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Biased human decisions have consequential impacts across various domains, yielding unfair treatment of individuals and resulting in suboptimal outcomes for organizations and society. In recognition of this fact, organizations regularly design and deploy interventions aimed at mitigating these biases. However, measuring human decision biases remains an important but elusive task. Organizations are frequently concerned with mistaken decisions disproportionately affecting one group. In practice, however, this is typically not possible to assess due to the scarcity of a gold standard: a label that indicates what the correct decision would have been. In this work, we propose a machine learning-based framework to assess bias in human-generated decisions when gold standard labels are scarce. We provide theoretical guarantees and empirical evidence demonstrating the superiority of our method over existing alternatives. This proposed methodology establishes a foundation for transparency in human decision-making, carrying substantial implications for managerial duties, and offering potential for alleviating algorithmic biases when human decisions are used as labels to train algorithms.

Proxima Cen (GJ 551; dM5.5e) is one of only about a dozen fully convective stars known to have a stellar cycle, and the only one to have long-term X-ray monitoring. A previous analysis found that X-ray and mid-UV observations, particularly two epochs of data from Swift, were consistent with a well sampled 7 yr optical cycle seen in ASAS data, but not convincing by themselves. The present work incorporates several years of new ASAS-SN optical data and an additional five years of Swift XRT and UVOT observations, with Swift observations now spanning 2009 to 2021 and optical coverage from late 2000. X-ray observations by XMM-Newton and Chandra are also included. Analysis of the combined data, which includes modeling and adjustments for stellar contamination in the optical and UV, now reveals clear cyclic behavior in all three wavebands with a period of 8.0 yr. We also show that UV and X-ray intensities are anti-correlated with optical brightness variations caused by the cycle and by rotational modulation, discuss possible indications of two coronal mass ejections, and provide updated results for the previous finding of a simple correlation between X-ray cycle amplitude and Rossby number over a wide range of stellar types and ages., Comment: 21 pages, 11 figures, 5 tables. Accepted by The Astrophysical Journal on 5 Nov 2024

We derive a simple model of actin waves consisting of three partial differential equations (PDEs) for active and inactive GTPase promoting growth of filamentous actin (F-actin, $F$). The F-actin feeds back to inactivate the GTPase at rate $sF$, where $s\ge 0$ is a ``negative feedback'' parameter. In contrast to previous models for actin waves, the simplicity of this model and its geometry (1D periodic cell perimeter) permits a full PDE bifurcation analysis. Based on a combination of continuation methods, linear stability analysis, and PDE simulations, we explore the existence, stability, interactions, and transitions between homogeneous steady states (resting cells), wave-pinning (polar cells), and travelling waves (cells with ruffling protrusions). We show that the value of $s$ and the size of the cell (length of perimeter) can affect the existence, coexistence, and stability of the patterns, as well as dominance of the one or another cell state. Implications to motile cells are discussed., Comment: 25 pages, 13 figures

Consumers on a shopping mission often leverage both product search and information seeking systems, such as web search engines and Question Answering (QA) systems, in an iterative process to improve their understanding of available products and reach a purchase decision. While product search is useful for shoppers to find the actual products meeting their requirements in the catalog, information seeking systems can be utilized to answer any questions they may have to refine those requirements. The recent success of Large Language Models (LLMs) has opened up an opportunity to bridge the gap between the two tasks to help customers achieve their goals quickly and effectively by integrating conversational QA within product search. In this paper, we propose to recommend users Question-Answer (Q&A) pairs that are relevant to their product search and can help them make a purchase decision. We discuss the different aspects of the problem including the requirements and characteristics of the Q&A pairs, their generation, and the optimization of the Q&A recommendation task. We highlight the challenges, open problems, and suggested solutions to encourage future research in this emerging area.

Male-dominated contexts pose a dilemma: they increase the benefits of repeated collaboration among women, yet at the same time, make such collaborations less likely. This paper seeks to understand the conditions that foster repeated collaboration among women versus men in male-dominated settings by examining the critical role of status hierarchies. Using collaboration data on 8,232,769 computer science research teams, we found that when a woman holds the top-ranking position in a steep status hierarchy, other women on that team are more likely than men to collaborate again, as compared to when the hierarchy is flat, and compared to when men occupy the top-ranking position. In steep hierarchies, top-ranking women but not top-ranking men foster conditions in which junior women are more likely to collaborate again than junior men of similar status levels. Our research suggests that whereas status hierarchies are especially detrimental to repeated collaboration among underrepresented individuals, top-ranking women in steep status hierarchies mitigate these negative impacts between women in male-dominated settings.

This work focuses on the analysis of the mean longitudinal magnetic field as a stellar activity tracer in the context of small exoplanet detection and characterisation in radial-velocity (RV) surveys. We use SDO/HMI filtergrams to derive Sun-as-a-star magnetic field measurements, and show that the mean longitudinal magnetic field is an excellent rotational period detector and a useful tracer of the solar magnetic cycle. To put these results into context, we compare the mean longitudinal magnetic field to three common activity proxies derived from HARPS-N Sun-as-a-star data: the full-width at half-maximum, the bisector span and the S-index. The mean longitudinal magnetic field does not correlate with the RVs and therefore cannot be used as a one-to-one proxy. However, with high cadence and a long baseline, the mean longitudinal magnetic field outperforms all other considered proxies as a solar rotational period detector, and can be used to inform our understanding of the physical processes happening on the surface of the Sun. We also test the mean longitudinal magnetic field as a "stellar proxy" on a reduced solar dataset to simulate stellar-like observational sampling. With a Gaussian Process regression analysis, we confirm that the solar mean longitudinal magnetic field is the most effective of the considered indicators, and is the most efficient rotational period indicator over different levels of stellar activity. This work highlights the need for polarimetric time series observations of stars., Comment: 23 pages, 27 figures

We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs. These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision. We believe that the community can benefit from these models in various research studies and commercial applications, especially for generating synthetic data to train smaller language models. Notably, over 98% of data used in our model alignment process is synthetically generated, showcasing the effectiveness of these models in generating synthetic data. To further support open research and facilitate model development, we are also open-sourcing the synthetic data generation pipeline used in our model alignment process.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Stellar photospheric inhomogeneities are a significant source of noise which currently precludes the discovery of Earth-mass planets orbiting Sun-like stars with the radial-velocity (RV) method. To complement several previous studies which have used ground- and spaced-based facilities to characterize the RV of the Sun, we here characterize the center-to-limb variability (CLV) of solar RVs arising from various solar-surface inhomogeneities observed by SDO/HMI and SDO/AIA. By using various SDO observables to classify pixels and calculate line-of-sight velocities as a function of pixel classification and limb angle, we show that each identified feature type, including the umbrae and penumbrae of sunspots, quiet-Sun magnetoconvective cells, magnetic network, and plage, exhibit distinct and complex CLV signatures, including a notable limb-angle dependence in the observed suppression of convective blueshift for magnetically active regions. We discuss the observed distributions of velocities by identified region type and limb angle, offer interpretations of the physical phenomena that shape these distributions, and emphasize the need to understand the RV signatures of these regions as astrophysical signals, rather than simple (un)correlated noise processes., Comment: 21 pages, 10 figures, 2 tables, accepted for publication in ApJ

We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) in the finite blocklength regime that provides Post-Quantum (PQ) security at high communication rates. Recently, hybrid cryptosystems offered PQ security by premixing the data using secure coding schemes and encrypting only a small portion of it, assuming the data is uniformly distributed. An assumption that is often challenging to enforce. Standard fixed-length lossless source coding and compression schemes guarantee a uniform output in normalized divergence. Yet, his is not sufficient to guarantee security. We consider an efficient almost uniform compression scheme in non-normalized variational distance for the proposed hybrid cryptosystem, that by utilizing uniform sub-linear shared seed, guarantees PQ security. Specifically, for the proposed PQ cryptosystem, first, we provide an end-to-end coding scheme, NU-HUNCC, for non-uniform messages. Second, we show that NU-HUNCC is information-theoretic individually secured (IS) against an eavesdropper with access to any subset of the links. Third, we introduce a modified security definition, individually semantically secure under a chosen ciphertext attack (ISS-CCA1), and show that against an all-observing eavesdropper, NU-HUNCC satisfies its conditions. Finally, we provide an analysis that shows the high communication rate of NU-HUNCC and the negligibility of the shared seed size.

The temporal analysis of stellar activity evolution is usually dominated by a complex trade-off between model complexity and interpretability, often by neglecting the non-stationary nature of the process. Recent studies appear to indicate that the presence of multiple coexisting cycles in a single star is more common than previously thought. The correct identification of physically meaningful cyclic components in spectroscopic time series is therefore a crucial task, which cannot overlook local behaviors. Here we propose a decomposition technique which adaptively recovers amplitude- and frequency-varying components. We present our results for the solar activity as measured both by the sunspot number and the $K$-line emission index, and we consistently recover the Schwabe and Gleissberg cycles as well as the Gnevyshev-Ohl pattern probably related to the Hale cycle. We also recover the known 8-year cycle for 61 Cygni A, in addition to evidence of a three-cycles long pattern reminiscent of the Gnevyshev-Ohl rule. This is particularly interesting as we cannot discard the possibility of a relationship between the measured field polarity reversals and this Hale-like periodicity., Comment: 8 pages, 3 figures

The bright star $\lambda$ Ser hosts a hot Neptune with a minimum mass of 13.6 $M_\oplus$ and a 15.5 day orbit. It also appears to be a solar analog, with a mean rotation period of 25.8 days and surface differential rotation very similar to the Sun. We aim to characterize the fundamental properties of this system, and to constrain the evolutionary pathway that led to its present configuration. We detect solar-like oscillations in time series photometry from the Transiting Exoplanet Survey Satellite (TESS), and we derive precise asteroseismic properties from detailed modeling. We obtain new spectropolarimetric data, and we use them to reconstruct the large-scale magnetic field morphology. We reanalyze the complete time series of chromospheric activity measurements from the Mount Wilson Observatory, and we present new X-ray and ultraviolet observations from the Chandra and Hubble space telescopes. Finally, we use the updated observational constraints to assess the rotational history of the star and to estimate the wind braking torque. We conclude that the remaining uncertainty on stellar age currently prevents an unambiguous interpretation of the properties of $\lambda$ Ser, and that the rate of angular momentum loss appears to be higher than for other stars with similar Rossby number. Future asteroseismic observations may help to improve the precision of the stellar age., Comment: 19 pages including 9 figures and 6 tables. Astronomical Journal, accepted

Problem Definition. Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/Results. In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk. Specifically, we combine counterfactual inference, machine learning, and optimization techniques to build a scalable decision model that can exploit high-dimensional medical data, such as the data found in modern electronic health records. Our decision model is evaluated based on electronic health records from 89,191 prediabetic patients. We compare the allocation of preventive treatments (metformin) prescribed by our data-driven decision model with that of current practice. We find that if our approach is applied to the U.S. population, it can yield annual savings of $1.1 billion. Finally, we analyze the cost-effectiveness under varying budget levels. Managerial Implications. Our work supports decision-making in health management, with the goal of achieving effective disease prevention at lower costs. Importantly, our decision model is generic and can thus be used for effective allocation of preventive care for other preventable diseases., Comment: Accepted by Manufacturing & Service Operations Management

Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular, human-generated labels are prone to encoding societal biases. While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias. After illustrating its performance on a synthetic dataset, we apply DeCoLe in the context of hate speech detection, where label bias has been recognized as an important challenge, and show that it successfully identifies biased labels and outperforms competing approaches., Comment: AI & HCI Workshop at the 40th International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA. 2023

During the concept design of complex networked systems, concept developers have to ensure that the choice of hardware modules and the topology of the target platform will provide adequate resources to support the needs of the application. For example, future-generation aerospace systems need to consider multiple requirements, with many trade-offs, foreseeing rapid technological change and a long period for realization and service. For that purpose, we introduce NetGAP, an automated 3-phase approach to synthesize network topologies and support the exploration and concept design of networked systems with multiple requirements including dependability, security, and performance. NetGAP represents the possible interconnections between hardware modules using a graph grammar and uses a Monte Carlo Tree Search optimization to generate candidate topologies from the grammar while aiming to satisfy the requirements. We apply the proposed approach to a synthetic version of a realistic avionics application use case. It includes 99 processes and 660 messages. The experiment shows the merits of the solution to support the early-stage exploration of alternative candidate topologies. The method vividly characterizes the topology-related trade-offs between requirements stemming from security, fault tolerance, timeliness, and the "cost" of adding new modules or links. We also create a scaled-up version of the problem (267 processes, 1887 messages) to illustrate scalability. Finally, we discuss the flexibility of using the approach when changes in the application and its requirements occur.

We analyze the asymptotic behaviour of the coupled cluster many-body wave-function in the limit of highly excited two- and three-particles states. We find that in this limit the different coupled cluster amplitudes exhibit a recurring behaviour, factorizing into a common asymptotic two- or three-body term. These asymptotic terms depend on the potential and in general are system specific. We also suggest that the knowledge of the asymptotic behaviour can potentially help solving the coupled cluster equations in a more efficient way.