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Monitoring in-class programming exercises can help instructors identify struggling students and common challenges. However, understanding students' progress can be prohibitively difficult, particularly for multi-faceted problems that include multiple steps with complex interdependencies, have no predictable completion order, or involve evaluation criteria that are difficult to summarize across many students (e.g., exercises building interactive web-based user interfaces). We introduce SPARK, a coding exercise monitoring dashboard designed to address these challenges. SPARK allows instructors to flexibly group substeps into checkpoints based on exercise requirements, suggests automated tests for these checkpoints, and generates visualizations to track progress across steps. SPARK also allows instructors to inspect intermediate outputs, providing deeper insights into solution variations. We also construct a dataset of 40-minute keystroke coding data from N=22 learners solving two web programming exercises and provide empirical insights into the perceived usefulness of SPARK through a within-subjects evaluation with 16 programming instructors.

Custom policy-learning pipelines in Spark fail for two coupled systems reasons: rowwise Python execution makes inference impractical, and driver-side candidate materialization makes split search fragile at feature scale. We present Spark Policy Toolkit, a semantics-governed systems toolkit for scalable policy learning in Spark. The toolkit provides two Spark-native primitives: partition-initialized vectorized inference through mapInPandas and mapInArrow, and collect-less split search that scores candidates on executors. Both primitives are governed by one fixed-input semantic contract: the same rows, feature order, treatment vocabulary, preprocessing manifest, and split boundaries must preserve per-row score vectors, best-split decisions, and end-to-end learned policy outputs. The evaluation combines practical baseline ladders, backend parity checks, measured split-search scale results, synthetic and Hillstrom end-to-end policy preservation, missingness stress, partition and order perturbation tests, quantile-boundary sensitivity, and a concrete adversarial failure catalog. On a 40-worker Databricks cluster, mapInArrow reaches 4.72M rows/s at 10M matched rows and 7.23M rows/s at 50

This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a comprehensive benchmark designed to ensure safety in humanoid autonomy and teleoperation. Humanoid robots pose significant safety risks due to their physical capabilities of interacting with complex environments. The physical structures of humanoid robots further add complexity to the design of general safety solutions. To facilitate safe deployment of complex robot systems, SPARK can be used as a toolbox that comes with state-of-the-art safe control algorithms in a modular and composable robot control framework. Users can easily configure safety criteria and sensitivity levels to optimize the balance between safety and performance. To accelerate humanoid safety research and development, SPARK provides simulation benchmarks that compare safety approaches in a variety of environments, tasks, and robot models. Furthermore, SPARK allows quick deployment of synthesized safe controllers on real robots. For hardware deployment, SPARK supports Apple Vision Pro (AVP) or a Motion Capture System as external sensors, while offering interfaces for seamless integration with alternative hardware setups at the same time.

Robot teleoperation enables human control over robotic systems in environments where full autonomy is challenging. Recent advancements in low-cost teleoperation devices and VR/AR technologies have expanded accessibility, particularly for bimanual robot manipulators. However, transitioning from in-person to remote teleoperation presents challenges in task performance. We introduce SPARK, a kinematically scaled, low-cost teleoperation system for operating bimanual robots. Its effectiveness is compared to existing technologies like the 3D SpaceMouse and VR/AR controllers. We further extend SPARK to SPARK-Remote, integrating sensor-based force feedback using haptic gloves and a force controller for remote teleoperation. We evaluate SPARK and SPARK-Remote variants on 5 bimanual manipulation tasks which feature operational properties - positional precision, rotational precision, large movements in the workspace, and bimanual collaboration - to test the effective teleoperation modes. Our findings offer insights into improving low-cost teleoperation interfaces for real-world applications. For supplementary materials, additional experiments, and qualitative results, visit the project webpag

Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framework, along with the evidence that informed our launch decision. We then discuss additional considerations, such as Muse Spark's broader content safety and behavioral profile, that are relevant to overall safety but fall outside the catastrophic risk domains governed by the Framework. Our preparedness results covering Chemical and Biological, Cybersecurity, and Loss of Control risks assess Muse Spark's deployment within Meta AI as presenting acceptable levels of residual risks under our Advanced AI Scaling Framework. We conducted a broad set of evaluations targeting dual-use and high-risk capabilities across these catastrophic risk domains. Those evaluations identified elevated risks prior to mitigations, with Chemical and Biological capabilities assessed as likely reaching the "high risk" category under the Advanced AI Scaling Framework before safeguards were applied. We have implemented a multi-layered set of mitigations that address the identified risks, and Muse Spark demonstrates state-of-the-art refus

Donoho and Elad \textit{[Proc. Natl. Acad. Sci. USA, 2003]} introduced the important notion of the spark of a frame, using which they derived a fundamental uncertainty principle. Based on spark, they also provided a necessary and sufficient condition for the uniqueness of sparse solutions to the NP-hard $\ell_0$-minimization problem. In this nano note, we show that the notion of spark can be extended to linear maps whose domains are measure spaces. Using this generalization, we derive an uncertainty principle and provide a sufficient condition for the existence of sparse solutions to linear systems on measure spaces.

As Spark becomes a common big data analytics platform, its growing complexity makes automatic tuning of numerous parameters critical for performance. Our work on Spark parameter tuning is particularly motivated by two recent trends: Spark's Adaptive Query Execution (AQE) based on runtime statistics, and the increasingly popular Spark cloud deployments that make cost-performance reasoning crucial for the end user. This paper presents our design of a Spark optimizer that controls all tunable parameters of each query in the new AQE architecture to explore its performance benefits and, at the same time, casts the tuning problem in the theoretically sound multi-objective optimization (MOO) setting to better adapt to user cost-performance preferences. To this end, we propose a novel hybrid compile-time/runtime approach to multi-granularity tuning of diverse, correlated Spark parameters, as well as a suite of modeling and optimization techniques to solve the tuning problem in the MOO setting while meeting the stringent time constraint of 1-2 seconds for cloud use. Evaluation results using TPC-H and TPC-DS benchmarks demonstrate the superior performance of our approach: (i) When prioritizi

There is increasing interest in building computational models of moral reasoning by people to enable effective interaction by Artificial Intelligence (AI) agents. We examine interactions on social media to understand human moral judgments in real-life ethical scenarios. Specifically, we examine posts from a popular Reddit subreddit (i.e., a subcommunity) called r/AmITheAsshole, where authors and commenters share their moral judgments on who (i.e., which participant of the described scenario) is blameworthy. To investigate the underlying reasoning influencing moral judgments, we focus on excerpts-which we term moral sparks-from original posts that some commenters include to indicate what motivates their judgments. To this end, we examine how (1) events activating social commonsense and (2) linguistic signals affect the identified moral sparks and their subsequent judgments. By examining over 24672 posts and 175988 comments, we find that event-related negative character traits (e.g., immature and rude) attract attention and stimulate blame, implying a dependent relationship between character traits and moral values. Specifically, we focus on causal graphs involving events (c-events)

We investigate the emergence of synchronization in heterogeneous networks of chaotic maps. Our findings reveal that a small cluster of highly connected maps is responsible for triggering the spark of synchronization. After the spark, the synchronized cluster grows in size and progressively moves to less connected maps, eventually reaching a cluster that may remain synchronized over time. We explore how the shape of the network's degree distribution affects the onset of synchronization and derive an expression based on the network's construction that determines the expected time for a network to synchronize. Understanding how the network structure affects the spark of synchronization is particularly important for the control and design of more robust systems that require some level of coherence between a subset of units for better functioning. Numerical simulations in finite-sized networks are consistent with this analysis.

We investigate the sparsity of null vectors of real symmetric matrices whose off-diagonal pattern of zero and nonzero entries is described by the adjacencies of a graph. We use the definition of the spark of a matrix, the smallest number of nonzero coordinates of any null vector, to define the spark of a graph as the smallest possible spark of a corresponding matrix. We study connections of graph spark to well-known concepts including minimum rank, forts, orthogonal representations, Parter and Fiedler vertices, and vertex connectivity.

Volcanic jet flows in explosive eruptions emit radio frequency signatures, indicative of their fluid dynamic and electrostatic conditions. The emissions originate from sparks supported by an electric field built up by the ejected charged volcanic particles. When shock-defined, low-pressure regions confine the sparks, the signatures may be limited to high-frequency content corresponding to the early components of the avalanche-streamer-leader hierarchy. Here, we image sparks and a standing shock together in a transient supersonic jet of micro-diamonds entrained in argon. Fluid dynamic and kinetic simulations of the experiment demonstrate that the observed sparks originate upstream of the standing shock. The sparks are initiated in the rarefaction region, and cut off at the shock, which would limit their radio frequency emissions to a tell-tale high-frequency regime. We show that sparks transmit an impression of the explosive flow, and open the way for novel instrumentation to diagnose currently inaccessible explosive phenomena.

Skyline queries are frequently used in data analytics and multi-criteria decision support applications to filter relevant information from big amounts of data. Apache Spark is a popular framework for processing big, distributed data. The framework even provides a convenient SQL-like interface via the Spark SQL module. However, skyline queries are not natively supported and require tedious rewriting to fit the SQL standard or Spark's SQL-like language. The goal of our work is to fill this gap. We thus provide a full-fledged integration of the skyline operator into Spark SQL. This allows for a simple and easy to use syntax to input skyline queries. Moreover, our empirical results show that this integrated solution of skyline queries by far outperforms a solution based on rewriting into standard SQL.

We have investigated the evolution of a system of sparking discharges in the inner acceleration region (IAR) above the pulsar polar cap. The surface of the polar cap is heated to temperatures around $10^6$ K and forms a partially screened gap (PSG) due to thermionic emission of positively charged ions from the stellar surface. The sparks lag behind the co-rotation speed during their lifetimes due to variable $E$x$B$ drift. In a PSG the sparking discharges arise in locations where the surface temperatures go below the critical level ($T_i$) for ions to freely flow from the surface. The sparking commences due to the large potential drop developing along the magnetic field lines in these lower temperature regions and subsequently the back streaming particles heat the surface to $T_i$. The temperature regulation requires the polar cap to be tightly filled with sparks and a continuous presence of sparks is required around its boundary since no heating is possible from the closed field line region. We have estimated the time evolution of the sparking system in the IAR which shows a gradual shift in the spark formation along two distinct directions resembling clockwise and anti-clockwise

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