Miracle-dz/UniFAR-FTUs huggingface dataset:
Origin of the Gabbro–Peridotite Association from the Northern Apennine Ophiolites (Italy)

The Northern Apennine ophiolites are remnants of the Middle Jurassic---Early Cretaceous lithosphere from the Ligurian Tethys. New trace element and Nd---Sr isotope investigations were performed on: (1) the rare gabbros associated with the subcontinental mantle rocks from the External Liguride ophiolites; (2) the gabbro--peridotite association from the poorly known ophiolitic bodies from Cecina valley (Southern Tuscany). Clinopyroxenes from the External Liguride and Cecina valley gabbros have similar trace element compositions, which are consistent with formation from normal mid-ocean ridge basalt (N-MORB) magmas. Sm---Nd mineral isochron ages are 179 9Ma for an External Liguride gabbro and 170 13Ma and 173 5 4 8Ma for two different gabbroic bodies from the Cecina valley ophiolites. These ages are interpreted to date the igneous crystallization of the gabbros and are slightly older than the oldest pelagic sediments of the Ligurian Tethys. Initial eNd (þ8 5 toþ8 9) and Sr/Sr of clinopyroxene are consistent with the interpretation that the studied gabbros were derived from N-MORB magmas. The least serpentinized mantle rocks from the Cecina valley ophiolites are porphyroclastic spinel lherzolites displaying a residual geochemical signature. They are similar to the least depleted residual peridotites from modern oceans. Nd and Sr isotopic ratios for separated mantle clinopyroxene are respectively higher (e.g. eNd 1⁄4 þ11) and lower than those of clinopyroxene from associated gabbros at the time of the gabbro intrusion. The gabbro---peridotite associations from the Northern Apennine ophiolites record the progression of the rifting process that led to opening of the Ligurian Tethys.
Adaptive Transfer of Graph Neural Networks for Few-Shot Molecular Property Prediction

Few-Shot Molecular Property Prediction (FSMPP) is an improtant task on drug discovery, which aims to learn transferable knowledge from base property prediction tasks with sufficient data for predicting novel properties with few labeled molecules. Its key challenge is how to alleviate the data scarcity issue of novel properties. Pretrained Graph Neural Network (GNN) based FSMPP methods effectively address the challenge by pre-training a GNN from large-scale self-supervised tasks and then finetuning it on base property prediction tasks to perform novel property prediction. However, in this paper, we find that the GNN finetuning step is not always effective, which even degrades the performance of pretrained GNN on some novel properties. This is because these molecule-property relationships among molecules change across different properties, which results in the finetuned GNN overfits to base properties and harms the transferability performance of pretrained GNN on novel properties. To address this issue, in this paper, we propose a novel Adaptive Transfer framework of GNN for FSMPP, called ATGNN, which transfers the knowledge of pretrained and finetuned GNNs in a task-adaptive manner to adapt novel properties. Specifically, we first regard the pretrained and finetuned GNNs as model priors of target-property GNN. Then, a task-adaptive weight prediction network is designed to leverage these priors to predict target GNN weights for novel properties. Finally, we combine our ATGNN framework with existing FSMPP methods for FSMPP. Extensive experiments on four real-world datasets, i.e., Tox21, SIDER, MUV, and ToxCast, show the effectiveness of our ATGNN framework.
Improved Culture Methods for Human Coronaviruses HCoV‐OC43, HCoV‐229E, and HCoV‐NL63

HCoV‐OC43, HCoV‐229E, HCoV‐NL63, and HCoV‐HKU1 are four of the seven known human coronaviruses (HCoVs) and, unlike the highly pathogenic SARS‐CoV, MERS‐CoV, and SARS‐CoV‐2, these four so‐called seasonal HCoVs generally cause mild upper‐respiratory‐tract illness. As Biosafety Level 2 (BSL‐2) pathogens, the seasonal HCoVs are more accessible and can be used as surrogates for studying the highly pathogenic HCoVs. However, scientists have for many years found these difficult to study because of the lack of a universal culture system and the inability of typical culture methods to yield high‐titer infectious stocks. We have developed assays to grow and quantify infectious virus and viral RNA for HCoV‐OC43, ‐229E, and ‐NL63. We identified which immortalized cell lines should be used to optimize the replication of HCoV‐OC43, ‐229E, and ‐NL63 in order to generate high titers (Vero E6, Huh‐7, and LLC‐MK2 cells, respectively). Here we present protocols for improved propagation and quantification of each seasonal HCoV. © 2023 Wiley Periodicals LLC.
MICA: A multi-omics method to predict gene regulatory networks in 1 early human embryos 2

20 21 Recent advances in single cell omics have been transformative in the characterisation of 22 challenging to study biological contexts, including when the source material is precious, such 23 as the early human embryo. Single cell datasets are technically challenging to infer 24 transcription factor-gene regulatory interactions, due to low read-depth leading to zero inflated 25 data. Here we have systematically assessed the application of four different machine learning 26 linear or non-linear gene regulatory network prediction strategies to single cell simulated and 27 human embryo transcriptome datasets. We have also compared how the method of gene 28 expression normalisation impacts on regulatory network predictions. Integrating chromatin 29 accessibility together with transcript expression datasets improved the reproducibility of the 30 predicted gene regulatory networks. We found that the application of a non-linear network 31 prediction method based on mutual information (MI) to single cell transcriptome datasets 32 refined with chromatin accessibility
Characterizing Conical Intersections in DNA/RNA Nucleobases with Multiconfigurational Wave Functions of Varying Active Space Size

We characterize the photochemically relevant conical intersections between the lowest-lying accessible electronic excited states of the different DNA/RNA nucleobases using Cholesky decomposition-based complete active space self-consistent field (CASSCF) algorithms. We benchmark two different basis set contractions and several active spaces for each nucleobase and conical intersection type, measuring for the first time how active space size affects conical intersection topographies in these systems and the potential implications these may have toward their description of photoinduced phenomena. Our results show that conical intersection topographies are highly sensitive to the electron correlation included in the model: by changing the amount (and type) of correlated orbitals, conical intersection topographies vastly change, and the changes observed do not follow any converging pattern toward the topographies obtained with the largest and most correlated active spaces. Comparison across systems shows analogous topographies for almost all intersections mediating population transfer to the dark 1nO/Nπ* states, while no similarities are observed for the “ethylene-like” conical intersection ascribed to mediate the ultrafast decay component to the ground state in all DNA/RNA nucleobases. Basis set size seems to have a minor effect, appearing to be relevant only for purine-based derivatives. We rule out structural changes as a key factor in classifying the different conical intersections, which display almost identical geometries across active space and basis set change, and we highlight instead the importance of correctly describing the electronic states involved at these crossing points. Our work shows that careful active space selection is essential to accurately describe conical intersection topographies and therefore to adequately account for their active role in molecular photochemistry.
Differential heat‐response characteristics of two plastid isoforms of triose phosphate isomerase in tomato

Summary Heat stress causes dysfunction of the carbon‐assimilation metabolism. As a member of Calvin–Benson–Bassham (CBB) cycle, the chloroplast triose phosphate isomerases (TPI) catalyse the interconversion of glyceraldehyde 3‐phosphate (GAP) and dihydroxyacetone phosphate (DHAP). The tomato (Solanum lycopersicum) genome contains two individual SlTPI genes, Solyc10g054870 and Solyc01g111120, which encode the chloroplast‐located proteins SlTPI1 and SlTPI2, respectively. The tpi1 and tpi2 single mutants had no visible phenotypes, but the leaves of their double mutant lines tpi1tpi2 had obviously reduced TPI activity and displayed chlorotic variegation, dysplasic chloroplasts and lower carbon‐assimilation efficiency. In addition to altering carbon metabolism, proteomic data showed that the loss of both SlTPI1 and SlTPI2 severely affected photosystem proteins, reducing photosynthetic capacity. None of these phenotypes was evident in the tpi1 or tpi2 single mutants, suggesting that SlTPI1 and SlTPI2 are functionally redundant. However, the two proteins differed in their responses to heat stress; the protein encoded by the heat‐induced SlTPI2 showed a higher level of thermotolerance than that encoded by the heat‐suppressed SlTPI1. Notably, heat‐induced transcription factors, SlWRKY21 and SlHSFA2/7, which negatively regulated SlTPI1 expression and positively regulated SlTPI2 expression, respectively. Our findings thus reveal that SlTPI1 and SlTPI2 have different thermostabilities and expression patterns in response to heat stress, which have the potential to be applied in thermotolerance strategies in crops.
Panoramic Ray Tracing for Interactive Mixed Reality Rendering Based on 360° RGBD Video

This article presents an interactive panoramic ray tracing method for rendering real-time realistic lighting and shadow effects when virtual objects are inserted in 360$^{\circ }$∘ RGBD videos. First, we approximate the geometry of the real scene. We propose a sparse sampling ray generation method to speed up the tracing process by reducing the number of rays that need to be emitted in ray tracing. After that, an irradiance estimation channel is introduced to generate noisy Monte Carlo images. Finally, the final result is smoothed and synthesized by interpolation, temporal filtering, and differential rendering. We tested our method in a number of natural and synthesized scenes and compared our method with results from ground truth and image-based illumination methods. The results show that our method can generate visually realistic frames for dynamic virtual objects in 360$^{\circ }$∘ RGBD videos in real time, making the rendering results more natural and believable.
pysentimiento: A Python Toolkit for Opinion Mining and Social NLP tasks

In recent years, the extraction of opinions and information from user-generated text has attracted a lot of interest, largely due to the unprecedented volume of content in Social Media. However, social researchers face some issues in adopting cutting-edge tools for these tasks, as they are usually behind commercial APIs, unavailable for other languages than English, or very complex to use for non-experts. To address these issues, we present pysentimiento, a comprehensive multilingual Python toolkit designed for opinion mining and other Social NLP tasks. This open-source library brings state-of-the-art models for Spanish, English, Italian, and Portuguese in an easy-to-use Python library, allowing researchers to leverage these techniques. We present a comprehensive assessment of performance for several pre-trained language models across a variety of tasks, languages, and datasets, including an evaluation of fairness in the results.
Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning

While bisimulation-based approaches hold promise for learning robust state representations for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up to par. In some instances, their performance has even significantly underperformed alternative methods. We aim to understand why bisimulation methods succeed in online settings, but falter in offline tasks. Our analysis reveals that missing transitions in the dataset are particularly harmful to the bisimulation principle, leading to ineffective estimation. We also shed light on the critical role of reward scaling in bounding the scale of bisimulation measurements and of the value error they induce. Based on these findings, we propose to apply the expectile operator for representation learning to our offline RL setting, which helps to prevent overfitting to incomplete data. Meanwhile, by introducing an appropriate reward scaling strategy, we avoid the risk of feature collapse in representation space. We implement these recommendations on two state-of-the-art bisimulation-based algorithms, MICo and SimSR, and demonstrate performance gains on two benchmark suites: D4RL and Visual D4RL. Codes are provided at \url{https://github.com/zanghyu/Offline_Bisimulation}.
The Not-So-Secret Fourth Parameter of Quantum Codes

A quantum code is usually referred to using three parameters: the number of physical qubits $n$, the dimension of the codespace $K$, and the distance of the code $d$. But these three parameters are not the only invariants of a quantum code under code equivalence - there is also a ``not-so-secret fourth parameter"$\G$, the transversal group, which is of fundamental importance for fault tolerance. It was shown in a recent paper of Omanakuttan and Gross that in certain cases the Dicke state mapping from spin codes to multiqubit codes preserves distance and logical gates. We prove this result in general, and use it to construct three families of non-additive codes that strictly outperform stabilizer codes with regards to the ``new"parameter $\G$. Our first family implements transversal gates which are impossible for any stabilizer code. Our second family has smaller length $n$ than any stabilizer code with the same distance $d$ and transversal group $\G$. And our third family implements the famous $ T $ gate transversally for larger distance $ d $ and shorter length $ n $ than any known stabilizer codes.
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants

In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose STEER, a steering detection model that predicts whether a follow-up turn is a user's attempt to steer the previous command. Constructing a training dataset for steering use cases poses challenges due to the cold-start problem. To overcome this, we developed heuristic rules to sample opt-in usage data, approximating positive and negative samples without any annotation. Our experimental results show promising performance in identifying steering intent, with over 95% accuracy on our sampled data. Moreover, STEER, in conjunction with our sampling strategy, aligns effectively with real-world steering scenarios, as evidenced by its strong zero-shot performance on a human-graded evaluation set. In addition to relying solely on user transcripts as input, we introduce STEER+, an enhanced version of the model. STEER+ utilizes a semantic parse tree to provide more context on out-of-vocabulary words, such as named entities that often occur at the sentence boundary. This further improves model performance, reducing error rate in domains where entities frequently appear, such as messaging. Lastly, we present a data analysis that highlights the improvement in user experience when voice assistants support steering use cases.
Datastore Design for Analysis of Police Broadcast Audio at Scale

With policing coming under greater scrutiny in recent years, researchers have begun to more thoroughly study the effects of contact between police and minority communities. Despite data archives of hundreds of thousands of recorded Broadcast Police Communications (BPC) being openly available to the public, a closer look at a large-scale analysis of the language of policing has remained largely unexplored. While this research is critical in understanding a"pre-reflective"notion of policing, the large quantity of data presents numerous challenges in its organization and analysis. In this paper, we describe preliminary work towards enabling Speech Emotion Recognition (SER) in an analysis of the Chicago Police Department's (CPD) BPC by demonstrating the pipelined creation of a datastore to enable a multimodal analysis of composed raw audio files.
Explainable Gated Bayesian Recurrent Neural Network for Non-Markov State Estimation

The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely an explainable gated Bayesian recurrent neural network specifically designed to state estimation under model mismatches. Firstly, we transform the non-Markov state-space model into an equivalent first-order Markov model with memory. It is a generalized transformation that overcomes the limitations of the first-order Markov property and enables recursive filtering. Secondly, by deriving a data-assisted joint state-memory-mismatch Bayesian filtering, we design a Bayesian gated framework that includes a memory update gate for capturing the temporal regularities in state evolution, a state prediction gate with the evolution mismatch compensation, and a state update gate with the observation mismatch compensation. The Gaussian approximation implementation of the filtering process within the gated framework is derived, taking into account the computational efficiency. Finally, the corresponding internal neural network structures and end-to-end training methods are designed. The Bayesian filtering theory enhances the interpretability of the proposed gated network, enabling the effective integration of offline data and prior models within functionally explicit gated units. In comprehensive experiments, including simulations and real-world datasets, the proposed gated network demonstrates superior estimation performance compared to benchmark filters and state-of-the-art deep learning filtering methods.
Model-Based Runtime Monitoring with Interactive Imitation Learning

Robot learning methods have recently made great strides, but generalization and robustness challenges still hinder their widespread deployment. Failing to detect and address potential failures renders state-of-the-art learning systems not combat-ready for high-stakes tasks. Recent advances in interactive imitation learning have presented a promising framework for human-robot teaming, enabling the robots to operate safely and continually improve their performances over long-term deployments. Nonetheless, existing methods typically require constant human supervision and preemptive feedback, limiting their practicality in realistic domains. This work aims to endow a robot with the ability to monitor and detect errors during task execution. We introduce a model-based runtime monitoring algorithm that learns from deployment data to detect system anomalies and anticipate failures. Unlike prior work that cannot foresee future failures or requires failure experiences for training, our method learns a latent-space dynamics model and a failure classifier, enabling our method to simulate future action outcomes and detect out-of-distribution and high-risk states preemptively. We train our method within an interactive imitation learning framework, where it continually updates the model from the experiences of the human-robot team collected using trustworthy deployments. Consequently, our method reduces the human workload needed over time while ensuring reliable task execution. Our method outperforms the baselines across system-level and unit-test metrics, with 23% and 40% higher success rates in simulation and on physical hardware, respectively. More information at https://ut-austin-rpl.github. io/sirius-runtime-monitor/
Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer

A good supervised embedding for a specific machine learning task is only sensitive to changes in the label of interest and is invariant to other confounding factors. We leverage the concept of repeatability from measurement theory to describe this property and propose to use the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. We then propose a novel regularizer, the ICC regularizer, as a complementary component for contrastive losses to guide deep neural networks to produce embeddings with higher repeatability. We use simulated data to explain why the ICC regularizer works better on minimizing the intra-class variance than the contrastive loss alone. We implement the ICC regularizer and apply it to three speech tasks: speaker verification, voice style conversion, and a clinical application for detecting dysphonic voice. The experimental results demonstrate that adding an ICC regularizer can improve the repeatability of learned embeddings compared to only using the contrastive loss; further, these embeddings lead to improved performance in these downstream tasks.
Unveiling microstructural damage for leakage current degradation in SiC Schottky diode after heavy ions irradiation under 200 V

Single-event burnout and single-event leakage current (SELC) in SiC power devices induced by heavy ions severely limit their space application, and the underlying mechanism is still unclear. One fundamental problem is lack of high-resolution characterization of radiation damage in the irradiated SiC power devices, which is a crucial indicator of the related mechanism. In this letter, high-resolution transmission electron microscopy (TEM) was used to characterize the radiation damage in the 1437.6 MeV 181Ta-irradiated SiC junction barrier Schottky diode under 200 V. The amorphous radiation damage with about 52 nm in diameter and 121 nm in length at the Schottky metal (Ti)-semiconductor (SiC) interface was observed. More importantly, in the damage site the atomic mixing of Ti, Si, and C was identified by electron energy loss spectroscopy and high-angle annular dark-field scanning TEM. It indicates that the melting of the Ti-SiC interface induced by localized Joule heating is responsible for the amorphization and the formation of titanium silicide, titanium carbide, or ternary phases. These modifications at nanoscale in turn cause the localized degradation of the Schottky contact, resulting in the permanent increase in leakage current. This experimental study provides very valuable clues to thorough understanding of the SELC mechanism in SiC diode.
Coreset Markov Chain Monte Carlo

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during inference in order to reduce computational cost. However, state of the art methods for tuning coreset weights are expensive, require nontrivial user input, and impose constraints on the model. In this work, we propose a new method -- Coreset MCMC -- that simulates a Markov chain targeting the coreset posterior, while simultaneously updating the coreset weights using those same draws. Coreset MCMC is simple to implement and tune, and can be used with any existing MCMC kernel. We analyze Coreset MCMC in a representative setting to obtain key insights about the convergence behaviour of the method. Empirical results demonstrate that Coreset MCMC provides higher quality posterior approximations and reduced computational cost compared with other coreset construction methods. Further, compared with other general subsampling MCMC methods, we find that Coreset MCMC has a higher sampling efficiency with competitively accurate posterior approximations.