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The Standard Model predicts a long-range force mediated by a pair of neutrinos, known as "the neutrino force." We derive an expression for this force that is valid for all $r$. For large $r$, it reduces to the known $G_F^2/r^5$ form, while for small $r$, it scales as $1/r$. We explore the implications of this result for atomic parity violation (APV) experiments. A key feature of the neutrino force is that it is a long-range effect, meaning it cannot be treated as a correction to the tree-level exchange $Z$ diagram. We calculate the effects in muonium and positronium, finding that the neutrino force contributes about 4% and 16%, respectively, compared to the leading $ Z$ exchange. This indicates a significant impact on APV, with important implications for detecting the neutrino force and measuring the weak mixing angle in APV experiments., Comment: 46 pages, 9 figures, 3 tables

We consider two-dimensional periodically driven systems of fermions with particle-hole symmetry. Such systems support non-trivial topological phases, including ones that cannot be realized in equilibrium. We show that a space-time defect in the driving Hamiltonian, dubbed a ``time vortex,'' can bind $\pi$ Majorana modes. A time vortex is a point in space around which the phase lag of the Hamiltonian changes by a multiple of $2\pi$. We demonstrate this behavior on a periodically driven version of Kitaev's honeycomb spin model, where $\mathbb{Z}_2$ fluxes and time vortices can realize any combination of $0$ and $\pi$ Majorana modes. We show that a time vortex can be created using Clifford gates, simplifying its realization in near-term quantum simulators., Comment: 11 pages, 6 figures

A tournament is an orientation of a complete graph. A vertex that can reach every other vertex within two steps is called a \emph{king}. We study the complexity of finding $k$ kings in a tournament graph. We show that the randomized query complexity of finding $k \le 3$ kings is $O(n)$, and for the deterministic case it takes the same amount of queries (up to a constant) as finding a single king (the best known deterministic algorithm makes $O(n^{3/2})$ queries). On the other hand, we show that finding $k \ge 4$ kings requires $\Omega(n^2)$ queries, even in the randomized case. We consider the RAM model for $k \geq 4$. We show an algorithm that finds $k$ kings in time $O(kn^2)$, which is optimal for constant values of $k$. Alternatively, one can also find $k \ge 4$ kings in time $n^{\omega}$ (the time for matrix multiplication). We provide evidence that this is optimal for large $k$ by suggesting a fine-grained reduction from a variant of the triangle detection problem.

Novice programmers are increasingly relying on Large Language Models (LLMs) to generate code for learning programming concepts. However, this interaction can lead to superficial engagement, giving learners an illusion of learning and hindering skill development. To address this issue, we conducted a systematic design exploration to develop seven cognitive engagement techniques aimed at promoting deeper engagement with AI-generated code. In this paper, we describe our design process, the initial seven techniques and results from a between-subjects study (N=82). We then iteratively refined the top techniques and further evaluated them through a within-subjects study (N=42). We evaluate the friction each technique introduces, their effectiveness in helping learners apply concepts to isomorphic tasks without AI assistance, and their success in aligning learners' perceived and actual coding abilities. Ultimately, our results highlight the most effective technique: guiding learners through the step-by-step problem-solving process, where they engage in an interactive dialog with the AI, prompting what needs to be done at each stage before the corresponding code is revealed., Comment: 19 pages, 6 figures

NLP research on aligning lexical representation spaces to one another has so far focused on aligning language spaces in their entirety. However, cognitive science has long focused on a local perspective, investigating whether translation equivalents truly share the same meaning or the extent that cultural and regional influences result in meaning variations. With recent technological advances and the increasing amounts of available data, the longstanding question of cross-lingual lexical alignment can now be approached in a more data-driven manner. However, developing metrics for the task requires some methodology for comparing metric efficacy. We address this gap and present a methodology for analyzing both synthetic validations and a novel naturalistic validation using lexical gaps in the kinship domain. We further propose new metrics, hitherto unexplored on this task, based on contextualized embeddings. Our analysis spans 16 diverse languages, demonstrating that there is substantial room for improvement with the use of newer language models. Our research paves the way for more accurate and nuanced cross-lingual lexical alignment methodologies and evaluation.

Research ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on idea broad generation, yet offer little support for iterative specification, refinement, and evaluation needed to further develop initial ideas. To bridge this gap, we introduce IdeaSynth, a research idea development system that uses LLMs to provide literature-grounded feedback for articulating research problems, solutions, evaluations, and contributions. IdeaSynth represents these idea facets as nodes on a canvas, and allow researchers to iteratively refine them by creating and exploring variations and composing them. Our lab study (N=20) showed that participants, while using IdeaSynth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline. Our deployment study (N=7) demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher's workflows.

We apply numerical optimization and linear algebra algorithms for classical computers to the problem of automatically synthesizing algorithms for quantum computers. Using our framework, we apply several common techniques from these classical domains and numerically examine their suitability for and performance on this problem. Our methods are evaluated on single-qubit systems as well as on larger systems. While the first part of our proposed method outputs a single unitary matrix representing the composite effects of a quantum circuit or algorithm, we use existing tools - and assess the performance of these - to factor such a matrix into a product of elementary quantum gates. This enables our pipeline to be truly end-to-end: starting from desired input/output examples, our code ultimately results in a quantum circuit diagram. We release our code to the research community (upon acceptance)., Comment: 17 pages, 9 figures

Mixed Reality enables hybrid workspaces where physical and virtual monitors are adaptively created and moved to suit the current environment and needs. However, in shared settings, individual users' workspaces are rarely aligned and can vary significantly in the number of monitors, available physical space, and workspace layout, creating inconsistencies between workspaces which may cause confusion and reduce collaboration. We present Desk2Desk, an optimization-based approach for remote collaboration in which the hybrid workspaces of two collaborators are fully integrated to enable immersive side-by-side collaboration. The optimization adjusts each user's workspace in layout and number of shared monitors and creates a mapping between workspaces to handle inconsistencies between workspaces due to physical constraints (e.g. physical monitors). We show in a user study how our system adaptively merges dissimilar physical workspaces to enable immersive side-by-side collaboration, and demonstrate how an optimization-based approach can effectively address dissimilar physical layouts., Comment: ACM Symposium on User Interface Software and Technology (UIST '24)

Within the Standard Model, the branching fraction of the $B_s\to K^0\overline{K}{}^0$ decay is related to other decay rates and CP asymmetries through the approximate $SU(3)$ flavor symmetry of the strong interactions. Three such relations were shown to be violated at a level of about $3\sigma$ each. By means of a systematic search for new physics explanations of these puzzles, we find that possible solutions are highly fine-tuned and rather implausible., Comment: 16 pages, 3 figures

It is a fundamental behavior that different individuals see the world in a largely similar manner. This is an essential basis for humans' ability to cooperate and communicate. However, what are the neuronal properties that underlie these inter-subject commonalities of our visual world? Finding out what aspects of neuronal coding remain invariant across individuals' brains will shed light not only on this fundamental question but will also point to the neuronal coding scheme as the basis of visual perception. Here, we address this question by obtaining intracranial recordings from three cohorts of patients taking part in a different visual recognition task (overall 19 patients and 244 high-order visual contacts included in the analyses) and examining the neuronal coding scheme most consistent across individuals' visual cortex. Our results highlight relational coding - expressed by the set of similarity distances between profiles of pattern activations - as the most consistent representation across individuals. Alternative coding schemes, such as population vector coding or linear coding, failed to achieve similar inter-subject consistency. Our results thus support relational coding as the central neuronal code underlying individuals' shared perceptual content in the human brain.

Formation of the giant Chicxulub crater off Mexico's Yucatan Peninsula coincided with deposition of the global Ir-rich Cretaceous-Tertiary (K-T) stratigraphic boundary layer at ca. 65 Ma. The boundary is marked most sharply by abundant spherules containing unaltered grains of magnesioferrite spinel. Here we predict for the first time the sequential condensation of solids and liquids from the plume of vaporized rock expected from oblique K-T impacts. We predict highly oxidizing plumes that condense silicate liquid droplets bearing spinel grains whose compositions closely match those marking the actual boundary. Systematic global variations in spinel composition are consistent with higher condensation temperatures for spinels found at Atlantic and European sites than for those in the Pacific., Comment: 24 pages, 3 figures, 1 table, 2 supplemental tables included

LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the challenging task of data analysis programming, which requires expertise in data processing, programming, and statistics. However, our formative study (n=15) uncovered serious challenges in verifying AI-generated results and steering the AI (i.e., guiding the AI system to produce the desired output). We developed two contrasting approaches to address these challenges. The first (Stepwise) decomposes the problem into step-by-step subgoals with pairs of editable assumptions and code until task completion, while the second (Phasewise) decomposes the entire problem into three editable, logical phases: structured input/output assumptions, execution plan, and code. A controlled, within-subjects experiment (n=18) compared these systems against a conversational baseline. Users reported significantly greater control with the Stepwise and Phasewise systems, and found intervention, correction, and verification easier, compared to the baseline. The results suggest design guidelines and trade-offs for AI-assisted data analysis tools., Comment: Published at UIST 2024; 19 pages, 9 figures, and 2 tables

The $\nu_M{\rm SM}$ is defined as the SM extended to include dimension-5 operators. In this model neutrino masses violate lepton number, and two parameters of the lepton mixing matrix, the Majorana phases, are yet to be constrained. One combination of these phases and the neutrino masses, often denoted by $m_{ee}$, is probed by neutrinoless double beta decays ($0\nu\beta\beta$). We explore what information may be obtained beyond $0\nu\beta\beta$, and how it depends on the lightest neutrino mass. We point out that with current central values of the mixing parameters, $\Delta L_e=2$ and $\Delta L_e=\Delta L_\mu = 1$ (or $\Delta L_e=2$ and $\Delta L_\mu = 2$) processes cannot simultaneously vanish, providing a no-lose theorem, in principle, for excluding the $\nu_M{\rm SM}$, even in the case of normal mass ordering.

The size and complexity of software applications is increasing at an accelerating pace. Source code repositories (along with their dependencies) require vast amounts of labor to keep them tested, maintained, and up to date. As the discipline now begins to also incorporate automatically generated programs, automation in testing and tuning is required to keep up with the pace - let alone reduce the present level of complexity. While machine learning has been used to understand and generate code in various contexts, machine learning models themselves are trained almost exclusively on static code without inputs, traces, or other execution time information. This lack of training data limits the ability of these models to understand real-world problems in software. In this work we show that inputs, like code, can be generated automatically at scale. Our generated inputs are stateful, and appear to faithfully reproduce the arbitrary data structures and system calls required to rerun a program function. By building our tool within the compiler, it both can be applied to arbitrary programming languages and architectures and can leverage static analysis and transformations for improved performance. Our approach is able to produce valid inputs, including initial memory states, for 90% of the ComPile dataset modules we explored, for a total of 21.4 million executable functions. Further, we find that a single generated input results in an average block coverage of 37%, whereas guided generation of five inputs improves it to 45%.

The neutrino force results from the exchange of a pair of neutrinos. A neutrino background can significantly influence this force. In this work, we present a comprehensive calculation of the neutrino force in various neutrino backgrounds with spin dependence taken into account. In particular, we calculate the spin-independent and spin-dependent parity-conserving neutrino forces, in addition to the spin-dependent parity-violating neutrino forces with and without the presence of a neutrino background for both isotropic and anisotropic backgrounds. Compared with the vacuum case, the neutrino background can effectively violate Lorentz invariance and lead to additional parity-violating terms that are not suppressed by the velocity of external particles. We estimate the magnitude of the effect of atomic parity-violation experiments, and it turns out to be well below the current experimental sensitivity., Comment: 34 pages, 3 figures, 2 tables

The progression to "Pervasive Augmented Reality" envisions easy access to multimodal information continuously. However, in many everyday scenarios, users are occupied physically, cognitively or socially. This may increase the friction to act upon the multimodal information that users encounter in the world. To reduce such friction, future interactive interfaces should intelligently provide quick access to digital actions based on users' context. To explore the range of possible digital actions, we conducted a diary study that required participants to capture and share the media that they intended to perform actions on (e.g., images or audio), along with their desired actions and other contextual information. Using this data, we generated a holistic design space of digital follow-up actions that could be performed in response to different types of multimodal sensory inputs. We then designed OmniActions, a pipeline powered by large language models (LLMs) that processes multimodal sensory inputs and predicts follow-up actions on the target information grounded in the derived design space. Using the empirical data collected in the diary study, we performed quantitative evaluations on three variations of LLM techniques (intent classification, in-context learning and finetuning) and identified the most effective technique for our task. Additionally, as an instantiation of the pipeline, we developed an interactive prototype and reported preliminary user feedback about how people perceive and react to the action predictions and its errors., Comment: Paper accepted to the 2024 CHI Conference on Human Factors in Computing Systems (CHI 2024)

Cloud-based data commons, data meshes, data hubs, and other data platforms are important ways to manage, analyze and share data to accelerate research and to support reproducible research. This is an annotated glossary of some of the more common terms used in articles and discussions about these platforms., Comment: 6 pages

A 2014 report on global human capital trends found that many employers across the United States had difficulties maintaining a skilled and committed workforce. Yet unemployment among young people ages 16 to 29 years was well above the overall unemployment rate. In 2016, 8.2 percent of this group were unemployed, while the national average was 4.9 percent. However, among Black young adults it was even higher: 13.8 percent. Indeed, unemployment among young Black men was more than triple the national average. To help address racial inequity and increase employment among young people, workforce development systems and other systems that serve young adults would need to change. Given this context, in 2016, the Annie E. Casey Foundation launched Generation Work™ to connect more young adults--especially young people of color from families with low incomes--with meaningful employment by changing the way public and private systems prepare them and support their search for jobs. As part of the initiative, it asked partnerships made up of key workforce organizations in five cities to change how their workforce systems operated by better aligning education, training, and support services across organizations, and weaving into their systems best practices related to employers and youth. This report examines the first five years of the initiative as implemented by five partnerships.

Large language models (LLMs) have profoundly transformed natural language applications, with a growing reliance on instruction-based definitions for designing chatbots. However, post-deployment the chatbot definitions are fixed and are vulnerable to attacks by malicious users, emphasizing the need to prevent unethical applications and financial losses. Existing studies explore user prompts' impact on LLM-based chatbots, yet practical methods to contain attacks on application-specific chatbots remain unexplored. This paper presents System Prompt Meta Language (SPML), a domain-specific language for refining prompts and monitoring the inputs to the LLM-based chatbots. SPML actively checks attack prompts, ensuring user inputs align with chatbot definitions to prevent malicious execution on the LLM backbone, optimizing costs. It also streamlines chatbot definition crafting with programming language capabilities, overcoming natural language design challenges. Additionally, we introduce a groundbreaking benchmark with 1.8k system prompts and 20k user inputs, offering the inaugural language and benchmark for chatbot definition evaluation. Experiments across datasets demonstrate SPML's proficiency in understanding attacker prompts, surpassing models like GPT-4, GPT-3.5, and LLAMA. Our data and codes are publicly available at: https://prompt-compiler.github.io/SPML/.

Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may hinder deep conceptual engagement. We developed CodeAid, an LLM-powered programming assistant delivering helpful, technically correct responses, without revealing code solutions. CodeAid answers conceptual questions, generates pseudo-code with line-by-line explanations, and annotates student's incorrect code with fix suggestions. We deployed CodeAid in a programming class of 700 students for a 12-week semester. A thematic analysis of 8,000 usages of CodeAid was performed, further enriched by weekly surveys, and 22 student interviews. We then interviewed eight programming educators to gain further insights. Our findings reveal four design considerations for future educational AI assistants: D1) exploiting AI's unique benefits; D2) simplifying query formulation while promoting cognitive engagement; D3) avoiding direct responses while encouraging motivated learning; and D4) maintaining transparency and control for students to asses and steer AI responses., Comment: CHI 2024 Paper - The paper includes 17 pages, 8 figures, 2 tables, along with a 2-page appendix

Suppose you are given a function $f\colon [n] \to [n]$ via (black-box) query access to the function. You are looking to find something local, like a collision (a pair $x \neq y$ s.t. $f(x)=f(y)$). The question is whether knowing the "shape" of the function helps you or not (by shape we mean that some permutation of the function is known). Formally, we investigate the unlabeled instance optimality of substructure detection problems in graphs and functions. A problem is $g(n)$-instance optimal if it admits an algorithm $A$ satisfying that for any possible input, the (randomized) query complexity of $A$ is at most $g(n)$ times larger than the query complexity of any algorithm $A'$ which solves the same problem while holding an unlabeled copy of the input (i.e., any $A'$ that "knows the structure of the input"). Our results point to a trichotomy of unlabeled instance optimality among substructure detection problems in graphs and functions: 1. A few very simple properties have an $O(1)$-instance optimal algorithm. 2. Most properties of graphs and functions, with examples such as containing a fixed point or a $3$-collision in functions, or a triangle in graphs, are $n^{\Omega(1)}$-far from instance optimality. 3. The problems of collision detection in functions and finding a claw in a graph serve as a middle ground between the two regimes. We show that these two properties are $\Omega(\log n)$-far from instance optimality, and conjecture that this bound is tight. We provide evidence towards this conjecture, by proving that finding a claw in a graph is $O(\log(n))$-instance optimal among all input graphs for which the query complexity of an algorithm holding an unlabeled certificate is $O\left(\sqrt{\frac{n}{\log n}}\right)$.

We study the penguin over tree ratio in $D\rightarrow \pi\pi$ decays. This ratio can serve as a probe for rescattering effects. Assuming the Standard Model and in the isospin limit, we derive expressions that relate both the magnitude and the phase of this ratio to direct CP asymmetries and branching fractions. We find that the current data suggest that rescattering is large. A dedicated experimental analysis with current and future data will be able to significantly reduce the errors on these determinations, and enable us to check if indeed there is significant rescattering in $D \to \pi \pi$ decays., Comment: 9 pages. Matches published version

Synthesizers are powerful tools that allow musicians to create dynamic and original sounds. Existing commercial interfaces for synthesizers typically require musicians to interact with complex low-level parameters or to manage large libraries of premade sounds. To address these challenges, we implement SynthScribe -- a fullstack system that uses multimodal deep learning to let users express their intentions at a much higher level. We implement features which address a number of difficulties, namely 1) searching through existing sounds, 2) creating completely new sounds, 3) making meaningful modifications to a given sound. This is achieved with three main features: a multimodal search engine for a large library of synthesizer sounds; a user centered genetic algorithm by which completely new sounds can be created and selected given the users preferences; a sound editing support feature which highlights and gives examples for key control parameters with respect to a text or audio based query. The results of our user studies show SynthScribe is capable of reliably retrieving and modifying sounds while also affording the ability to create completely new sounds that expand a musicians creative horizon.

Instance-level image classification tasks have traditionally relied on single-instance labels to train models, e.g., few-shot learning and transfer learning. However, set-level coarse-grained labels that capture relationships among instances can provide richer information in real-world scenarios. In this paper, we present a novel approach to enhance instance-level image classification by leveraging set-level labels. We provide a theoretical analysis of the proposed method, including recognition conditions for fast excess risk rate, shedding light on the theoretical foundations of our approach. We conducted experiments on two distinct categories of datasets: natural image datasets and histopathology image datasets. Our experimental results demonstrate the effectiveness of our approach, showcasing improved classification performance compared to traditional single-instance label-based methods. Notably, our algorithm achieves 13% improvement in classification accuracy compared to the strongest baseline on the histopathology image classification benchmarks. Importantly, our experimental findings align with the theoretical analysis, reinforcing the robustness and reliability of our proposed method. This work bridges the gap between instance-level and set-level image classification, offering a promising avenue for advancing the capabilities of image classification models with set-level coarse-grained labels.

Kaon physics is at a turning point -- while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop., Comment: 55 pages, Summary of Kaons@CERN 23 workshop, references updated, typos fixed, version as published in EPJC

We give a higher-algebraic interpretation of complex orientations of ring spectra as "$\mathbb{E}_2$ strictifications" of the identity element. We show that higher strictifications do not exist for most ring spectra of interest in chromatic homotopy theory., Comment: Added info on how the main results explain the role of even-periodicity in chromatic homotopy

Hybrid organic-inorganic nanomaterials can exhibit transitional behavior that deviates from models developed for all-organic or all-inorganic materials systems. Here, we reveal the complexity of exciton-phonon interactions in a recently discovered 2D layered hybrid organic-inorganic semiconductor, silver phenylselenolate (AgSePh). Using femtosecond resonant impulsive vibrational spectroscopy and non-resonant Raman scattering, we measure multiple hybrid organic-inorganic vibrational modes and identify a subset of these modes that strongly couple to the electronic excited state. Calculations by density functional perturbation theory show that these strongly coupled modes exhibit large out-of-plane silver atomic motions and silver-selenium spacing displacements. Moreover, analysis of photoluminescence fine-structure splitting and temperature-dependent peak-shifting/linewidth-broadening suggests that light emission in AgSePh is most strongly affected by a compound 100 cm-1 mode involving the wagging motion of phenylselenolate ligands and accompanying metal-chalcogen stretching. Finally, red-shifting of vibrational modes with increasing temperature reveals a high degree of anharmonicity arising from non-covalent interactions between phenyl rings. These findings reveal the unique effects of hybrid vibrational modes in organic-inorganic semiconductors and motivate future work aimed at specifically engineering such interactions through chemical and structural modification.

Perceptive deep reinforcement learning (DRL) has lead to many recent breakthroughs for complex AI systems leveraging image-based input data. Applications of these results range from super-human level video game agents to dexterous, physically intelligent robots. However, training these perceptive DRL-enabled systems remains incredibly compute and memory intensive, often requiring huge training datasets and large experience replay buffers. This poses a challenge for the next generation of field robots that will need to be able to learn on the edge in order to adapt to their environments. In this paper, we begin to address this issue through differentially encoded observation spaces. By reinterpreting stored image-based observations as a video, we leverage lossless differential video encoding schemes to compress the replay buffer without impacting training performance. We evaluate our approach with three state-of-the-art DRL algorithms and find that differential image encoding reduces the memory footprint by as much as 14.2x and 16.7x across tasks from the Atari 2600 benchmark and the DeepMind Control Suite (DMC) respectively. These savings also enable large-scale perceptive DRL that previously required paging between flash and RAM to be run entirely in RAM, improving the latency of DMC tasks by as much as 32%., Comment: 7 pages, 4 figures, 2 tables

Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers' flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI co-writing tasks. With ABScribe, users can swiftly modify variations using LLM prompts, which are auto-converted into reusable buttons. Variations are stored adjacently within text fields for rapid in-place comparisons using mouse-over interactions on a popup toolbar. Our user study with 12 writers shows that ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances user perceptions of the revision process (d = 2.41, p < 0.001) compared to a popular baseline workflow, and provides insights into how writers explore variations using LLMs., Comment: CHI 2024

Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data. Recent works have extended GPs to model scalar and vector quantities distributed over non-Euclidean domains, including smooth manifolds appearing in numerous fields such as computer vision, dynamical systems, and neuroscience. However, these approaches assume that the manifold underlying the data is known, limiting their practical utility. We introduce RVGP, a generalisation of GPs for learning vector signals over latent Riemannian manifolds. Our method uses positional encoding with eigenfunctions of the connection Laplacian, associated with the tangent bundle, readily derived from common graph-based approximation of data. We demonstrate that RVGP possesses global regularity over the manifold, which allows it to super-resolve and inpaint vector fields while preserving singularities. Furthermore, we use RVGP to reconstruct high-density neural dynamics derived from low-density EEG recordings in healthy individuals and Alzheimer's patients. We show that vector field singularities are important disease markers and that their reconstruction leads to a comparable classification accuracy of disease states to high-density recordings. Thus, our method overcomes a significant practical limitation in experimental and clinical applications., Comment: ICLR 2024 conference paper. Associated code: https://github.com/agosztolai/RVGP

Code is increasingly becoming a core data modality of modern machine learning research impacting not only the way we write code with conversational agents like OpenAI's ChatGPT, Google's Bard, or Anthropic's Claude, the way we translate code from one language into another, but also the compiler infrastructure underlying the language. While modeling approaches may vary and representations differ, the targeted tasks often remain the same within the individual classes of models. Relying solely on the ability of modern models to extract information from unstructured code does not take advantage of 70 years of programming language and compiler development by not utilizing the structure inherent to programs in the data collection. This detracts from the performance of models working over a tokenized representation of input code and precludes the use of these models in the compiler itself. To work towards the first intermediate representation (IR) based models, we fully utilize the LLVM compiler infrastructure, shared by a number of languages, to generate a 182B token dataset of LLVM IR. We generated this dataset from programming languages built on the shared LLVM infrastructure, including Rust, Swift, Julia, and C/C++, by hooking into LLVM code generation either through the language's package manager or the compiler directly to extract the dataset of intermediate representations from production grade programs. Statistical analysis proves the utility of our dataset not only for large language model training, but also for the introspection into the code generation process itself with the dataset showing great promise for machine-learned compiler components.