105 research outputs found
Wardrop Equilibrium Can Be Boundedly Rational: A New Behavioral Theory of Route Choice
As one of the most fundamental concepts in transportation science, Wardrop
equilibrium (WE) has always had a relatively weak behavioral underpinning. To
strengthen this foundation, one must reckon with bounded rationality in human
decision-making processes, such as the lack of accurate information, limited
computing power, and sub-optimal choices. This retreat from behavioral
perfectionism in the literature, however, was typically accompanied by a
conceptual modification of WE. Here we show that giving up perfect rationality
need not force a departure from WE. On the contrary, WE can be reached with
global stability in a routing game played by boundedly rational travelers. We
achieve this result by developing a day-to-day (DTD) dynamical model that
mimics how travelers gradually adjust their route valuations, hence choice
probabilities, based on past experiences. Our model, called cumulative logit
(CULO), resembles the classical DTD models but makes a crucial change: whereas
the classical models assume routes are valued based on the cost averaged over
historical data, ours values the routes based on the cost accumulated. To
describe route choice behaviors, the CULO model only uses two parameters, one
accounting for the rate at which the future route cost is discounted in the
valuation relative to the past ones and the other describing the sensitivity of
route choice probabilities to valuation differences. We prove that the CULO
model always converges to WE, regardless of the initial point, as long as the
behavioral parameters satisfy certain mild conditions. Our theory thus upholds
WE's role as a benchmark in transportation systems analysis. It also resolves
the theoretical challenge posed by Harsanyi's instability problem by explaining
why equally good routes at WE are selected with different probabilities
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
The field of protein folding research has been greatly advanced by deep
learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance
and atomic-level precision. As co-evolution is integral to protein structure
prediction, AF2's accuracy is significantly influenced by the depth of multiple
sequence alignment (MSA), which requires extensive exploration of a large
protein database for similar sequences. However, not all protein sequences
possess abundant homologous families, and consequently, AF2's performance can
degrade on such queries, at times failing to produce meaningful results. To
address this, we introduce a novel generative language model, MSA-Augmenter,
which leverages protein-specific attention mechanisms and large-scale MSAs to
generate useful, novel protein sequences not currently found in databases.
These sequences supplement shallow MSAs, enhancing the accuracy of structural
property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter
can generate de novo sequences that retain co-evolutionary information from
inferior MSAs, thereby improving protein structure prediction quality on top of
strong AF2
Multimodal Large Language Models: A Survey
The exploration of multimodal language models integrates multiple data types,
such as images, text, language, audio, and other heterogeneity. While the
latest large language models excel in text-based tasks, they often struggle to
understand and process other data types. Multimodal models address this
limitation by combining various modalities, enabling a more comprehensive
understanding of diverse data. This paper begins by defining the concept of
multimodal and examining the historical development of multimodal algorithms.
Furthermore, we introduce a range of multimodal products, focusing on the
efforts of major technology companies. A practical guide is provided, offering
insights into the technical aspects of multimodal models. Moreover, we present
a compilation of the latest algorithms and commonly used datasets, providing
researchers with valuable resources for experimentation and evaluation. Lastly,
we explore the applications of multimodal models and discuss the challenges
associated with their development. By addressing these aspects, this paper aims
to facilitate a deeper understanding of multimodal models and their potential
in various domains.Comment: IEEE BigData 2023. 10 page
Emerging Applications of Reversible Data Hiding
Reversible data hiding (RDH) is one special type of information hiding, by
which the host sequence as well as the embedded data can be both restored from
the marked sequence without loss. Beside media annotation and integrity
authentication, recently some scholars begin to apply RDH in many other fields
innovatively. In this paper, we summarize these emerging applications,
including steganography, adversarial example, visual transformation, image
processing, and give out the general frameworks to make these operations
reversible. As far as we are concerned, this is the first paper to summarize
the extended applications of RDH.Comment: ICIGP 201
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