Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision-makers Processing of AI Algorithmic Advice: Automation Bias versus Selective Adherence

Artificial intelligence algorithms are increasingly adopted as decisional aides by public organisations, with the promise of overcoming biases of human decision-makers. At the same time, the use of algorithms may introduce new biases in the human-algorithm interaction. A key concern emerging from psychology studies regards human overreliance on algorithmic advice even in the face of “warning signals” and contradictory information from other sources (automation bias). A second concern regards decision-makers’ inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes (selective adherence). To date, we lack rigorous empirical evidence about the prevalence of these biases in a public sector context. We assess these via two pre-registered experimental studies (N=1,509), simulating the use of algorithmic advice in decisions pertaining to the employment of school teachers in the Netherlands. In study 1, we test automation bias by exploring participants’ adherence to a prediction of teachers’ performance, which contradicts additional evidence, while comparing between two types of predictions: algorithmic v. human-expert. We do not find evidence for automation bias. In study 2, we replicate these findings, and we also test selective adherence by manipulating the teachers’ ethnic background. We find a propensity for adherence when the advice predicts low performance for a teacher of a negatively stereotyped ethnic minority, with no significant differences between algorithmic and human advice. Overall, our findings of selective, biased adherence belie the promise of neutrality that has propelled algorithm use in the public sector.
Random Feature Attention

Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their core is an attention function which models pairwise interactions between the inputs at every timestep. While attention is powerful, it does not scale efficiently to long sequences due to its quadratic time and space complexity in the sequence length. We propose RFA, a linear time and space attention that uses random feature methods to approximate the softmax function, and explore its application in transformers. RFA can be used as a drop-in replacement for conventional softmax attention and offers a straightforward way of learning with recency bias through an optional gating mechanism. Experiments on language modeling and machine translation demonstrate that RFA achieves similar or better performance compared to strong transformer baselines. In the machine translation experiment, RFA decodes twice as fast as a vanilla transformer. Compared to existing efficient transformer variants, RFA is competitive in terms of both accuracy and efficiency on three long text classification datasets. Our analysis shows that RFA's efficiency gains are especially notable on long sequences, suggesting that RFA will be particularly useful in tasks that require working with large inputs, fast decoding speed, or low memory footprints.
Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary

The key challenge of unsupervised vehicle re-identification (Re-ID) is learning discriminative features from unlabelled vehicle images. Numerous methods using domain adaptation have achieved outstanding performance, but those methods still need a labelled dataset as a source domain. This paper addresses an unsupervised vehicle Re-ID method, which no need any types of a labelled dataset, through a Self-supervised Metric Learning (SSML) based on a feature dictionary. Our method initially extracts features from vehicle images and stores them in a dictionary. Thereafter, based on the dictionary, the proposed method conducts dictionary-based positive label mining (DPLM) to search for positive labels. Pair-wise similarity, relative-rank consistency, and adjacent feature distribution similarity are jointly considered to find images that may belong to the same vehicle of a given probe image. The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively. The iterative process with DPLM and DTL boosts the performance of unsupervised vehicle Re-ID. Experimental results demonstrate the effectiveness of the proposed method by producing promising vehicle Re-ID performance without a pre-labelled dataset. The source code for this paper is publicly available on https://github.com/andreYoo/VeRI_SSML_FD. git.
The values of the Dedekind–Rademacher cocycle at real multiplication points

The values of the so-called {\em Dedekind--Rademacher cocycle} at certain real quadratic arguments are shown to be global $p$-units in the narrow Hilbert class field of the associated real quadratic field, as predicted by conjectures of Darmon, Dasgupta, and Vonk. The strategy for proving this result combines an approach of Darmon-Pozzi-Vonk with one crucial extra ingredient: the study of infinitesimal deformations of irregular Hilbert Eisenstein series of weight one in the anti-parallel direction, building on the techniques in earlier work of Betina, Dimitrov, and Pozzi.
Parametric Complexity Bounds for Approximating PDEs with Neural Networks

Recent experiments have shown that deep networks can approximate solutions to high-dimensional PDEs, seemingly escaping the curse of dimensionality. However, questions regarding the theoretical basis for such approximations, including the required network size, remain open. In this paper, we investigate the representational power of neural networks for approximating solutions to linear elliptic PDEs with Dirichlet boundary conditions. We prove that when a PDE's coefficients are representable by small neural networks, the parameters required to approximate its solution scale polynomially with the input dimension $d$ and proportionally to the parameter counts of the coefficient networks. To this we end, we develop a proof technique that simulates gradient descent (in an appropriate Hilbert space) by growing a neural network architecture whose iterates each participate as sub-networks in their (slightly larger) successors, and converge to the solution of the PDE. We bound the size of the solution, showing a polynomial dependence on $d$ and no dependence on the volume of the domain.
Sequence-Based Filtering for Visual Route-Based Navigation: Analyzing the Benefits, Trade-Offs and Design Choices

Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place using visual information under environmental, viewpoint and appearance changes. An emerging trend in VPR is the use of sequence-based filtering methods on top of single-frame-based place matching techniques for route-based navigation. The combination leads to varying levels of potential place matching performance boosts at increased computational costs. This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods? How does sequence matching length affect the performance curve? Which specific combinations provide a good trade-off between performance and computation? However, there is lack of previous work looking at these important questions and most of the sequence-based filtering work to date has been used without a systematic approach. To bridge this research gap, this paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods. It analyzes individual trade-offs, properties and limitations for different combinations of single-frame-based and sequential techniques. The experiments conducted in this study demonstrate the benefits of sequence-based filtering over the single-frame-based approach using various VPR techniques. We found that applying sequence-based filtering to a lightweight descriptor can enable higher VPR accuracy than state-of-the-art methods such as NetVLAD, while running in shorter time. For example, matching a sequence of 16 images, CALC descriptor outperforms NetVLAD on Campus Loop dataset while taking about 22% less time to perform VPR.
A numerical method for computing the Jordan Canonical Form

The Jordan Canonical Form of a matrix is highly sensitive to perturbations, and its numerical computation remains a formidable challenge. This paper presents a regularization theory that establishes a well-posed least squares problem of finding the nearest staircase decomposition in the matrix bundle of the highest codimension. A two-staged algorithm is developed for computing the numerical Jordan Canonical Form. At the first stage, the method calculates the Jordan structure of the matrix and an initial approximation to the multiple eigenvalues. The staircase decomposition is then constructed by an iterative algorithm at the second stage. As a result, the numerical Jordan Canonical decomposition along with multiple eigenvalues can be computed with high accuracy even if the underlying matrix is perturbed. keywords Jordan canonical form, eigenvalue, staircase form,
Online Adversarial Attacks

Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model, and the decisions made by the attacker are irrevocable since they operate on a transient data stream. We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied $k$-secretary problem in theoretical computer science and propose Virtual+, a simple yet practical online algorithm. Our main theoretical result shows Virtual+ yields provably the best competitive ratio over all single-threshold algorithms for $k<5$ -- extending the previous analysis of the $k$-secretary problem. We also introduce the \textit{stochastic $k$-secretary} -- effectively reducing online blackbox transfer attacks to a $k$-secretary problem under noise -- and prove theoretical bounds on the performance of Virtual+ adapted to this setting. Finally, we complement our theoretical results by conducting experiments on MNIST, CIFAR-10, and Imagenet classifiers, revealing the necessity of online algorithms in achieving near-optimal performance and also the rich interplay between attack strategies and online attack selection, enabling simple strategies like FGSM to outperform stronger adversaries.
Nonthermal phenomena in the center of Abell 1775

Context. Thermal gas in the center of galaxy clusters can show substantial motions that generate surface-brightness and temperature discontinuities known as cold fronts. The motions may be triggered by minor or off-axis mergers that preserve the cool core of the system. The dynamics of the thermal gas can also generate radio emission from the intra-cluster medium (ICM) and impact the evolution of clusters’ radio sources. Aims. We aim to study the central region of Abell 1775, a system in an ambiguous dynamical state at z = 0.072 which is known to host an extended head-tail radio galaxy, with the goal of investigating the connection between thermal and nonthermal components in its center. Methods. We made use of a deep (100 ks) Chandra observation accompanied by LOFAR 144 MHz, GMRT 235 MHz and 610 MHz, and VLA 1.4 GHz radio data. Results. We find a spiral-like pattern in the X-ray surface brightness that is mirrored in the temperature and pseudo-entropy maps. Additionally, we characterize an arc-shaped cold front in the ICM. We interpret these features in the context of a slingshot gas tail scenario. The structure of the head-tail radio galaxy “breaks” at the position of the cold front, showing an extension that is detected only at low frequencies, likely due to its steep and curved spectrum. We speculate that particle reacceleration is occurring in the outer region of this tail, which in total covers a projected size of ∼800 kpc. We also report the discovery of revived fossil plasma with ultra-steep spectrum radio emission in the cluster core together with a central diffuse radio source that is bounded by the arc-shaped cold front. Conclusions. The results reported in this work demonstrate the interplay between thermal and nonthermal components in the cluster center and the presence of ongoing particle reacceleration in the ICM on different scales.
Capelin: Data-Driven Capacity Procurement for Cloud Datacenters using Portfolios of Scenarios - Extended Technical Report

Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners with merely rule-of-thumb judgement. We derive requirements from a unique survey of experts in charge of diverse datacenters in several countries. We propose Capelin, a data-driven, scenario-based capacity planning system for mid-tier cloud datacenters. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. Capelin compares alternative topologies and for each gives detailed quantitative operational information, which could facilitate human decisions of capacity planning. We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. The results give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption.
Galactic spiral structure revealed by Gaia EDR3

Using the astrometry and integrated photometry from the Gaia Early Data Release 3, we map the density variations in the distribution of young upper main sequence (UMS) stars, open clusters, and classical Cepheids in the Galactic disc within several kiloparsecs of the Sun. We derive maps of relative over- and under-dense regions for UMS stars in the Galactic disc using both bivariate kernel density estimators and wavelet transformations. The resulting overdensity maps exhibit large-scale arches that extend in a clumpy but coherent way over the entire sampled volume, indicating the location of the spiral arm segments in the vicinity of the Sun. Peaks in the UMS overdensity are well matched by the distribution of young and intrinsically bright open clusters. By applying a wavelet transformation to a sample of classical Cepheids, we find that their overdensities possibly extend the spiral arm segments on a larger scale (≃10 kpc from the Sun). While the resulting map based on the UMS sample is generally consistent with previous models of the Sagittarius-Carina spiral arm, the geometry of the arms in the III quadrant (galactic longitudes 180° < l < 270°) differs significantly from that suggested by many previous models. In particular, we find that our maps favour a larger pitch angle for the Perseus arm, and that the Local Arm extends into the III quadrant at least 4 kpc past the position of the Sun, giving it a total length of at least 8 kpc.
Search for Magnetic Accretion in SW Sextantis Systems

SW Sextantis systems are nova-like cataclysmic variables that have unusual spectroscopic properties, which are thought to be caused by an accretion geometry having part of the mass flux trajectory out of the orbital plane. Accretion onto a magnetic white dwarf is one of the proposed scenarios for these systems. To verify this possibility, we analyzed photometric and polarimetric time-series data for a sample of six SW Sex stars. We report possible modulated circular polarization in BO Cet, SW Sex, and UU Aqr with periods of 11.1, 41.2, and 25.7 minutes, respectively, and less significant periodicities for V380 Oph at 22 minutes and V442 Oph at 19.4 minutes. We confirm previous results that LS Peg shows variable circular polarization. However, we determine a period of 18.8 minutes, which is different from the earlier reported value. We interpret these periods as the spin periods of the white dwarfs. Our polarimetric results indicate that 15% of the SW Sex systems have direct evidence of magnetic accretion. We also discuss SW Sex objects within the perspective of being magnetic systems, considering the latest findings about the demography, formation, and evolution of cataclysmic variables.
Performance Characteristics of Mixed Irradiated n-MCz Thin Silicon Microstrip Detector for the HL-LHC Experiments

Within CERN RD50 Collaboration, MCz Si was identified as a best material for the detector used in the high luminosity collider experiments. The n in p Si detectors were utmost radiation hard detectors, which can be used for the phase 2 upgrade plan of the new Compact Muon Solenoid tracker detector in 2026. The choice of n or p-MCz Si material depends upon the high electrical charge collection efficiency performance of these detectors over n or p-FZSi detectors. The bulk radiation damage model for n, pMCz Si need to develop for the designing, development and optimization of the advanced detectors for the next generation High Energy Physics experiments. In this work, an advanced four level deep-trap mixed irradiation model (E5, CiOi, E(30K), H(152K)) for n-MCz Si is proposed by the comparison of experimental data on the full depletion voltage and leakage current to the Shockley Read Hall recombination statistics results on the mixed irradiated n-MCz Si PAD detector. Prediction uncertainty in the radiation damage mixed irradiation model considered in the full depletion voltage and leakage current, which was due to the uncertainty in macroscopic results from an experimental measurement on the mixed irradiated n-MCz Si pad detector. A very good agreement is observed in the experimental and SRH results on the full depletion voltage and leakage current. This model is also used to extrapolate the value of the full depletion voltage at different mixed (proton + neutron) higher irradiation fluences for the thin n-MCz Si microstrip detector.
University of Copenhagen Participation in TREC Health Misinformation Track 2020

In this paper, we describe our participation in the TREC Health Misinformation Track 2020. We submitted $11$ runs to the Total Recall Task and 13 runs to the Ad Hoc task. Our approach consists of 3 steps: (1) we create an initial run with BM25 and RM3; (2) we estimate credibility and misinformation scores for the documents in the initial run; (3) we merge the relevance, credibility and misinformation scores to re-rank documents in the initial run. To estimate credibility scores, we implement a classifier which exploits features based on the content and the popularity of a document. To compute the misinformation score, we apply a stance detection approach with a pretrained Transformer language model. Finally, we use different approaches to merge scores: weighted average, the distance among score vectors and rank fusion.
Personal Productivity and Well-being - Chapter 2 of the 2021 New Future of Work Report

We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to evolve to help people adapt to the challenges of remote and hybrid work.
Minimax MSE Bounds and Nonlinear VAR Prewhitening for Long-Run Variance Estimation Under NonstationarityWe thank Whitney Newey and Tim Vogelsang for helpful discussions. We also thank Federico Belotti, Leopoldo Catania and Stefano Grassi for generous help with computer programming.

We establish new mean-squared error (MSE) bounds for long-run variance (LRV) estimation, valid for both stationary and nonstationary sequences that are sharper than previously established. The key element is to use restrictions on nonstationarity. Unlike previous bounds, they show how nonstationarity influences the bias-variance trade-off. We use them to construct new data-dependent methods for the selection of bandwidths for (double) kernel heteroskedasticity autocorrelation consistent (DK-HAC) estimators. These account more flexibly for nonstationarity and lead to tests with good finite-sample performance, especially good power when existing LRV estimators lead to tests having little or no power. The second contribution is to introduce a nonparametric nonlinear VAR prewhitened LRV estimator. This accounts explicitly for nonstationarity unlike previous prewhitened procedures which are known to be unstable. Its consistency, rate of convergence and MSE bounds are established. The prewhitened DK-HAC estimators lead to tests with good finitesample size while maintaining good monotonic power.
Probing the Solar Meridional Circulation Using Fourier Legendre Decomposition

We apply the helioseismic methodology of Fourier Legendre decomposition to 88 months of Dopplergrams obtained by the Helioseismic and Magnetic Imager (HMI) as the basis of inferring the depth variation of the mean meridional flow, as averaged between 20° and 60° latitude and in time, in both the northern and southern hemispheres. We develop and apply control procedures designed to assess and remove center-to-limb artifacts using measurements obtained by performing the analysis with respect to artificial poles at the east and west limbs. Forward modeling is carried out using sensitivity functions proportional to the mode kinetic energy density to evaluate the consistency of the corrected frequency shifts with models of the depth variation of the meridional circulation in the top half of the convection zone. The results, taken at face value, imply substantial differences between the meridional circulation in the northern and southern hemispheres. The inferred presence of a return (equatorward propagating) flow at a depth of approximately 40 Mm below the photosphere in the northern hemisphere is surprising and appears to be inconsistent with many other helioseismic analyses. This discrepancy may be the result of the inadequacy of our methodology to remove systematic errors in HMI data. Our results appear to be at least qualitatively similar to those by Gizon et al., which point to an anomaly in HMI data that is not present in MDI or GONG data.