Miracle-dz/UniFAR-FTUs huggingface dataset:
PG-Schema: Schemas for Property Graphs

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is anticipated that the second version of the GQL Standard will include a rich DDL. Aiming to inspire the development of GQL and enhance the capabilities of graph database systems, we propose PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Schema with flexible type definitions supporting multi-inheritance, as well as expressive constraints based on the recently proposed PG-Keys formalism. We provide the formal syntax and semantics of PG-Schema, which meet principled design requirements grounded in contemporary property graph management scenarios, and offer a detailed comparison of its features with those of existing schema languages and graph database systems.
Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.
Non-Coherent Over-the-Air Decentralized Gradient Descent

Decentralized Gradient Descent (DGD) is a popular algorithm used to solve decentralized optimization problems in diverse domains such as remote sensing, distributed inference, multi-agent coordination, and federated learning. Yet, executing DGD over wireless systems affected by noise, fading and limited bandwidth presents challenges, requiring scheduling of transmissions to mitigate interference and the acquisition of topology and channel state information -- complex tasks in wireless decentralized systems. This paper proposes a DGD algorithm tailored to wireless systems. Unlike existing approaches, it operates without inter-agent coordination, topology information, or channel state information. Its core is a Non-Coherent Over-The-Air (NCOTA) consensus scheme, exploiting a noisy energy superposition property of wireless channels. With a randomized transmission strategy to accommodate half-duplex operation, transmitters map local optimization signals to energy levels across subcarriers in an OFDM frame, and transmit concurrently without coordination. It is shown that received energies form a noisy consensus signal, whose fluctuations are mitigated via a consensus stepsize. NCOTA-DGD leverages the channel pathloss for consensus formation, without explicit knowledge of the mixing weights. It is shown that, for the class of strongly-convex problems, the expected squared distance between the local and globally optimum models vanishes with rate $\mathcal O(1/\sqrt{k})$ after $k$ iterations, with a proper design of decreasing stepsizes. Extensions address a broad class of fading models and frequency-selective channels. Numerical results on an image classification task depict faster convergence vis-\`a-vis running time than state-of-the-art schemes, especially in densely deployed networks.
General effective field theory of teleparallel gravity

We construct the effective field theory (EFT) of the teleparallel equivalent of general relativity (TEGR). Firstly, we present the necessary field redefinitions of the scalar field and the tetrads. Then we provide all the terms at next-to-leading-order, containing the torsion tensor and its derivatives, and derivatives of the scalar field, accompanied by generic scalar-field-dependent couplings, where all operators are suppressed by a scale Λ. Removing all redundant terms using the field redefinitions we result to the EFT of TEGR, which includes significantly more terms comparing to the EFT of general relativity (GR). Finally, we present an application in a cosmological framework. Interestingly enough, although GR and TEGR are completely equivalent at the level of classical equations, we find that their corresponding EFTs possess minor but non-zero differences. Hence, we do verify that at higher energies the excitation and the features of the extra degrees of freedom are slightly different in the two theories, thus making them theoretically distinguishable. Nevertheless, we mention that these differences are suppressed by the heavy mass scale Λ and thus it is not guaranteed that they could be measured in future experiments and observations.
Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss

In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added spatial tag for all situations is not always feasible, and regularization terms can be difficult to tune. Thus, we propose a new set of statistics for texture synthesis based on Sliced Wasserstein Loss, create a multi-scale method to synthesize textures without a user-added spatial tag, study the ability of our proposed method to capture long range constraints, and compare our results to other optimization-based, single texture synthesis algorithms.
Automorphisms and derivations of affine commutative and PI-algebras

We prove analogs of A. Selberg’s result for ﬁnitely generated sub- groups of Aut( A ) and of Engel’s theorem for subalgebras of Der( A ) for a ﬁnitely generated associative commutative algebra A over an associative commutative ring. We prove also an analog of the theorem of W. Burnside and I. Schur about locally ﬁniteness of torsion subgroups of Aut( A ).
Overfreezing Meets Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks

We study the generalization behavior of transfer learning of deep neural networks (DNNs). We adopt the over-parameterization perspective — featuring interpolation of the training data (i.e., approximately zero train error) and the double descent phenomenon — to explain the delicate effect of the transfer learning setting on generalization performance. We study how the generalization behavior of transfer learning is affected by the dataset size in the source and target tasks, the number of transferred layers that are kept frozen in the target DNN training, and the similarity between the source and target tasks. We show that the test error evolution during the target DNN training has a more signiﬁcant double descent effect when the target training dataset is sufﬁciently large with some label noise. In addition, a larger source training dataset can delay the arrival to interpolation and double descent peak in the target DNN training. Moreover, we demonstrate that the number of frozen layers can determine whether the transfer learning is effectively underparameterized or overparameterized and, in turn, this may affect the relative success or failure of learning. Speciﬁcally, we show that too many frozen layers may make a transfer from a less related source task better or on par with a transfer from a more related source task; we call this case overfreezing . We establish our results using image classiﬁcation experiments with the residual network (ResNet) and vision transformer (ViT) architectures.
ReInform: Selecting paths with reinforcement learning for contextualized link prediction

We propose to use reinforcement learning to inform transformer-based contextualized link prediction models by providing paths that are most useful for predicting the correct answer. This is in contrast to previous approaches, that either used reinforcement learning (RL) to directly search for the answer, or based their prediction on limited or randomly selected context. Our experiments on WN18RR and FB15k-237 show that contextualized link prediction models consistently outperform RL-based answer search, and that additional improvements (of up to 13.5% MRR) can be gained by combining RL with a link prediction model. The PyTorch implementation of the RL agent is available at https://github.com/marina-sp/reinform
Collisionless magnetohydrodynamics with gyrokinetic effects

Anisotropic magnetohydrodynamics equations, which also capture the dynamics of quasi-transverse small scales obeying the gyrokinetic ordering, are derived using fourth-rank moment closures, based on a refined description of linear Landau damping and finite Larmor radius (FLR) corrections. This “FLR-Landau fluid model” reproduces the dispersion relation of low-frequency waves, up to scales that, in the case of quasi-transverse kinetic Alfven waves, can be much smaller than the ion gyroradius. The mirror instability, which requires temperature anisotropy, is also captured, together with its quenching at small scales. This model that accurately reproduces the collisionless dissipation of low-frequency modes, should provide an efficient tool to simulate mesoscale turbulence in a magnetized collisionless plasma.
Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval

In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval. We investigate how to optimally combine multiple diverse textual and visual features into feature pairs that lead to generating multiple joint feature spaces, which encode text-video pairs into comparable representations. To learn these representations our proposed network architecture is trained by following a multiple space learning procedure. Moreover, at the retrieval stage, we introduce additional softmax operations for revising the inferred query-video similarities. Extensive experiments in several setups based on three large-scale datasets (IACC.3, V3C1, and MSR-VTT) lead to conclusions on how to best combine text-visual features and document the performance of the proposed network. Source code is made publicly available at: https://github.com/bmezaris/TextToVideoRetrieval-TtimesV
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models

Multivariate signals are prevalent in various domains, such as healthcare, transportation systems, and space sciences. Modeling spatiotemporal dependencies in multivariate signals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between sensors. To address these challenges, we propose representing multivariate signals as graphs and introduce GraphS4mer , a general graph neural network (GNN) architecture that captures both spatial and temporal dependencies in multivariate signals. Speciﬁcally, (1) we leverage Structured State Spaces model (S4), a state-of-the-art sequence model, to capture long-term temporal dependencies and (2) we propose a graph structure learning layer in GraphS4mer to learn dynamically evolving graph structures in the data. We evaluate our proposed model on three distinct tasks and show that GraphS4mer consistently improves over existing models, including (1) seizure detection from electroencephalography signals, outperforming a previous GNN with self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from polysomnography signals, a 4.1 points improvement in macro-F1 score compared to existing sleep staging models; and (3) traﬃc forecasting, reducing MAE by 8.8% compared to existing GNNs and by 1.4% compared to Transformer-based models.
Interior Structures and Tidal Heating in the TRAPPIST-1 Planets

With seven planets, the TRAPPIST-1 system has the largest number of exoplanets discovered in a single system so far. The system is of astrobiological interest, because three of its planets orbit in the habitable zone of the ultracool M dwarf. Assuming the planets are composed of non-compressible iron, rock, and H$_2$O, we determine possible interior structures for each planet. To determine how much tidal heat may be dissipated within each planet, we construct a tidal heat generation model using a single uniform viscosity and rigidity for each planet based on the planet's composition. With the exception of TRAPPIST-1c, all seven of the planets have densities low enough to indicate the presence of significant H$_2$O in some form. Planets b and c experience enough heating from planetary tides to maintain magma oceans in their rock mantles; planet c may have eruptions of silicate magma on its surface, which may be detectable with next-generation instrumentation. Tidal heat fluxes on planets d, e, and f are lower, but are still twenty times higher than Earth's mean heat flow. Planets d and e are the most likely to be habitable. Planet d avoids the runaway greenhouse state if its albedo is $\gtrsim$ 0.3. Determining the planet's masses within $\sim0.1$ to 0.5 Earth masses would confirm or rule out the presence of H$_2$O and/or iron in each planet, and permit detailed models of heat production and transport in each planet. Understanding the geodynamics of ice-rich planets f, g, and h requires more sophisticated modeling that can self-consistently balance heat production and transport in both rock and ice layers.
FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields

This paper presents the first significant work on directly predicting 3D face landmarks on neural radiance fields (NeRFs). Our 3D coarse-to-fine Face Landmarks NeRF (FLNeRF) model efficiently samples from a given face NeRF with individual facial features for accurate landmarks detection. Expression augmentation is applied to facial features in a fine scale to simulate large emotions range including exaggerated facial expressions (e.g., cheek blowing, wide opening mouth, eye blinking) for training FLNeRF. Qualitative and quantitative comparison with related state-of-the-art 3D facial landmark estimation methods demonstrate the efficacy of FLNeRF, which contributes to downstream tasks such as high-quality face editing and swapping with direct control using our NeRF landmarks. Code and data will be available. Github link: https://github.com/ZHANG1023/FLNeRF.
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation

Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward function of new tasks to infer the task objective, which is not realistic in many practical applications. To bridge this gap, we study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning. We develop a meta-RL algorithm that enables fast policy adaptation with preference-based feedback. The agent can adapt to new tasks by querying human's preference between behavior trajectories instead of using per-step numeric rewards. By extending techniques from information theory, our approach can design query sequences to maximize the information gain from human interactions while tolerating the inherent error of non-expert human oracle. In experiments, we extensively evaluate our method, Adaptation with Noisy OracLE (ANOLE), on a variety of meta-RL benchmark tasks and demonstrate substantial improvement over baseline algorithms in terms of both feedback efficiency and error tolerance.
Font Representation Learning via Paired-glyph Matching

Fonts can convey profound meanings of words in various forms of glyphs. Without typography knowledge, manually selecting an appropriate font or designing a new font is a tedious and painful task. To allow users to explore vast font styles and create new font styles, font retrieval and font style transfer methods have been proposed. These tasks increase the need for learning high-quality font representations. Therefore, we propose a novel font representation learning scheme to embed font styles into the latent space. For the discriminative representation of a font from others, we propose a paired-glyph matching-based font representation learning model that attracts the representations of glyphs in the same font to one another, but pushes away those of other fonts. Through evaluations on font retrieval with query glyphs on new fonts, we show our font representation learning scheme achieves better generalization performance than the existing font representation learning techniques. Finally on the downstream font style transfer and generation tasks, we confirm the benefits of transfer learning with the proposed method. The source code is available at https://github.com/junhocho/paired-glyph-matching.
Non-stationary resonant Alfvén surface waves in one-dimensional magnetic plasmas

This paper uses incompressible visco-resistive MHD to study the propagation of linear resonant waves in an inhomogeneous plasma. The background density and magnetic field are assumed to depend only on one spartial Cartesian coordinate, and the magnetic field is taken to be unidirectional and perpendicular to the direction of inhomogeneity. The equation that governs the component of the velocity normal to the plane formed by the direction of the inhomogeneity and the magnetic field is derived under the assumption that the coefficients of viscosity and resistivity are sufficiently small that dissipation of energy is confined to a narrow dissipative layer. The solutions to this equation are obtained in the form of decaying normal surface modes with wavelengths much larger than the characteristic scale of the inhomogeneity. The effect of non-stationarity inside the dissipative layer is taken into account, and valid solutions are found even when the ratio of the thickness of the dissipative layer to the inhomogeneity length scale is of the order of or smaller than the ratio of the inhoinogeneity length scale to the wavelength. These solutions are the generalization of the solutions obtained by Mok and Einaudi, which are only valid when the first ratio is much larger than the second. The rate of wave damping is shown to be independent of the values of the viscosity and the resistivity. However, the behaviour of the solutions in the dissipative layer depends strongly on the viscosity and the resistivity. In the case that the effect of dissipation dominates the effect of non-stationarity, the solutions behave in the dissipative layer as found by Mok and Einaudi. When the effect of dissipation is steadily decreased in comparison with the effect of nonstationarity, the solutions become more and more oscillatory, and their amplitudes grow very rapidly in the dissipative layer. Eventually, when nonstationarity dominates dissipation, the amplitudes of the solutions become so large in the dissipative layer in comparison with those outside the dissipative layer that practically all the energy of the perturbations is concentrated in the dissipative layer.
Graded local cohomology of modules over semigroup rings

We give a combinatorial description of local cohomology modules of a graded module over a semigroup ring, with support at the graded maximal ideal. This combinatorial framework yields Hochster-type formulas for the Hilbert series of such local cohomology modules in terms of the homology of finitely many polyhedral cell complexes. A Cohen--Macaulay criterion immediately follows. We also provide an alternative proof of a result of [18] characterizing Cohen--Macaulay affine semigroup rings.
A Bias-free Cosmological Analysis with Quasars Alleviating H 0 Tension

Cosmological models and their parameters are widely debated because of theoretical and observational mismatches of the standard cosmological model, especially the current discrepancy between the value of the Hubble constant, H 0, obtained by Type Ia supernovae (SNe Ia), and the cosmic microwave background radiation (CMB). Thus, considering high-redshift probes like quasars (QSOs), having intermediate redshifts between SNe Ia and CMB, is a necessary step. In this work, we use SNe Ia and the most updated QSO sample, reaching redshifts up to z ∼ 7.5, applying the Risaliti–Lusso QSO relation based on a nonlinear relation between ultraviolet and X-ray luminosities. We consider this relation both in its original form and corrected for selection biases and evolution in redshift through a reliable statistical method also accounting for the circularity problem. We also explore two approaches: with and without calibration on SNe Ia. We then investigate flat and nonflat standard cosmological models and a flat wCDM model, with a constant dark energy equation-of-state parameter w. Remarkably, when correcting for the evolution as a function of cosmology, we obtain closed constraints on Ω M using only noncalibrated QSOs. We find that considering noncalibrated QSOs combined with SNe Ia and accounting for the same correction, our results are compatible with a flat ΛCDM model with Ω M = 0.3 and H 0 = 70 km s−1 Mpc−1. Intriguingly, the H 0 values obtained are placed halfway between the one from SNe Ia and CMB, paving the way for new insights into the H 0 tension.
Remarks on E11 approach

We consider a few topics in E 11 approach to superstring/M-theory: even subgroups ( Z 2 orbifolds) of E n , n=11,10,9 and their connection to Kac-Moody algebras; EE 11 subgroup of E 11 and coincidence of one of its weights with the l 1 weight of E 11 , known to contain brane charges; possible form of supersymmetry relation in E 11 ; decomposition of l 1 w.r. t. the SO (10 , 10) and its square root at ﬁrst few levels; particle orbit of l 1 ⋉ E 11 . Possible relevance of coadjoint orbits method is noticed, based on a self-duality form of equations of motion in E 11 .
Continuous sparse domination and dimensionless weighted estimates for the Bakry Riesz vector

We present a fundamentally new proof of the dimensionless Lp boundedness of the Bakry Riesz vector on manifolds with bounded geometry. Our proof has the significant advantage that it allows for a much stronger conclusion than previous arguments, namely that of some new dimensionless weighted estimates with optimal exponent. Part of the importance of this task lies in the novelty of the techniques: we develop the self similarity argument known as sparse domination in the setting of uniformly integrable cadlag Hilbert space valued martingales and extend the domination to a process with infinite memory. We provide a range of optimal weighted estimates and weak type estimates for these stochastic processes. Previous geometric Riesz transform estimates relied on Bellman functions and did not provide this range of weighted estimates. The development of sparse domination in this probabilistic setting and its use for high dimensional problems is new.