Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Moral Dimensions in Social Media with Mformer

The ever-growing textual records of contemporary social issues, often discussed online with moral rhetoric, present both an opportunity and a challenge for studying how moral concerns are debated in real life. Moral foundations theory is a taxonomy of intuitions widely used in data-driven analyses of online content, but current computational tools to detect moral foundations suffer from the incompleteness and fragility of their lexicons and from poor generalization across data domains. In this paper, we fine-tune a large language model to measure moral foundations in text based on datasets covering news media and long- and short-form online discussions. The resulting model, called Mformer, outperforms existing approaches on the same domains by 4--12% in AUC and further generalizes well to four commonly used moral text datasets, improving by up to 17% in AUC. We present case studies using Mformer to analyze everyday moral dilemmas on Reddit and controversies on Twitter, showing that moral foundations can meaningfully describe people's stance on social issues and such variations are topic-dependent. Pre-trained model and datasets are released publicly. We posit that Mformer will help the research community quantify moral dimensions for a range of tasks and data domains, and eventually contribute to the understanding of moral situations faced by humans and machines.
Alignment via friction for nonisothermal multicomponent fluid systems

The derivation of an approximate Class-I model for nonisothermal multicomponent systems of fluids, as the high-friction limit of a Class-II model is justified, by validating the Chapman-Enskog expansion performed from the Class-II model towards the Class-I model. The analysis proceeds by comparing two thermomechanical theories via relative entropy.
Multi-phase characterization of AGN winds in 5 local type-2 quasars

We present MEGARA (Multi-Espectr\'ografo en GTC de Alta Resoluci\'on para Astronom\'ia) Integral Field Unit (IFU) observations of 5 local type-2 quasars (QSO2s, z $\sim 0.1$) from the Quasar Feedback (QSOFEED) sample. These active galactic nuclei (AGN) have bolometric luminosities of 10$^{45.5-46}$ erg/s and stellar masses of $\sim$10$^{11}$ M$_{\odot}$. We explore the kinematics of the ionized gas through the [O~III]$\lambda$5007 $\r{A}$ emission line. The nuclear spectra of the 5 QSO2s, extracted in a circular aperture of $\sim$ 1.2"($\sim$ 2.2 kpc) in diameter, show signatures of high velocity winds in the form of broad (full width at half maximum; 1300$\leq$FWHM$\leq$2240 km/s and blueshifted components. We find that 4 out of the 5 QSO2s present outflows that we can resolve with our seeing-limited data, and they have radii ranging from 3.1 to 12.6 kpc. In the case of the two QSO2s with extended radio emission, we find that it is well-aligned with the outflows, suggesting that low-power jets might be compressing and accelerating the ionized gas in these radio-quiet QSO2s. In the four QSO2s with spatially resolved outflows, we measure ionized mass outflow rates of 3.3-6.5 Msun/yr when we use [S~II]-based densities, and of 0.7-1.6 Msun/yr when trans-auroral line-based densities are considered instead. We compare them with the corresponding molecular mass outflow rates (8 - 16 Msun/yr), derived from CO(2-1) ALMA observations at 0.2"resolution. Both phases show lower outflow mass rates than those expected from observational scaling relations where uniform assumptions on the outflow properties were adopted. This might be indicating that the AGN luminosity is not the only driver of massive outflows and/or that these relations need to be re-scaled using accurate outflow properties. We do not find a significant impact of the outflows on the global star formation rates.
Witnessing quantum coherence with prior knowledge of observables

Quantum coherence is the key resource in quantum technologies including faster computing, secure communication and advanced sensing. Its quantification and detection are, therefore, paramount within the context of quantum information processing. Having certain priori knowledge on the observables may enhance the efficiency of coherence detection. In this work, we posit that the trace of the observables is a known quantity. Our investigation confirms that this assumption indeed extends the scope of coherence detection capabilities. Utilizing this prior knowledge of the trace of the observables, we establish a series of coherence detection criteria. We investigate the detection capabilities of these coherence criteria from diverse perspectives and ultimately ascertain the existence of four distinct and inequivalent criteria. These findings contribute to the deepening of our understanding of coherence detection methodologies, thereby potentially opening new avenues for advancements in quantum technologies.
Scaling of the Cumulative Weights of the Invasion Percolation Cluster on a Branching Process Tree

We analyse the scaling of the weights added by invasion percolation on a branching process tree. This process is a paradigm model of self-organised criticality, where criticality is approach without a prespecified parameter. In this paper, we are interested in the invasion percolation cluster (IPC), obtained by performing invasion percolation for $n$ steps and letting $n\to\infty$. The volume scaling of the IPC was discussed in detail in (G\"undlach and van der Hofstad 2023) and in this work, we extend this analysis to the scaling of the cumulative weights of the IPC. We assume a power-law offspring distribution on the branching process tree with exponent $\alpha$. In the regimes $\alpha>2$ and $\alpha\in(1,2)$, we observe a natural law-of-large-numbers result, where the cumulative weights have the same scaling as the volume, but converge to a different limit. In the case $\alpha<1$, where the weights added by invasion percolation vanish, the scaling regimes change significantly. For $\alpha\in(1/2,1)$, the weights scale exponentially but with a different parameter than the volume scaling, while for $\alpha\in(0,1/2)$ it turns out that the weights are summable without any scaling. Such a phase transition at $\alpha=1/2$ of the cumulative weights is novel and unexpected as there is no significant change in the neighbourhood scaling of the IPC at $\alpha=1/2$.
The Newton polytope of the Kronecker product

We study the Kronecker product of two Schur functions $s_\lambda\ast s_\mu$, defined as the image of the characteristic map of the product of two $S_n$ irreducible characters. We prove special cases of a conjecture of Monical--Tokcan--Yong that its monomial expansion has a saturated Newton polytope. Our proofs employ the Horn inequalities for positivity of Littlewood-Richardson coefficients and imply necessary conditions for the positivity of Kronecker coefficients.
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference

We present multimodal neural posterior estimation (MultiNPE), a method to integrate heterogeneous data from different sources in simulation-based inference with neural networks. Inspired by advances in deep fusion learning, it empowers researchers to analyze data from different domains and infer the parameters of complex mathematical models with increased accuracy. We formulate multimodal fusion approaches for \hbox{MultiNPE} (early, late, hybrid) and evaluate their performance in three challenging experiments. MultiNPE not only outperforms single-source baselines on a reference task, but also achieves superior inference on scientific models from neuroscience and cardiology. We systematically investigate the impact of partially missing data on the different fusion strategies. Across our experiments, late and hybrid fusion techniques emerge as the methods of choice for practical applications of multimodal simulation-based inference.
The 23.01 Release of Cloudy

We announce the C23.01 update of cloudy. This corrects a simple coding error, present since ∼1990, in one routine that required a conversion from the line-center to the mean normalization of the Lyα optical depth. This affects the destruction of H i Lyα by background opacities. Its largest effect is upon the Lyα intensity in high-ionization dusty clouds, where the predicted intensity is now up to three times stronger. Other properties that depend on Lyα destruction, such as grain infrared emission, change in response.
AQUATK: An Audio Quality Assessment Toolkit

Recent advancements in Neural Audio Synthesis (NAS) have outpaced the development of standardized evaluation methodologies and tools. To bridge this gap, we introduce AquaTk, an open-source Python library specifically designed to simplify and standardize the evaluation of NAS systems. AquaTk offers a range of audio quality metrics, including a unique Python implementation of the basic PEAQ algorithm, and operates in multiple modes to accommodate various user needs.
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education

Due to the empirical success of reinforcement learning, an increasing number of students study the subject. However, from our practical teaching experience, we see students entering the field (bachelor, master and early PhD) often struggle. On the one hand, textbooks and (online) lectures provide the fundamentals, but students find it hard to translate between equations and code. On the other hand, public codebases do provide practical examples, but the implemented algorithms tend to be complex, and the underlying test environments contain multiple reinforcement learning challenges at once. Although this is realistic from a research perspective, it often hinders educational conceptual understanding. To solve this issue we introduce EduGym, a set of educational reinforcement learning environments and associated interactive notebooks tailored for education. Each EduGym environment is specifically designed to illustrate a certain aspect/challenge of reinforcement learning (e.g., exploration, partial observability, stochasticity, etc.), while the associated interactive notebook explains the challenge and its possible solution approaches, connecting equations and code in a single document. An evaluation among RL students and researchers shows 86% of them think EduGym is a useful tool for reinforcement learning education. All notebooks are available from https://www.edugym. org/, while the full software package can be installed from https://github.com/RLG-Leiden/edugym.
Comparison of surface reflectance values from the USGS Landsat 5 TM climate data record (CDR) with values generated using a simple dark object subtraction (DOS) method in an Alpine watershed

Extraction of relevant information from remotely sensed imagery is essential for the identification of changes in the earth's environment. Methods for converting the data collected at the sensor to surface reflectance have been under constant improvement since the beginning of the Landsat program. The time and effort needed to perform this task has recently been eliminated with the publication of the USGS Landsat CDR. This paper compares the data available from the USGS with a simple dark object subtraction method for determining surface reflectance. Our goal is to determine if the USGS data set is comparable to previous methods. We find that the USGS data set is strongly correlated with the simpler DOS method. While clear differences in absolute surface reflectance are observed in the visible and near-IR bands, the trends in the data over time are consistent. This suggests that previous trend studies using the simpler methods do not need to be revisited using the newer data. The findings also suggest that researchers no longer need to perform the labor intensive step of converting raw data to surface reflectance by making use of the USGS surface reflectance data instead.
From “Thumbs Up” to “10 out of 10”: Reconsidering Scalar Feedback in Interactive Reinforcement Learning

Learning from human feedback is an effective way to improve robotic learning in exploration-heavy tasks. Compared to the wide application of binary human feedback, scalar human feedback has been used less because it is believed to be noisy and unstable. In this paper, we compare scalar and binary feedback, and demonstrate that scalar feedback benefits learning when properly handled. We collected binary or scalar feedback respectively from two groups of crowdworkers on a robot task. We found that when considering how consistently a participant labeled the same data, scalar feedback led to less consistency than binary feedback; however, the difference vanishes if small mismatches are allowed. Additionally, scalar and binary feedback show no significant differences in their correlations with key Reinforcement Learning targets. We then introduce Stabilizing TEacher Assessment DYnamics (STEADY) to improve learning from scalar feedback. Based on the idea that scalar feedback is muti-distributional, STEADY reconstructs underlying positive and negative feedback distributions and re-scales scalar feedback based on feedback statistics. We show that models trained with scalar feedback + STEADY outperform baselines, including binary feedback and raw scalar feedback, in a robot reaching task with non-expert human feedback. Our results show that both binary feedback and scalar feedback are dynamic, and scalar feedback is a promising signal for use in interactive Reinforcement Learning.
The high/low frequency balance drives the perception of noisy vibrations

Noisy vibrotactile signals transmitted during tactile explorations of an object provide precious information on the nature of its surface. Linking the properties of such vibrotactile signals to the way they are interpreted by the haptic sensory system remains challenging. In this study, we investigated humans' perception of noisy, stationary vibrations recorded during exploration of textures and reproduced using a vibrotactile actuator. Since intensity is a well-established essential perceptual attribute, an intensity equalization was first conducted, providing a model for its estimation. The equalized stimuli were further used to identify the most salient spectral features in a second experiment using dissimilarity estimations between pairs of vibrations. Based on dimensionally reduced spectral representations, linear models of dissimilarity prediction showed that the balance between low and high frequencies was the most important cue. Formal validation of this result was achieved through a Mushra experiment, where participants assessed the fidelity of resynthesized vibrations with various distorted frequency balances. These findings offer valuable insights into human vibrotactile perception and establish a computational framework for analyzing vibrations as humans do. Moreover, they pave the way for signal synthesis and compression based on sparse representations, holding significance for applications involving complex vibratory feedback.
General distance-like functions on the Wasserstein space

Viscosity solutions to the eikonal equations play a fundamental role to study the geometry, topology and geodesic flows. The classical definition of viscosity solution depends on the differential structure and can not extend directly to a general metric space. However, the distance-like functions, which are exactly viscosity solutions of the eikonal equation on a Riemannian manifold, is independent of the differential structure and well-defined on a non-compact, complete, separable metric space. In this paper, we study the viscosity solutions of the eikonal equation on Wasserstein space Pp(X) (p>1), whose ambient space X is complete, separable, non-compact, locally compact length space. But the Wasserstein space is not locally compact, the co-rays (or calibrated curves) of the distance-like functions may not exist. Fortunately, we show that if the distance-like function is induced by a sequence of closed subset diverging to infinity consisting of Dirac probability measures in the Wasserstein space, the co-rays do indeed exist. The concrete conditions of the existence of the co-rays are also provided. We also show that a distance-like function on the ambient space can induce a distance-like function on the associated Wasserstein space.
Quantum Enhancement in Dark Matter Detection with Quantum Computation

We propose a novel method to significantly enhance the signal rate in the qubit-based dark matter detection experiments with the help of quantum interference. Various quantum sensors possess ideal properties for detecting wave-like dark matter, and qubits, commonly employed in quantum computers, are excellent candidates for dark matter detectors. We demonstrate that, by designing an appropriate quantum circuit to manipulate the qubits, the signal rate scales proportionally to $n_{\rm q}^2$, with $n_{\rm q}$ being the number of sensor qubits, rather than linearly with $n_{\rm q}$. Consequently, in the dark matter detection with a substantial number of sensor qubits, a significant increase in the signal rate can be expected. We provide a specific example of a quantum circuit that achieves this enhancement by coherently combining the phase evolution in each individual qubit due to its interaction with dark matter. We also demonstrate that the circuit is fault tolerant to de-phasing noises, a critical quantum noise source in quantum computers. The enhancement mechanism proposed here is applicable to various modalities for quantum computers, provided that the quantum operations relevant to enhancing the dark matter signal can be applied to these devices.
LenSiam: Self-Supervised Learning on Strong Gravitational Lens Images

Self-supervised learning has been known for learning good representations from data without the need for annotated labels. We explore the simple siamese (SimSiam) architecture for representation learning on strong gravitational lens images. Commonly used image augmentations tend to change lens properties; for example, zoom-in would affect the Einstein radius. To create image pairs representing the same underlying lens model, we introduce a lens augmentation method to preserve lens properties by fixing the lens model while varying the source galaxies. Our research demonstrates this lens augmentation works well with SimSiam for learning the lens image representation without labels, so we name it LenSiam. We also show that a pre-trained LenSiam model can benefit downstream tasks. We open-source our code and datasets at https://github.com/kuanweih/LenSiam .
Wildfire Smoke Detection with Cross Contrast Patch Embedding

The Transformer-based deep networks have increasingly shown significant advantages over CNNs. Some existing work has applied it in the field of wildfire recognition or detection. However, we observed that the vanilla Transformer is not friendly for extracting smoke features. Because low-level information such as color, transparency and texture is very important for smoke recognition, and transformer pays more attention to the semantic relevance between middle- or high-level features, and is not sensitive to the subtle changes of low-level features along the space. To solve this problem, we propose the Cross Contrast Patch Embedding(CCPE) module based on the Swin Transformer, which uses the multi-scales spatial frequency contrast information in both vertical and horizontal directions to improve the discrimination of the network on the underlying details. The fuzzy boundary of smoke makes the positive and negative label assignment for instances in a dilemma, which is another challenge for wildfires detection. To solve this problem, a Separable Negative Sampling Mechanism(SNSM) is proposed. By using two different negative instance sampling strategies on positive images and negative images respectively, the problem of supervision signal confusion caused by label diversity in the process of network training is alleviated. This paper also releases the RealFire Test, the largest real wildfire test set so far, to evaluate the proposed method and promote future research. It contains 50,535 images from 3,649 video clips. The proposed method has been extensively tested and evaluated on RealFire Test dataset, and has a significant performance improvement compared with the baseline detection models.
Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers

This work presents an analysis of the effectiveness of using standard shallow feed-forward networks to mimic the behavior of the attention mechanism in the original Transformer model, a state-of-the-art architecture for sequence-to-sequence tasks. We substitute key elements of the attention mechanism in the Transformer with simple feed-forward networks, trained using the original components via knowledge distillation. Our experiments, conducted on the IWSLT2017 dataset, reveal the capacity of these ”attentionless Transformers” to rival the performance of the original architecture. Through rigorous ablation studies, and experimenting with various replacement network types and sizes, we offer insights that support the viability of our approach. This not only sheds light on the adaptability of shallow feed-forward networks in emulating attention mechanisms but also underscores their potential to streamline complex architectures for sequence-to-sequence tasks.
Reduced Cristoffel's tensor and acoustic axes: Necessary and sufficient condition

Acoustic axes are spatial directions in media (often crystals) where at least two of the three acoustic waves have the same phase velocity. Identification of such directions for materials with specific elasticity parameters is both theoretically fascinating and practical in acoustic applications. In this paper, we introduce the notion of the reduced acoustic tensor. In contrast to the ordinary Cristoffel's tensor, this tensor is traceless. Then it has only five independent components and fulfills the depressed cubic characteristic equation. We show that the conditions for the existence of an acoustic axes in a given propagation direction are determined solely by the reduced acoustic tensor. The necessary and sufficient conditions takes a simple compact invariant form. We also present the expression for the wave velocities along the acoustic axis. The conditions for the oblate/prolate types of the axis are derived in terms of reduced acoustic tensor.
The Impact of Familiarity on Naming Variation: A Study on Object Naming in Mandarin Chinese

Different speakers often produce different names for the same object or entity (e.g., “woman” vs. “tourist” for a female tourist). The reasons behind variation in naming are not well understood. We create a Language and Vision dataset for Mandarin Chinese that provides an average of 20 names for 1319 naturalistic images, and investigate how familiarity with a given kind of object relates to the degree of naming variation it triggers across subjects. We propose that familiarity influences naming variation in two competing ways: increasing familiarity can either expand vocabulary, leading to higher variation, or promote convergence on conventional names, thereby reducing variation. We find evidence for both factors being at play. Our study illustrates how computational resources can be used to address research questions in Cognitive Science.