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This study examines the impact of statewide Restorative Justice (RJ) policy reforms in Michigan and Texas on student disciplinary outcomes and behavior, in light of increasing concerns over the negative effects of zero-tolerance policies. As schools move away from exclusionary discipline practices, this research focuses on three primary questions: (1) Are these policies effectively implemented statewide? (2) Do they contribute to a reduction in problematic behaviors, such as bullying? (3) Does the distinction in policy implementation--Michigan's prescriptive approach versus Texas's permissive framework--affect outcomes? Utilizing school district-level data and a penalized synthetic control estimator for multiple treated districts, the analysis reveals that Texas has an overall reduction in out-of-school suspensions and bullying incidents, while Michigan shows an increase. However, taking these as a main takeaway would be misleading, as at a fine-grained level, more than half of the Michigan school districts show a reduction in bullying incidents. The results are further discussed, revealing patterns of racial composition in the districts with respect to their success in implementation of reforms. The findings highlight the critical role of implementation fidelity and the importance of local context in assessing the success of RJ initiatives while also filling a critical gap in understanding the multifaceted consequences of RJ practices.

In-situ control over band mass inversion is crucial for developing materials with topologically protected edge modes. In this Letter, we report the direct observation of displacement field $D$ control of band mass and Berry phase in Bernal stacked trilayer graphene (TLG) in the region where trigonal warping distorts the quadratic band into off-center Dirac points, referred to as `Dirac Gullies.' Using Shubnikov-de-Haas (SdH) oscillations, we map the Fermi surface contours of the Dirac gullies and the $D$-dependent band structure. With increasing $D$-field, the Berry phase undergoes multiple transitions from $\Phi_B=2\pi$ $\rightarrow$ $\pi$ $\rightarrow$ $2\pi$ as $D$ is varied. Concurrently, measurement of the effective mass reveals a series of transitions between massless and massive bands, signaling the closure and reopening (accompanied by a possible band inversion) of the band gap at a critical value of $D$. Interestingly, the expected Dirac-like behavior of the Dirac gullies ($\Phi_B=\pi$) persists only over a narrow range of $D$. Our study directly confirms recent predictions of $ D$-field-induced band inversion in the low-energy regions of TLG. It is a significant step towards achieving control over pure valley transport in multilayer graphene., Comment: 16 pages, comments and suggestions most welcome

Climate change significantly impacts public health, driving the emergence and spread of epidemics. Climate health models are essential for assessing and predicting disease outbreaks influenced by climatic variables like temperature and precipitation. For instance, dengue and malaria correlate with temperature changes, while cholera is linked to precipitation anomalies. Advances in AI-enabled weather prediction (AI-NWP) have improved forecasting, but integrating climate models with health systems is hindered by the lack of comprehensive, granular health datasets. This study introduces EpiClim: India's Epidemic-Climate Dataset, the first weekly district-wise dataset for major epidemics in India from 2009 to the present, sourced from the Integrated Disease Surveillance Programme (IDSP). The dataset, covering diseases like dengue, malaria, and acute-diarrheal disease, bridges the gap between climate and health data, enabling the integration of climate forecasts with epidemic prediction models. This work lays the foundation for coupling predictive climate health models with weather and climate models, advancing efforts to mitigate climate-induced public health crises.

In this paper, we examine the use of Conformal Language Modelling (CLM) alongside Answer Set Programming (ASP) to enhance the performance of standard open-weight LLMs on complex multi-step reasoning tasks. Using the StepGame dataset, which requires spatial reasoning, we apply CLM to generate sets of ASP programs from an LLM, providing statistical guarantees on the correctness of the outputs. Experimental results show that CLM significantly outperforms baseline models that use standard sampling methods, achieving substantial accuracy improvements across different levels of reasoning complexity. Additionally, the LLM-as-Judge metric enhances CLM's performance, especially in assessing structurally and logically correct ASP outputs. However, calibrating CLM with diverse calibration sets did not improve generalizability for tasks requiring much longer reasoning steps, indicating limitations in handling more complex tasks.

The forthcoming Wide Area Vista Extragalactic Survey (WAVES) on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) has a key science goal of probing the halo mass function to lower limits than possible with previous surveys. For that purpose, in its Wide component, galaxies targetted by WAVES will be flux-limited to $Z<21.1$ mag and will cover the redshift range of $z<0.2$, at a spectroscopic success rate of $\sim95\%$. Meeting this completeness requirement, when the redshift is unknown a priori, is a challenge. We solve this problem with supervised machine learning to predict the probability of a galaxy falling within the WAVES-Wide redshift limit, rather than estimate each object's redshift. This is done by training an XGBoost tree-based classifier to decide if a galaxy should be a target or not. Our photometric data come from 9-band VST+VISTA observations, including KiDS+VIKING surveys. The redshift labels for calibration are derived from an extensive spectroscopic sample overlapping with KiDS and ancillary fields. Our current results indicate that with our approach, we should be able to achieve the completeness of $\sim95\%$, which is the WAVES success criterion., Comment: 5 pages, 1 figure, ML4ASTRO2 conference proceedings

This paper is the extension of our previous work [arXiv:2108.03296] entitled Searching a systematics for nonfactorizable contributions to B hadronic decays. In order to realize the full impact of isospin analysis, and to relate decays of strange bottom meson with those of nonstrange bottom mesons, we generalize it to the SU(3) flavor symmetry to investigate the nonfactorizable contributions to CKM-enhanced and CKM-suppressed decays. We start with expressing total weak decay amplitude as sum of the factorizable and nonfactorizable parts, then obtain the factorizable part of the decay amplitude numerically using the known meson decay constants and relevant form-factors, then express the nonfactorizable parts in terms of SU(3)-reduced matrix elements. Using measured branching fractions of a few CKM-favored modes, we fix the reduced matrix elements, and predict branching ratios of the remaining CKM-favored decays and all CKM-suppressed B to PP decays processes.We find that the measured branching fractions agree well with our results, and other predicted values may be tested in the future experiments., Comment: 21. arXiv admin note: substantial text overlap with arXiv:2311.12483

Hardware impairments and system non-linearities impacting communication signal is one of key aspect for having harmonics and RF desense which overall causing the lower quality and integrity of the modulated signal, resulting in I/Q imbalance, further bit error and spectral efficiency degradation. This presentation outlines the RF Desense results, EVM Measurement and it impact at the almost noise floor with step size by 1 dB in QPSK at LTE Bands, Note for mmW 3GPP 38.521-2 clause 6.4.2.1 indicates single polarization., Comment: 3 pages, 1 table, 4 figures

The mechanical properties of micro-scale bio-entities are fundamental for understanding their functions and pathological states. However, current methods for assessing elastic properties at single-particle level such as Brillouin and atomic force microscopies exhibit intrinsic limitations, including being often slow, having poor resolution, or involving complicated and invasive setups. In this study, we explore Photon Absorption Remote Sensing (PARS) microscopy as a unique solution for mechanical sensing of single micro-objects. PARS uses probe beam scattering/reflectivity measurements to capture non-radiative relaxation process following the absorption of a pulse of light by a micro-object. In particular, we demonstrate that, when operating at GHz-range bandwidth, PARS can trace the sub-nanosecond dynamics of non-radiative relaxation in individual micro-objects, capturing both photoacoustic (PA) pressure propagation and thermal diffusion. This GHz-range measurement, in conjunction with a developed descriptive model, enables the experimental extraction of a minimally distorted PA temporal profile. The PA temporal profile contain information on the ratio between the absorbing object's sound speed and its characteristic diameter, offering a new dimension in PARS microscopy. This enables the assessment of the object's elastic properties, deduced from its speed of sound. Additionally, it offers the potential for sizing objects with known sound speeds. The proof of principle experiments was conducted using spherical polystyrene absorbers, ranging in size from 1 to 10 micrometers with known properties, embedded in a Polydimethylsiloxane (PDMS) matrix. This technique expands the scope of PARS imaging, opening new perspectives for clinical applications in mechanobiology by demonstrating its potential for mechanical imaging.

We use the Karl G. Jansky Very Large Array (VLA) and the Atacama Large Millimeter/submillimeter Array (ALMA) to detect $\rm CO(1-0)$, $\rm CO(3-2)$, and rest-frame 349-GHz continuum emission from an HI-selected galaxy, DLA1020+2733g, at $z\approx2.3568$ in the field of the $z=2.3553$ damped Lyman-$\alpha$ absorber (DLA) towards QSO J1020+2733. The VLA $\rm CO(1-0)$ detection yields a molecular gas mass of $(2.84\pm0.42)\times10^{11}\times(\alpha_{\rm CO}/4.36)\,\rm{M_\odot}$, the largest ever measured in an HI-selected galaxy. The DLA metallicity is $+0.28 \pm 0.16$, from the ZnII$\, \lambda2026$\r{A} absorption line detected in a Keck Echellette Spectrograph and Imager spectrum. This continues the trend of high-metallicity DLAs being frequently associated with massive galaxies. We obtain a star-formation rate (SFR) of $\lesssim400~$M$_\odot~$yr$^{-1}$ from the rest-frame 349-GHz continuum emission, and a relatively long molecular gas depletion timescale of $\gtrsim0.6~$Gyr. The excitation of the J=3 rotational level is sub-thermal, with $r_{31}\equiv{L'_{\rm{CO(3-2)}}/L'_{\rm{CO(1-0)}}}=0.513\pm0.081$, suggesting that DLA1020+2733g has a low SFR surface density. The large velocity spread of the CO lines, $\approx500~\rm km~s^{-1}$, and the long molecular gas depletion timescale suggest that DLA1020+2733g is likely to be a cold rotating-disk galaxy., Comment: Accepted for publication in ApJ Letters; 11 pages, 6 figures, and 2 Tables

Microbes and pathogens play a detrimental role in healing wounds, causing infections like impetigo through bodily fluids and skin and entering the bloodstream through the wounds, thereby hindering the healing process and tissue regeneration. Clay, known for its long history of natural therapeutic use, has emerged as one of the most promising candidates for biomedical applications due to its non-toxic nature, porosity, high surface area, ubiquity, and excellent cation exchange capacity. This study demonstrates an innovative approach to engineering an organo-functionalized, infection-resistant, easy-to-use bandage material from clay, an environmentally benign and sustainable material. The hybrid membranes have been developed using clays, zwitterions, silver ions, and terbinafine hydrochloride (TBH) to impart antibacterial and antifungal efficacy. A critical aspect of this study is embedding organic molecules and metal ions with the clays and releasing them to resist the growth and kill the pathogens. The antimicrobial efficacy of the membranes has been tested using a zone of inhibition study against the most common microbes in skin wounds, viz. S. aureus, E. coli, and C. albicans. Results from our studies not only demonstrate the potential of these hybrid clay membranes as a cost-effective, scalable, and effective solution for treating microbial infections but also instill newer avenues for point-of-care wound-healing treatments, offering hope for improved patient outcomes.

We introduce two new families of permutation group polynomials over finite fields of arbitrary characteristic, which are special types of bivariate local permutation polynomials. For each family, we explicitly construct their companions. Furthermore, we precisely determine the total number of permutation group polynomials equivalent to the proposed families. Moreover, we resolve the problem of enumerating permutation group polynomials that are equivalent to $e$-Klenian polynomials over finite fields for $e\geq 1$, a problem previously noted as nontrivial by Gutierrez and Urroz (2023)., Comment: 32 pages

Let $f$ be a normalized primitive Hecke eigen cusp form of even integral weight $k$ for the full modular group $SL(2,\mathbb{Z})$. For integers $j \geq 2$, let $\lambda_{sym^j f}(m)$ denote the $m$th Fourier coefficient of the $j$th symmetric power $L$-function associated with $f$. We give a quantitative result on the number of sign changes of $\lambda_{sym^j f}(m)$ for the indices $m$ that are the sum of two squares in the interval $[1,x]$ for sufficiently large $x$.

Knowledge Graphs (KGs) are valuable tools for representing relationships between entities in a structured format. Traditionally, these knowledge bases are queried to extract specific information. However, question-answering (QA) over such KGs poses a challenge due to the intrinsic complexity of natural language compared to the structured format and the size of these graphs. Despite these challenges, the structured nature of KGs can provide a solid foundation for grounding the outputs of Large Language Models (LLMs), offering organizations increased reliability and control. Recent advancements in LLMs have introduced reasoning methods at inference time to improve their performance and maximize their capabilities. In this work, we propose integrating these reasoning strategies with KGs to anchor every step or "thought" of the reasoning chains in KG data. Specifically, we evaluate both agentic and automated search methods across several reasoning strategies, including Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT), using GRBench, a benchmark dataset for graph reasoning with domain-specific graphs. Our experiments demonstrate that this approach consistently outperforms baseline models, highlighting the benefits of grounding LLM reasoning processes in structured KG data.

Foundation Models (FMs) have improved time series forecasting in various sectors, such as finance, but their vulnerability to input disturbances can hinder their adoption by stakeholders, such as investors and analysts. To address this, we propose a causally grounded rating framework to study the robustness of Foundational Models for Time Series (FMTS) with respect to input perturbations. We evaluate our approach to the stock price prediction problem, a well-studied problem with easily accessible public data, evaluating six state-of-the-art (some multi-modal) FMTS across six prominent stocks spanning three industries. The ratings proposed by our framework effectively assess the robustness of FMTS and also offer actionable insights for model selection and deployment. Within the scope of our study, we find that (1) multi-modal FMTS exhibit better robustness and accuracy compared to their uni-modal versions and, (2) FMTS pre-trained on time series forecasting task exhibit better robustness and forecasting accuracy compared to general-purpose FMTS pre-trained across diverse settings. Further, to validate our framework's usability, we conduct a user study showcasing FMTS prediction errors along with our computed ratings. The study confirmed that our ratings reduced the difficulty for users in comparing the robustness of different systems.

Magnetic frustration is a key ingredient to prevent conventional ordering and, in combination with enhanced fluctuations in low dimensional quantum magnets, to stabilize the long sought spin liquid ground states. Most experimental spin liquid candidates inevitably deviate from perfectly frustrated models due to chemical and structural disorder, blurring the nature and origin of their ground states. Y-Kapellasite Y$_3$Cu$_9$(OH)$_{19}$Cl$_{8}$ realizes a chemically clean system with an underlying kagome sub-lattice. The crystals undergo the theoretically predicted magnetic in-plane (1/3 1/3) order from a subtle release of frustration due to a distortion of the kagome system realised by displaced Y atoms. By studying the effect of pressure on the structure and optical phonons, we show that this distortion is partially lifted by compression towards a more isotropic model of interactions, in a gradual manner, without structural transition. Using $\mu$SR, we further show that the magnetic ground state is surprisingly sensitive to the applied pressure for a mineral, and that under a moderate 2 GPa pressure, the frustration increase is sufficient to induce a fluctuating ground state. Suppressing long-range magnetic order via frustration tuning with pressure, in an ultraclean material, establishes a major step towards realizing a well-controlled spin liquid ground state., Comment: 18 pages, 11 figures

The fractional quantum Hall (FQH) effect is a macroscopic manifestation of strong electron-electron interactions. Even denominator FQH states (FQHSs) at half-filling are particularly interesting as they are predicted to host non-Abelian excitations with non-trivial braiding statistics. Such states are predominantly observed in the $N=1$ Landau level (LL) of semiconductors such as GaAs. In this Letter, we report the unanticipated observation of even-denominator FQHSs in the $N=0$ LL of ABA trilayer graphene (TLG), a system characterized by tunable LL mixing and the absence of inversion symmetry. Notably, we find robust FQHSs at $\nu=5/2$ and $\nu=7/2$ when two LLs, originating from a monolayer-like band of TLG with different isospin indices, cross each other. These are flanked by the Levin-Halperin daughter states at $\nu=7/13$ and $\nu=9/17$, respectively, and further away, the standard series of Jain-sequence of composite fermions (CFs) is observed. The even-denominator FQHSs and their accompanying daughter states become stronger with increasing magnetic fields, while concomitantly, a weakening of the CF states is observed. We posit that the absence of inversion symmetry in the system gives rise to additional isospin interactions, which enhance LL mixing and soften the short-range part of the Coulomb repulsion, stabilizing the even-denominator FQHSs. In addition, we demonstrate that these states, along with their daughter states, can be finely tuned with an external displacement field that serves as an important tool to control the LL mixing in the system., Comment: 24 pages, comments and suggestions are very welcome

The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver, British Columbia, Canada. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant topics for the ML4H community. The organization of the research roundtables at the conference involved 13 senior and 27 junior chairs across 13 tables. Each roundtable session included an invited senior chair (with substantial experience in the field), junior chairs (responsible for facilitating the discussion), and attendees from diverse backgrounds with an interest in the session's topic.

The charge state distributions (CSD) of projectile ions through gaseous or solid targets in the energy range of tandem accelerators (1 MeV/u < E < 5 MeV/u) have a major impact on ion-atom collision and accelerator physics. Theoretically, it is possible to calculate the CSD for up to Ni-like ions, but empirical models do not have such bounds. In recent decades, the mean charge states have mostly been obtained from an empirical formula [Schiwietz et al., Nucl. Inst. Meths. , 225, 4(2004)]. But no description on CSD is found there. To estimate the CSD, we have used a Gaussian distribution function having distribution width given by [Novikov and Teplove, Phys. Lett. 378, 1286(2014)]. The results obtained are compared with those obtained with the measured CSD for heavy projectile ions. The merits and demerits of such empirical formulae are discussed., Comment: 6 pages, 4 figures, soon submitting in journal

State estimation involves finding the states of a noisy dynamical system with a noisy initial condition using noisy measurements, given a model of the system. Many methods exist for state estimation of dynamical systems. For linear dynamical systems with white Gaussian noises of known mean and variance, Kalman filtering is an optimal state estimation algorithm. While several extensions of the Kalman filter to nonlinear systems exist, either the method is not optimal or calculation is not feasible. Several approaches using neural networks have been developed in recent years. While these approaches seem to work numerically, stability and convergence guarantees do not exist. In this paper, we propose to use a Jordan recurrent neural network (JRN) for state estimation. An input-to-state stability (ISS) analysis of the error dynamics is used. Our results are compared to the Kalman filter for the linear case and the extended Kalman filter for the nonlinear case., Comment: 8 pages, 5 figures

State estimation of a dynamical system refers to estimating the state of a system given an imperfect model, noisy measurements and some or no information about the initial state. While Kalman filtering is optimal for estimation of linear systems with Gaussian noises, calculation of optimal estimators for nonlinear systems is challenging. We focus on establishing a pathway to optimal estimation of high-order systems by using recurrent connections motivated by Jordan recurrent neural networks(JRNs). The results are compared to the corresponding Elman structure based long short-term memory network(ELSTM) and the KF for linear and EKF for nonlinear systems. The results suggest that for nonlinear systems, the use of long short-term memory networks can improve estimation error and also computation time. Also, the Jordan based long short-term memory networks(JLSTMs) require less training to achieve performance similar to ELSTMs., Comment: 20 pages, 10 figures, submitted to International Journal of Control

Testing and debugging quantum software pose significant challenges due to the inherent complexities of quantum mechanics, such as superposition and entanglement. One challenge is indeterminacy, a fundamental characteristic of quantum systems, which increases the likelihood of flaky tests in quantum programs. To the best of our knowledge, there is a lack of comprehensive studies on quantum flakiness in the existing literature. In this paper, we present a novel machine learning platform that leverages multiple machine learning models to automatically detect flaky tests in quantum programs. Our evaluation shows that the extreme gradient boosting and decision tree-based models outperform other models (i.e., random forest, k-nearest neighbors, and support vector machine), achieving the highest F1 score and Matthews Correlation Coefficient in a balanced dataset and an imbalanced dataset, respectively. Furthermore, we expand the currently limited dataset for researchers interested in quantum flaky tests. In the future, we plan to explore the development of unsupervised learning techniques to detect and classify quantum flaky tests more effectively. These advancements aim to improve the reliability and robustness of quantum software testing., Comment: 8 pages, 1 figure, accepted by Q-SANER 2025

We compute two-loop helicity amplitudes in QCD for diphoton production through quark- and gluon-initiated channels, accounting for a massive internal quark loop by keeping its full mass dependence. Using physical projectors, we directly decompose the amplitude into its helicity components. By renormalising the heavy quark mass in on-shell, and other quantities in $\overline{\rm MS}$ schemes, we obtain finite remainders. This work paves the way for calculating the cross-section for diphoton production at higher orders in QCD with a massive quark loop, employing different subtraction schemes. The effect of a heavy quark is expected to play a crucial role in high-luminosity LHC., Comment: Ancillary files at DOI: https://zenodo.org/records/14809206

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments., Comment: Accepted for presentation at SysCon 2025

Far Infrared interferometry is the main diagnostics method for electron density measurements in medium sized tokamaks. The transverse density gradients of plasma produce refractive effect, and the probing radiation does not propagate along a straight line and gets deflected. Therefore, it is necessary to evaluate the plasma effect on beam direction to verify if the used wavelength is compatible with the machine geometry. This paper presents the analytical results of refractive bending of THz beam due to transverse density gradients for circular and elliptical cross section plasma. The results from ray tracing integration to calculate refractive bending in D-shaped plasmas are also presented.

We calculate the valence quark generalized parton distributions (GPDs) of the lightest pseudoscalar meson, pion, in an isospin asymmetric nuclear matter at zero temperature by employing a light-cone quark model. The medium modifications in the unpolarized GPDs have been incorporated by taking inputs from the chiral SU($3$) quark mean field model. The electromagnetic form factors (EMFFs) and charge radii have been calculated for both the vacuum and in-medium cases. These results are found to be in agreement with the available experimental data and other model predictions., Comment: 9 pages and 4 figures

In this paper, we derive Wintgen type inequality for bi-slant submanifolds in metallic Riemannian space forms, offering a new geometric perspective in this setting. Notable special cases, including hemi-slant, semi-slant, CR, and slant submanifolds, are further investigated under the same ambient framework. Additionally, we extend our analysis to manifolds with the golden structure, providing valuable insights into their geometry., Comment: 14 pages

Micromagnetic modeling is employed to optimize the design of artificial synapse devices based on the spin-orbit-torque (SOT) driven domain wall (DW) motion along a nanotrack with triangular notches. Key attributes, such as the thermal stability of the pinned DW and depinning currents, are obtained for varied nanotrack geometry and pinning strength. Depinning probability as a function of SOT current density and pulse width is studied using finite temperature micromagnetic simulations for varying pinning potential. Results show that wider notches provide better thermal stability - depinning current trade-off. On the other hand, narrow notches exhibit less variation in the depinning times at finite temperatures. It is observed that the DW position can be set precisely to any desired location by the SOT current pulse if the thermal stability is sufficiently high. It is also observed that the meta-plastic functionality can be obtained by adding notches of progressively higher depinning currents along the rectangular nanotrack., Comment: 12 pages, 6 figures

This paper addresses the challenge of scaling quantum computing by employing distributed quantum algorithms across multiple processors. We propose a novel circuit partitioning method that leverages graph partitioning to optimize both qubit and gate teleportation, minimizing the required Einstein-Podolsky-Rosen (EPR) pairs for executing general quantum circuits. Additionally, we formulate an integer linear program to further reduce entanglement requirements by mapping the logical resources of partitioned circuits to the physical constraints of the quantum network. Finally, we analyze the entanglement cost of implementing the Quantum Fourier Transform (QFT) across multiple QPUs, exploiting the circuit's structure to minimize total entanglement consumption., Comment: 12 pages, 9 figures

The half-life of $^{39}$Ar is measured using the DEAP-3600 detector located 2 km underground at SNOLAB. In 2016-2020, DEAP-3600 used a target mass of (3269 $\pm$ 24) kg of liquid argon distilled from the atmosphere in a direct-detection dark matter search. Such an argon mass also enables direct measurements of argon isotope properties. The decay of $^{39}$Ar in DEAP-3600 is the dominant source of triggers by two orders of magnitude, ensuring high statistics and making DEAP-3600 well-suited for measuring this isotope's half-life. Use of the pulse-shape discrimination technique in DEAP-3600 allows for powerful discrimination between nuclear recoils and electron recoils, resulting in the selection of a clean sample of $^{39}$Ar decays. Observing over a period of 3.4 years, the $^{39}$Ar half-life is measured to be $(302 \pm 8_{\rm stat} \pm 6_{\rm sys})$ years. This new direct measurement suggests that the half-life of $^{39}$Ar may be significantly longer than the accepted value, with potential implications for measurements using this isotope's half-life as input., Comment: 11 pages, 8 figures

We show that the holographic Schr\"odinger equation of light-front chiral QCD, together with the 't Hooft equation of (1+1)-dimensional QCD in the large $N_c$ limit, can simultaneously describe the $\phi$-meson mass spectroscopy as well as diffractive cross-section. We compute the $\phi$-meson diffractive cross-section by utilizing its resulting light-front wave functions (LFWFs), in conjunction with the color glass condensate (CGC) dipole scattering amplitude. Our predictions for the diffractive cross sections show good agreement with the existing experimental data from HERA at various energies from H1 and ZEUS collaborations. Additionally, we show that the obtained $\phi$-meson LFWFs effectively describe its various properties, including the decay constant, distribution amplitudes, electromagnetic form factors, charge radius, and magnetic and quadrupole moments., Comment: 17 pages, 9 figures, 3 tables

Standard conformal prediction offers a marginal guarantee on coverage, but for prediction sets to be truly useful, they should ideally ensure coverage conditional on each test point. Unfortunately, it is impossible to achieve exact, distribution-free conditional coverage in finite samples. In this work, we propose an alternative conformal prediction algorithm that targets coverage where it matters most--in instances where a classifier is overconfident in its incorrect predictions. We start by dissecting miscoverage events in marginally-valid conformal prediction, and show that miscoverage rates vary based on the classifier's confidence and its deviation from the Bayes optimal classifier. Motivated by this insight, we develop a variant of conformal prediction that targets coverage conditional on a reduced set of two variables: the classifier's confidence in a prediction and a nonparametric trust score that measures its deviation from the Bayes classifier. Empirical evaluation on multiple image datasets shows that our method generally improves conditional coverage properties compared to standard conformal prediction, including class-conditional coverage, coverage over arbitrary subgroups, and coverage over demographic groups.

The Alzheimer's Disease Analysis Model Generation 1 (ADAM) is a multi-agent large language model (LLM) framework designed to integrate and analyze multi-modal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and detection of Alzheimer's disease (AD). By leveraging retrieval-augmented generation (RAG) techniques along with its multi-agent architecture, ADAM-1 synthesizes insights from diverse data sources and contextualizes findings using literature-driven evidence. Comparative evaluation against XGBoost revealed similar mean F1 scores but significantly reduced variance for ADAM-1, highlighting its robustness and consistency, particularly in small laboratory datasets. While currently tailored for binary classification tasks, future iterations aim to incorporate additional data modalities, such as neuroimaging and biomarkers, to broaden the scalability and applicability for Alzheimer's research and diagnostics., Comment: 16 pages, 16 figures

In this article, we define quasiprimitive quandles and describe them with the help of quasiprimitive permutation groups. As a consequence, we enumerate finite non-affine simple quandles up to order $4096$., Comment: v1 extended to simple quandles

This paper presents the design of scalable quantum networks that utilize optical switches to interconnect multiple quantum processors, facilitating large-scale quantum computing. By leveraging these novel architectures, we aim to address the limitations of current quantum processors and explore the potential of quantum data centers. We provide an in-depth analysis of these architectures through the development of simulation tools and performance metrics, offering a detailed comparison of their advantages and trade-offs. We hope this work serves as a foundation for the development of efficient and resilient quantum networks, designed to meet the evolving demands of future quantum computing applications., Comment: 22 pages, 10 figures, 4 appendices

Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples, inadvertently overlooking the needs of diverse user populations. The performance disparity among various populations can harm the model's robustness to sub-populations due to the varying user properties. While large language models (LLMs) show promise in enhancing RS performance, their practical applicability is hindered by high costs, inference latency, and degraded performance on long user queries. To address these challenges, we propose a hybrid task allocation framework designed to promote social good by equitably serving all user groups. By adopting a two-phase approach, we promote a strategic assignment of tasks for efficient and responsible adaptation of LLMs. Our strategy works by first identifying the weak and inactive users that receive a suboptimal ranking performance by RSs. Next, we use an in-context learning approach for such users, wherein each user interaction history is contextualized as a distinct ranking task. We evaluate our hybrid framework by incorporating eight different recommendation algorithms and three different LLMs -- both open and close-sourced. Our results on three real-world datasets show a significant reduction in weak users and improved robustness to subpopulations without disproportionately escalating costs., Comment: arXiv admin note: text overlap with arXiv:2405.00824

Hecke modifications of vector bundles have played a significant role in several areas of mathematics. They appear in subjects ranging from number theory to complex geometry. This article intends to be a friendly introduction to the subject. We give an overview of how Hecke modifications appear in the literature, explain their origin and their importance in number theory and classical algebraic geometry. Moreover, we report the progress made in describing Hecke modifications explicitly and why these explicit descriptions are important. We describe all the Hecke modifications of the trivial rank $2$ vector bundle over a closed point of degree $5$ in the projective line, as well as all the vector bundles over a certain elliptic curve, which admit a rank $2$ and degree $0$ trace bundle as a Hecke modification. This result is not present in existing literature., Comment: Comments are welcome!

The role of asymmetric nuclear medium on the properties of kaon is investigated at zero and finite temperature employing a hybrid approach integrating the light cone quark model (LCQM) and the chiral SU(3) quark mean field (CQMF) model. The in-medium quark masses are calculated within the CQMF model and are used as inputs to study the medium modifications in the kaon properties. In particular, we have analysed the impact of baryonic density, isospin asymmetry and temperature on the weak decay constant, distribution amplitudes (DAs) and parton quark distributions (PDFs) of valence quark structure of kaons. The effects of isospin asymmetry on the kaon doublet $K =\left(\begin{array}{c} K^{+} \\ K^{0} \end{array} \right)$ and antikaon doublet $\bar{K}$= ($K^-, \bar{K}^0$) are also studied. In order to compare with future experiments, we have also evolved the in-medium DAs and PDFs of kaons to $Q^2=16$ GeV$^2$. As compared to the temperature and isospin asymmetry, change in baryonic density of the nuclear medium makes more significant changes to the DAs and PDFs of kaons., Comment: 33 pages, 16 figures