Miracle-dz/UniFAR-FTUs huggingface dataset:
Modal parameter identification using state space mode isolation

Multiple degree of freedom (MDOF) methods for modal parameter extraction in current use deal with all of the modal parameters in a simultaneous fashion in the process of matching measured data to analytical forms. In contrast, the mode isolation algorithm exploits the fact that each mode has unique characteristics that can be used to isolate it from the contribution of other modes. The present work extends the algorithm to use state-space modes as the analytical foundation. The paper describes how modal parameters are identified and then refined in a recursive manner. One of the issues of primary importance for a modal identification is the number of modes that significantly contribute to the measured response. Most MDOF algorithms require an a priori guess of the number of significant modes, or degrees of freedom, of the system. Several MDOF methods address this matter by utilizing peak counting of amplitude-frequency plots to identify the number of modes. However, such estimation becomes problematic for systems that exhibit modal coupling. In contrast, the mode isolation method offers a simple and consistent modal extraction methodology that is inherently automatic in nature. A prototypical system is used to demonstrate that it is not limited by the presence of modal coupling. A cantilever beam with three attached spring-mass-damper systems is tuned such that the bandwidths of two resonances are commensurate with the natural frequency separation. Differing levels of noise are imposed on the simulated time-domain response, which is then transformed to the frequency domain. The simulated data is used to illustrate the results of the algorithmic steps. The natural frequencies and damping ratios that result from the procedure are found to match the analytical system properties, with an error that is less than the noise level that was added.
An Overview of Color Transfer and Style Transfer for Images and Videos

—Image or video appearance features (e.g., color, tex- ture, tone, illumination, and so on) reﬂect one’s visual perception and direct impression of an image or video. Given a source image (video) and a target image (video), the image (video) color transfer technique aims to process the color of the source image or video (note that the source image or video is also referred to the reference image or video in some literature) to make it look like that of the target image or video, i.e., transferring the appearance of the target image or video to that of the source image or video, which can thereby change one’s perception of the source image or video. As an extension of color transfer, style transfer refers to rendering the content of a target image or video in the style of an artist with either a style sample or a set of images through a style transfer model. As an emerging ﬁeld, the study of style transfer has attracted the attention of a large number of researchers. After decades of development, it has become a highly interdisciplinary research with a variety of artistic expression styles can be achieved. This paper provides an overview of color transfer and style transfer methods over the past years.
Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks

Stock prices are highly volatile and sudden changes in trends are often very problematic for traditional forecasting models to handle. The standard Long Short Term Memory (LSTM) networks are regarded as the state-of-the-art models for such predictions. But, these models fail to handle sudden and drastic changes in the price trend. Moreover, there are some inherent constraints with the open, high, low and close (OHLC) prices of the stocks. Literature lacks the study on the inherent property of OHLC prices. We argue that predicting the OHLC prices for the next day is much more informative than predicting the trends of the stocks as the trend is mostly calculated using these OHLC prices only. The problem mainly is focused on Buy-Today Sell-Tomorrow (BTST) trading. In this regard, AEs when pre-trained with the stock prices, may be beneficial. A novel framework is proposed where a pre-trained encoder is cascaded in front of the multi-task predictor network. This hybrid network can leverage the power of a combination of networks and can both handle the OHLC constraints as well as capture any sudden drastic changes in the prices. It is seen that such a network is much more efficient at predicting stock prices. The experiments have been extended to recommend the most profitable and most overbought stocks on the next day. The model has been tested for multiple Indian companies and it is found that the recommendations from the proposed model have not resulted in a single loss for a test period of 300 days.
Surface critical properties of the three-dimensional clock model

Using Monte Carlo simulations and finite-size scaling analysis, we show that the $q$-state clock model with $q=6$ on the simple cubic lattice with open surfaces has a rich phase diagram; in particular, it has an extraordinary-log phase, besides the ordinary and extraordinary transitions at the bulk critical point. We prove numerically that the presence of the intermediate extraordinary-log phase is due to the emergence of an O(2) symmetry in the surface state before the surface enters the $Z_{q}$ symmetry-breaking region as the surface coupling is increased at the bulk critical point, while O(2) symmetry emerges for the bulk. The critical behaviors of the extraordinary-log transition, as well as the ordinary and the special transition separating the ordinary and the extraordinary-log transition are obtained.
Standardized Evaluation of Machine Learning Methods for Evolving Data Streams

Due to the unspeciﬁed and dynamic nature of data streams, online machine learning requires powerful and ﬂexible solutions. However, evaluating online machine learning methods under realistic conditions is diﬃcult. Existing work therefore often draws on diﬀerent heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quality machine learning in evolving data streams. In particular, we discuss sensible performance measures and evaluation strategies for online predictive modelling, online feature selection and concept drift detection. As one of the ﬁrst works, we also look at the interpretability of online learning methods. The proposed evaluation standards are provided in a new Python framework called ﬂoat . Float is completely modular and allows the simultaneous integration of common libraries, such as scikit-multiﬂow or river, with custom code. Float is open-sourced and can be accessed at https://github.com/haugjo/float . In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.
Cosmic strings from pure Yang–Mills theory

We discuss the formation of cosmic strings or macroscopic color ﬂux tubes at the phase transition from the deconﬁnement to conﬁnement phase in pure Yang–Mills (YM) theory, such as SU( N ) , Sp( N ) , SO( N ) , and Spin( N ) , based on the current understanding of theoretical physics. According to the holographic dual descriptions, the cosmic strings are dual to fundamental strings or wrapped D-branes in the gravity side depending on the structure of the gauge group, and the reconnection probability is suppressed by O ( N − 2 ) and e −O ( N ) , respectively. The pure YM theory thus provides a simple realization of cosmic F- and D-strings without the need for a brane-inﬂationary scenario or extra dimension. We also review the stability of cosmic strings based on the concept of 1-form symmetry, which further implies the existence of a baryon vertex in some YM theory. We calculate the gravitational wave spectrum that is emitted from the cosmic strings based on an extended velocity-dependent one-scale model and discuss its detectability based on ongoing and planned gravitational-wave experiments. In particular, the SKA and LISA can observe gravitational signals if the conﬁnement scale is higher than O (10 12 ) GeV and O (10 10 ) GeV for SU( N ) with N = O (1) , respectively.
Improved cutoff functions for short-range potentials and the Wolf summation

A class of radial, polynomial cutoﬀ functions f c n ( r ) for short-ranged pair potentials or related expressions is proposed. Their derivatives up to order n and n + 1 vanish at the outer cutoﬀ r c and an inner radius r i , respectively. Moreover, f c n ( r ≤ r i ) = 1 and f c n ( r ≥ r c ) = 0. It is shown that the used order n can qualitatively aﬀect results: stress and bulk moduli of ideal crystals are unavoidably discontinuous with density for n = 0 and n = 1, respectively. Systematic errors on energies and computing times decrease by approximately 25% for Lennard-Jones with n = 2 or n = 3 compared to standard cutting procedures. Another cutoﬀ function turns out beneﬁcial to compute Coulomb interactions using the Wolf summation, which is shown to not properly converge when local charge neutrality is obeyed only in a stochastic sense. However, for all investigated homogeneous systems with thermal noise (ionic crystals and liquids), the modiﬁed Wolf summation, despite being inﬁnitely diﬀerentiable at r c , converges similarly quickly as the original summation. Finally, it is discussed how to reduce the computational cost of numerically exact Monte Carlo simulations using the Wolf summation even when it does not properly converge.
A Closer Look at Branch Classifiers of Multi-exit Architectures

Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks. In this paper, we analyze different branching patterns that vary in their allocation of computational complexity for the branch classifiers. Constant-complexity branching keeps all branches the same, while complexity-increasing and complexity-decreasing branching place more complex branches later or earlier in the backbone respectively. Through extensive experimentation on multiple backbones and datasets, we find that complexity-decreasing branches are more effective than constant-complexity or complexity-increasing branches, which achieve the best accuracy-cost trade-off. We investigate a cause by using knowledge consistency to probe the effect of adding branches onto a backbone. Our findings show that complexity-decreasing branching yields the least disruption to the feature abstraction hierarchy of the backbone, which explains the effectiveness of the branching patterns.
Semantic Communication: An Information Bottleneck View

—Motivated by recent success of machine learning tools at the PHY layer and driven by high bandwidth demands of the next wireless communication standard 6G, the old idea of semantic communication by Weaver from 1949 has received considerable attention. It breaks with the classic design paradigm according to Shannon by aiming to transmit the meaning of a message rather than its exact copy and thus potentially allows for savings in bandwidth. In this work, inspired by Weaver, we propose an information- theoretic framework where the semantic context is explicitly introduced into probabilistic models. In particular, for bandwidth efﬁcient transmission, we deﬁne semantic communication system design as an Information Bottleneck optimization problem and consider important implementation aspects. Further, we uncover the restrictions of the classic 5G communication system design w.r. t. semantic context. Notably, based on the example of distributed image classiﬁcation, we reveal the huge potential of a semantic communication system design. Numerical results show a tremendous saving in bandwidth of 20 dB with our proposed approach ISCNet compared to a classic PHY layer design.
Persistent sheaf cohomology

We expand the toolbox of (co)homological methods in computational topology by applying the concept of persistence to sheaf cohomology. Since sheaves (of modules) combine topological information with algebraic information, they allow for variation along an algebraic dimension and along a topological dimension. Consequently, we introduce two diﬀerent constructions of sheaf cohomology (co)persistence modules. One of them can be viewed as a natural generalization of the construction of simplicial or singular cohomology copersistence modules. We discuss how both constructions relate to each other and show that, in some cases, we can reduce one of them to the other. Moreover, we show that we can combine both constructions to obtain two-dimensional (co)persistence modules with a topological and an algebraic dimension. We also show that some classical results and methods from persistence theory can be generalized to sheaves. Our results open up a new perspective on persistent cohomology of ﬁltrations of simplicial complexes.
Viscous inertial modes on a differentially rotating sphere: Comparison with solar observations

Context. In a previous paper, we studied the e ﬀ ect of latitudinal rotation on solar equatorial Rossby modes in the β -plane approximation. Since then, a rich spectrum of inertial modes has been observed on the Sun, which is not limited to the equatorial Rossby modes and includes high-latitude modes. Aims. Here we extend the computation of toroidal modes in 2D to spherical geometry using realistic solar di ﬀ erential rotation and including viscous damping. The aim is to compare the computed mode spectra with the observations and to study mode stability. Methods. At a ﬁxed radius, we solved the eigenvalue problem numerically using a spherical harmonics decomposition of the velocity stream function. Results. Due to the presence of viscous critical layers, the spectrum consists of four di ﬀ erent families: Rossby modes, high-latitude modes, critical-latitude modes, and strongly damped modes. For each longitudinal wavenumber m ≤ 3, up to three Rossby-like modes are present on the sphere, in contrast to the equatorial β plane where only the equatorial Rossby mode is present. The least damped modes in the model have eigenfrequencies and eigenfunctions that resemble the observed modes; the comparison improves when the radius is taken in the lower half of the convection zone. For radii above 0 . 75 R (cid:12) and Ekman numbers E < 10 − 4 , at least one mode is unstable. For either m = 1 or m = 2, up to two Rossby modes (one symmetric and one antisymmetric) are unstable when the radial dependence of the Ekman number follows a quenched di ﬀ usivity model ( E ≈ 2 × 10 − 5 at the base of the convection zone). For m = 3, up to two Rossby modes can be unstable, including the equatorial Rossby mode. Conclusions. Although the 2D model discussed here is highly simpliﬁed, the spectrum of toroidal modes appears to include many of the observed solar inertial modes. The self-excited modes in the model have frequencies close to those of the observed modes with the largest amplitudes.
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task

Time series classification is one of the very popular machine learning tasks. In this paper, we explore the application of Hidden Markov Model (HMM) for time series classification. We distinguish between two modes of HMM application. The first, in which a single model is built for each class. The second, in which one HMM is built for each time series. We then transfer both approaches for classifier construction to the domain of Fuzzy Cognitive Maps. The identified four models, HMM NN (HMM, one per series), HMM 1C (HMM, one per class), FCM NN, and FCM 1C are then studied in a series of experiments. We compare the performance of different models and investigate the impact of their hyperparameters on the time series classification accuracy. The empirical evaluation shows a clear advantage of the one-model-per-series approach. The results show that the choice between HMM and FCM should be dataset-dependent.
Two Decades of Colorization and Decolorization for Images and Videos

—Colorization is a computer-aided process, which aims to give color to a gray image or video. It can be used to enhance black-and-white images, including black-and-white photos, old-fashioned ﬁlms, and scientiﬁc imaging results. On the contrary, decolorization is to convert a color image or video into a grayscale one. A grayscale image or video refers to an image or video with only brightness information without color information. It is the basis of some downstream image processing applications such as pattern recognition, image segmentation, and image enhancement. Different from image decolorization, video decolorization should not only consider the image contrast preservation in each video frame, but also respect the temporal and spatial consistency between video frames. Researchers were devoted to develop decolorization methods by balancing spatial-temporal consistency and algorithm efﬁciency. With the prevalance of the digital cameras and mobile phones, image and video colorization and decolorization have been paid more and more attention by researchers. This paper gives an overview of the progress of image and video colorization and decolorization methods in the last two decades.
The Paper Pile at Home: Adopting Personal Electronic Records

Research has found that if respondents do not manage their personal records such as bills, receipts and tax-related documents efficiently, they risk not being able to re-find them when needed, resulting in significant problems. A significant gap in understanding and addressing this problem stems from a lack of knowledge of the format of these records, particularly in the context of the COVID-19 pandemic, that may have caused an increase in managing personal records in an electronic format, rather than by hardcopy. This paper provides results of quantitative research conducted in 2018, thereby providing a valuable benchmark for future research on the same and related topics. This measurement was achieved by means of an online survey distributed via social media amongst 205 respondents. The results revealed that nearly all respondents (97%) retained at least some records, and more than 80% kept some of those records in an electronic format, particularly travel reservations and payslips. Conversely, only 10% of respondents kept receipts and warranties for appliances or medical records in an electronic format. The reason for these differences in propensity of keeping various records in an electronic or hardcopy format will require further research.
Improving the Robustness of Federated Learning for Severely Imbalanced Datasets

With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and asynchronous weight update. In this manuscript, we have explored very simplistic synchronous weight update mechanisms. It has been seen that with an increasing number of worker nodes, the performance degrades drastically. This effect has been studied in the context of extreme imbalanced classification (e.g. outlier detection). In practical cases, the assumed conditions of i.i. d. may not be fulfilled. There may also arise global class imbalance situations like that of outlier detection where the local servers receive severely imbalanced data and may not get any samples from the minority class. In that case, the DNNs in the local servers will get completely biased towards the majority class that they receive. This would highly impact the learning at the parameter server (which practically does not see any data). It has been observed that in a parallel setting if one uses the existing federated weight update mechanisms at the parameter server, the performance degrades drastically with the increasing number of worker nodes. This is mainly because, with the increasing number of nodes, there is a high chance that one worker node gets a very small portion of the data, either not enough to train the model without overfitting or having a highly imbalanced class distribution. The chapter, hence, proposes a workaround to this problem by introducing the concept of adaptive cost-sensitive momentum averaging. It is seen that for the proposed system, there was no to minimal degradation in performance while most of the other methods hit their bottom performance before that.
Music Enhancement via Image Translation and Vocoding

Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ. This paper presents a deep learning approach to enhance low-quality music recordings by combining (i) an image-to-image translation model for manipulating audio in its mel-spectrogram representation and (ii) a music vocoding model for mapping synthetically generated mel-spectrograms to perceptually realistic waveforms. We find that this approach to music enhancement outperforms baselines which use classical methods for mel-spectrogram inversion and an end-to-end approach directly mapping noisy waveforms to clean waveforms. Additionally, in evaluating the proposed method with a listening test, we analyze the reliability of common audio enhancement evaluation metrics when used in the music domain.
Disc cloaking: Establishing a lower limit to the number density of local compact massive spheroids/bulges and the potential fate of some high-z red nuggets

The near-absence of compact massive quiescent galaxies in the local Universe implies a size evolution since $z\sim2.5$. It is often theorised that such `red nuggets' have evolved into today's elliptical (E) galaxies via an E-to-E transformation. We examine an alternative scenario in which a red nugget develops a rotational disc through mergers and accretion, say, at $1\lesssim z\lesssim2$, thereby cloaking the nugget as the extant bulge/spheroid component of a larger, now old, galaxy. We have performed detailed, physically-motivated, multi-component decompositions of a volume-limited sample of 103 massive ($M_*/\rm M_{\odot} \gtrsim 1\times 10^{11}$) galaxies within 110\,Mpc. Among our 28 galaxies with existing elliptical classifications, we found that 18 have large-scale discs, and two have intermediate-scale discs, and are reclassified here as lenticulars (S0) and elliculars (ES). The local spheroid stellar mass function, size-mass diagram and bulge-to-total ($B/T$) flux ratio are presented. We report lower-limits for the volume number density of compact massive spheroids, $n_\mathrm{c,Sph}\sim (0.17$-$1.2) \times 10^{-4}\,\rm Mpc^{-3}$, based on different definitions of `red nuggets' in the literature. Similar number densities of local compact massive bulges were reported by de la Rosa et al. using automated two-component decompositions and their existence is now abundantly clear with our multi-component decompositions. We find disc-cloaking to be a salient alternative for galaxy evolution. In particular, instead of an E-to-E process, disc growth is the dominant evolutionary pathway for at least low-mass ($1\times10^{10}<M_*/\rm M_{\odot} \lessapprox 4 \times 10^{10}$) red nuggets, while our current lower-limits are within an alluring factor of a few of the peak abundance of high-mass red nuggets at $1\lesssim z\lesssim2$.