Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Graph Generator for Multi-View Graph Clustering

Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose Variational Graph Generator for Multi-View Graph Clustering (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then a simple yet effective graph encoder in conjunction with the multi-view clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of VGMGC by analyzing the uncertainty of the inferred consensus graph with information bottleneck principle. Extensive experiments demonstrate the superior performance of our VGMGC over SOTAs.
Evidence of unconventional pairing in the quasi-two-dimensional 
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The CuIr$_{2-x}$Ru$_x$Te$_4$ superconductors (with a $T_c$ around 2.8 K) can host charge-density waves, whose onset and interplay with superconductivity are not well known at a microscopic level. Here, we report a comprehensive study of the $x$ = 0 and 0.05 cases, whose superconductivity was characterized via electrical-resistivity-, magnetization-, and heat-capacity measurements, while their microscopic superconducting properties were studied via muon-spin rotation and relaxation ($\mu$SR). In CuIr$_{2-x}$Ru$_x$Te$_4$, both the temperature-dependent electronic specific heat and the superfluid density (determined via transverse-field $\mu$SR) are best described by a two-gap (s+d)-wave model, comprising a nodeless gap and a gap with nodes. The multigap superconductivity is also supported by the temperature dependence of the upper critical field $H_\mathrm{c2}(T)$. However, under applied pressure, a charge-density-wave order starts to develop and, as a consequence, the superconductivity of CuIr$_2$Te$_4$ achieves a more conventional s-wave character. From a series of experiments, we provide ample evidence that the CuIr$_{2-x}$Ru$_x$Te$_4$ family belongs to the rare cases, where an unconventional superconducting pairing is found near a charge-density-wave quantum critical point.
Probing the magnetic fields of massive star-forming regions with methanol maser polarization

Methanol masers can provide valuable insight into the processes involved in high mass star formation; however, the local environment in which they form is still unclear. Four primary, yet conflicting, models have emerged to explain the commonly observed methanol maser structures at 6.67 GHz. These suggest that masers trace accretion discs, outflows, shock fronts or discs dominated by infall/outflows. One proposed means of testing these models is through mapping the local magnetic field structures around maser sources, which were predicted to lie parallel to shock and outflows and perpendicular to accretion discs. To follow up this suggestion, we have determined magnetic field directions from full polarization observations of 10 6.67-GHz sources. We find morphology that is parallel to the source structure, indicative of shocks or outflows, in five sources and perpendicular morphology indicative of discs in three sources. These results do not support any of the expected models and the diverse morphologies observed indicate that the masers could be emitting from different evolutionary stages or environments, or from a common local environment with complex associated magnetic fields. To resolve this conflict, we suggest a new approach that will search the simulations of massive star formation, which are just becoming available, for suitable sites for maser emission.
Switch-based Hybrid Beamforming Transceiver Design for Wideband Communications with Beam Squint

Hybrid beamforming (HBF) transceiver architectures based on frequency-independent phase shifters (PS-HBF) are sensitive to the phases and physical directions with limited capability to compensate for the detrimental effects of the beam squint. Motivated by the fact that switches are phase-independent and more power/cost efficient than PSs, we consider the switch-based HBF (SW-HBF) for wideband large-scale multiple-input multiple-output systems in this paper. We first derive a closed-form expression of the beam squint ratio and compare the expected array gains of both SW-HBF and PS-HBF architectures. The results show that SW-HBF is more robust to the beam squint effect. We then focus on the SW-HBF designs to maximize the spectral efficiency (SE) in both single-user and multiuser systems, which are both non-convex mixed-integer problems. For the former, by combining the tabu search (TS) method and projected gradient ascend (PGA), we propose an efficient heuristic PGA-TS algorithm to design analog beamformers while the digital ones admit closed-form solutions. For the latter, we develop a two-step algorithm based on fractional programming and the PGA-TS method. Simulations show that the proposed SW-HBF schemes are efficient and can outperform PS-based HBF architectures in terms of both SE and energy efficiency in terahertz communication systems.
Anderson localization of electron states in graphene in different types of disorder

Anderson localization of electron states on graphene lattice with diagonal and off-diagonal (OD) disorders in the absence of magnetic field is investigated by using the standard finite-size scaling analysis. In the presence of diagonal disorder all states are localized as predicted by the scaling theory for two-dimensional systems. In the case of OD disorder, the states at the Dirac point $(E=0)$ are shown to be delocalized due to the specific chiral symmetry, although other states $(E\ensuremath{\ne}0)$ are still localized. In OD disorder the conductance at $E=0$ in an $M\ifmmode\times\else\texttimes\fi{}L$ rectangular system at the thermodynamical limit is calculated with the transfer-matrix technique for various values of ratio $M∕L$ and different types of distribution functions of the OD elements ${t}_{n{n}^{\ensuremath{'}}}$. It is found that if all the ${t}_{n{n}^{\ensuremath{'}}}$'s are positive the conductance is independent of $L∕M$ as restricted by two delocalized channels at $E=0$. If the distribution function includes the sign randomness of elements ${t}_{n{n}^{\ensuremath{'}}}$, the conductivity, rather than the conductance, becomes $L∕M$ independent. The calculated value of the conductivity is around $\frac{4{e}^{2}}{h}$, in consistence with the experiments.
Rigorous dynamical mean field theory for stochastic gradient descent methods

We prove closed-form equations for the exact high-dimensional asymptotics of a family of first order gradient-based methods, learning an estimator (e.g. M-estimator, shallow neural network, .) from observations on Gaussian data with empirical risk minimization. This includes widely used algorithms such as stochastic gradient descent (SGD) or Nesterov acceleration. The obtained equations match those resulting from the discretization of dynamical mean-field theory (DMFT) equations from statistical physics when applied to gradient flow. Our proof method allows us to give an explicit description of how memory kernels build up in the effective dynamics, and to include non-separable update functions, allowing datasets with non-identity covariance matrices. Finally, we provide numerical implementations of the equations for SGD with generic extensive batch-size and with constant learning rates.
Low-resource Neural Machine Translation with Cross-modal Alignment

How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpus, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality. Specifically, we propose a cross-modal contrastive learning method to learn a shared space for all languages, where both a coarse-grained sentence-level objective and a fine-grained token-level one are introduced. Experimental results and further analysis show that our method can effectively learn the cross-modal and cross-lingual alignment with a small amount of image-text pairs, and achieves significant improvements over the text-only baseline under both zero-shot and few-shot scenarios.
A Bayesian analysis of the time through the order penalty in baseball

Abstract As a baseball game progresses, batters appear to perform better the more times they face a particular pitcher. The apparent drop-off in pitcher performance from one time through the order to the next, known as the Time Through the Order Penalty (TTOP), is often attributed to within-game batter learning. Although the TTOP has largely been accepted within baseball and influences many managers’ in-game decision making, we argue that existing approaches of estimating the size of the TTOP cannot disentangle continuous evolution in pitcher performance over the course of the game from discontinuities between successive times through the order. Using a Bayesian multinomial regression model, we find that, after adjusting for confounders like batter and pitcher quality, handedness, and home field advantage, there is little evidence of strong discontinuity in pitcher performance between times through the order. Our analysis suggests that the start of the third time through the order should not be viewed as a special cutoff point in deciding whether to pull a starting pitcher.
Emergence of Shared Sensory-motor Graphical Language from Visual Input

The framework of Language Games studies the emergence of languages in populations of agents. Recent contributions relying on deep learning methods focused on agents communicating via an idealized communication channel, where utterances produced by a speaker are directly perceived by a listener. This comes in contrast with human communication, which instead relies on a sensory-motor channel , where motor commands produced by the speaker (e.g. vocal or gestural articula-tors) result in sensory effects perceived by the listener (e.g. audio or visual). Here, we investigate if agents can evolve a shared language when they are equipped with a continuous sensory-motor system to produce and perceive signs, e.g. drawings. To this end, we introduce the Graphical Referential Game ( GREG ) where a speaker must produce a graphical utterance to name a visual referent object consisting of combinations of MNIST digits while a listener has to select the corresponding object among distractor referents, given the produced message. The utterances are drawing images produced using dynamical motor primitives combined with a sketching library. To tackle GREG we present CURVES : a multimodal contrastive deep learning mechanism that represents the energy (alignment) between named referents and utterances generated through gradient ascent on the learned energy landscape. We, then, present a set of experiments and metrics based on a systematic compositional dataset to evaluate the resulting language. We show that our method allows the emergence of a shared, graphical language with compositional properties.
Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps

Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 µm 2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.
Codes from incidence matrices of hypergraphs

Binary codes are constructed from incidence matrices of hypergraphs. A combina-troial description is given for the minimum distances of such codes via a combinatorial tool called “eonv”. This combinatorial approach provides a faster alternative method of ﬁnding the minimum distance, which is known to be a hard problem. This is demonstrated on several classes of codes from hypergraphs. Moreover, self-duality and self-orthogonality conditions are also studied through hypergraphs. or its subsidiaries. Other names and brands may be claimed as the property of others.
Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory

NASA’s Solar Dynamics Observatory (SDO) mission gathers 1.4 terabytes of data each day from its geosynchronous orbit in space. SDO data includes images of the Sun captured at different wavelengths, with the primary scientific goal of understanding the dynamic processes governing the Sun. Recently, end-to-end optimized artificial neural networks (ANN) have shown great potential in performing image compression. ANN-based compression schemes have outperformed conventional hand-engineered algorithms for lossy and lossless image compression. We have designed an ad-hoc ANN-based image compression scheme to reduce the amount of data needed to be stored and retrieved on space missions studying solar dynamics. In this work, we propose an attention module to make use of both local and non-local attention mechanisms in an adversarially trained neural image compression network. We have also demonstrated the superior perceptual quality of this neural image compressor. Our proposed algorithm for compressing images downloaded from the SDO spacecraft performs better in rate-distortion tradeoff than the popular currently-in-use image compression codecs such as JPEG and JPEG2000. In addition we have shown that the proposed method outperforms state-of-the art lossy transform coding compression codec, i.e., BPG.
Cosmology with the lights off: Standard sirens in the Einstein Telescope era

We explore the prospects for constraining cosmology using gravitational-wave (GW) observations of neutron-star binaries by the proposed Einstein Telescope (ET), exploiting the narrowness of the neutron-star mass function. Double neutron-star (DNS) binaries are expected to be one of the first sources detected after "first-light" of Advanced LIGO and are expected to be detected at a rate of a few tens per year in the advanced era. However the proposed ET could catalog tens of thousands per year. Combining the measured source redshift distributions with GW-network distance determinations will permit not only the precision measurement of background cosmological parameters, but will provide an insight into the astrophysical properties of these DNS systems. Of particular interest will be to probe the distribution of delay times between DNS-binary creation and subsequent merger, as well as the evolution of the star-formation rate density within ET's detection horizon. Keeping H_0, \Omega_{m,0} and \Omega_{\Lambda,0} fixed and investigating the precision with which the dark-energy equation-of-state parameters could be recovered, we found that with 10^5 detected DNS binaries we could constrain these parameters to an accuracy similar to forecasted constraints from future CMB+BAO+SNIa measurements. Furthermore, modeling the merger delay-time distribution as a power-law, and the star-formation rate (SFR) density as a parametrized version of the Porciani and Madau SF2 model, we find that the associated astrophysical parameters are constrained to within ~ 10%. All parameter precisions scaled as 1/sqrt(N), where N is the number of cataloged detections. We also investigated how precisions varied with the intrinsic underlying properties of the Universe and with the distance reach of the network (which may be affected by the low-frequency cutoff of the detector).
Extending the sunspot area series from Kodaikanal Solar Observatory

Kodaikanal Solar Observatory (KoSO) possesses one of world’s longest and homogeneous records of sunspot observations that span more than a century (1904–2017). Interestingly, these observations (originally recorded in photographic plates/films) were taken with the same setup over this entire time period which makes this data unique and best suitable for long-term solar variability studies. A large part of this data, between 1921–2011, were digitized earlier and a catalog containing the detected sunspot parameters (e.g., area and location) was published in Mandal et al. (2017). In this article, we extend the earlier catalog by including new sets of data between 1904–1921 and 2011–2017. To this end, we digitize and calibrate these new datasets which include resolving the issue of random image orientation. We fix this by comparing the KoSO images with co-temporal data from Royal Greenwich Observatory. Following that, a semi-automated sunspot detection and automated umbra detection algorithm are implemented onto these calibrated images to detect sunspots and umbra. Additionally, during this catalog updation, we also filled data gaps in the existing KoSO sunspot catalog (1921–2011) by virtue of re-calibrating the “rouge” plates. This updated sunspot area series covering nearly 115 years (1904–2017) are being made available to the community and will be a unique source to study the long term variability of the Sun.
Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation

Most research on natural language processing treats bias as an absolute concept: Based on a (probably complex) algorithmic analysis, a sentence, an article, or a text is classified as biased or not. Given the fact that for humans the question of whether a text is biased can be difficult to answer or is answered contradictory, we ask whether an"absolute bias classification"is a promising goal at all. We see the problem not in the complexity of interpreting language phenomena but in the diversity of sociocultural backgrounds of the readers, which cannot be handled uniformly: To decide whether a text has crossed the proverbial line between non-biased and biased is subjective. By asking"Is text X more [less, equally] biased than text Y? "we propose to analyze a simpler problem, which, by its construction, is rather independent of standpoints, views, or sociocultural aspects. In such a model, bias becomes a preference relation that induces a partial ordering from least biased to most biased texts without requiring a decision on where to draw the line. A prerequisite for this kind of bias model is the ability of humans to perceive relative bias differences in the first place. In our research, we selected a specific type of bias in argumentation, the stance bias, and designed a crowdsourcing study showing that differences in stance bias are perceptible when (light) support is provided through training or visual aid.
Retrospectives on the Embodied AI Workshop

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.
Scalability in Visualization

We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990 to 2020 to characterize the different notions of scalability in these works. While many papers have addressed scalability issues, our survey identifies a lack of consistency in the use of the term in the visualization research community. We address this issue by introducing a consistent terminology meant to help visualization researchers better characterize the scalability aspects in their research. It also helps in providing multiple methods for supporting the claim that a work is "scalable. " Our model is centered around an effort function with inputs and outputs. The inputs are the problem size and resources, whereas the outputs are the actual efforts, for instance, in terms of computational run time or visual clutter. We select representative examples to illustrate different approaches and facets of what scalability can mean in visualization literature. Finally, targeting the diverse crowd of visualization researchers without a scalability tradition, we provide a set of recommendations for how scalability can be presented in a clear and consistent way to improve fair comparison between visualization techniques and systems and foster reproducibility.
An Analysis Method for Metric-Level Switching in Beat Tracking

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this letter, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when being utilized alongside existing metrics, and discuss the new insights that can be gained.
Soundness and Completeness of SPARQL Query Containment Solver SpeCS

Tool S PE CS implements an eﬃcient automated approach for reasoning about the SPARQL query con- tainment problem. In this paper, we prove the correctness of this approach. We give precise semantics of the core subset of SPARQL language. We brieﬂy discuss the procedure used for reducing the query containment problem into a formal logical framework. We prove that such reduction is both sound and complete for conjunctive queries, and also for some important cases of non-conjunctive queries containing operator union , operator optional , and subqueries. Soundness and completeness proofs are considered in both containment and subsumption forms.