Miracle-dz/UniFAR-FTUs huggingface dataset:
The Athens Earthquake of 7 September 1999

Based on detailed aftershock monitoring, the first model of the Athens earthquake is formulated, which is consistent with global, regional, and local strong-motion data of the mainshock, and fits with geological setting. The 30-station temporary network located 450 aftershocks. During the first 20 observation days the aftershocks identified the mainshock fault plane dipping 52° and striking 117°, consistently with the teleseismic fault-plane solution. A formal upward continuation of the fault plane intersects the surface close to the Fili fault. Numerical modeling of the broadband regional data at 10 stations (epicentral distances 140 to 370 km) estimates the centroidal source depth of 10 km and yields an average source duration of 5 to 6 sec. The interstation variability of the apparent duration indicates source directivity. The empirical Green9s function modeling at the closest broadband station suggests a fault length of 20 or 10 km. Both the numerical and empirical modelings give a very short rise time of 0.1 to 0.3 sec. The short rise time seems to favor the nearly complete stress release of an asperity. A 10 km asperity (stress drop of 2.7 M Pa) is in agreement with a gap, identified during the first 12 observation days between two aftershock clusters. The strong-motion accelerograms in Athens also indicate a short apparent duration due to directivity (about 3 sec), and confirm an abrupt rupture beginning. There is no evidence for an abrupt stopping. The short rise time and short apparent source duration were two principal factors determining the damaging ground motions in Athens.
Data-Centric Long-Tailed Image Recognition

In the context of the long-tail scenario, models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to improve model performance. Among these approaches, information augmentation has been progressively introduced as a crucial category. It achieves a balance in model performance by augmenting the richness and quantity of samples in the tail classes. However, there is currently a lack of research into the underlying mechanisms explaining the effectiveness of information augmentation methods. Consequently, the utilization of information augmentation in long-tail recognition tasks relies heavily on empirical and intricate fine-tuning. This work makes two primary contributions. Firstly, we approach the problem from the perspectives of feature diversity and distribution shift, introducing the concept of Feature Diversity Gain (FDG) to elucidate why information augmentation is effective. We find that the performance of information augmentation can be explained by FDG, and its performance peaks when FDG achieves an appropriate balance. Experimental results demonstrate that by using FDG to select augmented data, we can further enhance model performance without the need for any modifications to the model's architecture. Thus, data-centric approaches hold significant potential in the field of long-tail recognition, beyond the development of new model structures. Furthermore, we systematically introduce the core components and fundamental tasks of a data-centric long-tail learning framework for the first time. These core components guide the implementation and deployment of the system, while the corresponding fundamental tasks refine and expand the research area.
Generalized regular Tur\'an numbers

We combine two generalizations of ordinary Tur\'an problems. Given graphs $H$ and $F$ and a positive integer $n$, we study $rex(n, H, F )$, which is the largest number of copies of $H$ in $F$-free regular $n$-vertex graphs.
Learning to Augment Distributions for Out-of-Distribution Detection

Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD detection. Advanced works, despite their promising progress, may still fail in the open world, owing to the lack of knowledge about unseen OOD data in advance. Although one can access auxiliary OOD data (distinct from unseen ones) for model training, it remains to analyze how such auxiliary data will work in the open world. To this end, we delve into such a problem from a learning theory perspective, finding that the distribution discrepancy between the auxiliary and the unseen real OOD data is the key to affecting the open-world detection performance. Accordingly, we propose Distributional-Augmented OOD Learning (DAL), alleviating the OOD distribution discrepancy by crafting an OOD distribution set that contains all distributions in a Wasserstein ball centered on the auxiliary OOD distribution. We justify that the predictor trained over the worst OOD data in the ball can shrink the OOD distribution discrepancy, thus improving the open-world detection performance given only the auxiliary OOD data. We conduct extensive evaluations across representative OOD detection setups, demonstrating the superiority of our DAL over its advanced counterparts.
Passive elasticity properties of $\textit{Octopus rubescens}$ arm

In this report, passive elasticity properties of $\textit{Octopus rubescens}$ arm tissue are investigated using a multidisciplinary approach encompassing biomechanical experiments, computational modeling, and analyses. Tensile tests are conducted to obtain stress-strain relationships of the arm under axial stretch. Rheological tests are also performed to probe into dynamic shear response of the arm tissue. Based on these tests, comparisons against three different viscoelasticity models are reported.
Quantitative convergence in relative entropy for a moderately interacting particle system on $\mathbb{R}^d$

This article shows how to combine the relative entropy method by D. Bresch, P.-E. Jabin, and Z. Wang in arXiv:1706.09564, arXiv:1906.04093 and the regularized $L^2(\mathbb{R}^d)$-estimate by Oelschl\"ager (Probability theory and related fields, 1987) to prove a strong propagation of chaos result for the viscous porous medium equation from a moderately interacting particle system in $L^\infty(0,T; L^1(\mathbb{R}^d))$-norm. In the moderate interacting setting, the interacting potential is a smoothed Dirac Delta distribution, however, current results regarding the relative entropy methods for singular potentials do not apply. The result holds on $\mathbb{R}^d$ for any dimension $d\geq 1$ and provides a quantitative result where the rate of convergence depends on the moderate scaling parameter and the dimension $d\geq 1$.
Faithful and Robust Local Interpretability for Textual Predictions

Interpretability is essential for machine learning models to be trusted and deployed in critical domains. However, existing methods for interpreting text models are often complex, lack solid mathematical foundations, and their performance is not guaranteed. In this paper, we propose FRED (Faithful and Robust Explainer for textual Documents), a novel method for interpreting predictions over text. FRED identifies key words in a document that significantly impact the prediction when removed. We establish the reliability of FRED through formal definitions and theoretical analyses on interpretable classifiers. Additionally, our empirical evaluation against state-of-the-art methods demonstrates the effectiveness of FRED in providing insights into text models.
Spectral Properties of Elementwise-Transformed Spiked Matrices

This work concerns elementwise-transformations of spiked matrices: $Y_n = n^{-1/2} f( \sqrt{n} X_n + Z_n)$. Here, $f$ is a function applied elementwise, $X_n$ is a low-rank signal matrix, and $Z_n$ is white noise. We find that principal component analysis is powerful for recovering signal under highly nonlinear or discontinuous transformations. Specifically, in the high-dimensional setting where $Y_n$ is of size $n \times p$ with $n,p \rightarrow \infty$ and $p/n \rightarrow \gamma>0$, we uncover a phase transition: for signal-to-noise ratios above a sharp threshold -- depending on $f$, the distribution of elements of $Z_n$, and the limiting aspect ratio $\gamma$ -- the principal components of $Y_n$ (partially) recover those of $X_n$. Below this threshold, the principal components of $Y_n$ are asymptotically orthogonal to the signal. In contrast, in the standard setting where $X_n + n^{-1/2}Z_n$ is observed directly, the analogous phase transition depends only on $\gamma$. A similar phenomenon occurs with $X_n$ square and symmetric and $Z_n$ a generalized Wigner matrix.
A Direct Comparison between the Use of Double Gray and Multiwavelength Radiative Transfer in a General Circulation Model with and without Radiatively Active Clouds

Inhomogeneous cloud formation and wavelength-dependent phenomena are expected to shape hot Jupiter atmospheres. We present a general circulation model with multiwavelength “picket fence” radiative transfer and radiatively active, temperature-dependent clouds, and compare the results to those of a double gray routine. The double gray method inherently fails to model polychromatic effects in hot Jupiter atmospheres, while picket fence captures these non-gray aspects and performs well compared to fully wavelength-dependent methods. We compare both methods with radiatively active clouds and cloud-free models, assessing the limitations of the double gray method. Although there are broad similarities, the picket fence models have larger dayside–nightside temperature differences, nonisothermal upper atmospheres, and multiwavelength effects in the presence of radiatively active clouds. We model the well-known hot Jupiters HD 189733 b and HD 209458 b. For the hotter HD 209458 b, the picket fence method prevents clouds from thermostating dayside temperatures, resulting in hotter upper atmospheres and the dissipation of dayside clouds. Differences in the temperature structures are then associated with nuanced differences in the circulation patterns and clouds. Models of the cooler HD 189733 b have global cloud coverage, regardless of the radiative transfer scheme, whereas there are larger differences in the models of HD 209458 b, particularly in the extent of the partial cloud coverage on its dayside. This results in minor changes to the thermal and reflected light phase curves of HD 189733 b, but more significant differences for the picket fence and double gray versions of HD 209458 b.
Total Variation Meets Differential Privacy

The framework of approximate differential privacy is considered, and augmented by introducing the notion of"the total variation of a (privacy-preserving) mechanism"(denoted by $\eta$-TV). With this refinement, an exact composition result is derived, and shown to be significantly tighter than the optimal bounds for differential privacy (which do not consider the total variation). Furthermore, it is shown that $(\varepsilon,\delta)$-DP with $\eta$-TV is closed under subsampling. The induced total variation of commonly used mechanisms are computed. Moreover, the notion of total variation of a mechanism is extended to the local privacy setting and privacy-utility tradeoffs are investigated. In particular, total variation distance and KL divergence are considered as utility functions and upper bounds are derived. Finally, the results are compared and connected to the (purely) locally differentially private setting.
On Hand-Held Grippers and the Morphological Gap in Human Manipulation Demonstration

Collecting manipulation demonstrations with robotic hardware is tedious - and thus difficult to scale. Recording data on robot hardware ensures that it is in the appropriate format for Learning from Demonstrations (LfD) methods. By contrast, humans are proficient manipulators, and recording their actions would be easy to scale, but it is challenging to use that data format with LfD methods. The question we explore is whether there is a method to collect data in a format that can be used with LfD while retaining some of the attractive features of recording human manipulation. We propose equipping humans with hand-held, hand-actuated parallel grippers and a head-mounted camera to record demonstrations of manipulation tasks. Using customised and reproducible grippers, we collect an initial dataset of common manipulation tasks. We show that there are tasks that, against our initial intuition, can be performed using parallel grippers. Qualitative insights are obtained regarding the impact of the difference in morphology on LfD by comparing the strategies used to complete tasks with human hands and grippers. Our data collection method bridges the gap between robot- and human-native manipulation demonstration. By making the design of our gripper prototype available, we hope to reduce other researchers effort to collect manipulation data.
First Detection of an Overmassive Black Hole Galaxy UHZ1: Evidence for Heavy Black Hole Seed Formation from Direct Collapse

The recent Chandra-JWST discovery of a quasar in the z ≈ 10.1 galaxy UHZ1 reveals that accreting supermassive black holes were already in place 470 million years after the Big Bang. The Chandra X-ray source detected in UHZ1 is a Compton-thick quasar with a bolometric luminosity of L bol ∼ 5 × 1045 erg s−1, which corresponds to an estimated black hole (BH) mass of ∼4 × 107 M ⊙, assuming accretion at the Eddington rate. JWST NIRCAM and NIRSpec data yield a stellar mass estimate for UHZ1 comparable to its BH mass. These characteristics are in excellent agreement with prior theoretical predictions for a unique class of transient, high-redshift objects, overmassive black hole galaxies (OBGs) by Natarajan et al., that harbor a heavy initial black hole seed that likely formed from the direct collapse of the gas. Given the excellent agreement between the observed multiwavelength properties of UHZ1 and theoretical model template predictions, we suggest that UHZ1 is the first detected OBG candidate. Our assertion rests on multiple lines of concordant evidence between model predictions and the following observed properties of UHZ1: its X-ray detection and the estimated ratio of the X-ray flux to the IR flux, which is consistent with theoretical expectations for a heavy initial BH seed; its high measured redshift of z ≈ 10.1, as predicted for the transient OBG stage (9 < z < 12); the amplitude and shape of the detected JWST spectral energy distribution (SED) between 1 and 5 μm, which is in very good agreement with simulated template SEDs for OBGs; and the extended JWST morphology of UHZ1, which is suggestive of a recent merge and is also expected for the formation of transient OBGs. As the first OBG candidate, UHZ1 provides compelling evidence for the formation of heavy initial seeds from direct collapse in the early Universe.
Numerical influence of ReLU'(0) on backpropagation

In theory, the choice of ReLU'(0) in [0, 1] for a neural network has a negligible influence both on backpropagation and training. Yet, in the real world, 32 bits default precision combined with the size of deep learning problems makes it a hyperparameter of training methods. We investigate the importance of the value of ReLU'(0) for several precision levels (16, 32, 64 bits), on various networks (fully connected, VGG, ResNet) and datasets (MNIST, CIFAR10, SVHN). We observe considerable variations of backpropagation outputs which occur around half of the time in 32 bits precision. The effect disappears with double precision, while it is systematic at 16 bits. For vanilla SGD training, the choice ReLU'(0) = 0 seems to be the most efficient. We also evidence that reconditioning approaches as batch-norm or ADAM tend to buffer the influence of ReLU'(0)'s value. Overall, the message we want to convey is that algorithmic differentiation of nonsmooth problems potentially hides parameters that could be tuned advantageously.
Role of element solubility on the kinetics of element partitioning: In situ observations and a thermodynamic kinetic model

[1] To elucidate the nature of element exchange between crystals via the mediation of a fluid phase, the mechanism and kinetics of the exchange reaction: 2Fe(Metal) + Mg2SiO4(Ol) = 2 Mg(Gas) + Fe2SiO4(Ol) were studied through a set of Knudsen cell mass spectrometry experiments. The reaction progress was monitored in situ by measuring vapor pressures of individual species (mainly Mg, SiO, and Fe) during experiments as well as by measurement of compositional profiles in crystals (Fe-rich rims) after the anneals. The experimental observations were reproduced by a numerical model which accounted for compositionally dependent diffusion in olivine, surface-controlled evaporation/condensation reactions (moving boundaries) and transport out of the system (open system behavior). The main finding of the study is that the composition (Fe/Mg ratio) at the surface of olivine varies with time at constant temperature, i.e., the final surface composition was not achieved instantaneously compared to the diffusion time scale, even during exchange with a vapor phase at high temperatures. The low concentration of Mg in the gas phase explains this behavior. On the basis of these observations a simple general model for element exchange between a solid and a fluid phase could be formulated and analytically solved. A single parameter may be defined combining thermodynamic and kinetic factors. The value of this parameter demarcates three domains: (1) disequilibrium at the surface, (2) equilibrium at the surface, and (3) no observable reaction, i.e., frozen system. It is shown that all three classes of behavior may be observed in natural fluids such as an ideal gas, aqueous solutions, and silicate melts.
Phenological Changes in Alpine Plants in Response to Increased Snowpack, Temperature, and Nitrogen

Abstract Modified environmental conditions are driving phenological changes in ecosystems around the world. Many plants have already responded to warmer temperatures by flowering earlier and sustaining longer periods of growth. Changes in other environmental factors, like precipitation and atmospheric nitrogen (N) deposition, may also influence phenology but have been less studied. Alpine plants may be good predictors of phenological response patterns because environmental changes are amplified in mountain ecosystems and extreme conditions may make alpine plants particularly sensitive to changes in limiting factors like precipitation, temperature, and N. We tested the effects of increased snowpack, temperature, and N on alpine tundra plant phenology, using snow fence, open-top warming chamber, and N fertilization treatments at the Niwot Ridge Long Term Ecological Research (LTER) site. Flowering phenology of three abundant species was recorded during two growing seasons. Treatment responses varied among species and functional types. Forbs responded to warming by flowering earlier and responded to snowpack and N by flowering later; however, when both snow and N were increased simultaneously, phenology was unchanged. Graminoids flowered earlier in response to N addition. Our results demonstrate that changing environmental conditions influence plant phenology, and specifically highlight that N and multiple factor interactions can yield stronger responses than warming alone.
Transcriptome analysis of early-onset familial Alzheimer’s disease and Sanfilippo syndrome zebrafish models reveals commonalities in disease mechanisms

Background Autosomal recessive inheritance of Sanfilippo syndrome (mucopolysaccharidosis type III, MPS III) causes childhood dementia, while Alzheimer’s disease is the most common type of adult-onset dementia. There are no approved treatments for Sanfilippo patients, and few options exist for those with Alzheimer’s disease. Increasing evidence suggests commonalities in the disease processes. However, a direct comparison of animal models with the two disorders has never been performed. Method We used RNA-seq to compare the transcriptome of zebrafish with early-onset familial Alzheimer’s disease (EOfAD, psen1 Q96_K97del/+), or MPS IIIB (naglu A603fs/A603fs) with their wild type siblings at 7 days post fertilisation and at 6 months of age (n = 8 fish per genotype). Results Differential gene expression and pathway analysis at each age revealed substantially more differentially expressed genes and pathways in MPS IIIB zebrafish relative to wild type than in the EOfAD-like zebrafish, consistent with MPS IIIB being a more severe, rapidly progressing and earlier onset form of dementia. Similar changes in gene expression were detected between the models in the extracellular matrix receptor interaction pathway in zebrafish larvae, and oxidative phosphorylation, ribosome and lysosome pathways in 6-month-old adult zebrafish brains. Cell type-specific changes in gene expression were detected for MPS IIIB zebrafish brains at 6 months of age, possibly reflecting altered proportions of oligodendrocytes, neural stem cells and inflammatory cells. Conclusions Our transcriptome analyses have illuminated possible shared disease mechanisms between EOfAD due to PSEN1 mutations and MPS IIIB. Future work will investigate the nature of these commonalities.
Motivational context determines the impact of aversive outcomes on mental effort allocation

It is well known that people will exert effort on a task if sufficiently motivated, but how they distribute these efforts across different strategies (e.g., efficiency vs. caution) remains uncertain. Past work has shown that people invest effort differently for potential positive outcomes (rewards) versus potential negative outcomes (penalties). However, this research failed to account for differences in the context in which negative outcomes motivate someone - either as punishment or reinforcement. It is therefore unclear whether effort profiles differ as a function of outcome valence, motivational context, or both. Using computational modeling and our novel Multi-Incentive Control Task, we show that the influence of aversive outcomes on one’s effort profile is entirely determined by their motivational context. Participants (N:91) favored increased caution in response to larger penalties for incorrect responses, and favored increased efficiency in response to larger reinforcement for correct responses, whether positively or negatively incentivized. Statement of Relevance People have to constantly decide how to allocate their mental effort, and in doing so can be motivated by both the positive outcomes that effort accrues and the negative outcomes that effort avoids. For example, someone might persist on a project for work in the hopes of being promoted or to avoid being reprimanded or even fired. Understanding how people weigh these different types of incentives is critical for understanding variability in human achievement as well as sources of motivational impairments (e.g., in major depression). We show that people not only consider both potential positive and negative outcomes when allocating mental effort, but that the profile of effort they engage under negative incentives differs depending on whether that outcome is contingent on sustaining good performance (negative reinforcement) or avoiding bad performance (punishment). Clarifying the motivational factors that determine effort exertion is an important step for understanding motivational impairments in psychopathology.