Miracle-dz/UniFAR-FTUs huggingface dataset:
Information thermodynamics of transition paths between multiple mesostates

A central concern across the natural sciences is a quantitative understanding of the mechanism governing rare transitions between two metastable states. Recent research has uncovered a fundamental equality between the time-reversal asymmetry of the ensemble of such transition paths and the informativeness of system dynamics about the reactivity of a given trajectory, immediately leading to quantitative criteria for judging the importance of distinct system coordinates for the transition. Here we generalize this framework to multiple mesostates. We find that the main system-wide and coordinate-specific results generalize intuitively, while the combinatorial diversity of pairwise transitions raises new questions and points to new concepts. This work increases the previous framework's generality and applicability and forges connections to enhanced-sampling and coarse-grained dynamical approaches such as milestoning and Markov-state models.
Study on Ecological Compensation Mechanism in the Development of Mineral Resources

With the rapid development of mineral resources in China, ecology compensation question appears day by day in the de-velopment of mineral resources. The article expounded some aspects about ecology compensation, such as connotation, theoretical princi-ple, basic principle, realistic demand, practical significance and domestic valuable experience derived from ecological compensation practice in recent years. The implementation standard, compensation mode and way of ecology compensation were further discussed, and the development and use of coal resources were taken as an example to discuss the standard system of ecology compensation specifically.
Heterogeneous structure and dynamics of water in a hydrated collagen microfibril

Collagen, type I in particular, is the protein of choice by Nature to design structural materials. Its hierarchical fibrillar structure confers to collagen its outstanding mechanical properties. Nonetheless, fibrillar collagen may rather be viewed as a composite material made of protein, macromolecules (such as glycosaminoglycans and proteoglycans) and water. Yet, the properties of water and the fine interactions of water with the protein constituent of these nanocomposites have only received limited attention. Here, we propose to investigate in-depth water structure and dynamics confined within the microfibril crystal structure of collagen type I to establish its impact on the properties of collagen. We found that the properties of water vary strongly with the level of hydration of the crystal, and spatially along the long axis of the crystal, namely moving from the so-called gap region to the so-called overlap region. In short, at low hydration, water acts as a glue between protein chains; while at high hydration, water acts as a lubricant. Beyond self-assembly and properties of fibrillar collagen, such heterogeneous structure and anisotropic dynamics may control its biomineralization and the properties of biological tissues such as bone.
Gravity-gauge Anomaly Constraints on the Energy-momentum Tensor

We derive constraints on the four dimensional energy-momentum tensor from gravitational and gauge anomalies. Our work can be considered an extension of Duff's analysis [1] to include parity-odd terms and explicit symmetry breaking. The constraints imply the absence of the parity-odd $R\tilde R$-term in a model-independent way. Remarkably, even in the case of explicit symmetry breaking the $\Box R$-anomaly is found to be finite and unambiguous after applying the symmetry constraints. We compute mixed gravity-gauge anomalies at leading order and deduce phenomenological consequences for vector bosons associated with global chiral symmetries.
Kaluza-Klein spectroscopy from neutron oscillations into hidden dimensions

Neutrons and neutrinos are natural probes for new physics. Since they carry no conserved gauge quantum numbers, both can easily mix with the fermions from hidden sectors. A particularly interesting effect is the oscillation of a neutron or a neutrino into a fermion propagating in large extra dimensions. In fact, such a mixing has been identified as the possible origin of small neutrino mass. In this paper, we study neutron oscillations into an extra-dimensional fermion and show that this effect provides a resonance imaging of the Kaluza-Klein tower. The remarkable feature of this phenomenon is its generic nature: because of a fine spacing of the Kaluza-Klein tower, neutrons at a variety of energy levels, both free or within nuclei, find a bulk oscillation partner. In particular, the partner can be a Kaluza-Klein mode of the same species that gives mass to the neutrino. The existence of bulk states matching the neutron energy levels of nuclear spectra gives rise to tight constraints as well as to potentially observable effects. For a free neutron, we predict recurrent resonant oscillations occurring with the values of the magnetic field correlated with the KK levels. We derive bounds on extra dimensions from ultra-cold neutron experiments and suggest signatures for refined measurements, which, in particular, can probe the parameter space motivated by the Hierarchy Problem. Ultra-cold neutron experiments offer a unique way of Kaluza-Klein spectroscopy.
Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC) - an end-to-end model for characterizing severity and diagnosis

Automated classification of cancer pathology reports can extract information from unstructured reports and categorize each report into structured diagnosis and severity categories. Thus, such system can reduce the burden for populating tumor registries, help registration for clinical trial as well as developing large dataset for deep learning model development using true pathologic ground truth. However, the content of breast pathology reports can be difficult for categorize due to the high linguistic variability in content and wide variety of potential diagnoses>50. Existing NLP models are primarily focused on developing classifier for primary breast cancer types (e.g. IDC, DCIS, ILC) and tumor characteristics, and ignore the rare diagnosis of cancer subtypes. We then developed a hierarchical hybrid transformer-based pipeline (59 labels) - Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC), which utilizes the potential of the transformer context-preserving NLP technique and compared our model to several state of the art ML and DL models. We trained the model on the EUH data and evaluated our model's performance on two external datasets - MGH and Mayo Clinic. We publicly release the code and a live application under Huggingface spaces repository
Zero-shot Building Attribute Extraction from Large-Scale Vision and Language Models

Existing building recognition methods, exemplified by BRAILS, utilize supervised learning to extract information from satellite and street-view images for classification and segmentation. However, each task module requires human-annotated data, hindering the scalability and robustness to regional variations and annotation imbalances. In response, we propose a new zero-shot workflow for building attribute extraction that utilizes large-scale vision and language models to mitigate reliance on external annotations. The proposed workflow contains two key components: image-level captioning and segment-level captioning for the building images based on the vocabularies pertinent to structural and civil engineering. These two components generate descriptive captions by computing feature representations of the image and the vocabularies, and facilitating a semantic match between the visual and textual representations. Consequently, our framework offers a promising avenue to enhance AI-driven captioning for building attribute extraction in the structural and civil engineering domains, ultimately reducing reliance on human annotations while bolstering performance and adaptability.
Rank-based Bayesian clustering via covariate-informed Mallows mixtures

Data in the form of rankings, ratings, pair comparisons or clicks are frequently collected in diverse fields, from marketing to politics, to understand assessors' individual preferences. Combining such preference data with features associated with the assessors can lead to a better understanding of the assessors' behaviors and choices. The Mallows model is a popular model for rankings, as it flexibly adapts to different types of preference data, and the previously proposed Bayesian Mallows Model (BMM) offers a computationally efficient framework for Bayesian inference, also allowing capturing the users' heterogeneity via a finite mixture. We develop a Bayesian Mallows-based finite mixture model that performs clustering while also accounting for assessor-related features, called the Bayesian Mallows model with covariates (BMMx). BMMx is based on a similarity function that a priori favours the aggregation of assessors into a cluster when their covariates are similar, using the Product Partition models (PPMx) proposal. We present two approaches to measure the covariate similarity: one based on a novel deterministic function measuring the covariates' goodness-of-fit to the cluster, and one based on an augmented model as in PPMx. We investigate the performance of BMMx in both simulation experiments and real-data examples, showing the method's potential for advancing the understanding of assessor preferences and behaviors in different applications.
The spectra of Laplace operators on covering simplicial complexes

In this paper, by the representation theory of symmetric group, we give a decomposition of the Laplace operator (in matrix form) of a covering simplicial complex into the direct sum of some matrices, including the Laplace operator of the underlying simplicial complex. So, the spectrum of a covering simplicial complex can be expressed into a union of the spectrum of the underlying simplicial complex and the spectra of some other matrices, which implies a result of Horak and Jost. In particular, we show that the spectrum of a $2$-fold covering simplicial complex is the union the that of the underlying simplicial complex and that of an incidence-signed simplicial complex, which is an analog of Bilu and Linial's result on graphs. Finally we show that the dimension of the cohomology group of a covering simplicial complex is greater than or equal to that of the underlying simplicial complex.
ODIN: Object Density Aware Index for CkNN Queries over Moving Objects on Road Networks

We study the problem of processing continuous k nearest neighbor (CkNN) queries over moving objects on road networks, which is an essential operation in a variety of applications. We are particularly concerned with scenarios where the object densities in different parts of the road network evolve over time as the objects move. Existing methods on CkNN query processing are ill-suited for such scenarios as they utilize index structures with fixed granularities and are thus unable to keep up with the evolving object densities. In this paper, we directly address this problem and propose an object density aware index structure called ODIN that is an elastic tree built on a hierarchical partitioning of the road network. It is equipped with the unique capability of dynamically folding/unfolding its nodes, thereby adapting to varying object densities. We further present the ODIN-KNN-Init and ODIN-KNN-Inc algorithms for the initial identification of the kNNs and the incremental update of query result as objects move. Thorough experiments on both real and synthetic datasets confirm the superiority of our proposal over several baseline methods.
Toda brackets in n-angulated categories

We introduce Toda brackets for n-angulated categories and show that the various definitions of Toda brackets coincide. We prove juggling formulas for these Toda brackets generalizing the triangulated case. Following that, we generalize a theorem due to Heller in the triangulated setting to the setting of n-angulated categories. We also provide several examples of computing Toda brackets for n-angulated categories. Finally, for an n-angulated category sitting in a triangulated category as in the setup of Geiss, Keller and Oppermann, we show that Toda brackets in the n-angulated sense coincide with n-fold Toda brackets in the triangulated sense up to an explicit sign.
A refinement of the argument of local realism versus quantum mechanics by algorithmic randomness

The notion of probability plays a crucial role in quantum mechanics. It appears in quantum mechanics as the Born rule. In modern mathematics which describes quantum mechanics, however, probability theory means nothing other than measure theory, and therefore any operational characterization of the notion of probability is still missing in quantum mechanics. In our former works [K. Tadaki, arXiv:1804.10174], based on the toolkit of algorithmic randomness, we presented a refinement of the Born rule, called the principle of typicality, for specifying the property of results of measurements in an operational way. In this paper, we make an application of our framework to the argument of local realism versus quantum mechanics for refining it, in order to demonstrate how properly our framework works in practical problems in quantum mechanics.
Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers

This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO. This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.
Impact of Correlations on the Modeling and Inference of Beyond Vacuum-GR Effects in Extreme-Mass-Ratio Inspirals

In gravitational-wave astronomy, extreme-mass-ratio-inspiral (EMRI) sources for the upcoming LISA observatory have the potential to serve as high-precision probes of astrophysical environments in galactic nuclei, and of potential deviations from general relativity (GR). Such ``beyond vacuum-GR'' effects are often modeled as perturbations to the evolution of vacuum EMRIs under GR. Previous studies have reported unprecedented constraints on these effects by examining the inference of one effect at a time. However, a more realistic analysis would require the simultaneous inference of multiple beyond vacuum-GR effects. The parameters describing such effects are generally significantly correlated with each other and the vacuum EMRI parameters. We explicitly show how these correlations remain even if any modeled effect is absent in the actual signal, and how they cause inference bias when any effect in the signal is absent in the analysis model. This worsens the overall measurability of the whole parameter set, challenging the constraints found by previous studies, and posing a general problem for the modeling and inference of beyond vacuum-GR effects in EMRIs.
Performance rating in chess, tennis, and other contexts

In this note, I introduce Estimated Performance Rating (PR$^e$), a novel system for evaluating player performance in sports and games. PR$^e$ addresses a key limitation of the Tournament Performance Rating (TPR) system, which is undefined for zero or perfect scores in a series of games. PR$^e$ is defined as the rating that solves an optimization problem related to scoring probability, making it applicable for any performance level. The main theorem establishes that the PR$^e$ of a player is equivalent to the TPR whenever the latter is defined. I then apply this system to historically significant win-streaks in association football, tennis, and chess. Beyond sports, PR$^e$ has broad applicability in domains where Elo ratings are used, from college rankings to the evaluation of large language models.
Unveiling Spaces: Architecturally meaningful semantic descriptions from images of interior spaces

There has been a growing adoption of computer vision tools and technologies in architectural design workflows over the past decade. Notable use cases include point cloud generation, visual content analysis, and spatial awareness for robotic fabrication. Multiple image classification, object detection, and semantic pixel segmentation models have become popular for the extraction of high-level symbolic descriptions and semantic content from two-dimensional images and videos. However, a major challenge in this regard has been the extraction of high-level architectural structures (walls, floors, ceilings windows etc.) from diverse imagery where parts of these elements are occluded by furniture, people, or other non-architectural elements. This project aims to tackle this problem by proposing models that are capable of extracting architecturally meaningful semantic descriptions from two-dimensional scenes of populated interior spaces. 1000 virtual classrooms are parametrically generated, randomized along key spatial parameters such as length, width, height, and door/window positions. The positions of cameras, and non-architectural visual obstructions (furniture/objects) are also randomized. A Generative Adversarial Network (GAN) for image-to-image translation (Pix2Pix) is trained on synthetically generated rendered images of these enclosures, along with corresponding image abstractions representing high-level architectural structure. The model is then tested on unseen synthetic imagery of new enclosures, and outputs are compared to ground truth using pixel-wise comparison for evaluation. A similar model evaluation is also carried out on photographs of existing indoor enclosures, to measure its performance in real-world settings.
Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores

The theoretical oscillation frequencies of even the best asteroseismic models of solar-like oscillators show significant differences from observed oscillation frequencies. Structure inversions seek to use these frequency differences to infer the underlying differences in stellar structure. While used extensively to study the Sun, structure inversion results for other stars have so far been limited. Applying sound speed inversions to more stars allows us to probe stellar theory over a larger range of conditions, as well as look for overall patterns that may hint at deficits in our current understanding. To that end, we present structure inversion results for 12 main-sequence solar-type stars with masses between 1 and 1.15 M ⊙. Our inversions are able to infer differences in the isothermal sound speed in the innermost 30% by radius of our target stars. In half of our target stars, the structure of our best-fit model fully agrees with the observations. In the remainder, the inversions reveal significant differences between the sound speed profile of the star and that of the model. We find five stars where the sound speed in the core of our stellar models is too low and one star showing the opposite behavior. For the two stars in which our inversions reveal the most significant differences, we examine whether changing the microphysics of our models improves them and find that changes to nuclear reaction rates or core opacities can reduce, but do not fully resolve, the differences.
LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces

Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces. To combine the controllability of VAE latent spaces with the state-of-the-art performance of recent large language models (LLMs), we present in this work LlaMaVAE, which combines expressive encoder and decoder models (sentenceT5 and LlaMA) with a VAE architecture, aiming to provide better text generation control to LLMs. In addition, to conditionally guide the VAE generation, we investigate a new approach based on flow-based invertible neural networks (INNs) named Invertible CVAE. Experimental results reveal that LlaMaVAE can outperform the previous state-of-the-art VAE language model, Optimus, across various tasks, including language modelling, semantic textual similarity and definition modelling. Qualitative analysis on interpolation and traversal experiments also indicates an increased degree of semantic clustering and geometric consistency, which enables better generation control.
A short note about the dynamical description of the measurement process in quantum physics

The measurement process of observables in a quantum system comes out to be an unsovable problem which started in the early times of the development of the theory. In the present note we consider the measured system part of an open system interacting with the measuring device and show under which ideal conditions the measure process may ideally work. Our procedure leads to the conclusion that there is no hope that any experimental procedure will be able to lead to a clean solution of the process, except maybe in very specific cases. The reasons for this situation are deeply rooted in the fundamental properties of quantum theory.
Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case study made those decisions, (iv) and identify existing gaps that could potentially be solved by the development of new methods. Overall, causal discovery is a promising approach for analyzing fMRI data, and multiple successful applications have indicated that it is superior to traditional fMRI functional connectivity methods, but current causal discovery methods for fMRI leave room for improvement.
DynaLay: An Introspective Approach to Dynamic Layer Selection for Deep Networks

Deep learning models have become increasingly computationally intensive, requiring extensive computational resources and time for both training and inference. A significant contributing factor to this challenge is the uniform computational effort expended on each input example, regardless of its complexity. We introduce \textbf{DynaLay}, an alternative architecture that features a decision-making agent to adaptively select the most suitable layers for processing each input, thereby endowing the model with a remarkable level of introspection. DynaLay reevaluates more complex inputs during inference, adjusting the computational effort to optimize both performance and efficiency. The core of the system is a main model equipped with Fixed-Point Iterative (FPI) layers, capable of accurately approximating complex functions, paired with an agent that chooses these layers or a direct action based on the introspection of the models inner state. The model invests more time in processing harder examples, while minimal computation is required for easier ones. This introspective approach is a step toward developing deep learning models that"think"and"ponder", rather than"ballistically'' produce answers. Our experiments demonstrate that DynaLay achieves accuracy comparable to conventional deep models while significantly reducing computational demands.