Miracle-dz/UniFAR-FTUs huggingface dataset:
General structure of Thomas-Whitehead gravity

Thomas-Whitehead (TW) gravity is a projectively invariant model of gravity over a d-dimensional manifold that is intimately related to string theory through reparameterization invariance. Unparameterized geodesics are the ubiquitous structure that ties together string theory and higher dimensional gravitation. This is realized through the projective geometry of Tracy Thomas. The projective connection, due to Thomas and later Whitehead, admits a component that in one dimension is in one-to-one correspondence with the coadjoint elements of the Virasoro algebra. This component is called the diffeomorphism field $\mathcal{D}_{ab }$ in the literature. It also has been shown that in four dimensions, the TW\ action collapses to the Einstein-Hilbert action with cosmological constant when $\mathcal{D}_{ab}$ is proportional to the Einstein metric. These previous results have been restricted to either particular metrics, such as the Polyakov 2D\ metric, or were restricted to coordinates that were volume preserving. In this paper, we review TW gravity and derive the gauge invariant TW action that is explicitly projectively invariant and general coordinate invariant. We derive the covariant field equations for the TW action and show how fermionic fields couple to the gauge invariant theory. The independent fields are the metric tensor $g_{ab}$, the fundamental projective invariant $\Pi^{a}_{\,\,\,bc}$, and the diffeomorphism field $\mathcal D_{ab}$.
Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach

This article presents our unimodal privacy-safe and non-individual proposal for the audio-video group emotion recognition subtask at the Emotion Recognition in the Wild (EmotiW) Challenge 2020. This sub challenge aims to classify in the wild videos into three categories: Positive, Neutral and Negative. Recent deep learning models have shown tremendous advances in analyzing interactions between people, predicting human behavior and affective evaluation. Nonetheless, their performance comes from individual-based analysis, which means summing up and averaging scores from individual detections, which inevitably leads to some privacy issues. In this research, we investigated a frugal approach towards a model able to capture the global moods from the whole image without using face or pose detection, or any individual-based feature as input. The proposed methodology mixes state-of-the-art and dedicated synthetic corpora as training sources. With an in-depth exploration of neural network architectures for group-level emotion recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF test set (eleventh place of the challenge). Given that the analysis is unimodal based only on global features and that the performance is evaluated on a real-world dataset, these results are promising and let us envision extending this model to multimodality for classroom ambiance evaluation, our final target application.
An Exact, Generalized Laplace Resonance in the HR 8799 Planetary System

A system of four super-Jupiter planets around HR 8799 is the first multiplanet configuration discovered via the direct imaging technique. Despite over a decade of research, the system’s architecture is still not fully resolved. The main difficulty comes from a still narrow observing window of ∼20 yr that covers small arcs of orbits with periods from roughly 50 to 500 yr. Soon after the discovery, it became clear that unconstrained best-fitting astrometric configurations self-disrupt rapidly due to strong mutual gravitational interactions between the companions of ≃10 Jupiter mass. Recently, we showed that the HR 8799 system may be long-term stable when locked in a generalized Laplace 8:4:2:1 mean-motion resonance (MMR) chain, and we constrained its orbits through the planetary migration. Here we qualitatively improve this approach by considering the MMR in terms of an exactly periodic configuration. This assumption enables us to construct for the first time the self-consistent N-body model of the long-term stable orbital architecture using only available astrometric positions of the planets relative to the star. We independently determine the planetary masses, which are consistent with thermodynamic evolution, and the parallax overlapping to 1σ with the most recent Gaia DR2 value. We also determine the global structure of the inner and outer debris disks in the [8, 600] au range, consistent with the updated orbital solution.
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product

Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images. We argue that product attributes and values are highly correlated, e.g., it will be easier to extract the values on condition that the product attributes are given. Thus, we jointly model the attribute prediction and value extraction tasks from multiple aspects towards the interactions between attributes and values. Moreover, product images have distinct effects on our tasks for different product attributes and values. Thus, we selectively draw useful visual information from product images to enhance our model. We annotate a multimodal product attribute value dataset that contains 87,194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task. Our code and dataset will be released to the public.
Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography

Recovering a high-quality image from noisy indirect measurements is an important problem with many applications. For such inverse problems, supervised deep convolutional neural network (CNN)-based denoising methods have shown strong results, but the success of these supervised methods critically depends on the availability of a high-quality training dataset of similar measurements. For image denoising, methods are available that enable training without a separate training dataset by assuming that the noise in two different pixels is uncorrelated. However, this assumption does not hold for inverse problems, resulting in artifacts in the denoised images produced by existing methods. Here, we propose Noise2Inverse, a deep CNN-based denoising method for linear image reconstruction algorithms that does not require any additional clean or noisy data. Training a CNN-based denoiser is enabled by exploiting the noise model to compute multiple statistically independent reconstructions. We develop a theoretical framework which shows that such training indeed obtains a denoising CNN, assuming the measured noise is element-wise independent, and zero-mean. On simulated CT datasets, Noise2Inverse demonstrates an improvement in peak signal-to-noise ratio and structural similarity index compared to state-of-the-art image denoising methods, and conventional reconstruction methods, such as Total-Variation Minimization. We also demonstrate that the method is able to significantly reduce noise in challenging real-world experimental datasets.
Winter feeding ecology of scaup and common goldeneye in Michigan

The feeding ecology of wintering greater scaup (Aythya marila), lesser scaup (A. affinis), and common goldeneye (Bucephala clangula) was studied on the Detroit River in southeast Michigan during 1980-81. Aquatic oligochaetes, wildcelery (Vallisneria americana), and pondweeds (Potamogeton spp.) were the principal foods consumed by all 3 species. Ducks foraged almost exclusively at sites with water depths -2 m. Macroinvertebrate densities were greater (P 31,000 in January and >16,000 in February. Waterfowl use of the Detroit River in winter is attributable to thermal and chemical discharges from power plants and factories that have increased amounts of ice-free water and to changes in current velocity in parts of the river caused by channel modifications. Maintaining winter shipping channels for transport of cargo would further open previously frozen habitats. Currently, there is little information available to develop effective management plans or to assess the effects of further alteration of wintering habitats. Temperature stress caused by low ambient temperatures could influence both survival and reproductive condition of waterfowl wintering at northern extremes of their range. Owen and Reinecke (1979) and Prince (1979) suggested energy expenditure for thermoregulation was high for waterfowl wintering at northern latitudes. Jorde et al. (1983) concluded that mallards (Anas platyrhynchos) were able to survive winters on Nebraska's Platte River by feeding on waste corn in nearby agricultural fields; however, this option is not available to diving ducks. Albright (1981) showed that body condition of female American black ducks (A. rubripes) wintering in Maine was adversely affected by low temperatures and wind chill. Habitat availability which was dependent on ice cover, also had an effect on condition of female black
Correlations between supermassive black holes and hot gas atmospheres in IllustrisTNG and X-ray observations

Recent X-ray observations have revealed remarkable correlations between the masses of central supermassive black holes (SMBHs) and the X-ray properties of the hot atmospheres permeating their host galaxies, thereby indicating the crucial role of the atmospheric gas in tracing SMBH growth in the high-mass regime. We examine this topic theoretically using the IllustrisTNG cosmological simulations and provide insights to the nature of this SMBH – gaseous halo connection. By carrying out a mock X-ray analysis for a mass-selected sample of TNG100 simulated galaxies at $z$ = 0, we inspect the relationship between the masses of SMBHs and the hot gas temperatures and luminosities at various spatial and halo scales – from galactic (∼Re) to group/cluster scales (∼R500c). We find strong SMBH-X-ray correlations mostly in quenched galaxies and find that the correlations become stronger and tighter at larger radii. Critically, the X-ray temperature (kBTX) at large radii (r ≳ 5Re) traces the SMBH mass with a remarkably small scatter (∼0.2 dex). The relations emerging from IllustrisTNG are broadly consistent with those obtained from recent X-ray observations. Overall, our analysis suggests that, within the framework of IllustrisTNG, the present-time MBH–kBTX correlations at the high-mass end (MBH ≳ 108M⊙) are fundamentally a reflection of the SMBH mass–halo mass relation, which at such high masses is set by the hierarchical assembly of structures. The exact form, locus, and scatter of those scaling relations are, however, sensitive to feedback processes such as those driven by star formation and SMBH activity.
High-order Refining for End-to-end Chinese Semantic Role Labeling

Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.
A note on convergence of noncompact nonsingular solutions of the Ricci flow

We extend some convergence results on nonsingular compact Ricci flows in the papers by Hamilton [Comm. Anal. Geom. 7 (1999), pp. 695–729], Sesum [Math. Res. Lett. 12 (2005), pp. 623–632] and Fang, Zhang, and Zhang [J. Geom. Anal. 20 (2010), pp. 592–608] to certain infinite volume noncompact cases which are “partially” nonsingular. As an application, for a finite time singularity which is partially type I, it is shown that a blow up limit is a gradient shrinking soliton.
Attention-Aware Inference for Neural Abstractive Summarization

Inspired by Google's Neural Machine Translation (NMT) \cite{Wu2016Google} that models the one-to-one alignment in translation tasks with an optimal uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source paragraphs/sentences with the optimal coverage. Unlike NMT, the attention-aware inference of NAS requires the prediction of the optimal attention distribution. Therefore, an attention-prediction model is constructed to learn the dependency between attention weights and sources. To apply the attention-aware inference on multi-document summarization, a Hierarchical Transformer (HT) is developed to accept lengthy inputs at the same time project cross-document information. Experiments on WikiSum \cite{liu2018generating} suggest that the proposed HT already outperforms other strong Transformer-based baselines. By refining the regular beam search with the attention-aware inference, significant improvements on the quality of summaries could be further observed. Last but not the least, the attention-aware inference could be adopted to single-document summarization with straightforward modifications according to the model architecture.
Motivic invariants of symmetric powers

We study the structure of various invariants of the symmetric powers of a smooth projective curve in terms of that of Jacobian of the curve. We generalize the results of Macdonald and Collino to various invariants including Weil-cohomology theory, higher Chow groups, Additive higher Chow groups and rational $K$-groups.
XMM–Newton study of X-ray sources in the field of Willman 1 dwarf spheroidal galaxy★

We present the results of the analysis of three XMM-Newton observations of the Willman 1 dwarf spheroidal galaxy (Wil 1). X-ray sources are classified on the basis of spectral analysis, hardness ratios, X-ray-to-optical flux ratio, X-ray variability, plus cross-correlation with available catalogues in optical and infrared wavelengths. We catalogued 97 sources in the field of Wil 1. Our classification shows the presence of a $\beta$-type symbiotic star in Wil 1. We classified one M dwarf foreground star in the field of Wil 1. Moreover, fifty-four sources are classified as background AGNs and galaxies. Our study shows that the luminosity of the X-ray sources of Wil 1 does not exceed $\sim$10$^{34}$ erg s$^{-1}$ in the energy range of 0.2--12.0 keV, which is similar to observed luminosities of sources in nearby dwarf spheroidal galaxies.
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation

Cross domain recommendation (CDR) has been proposed to tackle the data sparsity problem in recommender systems. This paper focuses on a common scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored shared-user representation to transfer knowledge across different domains. However, the idea of shared-user representation resorts to learn the overlapped properties of user preferences across different domains and suppresses the domain-specific properties of user preferences. In this paper, we attempt to learn both properties of user preferences for CDR, i.e. capturing both the overlapped and domain-specific properties. In particular, we assume that each user's preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformations. Based on this assumption, we propose an equivalent transformation learner (ETL) which models the joint distribution of user behaviors across different domains. The equivalent transformations in ETL relax the idea of shared-user representation and allow the learned preferences in different domains to have the capacity of preserving the domain-specific properties as well as the overlapped properties. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods.
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks

Adversarial attacks have exposed serious vulnerabilities in deep neural networks (DNNs), causing misclassifications through human-imperceptible perturbations to DNN inputs. We explore a new direction in the field of adversarial attacks by suggesting attacks that aim to degrade the energy or latency of DNNs rather than their classification accuracy. As a specific embodiment of this new threat vector, we propose and demonstrate adversarial sparsity attacks, which modify a DNN’s inputs so as to reduce sparsity (or the incidence of zeros) in its internal activation values. Exploiting sparsity in hardware and software has emerged as a popular approach to improve DNN efficiency in resource-constrained systems. The proposed attack, therefore, increases the execution time and energy consumption of sparsity-optimized DNN implementations, raising concern over their deployment in latency and energy-critical applications. We propose a systematic methodology to generate adversarial inputs for sparsity attacks by formulating an objective function that quantifies the network’s activation sparsity and minimizing this function using iterative gradient-descent techniques. To prevent easy detection of the attack, we further ensure that the perturbation magnitude is within a specified constraint and that the perturbation does not affect classification accuracy. We launch both white-box and black-box versions of adversarial sparsity attacks on image recognition DNNs and demonstrate that they decrease activation sparsity by <inline-formula> <tex-math notation="LaTeX">$1.16\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$1.82\times $ </tex-math></inline-formula>. On a sparsity-optimized DNN accelerator, the attack results in degradations of <inline-formula> <tex-math notation="LaTeX">$1.12\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$1.59\times $ </tex-math></inline-formula> in latency and <inline-formula> <tex-math notation="LaTeX">$1.18\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$1.99\times $ </tex-math></inline-formula> in energy-delay product (EDP). Additionally, we analyze the impact of various hyperparameters and constraints on the attack’s efficacy. Finally, we evaluate defense techniques, such as activation thresholding and input quantization and demonstrate that the proposed attack is able to withstand them, highlighting the need for further efforts in this new direction within the field of adversarial machine learning.
NGC 4869 in the Coma Cluster: Twist, Wrap, Overlap, and Bend

The upgraded Giant Metrewave Radio Telescope (GMRT) has been used to image the head–tail radio galaxy NGC 4869 in the Coma cluster with an angular resolution of 6. ″26 at 250–500 MHz and 2. ″18 at the 1050–1450 MHz bands. The archival legacy GMRT data have also been used to image the source with angular resolutions from 4. ″9 to 21. ″8 at 610 MHz, 325 MHz, 240 MHz, and 150 MHz. We find that the ∼200 kpc scale radio morphology consists of five distinct regions with the clear presence of a pinch at ≈1. ′4 (=38.8 kpc) and a ridge at ≈3. ′4 (=94.2 kpc) from the head. The sharp bend by ∼70° at ∼3. ′5 (=97 kpc) from the head is possibly due to projection effects. The radio spectra show progressive spectral steepening as a function of distance from the head and there is possibly re-acceleration of the synchrotron electrons and perhaps also magnetic field regeneration in the 6″–208″ (=2.8–96.1 kpc) region of the jet. We report a steep spectrum sheath layer enveloping a flat spectrum spine, hinting at a transverse velocity structure with a fast-moving spine surrounded by a slow-moving sheath layer. We also derive the lifetimes of the radiating electrons and equipartition parameters. A plausible explanation for the characteristic feature, a ridge of emission perpendicular to the direction of tail is the flaring of a straight, collimated radio jet as it crosses a surface brightness edge due to Kelvin–Helmholtz instabilities.
A Stochastic Theory of the Hierarchical Clustering. I. Halo Mass Function

We present a new theory for the hierarchical clustering of dark matter (DM) halos, based on stochastic differential equations, that constitutes a change of perspective with respect to existing frameworks (e.g., the excursion set approach); this work is specifically focused on the halo mass function. First, we present a stochastic differential equation that describes fluctuations in the mass growth of DM halos, as driven by a multiplicative white (Gaussian) noise dependent on the spherical collapse threshold and on the power spectrum of DM perturbations. We demonstrate that such a noise yields an average drift of the halo population toward larger masses, that quantitatively renders the standard hierarchical clustering. Then, we solve the Fokker–Planck equation associated to the stochastic dynamics, and obtain the Press & Schechter mass function as a (stationary) solution. Moreover, generalizing our treatment to a mass-dependent collapse threshold, we obtain an exact analytic solution capable of fitting remarkably well the N-body mass function over a wide range in mass and redshift. All in all, the new perspective offered by the theory presented here can contribute to a better understanding of the gravitational dynamics leading to the formation, evolution, and statistics of DM halos across cosmic times.
Autonomous learning of features for control: Experiments with embodied and situated agents

The efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including an additional neural network dedicated to features extraction trained through self-supervision. In this paper we introduce a method that permits to continue the training of the features extracting network during the training of the control network. We demonstrate that the parallel training of the two networks is crucial in the case of agents that operate on the basis of egocentric observations and that the extraction of features provides an advantage also in problems that do not benefit from dimensionality reduction. Finally, we compare different feature extracting methods and we show that sequence-to-sequence learning outperforms the alternative methods considered in previous studies.