Miracle-dz/UniFAR-FTUs huggingface dataset:
From Human-Computer Interaction to Human-AI Interaction: New Challenges and Opportunities for Enabling Human-Centered AI

While AI has benefited humans, it may also harm humans if not appropriately developed. We conducted a literature review of current related work in developing AI systems from an HCI perspective. Different from other approaches, our focus is on the unique characteristics of AI technology and the differences between non-AI computing systems and AI systems. We further elaborate on the human-centered AI (HCAI) approach that we proposed in 2019. Our review and analysis highlight unique issues in developing AI systems which HCI professionals have not encountered in non-AI computing systems. To further enable the implementation of HCAI, we promote the research and application of human-AI interaction (HAII) as an interdisciplinary collaboration. There are many opportunities for HCI professionals to play a key role to make unique contributions to the main HAII areas as we identified. To support future HCI practice in the HAII area, we also offer enhanced HCI methods and strategic recommendations. In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI, enabling HCI professionals to address the unique issues of AI systems and develop human-centered AI systems.
Quantum State Control of a Bose-Einstein Condensate in an Optical Lattice

We report on the efficient design of quantum optimal control protocols to manipulate the motional states of an atomic Bose-Einstein condensate (BEC) in a one-dimensional optical lattice. Our protocols operate on the momentum comb associated with the lattice. In contrast to previous works also dealing with control in discrete and large Hilbert spaces, our control schemes allow us to reach a wide variety of targets by varying a single parameter, the lattice position. With this technique, we experimentally demonstrate a precise, robust and versatile control: we optimize the transfer of the BEC to a single or multiple quantized momentum states with full control on the relative phase between the different momentum components. This also allows us to prepare the BEC in a given eigenstate of the lattice band structure, or superposition thereof.
When Does Contrastive Visual Representation Learning Work?

Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification. While the particulars of pretraining on ImageNet are now relatively well understood, the field still lacks widely accepted best practices for replicating this success on other datasets. As a first step in this direction, we study contrastive self-supervised learning on four diverse large-scale datasets. By looking through the lenses of data quantity, data domain, data quality, and task granularity, we provide new insights into the necessary conditions for successful self-supervised learning. Our key findings include observations such as: (i) the benefit of additional pretraining data beyond 500k images is modest, (ii) adding pretraining images from another domain does not lead to more general representations, (iii) corrupted pretraining images have a disparate impact on supervised and self-supervised pretraining, and (iv) contrastive learning lags far behind supervised learning on finegrained visual classification tasks.
On the photometric metallicity of Oosterhoff-type I and II RRab stars

We are revising the consistency of photometric metallicity formulae widely used for fundamental-mode RR Lyrae (RRab) variables, based on their V- and I-band light curves, published by Jurcsik & Kovács (1996) and Smolec (2005), respectively. 293 RRab variables belonging to 10 globular clusters, all simultaneously containing stars of both Oosterhoff types, are selected for this purpose. We find that, on average, the V-band formula results in higher estimated metallicities by about 0.05 dex than the I-band formula. Moreover, we detect a dependence on the Oosterhoff class of the variables for both formulae, as well. Using the V-band formula, Oo I stars are 0.05–0.10 dex more metal rich than Oo II stars of the same cluster. Although with less significance, but the I-band results indicate a reversed trend. Therefore, we surmise that the average difference we have found between the V- and I-band formulae is the consequence of the total sample being dominated by Oo I variables.
Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization

Multimodal manifold modeling methods extend the spectral geometry-aware data analysis to learning from several related and complementary modalities. Most of these methods work based on two major assumptions: 1) there are the same number of homogeneous data samples in each modality, and 2) at least partial correspondences between modalities are given in advance as prior knowledge. This work proposes two new multimodal modeling methods. The first method establishes a general analyzing framework to deal with the multimodal information problem for heterogeneous data without any specific prior knowledge. For this purpose, first, we identify the localities of each manifold by extracting local descriptors via spectral graph wavelet signatures (SGWS). Then, we propose a manifold regularization framework based on the functional mapping between SGWS descriptors (FMBSD) for finding the pointwise correspondences. The second method is a manifold regularized multimodal classification based on pointwise correspondences (M$^2$CPC) used for the problem of multiclass classification of multimodal heterogeneous, which the correspondences between modalities are determined based on the FMBSD method. The experimental results of evaluating the FMBSD method on three common cross-modal retrieval datasets and evaluating the (M$^2$CPC) method on three benchmark multimodal multiclass classification datasets indicate their effectiveness and superiority over state-of-the-art methods.
Time-domain metric reconstruction for hyperbolic scattering

Self-force methods can be applied in calculations of the scatter angle in two-body hyperbolic encounters, working order by order in the mass ratio (assumed small) but with no recourse to a weak-field approximation. This, in turn, can inform ongoing efforts to construct an accurate model of the general-relativistic binary dynamics via an effective-one-body description and other semi-analytical approaches. Existing self-force methods are to a large extent specialised to bound, inspiral orbits. Here we develop a technique for (numerical) self-force calculations that can efficiently tackle scatter orbits. The method is based on a time-domain reconstruction of the metric perturbation from a scalar-like Hertz potential that satisfies the Teukolsky equation, an idea pursued so far only for bound orbits. The crucial ingredient in this formulation are certain jump conditions that (each multipole mode of) the Hertz potential must satisfy along the orbit, in a 1+1-dimensional multipole reduction of the problem. We obtain a closed-form expression for these jumps, for an arbitrary geodesic orbit in Schwarzschild spacetime, and present a full numerical implementation for a scatter orbit. In this paper we focus on method development, and go only as far as calculating the Hertz potential; a calculation of the self-force and its physical effects on the scatter orbit will be the subject of forthcoming work.
Robust Inference on Income Inequality: t-Statistic Based Approaches

Empirical analyses on income and wealth inequality and those in other fields in economics and finance often face the difficulty that the data is heterogeneous, heavy-tailed or correlated in some unknown fashion. The paper focuses on applications of the recently developed \textit{t}-statistic based robust inference approaches in the analysis of inequality measures and their comparisons under the above problems. Following the approaches, in particular, a robust large sample test on equality of two parameters of interest (e.g., a test of equality of inequality measures in two regions or countries considered) is conducted as follows: The data in the two samples dealt with is partitioned into fixed numbers $q_1, q_2\ge 2$ (e.g., $q_1=q_2=2, 4, 8$) of groups, the parameters (inequality measures dealt with) are estimated for each group, and inference is based on a standard two-sample $t-$test with the resulting $q_1, q_2$ group estimators. Robust $t-$statistic approaches result in valid inference under general conditions that group estimators of parameters (e.g., inequality measures) considered are asymptotically independent, unbiased and Gaussian of possibly different variances, or weakly converge, at an arbitrary rate, to independent scale mixtures of normal random variables. These conditions are typically satisfied in empirical applications even under pronounced heavy-tailedness and heterogeneity and possible dependence in observations. The methods dealt with in the paper complement and compare favorably with other inference approaches available in the literature. The use of robust inference approaches is illustrated by an empirical analysis of income inequality measures and their comparisons across different regions in Russia.
Photometric and Kinematic Study of the Open Clusters SAI 44 and SAI 45

We carry out a detailed photometric and kinematic study of the poorly studied sparse open clusters SAI 44 and SAI 45 using ground-based BVR c I c data supplemented by archival data from Gaia eDR3 and Pan-STARRS. The stellar memberships are determined using a statistical method based on Gaia eDR3 kinematic data, and we found 204 members in SAI 44 while only 74 members are identified in SAI 45. The average distances to SAI 44 and SAI 45 are calculated to be 3670 ± 184 and 1668 ± 47 pc. The logarithmic age of the clusters are determined to be 8.82 ± 0.10 and 9.07 ± 0.10 yr for SAI 44 and SAI 45, respectively. The color–magnitude diagram of SAI 45 hosts an extended main-sequence turnoff (eMSTO). The apparent age spread is found to be similar to the apparent age spread predicted on the basis of the age spread and cluster age relation predicted by rotation models. This indicates that eMSTO is a stellar evolution rather than star formation phenomenon in SAI 45. We conclude that eMSTO in SAI 45 is mainly caused by the different rotation rates of stars as the SYCLIST synthetic population with different rotation rates was able to reproduce the observed eMSTO, and stars in the red part of the eMSTO were preferentially concentrated in the inner region, which again hints at different rotations being the reason for the extension in the upper MS. This finding supports the theory attributing the origin of eMSTO to the different rotations of eMSTO stars. The mass function slopes are obtained as −2.24 ± 0.66 and −2.58 ± 3.20 in the mass rages 2.426–0.990 M⊙ and 2.167–1.202 M⊙ for SAI 44 and SAI 45, respectively. SAI 44 exhibits the signature of mass segregation while we found weak evidence of mass segregation in SAI 45 possibly due to tidal stripping. The dynamical relaxation times of these clusters indicate that both clusters are in a dynamically relaxed state. Using the AD-diagram method, the apex coordinates are found to be ( 69.°79±0.°11 , − 30.°82±0.°15 ) for SAI 44 and (− 56.°22±0.°13 , − 56.°62±0.°13 ) for SAI 45. The average space velocity components of the clusters SAI 44 and SAI 45 are calculated in units of km s−1 as (−15.14 ± 3.90, −19.43 ± 4.41, −20.85 ± 4.57) and (28.13 ± 5.30, −9.78 ± 3.13, −19.59 ± 4.43), respectively.
Segmenter: Transformer for Semantic Segmentation

Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a transformer model for semantic segmentation. In contrast to convolution-based methods, our approach allows to model global context already at the first layer and throughout the network. We build on the recent Vision Transformer (ViT) and extend it to semantic segmentation. To do so, we rely on the output embeddings corresponding to image patches and obtain class labels from these embed-dings with a point-wise linear decoder or a mask trans-former decoder. We leverage models pre-trained for image classification and show that we can fine-tune them on moderate sized datasets available for semantic segmentation. The linear decoder allows to obtain excellent results already, but the performance can be further improved by a mask transformer generating class masks. We conduct an extensive ablation study to show the impact of the different parameters, in particular the performance is better for large models and small patch sizes. Segmenter attains excellent results for semantic segmentation. It outperforms the state of the art on both ADE20K and Pascal Context datasets and is competitive on Cityscapes.
A Fast Deep Learning Network for Automatic Image Auto-Straightening

Rectifying the orientation of images represents a daily task for every photographer. This task may be complicated even for the human eye, especially when the horizon or other horizontal and vertical lines in the image are missing. In this paper we address this problem and propose a new deep learning network specially adapted for image rotation correction: we introduce the rectangle-shaped depthwise convolutions which are specialized in detecting long lines from the image and a new adapted loss function that addresses the problem of orientation errors. Compared to other methods that are able to detect rotation errors only on few image categories, like man-made structures, the proposed method can be used on a larger variety of photographs e.g., portraits, landscapes, sport, night photos etc. Moreover, the model is adapted to mobile devices and can be run in real time, both for pictures and for videos. An extensive evaluation of our model on different datasets shows that it remarkably generalizes, not being dependent on any particular type of image. Finally, we significantly outperform the state-of-the-art methods, providing superior results.
Effect of gas flow rate on oxidation behaviour of alloy 625 in wet air in the temperature range 900–1000 °C

In many industrial processes, the flow rate of hot gaseous service environments may be in the range of several m/s. In the present paper, the effect of gas flow rate on the high temperature oxidation behaviour of alloy 625 in wet air is presented. The gas velocity was varied from near static conditions to linear gas flow rates up to 6 m/s. The oxidation kinetics were studied by gravimetry during cyclic/discontinuous testing at 900 and 1000 °C. The oxide scales and the subsurface depletion zones formed during exposure were studied by light optical microscopy, scanning electron microscopy with energy dispersive X‐ray analysis and, for selected specimens, by electron backscatter diffraction. It was found that Cr loss due to formation of volatile species is substantially enhanced by high gas flow rates thus significantly influencing the oxidation limited life time of the oxidising component. Within the studied range of flow rates no plateau value was reached, the Cr loss being substantially larger at a flow of 6 m/s than at 0.7 m/s. Additionally, it was found that geometrical factors of the test specimen substantially affected the extent of volatile species formation. Especially the leading edge of the specimen exhibited more extensive Cr loss than the other specimen areas.
Strong lensing signatures of self-interacting dark matter in low-mass haloes

Core formation and runaway core collapse in models with self-interacting dark matter (SIDM) significantly alter the central density profiles of collapsed haloes. Using a forward modelling inference framework with simulated data-sets, we demonstrate that flux ratios in quadruple image strong gravitational lenses can detect the unique structural properties of SIDM haloes, and statistically constrain the amplitude and velocity dependence of the interaction cross-section in haloes with masses between 106 and 1010 M⊙. Measurements on these scales probe self-interactions at velocities below $30 \ \rm {km} \ \rm {s^{-1}}$, a relatively unexplored regime of parameter space, complimenting constraints at higher velocities from galaxies and clusters. We cast constraints on the amplitude and velocity dependence of the interaction cross-section in terms of σ20, the cross-section amplitude at $20 \ \rm {km} \ \rm {s^{-1}}$. With 50 lenses, a sample size available in the near future, and flux ratios measured from spatially compact mid-IR emission around the background quasar, we forecast $\sigma _{20} \lt 11\rm {\small {--}}23 \ \rm {cm^2} \rm {g^{-1}}$ at $95 {{\ \rm per\ cent}}$ CI, depending on the amplitude of the subhalo mass function, and assuming cold dark matter (CDM). Alternatively, if $\sigma _{20} = 19.2 \ \rm {cm^2}\rm {g^{-1}}$ we can rule out CDM with a likelihood ratio of 20:1, assuming an amplitude of the subhalo mass function that results from doubly efficient tidal disruption in the Milky Way relative to massive elliptical galaxies. These results demonstrate that strong lensing of compact, unresolved sources can constrain SIDM structure on sub-galactic scales across cosmological distances, and the evolution of SIDM density profiles over several Gyr of cosmic time.
Causal Networks and Freedom of Choice in Bell’s Theorem

Bell's theorem is typically understood as the proof that quantum theory is incompatible with local-hidden-variable models. More generally, we can see the violation of a Bell inequality as witnessing the impossibility of explaining quantum correlations with classical causal models. The violation of a Bell inequality, however, does not exclude classical models where some level of measurement dependence is allowed, that is, the choice made by observers can be correlated with the source generating the systems to be measured. Here, we show that the level of measurement dependence can be quantitatively upper bounded if we arrange the Bell test within a network. Furthermore, we also prove that these results can be adapted in order to derive nonlinear Bell inequalities for a large class of causal networks and to identify quantumly realizable correlations that violate them.
Do galaxies die? Different views from simulations and observations in the local Universe

For years, the extragalactic community has divided galaxies in two distinct populations. One of them, featuring blue colours, is actively forming stars, while the other is made up of ‘red-and-dead’ objects with negligible star formation. Yet, are these galaxies really dead? Here, we would like to highlight that, as previously reported by several independent groups, state-of-the-art cosmological numerical simulations (EAGLE, IllustrisTNG, MAGNETICUM, and SIMBA) predict the existence of a large number of quenched galaxies that have not formed any star over the last few Gyr. In contrast, observational measurements of large galaxy samples in the nearby Universe (GAMA, SDSS) suggest that even the most passive systems still form stars at some residual level close to sSFR ∼ 10−12 yr−1. Unfortunately, extremely low star formation poses a challenge for both approaches. We conclude that, at present, the fraction of truly dead galaxies is still an important open question that must be addressed in order to understand galaxy formation and evolution.
Collaborative Regression of Expressive Bodies using Moderation

Recovering expressive humans from images is essential for understanding human behavior. Methods that estimate 3D bodies, faces, or hands have progressed significantly, yet separately. Face methods recover accurate 3D shape and geometric details, but need a tight crop and struggle with extreme views and low resolution. Whole-body methods are robust to a wide range of poses and resolutions, but provide only a rough 3D face shape without details like wrinkles. To get the best of both worlds, we introduce PIXIE, which produces animatable, whole-body 3D avatars with realistic facial detail, from a single image. For this, PIXIE uses two key observations. First, existing work combines independent estimates from body, face, and hand experts, by trusting them equally. PIXIE introduces a novel moderator that merges the features of the experts, weighted by their confidence. All part experts can contribute to the whole, using SMPL-X’s shared shape space across all body parts. Second, human shape is highly correlated with gender, but existing work ignores this. We label training images as male, female, or non-binary, and train PIXIE to infer “gendered” 3D body shapes with a novel shape loss. In addition to 3D body pose and shape parameters, PIXIE estimates expression, illumination, albedo and 3D facial surface displacements. Quantitative and qualitative evaluation shows that PIXIE estimates more accurate whole-shape and detailed face shape than the state of the art. Models and code are available at pixie.is. tue.mpg. de.
Surgical Management of Superior Labral Tears in Athletes: Focus on Biceps Tenodesis

Abstract Superior labrum anterior-posterior (SLAP) lesions are common in overhead athletes. Though some patients have asymptomatic lesions, many tears cause pain and diminished athletic performance. Accurate diagnosis of SLAP lesions can be challenging as the sensitivity and specificity of both the physical exam and advanced imaging is questionable. Management is also difficult, as treatment can be life-altering or career-ending for many athletes. If first-line nonoperative treatment fails, surgical options may be considered. The optimal surgical management of SLAP lesions in athletes is debated. Historically, return to play (RTP) rates among athletes who have undergone arthroscopic SLAP repair have been unsatisfactory, prompting clinicians to seek alternate surgical options. Biceps tenodesis (BT) has been postulated to eliminate biceps tendon-related pain in the shoulder and is increasingly used as a primary procedure for SLAP lesions. The purpose of this text is to review the current literature on the surgical management of SLAP lesions in athletes with an emphasis on the role of BT.