arXiv research

The study examines how quantum resources enhance the complexity of quantum circuits.

Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predic…

A model is presented in this work for simulating endogenously the evolution of the marginal costs of production of energy carriers from non-renewable resources, their consumption, depletion pathways and timescales. Such marginal costs can be used to simulate the long term average price formation of energy commodities. …

Study efficient resource allocation for detecting extreme values.

New algorithm solves online resource allocation problems efficiently.

These notes grew out of a lecture course on mathematical methods of classical physics for students of mathematics and mathematical physics at the master's level. Also, physicists with a strong interest in mathematics may find this text useful as a resource complementary to existing textbooks on classical physics. Topic…

The holy grail of deep learning is to come up with an automatic method to design optimal architectures for different applications. In other words, how can we effectively dimension and organize neurons along the network layers based on the computational resources, input size, and amount of training data? We outline prom…

This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.

Proposes TgNN-LD to improve neural network effectiveness and efficiency.

New models improve morpheme segmentation in low-resource languages.

A new algorithm for resource-aware multi-armed bandits minimizes regret.

We introduce an irreversible discrete multiplicative process that undergoes Bose-Einstein condensation as a generic model of competition. New players with different abilities successively join the game and compete for limited resources. A player's future gain is proportional to its ability and its current gain. The the…

Optimal resource allocation in censored semi-bandits with unknown thresholds.

PePR scores assess DL model performance per resource unit, promoting smaller, more efficient models.

Method learns software resource usage from snapshots.

Proposes a new framework for resource-limited recommendation.

Unified framework for complex financial networks using lattice theory.

Paper tackles pandemic resource allocation challenges.

The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommen…

PASHA optimizes model tuning for large datasets with limited resources.

Study resource allocation strategies in sequential decisions with unknown rewards.

This paper presents an assessment of global economic energy potentials for all major natural energy resources. This work is based on both an extensive literature review and calculations using natural resource assessment data. Economic potentials are presented in the form of cost-supply curves, in terms of energy flows …

Generative profiling improves real-time task timing for varied resource contexts.

Paper tackles non-monotonic resource utilization in sequential decision-making.

New loss function reduces outage probability in ML-assisted resource allocation.

PACC Discovery improves causal inference from limited data.

Deep learning enhances water resources management through data analysis.

Sequence-to-sequence models predict resource usage for co-scheduled jobs in data centers.

We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting. This forms part of a United Nations effort using keyword spotting to support humanitarian relief programmes in parts of Africa where languages are severely under-resourced. We use 1920 isolated keywords (40 types, 34 minutes)…

Unified formula for arbitrary liquidity operations in weighted AMMs

This paper optimizes revenue and resource balance in network revenue management.

As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are de…

Caratheodory's axiom limits arbitrage in resource-limited systems.

Study introduces KorFinMTEB for Korean financial texts, revealing model limitations.

Meta-DRL improves resource allocation in O-RAN networks.

Review of efficient neural networks for TinyML on resource-constrained devices.

RL enhances cloud autoscaling by learning optimal resource allocation.

In urban environments, supply resources have to be constantly matched to the "right" locations (where customer demand is present) so as to improve quality of life. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in EMS (Emergency Management Sy…

Resource allocation improved using machine learning from terminal positions.

Method predicts hardware resource usage by control software with guaranteed linear convergence.

The study examines how alternative resource adequacy contract designs affect market participants' risk profiles and resource mix.

We study classification problems where features are corrupted by noise and where the magnitude of the noise in each feature is influenced by the resources allocated to its acquisition. This is the case, for example, when multiple sensors share a common resource (power, bandwidth, attention, etc.). We develop a method f…

RL optimizes resource allocation in MG by balancing experience and exploration.

The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurat…

A new pricing controller handles resource constraints to infer target prices effectively.

Procedure for determining less discriminatory alternatives in AI audits with limited resources.

The abstract discusses open data resources for studying and controlling the spread of COVID-19.

Algorithm tackles clustered contextual bandits with resource constraints.