Miracle-dz/UniFAR-FTUs huggingface dataset:
MoFusion: A Framework for Denoising-Diffusion-Based Motion Synthesis

Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can synthesise long, temporally plausible, and semantically accurate motions based on a range of conditioning contexts (such as music and text). We also present ways to introduce well-known kinematic losses for motion plausibility within the motion-diffusion framework through our scheduled weighting strategy. The learned latent space can be used for several interactive motion-editing applications like in-betweening, seed-conditioning, and text-based editing, thus, providing crucial abilities for virtual-character animation and robotics. Through comprehensive quantitative evaluations and a perceptual user study, we demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature. We urge the reader to watch our supplementary video at https://vcai.mpi-inf.mpg. de/projects/MoFusion/.
The X-ray spectra of galaxies. II - Average spectral properties and emission mechanisms

The Imaging Proportional Counter data in the Einstein database is used to study the X-ray spectra of normal galaxies. The X-ray emission temperature of spirals is found to be higher on the average than that of ellipticals. This supports the idea that accreting binaries are a major source of X-rays in spirals, while a hot interstellar medium (ISM) is present in ellipticals. The X-ray spectra of Sa galaxies are intermediate between those of ellipticals and spirals, suggesting that these galaxies contain hot gaseous emission as well as emission from accreting binaries. In E and SO galaxies the emission temperature becomes higher with a decreasing X-ray to optical luminosity ratio, which suggests that the emission of X-ray faint early-type galaxies consists of a large evolved stellar component, while the gaseous emission becomes dominant in X-ray brighter galaxies. The group with the lowest X-ray to optical ratio does not follow this trend; in these galaxies a very soft X-ray component, amounting to about half the total X-ray emission, is found in addition to the hard X-ray component. Possible explanations are integrated emission of M stars and a relatively cool ISM. A very soft component is also found in several spiral galaxies. This may indicate that some spirals contain hot gaseous components similar to those seen in NGC 253 and M82.
Implicit causality in GPT-2: a case study

This case study investigates the extent to which a language model (GPT-2) is able to capture native speakers’ intuitions about implicit causality in a sentence completion task. Study 1 reproduces earlier results (showing that the model’s surprisal values correlate with the implicit causality bias of the verb; Davis and van Schijndel 2021), and then examine the effects of gender and verb frequency on model performance. Study 2 examines the reasoning ability of GPT-2: Is the model able to produce more sensible motivations for why the subject VERBed the object if the verbs have stronger causality biases? For this study we took care to avoid human raters being biased by obscenities and disfluencies generated by the model.
On the Probability for Sub-Halo Detection through Quasar Image Splitting

Abstract Dark galaxies may in principle be detectable through strong-lensing image splitting of quasars on small angular scales (milliarcseconds or below). Here, we estimate the overall probabilities for such detections under the assumption that the quasars can be treated as point sources. Due to the very low probabilities derived, we conclude that it is currently not feasable to use this strategy to put the CDM predictions for the dark galaxy population to the test.
A Better Test of Choice Overload

Choice overload - by which larger choice sets are detrimental to a chooser's wellbeing - is potentially of great importance to the design of economic policy. Yet the current evidence on its prevalence is inconclusive. We argue that existing tests are likely to be underpowered and hence that choice overload may occur more often than the literature suggests. We propose more powerful tests based on richer data and characterization theorems for the Random Utility Model. These new approaches come with significant econometric challenges, which we show how to address. We apply our tests to new experimental data and find strong evidence of choice overload that would likely be missed using current approaches.
DP-RAFT: A Differentially Private Recipe for Accelerated Fine-Tuning

A major direction in differentially private machine learning is differentially private fine-tuning: pretraining a model on a source of"public data"and transferring the extracted features to downstream tasks. This is an important setting because many industry deployments fine-tune publicly available feature extractors on proprietary data for downstream tasks. In this paper, we carefully integrate techniques, both new and from prior work, to solve benchmark tasks in computer vision and natural language processing using differentially private fine-tuning. Our key insight is that by accelerating training with the choice of key hyperparameters, we can quickly drive the model parameters to regions in parameter space where the impact of noise is minimized. We obtain new state-of-the art performance on CIFAR10, CIFAR100, FashionMNIST, STL10, and PersonaChat, including $99 \%$ on CIFAR10 for $\varepsilon=1, \delta=1e-5$-DP.
Fractionally integrated curve time series with cointegration

We introduce methods and theory for fractionally cointegrated curve time series. We develop a variance ratio test to determine the dimensions associated with the nonstationary and stationary subspaces. For each subspace, we apply a local Whittle estimator to estimate the long-memory parameter and establish its consistency. A Monte Carlo study of ﬁnite-sample performance is included, along with an empirical application
A probabilistic peridynamic framework with an application to the study of the statistical size effect

Mathematical models are essential for understanding and making predictions about systems arising in nature and engineering. Yet, mathematical models are a simplification of true phenomena, thus making predictions subject to uncertainty. Hence, the ability to quantify uncertainties is essential to any modelling framework, enabling the user to assess the importance of certain parameters on quantities of interest and have control over the quality of the model output by providing a rigorous understanding of uncertainty. Peridynamic models are a particular class of mathematical models that have proven to be remarkably accurate and robust for a large class of material failure problems. However, the high computational expense of peridynamic models remains a major limitation, hindering outer-loop applications that require a large number of simulations, for example, uncertainty quantification. This contribution provides a framework to make such computations feasible. By employing a Multilevel Monte Carlo (MLMC) framework, where the majority of simulations are performed using a coarse mesh, and performing relatively few simulations using a fine mesh, a significant reduction in computational cost can be realised, and statistics of structural failure can be estimated. The results show a speed-up factor of 16x over a standard Monte Carlo estimator, enabling the forward propagation of uncertain parameters in a computationally expensive peridynamic model. Furthermore, the multilevel method provides an estimate of both the discretisation error and sampling error, thus improving the confidence in numerical predictions. The performance of the approach is demonstrated through an examination of the statistical size effect in quasi-brittle materials.
On the Role of Dynamical Cooling in the Dynamics of Circumbinary Disks

Hydrodynamical interactions between binaries and circumbinary disks (CBDs) play an important role in a variety of astrophysical systems, from young stellar binaries to supermassive black hole binaries. Previous simulations of CBDs have mostly employed locally isothermal equations of state. We carry out 2D viscous hydrodynamic simulations of CBDs around equal-mass, circular binaries, treating the gas thermodynamics by thermal relaxation toward equilibrium temperature (the constant-β cooling ansatz, where β is the cooling time in units of the local Keplerian time). As an initial study, we use the grid-based code Athena++ on a polar grid, covering an extended disk outside the binary co-orbital region. We find that with a longer cooling time, the accretion variability is gradually suppressed, and the morphology of the CBD becomes more symmetric. The disk also shows evidence of hysteresis behavior depending on the initial conditions. Gas cooling also affects the rate of angular momentum transfer between the binary and the CBD, where given our adopted disk thickness and viscosity (H/r ∼ 0.1 and α ∼ 0.1), the binary orbit expands while undergoing accretion for most β values between 0 and 4.0 except over a narrow range of intermediate β values. The validity of using a polar grid excising the central domain is also discussed.
Bio-Heat Transfer and Monte Carlo Measurement of Near-Infrared Transcranial Stimulation of Human Brain

Transcranial photobiomodulation is an optical method for non-invasive brain stimulation. The method projects red and near-infrared light through the scalp within 600-1100 nm and low energy within the 1-20 J/cm2 range. Recent studies have been optimistic about replacing this method with pharmacotherapy and invasive brain stimulation. However, concerns and ambiguities exist regarding the light penetration depth and possible thermal side effects. While the literature survey indicates that the skin temperature rises after experimental optical brain stimulation, inadequate evidence supports a safe increase in temperature or the amount of light penetration in the cortex. Therefore, we aimed to conduct a comprehensive study on the heat transfer of near-infrared stimulation for the human brain. Our research considers the transcranial photobiomodulation over the human brain model by projecting 810 nm light with 100 mW/cm2 power density to evaluate its thermal and optical effects using bioheat transfer and radiative transfer equation. Our results confirm that the near-infrared light spectrum has a small incremental impact on temperature and approximately penetrates 1 cm, reaching the cortex. A time-variant study of the heat transfer was also carried out to measure the temperature changes during optical stimulation.
Boundary‐layer moisture regimes

Data from fifty-two aircraft flight legs 100−150m above ground from HAPEX and FIFE are analysed to estimate characteristics of boundary-layer moisture fluctuations in boundary layers with different bulk stability and surface energy regimes. In HAPEX, considerable effort was devoted to the quality of measurement of moisture fluctuations. The data include repeated 120km flight legs flown at 150m over the same relatively homogeneous terrain, allowing statistical examination of motion characteristics on horizontal scales up to 10km. This study finds two prototype boundary-layer regimes. With significant boundary-layer instability, relatively weak surface evaporation and drier air aloft, the entrainment-drying boundary layer may develop. This boundary layer is characterized by vertical divergence of the moisture flux and significant diffusion of dry air from the top downwards (top-down). In this type of boundary layer, dry air occasionally reaches the lower boundary layer, leading to negative moisture skewness in spite of positive temperature and vertical velocity skewness associated with warm moist updraughts. In contrast, the moistening boundary layer associated with greater surface evaporation is characterized by positive moisture skewness near the surface. In addition to the above turbulent moisture fluctuations, some of the above data are characterized by 10km moisture variations with horizontal gradients often concentrated in narrow zones of horizontal convergence. Since corresponding signatures of vertical velocity and temperature are weaker, these zones are referred to as ‘mesoscale’ moisture fronts. As a more general feature, moisture variations on scales of 5km and greater are negatively correlated with temperature variations associated with cool moist regions and warm dry regions. On scales of tens of kilometres and greater, such negative correlation may be due to inhomogeneity of the surface energy budget. The negative moisture-temperature correlation leads to large mesoscale variations of relative humidity and the lifted condensation level.
Educational Production Functions

This research examines scholastic performance within the context of an individual’s production function. A constant partial elasticity of substitution production function for academic achievement is presented and estimated with non linear maximum likelihood methods. We find that ability and time devoted to various aspects of the learning process are the most important determinants of students’ accomplishments. Our results underscore the potential for students to compensate for relatively “poor” educational backgrounds by spending more time on study and class attendance.
Detection of Dipole Modulation in CMB Temperature Anisotropy Maps from WMAP and Planck using Artificial Intelligence

Breakdown of rotational invariance of the primordial power spectrum manifests in the statistical anisotropy of the observed Cosmic Microwave Background (CMB) radiation. Hemispherical power asymmetry in the CMB may be caused due to a dipolar modulation, indicating the presence of a preferred direction. Appropriately rescaled local variance maps of the CMB temperature anisotropy data effectively encapsulate this dipolar pattern. As a first-of-its-kind method, we train Artificial Neural Networks (ANNs) with such local variances as input features to distinguish statistically isotropic CMB maps from dipole-modulated ones. Our trained ANNs are able to predict components of the amplitude times the unit vector of the preferred direction for mixed sets of modulated and unmodulated maps, with goodness-of-fit (R 2) scores >0.97 for full sky and >0.96 for partial sky coverage. On all observed foreground-cleaned CMB maps, the ANNs detect the dipolar modulation signal with overall consistent values of amplitudes and directions. This detection is significant at 97.21%–99.38% C.L. for all full sky maps, and at 98.34%–100% C.L. for all partial sky maps. Robustness of the signal holds across full and partial skies, various foreground cleaning methods, inpainting algorithms, instruments, and all the different periods of observation for Planck and WMAP satellites. The significant and robust detection of the signal, in addition to the consistency of values of amplitude and directions, as found independent of any preexisting methods, further mitigates the criticisms of look-elsewhere effects and a posteriori inferences for the preferred dipole direction in the CMB.
Performance Characterization of AutoNUMA Memory Tiering on Graph Analytics

Non-Volatile Memory (NVM) can deliver higher density and lower cost per bit when compared with DRAM. Its main drawback is that it is slower than DRAM. On the other hand, DRAM has scalability problems due to its cost and energy consumption. NVM will likely coexist with DRAM in computer systems and the biggest challenge is to know which data to allocate on each type of memory. A state-of-the-art approach is AutoNUMA, in the Linux kernel. Prior work is limited to measuring AutoNUMA solely in terms of the application execution time, without understanding AutoNUMA’s behavior. In this work we provide a more in-depth characterization of AutoNUMA, for instance, identifying where exactly a set of pages are allocated, while keeping track of promotion and demotion decisions performed by AutoNUMA. Our analysis shows that AutoNUMA’s benefits can be modest when running graph processing applications, or graph analytics, because most pages have only one access over the entire execution time and other pages accesses have no temporal locality. We make a case for exploring application characteristics using object-level mappings between DRAM and NVM. Our preliminary experiments show that an object-level memory tiering can better capture the application behavior and reduce the execution time of graph analytics by 21% (avg) and 51parcent (max), when compared to AutoNUMA, while significantly reducing the number of memory accesses in NVM.
Counterfactuals for the Future

Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the structure and stability of exogenous variables in the system being modeled --- an assumption that is reasonable in many settings where counterfactuals are used. In this work, we consider cases where we might reasonably make a different assumption about exogenous variables; namely, that the exogenous noise terms of each unit do exhibit some unit-specific structure and/or stability. This leads us to a different use of counterfactuals --- a forward-looking rather than retrospective counterfactual. We introduce "counterfactual treatment choice," a type of treatment choice problem that motivates using forward-looking counterfactuals. We then explore how mismatches between interventional versus forward-looking counterfactual approaches to treatment choice, consistent with different assumptions about exogenous noise, can lead to counterintuitive results.
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation

Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods for NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization. In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model. Specifically, for each training instance of the child model, ConsistTL constructs the semantically-equivalent instance for the parent model and encourages prediction consistency between the parent and child for this instance, which is equivalent to the child model learning each instance under the guidance of the parent model. Experimental results on five low-resource NMT tasks demonstrate that ConsistTL results in significant improvements over strong transfer learning baselines, with a gain up to 1.7 BLEU over the existing back-translation model on the widely-used WMT17 Turkish-English benchmark. Further analysis reveals that ConsistTL can improve the inference calibration of the child model. Code and scripts are freely available at https://github.com/NLP2CT/ConsistTL.
PERFECT PACKING OF SQUARES

It is known that ∞P i=1 1/i2 = π2/6. Meir and Moser asked what is the smallest ϵ such that all the squares of sides of length 1, 1/2, 1/3, . can be packed into a rectangle of area π2/6 + ϵ. A packing into a rectangle of the right area is called perfec
Group $\mathcal{H}$-classes of finitely presented special inverse monoids

We study the group H -classes (also known as maximal subgroups, in the sense of semigroup theory) of ﬁnitely presented special inverse monoids. We show that the group H -classes which can arise in such monoids are exactly the recursively presented groups, and moreover every such group H -class can also arise in the E -unitary case. We also prove that the possible groups of units are exactly the ﬁnitely generated recursively presented groups; this improves upon a result of, and answers a question of, the ﬁrst author and Ruˇskuc. These results give the ﬁrst signiﬁcant insight into the group H -classes of such monoids beyond the group of units, and the results together demonstrate that (perhaps surprisingly) it is possible for the subgroup structure to have a complexity which signiﬁcantly exceeds that of the group of units. We also observe that a ﬁnitely presented special inverse monoid (even an E -unitary one) may have inﬁnitely many pairwise non-isomorphic group H -classes; this contrasts sharply with the case of (non-inverse) special monoids, where Malheiro showed that all idempotents lie in the D -class of 1, from which it follows that all group H -classes are isomorphic.
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation

As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real world have received increasing attention. Data augmentation is a widely used method to improve model performance, and some recent works have also confirmed its positive effect on the robustness of AI models. However, most of the existing data augmentation methods are heuristic, lacking the exploration of their internal mechanisms. We apply the explainable artificial intelligence (XAI) method, explore the internal mechanisms of popular data augmentation methods, analyze the relationship between game interactions and some widely used robustness metrics, and propose a new proxy for model robustness in the open-set environment. Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches. Experiments show that our method can be widely applied to many popular data augmentation methods. Different from the adversarial training, our boosting method not only significantly improves the robustness of models, but also improves the accuracy of test sets. Our code is available at \url{https://github.com/Anonymous_for_submission}.
Training Adaptive Reconstruction Networks for Blind Inverse Problems

Neural networks allow solving many ill-posed inverse problems with unprecedented performance. Physics informed approaches already progressively replace carefully hand-crafted reconstruction algorithms in real applications. However, these networks suffer from a major defect: when trained on a given forward operator, they do not generalize well to a different one. The aim of this paper is twofold. First, we show through various applications that training the network with a family of forward operators allows solving the adaptivity problem without compromising the reconstruction quality significantly.Second, we illustrate that this training procedure allows tackling challenging blind inverse problems.Our experiments include partial Fourier sampling problems arising in magnetic resonance imaging (MRI) with sensitivity estimation and off-resonance effects, computerized tomography (CT) with a tilted geometry and image deblurring with Fresnel diffraction kernels.
On algebraic central division algebras over Henselian fields of finite absolute Brauer $p$-dimensions and residually arithmetic type

Let ( K, v ) be a Henselian ﬁeld with a residue ﬁeld b K and a value group v ( K ), and let P be the set of prime numbers. This paper ﬁnds conditions on K , v ( K ) and b K under which every algebraic associative central division K -algebra R contains a K -subalgebra e R de-composable into a tensor product of central K -subalgebras R p , p ∈ P , of ﬁnite p -primary degrees, such that each ﬁnite-dimensional K -subalgebra ∆ of R is isomorphic to a K -subalgebra e ∆ of e R .