Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition

Spatial and temporal modeling is one of the most core aspects of few-shot action recognition. Most previous works mainly focus on long-term temporal relation modeling based on high-level spatial representations, without considering the crucial low-level spatial features and short-term temporal relations. Actually, the former feature could bring rich local semantic information, and the latter feature could represent motion characteristics of adjacent frames, respectively. In this paper, we propose SloshNet, a new framework that revisits the spatial and temporal modeling for few-shot action recognition in a finer manner. First, to exploit the low-level spatial features, we design a feature fusion architecture search module to automatically search for the best combination of the low-level and high-level spatial features. Next, inspired by the recent transformer, we introduce a long-term temporal modeling module to model the global temporal relations based on the extracted spatial appearance features. Meanwhile, we design another short-term temporal modeling module to encode the motion characteristics between adjacent frame representations. After that, the final predictions can be obtained by feeding the embedded rich spatial-temporal features to a common frame-level class prototype matcher. We extensively validate the proposed SloshNet on four few-shot action recognition datasets, including Something-Something V2, Kinetics, UCF101, and HMDB51. It achieves favorable results against state-of-the-art methods in all datasets.
Spin rotons and supersolids in binary antidipolar condensates

We present a theoretical study of a mixture of antidipolar and nondipolar Bose-Einstein condensates confined to an infinite tube. We predict the presence of a spin roton and its associated instability, which triggers a continuous unmodulated-to-supersolid phase transition. We characterize the phase diagram of the binary system, ranging from the quasi-1D to the radial Thomas-Fermi (elongated 3D) regimes. We also present the dynamic formation of supersolids following a quench from the uniform miscible phase, which maintains phase coherence across the system.
Convergence beyond the over-parameterized regime using Rayleigh quotients

In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centered on the notion of Rayleigh quotients in order to prove Kurdyka-{\L}ojasiewicz inequalities for a broader set of neural network architectures and loss functions. We show that Rayleigh quotients provide a unified view for several convergence analysis techniques in the literature. Our strategy produces a proof of convergence for various examples of parametric learning. In particular, our analysis does not require the number of parameters to tend to infinity, nor the number of samples to be finite, thus extending to test loss minimization and beyond the over-parameterized regime.
Nearly Computable Real Numbers

We call a sequence ( a n ) n of elements of a metric space nearly computably Cauchy if for every increasing computable function r : N → N the sequence ( d ( a r ( n + 1 ) , a r ( n ) ) ) n converges computably to 0. We show that there exists an increasing sequence of rational numbers that is nearly computably Cauchy and unbounded. Then we call a real number α nearly computable if there exists a computable sequence ( a n ) n of rational numbers that converges to α and is nearly computably Cauchy. It is clear that every computable real number is nearly computable, and it follows from a result by Downey and LaForte (Theoretical Computer Science 284 (2002) 539–555) that there exists a nearly computable and left-computable number that is not computable. We observe that the set of nearly computable real numbers is a real closed field and closed under computable real functions with open domain, but not closed under arbitrary computable real functions. Among other things we strengthen results by Hoyrup (Theory of Computing Systems 60 (2017) 396–420) and by Stephan and Wu (In New computational paradigms. First conference on computability in Europe, CiE 2005, Proceedings (2005) 461–469 Springer) by showing that any nearly computable real number that is not computable is weakly 1-generic (and, therefore, hyperimmune and not Martin-Löf random) and strongly Kurtz random (and, therefore, not K-trivial), and we strengthen a result by Downey and LaForte (Theoretical Computer Science 284 (2002) 539–555) by showing that no promptly simple set can be Turing reducible to a nearly computable real number.
Community-Based Projects in Applied Statistics

Community-based projects can enhance student understanding of descriptive statistics and of the use and value of inferential statistics. This article relates the authors' experiences with and reections on three semesters of elementary college-level statistics courses that included an optional service-learning component. Reasons to try community-based projects in mathematics, and suggestions for identifying and implementing them, are offered.
A Wasserstein-type metric for generic mixture models, including location-scatter and group invariant measures

In this article, we study Wasserstein-type metrics and corresponding barycenters for mixtures of a chosen subset of probability measures called atoms hereafter. In particular, this works extends what was proposed by Delon and Desolneux [A Wasserstein-Type Distance in the Space of Gaussian Mixture Models. SIAM J. Imaging Sci. 13, 936-970 (2020)] for mixtures of gaussian measures to other mixtures. We first prove in a general setting that for a set of atoms equipped with a metric that defines a geodesic space, the set of mixtures based on this set of atoms is also geodesic space for the defined modified Wasserstein metric. We then focus on two particular cases of sets of atoms: (i) the set of location-scatter atoms and (ii) the set of measures that are invariant with respect to some symmetry group. Both cases are particularly relevant for various applications among which electronic structure calculations. Along the way, we also prove some sparsity and symmetry properties of optimal transport plans between measures that are invariant under some well-chosen symmetries.
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning

In cross-domain few-shot learning, the core issue is that the model trained on source domains struggles to generalize to the target domain, especially when the domain shift is large. Motivated by the observation that the domain shift between training tasks and target tasks usually can reflect in their style variation, we propose Task Augmented Meta-Learning (TAML) to conduct style transfer-based task augmentation to improve the domain generalization ability. Firstly, Multi-task Interpolation (MTI) is introduced to fuse features from multiple tasks with different styles, which makes more diverse styles available. Furthermore, a novel task-augmentation strategy called Multi-Task Style Transfer (MTST) is proposed to perform style transfer on existing tasks to learn discriminative style-independent features. We also introduce a Feature Modulation module (FM) to add random styles and improve generalization of the model. The proposed TAML increases the diversity of styles of training tasks, and contributes to training a model with better domain generalization ability. The effectiveness is demonstrated via theoretical analysis and thorough experiments on two popular cross-domain few-shot benchmarks.
Symmetry classification of typical quantum entanglement

Entanglement entropy of typical quantum states, also known as the Page curve, plays an important role in quantum many-body systems and quantum gravity. However, little has hitherto been understood about the role of symmetry in quantum entanglement. Here, we establish the comprehensive classification of typical quantum entanglement for free fermions, or equivalently the quadratic Sachdev-Ye-Kitaev model with symmetry, on the basis of the tenfold fundamental symmetry classes of time reversal, charge conjugation, and chiral transformation. Through both analytical and numerical calculations of random matrix theory, we show that the volume-law contribution to average entanglement entropy is robust and remains unaffected by symmetry. Conversely, we uncover that the constant terms of the average and variance of entanglement entropy yield tenfold universal values unique to each symmetry class. These constant terms originate from the combination of a global scaling of the entanglement spectrum due to time-reversal symmetry and a singular peak at the center of the entanglement spectrum due to chiral or particle-hole symmetry. Our work elucidates the interplay of symmetry and entanglement in quantum physics and provides characterization of symmetry-enriched quantum chaos.
Parametrized stability and the universal property of global spectra

We develop a framework of parametrized semiadditivity and stability with respect to so-called atomic orbital subcategories of an indexing $\infty$-category $T$, extending work of Nardin. Specializing this framework, we introduce global $\infty$-categories and the notions of equivariant semiadditivity and stability, yielding a higher categorical version of the notion of a Mackey 2-functor studied by Balmer-Dell'Ambrogio. As our main result, we identify the free presentable equivariantly stable global $\infty$-category with a natural global $\infty$-category of global spectra for finite groups, in the sense of Schwede and Hausmann.
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applications

Contrastive learning is widely used for sentence representation learning. Despite this prevalence, most studies have focused exclusively on English and few concern domain adaptation for domain-specific downstream tasks, especially for low-resource languages like Japanese, which are characterized by insufficient target domain data and the lack of a proper training strategy. To overcome this, we propose a novel Japanese sentence representation framework, JCSE (derived from ``Contrastive learning of Sentence Embeddings for Japanese''), that creates training data by generating sentences and synthesizing them with sentences available in a target domain. Specifically, a pre-trained data generator is finetuned to a target domain using our collected corpus. It is then used to generate contradictory sentence pairs that are used in contrastive learning for adapting a Japanese language model to a specific task in the target domain. Another problem of Japanese sentence representation learning is the difficulty of evaluating existing embedding methods due to the lack of benchmark datasets. Thus, we establish a comprehensive Japanese Semantic Textual Similarity (STS) benchmark on which various embedding models are evaluated. Based on this benchmark result, multiple embedding methods are chosen and compared with JCSE on two domain-specific tasks, STS in a clinical domain and information retrieval in an educational domain. The results show that JCSE achieves significant performance improvement surpassing direct transfer and other training strategies. This empirically demonstrates JCSE's effectiveness and practicability for downstream tasks of a low-resource language.
Multi-target multi-camera vehicle tracking using transformer-based camera link model and spatial-temporal information

Multi-target multi-camera tracking (MTMCT) of vehicles, i.e. tracking vehicles across multiple cameras, is a crucial application for the development of smart city and intelligent traffic system. The main challenges of MTMCT of vehicles include the intra-class variability of the same vehicle and inter-class similarity between different vehicles and how to associate the same vehicle accurately across different cameras under large search space. Previous methods for MTMCT usually use hierarchical clustering of trajectories to conduct cross camera association. However, the search space can be large and does not take spatial and temporal information into consideration. In this paper, we proposed a transformer-based camera link model with spatial and temporal filtering to conduct cross camera tracking. Achieving 73.68% IDF1 on the Nvidia Cityflow V2 dataset test set, showing the effectiveness of our camera link model on multi-target multi-camera tracking.
DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies

We introduce an information-theoretic quantity with similar properties to mutual information that can be estimated from data without making explicit assumptions on the underlying distribution. This quantity is based on a recently proposed matrix-based entropy that uses the eigenvalues of a normalized Gram matrix to compute an estimate of the eigenvalues of an uncentered covariance operator in a reproducing kernel Hilbert space. We show that a difference of matrix-based entropies (DiME) is well suited for problems involving the maximization of mutual information between random variables. While many methods for such tasks can lead to trivial solutions, DiME naturally penalizes such outcomes. We compare DiME to several baseline estimators of mutual information on a toy Gaussian dataset. We provide examples of use cases for DiME, such as latent factor disentanglement and a multiview representation learning problem where DiME is used to learn a shared representation among views with high mutual information.
A Survey for Pulsating Hot B Subdwarfs in the Northern Hemisphere

We present the final results of a survey to search for pulsating hot B subdwarf (sdB) stars. We picked our candidates on the basis of their values of Teff and log g, as estimated from available quantitative optical spectroscopy. With the help of a three-channel photometer, we gathered "white-light" light curves using a uniform sampling time of 10 s for a total of 74 stars. Our survey had a yield of ~5.4%, leading to the discovery of detectable luminosity variations in four objects: PG 1047+003, KPD 2109+4401, KPD 1930+2752, and PG 0014+067. For the light curves of the other 70 stars, we derive upper limits on the amplitudes of possible periodic signals (in the period window 20-1000 s) that range from ~0.05% of the mean brightness of the star at an apparent magnitude of y ~ 12 to ~0.20% at y ~ 15. We use our survey results as well as those of others to discuss the location and extent of the empirical instability "strip" for pulsating sdB stars in the log g-Teff diagram. We also address the question of the coexistence of pulsators and nonvariable stars in the same region of that diagram.
Statistical Inference for Noisy Incomplete Binary Matrix

We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categorical matrix completion literature focuses on point estimation and prediction. This paper moves one step further toward the statistical inference for binary matrix completion. Under a popular nonlinear factor analysis model, we obtain a point estimator and derive its asymptotic normality. Moreover, our analysis adopts a flexible missing-entry design that does not require a random sampling scheme as required by most of the existing asymptotic results for matrix completion. Under reasonable conditions, the proposed estimator is statistically efficient and optimal in the sense that the Cramer-Rao lower bound is achieved asymptotically for the model parameters. Two applications are considered, including (1) linking two forms of an educational test and (2) linking the roll call voting records from multiple years in the United States Senate. The first application enables the comparison between examinees who took different test forms, and the second application allows us to compare the liberal-conservativeness of senators who did not serve in the Senate at the same time.
The throughput in multi-channel (slotted) ALOHA: large deviations and analysis of bad events

We consider ALOHA and slotted ALOHA protocols as medium access rules for a multi-channel message delivery system. Users decide randomly and independently with a minimal amount of knowledge about the system at random times to make a message emission attempt. We consider the two cases that the system has a fixed number of independent available channels, and that interference constraints make the delivery of too many messages at a time impossible. We derive probabilistic formulas for the most important quantities like the number of successfully delivered messages and the number of emission attempts, and we derive large-deviation principles for these quantities in the limit of many participants and many emission attempts. We analyse the rate functions and their minimizers and derive laws of large numbers for the throughput. We optimize it over the probability parameter. Furthermore, we are interested in questions like ``if the number of successfully delivered messages is significantly lower than the expectation, was the reason that too many or too few sending attempts were made? ''. Our main tools are basic tools from probability and the theory of (the probabilities of) large deviations.
Analysis of the Magnetic Effect on Entropy Generation in an Inclined Channel Partially Filled with a Porous Medium

The major objective of this work is to investigate the magnetic effect on heat-fluid and entropy generation interactions in a porous medium for a laminar, incompressible, non-Dracy model flow in an inclined channel. The flow field considered is composed of porous and clear viscous layers. The constant magnetic force is assumed to be acting parallel to the y-axis perpendicular to the walls. The governing equations related to flow and thermal fields, which are coupled and nonlinear, are solved for both clear fluid and porous regions by implementing the semi numerical-analytical techniques differential transform method (DTM) and generalized differential quadrature method (GDQM). While keeping the channel walls at different constant temperatures (isothermal walls), the influence of the applied magnetic field on velocity, temperature, and entropy generation are investigated and presented graphically with the corresponding physical interpretations. Additionally, the effect of dimensionless parameters such as the Hartmann number (Ha), formation factor (F), porous parameter (σ), Brinkman number (Br), and the angle of inclination (ϕ) on velocity and temperature fields are examined. The entropy generation (N s ) number for the physical system is derived and plotted using velocity and temperature profiles and dimensionless quantities. One of the main advantages of this study compared to similar studies is to give a straightforward open form solution by using DTM and GDQM. By applying these techniques it is possible to obtain a tractable and easily applicable recurative form of nonlinear field equations. In many similar studies it is said that the equations have been solved; however, neither solution procedure provides neither accuracy nor, even more important than these, clarity of applicability cases and limits of using the technique to the reader. In this work, these are presented in a simple way.
Temperature dissipation fluctuations in a turbulent boundary layer

All three components of the dissipation rate of the fluctuating temperature ϑ are measured simultaneously in the inner region of a fully developed turbulent boundary layer at a moderate Reynolds number. Measurements are made with a probe of four cold wires consisting of two closely spaced parallel vertical wires mounted a small distance upstream of two closely spaced parallel horizontal wires. In the inner region of the layer, local isotropy is not closely approximated (∂ϑ/∂z)2〉 (∂ϑ/∂y)2〉 (∂ϑ/∂x)2. The spectral density of the sum χ[= (∂ϑ/∂x)2+(∂ϑ/∂y)2+ (∂ϑ/∂z)2] is similar in shape to that of (∂ϑ/∂y)2 or (∂ϑ/∂z)2 but not as rich in high frequency content as that of (∂ϑ/∂x)2. The probability density of χ has a lower skewness and flatness factor and is more closely log‐normal than those of the individual components. This is true regardless of whether χ and its components are unaveraged or locally averaged over a linear dimension r. When averaging is applied, departures from log‐normality are diminished but .
An Isothermal Titration Calorimeter

A new isothermal titration calorimeter has been designed to facilitate the study of heats of reaction and heats of solution at constant temperature. With this calorimeter, endothermic or exothermic processes can be studied at temperatures at a given point in the calorimeter constant to ±0.0002 C°. Heat effects are compensated by balancing the heat effect of the process taking place in the calorimeter against a variable heater and a constant cooling Peltier device. The calorimeter was tested by measuring the heat of ionization of water and the heat of dilution of aqueous HClO4 solutions. For these systems heats were measured to an accuracy of ±0.02 cal.
Quality Control of Nitrogen Multiple Breath Washout in a Multicenter Pediatric Asthma Study

Abstract Background Nitrogen multiple breath washout (N2MBW) is a lung function test increasingly used in small airway diseases. Quality criteria have not yet been globally implemented and time-consuming retrospective overreading is necessary. Little data has been published on children with recurrent wheeze or asthma from multicentered studies. Methods Children with wheeze or asthma and healthy controls were included in the longitudinal All Age Asthma Cohort (ALLIANCE). To assess ventilation inhomogeneity, N2MBW tests were performed in five centers from 2013 until 2020. All N2MBW tests were centrally overread by one center. Multiple washout procedures (trials) at the visit concluded to one test occasion. Tests were accepted if trials were technically sound (started correctly, terminated correctly, no leak, regular breathing pattern) and repeatable within one test occasion. Signal misalignment was retrospectively corrected. Factors that may impact test quality were analyzed, such as experience level. Results N2MBW tests of n=561 participants were analyzed leading to n=949 (68.3%) valid tests of n=1,390 in total. Inter-center test acceptability ranged from 27.6% to 77.8%. End-of-test criterion and leak were identified to be the most common reasons for rejection. Data loss and uncorrectable signal misalignment led to rejection of 58% of trials in one center. In preschool children, significant improvement of test acceptability was found longitudinally (χ2(8)=18.6; p=0.02). Conclusion N2MBW is feasible in a multicenter asthma study in children. However, the quality of this time-consuming procedure is dependent on experience level of staff in preschool children and still requires retrospective overreading for all age groups.