8,935 research outputs found
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.Comment: To appear in Journal of Computer and System Science
Integrating PPC and Flat Fee Pricing Schemes to Optimize the Internal Search Engine Revenue in the Electronic Market
Currently, the predominant pricing plan for the search engine (SE) advertising services in a proprietary electronic market is a flat fee (FF) pricing. These services have faced the challenge of customer attrition recently since FF pricing results in the inequality of service surplus among subscribers. A more sustainable and profitable pricing model would be to distinguish advertising resources by providing an additional usage-based pricing for certain user groups to transfer the service surplus among subscribers. We conceive a hybrid model integrating Pay-Per-Click (PPC) pricing into FF pricing. This proposed scheme can offer an incentive-compatible mechanism to attract more subscribers by relieving the inequity of service surplus, and eventually result in the increasing revenue of service providers
Multi-modal preference alignment remedies regression of visual instruction tuning on language model
In production, multi-modal large language models (MLLMs) are expected to
support multi-turn queries of interchanging image and text modalities. However,
the current MLLMs trained with visual-question-answering (VQA) datasets could
suffer from degradation, as VQA datasets lack the diversity and complexity of
the original text instruction datasets which the underlying language model had
been trained with. To address this challenging degradation, we first collect a
lightweight (6k entries) VQA preference dataset where answers were annotated by
Gemini for 5 quality metrics in a granular fashion, and investigate standard
Supervised Fine-tuning, rejection sampling, Direct Preference Optimization
(DPO), and SteerLM. Our findings indicate that the with DPO we are able to
surpass instruction-following capabilities of the language model, achieving a
6.73 score on MT-Bench, compared to Vicuna's 6.57 and LLaVA's 5.99 despite
small data scale. This enhancement in textual instruction proficiency
correlates with boosted visual instruction performance (+4.9\% on MM-Vet, +6\%
on LLaVA-Bench), with minimal alignment tax on visual knowledge benchmarks
compared to previous RLHF approach. In conclusion, we propose a
distillation-based multi-modal alignment model with fine-grained annotations on
a small dataset that reconciles the textual and visual performance of MLLMs,
restoring and boosting language capability after visual instruction tuning
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