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Time synchronization of devices in Internet-of-Things (IoT) networks is one of the challenging problems and a pre-requisite for the design of low-latency applications. Although many existing solutions have tried to address this problem, almost all solutions assume all the devices (nodes) in the network are faultless. Furthermore, these solutions exchange a large number of messages to achieve synchronization, leading to significant communication and energy overhead. To address these shortcomings, we propose C-sync, a clustering-based decentralized time synchronization protocol that provides resilience against several types of faults with energy-efficient communication. C-sync achieves scalability by introducing multiple reference nodes in the network that restrict the maximum number of hops any node can have to its time source. The protocol is designed with a modular structure on the Contiki platform to allow application transitions. We evaluate C-sync on a real testbed that comprises over 40 Tmote Sky hardware nodes distributed across different levels in a building and show through experiments the fault resilience, energy efficiency, and scalability of the protocol. C-sync detects and isolates faults to a cluster and recovers quickly. The evaluation makes a qualitative comparison with state-of-the-art protocols and a quantitative comparison with a class of decentralized protocols (derived from GTSP) that provide synchronization with no/limited fault-tolerance. Results also show a reduction of 56.12% and 75.75% in power consumption in the worst-case and best-case scenarios, respectively, compared to GTSP, while achieving similar accuracy.

The rapid proliferation of Internet-of-things (IoT) as well as mobile devices such as Electric Vehicles (EVs), has led to unpredictable load at the grid. The demand to supply ratio is particularly exacerbated at a few grid aggregators (charging stations) with excessive demand due to the geographic location, peak time, etc. Existing solutions on demand response cannot achieve significant improvements based only on time-shifting the loads without considering the device properties such as charging modes and movement capabilities to enable geographic migration. Additionally, the information on the spare capacity at a few aggregators can aid in re-channeling the load from other aggregators facing excess demand to allow migration of devices. In this paper, we model these flexible properties of the devices as a mixed-integer non-linear problem (MINLP) to minimize excess load and the improve the utility (benefit) across all devices. We propose an online distributed low-complexity heuristic that prioritizes devices based on demand and deadlines to minimize the cumulative loss in utility. The proposed heuristic is tested on an exhaustive set of synthetic data and compared with solutions from a solver/optimization tool for the same runtime to show the impracticality of using a solver. A real-world EV testbed data is also tested with our proposed solution and other scheduling solutions to show the practicality of generating a feasible schedule and a loss improvement of at least 57.23%.

Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To this end, we aim to address the problem by annotating a large-scale `in-the-wild' dataset for identifying human perceptions about the `Most Important Person (MIP)' in an image. The paper provides a thorough description of our proposed Multimodal Large Language Model (MLLM) based data annotation strategy, and a thorough data quality analysis. Further, we perform a comprehensive benchmarking of the proposed dataset utilizing state-of-the-art MIP localization methods, indicating a significant drop in performance compared to existing datasets. The performance drop shows that the existing MIP localization algorithms must be more robust with respect to `in-the-wild' situations. We believe the proposed dataset will play a vital role in building the next-generation social situation understanding methods. The code and data is available at https://github.com/surbhimadan92/MIP-GAF., Comment: Accepted for publication at WACV 2025

The outcomes of local measurements on entangled quantum systems can be certified to be genuinely random through the violation of a Bell Inequality. The randomness of the outcomes with respect to an adversary is quantified by the guessing probability, conditioned upon the observation of a specific amount of Bell violation or upon the observation of the entire input-output behavior. It has been an open question whether standard device-independent randomness generation protocols against classical or quantum adversaries can be constructed on the basis of any arbitrary Bell inequality, i.e., does there exist a Bell inequality for which the guessing probability is one for any chosen input even upon observing the maximal violation of the inequality? A strengthened version of the question asks whether there exists a quantum behavior that exhibits maximum non-locality but zero certifiable randomness for any arbitrary input. In this paper, we present an affirmative answer to both questions by constructing families of $n$-player non-local games for $n \geq 2$ and families of non-local behaviors on the quantum boundary that do not allow to certify any randomness against a classical adversary. Our results show the existence of a form of bound randomness against classical adversaries, highlighting that device-independent randomness and quantum non-locality can be maximally inequivalent resources., Comment: 4.5+23 pages; 3+9 figures

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

The change in the color pattern of the petals of Passiflora incarnata is studied using the chaos theory in the form of logistic maps and plotted using the corresponding bifurcation diagram. Based on a colorful inspection of the beginning of violet-colored dots along the filament of the flower's bud stage and the emergence of alternating bands of violet and white color in the matured bloom, it is possible to deduce that a two-degree model for polynomial mapping can be used to study color oscillations in the flower., Comment: 12 pages, 5 figures, 3 tables

In this study, we address the issue of API hallucinations in various software engineering contexts. We introduce CloudAPIBench, a new benchmark designed to measure API hallucination occurrences. CloudAPIBench also provides annotations for frequencies of API occurrences in the public domain, allowing us to study API hallucinations at various frequency levels. Our findings reveal that Code LLMs struggle with low frequency APIs: for e.g., GPT-4o achieves only 38.58% valid low frequency API invocations. We demonstrate that Documentation Augmented Generation (DAG) significantly improves performance for low frequency APIs (increase to 47.94% with DAG) but negatively impacts high frequency APIs when using sub-optimal retrievers (a 39.02% absolute drop). To mitigate this, we propose to intelligently trigger DAG where we check against an API index or leverage Code LLMs' confidence scores to retrieve only when needed. We demonstrate that our proposed methods enhance the balance between low and high frequency API performance, resulting in more reliable API invocations (8.20% absolute improvement on CloudAPIBench for GPT-4o).

When training deep learning models, the performance depends largely on the selected hyperparameters. However, hyperparameter optimization (HPO) is often one of the most expensive parts of model design. Classical HPO methods treat this as a black-box optimization problem. However, gray-box HPO methods, which incorporate more information about the setup, have emerged as a promising direction for more efficient optimization. For example, using intermediate loss evaluations to terminate bad selections. In this work, we propose an HPO method for neural networks using logged checkpoints of the trained weights to guide future hyperparameter selections. Our method, Forecasting Model Search (FMS), embeds weights into a Gaussian process deep kernel surrogate model, using a permutation-invariant graph metanetwork to be data-efficient with the logged network weights. To facilitate reproducibility and further research, we open-source our code at https://github.com/NVlabs/forecasting-model-search., Comment: See the project website at https://research.nvidia.com/labs/toronto-ai/FMS/

Battery-less embedded devices powered by energy harvesting are increasingly being used in wireless sensing applications. However, their limited and often uncertain energy availability challenges designing application programs. To examine if BDI-based agent programming can address this challenge, we used it for a real-life application involving an environmental sensor that works on energy harvested from ambient light. This yielded the first ever implementation of a BDI agent on a low-power battery-less and energy-harvesting embedded system. Furthermore, it uncovered conceptual integration challenges between embedded systems and BDI-based agent programming that, if overcome, will simplify the deployment of more autonomous systems on low-power devices with non-deterministic energy availability. Specifically, we (1) mapped essential device states to default \textit{internal} beliefs, (2) recognized and addressed the need for beliefs in general to be \textit{short-} or \textit{long-term}, and (3) propose dynamic annotation of intentions with their run-time energy impact. We show that incorporating these extensions not only simplified the programming but also improved code readability and understanding of its behavior.

Electromechanical systems manage physical processes through a network of inter-connected components. Today, programming the interactions required for coordinating these components is largely a manual process. This process is time-consuming and requires manual adaptation when system features change. To overcome this issue, we use autonomous software agents that process semantic descriptions of the system to determine coordination requirements and constraints; on this basis, they then interact with one another to control the system in a decentralized and coordinated manner.Our core insight is that coordination requirements between individual components are, ultimately, largely due to underlying physical interdependencies between the components, which can be (and, in many cases, already are) semantically modeled in automation projects. Agents then use hypermedia to discover, at run time, the plans and protocols required for enacting the coordination. A key novelty of our approach is the use of hypermedia-driven interaction: it reduces coupling in the system and enables its run-time adaptation as features change.

Analyzing sequential data is crucial in many domains, particularly due to the abundance of data collected from the Internet of Things paradigm. Time series classification, the task of categorizing sequential data, has gained prominence, with machine learning approaches demonstrating remarkable performance on public benchmark datasets. However, progress has primarily been in designing architectures for learning representations from raw data at fixed (or ideal) time scales, which can fail to generalize to longer sequences. This work introduces a \textit{compositional representation learning} approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space, an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification. We demonstrate its effectiveness through extensive experiments on publicly available time series classification benchmarks. Evaluating the coherence of segmented components shows its competitive performance on the unsupervised segmentation task., Comment: 15 pages, 2 figures, 4 tables

Buildings contain electro-mechanical systems that ensure the occupants' comfort, health, and safety. The functioning of these systems is automated through control programs, which are often available as reusable artifacts in a software library. However, matching these reusable control programs to the installed technical systems requires manual effort and adds engineering cost. In this article, we show that such matching can be accomplished fully automatically through logical rules and based on the creation of semantic relationships between descriptions of \emph{physical processes} and descriptions of technical systems and control programs. For this purpose, we propose a high-level bridging ontology that enables the desired rule-based matching and equips digital twins of the technical systems with the required knowledge about the underlying physical processes in a self-contained manner. We evaluated our approach in a real-life building automation project with a total of 34 deployed air handling units. Our data show that rules based on our bridging ontology enabled the system to infer the suitable choice of control programs automatically in more than 90\% of the cases while avoiding almost an hour of manual work for each such match.

`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, `scores` primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. `scores` not only includes common scores (e.g., Mean Absolute Error), it also includes novel scores not commonly found elsewhere (e.g., FIxed Risk Multicategorical (FIRM) score, Flip-Flop Index), complex scores (e.g., threshold-weighted continuous ranked probability score), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools. All of the scores and statistical techniques in this package have undergone a thorough scientific and software review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice. `scores` supports `xarray` datatypes, allowing it to work with Earth system data in a range of formats including NetCDF4, HDF5, Zarr and GRIB among others. `scores` uses Dask for scaling and performance. Support for `pandas` is being introduced. The `scores` software repository can be found at https://github.com/nci/scores/, Comment: Minor revisions to text and table. Updated title. 6 pages, 1 table. Software repository at https://github.com/nci/scores/

The next generation of rare-event searches, such as those aimed at determining the nature of particle dark matter or in measuring fundamental neutrino properties, will benefit from particle detectors with thresholds at the meV scale, 100-1000x lower than currently available. Quantum parity detectors (QPDs) are a novel class of proposed quantum devices that use the tremendous sensitivity of superconducting qubits to quasiparticle tunneling events as their detection concept. As envisioned, phonons generated by particle interactions within a crystalline substrate cause an eventual quasiparticle cascade within a surface patterned superconducting qubit element. This process alters the fundamental charge parity of the device in a binary manner, which can be used to deduce the initial properties of the energy deposition. We lay out the operating mechanism, noise sources, and expected sensitivity of QPDs based on a spectrum of charge-qubit types and readout mechanisms and detail an R&D pathway to demonstrating sensitivity to sub-eV energy deposits., Comment: 18 pages, 9 figures, 4 appendices

Deep learning has become a de facto method of choice for speech enhancement tasks with significant improvements in speech quality. However, real-time processing with reduced size and computations for low-power edge devices drastically degrades speech quality. Recently, transformer-based architectures have greatly reduced the memory requirements and provided ways to improve the model performance through local and global contexts. However, the transformer operations remain computationally heavy. In this work, we introduce WaveUNet squeeze-excitation Res2 (WSR)-based metric generative adversarial network (WSR-MGAN) architecture that can be efficiently implemented on low-power edge devices for noise suppression tasks while maintaining speech quality. We utilize multi-scale features using Res2Net blocks that can be related to spectral content used in speech-processing tasks. In the generator, we integrate squeeze-excitation blocks (SEB) with multi-scale features for maintaining local and global contexts along with gated recurrent units (GRUs). The proposed approach is optimized through a combined loss function calculated over raw waveform, multi-resolution magnitude spectrogram, and objective metrics using a metric discriminator. Experimental results in terms of various objective metrics on VoiceBank+DEMAND and DNS-2020 challenge datasets demonstrate that the proposed speech enhancement (SE) approach outperforms the baselines and achieves state-of-the-art (SOTA) performance in the time domain.

Recent reports reveal that isothermal chemical doping of hydrogen in correlated complex oxides such as perovskite nickelates (e.g. NdNiO3) can induce a metal-to-insulator transition (MIT) without the need for temperature modulation. In this work, we interrogate the magnitude change in temperature dependence of thermal conductivity upon chemical doping of hydrogen, as any changes to the thermal properties upon doping offer a route to solid-state thermal switches as well as another potential signal to monitor in a diverse set of sensing, electronic, and optical applications. Using frequency-domain thermoreflectance, we demonstrate that a large concentration of hydrogen (~ 0.1 - 0.5 H/unit cell) completely suppresses the electronic contribution to thermal conductivity in NdNiO3 thin films and reduces the phononic contribution by a factor of 2. These results are critical for the design of next-generation solid-state thermal switches, sensors, extreme environment electronics and neuromorphic memory architectures., Comment: preprint

Code generation models are not robust to small perturbations, which often lead to inconsistent and incorrect generations and significantly degrade the performance of these models. Improving the robustness of code generation models is crucial to better user experience when these models are deployed in real-world applications. However, existing efforts have not addressed this issue for code generation models. To fill this gap, we propose CodeFort, a framework to improve the robustness of code generation models, generalizing a large variety of code perturbations to enrich the training data and enabling various robust training strategies, mixing data augmentation, batch augmentation, adversarial logits pairing, and contrastive learning, all carefully designed to support high-throughput training. Extensive evaluations show that we improve the average robust pass rates of baseline CodeGen models from 14.79 to 21.74. Notably, the improvement in robustness against code-syntax perturbations is evidenced by a significant decrease in pass rate drop from 95.04% to 53.35%

Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion. However, the invariable use of retrieval in existing methods exposes issues in both efficiency and robustness, with a large proportion of the retrieved contexts proving unhelpful or harmful to code language models (code LMs). In this paper, we propose a selective RAG framework to avoid retrieval when unnecessary. To power this framework, we design a self-supervised learning approach to enable a code LM to accurately self-evaluate whether retrieval can improve its output quality and robustly leverage the potentially noisy retrieved contexts. Using this LM as both the selective RAG policy and the generation model, our framework achieves state-of-the-art repository-level code completion performance on diverse benchmarks including RepoEval, CrossCodeEval, and CrossCodeLongEval, a new long-form code completion benchmark. Meanwhile, our analyses show that selectively retrieving brings as much as 70% inference speedup in the online serving setting without harming the performance. We further demonstrate that our framework is able to accommodate different generation models, retrievers, and programming languages. These advancements position our framework as an important step towards more accurate and efficient repository-level code completion., Comment: ICML 2024

This paper presents a novel application of explainable AI (XAI) for root-causing performance degradation in machine learning models that learn continuously from user engagement data. In such systems a single feature corruption can cause cascading feature, label and concept drifts. We have successfully applied this technique to improve the reliability of models used in personalized advertising. Performance degradation in such systems manifest as prediction anomalies in the models. These models are typically trained continuously using features that are produced by hundreds of real time data processing pipelines or derived from other upstream models. A failure in any of these pipelines or an instability in any of the upstream models can cause feature corruption, causing the model's predicted output to deviate from the actual output and the training data to become corrupted. The causal relationship between the features and the predicted output is complex, and root-causing is challenging due to the scale and dynamism of the system. We demonstrate how temporal shifts in the global feature importance distribution can effectively isolate the cause of a prediction anomaly, with better recall than model-to-feature correlation methods. The technique appears to be effective even when approximating the local feature importance using a simple perturbation-based method, and aggregating over a few thousand examples. We have found this technique to be a model-agnostic, cheap and effective way to monitor complex data pipelines in production and have deployed a system for continuously analyzing the global feature importance distribution of continuously trained models., Comment: Submitted to The 2nd World Conference on eXplainable Artificial Intelligence, 17-19 July, 2024, Malta, Valletta

We design a deterministic subexponential time algorithm that takes as input a multivariate polynomial $f$ computed by a constant-depth circuit over rational numbers, and outputs a list $L$ of circuits (of unbounded depth and possibly with division gates) that contains all irreducible factors of $f$ computable by constant-depth circuits. This list $L$ might also include circuits that are spurious: they either do not correspond to factors of $f$ or are not even well-defined, e.g. the input to a division gate is a sub-circuit that computes the identically zero polynomial. The key technical ingredient of our algorithm is a notion of the pseudo-resultant of $f$ and a factor $g$, which serves as a proxy for the resultant of $g$ and $f/g$, with the advantage that the circuit complexity of the pseudo-resultant is comparable to that of the circuit complexity of $f$ and $g$. This notion, which might be of independent interest, together with the recent results of Limaye, Srinivasan and Tavenas, helps us derandomize one key step of multivariate polynomial factorization algorithms - that of deterministically finding a good starting point for Newton Iteration for the case when the input polynomial as well as the irreducible factor of interest have small constant-depth circuits.

The Kochen-Specker (KS) theorem is a cornerstone result in quantum foundations, establishing that quantum correlations in Hilbert spaces of dimension $d \geq 3$ cannot be explained by (consistent) hidden variable theories that assign a single deterministic outcome to each measurement. Specifically, there exist finite sets of vectors in these dimensions such that no non-contextual deterministic ($\{0,1\}$) outcome assignment is possible obeying the rules of exclusivity and completeness - that the sum of value assignments to any $d$ mutually orthogonal vectors be equal to $1$. Another central result in quantum foundations is Gleason's theorem that justifies the Born rule as a mathematical consequence of the quantum formalism. The KS theorem can be seen as a consequence of Gleason's theorem and the logical compactness theorem. Notably, Gleason's theorem also indicates the existence of KS-type finite vector constructions to rule out other finite alphabet outcome assignments beyond the $\{0,1\}$ case. Here, we propose a generalisation of the KS theorem that rules out hidden variable theories with outcome assignments in the set $\{0, p, 1-p, 1\}$ for $p \in [0,1/d) \cup (1/d, 1/2]$. The case $p = 1/2$ is especially physically significant. We show that in this case the result rules out (consistent) hidden variable theories that are fundamentally binary, i.e., theories where each measurement has fundamentally at most two outcomes (in contrast to the single deterministic outcome per measurement ruled out by KS). We present a device-independent application of this generalised KS theorem by constructing a two-player non-local game for which a perfect quantum winning strategy exists (a Pseudo-telepathy game) while no perfect classical strategy exists even if the players are provided with additional no-signaling resources of PR-box type (with marginal probabilities in $\{0,1/2,1\}$)., Comment: 14 pages, 5 figures

Psychological studies observe that emotions are rarely expressed in isolation and are typically influenced by the surrounding context. While recent studies effectively harness uni- and multimodal cues for emotion inference, hardly any study has considered the effect of long-term affect, or \emph{mood}, on short-term \emph{emotion} inference. This study (a) proposes time-continuous \emph{valence} prediction from videos, fusing multimodal cues including \emph{mood} and \emph{emotion-change} ($\Delta$) labels, (b) serially integrates spatial and channel attention for improved inference, and (c) demonstrates algorithmic generalisability with experiments on the \emph{EMMA} and \emph{AffWild2} datasets. Empirical results affirm that utilising mood labels is highly beneficial for dynamic valence prediction. Comparing \emph{unimodal} (training only with mood labels) vs \emph{multimodal} (training with mood and $\Delta$ labels) results, inference performance improves for the latter, conveying that both long and short-term contextual cues are critical for time-continuous emotion inference., Comment: 5 figures, 5 tables

Microwave Kinetic Inductance Detectors (MKIDs) have been demonstrated as capable phonon sensors when coupled to crystalline substrates, and have been proposed as detectors for next-generation rare-event searches such as for the direct detection of dark matter. These Kinetic Inductance Phonon Mediated (KIPM) detector designs, favoring large superconducting absorber volumes and high readout powers, are oftentimes limited in their sensitivity by low temperature amplifier noise introduced in the signal readout chain. We report here an effort to couple a wideband Kinetic Inductance Travelling Wave Parametric Amplifier (KI-TWPA), operated near the Standard Quantum Limit of minimal added amplifier noise, to sensors spanning a 70 MHz bandwidth at 3.5 GHz. This results in a ~5x improvement in the inferred detector energy resolution in the best sensor and highlights the potential of constructing O(100) meV resolving phonon-mediated particle detectors. We detail limitations introduced by lossy passive components, degraded RF responsivity, and microphysical noise sources like two-level systems (TLS), in achieving ultimate quantum-limited system noise levels., Comment: 7 pages, 4 figures

Spin pumping has significant implications for spintronics, providing a mechanism to manipulate and transport spins for information processing. Understanding and harnessing spin currents through spin pumping is critical for the development of efficient spintronic devices. The use of a magnetic insulator with low damping, enhances the signal-to-noise ratio in crucial experiments such as spin-torque ferromagnetic resonance (FMR) and spin pumping. A magnetic insulator coupled with a heavy metal or quantum material offers a more straight forward model system, especially when investigating spin-charge interconversion processes to greater accuracy. This simplicity arises from the absence of unwanted effects caused by conduction electrons unlike in ferromagnetic metals. Here, we investigate the spin pumping in coupled ferrimagnetic (FiM) Y3Fe5O12 (YIG)/Tm3Fe5O12 (TmIG) bilayers combined with heavy-metal (Pt) using the inverse spin Hall effect (ISHE). It is observed that magnon transmission occurs at both of the FiMs FMR positions. The enhancement of spin pumping voltage (Vsp) in the FiM garnet heterostructures is attributed to the strong interfacial exchange coupling between FiMs. The modulation of Vsp is achieved by tuning the bilayer structure. Further, the spin mixing conductance for these coupled systems is found to be 10^18 m^-2. Our findings describe a novel coupled FiM system for the investigation of magnon coupling providing new prospects for magnonic devices.

Microcalorimeters that leverage microwave kinetic inductance detectors to read out phonon signals in the particle-absorbing target, referred to as kinetic inductance phonon-mediated (KIPM) detectors, offer an attractive detector architecture to probe dark matter (DM) down to the fermionic thermal relic mass limit. A prototype KIPM detector featuring a single aluminum resonator patterned onto a 1-gram silicon substrate was operated in the NEXUS low-background facility at Fermilab for characterization and evaluation of this detector architecture's efficacy for a dark matter search. An energy calibration was performed by exposing the bare substrate to a pulsed source of 470 nm photons, resulting in a baseline resolution on the energy absorbed by the phonon sensor of $2.1\pm0.2$ eV, a factor of two better than the current state-of-the-art, enabled by millisecond-scale quasiparticle lifetimes. However, due to the sub-percent phonon collection efficiency, the resolution on energy deposited in the substrate is limited to $\sigma_E=318 \pm 28$ eV. We further model the signal pulse shape as a function of device temperature to extract quasiparticle lifetimes, as well as the observed noise spectra, both of which impact the baseline resolution of the sensor.

The experimental violation of a Bell inequality implies that at least one of a set of assumptions fails in nature. However, existing tests are inconclusive about which of the assumptions is the one that fails. Here, we show that there are quantum correlations that cannot be simulated with hidden variables that allow the slightest free will (or, equivalently, that limit, even minimally, retrocausal influences) or restrict, even minimally, actions at a distance. This result goes beyond Bell's theorem and demolishes the arguably most attractive motivation for considering hidden-variable theories with measurement dependence or actions at distance, namely, that simulating quantum correlations typically requires a small amount of these resources. We show that there is a feasible experiment that can discard any hidden-variable theory allowing for arbitrarily small free will and having arbitrarily small limitations to actions at a distance. The experiment involves two observers, each of them choosing between two measurements with $2^N$ outcomes. The larger $N$ for which a specific Bell-like inequality is violated, the larger the set of excluded hidden-variable theories. In the limit of $N$ tending to infinity, the only alternatives to the absence of hidden variables are complete superdeterminism or complete parameter dependence. We also explore the implications of this result for quantum information., Comment: 6+18 pages, 1 figure, 1 table