Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery Conditions of Sparse Signals Using Orthogonal Least Squares-Type Algorithms

Orthogonal least squares (OLS)-type algorithms are efficient in reconstructing sparse signals, which include the well-known OLS, multiple OLS (MOLS) and block OLS (BOLS). In this paper, we first investigate the noiseless exact recovery conditions of these algorithms. Specifically, based on mutual incoherence property (MIP), we provide theoretical analysis of OLS and MOLS to ensure that the correct nonzero support can be selected during the iterative procedure. Nevertheless, theoretical analysis for BOLS utilizes the block-MIP to deal with the block sparsity. Furthermore, the noiseless MIP-based analyses are extended to the noisy scenario. Our results indicate that for <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-sparse signals, when MIP or SNR satisfies certain conditions, OLS and MOLS obtain reliable reconstruction in at most <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> iterations, while BOLS succeeds in at most <inline-formula><tex-math notation="LaTeX">$(K/d)$</tex-math></inline-formula> iterations where <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the block length. It is shown that our derived theoretical results improve the existing ones, which are verified by simulation tests.
Strong [O iii] λ5007 Emission-line Compact Galaxies in LAMOST DR9: Blueberries, Green Peas, and Purple Grapes

Green Pea and Blueberry galaxies are well known for their compact size, low mass, strong emission lines, and analogs to high-z Lyα-emitting galaxies. In this study, 1547 strong [O iii] λ5007 emission-line compact galaxies with 1694 spectra are selected from LAMOST DR9 at the redshift range from 0.0 to 0.59. According to the redshift distribution, these samples can be separated into three groups: Blueberries, Green Peas, and Purple Grapes. Optical [Mg ii] λ2800 line feature, BPT diagram, multiwavelength spectral energy distribution (SED) fitting, mid-IR (MIR) color, and MIR variability are deployed to identify 23 active galactic nucleus candidates from these samples, which are excluded for the following star formation rate (SFR) discussions. We perform the multiwavelength SED fitting with GALEX UV and WISE MIR data. Color excess from the Balmer decrement shows that these strong [O iii] λ5007 emission-line compact galaxies are not highly reddened. The stellar mass of the galaxies is obtained by fitting LAMOST calibrated spectra with the emission lines masked. We find that the SFR is increasing with the increase of redshift, while for the sources within the same redshift bin the SFR increases with mass with a similar slope to the star-forming main sequence. These samples have a median metallicity of 12 + log(O/H) of 8.10. The metallicity increases with mass, and all the sources are below the mass–metallicity relation. The direct-derived T e -based metallicity from the [O iii] λ4363 line agrees with the empirical N2-based empirical gas-phase metallicity. Moreover, these compact strong [O iii] λ5007 lines are mostly in a less dense environment.
Numerical study of three-dimensional single-mode Rayleigh-Taylor instability in turbulent mixing stage

Abstract Rayleigh-Taylor instability (RTI) as a multi-scale, strongly nonlinear physical phenomenon which plays an important role in the engineering applications and scientific research. In this paper, the mesoscopic lattice Boltzmann method is used to numerically study the late-time evolutional mechanism of three-dimensional (3D) single-mode RTI and the influences of extensive dimensionless Reynolds number and Atwood number on phase interfacial dynamics, spike and bubble growth are investigated in details. For a high Reynolds number, it is reported that the development of 3D single-mode RTI would undergo four different stages: linear growth stage, saturated velocity growth stage, reacceleration stage and turbulent mixing stage. A series of complex interfacial structures with large topological changes can be observed at the turbulent mixing stage, which always preserve the symmetries with respect to the middle axis at a low Atwood number, and the lines of symmetry within spike and bubble are broken as the Atwood number is increased. Five statistical methods for computing the spike and bubble growth rates are then analyzed to reveal the growth law of 3D single-mode RTI in turbulent mixing stage. It is found that the spike late-time growth rate shows an overall increase with the Atwood number, while the bubble growth rate first decreases slightly with the Atwood number and then approaches a constant of around 0.1. When the Reynolds number decreases, the later stages cannot be reached gradually and the evolution of phase interface presents a laminar flow state.
On Neural Network Kernels and the Storage Capacity Problem

Abstract In this short note, we reify the connection between work on the storage capacity problem in wide two-layer treelike neural networks and the rapidly growing body of literature on kernel limits of wide neural networks. Concretely, we observe that the “effective order parameter” studied in the statistical mechanics literature is exactly equivalent to the infinite-width neural network gaussian process kernel. This correspondence connects the expressivity and trainability of wide two-layer neural networks.
The Recurrent Reinforcement Learning Crypto Agent

We demonstrate a novel application of online transfer learning for a digital assets trading agent. This agent makes use of a powerful feature space representation in the form of an echo state network, the output of which is made available to a direct, recurrent reinforcement learning agent. The agent learns to trade the XBTUSD (Bitcoin versus US Dollars) perpetual swap derivatives contract on BitMEX on an intraday basis. By learning from the multiple sources of impact on the quadratic risk-adjusted utility that it seeks to maximise, the agent avoids excessive over-trading, captures a funding profit, and can predict the market’s direction. Overall, our crypto agent realises a total return of 350%, net of transaction costs, over roughly five years, 71% of which is down to funding profit. The annualised information ratio that it achieves is 1.46.
Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph via Dual Attention Networks

Stock Movement Prediction (SMP) aims at predicting listed companies’ stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.
Quartic Regularity

In this paper, we propose new linearly convergent second-order methods for minimizing convex quartic polynomials. This framework is applied for designing optimization schemes, which can solve general convex problems satisfying a new condition of quartic regularity. It assumes positive definiteness and boundedness of the fourth derivative of the objective function. For such problems, an appropriate quartic regularization of Damped Newton Method has global linear rate of convergence. We discuss several important consequences of this result. In particular, it can be used for constructing new second-order methods in the framework of high-order proximal-point schemes [20, 21]. These methods have convergence rate Õ(k−p), where k is the iteration counter, p is equal to 3, 4, or 5, and tilde indicates the presence of logarithmic factors in the complexity bounds for the auxiliary problems, which are solved at each iteration of the schemes.
Insights into the Evolution of Five Isolated Galaxies

Galaxy evolution is believed to be conditioned by the environment. Isolated galaxies or galaxies in poor groups are an excellent laboratory to study evolutionary mechanisms where effects of the environment are minimal. We present new Swift-UVOT data in six filters, three in the ultraviolet (UV), of five isolated galaxies aiming at shedding light into their evolution. For all of our targets, we present new UV integrated fluxes, and for some of them also new UBV magnitudes. Our observations allow us to improve their multiwavelength spectral energy distributions, extending them over about three orders of magnitude in wavelength. We exploit our smooth particle hydrodynamical simulations with chemo-photometric implementation anchored, a posteriori, to the global multiwavelength properties of our targets, to give insight into their evolution. Then we compare their evolutionary properties with those previously derived for several galaxies in groups. The evolution of our targets is driven by a merger that occurred several gigayears ago, in the redshift range 0.5 ≤ z ≤ 4.5, not unlike what we have already found for galaxies in groups. The merger shapes the potential well where the gas is accreting, driving the star formation rate and the galaxy evolution. Isolated galaxies should not have suffered from interactions for at least 3 Gyr. However, the initial merger is still leaving its signature on the properties of our targets. Several rejuvenation episodes, triggered by in situ accretion, are highlighted. Moreover, jellyfish morphologies appear as these galaxies achieve their maximum star formation rate, before their quenching phase.
The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition

In this paper, we investigate several existing and a new state-of-the-art generative adversarial network-based (GAN) voice conversion method for enhancing dysarthric speech for improved dysarthric speech recognition. We compare key components of existing methods as part of a rigorous ablation study to find the most effective solution to improve dysarthric speech recognition. We find that straightforward signal processing methods such as stationary noise removal and vocoder-based time stretching lead to dysarthric speech recognition results comparable to those obtained when using state-of-the-art GAN-based voice conversion methods as measured using a phoneme recognition task. Additionally, our proposed solution of a combination of MaskCycleGAN-VC and time stretched enhancement is able to improve the phoneme recognition results for certain dysarthric speakers compared to our time stretched baseline.
GradMax: Growing Neural Networks using Gradient Information

The architecture and the parameters of neural networks are often optimized independently, which requires costly retraining of the parameters whenever the architecture is modified. In this work we instead focus on growing the architecture without requiring costly retraining. We present a method that adds new neurons during training without impacting what is already learned, while improving the training dynamics. We achieve the latter by maximizing the gradients of the new weights and find the optimal initialization efficiently by means of the singular value decomposition (SVD). We call this technique Gradient Maximizing Growth (GradMax) and demonstrate its effectiveness in variety of vision tasks and architectures.
Online State Estimation for Supervisor Synthesis in Discrete-Event Systems With Communication Delays and Losses

In the context of networked discrete-event systems (DESs), communication delays and losses exist between the plant and the supervisor for observation and between the supervisor and the actuator for control. In this article, we first introduce a new framework for supervisory control of networked DESs. Under the introduced framework, we address the state estimation problem for supervisor synthesis of networked DESs with both communication delays and losses. The estimation algorithm considers the effect of the controls imposed on the system. In addition, the estimation algorithm is based on the control decisions available up to the moment, and all future control decisions are assumed to be unknowable. Two notions, called “observation channel configuration” for tracking observation delays and losses and “control channel configuration” for tracking control delays and losses, are defined. Then, we introduce an online approach for state estimation of the controlled system. Compared with the existing approach, the proposed approach under the introduced framework can estimate the states of the controlled system more accurately. As an application of the proposed approach, we finally show that the existing methods can be applied to synthesize maximally permissible and safe networked supervisors.
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicability in performance-critical settings. Building on prior theoretical insights from ReLIC [Mitrovic et al., 2021], we include additional inductive biases into self-supervised learning. We propose a new self-supervised representation learning method, ReLICv2, which combines an explicit invariance loss with a contrastive objective over a varied set of appropriately constructed data views to avoid learning spurious correlations and obtain more informative representations. ReLICv2 achieves $77.1\%$ top-$1$ accuracy on ImageNet under linear evaluation on a ResNet50, thus improving the previous state-of-the-art by absolute $+1.5\%$; on larger ResNet models, ReLICv2 achieves up to $80.6\%$ outperforming previous self-supervised approaches with margins up to $+2.3\%$. Most notably, ReLICv2 is the first unsupervised representation learning method to consistently outperform the supervised baseline in a like-for-like comparison over a range of ResNet architectures. Using ReLICv2, we also learn more robust and transferable representations that generalize better out-of-distribution than previous work, both on image classification and semantic segmentation. Finally, we show that despite using ResNet encoders, ReLICv2 is comparable to state-of-the-art self-supervised vision transformers.
PDRs4All: A JWST Early Release Science Program on Radiative Feedback from Massive Stars

Massive stars disrupt their natal molecular cloud material through radiative and mechanical feedback processes. These processes have profound effects on the evolution of interstellar matter in our Galaxy and throughout the universe, from the era of vigorous star formation at redshifts of 1–3 to the present day. The dominant feedback processes can be probed by observations of the Photo-Dissociation Regions (PDRs) where the far-ultraviolet photons of massive stars create warm regions of gas and dust in the neutral atomic and molecular gas. PDR emission provides a unique tool to study in detail the physical and chemical processes that are relevant for most of the mass in inter- and circumstellar media including diffuse clouds, proto-planetary disks, and molecular cloud surfaces, globules, planetary nebulae, and star-forming regions. PDR emission dominates the infrared (IR) spectra of star-forming galaxies. Most of the Galactic and extragalactic observations obtained with the James Webb Space Telescope (JWST) will therefore arise in PDR emission. In this paper we present an Early Release Science program using the MIRI, NIRSpec, and NIRCam instruments dedicated to the observations of an emblematic and nearby PDR: the Orion Bar. These early JWST observations will provide template data sets designed to identify key PDR characteristics in JWST observations. These data will serve to benchmark PDR models and extend them into the JWST era. We also present the Science-Enabling products that we will provide to the community. These template data sets and Science-Enabling products will guide the preparation of future proposals on star-forming regions in our Galaxy and beyond and will facilitate data analysis and interpretation of forthcoming JWST observations.
Expression of Prostate Specific Membrane Antigen (PSMA) in Breast Cancer

Abstract Background Prostate specific membrane antigen (PSMA) is a promising protein for breast cancer patients. It has not only been detected in prostate cancer but is also expressed by tumor cells and the endothelial cells of tumor vessels in breast cancer patients. PSMA plays a role in tumor progression and tumor angiogenesis. For this reason, a number of diagnostic and therapeutic methods to target PSMA have been developed. Method This paper provides a general structured overview of PSMA and its oncogenic potential, with a special focus on its role in breast cancer. This narrative review is based on a selective literature search carried out in PubMed and the library of Freiburg University Clinical Center. The following key words were used for the search: “PSMA”, “PSMA and breast cancer”, “PSMA PET/CT”, “PSMA tumor progression”. Relevant articles were explicitly read through, processed, and summarized. Conclusion PSMA could be a new diagnostic and therapeutic alternative, particularly for triple-negative breast cancer. It appears to be a potential predictive and prognostic marker. Zusammenfassung Hintergrund Das prostataspezifische Membranantigen (PSMA) ist für Mammakarzinompatientinnen ein vielversprechendes Protein. Es wurde bisher nicht nur beim Prostatakarzinom nachgewiesen, sondern wird auch von den Tumor- sowie Endothelzellen der Tumorgefäße des Mammakarzinoms exprimiert. Das PSMA hat eine Rolle bei der Tumorprogression und Neubildung von Gefäßen. Deshalb wurden bereits viele PSMA-gerichtete diagnostische und therapeutische Verfahren entwickelt. Methode Diese Arbeit ist eine allgemeine und gegliederte Übersicht über das PSMA und dessen onkogenetisches Potenzial, mit dem Fokus auf dessen Rolle beim Mammakarzinom. Für dieses narrative Review wurde eine selektive Literaturrecherche via PubMed und der Bibliothek des Universitätsklinikums Freiburg angefertigt. Hierbei wurden folgende Schlüsselwörter verwendet: „PSMA“, „PSMA and breast cancer“, „PSMA PET/CT“, „PSMA tumor progression“. Relevante Artikel wurden explizit durchgelesen, bearbeitet und zusammengefasst. Schlussfolgerung Das PSMA könnte, insbesondere beim triple-negativen Mammakarzinom, eine neue diagnostische und therapeutische Alternative darstellen. Es scheint ein möglicher prädiktiver und prognostischer Marker zu sein.
Two components of body-image disturbance are differentially associated with distinct eating disorder characteristics in healthy young women

Body-image disturbance comprises two components. The first is perceptual in nature, and is measured by a discrepancy between one’s actual body and perceived self-image (“perceived–actual discrepancy”). The other component is affective, and is measured by a discrepancy between one’s perceived self-image and ideal body image (“perceived–ideal discrepancy”). The present study evaluated the relationships between body-image disturbance and characteristics of eating disorders such as symptoms and related personality traits. In a psychophysiological experiment, female university students (mean ± SD age = 21.0 ± 1.38 years) were presented with silhouette images of their own bodies that were distorted in terms of width. The participants were asked whether each silhouette image was more overweight than their actual or ideal body images. Eating-disorder characteristics were assessed using six factors from the Japanese version of the Eating Disorder Inventory 2 (EDI2). We found that perceived–actual discrepancies correlated with negative self-evaluation (i.e., factor 3 of the EDI2), whereas perceived–ideal discrepancies correlated with dissatisfaction with one’s own body (i.e., factor 2 of EDI2). These results imply that distinct psychological mechanisms underlie the two components of body-image disturbance.
A Network Analysis of the Association Between Intergroup Contact and Intergroup Relations

Purpose Intergroup contact is an effective strategy to improve intergroup relationships. Although intergroup relationships have been studied extensively, the individual roles of quantity and quality of contact in relationships with cognition, emotion, and intention of behavior toward other ethnic minority groups are not fully understood. This study explores the situation via network analysis among Zhuang and Yao ethnic minorities in Southwest China. Methods We investigated the quantity and quality of intergroup contact and cognition, emotion, and intention of behavior among a sample of 543 Zhuang and 490 Yao ethnic group members. Data were analyzed using the R-package. Network structures were analyzed via the Qgraph package, and the accuracy and stability of the network were measured via the Bootnet package; communities were detected via the Igraph package; bridge analyses were conducted via the Networktools package; and the network difference was compared via the Network Comparison Test package. Results The results indicated perceived intimacy is the central node. Quantity of contact constructed a community with “perceived connection,” “sense of community,” “knowledge about out-group,” and “perceived similarity. ” Meanwhile, quality of contact constructed a community with “intergroup attitude” and a “feeling thermometer. ” The remainder of the nodes constructed two additional communities. The network global connectivity and structure between the two ethnic groups were highly similar. Conclusion The study examined the quantity and quality of intergroup contact via network analysis for two ethnic minority groups. It was shown that the two groups’ global network structures of intergroup contact and their effects are highly similar. Specifically, quantity and quality of contact produce different effects on intergroup relations. Quantity of contact has proximal effects, including instant cognitive and emotional response without depth cognition, while quality of contact has proximal effects that may change deep-seated cognition and subsequently improve intergroup relations.