382 research outputs found
HySIM: A Hybrid Spectrum and Information Market for TV White Space Networks
We propose a hybrid spectrum and information market for a database-assisted
TV white space network, where the geo-location database serves as both a
spectrum market platform and an information market platform. We study the
inter- actions among the database operator, the spectrum licensee, and
unlicensed users systematically, using a three-layer hierarchical model. In
Layer I, the database and the licensee negotiate the commission fee that the
licensee pays for using the spectrum market platform. In Layer II, the database
and the licensee compete for selling information or channels to unlicensed
users. In Layer III, unlicensed users determine whether they should buy the
exclusive usage right of licensed channels from the licensee, or the
information regarding unlicensed channels from the database. Analyzing such a
three-layer model is challenging due to the co-existence of both positive and
negative network externalities in the information market. We characterize how
the network externalities affect the equilibrium behaviours of all parties
involved. Our numerical results show that the proposed hybrid market can
improve the network profit up to 87%, compared with a pure information market.
Meanwhile, the achieved network profit is very close to the coordinated
benchmark solution (the gap is less than 4% in our simulation).Comment: This manuscript serves as the online technical report of the article
published in IEEE International Conference on Computer Communications
(INFOCOM), 201
Analyzing Character and Consciousness in AI-Generated Social Content: A Case Study of Chirper, the AI Social Network
This paper delves into an intricate analysis of the character and
consciousness of AI entities, with a particular focus on Chirpers within the AI
social network. At the forefront of this research is the introduction of novel
testing methodologies, including the Influence index and Struggle Index Test,
which offers a fresh lens for evaluating specific facets of AI behavior. The
study embarks on a comprehensive exploration of AI behavior, analyzing the
effects of diverse settings on Chirper's responses, thereby shedding light on
the intricate mechanisms steering AI reactions in different contexts.
Leveraging the state-of-the-art BERT model, the research assesses AI's ability
to discern its own output, presenting a pioneering approach to understanding
self-recognition in AI systems. Through a series of cognitive tests, the study
gauges the self-awareness and pattern recognition prowess of Chirpers.
Preliminary results indicate that Chirpers exhibit a commendable degree of
self-recognition and self-awareness. However, the question of consciousness in
these AI entities remains a topic of debate. An intriguing aspect of the
research is the exploration of the potential influence of a Chirper's handle or
personality type on its performance. While initial findings suggest a possible
impact, it isn't pronounced enough to form concrete conclusions. This study
stands as a significant contribution to the discourse on AI consciousness,
underscoring the imperative for continued research to unravel the full spectrum
of AI capabilities and the ramifications they hold for future human-AI
interactions
Music Source Separation with Band-split RNN
The performance of music source separation (MSS) models has been greatly
improved in recent years thanks to the development of novel neural network
architectures and training pipelines. However, recent model designs for MSS
were mainly motivated by other audio processing tasks or other research fields,
while the intrinsic characteristics and patterns of the music signals were not
fully discovered. In this paper, we propose band-split RNN (BSRNN), a
frequency-domain model that explictly splits the spectrogram of the mixture
into subbands and perform interleaved band-level and sequence-level modeling.
The choices of the bandwidths of the subbands can be determined by a priori
knowledge or expert knowledge on the characteristics of the target source in
order to optimize the performance on a certain type of target musical
instrument. To better make use of unlabeled data, we also describe a
semi-supervised model finetuning pipeline that can further improve the
performance of the model. Experiment results show that BSRNN trained only on
MUSDB18-HQ dataset significantly outperforms several top-ranking models in
Music Demixing (MDX) Challenge 2021, and the semi-supervised finetuning stage
further improves the performance on all four instrument tracks
- …