Miracle-dz/UniFAR-FTUs huggingface dataset:
Wide-angle effects in multi-tracer power spectra with Doppler corrections

We examine the computation of wide-angle corrections to the galaxy power spectrum including redshift-space distortions and relativistic Doppler corrections, and also including multiple tracers with differing clustering, magnification and evolution biases. We show that the inclusion of the relativistic Doppler contribution, as well as radial derivative terms, are crucial for a consistent wide-angle expansion for large-scale surveys, both in the single and multi-tracer cases. We also give for the first time the wide-angle cross-power spectrum associated with the Doppler magnification-galaxy cross correlation, which has been shown to be a new way to test general relativity. In the full-sky power spectrum, the wide-angle expansion allows integrals over products of spherical Bessel functions to be computed analytically as distributional functions, which are then relatively simple to integrate over. We give for the first time a complete discussion and new derivation of the finite part of the divergent integrals of the form ∫0 ∞ drrnjℓ (kr) jℓ' (qr), which are necessary to compute the wide-angle corrections when a general window function is included. This facilitates a novel method for integrating a general analytic function against a pair of spherical Bessel functions.
Splitting algorithms and circuit analysis

The splitting algorithms of monotone operator theory find zeros of sums of relations. This corresponds to solving series or parallel one-port electrical circuits, or the negative feedback interconnection of two subsystems. One-port circuits with series and parallel interconnections, or block diagrams with multiple forward and return paths, give rise to current-voltage relations consisting of nested sums and inverses. In this extended abstract, we present new splitting algorithms specially suited to these structures, for interconnections of monotone and anti-monotone relations.
Green Software Lab: Towards an Engineering Discipline for Green Software

This report describes the research goals and results of the Green Software Lab (GSL) research project. This was a project funded by Funda\c{c}\~ao para a Ci\^encia e a Tecnologia (FCT) -- the Portuguese research foundation -- under reference POCI-01-0145-FEDER-016718, that ran from January 2016 till July 2020. This report includes the complete document reporting the results achieved during the project execution, which was submitted to FCT for evaluation on July 2020. It describes the goals of the project, and the different research tasks presenting the deliverables of each of them. It also presents the management and result dissemination work performed during the project's execution. The document includes also a self assessment of the achieved results, and a complete list of scientific publications describing the contributions of the project. Finally, this document includes the FCT evaluation report.
FERIA: Flat Envelope Model with Rotation and Infall under Angular Momentum Conservation

Radio observations of low-mass star formation in molecular spectral lines have rapidly progressed since the advent of the Atacama Large Millimeter/submillimeter Array (ALMA). The gas distribution and its kinematics within a few hundreds of astronomical unit scale around a Class 0–I protostar are spatially resolved, and the region where a protostellar disk is being formed is now revealed in detail. In such studies, it is essential to characterize the complex physical structure around a protostar consisting of an infalling envelope, a rotationally supported disk, and an outflow. For this purpose, we have developed a general-purpose computer code “FERIA” (Flat Envelope model with Rotation and Infall under Angular momentum conservation) to generate the image data cube based on the infalling-rotating envelope model and the Keplerian disk model, both of which are often used in observational studies. In this paper, we present the description and the user manual of FERIA and summarize caveats in actual applications. This program outputs cube FITS files, which can be used for direct comparison with observations. It can also be used to generate mock data for machine/deep learning. Examples of these applications are described and discussed to demonstrate how the model analyses work with actual observational data.
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding

Partial least square regression (PLSR) is a widely-used statistical model to reveal the linear relationships of latent factors that comes from the independent variables and dependent variables. However, traditional methods to solve PLSR models are usually based on the Euclidean space, and easily getting stuck into a local minimum. To this end, we propose a new method to solve the partial least square regression, named PLSR via optimization on bi-Grassmann manifold (PLSRbiGr). Specifically, we first leverage the three-factor SVD-type decomposition of the cross-covariance matrix defined on the bi-Grassmann manifold, converting the orthogonal constrained optimization problem into an unconstrained optimization problem on bi-Grassmann manifold, and then incorporate the Riemannian preconditioning of matrix scaling to regulate the Riemannian metric in each iteration. PLSRbiGr is validated with a variety of experiments for decoding EEG signals at motor imagery (MI) and steady-state visual evoked potential (SSVEP) task. Experimental results demonstrate that PLSRbiGr outperforms competing algorithms in multiple EEG decoding tasks, which will greatly facilitate small sample data learning.
ASR Error Correction with Constrained Decoding on Operation Prediction

Error correction techniques remain effective to refine outputs from automatic speech recognition (ASR) models. Existing end-to-end error correction methods based on an encoder-decoder architecture process all tokens in the decoding phase, creating undesirable latency. In this paper, we propose an ASR error correction method utilizing the predictions of correction operations. More specifically, we construct a predictor between the encoder and the decoder to learn if a token should be kept ("K"), deleted ("D"), or changed ("C") to restrict decoding to only part of the input sequence embeddings (the"C"tokens) for fast inference. Experiments on three public datasets demonstrate the effectiveness of the proposed approach in reducing the latency of the decoding process in ASR correction. It enhances the inference speed by at least three times (3.4 and 5.7 times) while maintaining the same level of accuracy (with WER reductions of 0.53% and 1.69% respectively) for our two proposed models compared to a solid encoder-decoder baseline. In the meantime, we produce and release a benchmark dataset contributing to the ASR error correction community to foster research along this line.
A Novel Faster All-Pair Shortest Path Algorithm Based on the Matrix Multiplication for GPUs

The shortest path problem is a class of typical problems in graph theory and network science, with a wide range of application scenarios. The state of the art all-pair shortest path algorithm is implemented by the parallel single source shortest path algorithms, since the Floyd algorithm is diﬃcult to accelerate by parallelism. We pro-pose a novel all-pair shortest path algorithm based on block matrix multiplication via GPUs, which transforms shortest path problems into the linear algebra problems and takes advantage of the GPUs’ superior performance in this regard. In the experiments, the novel algorithm achieves average of 41.257 × and the maximum of 89.919 × over widely used Dijkstra algorithm which implements the priority queue by the binary heap and is optimized via multi-threading.
A lensed protocluster candidate at z = 7:66 identified in JWST observations of the galaxy cluster SMACS0723-7327

According to the current paradigm of galaxy formation, the first galaxies have been likely formed within large dark matter haloes. The fragmentation of these massive haloes led to the formation of galaxy protoclusters, which are usually composed of one to a few bright objects, surrounded by numerous fainter (and less massive) galaxies. These early structures could have played a major role in reionising the neutral hydrogen within the first billion years of the Universe; especially, if their number density is significant.Taking advantage of the unprecedented sensitivity reached by the \textit{James Webb Space Telescope (JWST)}, galaxy protoclusters can now be identified and studied in increasing numbers beyond $z\geq\ $6. Characterising their contribution to the UV photon budget could supply new insights into the reionisation process. We analyse the first JWST dataset behind SMACS0723-7327 to search for protoclusters at $z\geq6$, combining the available spectroscopic and photometric data. We then compare our findings with semi-analytical models and simulations. In addition to two bright galaxies ($\leq$26.5 AB in F277W), separated by $\sim$11\arcsec and spectroscopically confirmed at $z_{spec}=7.66$, we identify 6 additional galaxies with similar colors in a $\theta\sim20$\arcsec radius around these (corresponding to R$\sim60-90$ kpc in the source plane). Using several methods, we estimate the mass of the dark matter halo of this protocluster, $\sim$3.3$\times$10$^{11}$M$_{\odot}$ accounting for magnification, consistent with various predictions. The physical properties of all protocluster members are also in excellent agreement with what has been previously found at lower redshifts: star-formation main sequence and protocluster size. This detection adds to just a few protoclusters currently known in the first billion years of the universe.
Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks

Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images. We integrate the linguistic information into the vision-based U-Structure networks designed for salient object detection tasks. The experiments are based on the newly created DUTS Cross Modal (DUTS-CM) dataset, which contains both visual and linguistic labels. We propose a new module called efficient Cross-Modal Self-Attention (eCMSA) to combine visual and linguistic features and improve the performance of the original U-structure networks. Meanwhile, to reduce the heavy burden of labeling, we employ a semi-supervised learning method by training an image caption model based on the DUTS-CM dataset, which can automatically label other datasets like DUT-OMRON and HKU-IS. The comprehensive experiments show that the performance of SOD can be improved with the natural language input and is competitive compared with other SOD methods.
Projected 
d
-wave superconducting state: A fermionic projected entangled pair state study

We investigate the physics of projected d-wave pairing states using their fermionic projected entangled pair state (fPEPS) representation. First, we approximate a d-wave Bardeen-Cooper-Schrieffer state using the Gaussian fPEPS. Next, we translate the resulting state into fPEPS tensors and implement the Gutzwiller projection which removes double occupancy by modifying the local tensor elements. The tensor network representation of the projected d-wave pairing state allows us to evaluate physical quantities in the thermodynamic limit without employing the Gutzwiller approximation. Despite having very few variational parameters, such physically motivated tensor network states are shown to exhibit competitive energies for the doped t-J model. We expect that such construction offers useful initial states and guidance for variational tensor network calculations.
On the Activation Function Dependence of the Spectral Bias of Neural Networks

Neural networks are universal function approximators which are known to generalize well despite being dramatically overparameterized. We study this phenomenon from the point of view of the spectral bias of neural networks. Our contributions are two-fold. First, we provide a theoretical explanation for the spectral bias of ReLU neural networks by leveraging connections with the theory of finite element methods. Second, based upon this theory we predict that switching the activation function to a piecewise linear B-spline, namely the Hat function, will remove this spectral bias, which we verify empirically in a variety of settings. Our empirical studies also show that neural networks with the Hat activation function are trained significantly faster using stochastic gradient descent and ADAM. Combined with previous work showing that the Hat activation function also improves generalization accuracy on image classification tasks, this indicates that using the Hat activation provides significant advantages over the ReLU on certain problems.
Monitoring the metering performance of an electronic voltage transformer on-line based on cyber-physics correlation analysis

Metering performance is the key parameter of an electronic voltage transformer (EVT), and it requires high accuracy. The conventional off-line calibration method using a standard voltage transformer is not suitable for the key equipment in a smart substation, which needs on-line monitoring. In this article, we propose a method for monitoring the metering performance of an EVT on-line based on cyber-physics correlation analysis. By the electrical and physical properties of a substation running in three-phase symmetry, the principal component analysis method is used to separate the metering deviation caused by the primary fluctuation and the EVT anomaly. The characteristic statistics of the measured data during operation are extracted, and the metering performance of the EVT is evaluated by analyzing the change in statistics. The experimental results show that the method successfully monitors the metering deviation of a Class 0.2 EVT accurately. The method demonstrates the accurate evaluation of on-line monitoring of the metering performance on an EVT without a standard voltage transformer.
CLIP-based Neural Neighbor Style Transfer for 3D Assets

We present a method for transferring the style from a set of images to a 3D object. The texture appearance of an asset is optimized with a differentiable renderer in a pipeline based on losses using pretrained deep neural networks. More specifically, we utilize a nearest-neighbor feature matching loss with CLIP-ResNet50 to extract the style from images. We show that a CLIP- based style loss provides a different appearance over a VGG-based loss by focusing more on texture over geometric shapes. Additionally, we extend the loss to support multiple images and enable loss-based control over the color palette combined with automatic color palette extraction from style images.
Convex polytopes from fewer points

Let ES d ( n ) be the smallest integer such that any set of ES d ( n ) points in R d in general position contains n points in convex position. In 1960, Erd˝os and Szekeres showed that ES 2 ( n ) > 2 n − 2 + 1 holds, and famously conjectured that their construction is optimal. This was nearly settled by Suk in 2017, who showed that ES 2 ( n ) 6 2 n + o ( n ) . In this paper, we prove that ES n ) holds for all d > 3. In particular, this establishes that, in higher dimensions, substantially fewer points are needed in order to ensure the presence of a convex polytope on n vertices, compared to how many are required in the plane.
Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams

Today's large-scale services ( e.g. , video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a "sketch of sketches" to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times.
Nuclear mass predictions with machine learning reaching the accuracy required by 
r
-process studies

Nuclear masses are predicted with the Bayesian neural networks by learning the mass surface of even-even nuclei and the correlation energies to their neighbouring nuclei. By keeping the known physics in various sophisticated mass models and performing the delicate design of neural networks, the proposed Bayesian machine learning (BML) mass model achieves an accuracy of $84$~keV, which crosses the accuracy threshold of the $100$~keV in the experimentally known region. It is also demonstrated the corresponding uncertainties of mass predictions are properly evaluated, while the uncertainties increase by about $50$~keV each step along the isotopic chains towards the unknown region. The shell structures in the known region are well described and several important features in the unknown region are predicted, such as the new magic numbers around $N = 40$, the robustness of $N = 82$ shell, the quenching of $N = 126$ shell, and the smooth separation energies around $N = 104$.
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering

Time series shapelets are discriminative subsequences that have been recently found effective for time series clustering (TSC). The shapelets are convenient for interpreting the clusters. Thus, the main challenge for TSC is to discover high-quality variable-length shapelets to discriminate different clusters. In this paper, we propose a novel autoencoder-shapelet approach (AUTOSHAPE), which is the first study to take the advantage of both autoencoder and shapelet for determining shapelets in an unsupervised manner. An autoencoder is specially designed to learn high-quality shapelets. More specifically, for guiding the latent representation learning, we employ the latest self-supervised loss to learn the unified embeddings for variable-length shapelet candidates (time series subsequences) of different variables, and propose the diversity loss to select the discriminating embeddings in the unified space. We introduce the reconstruction loss to recover shapelets in the original time series space for clustering. Finally, we adopt Davies Bouldin index (DBI) to inform AUTOSHAPE of the clustering performance during learning. We present extensive experiments on AUTOSHAPE. To evaluate the clustering performance on univariate time series (UTS), we compare AUTOSHAPE with 15 representative methods using UCR archive datasets. To study the performance of multivariate time series (MTS), we evaluate AUTOSHAPE on 30 UEA archive datasets with 5 competitive methods. The results validate that AUTOSHAPE is the best among all the methods compared. We interpret clusters with shapelets, and can obtain interesting intuitions about clusters in two UTS case studies and one MTS case study, respectively.
Dynamics of Large-Scale Solar-Wind Streams Obtained by the Double Superposed Epoch Analysis: 5. Influence of the Solar Activity Decrease

In solar cycles 23–24, solar activity noticeably decreased and, as a result, solar wind parameters decreased. Based on the measurements of the OMNI base for the period 1976–2019, the time profiles of the main solar wind parameters and magnetospheric indices for the main interplanetary drivers of magnetospheric disturbances (solar wind types CIR. Sheath, ejecta and MC) are studied using the double superposed epoch method. The main task of the research is to compare time profiles for the epoch of high solar activity at 21–22 SC and the epoch of low activity at 23–24 SC. The following results were obtained. (1) The analysis did not show a statistically significant change in driver durations during the epoch of minimum. (2) The time profiles of all parameters for all types of SW in the epoch of low activity have the same shape as in the epoch of high activity, but locate at lower values of the parameters. (3) In CIR events, the longitude angle of the solar wind flow has a characteristic S shape; but in the epoch of low activity, it varies in a larger range than in the previous epoch.
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

Human leukocyte antigen (HLA) is an important molecule family in the field of human immunity, which recognizes foreign threats and triggers immune responses by presenting peptides to T cells. In recent years, the synthesis of tumor vaccines to induce specific immune responses has become the forefront of cancer treatment. Computationally modeling the binding patterns between peptide and HLA can greatly accelerate the development of tumor vaccines. However, most of the prediction methods’ performance is very limited and they cannot fully take advantage of the analysis of existing biological knowledge as the basis of modeling. In this paper, we propose TripHLApan, a novel pan-specific prediction model, for HLA molecular peptide binding prediction. TripHLApan exhibits powerful prediction ability by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. The comprehensive evaluations demonstrate the effectiveness of TripHLApan in predicting HLA-I and HLA-II peptide binding in different test environments. The predictive power of HLA-I is further demonstrated in the latest data set. In addition, we show that TripHLApan has strong binding reconstitution ability in the samples of a melanoma patient. In conclusion, TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines.
Characterizing the 2022- Russo-Ukrainian Conflict Through the Lenses of Aspect-Based Sentiment Analysis: Dataset, Methodology, and Key Findings

Online social networks (OSNs) play a crucial role in modern society by supporting free expression, information sharing, and social movement organization. However, they are also the tool of choice to spread disinformation, hate speech, and support propaganda. As such, it is crucial to analyze OSNs, particularly during critical events such as elections, pandemics, and conflicts, when disinformation campaigns may seek to undermine the democratic values of a nation. This paper analyzes the general-public perception of the first phases of the 2022- Russo-Ukrainian conflict on Twitter. To this end, we developed a general methodology consisting of several steps. We built a dataset of 5.5+ million tweets related to the subject, generated by 1.8+ million unique users. Then, we cluster users into five categories, and combining statistical analysis and aspect-based sentiment analysis (ABSA), we quantitatively and qualitatively investigate the spread of information during the conflict. Our analysis revealed several important insights, including anomalies in the behavior of specific user categories and their sentiment trends and a spike in the daily account creation rate before the conflict. Other than being interesting on their own, our findings also have significant implications for future research on how disinformation campaigns are executed and on developing effective strategies to mitigate their impact.