Miracle-dz/UniFAR-FTUs huggingface dataset:
Global-aware Beam Search for Neural Abstractive Summarization

This study develops a calibrated beam-based algorithm with awareness of the global attention distribution for neural abstractive summarization, aiming to improve the local optimality problem of the original beam search in a rigorous way. Specifically, a novel global protocol is proposed based on the attention distribution to stipulate how a global optimal hypothesis should attend to the source. A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion. This novel design enjoys a distinctive property, i.e., the global attention distribution could be predicted before inference, enabling step-wise improvements on the beam search through the global scoring mechanism. Extensive experiments on nine datasets show that the global (attention)-aware inference significantly improves state-of-the-art summarization models even using empirical hyper-parameters. The algorithm is also proven robust as it remains to generate meaningful texts with corrupted attention distributions. The codes and a comprehensive set of examples are available.
Gyrokinetic simulations in stellarators using different computational domains

In this work, we compare gyrokinetic simulations in stellarators using different computational domains, namely, flux tube (FT), full-flux-surface (FFS), and radially global (RG) domains. Two problems are studied: the linear relaxation of zonal flows (ZFs) and the linear stability of ion temperature gradient (ITG) modes. Simulations are carried out with the codes EUTERPE, GENE, GENE-3D, and stella in magnetic configurations of LHD and W7-X using adiabatic electrons. The ZF relaxation properties obtained in different FTs are found to differ with each other and with the RG result, except for sufficiently long FTs, in general. The FT length required for convergence is configuration-dependent. Similarly, for ITG instabilities, different FTs provide different results, but the discrepancy between them diminishes with increasing FT length. FFS and FT simulations show good agreement in the calculation of the growth rate and frequency of the most unstable modes in LHD, while for W7-X differences in the growth rates are found between the FT and the FFS domains. RG simulations provide results close to the FFS ones. The radial scale of unstable ITG modes is studied in global and FT simulations finding that in W7-X, the radial scale of the most unstable modes depends on the binormal wavenumber, while in LHD no clear dependency is found.
The Black Hole Mass of NGC 4151 from Stellar Dynamical Modeling

The mass of a supermassive black hole (M BH) is a fundamental property that can be obtained through observational methods. Constraining M BH through multiple methods for an individual galaxy is important for verifying the accuracy of different techniques and for investigating the assumptions inherent in each method. However, there exist only a few galaxies where multiple M BH measurement techniques can be applied. NGC 4151 is one of these rare galaxies for which multiple methods can be used: stellar and gas dynamical modeling because of its proximity (D = 15.8 ± 0.4 Mpc from Cepheids), and reverberation mapping because of its active accretion. In this work, we reanalyzed H-band integral field spectroscopy of the nucleus of NGC 4151 from Gemini NIFS, improving the analysis at several key steps. We then constructed a wide range of axisymmetric dynamical models with the new orbit-superposition code Forstand. One of our primary goals is to quantify the systematic uncertainties in M BH arising from different combinations of the deprojected density profile, inclination, intrinsic flattening, and mass-to-light ratio. As a consequence of uncertainties on the stellar luminosity profile arising from the presence of the active galactic nucleus, our constraints on M BH are rather weak. Models with a steep central cusp are consistent with no black hole; however, in models with more moderate cusps, the black hole mass lies within the range of 0.25 × 107 M ⊙ ≲ M BH ≲ 3 × 107 M ⊙. This measurement is somewhat smaller than the earlier analysis presented by Onken et al. but agrees with previous M BH values from gas dynamical modeling and reverberation mapping. Future dynamical modeling of reverberation data, as well as IFU observations with JWST, will aid in further constraining the mass of the central supermassive black hole in NGC 4151.
Contact Tracing Information Improves the Performance of Group Testing Algorithms

Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In group testing, we are given $n$ samples, one per individual. These samples are arranged into $m<n$ pooled samples, where each pool is obtained by mixing a subset of the $n$ individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we use side information (SI) collected from contact tracing (CT) within nonadaptive/single-stage group testing algorithms. We generate CT SI data by incorporating characteristics of disease spread between individuals. These data are fed into two signal and measurement models for group testing, and numerical results show that our algorithms provide improved sensitivity and specificity. We also show how to incorporate CT SI into the design of the pooling matrix. That said, our numerical results suggest that the utilization of SI in the pooling matrix design based on the minimization of a weighted coherence measure does not yield significant performance gains beyond the incorporation of SI in the group testing algorithm.
Topologically induced changes in thermal properties of structurally balanced systems

The dynamics of social relations and the possibility of reaching the state of structural balance (Heider balance) are discussed for various topologies of the networks of interacting actors under the influence of the temperature modeling the social noise level. For that purpose, two main types of lattices are considered. The first is created by removing some links from a regular triangular lattice to produce a diluted triangular lattice or adding more links to create an enhanced triangular lattice. The second is the classical random graph. In both those cases, the full range of possible graph densities is discussed, limited by the extreme cases of networks which consist of a small number of separated triads and fully connected networks. The performed simulations indicate that for random lattices a transition between the balanced and imbalanced states is always possible and occurs at the critical temperature which depends on the graph density. On the other hand, it is shown that this transition and existence of the balanced state are not possible for networks based on the diluted triangular lattices if the average node degree is too close to the value characterizing the regular triangular lattice. The critical temperatures are size-independent only for the diluted triangular lattices and depend on the size of the system for the enhanced triangular lattices and random graphs.
Computing Second-Order Functional Derivatives with Respect to the External Potential

Following the increasing interest in the higher-order (functional) derivatives of conceptual density functional theory, we developed and implemented a method for calculating second-order functional derivatives with respect to the external potential. Our method is theoretically exact but involves two numerical approximations: the functional derivatives are expanded in a basis set, and the values of the corresponding expansion coefficients are determined by probing the molecular environment by a finite set of external potential perturbations. Exact solutions are obtained only in the limit of a complete basis set and an infinite number of distinct perturbations. We use this method to compute the atom-condensed linear response kernel for a series of six molecules and show that the results are comparable to the ones obtained by a previously proposed, approximate approach from second-order perturbation theory. The numerical error of the current implementation is about 0.01 au. Because the present method gives e.
Exploring Mutual Information between IRIS Spectral Lines. II. Calculating the Most Probable Response in all Spectral Windows

A three-dimensional picture of the solar atmosphere’s thermodynamics can be obtained by jointly analyzing multiple spectral lines that span many formation heights. In Paper I, we found strong correlations between spectral shapes from a variety of different ions during solar flares in comparison to the quiet Sun. We extend these techniques to address the following questions: which regions of the solar atmosphere are most connected during a solar flare, and what are the most likely responses across several spectral windows based on the observation of a single Mg ii spectrum? Our models are derived from several million IRIS spectra collected from 21 M- and X-class flares. We applied this framework to archetypal Mg ii flare spectra and analyzed the results from a multiline perspective. We find that (1) the line correlations from the photosphere to the transition region are highest in flare ribbons. (2) Blueshifted reversals appear simultaneously in Mg ii, C ii, and Si iv during the impulsive phase, with Si iv displaying possible optical depth effects. Fe ii shows signs of strong emission, indicating deep early heating. (3) The Mg ii line appears to typically evolve a blueshifted reversal that later returns to line center and becomes single peaked within 1–3 minutes. The widths of these single-peaked profiles slowly erode with time. During the later flare stages, strong red-wing enhancements indicating coronal rain are evident in Mg ii, C ii, and Si iv. Our framework is easily adaptable to any multiline data set and enables comprehensive statistical analyses of the atmospheric behavior in different spectral windows.
Relative Importance in Sentence Processing

Determining the relative importance of the elements in a sentence is a key factor for effortless natural language understanding. For human language processing, we can approximate patterns of relative importance by measuring reading fixations using eye-tracking technology. In neural language models, gradient-based saliency methods indicate the relative importance of a token for the target objective. In this work, we compare patterns of relative importance in English language processing by humans and models and analyze the underlying linguistic patterns. We find that human processing patterns in English correlate strongly with saliency-based importance in language models and not with attention-based importance. Our results indicate that saliency could be a cognitively more plausible metric for interpreting neural language models. The code is available on github: https://github.com/beinborn/relative_importance.
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning

In multi-label text classification (MLTC), each given document is associated with a set of correlated labels. To capture label correlations, previous classifier-chain and sequence-to-sequence models transform MLTC to a sequence prediction task. However, they tend to suffer from label order dependency, label combination over-fitting and error propagation problems. To address these problems, we introduce a novel approach with multi-task learning to enhance label correlation feedback. We first utilize a joint embedding (JE) mechanism to obtain the text and label representation simultaneously. In MLTC task, a document-label cross attention (CA) mechanism is adopted to generate a more discriminative document representation. Furthermore, we propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning: 1) Pairwise Label Co-occurrence Prediction (PLCP), and 2) Conditional Label Co-occurrence Prediction (CLCP). Experimental results on AAPD and RCV1-V2 datasets show that our method outperforms competitive baselines by a large margin. We analyze low-frequency label performance, label dependency, label combination diversity and coverage speed to show the effectiveness of our proposed method on label correlation learning.
Learning Video Models from Text: Zero-Shot Anticipation for Procedural Actions

—Can we teach a robot to recognize and make predictions for activities that it has never seen before? We tackle this problem by learning models for video from text. This paper presents a hierarchical model that generalizes instructional knowledge from large-scale text-corpora and transfers the knowledge to video. Given a portion of an instructional video, our model recognizes and predicts coherent and plausible actions multiple steps into the future, all in rich natural language. To demonstrate the capabilities of our model, we introduce the Tasty Videos Dataset V2 , a collection of 4022 recipes for zero-shot learning, recognition and anticipation. Extensive experiments with various evaluation metrics demonstrate the potential of our method for generalization, given limited video data for training models.
Los isómeros cis-9, trans-11 y trans-10, cis-12 de ácido linoleico conjugado y su relación con producción de leche de vacas Holstein-Friesian. Revisión Relationship between 9-cis, 11-trans and 10-trans, 12-cis conjugated linoleic acid isomers and milk yield in Holstein-Friesian cows. Review

The purpose of the present review was to explore the mechanisms of addition of 9-cis, 11-trans and 10-trans, 12-cis conjugated linoleic acid (ALC) isomers to dairy cow diets and their relationship to milk yield. Both isomers are formed by isomerization of linoleic acid by the rumen bacteria Butyrivibrio fibrisolvens. They can also be produced by desaturation of vaccenic acid (11-trans 18:1) in the small intestine and liver of dairy cows. In humans, consumption of these isomers is associated to hypocholesterolemic, antiatherogenic functions, stimulating the immune system, providing protection against certain types of cancer, as an antioxidant and stimulating drops in body weight too. In contrast, dairy cows fed with both isomers show an increase in milk yield associated to reduced milkfat content. Isomer 9-cis, 11-trans is responsible for increases in milk yield; whereas 10-trans, 12-cis is related to a drop in milk fat. Energy partition has a big impact on reducing postpartum negative energy balance. This is associated to an increase in cow reproductive activity and condition. Milk fat depression owing to either conjugated linoleic acid isomers or milk fat depressing diets can be explained through presence of fat synthesis inhibitors. In conclusion, addition of both isomers to dairy cow diets during lactation do not affect DMI, body weight and condition and protein yield. Quite the reverse, addition of both isomers to dairy cow diets during lactation increases milk yield and diminishes milk fat content.
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic reward (makespan) and learn ScheduleNet, a decentralized decision-making policy that can effectively coordinate multiple agents to complete tasks. The decision making procedure of ScheduleNet includes: (1) representing the state of a scheduling problem with the agent-task graph, (2) extracting node embeddings for agent and tasks nodes, the important relational information among agents and tasks, by employing the type-aware graph attention (TGA), and (3) computing the assignment probability with the computed node embeddings. We validate the effectiveness of ScheduleNet as a general learning-based scheduler for solving various types of multi-agent scheduling tasks, including multiple salesman traveling problem (mTSP) and job shop scheduling problem (JSP).
Oriented Object Detection with Transformer

Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN. However, the potential of DETR remains largely unexplored for the more challenging task of arbitrary-oriented object detection problem. We provide the first attempt and implement Oriented Object DEtection with TRansformer ($\bf O^2DETR$) based on an end-to-end network. The contributions of $\rm O^2DETR$ include: 1) we provide a new insight into oriented object detection, by applying Transformer to directly and efficiently localize objects without a tedious process of rotated anchors as in conventional detectors; 2) we design a simple but highly efficient encoder for Transformer by replacing the attention mechanism with depthwise separable convolution, which can significantly reduce the memory and computational cost of using multi-scale features in the original Transformer; 3) our $\rm O^2DETR$ can be another new benchmark in the field of oriented object detection, which achieves up to 3.85 mAP improvement over Faster R-CNN and RetinaNet. We simply fine-tune the head mounted on $\rm O^2DETR$ in a cascaded architecture and achieve a competitive performance over SOTA in the DOTA dataset.
Naked emergence of an anti-Hale active region

Aims. In order to understand the emergence of the active region, we investigate the emerging process and magnetic properties of a naked anti-Hale active region during the period between August 24 to 25, 2018. Methods. Using the data from Helioseismic and Magnetic Imager on board the Soar Dynamic Observatory and the New Vacuum Solar Telescope, we calculated different evolving parameters (such as pole separation, tilt angle) and magnetic parameters (such as vertical electric current, force-free parameter, relative magnetic helicity) during the emergence of the active region. With these calculated parameters and some reasonable assumptions, we use two different methods to estimate the twist of the active region. Results. The magnetic flux and pole separation continue increasing while the tilt angle exhibits a decreasing pattern during the emergence of the active region. The increase of the pole separation is mainly contributed as a result of the enhancement in the longitude direction. A power-law relationship between pole separation and total flux is found during the emergence of the active region. On the other hand, it is found that both the positive and negative electric currents increased equivalently and the average flux-weighted force-free parameter α̃ remains almost consistently positive, on the order of ∼10−8 m−1. The relative magnetic helicity is mainly contributed by the shear term, while the relative magnetic helicity injection flux of the shear term changes its sign at the latter stage of the emergence. The twist number of the whole active region remains on the order of 10−1 turns during the emergence of the active region. Conclusions. We find that the magnetic flux tube with low twist also could emerge into the solar atmosphere.
Comparison study on osmotic adjustment of accumulation of spring wheat seedlings in water and salt stress

Three different genetype spring wheats were treated with different concentrations of NaCl and iso osmotic PEG solutions; The leaf water content, stress susceptibility index, the content of proline, soluble sugar, Na\++ and K\++ of leaf and root were determined in 7 days after treatment.The results showed that under iso osmotic condition, the decline of leaf water content, the increase of salt stress susceptibility index, the content of proline, soluble sugar, Na\++ and K\++ under salt stress were larger than those under iso osmotic PEG stress. J3377 which had both more salt tolerance and more drought tolerance than other varieties under drought and salt stress was a improved variety; while hongmangmai which had less drought and salt tolerance than other varieties was a local variety. \;
Development of a long‐life vacuum‐packaged ready‐to‐eat meat product based on a traditional Portuguese seasoned meat

Summary Carne do alguidar is a Portuguese traditional pork fried meat, usually manufactured for self-consumption purposes. This study developed a ready-to-eat (RTE) meat product, to meet today's consumers’ convenience, manufactured at the industrial scale evaluating its quality and shelf life, assessing the effect of vacuum packaging and the use of an antioxidant (50 ppm BHT) to enhance oxidative stability. Physicochemical and microbiological parameters were assessed, and a sensory analysis was performed. Interestingly, no significant differences were recorded between control (non-BHT) and antioxidant (BHT) samples. Microbiological counts remained at low levels throughout the storage period, ensuring the product's required microbiological quality. At later storage stages, higher values of thiobarbituric acid-reactive substances arose and off flavours and aromas were perceived. Still, overall appreciation was not affected until 12 months of storage and a significant depreciation was perceived only after 15 months. Fibrousness and rising of off flavours were negatively correlated with overall appreciation.
Graph2Graph Learning with Conditional Autoregressive Models

We present a graph neural network model for solving graph-to-graph learning problems. Most deep learning on graphs considers ``simple'' problems such as graph classification or regressing real-valued graph properties. For such tasks, the main requirement for intermediate representations of the data is to maintain the structure needed for output, i.e., keeping classes separated or maintaining the order indicated by the regressor. However, a number of learning tasks, such as regressing graph-valued output, generative models, or graph autoencoders, aim to predict a graph-structured output. In order to successfully do this, the learned representations need to preserve far more structure. We present a conditional auto-regressive model for graph-to-graph learning and illustrate its representational capabilities via experiments on challenging subgraph predictions from graph algorithmics; as a graph autoencoder for reconstruction and visualization; and on pretraining representations that allow graph classification with limited labeled data.
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

With rapid progress in neural text-to-speech (TTS) models, personalized speech generation is now in high demand for many applications. For practical applicability, a TTS model should generate high-quality speech with only a few audio samples from the given speaker, that are also short in length. However, existing methods either require to fine-tune the model or achieve low adaptation quality without fine-tuning. In this work, we propose StyleSpeech, a new TTS model which not only synthesizes high-quality speech but also effectively adapts to new speakers. Specifically, we propose Style-Adaptive Layer Normalization (SALN) which aligns gain and bias of the text input according to the style extracted from a reference speech audio. With SALN, our model effectively synthesizes speech in the style of the target speaker even from single speech audio. Furthermore, to enhance StyleSpeech's adaptation to speech from new speakers, we extend it to Meta-StyleSpeech by introducing two discriminators trained with style prototypes, and performing episodic training. The experimental results show that our models generate high-quality speech which accurately follows the speaker's voice with single short-duration (1-3 sec) speech audio, significantly outperforming baselines.