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The "Fall 2024 Snapshot on International Student Enrollment" findings are based on responses from over 690 U.S. higher education institutions on current patterns in international student enrollment as of fall 2024. The report, conducted by the Institute of International Education (IIE) and ten partner higher education associations, complements the "Open Doors® Report on International Educational Exchange." The findings from the Fall 2024 Snapshot reflect a continued rise in international student enrollment numbers. A 3% growth in international student totals (undergraduate, graduate, non-degree, and Optional Practical Training [OPT]) builds on the 7% growth in 2023/24 and the 12% growth in 2022/23 according to "Open Doors." This marks four years of international student increases since the COVID-19 pandemic. 84% of reporting institutions indicate that financial support for their international student recruitment efforts is the same or higher than in the previous year. U.S. institutions report working with current international students, collaborating with agents, building international partnerships, and conducting in-person recruitment events to support outreach and recruitment. [This report was co-produced with the Association of International Enrollment Management (AIRC).]

This document shares valuable insights from peer mentorship programs at institutions that participated in APLU's Powered by Publics initiative and serves as a comprehensive resource for other institutions aiming to develop or enhance similar initiatives. By showcasing best practices, case examples, and practical recommendations, this report aims to empower universities to implement effective, evidence-based peer mentoring programs that significantly contribute to student success and equitable outcomes.

Over a 6-year period, a sociolinguistic and sociocultural project was undertaken by Alaska Native expert educators and linguists (aka the Yup'ik Expert Group) from the Yup'ik community in the Lower Kuskokwim School District, Central Alaska. The native experts developed their own culturally sustainable, valid, and reliable Kindergarten through Grade 6 Alaska Native language (Yugtun) assessment. Yup'ik experts named their assessment the Yugtun Piciryaranek Qaneryaranek-llu Cuqyun (aka Yup'ik Culture and Language Measurement). This paper focuses on a foundational component of the assessment, the Yup'ik Cultural Awareness subtest, which has two components, Nonverbal Communication and Yup'ik Worldview.

Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts. By mapping the spatial ranges of all species, we would obtain deeper insights into how global biodiversity is affected by climate change and habitat loss. However, accurate range estimates are only available for a relatively small proportion of all known species. For the majority of the remaining species, we often only have a small number of records denoting the spatial locations where they have previously been observed. We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data. During inference, our model takes a set of spatial locations as input, along with optional metadata such as text or an image, and outputs a species encoding that can be used to predict the range of a previously unseen species in feed-forward manner. We validate our method on two challenging benchmarks, where we obtain state-of-the-art range estimation performance, in a fraction of the compute time, compared to recent alternative approaches.

We present an open-source benchmark and evaluation framework for assessing emotional boundary handling in Large Language Models (LLMs). Using a dataset of 1156 prompts across six languages, we evaluated three leading LLMs (GPT-4o, Claude-3.5 Sonnet, and Mistral-large) on their ability to maintain appropriate emotional boundaries through pattern-matched response analysis. Our framework quantifies responses across seven key patterns: direct refusal, apology, explanation, deflection, acknowledgment, boundary setting, and emotional awareness. Results demonstrate significant variation in boundary-handling approaches, with Claude-3.5 achieving the highest overall score (8.69/10) and producing longer, more nuanced responses (86.51 words on average). We identified a substantial performance gap between English (average score 25.62) and non-English interactions (< 0.22), with English responses showing markedly higher refusal rates (43.20% vs. < 1% for non-English). Pattern analysis revealed model-specific strategies, such as Mistral's preference for deflection (4.2%) and consistently low empathy scores across all models (< 0.06). Limitations include potential oversimplification through pattern matching, lack of contextual understanding in response analysis, and binary classification of complex emotional responses. Future work should explore more nuanced scoring methods, expand language coverage, and investigate cultural variations in emotional boundary expectations. Our benchmark and methodology provide a foundation for systematic evaluation of LLM emotional intelligence and boundary-setting capabilities.

Fermi's Golden Rule (FGR) is one of the most impactful formulas in quantum mechanics, providing a link between easy-to-measure observables - such as transition rates - and fundamental microscopic properties - such as density of states or spectral functions. Its validity relies on three key assumptions: the existence of a continuum, an appropriate time window, and a weak coupling. Understanding the regime of validity of FGR is critical for the proper interpretation of most spectroscopic experiments. While the assumptions underlying FGR are straightforward to analyze in simple models, their applicability is significantly more complex in quantum many-body systems. Here, we observe the emergence and breakdown of FGR, using a strongly interacting homogeneous spin-$1/2$ Fermi gas coupled to a radio-frequency (rf) field. Measuring the transition probability into an outcoupled internal state, we map the system's dynamical response diagram versus the rf-pulse duration $t$ and Rabi frequency $\Omega_0$. For weak drives, we identify three regimes: an early-time regime where the transition probability takes off as $t^2$, an intermediate-time FGR regime, and a long-time non-perturbative regime. Beyond a threshold Rabi frequency, Rabi oscillations appear. Our results provide a blueprint for the applicability of linear response theory to the spectroscopy of quantum many-body systems.

We report a measurement of the inelasticity distribution in the scattering of neutrinos of energy $80-560$ GeV off nucleons, which is sensitive to the inclusive differential cross section. This analysis is based on a sample of atmospheric muon neutrinos detected in the IceCube sub-array DeepCore during 2012-2021, and is the first such measurement in this energy range. Our measurement extends to energies where accelerator data is not available, hence we compare our results to predictions from perturbative QCD calculations, finding good agreement., Comment: 17 pages, 19 figures

We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap encompasses four generations of devices: a single-qubit device that enables a measurement-based qubit benchmarking protocol; a two-qubit device that uses measurement-based braiding to perform single-qubit Clifford operations; an eight-qubit device that can be used to show an improvement of a two-qubit operation when performed on logical qubits rather than directly on physical qubits; and a topological qubit array supporting lattice surgery demonstrations on two logical qubits. Devices that enable this path require a superconductor-semiconductor heterostructure that supports a topological phase, quantum dots and coupling between those quantum dots that can create the appropriate loops for interferometric measurements, and a microwave readout system that can perform fast, low-error single-shot measurements. We describe the key design components of these qubit devices, along with the associated protocols for demonstrations of single-qubit benchmarking, Clifford gate execution, quantum error detection, and quantum error correction, which differ greatly from those in more conventional qubits. Finally, we comment on implications and advantages of this architecture for utility-scale quantum computation., Comment: 11+6 pages, 8+5 figures

The process of carbon burning is vital to understanding late stage stellar evolution of massive stars and the conditions of certain supernovae. Carbon burning is a complex problem, involving quantum tunnelling and nuclear molecular states. Quantum dynamical calculations of carbon burning are presented, combining the time-dependent wave-packet method and the density-constrained time-dependent Hartree-Fock (DC-TDHF) approach. By limiting the contribution of triaxial molecular configurations to fusion, we demonstrate that the DC-TDHF interaction potential successfully explains the appearance of some resonant structures in the sub-barrier fusion cross-section. This result shows the critical role of nucleon-nucleon interactions in the 12C + 12C fusion resonances observed at astrophysical energies., Comment: 8 pages, 5 figures

A global continuous-variable quantum key distribution (CV-QKD) network can be established using a series of satellite-to-Earth channels. Increased performance in such a network is provided by performing coherent measurement of the optical quantum signals using a real local oscillator, calibrated locally by encoding known information on transmitted reference pulses and using signal phase error estimation algorithms. The speed and accuracy of the signal phase error estimation algorithm are vital to practical CV-QKD implementation. Our work provides a framework to analyze long short-term memory neural network (NN) architecture parameterization, with respect to the quantum Cram\'er-Rao uncertainty bound of the signal phase error estimation, with a focus on reducing the model complexity. More specifically, we demonstrate that signal phase error estimation can be achieved using a low-complexity NN architecture, without significantly sacrificing accuracy. Our results significantly improve the real-time performance of practical CV-QKD systems deployed over satellite-to-Earth channels, thereby contributing to the ongoing development of the Quantum Internet.

The observation of neutrino oscillations has established that neutrinos have non-zero masses. This phenomenon is not explained by the Standard Model of particle physics, but one viable explanation to this dilemma involves the existence of heavy neutral leptons in the form of right-handed neutrinos. This work presents the first search for heavy neutral leptons with the IceCube Neutrino Observatory. The standard three flavor neutrino model is extended by adding a fourth GeV-scale mass state allowing mixing with the $\tau$ sector through the parameter $|U_{\tau4}|^2$. The analysis is performed by searching for signatures of heavy neutral leptons that are directly produced via up-scattering of atmospheric $\nu_\tau$'s inside the IceCube detection volume. Three heavy neutral lepton mass values, $m_4$, of 0.3 GeV, 0.6 GeV, and 1.0 GeV are tested using ten years of data, collected between 2011 and 2021. No significant signal of heavy neutral leptons is observed for any of the tested masses. The resulting constraints for the mixing parameter are $|U_{\tau4}|^2 < 0.19$ ($m_4 = 0.3$ GeV), $|U_{\tau4}|^2 < 0.36$ ($m_4 = 0.6$ GeV), and $|U_{\tau4}|^2 < 0.40$ ($m_4 = 1.0$ GeV) at the 90% confidence level. This analysis serves as proof-of-concept for heavy neutral lepton searches in IceCube. The heavy neutral lepton event generator, developed in this work, and the analysis of the expected signatures lay the fundamental groundwork for future searches thereof.

Given a fixed integer $n\geq 2$, we construct two new finite lattices that we call the cyclic Tamari lattice and the affine Tamari lattice. The cyclic Tamari lattice is a sublattice and a quotient lattice of the cyclic Dyer lattice, which is the infinite lattice of translation-invariant total orders under containment of inversion sets. The affine Tamari lattice is a quotient of the Dyer lattice, which in turn is a quotient of the cyclic Dyer lattice and is isomorphic to the collection of biclosed sets of the root system of type $\widetilde{A}_{n-1}$ under inclusion. We provide numerous combinatorial and algebraic descriptions of these lattices using translation-invariant total orders, translation-invariant binary in-ordered trees, noncrossing arc diagrams, torsion classes, triangulations, and translation-invariant noncrossing partitions. The cardinalities of the cyclic and affine Tamari lattices are the Catalan numbers of types $B_n$ and $D_n$, respectively. We show that these lattices are self-dual and semidistributive, and we describe their decompositions coming from the Fundamental Theorem of Finite Semidistributive Lattices. We also show that the rowmotion operators on these lattices have well-behaved orbit structures, which we describe via the cyclic sieving phenomenon. Our new combinatorial framework allows us to prove that the lengths of maximal green sequences for the completed path algebra of the oriented $n$-cycle are precisely the integers in the interval $[2n-1,\binom{n+1}{2}]$., Comment: 45 pages, 18 figures

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

The extended weak order on a Coxeter group $W$ is the poset of biclosed sets in its root system. In (Barkley-Speyer 2024), it was shown that when $W=\widetilde{S}_n$ is the affine symmetric group, then the extended weak order is a quotient of the lattice $L_n$ of translation-invariant total orderings of the integers. In this article, we give a combinatorial introduction to $L_n$ and the extended weak order on $\widetilde{S}_n$. We show that $L_n$ is an algebraic completely semidistributive lattice. We describe its canonical join representations using a cyclic version of Reading's non-crossing arc diagrams. We also show analogous statements for the lattice of all total orders of the integers, which is the extended weak order on the symmetric group $S_\infty$. A key property of both of these lattices is that they are profinite; we also prove that a profinite lattice is join semidistributive if and only if its compact elements have canonical join representations. We conjecture that the extended weak order of any Coxeter group is a profinite semidistributive lattice., Comment: v1: Preliminary version, comments welcome

Monoclonal antibodies (mAb) represent an important class of biologic therapeutics that can treat a variety of diseases including cancer, autoimmune disorders or respiratory conditions (e.g. COVID-19). However, throughout their development, mAb are exposed to air-water or oil-water interfaces that may trigger mAb partial unfolding that can lead to the formation of proteinaceous aggregates. Using a combination of dynamic surface tensiometry and spatially resolved 1D 1H NMR spectroscopy, this study investigates if adsorption of a model IgG2a-\k{appa} mAb to the oil-water interface affects its structure. Localized NMR spectroscopy was performed using voxels of 375 um, incrementally approaching the oil-water interface. Dynamic interfacial tension progressively decreases at the oil-water interface over time, confirming mAb adsorption to the interface. Localized NMR spectroscopy results indicate that, while the number of mAb-related chemical resonances and chemical shift frequencies remain unaffected, spectral line broadening is observed as voxels incrementally approach the oil-water interface. Moreover, the spin-spin (T2) relaxation of the mAb molecule was measured for a voxel centered at the interface and shown to be affected differentially across the mAb resonances, indicating a rotational restriction for mAb molecules due to presence of the interface. Finally, the apparent diffusion coefficient of the mAb for the voxel centered at the interface is lower than the bulk mAb. These results suggest that this specific mAb interacts with and may be in exchange with bulk mAb phase in the vicinity of the interface. As such, these localized NMR techniques offer the potential to probe and quantify alterations of mAb properties near interfacial layers.

Accurate estimates of salmon escapement - the number of fish migrating upstream to spawn - are key data for conservation and fishery management. Existing methods for salmon counting using high-resolution imaging sonar hardware are non-invasive and compatible with computer vision processing. Prior work in this area has utilized object detection and tracking based methods for automated salmon counting. However, these techniques remain inaccessible to many sonar deployment sites due to limited compute and connectivity in the field. We propose an alternative lightweight computer vision method for fish counting based on analyzing echograms - temporal representations that compress several hundred frames of imaging sonar video into a single image. We predict upstream and downstream counts within 200-frame time windows directly from echograms using a ResNet-18 model, and propose a set of domain-specific image augmentations and a weakly-supervised training protocol to further improve results. We achieve a count error of 23% on representative data from the Kenai River in Alaska, demonstrating the feasibility of our approach., Comment: ECCV 2024. 6 pages, 2 figures

The Herschel open-time key program Disc Emission via a Bias-free Reconnaissance in the Infrared and Sub-millimeter (DEBRIS) is an unbiased survey of the nearest ~100 stars for each stellar type A-M observed with a uniform photometric sensitivity to search for cold debris disks around them. The analysis of the Photoconductor Array Camera and Spectrometer (PACS) photometric observations of the 94 DEBRIS M dwarfs of this program is presented in this paper, following upon two companion papers on the DEBRIS A-star and FGK-star subsamples. In the M-dwarf subsample, two debris disks have been detected, around the M3V dwarf GJ581 and the M4V dwarf FomalhautC (LP876-10). This result gives a disk detection rate of 2.1^{+2.7}_{-0.7}% at the 68% confidence level, significantly less than measured for earlier stellar types in the DEBRIS program. However, we show that the survey of the DEBRIS M-dwarf subsample is about ten times shallower than the surveys of the DEBRIS FGK subsamples when studied in the physical parameter space of the disk's fractional dust luminosity versus blackbody radius. Furthermore, had the DEBRIS K-star subsample been observed at the same shallower depth in this parameter space, its measured disk detection rate would have been statistically consistent with the one found for the M-dwarf subsample. Hence, the incidence of debris disks does not appear to drop from the K subsample to the M subsample of the DEBRIS program, when considering disks in the same region of physical parameter space. An alternative explanation is that the only two bright disks discovered in the M-dwarf subsample would not, in fact, be statistically representative of the whole population., Comment: 12 pages, long Table 3 included, accepted for publication in Astronomy & Astrophysics

While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods used towards understanding this is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations of the blazar B3 2247+381, taken in response to an IceCube multiplet alert for a cluster of muon neutrino events compatible with the source location between May 20, 2022 and November 10, 2022. B3 2247+381 was not detected with VERITAS during this time period. The source was found to be in a low-flux state in the optical, ultraviolet and gamma-ray bands for the time interval corresponding to the neutrino event, but was detected in the hard X-ray band with NuSTAR during this period. We find the multiwavelength spectral energy distribution is well described using a simple one-zone leptonic synchrotron self-Compton radiation model. Moreover, assuming the neutrinos originate from hadronic processes within the jet, the neutrino flux would be accompanied by a photon flux from the cascade emission, and the integrated photon flux required in such a case would significantly exceed the total multiwavelength fluxes and the VERITAS upper limits presented here. The lack of flaring activity observed with VERITAS, combined with the low multiwavelength flux levels, and given the significance of the neutrino excess is at 3$\sigma$ level (uncorrected for trials), makes B3 2247+381 an unlikely source of the IceCube multiplet. We conclude that the neutrino excess is likely a background fluctuation., Comment: 26 pages, 5 figures. Accepted for publication in the Astrophysical Journal (ApJ)

We present synthetic optical light curves of the hierarchical HD98800 quadruple system over a decade-long period when the circumbinary disc encircling the system's B binary is expected to eclipse the light from the A binary. We produce and compare light curves of this transit event using hydrodynamical models with different values of the disc's gas mass, dust mass, and $\alpha$-viscosity to determine the observable effect of each parameter. These comparisons provide insight that could aid in the analysis of observational data from the system when the real transit occurs and provide recommendations for how such observations should be made. We find that a higher dust mass or higher value of $\alpha$ correspond to a longer transit, with the gas mass having a more minor effect on the overall shape and duration of the transit. A higher $\alpha$ has an observable effect on the viscous spreading at the outer edge of the disc, though is countered through truncation by the outer binary. It is also shown that long-term interactions between the outer binary and disc can excite spiral arms in the disc, which introduce observable asymmetries to the light curve. Our models suggest that the transit should have begun at the time of writing, but no dimming has yet been observed. It is likely that the disc has a smaller radial extent than our models, due to a lower viscosity than can be simulated with SPH. The transit is expected to last 8-11 years, ending in late 2034 at the latest., Comment: 12 pages, 13 figures

The outstanding opto-electronic properties of lead halide perovskites have been related to the formation of polarons. Nevertheless, the observation of the atomistic deformation brought about by one electron-hole pair in these materials has remained elusive. Here, we measure the diffraction patterns of single CsPbBr$_3$ quantum dots (QDs) with and without resonant excitation in the single exciton limit using serial femtosecond crystallography (SFX). By reconstructing the 3D differential diffraction pattern, we observe small shifts of the Bragg peaks indicative of a crystal-wide deformation field. Building on DFT calculations, we show that these shifts are consistent with the lattice distortion induced by a delocalized electron and a localized hole, forming a mixed large/small exciton polaron. This result creates a clear picture of the polaronic deformation in CsPbBr$_3$ QDs, highlights the exceptional sensitivity of SFX to lattice distortions in few-nanometer crystallites, and establishes an experimental platform for future studies of electron-lattice interactions., Comment: Main: 12 pages, 5 figures; Supplemental: 21 pages, 11 figures

We present a search for the diffuse extremely-high-energy neutrino flux using $12.6$ years of IceCube data. The non-observation of neutrinos with energies well above $10 \, \mathrm{PeV}$ constrains the all-flavor neutrino flux at $10^{18} \, \mathrm{eV}$ to a level of $E^2 \Phi_{\nu_e + \nu_\mu + \nu_\tau} \simeq 10^{-8} \, \mathrm{GeV} \, \mathrm{cm}^{-2} \, \mathrm{s}^{-1} \, \mathrm{sr}^{-1}$, the most stringent limit to date. Using this data, we constrain the proton fraction of ultra-high-energy cosmic rays (UHECRs) above $\simeq 30 \, \mathrm{EeV}$ to be $\lesssim 70\,$% (at $90\,$% CL) if the cosmological evolution of the sources is comparable to or stronger than the star formation rate. This result complements direct air-shower measurements by being insensitive to uncertainties associated with hadronic interaction models. It is the first such result to disfavor the ``proton-only" hypothesis for UHECRs using neutrino data.

We study the group strategic behaviors in Bayesian games. Equilibria in previous work do not consider group strategic behaviors with bounded sizes and are too ``strong'' to exist in many scenarios. We propose the ex-ante Bayesian $k$-strong equilibrium and the Bayesian $k$-strong equilibrium, where no group of at most $k$ agents can benefit from deviation. The two solution concepts differ in how agents calculate their utilities when contemplating whether a deviation is beneficial. Intuitively, agents are more conservative in the Bayesian $k$-strong equilibrium than in the ex-ante Bayesian $k$-strong equilibrium. With our solution concepts, we study collusion in the peer prediction mechanisms, as a representative of the Bayesian games with group strategic behaviors. We characterize the thresholds of the group size $k$ so that truthful reporting in the peer prediction mechanism is an equilibrium for each solution concept, respectively. Our solution concepts can serve as criteria to evaluate the robustness of a peer prediction mechanism against collusion. Besides the peer prediction problem, we also discuss two other potential applications of our new solution concepts, voting and Blotto games, where introducing bounded group sizes provides more fine-grained insights into the behavior of strategic agents., Comment: Accepted by TheWebConf 2025 (WWW'25). 23 pages

Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis. However, their deployment faces challenges due to the high dimensionality of the state space, necessitating the development of efficient inference algorithms. Current inference approaches mainly fall into two categories: exact simulation and approximate methods such as $\tau$-leaping. While exact methods suffer from unpredictable inference time and redundant function evaluations, $\tau$-leaping is limited by its first-order accuracy. In this work, we advance the latter category by tailoring the first extension of high-order numerical inference schemes to discrete diffusion models, enabling larger step sizes while reducing error. We rigorously analyze the proposed schemes and establish the second-order accuracy of the $\theta$-trapezoidal method in KL divergence. Empirical evaluations on GPT-2 level text and ImageNet-level image generation tasks demonstrate that our method achieves superior sample quality compared to existing approaches under equivalent computational constraints., Comment: 38 pages, 7 figures

We introduce a prescription for estimating the flux of the 7.7 micron and 11.3 micron\ polycyclic aromatic hydrocarbon (PAH) features from broadband JWST/MIRI images. Probing PAH flux with MIRI imaging data has advantages in field of view, spatial resolution, and sensitivity compared with MIRI spectral maps, but comparisons with spectra are needed to calibrate these flux estimations over a wide variety of environments. For 267 MIRI/MRS spectra from independent regions in the four luminous infrared galaxies (LIRGs) in the Great Observatories All-sky LIRG Survey (GOALS) early release science program, we derive synthetic filter photometry and directly compare estimated PAH fluxes to those measured from detailed spectral fits. We find that for probing PAH 7.7 micron, the best combination of filters is F560W, F770W, and either F1500W or F2100W, and the best for PAH 11.3 micron is F560W, F1000W, F1130W, and F1500W. The prescription with these combinations yields predicted flux densities that typically agree with values from spectral decomposition within ~7% and ~5% for PAH 7.7 and 11.3 micron, respectively., Comment: Submitted to ApJ

Supermassive black holes (SMBH) are thought to grow through accretion of matter and mergers. Models of SMBH mergers have long suffered the final parsec problem, where SMBH binaries may stall before energy loss from gravitational waves (GW) becomes significant, leaving the pair unmerged. Direct evidence of coalesced SMBH remains elusive. Theory predicts that GW recoiling black holes can occur following a black hole merger. Here we present new and conclusive spectroscopic evidence that both the accretion disk and the broad line region in the spatially offset quasar 3C 186 are blue-shifted by the same velocity relative to the host galaxy, with a line of sight velocity of (-1310 +- 21) km/s. This is best explained by the GW recoil super-kick scenario. This confirmation of the ejection process implies that the final parsec problem is resolved in nature, providing evidence that even the most massive black holes can merge., Comment: 32 pages, 10 figures, submitted

Localized receptive fields -- neurons that are selective for certain contiguous spatiotemporal features of their input -- populate early sensory regions of the mammalian brain. Unsupervised learning algorithms that optimize explicit sparsity or independence criteria replicate features of these localized receptive fields, but fail to explain directly how localization arises through learning without efficient coding, as occurs in early layers of deep neural networks and might occur in early sensory regions of biological systems. We consider an alternative model in which localized receptive fields emerge without explicit top-down efficiency constraints -- a feedforward neural network trained on a data model inspired by the structure of natural images. Previous work identified the importance of non-Gaussian statistics to localization in this setting but left open questions about the mechanisms driving dynamical emergence. We address these questions by deriving the effective learning dynamics for a single nonlinear neuron, making precise how higher-order statistical properties of the input data drive emergent localization, and we demonstrate that the predictions of these effective dynamics extend to the many-neuron setting. Our analysis provides an alternative explanation for the ubiquity of localization as resulting from the nonlinear dynamics of learning in neural circuits., Comment: Appeared at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024); spotlight presentation

In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and muons produced in cosmic-ray interactions. IceCube event selections used to isolate the astrophysical neutrino signal often focus on t/he morphology of the light patterns recorded by the detector. The analyses presented here use the new IceCube Enhanced Starting Track Event Selection (ESTES), which identifies events likely generated by muon neutrino interactions within the detector geometry, focusing on neutrino energies of 1-500 TeV with a median angular resolution of 1.4{\deg}. Selecting for starting track events filters out not only the atmospheric-muon background, but also the atmospheric-neutrino background in the southern sky. This improves IceCube's muon neutrino sensitivity to southern-sky neutrino sources, especially for Galactic sources that are not expected to produce a substantial flux of neutrinos above 100 TeV. In this work, the ESTES sample was applied for the first time to searches for astrophysical sources of neutrinos, including a search for diffuse neutrino emission from the Galactic plane. No significant excesses were identified from any of the analyses; however, constraining limits are set on the hadronic emission from TeV gamma-ray Galactic plane objects and models of the diffuse Galactic plane neutrino flux., Comment: 23 pages, 8 figures, 4 tables. Submitted to ApJ

We introduce a protocol called ENCORE which simultaneously compresses and encrypts data in a one-pass process that can be implemented efficiently and possesses a number of desirable features as a streaming encoder/decoder. Motivated by the observation that both lossless compression and encryption consist of performing an invertible transformation whose output is close to a uniform distribution over bit streams, we show that these can be done simultaneously, at least for ``typical'' data with a stable distribution, i.e., approximated reasonably well by the output of a Markov model. The strategy is to transform the data into a dyadic distribution whose Huffman encoding is close to uniform, and then store the transformations made to said data in a compressed secondary stream interwoven into the first with a user-defined encryption protocol. The result is an encoding which we show exhibits a modified version of Yao's ``next-bit test'' while requiring many fewer bits of entropy than standard encryption. Numerous open questions remain, particularly regarding results that we suspect can be strengthened considerably., Comment: 15 pages

In this report we summarize the activities of the International Astronomical Consortium for High Energy Calibration (IACHEC) from the 16th IACHEC Workshop at Parador de La Granja, Spain. Sixty-one scientists directly involved in the calibration of operational and future high-energy missions gathered during 3.5 days to discuss the status of the cross-calibration between the current international complement of X-ray observatories, and the possibilities to improve it. This summary consists of reports from the Working Groups with topics ranging across: the identification and characterization of standard calibration sources, multi-observatory cross-calibration campaigns, appropriate and new statistical techniques, calibration of instruments and characterization of background, preservation of knowledge, and results for the benefit of the astronomical community., Comment: 11 pages; Summary of the 16th IACHEC meeting (May 2024). arXiv admin note: text overlap with arXiv:2407.09432

Fashion content generation is an emerging area at the intersection of artificial intelligence and creative design, with applications ranging from virtual try-on to culturally diverse design prototyping. Existing methods often struggle with cultural bias, limited scalability, and alignment between textual prompts and generated visuals, particularly under weak supervision. In this work, we propose a novel framework that integrates Large Language Models (LLMs) with Latent Diffusion Models (LDMs) to address these challenges. Our method leverages LLMs for semantic refinement of textual prompts and introduces a weak supervision filtering module to effectively utilize noisy or weakly labeled data. By fine-tuning the LDM on an enhanced DeepFashion+ dataset enriched with global fashion styles, the proposed approach achieves state-of-the-art performance. Experimental results demonstrate that our method significantly outperforms baselines, achieving lower Frechet Inception Distance (FID) and higher Inception Scores (IS), while human evaluations confirm its ability to generate culturally diverse and semantically relevant fashion content. These results highlight the potential of LLM-guided diffusion models in driving scalable and inclusive AI-driven fashion innovation.

Multi-modal retrieval-augmented Question Answering (MRAQA), integrating text and images, has gained significant attention in information retrieval (IR) and natural language processing (NLP). Traditional ranking methods rely on small encoder-based language models, which are incompatible with modern decoder-based generative large language models (LLMs) that have advanced various NLP tasks. To bridge this gap, we propose RAMQA, a unified framework combining learning-to-rank methods with generative permutation-enhanced ranking techniques. We first train a pointwise multi-modal ranker using LLaVA as the backbone. Then, we apply instruction tuning to train a LLaMA model for re-ranking the top-k documents using an innovative autoregressive multi-task learning approach. Our generative ranking model generates re-ranked document IDs and specific answers from document candidates in various permutations. Experiments on two MRAQA benchmarks, WebQA and MultiModalQA, show significant improvements over strong baselines, highlighting the effectiveness of our approach. Code and data are available at: https://github.com/TonyBY/RAMQA, Comment: Accepted by NAACL 2025 Findings

A next-generation medium-energy gamma-ray telescope targeting the MeV range would address open questions in astrophysics regarding how extreme conditions accelerate cosmic-ray particles, produce relativistic jet outflows, and more. One concept, AMEGO-X, relies upon the mission-enabling CMOS Monolithic Active Pixel Sensor silicon chip AstroPix. AstroPix is designed for space-based use, featuring low noise, low power consumption, and high scalability. Desired performance of the device include an energy resolution of 5 keV (or 10% FWHM) at 122 keV and a dynamic range per-pixel of 25-700 keV, enabled by the addition of a high-voltage bias to each pixel which supports a depletion depth of 500 um. This work reports on the status of the AstroPix development process with emphasis on the current version under test, version three (v3), and highlights of version two (v2). Version 3 achieves energy resolution of 10.4 +\- 3.2 % at 59.5 keV and 94 +\- 6 um depletion in a low-resistivity test silicon substrate., Comment: 20 pages, 13 figures

The recent advancements in Generative Artificial intelligence (GenAI) technology have been transformative for the field of education. Large Language Models (LLMs) such as ChatGPT and Bard can be leveraged to automate boilerplate tasks, create content for personalised teaching, and handle repetitive tasks to allow more time for creative thinking. However, it is important to develop guidelines, policies, and assessment methods in the education sector to ensure the responsible integration of these tools. In this article, thematic analysis has been performed on seven essays obtained from professionals in the education sector to understand the advantages and pitfalls of using GenAI models such as ChatGPT and Bard in education. Exploratory Data Analysis (EDA) has been performed on the essays to extract further insights from the text. The study found several themes which highlight benefits and drawbacks of GenAI tools, as well as suggestions to overcome these limitations and ensure that students are using these tools in a responsible and ethical manner.

Towards clinical interpretations, this paper presents a new ''output-with-confidence'' segmentation neural network with multiple input images and multiple output segmentation maps and their pairwise relations. A confidence score of the test image without ground-truth can be estimated from the difference among the estimated relation maps. We evaluate the method based on the widely used vanilla U-Net for segmentation and our new model is named Relation U-Net which can output segmentation maps of the input images as well as an estimated confidence score of the test image without ground-truth. Experimental results on four public datasets show that Relation U-Net can not only provide better accuracy than vanilla U-Net but also estimate a confidence score which is linearly correlated to the segmentation accuracy on test images., Comment: ISIB 2025

We report a search for high-energy astrophysical neutrino multiplets, detections of multiple neutrino clusters in the same direction within 30 days, based on an analysis of 11.4 years of IceCube data. A new search method optimized for transient neutrino emission with a monthly time scale is employed, providing a higher sensitivity to neutrino fluxes. This result is sensitive to neutrino transient emission, reaching per-flavor flux of approximately $10^{-10}\ {\rm erg}\ {\rm cm}^{-2}\ {\rm sec}^{-1}$ from the Northern sky in the energy range $E\gtrsim 50$~TeV. The number of doublets and triplets identified in this search is compatible with the atmospheric background hypothesis, which leads us to set limits on the nature of neutrino transient sources with emission timescales of one month.

What types of numeric representations emerge in Neural Networks (NNs)? To what degree do NNs induce abstract, mutable, slot-like numeric variables, and in what situations do these representations emerge? How do these representations change over learning, and how can we understand the neural implementations in ways that are unified across different NNs? In this work, we approach these questions by first training sequence based neural systems using Next Token Prediction (NTP) objectives on numeric tasks. We then seek to understand the neural solutions through the lens of causal abstractions or symbolic algorithms. We use a combination of causal interventions and visualization methods to find that artificial neural models do indeed develop analogs of interchangeable, mutable, latent number variables purely from the NTP objective. We then ask how variations on the tasks and model architectures affect the models' learned solutions to find that these symbol-like numeric representations do not form for every variant of the task, and transformers solve the problem in a notably different way than their recurrent counterparts. We then show how the symbol-like variables change over the course of training to find a strong correlation between the models' task performance and the alignment of their symbol-like representations. Lastly, we show that in all cases, some degree of gradience exists in these neural symbols, highlighting the difficulty of finding simple, interpretable symbolic stories of how neural networks perform numeric tasks. Taken together, our results are consistent with the view that neural networks can approximate interpretable symbolic programs of number cognition, but the particular program they approximate and the extent to which they approximate it can vary widely, depending on the network architecture, training data, extent of training, and network size.

Transductive zero-shot learning with vision-language models leverages image-image similarities within the dataset to achieve better classification accuracy compared to the inductive setting. However, there is little work that explores the structure of the language space in this context. We propose GTA-CLIP, a novel technique that incorporates supervision from language models for joint transduction in language and vision spaces. Our approach is iterative and consists of three steps: (i) incrementally exploring the attribute space by querying language models, (ii) an attribute-augmented transductive inference procedure, and (iii) fine-tuning the language and vision encoders based on inferred labels within the dataset. Through experiments with CLIP encoders, we demonstrate that GTA-CLIP, yields an average performance improvement of 8.6% and 3.7% across 12 datasets and 3 encoders, over CLIP and transductive CLIP respectively in the zero-shot setting. We also observe similar improvements in a few-shot setting. We present ablation studies that demonstrate the value of each step and visualize how the vision and language spaces evolve over iterations driven by the transductive learning., Comment: Code will be released at https://github.com/cvl-umass/GTA-CLIP

When can we say that two neural systems are the same? The answer to this question is goal-dependent, and it is often addressed through correlative methods such as Representational Similarity Analysis (RSA) and Centered Kernel Alignment (CKA). How do we target functionally relevant similarity, and how do we isolate specific causal aspects of the representations? In this work, we introduce Model Alignment Search (MAS), a method for causally exploring distributed representational similarity. The method learns invertible linear transformations that align a subspace between two distributed networks' representations where causal information can be freely interchanged. We first show that the method can be used to transfer values of specific causal variables -- such as the number of items in a counting task -- between networks with different training seeds. We then explore open questions in number cognition by comparing different types of numeric representations in models trained on structurally different tasks. We then explore differences between MAS vs preexisting causal similarity methods, and lastly, we introduce a counterfactual latent auxiliary loss function that helps shape causally relevant alignments even in cases where we do not have causal access to one of the two models for training.

ALMA has revealed that the millimetre dust structures of protoplanetary discs are extremely diverse. It has been proposed that the strength of H$_2$O emission in the inner disc particularly depends on the influx of icy pebbles from the outer disc, a process that would correlate with the outer dust disc radius, and that could be prevented by pressure bumps. This work aims to assess the influence of pressure bumps on the inner disc's molecular reservoirs. Using JWST's MIRI/MRS, we compared the observational emission properties of H$_2$O, HCN, C$_2$H$_2$, and CO$_2$ with the outer dust disc structure from ALMA observations, in eight discs with confirmed gaps in ALMA observations, and two discs with gaps of tens of astronomical units in width, around stars with $M_\star \geq 0.45M_{\odot}$. We used new visibility plane fits of the ALMA data to determine the outer dust disc radius and identify substructures in the discs. We find that the presence of a dust gap does not necessarily result in weak H$_2$O emission. Furthermore, the relative lack of colder H$_2$O-emission seems to go hand in hand with elevated emission from carbon-bearing species. The discs with cavities and extremely wide gaps appear to behave as a somewhat separate group, with stronger cold H$_2$O emission and weak warm H$_2$O emission. We conclude that fully blocking radial dust drift from the outer disc seems difficult to achieve. However, there does seem to be a dichotomy between discs that show a strong cold H$_2$O excess and ones that show strong emission from HCN and C$_2$H$_2$. Better constraints on the influence of the outer dust disc structure and inner disc composition require more information on substructure formation timescales and disc ages, along with the importance of trapping of volatiles like CO and CO$_2$ into more strongly bound ices like H$_2$O and chemical transformation of CO into less volatile species., Comment: Accepted for publication in A&A. 29 pages, 12 figures and 10 tables

Quantum dot (QD) light-emitting diodes (QLEDs) are promising candidates for next-generation displays because of their high efficiency, brightness, broad color gamut, and solution-processability. Large-scale solution-processing of electroluminescent QLEDs poses significant challenges, particularly concerning the precise control of the active layer's thickness and uniformity. These obstacles directly impact charge transport, leading to current leakage and reduced overall efficiency. Blade-coating is a prevalent and scalable solution processing technique known for its speed and minimal waste. Additionally, it allows for continuous "roll-to-roll" processing, making it highly adaptable in various applications. In this study, we demonstrate the precise control of blade speed in the Landau-Levich regime to create a uniform QD emission layer, using a commercial CdSe/ZnS QD as a representative example. QDs assemble into different morphologies on glass and the underlying layers of the QLED device due to variations in interaction energy. The QD film thickness can be modified from monolayer to multilayer by adjusting blade speed, which can be predicted by fitting the Landau-Levich-Derjaguin theory. The optimal speed at 7 mm/s results in a QD film with a surface coverage of around 163% and low roughness (1.57 nm mean square height). The QLED external quantum efficiency (EQE) of approximately 1.5% was achieved using commercially available CdSe/ZnS QDs with low photoluminescence quantum yield (PLQY), and an EQE of around 7% has been obtained using lab-made InP/ZnSe/ZnS QDs having a solution PLQY of 74%. All-blade-coated CdSe-QLEDs are further demonstrated by adopting the optimized speed for the QD layer. This method demonstrates significant potential for developing low-cost, reproducible, and scalable QLED technologies with uniform emission characteristics and low-waste production., Comment: 36 pages, 17 figures

The battery performance and lifespan of electric vehicles (EVs) degrade significantly in cold climates, requiring a considerable amount of energy to heat up the EV batteries. This paper proposes a novel technology, namely temperature-controlled smart charging, to coordinate the heating/charging power and reduce the total energy use of a solar-powered EV charging station. Instead of fixing the battery temperature setpoints, we analyze the thermal dynamics and inertia of EV batteries, and decide the optimal timing and proper amount of energy allocated for heating. In addition, a temperature-sensitive charging model is formulated with consideration of dynamic charging rates as well as battery health. We further tailor acceleration algorithms for large-scale EV charging, including the reduced-order dual decomposition and vehicle rescheduling. Simulation results demonstrate that the proposed temperature-controlled smart charging is superior in capturing the flexibility value of EV batteries and making full use of the rooftop solar energy. The proposed model typically achieves a 12.5--18.4% reduction in the charging cost and a 0.4--6.8% drop in the overhead energy use for heating., Comment: 11 pages, accepted by IEEE Transactions on Smart Grid

Atomic and trapped-ion systems are the backbone of a new generation of quantum-based positioning, navigation, and timing (PNT) technologies. The miniaturization of such quantum systems offers tremendous technological advantages, especially the reduction of system size, weight, and power consumption. Yet, this has been limited by the absence of compact, standalone photonic integrated circuits (PICs) at the wavelengths suitable for these instruments. Mobilizing such photonic systems requires development of fully integrated, on-chip, active components at sub-micrometer wavelengths. We demonstrate heterogeneous photonic integrated circuits operating at 980 nm based on wafer-scale bonding of InGaAs quantum well active regions to tantalum pentoxide passive components. This high-yield process provides > 95 % surface area yield and enables integration of > 1300 active components on a 76.2 mm (3 inch) silicon wafer. We present a diverse set of functions, including semiconductor optical amplifiers, Fabry-Perot lasers, and distributed feedback lasers with 43 dB side-mode suppression ratio and > 250 GHz single-mode tuning range. We test the precise wavelength control and system level functionality of the on-chip lasers by pumping optical parametric oscillation processes in microring resonators fabricated on the same platform, generating short-wavelength signals at 778 nm and 752 nm. These results provide a pathway to realize fully functional integrated photonic engines for operation of compact quantum sensors based on atomic and trapped-ion systems., Comment: 12 pages, 5 figures

Open string amplitudes at tree level have been studied for over fifty years, but there is no known analytic form for general $n$-point amplitudes, and their conventional representation in terms of worldsheet integrals does not make many of their most basic physical properties manifest. Recently, a formulation of these amplitudes exposing the underlying "binary geometry" via the use of "$u$" variables has given us many insights into their basic features. In this paper, we initiate a systematic exploration of fundamental aspects of open string amplitudes from this new point of view. We begin by giving explicit expressions for the factorization of amplitudes at general massive levels. We then study the asymptotic behavior when subsets of kinematic variables become large, delineating regimes with exponential (generalized hard scattering) and power-law (generalized Regge) behavior. We also give precise expressions for the asymptotics, which reveal another example of the recently observed property of factorization away from poles. We finally derive new recursion relations and infinite series representations for the amplitude, and for five points, we present a new closed-form expression for the amplitude that for the first time gives its analytic continuation to all of kinematic space., Comment: 54 pages, 17 figures

A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this process is selecting a forecasting strategy, however, with no existing frameworks to map out the space of strategies, practitioners are left with ad-hoc methods for strategy selection. In this work, we propose Stratify, a parameterised framework that addresses multi-step forecasting, unifying existing strategies and introducing novel, improved strategies. We evaluate Stratify on 18 benchmark datasets, five function classes, and short to long forecast horizons (10, 20, 40, 80). In over 84% of 1080 experiments, novel strategies in Stratify improved performance compared to all existing ones. Importantly, we find that no single strategy consistently outperforms others in all task settings, highlighting the need for practitioners explore the Stratify space to carefully search and select forecasting strategies based on task-specific requirements. Our results are the most comprehensive benchmarking of known and novel forecasting strategies. We make code available to reproduce our results., Comment: 30 pages, 9 figures, journal

We model the total mass and galactic substructure in the strong lensing galaxy cluster MACS J0138.0-2155 using a combination of Chandra X-ray data, Multi-Unit Spectroscopic Explorer (MUSE) spectroscopy, and Hubble Space Telescope imaging. MACS J0138.0-2155 lenses a source galaxy at z=1.95 which hosts two strongly lensed supernovae, Requiem and Encore. We find MACS J0138.0-2155 to have an X-ray temperature of 6.7 +/- 0.4 keV and a velocity dispersion of cluster member galaxies of 718^{+132}_{-182} km/s, which indicate a cluster mass of ~5 x 10^{14} solar masses. The round morphology of the X-ray emission indicates that this cluster is relaxed with an ellipticity within the lensing region of e=0.12 +/- 0.03. Using 18 of the brightest, non-blended, quiescent galaxies, we fit the cluster specific Faber-Jackson relation, including a set of 81 variations in the analysis choices to estimate the systematic uncertainties in our results. We find a slope of alpha = 0.26 +/- 0.06 (stat.) +/- 0.03 (sys.) with an intrinsic scatter of 31^{+8}_{-6} (stat.) +/- 4 (sys.) km/s at a reference velocity dispersion of ~220 km/s. We also report on significant galaxies along the line-of-sight potentially impacting the lens modeling, including a massive galaxy with stellar velocity dispersion of 291 +/- 3 km/s$ which lies close in projection to the central cluster galaxy. This galaxy is part of a small group at a slightly higher redshift than the cluster., Comment: submitted to Open Journal of Astrophysics

Near the horizon of extremal charged black holes, an accidental symmetry is known to act on zero-temperature perturbations, transforming them into finite-temperature ones. In this paper, we uncover the corresponding accidental symmetry of the vacuum Einstein equation near the horizon of extremal spinning black holes. To do so, we first devise a new method of deriving the symmetry near the ${\rm AdS}_2 \times S^2$ near-horizon geometry of extreme Reissner-Nordstr\"om, using a new scaling coordinate transformation that unifies the near-horizon limits of extremal and near-extremal black holes in a way that is regular at zero temperature. We use our new method to obtain the accidental symmetry in the near-horizon of extreme Kerr (NHEK). We show that accidental symmetries combine neatly with the near-horizon isometries inside a Virasoro algebra., Comment: 18 pages

Fine-resolution maps of forest aboveground biomass (AGB) effectively represent spatial patterns and can be flexibly aggregated to map subregions by computing spatial averages or totals of pixel-level predictions. However, generalized model-based uncertainty estimation for spatial aggregates requires computationally expensive processes like iterative bootstrapping and computing pixel covariances. Uncertainty estimation for map subregions is critical for enhancing practicality and eventual adoption of model-based data products, as this capability would empower users to produce estimates at scales most germane to management: individual forest stands and ownership parcels. In this study we produced estimates of standard error (SE) associated with spatial averages of AGB predictions for ownership parcels in New York State (NYS). This represents the first model-based uncertainty estimation study to include all four types of uncertainty (reference data, sample variability, residual variability, and auxiliary data), incorporate spatial autocorrelation of model residuals, and use methods compatible with algorithmic modeling. We found that uncertainty attributed to residual variance, largely resulting from spatial correlation of residuals, dominated all other sources for most parcels in the study. These results suggest that improvements to model accuracy will yield the greatest reductions to total uncertainty in regions like the northeastern and midwestern United States where forests are divided into smaller spatial units. Further, we demonstrated that log-log regression relating parcel characteristics (area, perimeter, AGB density, forest cover) to parcel-level SE can accurately estimate uncertainty for map subregions, thus providing a convenient means to empower map users. These findings support transparency in future regional-scale model-based forest carbon accounting and monitoring efforts., Comment: 22 pages, 8 figures, 4 tables

The Arcus Probe mission concept provides high-resolution soft X-ray and UV spectroscopy to reveal feedback-driven structure and evolution throughout the universe with an agile response capability ideal for probing the physics of time-dependent phenomena. The X-ray Spectrograph (XRS) utilizes two nearly identical CCD focal planes to detect and record X-ray photons from the dispersed spectra and zero-order of the critical angle transmission gratings. In this paper we describe the Arcus focal plane instrument and the CCDs, including laboratory performance results, which meet observatory requirements., Comment: 11 pages, 7 figures; to be published in JATIS

We report the detection of the [O III] auroral line in 42 galaxies within the redshift range of $3 < z < 10$. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMALsurveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in the high-ionization regions of these galaxies range from $T_e$([O III]) = 12,000 to 24,000 K, consistent with temperatures observed in local metal-poor galaxies and previous JWST studies. In 10 galaxies, we also detect the [O II] auroral line, allowing us to determine electron temperatures in the low-ionization regions, which range between $T_e$([O II]) = 10,830 and 20,000 K. The direct-$T_e$-based metallicities of our sample span from 12 + log(O/H) = 7.2 to 8.4, indicating these high-redshift galaxies are relatively metal-poor. By combining our sample with 25 galaxies from the literature, we expand the dataset to a total of 67 galaxies within $3 < z < 10$, effectively more than doubling the previous sample size for direct-$T_e$ based metallicity studies. This larger dataset allow us to derive empirical metallicity calibration relations based exclusively on high-redshift galaxies, using six key line ratios: R3, R2, R23, Ne3O2, O32, and O3N2. Notably, we derive a novel metallicity calibration relation for the first time using high-redshift $T_e$-based metallicities: $\hat{R}$ = 0.18log $R2$ + 0.98log $R3$. This new calibration significantly reduces the scatter in high-redshift galaxies compared to the $\hat{R}$ relation previously calibrated for low-redshift galaxies., Comment: 23 pages, 10 figures, submitted to ApJ