Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of sex differences in pelvis and femur x‐ray images in British shorthair cats by geometric morphometric method

The study covers British Shorthair cats, which is a popular domestic cat breed in our country and defined as ‘brachycephalic’ in terms of skull structure. Article focuses on the pelvis and femur region of cats which gynaecological and orthopaedic diseases of this cat breed, which are frequently encountered in clinical practice, is found to be important. It is aimed to reveal the shape differences between male and female individuals using geometric morphometric method. X‐ray images were used to examine the pelvis and femurs of cats via geometric method. Radiological images were obtained from a total of 40 British Shorthair cats (20 females and 20 males) in two positions, ventro‐dorsal (VD) and latero‐lateral (LL), and shape differences between the sexes were examined. As result, it is seen that the measurement points taken from the males are more outward and have sharper angles than the females. Study also shows pelvis is a better choice than femur when it comes to sex discrimination. Although traditional morphometric methods are more useful in clinical practice, it is thought that is important to support or re‐evaluate the data with the geometric morphometric method in academic field for a further approach.
The First Karenitid (Therapsida, Therocephalia) from the Upper Permian of Gondwana and the Biogeography of Permo-Triassic Therocephalians

ABSTRACT Therocephalians were an ecologically diverse group of therapsids whose long stratigraphic record and widespread distribution during Permian and Triassic times make them important for understanding biogeographic patterns during a major faunal transition. Here, we describe a new therocephalian, Mupashi migrator gen. et sp. nov., from the upper Madumabisa Mudstone Formation (upper Permian) of Zambia's Luangwa Basin. The specimen has a long snout with a maxilla-vomerine contact on the hard palate, a high antecanine tooth count and numerous postcanines, and a conspicuous boss on the dentary angle whose structure is identical to that of the Russian baurioid Karenites. Additional endocranial anatomy, including palatal and braincase features, are revealed by high-resolution X-ray computed tomography and described in detail in light of the growing availability of tomographic data for therapsids. A phylogenetic analysis of 56 therapsid taxa and 136 cranial and postcranial characters recovers Mupashi as the sister taxon to Karenites, placing the pair between basal ictidosuchid-grade baurioids and the later Triassic bauriamorphs. Parsimony optimizations of geographic occurrences are ambiguous regarding the origination centers of therocephalian subclades. However, the patterns are suggestive of either (1) rapid, early dispersal events of Eutherocephalia and its major subgroups from a Laurasian center during the early-late Permian or, more likely, (2) within-province diversifications with occasional dispersal events occurring between provinces. Regardless, these associations strengthen the hypothesis that unknown but effective dispersal routes to high latitudes were available to therapsids and other tetrapods at least until early-late Permian times.
Derivation of sixth-order exponential Runge-Kutta methods for stiff systems

This work constructs the first-ever sixth-order exponential Runge--Kutta (ExpRK) methods for the time integration of stiff parabolic PDEs. First, we leverage the exponential B-series theory to restate the stiff order conditions for ExpRK methods of arbitrary order based on an essential set of trees only. Then, we explicitly provide the 36 order conditions required for sixth-order methods and present convergence results. In addition, we are able to solve the 36 stiff order conditions in both their weak and strong forms, resulting in two families of sixth-order parallel stages ExpRK schemes. Interestingly, while these new schemes require a high number of stages, they can be implemented efficiently similar to the cost of a 6-stage method. Numerical experiments are given to confirm the accuracy and efficiency of the new schemes.
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations

Self-supervised representation learning often uses data augmentations to induce some invariance to"style"attributes of the data. However, with downstream tasks generally unknown at training time, it is difficult to deduce a priori which attributes of the data are indeed"style"and can be safely discarded. To address this, we introduce a more principled approach that seeks to disentangle style features rather than discard them. The key idea is to add multiple style embedding spaces where: (i) each is invariant to all-but-one augmentation; and (ii) joint entropy is maximized. We formalize our structured data-augmentation procedure from a causal latent-variable-model perspective, and prove identifiability of both content and (multiple blocks of) style variables. We empirically demonstrate the benefits of our approach on synthetic datasets and then present promising but limited results on ImageNet.
On the area between a L\'evy process with secondary jump inputs and its reflected version

We study the the stochastic properties of the area under some function of the difference between (i) a spectrally positive L\'evy process $W_t^x$ that jumps to a level $x>0$ whenever it hits zero, and (ii) its reflected version $W_t$. Remarkably, even though the analysis of each of these processes is challenging, we succeed in attaining explicit expressions for their difference. The main result concerns the Laplace-Stieltjes transform of the integral $A_x$ of (a function of) the distance between $W_t^x$ and $W_t$ until $W_t^x$ hits zero. This result is extended in a number of directions, including the area between $A_x$ and $A_y$ and a Gaussian limit theorem. We conclude the paper with an inventory problem for which our results are particularly useful.
Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models

Abstract grammatical knowledge - of parts of speech and grammatical patterns - is key to the capacity for linguistic generalization in humans. But how abstract is grammatical knowledge in large language models? In the human literature, compelling evidence for grammatical abstraction comes from structural priming. A sentence that shares the same grammatical structure as a preceding sentence is processed and produced more readily. Because confounds exist when using stimuli in a single language, evidence of abstraction is even more compelling from crosslingual structural priming, where use of a syntactic structure in one language primes an analogous structure in another language. We measure crosslingual structural priming in large language models, comparing model behavior to human experimental results from eight crosslingual experiments covering six languages, and four monolingual structural priming experiments in three non-English languages. We find evidence for abstract monolingual and crosslingual grammatical representations in the models that function similarly to those found in humans. These results demonstrate that grammatical representations in multilingual language models are not only similar across languages, but they can causally influence text produced in different languages.
Correlation-aware active learning for surgery video segmentation

Semantic segmentation is a complex task that relies heavily on large amounts of annotated image data. However, annotating such data can be time-consuming and resource-intensive, especially in the medical domain. Active Learning (AL) is a popular approach that can help to reduce this burden by iteratively selecting images for annotation to improve the model performance. In the case of video data, it is important to consider the model uncertainty and the temporal nature of the sequences when selecting images for annotation. This work proposes a novel AL strategy for surgery video segmentation, COWAL, COrrelation-aWare Active Learning. Our approach involves projecting images into a latent space that has been fine-tuned using contrastive learning and then selecting a fixed number of representative images from local clusters of video frames. We demonstrate the effectiveness of this approach on two video datasets of surgical instruments and three real-world video datasets. The datasets and code will be made publicly available upon receiving necessary approvals.
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints

Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale data, so that training only on the subset practically performs on par with full data. Practitioners regularly desire to identify the smallest possible coreset in realistic scenes while maintaining comparable model performance, to minimize costs and maximize acceleration. Motivated by this desideratum, for the first time, we pose the problem of refined coreset selection, in which the minimal coreset size under model performance constraints is explored. Moreover, to address this problem, we propose an innovative method, which maintains optimization priority order over the model performance and coreset size, and efficiently optimizes them in the coreset selection procedure. Theoretically, we provide the convergence guarantee of the proposed method. Empirically, extensive experiments confirm its superiority compared with previous strategies, often yielding better model performance with smaller coreset sizes.
Social Bias Probing: Fairness Benchmarking for Language Models

Large language models have been shown to encode a variety of social biases, which carries the risk of downstream harms. While the impact of these biases has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, offering a constrained view of the nature of societal biases within language models. In this paper, we propose an original framework for probing language models for societal biases. We collect a probing dataset to analyze language models' general associations, as well as along the axes of societal categories, identities, and stereotypes. To this end, we leverage a novel perplexity-based fairness score. We curate a large-scale benchmarking dataset addressing drawbacks and limitations of existing fairness collections, expanding to a variety of different identities and stereotypes. When comparing our methodology with prior work, we demonstrate that biases within language models are more nuanced than previously acknowledged. In agreement with recent findings, we find that larger model variants exhibit a higher degree of bias. Moreover, we expose how identities expressing different religions lead to the most pronounced disparate treatments across all models.
Conservation Laws Reveal the Quantumness of Gravity

Adopting a general framework for quantum-classical dynamics, we analyze the interaction between quantum matter and a classical gravitational field. We point out that, assuming conservation of momentum or energy, and assuming that the dynamics obeys a particular decomposition property set out in the paper, the classical gravitational field cannot change the momentum or energy of the quantum system, whereas the quantum gravitational field can do so. Drawing upon the fundamental relationship between conservation laws and the quantum properties of objects, our analysis offers new perspectives for the study of quantum gravity and provides a novel interpretation of existing experimental observations, such as free fall.
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning

Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classes while alleviating the catastrophic forgetting. In the self-supervised manner, it becomes more challenging to avoid the conflict between the feature embedding spaces of novel classes and old ones without any class labels. To address the problem, we propose a self-supervised CIL framework CPPF, meaning Combining Past, Present and Future. In detail, CPPF consists of a prototype clustering module (PC), an embedding space reserving module (ESR) and a multi-teacher distillation module (MTD). 1) The PC and the ESR modules reserve embedding space for subsequent phases at the prototype level and the feature level respectively to prepare for knowledge learned in the future. 2) The MTD module maintains the representations of the current phase without the interference of past knowledge. One of the teacher networks retains the representations of the past phases, and the other teacher network distills relation information of the current phase to the student network. Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate that our proposed method boosts the performance of self-supervised class incremental learning. We will release code in the near future.
New Graph Decompositions and Combinatorial Boolean Matrix Multiplication Algorithms

We revisit the fundamental Boolean Matrix Multiplication (BMM) problem. With the invention of algebraic fast matrix multiplication over 50 years ago, it also became known that BMM can be solved in truly subcubic $O(n^\omega)$ time, where $\omega<3$; much work has gone into bringing $\omega$ closer to $2$. Since then, a parallel line of work has sought comparably fast combinatorial algorithms but with limited success. The naive $O(n^3)$-time algorithm was initially improved by a $\log^2{n}$ factor [Arlazarov et al.; RAS'70], then by $\log^{2.25}{n}$ [Bansal and Williams; FOCS'09], then by $\log^3{n}$ [Chan; SODA'15], and finally by $\log^4{n}$ [Yu; ICALP'15]. We design a combinatorial algorithm for BMM running in time $n^3 / 2^{\Omega(\sqrt[7]{\log n})}$ -- a speed-up over cubic time that is stronger than any poly-log factor. This comes tantalizingly close to refuting the conjecture from the 90s that truly subcubic combinatorial algorithms for BMM are impossible. This popular conjecture is the basis for dozens of fine-grained hardness results. Our main technical contribution is a new regularity decomposition theorem for Boolean matrices (or equivalently, bipartite graphs) under a notion of regularity that was recently introduced and analyzed analytically in the context of communication complexity [Kelley, Lovett, Meka; arXiv'23], and is related to a similar notion from the recent work on $3$-term arithmetic progression free sets [Kelley, Meka; FOCS'23].
Variational manifolds for ground states and scarred dynamics of blockade-constrained spin models on two and three dimensional lattices

We introduce a variational manifold of simple tensor network states for the study of a family of constrained models that describe spin-1/2 systems as realized by Rydberg atom arrays. Our manifold permits analytical calculation via perturbative expansion of one- and two-point functions in arbitrary spatial dimensions and allows for efficient computation of the matrix elements required for variational energy minimization and variational time evolution in up to three dimensions. We apply this framework to the PXP model on the hypercubic lattice in 1D, 2D, and 3D, and show that, in each case, it exhibits quantum phase transitions breaking the sub-lattice symmetry in equilibrium, and hosts quantum many body scars out of equilibrium. We demonstrate that our variational ansatz qualitatively captures all these phenomena and predicts key quantities with an accuracy that increases with the dimensionality of the lattice, and conclude that our method can be interpreted as a generalization of mean-field theory to constrained spin models.
Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual Speech

Recognizing emotions in spoken communication is crucial for advanced human-machine interaction. Current emotion detection methodologies often display biases when applied cross-corpus. To address this, our study amalgamates 16 diverse datasets, resulting in 375 hours of data across languages like English, Chinese, and Japanese. We propose a soft labeling system to capture gradational emotional intensities. Using the Whisper encoder and data augmentation methods inspired by contrastive learning, our method emphasizes the temporal dynamics of emotions. Our validation on four multilingual datasets demonstrates notable zero-shot generalization. We publish our open source model weights and initial promising results after fine-tuning on Hume-Prosody.
Co-ML: Collaborative Machine Learning Model Building for Developing Dataset Design Practices

Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect for data quality. To this end, we outline a set of four data design practices (DDPs) for designing inclusive ML models and share how we designed a tablet-based application called Co-ML to foster learning of DDPs through a collaborative ML model building experience. With Co-ML, beginners can build image classifiers through a distributed experience where data is synchronized across multiple devices, enabling multiple users to iteratively refine ML datasets in discussion and coordination with their peers. We deployed Co-ML in a 2-week-long educational AIML Summer Camp, where youth ages 13-18 worked in groups to build custom ML-powered mobile applications. Our analysis reveals how multi-user model building with Co-ML, in the context of student-driven projects created during the summer camp, supported development of DDPs including incorporating data diversity, evaluating model performance, and inspecting for data quality. Additionally, we found that students’ attempts to improve model performance often prioritized learnability over class balance. Through this work, we highlight how the combination of collaboration, model testing interfaces, and student-driven projects can empower learners to actively engage in exploring the role of data in ML systems.
Are Normalizing Flows the Key to Unlocking the Exponential Mechanism? A Path through the Accuracy-Privacy Ceiling Constraining Differentially Private ML

The state of the art and de facto standard for differentially private machine learning (ML) is differentially private stochastic gradient descent (DPSGD). Yet, the method is inherently wasteful. By adding noise to every gradient, it diminishes the overall privacy with every gradient step. Despite 15 years of fruitful research advancing the composition theorems, sub-sampling methods, and implementation techniques, adequate accuracy and privacy is often unattainable with current private ML methods. Meanwhile, the Exponential Mechanism (ExpM), designed for private optimization, has been historically sidelined from privately training modern ML algorithms primarily because ExpM requires sampling from a historically intractable density. Despite the recent discovery of Normalizing Flow models (NFs), expressive deep networks for approximating intractable distributions, ExpM remains in the background. Our position is that leveraging NFs to circumvent historic obstructions of ExpM is a potentially transformational solution for differentially private ML worth attention. We introduce a new training method, ExpM+NF, as a potential alternative to DPSGD, and we provide experiment with logistic regression and a modern deep learning model to test whether training via ExpM+NF is viable with"good"privacy parameters. Under the assumption that the NF output distribution is the ExpM distribution, we are able to achieve $\varepsilon$ a low as $1\mathrm{e}{-3}$ -- three orders of magnitude stronger privacy with similar accuracy. This work outlines a new avenue for advancing differentially private ML, namely discovering NF approximation guarantees. Code to be provided after review.
Scale-Free Chaos in the 2D Harmonically Confined Vicsek Model

Animal motion and flocking are ubiquitous nonequilibrium phenomena that are often studied within active matter. In examples such as insect swarms, macroscopic quantities exhibit power laws with measurable critical exponents and ideas from phase transitions and statistical mechanics have been explored to explain them. The widely used Vicsek model with periodic boundary conditions has an ordering phase transition but the corresponding homogeneous ordered or disordered phases are different from observations of natural swarms. If a harmonic potential (instead of a periodic box) is used to confine particles, then the numerical simulations of the Vicsek model display periodic, quasiperiodic, and chaotic attractors. The latter are scale-free on critical curves that produce power laws and critical exponents. Here, we investigate the scale-free chaos phase transition in two space dimensions. We show that the shape of the chaotic swarm on the critical curve reflects the split between the core and the vapor of insects observed in midge swarms and that the dynamic correlation function collapses only for a finite interval of small scaled times. We explain the algorithms used to calculate the largest Lyapunov exponents, the static and dynamic critical exponents, and compare them to those of the three-dimensional model.
Generalised eigenfunction expansion and singularity expansion methods for canonical time-domain wave scattering problems

The generalised eigenfunction expansion method (GEM) and the singularity expansion method (SEM) are applied to solve the canonical problem of wave scattering on an infinite stretched string in the time domain. The GEM, which is shown to be equivalent to d'Alembert's formula when no scatterer is present, is also derived in the case of a point-mass scatterer coupled to a spring. The discrete GEM, which generalises the discrete Fourier transform, is shown to reduce to matrix multiplication. The SEM, which is derived from the Fourier transform and the residue theorem, is also applied to solve the problem of scattering by the mass-spring system. The GEM and SEM are also applied to the problem of scattering by a mass positioned a fixed distance from an anchor point, which supports more complicated resonant behavior.
AdapterShadow: Adapting Segment Anything Model for Shadow Detection

Segment anything model (SAM) has shown its spectacular performance in segmenting universal objects, especially when elaborate prompts are provided. However, the drawback of SAM is twofold. On the first hand, it fails to segment specific targets, e.g., shadow images or lesions in medical images. On the other hand, manually specifying prompts is extremely time-consuming. To overcome the problems, we propose AdapterShadow, which adapts SAM model for shadow detection. To adapt SAM for shadow images, trainable adapters are inserted into the frozen image encoder of SAM, since the training of the full SAM model is both time and memory consuming. Moreover, we introduce a novel grid sampling method to generate dense point prompts, which helps to automatically segment shadows without any manual interventions. Extensive experiments are conducted on four widely used benchmark datasets to demonstrate the superior performance of our proposed method. Codes will are publicly available at https://github.com/LeipingJie/AdapterShadow.