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arXiv research

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.

168,695 papers · 148 categories

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8172533 · Jun 202019922001200920172026
48 results for digital footprints

Carbontracker tracks and predicts training DL models' carbon footprint.

problem Exponential growth in energy consumption for training deep learning models.
method Carbontracker tool for tracking and predicting energy and carbon footprint.
result Promotes responsible computing and encourages energy-efficient deep learning.

This paper analyzes energy and carbon footprints in distributed and federated learning.

problem High energy costs and carbon emissions in centralized AI methods.
method A novel framework quantifying energy and carbon footprints in vanilla and consensus-based FL methods.
result Optimal bounds and operational points for green FL designs and sustainability assessment.

Persistent homology reveals geometric features of metric spaces, especially geodesic circles.

problem Detecting geometric features in metric spaces using persistent homology.
method Analyzing algebraic elements (footprints) in persistent homology of metric spaces and subspace.
result Higher-dimensional persistent homology captures lower-dimensional geometric features.

A new Input-Output model, called the Multi-Entity Input-Output (MEIO) model, is introduced to estimate the responsibility of entities of an ecosystem on the footprint of each other. It assumed that the ecosystem is comprised of end users, service providers, and utilities. The proposed MEIO modeling approach can be seen…

2014-04-24abs ↗pdf ↗

A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital f…

2018-03-23abs ↗pdf ↗

SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.

problem Personalized models for small samples and high-dimensional features with interpretability.
method Sparse Deep Additive Model with Interactions (SDAMI) combining sparsity-driven feature selection and deep subnetworks.
result SDAMI successfully identifies pure interactions with near-zero false positive rates.

We propose a statistical model to understand people's perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we design a system to probe people's perception from simple pairwise comparisons of the relative carbon footprint of their actions. The formulation…

2019-11-26abs ↗pdf ↗

We construct Peano curves γ:[0,)R2γ: [0,\infty) \to \mathbb{R}^2 whose "footprints" γ([0,t])γ([0,t]), t>0t>0, have CC^\infty boundaries and are tangent to a common continuous line field on the punctured plane R2{γ(0)}\mathbb{R}^2 \setminus \{γ(0)\}. Moreover, these boundaries can be taken CC^\infty-close to any prescribed smooth family…

2014-07-19abs ↗pdf ↗

Serenity optimizes neural network execution for edge devices by scheduling with optimal memory footprint.

problem Order of nodes in irregular neural networks affects memory footprint, complicating execution under resource constraints.
method Memory-aware compiler using dynamic programming and graph rewriting to find optimal schedules.
result Achieves optimal peak memory and further improves it with graph rewriting, reducing memory usage by 1.68x-1.86x compared to TensorFlow Lite.

This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.

problem Balancing financial performance with environmental responsibilities in the context of climate risks.
method Develops decarbonized indices using mean-VaR and mean-ES optimization methods.
result Optimized indices reduce financial risk and carbon footprint, providing a balanced investment option.

Paper proposes a KGE framework that reduces training time and carbon footprint.

problem Efficient KGE learning with reduced computational cost and environmental impact.
method Full batch learning, Orthogonal Procrustes Analysis, non-negative-sampling training.
result Significant reduction in training time and carbon footprint compared to state-of-the-art approaches.

Automates building structural design with reduced mass and carbon footprint.

problem Time-consuming and laborious manual design process for buildings.
method Formulated building structures as graphs, trained end-to-end pipeline with a differentiable simulator.
result Optimal structural designs comparable to GA, with reduced building mass and carbon footprint.

In this paper, we show how to construct graph theoretical models of n-dimensional continuous objects and manifolds. These models retain topological properties of their continuous counterparts. An LCL collection of n-cells in Euclidean space is introduced and investigated. If an LCL collection of n-cells is a cover of a…

2017-05-02abs ↗pdf ↗

Incorrect fixed point assertions in digital topology are discussed.

problem Incorrect, incorrectly proven, or trivial fixed point assertions in digital topology.
method Continues earlier work on identifying and critiquing bad fixed point assertions.
result Clarifies the nature and extent of incorrect fixed point assertions in digital topology.

This paper proposes an alternative to E2E training for deep networks, reducing memory footprint.

problem High GPUs memory footprint in end-to-end training of deep networks.
method Locally supervised learning with information propagation loss to avoid information collapse.
result The proposed method achieves competitive performance with less than 40% memory footprint compared to E2E training.

A variety of approaches have been proposed to automatically infer the profiles of users from their digital footprint in social media. Most of the proposed approaches focus on mining a single type of information, while ignoring other sources of available user-generated content (UGC). In this paper, we propose a mechanis…

2020-01-05abs ↗pdf ↗

SGQuant reduces GNN memory usage without significant accuracy loss.

problem High memory consumption in GNNs limits their applicability on memory-constrained devices.
method Proposes a specialized GNN quantization scheme (SGQuant) with a quantization algorithm, fine-tuning scheme, and multi-granularity strategy.
result SGQuant reduces GNN memory footprint from 4.25x to 31.9x with minimal accuracy loss.

A new method streamlines digital payment programming using smart contracts.

problem High costs and security challenges in programming smart contracts for digital payments.
method Transforming digital currencies into token streams and using configurable templates to generate specialized smart contracts.
result Reduces payment programming costs and enhances security, self-enforcement, adaptability, and controllability.

The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough for kernel methods? Here we propose an approach that scales up kernel methods using a novel concept called "doubly stochastic functional grad…

2014-07-21abs ↗pdf ↗

New framework explains leading digit patterns without probabilistic assumptions.

problem Explaining leading digit distributions without relying on probabilistic models.
method Shift-invariant functional equation and affine-plus-periodic formulas.
result Unified mathematical foundation for understanding digit distributions.