Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection

Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and inter-image correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three co-saliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.
Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition

Dialogue act recognition is a fundamental task for an intelligent dialogue system. Previous work models the whole dialog to predict dialog acts, which may bring the noise from unrelated sentences. In this work, we design a hierarchical model based on self-attention to capture intra-sentence and inter-sentence information. We revise the attention distribution to focus on the local and contextual semantic information by incorporating the relative position information between utterances. Based on the found that the length of dialog affects the performance, we introduce a new dialog segmentation mechanism to analyze the effect of dialog length and context padding length under online and offline settings. The experiment shows that our method achieves promising performance on two datasets: Switchboard Dialogue Act and DailyDialog with the accuracy of 80.34\% and 85.81\% respectively. Visualization of the attention weights shows that our method can learn the context dependency between utterances explicitly.
Optimal Control of Boolean Control Networks with Discounted Cost: An Efficient Approach based on Deterministic Markov Decision Process

This paper deals with the infinite-horizon optimal control problem for Boolean control networks (BCNs) with a discounted-cost criterion. This problem has been investigated in existing studies with algorithms characterized by high computational complexity. We thus attempt to develop more efficient approaches for this problem from a deterministic Markov decision process (DMDP) perspective. First, we show the eligibility of a DMDP to model the control process of a BCN and the existence of an optimal solution. Next, two approaches are developed to handle the optimal control problem in a DMDP. One approach adopts the well-known value iteration algorithm, and the other resorts to the Madani’s algorithm specifically designed for DMDPs. The latter approach can find an exact optimal solution and outperform existing methods in terms of time efficiency, while the former value iteration based approach usually obtains a near-optimal solution much faster than all others. The 9-state-4-input ara operon network of the bacteria E. coli is used to verify the effectiveness and performance of our approaches. Results show that both approaches can reduce the running time dramatically by several orders of magnitude compared with existing work.
Statistical deprojection of intervelocities, interdistances, and masses in the Isolated Galaxy Pair Catalog

Aims. In order to study the internal dynamics of actual galaxy pairs, we need to derive the probability distribution function (PDF) of true 3D, orbital intervelocities and interdistances between pair members from their observed projected values along with the pair masses from Kepler’s third law. For this research, we used 13 114 pairs from the Isolated Galaxy Pair Catalog (IGPC). Methods. The algorithms of statistical deprojection previously elaborated were applied to these observational data. We derived the orbital velocity PDFs for the whole catalog and for several selected subsamples. The interdistance PDF is deprojected and compared to the analytical profiles expected from semi-theoretical arguments. Results. The PDF of deprojected pair orbital velocities is characterized by the existence of a main probability peak around ≈150 km s−1 for all subsamples of the IGPC as well as for the Uppsala Galaxy Pair Catalog. The interdistance PDFs of both the projected and deprojected data are described at large distances by the same power law with exponent ≈ − 2. The whole distributions, including their cores, are fairly fitted by King profiles. The mass deprojection yields a mass/luminosity ratio for the pairs of M/L = (30 ± 5) in solar units. Conclusions. The orbital velocity probability peak is observed at the same value, ≈150 km s−1, as the main exoplanet velocity peak, which points toward a possible universality of Keplerian structures, whatever the scale. The pair M/L ratio is just seven times the standard ratio for luminous matter, which does not require the existence of nonbaryonic dark matter in these systems.
A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction

It remains challenging to automatically predict the multi-agent trajectory due to multiple interactions including agent to agent interaction and scene to agent interaction. Although recent methods have achieved promising performance, most of them just consider spatial influence of the interactions and ignore the fact that temporal influence always accompanies spatial influence. Moreover, those methods based on scene information always require extra segmented scene images to generate multiple socially acceptable trajectories. To solve these limitations, we propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC). First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity. Experiments are performed on the two widely used ETH-UCY datasets and demonstrate that the proposed model achieves state-of-the-art prediction accuracy and handles more complex scenarios.
Hunting for hot corinos and WCCC sources in the OMC-2/3 filament

Context. Solar-like protostars are known to be chemically rich, but it is not yet clear how much their chemical composition can vary and why. So far, two chemically distinct types of Solar-like protostars have been identified: hot corinos, which are enriched in interstellar Complex Organic Molecules, such as methanol (CH3OH) or dimethyl ether (CH3OCH3), and warm carbon chain chemistry (WCCC) objects, which are enriched in carbon chain molecules, such as butadiynyl (C4H) or ethynyl radical (CCH). However, none of these have been studied so far in environments similar to that in which our Sun was born, that is, one that is close to massive stars. Aims. In this work, we search for hot corinos and WCCC objects in the closest analogue to the Sun’s birth environment, the Orion Molecular Cloud 2/3 (OMC-2/3) filament located in the Orion A molecular cloud. Methods. We obtained single-dish observations of CCH and CH3OH line emission towards nine Solar-like protostars in this region. As in other similar studies of late, we used the [CCH]/[CH3OH] abundance ratio in order to determine the chemical nature of our protostar sample. Results. Unexpectedly, we found that the observed methanol and ethynyl radical emission (over a few thousands au scale) does not seem to originate from the protostars but rather from the parental cloud and its photo-dissociation region, illuminated by the OB stars of the region. Conclusions. Our results strongly suggest that caution should be taken before using [CCH]/[CH3OH] from single-dish observations as an indicator of the protostellar chemical nature and that there is a need for other tracers or high angular resolution observations for probing the inner protostellar layers.
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space

As an essential step towards computer creativity, automatic poetry generation has gained increasing attention these years. Though recent neural models make prominent progress in some criteria of poetry quality, generated poems still suffer from the problem of poor diversity. Related literature researches show that different factors, such as life experience, historical background, etc., would influence composition styles of poets, which considerably contributes to the high diversity of human-authored poetry. Inspired by this, we propose MixPoet, a novel model that absorbs multiple factors to create various styles and promote diversity. Based on a semi-supervised variational autoencoder, our model disentangles the latent space into some subspaces, with each conditioned on one influence factor by adversarial training. In this way, the model learns a controllable latent variable to capture and mix generalized factor-related properties. Different factor mixtures lead to diverse styles and hence further differentiate generated poems from each other. Experiment results on Chinese poetry demonstrate that MixPoet improves both diversity and quality against three state-of-the-art models.
Iterative Retraining of Quantum Spin Models Using Recurrent Neural Networks

Modeling quantum many-body systems is enormously challenging due to the exponential scaling of Hilbert dimension with system size. Finding efficient compressions of the wavefunction is key to building scalable models. Here, we introduce iterative retraining, an approach for simulating bulk quantum systems that uses recurrent neural networks (RNNs). By mapping translations in the lattice vector to the time index of an RNN, we are able to efficiently capture the near translational invariance of large lattices. We show that we can use this symmetry mapping to simulate very large systems in one and two dimensions. We do so by 'growing' our model, iteratively retraining the same model on progressively larger lattices until edge effects become negligible. We argue that this scheme generalizes more naturally to higher dimensions than Density Matrix Renormalization Group.
Constraining SMEFT operators with associated $h\gamma$ production in Weak Boson Fusion

We consider the associated production of a Higgs boson and a photon in weak boson fusion in the Standard Model (SM) and the Standard Model Effective Theory (SMEFT), with the Higgs boson decaying to a pair of bottom quarks. Analysing events in a cut-based analysis and with multivariate techniques we determine the sensitivity of this process to the bottom-Yukawa coupling in the SM and to possible CP-violation mediated by dimension-6 operators in the SMEFT.
Using word embeddings to improve the discriminability of co-occurrence text networks

Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios. Despite the relative success in several applications, traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text. Here we investigate whether the use of word embeddings as a tool to create virtual links in co-occurrence networks may improve the quality of classification systems. Our results revealed that the discriminability in the stylometry task is improved when using Glove, Word2Vec and FastText. In addition, we found that optimized results are obtained when stopwords are not disregarded and a simple global thresholding strategy is used to establish virtual links. Because the proposed approach is able to improve the representation of texts as complex networks, we believe that it could be extended to study other natural language processing tasks. Likewise, theoretical languages studies could benefit from the adopted enriched representation of word co-occurrence networks.
Pyramidal Edge-maps based Guided Thermal Super-resolution

Thermal imaging is a robust sensing technique but its consumer applicability is limited by the high cost of thermal sensors. Nevertheless, low-resolution thermal cameras are relatively affordable and are also usually accompanied by a high-resolution visible-range camera. This visible-range image can be used as a guide to reconstruct a high-resolution thermal image using guided super-resolution(GSR) techniques. However, the difference in wavelength-range of the input images makes this task challenging. Improper processing can introduce artifacts such as blur and ghosting, mainly due to texture and content mismatch. To this end, we propose a novel algorithm for guided super-resolution that explicitly tackles the issue of texture-mismatch caused due to multimodality. We propose a two-stage network that combines information from a low-resolution thermal and a high-resolution visible image with the help of multi-level edge-extraction and integration. The first stage of our network extracts edge-maps from the visual image at different pyramidal levels and the second stage integrates these edge-maps into our proposed super-resolution network at appropriate layers. Extraction and integration of edges belonging to different scales simplifies the task of GSR as it provides texture to object-level information in a progressive manner. Using multi-level edges also allows us to adjust the contribution of the visual image directly at the time of testing and thus provides controllability at test-time. We perform multiple experiments and show that our method performs better than existing state-of-the-art guided super-resolution methods both quantitatively and qualitatively.
Interaction Graphs for Object Importance Estimation in On-road Driving Videos

A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver’s decisions and actions. Learning to estimate the importance of each object on the driver’s real-time decision-making may help better understand human driving behavior and lead to more reliable autonomous driving systems. Solving this problem requires models that understand the interactions between the ego-vehicle and the surrounding objects. However, interactions among other objects in the scene can potentially also be very helpful, e.g., a pedestrian beginning to cross the road between the ego-vehicle and the car in front will make the car in front less important. We propose a novel framework for object importance estimation using an interaction graph, in which the features of each object node are updated by interacting with others through graph convolution. Experiments show that our model outperforms state-of-the-art baselines with much less input and pre-processing.
Harmonizing Transferability and Discriminability for Adapting Object Detectors

Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline. Whilst adversarial adaptation significantly enhances the transferability of feature representations, the feature discriminability of object detectors remains less investigated. Moreover, transferability and discriminability may come at a contradiction in adversarial adaptation given the complex combinations of objects and the differentiated scene layouts between domains. In this paper, we propose a Hierarchical Transferability Calibration Network (HTCN) that hierarchically (local-region/image/instance) calibrates the transferability of feature representations for harmonizing transferability and discriminability. The proposed model consists of three components: (1) Importance Weighted Adversarial Training with input Interpolation (IWAT-I), which strengthens the global discriminability by re-weighting the interpolated image-level features; (2) Context-aware Instance-Level Alignment (CILA) module, which enhances the local discriminability by capturing the underlying complementary effect between the instance-level feature and the global context information for the instance-level feature alignment; (3) local feature masks that calibrate the local transferability to provide semantic guidance for the following discriminative pattern alignment. Experimental results show that HTCN significantly outperforms the state-of-the-art methods on benchmark datasets.
Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks

This paper explores the use of Deep Learning methods for automatic estimation of quality of human translations. Automatic estimation can provide useful feedback for translation teaching, examination and quality control. Conventional methods for solving this task rely on manually engineered features and external knowledge. This paper presents an end-to-end neural model without feature engineering, incorporating a cross attention mechanism to detect which parts in sentence pairs are most relevant for assessing quality. Another contribution concerns oprediction of fine-grained scores for measuring different aspects of translation quality, such as terminological accuracy or idiomatic writing. Empirical results on a large human annotated dataset show that the neural model outperforms feature-based methods significantly. The dataset and the tools are available.
On the effectiveness of convolutional autoencoders on image-based personalized recommender systems

Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users from all over the world seek and share their opinions based on all types of products. Specifically, millions of images tagged with users’ tastes are available on the web. Therefore, the application of deep learning techniques to solve these types of tasks has become a key issue, and there is a growing interest in the use of images to solve them, particularly through feature extraction. This work explores the potential of using only images as sources of information for modeling users’ tastes and proposes a method to provide gastronomic recommendations based on them. To achieve this, we focus on the pre-processing and encoding of the images, proposing the use of a pre-trained convolutional autoencoder as feature extractor. We compare our method with the standard approach of using convolutional neural networks and study the effect of applying transfer learning, reflecting how it is better to use only the specific knowledge of the target domain in this case, even if fewer examples are available.
The Fraction of Gamma-Ray Bursts with an Observed Photospheric Emission Episode

There is no complete description of the emission physics during the prompt phase in gamma-ray bursts. Spectral analyses, however, indicate that many spectra are narrower than what is expected for nonthermal emission models. Here, we reanalyze the sample of 37 bursts in Yu et al. by fitting the narrowest time-resolved spectrum in each burst. We perform a model comparison between photospheric and synchrotron emission models based on Bayesian evidence. We compare the shapes of the narrowest expected spectra: emission from the photosphere in a non-dissipative flow and slow cooled synchrotron emission from a narrow electron distribution. We find that the photospheric spectral shape is preferred by 54% ± 8% of the spectra (20/37), while 38% ± 8% of the spectra (14/37) prefer the synchrotron spectral shape; three spectra are inconclusive. We hence conclude that GRB spectra are indeed very narrow and that more than half of the bursts have a photospheric emission episode. We also find that a third of all analyzed spectra, not only prefer, but are also compatible with a non-dissipative photosphere, confirming previous similar findings. Furthermore, we notice that the spectra that prefer the photospheric model all have low-energy power-law indices α ≳ −0.5. This means that α is a good estimator for which model is preferred by the data. Finally, we argue that the spectra that statistically prefer the synchrotron model could equally as well be caused by subphotospheric dissipation. If that is the case, photospheric emission during the early, prompt phase would be even more dominant.
The use of neural networks and nonlinear finite element models to simulate the temperature-dependent stress response of thermoplastic elastomers

In this study, a methodology that combines artificial neural networks and nonlinear hyperelastic finite element modeling to simulate the temperature-dependent stress response of elastomer solids is presented. The methodology is verified by a discrete model of a tensile test specimen, which is used to generate stress–strain pairs of existent experimental data. The proposed method is also tested with a benchmark problem of a rubber-like cylinder under compression. Three grades of an elastomer used for diverse engineering applications are used throughout the study. On this basis, three neural network architecture with 10 hidden neurons are implemented as constitutive models to reproduce the experimental data of the materials. The validation results show that the proposed methodology can reproduce tensile tests with an error of 5% of less than regarding experimental data for elastomers that present no yielding point. The benchmark problem results were at the range expected for the elastomer materials with no yielding, where it was possible to derive force temperature-dependent responses. These results suggest that the methodology helps the prediction of the material response when only material stress–strain curves at different temperatures exist. Therefore, the presented approach in this contribution helps to simulate the temperature-dependent stress responses of elastomeric solids with no defined yielding point.
Nonorthogonal Multiple Access With Orthogonal Time–Frequency Space Signal Transmission

Orthogonal time–frequency space (OTFS) is being pursued in recent times as a suitable wireless transmission technology for use in high-mobility scenarios. In this article, we propose nonorthogonal multiple access (NOMA) based OTFS which may be called NOMA-OTFS system and evaluate its performance from a system-level and link-level perspective. The challenge lies in the fact that while OTFS transmission technology is known for its resilience to high-mobility conditions, while NOMA is known to yield high spectral efficiency (SE) in low-mobility scenarios in comparison to orthogonal multiple access (OMA). We present a minimum mean square error (MMSE)-successive interference cancellation based receiver for NOMA-OTFS, for which we derive expression for symbol-wise postprocessing SINR in order to evaluate system sum SE. We develop power allocation schemes to maximize the sum SE in the high-mobility version of NOMA. We further design a realizable codeword-level SIC (CWIC) receiver using low-density parity check (LDPC) codes along with MMSE equalization for evaluating link-level performance of such practical NOMA-OTFS system. The system-level and link-level performance of the proposed NOMA-OTFS system are compared against benchmark OMA-OTFS, OMA-orthogonal frequency division multiplexing (OMA-OFDM) and NOMA-OFDM schemes. From system-level performance evaluation, we observe interestingly that NOMA-OTFS provides higher system sum SE than OMA-OTFS. When compared to NOMA-OFDM, we find that outage SE of NOMA-OTFS is improved at the cost of decrease in mean SE. The link-level results additionally show that the developed CWIC-based NOMA-OTFS receiver performs significantly better than NOMA-OFDM in terms of block error rate, goodput, and throughout.