Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing fair AI for managing employees in organizations: a review, critique, and design agenda

ABSTRACT Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.
Bruhat inversions in Weyl groups and torsion-free classes over preprojective algebras

Abstract For an element w of a simply-laced Weyl group, Buan-Iyama-Reiten-Scott defined a subcategory of the module category over the preprojective algebra of Dynkin type. This paper studies categorical properties of using the root system. We show that simple objects in bijectively correspond to Bruhat inversion roots of w, and obtain a combinatorial criterion for to satisfy the Jordan-Hölder property (JHP). For type A case, we give a diagrammatic construction of simple objects, and show that (JHP) can be characterized via a forest-like permutation, introduced by Bousquet-Mélou and Butler in the study of Schubert varieties.
Private Mean Estimation of Heavy-Tailed Distributions

We give new upper and lower bounds on the minimax sample complexity of differentially private mean estimation of distributions with bounded $k$-th moments. Roughly speaking, in the univariate case, we show that $n = \Theta\left(\frac{1}{\alpha^2} + \frac{1}{\alpha^{\frac{k}{k-1}}\varepsilon}\right)$ samples are necessary and sufficient to estimate the mean to $\alpha$-accuracy under $\varepsilon$-differential privacy, or any of its common relaxations. This result demonstrates a qualitatively different behavior compared to estimation absent privacy constraints, for which the sample complexity is identical for all $k \geq 2$. We also give algorithms for the multivariate setting whose sample complexity is a factor of $O(d)$ larger than the univariate case.
Studying the ISM at ∼10 pc scale in NGC 7793 with MUSE

Context.Studies of nearby galaxies reveal that around 50% of the total Hαluminosity in late-type spirals originates from diffuse ionised gas (DIG), which is a warm, diffuse component of the interstellar medium that can be associated with various mechanisms, the most important ones being “leaking” HII regions, evolved field stars, and shocks.Aims. Using MUSE Wide Field Mode adaptive optics-assisted data, we study the condition of the ionised medium in the nearby (D = 3.4 Mpc) flocculent spiral galaxy NGC 7793 at a spatial resolution of ∼10 pc. We construct a sample of HII regions and investigate the properties and origin of the DIG component.Methods. We obtained stellar and gas kinematics by modelling the stellar continuum and fitting the Hαemission line. We identified the boundaries of resolved HII regions based on their Hαsurface brightness. As a way of comparison, we also selected regions according to the Hα/[SII] line ratio; this results in more conservative boundaries. Using characteristic line ratios and the gas velocity dispersion, we excluded potential contaminants, such as supernova remnants (SNRs) and planetary nebulae (PNe). The continuum subtracted HeII map was used to spectroscopically identify Wolf Rayet stars (WR) in our field of view. Finally, we computed electron densities and temperatures using the line ratio [SII]6716/6731 and [SIII]6312/9069, respectively. We studied the properties of the ionised gas through “BPT” emission line diagrams combined with velocity dispersion of the gas.Results. We spectroscopically confirm two previously detected WR and SNR candidates and report the discovery of the other seven WR candidates, one SNR, and two PNe within our field of view. The resulting DIG fraction is between ∼27 and 42% depending on the method used to define the boundaries of the HII regions (flux brightness cut in Hα= 6.7 × 10−18erg s−1cm−2or Hα/[SII] = 2.1, respectively). In agreement with previous studies, we find that the DIG exhibits enhanced [SII]/Hαand [NII]/Hαratios and a median temperature that is ∼3000 K higher than in HII regions. We also observe an apparent inverse correlation between temperature and Hαsurface brightness. In the majority of our field of view, the observed [SII]6716/6731 ratio is consistent within 1σwithne < 30 cm−3, with an almost identical distribution for the DIG and HII regions. The velocity dispersion of the ionised gas indicates that the DIG has a higher degree of turbulence than the HII regions. Comparison with photoionisation and shock models reveals that, overall, the diffuse component can only partially be explained via shocks and that it is most likely consistent with photons leaking from density bounded HII regions or with radiation from evolved field stars. Further investigation will be conducted in a follow-up paper.
SummaryNet: A Multi-Stage Deep Learning Model for Automatic Video Summarisation

Video summarisation can be posed as the task of extracting important parts of a video in order to create an informative summary of what occurred in the video. In this paper we introduce SummaryNet as a supervised learning framework for automated video summarisation. SummaryNet employs a two-stream convolutional network to learn spatial (appearance) and temporal (motion) representations. It utilizes an encoder-decoder model to extract the most salient features from the learned video representations. Lastly, it uses a sigmoid regression network with bidirectional long short-term memory cells to predict the probability of a frame being a summary frame. Experimental results on benchmark datasets show that the proposed method achieves comparable or significantly better results than the state-of-the-art video summarisation methods.
New SAV-pressure correction methods for the Navier-Stokes equations: stability and error analysis

We construct new first- and second-order pressure correction schemes using the scalar auxiliary variable (SAV) approach for the Navier-Stokes equations. These schemes are linear, decoupled and only require a sequence of solving Poisson type equations at each time step. Furthermore, they are unconditionally energy stable. We also establish rigorous error estimates for the velocity and pressure approximation of the first-order scheme without any condition on the time step.
Crowdsourced Collective Entity Resolution with Relational Match Propagation

Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity resolution (ER) aims to identify entities in KBs which refer to the same real-world object. Recent studies have shown significant benefits of involving humans in the loop of ER. They often resolve entities with pairwise similarity measures over attribute values and resort to the crowds to label uncertain ones. However, existing methods still suffer from high labor costs and insufficient labeling to some extent. In this paper, we propose a novel approach called crowdsourced collective ER, which leverages the relationships between entities to infer matches jointly rather than independently. Specifically, it iteratively asks human workers to label picked entity pairs and propagates the labeling information to their neighbors in distance. During this process, we address the problems of candidate entity pruning, probabilistic propagation, optimal question selection and error-tolerant truth inference. Our experiments on real-world datasets demonstrate that, compared with state-of-the-art methods, our approach achieves superior accuracy with much less labeling.
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond

Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade re-ID performance in real-world scenarios. To overcome this problem, we propose a novel generative adversarial network to address cross-resolution person re-ID, allowing query images with varying resolutions. By advancing adversarial learning techniques, our proposed model learns resolution-invariant image representations while being able to recover the missing details in low-resolution input images. The resulting features can be jointly applied for improving re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Extensive experimental results on five standard person re-ID benchmarks confirm the effectiveness of our method and the superiority over the state-of-the-art approaches, especially when the input resolutions are not seen during training. Furthermore, the experimental results on two vehicle re-ID benchmarks also confirm the generalization of our model on cross-resolution visual tasks. The extensions of semi-supervised settings further support the use of our proposed approach to real-world scenarios and applications.
Robustness from Simple Classifiers

Despite the vast success of Deep Neural Networks in numerous application domains, it has been shown that such models are not robust i.e., they are vulnerable to small adversarial perturbations of the input. While extensive work has been done on why such perturbations occur or how to successfully defend against them, we still do not have a complete understanding of robustness. In this work, we investigate the connection between robustness and simplicity. We find that simpler classifiers, formed by reducing the number of output classes, are less susceptible to adversarial perturbations. Consequently, we demonstrate that decomposing a complex multiclass model into an aggregation of binary models enhances robustness. This behavior is consistent across different datasets and model architectures and can be combined with known defense techniques such as adversarial training. Moreover, we provide further evidence of a disconnect between standard and robust learning regimes. In particular, we show that elaborate label information can help standard accuracy but harm robustness.
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines

This paper proposes a simple yet effective method for human action recognition in video. The proposed method separately extracts local appearance and motion features using state-of-the-art three-dimensional convolutional neural networks from sampled snippets of a video. These local features are then concatenated to form global representations which are then used to train a linear SVM to perform the action classification using full context of the video, as partial context as used in previous works. The videos undergo two simple proposed preprocessing techniques, optical flow scaling and crop filling. We perform an extensive evaluation on three common benchmark dataset to empirically show the benefit of the SVM, and the two preprocessing steps.
First combined measurement of the muon neutrino and antineutrino charged-current cross section without pions in the final state at T2K

This paper presents the first combined measurement of the double-differential muon neutrino and antineutrino charged-current cross sections with no pions in the final state on hydrocarbon at the off-axis near detector of the T2K experiment. The data analyzed in this work comprise 5.8$\times$10$^{20}$ and 6.3$\times$10$^{20}$ protons on target in neutrino and antineutrino mode respectively, at a beam energy peak of 0.6 GeV. Using the two measured cross sections, the sum, difference and asymmetry were calculated with the aim of better understanding the nuclear effects involved in such interactions. The extracted measurements have been compared with the prediction from different Monte Carlo generators and theoretical models showing that the difference between the two cross sections have interesting sensitivity to nuclear effects.
Structural and magnetic properties of antiferromagneticCe2IrGa12

We report a study of the structural and magnetic properties of single crystals of Ce$_2$IrGa$_{12}$. Ce$_2$IrGa$_{12}$ crystallizes in a layered tetragonal structure, and undergoes an antiferromagnetic transition below 3.1 K. We characterize the temperature-field phase diagrams of Ce$_2$IrGa$_{12}$ for fields both within the $ab$-plane and along the $c$-axis, where the presence of a field-induced magnetic phase is found for in-plane fields. The ordering temperature is moderately enhanced upon the application of pressures up to 2.3~GPa, suggesting that Ce$_2$IrGa$_{12}$ corresponds to the well localized region of the Doniach phase diagram.
Extremum Seeking with Vanishing Control Oscillations

Extremum seeking control is an adaptive control technique that stabilizes a dynamical system around a neighborhood of the minimum point of a cost function without direct access to gradient information. Traditional extremum seeking suffers from persistent control oscillations. In recent years, an approach to extremum seeking made it possible to design control vector fields for extremum seeking that vanish around the minimum point, leading to asymptotic stability/convergence. One of the main issues with this approach is that it requires knowledge of the minimum value of the function. While other recent extremum seeking approaches offer a simultaneous online estimation of the minimum value of the cost function by adapting the frequency of oscillation, they typically suffer from unbounded update rate in the frequency and amplitude of oscillation, as the minimum point is approached. In this work we aim to resolve these two issues. In particular, we propose an extremum seeking control system that converges to the minimum point with vanishing control oscillations, without access to the minimum value of the cost function, while maintaining a bounded update rate.
Magnetic-field-induced topological phase transition in Fe-doped 
(Bi,Sb)2Se3
 heterostructures

Three-dimensional topological insulators (3D-TIs) possess a specific topological order of electronic bands, resulting in gapless surface states via bulk-edge correspondence. Exotic phenomena have been realized in ferromagnetic TIs, such as the quantum anomalous Hall (QAH) effect with a chiral edge conduction and a quantized value of the Hall resistance ${R_{yx}}$. Here, we report on the emergence of distinct topological phases in paramagnetic Fe-doped (Bi,Sb)${_2}$Se${_3}$ heterostructures with varying structure architecture, doping, and magnetic and electric fields. Starting from a 3D-TI, a two-dimensional insulator appears at layer thicknesses below a critical value, which turns into an Anderson insulator for Fe concentrations sufficiently large to produce localization by magnetic disorder. With applying a magnetic field, a topological transition from the Anderson insulator to the QAH state occurs, which is driven by the formation of an exchange gap owing to a giant Zeeman splitting and reduced magnetic disorder. Topological phase diagram of (Bi,Sb)${_2}$Se${_3}$ allows exploration of intricate interplay of topological protection, magnetic disorder, and exchange splitting.
Locally Private Hypothesis Selection

We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution $p$ and a set of $k$ probability distributions $\mathcal{Q}$, we aim to output, under the constraints of $\varepsilon$-local differential privacy, a distribution from $\mathcal{Q}$ whose total variation distance to $p$ is comparable to the best such distribution. This is a generalization of the classic problem of $k$-wise simple hypothesis testing, which corresponds to when $p \in \mathcal{Q}$, and we wish to identify $p$. Absent privacy constraints, this problem requires $O(\log k)$ samples from $p$, and it was recently shown that the same complexity is achievable under (central) differential privacy. However, the naive approach to this problem under local differential privacy would require $\tilde O(k^2)$ samples. We first show that the constraint of local differential privacy incurs an exponential increase in cost: any algorithm for this problem requires at least $\Omega(k)$ samples. Second, for the special case of $k$-wise simple hypothesis testing, we provide a non-interactive algorithm which nearly matches this bound, requiring $\tilde O(k)$ samples. Finally, we provide sequentially interactive algorithms for the general case, requiring $\tilde O(k)$ samples and only $O(\log \log k)$ rounds of interactivity. Our algorithms are achieved through a reduction to maximum selection with adversarial comparators, a problem of independent interest for which we initiate study in the parallel setting. For this problem, we provide a family of algorithms for each number of allowed rounds of interaction $t$, as well as lower bounds showing that they are near-optimal for every $t$. Notably, our algorithms result in exponential improvements on the round complexity of previous methods.
Study of the Decay Rates of the Umbral Area of Sunspot Groups Using a High-resolution Database

The emergence and decay of the sunspot groups are important components of the solar dynamo models. There are two different types of studies on the evolution of active regions. One of them is based on fewer data with higher spatial resolution, the other one uses more data with lower spatial resolution. The input data of the present study allow the investigation with high resolution both spatially and temporally. The temporal resolution of the Solar and Heliospheric Observatory Debrecen sunspot database is one and a half hours, and it also makes it possible to identify all individual sunspots with the position, area, and magnetic polarity. More than 200 sunspot groups have been selected, which have clear maxima on the solar disk, and the decrease of their umbral area is observable during at least four days. The decay rates were calculated by using two data: the umbral area and the number of contained sunspots—these decay rates were computed for the total umbral area of sunspot groups and their leading and following parts. The decay rate has a linear area dependency, and it is higher for the following part than for the leading one.
Gas fractions and depletion times in galaxies with different degrees of interaction

Context. A moderate enhancement of the star formation rates (SFR) in local interacting galaxies has been reported, but the physical mechanisms leading to this increase are not clear. Aims. We study the atomic gas content and the central stellar mass concentration for a sample of almost 1500 nearby galaxies to further investigate the nature of starbursts and the influence of galaxy-galaxy interactions on star formation. Methods. We used a sample of catalogued interacting and non-interacting galaxies in the S4G survey – along with archival H I gas masses, stellar masses (M*), and SFRs from IRAS far-infrared fluxes – and calculate depletion times (τ) and gas fractions. We traced the central stellar mass concentration from the inner slope of the stellar component of the rotation curves, dRv*(0). Starbursts are defined as galaxies with a factor > 4 enhanced SFR relative to a control sample of non-interacting galaxies which are ±0.2 dex in stellar mass and ±1 in T-type. Results. Starbursts are mainly early-type (T ≲ 5), massive spiral galaxies (M* ≳ 1010 M⊙) that are not necessarily interacting. For a given stellar mass bin, starbursts are characterised by lower gas depletion times, similar gas fractions, and larger central stellar mass concentrations than non-starburst galaxies. The global distributions of gas fraction and gas depletion time of interacting galaxies are not statistically different from those of their non-interacting counterparts. However, in the case of currently merging galaxies, the median gas depletion time is a factor of 0.4 ± 0.2 that of control sample galaxies, and their SFRs are a factor of 1.9 ± 0.5 enhanced, even though the median gas fraction is similar. Conclusions. Starbursts present long-lasting star formation in circumnuclear regions, which causes an enhancement of the central stellar density at z ≈ 0 in both interacting and non-interacting systems. Starbursts have low gas depletion timescales, yet similar gas fractions as normal main-sequence galaxies. Galaxy mergers cause a moderate enhancement of the star formation efficiency.
Probing UV-sensitive Pathways for CN and HCN Formation in Protoplanetary Disks with the Hubble Space Telescope

The UV radiation field is a critical regulator of gas-phase chemistry in surface layers of disks around young stars. In an effort to understand the relationship between photocatalyzing UV radiation fields and gas emission observed at infrared and submillimeter wavelengths, we present an analysis of new and archival Hubble Space Telescope (HST), Spitzer, ALMA, IRAM, and SMA data for five targets in the Lupus cloud complex and 14 systems in Taurus-Auriga. The HST spectra were used to measure Lyα and far-UV (FUV) continuum fluxes reaching the disk surface, which are responsible for dissociating relevant molecular species (e.g., HCN, N2). Semi-forbidden C ii] λ2325 and UV-fluorescent H2 emission were also measured to constrain inner disk populations of C+ and vibrationally excited H2. We find a significant positive correlation between 14 μm HCN emission and fluxes from the FUV continuum and C ii] λ2325, consistent with model predictions requiring N2 photodissociation and carbon ionization to trigger the main CN/HCN formation pathways. We also report significant negative correlations between submillimeter CN emission and both C ii] and FUV continuum fluxes, implying that CN is also more readily dissociated in disks with stronger FUV irradiation. No clear relationships are detected between either CN or HCN and Lyα or UV-H2 emission. This is attributed to the spatial stratification of the various molecular species, which span several vertical layers and radii across the inner and outer disk. We expect that future observations with the James Webb Space Telescope will build on this work by enabling more sensitive IR surveys than were possible with Spitzer.
An end-to-end approach for the verification problem: learning the right distance

In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn for the learned distance-like model's output. We first show it approximates a likelihood ratio which can be used for hypothesis tests, and that it further induces a large divergence across the joint distributions of pairs of examples from the same and from different classes. Evaluation is performed under the verification setting consisting of determining whether sets of examples belong to the same class, even if such classes are novel and were never presented to the model during training. Empirical evaluation shows such method defines an end-to-end approach for the verification problem, able to attain better performance than simple scorers such as those based on cosine similarity and further outperforming widely used downstream classifiers. We further observe training is much simplified under the proposed approach compared to metric learning with actual distances, requiring no complex scheme to harvest pairs of examples.