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Using foot traffic data for over 150,000 points of interest (POIs) near the sites of 42 mass shootings (2018-2022, U.S.), we evaluate the spatial-temporal impact of the tragic events on community mobility and relocation of economic activities. Visits to nearby POIs decrease, while farther away POIs experience increased foot traffic, implying that communities shift their activities away from the shooting sites. The impact is stronger when stronger trauma responses are expected. Our results suggest that mass shootings drive significant displacements of economic activities and can consequently lead to welfare losses due to distortions in optimal choices of time and location.

Forensic examiners and attorneys need to know how to express evidence in favor or against a prosecutor's hypothesis in a way that avoids the prosecutor's fallacy and follows the modern reporting standards for forensic evidence. This article delves into the inherent conflict between legal and scientific principles, exacerbated by the prevalence of alternative facts in contemporary discourse. Courts grapple with contradictory expert testimonies, leading to a surge in erroneous rulings based on flawed amicus briefs and testimonies, notably the persistent prosecutor's fallacy. The piece underscores the necessity for legal practitioners to navigate this fallacy within the modern forensic science framework, emphasizing the importance of reporting likelihood ratios (LRs) over posterior probabilities. Recognizing the challenge of lay comprehension of LRs, the article calls for updated recommendations to mitigate the prosecutor's fallacy. Its contribution lies in providing a detailed analysis of the fallacy using LRs and advocating for a sound interpretation of evidence. Illustrated through a modified real case, this article serves as a valuable guide for legal professionals, offering insights into avoiding fallacious reasoning in forensic evidence assessment.

We investigate the presence and kinematics of NV absorption proximate to high redshift quasars selected upon the presence of strong $H_{2}$ and HI absorption at the quasar redshift. Our spectroscopic observations with X-shooter at the VLT reveal a 70% detection rate of NV (9 of 13 quasars with 2.5 < z < 3.3), remarkably higher than the 10% detection rate in intervening DLA systems and the 30% rate observed within a few thousand km/s of the source in the general quasar population. While many NV components lie within the velocity range of the neutral gas, the kinematic profiles of high-ionization species appear decoupled from those of low-ionization species, with the former extending over much larger velocity ranges, particularly towards bluer velocities. We also observe significant variations in the NV/SiIV, which we attribute to varying ionization conditions, with a velocity-dependent trend: blueshifted NV components systematically exhibit higher ionization parameters compared to those near the quasar's systemic redshift. Furthermore, the most redshifted systems relative to the quasar show no evidence of NV absorption. The results suggest that proximate $H_{2}$ absorption systems select critical stages of quasar evolution, during which the quasar remains embedded in a rich molecular environment. Redshifted systems trace infalling gas, potentially associated with mergers, preceding the onset of outflows. Such outflows may reach or even carry out neutral and molecular gas.This latter stage would correspond to proximate $H_{2}$ systems located around or blueshifted relative to the quasar's systemic z. Finally, the only case in our sample featuring highly blueshifted neutral gas shows no evidence of an association with the quasar.Our findings highlight the need to account for the ionization state when defining a velocity threshold to distinguish quasar-associated systems from intervening., Comment: 20 pages, 22 figures, accepted for publication in A&A

The assumption of fingerprint uniqueness is foundational in forensic science and central to criminal identification practices. However, empirical evidence supporting this assumption is limited, and recent findings from artificial intelligence challenge its validity. This paper uses a probabilistic approach to examine whether fingerprint patterns remain unique across large populations. We do this by drawing on Francis Galton's 1892 argument and applying the birthday paradox to estimate the probability of fingerprint repetition. Our findings indicate that there is a 50\% probability of coincidental fingerprint matches in populations of 14 million, rising to near certainty at 40 million, which contradicts the traditional view of fingerprints as unique identifiers. We introduce the concept of a Random Overlap Probability (ROP) to assess the likelihood of fingerprint repetition within specific population sizes. We recommend a shift toward probabilistic models for fingerprint comparisons that account for the likelihood of pattern repetition. This approach could strengthen the reliability and fairness of fingerprint comparisons in the criminal justice system.

The diagnosis of Shaken Baby Syndrome/Abusive Head Trauma (SBS/AHT) is fraught with controversy due to critical statistical deficiencies in the data underpinning these diagnoses. This paper examines the reliability and scientific foundation of SBS/AHT through a statistical lens, highlighting the lack of independently verified ground truth, contextual biases, data circularity, and diagnostic heterogeneity. These issues render current methodologies inadequate and complicate evaluations of diagnostic accuracy, particularly when legal determinations are integrated into medical assessments. Without empirical evidence validating the specificity of symptoms like subdural hematoma, retinal hemorrhage, and brain swelling, the diagnosis remains untested and its foundational validity unproven. We recommend that physicians focus on reporting observed clinical signs and avoid making determinations of abuse, which should remain within the legal domain. Addressing these challenges requires comprehensive, high-quality data collection encompassing contextual, medical, and legal information to evaluate the accuracy, repeatability, and reproducibility of SBS/AHT diagnoses. These efforts are essential to protect vulnerable children while ensuring fairness and accuracy in legal proceedings involving allegations of abuse.

Despite calls for reform to enhance forensic science, insufficient attention has been paid to the potential errors arising from exclusions. Often based on intuitive judgment rather than empirical evidence, exclusions can lead to significant errors. Additionally, exclusions can imply inclusions, especially when ancillary information narrows the pool of suspects in an investigation. Without empirical evidence demonstrating that class characteristics alone can consistently be used for comparison with high repeatability, replicability, and accuracy, these characteristics should not be employed for exclusions. Errors in exclusions require the same scrutiny as errors in inclusions. Validity studies and reports of the accuracy of a forensic method must include both false positive rates and false negative rates, since only focusing on false positive rates can lead, and has led, to errors and subsequent miscarriages of justice.

Artificial Intelligence (AI), like any transformative technology, has the potential to be a double-edged sword, leading either toward significant advancements or detrimental outcomes for society as a whole. As is often the case when it comes to widely-used technologies in market economies (e.g., cars and semiconductor chips), commercial interest tends to be the predominant guiding factor. The AI community is at risk of becoming polarized to either take a laissez-faire attitude toward AI development, or to call for government overregulation. Between these two poles we argue for the community of AI practitioners to consciously and proactively work for the common good. This paper offers a blueprint for a new type of innovation infrastructure including 18 concrete milestones to guide AI research in that direction. Our view is that we are still in the early days of practical AI, and focused efforts by practitioners, policymakers, and other stakeholders can still maximize the upsides of AI and minimize its downsides. We talked to luminaries such as recent Nobelist John Jumper on science, President Barack Obama on governance, former UN Ambassador and former National Security Advisor Susan Rice on security, philanthropist Eric Schmidt on several topics, and science fiction novelist Neal Stephenson on entertainment. This ongoing dialogue and collaborative effort has produced a comprehensive, realistic view of what the actual impact of AI could be, from a diverse assembly of thinkers with deep understanding of this technology and these domains. From these exchanges, five recurring guidelines emerged, which form the cornerstone of a framework for beginning to harness AI in service of the public good. They not only guide our efforts in discovery but also shape our approach to deploying this transformative technology responsibly and ethically.

Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to 50%. However, challenges persist, especially with the advent of robots and the need to humanize machines for improved Human-AI collaboration. In this experiment, three GPT agents with varying levels of agreeableness (disagreeable, neutral, agreeable) based on the Big Five Inventory were tested in a Turing Test. All exceeded a 50% confusion rate, with the highly agreeable AI agent surpassing 60%. This agent was also recognized as exhibiting the most human-like traits. Various explanations in the literature address why these GPT agents were perceived as human, including psychological frameworks for understanding anthropomorphism. These findings highlight the importance of personality engineering as an emerging discipline in artificial intelligence, calling for collaboration with psychology to develop ergonomic psychological models that enhance system adaptability in collaborative activities., Comment: 25 pages, 2 figures, 7 tables

We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin foil coupled with high-speed infrared thermography. The measurement technique is challenged by the thermal inertia of the foil, the high frequency of turbulent fluctuations, and the measurement noise of the infrared camera. We discuss in detail the advantages and drawbacks of all the design choices that were made, thereby providing a successful implementation strategy to obtain high-quality data. This experimental approach could be useful for experimental studies employing wall-based measurements of turbulence, such as flow control applications in wall-bounded turbulence.

We present VLT/MUSE integral-field spectroscopy ($R\approx 1\,800$) of four giant gravitational arcs exhibiting strong C IV absorption at 8 intervening redshifts, $z_{abs}\approx 2.0$--$2.5$. We detect C IV absorption in a total of 222 adjacent and seeing-uncorrelated sightlines, whose spectra sample beams of ("de-lensed") linear size $\approx 1$ kpc. Our data show that (1) absorption velocities cluster at all probed transverse scales, $\Delta r_\perp\approx0$--$15$ kpc, depending on system; (2) the (transverse) velocity dispersion never exceeds the mean (line-of-sight) absorption spread; and (3) the (transverse) velocity autocorrelation function does not resolve kinematic patterns at the above spatial scales, but its velocity projection, $\xi^{arc}(\Delta v)$, exhibits a similar shape to the known two-point correlation function toward quasars, $\xi^{QSO}(\Delta v)$. An empirical kinematic model suggests that these results are a natural consequence of wide-beam observations of an unresolved clumpy medium. Our model recovers both the underlying velocity dispersion of the clumps ($70$--$170$ \kms) and the mean number of clumps per unit area ($2$--$13$ kpc$^{-2}$). The latter constrains the projected mean inter-clump distance to within $\approx0.3$--$0.8$ kpc, which we argue is a measure of clump size for near-unity covering fraction. The model is also able to predict $\xi^{arc}(\Delta v)$ from $\xi^{QSO}(\Delta v)$, suggesting that the strong systems that shape the former and the line-of-sight velocity components that define the latter trace the same kinematic population. Consequently, the clumps must possess an internal density structure that generates both weak and strong components. We discuss how our interpretation is consistent with previous observations using background galaxies and multiple quasars., Comment: Accepted for publication in A&A

Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However, workers themselves offer a more optimistic, nuanced perspective. Drawing on a far-reaching 2024 survey of more than 9,000 workers across nine countries, this paper finds that more workers report potential benefits from new technologies like robots and AI for their safety and comfort at work, their pay, and their autonomy on the job than report potential costs. Workers with jobs that ask them to solve complex problems, workers who feel valued by their employers, and workers who are motivated to move up in their careers are all more likely to see new technologies as beneficial. In contrast to assumptions in previous research, more formal education is in some cases associated with more negative attitudes toward automation and its impact on work. In an experimental setting, the prospect of financial incentives for workers improve their perceptions of automation technologies, whereas the prospect of increased input about how new technologies are used does not have a significant effect on workers' attitudes toward automation.

In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Re{\tau} = 200. The estimation technique is based on a 3D generative adversarial network (3D-GAN). We recently demonstrated that 3D-GANs are capable of estimating fields with good accuracy by employing fully-resolved wall quantities (pressure and streamwise/spanwise wall shear stress on a grid with DNS resolution). However, the practical implementation in an experimental setting is challenging due to the large number of sensors required. In this work, we aim to estimate the flow fields with substantially fewer sensors. The impact of the reduction of the number of sensors on the quality of the flow reconstruction is assessed in terms of accuracy degradation and spectral length-scales involved. It is found that the accuracy degradation is mainly due to the spatial undersampling of scales, rather than the reduction of the number of sensors per se. We explore the performance of the estimator in case only one wall quantity is available. When a large number of sensors is available, pressure measurements provide more accurate flow field estimations. Conversely, the elongated patterns of the streamwise wall shear stress make this quantity the most suitable when only few sensors are available. As a further step towards a real application, the effect of sensor noise is also quantified. It is shown that configurations with fewer sensors are less sensitive to measurement noise., Comment: in Theoretical and Computational Fluid Dynamics (2024)

Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint.

We propose a set of causal estimands that we call the "mediated probabilities of causation." These estimands quantify the probabilities that an observed negative outcome was induced via a mediating pathway versus a direct pathway in a stylized setting involving a binary exposure or intervention, a single binary mediator, and a binary outcome. We outline a set of conditions sufficient to identify these effects given observed data, and propose a doubly-robust projection based estimation strategy that allows for the use of flexible non-parametric and machine learning methods for estimation. We argue that these effects may be more relevant than the probability of causation, particularly in settings where we observe both some negative outcome and negative mediating event, and we wish to distinguish between settings where the outcome was induced via the exposure inducing the mediator versus the exposure inducing the outcome directly. We motivate these estimands by discussing applications to legal and medical questions of causal attribution.

The bachelor's degree remains a fundamental path to economic opportunity in the United States. Critical for policymakers, then, is ensuring equitable access to such benefits--a task often constrained by long-standing structural barriers. One of the most obvious structural impediments for bachelor's degree-aspirants in California (CA) is the current version of the CA Master Plan for Higher Education. With a robust public higher education system that is continually forced to adapt to demographic shifts and evolving labor markets, California is an especially important region for exploring the future of postsecondary education and economic growth. This report describes what we currently know about the California Community College Baccalaureate (CCB) program that was launched in 2017 and expanded in 2021. The program shows promising outcomes and that state education leaders can leverage existing components of the state's education ecosystem to meet the economic and social demands of 40 million diverse Californians and to close existing racial equity gaps. This report was published as part of a new research series, "A Civil Rights Agenda for California's Next Quarter Century," in commemoration of the Civil Rights Project's 25th anniversary.

Reviews conducted by the National Academy of Sciences (2009) and the President's Council of Advisors on Science and Technology (2016) concluded that the field of forensic firearm comparisons has not been demonstrated to be scientifically valid. Scientific validity requires adequately designed studies of firearm examiner performance in terms of accuracy, repeatability, and reproducibility. Researchers have performed ``black-box'' studies with the goal of estimating these performance measures. As statisticians with expertise in experimental design, we conducted a literature search of such studies to date and then evaluated the design and statistical analysis methods used in each study. Our conclusion is that all studies in our literature search have methodological flaws that are so grave that they render the studies invalid, that is, incapable of establishing scientific validity of the field of firearms examination. Notably, error rates among firearms examiners, both collectively and individually, remain unknown. Therefore, statements about the common origin of bullets or cartridge cases that are based on examination of ``individual" characteristics do not have a scientific basis. We provide some recommendations for the design and analysis of future studies.

The LC method described in this work seeks to approximate the roots of polynomial equations in one variable. This book allows you to explore the LC method, which uses geometric structures of Lines L and Circumferences C in the plane of complex numbers, based on polynomial coefficients. These structures depend on the inclination angle of a line with fixed point that seeks to contain one of the roots; they are associated with an error measure that indicates the degree of proximity to that root, without knowing a priori its location. Using a computer with parallel processing capabilities, it is feasible to construct several of these geometric structures at the same time, varying the inclination angle of the lines with fixed point, in order to obtain an error measure map, with which it is possible to identify, approximately, the location of all polynomial roots. To show how the LC method works, this book includes numerical examples for quadratic, cubic, and quartic polynomials, and also for polynomials of degree greater than or equal to 5; this book also includes R programs that allow you to reproduce the results of the examples on a typical personal computer; these R programs use vectorization of operations instead of loops, which can be seen as a basic and accessible form of parallel processing. This book, in the end, invites us to explore beyond the basic ideas and concepts described here, motivating the development of a more efficient and complete computational implementation of the LC method., Comment: PDF document with 270 pages

This paper describes a geometrical method for finding the roots $r_1$, $r_2$ of a quadratic equation in one complex variable of the form $x^2+c_1 x+c_2=0$, by means of a Line $L$ and a Circumference $C$ in the complex plane, constructed from known coefficients $c_1$, $c_2$. This Line-Circumference (LC) geometric structure contains the sought roots $r_1$, $r_2$ at the intersections of its component elements $L$ and $C$. Line $L$ is mapped onto Circumference $C$ by a Mobius transformation. The location and inclination angle of $L$ can be computed directly from coefficients $c_1$, $c_2$, while $C$ is constructed by dividing the constant term $c_2$ by each point from $L$. This paper describes the technical details for the quadratic LC method, and then shows how the quadratic LC method works through a numerical example. The quadratic LC method described here, although more elaborate than the traditional quadratic formula, can be extended to find initial approximations to the roots of polynomials in one variable of degree $n \geq 3$. As an additional feature, this paper also studies an interesting property of the rectilinear segments connecting key points in a quadratic LC structure., Comment: 7 pages, 2 figures

Forensic toolmark analysis traditionally relies on subjective human judgment, leading to inconsistencies and lack of transparency. The multitude of variables, including angles and directions of mark generation, further complicates comparisons. To address this, we first generate a dataset of 3D toolmarks from various angles and directions using consecutively manufactured slotted screwdrivers. By using PAM clustering, we find that there is clustering by tool rather than angle or direction. Using Known Match and Known Non-Match densities, we establish thresholds for classification. Fitting Beta distributions to the densities, we allow for the derivation of likelihood ratios for new toolmark pairs. With a cross-validated sensitivity of 98% and specificity of 96%, our approach enhances the reliability of toolmark analysis. This approach is applicable to slotted screwdrivers, and for screwdrivers that are made with a similar production method. With data collection of other tools and factors, it could be applied to compare toolmarks of other types. This empirically trained, open-source solution offers forensic examiners a standardized means to objectively compare toolmarks, potentially decreasing the number of miscarriages of justice in the legal system., Comment: Revised text, results unchanged

Swirling spin textures, including topologically non-trivial states, such as skyrmions, chiral domain walls, and magnetic vortices, have garnered significant attention within the scientific community due to their appeal from both fundamental and applied points of view. However, their creation, controlled manipulation, and stability are typically constrained to certain systems with specific crystallographic symmetries, bulk, or interface interactions, and/or a precise stacking sequence of materials. Here, we make use of the stray field of YBa2Cu3O7-{\delta} superconducting microstructures in ferromagnet/superconductor hybrids to imprint magnetic radial vortices in permalloy at temperatures below the superconducting transition temperature (TC), a method easily extended to other ferromagnets with in-plane magnetic anisotropy. We examine the size dependence and temperature stability of the imprinted magnetic configurations. We show that above TC, magnetic domains retain memory of the imprinted spin texture. Micromagnetic modelling coupled with a SC field model reveals that the stabilization mechanism leading to this memory effect is mediated by microstructural defects. Superconducting control of swirling spin textures below and above the superconducting transition temperature holds promising prospects for shaping spintronics based on magnetic textures.

The normal-state conductivity and superconducting critical temperature of oxygen-deficient YBa2Cu3O7-x can be persistently enhanced by illumination. Strongly debated for years, the origin of those effects -- termed persistent photoconductivity (PPC) and photosuperconductivity (PPS) -- has remained an unsolved critical problem, whose comprehension may provide key insights to harness the origin of high-temperature superconductivity itself. Here we make essential steps toward understanding PPS. While the models proposed so far assume that it is caused by a carrier-density increase (photodoping) observed concomitantly, our experiments contradict such conventional belief: we demonstrate that it is instead linked to a photo-induced decrease of the electronic scattering rate. Furthermore, we find that the latter effect and photodoping are completely disconnected and originate from different microscopic mechanisms since they present different wavelength and oxygen-content dependencies as well as strikingly different relaxation dynamics. Besides helping disentangle photodoping, PPC, and PPS, our results provide new evidence for the intimate relation between critical temperature and scattering rate, a key ingredient in modern theories on high-temperature superconductivity.

Since the National Academy of Sciences released their report outlining paths for improving reliability, standards, and policies in the forensic sciences NAS (2009), there has been heightened interest in evaluating and improving the scientific validity within forensic science disciplines. Guyll et al. (2023) seek to evaluate the validity of forensic cartridge-case comparisons. However, they make a serious statistical error that leads to highly inflated claims about the probability that a cartridge case from a crime scene was fired from a reference gun, typically a gun found in the possession of a defendant. It is urgent to address this error since these claims, which are generally biased against defendants, are being presented by the prosecution in an ongoing homicide case where the defendant faces the possibility of a lengthy prison sentence (DC Superior Court, 2023).

A Golomb ruler is a sequence of integers whose pairwise differences, or equivalently pairwise sums, are all distinct. This definition has been generalized in various ways to allow for sums of h integers, or to allow up to g repetitions of a given sum or difference. Beck, Bogart, and Pham applied the theory of inside-out polytopes of Beck and Zaslavsky to prove structural results about the counting functions of Golomb rulers. We extend their approach to the various types of generalized Golomb rulers., Comment: 15 pages, 2 figures

This paper provides asymptotically valid tests for the null hypothesis of no treatment effect heterogeneity. Importantly, I consider the presence of heterogeneity that is not explained by observed characteristics, or so-called idiosyncratic heterogeneity. When examining this heterogeneity, common statistical tests encounter a nuisance parameter problem in the average treatment effect which renders the asymptotic distribution of the test statistic dependent on that parameter. I propose an asymptotically valid test that circumvents the estimation of that parameter using the empirical characteristic function. A simulation study illustrates not only the test's validity but its higher power in rejecting a false null as compared to current tests. Furthermore, I show the method's usefulness through its application to a microfinance experiment in Bosnia and Herzegovina. In this experiment and for outcomes related to loan take-up and self-employment, the tests suggest that treatment effect heterogeneity does not seem to be completely accounted for by baseline characteristics. For those outcomes, researchers could potentially try to collect more baseline characteristics to inspect the remaining treatment effect heterogeneity, and potentially, improve treatment targeting., Comment: 50 pages (31 pages main text), 6 tables (5 main tables and 1 table in the appendix)