Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering substantive disagreement with review articles?

Disagreements help drive science. How does one identify and track them in scholarly literature? We ask the research question will searching review articles (RA) will be more time efficient for this purpose than searching non-review ones (NRA). This is especially so to the extent NRAs exceed RAs in a given field. We also discuss a metric for whether RAs report more substantive disagreements than NRAs.
Two-sided Matrix Regression

The two-sided matrix regression model $Y = A^*X B^* +E$ aims at predicting $Y$ by taking into account both linear links between column features of $X$, via the unknown matrix $B^*$, and also among the row features of $X$, via the matrix $A^*$. We propose low-rank predictors in this high-dimensional matrix regression model via rank-penalized and nuclear norm-penalized least squares. Both criteria are non jointly convex; however, we propose explicit predictors based on SVD and show optimal prediction bounds. We give sufficient conditions for consistent rank selector. We also propose a fully data-driven rank-adaptive procedure. Simulation results confirm the good prediction and the rank-consistency results under data-driven explicit choices of the tuning parameters and the scaling parameter of the noise.
Complexity and scalability of defeasible reasoning in many-valued weighted knowledge bases with typicality

Weighted knowledge bases for description logics with typicality under a"concept-wise"multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a $\Pi^p_2$ upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfils the lack by providing a $P^{NP[log]}$-completeness result and new ASP encodings that deal with weighted knowledge bases with large search spaces.
Computing with Categories in Machine Learning

Category theory has been successfully applied in various domains of science, shedding light on universal principles unifying diverse phenomena and thereby enabling knowledge transfer between them. Applications to machine learning have been pursued recently, and yet there is still a gap between abstract mathematical foundations and concrete applications to machine learning tasks. In this paper we introduce DisCoPyro as a categorical structure learning framework, which combines categorical structures (such as symmetric monoidal categories and operads) with amortized variational inference, and can be applied, e.g., in program learning for variational autoencoders. We provide both mathematical foundations and concrete applications together with comparison of experimental performance with other models (e.g., neuro-symbolic models). We speculate that DisCoPyro could ultimately contribute to the development of artificial general intelligence.
ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification

ChatGPT has shown strong capabilities in natural language generation tasks, which naturally leads researchers to explore where its abilities end. In this paper, we examine whether ChatGPT can be used for zero-shot text classification, more specifically, automatic genre identification. We compare ChatGPT with a multilingual XLM-RoBERTa language model that was fine-tuned on datasets, manually annotated with genres. The models are compared on test sets in two languages: English and Slovenian. Results show that ChatGPT outperforms the fine-tuned model when applied to the dataset which was not seen before by either of the models. Even when applied on Slovenian language as an under-resourced language, ChatGPT's performance is no worse than when applied to English. However, if the model is fully prompted in Slovenian, the performance drops significantly, showing the current limitations of ChatGPT usage on smaller languages. The presented results lead us to questioning whether this is the beginning of an end of laborious manual annotation campaigns even for smaller languages, such as Slovenian.
Quantifying optical rogue waves

This work presents two distinct approaches to estimating the exponent related to the distribution of optical rogue waves observed in supercontinuum generated in a single-mode fiber, that is, to quantifying the rogueness. The first is a generalization of the well-known Hill estimator, and the second relies on estimating all parameters of a multi-parameter model. We show that the model shows a good correspondence with experimental data, and that the two estimating approaches provide consistent results, which are significantly more accurate than those obtained with earlier methods of estimation. Furthermore, alternative visualization through the tail function revealed the presence of pump depletion as well as detector saturation leading to the breakdown of power-law behavior for the largest observations. We characterized this breakdown via a combination of an exponential and a generalized Pareto distribution. Additionally, we have uncovered a weak memory effect in the data, which can be attributed to changes of the refractive index in the single-mode fiber.
The Black Hole Candidate Swift J1728.9–3613 and the Supernova Remnant G351.9–0.9

A number of neutron stars have been observed within the remnants of the core-collapse supernova explosions that created them. In contrast, black holes are not yet clearly associated with supernova remnants (SNRs). Indeed, some observations suggest that black holes are “born in the dark,” i.e., without a supernova explosion. Herein, we present a multiwavelength analysis of the X-ray transient Swift J1728.9−3613, based on observations made with Chandra, ESO-VISTA, MeerKAT, NICER, NuSTAR, Swift, and XMM-Newton. Three independent diagnostics indicate that the system likely harbors a black hole primary. Infrared imaging signals a massive companion star that is broadly consistent with an A or B spectral type. Most importantly, the X-ray binary lies within the central region of the cataloged SNR G351.9−0.9. Our deep MeerKAT image at 1.28 GHz signals that the remnant is in the Sedov phase; this fact and the nondetection of the soft X-ray emission expected from such a remnant argue that it lies at a distance that could coincide with the black hole. Utilizing a formal measurement of the distance to Swift J1728.9−3613 (d = 8.4 ± 0.8 kpc), a lower limit on the distance to G351.9−0.9 (d ≥ 7.5 kpc), and the number and distribution of black holes and SNRs within the Milky Way, extensive simulations suggest that the probability of a chance superposition is <1.7% (99.7% credible interval). The discovery of a black hole within an SNR would support numerical simulations that produce black holes and remnants, and thus provide clear observational evidence of distinct black hole formation channels. We discuss the robustness of our analysis and some challenges to this interpretation.
Interface characteristics of n-n and p-n Ge/SiC heterojunction diodes formed by molecular beam epitaxy deposition

In this article, we report on the physical and electrical nature of Ge/SiC heterojunction layers that have been formed by molecular beam epitaxy (MBE) deposition. Using x-ray diffraction, atomic force microscopy, and helium ion microscopy, we perform a thorough analysis of how MBE growth conditions affect the Ge layers. We observe the layers developing from independent islands at thicknesses of 100 nm to flat surfaces at 300 nm. The crystallinity and surface quality of the layer is shown to be affected by the deposition parameters and, using a high temperature deposition and a light dopant species, the layers produced have large polycrystals and hence a low resistance. The p-type and n-type layers, 300 nm thick are formed into Ge/SiC heterojunction mesa diodes and these are characterized electrically. The polycrystalline diodes display near ideal diode characteristics (n<1.05), low on resistance and good reverse characteristics. Current-voltage (I-V) measurements at varying temperature prove that all the layers have two-dimensional fluctuations in the Schottky barrier height (SBH) due to inhomogeneities at the heterojunction interface. Capacitance-voltage analysis and the SBH size extracted from I-V analysis suggest strongly that interface states are present at the surface causing Fermi-level pinning throughout the bands. A simple model is used to quantify the concentration of interface states at the surface.
Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima

Computational simulations are a popular method for testing hypotheses about the emergence of communication. This kind of research is performed in a variety of traditions including language evolution, developmental psychology, cognitive science, machine learning, robotics, etc. The motivations for the models are different, but the operationalizations and methods used are often similar. We identify the assumptions and explanatory targets of several most representative models and summarise the known results. We claim that some of the assumptions -- such as portraying meaning in terms of mapping, focusing on the descriptive function of communication, modelling signals with amodal tokens -- may hinder the success of modelling. Relaxing these assumptions and foregrounding the interactions of embodied and situated agents allows one to systematise the multiplicity of pressures under which symbolic systems evolve. In line with this perspective, we sketch the road towards modelling the emergence of meaningful symbolic communication, where symbols are simultaneously grounded in action and perception and form an abstract system.
Energy-distance relation for fcc transition metal nanocrystals

First-principles calculations have been performed to obtain the size- and shape-dependent energetics of the unary nanocrystals made of transition metal elements Ni, Cu, Rh, Pd, Ag, Ir, Pt, or Au. The variation of the nanocrystal chemical potential μ with the interatomic distance d has been studied, leading to derivation of an energy-distance (μ-d) relation. A variety of nanocrystal morphologies have been employed to explore general features of this relation. It is found that the curves representing the μ-d relationship for the entire set of nanocrystals could all be represented by a single universal function, regardless of the atomic species or the nanocrystal morphology or magnetic ground-state.
Large Interferometer For Exoplanets (LIFE). IX. Assessing the impact of clouds on atmospheric retrievals at mid-infrared wavelengths with a Venus-twin exoplanet

The Large Interferometer For Exoplanets (LIFE) initiative aims to develop a space based mid-infrared (MIR) nulling interferometer to measure the thermal emission spectra of temperate terrestrial exoplanets. We investigate how well LIFE could characterize a cloudy Venus-twin exoplanet to: (1) test our retrieval routine on a realistic non-Earth-like MIR spectrum of a known planet, (2) investigate how clouds impact retrievals, (3) refine the LIFE requirements derived in previous Earth-centered studies. We run retrievals for simulated LIFE observations of a Venus-twin exoplanet orbiting a Sun-like star located 10 pc from the observer. By assuming different models (cloudy and cloud-free) we analyze the performance as a function of the quality of the LIFE observation. This allows us to determine how well atmosphere and clouds are characterizable depending on the quality of the spectrum. Our study shows that the current minimal resolution ($R=50$) and signal-to-noise ($S/N=10$ at $11.2\mu$m) requirements for LIFE suffice to characterize the structure and composition of a Venus-like atmosphere above the cloud deck if an adequate model is chosen. However, we cannot infer cloud properties. The accuracy of the retrieved planet radius ($R_{pl}$), equilibrium temperature ($T_{eq}$), and Bond albedo ($A_B$) depend on the choice of model. Generally, a cloud-free model performs best and thus the presence of clouds cannot be inferred. This model dependence of retrieval results emphasizes the importance of developing a community-wide best-practice for atmospheric retrieval studies. If we consider higher quality spectra (especially $S/N=20$), we can infer the presence of clouds and pose first constraints on their structure.
Six Permutation Patterns Force Quasirandomness

A sequence $\pi_1,\pi_2,\dots$ of permutations is said to be"quasirandom"if the induced density of every permutation $\sigma$ in $\pi_n$ converges to $1/|\sigma|! $ as $n\to\infty$. We prove that $\pi_1,\pi_2,\dots$ is quasirandom if and only if the density of each permutation $\sigma$ in the set $$\{123,321,2143,3412,2413,3142\}$$ converges to $1/|\sigma|! $. Previously, the smallest cardinality of a set with this property, called a"quasirandom-forcing"set, was known to be between four and eight. In fact, we show that there is a single linear expression of the densities of the six permutations in this set which forces quasirandomness and show that this is best possible in the sense that there is no shorter linear expression of permutation densities with positive coefficients with this property. In the language of theoretical statistics, this expression provides a new nonparametric independence test for bivariate continuous distributions related to Spearman's $\rho$.
Phase structure of quantum improved Schwarzschild-(Anti)de Sitter black holes

We study the phase structure of quantum improved Schwarzschild–(A)dS black holes in asymptotically safe gravity. Our results confirm some of the well-known properties of quantum black holes. For example, the quantum effect provides a repulsive force in the core region near singularity which stabilizes the thermodynamically unstable small black holes, and also creates a zero temperature state with finite size. We suggest that this could be a candidate for dark matter. We find a new second order phase transition between small and large black holes for quantum improved Schwarzschild-Anti de Sitter black holes. We also discuss the black holes with different spatial topologies and find a notable duality.
Optimal Solutions of Well-Posed Linear Systems via Low-Precision Right-Preconditioned GMRES with Forward and Backward Stabilization

Linear systems in applications are typically well-posed, and yet the coefficient matrices may be nearly singular in that the condition number $\kappa(\boldsymbol{A})$ may be close to $1/\varepsilon_{w}$, where $\varepsilon_{w}$ denotes the unit roundoff of the working precision. It is well known that iterative refinement (IR) can make the forward error independent of $\kappa(\boldsymbol{A})$ if $\kappa(\boldsymbol{A})$ is sufficiently smaller than $1/\varepsilon_{w}$ and the residual is computed in higher precision. We propose a new iterative method, called Forward-and-Backward Stabilized Minimal Residual or FBSMR, by conceptually hybridizing right-preconditioned GMRES (RP-GMRES) with quasi-minimization. We develop FBSMR based on a new theoretical framework of essential-forward-and-backward stability (EFBS), which extends the backward error analysis to consider the intrinsic condition number of a well-posed problem. We stabilize the forward and backward errors in RP-GMRES to achieve EFBS by evaluating a small portion of the algorithm in higher precision while evaluating the preconditioner in lower precision. FBSMR can achieve optimal accuracy in terms of both forward and backward errors for well-posed problems with unpolluted matrices, independently of $\kappa(\boldsymbol{A})$. With low-precision preconditioning, FBSMR can reduce the computational, memory, and energy requirements over direct methods with or without IR. FBSMR can also leverage parallelization-friendly classical Gram-Schmidt in Arnoldi iterations without compromising EFBS. We demonstrate the effectiveness of FBSMR using both random and realistic linear systems.
Mechanical squeezing via detuning-switched driving

Generation of mechanical squeezing has attracted a lot of interest for its nonclassical properties, applications in quantum information, and high-sensitivity measurement. Here we propose a detuning-switched method that can rapidly generate strong and stationary mechanical squeezing. The pulsed driving can dynamically transpose the optomechanical coupling into a linear optical force and maintain an effective mechanical frequency, which can introduce strong mechanical squeezing in a short time. Moreover, we show the obtained strong mechanical squeezing can be frozen by increasing the pulse intervals, leading to stationary mechanical squeezing with a fixed squeezing angle. Thus, our proposal provides fascinating insights and applications of modulated optomechanical systems.
Nonequilibrium correlation dynamics in the one-dimensional Fermi-Hubbard model: A testbed for the two-particle reduced density matrix theory

We explore the non-equilibrium dynamics of a one-dimensional Fermi-Hubbard system as a sensitive testbed for the capabilities of the time-dependent two-particle reduced density matrix (TD2RDM) theory to accurately describe time-dependent correlated systems. We follow the time evolution of the out-of-equilibrium finite-size Fermi-Hubbard model initialized by a quench over extended periods of time. By comparison with exact calculations for small systems and with matrix product state (MPS) calculations for larger systems but limited to short times, we demonstrate that the TD2RDM theory can accurately account for the non-equilibrium dynamics in the regime from weak to moderately strong inter-particle correlations. We find that the quality of the approximate reconstruction of the three-particle cumulant (or correlation) required for the closure of the equations of motion for the reduced density matrix is key to the accuracy of the numerical TD2RDM results. We identify the size of the dynamically induced three-particle correlations and the amplitude of cross correlations between the two- and three-particle cumulants as critical parameters that control the accuracy of the TD2RDM theory when current state-of-the art reconstruction functionals are employed.
Nucleosynthesis of Binary-stripped Stars

The cosmic origin of the elements, the fundamental chemical building blocks of the universe, is still uncertain. Binary interactions play a key role in the evolution of many massive stars, yet their impact on chemical yields is poorly understood. Using the MESA stellar evolution code, we predict the chemical yields ejected in wind mass loss and the supernovae of single and binary-stripped stars. We do this with a large 162-isotope nuclear network at solar metallicity. We find that binary-stripped stars are more effective producers of the elements than single stars, due to their increased mass loss and an increased chance to eject their envelopes during a supernova. This increased production by binaries varies across the periodic table, with F and K being more significantly produced by binary-stripped stars than single stars. We find that the 12C/13C could be used as an indicator of the conservativeness of mass transfer, as 13C is preferentially ejected during mass transfer while 12C is preferentially ejected during wind mass loss. We identify a number of gamma-ray-emitting radioactive isotopes that may be used to help constrain progenitor and explosion models of core-collapse supernovae with next-generation gamma-ray detectors. For single stars we find that 44V and 52Mn are strong probes of the explosion model, while for binary-stripped stars it is 48Cr. Our findings highlight that binary-stripped stars are not equivalent to two single stars and that detailed stellar modeling is needed to predict their final nucleosynthetic yields.
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant attention from society. As a result, many individuals have become interested in related resources and are seeking to uncover the background and secrets behind its impressive performance. In fact, ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC), which involves the creation of digital content, such as images, music, and natural language, through AI models. The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace. AIGC is achieved by extracting and understanding intent information from instructions provided by human, and generating the content according to its knowledge and the intent information. In recent years, large-scale models have become increasingly important in AIGC as they provide better intent extraction and thus, improved generation results. With the growth of data and the size of the models, the distribution that the model can learn becomes more comprehensive and closer to reality, leading to more realistic and high-quality content generation. This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimodal interaction and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, we discuss the existing open problems and future challenges in AIGC.
Loss-Curvature Matching for Dataset Selection and Condensation

Training neural networks on a large dataset requires substantial computational costs. Dataset reduction selects or synthesizes data instances based on the large dataset, while minimizing the degradation in generalization performance from the full dataset. Existing methods utilize the neural network during the dataset reduction procedure, so the model parameter becomes important factor in preserving the performance after reduction. By depending upon the importance of parameters, this paper introduces a new reduction objective, coined LCMat, which Matches the Loss Curvatures of the original dataset and reduced dataset over the model parameter space, more than the parameter point. This new objective induces a better adaptation of the reduced dataset on the perturbed parameter region than the exact point matching. Particularly, we identify the worst case of the loss curvature gap from the local parameter region, and we derive the implementable upper bound of such worst-case with theoretical analyses. Our experiments on both coreset selection and condensation benchmarks illustrate that LCMat shows better generalization performances than existing baselines.
CALPHAD-Based Modelling of the Temperature–Composition–Structure Relationship during Physical Vapor Deposition of Mg-Ca Thin Films

The temperature-dependent composition and phase formation during the physical vapor deposition (PVD) of Mg-Ca thin films is modeled using a CALPHAD-based approach. Considering the Mg and Ca sublimation fluxes calculated based on the vapor pressure obtained by employing thermochemical equilibrium calculations, the experimentally observed synthesis-temperature trends in the thin-film composition and phase formation were reproduced. The model is a significant step towards understanding how synthesis parameters control composition and, therefore, phase formation in the PVD of metals with high vapor pressures.