has been tested so far with limited setups for disk structure and supernova distance, which have treated disk disruption and 26Al injection separately. Here, we revisit this problem, to investigate whether there are self-consistent conditions under which the surviving disk radius can receive enough 26Al to account for the abundance in the early solar system. We also consider a range of disk masses and structures, 26Al yields from supernova, and a large dust mass fraction η d. We find that 26Al yields of supernova are required as ≳ 2.1 × 10 − 3 M ⊙ ( η d / 0.2 ) − 1 , which are challenging to achieve with the known possible 26Al ejection and dust mass fraction ranges. Furthermore, we find that even if the above conditions are met, the supernova flow changes the disk temperature, which may not be consistent with the solar system record. Our results place a strong constraint on the disk injection scenario. Rather, we suggest that the fresh 26Al of the early solar system must have been synthesized/injected in other ways.
Prospects for probing small-scale dark matter models with pulsars around Sagittarius 
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Future observations with next-generation large-area radio telescopes are expected to discover radio pulsars (PSRs) closely orbiting around Sagittarius~A* (Sgr~A*), the supermassive black hole (SMBH) dwelling at our Galactic Center (GC). Such a system can provide a unique laboratory for testing General Relativity (GR), as well as the astrophysics around the GC. In this paper, we provide a numerical timing model for PSR-SMBH systems based on the post-Newtonian (PN) equation of motion, and use it to explore the prospects of measuring the black hole (BH) properties with pulsar timing. We further consider the perturbation caused by the dark matter (DM) distribution around Sgr~A*, and the possibility of constraining DM models with PSR-SMBH systems. Assuming a 5-year observation of a normal pulsar in an eccentric ($e=0.8$) orbit with an orbital period $P_b = 0.5\,$yr, we find that -- with weekly recorded times of arrival (TOAs) and a timing precision of 1 ms -- the power-law index of DM density distribution near the GC can be constrained to about 20%. Such a measurement is comparable to those measurements at the Galactic length scale but can reveal small-scale properties of the DM.
ADT: Agent-based Dynamic Thresholding for Anomaly Detection

The complexity and scale of IT systems are increasing dramatically, posing many challenges to real-world anomaly detection. Deep learning anomaly detection has emerged, aiming at feature learning and anomaly scoring, which has gained tremendous success. However, little work has been done on the thresholding problem despite it being a critical factor for the effectiveness of anomaly detection. In this paper, we model thresholding in anomaly detection as a Markov Decision Process and propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network. The proposed method can be integrated into many systems that require dynamic thresholding. An auto-encoder is utilized in this study to obtain feature representations and produce anomaly scores for complex input data. ADT can adjust thresholds adaptively by utilizing the anomaly scores from the auto-encoder and significantly improve anomaly detection performance. The properties of ADT are studied through experiments on three real-world datasets and compared with benchmarks, hence demonstrating its thresholding capability, data-efficient learning, stability, and robustness. Our study validates the effectiveness of reinforcement learning in optimal thresholding control in anomaly detection.
SymNoise: Advancing Language Model Fine-tuning with Symmetric Noise

In this paper, we introduce a novel fine-tuning technique for language models, which involves incorporating symmetric noise into the embedding process. This method aims to enhance the model's function by more stringently regulating its local curvature, demonstrating superior performance over the current method, NEFTune. When fine-tuning the LLaMA-2-7B model using Alpaca, standard techniques yield a 29.79% score on AlpacaEval. However, our approach, SymNoise, increases this score significantly to 69.04%, using symmetric noisy embeddings. This is a 6.7% improvement over the state-of-the-art method, NEFTune~(64.69%). Furthermore, when tested on various models and stronger baseline instruction datasets, such as Evol-Instruct, ShareGPT, OpenPlatypus, SymNoise consistently outperforms NEFTune. The current literature, including NEFTune, has underscored the importance of more in-depth research into the application of noise-based strategies in the fine-tuning of language models. Our approach, SymNoise, is another significant step towards this direction, showing notable improvement over the existing state-of-the-art method.
Regularity as Intrinsic Reward for Free Play

We propose regularity as a novel reward signal for intrinsically-motivated reinforcement learning. Taking inspiration from child development, we postulate that striving for structure and order helps guide exploration towards a subspace of tasks that are not favored by naive uncertainty-based intrinsic rewards. Our generalized formulation of Regularity as Intrinsic Reward (RaIR) allows us to operationalize it within model-based reinforcement learning. In a synthetic environment, we showcase the plethora of structured patterns that can emerge from pursuing this regularity objective. We also demonstrate the strength of our method in a multi-object robotic manipulation environment. We incorporate RaIR into free play and use it to complement the model's epistemic uncertainty as an intrinsic reward. Doing so, we witness the autonomous construction of towers and other regular structures during free play, which leads to a substantial improvement in zero-shot downstream task performance on assembly tasks.
New method for SISO strong stabilization with advantages over existing methods

We address stabilization of linear time-invariant (LTI), single-input single-output (SISO) systems in the Laplace domain, with a stable controller in a single feedback loop. Such stabilization is called strong. Plants that satisfy a parity interlacing property are known to be strongly stabilizable. Finding such controllers is a well known difficult problem. Existing general methods are based on either manual search or a clever use of Nevanlinna-Pick interpolation with polynomials of possibly high integer order. Here we present a new, simple, and general method for strongly stabilizing systems of relative degree less than 3. We call our method Real to Integers (RTI). Our theoretical contributions constitute proposing the functional form used, which involves a product of several terms of the form $\displaystyle \left ( \frac{s+a}{s+b} \right )^m$, showing that real $m$'s will arise whenever the plant is strongly stabilizable, and proving that integer $m$'s can be obtained by continuously varying free parameters (i.e., the $a$'s and $b$'s). Our practical contributions include demonstrating a simple way, based on a trigonometric trick, to adjust the fractional powers until they take reasonable integer values. We include brief but necessary associated discussion to make the paper accessible to a broad audience. We also present ten numerical examples of successful control design with varying levels of difficulty, including plants whose transfer functions have relative degrees of 0, 1 or 2; and with right half plane zeros of multiplicity possibly exceeding one.
Nonstandard finite difference methods preserving general quadratic Lyapunov functions

In this work, we consider a class of dynamical systems described by ordinary differential equations under the assumption that the global asymptotic stability (GAS) of equilibrium points is established based on the Lyapunov stability theory with the help of quadratic Lyapunov functions. We employ the Micken's methodology to construct a family of explicit nonstandard finite difference (NSFD) methods preserving any given quadratic Lyapunov function $V$, i.e. they admit $V$ as a discrete Lyapunov function. Here, the proposed NSFD methods are derived from a novel non-local approximation for the zero vector function. Through rigorous mathematical analysis, we show that the constructed NSFD methods have the ability to preserve any given quadratic Lyapunov functions regardless of the values of the step size. As an important consequence, they are dynamically consistent with respect to the GAS of continuous-time dynamical systems. On the other hand, the positivity of the proposed NSFD methods is investigated. It is proved that they can also preserve the positivity of solutions of continuous-time dynamical systems. Finally, the theoretical findings are supported by a series of illustrative numerical experiments, in which advantages of the NSFD methods are demonstrated.
SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images

Accurate identification and precise delineation of regions of significance, such as tumors or lesions, is a pivotal goal in medical imaging analysis. This paper proposes SPEEDNet, a novel architecture for precisely segmenting lesions within colonoscopy images. SPEEDNet uses a novel block named Dilated-Involutional Pyramidal Convolution Fusion (DIPC). A DIPC block combines the dilated involution layers pairwise into a pyramidal structure to convert the feature maps into a compact space. This lowers the total number of parameters while improving the learning of representations across an optimal receptive field, thereby reducing the blurring effect. On the EBHISeg dataset, SPEEDNet outperforms three previous networks: UNet, FeedNet, and AttesResDUNet. Specifically, SPEEDNet attains an average dice score of 0.952 and a recall of 0.971. Qualitative results and ablation studies provide additional insights into the effectiveness of SPEEDNet. The model size of SPEEDNet is 9.81 MB, significantly smaller than that of UNet (22.84 MB), FeedNet(185.58 MB), and AttesResDUNet (140.09 MB).
The Use of Cesium-137 Measurements of Soil Erosion and Farmers' Perceptions to Indicate Land Degradation amongst Shifting Cultivators in Northern Thailand

Upland agriculture in northern Thailand has been claimed to cause declining soil fertility and to lead to sedimentation and water shortages in the lowlands. Research was undertaken in a Yao community of Chiang Rai province to identify the extent of soil degradation and to what extent physical changes in soil properties were perceived by farmers. GIS was used to classify land susceptibility and the distribution of cultivation in historic times. Historic soil erosion was measured using the Cesium-137 technique. Farmers were questioned to identify the perceived importance of erosion. Results indicate that erosion of between 28 and 64 t ha' yroccurred on steep slopes during the period 1963-1991; these amounts are consistent with those measured on land underlain by granite elsewhere in the world. Farmers perceive erosion as a hazard and have generally avoided cultivating on steep slopes. In addition, much erosion apparently results from naturally-occurring gullies. Evidence therefore suggests that the influence of upland agriculture on erosion is overstated. However, frequent cultivation is still resulting in declining soil fertility in the uplands. R9SUMI Utilisation des mesures d'rosion du sol avec le cisium-137, et des perceptions des fermiers, pour l'ivaluation de la digradation des termrs liie a l'agriculture itinerante du Nord de la Thailande. L'agriculture des hautes terres du Nord de la Thailande est souvent considere ces valeurs sont coherentes avec celles obtenues pour des terres avec soubassement de granite situe diese Werte stimmen mit denen fiberein, die auf Landflichen mit Granituntergrund an anderen Plitzen der Welt gemessen wurden. Die Bauern vermeiden gew6hnlich die Bestellung auf steilen Hfingen, um der Erosionsgefahr zu entgehen. Erhebliche Erosion entsteht in naturgeschaffenen Rinnen. Das Beweismaterial liBt den SchluB zu, daB die von der Landwirtschaft in den Hochlanden hervorgerufene Erosion fiberbewertet wird. Fest steht, daB intensives Bestellen zu verminderter Bodenfruchtbarkeit in den Hochlanden fiihrt.
Online Dominating Set and Coloring for Geometric Intersection Graphs

We present online deterministic algorithms for minimum coloring and minimum dominating set problems in the context of geometric intersection graphs. We consider a graph parameter: the independent kissing number $\zeta$, which is a number equal to `the size of the largest induced star in the graph $-1$'. For a graph with an independent kissing number at most $\zeta$, we show that the famous greedy algorithm achieves an optimal competitive ratio of $\zeta$ for the minimum dominating set and the minimum independent dominating set problems. However, for the minimum connected dominating set problem, we obtain a competitive ratio of at most $2\zeta$. To complement this, we prove that for the minimum connected dominating set problem, any deterministic online algorithm has a competitive ratio of at least $2(\zeta-1)$ for the geometric intersection graph of translates of a convex object in $\mathbb{R}^2$. Next, for the minimum coloring problem, we obtain algorithms having a competitive ratio of $O\left({\zeta'}{\log m}\right)$ for geometric intersection graphs of bounded scaled $\alpha$-fat objects in $\mathbb{R}^d$ having widths in the interval $[1,m]$, where $\zeta'$ is the independent kissing number of the geometric intersection graph of bounded scaled $\alpha$-fat objects having widths in the interval $[1,2]$. Finally, we investigate the value of $\zeta$ for geometric intersection graphs of various families of geometric objects.