Miracle-dz/UniFAR-FTUs huggingface dataset:
Rational ellipticity of $G$-manifolds from their quotients

We prove that if a compact, simply connected Riemannian $G$-manifold $M$ has orbit space $M/G$ isometric to some other quotient $N/H$ with $N$ having zero topological entropy, then $M$ is rationally elliptic. This result, which generalizes most conditions on rational ellipticity, is a particular case of a more general result involving manifold submetries.
Gompf's cork and Heegaard Floer homology

Gompf showed that for $K$ in a certain family of double-twist knots, the swallow-follow operation makes $1/n$-surgery on $K \# -K$ into a cork boundary. We derive a general Floer-theoretic condition on $K$ under which this is the case. Our formalism allows us to produce many further examples of corks, partially answering a question of Gompf. Unlike Gompf's method, our proof does not rely on any closed 4-manifold invariants or effective embeddings, and also generalizes to other diffeomorphisms.
Cross Fertilizing Empathy from Brain to Machine as a Value Alignment Strategy

AI Alignment research seeks to align human and AI goals to ensure independent actions by a machine are always ethical. This paper argues empathy is necessary for this task, despite being often neglected in favor of more deductive approaches. We offer an inside-out approach that grounds morality within the context of the brain as a basis for algorithmically understanding ethics and empathy. These arguments are justified via a survey of relevant literature. The paper concludes with a suggested experimental approach to future research and some initial experimental observations.
A Census from JWST of Extreme Emission Line Galaxies Spanning the Epoch of Reionization in CEERS

We present a sample of 1165 extreme emission-line galaxies (EELGs) at 4<z<9 selected using James Webb Space Telescope (JWST) NIRCam photometry in the Cosmic Evolution Early Release Science (CEERS) program. We use a simple method to photometrically identify EELGs with Hb + [OIII] (combined) or Ha emission of observed-frame equivalent width EW>5000 AA. JWST/NIRSpec spectroscopic observations of a subset (34) of the photometrically selected EELGs validate our selection method: all spectroscopically observed EELGs confirm our photometric identification of extreme emission, including some cases where the SED-derived photometric redshifts are incorrect. We find that the medium-band F410M filter in CEERS is particularly efficient at identifying EELGs, both in terms of including emission lines in the filter and in correctly identifying the continuum between Hb + [OIII] and Ha in the neighboring broad-band filters. We present examples of EELGs that could be incorrectly classified at ultra-high redshift (z>12) as a result of extreme Hb + [OIII] emission blended across the reddest photometric filters. We compare the EELGs to the broader (sub-extreme) galaxy population in the same redshift range and find that they are consistent with being the bluer, high equivalent width tail of a broader population of emission-line galaxies. The highest-EW EELGs tend to have more compact emission-line sizes than continuum sizes, suggesting that active galactic nuclei are responsible for at least some of the most extreme EELGs. Photometrically inferred emission-line ratios are consistent with ISM conditions with high ionization and moderately low metallicity, consistent with previous spectroscopic studies.
An Online, Adaptive and Unsupervised Regression Framework with Drift Detection for Label Scarcity Contexts

In scenarios where obtaining real-time labels proves challenging, conventional approaches may result in sub-optimal performance. This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression. The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism to enable dynamic model adaptations in response to evolving patterns in the data. To enhance adaptability, we integrate the ADWIN (ADaptive WINdowing) algorithm with error generalization based on Root Mean Square Error (RMSE). ADWIN facilitates real-time drift detection, while RMSE provides a robust measure of model prediction accuracy. This combination enables our multivariate method to effectively navigate the challenges of streaming data, continuously adapting to changing patterns while maintaining a high level of predictive precision. Finally, we evaluate the performance of our multivariate method across various public datasets, comparing it to non-adapting baselines. Through comprehensive assessments, we demonstrate the superior efficacy of our adaptive regression technique for tasks where obtaining labels in real-time is a significant challenge. The results underscore the method's capacity to outperform traditional approaches and highlight its potential in scenarios characterized by label scarcity and evolving data patterns.
New High-Order Numerical Methods for Hyperbolic Systems of Nonlinear PDEs with Uncertainties

In this paper, we develop new high-order numerical methods for hyperbolic systems of nonlinear partial differential equations (PDEs) with uncertainties. The new approach is realized in the semi-discrete finite-volume framework and is based on fifth-order weighted essentially non-oscillatory (WENO) interpolations in (multidimensional) random space combined with second-order piecewise linear reconstruction in physical space. Compared with spectral approximations in the random space, the presented methods are essentially non-oscillatory as they do not suffer from the Gibbs phenomenon while still achieving high-order accuracy. The new methods are tested on a number of numerical examples for both the Euler equations of gas dynamics and the Saint-Venant system of shallow-water equations. In the latter case, the methods are also proven to be well-balanced and positivity-preserving.
The Global Asteroseismology Project Proof of Concept: Asteroseismology of Massive Stars with Continuous Ground-based Observations

Massive (≳8M ⊙) stars are the progenitors of many astrophysical systems, yet key aspects of their structure and evolution are poorly understood. Asteroseismology has the potential to solve these open puzzles; however, sampling both the short period pulsations and long period beat patterns of massive stars poses many observational challenges. Ground-based single-site observations require years or decades to discern the main oscillation modes. Multisite campaigns were able to shorten this time span, but have not been able to scale up to population studies on sample of objects. Space-based observations can achieve both continuous sampling and observe large numbers of objects; however, most lack the multiband data that is often necessary for mode identification and removing model degeneracies. Here, we develop and test a new ground-based observational strategy for discerning and identifying the main oscillation modes of a massive star in a few months, in a way that can be scaled to large samples. We do so using the Las Cumbres Observatory—a unique facility consisting of robotic, homogeneous telescopes operating as a global network, overcoming most of the challenges of previous multisite efforts, but presenting new challenges which we tailor our strategy to address. This work serves as the proof of concept for the Global Asteroseismology Project, which aims to move massive star asteroseismology from single-objects to bulk studies, unleashing its full potential in constraining stellar structure and evolution models. This work also demonstrates the ability of the Las Cumbres Observatory to perform multisite continuous observations for various science goals.
Early Dark Energy and Dark Photon Dark Matter from Waterfall Symmetry Breaking

We investigate a cosmological model wherein a waterfall symmetry breaking occurs during the radiation-dominated era. The model comprises a complex waterfall field, an axion field, and the gauge field (dark photon) generated through a tachyonic instability due to the Chern-Simons interaction. Prior to symmetry breaking, the total energy density incorporates a vacuum energy from the waterfall field, establishing a novel scenario for Early Dark Energy (EDE). Subsequent to the symmetry breaking, the dark photon dynamically acquires mass via the Higgs mechanism, potentially contributing to the dark matter abundance. Hence, our model can simultaneously address the $H_0$ tension and the origin of dark matter.
A non-perturbative second law of black hole mechanics in effective field theory

We describe a method for defining dynamical black hole entropy in gravitational effective field theories (EFTs). The entropy is constructed order by order in derivatives. For any fixed number of derivatives, the entropy satisfies a non-perturbative second law of black hole mechanics if the black hole remains within the regime of validity of EFT. In equilibrium the entropy reduces to the Wald entropy. It reduces to the entropy defined by Hollands et al in theories of vacuum gravity with up to 10 derivatives.
On the calculation of irregular solutions of the Schr\"odinger equation for non-spherical potentials

The irregular solutions of the stationary Schr\"odinger equation are important for the fundamental formal development of scattering theory. They are also necessary for the analytical properties of the Green function, which in practice can speed up calculations enormously. Despite these facts they are seldom considered in numerical treatments. The reason for this is their divergent behavior at the origin. This divergence demands high numerical precision that is difficult to achieve, in particular, for non-spherical potentials which lead to different divergence rates in the coupled angular momentum channels. Based on an unconventional treatment of boundary conditions, an integral-equation method is developed, which is capable to deal with this problem. The available precision is illustrated by electron-density calculations for NiTi in its monoclinic B19' structure.
GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time

The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP hierarchically partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance on large-scale routing problems, including TSP, ATSP, CVRP, and PCTSP. Our code is available at: https://github.com/henry-yeh/GLOP.
Morse predecomposition of an ivariant set

Motivated by the study of the recurrent orbits in a Morse set of a Morse decomposition, we introduce the concept of Morse predecomposition of an isolated invariant set in the setting of combinatorial and classical dynamical systems. We prove that a Morse predecomposition indexed by a poset is a Morse decomposition and we show how a Morse predecomposition may be condensed back to a Morse decomposition.
On the ground state of lattice Schr\"{o}dinger operators

We prove necessary and sufficient conditions for lattice Schr\"{o}dinger operators to have a zero energy bound state in arbitrary dimension. The two criteria are sharp, complementary, and depend crucially on both the dimension and asymptotic behaviour of the potential. The method relies on a discrete variant of Agmon's comparison principle which is also proven. Our results represent a discrete variant of the recent criteria obtained in the continuous setting by D. Hundertmark, M. Jex, and M. Lange [$\textit{Forum Mathematics, Sigma}$ $\textbf{11}$ (2023)].
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning

Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction relies on reliable depth estimation for multi-camera images. However, existing approaches exhaustively predict depths for every pixel without prioritizing objects, which are precisely the entities requiring detection in the 3D space. To this end, we propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector. First, a category-specific structural priors mining approach is proposed for enhancing the efficacy of monocular depth generation. Besides, a self-boosting learning strategy is further proposed to encourage the model to place more emphasis on challenging objects in computation-expensive temporal stereo matching. Together they provide advanced depth estimation results for high-quality BEV features construction, benefiting the ultimate 3D detection. The proposed method achieves state-of-the-art performances on the challenging nuScenes benchmark, and extensive experimental results demonstrate the effectiveness of our designs.
The pseudospectra of black holes in AdS

We study the stability of quasinormal modes (QNMs) in electrically charged black brane spacetimes that asymptote to AdS by means of the pseudospectrum. Methodologically, we adopt ingoing Eddington-Finkelstein coordinates to cast QNMs in terms of a generalised eigenvalue problem involving a non-selfadjoint operator; this simplifies the computation significantly in comparison with previous results in the literature. Our analysis reveals spectral instability for (neutral) scalar as well as gravitoelectric perturbations. This is significant as it contains information about the dual strongly coupled system via the gauge/gravity correspondence. Particular attention is given on the hydrodynamic modes, which are found to be the least unstable. In contrast with computations in hyperboloidal coordinates, we find that the pseudospectral contour lines cross to the upper half plane. This indicates the existence of pseudo-resonances as well as the possibility of transient instabilities. We also investigate the asymptotic structure of pseudospectral contour levels and we find remarkable universality across all sectors, persistent in the extremal limit.
High-density Electromyography for Effective Gesture-based Control of Physically Assistive Mobile Manipulators

Injury to the cervical spinal cord can cause quadriplegia, impairing muscle function in all four limbs. People with impaired hand function and mobility encounter significant difficulties in carrying out essential self-care and household tasks. Despite the impairment of their neural drive, their volitional myoelectric activity is often partially preserved. High-density electromyography (HDEMG) can detect this myoelectric activity, which can serve as control inputs to assistive devices. Previous HDEMG-controlled robotic interfaces have primarily been limited to controlling table-mounted robot arms. These have constrained reach capabilities. Instead, the ability to control mobile manipulators, which have no such workspace constraints, could allow individuals with quadriplegia to perform a greater variety of assistive tasks, thus restoring independence and reducing caregiver workload. In this study, we introduce a non-invasive wearable HDEMG interface with real-time myoelectric hand gesture recognition, enabling both coarse and fine control over the intricate mobility and manipulation functionalities of an 8 degree-of-freedom mobile manipulator. Our evaluation, involving 13 participants engaging in challenging self-care and household activities, demonstrates the potential of our wearable HDEMG system to profoundly enhance user independence by enabling non-invasive control of a mobile manipulator.
Improving parallel efficiency for asynchronous graph analytics using Gauss‐Seidel‐based matrix computation

Graph analytics is extensively used in big‐data applications such as social networks, web analysis, bio‐informatics, etc. Most graph processing frameworks adopt vertex‐centric model due to its ease of use and programming. However, when dealing with asynchronous graph analytics, frameworks based on vertex programming perform inefficiently. The reason is that first, vertex programming must guarantee the sequential consistency, which means frequent use of locks or atomic operations, and second, the algorithms are parallelized in vertex level and latent parallelism of the algorithms cannot be exploited. To improve parallel efficiency of asynchronous graph processing, the Gauss‐Seidel style algorithms in particular, this paper proposes a scheduling model using Gauss‐Seidel‐based matrix computation, which converts the vertex programming into two main matrix operations and then algorithms are parallelized by row and column vectors. Compared to vertex programming, our model parallelizes algorithms in a finer way to exploit more latent parallelism, while retains the ease‐of‐programming advantage of vertex programming. Instead of using locks to guarantee the sequential consistency, our model uses a hybrid synchronization policy to reduce serializability among threads and overheads of context switching. Furthermore, this model strengthens locality of the program. Experiment results show that our model outperforms vertex‐centric asynchronous frameworks in both performance and scalability. Moreover, it even surpasses the matrix‐based synchronous framework GraphMat with some non‐Gauss‐Seidel style algorithms.
Large Language Model Enhanced Multi-Agent Systems for 6G Communications

The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task requirements to LLMs by nature language. However, directly applying native LLMs in 6G encounters various challenges, such as a lack of private communication data and knowledge, limited logical reasoning, evaluation, and refinement abilities. Integrating LLMs with the capabilities of retrieval, planning, memory, evaluation and reflection in agents can greatly enhance the potential of LLMs for 6G communications. To this end, we propose a multi-agent system with customized communication knowledge and tools for solving communication related tasks using natural language, comprising three components: (1) Multi-agent Data Retrieval (MDR), which employs the condensate and inference agents to refine and summarize communication knowledge from the knowledge base, expanding the knowledge boundaries of LLMs in 6G communications; (2) Multi-agent Collaborative Planning (MCP), which utilizes multiple planning agents to generate feasible solutions for the communication related task from different perspectives based on the retrieved knowledge; (3) Multi-agent Evaluation and Reflecxion (MER), which utilizes the evaluation agent to assess the solutions, and applies the reflexion agent and refinement agent to provide improvement suggestions for current solutions. Finally, we validate the effectiveness of the proposed multi-agent system by designing a semantic communication system, as a case study of 6G communications.
Prompting LLMs with content plans to enhance the summarization of scientific articles

This paper presents novel prompting techniques to improve the performance of automatic summarization systems for scientific articles. Scientific article summarization is highly challenging due to the length and complexity of these documents. We conceive, implement, and evaluate prompting techniques that provide additional contextual information to guide summarization systems. Specifically, we feed summarizers with lists of key terms extracted from articles, such as author keywords or automatically generated keywords. Our techniques are tested with various summarization models and input texts. Results show performance gains, especially for smaller models summarizing sections separately. This evidences that prompting is a promising approach to overcoming the limitations of less powerful systems. Our findings introduce a new research direction of using prompts to aid smaller models.
MedYOLO: A Medical Image Object Detection Framework

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed Tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on commonly present medium and large-sized structures such as the heart, liver, and pancreas even without hyperparameter tuning. However, the models struggle with very small or rarely present structures.
A New Perspective On Denoising Based On Optimal Transport

In the standard formulation of the denoising problem, one is given a probabilistic model relating a latent variable $\Theta \in \Omega \subset \mathbb{R}^m \; (m\ge 1)$ and an observation $Z \in \mathbb{R}^d$ according to: $Z \mid \Theta \sim p(\cdot\mid \Theta)$ and $\Theta \sim G^*$, and the goal is to construct a map to recover the latent variable from the observation. The posterior mean, a natural candidate for estimating $\Theta$ from $Z$, attains the minimum Bayes risk (under the squared error loss) but at the expense of over-shrinking the $Z$, and in general may fail to capture the geometric features of the prior distribution $G^*$ (e.g., low dimensionality, discreteness, sparsity, etc.). To rectify these drawbacks, in this paper we take a new perspective on this denoising problem that is inspired by optimal transport (OT) theory and use it to propose a new OT-based denoiser at the population level setting. We rigorously prove that, under general assumptions on the model, our OT-based denoiser is well-defined and unique, and is closely connected to solutions to a Monge OT problem. We then prove that, under appropriate identifiability assumptions on the model, our OT-based denoiser can be recovered solely from information of the marginal distribution of $Z$ and the posterior mean of the model, after solving a linear relaxation problem over a suitable space of couplings that is reminiscent of a standard multimarginal OT (MOT) problem. In particular, thanks to Tweedie's formula, when the likelihood model $\{ p(\cdot \mid \theta) \}_{\theta \in \Omega}$ is an exponential family of distributions, the OT-based denoiser can be recovered solely from the marginal distribution of $Z$. In general, our family of OT-like relaxations is of interest in its own right and for the denoising problem suggests alternative numerical methods inspired by the rich literature on computational OT.