Miracle-dz/UniFAR-FTUs huggingface dataset:
The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Abstract The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets. The purpose of this review is thus two-fold. We first aim at summarising, in a common document, the main applications of deep learning for galaxy surveys that have emerged so far. We then extract the major achievements and lessons learned and highlight key open questions and limitations, which in our opinion, will require particular attention in the coming years. Overall, state-of-the-art deep learning methods are rapidly adopted by the astronomical community, reflecting a democratisation of these methods. This review shows that the majority of works using deep learning up to date are oriented to computer vision tasks (e.g. classification, segmentation). This is also the domain of application where deep learning has brought the most important breakthroughs so far. However, we also report that the applications are becoming more diverse and deep learning is used for estimating galaxy properties, identifying outliers or constraining the cosmological model. Most of these works remain at the exploratory level though which could partially explain the limited impact in terms of citations. Some common challenges will most likely need to be addressed before moving to the next phase of massive deployment of deep learning in the processing of future surveys; for example, uncertainty quantification, interpretability, data labelling and domain shift issues from training with simulations, which constitutes a common practice in astronomy.
Manipulation and Peer Mechanisms: A Survey

In peer mechanisms, the competitors for a prize also determine who wins. Each competitor may be asked to rank, grade, or nominate peers for the prize. Since the prize can be valuable, such as financial aid, course grades, or an award at a conference, competitors may be tempted to manipulate the mechanism. We survey approaches to prevent or discourage the manipulation of peer mechanisms. We conclude our survey by identifying several important research challenges
Twisted equivariant HKR theorem for torus action and the small quantum group

We show that when a torus T acts on a smooth variety X , the twisted HKR isomorphism is equivariant. The main consequence is that the Bezrukavnikov-Lachowska isomorphism, relating the Hochschild cohomology of the principal block of the small quantum group to certain sheaf cohomology groups on the Springer resolution e N , can be upgraded to a ring isomorphism by a twist.
On the Use of Deep Learning in Software Defect Prediction

Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome, time consuming and hardly capture the semantic information reported in bug reporting tools. Deep learning (DL) techniques provide practitioners with the opportunities to automatically extract and learn from more complex and high-dimensional data. Objective: The purpose of this study is to systematically identify, analyze, summarize, and synthesize the current state of the utilization of DL algorithms for SDP in the literature. Method: We systematically selected a pool of 102 peer-reviewed studies and then conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: Main highlights include: (1) most studies applied supervised DL; (2) two third of the studies used metrics as an input to DL algorithms; (3) Convolutional Neural Network is the most frequently used DL algorithm. Conclusion: Based on our findings, we propose to (1) develop more comprehensive DL approaches that automatically capture the needed features; (2) use diverse software artifacts other than source code; (3) adopt data augmentation techniques to tackle the class imbalance problem; (4) publish replication packages.
Jitter Does Matter: Adapting Gaze Estimation to New Domains

Deep neural networks have demonstrated superior performance on appearance-based gaze estimation tasks. However, due to variations in person, illuminations, and background, performance degrades dramatically when applying the model to a new domain. In this paper, we discover an interesting gaze jitter phenomenon in cross-domain gaze estimation, i.e., the gaze predictions of two similar images can be severely deviated in target domain. This is closely related to cross-domain gaze estimation tasks, but surprisingly, it has not been noticed yet previously. Therefore, we innovatively propose to utilize the gaze jitter to analyze and optimize the gaze domain adaptation task. We find that the high-frequency component (HFC) is an important factor that leads to jitter. Based on this discovery, we add high-frequency components to input images using the adversarial attack and employ contrastive learning to encourage the model to obtain similar representations between original and perturbed data, which reduces the impacts of HFC. We evaluate the proposed method on four cross-domain gaze estimation tasks, and experimental results demonstrate that it significantly reduces the gaze jitter and improves the gaze estimation performance in target domains.
Calibrating the Cepheid Period–Wesenheit Relation in the Gaia Bands Using Galactic Open-cluster Cepheids

Establishing the period–Wesenheit relation requires independent and accurate distance measurements of classical Cepheids (DCEPs). The precise distances provided by associated open clusters independently calibrate the period–Wesenheit relations of DCEPs. 51 DCEPs associated with open clusters are compiled using the constraints of five-dimensional astrometric information. By directly using Gaia data release 3 parallaxes, the period–Wesenheit relation in the Gaia G band is calibrated as WG=(−3.06±0.11)logP+(−2.96±0.10) . Compared with the results derived from directly adopting the DCEP parallaxes or from using distance moduli, the Wesenheit magnitudes based on the cluster-averaged parallaxes exhibit a tighter relation with the period. Besides, there is a systematic offset between the observed Wesenheit absolute magnitudes of distant open-cluster DCEPs and their fitted magnitudes. After considering a parallax zeropoint correction, the systematic offset can be reduced, yielding a probably better period–Wesenheit relation of WG=(−2.94±0.12)logP+(−2.93±0.11) .
Probabilistic representation of viscosity solutions to quasi-variational inequalities with non-local drivers

We consider quasi-variational inequalities (QVIs) with general non-local drivers and related systems of reflected backward stochastic differential equations (BSDEs) in a Brownian filtration. We show existence and uniqueness of viscosity solutions to the QVIs by first considering the standard (local) setting and then applying a contraction argument. In addition, the contraction argument yields existence and uniqueness of solutions to the related systems of reflected BSDEs and extends the theory of probabilistic representations of PDEs in terms of BSDEs to our specific setting.
Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase

Nowadays, AI companies improve service quality by aggressively collecting users’ data generated by edge devices, which jeopardizes data privacy. To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users’ raw data to servers. However, for machine-learning applications on edge devices that have hard memory constraints, implementing a large model using FL is infeasible. To meet the memory requirement, a recent collaborative learning scheme named split federal learning is a potential solution since it keeps a small model on the device and keeps the rest of the model on the server. In this work, we implement a simply SFL framework on the Arduino board and verify its correctness on the Chinese digits audio dataset for keyword spotting application with over 90% accuracy. Furthermore, on the English digits audio dataset, our SFL implementation achieves 13.89% higher accuracy compared to a state-of-the-art FL implementation.
Ground-state multiquantum vortices in rotating two-species superfluids

We show numerically that a rotating, harmonically trapped mixture of two Bose-Einstein-condensed superfluids can, contrary to its single-species counterpart, contain a multiply quantized vortex in the ground state of the system. This giant vortex can occur without any accompanying single-quantum vortices, may either be coreless or have an empty core, and can be realized in a $^{87}$Rb-$^{41}$K Bose-Einstein condensate. Our results not only provide a rare example of a stable, solitary multiquantum vortex but also reveal exotic physics stemming from the coexistence of multiple, compositionally distinct condensates in one system.
An approximate local modular quantum energy inequality in general quantum field theory

For every local quantum ﬁeld theory on a static, globally hyperbolic spacetime of arbitrary dimension, assuming the Reeh-Schlieder property, local preparability of states, and the existence of an energy density as operator-valued distribution, we prove an approximate quantum energy inequality for a dense set of vector states. The quantum ﬁeld theory is given by a net of von Neumann algebras of observables, and the energy density is assumed to fulﬁll polynomial energy bounds and to locally generate the time translations. While being approximate in the sense that it is controlled by a small parameter that depends on the respective state vector, the derived lower bound on the expectation value of the spacetime averaged energy density has a universal structure. In particular, the bound is directly related to the Tomita-Takesaki modular operators associated to the local von Neumann algebras. This reveals general, model-independent features of quantum energy inequalities for a large class of quantum ﬁeld theories on static spacetimes.
A Farewell to Falsifiability

Some of the most obviously correct physical theories - namely string theory and the multiverse - make no testable predictions, leading many to question whether we should accept something as scientific even if it makes no testable predictions and hence is not refutable. However, some far-thinking physicists have proposed instead that we should give up on the notion of Falsifiability itself. We endorse this suggestion but think it does not go nearly far enough. We believe that we should also dispense with other outdated ideas, such as Fidelity, Frugality, Factuality and other "F" words. And we quote a lot of famous people to support this view.
Optimization-Informed Neural Networks

Solving constrained nonlinear optimization problems (CNLPs) is a longstanding problem that arises in various fields, e.g., economics, computer science, and engineering. We propose optimization-informed neural networks (OINN), a deep learning approach to solve CNLPs. By neurodynamic optimization methods, a CNLP is first reformulated as an initial value problem (IVP) involving an ordinary differential equation (ODE) system. A neural network model is then used as an approximate solution for this IVP, with the endpoint being the prediction to the CNLP. We propose a novel training algorithm that directs the model to hold the best prediction during training. In a nutshell, OINN transforms a CNLP into a neural network training problem. By doing so, we can solve CNLPs based on deep learning infrastructure only, without using standard optimization solvers or numerical integration solvers. The effectiveness of the proposed approach is demonstrated through a collection of classical problems, e.g., variational inequalities, nonlinear complementary problems, and standard CNLPs.
MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial-temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.
Weak lensing of gravitational waves in wave optics: Beyond the Born approximation

The Universe's matter inhomogeneity gravitationally affects the propagation of gravitational waves (GWs), causing the lensing effect. Particularly, the weak lensing of GWs has been studied within the range of the Born approximation to constrain the small-scale power spectrum. In this work, the validity of the Born approximation is investigated by accounting for the higher-order terms in the gravitational potential $\Phi$. To do so, we formulate the post-Born approximation and derive the magnification $K$ and the phase modulation $S$ up to third order in $\Phi$. We find that the average of $S$ and $K$ is non-zero and that the average of $S$ depends on the size of the point mass. Due to this size dependency, the signal is enhanced, and the number of GW events required for detecting the average of $S$ decreases. We find that this number can become comparable to or even smaller than the number required for detecting the variance of $S$ in certain scenarios. In addition, it is verified that, for lensing by dark low-mass halos, the post-Born corrections are a few orders of magnitude smaller than the Born approximation at $f\geq0.01$~Hz. However, in the presence of the point mass, there is a condition under which the Born approximation fails. We derive the correction terms to the Born approximation and identify the condition under which the Born approximation no longer holds. For the magnification, the Born approximation is valid as long as the wavelength of GWs is larger than the Schwarzschild radius of lenses, while for the phase modulation, this condition is modified due to the physical size of the point mass.
Using Full-Text Content to Characterize and Identify Best Seller Books

Artistic pieces can be studied from several perspectives, one example being their reception among readers over time. In the present work, we approach this interesting topic from the standpoint of literary works, particularly assessing the task of predicting whether a book will become a best seller. Dissimilarly from previous approaches, we focused on the full content of books and considered visualization and classification tasks. We employed visualization for the preliminary exploration of the data structure and properties, involving SemAxis and linear discriminant analyses. Then, to obtain quantitative and more objective results, we employed various classifiers. Such approaches were used along with a dataset containing (i) books published from 1895 to 1924 and consecrated as best sellers by the Publishers Weekly Bestseller Lists and (ii) literary works published in the same period but not being mentioned in that list. Our comparison of methods revealed that the best-achieved result - combining a bag-of-words representation with a logistic regression classifier - led to an average accuracy of 0.75 both for the leave-one-out and 10-fold cross-validations. Such an outcome suggests that it is unfeasible to predict the success of books with high accuracy using only the full content of the texts. Nevertheless, our findings provide insights into the factors leading to the relative success of a literary work.
Ultrafast carrier dynamics in thin-films of the topological insulator Bi2Se3

Transient reflectivity measurements of thin films, ranging from 6 to 40 nm in thickness, of the topological insulator Bi2Se3 reveal a strong dependence of the carrier relaxation time on the film thickness. For thicker films, the relaxation dynamics are similar to those of bulk Bi2Se3, where the contribution of the bulk insulating phase dominates over that of the surface metallic phase. The carrier relaxation time shortens with decreasing film thickness, reaching values comparable to those of noble metals. This effect may result from the hybridization of Dirac cone states at the opposite surfaces for the thinnest films.
The spectrum of Grothendieck monoid: classifying Serre subcategories and reconstruction theorem

The Grothendieck monoid of an exact category is a monoid version of the Grothendieck group. We use it to classify Serre subcategories of an exact category and to reconstruct the topology of a noetherian scheme. We ﬁrst construct bijections between (i) the set of Serre subcategories of an exact category, (ii) the set of faces of its Grothendieck monoid, and (iii) the monoid spectrum of its Grothendieck monoid. By using (ii), we classify Serre subcategories of exact categories related to a ﬁnite dimensional algebra and a smooth projective curve. For this, we determine the Grothendieck monoid of the category of coherent sheaves on a smooth projective curve. By using (iii), we introduce a topology on the set of Serre subcategories. As a consequence, we recover the topology of a noetherian scheme from the Grothendieck monoid.
Langevin Dynamics With General Kinetic Energies

We study Langevin dynamics with a kinetic energy different from the standard, quadratic one in order to accelerate the sampling of Boltzmann-Gibbs distributions. In particular, this kinetic energy can be nonglobally Lipschitz, which raises issues for the stability of discretizations of the associated Langevin dynamics. We first prove the exponential convergence of the law of the continuous process to the Boltzmann-Gibbs measure by a hypocoercive approach and characterize the asymptotic variance of empirical averages over trajectories. We next develop numerical schemes which are stable and of weak order two by considering splitting strategies where the discretizations of the fluctuation/dissipation are corrected by a Metropolis procedure. We use the newly developed schemes for two applications: optimizing the shape of the kinetic energy for the so-called adaptively restrained Langevin dynamics (which considers perturbations of standard quadratic kinetic energies vanishing around the origin) and reducing the metastability of some toy models using nonglobally Lipschitz kinetic energies.
Non-ideal instabilities in sinusoidal shear flows with a streamwise magnetic field

Abstract We investigate the linear stability of a sinusoidal shear flow with an initially uniform streamwise magnetic field in the framework of incompressible magnetohydrodynamics (MHD) with finite resistivity and viscosity. This flow is known to be unstable to the Kelvin–Helmholtz instability in the hydrodynamic case. The same is true in ideal MHD, where dissipation is neglected, provided the magnetic field strength does not exceed a critical threshold beyond which magnetic tension stabilizes the flow. Here, we demonstrate that including viscosity and resistivity introduces two new modes of instability. One of these modes, which we refer to as an Alfvénic Dubrulle–Frisch instability, exists for any non-zero magnetic field strength as long as the magnetic Prandtl number ${{{Pm}}} < 1$. We present a reduced model for this instability that reveals its excitation mechanism to be the negative eddy viscosity of periodic shear flows described by Dubrulle & Frisch (Phys. Rev. A, vol. 43, 1991, pp. 5355–5364). Finally, we demonstrate numerically that this mode saturates in a quasi-stationary state dominated by counter-propagating solitons.
A novel soft-rigid wheeled crawling robot with high payload and passing capability

Abstract Soft crawling robots have been significantly studied in recent decades. However, moving in amphibious environment, high payload capability, and passing through complex ground have always been challenges for soft crawling robots. For these problems, this article presents an amphibious soft-rigid wheeled crawling robot (SRWCR) consists of a soft-rigid body actuated by two soft pneumatic actuators (SPAs), four wheels, and four annular soft bladders (ASBs) as brakes. By programming the actuation sequences of the two SPAs and four ASBs, SRWCR can achieve two basic modes of locomotion: linear motion and turning. Based on the energy conservation law, we have developed analytical models to interpret the static actuation performance of SPA, including linear and bending deformations. Furthermore, with the help of fast response and waterproof of SPA and ASB, SRWCR can achieve a linear speed of 14.97 mm/s, a turning speed of 5.63°/s, and an underwater locomotion speed of 13 mm/s, which demonstrates the excellent locomotion performance of SRWCR in amphibious environment. In addition, SRWCR can also achieve multiple impressive functions, including carrying a payload of 2 kg at the moving speed of 11.18 mm/s, passing through various complex ground such as the grass ground and sand ground, and so on, obstacle navigation in confined space. Compared with the existing soft crawling robots, with the help of the soft-rigid body and wheeled structure, SRWCR has the best payload and passing capability, which indicates the potential advantage of SRWCR in the design of functional robots.