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This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Formal verification using proof assistants, such as Coq, is an effective way of improving software quality, but it is expensive. Writing proofs manually requires both significant effort and expertise. Recent research has used machine learning to automatically synthesize proofs, reducing verification effort, but these tools are able to prove only a fraction of the desired software properties. We introduce Cobblestone, a new proof-synthesis approach that improves on the state of the art by taking advantage of partial progress in proof synthesis attempts. Unlike prior tools, Cobblestone can produce multiple unsuccessful proofs using a large language model (LLM), identify the working portions of those proofs, and combine them into a single, successful proof, taking advantage of internal partial progress. We evaluate Cobblestone on two benchmarks of open-source Coq projects, controlling for training data leakage in LLM datasets. Fully automatically, Cobblestone can prove 48% of the theorems, while Proverbot9001, the previous state-of-the-art, learning-based, proof-synthesis tool, can prove 17%. Cobblestone establishes a new state of the art for fully automated proof synthesis tools for Coq. We also evaluate Cobblestone in a setting where it is given external partial proof progress from oracles, serving as proxies for a human proof engineer or another tool. When the theorem is broken down into a set of subgoals and Cobblestone is given a set of relevant lemmas already proven in the project, it can prove up to 58% of the theorems. We qualitatively study the theorems Cobblestone is and is not able to prove to outline potential future research directions to further improve proof synthesis, including developing interactive, semi-automated tools. Our research shows that tools can make better use of partial progress made during proof synthesis to more effectively automate formal verification., Comment: 13 pages, 10 figures

Demand for streaming services, including satellite, continues to exhibit unprecedented growth. Internet Service Providers find themselves at the crossroads of technological advancements and rising customer expectations. To stay relevant and competitive, these ISPs must ensure their networks deliver optimal video streaming quality, a key determinant of user satisfaction. Towards this end, it is important to have accurate Quality of Experience prediction models in place. However, achieving robust performance by these models requires extensive data sets labeled by subjective opinion scores on videos impaired by diverse playback disruptions. To bridge this data gap, we introduce the LIVE-Viasat Real-World Satellite QoE Database. This database consists of 179 videos recorded from real-world streaming services affected by various authentic distortion patterns. We also conducted a comprehensive subjective study involving 54 participants, who contributed both continuous-time opinion scores and endpoint (retrospective) QoE scores. Our analysis sheds light on various determinants influencing subjective QoE, such as stall events, spatial resolutions, bitrate, and certain network parameters. We demonstrate the usefulness of this unique new resource by evaluating the efficacy of prevalent QoE-prediction models on it. We also created a new model that maps the network parameters to predicted human perception scores, which can be used by ISPs to optimize the video streaming quality of their networks. Our proposed model, which we call SatQA, is able to accurately predict QoE using only network parameters, without any access to pixel data or video-specific metadata, estimated by Spearman's Rank Order Correlation Coefficient (SROCC), Pearson Linear Correlation Coefficient (PLCC), and Root Mean Squared Error (RMSE), indicating high accuracy and reliability.

Modern cryptographic methods for implementing privacy-preserving LLMs such as Homomorphic Encryption (HE) require the LLMs to have a polynomial form. Forming such a representation is challenging because Transformers include non-polynomial components, such as Softmax and layer normalization. Previous approaches have either directly approximated pre-trained models with large-degree polynomials, which are less efficient over HE, or replaced non-polynomial components with easier-to-approximate primitives before training, e.g., Softmax with pointwise attention. The latter approach might introduce scalability challenges. We present a new HE-friendly variant of self-attention that offers a stable form for training and is easy to approximate with polynomials for secure inference. Our work introduces the first polynomial LLMs with 32 layers and over a billion parameters, exceeding the size of previous models by more than tenfold. The resulting models demonstrate reasoning and in-context learning (ICL) capabilities comparable to standard transformers of the same size, representing a breakthrough in the field. Finally, we provide a detailed latency breakdown for each computation over encrypted data, paving the way for further optimization, and explore the differences in inductive bias between transformers relying on our HE-friendly variant and standard transformers. Our code is attached as a supplement.

Sparse Autoencoders for transformer-based language models are typically defined independently per layer. In this work we analyze statistical relationships between features in adjacent layers to understand how features evolve through a forward pass. We provide a graph visualization interface for features and their most similar next-layer neighbors, and build communities of related features across layers. We find that a considerable amount of features are passed through from a previous layer, some features can be expressed as quasi-boolean combinations of previous features, and some features become more specialized in later layers.

Arecibo 305-m Telescope observations between 2008 and 2018 detected a radio continuum and spectral-line outburst in the nearby galaxy, NGC 660. Excited-OH maser emission/absorption lines near 4.7 GHz, and H$_2$CO absorption at 4.83 GHz varied on time-scales of months. Simultaneously, a continuum outburst occurred in which a new compact component appeared, with a GHz-peaked spectrum and a 5-GHz flux density that rose to a peak value of about 500 mJy from 2008.0 to 2012.0. Follow-up interferometric continuum images from theVery Large Array (VLA) at 10 GHz of this new continuum component determined it to be located at the nucleus of NGC 660. Subsequent High Sensitivity Array (HSA) line and continuum VLBI observations of the NGC 660 nucleus revealed a morphology that appears to be consistent with rapidly-precessing, mildly-relativistic jets from the central black hole. While requiring detailed modeling, this strongly suggests that the outburst is due to nuclear activity. From its time-scale, the shape of the continuum light-curve, and the milliarcsec radio structure, the most likely cause of the outburst is AGN-type activity of accretion of a gas cloud onto the central black hole., Comment: 18 pages, 7 figures, accepted for publication in The Astronomical Journal

The classical Muckenhoupt's $A_p$ condition is necessary and sufficient for the boundedness of the maximal operator $M$ on $L^p(w)$ spaces. In this paper we obtain another characterization of the $A_p$ condition. As a result, we show that some strong versions of the weighted $L^p(w)$ Coifman--Fefferman and Fefferman--Stein inequalities hold if and only if $w\in A_p$. We also give new examples of Banach function spaces $X$ such that $M$ is bounded on $X$ but not bounded on the associate space $X'$., Comment: v2: typos corrected

We use a c-GAN (convolutional generative adversarial) neural network to analyze transliterated text fragments of extant, dead comprehensible, and one dead non-deciphered (Cypro-Minoan) language to establish linguistic affinities. The paper is agnostic with respect to translation and/or deciphering. However, there is hope that the proposed approach can be useful for decipherment with more sophisticated neural network techniques., Comment: First draft. Comments are welcome

Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic Health Records (EHRs). Because a lot of the clinical information of EHRs are lying in texts, phenotyping from text takes an important role in studies that rely on the secondary use of EHRs. However, the heterogeneity and highly specialized aspect of both the content and form of clinical texts makes this task particularly tedious, and is the source of time and cost constraints in observational studies. To facilitate the development, evaluation and reproductibility of phenotyping pipelines, we developed an open-source Python library named medkit. It enables composing data processing pipelines made of easy-to-reuse software bricks, named medkit operations. In addition to the core of the library, we share the operations and pipelines we already developed and invite the phenotyping community for their reuse and enrichment. medkit is available at https://github.com/medkit-lib/medkit

While research on adolescent smoking is extensive, little attention has been given to smoking behaviors among rural middle-aged and older adults. This study examines the role of personal networks and sociodemographic factors in predicting smoking status in a rural Romanian community. Using a link-tracing sampling method, we gathered data from 76 participants out of 83 in Leresti, Arges County. Face-to-face interviews collected sociodemographic data and network information, including smoking status and relational dynamics. We applied multilevel logistic regression models to predict smoking behaviors (current smokers, former smokers, and non-smokers) based on individual characteristics and network influences. Results indicate that social networks significantly influence smoking behaviors. For current smokers, having a smoking family member greatly increased the odds of smoking (OR = 2.51, 95% CI: 1.62, 3.91, p < 0.001). Similarly, non-smoking family members increased the likelihood of being a non-smoker (OR = 1.64, 95% CI: 1.04, 2.61, p < 0.05). Women were less likely to smoke, highlighting sex differences in behavior. These findings emphasize the critical role of social networks in shaping smoking habits, advocating for targeted interventions in rural areas., Comment: 27 pages; 1 figure; 6 tables

Understanding why researchers cite each other has been a longstanding conjecture in studying scientific networks. Prior research suggests relevance, group cohesion, or honest source crediting as possible factors. However, the dual nature of cognitive and social dimensions underlying citation is often overlooked by not considering the intermediary steps leading up to a citation. For one work to be cited by another, it must first be published by a set of authors. Therefore, we investigate the reasons behind researchers' citations, explicitly examining the interplay of socio-cognitive ties through the interdependence of coauthorship and citation networks. We assess our claims in an empirical analysis by employing the Author-Oriented Relational HyperEvent Model (AuthRHEM) to study Chilean astronomers' citation and collaboration behavior between 2013 and 2015 in a joint framework. We find evidence that when deciding which work to cite, authors prefer other work with novelty and cognitive ties, such as work-to-work relations. At the same time, coherent groups are relevant because coauthors are cocited more frequently in subsequent publications.

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

There is a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences. We consider the setting of fairness in content moderation, in which human feedback is used to determine how two comments -- referencing different sensitive attribute groups -- should be treated in comparison to one another. With a novel dataset collected from Prolific and MTurk, we find significant gaps in fairness preferences depending on the race, age, political stance, educational level, and LGBTQ+ identity of annotators. We also demonstrate that demographics mentioned in text have a strong influence on how users perceive individual fairness in moderation. Further, we find that differences also exist in downstream classifiers trained to predict human preferences. Finally, we observe that an ensemble, giving equal weight to classifiers trained on annotations from different demographics, performs better for different demographic intersections; compared to a single classifier that gives equal weight to each annotation., Comment: To appear in the Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

We explore the new proofs and extensions of the Heisenberg Uncertainty Principle introduced by A.~Widgerson & Y.~Widgerson in [MR4229152], developed in [MR4453622] by N.C.~Dias, F.~Luef and J.N.~Prata and also in [MR4337266] by Y.~Tang. In particular we give here a proof of the Uncertainty Principle for operators in the Metaplectic group in any dimension., Comment: 48 pages, 28 references

The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained foundation models like DINOv2 for the VPR task. However, these models are often deemed inadequate for VPR without further fine-tuning on task-specific data. In this paper, we propose a simple yet powerful approach to better exploit the potential of a foundation model for VPR. We first demonstrate that features extracted from self-attention layers can serve as a powerful re-ranker for VPR. Utilizing these features in a zero-shot manner, our method surpasses previous zero-shot methods and achieves competitive results compared to supervised methods across multiple datasets. Subsequently, we demonstrate that a single-stage method leveraging internal ViT layers for pooling can generate global features that achieve state-of-the-art results, even when reduced to a dimensionality as low as 128D. Nevertheless, incorporating our local foundation features for re-ranking, expands this gap. Our approach further demonstrates remarkable robustness and generalization, achieving state-of-the-art results, with a significant gap, in challenging scenarios, involving occlusion, day-night variations, and seasonal changes.

We consider how the absence of thermalisation affects the classical Coulomb blockade regime in quantum dots. By solving the quantum kinetic equation in the experimentally accessible regime when the dot has two relevant occupation states, we calculate the current-voltage characteristics for arbitrary coupling to the leads. If the couplings are strongly asymmetric, we have found that the Coulomb staircase practically reduces to the first step independent of the charging energy when the latter is larger than the Fermi energy, while the standard thermalised results are recovered in the opposite case. If the couplings are of the same order, the absence of thermalisation has a new, striking signature - a robust additional peak in the differential conductance., Comment: 16 pages, 4 figures

We briefly test the key claims made in [Nat. Commun. 15, 3107 (2024)] regarding the scaling of the low-frequency tail of the nonphononic VDoS of glasses, and its connections to glass formation history and to the form of the distribution of weak local stresses., Comment: 3 pages, 3 figures

BitVMX is a new design for a virtual CPU to optimistically execute arbitrary programs on Bitcoin based on a challenge response game introduced in BitVM. Similar to BitVM1 we create a general-purpose CPU to be verified in Bitcoin script. Our design supports common architectures, such as RISC-V or MIPS. Our main contribution to the state of the art is a design that uses hash chains of program traces, memory mapped registers, and a new challenge-response protocol. We present a new message linking protocol as a means to allow authenticated communication between the participants. This protocol emulates stateful smart contracts by sharing state between transactions. This provides a basis for our verification game which uses a graph of pre-signed transactions to support challenge-response interactions. In case of a dispute, the hash chain of program trace is used with selective pre-signed transactions to locate (via $n$-ary search) and then recover the precise nature of errors in the computation. Unlike BitVM1, our approach does not require the creation of Merkle trees for CPU instructions or memory words. Additionally, it does not rely on signature equivocations. These differences help avoid complexities associated with BitVM1 and make BitVMX a compelling alternative to BitVM2. Our approach is quite flexible, BitVMX can be instantiated to balance transaction cost vs round complexity, prover cost vs verifier cost, and precomputations vs round complexity.

Proof assistants enable users to develop machine-checked proofs regarding software-related properties. Unfortunately, the interactive nature of these proof assistants imposes most of the proof burden on the user, making formal verification a complex, and time-consuming endeavor. Recent automation techniques based on neural methods address this issue, but require good programmatic support for collecting data and interacting with proof assistants. This paper presents CoqPyt, a Python tool for interacting with the Coq proof assistant. CoqPyt improves on other Coq-related tools by providing novel features, such as the extraction of rich premise data. We expect our work to aid development of tools and techniques, especially LLM-based, designed for proof synthesis and repair. A video describing and demonstrating CoqPyt is available at: https://youtu.be/fk74o0rePM8., Comment: Accepted to FSE '24 Demonstrations Track

This paper expands on existing learned models of human behavior via a measured step in structured irrationality. Specifically, by replacing the suboptimality constant $\beta$ in a Boltzmann rationality model with a function over states $\beta(s)$, we gain natural expressivity in a computationally tractable manner. This paper discusses relevant mathematical theory, sets up several experimental designs, presents limited preliminary results, and proposes future investigations.

Precise object manipulation and placement is a common problem for household robots, surgery robots, and robots working on in-situ construction. Prior work using computer vision, depth sensors, and reinforcement learning lacks the ability to reactively recover from planning errors, execution errors, or sensor noise. This work introduces a method that uses force-torque sensing to robustly place objects in stable poses, even in adversarial environments. On 46 trials, our method finds success rates of 100% for basic stacking, and 17% for cases requiring adjustment.

It is now established that glasses feature low-frequency, nonphononic excitations, in addition to phonons that follow Debye's vibrational density of state (VDoS). Extensive computer studies demonstrated that these nonphononic, glassy excitations follow a universal non-Debye VDoS ${\cal D}_{\rm G}(\omega)\!\sim\!\omega^4$, at low frequencies $\omega$. Yet, due to intrinsic difficulties in disentangling ${\cal D}_{\rm G}(\omega)$ from the total VDoS ${\cal D}(\omega)$, which is experimentally accessible through various scattering techniques, the $\omega^4$ tail of ${\cal D}_{\rm G}(\omega)$ lacked direct experimental support. We develop a procedure to extract ${\cal D}_{\rm G}(\omega)$ from the measured ${\cal D}(\omega)$, based on recent advances in understanding low-frequency excitations in glasses, and apply it to available datasets for diverse glasses. The resulting analysis shows that the $\omega^4$ tail of the nonphononic vibrational spectra of glasses is nontrivially consistent with a broad range of experimental observations. It also further supports that ${\cal D}_{\rm G}(\omega)$ makes an additive contribution to ${\cal D}(\omega)$., Comment: 5 pages, 3 figures + Supplemental materials + Bibliographical list; see related work at: https://doi.org/10.48550/arXiv.2404.12735

In this note we answer positively to two conjectures proposed by Nieraeth (2023) about the maximal operator on rescaled Banach function spaces. We also obtain a new criterion saying when the maximal operator bounded on a Banach function space $X$ is also bounded on the associate space $X'$., Comment: 13 pages

In addition to Goldstone phonons that generically emerge in the low-frequency vibrational spectrum of any solid, crystalline or glassy, structural glasses also feature other low-frequency vibrational modes. The nature and statistical properties of these modes -- often termed `excess modes' -- have been the subject of decades-long investigation. Studying them, even using well-controlled computer glasses, has proven challenging due to strong spatial hybridization effects between phononic and nonphononic excitations, which hinder quantitative analyses of the nonphononic contribution ${\cal D}_{\rm G}(\omega)$ to the total spectrum ${\cal D}(\omega)$, per frequency $\omega$. Here, using recent advances indicating that ${\cal D}_{\rm G}(\omega)\!=\!{\cal D}(\omega)-{\cal D}_{\rm D}(\omega)$, where ${\cal D}_{\rm D}(\omega)$ is Debye's spectrum of phonons, we present a simple and straightforward scheme to enumerate nonphononic modes in computer glasses. Our analysis establishes that nonphononic modes in computer glasses indeed make an additive contribution to the total spectrum, including in the presence of strong hybridizations. Moreover, it cleanly reveals the universal ${\cal D}_{\rm G}(\omega)\!\sim\!\omega^4$ tail of the nonphononic spectrum, and opens the way for related analyses of experimental spectra of glasses., Comment: 7 pages, 5 figures. V2: a reference added to a closely related analysis of experimental data, see arXiv:2404.16996

In 1977, Yu. V. Matiyasevich proposed a formula expressing the chromatic polynomial of an arbitrary graph as a linear combination of flow polynomials of subgraphs of the original graph. In this paper, we prove that this representation is a particular case of one (easily verifiable) formula, namely, the representation of the characteristic polynomial of an arbitrary matroid as a linear combination of characteristic polynomials of dual matroids. As an application, we represent the flow polynomial of a complete graph with $n$ vertices as the sum of elementary terms with respect to all partitions of positive integer $n$. Since the growth rate of the number of all partitions is less than exponential, this technique allows us to evaluate the flow polynomial for values of $n\approx 50$. We also get an explicit expression for the characteristic polynomial of the matroid dual to the matroid of the projective geometry over a finite field. We prove, in particular, that major coefficients of all these polynomials coincide with the beginning of the row in the Pascal triangle, whose number equals the quantity of elements in the corresponding matroid. At the end part of the paper, we consider one more approach, which allows us to obtain the same results of application of our main theoren by using properties of the Tutte polynomial and the classical Rota formula for coefficients of the characteristic polynomial of a matroid. In addition, we describe the connection between the matroid variant of the Matiyasevich formula and convolution formulas for Tutte polynomials., Comment: 18 pages

Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states. The current model, while trained solely on successful proof paths, faces a discrepancy at the inference stage, as it must sample and try various tactics at each proof state until finding success, unlike its training which does not incorporate learning from failed attempts. Intuitively, a tactic that leads to a failed search path would indicate that similar tactics should receive less attention during the following trials. In this paper, we demonstrate the benefit of training models that additionally learn from failed search paths. Facing the lack of such trial-and-error data in existing open-source theorem-proving datasets, we curate a dataset on intuitionistic propositional logic theorems and formalize it in Lean, such that we can reliably check the correctness of proofs. We compare our model trained on relatively short trial-and-error information (TrialMaster) with models trained only on the correct paths and discover that the former solves more unseen theorems with lower trial searches., Comment: Accepted as a main conference paper at ACL 2024

Materials typically fail under complex stress states, essentially involving dilatational (volumetric) components that eventually lead to material decohesion/separation. It is therefore important to understand dilatational irreversible deformation -- i.e., dilatational plasticity -- en route to failure. In the context of glasses, much focus has been given to shear (volume-preserving) plasticity, both in terms of the stress states considered and the corresponding material response. Here, using a recently-developed methodology and extensive computer simulations, we shed basic light on the elementary processes mediating dilatational plasticity in glasses. We show that plastic instabilities, corresponding to singularities of the glass Hessian, generically feature both dilatational and shear irreversible strain components. The relative magnitude and statistics of the strain components depend both on the symmetry of the driving stress (e.g., shear vs.~hydrostatic tension) and on the cohesive (attractive) part of the interatomic interaction. We further show that the tensorial shear component of the plastic strain is generally non-planar and also extract the characteristic volume of plastic instabilities. Elucidating the fundamental properties of the elementary micro-mechanical building blocks of plasticity in glasses sets the stage for addressing larger-scale, collective phenomena in dilatational plasticity such as topological changes in the form of cavitation and ductile-to-brittle transitions. As a first step in this direction, we show that the elastic moduli markedly soften during dilatational plastic deformation approaching cavitation., Comment: Slightly revised presentation, no change in content, fixed bibliographical list

In this paper, we establish that FFLV polytopes, which describe monomial bases compatible with the PBW filtration on finite-dimensional simple modules for $\lie{sl}_n$ and $\lie{sp}_n$, are actually string polytopes as described by Littelmann and Berenstein-Zelevinsky for Demazure modules of higher-rank Lie algebras.

In this paper we obtain the sharp quantitative matrix weighted weak type bounds for the Christ--Goldberg maximal operator $M_{W,p}$ in the case $1

There have been some interesting recent advances in understanding the notion of mechanical disorder in structural glasses and the statistical mechanics of these systems' low-energy excitations. Here we contribute to these advances by studying a minimal model for structural glasses' elasticity in which the degree of mechanical disorder -- as characterized by recently introduced dimensionless quantifiers -- is readily tunable over a very large range. We comprehensively investigate a number of scaling laws observed for various macro-, meso- and microscopic elastic properties, and rationalize them using scaling arguments. Interestingly, we demonstrate that the model features the universal quartic glassy vibrational density of states as seen in many atomistic and molecular models of structural glasses formed by cooling a melt. The emergence of this universal glassy spectrum highlights the role of self-organization (towards mechanical equilibrium) in its formation, and elucidates why models featuring structural frustration alone do not feature the same universal glassy spectrum. Finally, we discuss relations to existing work in the context of strain-stiffening of elastic networks and of low-energy excitations in structural glasses, in addition to future research directions., Comment: 9 pages, 7 figures

Knowledge-based Visual Question Answering about Named Entities is a challenging task that requires retrieving information from a multimodal Knowledge Base. Named entities have diverse visual representations and are therefore difficult to recognize. We argue that cross-modal retrieval may help bridge the semantic gap between an entity and its depictions, and is foremost complementary with mono-modal retrieval. We provide empirical evidence through experiments with a multimodal dual encoder, namely CLIP, on the recent ViQuAE, InfoSeek, and Encyclopedic-VQA datasets. Additionally, we study three different strategies to fine-tune such a model: mono-modal, cross-modal, or joint training. Our method, which combines mono-and cross-modal retrieval, is competitive with billion-parameter models on the three datasets, while being conceptually simpler and computationally cheaper.