Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-repairing high entropy oxides

All biological organisms, from plants to living creatures, can heal minor wounds and damage. The realization of a similar self-healing capacity in inorganic materials has been a design target for many decades. This would represent a breakthrough in materials engineering, enabling many novel technological applications, since such materials would be able to resist damage caused by electromagnetic irradiation and/or mechanical impact. Here we demonstrate that a high-entropy oxide is intrinsically capable of undergoing an autonomous self-repairing process. Transmission electron microscopy revealed that the spinel structure of (AlCoCrCu0.5FeNi)3O4 can regrow and repair itself at the atomic level when damaged. Density functional theory calculations reveal that the extra enthalpy stored in the high entropy material during fabrication can be released to effectively heal macroscopic defects by regrowing into a partially ordered state. This extraordinary self-repairing phenomenon makes this new material highly desirable as a coating, enabling structures used in harsh environments to better withstand damage, such as cosmic irradiation in space, nuclear irradiation in nuclear power facilities, or tribological damage. Most importantly, our results set the general design principles for the synthesis of self-repairing materials. One-Sentence Summary: Experimental results and theoretical calculations are presented to establish principles for the design of a new class of high entropy oxide materials with an extraordinary capacity for self-repair following damage caused by electromagnetic irradiation or mechanical impact.
A Comparison of Bayesian Inference Techniques for Sparse Factor Analysis

Dimension reduction algorithms aim to discover latent variables which describe underlying structures in high-dimensional data. Methods such as factor analysis and principal component analysis have the downside of not offering much interpretability of its inferred latent variables. Sparse factor analysis addresses this issue by imposing sparsity on its factor loadings, allowing each latent variable to be related to only a subset of features, thus increasing interpretability. Sparse factor analysis has been used in a wide range of areas including genomics, signal processing, and economics. We compare two Bayesian inference techniques for sparse factor analysis, namely Markov chain Monte Carlo (MCMC), and variational inference (VI). VI is computationally faster than MCMC, at the cost of a loss in accuracy. We derive MCMC and VI algorithms and perform a comparison using both simulated and biological data, demonstrating that the higher computational efficiency of VI is desirable over the small gain in accuracy when using MCMC. Our implementation of MCMC and VI algorithms for sparse factor analysis is available at https://github.com/ysfoo/sparsefactor.
Trees in transformers: a theoretical analysis of the Transformer's ability to represent trees

Transformer networks are the de facto standard architecture in natural language processing. To date, there are no theoretical analyses of the Transformer's ability to capture tree structures. We focus on the ability of Transformer networks to learn tree structures that are important for tree transduction problems. We first analyze the theoretical capability of the standard Transformer architecture to learn tree structures given enumeration of all possible tree backbones, which we define as trees without labels. We then prove that two linear layers with ReLU activation function can recover any tree backbone from any two nonzero, linearly independent starting backbones. This implies that a Transformer can learn tree structures well in theory. We conduct experiments with synthetic data and find that the standard Transformer achieves similar accuracy compared to a Transformer where tree position information is explicitly encoded, albeit with slower convergence. This confirms empirically that Transformers can learn tree structures.
Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?

Spiking neural networks (SNNs), that operate via binary spikes distributed over time, have emerged as a promising energy efficient ML paradigm for resource-constrained devices. However, the current state-of-the-art (SOTA) SNNs require multiple time steps for acceptable inference accuracy, increasing spiking activity and, consequently, energy consumption. SOTA training strategies for SNNs involve conversion from a non-spiking deep neural network (DNN). In this paper, we determine that SOTA conversion strategies cannot yield ultra low latency because they incorrectly assume that the DNN and SNN pre-activation values are uniformly distributed. We propose a new training algorithm that accurately captures these distributions, minimizing the error between the DNN and converted SNN. The resulting SNNs have ultra low latency and high activation sparsity, yielding significant improvements in compute efficiency. In particular, we evaluate our framework on image recognition tasks from CIFAR-10 and CIFAR-100 datasets on several VGG and ResNet architectures. We obtain top-1 accuracy of 64.19% with only 2 time steps on the CIFAR-100 dataset with ∼159.2× lower compute energy compared to an iso-architecture standard DNN. Compared to other SOTA SNN models, our models perform inference 2.5-8× faster (i.e., with fewer time steps).
Faster Convergence in Multiobjective Optimization Algorithms Based on Decomposition

Abstract The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in performance of MOEA/D with RA. This study investigates the effects of MOEA/D with the Partial Update Strategy (PS) in an extensive set of MOPs to generate insights into correspondences of MOEA/D with the partial update and MOEA/D with small population size and big population size. Our work undertakes an in-depth analysis of the populational dynamics behaviour considering their final approximation Pareto sets, anytime hypervolume performance, attained regions, and number of unique nondominated solutions. Our results indicate that MOEA/D with partial update progresses with the search as fast as MOEA/D with small population size and explores the search space as MOEA/D with big population size. MOEA/D with partial update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique nondominated solutions, and as the anytime performance and Empirical Attainment Function indicate.
Catch Me If You GAN: Using Artificial Intelligence for Fake Log Generation

With artificial intelligence (AI) becoming relevant in various parts of everyday life, other technologies are already widely influenced by the new way of handling large amounts of data. Although widespread already, AI has had only punctual influences on the cybersecurity field specifically. Many techniques and technologies used by cybersecurity experts function through manual labor and barely draw on automation, e.g., logs are often reviewed manually by system admins for potentially malicious keywords. This work evaluates the use of a special type of AI called generative adversarial networks (GANs) for log generation. More precisely, three different generative adversarial networks, SeqGAN, MaliGAN, and CoT, are reviewed in this research regarding their performance, focusing on generating new logs as a means of deceiving system admins for red teams. Although static generators for fake logs have been around for a while, their produces are usually easy to reveal as such. Using AI as an approach to this problem has not been widely researched. Identified challenges consist of formatting, dates and times, and overall consistency. Summing up the results, GANs seem not to be a good fit for generating fake logs. Their capability to detect fake logs, however, might be of use in practical scenarios.
A Graphical Method for the Design of Feedback Networks for Microwave Transistor Amplifiers: Theory and Applications

A new theory is presented that is very useful for the design of feedback transistor amplifiers, including considerations on stability, gain equalization, and matching. The theory is based on graphical feedback diagrams whose construction rides and practical circuit design techniques are described. The method provides insight into the effects of the feedback network elements and saves computer time and money. Three applications are presented: a tuned neutralized bipolar transistor amplifier; a broad-band medium power MESFET amplifier in the 3.7-4.2-GHz range; and a ultrawide-band matched MESFET amplifier covering the 0.1- 12-GHz frequency range.
Skilled motor control implies a low entropy of states but a high entropy of actions

The mastery of skills such as playing tennis or balancing an inverted pendulum implies a very accurate control of movements to achieve the task goals. Traditional accounts of skilled action control that focus on either routinization or perceptual control make opposite predictions about the ways we achieve mastery. The notion of routinization emphasizes the decrease of the variance of our actions, whereas the notion of perceptual control emphasizes the decrease of the variance of the states we visit, but not of the actions we execute. Here, we studied how participants managed control tasks of varying levels of complexity, which consisted in controlling inverted pendulums of different lengths. We used information-theoretic measures to compare the predictions of alternative theoretic accounts that focus on routinization and perceptual control, respectively. Our results indicate that the successful performance of the control task strongly correlates with the decrease of state variability and the increase of action variability. As postulated by perceptual control theory, the mastery of skills consists in achieving stable control goals by flexible means.
Beyond the castle:public space co-design, a case study and guidelines for designers

In this paper we describe a high profile project to reimagine a large green space in the heart of the city of Lancaster in the UK. This co-design project involved professional designers but also 2500 people with 700 of these making an active co-design contribution. This project forms the basis of a discussion of how we used a series of events to help participants reach their full creative co-design potential moving from doing to creating levels of creativity. From this case study we go on to develop a framework of recommendations to help designers reflect on their normal practice and how they need to operate within a co-design project. These recommendations seek to maximise the benefits of this approach and produce good design outcomes. This framework has been evaluated in a series of international workshops in the UK, Belgium and the Netherlands.
Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks

In this paper, we use The Quijote simulations in order to extract the cosmological parameters through Bayesian Neural Networks. This kind of model has a remarkable ability to estimate the associated uncertainty, which is one of the ultimate goals in the precision cosmology era. We demonstrate the advantages of BNNs for extracting more complex output distributions and non-Gaussianities information from the simulations.
On Shafarevich-Tate groups and analytic ranks in families of modular forms, II. Coleman families

This is the second article in a two-part project whose aim is to study algebraic and analytic ranks in $p$-adic families of modular forms. Let $f$ be a newform of weight $2$, square-free level $N$ and trivial character, let $A_f$ be the abelian variety attached to $f$, whose dimension will be denoted by $d_f$, and for every prime number $p\nmid N$ let $\boldsymbol f^{(p)}$ be a $p$-adic Coleman family through $f$ over a suitable open disc in the $p$-adic weight space. We prove that, for all but finitely many primes $p$ as above, if $A_f(\mathbb Q)$ has rank $r\in\{0,d_f\}$ and the $p$-primary part of the Shafarevich-Tate group of $A_f$ over $\mathbb Q$ is finite, then all classical specializations of $\boldsymbol f^{(p)}$ of weight congruent to $2$ modulo $2(p-1)$ and trivial character have finite $p$-primary Shafarevich-Tate group and $r/d_f$-dimensional image of the relevant $p$-adic \'etale Abel-Jacobi map. As a second contribution, assuming the non-degeneracy of certain height pairings \`a la Gillet-Soul\'e between Heegner cycles, we show that, for all but finitely many $p$, if $f$ has analytic rank $r\in\{0,1\}$, then all classical specializations of $\boldsymbol f^{(p)}$ of weight congruent to $2$ modulo $2(p-1)$ and trivial character have analytic rank $r$. This result provides some evidence for a conjecture of Greenberg on analytic ranks in families of modular forms.
The Complexity of Iterated Reversible Computation

We study a class of functional problems reducible to computing $f^{(n)}(x)$ for inputs $n$ and $x$, where $f$ is a polynomial-time bijection. As we prove, the definition is robust against variations in the type of reduction used in its definition, and in whether we require $f$ to have a polynomial-time inverse or to be computible by a reversible logic circuit. These problems are characterized by the complexity class $\mathsf{FP}^{\mathsf{PSPACE}}$, and include natural $\mathsf{FP}^{\mathsf{PSPACE}}$-complete problems in circuit complexity, cellular automata, graph algorithms, and the dynamical systems described by piecewise-linear transformations.
A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition

Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse analysis. Most existing methods train multiple models to predict multi-level labels independently, while ignoring the dependence between hierarchically structured labels. In this paper, we consider multi-level IDRR as a conditional label sequence generation task and propose a Label Dependence-aware Sequence Generation Model (LDSGM) for it. Specifically, we first design a label attentive encoder to learn the global representation of an input instance and its level-specific contexts, where the label dependence is integrated to obtain better label embeddings. Then, we employ a label sequence decoder to output the predicted labels in a top-down manner, where the predicted higher-level labels are directly used to guide the label prediction at the current level. We further develop a mutual learning enhanced training method to exploit the label dependence in a bottom-up direction, which is captured by an auxiliary decoder introduced during training. Experimental results on the PDTB dataset show that our model achieves the state-of-the-art performance on multi-level IDRR. We release our code at https://github.com/nlpersECJTU/LDSGM.
Gravitational wave imprints of left-right symmetric model with minimal Higgs sector

We study the gravitational wave imprints of left-right symmetric model equipped with universal seesaw mechanism allowing for the natural generation of hierarchical masses of the Standard Model fermions. The scalar sector of this model is the minimal one, consisting of only two Higgs doublets. Following the construction of the full thermal potential for this model, we perform a scan of the entire parameter space and identify the region in which the cosmic phase transition associated with the left-right symmetry breaking gives gravitational wave signals detectable by a variety of planned space-based interferometers. Then we also discuss the relevant collider implications of this beyond the Standard Model scenario.
Variational Quantum Soft Actor-Critic

Quantum computing has a superior advantage in tackling specific problems, such as integer factorization and Simon's problem. For more general tasks in machine learning, by applying variational quantum circuits, more and more quantum algorithms have been proposed recently, especially in supervised learning and unsupervised learning. However, little work has been done in reinforcement learning, arguably more important and challenging. Previous work in quantum reinforcement learning mainly focuses on discrete control tasks where the action space is discrete. In this work, we develop a quantum reinforcement learning algorithm based on soft actor-critic -- one of the state-of-the-art methods for continuous control. Specifically, we use a hybrid quantum-classical policy network consisting of a variational quantum circuit and a classical artificial neural network. Tested in a standard reinforcement learning benchmark, we show that this quantum version of soft actor-critic is comparable with the original soft actor-critic, using much less adjustable parameters. Furthermore, we analyze the effect of different hyper-parameters and policy network architectures, pointing out the importance of architecture design for quantum reinforcement learning.
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares

Many recent problems in signal processing and machine learning such as compressed sensing, image restoration, matrix/tensor recovery, and non-negative matrix factorization can be cast as constrained optimization. Projected gradient descent is a simple yet efficient method for solving such constrained optimization problems. Local convergence analysis furthers our understanding of its asymptotic behavior near the solution, offering sharper bounds on the convergence rate compared to global convergence analysis. However, local guarantees often appear scattered in problem-specific areas of machine learning and signal processing. This manuscript presents a unified framework for the local convergence analysis of projected gradient descent in the context of constrained least squares. The proposed analysis offers insights into pivotal local convergence properties such as the conditions for linear convergence, the region of convergence, the exact asymptotic rate of convergence, and the bound on the number of iterations needed to reach a certain level of accuracy. To demonstrate the applicability of the proposed approach, we present a recipe for the convergence analysis of projected gradient descent and demonstrate it via a beginning-to-end application of the recipe on four fundamental problems, namely, linear equality-constrained least squares, sparse recovery, least squares with the unit norm constraint, and matrix completion.
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.
BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer

This paper describes BACON1, a basic prototype of an automatic poetry generator with author linguistic style transfer. It combines concepts and techniques from finite state machinery, probabilistic models, artificial neural networks and deep learning, to write original poetry with rich aesthetic-qualities in the style of any given author. Extrinsic evaluation of the output generated by BACON shows that participants were unable to tell the difference between human and AI-generated poems in any statistically significant way.
An Absolute Calibration of the Near-infrared Period–Luminosity Relations of Type II Cepheids in the Milky Way and in the Large Magellanic Cloud

We present time-series photometry of 21 nearby type II Cepheids in the near-infrared J, H, and K s passbands. We use this photometry, together with the Third Gaia Early Data Release parallaxes, to determine for the first time period–luminosity relations (PLRs) for type II Cepheids from field representatives of these old pulsating stars in the near-infrared regime. We found PLRs to be very narrow for BL Herculis stars, which makes them candidates for precision distance indicators. We then use archival photometry and the most accurate distance obtained from eclipsing binaries to recalibrate PLRs for type II Cepheids in the Large Magellanic Cloud (LMC). Slopes of our PLRs in the Milky Way and in the LMC differ by slightly more than 2σ and are in a good agreement with previous studies of the LMC, Galactic bulge, and Galactic globular cluster type II Cepheids samples. We use PLRs of Milky Way type II Cepheids to measure the distance to the LMC, and we obtain a distance modulus of 18.540 ± 0.026(stat.) ± 0.034(syst.) mag in the W JK Wesenheit index. We also investigate the metallicity effect within our Milky Way sample, and we find a rather significant value of about −0.2 mag dex−1 in each band meaning that more metal-rich type II Cepheids are intrinsically brighter than their more metal-poor counterparts, in agreement with the value obtained from type II Cepheids in Galactic globular clusters. The main source of systematic error on our Milky Way PLRs calibration, and the LMC distance, is the current uncertainty of the Gaia parallax zero-point.