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The PANDAX-4T and XENONnT experiments present indications of Coherent Elastic Neutrino Nucleus Scattering (CE$\nu$NS) from ${}^{8}$B solar neutrinos at 2.6$\sigma$ and 2.7$\sigma$, respectively. This constitutes the first observation of the neutrino "floor" or "fog", an irreducible background that future dark matter searches in terrestrial detectors will have to contend with. Here, we first discuss the contributions from neutrino-electron scattering and from the Migdal effect in the region of interest of these experiments, and we argue that they are non-negligible. Second, we make use of the recent PANDAX-4T and XENONnT data to derive novel constraints on light scalar and vector mediators coupling to neutrinos and quarks. We demonstrate that these experiments already provide world-leading laboratory constraints on new light mediators in some regions of parameter space., Comment: 11 pages, 4 figures

Recent large language models (LLMs) have enabled the development of advanced agentic systems that can integrate various tools and APIs to fulfill user queries through function calling. However, the deployment of these LLMs on the edge has not been explored since they typically require cloud-based infrastructure due to their substantial model size and computational demands. To this end, we present TinyAgent, an end-to-end framework for training and deploying task-specific small language model agents capable of function calling for driving agentic systems at the edge. We first show how to enable accurate function calling for open-source models via the LLMCompiler framework. We then systematically curate a high-quality dataset for function calling, which we use to fine-tune two small language models, TinyAgent-1.1B and 7B. For efficient inference, we introduce a novel tool retrieval method to reduce the input prompt length and utilize quantization to further accelerate the inference speed. As a driving application, we demonstrate a local Siri-like system for Apple's MacBook that can execute user commands through text or voice input. Our results show that our models can achieve, and even surpass, the function-calling capabilities of larger models like GPT-4-Turbo, while being fully deployed at the edge. We open-source our dataset, models, and installable package and provide a demo video for our MacBook assistant agent., Comment: EMNLP 2024 Demo

The lifetime of superconducting qubits is limited by dielectric loss, and a major source of dielectric loss is the native oxide present at the surface of the superconducting metal. Specifically, tantalum-based superconducting qubits have been demonstrated with record lifetimes, but a major source of loss is the presence of two-level systems (TLSs) in the surface tantalum oxide. Here, we demonstrate a strategy for avoiding oxide formation by encapsulating the tantalum with noble metals that do not form native oxide. By depositing a few nanometers of Au or AuPd alloy before breaking vacuum, we completely suppress tantalum oxide formation. Microwave loss measurements of superconducting resonators reveal that the noble metal is proximitized, with a superconducting gap over 80% of the bare tantalum at thicknesses where the oxide is fully suppressed. We find that losses in resonators fabricated by subtractive etching are dominated by oxides on the sidewalls, suggesting total surface encapsulation by additive fabrication as a promising strategy for eliminating surface oxide TLS loss in superconducting qubits.

Spin Hamiltonians, like the Heisenberg model, are used to describe magnetic properties of exchange-coupled molecules and solids. For finite clusters, physical quantities such as heat capacities, magnetic susceptibilities or neutron-scattering spectra, can be calculated based on energies and eigenstates obtained by exact diagonalization (ED). Utilizing spinrotational symmetry SU(2) to factor the Hamiltonian with respect to total spin S facilitates ED, but the conventional approach to spin-adapting the basis is more intricate than selecting states with a given magnetic quantum number M (the spin z-component), as it relies on irreducible tensor-operator techniques and spin-coupling coefficients. Here, we present a simpler technique based on applying a spin projector to uncoupled basis states. As an alternative to L\"owdin's projection operator, we consider a group-theoretical formulation of the projector, which can be evaluated either exactly or approximately using an integration grid. An important aspect is the choice of uncoupled basis states. We present an extension of L\"owdin's theorem for s = 1/2 to arbitrary local spin quantum numbers s, which allows for the direct selection of configurations that span a complete, linearly independent basis in an S sector upon spin projection. We illustrate the procedure with a few examples., Comment: 23 pages, 9 figures

Spin models like the Heisenberg Hamiltonian effectively describe the interactions of open-shell transition-metal ions on a lattice and can account for various properties of magnetic solids and molecules. Numerical methods are usually required to find exact or approximate eigenstates, but for small clusters with spatial symmetry, analytical solutions exist, and a few Heisenberg systems have been solved in closed form. This paper presents a simple, generally applicable approach to analytically solve isotropic spin clusters, based on adapting the basis to both total-spin and point-group symmetry to factor the Hamiltonian matrix into sufficiently small blocks. We demonstrate applications to small rings and polyhedra, some of which are straightforward to solve by successive spin-coupling for Heisenberg terms only; additional interactions, such as biquadratic exchange or multi-center terms necessitate symmetry adaptation., Comment: 23 pages, 5 figures

In a simple, undirected graph G, an edge 2-coloring is a coloring of the edges such that no vertex is incident to edges with more than 2 distinct colors. The problem maximum edge 2-coloring (ME2C) is to find an edge 2-coloring in a graph G with the goal to maximize the number of colors. For a relevant graph class, ME2C models anti-Ramsey numbers and it was considered in network applications. For the problem a 2-approximation algorithm is known, and if the input graph has a perfect matching, the same algorithm has been shown to have a performance guarantee of 5/3. It is known that ME2C is APX-hard and that it is UG-hard to obtain an approximation ratio better than 1.5. We show that if the input graph has a perfect matching, there is a polynomial time 1.625-approximation and if the graph is claw-free or if the maximum degree of the input graph is at most three (i.e., the graph is subcubic), there is a polynomial time 1.5-approximation algorithm for ME2C, Comment: 20 pages, 6 figures, preliminary version published at WAOA 2023

We summarize the Fermilab Accelerator Complex Evolution (ACE) Science Workshop, held on June 14-15, 2023. The workshop presented the strategy for the ACE program in two phases: ACE Main Injector Ramp and Target (MIRT) upgrade and ACE Booster Replacement (BR) upgrade. Four plenary sessions covered the primary experimental physics thrusts: Muon Collider, Neutrinos, Charged Lepton Flavor Violation, and Dark Sectors. Additional physics and technology ideas were presented from the community that could expand or augment the ACE science program. Given the physics framing, a parallel session at the workshop was dedicated to discussing priorities for accelerator R\&D. Finally, physics discussion sessions concluded the workshop where experts from the different experimental physics thrusts were brought together to begin understanding the synergies between the different physics drivers and technologies. In December of 2023, the P5 report was released setting the physics priorities for the field in the next decade and beyond, and identified ACE as an important component of the future US accelerator-based program. Given the presentations and discussions at the ACE Science Workshop and the findings of the P5 report, we lay out the topics for study to determine the physics priorities and design goals of the Fermilab ACE project in the near-term.

Neutrino physics is advancing into a precision era with the construction of new experiments, particularly in the few GeV energy range. Within this energy range, neutrinos exhibit diverse interactions with nucleons and nuclei. This study delves in particular into neutrino--nucleus quasi-elastic cross sections, taking into account both standard and, for the first time, non-standard interactions, all within the framework of effective field theory (EFT). The main uncertainties in these cross sections stem from uncertainties in the nucleon-level form factors, and from the approximations necessary to solve the nuclear many-body problem. We explore how these uncertainties influence the potential of neutrino experiments to probe new physics introduced by left-handed, right-handed, scalar, pseudoscalar, and tensor interactions. For some of these interactions the cross section is enhanced, making long-baseline experiments an excellent place to search for them. Our results, including tabulated cross sections for all interaction types and all neutrino flavors, can serve as the foundation for such searches., Comment: 27 pages, 3 figures; v2: Corrected typos, as appears in JHEP

In this study, we address the critical challenge of balancing speed and accuracy while maintaining interpretablity in visual odometry (VO) systems, a pivotal aspect in the field of autonomous navigation and robotics. Traditional VO systems often face a trade-off between computational speed and the precision of pose estimation. To tackle this issue, we introduce an innovative system that synergistically combines traditional VO methods with a specifically tailored fully connected network (FCN). Our system is unique in its approach to handle each degree of freedom independently within the FCN, placing a strong emphasis on causal inference to enhance interpretability. This allows for a detailed and accurate assessment of relative pose error (RPE) across various degrees of freedom, providing a more comprehensive understanding of parameter variations and movement dynamics in different environments. Notably, our system demonstrates a remarkable improvement in processing speed without compromising accuracy. In certain scenarios, it achieves up to a 5% reduction in Root Mean Square Error (RMSE), showcasing its ability to effectively bridge the gap between speed and accuracy that has long been a limitation in VO research. This advancement represents a significant step forward in developing more efficient and reliable VO systems, with wide-ranging applications in real-time navigation and robotic systems., Comment: https://www.american-cse.org/csci2023 is website of conference and conference name is CSCI2023

The reasoning capabilities of the recent LLMs enable them to execute external function calls to overcome their inherent limitations, such as knowledge cutoffs, poor arithmetic skills, or lack of access to private data. This development has allowed LLMs to select and coordinate multiple functions based on the context to tackle more complex problems. However, current methods for function calling often require sequential reasoning and acting for each function which can result in high latency, cost, and sometimes inaccurate behavior. To address this, we introduce LLMCompiler, which executes functions in parallel to efficiently orchestrate multiple function calls. Drawing inspiration from the principles of classical compilers, LLMCompiler enables parallel function calling with three components: (i) a Function Calling Planner, formulating execution plans for function calling; (ii) a Task Fetching Unit, dispatching function calling tasks; and (iii) an Executor, executing these tasks in parallel. LLMCompiler automatically generates an optimized orchestration for the function calls and can be used with both open-source and closed-source models. We have benchmarked LLMCompiler on a range of tasks with different patterns of function calling. We observe consistent latency speedup of up to 3.7x, cost savings of up to 6.7x, and accuracy improvement of up to ~9% compared to ReAct. Our code is available at https://github.com/SqueezeAILab/LLMCompiler., Comment: ICML 2024

Feedback-driven optimization, such as traditional machine learning training, is a static process that lacks real-time adaptability of hyperparameters. Tuning solutions for optimization require trial and error paired with checkpointing and schedulers, in many cases feedback from the algorithm is overlooked. Adjusting hyperparameters during optimization usually requires the program to be restarted, wasting utilization and time, while placing unnecessary strain on memory and processors. We present LiveTune, a novel framework allowing real-time parameter adjustment of optimization loops through LiveVariables. Live Variables allow for continuous feedback-driven optimization by storing parameters on designated ports on the system, allowing them to be dynamically adjusted. Extensive evaluations of our framework on standard machine learning training pipelines show saving up to 60 seconds and 5.4 Kilojoules of energy per hyperparameter change. We also show the feasibility and value of LiveTune in a reinforcement learning application where the users change the dynamics of the reward structure while the agent is learning showing 5x improvement over the baseline. Finally, we outline a fully automated workflow to provide end-to-end, unsupervised feedback-driven optimization.

It is usually asserted that physical Hamiltonians for fermions must contain an even number of fermion operators. This is indeed true in electronic structure theory. However, when the Jordan-Wigner transformation is used to map physical spin Hamiltonians to Hamiltonians of spinless fermions, terms which contain an odd number of fermion operators may appear. The resulting fermionic Hamiltonian thus does not have number parity symmetry, and requires wave functions which do not have this symmetry either. In this work, we discuss the extension of standard Hartree-Fock-Bogoliubov (HFB) theory to the number-parity--nonconserving case. These ideas had appeared in the literature before, but, perhaps for lack of practical applications, had to the best of our knowledge never been employed. We here present a useful application for this more general HFB theory based on coherent states of the SO(2$M$ + 1) Lie group, where $M$ is the number of orbitals. We also show how using these unusual mean-field states can provide significant improvements when studying the Jordan-Wigner transformation of chemically relevant spin Hamiltonians., Comment: Resubmitted to J. Chem. Phys

Heavy neutral leptons (HNLs) are often among the hypothetical ingredients behind nonzero neutrino masses. If sufficiently light, they can be produced and detected in fixed-target-like experiments. We show that if the HNLs belong to a richer -- but rather generic -- dark sector, their production mechanism can deviate dramatically from expectations associated to the standard-model weak interactions. In more detail, we postulate that the dark sector contains an axion-like particle (ALP) that naturally decays into HNLs. Since ALPs mix with the pseudoscalar hadrons, the HNL flux might be predominantly associated to the production of neutral mesons (e.g., $\pi^0$, $\eta$) as opposed to charge hadrons (e.g., $\pi^\pm$, $K^\pm$). In this case, the physics responsible for HNL production and decay are not directly related and experiments like DUNE might be sensitive to HNLs that are too weakly coupled to the standard model to be produced via weak interactions, as is generically the case of HNLs that play a direct role in the type-I seesaw mechanism., Comment: 5 pages, 4 figures