What is ELT? ELT is a widely used concept in the data engineering world and stands for Extract, Load, and Transform. Extract – The process of reading raw data from various sources such as mainframes, databases, files, or communication platforms like emails and chats. Load – Involves storing the extracted data into a target system, such as a database or cloud storage solutions like Amazon S3, Azure Blob Storage, or Google Cloud Storage (GCS). Transform – The step where raw data is […]

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A Comprehensive Engineering and Operational Analysis of the Internet Archive Introduction: The Hum of History in the Fog If you stand quietly in the nave of the former Christian Science church on Funston Avenue in San Francisco’s Richmond District, you can hear the sound of the internet breathing. It is not the chaotic screech of a dial-up modem or the ping of a notification, but a steady, industrial hum—a low-frequency thrum generated by hundreds of spinning hard drives […]

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Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives. While single-policy MORL methods can learn fair policies for fixed user preferences using welfare functions such as the generalized Gini welfare function (GGF), they fail to provide the diverse set of policies necessary for dynamic or unknown user preferences. To address this limitation, we formalize the fair optimization problem in multi-policy MORL, where […]

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arXiv:2604.08592v1 Announce Type: new Abstract: Reservoir observers provide a data-driven approach to the inference of unmeasured variables from observed ones for nonlinear dynamical systems. While previous studies have demonstrated wide applicability, their performance may vary considerably with different input variables, even compromising reliability in the worst cases. To enhance the performance of inference, we integrate residual calibration and attention mechanism into the reservoir observer design. The residual calibration module leverages information from the estimation residuals to refine the […]

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Could you run an entire business from a markdown file? We’re about to find out. For five thousand years, the business ledger was a bulwark against the universe’s inevitable slide toward sludge. The merchants of Uruk pressed reeds into damp clay to count sheep; the bankers of Renaissance Genoa perfected double-entry bookkeeping to track the flow of florins. These records were static. They were memory made physical, a way to freeze the turbulent currents of commerce into legible […]

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arXiv:2603.12350v1 Announce Type: new Abstract: Text-speech joint spoken language modeling (SLM) aims at natural and intelligent speech-based interactions, but developing such a system may suffer from modality mismatch: speech unit sequences are much longer than text tokens. Prior work reduces this gap with text-aligned tokenization and embedding (TASTE), producing speech tokens that align in lengths with their textual counterparts. However, the dependence on an external ASR system and the use of a non-causal decoder limits streaming use. To […]

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After 35 years of multivendor storage work, I have learned that the real value is not slicing one box into smaller boxes. The real value is putting an intelligent control layer between applications and hardware so the business is no longer trapped by whichever vendor sold them the last shiny frame. Every few years, somebody asks a version of the same question: Is storage virtualization basically like VMware, where you take one piece of hardware and carve more […]

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arXiv:2601.20868v1 Announce Type: new Abstract: Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on manual design, this LLM-Driven Heuristic Design (LHD) process leverages LLMs to iteratively generate and refine solvers to achieve high performance. However, existing LHD frameworks face two critical limitations: (1) Endpoint-only evaluation, which ranks solvers solely by final quality, ignoring the convergence process and runtime efficiency; (2) High adaptation costs, where distribution shifts necessitate re-adaptation […]

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arXiv:2602.11204v1 Announce Type: new Abstract: The widespread use of publicly available pre-trained encoders from self-supervised learning (SSL) has exposed a critical vulnerability: their susceptibility to downstream-agnostic adversarial examples (DAEs), which are crafted without knowledge of the downstream tasks but capable of misleading downstream models. While several defense methods have been explored recently, they rely primarily on task-specific adversarial fine-tuning, which inevitably limits generalizability and causes catastrophic forgetting and deteriorates benign performance. Different with previous works, we propose a […]

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