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Operational technology (OT) environments, including programmable logic controllers (PLCs), industrial control systems (ICS), and supervisory control and data acquisition (SCADA) systems, are increasin

Filtering has not yet had its foundation-model moment: state estimation is still dominated either by online Bayesian filtering algorithms that require known dy- namics as inputs, or by learning-based

Mission, vision, and architecture of the AcaciaFund research synthesis platform.

We find no evidence of critical scaling in the Schelling segregation model, in either the Moore neighborhood or its dense-spectrum extension to Chebyshev radii up to $r_0 = 6$ ($k = 168$ neighbors). O

AcaciaFund as a DataOps pipeline — architecture, principles, and metrics.

Distributionally robust optimization (DRO) hedges data-driven two-stage stochastic programs against error in the distribution estimated from a finite record, and standard ambiguity sets differ in how

Machine learning is transforming AML surveillance by detecting complex patterns that rule-based systems miss, while reducing false positive rates. This module covers ML applications in transaction monitoring (anomaly detection, supervised classification, graph neural networks), model validation and governance (backtesting, explainability, bias detection), the AML model lifecycle, and 2025-2026 trends including generative AI for scenario generation, explainable ML for regulatory compliance, graph neural networks achieving breakthrough results in SAR quality, and the emerging regulatory guidance on AI model risk management in financial crime.

Analysis of the AI hardware investment boom as annual spending reaches $300B run rate, examining which companies capture value across the stack — from NVIDIA and AMD to custom ASIC designers and memory manufacturers.

The EGI Federation has provided federated e-infrastructure services to European and global science for over 15 years. We review EGI's early experiences with AI in the interTwin and iMagine Horizon Eur

Case study of how five major European banks deployed machine learning for AML transaction monitoring, achieving 80% false positive reduction while improving suspicious activity detection rates.

Comprehensive comparison of the three leading Python orchestrators: execution model, DAG vs asset paradigm, scaling characteristics, monitoring, and community ecosystem. Decision framework for greenfield and migration scenarios.

The Dataflow Model provides a unified framework for batch and stream processing by separating What, Where, When, and How — enabling exactly-once processing of unbounded, out-of-order data.

Algorithmic trading uses computer programs to execute trades based on predefined rules, accounting for market conditions, timing, and volume. This module covers strategy types (TWAP, VWAP, implementation shortfall, statistical arbitrage, market making), infrastructure requirements (co-location, market data feeds, order management systems), market impact models, and 2025-2026 trends including AI-driven execution algorithms, the rise of 0DTE options trading, and regulatory scrutiny of electronic trading practices.

Alternative data encompasses non-traditional information sources used to gain investment insights. This module covers data types (satellite imagery, credit card transactions, app downloads, web scraping, supply chain data), analysis methods (NLP, anomaly detection, nowcasting), data due diligence (provenance, survivorship bias, backtest overfitting), and 2025-2026 trends including the alternative data market exceeding $100B, regulatory scrutiny of data sourcing, and the integration of alternative data with traditional fundamental analysis.

Amihud develops the ILLIQ measure (absolute return / dollar volume) and shows that expected stock returns are positively related to illiquidity across stocks and over time.

An introduction to anti-money laundering: the three stages of money laundering, global regulatory bodies (FATF, FinCEN, FCA, EU), core concepts (KYC, CDD, SAR, PEP), red flags, and AML compliance program requirements.

Regulatory examinations of AML programs are increasingly rigorous, with examiners using data analytics to assess program effectiveness. This module covers the AML audit lifecycle (planning, scoping, testing, reporting), regulatory examination expectations (BSA/AML exam manual, risk-focused examinations), common findings (SAR timeliness, CDD gaps, independent testing deficiencies), building examination-ready documentation, and 2025-2026 trends including AI-assisted compliance testing, regulatory focus on model risk management for AML models, and the rise of automated regulatory reporting solutions.

Apply AML concepts to real-world enforcement cases: HSBC, Standard Chartered, BNP Paribas penalties. Learn the SAR filing process, building an AML compliance program, and scenario-based suspicious transaction evaluation.

Understand money laundering stages, the AML regulatory framework, KYC requirements, suspicious activity red flags, and real-world detection techniques.

Scenario-based quiz testing AML knowledge across money laundering stages, regulations, red flag detection, and compliance practices — paired with the AML Fundamentals lesson.

A culture of compliance extends beyond policies to encompass ongoing training, awareness, and ethical decision-making throughout an organization. This module covers AML training program design (role-based training, scenario-based learning, testing and certification), measuring compliance culture (surveys, governance metrics, whistleblower reports), the convergence of AML and ESG compliance (human trafficking, environmental crime, corruption), and 2025-2026 trends including the integration of AML and ESG risk assessment, regulator focus on conduct and culture, and the use of learning management systems with real-time compliance updates.

We study the strategyproof placement of \(k\) facilities on the real line for \(n\) agents who privately report their locations, without monetary transfers. For two facilities, the Proportional Mechan

HackerNews discussion (1708 points) about Anthropic S-1.

Apache Iceberg defines an open table format for petabyte-scale analytic datasets with ACID transactions, schema evolution, partition evolution, time travel, and snapshot isolation on cloud object storage.

How Apache Iceberg enables ACID transactions on data lakes: partitioning, hidden partitioning, time travel, snapshot isolation, and Iceberg REST catalog. Migration strategies from Hive-style tables and Parquet-only storage.

A defining feature of electroencephalography (EEG) data is its 1/ f -like spectral structure, whereby power decreases as frequency increases. The nonoscillatory, aperiodic activity underlying this structure has regained attention as an index of cortical excitation-inhibition balance or neural noise,...

Apply market analysis techniques to real scenarios: building investment theses with catalysts and moats, case study valuation of a SaaS company, portfolio construction using MPT and risk parity, sector rotation across economic cycles, and practical risk management framework with Kelly criterion.

The Lakehouse architecture combines data lake flexibility with warehouse reliability through a metadata/transaction layer providing ACID transactions, schema enforcement, and performance on cloud object storage.

We propose that financial institutions can act as asset insulators, holding assets for the long run to protect their valuations from consequences of exposure to financial markets. We demonstrate the empirical relevance of this theory for the balance sheet behavior of a large class of intermediaries,...

Does artificial intelligence substitute or complement high-skill labor? We study financial robo-advisors and their impact on the labor market for human advisors. Using novel data on robo-advisors and advisor employment, we find that robo-advisors increase, rather than reduce, human advisor employmen...