Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Correlations in BaZrO3: Structure and Dynamics on the Nanoscale

Barium zirconate BaZrO3 is one of few perovskites that is claimed to retain an average cubic structure down to 0K at ambient pressure, while being energetically very close to a tetragonal phase obtained by condensation of a soft phonon mode at the R-point. Previous studies suggest, however, that the local structure of BaZrO3 may change at low temperature forming nanodomains or a glass-like phase. Here, we investigate the global and local structure of BaZrO3 as a function of temperature and pressure via molecular dynamics simulations using a machine-learned potential with near density functional theory (DFT) accuracy. We show that the softening of the octahedral tilt mode at the R-point gives rise to weak diffuse superlattice reflections at low temperatures and ambient pressure, which are also observed experimentally. However, we do not observe any static nanodomains but rather soft dynamic fluctuations of the ZrO6 octahedra with a correlation length of 2 to 3nm over time-scales of about 1ps. This soft dynamic behaviour is the precursor of a phase transition and explains the emergence of weak superlattice peaks in measurements. On the other hand, when increasing the pressure at 300K we find a phase transition from the cubic to the tetragonal phase at around 16GPa, also in agreement with experimental studies.
Making Large Language Models Perform Better in Knowledge Graph Completion

Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs and enrich the KGs to become better web infrastructure, which can benefit a lot of web-based automatic services. However, research about LLM-based KGC is limited and lacks effective utilization of LLM's inference capabilities, which ignores the important structural information in KGs and prevents LLMs from acquiring accurate factual knowledge. In this paper, we discuss how to incorporate the helpful KG structural information into the LLMs, aiming to achieve structrual-aware reasoning in the LLMs. We first transfer the existing LLM paradigms to structural-aware settings and further propose a knowledge prefix adapter (KoPA) to fulfill this stated goal. KoPA employs structural embedding pre-training to capture the structural information of entities and relations in the KG. Then KoPA informs the LLMs of the knowledge prefix adapter which projects the structural embeddings into the textual space and obtains virtual knowledge tokens as a prefix of the input prompt. We conduct comprehensive experiments on these structural-aware LLM-based KGC methods and provide an in-depth analysis comparing how the introduction of structural information would be better for LLM's knowledge reasoning ability. Our code is released at https://github.com/zjukg/KoPA.
Large Language Models for Propaganda Detection

The prevalence of propaganda in our digital society poses a challenge to societal harmony and the dissemination of truth. Detecting propaganda through NLP in text is challenging due to subtle manipulation techniques and contextual dependencies. To address this issue, we investigate the effectiveness of modern Large Language Models (LLMs) such as GPT-3 and GPT-4 for propaganda detection. We conduct experiments using the SemEval-2020 task 11 dataset, which features news articles labeled with 14 propaganda techniques as a multi-label classification problem. Five variations of GPT-3 and GPT-4 are employed, incorporating various prompt engineering and fine-tuning strategies across the different models. We evaluate the models' performance by assessing metrics such as $F1$ score, $Precision$, and $Recall$, comparing the results with the current state-of-the-art approach using RoBERTa. Our findings demonstrate that GPT-4 achieves comparable results to the current state-of-the-art. Further, this study analyzes the potential and challenges of LLMs in complex tasks like propaganda detection.
Electrical Characteristics of the GEC Reference Cell with Impedance Matching: A Two-Dimensional PIC/MCC Modeling Study

In this paper, the electrical characteristics of the Gaseous Electronics Conference (GEC) reference cell with impedance matching are investigated through a two-dimensional electrostatic implicit Particle-in-Cell/Monte Carlo Collision (PIC/MCC) model in an axisymmetric coordinate system. The coupling between the complex reactor geometry and the external circuit is included via an equivalent capacitance calculated from the electric energy density. The results of this model are compared with experimental measurements and other model calculations and show good agreement. This simulation obtains the plasma kinetics of the capacitively coupled discharge process at low pressure and detailed external circuit responses, including power transmission, reflection, and higher-order harmonics in the circuit, which provides important insights for impedance-matching design in semiconductor plasma processing.
Experimental Designs for Heteroskedastic Variance

Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set of measurement vectors $\mathcal{X}\subset \mathbb{R}^d$ that can be probed to receive noisy linear responses of the form $y=x^{\top}\theta^{\ast}+\eta$. Here $\theta^{\ast}\in \mathbb{R}^d$ is an unknown parameter vector, and $\eta$ is independent mean-zero $\sigma_x^2$-sub-Gaussian noise defined by a flexible heteroskedastic variance model, $\sigma_x^2 = x^{\top}\Sigma^{\ast}x$. Assuming that $\Sigma^{\ast}\in \mathbb{R}^{d\times d}$ is an unknown matrix, we propose, analyze and empirically evaluate a novel design for uniformly bounding estimation error of the variance parameters, $\sigma_x^2$. We demonstrate the benefits of this method with two adaptive experimental design problems under heteroskedastic noise, fixed confidence transductive best-arm identification and level-set identification and prove the first instance-dependent lower bounds in these settings. Lastly, we construct near-optimal algorithms and demonstrate the large improvements in sample complexity gained from accounting for heteroskedastic variance in these designs empirically.
Automatic and Efficient Customization of Neural Networks for ML Applications

ML APIs have greatly relieved application developers of the burden to design and train their own neural network models -- classifying objects in an image can now be as simple as one line of Python code to call an API. However, these APIs offer the same pre-trained models regardless of how their output is used by different applications. This can be suboptimal as not all ML inference errors can cause application failures, and the distinction between inference errors that can or cannot cause failures varies greatly across applications. To tackle this problem, we first study 77 real-world applications, which collectively use six ML APIs from two providers, to reveal common patterns of how ML API output affects applications' decision processes. Inspired by the findings, we propose ChameleonAPI, an optimization framework for ML APIs, which takes effect without changing the application source code. ChameleonAPI provides application developers with a parser that automatically analyzes the application to produce an abstract of its decision process, which is then used to devise an application-specific loss function that only penalizes API output errors critical to the application. ChameleonAPI uses the loss function to efficiently train a neural network model customized for each application and deploys it to serve API invocations from the respective application via existing interface. Compared to a baseline that selects the best-of-all commercial ML API, we show that ChameleonAPI reduces incorrect application decisions by 43%.
Inversion of multimode surface wave data: evidence for sub-lithospheric anisotropy

Summary. Multiparameter inversions of multimode dispersion data are performed for two large regions: the Pacific Ocean and North America. Anisotropy is taken into account by considering transversely isotropic structures with a vertical axis of symmetry. Two fundamental questions are studied in detail: (1) how to make the inverted models consistent when using different sets of parameters, (2) what is the significance of transversely isotropic inversion for the actual Earth's structure? It is proved that full consistency of the inverted models can be achieved by properly taking into account some a priori informations on the model and it is shown that the use of transversely isotropic models with vertical axis of symmetry does not cause severe limitations when interpreting the data. The models we have obtained are discussed in the light of these investigations. Considering an olivine-rich upper mantle, we make a tentative interpretation of these models in terms of preferred orientation of the a-axis of the crystals in one fixed horizontal direction.
Guideline Learning for In-context Information Extraction

Large language models (LLMs) can perform a new task by merely conditioning on task instructions and a few input-output examples, without optimizing any parameters. This is called In-Context Learning (ICL). In-context Information Extraction (IE) has recently garnered attention in the research community. However, the performance of In-context IE generally lags behind the state-of-the-art supervised expert models. We highlight a key reason for this shortfall: underspecified task description. The limited-length context struggles to thoroughly express the intricate IE task instructions and various edge cases, leading to misalignment in task comprehension with humans. In this paper, we propose a Guideline Learning (GL) framework for In-context IE which reflectively learns and follows guidelines. During the learning phrase, GL automatically synthesizes a set of guidelines based on a few error cases, and during inference, GL retrieves helpful guidelines for better ICL. Moreover, we propose a self-consistency-based active learning method to enhance the efficiency of GL. Experiments on event extraction and relation extraction show that GL can significantly improve the performance of in-context IE.
HI-SLAM: Monocular Real-Time Dense Mapping With Hybrid Implicit Fields

In this letter, we present a neural field-based real-time monocular mapping framework for accurate and dense Simultaneous Localization and Mapping (SLAM). Recent neural mapping frameworks show promising results, but rely on RGB-D or pose inputs, or cannot run in real-time. To address these limitations, our approach integrates dense-SLAM with neural implicit fields. Specifically, our dense SLAM approach runs parallel tracking and global optimization, while a neural field-based map is constructed incrementally based on the latest SLAM estimates. For the efficient construction of neural fields, we employ multi-resolution grid encoding and signed distance function (SDF) representation. This allows us to keep the map always up-to-date and adapt instantly to global updates via loop closing. For global consistency, we propose an efficient $Sim(3)$-based pose graph bundle adjustment (PGBA) approach to run online loop closing and mitigate the pose and scale drift. To enhance depth accuracy further, we incorporate learned monocular depth priors. We propose a novel joint depth and scale adjustment (JDSA) module to solve the scale ambiguity inherent in depth priors. Extensive evaluations across synthetic and real-world datasets validate that our approach outperforms existing methods in accuracy and map completeness while preserving real-time performance.
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding

Developing text mining approaches to mine aspects from customer reviews has been well-studied due to its importance in understanding customer needs and product attributes. In contrast, it remains unclear how to predict the future emerging aspects of a new product that currently has little review information. This task, which we named product aspect forecasting, is critical for recommending new products, but also challenging because of the missing reviews. Here, we propose ForeSeer, a novel textual mining and product embedding approach progressively trained on temporal product graphs for this novel product aspect forecasting task. ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews. A key novelty of our method is to jointly provide review, product, and aspect embeddings that are both time-sensitive and less affected by extremely imbalanced aspect frequencies. We evaluated ForeSeer on a real-world product review system containing 11,536,382 reviews and 11,000 products over 3 years. We observe that ForeSeer substantially outperformed existing approaches with at least 49.1% AUPRC improvement under the real setting where aspect associations are not given. ForeSeer further improves future link prediction on the product graph and the review aspect association prediction. Collectively, Foreseer offers a novel framework for review forecasting by effectively integrating review text, product network, and temporal information, opening up new avenues for online shopping recommendation and e-commerce applications.
Transferable Availability Poisoning Attacks

We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its training data. Existing poisoning strategies can achieve the attack goal but assume the victim to employ the same learning method as what the adversary uses to mount the attack. In this paper, we argue that this assumption is strong, since the victim may choose any learning algorithm to train the model as long as it can achieve some targeted performance on clean data. Empirically, we observe a large decrease in the effectiveness of prior poisoning attacks if the victim uses a different learning paradigm to train the model and show marked differences in frequency-level characteristics between perturbations generated with respect to different learners and attack methods. To enhance the attack transferability, we propose Transferable Poisoning, which generates high-frequency poisoning perturbations by alternately leveraging the gradient information with two specific algorithms selected from supervised and unsupervised contrastive learning paradigms. Through extensive experiments on benchmark image datasets, we show that our transferable poisoning attack can produce poisoned samples with significantly improved transferability, not only applicable to the two learners used to devise the attack but also for learning algorithms and even paradigms beyond.
Simulación del flujo sanguíneo y su interacción con la pared arterial mediante modelos de elementos finitos

Resumen La biomecanica aplica las leyes de la mecanica a los seres vivos y en particular la hemodinamica estudia el movimiento de la sangre en el sistema cardiovascular. Una parte de las principales arterias son elasticas e interaccionan con el flujo sanguineo, por lo que es mas realista tratar de forma acoplada ambos fenomenos. En esta tesis se desarrolla un modelo de fluido newtoniano incompresible que simula la sangre y un modelo de solido hiperelastico incompresible para la pared arterial, obteniendose los parametros constitutivos de este ultimo con datos experimentales. Las ecuaciones se resuelven numericamente mediante el metodo de los elementos finitos con un tratamiento especifico de la condicion de incompresibilidad. La interaccion fluido-solido se plantea con un metodo particionado con acoplamiento fuerte en el que se utiliza la formulacion ALE para tener en cuenta el movimiento de los contornos del fluido. El modelo de interaccion se aplica a arterias coronarias con geometria real cuyas mallas de hexaedros se generan automaticamente a partir de la directriz y diametros de las arterias. Para simular la condicion de contorno absorbente en el extremo final se desarrolla un modelo unidimensional de interaccion, el cual se acopla al modelo tridimensional en dicho extremo. El modelo final propuesto tiene por tanto doble acoplamiento. Abstract The objective of biomechanics is to apply the laws of mechanics to living beings. In particular haemodynamics study the blood motion in the cardiovascular system. A group of the principal arteries are elastic and interact with the blood flow, so it’s more realistic to consider both phenomena in a coupled way. In this work an incompressible newtonian fluid model is developed to simulate the blood and an incompressible hyperelastic solid model for the arterial wall. The constitutive parameters of the solid are obtained from experimental data. The equations are solved numerically by using the finite element method with a special treatment of the incompressibility condition. Fluid-solid interaction is set through a partitioned method with strong coupling where the ALE formulation is used to take into account the movement of the fluid boundaries. The interaction model is applied to coronary arteries with real geometry whose meshes are generated automatically from the longitudinal profile and diameters of the arteries. In order to simulate absorbent boundary conditions at the outlet a one-dimensional interaction model is developed. This 1D model is coupled to the three-dimensional model at the outlet, so the final model consists in the interaction of two coupled systems. Keywords: haemodynamics, fluid-structure interaction, coupled models, newtonian fluid, hyperelasticity, finite element method, mesh generation.
Exploring the Maze of Multilingual Modeling

Multilingual language models have gained significant attention in recent years, enabling the development of applications that meet diverse linguistic contexts. In this paper, we present a comprehensive evaluation of three popular multilingual language models: mBERT, XLM-R, and GPT-3. We assess their performance across a diverse set of languages, with a focus on understanding the impact of resource availability (general and model-specific), language family, script type, and word order on model performance, under two distinct tasks - text classification and text generation. Our findings reveal that while the amount of language-specific pretraining data plays a crucial role in model performance, we also identify other factors such as general resource availability, language family, and script type, as important features. We hope that our study contributes to a deeper understanding of multilingual language models to enhance their performance across languages and linguistic contexts.
Linking the Metallicity Enrichment History to the Star Formation History: An SFH-regulated Chemical Evolution Model and Its Implications for the Gas Cycling Process

The metallicity enrichment history (MEH) of a galaxy is determined by its star formation history (SFH) and the gas cycling process. In this paper, we construct a chemical evolution model that is regulated by the SFH of the system. In this SFH-regulated model, the evolution of all other variables, including the MEH, can be determined by the SFH. We test this model on six locally isolated dwarf galaxies covering three dwarf types that were observed by the Local Cosmology from Isolated Dwarfs (LCID) project. The SFHs and MEHs of these LCID galaxies are relatively reliable because they have been measured from deep color–magnitude diagrams that reach the main-sequence turnoff stars with good photometric accuracy. With simple assumptions of the star formation law and the mass-dependent outflows, our SFH-regulated model successfully reproduces the MEHs of all six LCID galaxies from their SFHs, with only one free parameter, the wind efficiency η ∼ 1.0, for all six galaxies. This model provides a physically motivated link that directly connects the SFH and MEH of a galaxy, which will be useful to accommodate into the state-of-the-art stellar population synthesis models to help relieve the nuisance of the heavy degeneracy between the ages and metallicities of the stellar populations.
Floating binary planets from ejections during close stellar encounters

The discovery of planetary systems beyond our solar system has posed challenges to established theories of planetary formation. Planetary orbits display a variety of architectures not predicted by first principles, and free-floating planets appear ubiquitous. The recent discovery of candidate Jupiter Mass Binary Objects (JuMBOs) by the James Webb Space Telescope (JWST) further expanded this enigma. Here, by means of high-accuracy, direct $N$-body simulations, we evaluate the possibility that JuMBOs may form as a result of ejection after a close stellar flyby. We consider a system of two Jupiter-like planets moving in circular orbits with velocities $v_1$ and $v_2$ at distances $a_1$ and $a_2$ around a Sun-like star. The interloper is another Sun-like star approaching with asymptotic velocity $v_\infty$. We find that JuMBOs can indeed be formed upon ejection if the two planets are nearly aligned as the interloper reaches the closest approach. The ratio of the cross-section of JuMBOs production to that of single ejected free-floating planets can approach $\sim 20\%$ for $v_\infty/v_2\sim 0.1 - 0.2$ and $a_1/a_2\sim 0.65-0.7$. JuMBOs formed via this channel are expected to have an average semi-major axis comparable to $\Delta a~\sim 3(a_2-a_1)$ and high eccentricity, with a distinctive superthermal distribution which can help to observationally identify this formation channel and distinguish it from primordial formation. We determine an upper limit on the JuMBO formation efficiency per planetary system. In very dense star clusters like the Trapezium in the Orion Nebula, this efficiency can reach several tens of percent. If the ejection channel is confirmed for these or future JWST observations, these JuMBOs will directly inform us of the conditions where these giant planets formed in protoplanetary disks, putting stringent constraints on the giant planet formation theory.
Modeling motor control in continuous-time Active Inference: a survey

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matching internal predictions with sensations. Active Inference offers a modern formulation of these ideas, in terms of inferential mechanisms and prediction-error-based control, which can be linked to neural mechanisms of living organisms. This article provides a technical illustration of Active Inference models in continuous time and a brief survey of Active Inference models that solve four kinds of control problems; namely, the control of goal-directed reaching movements, active sensing, the resolution of multisensory conflict during movement and the integration of decision-making and motor control. Crucially, in Active Inference, all these different facets of motor control emerge from the same optimization process - namely, the minimization of Free Energy - and do not require designing separate cost functions. Therefore, Active Inference provides a unitary perspective on various aspects of motor control that can inform both the study of biological control mechanisms and the design of artificial and robotic systems.