Miracle-dz/UniFAR-FTUs huggingface dataset:
Aperture and Resolution Effects on Ultraviolet Star-forming Properties: Insights from Local Galaxies and Implications for High-redshift Observations*

We present an analysis of the effects of spectral resolution and aperture scale on derived galaxy properties using far-ultraviolet (FUV) spectra of local star-forming galaxies from the International Ultraviolet Explorer (R ∼ 250, field of view (FOV) ∼ 10″ × 20″) and Cosmic Origins Spectrograph on the Hubble Space Telescope (R ∼ 15,000, FOV ∼ 2.″5). Using these spectra, we measured FUV luminosities, spectral slopes, dust attenuation, and equivalent widths. We find that galaxies with one dominant stellar cluster have FUV properties that are independent of aperture size, while galaxies with multiple bright clusters are sensitive to the total light fraction captured by the aperture. Additionally, we find significant correlations between the strength of stellar and interstellar absorption lines and metallicity, indicating metallicity-dependent line-driven stellar winds and interstellar macroscopic gas flows shape stellar and interstellar spectral lines, respectively. The observed line strength versus metallicity relation of stellar-wind lines agrees with the prediction of population synthesis models for young starbursts. In particular, measurements of the strong stellar C iv λλ1548, 1550 line provide an opportunity to determine stellar abundances as a complement to gas-phase abundances. We provide a relation between the equivalent width of the C iv line and the oxygen abundance of the galaxy. We discuss this relation in terms of the stellar-wind properties of massive stars. As the driving lines in stellar winds are mostly ionized iron species, the C iv line may eventually offer a method to probe α-element-to-iron ratios in star-forming galaxies once consistent models with nonsolar abundance ratios are available. These results have important implications for the galaxy-scale, low-resolution observations of high-redshift galaxies from JWST (R ∼ 100–3500).
FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning

In this paper, we propose a real-world benchmark for studying robotic learning in the context of functional manipulation: a robot needs to accomplish complex long-horizon behaviors by composing individual manipulation skills in functionally relevant ways. The core design principles of our Functional Manipulation Benchmark (FMB) emphasize a harmonious balance between complexity and accessibility. Tasks are deliberately scoped to be narrow, ensuring that models and datasets of manageable scale can be utilized effectively to track progress. Simultaneously, they are diverse enough to pose a significant generalization challenge. Furthermore, the benchmark is designed to be easily replicable, encompassing all essential hardware and software components. To achieve this goal, FMB consists of a variety of 3D-printed objects designed for easy and accurate replication by other researchers. The objects are procedurally generated, providing a principled framework to study generalization in a controlled fashion. We focus on fundamental manipulation skills, including grasping, repositioning, and a range of assembly behaviors. The FMB can be used to evaluate methods for acquiring individual skills, as well as methods for combining and ordering such skills to solve complex, multi-stage manipulation tasks. We also offer an imitation learning framework that includes a suite of policies trained to solve the proposed tasks. This enables researchers to utilize our tasks as a versatile toolkit for examining various parts of the pipeline. For example, researchers could propose a better design for a grasping controller and evaluate it in combination with our baseline reorientation and assembly policies as part of a pipeline for solving multi-stage tasks. Our dataset, object CAD files, code, and evaluation videos can be found on our project website: https://functional-manipulation-benchmark.github. io
Prediction and experimental verification of field-enhanced molecular scattering in SEIRA spectroscopy

Field-enhanced molecular absorption is widely accepted as the origin of surface enhanced infrared absorption (SEIRA). Here, we present an interpretation of SEIRA in form of a scattering process that identifies molecular scattering as the quantity that is measured in SEIRA. Molecular scattering - so far assumed to be negligible - is found to be enhanced by an extraordinary 13 orders of magnitude chiefly due to interference with the incident field (in case of SEIRA extinction spectra) or with the direct antenna scattered field (in case of SEIRA scattering spectra). It is shown that interferometric field-enhanced molecular scattering alone fully describes the magnitude and shape of vibrational lines in SEIRA extinction and scattering spectra. The results are supported by a near-field study that revealed that field-enhanced molecular scattering can be measured, yields a dispersive line shape and scales with the fourth power of the local field enhancement. As an application, a SEIRA modality achieving up to 53% molecular contrast is demonstrated.
Survival Analysis of Young Triple-Negative Breast Cancer Patients

Breast cancer prognosis is crucial for effective treatment, with the disease more common in women over 40 years old but rare under 40 years old, where less than 5 percent of cases occur in the U.S. Studies indicate a worse prognosis in younger women, which varies by ethnicity. Breast cancers are classified based on receptors like estrogen, progesterone, and HER2. Triple-negative breast cancer (TNBC), lacking these receptors, accounts for about 15 percent of cases and is more prevalent in younger patients, often resulting in poorer outcomes. Nevertheless, the impact of age on TNBC prognosis remains unclear. Factors like age, race, tumor grade, size, and lymph node status are studied for their role in TNBC's clinical outcomes, but current research is inconclusive about age-related differences. This study uses SEER data set to examine the influence of younger age on survivability in TNBC patients, aiming to determine if age is a significant prognostic factor. Our experimental results on SEER dataset confirm the existing research reports that TNBC patients have worse prognosis compared to non-TNBC based on age. Our main goal was to investigate whether younger age has any significance on the survivability of TNBC patients. Experimental results do not show that younger age has any significance on the prognosis and survival rate of the TNBC patients
Continuous Time Continuous Space Homeostatic Reinforcement Learning (CTCS-HRRL) : Towards Biological Self-Autonomous Agent

Homeostasis is a biological process by which living beings maintain their internal balance. Previous research suggests that homeostasis is a learned behaviour. Recently introduced Homeostatic Regulated Reinforcement Learning (HRRL) framework attempts to explain this learned homeostatic behavior by linking Drive Reduction Theory and Reinforcement Learning. This linkage has been proven in the discrete time-space, but not in the continuous time-space. In this work, we advance the HRRL framework to a continuous time-space environment and validate the CTCS-HRRL (Continuous Time Continuous Space HRRL) framework. We achieve this by designing a model that mimics the homeostatic mechanisms in a real-world biological agent. This model uses the Hamilton-Jacobian Bellman Equation, and function approximation based on neural networks and Reinforcement Learning. Through a simulation-based experiment we demonstrate the efficacy of this model and uncover the evidence linked to the agent's ability to dynamically choose policies that favor homeostasis in a continuously changing internal-state milieu. Results of our experiments demonstrate that agent learns homeostatic behaviour in a CTCS environment, making CTCS-HRRL a promising framework for modellng animal dynamics and decision-making.
Algebraic solution of project scheduling problems with temporal constraints

New solutions for problems in optimal scheduling of activities in a project under temporal constraints are developed in the framework of tropical algebra, which deals with the theory and application of algebraic systems with idempotent operations. We start with a constrained tropical optimization problem that has an objective function represented as a vector form given by an arbitrary matrix, and that can be solved analytically in a closed but somewhat complicated form. We examine a special case of the problem when the objective function is given by a matrix of unit rank, and show that the solution can be sufficiently refined in this case, which results in an essentially simplified analytical form and reduced computational complexity of the solution. We exploit the obtained result to find complete solutions of project scheduling problems to minimize the project makespan and the maximum absolute deviation of start times of activities under temporal constraints. The constraint under consideration include ``start-start'', ``start-finish'' and ``finish-start'' precedence relations, release times, release deadlines and completion deadlines for activities. As an application, we consider optimal scheduling problems of a vaccination project in a medical centre.
Yielding and plasticity in amorphous solids

The physics of disordered media, from metallic glasses to colloidal suspensions, granular matter and biological tissues, offers difficult challenges because it often occurs far from equilibrium, in materials lacking symmetries and evolving through complex energy landscapes. Here, we review recent theoretical efforts to provide microscopic insights into the mechanical properties of amorphous media using approaches from statistical mechanics as unifying frameworks. We cover both the initial regime corresponding to small deformations, and the yielding transition marking a change between elastic response and plastic flow. We discuss the specific features arising for systems evolving near a jamming transition, and extend our discussion to recent studies of the rheology of dense biological and active materials.
Accurate Memory Kernel Extraction from Discretized Time Series Data

Memory effects emerge as a fundamental consequence of dimensionality reduction when low-dimensional observables are used to describe the dynamics of complex many-body systems. In the context of molecular dynamics (MD) data analysis, accounting for memory effects using the framework of the generalized Langevin equation (GLE) has proven efficient, accurate and insightful, particularly when working with high-resolution time series data. However, in experimental systems, high-resolution data is often unavailable, raising questions about the impact of the data resolution on the estimated GLE parameters. This study demonstrates that direct memory extraction remains accurate when the discretization time is below the memory time. To obtain memory functions reliably even when the discretization time exceeds the memory time, we introduce a Gaussian Process Optimization (GPO) scheme. This scheme minimizes the deviation of discretized two-point correlation functions between MD and GLE simulations and is able to estimate accurate memory kernels as long as the discretization time stays below the longest time scale in the data, typically the barrier crossing time.
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation

Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap between theory and practice leaving fundamental questions unanswered: Can data-related and model-related uncertainty really be separated in practice? Which components of an uncertainty method are essential for real-world performance? Which uncertainty method works well for which application? In this work, we link this research gap to a lack of systematic and comprehensive evaluation of uncertainty methods. Specifically, we identify three key pitfalls in current literature and present an evaluation framework that bridges the research gap by providing 1) a controlled environment for studying data ambiguities as well as distribution shifts, 2) systematic ablations of relevant method components, and 3) test-beds for the five predominant uncertainty applications: OoD-detection, active learning, failure detection, calibration, and ambiguity modeling. Empirical results on simulated as well as real-world data demonstrate how the proposed framework is able to answer the predominant questions in the field revealing for instance that 1) separation of uncertainty types works on simulated data but does not necessarily translate to real-world data, 2) aggregation of scores is a crucial but currently neglected component of uncertainty methods, 3) While ensembles are performing most robustly across the different downstream tasks and settings, test-time augmentation often constitutes a light-weight alternative. Code is at: https://github.com/IML-DKFZ/values
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition.
Interface Dynamics of Strongly interacting Binary Superfluids

Understanding the interface dynamics in non-equilibrium quantum systems remains a challenge. We study the interface dynamics of strongly coupled immiscible binary superfluids by using holographic duality. The full nonlinear evolution of the binary superfluids with a relative velocity shows rich nonlinear patterns toward quantum turbulence, which is reminiscent of the quantum Kelvin-Helmholtz instability. The wave number of the fast growing modes $k_0$ extracted from the interface pattern yields a non-monotonic dependence of the relative velocity, independent of the temperature and interaction. The value of $k_0$ first increases with the velocity difference and then decreases, which stands in sharp contrast to the results of mean-field theory described by the Gross-Pitaevskii equation and is confirmed by using the linear analyses on top of the stationary configuration. We uncover that the critical velocity associated with the maximum correspond to the case when the mean separation of vortices generated by interface instabilities becomes comparable to the vortex size, which could be a universal physical mechanism at strongly interacting superfluids and is directly testable in laboratory experiments.
Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting

Reconstructing an object from photos and placing it virtually in a new environment goes beyond the standard novel view synthesis task as the appearance of the object has to not only adapt to the novel viewpoint but also to the new lighting conditions and yet evaluations of inverse rendering methods rely on novel view synthesis data or simplistic synthetic datasets for quantitative analysis. This work presents a real-world dataset for measuring the reconstruction and rendering of objects for relighting. To this end, we capture the environment lighting and ground truth images of the same objects in multiple environments allowing to reconstruct the objects from images taken in one environment and quantify the quality of the rendered views for the unseen lighting environments. Further, we introduce a simple baseline composed of off-the-shelf methods and test several state-of-the-art methods on the relighting task and show that novel view synthesis is not a reliable proxy to measure performance. Code and dataset are available at https://github.com/isl-org/objects-with-lighting .
3D Human Pose Analysis via Diffusion Synthesis

Diffusion models have demonstrated remarkable success in generative modeling. In this paper, we propose PADS (Pose Analysis by Diffusion Synthesis), a novel framework designed to address various challenges in 3D human pose analysis through a unified pipeline. Central to PADS are two distinctive strategies: i) learning a task-agnostic pose prior using a diffusion synthesis process to effectively capture the kinematic constraints in human pose data, and ii) unifying multiple pose analysis tasks like estimation, completion, denoising, etc, as instances of inverse problems. The learned pose prior will be treated as a regularization imposing on task-specific constraints, guiding the optimization process through a series of conditional denoising steps. PADS represents the first diffusion-based framework for tackling general 3D human pose analysis within the inverse problem framework. Its performance has been validated on different benchmarks, signaling the adaptability and robustness of this pipeline.
Cluster categories for completed infinity-gons I: Categorifying triangulations

Paquette and Y{\i}ld{\i}r{\i}m recently introduced cluster-type categories for completed infinity-gons, which are discs with an infinite closed set of marked points on their boundary. They classify (weak) cluster-tilting subcategories, which turn out to be in bijection with very special triangulations of the disc. This is in contrast to Igusa and Todorov's earlier work in the uncompleted case, in which every triangulation corresponds to a weak cluster-tilting subcategory. In this paper, we replace the triangulated structure of Paquette and Y{\i}ld{\i}r{\i}m's category by an extriangulated substructure and prove that, with this structure, the weak cluster-tilting subcategories are once again in bijection with triangulations. We further show that functorial finiteness of such a subcategory is equivalent to a very mild condition on the triangulation. This condition also appears in \c{C}anak\c{c}{\i} and Felikson's study of infinite rank cluster algebras from Teichm\"uller theory. By comparison with the combinatorics of triangulations, we are also able to characterise when weak cluster-tilting subcategories can be mutated in this new extriangulated category.
Polygonal Sequence-driven Triangulation Validator: An Incremental Approach to 2D Triangulation Verification

Two-dimensional Delaunay triangulation is a fundamental aspect of computational geometry. This paper presents a novel algorithm that is specifically designed to ensure the correctness of 2D Delaunay triangulation, namely the Polygonal Sequence-driven Triangulation Validator (PSTV). Our research highlights the paramount importance of proper triangulation and the often overlooked, yet profound, impact of rounding errors in numerical computations on the precision of triangulation. The primary objective of the PSTV algorithm is to identify these computational errors and ensure the accuracy of the triangulation output. In addition to validating the correctness of triangulation, this study underscores the significance of the Delaunay property for the quality of finite element methods. Effective strategies are proposed to verify this property for a triangulation and correct it when necessary. While acknowledging the difficulty of rectifying complex triangulation errors such as overlapping triangles, these strategies provide valuable insights on identifying the locations of these errors and remedying them. The unique feature of the PSTV algorithm lies in its adoption of floating-point filters in place of interval arithmetic, striking an effective balance between computational efficiency and precision. This research sets a vital precedent for error reduction and precision enhancement in computational geometry.
Controllable distant interactions at bound state in the continuum

Distant interactions at arbitrary locations and their dynamic control are fundamentally important for realizing large-scale photonic and quantum circuits. Conventional approaches suffer from short coupling distance, poor controllability, fixed locations and low wavelength uniformity, significantly restricting the scalability of photonic and quantum networks. Here, we exploit the intrinsic advantages of optical bound state in the continuum (BIC) and demonstrate an all-in-one solution for dynamically controllable long-range interactions. BIC metasurface can support a series of finite-sized quasi-BIC microlasers at arbitrary locations. The quasi-BICs microlasers have the same wavelength and are inherently connected through BIC waveguide. Consequently, the coupling distances in experiment increase significantly from subwavelength to tens of micrometers. Such long-range interaction in BIC metasurface enables scaling to two-dimensional architectures and ultrafast control of internal laser actions, e.g., non-Hermitian zero-mode lasing and enhanced optical gain. This research shall facilitate the advancement of scalable and reconfigurable photonic networks.
Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence

Although data-driven artificial intelligence (AI) in medical image diagnosis has shown impressive performance in silico, the lack of interpretability makes it difficult to incorporate the"black box"into clinicians' workflows. To make the diagnostic patterns learned from data understandable by clinicians, we develop an interpretable model, knowledge-guided diagnosis model (KGDM), that provides a visualized reasoning process containing AI-based biomarkers and retrieved cases that with the same diagnostic patterns. It embraces clinicians' prompts into the interpreted reasoning through human-AI interaction, leading to potentially enhanced safety and more accurate predictions. This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness. The classification performance of KGDM is evaluated on a prospective validation dataset, an external testing dataset, and an publicly available testing dataset. The diagnostic odds ratios (DOR) of the interpreted AI-based biomarkers are effective, ranging from 3.011 to 35.233 and exhibit consistent diagnostic patterns with clinic experience. Moreover, a human-AI collaborative diagnosis test is conducted and the participants with collaboration achieved a performance exceeding that of both humans and AI. By synergistically integrating interpretability and interaction, this study facilitates the convergence of clinicians' expertise and data-driven intelligence. The promotion of inexperienced ophthalmologists with the aid of AI-based biomarkers, as well as increased AI prediction by intervention from experienced ones, demonstrate a promising diagnostic paradigm for infectious keratitis using KGDM, which holds the potential for extension to other diseases where experienced medical practitioners are limited and the safety of AI is concerned.