Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational discovery of microstructured composites with optimal stiffness-toughness trade-offs

The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and find microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously found through trial and error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics.
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias

We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature. Position bias captures the tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. Through numerous experiments on four diverse real-world datasets, we study position bias in multiple LLM models such as GPT 3.5-Turbo, Llama-2, and Dolly-v2, as well as state-of-the-art pretrained encoder-decoder abstractive summarization models such as Pegasus and BART. Our findings lead to novel insights and discussion on performance and position bias of models for zero-shot summarization tasks.
Anisotropy of the anomalous Hall effect in the altermagnet candidate Mn$_5$Si$_3$ films

Altermagnets are compensated magnets belonging to spin symmetry groups that allow alternating spin polarizations both in the coordinate space of the crystal and in the momentum space of the electronic structure. In these materials the anisotropic local crystal environment of the different sublattices lowers the symmetry of the system so that the opposite-spin sublattices are connected only by rotations, which results in an unconventional spin-polarized band structure in the momentum space. This low symmetry of the crystal structure is expected to be reflected in the anisotropy of the anomalous Hall effect. In this work, we study the anisotropy of the anomalous Hall effect in epitaxial thin films of Mn$_5$Si$_3$, an altermagnetic candidate material. We first demonstrate a change in the relative N\'eel vector orientation when rotating the external field orientation through systematic changes in both the anomalous Hall effect and the anisotropic longitudinal magnetoresistance. We then show that the anomalous Hall effect in this material is anisotropic with the N\'eel vector orientation relative to the crystal structure and that this anisotropy requires high crystal quality and unlikely correlates with the magnetocrystalline anisotropy. Our results provide further systematic support to the case for considering epitaxial thin films of Mn$_5$Si$_3$ as an altermagnetic candidate material.
Lightweight Fish Classification Model for Sustainable Marine Management: Indonesian Case

The enormous demand for seafood products has led to exploitation of marine resources and near-extinction of some species. In particular, overfishing is one the main issues in sustainable marine development. In alignment with the protection of marine resources and sustainable fishing, this study proposes to advance fish classification techniques that support identifying protected fish species using state-of-the-art machine learning. We use a custom modification of the MobileNet model to design a lightweight classifier called M-MobileNet that is capable of running on limited hardware. As part of the study, we compiled a labeled dataset of 37,462 images of fish found in the waters of the Indonesian archipelago. The proposed model is trained on the dataset to classify images of the captured fish into their species and give recommendations on whether they are consumable or not. Our modified MobileNet model uses only 50\% of the top layer parameters with about 42% GTX 860M utility and achieves up to 97% accuracy in fish classification and determining its consumability. Given the limited computing capacity available on many fishing vessels, the proposed model provides a practical solution to on-site fish classification. In addition, synchronized implementation of the proposed model on multiple vessels can supply valuable information about the movement and location of different species of fish.
A Survey Analyzing Generalization in Deep Reinforcement Learning

Reinforcement learning research obtained significant success and attention with the utilization of deep neural networks to solve problems in high dimensional state or action spaces. While deep reinforcement learning policies are currently being deployed in many different fields from medical applications to self driving vehicles, there are still ongoing questions the field is trying to answer on the generalization capabilities of deep reinforcement learning policies. In this paper, we will outline the fundamental reasons why deep reinforcement learning policies encounter overfitting problems that limit their robustness and generalization capabilities. Furthermore, we will formalize and unify the diverse solution approaches to increase generalization, and overcome overfitting in state-action value functions. We believe our study can provide a compact systematic unified analysis for the current advancements in deep reinforcement learning, and help to construct robust deep neural policies with improved generalization abilities.
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning

Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, lines) and textual components (e.g., labels, legends) poses challenges for general-purpose multimodal models. While vision-language models trained on chart data excel in comprehension, they struggle with generalization. To address these challenges, we propose ChartAssistant, a chart-based vision-language model for universal chart comprehension and reasoning. ChartAssistant leverages ChartSFT, a comprehensive dataset covering diverse chart-related tasks with basic (e.g. bars and pies) and specialized (e.g. radars, and bubbles) chart types. It undergoes a two-stage training process, starting with pre-training on chart-to-table parsing to align chart and text, followed by multitask instruction-following fine-tuning. This approach enables ChartAssistant to achieve competitive performance across various chart tasks. Experimental results demonstrate significant performance gains over the state-of-the-art UniChart and Chartllama method, especially outperforming them on real-world chart data with zero-shot setting. The code and data are available at https://github.com/OpenGVLab/ChartAst.
From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization

This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of ``offline"data examples. While recent years have seen a flurry of work on applying various machine learning techniques to the offline optimization problem, the majority of these work focused on learning a surrogate of the unknown objective function and then applying existing optimization algorithms. While the idea of modeling the unknown objective function is intuitive and appealing, from the learning point of view it also makes it very difficult to tune the objective of the learner according to the objective of optimization. Instead of learning and then optimizing the unknown objective function, in this paper we take on a less intuitive but more direct view that optimization can be thought of as a process of sampling from a generative model. To learn an effective generative model from the offline data examples, we consider the standard technique of ``re-weighting", and our main technical contribution is a probably approximately correct (PAC) lower bound on the natural optimization objective, which allows us to jointly learn a weight function and a score-based generative model. The robustly competitive performance of the proposed approach is demonstrated via empirical studies using the standard offline optimization benchmarks.
Can We Generate Realistic Hands Only Using Convolution?

The enduring inability of image generative models to recreate intricate geometric features, such as those present in human hands and fingers has been an ongoing problem in image generation for nearly a decade. While strides have been made by increasing model sizes and diversifying training datasets, this issue remains prevalent across all models, from denoising diffusion models to Generative Adversarial Networks (GAN), pointing to a fundamental shortcoming in the underlying architectures. In this paper, we demonstrate how this problem can be mitigated by augmenting convolution layers geometric capabilities through providing them with a single input channel incorporating the relative $n$-dimensional Cartesian coordinate system. We show that this drastically improves quality of hand and face images generated by GANs and Variational AutoEncoders (VAE).
Dust and Volatiles in the Disintegrating Comet C/2019 Y4 (ATLAS)

C/2019 Y4 (ATLAS) is an Oort cloud comet with an orbital period of ∼5895 yr. Starting in 2020 March, its nucleus underwent disintegration. In order to investigate the gas and dust properties of C/2019 Y4 (ATLAS) during its disintegration, we obtained long-slit spectra at 3600–8700 Å and BVRI multiband images with the Xinglong 2.16 m Telescope in 2020 April. Our observations revealed that C/2019 Y4 (ATLAS) exhibited strong emission bands of CN, C2, C3, and NH2, which are superimposed on a dust-scattering continuum, typical of cometary spectra in the optical. The production rates of CN, C2, and C3 derived using the Haser model and the corresponding C2/CN and C3/CN ratios suggest that C/2019 Y4 (ATLAS) is a “typical” Oort cloud comet under the A’Hearn classification, although it appears less dusty, as revealed by the Af ρ quantities. Its dust-scattering reflectivity is slightly red, with a gradient of ∼5% per 103Å. We model the reflectivity gradient in terms of porous dust and find that the red color is accounted for by porous dust.
Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation

This paper introduces a novel two-stage machine learning-based surrogate modeling framework to address inverse problems in scientific and engineering fields. In the first stage of the proposed framework, a machine learning model termed the"learner"identifies a limited set of candidates within the input design space whose predicted outputs closely align with desired outcomes. Subsequently, in the second stage, a separate surrogate model, functioning as an"evaluator,"is employed to assess the reduced candidate space generated in the first stage. This evaluation process eliminates inaccurate and uncertain solutions, guided by a user-defined coverage level. The framework's distinctive contribution is the integration of conformal inference, providing a versatile and efficient approach that can be widely applicable. To demonstrate the effectiveness of the proposed framework compared to conventional single-stage inverse problems, we conduct several benchmark tests and investigate an engineering application focused on the micromechanical modeling of fiber-reinforced composites. The results affirm the superiority of our proposed framework, as it consistently produces more reliable solutions. Therefore, the introduced framework offers a unique perspective on fostering interactions between machine learning-based surrogate models in real-world applications.
How does Observational Learning Impact Crowdfunding Outcomes for Backers, Project Creators and Platforms?

Reward-based crowdfunding platforms are becoming increasingly popular to finance projects proposing innovative products, e.g., Kickstarter. One important challenge of this form of financing is the uncertainty in the quality of projects. To mitigate the negative effects of this uncertainty for backers, platforms share information regarding the decisions of earlier backers visiting the project campaign pages. This allows backers not only to rely on their expertise to identify project qualities but also to learn from the decisions of their fellow backers who might be more informed. Current studies on observational learning (OL) in crowdfunding mainly focus on predicting the success chances of projects, and there is a lack of understanding of how OL affects crowdfunding dynamics for backers, project creators and platforms. This paper aims to fill this gap by using a theoretical OL model involving two projects competing for funding from backers who may have differentiated expertness in identifying project quality. By introducing various performance measures for backers, creators and platforms and comparing these measures under OL to the case without learning, we provide a thorough analysis of how OL impacts crowdfunding outcomes. We find that information sharing and OL always benefit backers, especially when the early backers are experts. Regarding the impact of OL on creators and platforms, our analysis reveals two understudied but important aspects: the tightness of the competition for projects according to the availability of funding, and the quality difference among the proposed projects. Additionally, we investigate how OL affects the quality decisions of creators and show that OL increases the incentive for high-quality products, especially in situations where funding is scarce.
Sensitivity of peptide conformational dynamics in carbon nanotubes to directional mechanical forces

In living organisms, proteins and peptides are often under the influence of mechanical forces, especially in confined spaces such as membrane channels, ribosome exit tunnel, or proteasome gate. Due to the directional nature of proteins as polymers with distinct ends, forces have the potential to influence protein conformational dynamics in a direction-dependent manner. In this study, we employed force-probe molecular dynamics simulations to investigate the impact of pulling a peptide through a confined environment versus pushing it in the same direction. Our model involves a carbon nanotube and one of three decapeptides with varying amino acid sequences. The simulations reveal that the difference between pulling the C-terminus and pushing the N-terminus is relatively minor when considering the conformational ensembles of the peptides. The loading rate of the force probe and the amino acid sequence of the peptide play a more important role. However, the application of force to the peptide significantly influences the relative motion of the peptides with respect to the nanotube. Pulling the peptide results in a smoother translocation through the nanotube compared to pushing, although the internal conformational dynamics of the peptide add complexity in either case. Our findings shed light on how short peptides navigate confined spaces within the cellular environment, emphasizing the importance of force-probe simulation studies in understanding these processes.
Long-term Evolution of Relativistic Unmagnetized Collisionless Shocks

We study a relativistic collisionless electron–positron shock propagating into an unmagnetized ambient medium using 2D particle-in-cell simulations of unprecedented duration and size. The shock generates intermittent magnetic structures of increasingly larger size as the simulation progresses. Toward the end of our simulation, at around 26,000 plasma times, the magnetic coherence scale approaches λ ∼ 100 plasma skin depths, both ahead and behind the shock front. We anticipate a continued growth of λ beyond the time span of our simulation, as long as the shock accelerates particles to increasingly higher energies. The post-shock field is concentrated in localized patches, which maintain a local magnetic energy fraction ε B ∼ 0.1. Particles randomly sampling the downstream fields spend most of their time in low field regions (ε B ≪ 0.1) but emit a large fraction of the synchrotron power in the localized patches with strong fields (ε B ∼ 0.1). Our results have important implications for models of gamma-ray burst afterglows.
Free space optical link to a tethered balloon for frequency transfer and chronometric geodesy.

We present the results of an optical link to a corner cube on board a tethered balloon at 300 m altitude including a Tip/Tilt compensation for the balloon tracking. Our experiment measures the carrier phase of a 1542 nm laser, which is the useful signal for frequency comparison of distant clocks. An active phase noise compensation of the carrier is implemented, demonstrating a fractional frequency stability of 8 × 10-19 after 16 s averaging, which slightly (factor ∼ 3) improves on best previous links via an airborne platform. This state-of-the-art result is obtained with a transportable set-up that enables a fast field deployment.
Topological transmission in Suzuki phase sonic crystals

This work reports topological extraordinary properties of sound transmission through topological states in sonic crystals denominated Suzuki phase, consisting of a rectangular lattice of vacancies created in a triangular lattice. These low-symmetry crystals exhibit unique properties due to the embedded lattice of vacancies. A generalized folding method explains the band structure and the quasi-type-II Dirac point in the Suzuki phase, which is related to the underlying triangular lattice. In analogy to the acoustic valley Hall effect, the Suzuki phase contains three types of topological edge states on the four possible interfaces separating two Suzuki phase crystals with distinct topological phases. The edge states have defined symmetries with inherent directionality, which affect the topological sound transmission and are different from chirality, valley vorticity or helicity. Particularly, the existence of topological deaf bands is here reported. The propagation of topological eigenmodes on the same interface is also different, which is quantified using the acoustic Shannon entropy, making the topological transport dependent on the frequency of the edge states. Based on the abundant topological edge states of Suzuki phase crystals, a multifunctional device with acoustic diodes, multi-channel transmission, and selective acoustic transmission can be designed. Numerical simulations and measurements demonstrate the topological transmission. Our work extends the research platform of acoustic topological states to lattices with low symmetry, which opens new avenues for enriching topological states with broad engineering applications.
A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems

In this paper, we explore a specific optimization problem that involves the combination of a differentiable nonconvex function and a nondifferentiable function. The differentiable component lacks a global Lipschitz continuous gradient, posing challenges for optimization. To address this issue and accelerate the convergence, we propose a Bregman proximal stochastic gradient method with extrapolation (BPSGE), which only requires smooth adaptivity of the differentiable part. Under variance reduction framework, we not only analyze the subsequential and global convergence of the proposed algorithm under certain conditions, but also analyze the sublinear convergence rate of the subsequence, and the complexity of the algorithm, revealing that the BPSGE algorithm requires at most O(epsilon\^\,(-2)) iterations in expectation to attain an epsilon-stationary point. To validate the effectiveness of our proposed algorithm, we conduct numerical experiments on three real-world applications: graph regularized nonnegative matrix factorization (NMF), matrix factorization with weakly-convex regularization, and NMF with nonconvex sparsity constraints. These experiments demonstrate that BPSGE is faster than the baselines without extrapolation. The code is available at: https://github.com/nothing2wang/BPSGE-Algorithm.
Mean-Field Assisted Deep Boltzmann Learning with Probabilistic Computers

Despite their appeal as physics-inspired, energy-based and generative nature, general Boltzmann Machines (BM) are considered intractable to train. This belief led to simplified models of BMs with restricted intralayer connections or layer-by-layer training of deep BMs. Recent developments in domain-specific hardware -- specifically probabilistic computers (p-computer) with probabilistic bits (p-bit) -- may change established wisdom on the tractability of deep BMs. In this paper, we show that deep and unrestricted BMs can be trained using p-computers generating hundreds of billions of Markov Chain Monte Carlo (MCMC) samples per second, on sparse networks developed originally for use in D-Wave's annealers. To maximize the efficiency of learning the p-computer, we introduce two families of Mean-Field Theory assisted learning algorithms, or xMFTs (x = Naive and Hierarchical). The xMFTs are used to estimate the averages and correlations during the positive phase of the contrastive divergence (CD) algorithm and our custom-designed p-computer is used to estimate the averages and correlations in the negative phase. A custom Field-Programmable-Gate Array (FPGA) emulation of the p-computer architecture takes up to 45 billion flips per second, allowing the implementation of CD-$n$ where $n$ can be of the order of millions, unlike RBMs where $n$ is typically 1 or 2. Experiments on the full MNIST dataset with the combined algorithm show that the positive phase can be efficiently computed by xMFTs without much degradation when the negative phase is computed by the p-computer. Our algorithm can be used in other scalable Ising machines and its variants can be used to train BMs, previously thought to be intractable.
Generating Rhythm Game Music with Jukebox

Music has always been thought of as a"human"endeavor -- when praising a piece of music, we emphasize the composer's creativity and the emotions the music invokes. Because music also heavily relies on patterns and repetition in the form of recurring melodic themes and chord progressions, artificial intelligence has increasingly been able to replicate music in a human-like fashion. This research investigated the capabilities of Jukebox, an open-source commercially available neural network, to accurately replicate two genres of music often found in rhythm games, artcore and orchestral. A Google Colab notebook provided the computational resources necessary to sample and extend a total of sixteen piano arrangements of both genres. A survey containing selected samples was distributed to a local youth orchestra to gauge people's perceptions of the musicality of AI and human-generated music. Even though humans preferred human-generated music, Jukebox's slightly high rating showed that it was somewhat capable at mimicking the styles of both genres. Despite limitations of Jukebox only using raw audio and a relatively small sample size, it shows promise for the future of AI as a collaborative tool in music production.