Miracle-dz/UniFAR-FTUs huggingface dataset:
Volatiles and trace elements content in melt inclusions from the zoned Green Tuff ignimbrite (Pantelleria, Sicily): petrological inferences

The island of Pantelleria is one of the best known localities of bimodal mafic-felsic magmatism (alkali basalt and trachyte-pantellerite). Among the felsic rocks, the coexistence in a single eruption of products of both trachyte and pantellerite compositions is limited to few occurrences, the Green Tuff (GT) ignimbrite being one of these. The GT is compositionally zoned from pantellerite (70.1 wt% SiO 2 , mol Na+K/Al = 1.86, 1871 ppm Zr) at the base to crystal-rich (>30 vol%) comenditic trachyte (63.4 wt% SiO 2 , mol Na+K/Al = 1.10, 265 ppm Zr) at the top, although the pantellertic compositions dominate the erupted volume. We present here new data on melt inclusions (MIs) from the pantellerite portions of the GT eruption and, most importantly, from the trachyte member, which have not been studied in-situ by previous work focused on the GT. We document the first occurrence of trachytic melt inclusions in the late-erupted member, whose importance resides in the fact that trachytes were known mostly as crystal-rich lavas or ignimbrites, all variably affected by crystal accumulation. Besides the obvious inferences on the interplay between parental-derivative magmas, this evidence adds also some helpful elements in understanding zoning of silicic and peralkaline (i.e. low-viscosity) magma chambers. Trace elements compositions of MIs reveal that trachyte melts are of two types : (i) a low-Ba , directly descending from basaltic melts by 60-70 % of fractional crystallisation, and (ii) a high-Ba that might be affected by processes of feldspar dissolution and entrainment of the resulting small-scale melts in some MIs. MIs hosted in the deep-seated trachyte body are H 2 O-poor (≤ 1.2 wt %) with respect to the early erupted (and shallower) pantellerite magma (≤ 4.2 wt %), raising the possibility that either trachyte magma was H 2 O-undesaturated, or clinopyroxene hosted melt inclusions which suffered consistent H 2 O loss.
Asymptotic-preserving and energy stable dynamical low-rank approximation for thermal radiative transfer equations

The thermal radiative transfer equations model temperature evolution through a background medium as a result of radiation. When a large number of particles are absorbed in a short time scale, the dynamics tend to a non-linear diffusion-type equation called the Rosseland approximation. The main challenges for constructing numerical schemes that exhibit the correct limiting behavior are posed by the solution's high-dimensional phase space and multi-scale effects. In this work, we propose an asymptotic-preserving and rank-adaptive dynamical low-rank approximation scheme based on the macro-micro decomposition of the particle density and a modified augmented basis-update \&Galerkin integrator. We show that this scheme, for linear particle emission by the material, dissipates energy over time under a step size restriction that captures the hyperbolic and parabolic CFL conditions. We demonstrate the efficacy of the proposed method in a series of numerical experiments.
Principled Architecture-aware Scaling of Hyperparameters

Training a high-quality deep neural network requires choosing suitable hyperparameters, which is a non-trivial and expensive process. Current works try to automatically optimize or design principles of hyperparameters, such that they can generalize to diverse unseen scenarios. However, most designs or optimization methods are agnostic to the choice of network structures, and thus largely ignore the impact of neural architectures on hyperparameters. In this work, we precisely characterize the dependence of initializations and maximal learning rates on the network architecture, which includes the network depth, width, convolutional kernel size, and connectivity patterns. By pursuing every parameter to be maximally updated with the same mean squared change in pre-activations, we can generalize our initialization and learning rates across MLPs (multi-layer perception) and CNNs (convolutional neural network) with sophisticated graph topologies. We verify our principles with comprehensive experiments. More importantly, our strategy further sheds light on advancing current benchmarks for architecture design. A fair comparison of AutoML algorithms requires accurate network rankings. However, we demonstrate that network rankings can be easily changed by better training networks in benchmarks with our architecture-aware learning rates and initialization.
TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics

Data-driven generation of molecules with desired properties, also known as inverse molecular design (IMD), has attracted significant attention in recent years. Despite the significant progress in the accuracy and diversity of solutions, existing IMD methods lag behind in terms of trustworthiness. The root issue is that the design process of these methods is increasingly more implicit and indirect, and this process is also isolated from the native forward process (NFP), the ground-truth function that models the molecular dynamics. Following this insight, we propose TrustMol, an IMD method built to be trustworthy. For this purpose, TrustMol relies on a set of technical novelties including a new variational autoencoder network. Moreover, we propose a latent-property pairs acquisition method to effectively navigate the complexities of molecular latent optimization, a process that seems intuitive yet challenging due to the high-frequency and discontinuous nature of molecule space. TrustMol also integrates uncertainty-awareness into molecular latent optimization. These lead to improvements in both explainability and reliability of the IMD process. We validate the trustworthiness of TrustMol through a wide range of experiments.
Ansible Lightspeed: A Code Generation Service for IT Automation

The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been released, almost all of them focus on general-purpose programming languages. Domain-specific languages, such as those crucial for IT automation, have not received much attention. Ansible is one such YAML-based IT automation-specific language. Red Hat Ansible Lightspeed with IBM Watson Code Assistant, further referred to as Ansible Lightspeed, is an LLM-based service designed explicitly for natural language to Ansible code generation. In this paper, we describe the design and implementation of the Ansible Lightspeed service and analyze feedback from thousands of real users. We examine diverse performance indicators, classified according to both immediate and extended utilization patterns along with user sentiments. The analysis shows that the user acceptance rate of Ansible Lightspeed suggestions is higher than comparable tools that are more general and not specific to a programming language. This remains true even after we use much more stringent criteria for what is considered an accepted model suggestion, discarding suggestions which were heavily edited after being accepted. The relatively high acceptance rate results in higher-than-expected user retention and generally positive user feedback. This paper provides insights on how a comparatively small, dedicated model performs on a domain-specific language and more importantly, how it is received by users.
Determinants of LLM-assisted Decision-Making

Decision-making is a fundamental capability in everyday life. Large Language Models (LLMs) provide multifaceted support in enhancing human decision-making processes. However, understanding the influencing factors of LLM-assisted decision-making is crucial for enabling individuals to utilize LLM-provided advantages and minimize associated risks in order to make more informed and better decisions. This study presents the results of a comprehensive literature analysis, providing a structural overview and detailed analysis of determinants impacting decision-making with LLM support. In particular, we explore the effects of technological aspects of LLMs, including transparency and prompt engineering, psychological factors such as emotions and decision-making styles, as well as decision-specific determinants such as task difficulty and accountability. In addition, the impact of the determinants on the decision-making process is illustrated via multiple application scenarios. Drawing from our analysis, we develop a dependency framework that systematizes possible interactions in terms of reciprocal interdependencies between these determinants. Our research reveals that, due to the multifaceted interactions with various determinants, factors such as trust in or reliance on LLMs, the user's mental model, and the characteristics of information processing are identified as significant aspects influencing LLM-assisted decision-making processes. Our findings can be seen as crucial for improving decision quality in human-AI collaboration, empowering both users and organizations, and designing more effective LLM interfaces. Additionally, our work provides a foundation for future empirical investigations on the determinants of decision-making assisted by LLMs.
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models

In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements. While the low-rank adaptation (LoRA) module within DreamBooth offers a reduction in trainable parameters, it introduces a pronounced sensitivity to hyperparameters, leading to a compromise between parameter efficiency and the quality of T2I personalized image synthesis. Addressing these constraints, we introduce \textbf{\textit{DiffuseKronA}}, a novel Kronecker product-based adaptation module that not only significantly reduces the parameter count by 35\% and 99.947\% compared to LoRA-DreamBooth and the original DreamBooth, respectively, but also enhances the quality of image synthesis. Crucially, \textit{DiffuseKronA} mitigates the issue of hyperparameter sensitivity, delivering consistent high-quality generations across a wide range of hyperparameters, thereby diminishing the necessity for extensive fine-tuning. Furthermore, a more controllable decomposition makes \textit{DiffuseKronA} more interpretable and even can achieve up to a 50\% reduction with results comparable to LoRA-Dreambooth. Evaluated against diverse and complex input images and text prompts, \textit{DiffuseKronA} consistently outperforms existing models, producing diverse images of higher quality with improved fidelity and a more accurate color distribution of objects, all the while upholding exceptional parameter efficiency, thus presenting a substantial advancement in the field of T2I generative modeling. Our project page, consisting of links to the code, and pre-trained checkpoints, is available at https://diffusekrona.github. io/.
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning

Recently, the application of Contrastive Representation Learning (CRL) in learning with noisy labels (LNL) has shown promising advancements due to its remarkable ability to learn well-distributed representations for better distinguishing noisy labels. However, CRL is mainly used as a pre-training technique, leading to a complicated multi-stage training pipeline. We also observed that trivially combining CRL with supervised LNL methods decreases performance. Using different images from the same class as negative pairs in CRL creates optimization conflicts between CRL and the supervised loss. To address these two issues, we propose an end-to-end PLReMix framework that avoids the complicated pipeline by introducing a Pseudo-Label Relaxed (PLR) contrastive loss to alleviate the conflicts between losses. This PLR loss constructs a reliable negative set of each sample by filtering out its inappropriate negative pairs that overlap at the top k indices of prediction probabilities, leading to more compact semantic clusters than vanilla CRL. Furthermore, a two-dimensional Gaussian Mixture Model (GMM) is adopted to distinguish clean and noisy samples by leveraging semantic information and model outputs simultaneously, which is expanded on the previously widely used one-dimensional form. The PLR loss and a semi-supervised loss are simultaneously applied to train on the GMM divided clean and noisy samples. Experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed method. Our proposed PLR loss is scalable, which can be easily integrated into other LNL methods and boost their performance. Codes will be available.
Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison

Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-year datasets. Moreover, the computational strategies of unsupervised dimensionality reduction often provide the basis for more complicated supervised techniques. Seven unsupervised dimensionality reduction algorithms are tested on hyperspectral data from the HYPSO-1 earth observation satellite. Each particular algorithm is chosen to be representative of a broader collection. The experiments probe the computational complexity, reconstruction accuracy, signal clarity, sensitivity to artifacts, and effects on target detection and classification of the different algorithms. No algorithm consistently outperformed the others across all tests, but some general trends regarding the characteristics of the algorithms did emerge. With half a million pixels, computational time requirements of the methods varied by 5 orders of magnitude, and the reconstruction error varied by about 3 orders of magnitude. A relationship between mutual information and artifact susceptibility was suggested by the tests. The relative performance of the algorithms differed significantly between the target detection and classification tests. Overall, these experiments both show the power of dimensionality reduction and give guidance regarding how to evaluate a technique prior to incorporating it into a processing pipeline.
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scaling properties) of different finetuning methods is still limited. To fill this gap, we conduct systematic experiments studying whether and how different scaling factors, including LLM model size, pretraining data size, new finetuning parameter size and finetuning data size, affect the finetuning performance. We consider two types of finetuning -- full-model tuning (FMT) and parameter efficient tuning (PET, including prompt tuning and LoRA), and explore their scaling behaviors in the data-limited regime where the LLM model size substantially outweighs the finetuning data size. Based on two sets of pretrained bilingual LLMs from 1B to 16B and experiments on bilingual machine translation and multilingual summarization benchmarks, we find that 1) LLM finetuning follows a powerbased multiplicative joint scaling law between finetuning data size and each other scaling factor; 2) LLM finetuning benefits more from LLM model scaling than pretraining data scaling, and PET parameter scaling is generally ineffective; and 3) the optimal finetuning method is highly task- and finetuning data-dependent. We hope our findings could shed light on understanding, selecting and developing LLM finetuning methods.
InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning

Jointly learning multiple tasks with a unified model can improve accuracy and data efficiency, but it faces the challenge of task interference, where optimizing one task objective may inadvertently compromise the performance of another. A solution to mitigate this issue is to allocate task-specific parameters, free from interference, on top of shared features. However, manually designing such architectures is cumbersome, as practitioners need to balance between the overall performance across all tasks and the higher computational cost induced by the newly added parameters. In this work, we propose \textit{InterroGate}, a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency. We employ a learnable gating mechanism to automatically balance the shared and task-specific representations while preserving the performance of all tasks. Crucially, the patterns of parameter sharing and specialization dynamically learned during training, become fixed at inference, resulting in a static, optimized MTL architecture. Through extensive empirical evaluations, we demonstrate SoTA results on three MTL benchmarks using convolutional as well as transformer-based backbones on CelebA, NYUD-v2, and PASCAL-Context.
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning

Benchmarks play a crucial role in the development and analysis of reinforcement learning (RL) algorithms. We identify that existing benchmarks used for research into open-ended learning fall into one of two categories. Either they are too slow for meaningful research to be performed without enormous computational resources, like Crafter, NetHack and Minecraft, or they are not complex enough to pose a significant challenge, like Minigrid and Procgen. To remedy this, we first present Craftax-Classic: a ground-up rewrite of Crafter in JAX that runs up to 250x faster than the Python-native original. A run of PPO using 1 billion environment interactions finishes in under an hour using only a single GPU and averages 90% of the optimal reward. To provide a more compelling challenge we present the main Craftax benchmark, a significant extension of the Crafter mechanics with elements inspired from NetHack. Solving Craftax requires deep exploration, long term planning and memory, as well as continual adaptation to novel situations as more of the world is discovered. We show that existing methods including global and episodic exploration, as well as unsupervised environment design fail to make material progress on the benchmark. We believe that Craftax can for the first time allow researchers to experiment in a complex, open-ended environment with limited computational resources.
NeuralSI: Neural Design of Semantic Interaction for Interactive Deep Learning

An increasing number of studies have utilized interactive deep learning as the analytic model of visual analytics systems for complex sensemaking tasks. In these systems, traditional interactive dimensionality reduction (DR) models are commonly utilized to build a bi-directional bridge between high-dimensional deep learning representations and low-dimensional visualizations. While these systems better capture analysts' intents in the context of human-in-the-loop interactive deep learning, traditional DR cannot support several desired properties for visual analytics, including out-of-sample extensions, stability, and real-time inference. To avoid this issue, we propose the neural design framework of semantic interaction for interactive deep learning. In our framework, we replace the traditional DR with a neural projection network and append it to the deep learning model as the task-specific output layer. Therefore, the analytic model (deep learning) and visualization method (interactive DR) form one integrated end-to-end trainable deep neural network. In order to understand the performance of the neural design in comparison to the state-of-the-art, we systematically performed two complementary studies, a human-centered qualitative case study and an algorithm-centered simulation-based quantitative experiment. The results of these studies indicate that the neural design can give semantic interaction systems substantial advantages while still keeping comparable inference ability compared to the state-of-the-art model.
Convergence to stationary measures for the half-space log-gamma polymer

We consider the point-to-point half-space log-gamma polymer model in the unbound phase. We prove that the free energy increment process on the anti-diagonal path converges to the top marginal of a two-layer Markov chain with an explicit description, which can be interpreted as two random walks conditioned softly never to intersect. This limiting law is a stationary measure for the polymer on the anti-diagonal path. The starting point of our analysis is an embedding of the free energy into the half-space log-gamma line ensemble recently constructed by Barraquand, Corwin, and Das. Given the Gibbsian line ensemble structure, the main contribution of our work lies in developing a route to access and prove convergence to stationary measures via line ensemble techniques. Our argument relies on a description of the limiting behavior of two softly non-intersecting random walk bridges around their starting point, a result established in this paper that may be of independent interest.
Testing The Weak Cosmic Censorship Conjecture in Short Haired Black Holes

The Weak Cosmic Censorship Conjecture is a hypothesis regarding the properties of event horizons and singularities during the formation of black holes, stating that singularities are always encompassed by the event horizons(TEH) of black holes, thus preventing naked singularities from affecting the causal structure of spacetime. In this paper, we explore the Weak Cosmic Censorship Conjecture in the context of rotating hairy black holes, aiming to understand the impact of hairiness on the conjecture for Kerr black holes. We investigate whether TEH of rotating hairy black holes can be disrupted by incoming test particles and scalar fields. When test particles and scalar fields incident on the rotating hairy black hole are found. In extreme cases, when considering a second order approximation, if the parameter $\kappa$ (The parameter here is a function related to hair strength $\kappa(Q_m^{2k})$. )falls within the range of $0<\kappa<\sqrt{1/3}$, TEH of a hairy black hole can be disrupted. Conversely, in the range of $\sqrt{1/3}<\kappa<1$, TEH of a hairy black hole cannot be disrupted. When considering the second order approximation in near extreme cases, the parameter $\kappa$, within the range of $0<\kappa<1$, can lead to the disruption of TEH in this spacetime. When an incident scalar field is present, in near extreme conditions, TEH of a rotating short hair black hole cannot be disrupted. Therefore, the value of the parameter $\kappa$ reveals the connection between rotating short hair black holes and the weak cosmic censorship conjecture, indicating that the presence of short hair significantly affects TEH of black holes. This will aid in further understanding the nature of rotating short hair black holes.