Miracle-dz/UniFAR-FTUs huggingface dataset:
NREL Phase VI wind turbine in the dusty environment

The meteorological conditions markedly affect the energy efficiencies and cost/power rate of the wind turbines. This study numerically investigates the performance of the National Renewable Energy Laboratory (NREL) Phase VI wind turbine, designed to be insusceptible to surface roughness, undergoing either clean or dusty air. First, the numerical approach is validated against the available experimental data for clean air. Following this, the model is developed into a Lagrangian-Eulerian multiphase approach to comprehensively analyze the effects of the dusty air. The dependence of aerodynamic performance on the wind speed (= 5-25 m/s), particle diameter dp (= 0.025-0.9 mm) and angle of attack (= 0o-44o) is investigated. It is found that the turbine performance generally deteriorates in dusty conditions. But it becomes relatively acute for dp>0.1 mm and post-stall state. As such, the generated power is reduced by 4.3% and 13.3% on average for the air with the dp = 0.05 and 0.9 mm, respectively. The particles change the flow field profoundly, declining the pressure difference between the suction/pressure sides of the blade-airfoil, advancing the boundary layer separation, and strengthening the recirculation zones. The above changes account for a lower lift coefficient and higher drag coefficient.
Optimizing persistent currents in a ring-shaped Bose-Einstein condensate using machine learning

We demonstrate a method for generating persistent currents in Bose-Einstein condensates by using a Gaussian process learner to experimentally control the stirring of the superfluid. The learner optimizes four different outcomes of the stirring process: (O.I) targeting and (O.II) maximization of the persistent current winding number; and (O.III) targeting and (O.IV) maximization with time constraints. The learner optimizations are determined based on the achieved winding number and the number of spurious vortices introduced by stirring. We find that the learner is successful in optimizing the stirring protocols, although the optimal stirring profiles vary significantly depending strongly on the choice of cost function and scenario. These results suggest that stirring is robust and persistent currents can be reliably generated through a variety of stirring approaches.
Sensing the Pulse of the Pandemic: Geovisualizing the Demographic Disparities of Public Sentiment toward COVID-19 through Social Media

Social media offers a unique lens to observe users emotions and subjective feelings toward critical events or topics and has been widely used to investigate public sentiment during crises, e.g., the COVID-19 pandemic. However, social media use varies across demographic groups, with younger people being more inclined to use social media than the older population. This digital divide could lead to biases in data representativeness and analysis results, causing a persistent challenge in research based on social media data. This study aims to tackle this challenge through a case study of estimating the public sentiment about the COVID-19 using social media data. We analyzed the pandemic-related Twitter data in the United States from January 2020 to December 2021. The objectives are: (1) to elucidate the uneven social media usage among various demographic groups and the disparities of their emotions toward COVID-19, (2) to construct an unbiased measurement for public sentiment based on social media data, the Sentiment Adjusted by Demographics (SAD) index, through the post-stratification method, and (3) to evaluate the spatially and temporally evolved public sentiment toward COVID-19 using the SAD index. The results show significant discrepancies among demographic groups in their COVID-19-related emotions. Female and under or equal to 18 years old Twitter users expressed long-term negative sentiment toward COVID-19. The proposed SAD index in this study corrected the underestimation of negative sentiment in 31 states, especially in Vermont. According to the SAD index, Twitter users in Wyoming (Vermont) posted the largest (smallest) percentage of negative tweets toward the pandemic.
SPColor: Semantic Prior Guided Exemplar-based Image Colorization

Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch. We summarize the difficulties in two aspects: (1) When the reference image only contains a part of objects related to target image, improper correspondence will be established in unrelated regions. (2) It is prone to get mismatch in regions where the shape or texture of the object is easily confused. To overcome these issues, we propose SPColor, a semantic prior guided exemplar-based image colorization framework. Different from previous methods, SPColor first coarsely classifies pixels of the reference and target images to several pseudo-classes under the guidance of semantic prior, then the correspondences are only established locally between the pixels in the same class via the newly designed semantic prior guided correspondence network. In this way, improper correspondence between different semantic classes is explicitly excluded, and the mismatch is obviously alleviated. Besides, to better reserve the color from reference, a similarity masked perceptual loss is designed. Noting that the carefully designed SPColor utilizes the semantic prior provided by an unsupervised segmentation model, which is free for additional manual semantic annotations. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively on public dataset.
A Gaia Data Release 3 View on the Tip of the Red Giant Branch Luminosity

The tip of the red giant branch (TRGB) is a standard candle that can be used to help refine the determination of the Hubble constant. Gaia Data Release 3 (DR3) provides synthetic photometry constructed from low-resolution BP/RP spectra for Milky Way field stars that can be used to directly calibrate the luminosity of the TRGB in the Johnson–Cousins I band, where the TRGB is least sensitive to metallicity. We calibrate the TRGB luminosity using a two-dimensional maximum likelihood algorithm with field stars and Gaia synthetic photometry and parallaxes. For a high-contrast and low-contrast break (characterized by the values of the contrast parameter R or the magnitude of the break β), we find M TRGB I = −4.02 and −3.92 mag respectively, or a midpoint of −3.970 −0.024+0.042 (sys) ± 0.062 (stat) mag. This measurement improves upon the TRGB measurement from Li et al., as the higher precision photometry based on Gaia DR3 allows us to constrain two additional free parameters of the luminosity function. We also investigate the possibility of using Gaia DR3 synthetic photometry to calibrate the TRGB luminosity with ω Centauri, but find evidence of blending within the inner region for cluster member photometry that precludes accurate calibration with Gaia DR3 photometry. We instead provide an updated TRGB measurement of m TRGB I = 9.82 ± 0.04 mag in ω Centauri using ground-based photometry from the most recent version of the database described in Stetson et al., which gives M TRGB I = −3.97 ± 0.04 (stat) ± 0.10 (sys) mag when tied to the Gaia EDR3 parallax distance from the consensus of Vasiliev & Baumgardt, Soltis et al., and Maíz Apellániz.
Skyline Operators for Document Spanners

When extracting a relation of spans (intervals) from a text document, a common practice is to filter out tuples of the relation that are deemed dominated by others. The domination rule is defined as a partial order that varies along different systems and tasks. For example, we may state that a tuple is dominated by tuples which extend it by assigning additional attributes, or assigning larger intervals. The result of filtering the relation would then be the skyline according to this partial order. As this filtering may remove most of the extracted tuples, we study whether we can improve the performance of the extraction by compiling the domination rule into the extractor. To this aim, we introduce the skyline operator for declarative information extraction tasks expressed as document spanners. We show that this operator can be expressed via regular operations when the domination partial order can itself be expressed as a regular spanner, which covers several natural domination rules. Yet, we show that the skyline operator incurs a computational cost (under combined complexity). First, there are cases where the operator requires an exponential blowup on the number of states needed to represent the spanner as a sequential variable-set automaton. Second, the evaluation may become computationally hard. Our analysis more precisely identifies classes of domination rules for which the combined complexity is tractable or intractable.
Time dependent Markovian master equation beyond the adiabatic limit

We develop a Markovian master equation that models the evolution of systems subject to arbitrary driving and control fields. Our approach combines time rescaling and weak-coupling limits for the system-environment interaction with a secular approximation. The derivation makes use of the adiabatic time evolution operator in a manner that allows for the efficient description of strong driving, while recovering the adiabatic master equation in the appropriate limit. To illustrate the effectiveness of our approach, we apply it to the paradigmatic case of a two-level (qubit) system subjected to a form of periodic driving that remains unsolvable using a Floquet representation. We demonstrate the reliability and broad scope of our approach by benchmarking the solutions of the derived reduced time evolution against numerically exact simulations using tensor networks. Our results provide rigorous conditions that must be satisfied by phenomenological master equations for driven systems that do not rely on first principles derivations.
Per-RMAP: Feasibility-Seeking and Superiorization Methods for Floorplanning with I/O Assignment

The feasibility-seeking approach provides a systematic scheme to manage and solve complex constraints for continuous problems, and we explore it for the floorplanning problems with increasingly heterogeneous constraints. The classic legality constraints can be formulated as the union of convex sets. However, the convergence of conventional projection-based algorithms is not guaranteed when the constraints sets are non-convex, which is the case with unions of convex sets. In this work, we propose a resetting strategy to greatly eliminate the divergence issue of the projection-based algorithm for the feasibility-seeking formulation. Furthermore, the superiorization methodology (SM), which lies between feasibility-seeking and constrained optimization, is firstly applied to floorplanning. The SM uses perturbations to steer the iterates of a feasibility-seeking algorithm to a feasible solution with shorter total wirelength. The proposed algorithmic flow is extendable to tackle various constraints and variants of floorplanning problems, e.g., floorplanning with I/O assignment problems. We have evaluated the proposed algorithm on the MCNC benchmarks. We can obtain legal floorplans only two times slower than the branch-and-bound method in its current prototype using MATLAB, with only 3 % wirelength inferior to the optimal results. We evaluate the effectiveness of the algorithmic flow by considering the constraints of I/O assignment, and our algorithm achieves 8 % improvement on wirelength.
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face inpainting

Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. However, current generative models for face inpainting often fail to preserve fine facial details and the identity of the person, despite creating aesthetically convincing image structures and textures. In this work, we propose Person Aware Tuning (PAT) of Mask-Aware Transformer (MAT) for face inpainting, which addresses this issue. Our proposed method, PATMAT 1, effectively preserves identity by incorporating reference images of a subject and fine-tuning a MAT architecture trained on faces. By using ~40 reference images, PATMAT creates anchor points in MAT’s style module, and tunes the model using the fixed anchors to adapt the model to a new face identity. Moreover, PATMAT’s use of multiple images per anchor during training allows the model to use fewer reference images than competing methods. We demonstrate that PATMAT outperforms state-of-the-art models in terms of image quality, the preservation of person-specific details, and the identity of the subject. Our results suggest that PATMAT can be a promising approach for improving the quality of personalized face inpainting 2.
Maximal fairness

Fairness in AI has garnered quite some attention in research, and increasingly also in society. The so-called "Impossibility Theorem" has been one of the more striking research results with both theoretical and practical consequences, as it states that satisfying a certain combination of fairness measures is impossible. To date, this negative result has not yet been complemented with a positive one: a characterization of which combinations of fairness notions are possible. This work aims to fill this gap by identifying maximal sets of commonly used fairness measures that can be simultaneously satisfied. The fairness measures used are demographic parity, equal opportunity, predictive equality, predictive parity, false omission rate parity, overall accuracy equality and treatment equality. We conclude that in total 12 maximal sets of these fairness measures are possible, among which are seven combinations of two measures, and five combinations of three measures. Our work raises interesting questions regarding the practical relevance of each of these 12 maximal fairness notions in various scenarios.
Supersolid stacks in antidipolar Bose-Einstein condensates

We theoretically investigate a novel supersolid structure taking the form of stacked, disk-shaped superfluid droplets connected via a dilute superfluid, in an antidipolar condensate. A phase diagram is determined for varying the particle number and scattering length, identifying the regions of a regular dipolar superfluid, supersolid stacks, and isolated stacked disk-shaped droplets in an experimentally realizable trapping potential. The collective Bogoliubov excitation spectrum across the superfluid-supersolid phase transition is studied, and the transition point is found to be associated with the breaking of the degeneracy of the two lowest-lying modes. The dynamical generation of the supersolid stacks is also investigated by ramping down the scattering length across the phase transition. Moreover, we have studied the impact of vortex-line penetration on the phase transition. We have found that the presence of a vortex line causes the supersolid region to move towards weaker contact interactions. Our detailed numerical simulations highlight that an antidipolar condensate can create such supersolid stacks within an experimentally reachable parameter regime.
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.
Noisy Correspondence Learning with Meta Similarity Correction

Despite the success of multimodal learning in crossmodal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions. Our code is publicly available.11https://github.com/hhc1997/MSCN
Quantification Couleur : Comparaisons Objectives et Subjectives de Différents Algorithmes

Dans cette contribution, nous proposons d'evaluer la qualite de differents algorithmes de reduction de la palette couleur d'une image. Deux techniques originales sont particulierement detaillees (avec deux variantes pour chacune d'elles): l'une basee sur la transformation du boulanger et l'autre employant la matrice des palettes locales. Dans la campagne d'evaluation, les resultats de ces deux techniques sont compares a ceux d'algorithmes standards tels que «median cut», «octree» et «split & merge». L'evaluation se veut a la fois objective (utilisation d'une metrique et de descripteurs locaux de qualite) et subjective (utilisation d'experiences psycho-visuelles). En effet, l'usage seul d'une metrique n'integre pas la notion de HVS (Human Visual System). La couleur etant davantage consideree comme propriete perceptuelle que comme donnee quantitative, une mesure classique ne peut decrire correctement l'alteration que subit une image lors d'une reduction des couleurs. Les resultats de la campagne d'evaluation psycho-visuelle montrent une fois de plus que les metriques classiques sont souvent en contradiction avec la perception humaine de la couleur.
Twisted Heating-Cooling Transition of Near-field Radiation in Drifted Metasurfaces

The magic angle twisted bilayer systems give rise to many exotic phenomena in two-dimensional electronic or photonic platforms. Here, we study the twisted near-field energy radiation between graphene metasurfaces with nonequilibrium drifted Dirac electrons. Anomalously, we find unconventional radiative flux that directs heat from cold to hot. This far-from-equilibrium phenomenon leads to a heating-cooling transition beyond a thermal magic twist angle, facilitated by twist-induced photonic topological transitions. The underlying mechanism is related to the spectrum match and mismatch caused by the cooperation between the non-reciprocal nature of drifted plasmon polaritons and their topological features. Furthermore, we report the unintuitive distance dependence of radiative energy flux under large twist angles. The near-field radiation becomes thermal insulating when increasing to a critical distance, and subsequently reverses the radiative flux to increase the cooling power as the distance increases further. Our results indicate the promising future of nonequilibrium drifted and twisted devices and pave the way towards tunable radiative thermal management.
CoVE: Towards Confidential Computing on RISC-V Platforms

Multi-tenant computing platforms are typically comprised of several software and hardware components including platform firmware, operating system, virtualization monitor, and tenant workloads (typically in a virtual machine, container, or application). This model is well established in large scale commercial deployments, but the downside is that all platform components and operators are in the Trusted Computing Base (TCB) of the tenant. This aspect is ill-suited for privacy-oriented workloads that aim to minimize the TCB. Confidential computing [1] presents a good stepping-stone towards providing a quantifiable TCB for computing. Confidential computing requires the use of a HW-attested Trusted Execution Environment for data-in-use protection. The RISC-V architecture presents a strong foundation for meeting the requirements for Confidential Computing in a clean slate manner. This paper describes a reference architecture and discusses ISA, non-ISA and System-on-Chip (SoC) requirements for confidential computing on RISC-V Platforms. It discusses proposed RISC-V ISA and non-ISA for Confidential Virtual-machine Extension, referred to as CoVE.
Decidability of Querying First-Order Theories via Countermodels of Finite Width

We propose a generic framework for establishing the decidability of a wide range of logical entailment problems (briefly called querying), based on the existence of countermodels that are structurally simple, gauged by certain types of width measures (with treewidth and cliquewidth as popular examples). As an important special case of our framework, we identify logics exhibiting width-finite finitely universal model sets, warranting decidable entailment for a wide range of homomorphism-closed queries, subsuming a diverse set of practically relevant query languages. As a particularly powerful width measure, we propose Blumensath's partitionwidth, which subsumes various other commonly considered width measures and exhibits highly favorable computational and structural properties. Focusing on the formalism of existential rules as a popular showcase, we explain how finite partitionwidth sets of rules subsume other known abstract decidable classes but - leveraging existing notions of stratification - also cover a wide range of new rulesets. We expose natural limitations for fitting the class of finite unification sets into our picture and provide several options for remedy.