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This paper investigates how teachers learn about race in the school context, with a particular focus on teachers' development of racial competency. Using in-depth, semi-structured interviews we find that teachers learn through three sources: from their peers, from years of experience, and from teacher preparation and in-service experiences. Furthermore, we find that learning occurs both informally and formally and that these sources of learning are moderated by three contextual factors: career status, school culture, and out-of-school factors We find that teachers rely most on informal avenues and encounters to develop racial competency.

Local teacher recruitment through "grow-your-own" programs is a prominent strategy to address workforce shortages and ensure that incoming teachers resemble, understand, and have strong connections to their communities. We exploit the staggered rollout of the Teacher Academy of Maryland career and technical education certificate program across public high schools, finding that exposed students were more likely to become teachers by 0.6 percentage points (pp), or 47%. Effects are concentrated among White girls (1.4pp/39%) and Black girls (0.7pp/80%). We also identify positive impacts on wages (5% on average/18% for Black girls), countering a prevailing narrative that teaching leaves one worse off financially relative to other labor market opportunities.

Mounting evidence supporting the advantages of a diverse teacher workforce prompts policymakers to scrutinize existing recruitment pathways. Following four cohorts of Maryland public high-school students over 12 years reveals several insights. Early barriers require timely interventions, aiding students of color in achieving educational milestones that are prerequisites for teacher candidacy (high school graduation, college enrollment). While alternative pathways that bypass traditional undergraduate teacher preparation may help, current approaches still show persistent racial disparities. Data simulations underscore the need for race-conscious policies specifically targeting or differentially benefiting students of color, as race-neutral strategies have minimal impact. Ultimately, multiple race-conscious policy solutions addressing various educational milestones must demonstrate significant effects--approximately 30% increases--to reshape the teacher workforce to align with student body demographics.

We report the discovery and characterization of a small planet, TOI-1408 c, on a 2.2-day orbit located interior to a previously known hot Jupiter, TOI-1408 b ($P=4.42$ d, $M=1.86\pm0.02\,M_\mathrm{Jup}$, $R=2.4\pm0.5\,R_\mathrm{Jup}$) that exhibits grazing transits. The two planets are near 2:1 period commensurability, resulting in significant transit timing variations (TTVs) for both planets and transit duration variations (TDVs) for the inner planet. The TTV amplitude for TOI-1408 c is 15% of the planet's orbital period, marking the largest TTV amplitude relative to the orbital period measured to date. Photodynamical modeling of ground-based radial velocity (RV) observations and transit light curves obtained with the Transiting Exoplanet Survey Satellite (TESS) and ground-based facilities leads to an inner planet radius of $2.22\pm0.06\,R_\oplus$ and mass of $7.6\pm0.2\,M_\oplus$ that locates the planet into the Sub-Neptune regime. The proximity to the 2:1 period commensurability leads to the libration of the resonant argument of the inner planet. The RV measurements support the existence of a third body with an orbital period of several thousand days. This discovery places the system among the rare systems featuring a hot Jupiter accompanied by an inner low-mass planet., Comment: Accepted to ApJL, 17 pages, 6 figures, 4 tables

We investigate magneto-optical trap loss spectroscopy of Rydberg excited $^{85}$Rb ($66\leq n \leq 68~S_{1/2}$) atoms, placed inside a tailored microwave cavity. The cavity frequency at 13.053 GHz is in resonance with the $67S_{1/2} \rightarrow 66P_{3/2}$ transition, inducing a ladder multiphoton microwave Rydberg absorption and emission. The observed spectra are modeled with an extended Jaynes-Cumming formalism that accounts for multiphoton absorption from and emission into the cavity, the loss from the trap due to Rydberg excitation, and cavity imperfection. We calculate the average photons in each spectral feature and find evidence for fractional photon emission into the cavity modes. The microwave cavity Rydberg spectroscopy in this work should inform the application and technology development of Rydberg based sensors and hybrid Rydberg atom-superconducting resonator quantum gates.

As Artificial Intelligence (AI) increasingly influences various aspects of society, there is growing public interest in its potential benefits and risks. In this paper we present results of public perception of AI from a survey conducted with 10,000 respondents spanning ten countries in four continents around the world. The results show that currently an equal percentage of respondents who believe AI will change the world as we know it, also believe AI needs to be heavily regulated. However, our findings also indicate that despite the general sentiment among the global public that AI will replace workers, if a company were to use AI to innovate to improve lives, the public would be more likely to think highly of the company, purchase from them and even be interested in a job in that company. Our results further reveal that the global public largely views AI as a tool for problem solving. These nuanced results underscore the importance of AI directed towards challenges that the public would like science and technology-based innovations to address. We draw on a multi-year 3M study of public perception of science to provide further context on what the public perceives as important problems to be solved.

We present in this paper the discovery, properties, and a catalog of 1165 high redshift $6.5 < z < 18$ galaxies found in deep JWST NIRCam imaging from the GTO PEARLS survey combined with data from JWST public fields. We describe our bespoke homogeneous reduction process and our analysis of these areas including the NEP, CEERS, GLASS, NGDEEP, JADES, and ERO SMACS-0723 fields with over 214 arcmin$^{2}$ imaged to depths of $\sim 30$ mag. We describe our rigorous methods for identifying these galaxies, involving the use of Lyman-break strength, detection significance criteria, visual inspection, and integrated photometric redshifts probability distributions predominately at high redshift. Our sample is a robust and highly pure collection of distant galaxies from which we also remove brown dwarf stars, and calculate completeness and contamination from simulations. We include a summary of the basic properties of these $z > 6.5$ galaxies, including their redshift distributions, UV absolute magnitudes, and star formation rates. Our study of these young galaxies reveals a wide range of stellar population properties as seen in their colors and SED fits which we compare to stellar population models, indicating a range of star formation histories, dust, AGN and/or nebular emission. We find a strong trend exists between stellar mass and $(U-V)$ color, as well as the existence of the `main-sequence' of star formation for galaxies as early as $z \sim 12$. This indicates that stellar mass, or an underlying variable correlating with stellar mass, is driving galaxy formation, in agreement with simulation predictions. We also discover ultra-high redshift candidates at $z > 12$ in our sample and describe their properties. Finally, we note a significant observed excess of galaxies compared to models at $z > 12$, revealing a tension between predictions and our observations., Comment: Submitted to AAS Journals, 37 pages

I present a novel analytic framework for \(SU(N)\) Yang-Mills theory in the four-dimensional continuum. Background and effective field theory techniques are used to include non-perturbative contributions from cubic and quartic interactions. This approach is inspired by Savvidy who claims that first-order contributions from quartic interactions stabilize IR divergence found at one-loop order, paving the way for IR finite Yang-Mills calculations. I assess the validity of this claim and discuss the implications of my findings.

The SKYSURF project constrained extragalactic background light (EBL) and diffuse light with the vast archive of Hubble Space Telescope (HST) images. Thermal emission from HST itself introduces an additional uncertain background and hinders accurate measurement of the diffuse light level. Here, we use archival WFC3/IR engineering data to investigate and model changes in the temperature of various components in HST's optical path as a function of time (solar cycle) and time of the year (Earth-Sun distance). We also specifically investigate changes in temperature with HST's orbital phase and time since Earth occultation. We investigate possible correlations between HST component temperature and year, and temperature and month. The thermal background changes by less than one Kelvin in the WFC3 pick-off mirror, one of the most important contributors to the thermal background. We model these data to describe the impact that orbital phase, year, and time of year have on the HST and WFC3 component temperatures, and use this to derive the impact on the thermal dark signal and the resulting diffuse light measurements. Based on this improved modeling, we provide new upper limits on the level of diffuse light of 21 nW m-2 sr-1, 32 nW m-2 sr-1, and 25 nW m-2 sr-1 for F125W, F140W, and F160W. Additionally, by accounting for all known sources of measurement uncertainty, we report lower limits on the level of diffuse light of 12 nW m-2 sr-1, 20 nW m-2 sr-1, and 2 nW m-2 sr-1 for F125W, F140W, and F160W., Comment: 18 pages, 12 figures

The volumetric rate of tidal disruption events (TDEs) encodes information on the still-unknown demographics of central massive black holes (MBHs) in low-mass galaxies ($\lesssim 10^9$~M$_\odot$). Theoretical TDE rates from model galaxy samples can extract this information, but this requires accurately defining the nuclear stellar density structures. This region is typically dominated by nuclear star clusters (NSCs), which have been shown to increase TDE rates by orders of magnitude. Thus, we assemble the largest available sample of pc-scale 3-D density profiles that include NSC components. We deproject the PSF-deconvolved surface brightness profiles of 91 nearby galaxies of varying morphology and combine these with nuclear mass-to-light ratios estimated from measured colors or spectral synthesis to create 3-D mass density profiles. We fit the inner 3-D density profile to find the best-fit power-law density profile in each galaxy. We compile this information as a function of galaxy stellar mass to fit new empirical density scaling relations. These fits reveal positive correlations between galaxy stellar mass and central stellar density in both early- and late-type galaxies. We find that early-type galaxies have somewhat higher densities and shallower profiles relative to late-type galaxies at the same mass. We also use the density profiles to estimate the influence radius of each galaxy's MBH and find that the sphere of influence was likely resolved in most cases. These new relations will be used in future works to build mock galaxy samples for dynamical TDE rate calculations, with the aim of constraining MBH demographics in low-mass galaxies., Comment: 18 pages, 6 figures, 1 table. Accepted by AAS Journals

We present a full analysis of galaxy major merger pair fractions, merger rates, and mass accretion rates, thus uncovering the role of mergers in galaxy formation at the earliest previously unexplored epoch of $4.5

Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this paper, we first integrate buoyancy and hydrodynamics models into a modern Reinforcement Learning framework to reduce training time. Next, we show how system identification coupled with domain randomization improves the RL agent performance and narrows the sim-to-real gap. Real-world experiments for the task of capturing floating waste show that our approach lowers energy consumption by 13.1\% while reducing task completion time by 7.4\%. These findings, supported by sharing our open-source implementation, hold the potential to impact the efficiency and versatility of ASVs, contributing to environmental conservation efforts., Comment: IROS 2024, IEEE, Oct 2024, Abu Dhabi, United Arab Emirates

Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are crucial for building these chatbots. However, creating effective enterprise chatbots is challenging and requires meticulous RAG pipeline engineering. This includes fine-tuning embeddings and LLMs, extracting documents from vector databases, rephrasing queries, reranking results, designing prompts, honoring document access controls, providing concise responses, including references, safeguarding personal information, and building orchestration agents. We present a framework for building RAG-based chatbots based on our experience with three NVIDIA chatbots: for IT/HR benefits, financial earnings, and general content. Our contributions are three-fold: introducing the FACTS framework (Freshness, Architectures, Cost, Testing, Security), presenting fifteen RAG pipeline control points, and providing empirical results on accuracy-latency tradeoffs between large and small LLMs. To the best of our knowledge, this is the first paper of its kind that provides a holistic view of the factors as well as solutions for building secure enterprise-grade chatbots.", Comment: 8 pages, 6 figures, 2 tables, Preprint submission to ACM CIKM 2024

The recent inference of sulphur dioxide (SO$_2$) in the atmosphere of the hot ($\sim$1100 K), Saturn-mass exoplanet WASP-39b from near-infrared JWST observations suggests that photochemistry is a key process in high temperature exoplanet atmospheres. This is due to the low ($<$1 ppb) abundance of SO$_2$ under thermochemical equilibrium, compared to that produced from the photochemistry of H$_2$O and H$_2$S (1-10 ppm). However, the SO$_2$ inference was made from a single, small molecular feature in the transmission spectrum of WASP-39b at 4.05 $\mu$m, and therefore the detection of other SO$_2$ absorption bands at different wavelengths is needed to better constrain the SO$_2$ abundance. Here we report the detection of SO$_2$ spectral features at 7.7 and 8.5 $\mu$m in the 5-12 $\mu$m transmission spectrum of WASP-39b measured by the JWST Mid-Infrared Instrument (MIRI) Low Resolution Spectrometer (LRS). Our observations suggest an abundance of SO$_2$ of 0.5-25 ppm (1$\sigma$ range), consistent with previous findings. In addition to SO$_2$, we find broad water vapour absorption features, as well as an unexplained decrease in the transit depth at wavelengths longer than 10 $\mu$m. Fitting the spectrum with a grid of atmospheric forward models, we derive an atmospheric heavy element content (metallicity) for WASP-39b of $\sim$7.1-8.0 $\times$ solar and demonstrate that photochemistry shapes the spectra of WASP-39b across a broad wavelength range., Comment: Published in Nature

Consider edge colorings of digraphs where edges $v_1 v_2$ and $v_2 v_3$ have different colors. This coloring induces a vertex coloring by sets of edge colors, in which edge $v_1 v_2$ in the graph implies that the set color of $v_1$ contains an element not in the set color of $v_2$, and conversely. We generalize to colorings of $k$(vertex)-walks, defined so two walks have different colors if one is the prefix $c_1$ and the other is the suffix $c_2$ of a common $(k+1)$-walk. Further, the colors can belong to a poset $P$ where $c_1$, $c_2$ must satisfy $c_1 \not\leq c_2$. This set construction generalizes the lower order ideal in $P$ from a set of $k$-walk colors; these order ideals are partially ordered by containment. We conclude that a $P$ coloring of $k$-walks exists iff there is a vertex coloring by $A$ iterated $k-1$ times on $P$, where Birkhoff's $A$ maps a poset to its poset of lower order ideals. Thus the directed chromatic index problem is generalized and reduced to poset coloring of vertices. This work uses ideas, results and motivations due to Cole and Vishkin on deterministic coin tossing and Becker and Simon on vertex covers for subsets of $(n-2)$-cubes., Comment: written in 1994, 17 pages

Satellite imagery has emerged as an important tool to analyse demographic, health, and development indicators. While various deep learning models have been built for these tasks, each is specific to a particular problem, with few standard benchmarks available. We propose a new dataset pairing satellite imagery and high-quality survey data on child poverty to benchmark satellite feature representations. Our dataset consists of 33,608 images, each 10 km $\times$ 10 km, from 19 countries in Eastern and Southern Africa in the time period 1997-2022. As defined by UNICEF, multidimensional child poverty covers six dimensions and it can be calculated from the face-to-face Demographic and Health Surveys (DHS) Program . As part of the benchmark, we test spatial as well as temporal generalization, by testing on unseen locations, and on data after the training years. Using our dataset we benchmark multiple models, from low-level satellite imagery models such as MOSAIKS , to deep learning foundation models, which include both generic vision models such as Self-Distillation with no Labels (DINOv2) models and specific satellite imagery models such as SatMAE. We provide open source code for building the satellite dataset, obtaining ground truth data from DHS and running various models assessed in our work., Comment: 15 pages, 1 figure

We present beam measurements of the CHIME telescope using a radio calibration source deployed on a drone payload. During test flights, the pulsing calibration source and the telescope were synchronized to GPS time, enabling in-situ background subtraction for the full $N^{2}$ visibility matrix for one CHIME cylindrical reflector. We use the autocorrelation products to estimate the primary beam width and centroid location, and compare these quantities to solar transit measurements and holographic measurements where they overlap on the sky. We find that the drone, solar, and holography data have similar beam parameter evolution across frequency and both spatial coordinates. This paper presents the first drone-based beam measurement of a large cylindrical radio interferometer. Furthermore, the unique analysis and instrumentation described in this paper lays the foundation for near-field measurements of experiments like CHIME., Comment: Submitted to IEEE OJAP June 30, 2024

If $G : \mathbb{R}_+ \to \mathbb{R}_+$, the $G$-moment of a vector $\mathbf{x}\in\mathbb{R}_+^n$ is $G(\mathbf{x}) = \sum_{v\in[n]} G(\mathbf{x}(v))$ and the $G$-sampling problem is to select an index $v_*\in [n]$ according to its contribution to the $G$-moment, i.e., such that $\Pr(v_*=v) = G(\mathbf{x}(v))/G(\mathbf{x})$. Approximate $G$-samplers may introduce multiplicative and/or additive errors to this probability, and some have a non-trivial probability of failure. In this paper we focus on the exact $G$-sampling problem, where $G$ is selected from the class $\mathcal{G}$ of Laplace exponents of non-negative, one-dimensional L\'evy processes, which includes several well studied classes such as $p$th moments $G(z)=z^p$, $p\in[0,1]$, logarithms $G(z)=\log(1+z)$, Cohen and Geri's soft concave sublinear functions, which are used to approximate concave sublinear functions, including cap statistics. We develop $G$-samplers for a vector $\mathbf{x} \in \mathbb{R}_+^n$ that is presented as an incremental stream of positive updates. In particular: * For any $G\in\mathcal{G}$, we give a very simple $G$-sampler that uses 2 words of memory and stores at all times a $v_*\in [n]$, such that $\Pr(v_*=v)$ is exactly $G(\mathbf{x}(v))/G(\mathbf{x})$. * We give a ``universal'' $\mathcal{G}$-sampler that uses $O(\log n)$ words of memory w.h.p., and given any $G\in \mathcal{G}$ at query time, produces an exact $G$-sample. With an overhead of a factor of $k$, both samplers can be used to $G$-sample a sequence of $k$ indices with or without replacement. Our sampling framework is simple and versatile, and can easily be generalized to sampling from more complex objects like graphs and hypergraphs.

Hallucinations -- the generation of untrue claims -- pose a challenge to the application of large language models (LLMs) [1] thereby motivating the development of metrics to evaluate factual precision. We observe that popular metrics using the Decompose-Then-Verify framework, such as FActScore [2], can be manipulated by adding obvious or repetitive claims to artificially inflate scores. We expand the FActScore dataset to design and analyze factual precision metrics, demonstrating that models can be trained to achieve high scores under existing metrics through exploiting the issues we identify. This motivates our new customizable plug-and-play subclaim selection component called Core, which filters down individual subclaims according to their uniqueness and informativeness. Metrics augmented by Core are substantially more robust as shown in head-to-head comparisons. We release an evaluation framework supporting the modular use of Core (https://github.com/zipJiang/Core) and various decomposition strategies, and we suggest its adoption by the LLM community. [1] Hong et al., "The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models", arXiv:2404.05904v2 [cs.CL]. [2] Min et al., "FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation", arXiv:2305.14251v2 [cs.CL].

This paper introduces UnSeenTimeQA, a novel time-sensitive question-answering (TSQA) benchmark that diverges from traditional TSQA benchmarks by avoiding factual and web-searchable queries. We present a series of time-sensitive event scenarios decoupled from real-world factual information. It requires large language models (LLMs) to engage in genuine temporal reasoning, disassociating from the knowledge acquired during the pre-training phase. Our evaluation of six open-source LLMs (ranging from 2B to 70B in size) and three closed-source LLMs reveal that the questions from the UnSeenTimeQA present substantial challenges. This indicates the models' difficulties in handling complex temporal reasoning scenarios. Additionally, we present several analyses shedding light on the models' performance in answering time-sensitive questions.

The theory of forbidden 0-1 matrices generalizes Turan-style (bipartite) subgraph avoidance, Davenport-Schinzel theory, and Zarankiewicz-type problems, and has been influential in many areas, such as discrete and computational geometry, the analysis of self-adjusting data structures, and the development of the graph parameter twin width. The foremost open problems in this area is to resolve the Pach-Tardos conjecture from 2005, which states that if a forbidden pattern $P\in\{0,1\}^{k\times l}$ is the bipartite incidence matrix of an acyclic graph (forest), then $\mathrm{Ex}(P,n) = O(n\log^{C_P} n)$, where $C_P$ is a constant depending only on $P$. This conjecture has been confirmed on many small patterns, specifically all $P$ with weight at most 5, and all but two with weight 6. The main result of this paper is a clean refutation of the Pach-Tardos conjecture. Specifically, we prove that $\mathrm{Ex}(S_0,n),\mathrm{Ex}(S_1,n) \geq n2^{\Omega(\sqrt{\log n})}$, where $S_0,S_1$ are the outstanding weight-6 patterns. We also prove sharp bounds on the entire class of alternating patterns $(P_t)$, specifically that for every $t\geq 2$, $\mathrm{Ex}(P_t,n)=\Theta(n(\log n/\log\log n)^t)$. This is the first proof of an asymptotically sharp bound that is $\omega(n\log n)$.

The three dynamically confirmed wind-fed black hole high-mass X-ray binaries (BH-HMXBs) are suggested to all contain a highly spinning black hole (BH). However, based on the theories of efficient angular momentum transport inside the stars, we expect that the first-born BHs in binary systems should have low spins, which is consistent with gravitational-wave observations. As a result, the origin of the high BH spins measured in wind-fed BH-HMXBs remains a mystery. In this paper, we conduct a binary population synthesis study on wind-fed BH-HMXBs at solar metallicity with the use of the newly developed code POSYDON, considering three scenarios for BH accretion: Eddington-limited, moderately super-Eddington, and fully conservative accretion. Taking into account the conditions for accretion-disk formation, we find that regardless of the accretion model, these systems are more likely to have already experienced a phase of Roche-lobe overflow after the BH formation. To account for the extreme BH spins, highly conservative accretion onto BHs is required, when assuming the accreted material carries the specific angular momentum at the innermost stable orbit. Besides, in our simulations we found that the systems with donor stars within the mass range of $10-20\,M_{\odot}$ are prevalent, posing a challenge in explaining simultaneously all observed properties of the BH-HMXB in our Galaxy, Cygnus X-1, and potentially hinting that the accretion efficiency onto non-degenerate stars, before the formation of the BH, is also more conservative than assumed in our simulations., Comment: Submitted to A&A. Comments are welcome!

The Near-Earth binary asteroid Didymos was the target of a planetary defense demonstration mission DART in September 2022. The smaller binary component, Dimorphos, was impacted by the spacecraft in order to measure momentum transfer in kinetic impacts into rubble piles. DART and associated Earth-based observation campaigns have provided a wealth of scientific data on the Didymos-Dimorphos binary. DART revealed a largely oblate and ellipsoidal shape of Dimorphos before the impact, while the post-impact observations suggest that Dimorphos now has a prolate shape. Here we add those data points to the known properties of small binary asteroids and propose new paradigms of the radiative binary YORP (BYORP) effect as well as tidal dissipation in small binaries. We find that relatively spheroidal bodies like Dimorphos made of small debris may experience a weaker and more size-dependent BYORP effect than previously thought. This could explain the observed values of period drift in several well-characterized binaries. We also propose that energy dissipation in small binaries is dominated by relatively brief episodes of large-scale movement of (likely surface) materials, rather than long-term steady-state tidal dissipation. We propose that one such episode was triggered on Dimorphos by the DART impact. Depending on the longevity of this high-dissipation regime, it is possible that Dimorphos will be more dynamically relaxed in time for the Hera mission than it was in the weeks following the impact., Comment: Accepted for PSJ

Numerical computations and methods have become increasingly crucial in the study of spin foam models across various regimes. This paper adds to this field by introducing new algorithms based on tensor network methods for computing amplitudes, focusing on topological SU(2) BF and Lorentzian EPRL spin foam models. By reorganizing the sums and tensors involved, vertex amplitudes are recast as a sequence of matrix contractions. This reorganization significantly reduces computational complexity and memory usage, allowing for scalable and efficient computations of the amplitudes for larger representation labels on standard consumer hardware--previously infeasible due to the computational demands of high-valent tensors. We apply these tensor network algorithms to analyze the characteristics of various vertex configurations, including Regge and vector geometries for the SU(2) BF theory, demonstrating consistent scaling behavior and differing oscillation patterns. Our benchmarks reveal substantial improvements in computational time and memory allocations, especially for large representation labels. Additionally, these tensor network methods are applicable to generic 2-complexes with multiple vertices, where we introduce partial-coherent vertex amplitudes to streamline the computations. The implementation of these algorithms is available on GitHub for further exploration and use., Comment: 31 pages, 8 figures, 3 tables

Biological neural networks can perform complex computations to predict their environment, far above the limited predictive capabilities of individual neurons. While conventional approaches to understanding these computations often focus on isolating the contributions of single neurons, here we argue that a deeper understanding requires considering emergent dynamics - dynamics that make the whole system "more than the sum of its parts". Specifically, we examine the relationship between prediction performance and emergence by leveraging recent quantitative metrics of emergence, derived from Partial Information Decomposition, and by modelling the prediction of environmental dynamics in a bio-inspired computational framework known as reservoir computing. Notably, we reveal a bidirectional coupling between prediction performance and emergence, which generalises across task environments and reservoir network topologies, and is recapitulated by three key results: 1) Optimising hyperparameters for performance enhances emergent dynamics, and vice versa; 2) Emergent dynamics represent a near sufficient criterion for prediction success in all task environments, and an almost necessary criterion in most environments; 3) Training reservoir computers on larger datasets results in stronger emergent dynamics, which contain task-relevant information crucial for performance. Overall, our study points to a pivotal role of emergence in facilitating environmental predictions in a bio-inspired computational architecture.

The observed Standard Model is consistent with the existence of vector-like species with electric charge a multiple of $e/6$. The discovery of a fractionally charged particle would provide nonperturbative information about Standard Model physics, and furthermore rule out some or all of the minimal theories of unification. We discuss the phenomenology of such particles and focus particularly on current LHC constraints, for which we reinterpret various searches to bound a variety of fractionally charged representations. We emphasize that in some circumstances the collider bounds are surprisingly low or nonexistent, which highlights the discovery potential for these species which have distinctive signatures and important implications. We additionally offer pedagogical discussions of the representation theory of gauge groups with different global structures, and separately of the modern framework of Generalized Global Symmetries, either of which serves to underscore the bottom-up importance of these searches., Comment: 43+12 pages, 10 figures

We revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion requests, one often-cited potential application for unlearning methods is to remove the effects of training on poisoned data. We experimentally demonstrate that, while existing unlearning methods have been demonstrated to be effective in a number of evaluation settings (e.g., alleviating membership inference attacks), they fail to remove the effects of data poisoning, across a variety of types of poisoning attacks (indiscriminate, targeted, and a newly-introduced Gaussian poisoning attack) and models (image classifiers and LLMs); even when granted a relatively large compute budget. In order to precisely characterize unlearning efficacy, we introduce new evaluation metrics for unlearning based on data poisoning. Our results suggest that a broader perspective, including a wider variety of evaluations, is required to avoid a false sense of confidence in machine unlearning procedures for deep learning without provable guarantees. Moreover, while unlearning methods show some signs of being useful to efficiently remove poisoned datapoints without having to retrain, our work suggests that these methods are not yet "ready for prime time", and currently provide limited benefit over retraining.

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types., Comment: Accepted at the Undergraduate Consortium at KDD 2024 (KDD-UC)

Monte Carlo (MC) neutron transport provides detailed estimates of radiological quantities within fission reactors. This method involves tracking individual neutrons through a computational geometry. CPU-based MC codes use multiple polymorphic tracker types with different tracking algorithms to exploit the repeated configurations of reactors, but virtual function calls have high overhead on the GPU. The Shift MC code was modified to support GPU-based tracking with three strategies: (1) dynamic polymorphism (DP) with virtual functions, (2) static polymorphism (SP), and (3) a single tracker (ST) type with tree-based acceleration. Results on the Frontier supercomputer show that the DP, SP, and ST methods achieve 77.8%, 91.2%, and 83.4% of the practical maximum tracking rate in the worst case, indicating that any of these methods can be used without incurring a significant performance penalty. The flexibility of the ST method is highlighted with a hexagonal-grid microreactor problem, performed without hexagonal-grid-specific tracking routines.

We present several algorithms for evaluating point containment in constructive solid geometry (CSG) trees with unbounded primitives. Three algorithms are presented based on postfix, prefix, and infix notations of the CSG binary expression tree. We show that prefix and infix notations enable short-circuiting logic, which reduces the number of primitives that must be checked during point containment. To evaluate the performance of the algorithms, each algorithm was implemented in the OpenMC Monte Carlo particle transport code, which relies on CSG to represent solid bodies through which subatomic particles travel. Two sets of tests were carried out. First, the execution time to generate a high-resolution rasterized image of a 2D slice of a detailed CSG model of the ITER tokamak was measured. Use of both prefix and infix notations offered significant speedup over the postfix notation that has traditionally been used in particle transport codes, with infix resulting in a 6$\times$ reduction in execution time relative to postfix. We then measured the execution time of neutron transport simulations of the same ITER model using each of the algorithms. The results and performance improvements reveal the same trends as for the rasterization test, with a 4.59$\times$ overall speedup using the infix notation relative to the original postfix notation in OpenMC.

We measure the high-mass stellar initial mass function (IMF) from resolved stars in M33 young stellar clusters. Leveraging \textit{Hubble Space Telescope's} high resolving power, we fully model the IMF probabilistically. We first model the optical CMD of each cluster to constrain its power-law slope $\Gamma$, marginalized over other cluster parameters in the fit (e.g., cluster age, mass, and radius). We then probabilistically model the distribution of MF slopes for a highly strict cluster sample of 9 clusters more massive than log(Mass/M$_{\odot}$)=3.6; above this mass, all clusters have well-populated main sequences of massive stars and should have accurate recovery of their MF slopes, based on extensive tests with artificial clusters. We find the ensemble IMF is best described by a mean high-mass slope of $\overline{\Gamma} = 1.49\pm0.18$, with an intrinsic scatter of $\sigma^{2}_{\Gamma} = 0.02^{+0.16}_{0.00}$, consistent with a universal IMF. We find no dependence of the IMF on environmental impacts such as the local star formation rate or galactocentric radius within M33, which serves as a proxy for metallicity. This $\overline{\Gamma}$ measurement is consistent with similar measurements in M31, despite M33 having a much higher star formation rate intensity. While this measurement is formally consistent with the canonical Kroupa ($\Gamma = 1.30$) IMF, as well as the Salpeter ($\Gamma = 1.35)$) value, it is the second Local Group cluster sample to show evidence for a somewhat steeper high-mass IMF slope. We explore the impacts a steeper IMF slope has on a number of astronomical sub-fields., Comment: Accepted for publication in ApJ. 9 Figures, 1 Table

Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a novel General-purpose Large Audio-Language Model (LALM) with Advanced Audio Understanding and Complex Reasoning Abilities. We build GAMA by integrating an LLM with multiple types of audio representations, including features from a custom Audio Q-Former, a multi-layer aggregator that aggregates features from multiple layers of an audio encoder. We fine-tune GAMA on a large-scale audio-language dataset, which augments it with audio understanding capabilities. Next, we propose CompA-R (Instruction-Tuning for Complex Audio Reasoning), a synthetically generated instruction-tuning (IT) dataset with instructions that require the model to perform complex reasoning on the input audio. We instruction-tune GAMA with CompA-R to endow it with complex reasoning abilities, where we further add a soft prompt as input with high-level semantic evidence by leveraging event tags of the input audio. Finally, we also propose CompA-R-test, a human-labeled evaluation dataset for evaluating the capabilities of LALMs on open-ended audio question-answering that requires complex reasoning. Through automated and expert human evaluations, we show that GAMA outperforms all other LALMs in literature on diverse audio understanding tasks by margins of 1%-84%. Further, GAMA IT-ed on CompA-R proves to be superior in its complex reasoning and instruction following capabilities., Comment: Project Website: https://sreyan88.github.io/gamaaudio/

The knowledge of scintillation quenching of $\alpha$-particles plays a paramount role in understanding $\alpha$-induced backgrounds and improving the sensitivity of liquid argon-based direct detection of dark matter experiments. We performed a relative measurement of scintillation quenching in the MeV energy region using radioactive isotopes ($^{222}$Rn, $^{218}$Po and $^{214}$Po isotopes) present in trace amounts in the DEAP-3600 detector and quantified the uncertainty of extrapolating the quenching factor to the low-energy region., Comment: Submitted to Eur. Phys. J. C

We introduce DafnyBench, the largest benchmark of its kind for training and evaluating machine learning systems for formal software verification. We test the ability of LLMs such as GPT-4 and Claude 3 to auto-generate enough hints for the Dafny formal verification engine to successfully verify over 750 programs with about 53,000 lines of code. The best model and prompting scheme achieved 68% success rate, and we quantify how this rate improves when retrying with error message feedback and how it deteriorates with the amount of required code and hints. We hope that DafnyBench will enable rapid improvements from this baseline as LLMs and verification techniques grow in quality., Comment: Code & dataset available at: https://github.com/sun-wendy/DafnyBench

In mathematical proof education, there remains a need for interventions that help students learn to write mathematical proofs. Research has shown that timely feedback can be very helpful to students learning new skills. While for many years natural language processing models have struggled to perform well on tasks related to mathematical texts, recent developments in natural language processing have created the opportunity to complete the task of giving students instant feedback on their mathematical proofs. In this paper, we present a set of training methods and models capable of autograding freeform mathematical proofs by leveraging existing large language models and other machine learning techniques. The models are trained using proof data collected from four different proof by induction problems. We use four different robust large language models to compare their performances, and all achieve satisfactory performances to various degrees. Additionally, we recruit human graders to grade the same proofs as the training data, and find that the best grading model is also more accurate than most human graders. With the development of these grading models, we create and deploy an autograder for proof by induction problems and perform a user study with students. Results from the study shows that students are able to make significant improvements to their proofs using the feedback from the autograder, but students still do not trust the AI autograders as much as they trust human graders. Future work can improve on the autograder feedback and figure out ways to help students trust AI autograders.

The effect of self-irradiation damage can influence many properties of a radioactive material. Actinide materials involving the decay through alpha radiation have been frequently studied using techniques such as transport, thermodynamics, and x-ray diffraction. The use of nuclear magnetic resonance (NMR) spectroscopy to study such effects, however, has seen relatively little attention. Here, we use $^{239}$Pu NMR to study the local influence of self-damage in a single crystal of the candidate topological insulator plutonium tetraboride (PuB$_{4}$). We first characterize the anisotropy of the $^{239}$Pu resonance in a single crystal and confirm the local axial site symmetry inferred from previous polycrystalline measurements. Aging effects are then evaluated over the timeframe of six years. We find that, though the static NMR spectra may show a slight modulation in their shape, their field-rotation pattern reveals no change in Pu local site symmetry over time, suggesting that aging has a surprisingly small impact on the spatial distribution of the static hyperfine field. By contrast, aging has a prominent impact on the NMR relaxation processes and signal intensity. Specifically, aging-induced damage manifests itself as an increase in the spin-lattice relaxation time $T_{1}$, an increased distribution of $T_{1}$, and a signal intensity that decreases linearly by 20 % per year. The spin-spin relaxation time $T_{2}$ in the aged sample shows a strong variation across the spectrum as well as a drastic shortening towards lower temperature, suggesting growth of slow fluctuations of the hyperfine field that are linked to radiation-damage-induced inhomogeneity and could be responsible for the signal wipeout that develops over time., Comment: 44 pages, 10 + 3 figures

Contact between languages has the potential to transmit vocabulary and other language features; however, this does not always happen. Here, an iterated learning model is used to examine, in a simple way, the resistance of languages to change during language contact. Iterated learning models are agent-based models of language change, they demonstrate that languages that are expressive and compositional arise spontaneously as a consequence of a language transmission bottleneck. A recently introduced type of iterated learning model, the Semi-Supervised ILM is used to simulate language contact. These simulations do not include many of the complex factors involved in language contact and do not model a population of speakers; nonetheless the model demonstrates that the dynamics which lead languages in the model to spontaneously become expressive and compositional, also cause a language to maintain its core traits even after mixing with another language., Comment: to appear ALIFE24

Topological photonics is an emergent research discipline which interlinks fundamental aspects of photonics, information processing and solid-state physics. Exciton-polaritons are a specifically interesting platform to study topological phenomena, since the coherent light matter coupling enables new degrees of freedom such as tunable non-linearities, chiralities and dissipation. Room-temperature operation of such exciton-polaritons relies on materials comprising both, large exciton binding energies and oscillator strength. We harness widely spectrally tunable, room temperature exciton-polaritons based on a WS2 monolayer in an open optical cavity to realize a polariton potential landscape which emulates the Su-Schrieffer-Heeger (SSH) Hamiltonian. It comprises a domain boundary hosting a topological, exponentially localized mode at the interface between two lattices characterized by different Zak-phases which features a spectral tunability over a range as large as 80 meV. Moreover, we utilize the unique tilt-tunability of our implementation, to transform the SSH-lattice into a Stark-ladder. This transformation couples the topologically protected defect mode to propagating lattice modes, and effectively changes the symmetry of the system. Furthermore, it allows us to directly quantify the Zak-phase difference $\Delta_{Zak}=(1.13\pm 0.11)\pi$ between the two topological phases. Our work comprises an important step towards in-situ tuning topological lattices to control and guide light on non-linear chips.

Mammalian brains handle complex reasoning by integrating information across brain regions specialized for particular sensory modalities. This enables improved robustness and generalization versus deep neural networks, which typically process one modality and are vulnerable to perturbations. While defense methods exist, they do not generalize well across perturbations. We developed a fusion model combining background and foreground features from CNNs trained on Imagenet and Places365. We tested its robustness to human-perceivable perturbations on MS COCO. The fusion model improved robustness, especially for classes with greater context variability. Our proposed solution for integrating multiple modalities provides a new approach to enhance robustness and may be complementary to existing methods., Comment: arXiv admin note: text overlap with arXiv:2011.09526

A large spectrum of problems in classical physics and engineering, such as turbulence, is governed by nonlinear differential equations, which typically require high-performance computing to be solved. Over the past decade, however, the growth of classical computing power has slowed down because the miniaturisation of chips has been approaching the atomic scale. This is marking an end to Moore's law, which calls for a new computing paradigm: Quantum computing is a prime candidate. In this paper, we offer a perspective on the current challenges that need to be overcome in order to use quantum computing for the simulation of nonlinear dynamics. We review and discuss progress in the development of both quantum algorithms for nonlinear equations and quantum hardware. We propose pairings between quantum algorithms for nonlinear equations and quantum hardware concepts. These avenues open new opportunities for the simulation of nonlinear systems and turbulence.

New JWST/NIRCam wide-field slitless spectroscopy provides redshifts for two z > 8 galaxies located behind the lensing cluster MACS J0416.1-2403. Both galaxies are strong [O iii]{\lambda}5007 emitters. For one galaxy, "Y1", the existing redshift z = 8.31, based on ALMA measurements of [O iii] 88 {\mu}m and [C ii] 157.7 {\mu}m lines, is confirmed. JWST/NIRCam images resolve this galaxy into three components of similar colors, and the whole system extends over ~3.4 kpc. The other galaxy, "JD", is at z = 8.34 instead of the previously claimed z = 9.28. It has a companion, "JD-N", at the same redshift with projected separation ~2.3 kpc. All objects are only moderately magnified and have intrinsic MUV ranging from -19.66 to -20.85 mag. Their eight-band NIRCam spectral energy distributions show that the galaxies are all very young with ages $\lesssim$11 Myr and stellar masses about 108 $M_{\odot}$. These infant galaxies are actively forming stars at rates of a few tens to a couple of hundred $M_{\odot} yr^{-1}$, but only one of them (JD) has a blue rest-frame UV slope. This slope indicates a high Lyman-continuum photon escape fraction that could contribute significantly to the cosmic hydrogen-reionizing background. The other two systems have much flatter slopes largely because their dust extinction is twice as high as JD's albeit only AV ~ 0.90 mag. The much lower indicated escape fractions show that even very young, actively star-forming galaxies can have negligible contribution to reionization when they quickly form dust throughout their bodies., Comment: 18 pages, 6 figures, submitted to ApJL

Advances in deep learning systems have allowed large models to match or surpass human accuracy on a number of skills such as image classification, basic programming, and standardized test taking. As the performance of the most capable models begin to saturate on tasks where humans already achieve high accuracy, it becomes necessary to benchmark models on increasingly complex abilities. One such task is forecasting the future outcome of events. In this work we describe experiments using a novel dataset of real world events and associated human predictions, an evaluation metric to measure forecasting ability, and the accuracy of a number of different LLM based forecasting designs on the provided dataset. Additionally, we analyze the performance of the LLM forecasters against human predictions and find that models still struggle to make accurate predictions about the future. Our follow-up experiments indicate this is likely due to models' tendency to guess that most events are unlikely to occur (which tends to be true for many prediction datasets, but does not reflect actual forecasting abilities). We reflect on next steps for developing a systematic and reliable approach to studying LLM forecasting.

Visual cues, like lip motion, have been shown to improve the performance of Automatic Speech Recognition (ASR) systems in noisy environments. We propose LipGER (Lip Motion aided Generative Error Correction), a novel framework for leveraging visual cues for noise-robust ASR. Instead of learning the cross-modal correlation between the audio and visual modalities, we make an LLM learn the task of visually-conditioned (generative) ASR error correction. Specifically, we instruct an LLM to predict the transcription from the N-best hypotheses generated using ASR beam-search. This is further conditioned on lip motions. This approach addresses key challenges in traditional AVSR learning, such as the lack of large-scale paired datasets and difficulties in adapting to new domains. We experiment on 4 datasets in various settings and show that LipGER improves the Word Error Rate in the range of 1.1%-49.2%. We also release LipHyp, a large-scale dataset with hypothesis-transcription pairs that is additionally equipped with lip motion cues to promote further research in this space, Comment: InterSpeech 2024. Code and Data: https://github.com/Sreyan88/LipGER

We consider the CH-KP-I equation. For this equation we prove the existence of steady solutions, which are solitary in one horizontal direction and periodic in the other. We show that such waves bifurcate from the line solitary wave solutions, i.e. solitary wave solutions to the Camassa-Holm equation, in a dimension-breaking bifurcation. This is achieved through reformulating the problem as a dynamical system for a perturbation of the line solitary wave solutions, where the periodic direction takes the role of time, then applying the Lyapunov-Iooss theorem., Comment: 11 pages

The signal from a transiting planet can be diluted by astrophysical contamination. In the case of circumstellar debris disks, this contamination could start in the mid-infrared and vary as a function of wavelength, which would then change the observed transmission spectrum for any planet in the system. The MIRI/LRS WASP-39b transmission spectrum shows an unexplained dip starting at $\sim$10 $\mu$m that could be caused by astrophysical contamination. The spectral energy distribution displays excess flux at similar levels to that which are needed to create the dip in the transmission spectrum. In this article, we show that this dip is consistent with the presence of a bright circumstellar debris disk, at a distance of $>$2 au. We discuss how a circumstellar debris disk like that could affect the atmosphere of WASP-39b. We also show that even faint debris disks can be a source of contamination in MIRI exoplanet spectra., Comment: accepted to ApJL

Recent advances in reinforcement learning (RL) heavily rely on a variety of well-designed benchmarks, which provide environmental platforms and consistent criteria to evaluate existing and novel algorithms. Specifically, in multi-agent RL (MARL), a plethora of benchmarks based on cooperative games have spurred the development of algorithms that improve the scalability of cooperative multi-agent systems. However, for the competitive setting, a lightweight and open-sourced benchmark with challenging gaming dynamics and visual inputs has not yet been established. In this work, we present FightLadder, a real-time fighting game platform, to empower competitive MARL research. Along with the platform, we provide implementations of state-of-the-art MARL algorithms for competitive games, as well as a set of evaluation metrics to characterize the performance and exploitability of agents. We demonstrate the feasibility of this platform by training a general agent that consistently defeats 12 built-in characters in single-player mode, and expose the difficulty of training a non-exploitable agent without human knowledge and demonstrations in two-player mode. FightLadder provides meticulously designed environments to address critical challenges in competitive MARL research, aiming to catalyze a new era of discovery and advancement in the field. Videos and code at https://sites.google.com/view/fightladder/home., Comment: ICML 2024

Interlayer excitons in transition-metal dichalcogenide heterobilayers combine high binding energy and valley-contrasting physics with long optical lifetime and strong dipolar character. Their permanent electric dipole enables electric-field control of emission energy, lifetime, and location. Device material and geometry impacts the nature of the interlayer excitons via their real- and momentum-space configurations. Here, we show that interlayer excitons in MoS$_{2}$/MoSe$_{2}$ heterobilayers are formed by charge carriers residing at the Brillouin zone edges, with negligible interlayer hybridization. We find that the moir\'e superlattice leads to the reversal of the valley-dependent optical selection rules, yielding a positively valued g-factor and cross-polarized photoluminescence. Time-resolved photoluminescence measurements reveal that the interlayer exciton population retains the optically induced valley polarization throughout its microsecond-long lifetime. The combination of long optical lifetime and valley polarization retention makes MoS$_{2}$/MoSe$_{2}$ heterobilayers a promising platform for studying fundamental bosonic interactions and developing excitonic circuits for optical information processing.

$\omega$ Centauri, the most massive globular cluster in the Milky Way, has long been suspected to be the stripped nucleus of a dwarf galaxy that fell into the Galaxy a long time ago. There is considerable evidence for this scenario including a large spread in metallicity and an unusually large number of distinct sub-populations seen in photometric studies. In this work, we use new MUSE spectroscopic and HST photometric catalogs to investigate the underlying metallicity distributions as well as the spatial variations of the populations within the cluster up to its half-light radius. Based on 11,050 member stars, the [M/H] distribution has a median of $ (-1.614 \pm 0.003)$ dex and a large spread of $\sim$ 1.37 dex reaching from $ -0.67$ dex to $ -2.04$ dex for 99.7 % of the stars. In addition, we show the chromosome map of the cluster, which separates the red giant branch stars into different sub-populations, and analyze the sub-populations of the metal-poorest component. Finally, we do not find any metallicity gradient within the half-light radius, and the different sub-populations are well mixed., Comment: 22 pages, 18 figures, and 3 tables. Accepted for publication in ApJ