Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding

Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However, understanding fine-grained visual-linguistic concepts, such as attributes and inter-object relationships, remains a significant challenge. While several benchmarks aim to evaluate VLMs in finer granularity, their primary focus remains on the linguistic aspect, neglecting the visual dimension. Here, we highlight the importance of evaluating VLMs from both a textual and visual perspective. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects. Utilizing this data engine, we carefully design a benchmark, SPEC, to diagnose the comprehension of object size, position, existence, and count. Subsequently, we conduct a thorough evaluation of four leading VLMs on SPEC. Surprisingly, their performance is close to random guess, revealing significant limitations. With this in mind, we propose a simply yet effective approach to optimize VLMs in fine-grained understanding, achieving significant improvements on SPEC without compromising the zero-shot performance. Results on two additional fine-grained benchmarks also show consistent improvements, further validating the transferability of our approach.
Rigidity Results for large displacement quotients of mapping class groups

We consider quotients of mapping class groups of orientable, finite type surfaces by subgroups whose action on the curve graph has large displacement. This class includes quotients by the normal closure of a pseudo-Anosov element, the mapping class group itself, and in view of forthcoming work of Abbott, Berlyne, Ng, and Rasmussen, also random quotients. First, we show that every automorphism of the corresponding quotient of the curve graph is induced by a mapping class, thus generalising Ivanov's Theorem about automorphisms of the curve graph. Then we use this to prove quasi-isometric rigidity under additional assumptions, satisfied by all aforementioned quotients. In the process, we clarify a proof of quasi-isometric rigidity of mapping class groups by Behrstock, Hagen, and Sisto. Finally, we show that the outer automorphisms groups of our quotients, as well as their abstract commensurators, are"the smallest possible".
Photo-induced charge carrier dynamics in a semiconductor-based ion trap investigated via motion-sensitive qubit transitions

Ion trap systems built upon microfabricated chips have emerged as a promising platform for quantum computing to achieve reproducible and scalable structures. However, photo-induced charging of materials in such chips can generate undesired stray electric fields that disrupt the quantum state of the ion, limiting high-fidelity quantum control essential for practical quantum computing. While crude understanding of the phenomena has been gained heuristically over the past years, explanations for the microscopic mechanism of photo-generated charge carrier dynamics remains largely elusive. Here, we present a photo-induced charging model for semiconductors, whose verification is enabled by a systematic interaction between trapped ions and photo-induced stray fields from exposed silicon surfaces in our chip. We use motion-sensitive qubit transitions to directly characterize the stray field and analyze its effect on the quantum dynamics of the trapped ion. In contrast to incoherent errors arising from the thermal motion of the ion, coherent errors are induced by the stray field, whose effect is significantly imprinted during the quantum control of the ion. These errors are investigated in depth and methods to mitigate them are discussed. Finally, we extend the implications of our study to other photo-induced charging mechanisms prevalent in ion traps.
FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting

Novel view synthesis from limited observations remains an important and persistent task. However, high efficiency in existing NeRF-based few-shot view synthesis is often compromised to obtain an accurate 3D representation. To address this challenge, we propose a few-shot view synthesis framework based on 3D Gaussian Splatting that enables real-time and photo-realistic view synthesis with as few as three training views. The proposed method, dubbed FSGS, handles the extremely sparse initialized SfM points with a thoughtfully designed Gaussian Unpooling process. Our method iteratively distributes new Gaussians around the most representative locations, subsequently infilling local details in vacant areas. We also integrate a large-scale pre-trained monocular depth estimator within the Gaussians optimization process, leveraging online augmented views to guide the geometric optimization towards an optimal solution. Starting from sparse points observed from limited input viewpoints, our FSGS can accurately grow into unseen regions, comprehensively covering the scene and boosting the rendering quality of novel views. Overall, FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets, including LLFF, Mip-NeRF360, and Blender. Project website: https://zehaozhu.github. io/FSGS/.
Carrier density crossover and quasiparticle mass enhancement in a doped 5$d$ Mott insulator

High-temperature superconductivity in cuprates emerges upon doping the parent Mott insulator. Robust signatures of the low-doped electronic state include a Hall carrier density that initially tracks the number of doped holes and the emergence of an anisotropic pseudogap; the latter characterised by disconnected Fermi arcs, closure at a critical doping level $p^* \approx 0.19$, and, in some cases, a strongly enhanced carrier effective mass. In Sr$_2$IrO$_4$, a spin-orbit-coupled Mott insulator often regarded as a 5$d$ analogue of the cuprates, surface probes have revealed the emergence of an anisotropic pseudogap and Fermi arcs under electron doping, though neither the corresponding $p^*$ nor bulk signatures of pseudogap closing have as yet been observed. Here, we report electrical transport and specific heat measurements on Sr$_{2-x}$La$_x$IrO$_4$ over an extended doping range 0 $\leq x \leq$ 0.20. The effective carrier density $n_{\rm H}$ at low temperatures exhibits a crossover from $n_{\rm H} \approx x$ to $n_{\rm H} \approx 1+x$ near $x$ = 0.16, accompanied by \textcolor{blue}{a five-orders-of-magnitude increase in conductivity} and a six-fold enhancement in the electronic specific heat. These striking parallels in the bulk pseudogap phenomenology, coupled with the absence of superconductivity in electron-doped Sr$_2$IrO$_4$, disfavour the pseudogap as a state of precursor pairing and thereby narrow the search for the key ingredient underpinning the formation of the superconducting condensate in doped Mott insulators.
On two-dimensional Dirac operators with $\delta$-shell interactions supported on unbounded curves with straight ends

In this paper we study the self-adjointness and spectral properties of two-dimensional Dirac operators with electrostatic, Lorentz scalar, and anomalous magnetic $\delta$-shell interactions with constant weights that are supported on a smooth unbounded curve that is straight outside a compact set and whose ends are rays that are not parallel to each other. For all possible combinations of interaction strengths we describe the self-adjoint realizations and compute their essential spectra. Moreover, we prove in different situations the existence of geometrically induced discrete eigenvalues.
Merlin: Empowering Multimodal LLMs with Foresight Minds

Humans possess the remarkable ability to foresee the future to a certain extent based on present observations, a skill we term as foresight minds. However, this capability remains largely under explored within existing Multimodal Large Language Models (MLLMs), hindering their capacity to learn the fundamental principles of how things operate and the intentions behind the observed subjects. To address this issue, we introduce the integration of future modeling into the existing learning frameworks of MLLMs. By utilizing the subject trajectory, a highly structured representation of a consecutive frame sequence, as a learning objective, we aim to bridge the gap between the past and the future. We propose two innovative methods to empower MLLMs with foresight minds, Foresight Pre-Training (FPT) and Foresight Instruction-Tuning (FIT), which are inspired by the modern learning paradigm of LLMs. Specifically, FPT jointly training various tasks centered on trajectories, enabling MLLMs to learn how to attend and predict entire trajectories from a given initial observation. Then, FIT requires MLLMs to first predict trajectories of related objects and then reason about potential future events based on them. Aided by FPT and FIT, we build a novel and unified MLLM named Merlin that supports multi-images input and analysis about potential actions of multiple objects for the future reasoning. Experimental results show Merlin powerful foresight minds with impressive performance on both future reasoning and visual comprehension tasks.
LATE NEOGENE (LATE HEMPHILLIAN) RODENTS FROM THE PIPE CREEK SINKHOLE, GRANT COUNTY, INDIANA

Abstract Spermophilus cf. howelli, Spermophilus sp. , Castor or Dipoides sp. , Geomys cf. adamsi, Symmetrodontomys daamsi, sp. nov., Bensonomys hershkovitzi, sp. nov., Ogmodontomys pipecreekensis, sp. nov., and Pliophenacomys koenigswaldi n. sp. are reported from the Pipe Creek Sinkhole of Grant County, Indiana. The Geomys sample demonstrates a variable sulcus morphology of the upper incisor, suggesting it stands near the Geomys—Pappogeomys boundary. Symmetrodontomys daamsi is characterized by its short lower molars and small mandible. Bensonomys hershkovitzi has a unique m1, with an anteroconid that with little wear connects deeply with the labial cingulum, and a mesolophid. O. pipecreekensis, with relatively low dentine tracts and no enamel atoll on m1, may be related to both Ogmodontomys sawrockensis and Ogmodontomys poaphagus, although it is probably not ancestral to either of them. Ogmodontomys Hibbard is considered an endemic North American genus distinguished from Mimomys Forsyth Major on the basis of a suite of dental features, including absence of a full lamellar enamel layer on leading edges of the molar triangles. The dentition of Pliophenacomys koenigswaldi exhibits a set of features suggesting a close relationship with Pliophenacomys finneyi of the early Blancan Fox Canyon local fauna of Kansas. Together with the rhinoceros Teleoceras, the rodent assemblage collectively suggests an early Pliocene (latest Hemphillian) age for the Pipe Creek biota. This is the first report of a late Neogene rodent fauna from Indiana.
aeons: approximating the end of nested sampling

This paper presents analytic results on the anatomy of nested sampling, from which a technique is developed to estimate the run-time of the algorithm that works for any nested sampling implementation. We test these methods on both toy models and true cosmological nested sampling runs. The method gives an order-of-magnitude prediction of the end point at all times, forecasting the true endpoint within standard error around the halfway point.
Reconstructing patchy helium reionization using the cosmic microwave background and large-scale structure

The intergalactic helium became fully ionized by the end of cosmic noon ($z\sim2$). Similarly to the reionization of hydrogen, helium reionization is expected to be patchy, driven by luminous quasars that ionize the intergalactic gas in their surrounding environment. Probing the morphology of ionized electrons during this epoch can provide crucial information about early structure formation, including the clustering and luminosities of quasars, the accretion rates, variability, and lifetimes of active galactic nuclei, as well as the growth and evolution of supermassive black holes. In this study, we present how measurements of the cosmic microwave background (CMB) can be used to reconstruct the optical-depth fluctuations resulting from patchy helium reionization. As helium reionization occurred at lower redshifts, upcoming probes of large-scale structure surveys will present a significant opportunity to enhance the prospects of probing this epoch by their combined analysis with the CMB. Using a joint information-matrix analysis of hydrogen and helium reionization, we show that near-future galaxy and CMB surveys will have enough statistical power to detect optical-depth fluctuations due to doubly-ionized helium, providing a way of measuring the redshift and duration of helium reionization to high significance. We also show that modeling uncertainties in helium reionization can impact the measurement precision of parameters characterizing hydrogen reionization.
AdaptiveConformal: An R Package for Adaptive Conformal Inference

Conformal Inference (CI) is a popular approach for generating finite sample prediction intervals based on the output of any point prediction method when data are exchangeable. Adaptive Conformal Inference (ACI) algorithms extend CI to the case of sequentially observed data, such as time series, and exhibit strong theoretical guarantees without having to assume exchangeability of the observed data. The common thread that unites algorithms in the ACI family is that they adaptively adjust the width of the generated prediction intervals in response to the observed data. We provide a detailed description of five ACI algorithms and their theoretical guarantees, and test their performance in simulation studies. We then present a case study of producing prediction intervals for influenza incidence in the United States based on black-box point forecasts. Implementations of all the algorithms are released as an open-source R package, AdaptiveConformal, which also includes tools for visualizing and summarizing conformal prediction intervals.
Growth, population age structure, and aspects of the reproductive biology of snowy grouper, Epinephelus niveatus, off North Carolina and South Carolina

Sagittae (n=2,263) and gonads (n=870) from snowy grouper, Epinephelus niveatus, caught primarily with longlines, Kali poles, snapper reels, and chevron traps off North Carolina and South Carolina were examined 1) to compare growth rates, population age structure, and sex ratio between two periods 1979–85 and 1993–94, and 2) to determine reproductive seasonality, size and age at maturity, and size and age at sex change. There were several indications that the population off North Carolina and South Carolina is overfished: 1) size at age of specimens caught with longlines and snapper reels has increased noticeably since the 1980s (possibly a a density-dependent population response to a high level of fishing mortality); 2) 81% of the fish caught with commercial longlines during 1993–94 were ages 1–6, the majority (56%) of which were immature females; 3) the percentage of males in the population appears to have decreased significantly, from 7–23% in the 1970s and 1980s to 1% in the 1990s; and 4) mean length of fish landed in the longline fishery has steadily decreased from 65–80 cm in the early 1980s to 50–60 cm in the mid-1990s. There was a positive trend between water depth and total length in fishery-independent samples. Histological examination of gonads revealed that mature gonads were present in 4% of the females at age 3, 52% at age 5, 95% at age 7, and 100% at ages >7 during 1993–94. The smallest mature female was 469 mm TL, and the largest immature female was 575 mm. Estimates of Length50 and Age50 were 541 mm (95% CI=529–553 mm) and 4.92 yr (95% CI=4.65–5.21 yr), respectively. Spawning females were caught during April through September on the upper continental slope off South Carolina at depths of 176–232 m. The size (767–1090 mm) and age (8–29 yr) of 97 male specimens and the capture of two specimens undergoing sex change provided conclusive evidence that snowy grouper are protogynous hermaphrodites.
Beyond the Screen: Reshaping the Workplace with Virtual and Augmented Reality

Although extended reality technologies have enjoyed an explosion in popularity in recent years, few applications are effectively used outside the entertainment or academic contexts. This work consists of a literature review regarding the effective integration of such technologies in the workplace. It aims to provide an updated view of how they are being used in that context. First, we examine existing research concerning virtual, augmented, and mixed-reality applications. We also analyze which have made their way to the workflows of companies and institutions. Furthermore, we circumscribe the aspects of extended reality technologies that determined this applicability.
Split, Merge, and Refine: Fitting Tight Bounding Boxes via Over-Segmentation and Iterative Search

Achieving tight bounding boxes of a shape while guaranteeing complete boundness is an essential task for efficient geometric operations and unsupervised semantic part detection. But previous methods fail to achieve both full coverage and tightness. Neural-network-based methods are not suitable for these goals due to the non-differentiability of the objective, while classic iterative search methods suffer from their sensitivity to the initialization. We propose a novel framework for finding a set of tight bounding boxes of a 3D shape via over-segmentation and iterative merging and refinement. Our result shows that utilizing effective search methods with appropriate objectives is the key to producing bounding boxes with both properties. We employ an existing pre-segmentation to split the shape and obtain over-segmentation. Then, we apply hierarchical merging with our novel tightness-aware merging and stopping criteria. To overcome the sensitivity to the initialization, we also define actions to refine the bounding box parameters in an Markov Decision Process (MDP) setup with a soft reward function promoting a wider exploration. Lastly, we further improve the refinement step with Monte Carlo Tree Search (MCTS) based multi-action space exploration. By thoughtful evaluation on diverse 3D shapes, we demonstrate full coverage, tightness, and an adequate number of bounding boxes of our method without requiring any training data or supervision. It thus can be applied to various downstream tasks in computer vision and graphics.
Functional Renormalization Group Study of Thermodynamic Geometry Around the Phase Transition of Quantum Chromodynamic

We investigate the thermodynamic geometry of the quark-meson model at finite temperature, $T$, and quark number chemical potential, $\mu$. We extend previous works by the inclusion of fluctuations exploiting the functional renormalization group approach. We use recent developments to recast the flow equation into the form of an advection-diffusion equation. We adopt the local potential approximation for the effective average action. We focus on the thermodynamic curvature, $R$, in the $(\mu,T)$ plane, in proximity of the chiral crossover, up to the critical point of the phase diagram. We find that the inclusion of fluctuations results in a smoother behavior of $R$ near the chiral crossover. Moreover, for small $\mu$, $R$ remains negative, signaling the fact that bosonic fluctuations reduce the capability of the system to completely overcome the fermionic statistical repulsion of the quarks. We investigate in more detail the small $\mu$ region by analyzing a system in which we artificially lower the pion mass, thus approaching the chiral limit in which the crossover is actually a second order phase transition. On the other hand, as $\mu$ is increased and the critical point is approached, we find that $R$ is enhanced and a sign change occurs, in agreement with mean field studies. Hence, we completely support the picture that $R$ is sensitive to a crossover and a phase transition, and provides information about the effective behavior of the system at the phase transition.
Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning

The quality control of fetal sonographic (FS) images is essential for the correct biometric measurements and fetal anomaly diagnosis. However, quality control requires professional sonographers to perform and is often labor-intensive. To solve this problem, we propose an automatic image quality assessment scheme based on multi-task learning to assist in FS image quality control. An essential criterion for FS image quality control is that all the essential anatomical structures in the section should appear full and remarkable with a clear boundary. Therefore, our scheme aims to identify those essential anatomical structures to judge whether an FS image is the standard image, which is achieved by three convolutional neural networks. The Feature Extraction Network aims to extract deep level features of FS images. Based on the extracted features, the Class Prediction Network determines whether the structure meets the standard and Region Proposal Network identifies its position. The scheme has been applied to three types of fetal sections, which are the head, abdominal, and heart. The experimental results show that our method can make a quality assessment of an FS image within less a second. Also, our method achieves competitive performance in both the detection and classification compared with state-of-the-art methods.
A Dynamical Systems Model of the Dansgaard–Oeschger Oscillation and the Origin of the Bond Cycle

Abstract A low-dimensional dynamical systems model of the North Atlantic thermohaline circulation has been developed in order to better understand the mechanism underlying the so-called Dansgaard–Oeschger oscillation that is so clearly evident during the late glacial period (oxygen isotope stage 3) of the most recent ice-age cycle. The reduced system is designed to describe the evolution of the salinity distribution in this region that has previously been analyzed using both two- and three-dimensional models of the deep ocean circulation. The drastically simplified model described herein is shown to accurately represent the essence of the Dansgaard–Oeschger oscillation as this was previously revealed through detailed analyses performed with a model in which the deep circulation was described using a set of linked interacting two-dimensional (latitude–depth) basins. The authors’ analyses with the reduced dynamical system reinforce their previous contention that the Dansgaard–Oeschger oscillation is driven .
Influences of the underlying dimensions of destination image on destination loyalty in a cultural heritage destination

ABSTRACT The “cognitive-affective-conative” theory was used to explain the relationship between the destination image and loyalty to a cultural heritage site. After a systematic synthesis of 34 pertinent studies and in-depth interviews, the study applied structural equation modeling to a dataset of 424 respondents. This study identified three components of the destination image: cognitive, affective, and conative. The underlying components of the cognitive image were validated using a second-order measurement model, which discovered significant associations. The results revealed that the cognitive image influenced the affective image and destination loyalty. This study contributes to the theory of heritage destination image formation.