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The purpose of the addendum is to document the findings from the Year 2 student achievement analyses of a teacher-level randomized experiment, which was designed to examine the implementation and impact of a scalable version of the 2-year MyTeachingPartner Secondary (MTP-S) program as part of a federally-funded Education Innovation and Research (EIR) project. Due to COVID-caused delays, we were unable to include findings about the impact of MTP-S on Year 2 student achievement in the final report produced at the end of the EIR project (Wayne et al., 2023). With the support of a grant from the William T. Grant Foundation, we were able to complete the collection and analyses of Year 2 student achievement data presented in this addendum. Similar to the Year 1 student achievement findings presented in our EIR final project report, the Year 2 student achievement findings did not reveal any significant program impact on students' math or English language arts achievement at the end of the 2-year program. Findings from this study need to be interpreted with caution given study limitations resulting largely from the influence of the pandemic.

The four-day school week is a school calendar that has become increasingly common following the COVID-19 pandemic. Proponents of the calendar often claim that offering teachers a regular 3-day weekend will help schools better retain existing teachers and recruit new teachers to their district without incurring additional costs due to higher salaries or other pecuniary benefits. However, there is scant empirical evidence assessing these claims. I use difference-in-differences and synthetic difference-in-differences models to assess the impact of four-day school week calendars on teacher retention and teacher quality in Arkansas. I find evidence that the calendar may help retain teachers who otherwise would have moved to another school and suggestive evidence that retention in non-adopting schools may be harmed by the four-day school week adoption in nearby districts. Results examining changes in teacher quality are inconclusive. These results have significant implications given the rapid growth in four-day school week calendars in recent years.

Attracting and retaining high-quality teachers is a pressing policy concern. Increasing teacher salaries and creating more attractive compensation packages are often proposed as a potential solution. Signed into law in March 2023, the LEARNS Act increased Arkansas's minimum teacher salary from $36,000 to $50,000, guaranteed all teachers a minimum raise of $2,000, and added flexibility allowing school districts to deviate from seniority-based traditional salary schedules. To study school districts' adjustments to the new legislation, we collected information about districts' teacher compensation policies one year before and the first year of implementation. We also integrated this data with teachers' administrative records to study patterns of teacher retention and mobility. Our results reveal a more equitable distribution of starting teacher salaries across districts, with minimal variation. The LEARNS Act notably increased funding for rural and high-poverty districts, mitigating the negative association between starting salaries and district poverty rates. However, the initial effects on teacher retention and mobility were modest. While some positive trends emerged, such as reduced probabilities of teachers transitioning to non-instructional roles and increased new teacher placement in geographic areas of shortage, broader impacts on retention and mobility were limited in the first year of implementation.

A later school start time policy has been recommended as a solution to adolescents' sleep deprivation. We estimated the impacts of later school start times on adolescents' sleep and substance use by leveraging a quasi-experiment in which school start time was delayed in some regions in South Korea. A later school start time policy was implemented in 2014 and 2015, which delayed school start times approximately 30-90 minutes. We applied difference-in-differences and event-study designs to longitudinal data on a nationally-representative cohort of adolescents from 2010 to 2015, which annually tracked sleep and substance use of 1,133 adolescents from grade 7 through grade 12. The adoption of a later school start time policy was initially associated with a 19-minute increase in sleep duration (95% CI, 5.52 to 32.04), driven by a delayed wake time and consistent bedtime. The policy was also associated with statistically significant reductions in monthly smoking and drinking frequencies. However, approximately a year after implementation, the observed increase in sleep duration shrank to 7-minute (95% CI, -12.60 to 25.86) and became statistically nonsignificant. Similarly, the observed reduction in smoking and drinking was attenuated a year after. Our findings suggest that policies that increase sleep in adolescents may have positive effects on health behaviors, but additional efforts may be required to sustain positive impacts over time. Physicians and education and health policymakers should consider the long-term effects of later school start times on adolescent health and well-being.

We use student-level data on elementary special education identification from Washington state to explore student identification rates in the months immediately after the onset of the COVID-19 pandemic and over 2 subsequent years. Special education identification rates dropped dramatically in March 2020 through the end of the 2019-20 school year and remained below historical norms through 2020-21 before returning to pre-pandemic levels early in 2021-22. The magnitude of these effects implies that over 8,000 fewer elementary students were identified for special education services during the pandemic in Washington than would have been expected based on prior trends.

The Quantum Gravity Mediated Entanglement (QGEM) protocol offers a novel method to probe the quantumness of gravitational interactions at non-relativistic scales. This protocol leverages the Stern-Gerlach effect to create $\mathcal{O}(\sim \mu m)$ spatial superpositions of two nanodiamonds (mass $\sim 10^{-15}$ kg) with NV spins, which are then allowed to interact and become entangled solely through the gravitational interaction. Since electromagnetic interactions such as Casimir-Polder and dipole-dipole interactions dominate at this scale, screening them to ensure the masses interact exclusively via gravity is crucial. In this paper, we propose using magnetic traps based on micro-fabricated wires, which provide strong gradients with relatively modest magnetic fields to trap nanoparticles for interferometric entanglement experiments. The design consists of a small trap to cool the center-of-mass motion of the nanodiamonds and a long trap with a weak direction suitable for creating macroscopic superpositions. In contrast to permanent-magnet-based long traps, the micro-fabricated wire-based approach allows fast switching of the magnetic trapping and state manipulation potentials and permits integrated superconducting shielding, which can screen both electrostatic and magnetic interactions between nanodiamonds in a gravitational entanglement experiment. The setup also provides a possible platform for other tests of quantum coherence in macroscopic systems and searches for novel short-range forces., Comment: 11 pages, 8 figures

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

We present the discovery and characterization of TOI-4364\,b, a young mini-Neptune in the tidal tails of the Hyades cluster, identified through TESS transit observations and ground-based follow-up photometry. The planet orbits a bright M dwarf ($K=9.1$\,mag) at a distance of 44\,pc, with an orbital period of 5.42\,days and an equilibrium temperature of $488^{+4}_{-4}$\,K. The host star's well-constrained age of 710\,Myr makes TOI-4364\,b an exceptional target for studying early planetary evolution around low-mass stars. We determined a planetary radius of $2.01^{+0.1}_{-0.08}$\,Earth radii, indicating that this planet is situated near the upper edge of the radius valley. This suggests that the planet retains a modest H/He envelope. As a result, TOI-4364\,b provides a unique opportunity to explore the transition between rocky super-Earths and gas-rich mini-Neptunes at the early stages of evolution. Its radius, which may still evolve as a result of ongoing atmospheric cooling, contraction, and photoevaporation, further enhances its significance for understanding planetary development. Furthermore, TOI-4364\,b possesses a moderately high Transmission Spectroscopy Metric of 44.2, positioning it as a viable candidate for atmospheric characterization with instruments such as JWST. This target has the potential to offer crucial insights into atmospheric retention and loss in young planetary systems., Comment: 18 pages, 9 figures

The nearby long gamma-ray burst (GRB) 190829A was observed using the HST/WFC3/IR grisms about four weeks to 500 days after the burst. We find the spectral features of its associated supernova, SN 2019oyw, are redshifted by several thousands km/s compared to the redshift of the large spiral galaxy on which it is superposed. This velocity offset is seen in several features but most clearly in Ca II NIR triplet $\lambda\lambda$ 8498, 8542, 8662 (CaIR3). We also analyze VLT/FORS and X-shooter spectra of the SN and find strong evolution with time of its P-Cygni features of CaIR3 from the blue to the red. However, comparison with a large sample of Type Ic-BL and Ic SNe shows no other object with the CaIR3 line as red as that of SN 2019oyw were it at the z = 0.0785 redshift of the disk galaxy. This implies that SN 2019oyw is either a highly unusual SN or is moving rapidly with respect to its apparent host. Indeed, using CaIR3 we find the redshift of SN 2019oyw is 0.0944 <= z <= 0.1156. The GRB-SN is superposed on a particularly dusty region of the massive spiral galaxy; therefore, while we see no sign of a small host galaxy behind the spiral, it could be obscured. Our work provides a surprising result on the origins of GRB 190829A, as well as insights into the time evolution of GRB-SNe spectra and a method for directly determining the redshift of a GRB-SN using the evolution of strong spectral features such as CaIR3., Comment: 21 pages, 16 figures

Polycyclic aromatic hydrocarbons (PAHs) are among the most ubiquitous compounds in the universe, accounting for up to ~25% of all interstellar carbon. Since most unsubstituted PAHs do not possess permanent dipole moments, they are invisible to radio astronomy. Constraining their abundances relies on the detection of polar chemical proxies, such as aromatic nitriles. We report the detection of 2- and 4-cyanopyrene, isomers of the recently detected 1-cyanopyrene. We find that these isomers are present in an abundance ratio of ~2:1:2, which mirrors the number of equivalent sites available for CN addition. We conclude that there is evidence that the cyanopyrene isomers formed by direct CN addition to pyrene under kinetic control in hydrogen-rich gas at 10 K and discuss constraints on the H/CN ratio for PAHs in TMC-1., Comment: Submitted version to comply with licensing agreements

Polycyclic aromatic hydrocarbons (PAHs) are expected to be the most abundant class of organic molecules in space. Their interstellar lifecycle is not well understood, and progress is hampered by difficulties detecting individual PAH molecules. Here, we present the discovery of CN-functionalized pyrene, a 4-ring PAH, in the dense cloud TMC-1 using the 100-m Green Bank Telescope. We derive an abundance of 1-cyanopyrene of ~1.52 x $10^{12}$ cm$^{-2}$, and from this estimate that the un-substituted pyrene accounts for up to ~0.03-0.3% of the carbon budget in the dense interstellar medium which trace the birth sites of stars and planets. The presence of pyrene in this cold (~10 K) molecular cloud agrees with its recent measurement in asteroid Ryugu where isotopic clumping suggest a cold, interstellar origin. The direct link to the birth site of our solar system is strengthened when we consider the solid state pyrene content in the pre-stellar materials compared to comets, which represent the most pristine material in the solar system. We estimate that solid state pyrene can account for 1% of the carbon within comets carried by this one single organic molecule. The abundance indicates pyrene is an "island of stability" in interstellar PAH chemistry and suggests a potential cold molecular cloud origin of the carbon carried by PAHs that is supplied to forming planetary systems, including habitable worlds such as our own., Comment: Version of manuscript revised to comply with licensing requirements

Superfast rotators (SFRs) are small solar system objects that rotate faster than generally possible for a cohesionless rubble pile. Their rotational characteristics allow us to make inferences about their interior structure and composition. Here, we present the methods and results from a preliminary search for SFRs in the DECam Ecliptic Exploration Project (DEEP) data set. We find three SFRs from a sample of 686 main-belt asteroids, implying an occurrence rate of 0.4 -0.3/+0.1 percent - a higher incidence rate than has been measured by previous studies. We suggest that this high occurrence rate is due to the small sub-kilometer size regime to which DEEP has access: the objects searched here were as small as 500 m. We compute the minimum required cohesive strength for each of these SFRs and discuss the implications of these strengths in the context of likely evolution mechanisms. We find that all three of these SFRs require strengths that are more than that of weak regolith but consistent with many cohesive asteroid strengths reported in the literature. Across the full DEEP data set, we have identified ~70,000 Main-Belt Asteroids and expect ~300 SFRs - a result that will be assessed in a future paper.

Despite being operational for only a short time, the Einstein Probe mission has already significantly advanced the study of rapid variability in the soft X-ray sky. We report the discovery of luminous and variable radio emission from the Einstein Probe fast X-ray transient EP240414a, the second such source with a radio counterpart. The radio emission at $3\,\rm{GHz}$ peaks at $\sim30$ days post explosion and with a spectral luminosity $\sim2\times10^{30}\,\rm{erg}\,\rm{s}^{-1}\,\rm{Hz}^{-1}$, similar to what is seen from long gamma-ray bursts, and distinct from other extra-galactic transients including supernovae and tidal disruption events, although we cannot completely rule out emission from engine driven stellar explosions e.g. the fast blue optical transients. An equipartition analysis of our radio data reveals that an outflow with at least a moderate bulk Lorentz factor ($\Gamma\gtrsim1.6$) with a minimum energy of $\sim10^{48}\,\rm{erg}$ is required to explain our observations. The apparent lack of reported gamma-ray counterpart to EP240414a could suggest that an off-axis or choked jet could be responsible for the radio emission, although a low luminosity gamma-ray burst may have gone undetected. Our observations are consistent with the hypothesis that a significant fraction of extragalactic fast X-ray transients are associated with the deaths of massive stars., Comment: 11 pages, 6 figures, submitted to ApJL

The characterization of young planets (< 300 Myr) is pivotal for understanding planet formation and evolution. We present the 3-5$\mu$m transmission spectrum of the 17 Myr, Jupiter-size ($R$ $\sim$10$R_{\oplus}$) planet, HIP 67522 b, observed with JWST/NIRSpec/G395H. To check for spot contamination, we obtain a simultaneous $g$-band transit with SOAR. The spectrum exhibits absorption features 30-50% deeper than the overall depth, far larger than expected from an equivalent mature planet, and suggests that HIP 67522 b's mass is $<$20 $M_{\oplus}$ irrespective of cloud cover and stellar contamination. A Bayesian retrieval analysis returns a mass constraint of $13.8\pm1.0M_{\oplus}$. This challenges the previous classification of HIP 67522 b as a hot Jupiter and instead, positions it as a precursor to the more common sub-Neptunes. With a density of $<$0.10g/cm$^{3}$, HIP 67522 b is one of the lowest density planets known. We find strong absorption from H$_{2}$O and CO$_{2}$ ($\ge7\sigma$), a modest detection of CO (3.5$\sigma$), and weak detections of H$_2$S and SO$_2$ ($\simeq2\sigma$). Comparisons with radiative-convective equilibrium models suggest supersolar atmospheric metallicities and solar-to-subsolar C/O ratios, with photochemistry further constraining the inferred atmospheric metallicity to 3$\times$10 Solar due to the amplitude of the SO$_2$ feature. These results point to the formation of HIP 67522 b beyond the water snowline, where its envelope was polluted by icy pebbles and planetesimals. The planet is likely experiencing substantial mass loss (0.01-0.03 M$_{\oplus}$ Myr$^{-1}$), sufficient for envelope destruction within a Gyr. This highlights the dramatic evolution occurring within the first 100 Myr of its existence., Comment: Accepted for publication in The Astronomical Journal; 32 pages, 18 figures, 7 tables

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

Using the FIRE-2 cosmological zoom-in simulations, we investigate the temporal evolution of gas-phase metallicity radial gradients of Milky Way-mass progenitors in the redshift range of $0.4

The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to highlight novel approaches for optimizing the construction and analysis of anonymized traffic matrices using over 100 billion network packets derived from the largest Internet telescope in the world (CAIDA). This challenge specifies the anonymization, construction, and analysis of these traffic matrices. A GraphBLAS reference implementation is provided, but the use of GraphBLAS is not required in this Graph Challenge. As with prior Graph Challenges the goal is to provide a well-defined context for demonstrating innovation. Graph Challenge participants are free to select (with accompanying explanation) the Graph Challenge elements that are appropriate for highlighting their innovations., Comment: Accepted to IEEE HPEC 2024

We present new Galactic reddening maps of the high Galactic latitude sky using DESI imaging and spectroscopy. We directly measure the reddening of 2.6 million stars by comparing the observed stellar colors in $g-r$ and $r-z$ from DESI imaging with the synthetic colors derived from DESI spectra from the first two years of the survey. The reddening in the two colors is on average consistent with the \cite{fitzpatrick_correcting_1999} extinction curve with $R_\mathrm{V}=3.1$. We find that our reddening maps differ significantly from the commonly used \cite{schlegel_maps_1998} (SFD) reddening map (by up to 80 mmag in $E(B-V)$), and we attribute most of this difference to systematic errors in the SFD map. To validate the reddening map, we select a galaxy sample with extinction correction based on our reddening map, and this yields significantly better uniformity than the SFD extinction correction. Finally, we discuss the potential systematic errors in the DESI reddening measurements, including the photometric calibration errors that are the limiting factor on our accuracy. The $E(g-r)$ and $E(g-r)$ maps presented in this work, and for convenience their corresponding $E(B-V)$ maps with SFD calibration, are publicly available., Comment: Submitted to the Open Journal of Astrophysics. Associated data files: https://data.desi.lbl.gov/public/papers/mws/desi_dust/y2/v1/maps/

Ultralight scalar fields can experience runaway `superradiant' amplification near spinning black holes, resulting in a macroscopic `axion cloud' which slowly dissipates via continuous monochromatic gravitational waves. For a particular range of boson masses, $\mathcal{O}(10^{-11}$ -- $10^{-10})$ eV, an axion cloud will radiate in the $10$ -- $100$ kHz band of the Levitated Sensor Detector (LSD). Using fiducial models of the mass, spin, and age distributions of stellar-origin black holes, we simulate the present-day Milky Way population of these hypothetical objects. As a first step towards assessing the LSD's sensitivity to the resultant ensemble of GW signals, we compute the corresponding signal-to-noise ratios which build up over a nominal integration time of $10^{7}$ s, assuming the projected sensitivity of the $1$-m LSD prototype currently under construction, as well as for future $10$-m and $100$-m concepts. For a $100$-m cryogenic instrument, hundreds of resolvable signals could be expected if the boson mass $\mu$ is around $3\times10^{-11}$ eV, and this number diminishes with increasing $\mu$ up to $\approx 5.5\times10^{-11}$ eV. The much larger population of unresolved sources will produce a confusion foreground which could be detectable by a $10$-m instrument if $\mu \in (3-4.5)\times10^{-11}$ eV, or by a $100$-m instrument if $\mu \in (3-6)\times10^{-11}$ eV.

Understanding what is normal is a key aspect of protecting a domain. Other domains invest heavily in observational science to develop models of normal behavior to better detect anomalies. Recent advances in high performance graph libraries, such as the GraphBLAS, coupled with supercomputers enables processing of the trillions of observations required. We leverage this approach to synthesize low-parameter observational models of anonymized Internet traffic with a high regard for privacy., Comment: Accepted to IEEE HPEC, 7 pages, 6 figures, 1 table, 41 references

Optically-levitated dielectric objects are promising for precision force, acceleration, torque, and rotation sensing due to their extreme environmental decoupling. While many levitated opto-mechanics experiments employ spherical objects, for some applications non-spherical geometries offer advantages. For example, rod-shaped or dumbbell shaped particles have been demonstrated for torque and rotation sensing and high aspect ratio plate-like particles can exhibit reduced photon recoil heating and may be useful for high-frequency gravitational wave detection or as high bandwidth accelerometers. To achieve optimal sensitivity, cooling, and quantum control in these systems, it is beneficial to achieve optimal displacement detection using scattered light. We describe and numerically implement a method based on Fisher information that is applicable to suspended particles of arbitrary geometry. We demonstrate the agreement between our method and prior methods employed for spherical particles, both in the Rayleigh and Lorentz-Mie regimes. As practical examples we analyze the optical detection limits of an optically-levitated high-aspect-ratio disc-like dielectric object and a rod-shaped object for configurations recently realized in experimental work., Comment: 8 pages, 6 figures

There has been a paradigm shift from accommodations to the identification of levels of accessibility (e.g., universal features, designated features, and accommodations). A consequence of this shift is that many students with disabilities who previously were assigned accommodations now access many of them as universal or designated features. This Brief highlights what states and Individualized Education Program (IEP) teams can do to avoid possible unintended consequences of the paradigm shift.

This paper describes Asterisk, a compact GPT-based model for generating text embeddings. The model uses a minimalist architecture with two layers, two attention heads, and 256 embedding dimensions. By applying knowledge distillation from larger pretrained models, we explore the trade-offs between model size and performance while minimizing computational and memory requirements. The model is primarily evaluated and optimized for classification tasks, with experimental results showing its moderate performance in zero-shot classification across various downstream applications. With additional configuration, the model performance can approach or even surpass that of larger architectures on specific classification tasks.

Conrey and Ghosh studied the second moment of the Riemann zeta function, evaluated at its local extrema along the critical line, finding the leading order behaviour to be $\frac{e^2 - 5}{2 \pi} T (\log T)^2$. This problem is closely related to a mixed moment of the Riemann zeta function and its derivative. We present a new approach which will uncover the lower order terms for the second moment as a descending chain of powers of logarithms in the asymptotic expansion., Comment: 22 pages, 9 figures, 2 tables

Conrey, Ghosh and Gonek studied the first moment of the derivative of the Riemann zeta function evaluated at the non-trivial zeros of the zeta function, resolving a problem known as Shanks' conjecture. Conrey and Ghosh studied the second moment of the Riemann zeta function evaluated at its local extrema along the critical line to leading order. In this paper we combine the two results, evaluating the first moment of the zeta function and its derivatives at the local extrema of zeta along the critical line, giving a full asymptotic. We also consider the factor from the functional equation for the zeta function at these extrema., Comment: 33 pages, 4 figures

Based on radial velocities, EXORAP photometry, and activity indicators, the HADES team reported a 16.3-day rotation period for the M dwarf GJ 3992. However, an RV--H$\alpha$ magnitude-squared coherence estimate has significant peaks at frequencies 1/16 cycles/day and 1/32 cycles/day. We re-analyze HADES data plus Hipparcos, SuperWASP, and TESS photometry to see whether the rotation period could be 32 days with 16-day harmonic. SuperWASP shows no significant periodicities, while the Hipparcos observing cadence is suboptimal for detecting 16- and 32-day periodicities. Although the average TESS periodogram has peaks at harmonics of 1/16 cycles/day, the harmonic sequence is not fully resolved according to the Rayleigh criterion. The TESS observations suggest a 1/16 cycles/day rotation frequency and a 1/32 cycles/day subharmonic, though resolution makes the TESS rotation detection ambiguous., Comment: 5 pages, 1 figure, submitted to RNAAS

To identify what types of non-Fermi liquid (NFL) behavior are most likely to occur in cubic metals due to doublet impurities, we derive every cubic symmetry-allowed, NFL, Kondo-type exchange coupling. We find three distinct types of NFL behavior: two-channel Kondo (2CK) behavior for a non-Kramers doublet impurity coupled to local $\Gamma_8$ conduction electrons; topological Kondo physics for a Kramers doublet impurity and $\Gamma_4$ or $\Gamma_5$ conduction electrons; and lastly, spin-half impurity spin-$\frac{3}{2}$ conduction electron Kondo behavior for a Kramers doublet impurity and $\Gamma_8$ conduction electrons. The first two critical behaviors are not straightforward to realize. In the first case, 2CK physics is not guaranteed, since cubic symmetry does not prevent an effective spatial anisotropy from exceeding the 2CK coupling, which restores a Fermi liquid behavior. In the second case, the topological Kondo interaction is guaranteed to dominate, however, the spin degeneracy of the conduction electrons needs to be lifted e.g. by a magnetic field$-$so that they can be represented by $\Gamma_4$ or $\Gamma_5$ triplets$-$which then also lifts the degeneracy of the Kramers doublet. We find that the spin-half impurity spin-$\frac{3}{2}$ conduction electron, NFL, Kondo behavior has the greatest chance of existing in diluted, cubic compounds. We compute the thermodynamics of the topological Kondo model using the numerical renormalization group, and discuss the thermodynamics of the spin-half impurity spin-$\frac{3}{2}$ conduction electron Kondo model. We also identify candidate materials where the corresponding NFL behaviors could be observed., Comment: 6 pages + 2 pages of supplemental material, 1 figure

We analysed microlensing data to uncover the nature of the anomaly that appeared near the peak of the short-timescale microlensing event KMT-2024-BLG-1044. Despite the anomaly's brief duration of less than a day, it was densely observed through high-cadence monitoring conducted by the KMTNet survey. Detailed modelling of the light curve confirmed the planetary origin of the anomaly and revealed two possible solutions, due to an inner--outer degeneracy. The two solutions provide different measured planet parameters: $(s, q)_{\rm inner} = [1.0883 \pm 0.0027, (3.125 \pm 0.248)\times 10^{-4}]$ for the inner solutions and $(s, q)_{\rm outer} = [1.0327 \pm 0.0054, (3.350 \pm 0.316)\times 10^{-4}]$ for the outer solutions. Using Bayesian analysis with constraints provided by the short event timescale ($t_{\rm E} \sim 9.1$~day) and the small angular Einstein radius ($\theta_{\rm E}\sim 0.16$~mas for the inner solution and $\sim 0.10$~mas for the outer solutio), we determined that the lens is a planetary system consisting of a host near the boundary between a star and a brown dwarf and a planet with a mass lower than that of Uranus. The discovery of the planetary system highlights the crucial role of the microlensing technique in detecting planets that orbit substellar brown dwarfs or very low-mass stars., Comment: 8 pages, 10 figures

With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the model in a compact form and is coupled with a decompression algorithm that returns it to its original form and size - namely lossless compression. We present ZipNN a lossless compression tailored to neural networks. Somewhat surprisingly, we show that specific lossless compression can gain significant network and storage reduction on popular models, often saving 33% and at times reducing over 50% of the model size. We investigate the source of model compressibility and introduce specialized compression variants tailored for models that further increase the effectiveness of compression. On popular models (e.g. Llama 3) ZipNN shows space savings that are over 17% better than vanilla compression while also improving compression and decompression speeds by 62%. We estimate that these methods could save over an ExaByte per month of network traffic downloaded from a large model hub like Hugging Face., Comment: arXiv admin note: substantial text overlap with arXiv:2404.15198

Biological systems transduce signals from their surroundings in numerous ways. This paper introduces a communication system using the light-gated ion channel Channelrhodopsin-2 (ChR2), which causes an ion current to flow in response to light. Our design includes a ChR2-based receiver along with encoding, modulation techniques and detection. Analyzing the resulting communication system, we discuss the effect of different parameters on the performance of the system. Finally, we discuss its potential design in the context of bio-engineering and light-based communication and show that the data rate scales up with the number of receptors, indicating that high-speed communication may be possible., Comment: 11 pages, 16 figures and 4 Tables

Recent advances in generative artificial intelligence (AI) have shown promise in accurately grading open-ended student responses. However, few prior works have explored grading handwritten responses due to a lack of data and the challenge of combining visual and textual information. In this work, we leverage state-of-the-art multi-modal AI models, in particular GPT-4o, to automatically grade handwritten responses to college-level math exams. Using real student responses to questions in a probability theory exam, we evaluate GPT-4o's alignment with ground-truth scores from human graders using various prompting techniques. We find that while providing rubrics improves alignment, the model's overall accuracy is still too low for real-world settings, showing there is significant room for growth in this task.

We construct four-variable $p$-adic $L$-functions for the spin Galois representation of a Siegel modular form of genus 2 twisted by the Galois representation of a cuspidal modular form as the modular forms vary in Coleman families. The main ingredient is the construction of a space of nearly overconvergent modular forms in the coherent cohomology of the Siegel threefold, extending the spaces of overconvergent modular forms appearing in higher Coleman theory. In addition to this, we construct $p$-adic distributions interpolating the Gan-Gross-Prasad automorphic periods for $(\mathrm{GSpin}(5), \mathrm{GSpin}(4))$ which, conditional on the local and global Gan-Gross-Prasad conjectures for this pair of groups, provides a construction of "square-root" $p$-adic $L$-functions for $\mathrm{GSp}(4) \times \mathrm{GL}(2) \times \mathrm{GL}(2)$ as the automorphic forms vary in Coleman families., Comment: 54 pages

The ubiquitous noise in quantum system hinders the advancement of quantum information processing and has driven the emergence of different hardware-efficient quantum error correction protocols. Among them, qubits with structured noise, especially with biased noise, are one of the most promising platform to achieve fault-tolerance due to the high error thresholds of quantum error correction codes tailored for them. Nevertheless, their quantum operations are challenging and the demonstration of their performance beyond the fault-tolerant threshold remain incomplete. Here, we leverage Schr\"odinger cat states in a scalable planar superconducting nonlinear oscillator to thoroughly characterize the high-fidelity single-qubit quantum operations with systematic quantum tomography and benchmarking tools, demonstrating the state-of-the-art performance of operations crossing the fault-tolerant threshold of the XZZX surface code. These results thus embody a transformative milestone in the exploration of quantum systems with structured error channels. Notably, our framework is extensible to other types of structured-noise systems, paving the way for systematic characterization and validation of novel quantum platforms with structured noise., Comment: 19 pages, 12 figures

Neutral atom arrays have emerged as a promising platform for both analog and digital quantum processing. Recently, devices capable of reconfiguring arrays during quantum processes have enabled new applications for these systems. Atom reconfiguration, or routing, is the core mechanism for programming circuits; optimizing this routing can increase processing speeds, reduce decoherence, and enable efficient implementations of highly non-local connections. In this work, we investigate routing models applicable to state-of-the-art neutral atom systems. With routing steps that can operate on multiple atoms in parallel, we prove that current designs require $\Omega(\sqrt N \log N)$ steps to perform certain permutations on 2D arrays with $N$ atoms and provide a protocol that achieves routing in $\mathcal O(\sqrt N \log N)$ steps for any permutation. We also propose a simple experimental upgrade and show that it would reduce the routing cost to $\Theta(\log N)$ steps., Comment: 21 pages, 9 figures

Axion-like particles may form a network of cosmic strings in the Universe today that can rotate the plane of polarization of cosmic microwave background (CMB) photons. Future CMB observations with improved sensitivity might detect this axion-string-induced birefringence effect, thereby revealing an as-yet unseen constituent of the Universe and offering a new probe of particles and forces that are beyond the Standard Model of Elementary Particle Physics. In this work, we explore how spherical convolutional neural networks (SCNNs) may be used to extract information about the axion string network from simulated birefringence maps. We construct a pipeline to simulate the anisotropic birefringence that would arise from an axion string network, and we train SCNNs to estimate three parameters related to the cosmic string length, the cosmic string abundance, and the axion-photon coupling. Our results demonstrate that neural networks are able to extract information from a birefringence map that is inaccessible with two-point statistics alone (i.e., the angular power spectrum). We also assess the impact of noise on the accuracy of our SCNN estimators, demonstrating that noise at the level anticipated for Stage IV (CMB-S4) measurements would significantly bias parameter estimation for SCNNs trained on noiseless simulated data, and necessitate modeling the noise in the training data., Comment: 23 pages, 7 figures, 4 tables

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

This document summarizes the discussions which took place during the PITT-PACC Workshop entitled "Non-Standard Cosmological Epochs and Expansion Histories," held in Pittsburgh, Pennsylvania, Sept. 5-7, 2024. Much like the non-standard cosmological epochs that were the subject of these discussions, the format of this workshop was also non-standard. Rather than consisting of a series of talks from participants, with each person presenting their own work, this workshop was instead organized around free-form discussion blocks, with each centered on a different overall theme and guided by a different set of Discussion Leaders. This document is not intended to serve as a comprehensive review of these topics, but rather as an informal record of the discussions that took place during the workshop, in the hope that the content and free-flowing spirit of these discussions may inspire new ideas and research directions., Comment: 33 pages, LaTeX, 2 figures

One of the key challenges of detecting AI-generated images is spotting images that have been created by previously unseen generative models. We argue that the limited diversity of the training data is a major obstacle to addressing this problem, and we propose a new dataset that is significantly larger and more diverse than prior work. As part of creating this dataset, we systematically download thousands of text-to-image latent diffusion models and sample images from them. We also collect images from dozens of popular open source and commercial models. The resulting dataset contains 2.7M images that have been sampled from 4803 different models. These images collectively capture a wide range of scene content, generator architectures, and image processing settings. Using this dataset, we study the generalization abilities of fake image detectors. Our experiments suggest that detection performance improves as the number of models in the training set increases, even when these models have similar architectures. We also find that detection performance improves as the diversity of the models increases, and that our trained detectors generalize better than those trained on other datasets., Comment: 15 pages

Continuously adapting pre-trained models to local data on resource constrained edge devices is the $\emph{last mile}$ for model deployment. However, as models increase in size and depth, backpropagation requires a large amount of memory, which becomes prohibitive for edge devices. In addition, most existing low power neural processing engines (e.g., NPUs, DSPs, MCUs, etc.) are designed as fixed-point inference accelerators, without training capabilities. Forward gradients, solely based on directional derivatives computed from two forward calls, have been recently used for model training, with substantial savings in computation and memory. However, the performance of quantized training with fixed-point forward gradients remains unclear. In this paper, we investigate the feasibility of on-device training using fixed-point forward gradients, by conducting comprehensive experiments across a variety of deep learning benchmark tasks in both vision and audio domains. We propose a series of algorithm enhancements that further reduce the memory footprint, and the accuracy gap compared to backpropagation. An empirical study on how training with forward gradients navigates in the loss landscape is further explored. Our results demonstrate that on the last mile of model customization on edge devices, training with fixed-point forward gradients is a feasible and practical approach.

Quantum symmetrization is the task of transforming a non-strictly increasing list of $n$ integers into an equal superposition of all permutations of the list (or more generally, performing this operation coherently on a superposition of such lists). This task plays a key role in initial state preparation for first-quantized simulations. A weaker version of symmetrization in which the input list is \emph{strictly} increasing has been extensively studied due to its application to fermionic systems, but the general symmetrization problem with repetitions in the input list has not been solved previously. We present the first quantum algorithm for the general symmetrization problem. Our algorithm, based on (quantum) sorting networks, uses $O(\log n)$ depth and $O(n\log m)$ ancilla qubits where $m$ is the greatest possible value of the list, enabling efficient simulation of bosonic quantum systems in first quantization. Furthermore, our algorithm can prepare (superpositions of) Dicke states of any Hamming weight in $O(\log n)$ depth using $O(n\log n)$ ancilla qubits. We also propose an $O(\log^2 n)$-depth quantum algorithm to transform second-quantized states to first-quantized states. Using this algorithm, QFT-based quantum telescope arrays can image brighter photon sources, extending quantum interferometric imaging systems to a new regime., Comment: 22 pages, 7 figures

Eigenvalue transformations, which include solving time-dependent differential equations as a special case, have a wide range of applications in scientific and engineering computation. While quantum algorithms for singular value transformations are well studied, eigenvalue transformations are distinct, especially for non-normal matrices. We propose an efficient quantum algorithm for performing a class of eigenvalue transformations that can be expressed as a certain type of matrix Laplace transformation. This allows us to significantly extend the recently developed linear combination of Hamiltonian simulation (LCHS) method [An, Liu, Lin, Phys. Rev. Lett. 131, 150603, 2023; An, Childs, Lin, arXiv:2312.03916] to represent a wider class of eigenvalue transformations, such as powers of the matrix inverse, $A^{-k}$, and the exponential of the matrix inverse, $e^{-A^{-1}}$. The latter can be interpreted as the solution of a mass-matrix differential equation of the form $A u'(t)=-u(t)$. We demonstrate that our eigenvalue transformation approach can solve this problem without explicitly inverting $A$, reducing the computational complexity., Comment: 29+7 pages

Hampering the interpretation of benchmark scores, evaluation data contamination has become a growing concern in the evaluation of LLMs, and an active area of research studies its effects. While evaluation data contamination is easily understood intuitively, it is surprisingly difficult to define precisely which samples should be considered contaminated and, consequently, how it impacts benchmark scores. We propose that these questions should be addressed together and that contamination metrics can be assessed based on whether models benefit from the examples they mark contaminated. We propose a novel analysis method called ConTAM, and show with a large scale survey of existing and novel n-gram based contamination metrics across 13 benchmarks and 7 models from 2 different families that ConTAM can be used to better understand evaluation data contamination and its effects. We find that contamination may have a much larger effect than reported in recent LLM releases and benefits models differently at different scales. We also find that considering only the longest contaminated substring provides a better signal than considering a union of all contaminated substrings, and that doing model and benchmark specific threshold analysis greatly increases the specificity of the results. Lastly, we investigate the impact of hyperparameter choices, finding that, among other things, both using larger values of n and disregarding matches that are infrequent in the pre-training data lead to many false negatives. With ConTAM, we provide a method to empirically ground evaluation data contamination metrics in downstream effects. With our exploration, we shed light on how evaluation data contamination can impact LLMs and provide insight into the considerations important when doing contamination analysis. We end our paper by discussing these in more detail and providing concrete suggestions for future work.

Maxwell's demon (MD) has proven an instructive vehicle by which to explore the relationship between information theory and thermodynamics, fueling the possibility of information driven machines. A long standing debate has been the concern of entropy violation, now resolved by the introduction of a quantum MD, but this theoretical suggestion has proven experimentally challenging. Here, we use classical vectorially structured light that is non-separable in spin and orbital angular momentum to emulate a quantum MD experiment. Our classically entangled light fields have all the salient properties necessary of their quantum counterparts but without the experimental complexity of controlling quantum entangled states. We use our experiment to show that the demon's entropy increases during the process while the system's entropy decreases, so that the total entropy is conserved through an exchange of information, confirming the theoretical prediction. We show that our MD is able to extract useful work from the system in the form of orbital angular momentum, opening a path to information driven optical spanners for the mechanical rotation of objects with light. Our synthetic dimensions of angular momentum can easily be extrapolated to other degrees of freedom, for scalable and robust implementations of MDs at both the classical and quantum realms, enlightening the role of a structured light MD and its capability to control and measure information., Comment: 14 pages, 6 Figures

Animals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts. Animals can then respond flexibly by selecting the appropriate task abstraction. However, how such flexible task abstractions may arise in neural systems remains unknown. Here, we analyze a linear gated network where the weights and gates are jointly optimized via gradient descent, but with neuron-like constraints on the gates including a faster timescale, nonnegativity, and bounded activity. We observe that the weights self-organize into modules specialized for tasks or sub-tasks encountered, while the gates layer forms unique representations that switch the appropriate weight modules (task abstractions). We analytically reduce the learning dynamics to an effective eigenspace, revealing a virtuous cycle: fast adapting gates drive weight specialization by protecting previous knowledge, while weight specialization in turn increases the update rate of the gating layer. Task switching in the gating layer accelerates as a function of curriculum block size and task training, mirroring key findings in cognitive neuroscience. We show that the discovered task abstractions support generalization through both task and subtask composition, and we extend our findings to a non-linear network switching between two tasks. Overall, our work offers a theory of cognitive flexibility in animals as arising from joint gradient descent on synaptic and neural gating in a neural network architecture.

This paper introduces a method to enhance Interactive Imitation Learning (IIL) by extracting touch interaction points and tracking object movement from video demonstrations. The approach extends current IIL systems by providing robots with detailed knowledge of both where and how to interact with objects, particularly complex articulated ones like doors and drawers. By leveraging cutting-edge techniques such as 3D Gaussian Splatting and FoundationPose for tracking, this method allows robots to better understand and manipulate objects in dynamic environments. The research lays the foundation for more effective task learning and execution in autonomous robotic systems., Comment: CoRL 2024, Workshop on Lifelong Learning for Home Robots, Munich, Germany