Miracle-dz/UniFAR-FTUs huggingface dataset:
"Multi-label classiﬁcation: do Hamming loss and subset accuracy really conﬂict with each other?"

where = [( ij)] ∈ {−1,+1}n×c in which each element ij is i.i. d. sampled from the Rademacher distribution Unif({+1,−1}) and f(xi) = [f1(xi), . , fc(xi)]. Besides, its deterministic counterpart is Rn(F) = ES∼Dn [R̂S(F)]. Theorem A.1 ([2]). Assume G be a family of functions from z to [0,M ]. Then, for any δ > 0, with probability at least 1− δ over the draw of an i.i. d. sample S of size n, the following generalization bound holds for all g ∈ G:
University of Dundee THM analysis of compacted clay liner response to baled municipal waste Ciantia, Matteo; Arroyo, Marcos; Isus, Andreu

Heat is generated in municipal waste containment facilities due to the chemical and biochemical evolution of waste. Sometimes it is required to check if the level of heat generated during the deposit lifetime may damage or reduce the isolating properties of the liners. That verification relies on appropriate evaluation of heat and moisture transfer through the barrier as well as the evolution of stress fields. This paper presents an example of such verification based on a coupled thermo-hydro-mechanical (THM) analysis for the case of a compacted clay liner designed to isolate a deposit of baled municipal waste. The evaluation of the different boundary conditions for the analysis is discussed in detail. The case study corresponds to a deposit of baled waste and the evaluation of thermal loadings on the lining is performed using thermal discrete elements.
Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image Versus Data Domain

Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame. As the latter is computationally more tractable, investigation to which extent this equivalence holds conducted in this paper is of immediate practical significance. To this end we formulate and compare <bold>DR</bold>, a two step approach based on the recovery of the complete volume of the photoacoustic data from the subsampled data followed by the acoustic inversion, and <inline-formula><tex-math notation="LaTeX">${p_0\bf {R}}$</tex-math></inline-formula>, a one step approach where the photoacoustic image (the initial pressure, <inline-formula><tex-math notation="LaTeX">$p_0$</tex-math></inline-formula>) is directly recovered from the subsampled data. Effective representation of the photoacoustic data requires basis defined on the range of the photoacoustic forward operator. To this end we propose a novel <italic>wedge-restriction</italic> of Curvelet transform which enables us to construct such basis. Both recovery problems are formulated in a variational framework. As the Curvelet frame is heavily overdetermined, we use reweighted <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> norm penalties to enhance the sparsity of the solution. The data reconstruction problem <bold>DR</bold> is a standard compressed sensing recovery problem, which we solve using an ADMM-type algorithm, SALSA. Subsequently, the initial pressure is recovered using time reversal as implemented in the k-Wave Toolbox. The <inline-formula><tex-math notation="LaTeX">$p_0$</tex-math></inline-formula> reconstruction problem, <inline-formula><tex-math notation="LaTeX">${p_0\bf {R}}$</tex-math></inline-formula>, aims to recover the photoacoustic image directly via FISTA, or ADMM when in addition including a non-negativity constraint. We compare and discuss the relative merits of the two approaches and illustrate them on 2D simulated and 3D real data in a fair and rigorous manner.
STR-GQN: Scene Representation and Rendering for Unknown Cameras Based on Spatial Transformation Routing

Geometry-aware modules are widely applied in recent deep learning architectures for scene representation and rendering. However, these modules require intrinsic camera information that might not be obtained accurately. In this paper, we propose a Spatial Transformation Routing (STR) mechanism to model the spatial properties without applying any geometric prior. The STR mechanism treats the spatial transformation as the message passing process, and the relation between the view poses and the routing weights is modeled by an end-to-end trainable neural network. Besides, an Occupancy Concept Mapping (OCM) framework is proposed to provide explainable rationals for scene-fusion processes. We conducted experiments on several datasets and show that the proposed STR mechanism improves the performance of the Generative Query Network (GQN). The visualization results reveal that the routing process can pass the observed information from one location of some view to the associated location in the other view, which demonstrates the advantage of the proposed model in terms of spatial cognition.
Through the Looking Glass: Insights Into Visualization Pedagogy Through Sentiment Analysis of Peer Review Text

Peer review is a widely utilized feedback mechanism for engaging students. As a pedagogical method, it has been shown to improve educational outcomes, but we have found limited empirical measurement of peer review in visualization courses. In addition to increasing engagement, peer review provides diverse feedback and reinforces recently learned course concepts through critical evaluation of others’ work. We discuss the construction and application of peer review in two visualization courses from different colleges at the University of South Florida. We then analyze student projects and peer review text via sentiment analysis to infer insights for visualization educators, including the focus of course content, engagement across student groups, student mastery of concepts, course trends over time, and expert intervention effectiveness. Finally, we provide suggestions for adapting peer review to other visualization courses to engage students and increase instructor understanding of the peer review process.
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them. Despite the promising results, we argue the None-or-All labeling strategy is suboptimal as the densely labeled individuals in each crowd image usually appear similar while the massive unlabeled crowd images may contain entirely diverse individuals. To this end, we propose to break the labeling chain of previous methods and make the first attempt to reduce spatial labeling redundancy for semi-supervised crowd counting. First, instead of annotating all the regions in each crowd image, we propose to annotate the representative ones only. We analyze the region representativeness from both vertical and horizontal directions, and formulate them as cluster centers of Gaussian Mixture Models. Additionally, to leverage the rich unlabeled regions, we exploit the similarities among individuals in each crowd image to directly supervise the unlabeled regions via feature propagation instead of the error-prone label propagation employed in the previous methods. In this way, we can transfer the original spatial labeling redundancy caused by individual similarities to effective supervision signals on the unlabeled regions. Extensive experiments on the widely-used benchmarks demonstrate that our method can outperform previous best approaches by a large margin.
Deep Residual Echo Suppression and Noise Reduction: A Multi-Input FCRN Approach in a Hybrid Speech Enhancement System

Deep neural network (DNN)-based approaches to acoustic echo cancellation (AEC) and hybrid speech enhancement systems have gained increasing attention recently, introducing significant performance improvements to this research field. Using the fully convolutional recurrent network (FCRN) architecture that is among state of the art topologies for noise reduction, we present a novel deep residual echo suppression and noise reduction with up to four input signals as part of a hybrid speech enhancement system with a linear frequency domain adaptive Kalman filter AEC. In an extensive ablation study, we reveal trade-offs with regard to echo suppression, noise reduction, and near-end speech quality, and provide surprising insights to the choice of the FCRN inputs: In contrast to often seen input combinations for this task, we propose not to use the loudspeaker reference signal, but the enhanced signal after AEC, the microphone signal, and the echo estimate, yielding improvements over previous approaches by more than 0.2PESQ points.
Constructing Orientable Sequences

This paper describes new, simple, recursive methods of construction for <italic>orientable sequences</italic>, i.e. periodic binary sequences in which any <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-tuple occurs at most once in a period in either direction. As has been previously described, such sequences have potential applications in automatic position-location systems, where the sequence is encoded onto a surface and a reader needs only examine <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> consecutive encoded bits to determine its location and orientation on the surface. The only previously described method of construction (due to Dai <italic>et al.</italic>) is somewhat complex, whereas the new techniques are simple to both describe and implement. The methods of construction cover both the standard ‘infinite periodic’ case, and also the aperiodic, finite sequence, case. Both the new methods build on the Lempel homomorphism, first introduced as a means of recursively generating de Bruijn sequences.
An Integrated Progressive Hedging and Benders Decomposition With Multiple Master Method to Solve the Brazilian Generation Expansion Problem

This paper exploits the decomposition structure of the large-scale hydrothermal generation expansion planning problem with an integrated modified Benders Decomposition and Progressive Hedging approach. We consider detailed and realistic data from the Brazilian power system to represent hourly chronological constraints based on typical days per month and year. Also, we represent the multistage stochastic nature of the optimal hydrothermal operational policy through co-optimized linear decision rules for individual reservoirs. Therefore, we ensure investment decisions compatible with a nonanticipative (implementable) operational policy. To solve the large-scale optimization problem, we propose an improved Benders Decomposition method with multiple instances of the master problem, each of which strengthened by primal cuts and new Benders cuts generated by each master’s trial solution. Additionally, our new approach allows using Progressive Hedging penalization terms for accelerating the convergence of the method. We show that our method is 60% faster than the benchmark. Finally, the consideration of a nonanticipative operational policy can save 7.64% of the total cost (16.18% of the investment costs) and significantly improve spot price profiles.
Quantum Meets the Minimum Circuit Size Problem

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory---its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reduction and self-reduction. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction.
Fast Algorithms and Error Analysis of Caputo Derivatives with Small Factional Orders

In this paper, we investigate fast algorithms in the small fraction order regime to approximate the Caputo derivative $^C_0D_t^\alpha u(t)$ when $\alpha$ is small. We focus on two fast algorithms, i.e. FIR and FIDR, both relying on the sum-of-exponential approximation to reduce the cost of evaluating the history part. FIR is the numerical scheme originally proposed in [16], and FIDR is an alternative scheme proposed in [26], and we show that the latter is superior when $\alpha$ is small. With quantitative estimates, we prove that given a certain error threshold, the computational cost of evaluating the history part of the Caputo derivative can be decreased as $\alpha$ gets small. Hence, only minimal cost for the fast evaluation is required in the small $\alpha$ regime, which matches prevailing protocols in engineering practice. We also present improved stability and error analysis of FIDR for solving linear fractional diffusion equations, which achieves clear dependence of the error bound on the fraction order $\alpha$. Finally, we carry out systematic numerical studies for the performances of both FIR and FIDR schemes, where we explore the trade-off between accuracy and efficiency when $\alpha$ is small.
Radio spectral properties of star-forming galaxies in the MIGHTEE-COSMOS field and their impact on the far-infrared-radio correlation

We study the radio spectral properties of 2094 star-forming galaxies (SFGs) by combining our early science data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey with VLA, GMRT radio data, and rich ancillary data in the COSMOS field. These SFGs are selected at VLA 3 GHz, and their flux densities from MeerKAT 1.3 GHz and GMRT 325 MHz imaging data are extracted using the ‘superdeblending’ technique. The median radio spectral index is $\alpha _{\rm 1.3\, GHz}^{\rm 3\, GHz}=-0.80\pm 0.01$ without significant variation across the rest-frame frequencies ∼1.3–10 GHz, indicating radio spectra dominated by synchrotron radiation. On average, the radio spectrum at observer-frame 1.3–3 GHz slightly steepens with increasing stellar mass with a linear fitted slope of β = −0.08 ± 0.01, which could be explained by age-related synchrotron losses. Due to the sensitivity of GMRT 325 MHz data, we apply a further flux density cut at 3 GHz ($S_{\rm 3\, GHz}\ge 50\, \mu$Jy) and obtain a sample of 166 SFGs with measured flux densities at 325 MHz, 1.3 GHz, and 3 GHz. On average, the radio spectrum of SFGs flattens at low frequency with the median spectral indices of $\alpha ^{\rm 1.3\, GHz}_{\rm 325\, MHz}=-0.59^{+0.02}_{-0.03}$ and $\alpha ^{\rm 3.0\, GHz}_{\rm 1.3\, GHz}=-0.74^{+0.01}_{-0.02}$. At low frequency, our stacking analyses show that the radio spectrum also slightly steepens with increasing stellar mass. By comparing the far-infrared-radio correlations of SFGs based on different radio spectral indices, we find that adopting $\alpha _{\rm 1.3\, GHz}^{\rm 3\, GHz}$ for k-corrections will significantly underestimate the infrared-to-radio luminosity ratio (qIR) for >17 per cent of the SFGs with measured flux density at the three radio frequencies in our sample, because their radio spectra are significantly flatter at low frequency (0.33–1.3 GHz).
Modeling the Hα Emission Surrounding Spica Using the Lyman Continuum from a Gravity-darkened Central Star

The large, faint Hα emission surrounding the early B-star binary Spica has been used to constrain the total hydrogen recombination rate of the nebula and indirectly probe the Lyman continuum luminosity of the primary star. Early analysis suggested that a stellar atmosphere model, consistent with Spica A’s spectral type, has a Lyman continuum luminosity about two times lower than required to account for the measured Hα surface brightness within the nebula. To more consistently model both the stellar and nebular emission, we have used a model atmosphere for Spica A that includes the effects of gravity darkening as input to photoionization models to produce synthetic Hα surface brightness distributions for comparison to data from the Southern Hα Sky Survey Atlas. This paper presents a method for the computation of projected surface brightness profiles from 1D volume emissivity models and constrains both stellar and nebular parameters. A mean effective temperature for Spica A of ≃24,800 K is sufficient to match both the observed absolute spectrophotometry, from the far-UV to the near-IR, and radial Hα surface brightness distributions. Model hydrogen densities increase with the distance from the star, more steeply and linearly toward the southeast. The northwest matter-bounded portion of the nebula is predicted to leak ∼17% of Lyman continuum photons. Model H ii region column densities are consistent with archival observations along the line of sight.
Attention-based fusion of semantic boundary and non-boundary information to improve semantic segmentation

This paper introduces a method for image semantic segmentation grounded on a novel fusion scheme, which takes place inside a deep convolutional neural network. The main goal of our proposal is to explore object boundary information to improve the overall segmentation performance. Unlike previous works that combine boundary and segmentation features, or those that use boundary information to regularize semantic segmentation, we instead propose a novel approach that embodies boundary information onto segmentation. For that, our semantic segmentation method uses two streams, which are combined through an attention gate, forming an end-to-end Y-model. To the best of our knowledge, ours is the first work to show that boundary detection can improve semantic segmentation when fused through a semantic fusion gate (attention model). We performed an extensive evaluation of our method over public data sets. We found competitive results on all data sets after comparing our proposed model with other twelve stateof-the-art segmenters, considering the same training conditions. Our proposed model achieved the best mIoU on the CityScapes, CamVid, and Pascal Context data sets, and the second best on Mapillary Vistas.
After The Fall: Resolving the Molecular Gas in Post-starburst Galaxies

Post-starburst (PSB), or “E + A,” galaxies represent a rapid transitional phase between major, gas-rich mergers and gas-poor, quiescent, early-type galaxies. Surprisingly, many PSBs have been shown to host a significant interstellar medium (ISM), despite theoretical predictions that the majority of the star-forming gas should be expelled in active galactic nuclei– or starburst-driven outflows. To date, the resolved properties of this surviving ISM have remained unknown. We present high-resolution ALMA continuum and CO(2–1) observations in six gas- and dust-rich PSBs, revealing for the first time the spatial and kinematic structure of their ISM on sub-kpc scales. We find extremely compact molecular reservoirs, with dust and gas surface densities rivaling those found in (ultra)luminous infrared galaxies. We observe spatial and kinematic disturbances in all sources, with some also displaying disk-like kinematics. Estimates of the internal turbulent pressure in the gas exceed those of normal star-forming disks by at least 2 orders of magnitude, and rival the turbulent gas found in local interacting galaxies, such as the Antennae. Though the source of this high turbulent pressure remains uncertain, we suggest that the high incidence of tidal disruption events in PSBs could play a role. The star formation in these PSBs’ turbulent central molecular reservoirs is suppressed, forming stars only 10% as efficiently as starburst galaxies with similar gas surface densities. “The fall” of star formation in these galaxies was not precipitated by complete gas expulsion or redistribution. Rather, this high-resolution view of PSBs’ ISM indicates that star formation in their remaining compact gas reservoirs is suppressed by significant turbulent heating.
Conditional Separation as a Binary Relation

The concept of d-separation holds a pivotal role in causality theory, serving as a fundamental tool for deriving conditional independence properties from causal graphs. Pearl defined the d-separation of two subsets conditionally on a third one. In this study, we present a novel perspective by showing i) how the d-separation can be extended beyond acyclic graphs, possibly infinite, and ii) how it can be expressed and characterized as a binary relation between vertices. Compared to the typical perspectives in causality theory, our equivalence opens the door to more compact and computational proofing techniques, because the language of binary relations is well adapted to equational reasoning. Additionally, and of independent interest, the proofs of the results presented in this paper are checked with the Coq proof assistant.
Stability of asymptotic behaviour within polarized T2-symmetric vacuum solutions with cosmological constant

We prove the nonlinear stability of the asymptotic behaviour of perturbations of subfamilies of Kasner solutions in the contracting time direction within the class of polarized T2-symmetric solutions of the vacuum Einstein equations with arbitrary cosmological constant Λ. This stability result generalizes the results proven in Ames E et al. (2022 Stability of AVTD Behavior within the Polarized T2-symmetric vacuum spacetimes. Ann. Henri Poincaré. (doi:10.1007/s00023-021-01142-0)), which focus on the Λ=0 case, and as in that article, the proof relies on an areal time foliation and Fuchsian techniques. Even for Λ=0, the results established here apply to a wider class of perturbations of Kasner solutions within the family of polarized T2-symmetric vacuum solutions than those considered in Ames E et al. (2022 Stability of AVTD Behavior within the Polarized T2-symmetric vacuum spacetimes. Ann. Henri Poincaré. (doi:10.1007/s00023-021-01142-0)) and Fournodavlos G et al. (2020 Stable Big Bang formation for Einstein’s equations: the complete sub-critical regime. Preprint. (http://arxiv.org/abs/2012.05888)). Our results establish that the areal time coordinate takes all values in (0,T0] for some T0>0, for certain families of polarized T2-symmetric solutions with cosmological constant. This article is part of the theme issue ‘The future of mathematical cosmology, Volume 1’.