219 research outputs found
Genetic programming hyper-heuristic with vehicle collaboration for uncertain capacitated arc routing problem
Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm
Thick film magnetic nanoparticulate composites and method of manufacture thereof
Thick film magnetic/insulating nanocomposite materials, with significantly reduced core loss, and their manufacture are described. The insulator coated magnetic nanocomposite comprises one or more magnetic components, and an insulating component. The magnetic component comprises nanometer scale particles (about 1 to about 100 nanometers) coated by a thin-layered insulating phase. While the intergrain interaction between the immediate neighboring magnetic nanoparticles separated by the insulating phase provides the desired soft magnetic properties, the insulating material provides high resistivity, which reduces eddy current loss
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs
Irregular data in real-world are usually organized as heterogeneous graphs
(HGs) consisting of multiple types of nodes and edges. To explore useful
knowledge from real-world data, both the large-scale encyclopedic HG datasets
and corresponding effective learning methods are crucial, but haven't been well
investigated. In this paper, we construct a large-scale HG benchmark dataset
named UniKG from Wikidata to facilitate knowledge mining and heterogeneous
graph representation learning. Overall, UniKG contains more than 77 million
multi-attribute entities and 2000 diverse association types, which
significantly surpasses the scale of existing HG datasets. To perform effective
learning on the large-scale UniKG, two key measures are taken, including (i)
the semantic alignment strategy for multi-attribute entities, which projects
the feature description of multi-attribute nodes into a common embedding space
to facilitate node aggregation in a large receptive field; (ii) proposing a
novel plug-and-play anisotropy propagation module (APM) to learn effective
multi-hop anisotropy propagation kernels, which extends methods of large-scale
homogeneous graphs to heterogeneous graphs. These two strategies enable
efficient information propagation among a tremendous number of multi-attribute
entities and meantimes adaptively mine multi-attribute association through the
multi-hop aggregation in large-scale HGs. We set up a node classification task
on our UniKG dataset, and evaluate multiple baseline methods which are
constructed by embedding our APM into large-scale homogenous graph learning
methods. Our UniKG dataset and the baseline codes have been released at
https://github.com/Yide-Qiu/UniKG.Comment: 9 pages, 4 figure
Unveiling A Core Linguistic Region in Large Language Models
Brain localization, which describes the association between specific regions
of the brain and their corresponding functions, is widely accepted in the field
of cognitive science as an objective fact. Today's large language models (LLMs)
possess human-level linguistic competence and can execute complex tasks
requiring abstract knowledge and reasoning. To deeply understand the inherent
mechanisms of intelligence emergence in LLMs, this paper conducts an analogical
research using brain localization as a prototype. We have discovered a core
region in LLMs that corresponds to linguistic competence, accounting for
approximately 1% of the total model parameters. This core region exhibits
significant dimension dependency, and perturbations to even a single parameter
on specific dimensions can lead to a loss of linguistic competence.
Furthermore, we observe that an improvement in linguistic competence does not
necessarily accompany an elevation in the model's knowledge level, which might
imply the existence of regions of domain knowledge that are dissociated from
the linguistic region. Overall, exploring the LLMs' functional regions provides
insights into the foundation of their intelligence. In the future, we will
continue to investigate knowledge regions within LLMs and the interactions
between them.Comment: Work on progres
Quantum Microscopy of Cancer Cells at the Heisenberg Limit
Entangled biphoton sources exhibit nonclassical characteristics and have been
applied to novel imaging techniques such as ghost imaging, quantum holography,
and quantum optical coherence tomography. The development of wide-field quantum
imaging to date has been hindered by low spatial resolutions, speeds, and
contrast-to-noise ratios (CNRs). Here, we present quantum microscopy by
coincidence (QMC) with balanced pathlengths, which enables super-resolution
imaging at the Heisenberg limit with substantially higher speeds and CNRs than
existing wide-field quantum imaging methods. QMC benefits from a configuration
with balanced pathlengths, where a pair of entangled photons traversing
symmetric paths with balanced optical pathlengths in two arms behave like a
single photon with half the wavelength, leading to 2-fold resolution
improvement. Concurrently, QMC resists stray light up to 155 times stronger
than classical signals. The low intensity and entanglement features of
biphotons in QMC promise nondestructive bioimaging. QMC advances quantum
imaging to the microscopic level with significant improvements in speed and CNR
toward bioimaging of cancer cells. We experimentally and theoretically prove
that the configuration with balanced pathlengths illuminates an avenue for
quantum-enhanced coincidence imaging at the Heisenberg limit.Comment: 20 pages, 4 figures; Supplementary Information 15 pages, 9 figure
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