Drifting icebergs in the polar oceans play a key role in the Earth's climate system, impacting freshwater fluxes into the ocean and regional ecosystems while also posing a challenge to polar navigation. However, accurately forecasting iceberg trajectories remains a formidable challenge, primarily due to the scarcity of spatiotemporal data and the complex, nonlinear nature of iceberg motion, which is also impacted by environmental variables. The iceberg motion is influenced by multiple dynamic environmental factors, creating a highly variable system that makes trajectory identification complex. These limitations hinder the ability of deep learning models to effectively capture the underlying dynamics and provide reliable predictive outcomes. To address these challenges, we propose a hybrid IDRIFTNET model, a physics-driven deep learning model that combines an analytical formulation of iceberg drift physics, with an augmented residual learning model. The model learns the pattern of mismatch between the analytical solution and ground-truth observations, which is combined with a rotate-augmented spectral neural network that captures both global and local patterns from the data to forec
A significant fraction (4%-13%) of Antarctic sea ice remains stationary as landfast sea-ice ("fast ice"), typically anchored by grounded icebergs. Current global climate models do not represent fast-ice formation due to iceberg grounding, as iceberg-sea-ice interaction mostly occurs at subgrid scales. We propose a novel subgrid-scale coupling mechanism between Lagrangian iceberg particles and an Eulerian sea-ice continuum model. This hybrid particle-continuum approach integrates feedback from icebergs into the sea-ice momentum equation via a Green's function, a Stokeslet, representing the drag exerted by a point force on the viscous-plastic medium. The coupled system, including the Stokeslet induced drag, is discretized using a finite-element method with piecewise linear basis functions. The approach assumes that individual icebergs have diameters smaller than the grid spacing. The presented finite-element discretization is compatible with existing unstructured-mesh ocean model frameworks such as FESOM and ICON, ensuring practical applicability in Earth system modeling. This work provides and analyzes, for the first time, a stable numerical framework to capture the effects of indiv
Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models. We present a Hybrid NARX-LLM framework that combines a nonlinear autoregressive model with exogenous inputs (NARX) and a large language model (LLM) for residual correction. We further propose a Physics-Informed Prompt (PIP) method that transforms unstructured physical knowledge into structured prompts for zero-shot in-context reasoning. The primary objective is to explore the corrective potential of this framework for modeling Greenland iceberg discharge, rather than merely optimizing predictive accuracy. The NARX component captures intrinsic temporal dependencies, while the LLM, guided by PIP, encodes glacier dynamics and environmental drivers and perceives key trend patterns to correct systematic prediction errors. This integration allows the model to reason about unmodeled factors and produce interpretable residuals, enhancing overall predictive accuracy. Applied to Greenland iceberg discharge time series, our approach addresses extreme events that are difficult to predict due to rare variations and nonstationary trends, a limitation often overlo
The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparative analysis of Delta Parquet, Iceberg, and Hudi using real-world time-series automotive telemetry data with fields such as vehicle ID, timestamp, location, and event metrics. The evaluation considers modeling strategies, partitioning, CDC support, query performance, scalability, data consistency, and ecosystem maturity. Key findings show Delta Parquet provides strong ML readiness and governance, Iceberg delivers high performance for batch analytics and cloud-native workloads, while Hudi is optimized for real-time ingestion and incremental processing. Each format exhibits tradeoffs in query efficiency, time-travel, and update semantics. The study offers insights for selecting or combining form
Artificial Intelligence is reshaping America's \$9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI transforms quality control tasks in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human-AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approx \$211 billion) represents only the tip of the iceberg. Technical capabi
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap of these models through pretraining on synthetic data and introduce Iceberg, a synthetic data augmentation approach that expands both large language model (LLM)-generated programs and weak labels of unseen design configurations. Our weak label generation method is integrated with an in-context model architecture, enabling meta-learning from actual and proximate labels. Iceberg improves the geometric mean modeling accuracy by $86.4\%$ when adapt to six real-world applications with few-shot examples and achieves a $2.47\times$ and a $1.12\times$ better offline DSE performance when adapting to two different test datasets. Our open-sourced code is here: https://github.com/UCLA-VAST/iceberg
Iceberg drift and decay and the associated freshwater release are increasingly seen as important processes in Earth's climate system, yet a detailed understanding of their dynamics has remained elusive. Here, an idealized model of iceberg drift is presented. The model is designed to include the most salient physical processes that determine iceberg motion while remaining sufficiently simple to facilitate physical insight into iceberg drift dynamics. We derive an analytical solution of the model, which helps build understanding and also enables the rapid computation of large numbers of iceberg trajectories. The long-standing empirical rule of thumb that icebergs drift at 2% of the wind velocity, relative to the ocean current, is derived here from physical first principles, and it is shown that this relation only holds in the limit of strong winds or small icebergs, which approximately applies for typical icebergs in the Arctic. It is demonstrated that the opposite limit of weak winds or large icebergs approximately applies for typical Antarctic tabular icebergs, and that in this case the icebergs simply move with the ocean surface current. It is furthermore found that when winds are
The rapid progress in quantum hardware is expected to make them viable tools for the study of quantum algorithms in the near term. The timeline to useful algorithmic experimentation can be accelerated by techniques that use many noisy shots to produce an accurate estimate of the observable of interest. One such technique is to encode the quantum circuit using an error detection code and discard the samples for which an error has been detected. An underexplored property of error-detecting codes is the flexibility in the circuit encoding and fault-tolerant gadgets, which enables their co-optimization with the algorthmic circuit. However, standard circuit optimization tools cannot be used to exploit this flexibility as optimization must preserve the fault-tolerance of the gadget. In this work, we focus on the $[[k+2, k, 2]]$ Iceberg quantum error detection code, which is tailored to trapped-ion quantum processors. We design new flexible fault-tolerant gadgets for the Iceberg code, which we then co-optimize with the algorithmic circuit for the quantum approximate optimization algorithm (QAOA) using tree search. By co-optimizing the QAOA circuit and the Iceberg gadgets, we achieve an im
Large Language Models (LLMs) have revolutionized how information are collected, aggregated, and reasoned. However, this enables a novel and accessible vector of privacy intrusion: the automated and in-depth personal profiling; this engenders a chilling effect of "peepers everywhere". Existing research primarily unfolds from the training pipeline of LLM, emphasizing the exposure of Personally Identifiable Information (PII) through memorization, while privacy studies from a human-centric perspective remain underexplored. To fill this void, we empirically investigate privacy perception in the real world through the lens of human awareness and the practices of LLM-integrated platforms, revealing a significant dissonance: platforms fail to technically or policy-wise address public privacy concerns. To facilitate a systematic and quantifiable study of privacy risk, we propose the PrivacyIceberg, which categorizes real-world human privacy risks into three tiers: explicitly searched, contextually inferred, and deeply aggregated, based on the sophistication of LLM exploitation. We developed IcebergExplorer to audit privacy exposure, utilizing minimal PII as a search seed to reconstruct high
We describe a design pattern and concrete implementation for embedding distributed approximate nearest neighbor indexes inside the Apache Iceberg table format, using the Puffin sidecar file as the storage container and the snapshot summary as the binding mechanism. Modern analytical query engines increasingly adopt a compute disaggregated architecture: executors are stateless, scale elastically, and read all data from object storage. Adding vector similarity search to such an engine traditionally requires a dedicated index storage layer with its own lifecycle, consistency model, and operational surface breaking the disaggregation in variant. We show that the Puffin format, originally introduced portable level statistics and deletion vectors, is sufficient to carry full Vamana graphs at billion vector scale, and that linking these blobs through the existing statistics file snapshot summary property reduces ANN index management to standard Iceberg snapshot operations. We present a binary layout for sharded graph indexes inside Puffin, a coordinator executor protocol for distributed index build, probe, and incremental refresh, the integration into the existing optimistic-concurrency c
Recent advancements in quantum computing have enabled practical use of quantum error detecting and correcting codes. However, current architectures and future proposals of quantum computer design suffer from limited qubit counts, necessitating the use of high-rate codes. Such codes, with their code parameters denoted as $[[n, k, d]]$, have more than $1$ logical qubit per code (i.e., $k > 1$). This leads to reduced error tolerance of the code, since $\lceil (d-1)/2\rceil$ errors on any of the $n$ physical qubits can affect the logical state of all $k$ logical qubits. Therefore, it becomes critical to optimally map the input qubits of a quantum circuit to these codes, in such a way that the circuit fidelity is maximized. \par However, the problem of mapping program qubits to logical qubits for high-rate codes has not been studied in prior work. A brute force search to find the optimal mapping is super exponential (scaling as $O(n!)$, where $n$ is the number of input qubits), making exhaustive search infeasible past a small number of qubits. We propose a framework that addresses this problem on two fronts: (1) for any given mapping, it performs logical-to-physical compilation that
Although iceberg models have been used for decades, they have received far more widespread attention in recent years, in part due to efforts to explicitly represent icebergs in climate models. This calls for increased scrutiny of all aspects of typical iceberg models. An important component of iceberg models is the representation of iceberg capsizing, or rolling. Rolling occurs spontaneously when the ratio of iceberg width to height falls below a critical threshold. Here we examine previously proposed representations of this threshold, and we find that there have been crucial errors in the representation of rolling in many modeling studies to date. We correct these errors and identify an accurate model representation of iceberg rolling. Next, we assess how iceberg rolling influences simulation results in a hierarchy of models. Rolling is found to substantially prolong the lifespan of individual icebergs and allow them to drift farther offshore, although it is found to have relatively small impacts on the large-scale freshwater distribution in comprehensive model simulations. The results suggest that accurate representations of iceberg rolling may be of particular importance for ope
Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequen
ICEBERG is a liquid argon time projection chamber at Fermilab for the purpose of testing detector components and software for the Deep Underground Neutrino Experiment (DUNE). The detector features a 1.15m x 1m anode plane following the specifications of the DUNE horizontal drift far detector and a newly installed X-ARAPUCA photodetector. The status of ICEBERG is reported along with analysis of noise, pulser, and cosmic ray data from the ninth run beginning May 2024 with the goal of advising the DUNE collaboration on the optimal wire readout electronics configuration. In addition, development of an absolute energy scale calibration method is currently underway using known sources such as cosmic ray muon Michel electrons at the ~10 MeV scale and $^{39}$Ar decay electrons at the ~100keV scale. Research into AI-based identification of such events at the data acquisition level is introduced.
Due to their large mass and small aspect ratio, icebergs pose a threat to boats and offshore structures. Small icebergs and bergy bits can cause harm to platform hulls and are more difficult to discover remotely. As icebergs are dynamic mediums, the study of icebergs in relation to safe human operations requires the rigorous analysis of the ice-ocean interaction, in particular with waves and currents. In this paper, we present iceberg towing experiments and analyze iceberg stability from GPS tracks and inertial motion unit data. The towline tension as well as the boat motion relative to the iceberg was measured. Different scenarios were investigated by changing the towing strategy with regards to towing speed, direction (straight or curved trajectory) and acceleration. Large amplitude roll oscillations with period of approximately 30 s were observed immediately after the load dropped and the iceberg returned to a stable static position. In two of the cases, the iceberg flipped over partly or entirely after some towing time. From the load cell, we observed oscillations in the system with periods of approximately 6 s, which were attributed to the rope elastic properties and the icebe
Graph Neural Networks (GNNs) have achieved great success in dealing with non-Euclidean graph-structured data and have been widely deployed in many real-world applications. However, their effectiveness is often jeopardized under class-imbalanced training sets. Most existing studies have analyzed class-imbalanced node classification from a supervised learning perspective, but they do not fully utilize the large number of unlabeled nodes in semi-supervised scenarios. We claim that the supervised signal is just the tip of the iceberg and a large number of unlabeled nodes have not yet been effectively utilized. In this work, we propose IceBerg, a debiased self-training framework to address the class-imbalanced and few-shot challenges for GNNs at the same time. Specifically, to figure out the Matthew effect and label distribution shift in self-training, we propose Double Balancing, which can largely improve the performance of existing baselines with just a few lines of code as a simple plug-and-play module. Secondly, to enhance the long-range propagation capability of GNNs, we disentangle the propagation and transformation operations of GNNs. Therefore, the weak supervision signals can p
Iceberg meltwater is a critical freshwater flux from the cryosphere to the oceans. Global climate simulations therefore require simple and accurate parameterisations of iceberg melting. Iceberg shape is an important but often neglected aspect of iceberg melting. Icebergs have an enormous range of shapes and sizes, and distinct processes dominate basal and side melting. We show how different iceberg aspect ratios and relative ambient water velocities affect melting using a combined experimental and numerical study. The experimental results show significant variations in melting between different iceberg faces, as well as within each iceberg face. These findings are reproduced and explained with novel multiphysics numerical simulations. At high relative ambient velocities melting is largest on the side facing the flow, and mixing during vortex generation causes local increases in basal melt rates of over 50%. Double-diffusive buoyancy effects become significant when the relative ambient velocity is low. Existing melting parameterisations do not reproduce our findings. We propose several corrections to capture the influence of aspect ratio on iceberg melting.
At near-grounded glacier termini, calving can lead to the capsize of kilometer-scale unstable icebergs. The transient contact force applied by the capsizing iceberg on the glacier front generates seismic waves that propagate over teleseismic distances. The inversion of this seismic signal is of great interest to get insight into actual and past capsize dynamics. However, the iceberg size, which is of interest for geophysical and climatic studies, cannot be recovered from the seismic amplitude alone. This is because the capsize is a complex process involving interactions between the iceberg, the glacier and the surrounding water. This paper presents a first step towards the construction of a complete model, and is focused on the capsize in the open ocean without glacier front nor ice-mélange. The capsize dynamics of an iceberg in the open ocean is captured by computational fluid dynamics (CFD) simulations, which allows assessing the complexity of the fluid motion around a capsizing iceberg and how far the ocean is affected by iceberg rotation. Expressing the results in terms of appropriate dimensionless variables, we show that laboratory scale and field scale capsizes can be directl
Machine Vision (MV) is essential for solving driving automation. This paper examines potential shortcomings in current MV testing strategies for highly automated driving (HAD) systems. We argue for a more comprehensive understanding of the performance factors that must be considered during the MV evaluation process, noting that neglecting these factors can lead to significant risks. This is not only relevant to MV component testing, but also to integration testing. To illustrate this point, we draw an analogy to a ship navigating towards an iceberg to show potential hidden challenges in current MV testing strategies. The main contribution is a novel framework for black-box testing which observes environmental relations. This means it is designed to enhance MV assessments by considering the attributes and surroundings of relevant individual objects. The framework provides the identification of seven general concerns about the object recognition of MV, which are not addressed adequately in established test processes. To detect these deficits based on their performance factors, we propose the use of a taxonomy called "granularity orders" along with a graphical representation. This all
Owens Valley Radio Observatory (OVRO) observations of supermassive black hole binary (SMBHB) candidate PKS~2131$-$021 revealed, for the first time, six likely characteristics of the phenomenology exhibited by SMBHB in blazars, of which the most unexpected and critical is sinusoidal flux density variations. We have now identified a second blazar, PKS~J0805$-$0111, showing significant sinusoidal variations, with an observed period that translates to $1.422 \pm 0.005$ yr in the rest frame of the $z = 1.388$ object. We generate $10^6$ simulated light curves to reproduce the radio variability characteristics of PKS~J0805$-$0111, and show that the global probability, considering the \textit{look-elsewhere effect}, indicates that the observed periodicity can be attributed to the red noise tail of the power spectral density, with a $p_0$ value of $7.8 \times 10^{-5}$ (i.e. 3.78$σ$). PKS J0805$-$0111 displays all six characteristics observed in PKS 2131$-$021. Taking into account the well-defined OVRO sample size, the false positive probability $\sim 0.22$, but the rare behavior makes this a strong SMBHB candidate. The discovery of a second SMBHB candidate exhibiting these rare characterist