Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Active Learning on Heteroskedastic Distributions

Models that can actively seek out the best quality training data hold the promise of more accurate, adaptable, and efficient machine learning. Active learning techniques often tend to prefer examples that are the most difficult to classify. While this works well on homogeneous datasets, we find that it can lead to catastrophic failures when performed on multiple distributions with different degrees of label noise or heteroskedasticity. These active learning algorithms strongly prefer to draw from the distribution with more noise, even if their examples have no informative structure (such as solid color images with random labels). To this end, we demonstrate the catastrophic failure of these active learning algorithms on heteroskedastic distributions and propose a fine-tuning-based approach to mitigate these failures. Further, we propose a new algorithm that incorporates a model difference scoring function for each data point to filter out the noisy examples and sample clean examples that maximize accuracy, outperforming the existing active learning techniques on the heteroskedastic datasets. We hope these observations and techniques are immediately helpful to practitioners and can help to challenge common assumptions in the design of active learning algorithms.
Human alignment of neural network representations

Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms differ in numerous ways from those that give rise to human vision. In this paper, we investigate the factors that affect the alignment between the representations learned by neural networks and human mental representations inferred from behavioral responses. We find that model scale and architecture have essentially no effect on the alignment with human behavioral responses, whereas the training dataset and objective function both have a much larger impact. These findings are consistent across three datasets of human similarity judgments collected using two different tasks. Linear transformations of neural network representations learned from behavioral responses from one dataset substantially improve alignment with human similarity judgments on the other two datasets. In addition, we find that some human concepts such as food and animals are well-represented by neural networks whereas others such as royal or sports-related objects are not. Overall, although models trained on larger, more diverse datasets achieve better alignment with humans than models trained on ImageNet alone, our results indicate that scaling alone is unlikely to be sufficient to train neural networks with conceptual representations that match those used by humans.
Where Do We Go From Here? Guidelines For Offline Recommender Evaluation

Various studies in recent years have pointed out large issues in the offline evaluation of recommender systems, making it difficult to assess whether true progress has been made. However, there has been little research into what set of practices should serve as a starting point during experimentation. In this paper, we examine four larger issues in recommender system research regarding uncertainty estimation, generalization, hyperparameter optimization and dataset pre-processing in more detail to arrive at a set of guidelines. We present a TrainRec, a lightweight and flexible toolkit for offline training and evaluation of recommender systems that implements these guidelines. Different from other frameworks, TrainRec is a toolkit that focuses on experimentation alone, offering flexible modules that can be can be used together or in isolation. Finally, we demonstrate TrainRec's usefulness by evaluating a diverse set of twelve baselines across ten datasets. Our results show that (i) many results on smaller datasets are likely not statistically significant, (ii) there are at least three baselines that perform well on most datasets and should be considered in future experiments, and (iii) improved uncertainty quantification (via nested CV and statistical testing) rules out some reported differences between linear and neural methods. Given these results, we advocate that future research should standardize evaluation using our suggested guidelines.
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints

Offline reinforcement learning (RL) learns policies entirely from static datasets, thereby avoiding the challenges associated with online data collection. Practical applications of offline RL will inevitably require learning from datasets where the variability of demonstrated behaviors changes non-uniformly across the state space. For example, at a red light, nearly all human drivers behave similarly by stopping, but when merging onto a highway, some drivers merge quickly, efficiently, and safely, while many hesitate or merge dangerously. Both theoretically and empirically, we show that typical offline RL methods, which are based on distribution constraints fail to learn from data with such non-uniform variability, due to the requirement to stay close to the behavior policy to the same extent across the state space. Ideally, the learned policy should be free to choose per state how closely to follow the behavior policy to maximize long-term return, as long as the learned policy stays within the support of the behavior policy. To instantiate this principle, we reweight the data distribution in conservative Q-learning (CQL) to obtain an approximate support constraint formulation. The reweighted distribution is a mixture of the current policy and an additional policy trained to mine poor actions that are likely under the behavior policy. Our method, CQL (ReDS), is simple, theoretically motivated, and improves performance across a wide range of offline RL problems in Atari games, navigation, and pixel-based manipulation.
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

Recent incremental learning for action recognition usually stores representative videos to mitigate catastrophic forgetting. However, only a few bulky videos can be stored due to the limited memory. To address this problem, we propose FrameMaker, a memory-efficient video class-incremental learning approach that learns to produce a condensed frame for each selected video. Specifically, FrameMaker is mainly composed of two crucial components: Frame Condensing and Instance-Specific Prompt. The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage. By this means, FrameMaker enables a remarkable reduction in memory but keep enough information that can be applied to following incremental tasks. Experimental results on multiple challenging benchmarks, i.e., HMDB51, UCF101 and Something-Something V2, demonstrate that FrameMaker can achieve better performance to recent advanced methods while consuming only 20% memory. Additionally, under the same memory consumption conditions, FrameMaker significantly outperforms existing state-of-the-arts by a convincing margin.
Long-Range Repulsion Between Chromosomes in Mammalian Oocyte Spindles

During eukaryotic cell division, a microtubule-based structure called the spindle exerts forces on chromosomes, thereby organizing and segregating them Extensive work demonstrates that the forces acting parallel to the spindle axis, including those responsible for separating sister chromatids, are generated by microtubule polymerization and depolymerization, and molecular-motors. In contrast, little is known about the forces acting perpendicular to the spindle axis, which determine the configuration of chromosomes at the metaphase plate, and thus impact nuclear localization and rates of segregation errors. Here, we use quantitative live-cell microscopy to show that metaphase chromosomes are spatially anti-correlated in mouse oocyte spindles, indicating the existence of hitherto unknown long-range forces acting perpendicular to the spindle axis. We explain this observation by first demonstrating that the spindle's microtubule network behaves as a nematic liquid crystal, and then arguing that deformation of the nematic field around embedded chromosomes causes long-range repulsion between them. Our work highlights the surprising relevance of materials physics in understanding the structure, dynamics, and mechanics of cellular structures, and presents a novel and potentially generic mode of chromosome organization in large spindles.
Shifted combinatorial Hopf algebras from $K$-theory

In prior joint work with Lewis, we developed a theory of enriched set-valued $P$-partitions to construct a $K$-theoretic generalization of the Hopf algebra of peak quasisymmetric functions. Here, we situate this object in a diagram of six Hopf algebras, providing a shifted version of the diagram of $K$-theoretic combinatorial Hopf algebras studied by Lam and Pylyavskyy. This allows us to describe new $K$-theoretic analogues of the classical peak algebra. We also study the Hopf algebras generated by Ikeda and Naruse's $K$-theoretic Schur $P$- and $Q$-functions, as well as their duals. Along the way, we derive several product, coproduct, and antipode formulas and outline a number of open problems and conjectures.
Linear optimization over homogeneous matrix cones

A convex cone is homogeneous if its automorphism group acts transitively on the interior of the cone. Cones that are homogeneous and self-dual are called symmetric. Conic optimization problems over symmetric cones have been extensively studied, particularly in the literature on interior-point algorithms, and as the foundation of modelling tools for convex optimization. In this paper we consider the less well-studied conic optimization problems over cones that are homogeneous but not necessarily self-dual. We start with cones of positive semidefinite symmetric matrices with a given sparsity pattern. Homogeneous cones in this class are characterized by nested block-arrow sparsity patterns, a subset of the chordal sparsity patterns. Chordal sparsity guarantees that positive define matrices in the cone have zero-fill Cholesky factorizations. The stronger properties that make the cone homogeneous guarantee that the inverse Cholesky factors have the same zero-fill pattern. We describe transitive subsets of the cone automorphism groups, and important properties of the composition of log-det barriers with the automorphisms. Next, we consider extensions to linear slices of the positive semidefinite cone, and review conditions that make such cones homogeneous. An important example is the matrix norm cone, the epigraph of a quadratic-over-linear matrix function. The properties of homogeneous sparse matrix cones are shown to extend to this more general class of homogeneous matrix cones. We then give an overview of the algebraic theory of homogeneous cones due to Vinberg and Rothaus. A fundamental consequence of this theory is that every homogeneous cone admits a spectrahedral (linear matrix inequality) representation. We conclude by discussing the role of homogeneous structure in primal–dual symmetric interior-point methods, contrasting this with the well-developed algorithms for symmetric cones that exploit the strong properties of self-scaled barriers, and with symmetric primal–dual methods for general convex cones.
Achieving one-dimensionality with attractive fermions

In this article we discuss the accuracy of effective one-dimensional theories used to describe the behavior of ultracold atomic ensembles confined in quantum wires by a harmonic trap. We derive within a fully many-body approach the effective Hamiltonian describing this class of systems and we calculate the beyond-mean field corrections to the energy of the ground state arising from virtual transitions towards excited state of the confining potential. We find that, due to the Pauli principle, effective finite-range corrections are one of magnitude larger than effective three-body interactions.By comparing to exact solutions of the purely 1D problem, we conclude that a 1D effective theory provides a good description of the ground state of the system for a rather large range of interaction parameters.
Fast Adaptive Federated Bilevel Optimization

Bilevel optimization is a popular hierarchical model in machine learning, and has been widely applied to many machine learning tasks such as meta learning, hyperparameter learning and policy optimization. Although many bilevel optimization algorithms recently have been developed, few adaptive algorithm focuses on the bilevel optimization under the distributed setting. It is well known that the adaptive gradient methods show superior performances on both distributed and non-distributed optimization. In the paper, thus, we propose a novel adaptive federated bilevel optimization algorithm (i.e.,AdaFBiO) to solve the distributed bilevel optimization problems, where the objective function of Upper-Level (UL) problem is possibly nonconvex, and that of Lower-Level (LL) problem is strongly convex. Specifically, our AdaFBiO algorithm builds on the momentum-based variance reduced technique and local-SGD to obtain the best known sample and communication complexities simultaneously. In particular, our AdaFBiO algorithm uses the unified adaptive matrices to flexibly incorporate various adaptive learning rates to update variables in both UL and LL problems. Moreover, we provide a convergence analysis framework for our AdaFBiO algorithm, and prove it needs the sample complexity of $\tilde{O}(\epsilon^{-3})$ with communication complexity of $\tilde{O}(\epsilon^{-2})$ to obtain an $\epsilon$-stationary point. Experimental results on federated hyper-representation learning and federated data hyper-cleaning tasks verify efficiency of our algorithm.
Kinematic Disturbances in Optical Rotation Curves among 89 Virgo Disk Galaxies

For 89 galaxies, mostly spirals, in the Virgo Cluster region, we have obtained optical long-slit major-axis spectra of the ionized gas. We find the following: (1) One-half of the Virgo galaxies we observed have regular rotation patterns, while the other half exhibit kinematic disturbances ranging from mild to major. Velocity complexities are generally consistent with those resulting from tidal encounters or accretion events. Since kinematic disturbances are expected to fade within ∼109 yr, many Virgo galaxies have experienced several significant kinematic disturbances during their lifetimes. (2) There is no strong correlation of rotation curve complexity with Hubble type, with galaxy luminosity, with local galaxy density, or with H I deficiency. (3) A few Virgo galaxies have ionized gas of limited extent, with velocities exceptionally low for their luminosities. In these galaxies the gas must be not rotationally supported. (4) There is a remarkable difference in the distribution of galaxy systemic velocity for galaxies with regular rotation curves and galaxies with disturbed rotation curves. Galaxies with regular rotation patterns show a flat distribution with velocities ranging from V0 = -300 km s-1 to V0 = +2500 km s-1; galaxies with disturbed kinematics have a Gaussian distribution that peaks at V0 = +1172 ± 100 km s-1, close to the cluster mean velocity. This latter distribution is virtually identical to the distribution of systemic velocity for elliptical galaxies in Virgo. However, disturbed galaxies are less concentrated to the cluster core than are the ellipticals; those near the periphery have velocities closer to the mean cluster velocity. Thus, spirals with disturbed kinematics are preferentially on radial orbits, which bring them to the denser core, where tidal interactions are strong and/or more common. Because they spend much time near apocenter, we observe them near the cluster periphery. Some may be falling into the core for the first time. These observations suggest that for a nonvirialized cluster like Virgo, galaxies may encounter either local (nearby galaxies) or global (cluster-related) interactions. These interactions may alter the morphology of the galaxy and may also play a role in driving the Virgo Cluster toward dynamical equilibrium.
Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Predictions of thunderstorm‐related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard types. The model can utilize multiple data sources; we use data from weather radar, lightning detection, satellite visible/infrared imagery, numerical weather prediction and digital elevation models. We demonstrate the ability of the model to predict lightning, hail and heavy precipitation probabilistically on a 1 km resolution grid, with a temporal resolution of 5 min and lead times up to 60 min. Shapley values quantify the importance of the different data sources, showing that the weather radar products are the most important predictors for all three hazard types.
High-field quantum spin liquid transitions and angle-field phase diagram of Kitaev magnet $\alpha$-RuCl$_3$

The pursuit of quantum spin liquid (QSL) in the Kitaev honeycomb magnets has drawn intensive attention recently. In particular, $\alpha$-RuCl$_3$ has been widely recognized as a promising candidate for the Kitaev QSL. Although the compound exhibits an antiferromagnetic order under zero field, it is believed to be endowed with fractionalized excitations, and can be driven to the QSL phase by magnetic fields. Here, based on a realistic $K$-$J$-$\Gamma$-$\Gamma'$ model for $\alpha$-RuCl$_3$ [1], we exploit the exponential tensor renormalization group approach to explore the phase diagram of the compound under magnetic fields. We calculate the thermodynamic quantities, including the specific heat, Gr\"uneisen parameter, magnetic torque, and the magnetotropic susceptibility, etc, under a magnetic field with a tilting angle $\theta$ to the $c^*$-axis perpendicular to the honeycomb plane. We find an extended QSL in the angle-field phase diagram determined with thermodynamic responses. The gapless nature of such field-induced QSL is identified from the specific heat and entropy data computed down to very low temperatures. The present study provides guidance for future high-field experiments for the QSL in $\alpha$-RuCl$_3$ and other candidate Kitaev magnets.
Translators of Galaxy Morphology Indicators between Observation and Simulation

Based on the recent advancements in numerical simulations of galaxy formation, we anticipate the achievement of realistic models of galaxies in the near future. Morphology is the most basic and fundamental property of galaxies, yet observations and simulations still use different methods to determine galaxy morphology, making it difficult to compare them. We hereby perform a test on the recent NewHorizon simulation, which has spatial and mass resolutions that are remarkably high for a large-volume simulation, to resolve the situation. We generate mock images for the simulated galaxies using SKIRT, which calculates complex radiative transfer processes in each galaxy. We measure morphological and kinematic indicators using photometric and spectroscopic methods following observers’ techniques. We also measure the kinematic disk-to-total ratios using the Gaussian mixture model and assume that they represent the true structural composition of galaxies. We found that spectroscopic indicators such as V/σ and λ R closely trace the kinematic disk-to-total ratios. In contrast, photometric disk-to-total ratios based on the radial profile fitting method often fail to recover the true kinematic structure of galaxies, especially small ones. We provide translating equations between various morphological indicators.
Effects of syndication network on specialisation and performance of venture capital firms

The Chinese venture capital (VC) market is a young and rapidly expanding financial subsector. Gaining a deeper understanding of the investment behaviours of VC firms is crucial for the development of a more sustainable and healthier market and economy. Contrasting evidence supports that either specialisation or diversification helps to achieve a better investment performance. However, the impact of the syndication network is overlooked. Syndication network has a great influence on the propagation of information and trust. By exploiting an authoritative VC dataset of thirty-five-year investment information in China, we construct a joint-investment network of VC firms and analyse the effects of syndication and diversification on specialisation and investment performance. There is a clear correlation between the syndication network degree and specialisation level of VC firms, which implies that the well-connected VC firms are diversified. More connections generally bring about more information or other resources, and VC firms are more likely to enter a new stage or industry with some new co-investing VC firms when compared to a randomised null model. Moreover, autocorrelation analysis of both specialisation and success rate on the syndication network indicates that clustering of similar VC firms is roughly limited to the secondary neighbourhood. When analysing local clustering patterns, we discover that, contrary to popular beliefs, there is no apparent successful club of investors. In contrast, investors with low success rates are more likely to cluster. Our discoveries enrich the understanding of VC investment behaviours and can assist policymakers in designing better strategies to promote the development of the VC industry.
Perfect matchings in random sparsifications of Dirac hypergraphs

For all integers $n \geq k>d \geq 1$, let $m_{d}(k,n)$ be the minimum integer $D \geq 0$ such that every $k$-uniform $n$-vertex hypergraph $\mathcal H$ with minimum $d$-degree $\delta_{d}(\mathcal H)$ at least $D$ has an optimal matching. For every fixed integer $k \geq 3$, we show that for $n \in k \mathbb{N}$ and $p = \Omega(n^{-k+1} \log n)$, if $\mathcal H$ is an $n$-vertex $k$-uniform hypergraph with $\delta_{k-1}(\mathcal H) \geq m_{k-1}(k,n)$, then a.a. s. \ its $p$-random subhypergraph $\mathcal H_p$ contains a perfect matching ($m_{k-1}(k,n)$ was determined by R\"{o}dl, Ruci\'nski, and Szemer\'edi for all large $n \in k\mathbb N$). Moreover, for every fixed integer $d0$, we show that the same conclusion holds if $\mathcal H$ is an $n$-vertex $k$-uniform hypergraph with $\delta_d(\mathcal H) \geq m_{d}(k,n) + \gamma\binom{n - d}{k - d}$. Both of these results strengthen Johansson, Kahn, and Vu's seminal solution to Shamir's problem and can be viewed as"robust"versions of hypergraph Dirac-type results. In addition, we also show that in both cases above, $\mathcal H$ has at least $\exp((1-1/k)n \log n - \Theta (n))$ many perfect matchings, which is best possible up to a $\exp(\Theta(n))$ factor.
Low-Resource Music Genre Classification with Advanced Neural Model Reprogramming

Transfer learning (TL) approaches have shown promising re-sults when handling tasks with limited training data. However, considerable memory and computational resources are often required for ﬁne-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) clas-siﬁcation based on the concept of Neural Model Reprogram-ming (NMR) . NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR , to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classiﬁcation by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform ﬁne-tuning-based TL methods on a small genre classiﬁcation dataset.