Conventional topological Hall effects (THE) require conducting magnets, leaving insulating systems largely inaccessible. Here we introduce the interfacial topological Hall effect (ITHE), where the noncoplanar spin textures of insulating magnets are imprinted onto an adjacent heavy metal via the magnetic proximity effect (MPE) and detected electrically. In Pt/h-LuFeO3 bilayers, h-LuFeO3 hosts a topological spin structure robust against high magnetic fields, arising from a 120° triangular spin lattice with small spin canting that yields nontrivial topology but minimal magnetization. This generates a giant Hall response in Pt up to 0.5% of the longitudinal resistivity and a Hall-conductivity/magnetization ratio above 2 V^{-1}, clearly distinguishable from the spin Hall Hanle effect background. Field- and temperature-dependent analysis further reveals that Pt nanoclusters inherit topological textures from h-LuFeO3 via MPE. Unlike the conventional THE narrow peak-and-dip features, ITHE in Pt/h-LuFeO3 persists across a broad magnetic field range up to 14 T, demonstrating the exceptional stability of the underlying topological spin structure. This establishes ITHE as a powerful and sensitive probe for topological magnetism in ultrathin insulating films and paves the way for new spintronic applications.

We investigate a cosmological model in which a fraction of the dark matter is atomic dark matter (ADM). This ADM consists of dark versions of the electron and of the proton, interacting with each other and with dark photons just as their light sector versions do, but interacting with everything else only gravitationally. We find constraints given current cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) data, with and without an $H_0$ prior, and with and without enforcing a big bang nucleosynthesis consistent helium abundance. We find that, at low dark photon temperature, one can have consistency with BAO and CMB data, with a fraction of dark matter that is ADM ($f_{\rm adm}$) as large as $\sim 0.1$. Such a large $f_{\rm adm}$ leads to a suppression of density fluctuations today on scales below about 60 Mpc that may be of relevance to the $\sigma_8$ tension. Our work motivates calculation of nonlinear corrections to matter power spectrum predictions in the ADM model. We forecast parameter constraints to come from future ground-based CMB surveys, and find that if ADM is indeed the cause of the $\sigma_8$ tension, the influence of the ADM, primarily on CMB lensing, will likely be detectable at high significance.

A growing number of introductory physics instructors are implementing active learning methods in their classrooms, and they are modifying the methods to fit their local instructional contexts. However, we lack a detailed framework for describing the range of what these instructor adaptations of active learning methods look like in practice. Existing studies apply structured protocols to classroom observations and report descriptive statistics, but this approach overlooks the complex nature of instruction. In this study, we apply network analysis to classroom observations to define a typology of active learning that considers the temporal and interactional nature of instructional practices. We use video data from 30 instructors at 27 institutions who implemented one of the following named active learning methods in their introductory physics or astronomy course: Investigative Science Learning Environment (ISLE), Peer Instruction, Tutorials, and Student-Centered Active Learning Environment with Upside-down Pedagogies (SCALE-UP). We identify five types of active learning instruction: clicker lecture, dialogic clicker lecture, dialogic lecture with short groupwork activities, short groupwork activities, and long groupwork activities. We find no significant relationship between these instruction types and the named active learning methods; instead, implementations of each of the four methods are spread across different instruction types. This result prompts a shift in the way we think and talk about active learning: the names of developed active learning methods may not actually reflect the specific activities that happen during instruction. We also find that student conceptual learning does not vary across the identified instruction types, suggesting that instructors may be flexible when modifying these methods without sacrificing effectiveness.

Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both single- and multi-view learning scenarios.

In some extensions of the standard model of particle physics, the values of the fundamental coupling constants vary in space and time. Some observations of quasars hint at time and spatial variation of the fine structure constant $\alpha$. Here, the Bekenstein-Sandvik-Barrow-Magueijo (BSBM) model (which posits the existence of a scalar field driving evolution in the fundamental electric charge $e$) is tested against quasar and Planck satellite cosmic microwave background (CMB) data. In this model, variations in $e$ are coupled to the matter density through a factor $\zeta_{\rm m}/{\omega}$, which is related to electromagnetic contributions to nucleon masses, and {the energy} scale of new physics. Simulations conducted here do not support claims that the electrostatic contribution to $\zeta_{m}$ is completely shielded. Other common approximations used in BSBM field evolution are found to be adequate. Principal components of the CMB data with respect to variations in $\alpha$ are used to obtain constraints of $\zeta_{\rm m}/{\omega}\lesssim 9.3 \times 10^{-9}$ for a massless field. A forecast anticipating the promise of the Simons Observatory (SO) CMB experiment shows that SO will be sensitive to values of $\zeta_{\rm m}/{\omega}\geq 2.2 \times 10^{-9}$.

This thesis investigates the psychological factors that influence belief in AI predictions, comparing them to belief in astrology- and personality-based predictions, and examines the "personal validation effect" in the context of AI, particularly with Large Language Models (LLMs). Through two interconnected studies involving 238 participants, the first study explores how cognitive style, paranormal beliefs, AI attitudes, and personality traits impact perceptions of the validity, reliability, usefulness, and personalization of predictions from different sources. The study finds a positive correlation between belief in AI predictions and belief in astrology- and personality-based predictions, highlighting a "rational superstition" phenomenon where belief is more influenced by mental heuristics and intuition than by critical evaluation. Interestingly, cognitive style did not significantly affect belief in predictions, while paranormal beliefs, positive AI attitudes, and conscientiousness played significant roles. The second study reveals that positive predictions are perceived as significantly more valid, personalized, reliable, and useful than negative ones, emphasizing the strong influence of prediction valence on user perceptions. This underscores the need for AI systems to manage user expectations and foster balanced trust. The thesis concludes with a proposal for future research on how belief in AI predictions influences actual user behavior, exploring it through the lens of self-fulfilling prophecy. Overall, this thesis enhances understanding of human-AI interaction and provides insights for developing AI systems across various applications.

The Dark Matter Time Projection Chamber collaboration recently reported a dark matter limit obtained with a 10 liter time projection chamber filled with CF4 gas. The 10 liter detector was capable of 2D tracking (perpendicular to the drift direction) and 2D fiducialization, and only used information from two CCD cameras when identifying tracks and rejecting backgrounds. Since that time, the collaboration has explored the potential benefits of photomultiplier tube and electronic charge readout to achieve 3D tracking, and particle identification for background rejection. The latest results of this effort is described here.

The complexity of game play in online multiplayer games has generated strong interest in modeling the different play styles or strategies used by players for success. We develop a hierarchical Bayesian regression approach for the online multiplayer game Battlefield 3 where performance is modeled as a function of the roles, game type, and map taken on by that player in each of their matches. We use a Dirichlet process prior that enables the clustering of players that have similar player-specific coefficients in our regression model, which allows us to discover common play styles amongst our sample of Battlefield 3 players. This Bayesian semi-parametric clustering approach has several advantages: the number of common play styles do not need to be specified, players can move between multiple clusters, and the resulting groupings often have a straight-forward interpretations. We examine the most common play styles among Battlefield 3 players in detail and find groups of players that exhibit overall high performance, as well as groupings of players that perform particularly well in specific game types, maps and roles. We are also able to differentiate between players that are stable members of a particular play style from hybrid players that exhibit multiple play styles across their matches. Modeling this landscape of different play styles will aid game developers in developing specialized tutorials for new participants as well as improving the construction of complementary teams in their online matching queues.

Aerosols have been found to be nearly ubiquitous in substellar atmospheres. The precise temperature at which these aerosols begin to form in exoplanets has yet to be observationally constrained. Theoretical models and observations of muted spectral features suggest that silicate clouds play an important role in exoplanets between at least 950 and 2,100 K. However, some giant planets are thought to be hot enough to avoid condensation altogether. Here, we present the near-UV transmission spectrum of an ultra-hot Jupiter, WASP-178b ($\sim$2,450~K), that exhibits significant NUV absorption. This short-wavelength absorption is among the largest spectral features ever observed in an exoplanet in terms of atmospheric scale heights. Bayesian retrievals indicate the presence of gaseous refractory species containing silicon and magnesium, which are the precursors to condensate clouds at lower temperatures. SiO in particular has not been detected in exoplanets before, but the presence of SiO in WASP-178b is consistent with theoretical expectation as the dominant Si-bearing species at high temperatures. These observations allow us to re-interpret previous observations of HAT-P-41b and WASP-121b that did not consider SiO to suggest that silicate cloud formation begins on exoplanets with equilibrium temperatures between 1,950 and 2,450~K.

We test whether replacing crowdsource workers with LLMs to write Natural Language Inference (NLI) hypotheses similarly results in annotation artifacts. We recreate a portion of the Stanford NLI corpus using GPT-4, Llama-2 and Mistral 7b, and train hypothesis-only classifiers to determine whether LLM-elicited hypotheses contain annotation artifacts. On our LLM-elicited NLI datasets, BERT-based hypothesis-only classifiers achieve between 86-96% accuracy, indicating these datasets contain hypothesis-only artifacts. We also find frequent "give-aways" in LLM-generated hypotheses, e.g. the phrase "swimming in a pool" appears in more than 10,000 contradictions generated by GPT-4. Our analysis provides empirical evidence that well-attested biases in NLI can persist in LLM-generated data.

We simulate the dynamics of Rydberg atoms resonantly exchanging energy via two-, three-, and four-body dipole-dipole interactions in a one-dimensional array. Using simplified models of a realistic experimental system, we study the initial state survival probability, mean level spacing, spread of entanglement, and properties of the energy eigenstates. By exploring a range of disorders and interaction strengths, we find regions in parameter space where the three- and four-body dynamics either fail to thermalize or do so slowly. The interplay between the stronger hopping and weaker field-tuned interactions gives rise to quantum many-body scar states, which play a critical role in slowing the dynamics of the three- and four-body interactions.

A normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The inherent complexity in this endocrine system is a challenge to identifying mechanisms of cycle disruption, particularly given the large number of unknown parameters in existing mathematical models. We develop a new endocrine model to limit model complexity and use simulated distributions of unknown parameters for model analysis. By employing a comprehensive model evaluation, we identify a collection of mechanisms that differentiate normal and abnormal phenotypes. We also discover an intermediate phenotype--displaying relatively normal hormone levels and cycle dynamics--that is grouped statistically with the irregular phenotype. Results provide insight into how clinical symptoms associated with ovulatory disruption may not be detected through hormone measurements alone.

For an integer $b\geq 2$, we call a positive integer $b$-anti-Niven if it is relatively prime to the sum of the digits in its base-$b$ representation. In this article, we investigate the maximum lengths of arithmetic progressions of $b$-anti-Niven numbers.

Permutation Entropy and statistiCal Complexity Analysis for astRophYsics (PECCARY) is a computationally inexpensive, statistical method by which any time-series can be characterized as predominantly regular, complex, or stochastic. Elements of the PECCARY method have been used in a variety of physical, biological, economic, and mathematical scenarios, but have not yet gained traction in the astrophysical community. This study introduces the PECCARY technique with the specific aims to motivate its use in and optimize it for the analysis of astrophysical orbital systems. PECCARY works by decomposing a time-dependent measure, such as the x-coordinate or orbital angular momentum time-series, into ordinal patterns. Due to its unique approach and statistical nature, PECCARY is well-suited for detecting preferred and forbidden patterns (a signature of chaos), even when the chaotic behavior is short-lived or when working with a relatively short duration time-series or small sets of time-series data. A variety of examples are used to demonstrate the capabilities of PECCARY. These include mathematical examples (sine waves, varieties of noise, sums of sine waves, well-known chaotic functions), a double pendulum system, and astrophysical tracer particle simulations with potentials of varying intricacies. Since the adopted timescale used to diagnose a given time-series can affect the outcome, a method is presented to identify an ideal sampling scheme, constrained by the overall duration and the natural timescale of the system. The accompanying PECCARY Python package and its usage are discussed.

A static electric field of a few V/cm shifts the energy levels of ultracold Rydberg atoms in a magneto-optical trap. For a given principle quantum number, most of the energy levels are nearly degenerate at zero field and fan out with increasing field to form a manifold. We excite Rydberg atoms to energy levels near the center of the manifold, where the spacing is nearly harmonic, and allow them to exchange energy via resonant dipole-dipole interactions. We measure the time evolution as energy spreads away from the center of the manifold, which reveals that the system fails to thermalize for long interaction times. A computational model that includes only a few essential features of the system qualitatively agrees with this result.

As galaxies evolve over time, the orbits of their constituent stars are expected to change in size and shape, moving stars away from their birth radius. Radial gas flows are also expected. Spiral arms and bars in galaxies are predicted to help drive this radial relocation, which may be possible to trace observationally via a flattening of metallicity gradients. We use data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, part of the fourth phase of the Sloan Digital Sky Surveys (SDSS-IV), to look for correlations of the steepness of gas-phase metallicity gradients with various galaxy morphological features (e.g. presence and pitch angle of spiral arms, presence of a large scale bar, bulge size). We select from MaNGA a sample of star forming galaxies for which gas phase metallicity trends can be measured, and use morphologies from Galaxy Zoo. We observe that at fixed galaxy mass (1) the presence of spiral structure correlates with steeper gas phase metallicity gradients; (2) spiral galaxies with larger bulges have both higher gas-phase metallicities and shallower gradients; (3) there is no observable difference with azimuthally averaged radial gradients between barred and unbarred spirals and (4) there is no observable difference in gradient between tight and loosely wound spirals, but looser wound spirals have lower average gas-phase metallicity values at fixed mass. We discuss the possible implications of these observational results.

We test whether NLP datasets created with Large Language Models (LLMs) contain annotation artifacts and social biases like NLP datasets elicited from crowd-source workers. We recreate a portion of the Stanford Natural Language Inference corpus using GPT-4, Llama-2 70b for Chat, and Mistral 7b Instruct. We train hypothesis-only classifiers to determine whether LLM-elicited NLI datasets contain annotation artifacts. Next, we use pointwise mutual information to identify the words in each dataset that are associated with gender, race, and age-related terms. On our LLM-generated NLI datasets, fine-tuned BERT hypothesis-only classifiers achieve between 86-96% accuracy. Our analyses further characterize the annotation artifacts and stereotypical biases in LLM-generated datasets.

While the two derivative action of gravitation is specified uniquely, higher derivative operators are also allowed with coefficients that are not specified uniquely by effective field theory. We focus on a four derivative operator in which the Riemann tensor couples directly to the electromagnetic field $a\,R_{\mu\nu\alpha\beta}F^{\mu\nu}F^{\alpha\beta}$. We compute the corresponding corrections to the Shapiro time delay in the solar system and compare this to data from the Cassini probe. We place an observational upper bound on the coefficient $a$ at 95% confidence |a|<26\,(1000\,\mbox{km})^2. We compare this to the weak gravity conjecture (WGC) prediction of a bound on the coefficients $a,\,b$ of four derivative operators involving the graviton and the photon; this includes the above term $a\,R_{\mu\nu\alpha\beta}F^{\mu\nu}F^{\alpha\beta}$ as well as $b\,F^4$. We show that by using the observed value of the $b$ coefficient from measurements of light by light scattering, which arises in the Standard Model from integrating out the electron, the WGC predicted bound for $a$ is $a\lesssim 7.8\,(1000\,\mbox{km})^2$. This is consistent with the above observational bound, but is intriguingly close and can be further probed in other observations.

This work explores the relationship between altruism and the genetic system of arrhenotoky through an evolutionary game theory (EGT)-inspired lens, using a dynamic model of beehive populations consisting of three castes: workers, drones, and the queen. Arrhenotoky is a form of asexual reproduction in which unfertilized eggs become males while fertilized eggs develop into females, leading to unusual patterns of genetic relatedness between family members. This mode of reproduction occurs in insects such as the Hymenoptera, including bees. In the hive environment, bees often display altruistic behavior, or actions taken by an organism that reduce its own fitness to increase the fitness of others. Eusociality, an elaborate form of social organization characterized by complex and altruistic social behaviors, is also observed in the Hymenoptera. To explore the interplay between altruism and the reproductive patterns of arrhenotoky, we employ a population dynamics model to simulate beehive populations over a range of parameters, controlling for altruism in workers and the queen. Our results show that altruistic behaviors are essential for beehive success, with optimal worker altruism corresponding to the division of labor observed in eusocial species. Furthermore, we find that modest altruism from the queen is also vital for hive survival, emphasizing the delicate balance that can exist in these complex social systems. Overall, our findings shed light on the co-evolution of altruism, arrhenotoky, and eusociality in the natural world.