A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method stabilizes tensegrity structures suitable for engineering.
Paper discusses challenges in deploying ML models for structural engineering.
Automates building structural design with reduced mass and carbon footprint.
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft? We present nn-dependability-kit, an open-source toolbox to support safety engineering of neural networks for autonomous driving systems. The rationale behind nn-dependability-kit is to consider…
ProGen models protein sequences for synthetic biology.
Transformer models can approximate smooth functions with prompts, enhancing LLMs' dynamic capabilities.
Deep learning models enhance engineering design automation.
New metrics quantify implementation risk in portfolio backtesting, revealing systematic differences in engine implementations.
Paper integrates ML with physics models for engineering and environmental challenges.
Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines. In this article, we analyze more than 250 thousand malware signatures generated by 61 different Anti-Virus engines after…
UQE uses LLMs to analyze unstructured data efficiently.
Paper proposes a new model for better engine control.
Explains equivariant neural networks for machine learning.
The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.]. But training machine learning models on data stored in relational databases requires significant data extraction and feature engineering efforts. These efforts are not …
We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice network model so we can architect the model structure to enhance interpretability, and add monotonicity constraints between inputs-and-outputs.…
A key phase in the bridge design process is the selection of the structural system. Due to budget and time constraints, engineers typically rely on engineering judgment and prior experience when selecting a structural system, often considering a limited range of design alternatives. The objective of this study was to e…
Paper proposes a holistic optimization for civil structures considering uncertainties.
Simple feature engineering beats complex models in financial prediction.
This review discusses challenges and solutions for AI in chemical engineering.
Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-stat…
Automates feature extraction from JSON data for machine learning.
Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for…
In many instances, information on engineering systems can be obtained through measurements, monitoring or direct observations of system performances and can be used to update the system reliability estimate. In structural reliability analysis, such information is expressed either by inequalities (e.g. for the observati…
Paper proposes verifier engineering for improving foundation models.
Engine predicts real-time air quality with high resolution.
The way developers collaborate inside and particularly across teams often escapes management's attention, despite a formal organization with designated teams being defined. Observability of the actual, organically formed engineering structure provides decision makers invaluable additional tools to manage their talent p…
Near real-time damage diagnosis of building structures after extreme events (e.g., earthquakes) is of great importance in structural health monitoring. Unlike conventional methods that are usually time-consuming and require human expertise, pattern recognition algorithms have the potential to interpret sensor recording…
High-dimensional models pose both safety benefits and risks.
Machine learning predicts molecular crystal stability.
Predicts coherence from quantum heat engine noise using machine learning.
As an application of `reverse engineering' technique introduced by R. Fintushel, D. Park and R. Stern \cite{FPS}, we construct an infinite family of fake (2n+2l-1)CP^2#(2n+4l-1)(-CP^2)'s for all n \ge 0, l \ge 1.
Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynam…
A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspect…
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications. Thus a critical task in material design is establishing the processing-structure relationship, which requires domain expertise and …
New method uses models from regularity structures as features in machine learning.
SAFE automates feature engineering for industrial tasks efficiently and scalably.
In recent years several trading platforms appeared which provide a backtest engine to calculate historic performance of self designed trading strategies on underlying candle data. The construction of a correct working backtest engine is, however, a subtle task as shown by Maier-Paape and Platen (cf. arXiv:1412.5558 [q-…
A new diffusion model generates novel protein backbones without relying on pretrained networks.
Factor Engine simplifies financial factor computation and analysis in Python.
Study uses machine learning to predict nonlinear seismic brace behavior.
Surrogate model construction for vector-valued outputs
Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supported by a suite of software tools including integrated development environments (IDE), hardware configurators, compilers, and runtimes. The…
The authors seek financial datasets to benchmark feature engineering methods on US market data.
Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much rese…
Paper proposes ARPHMM for fault detection and prognosis in aero-engines.