11,865 research outputs found
Interest communities and flow roles in directed networks: the Twitter network of the UK riots
Directionality is a crucial ingredient in many complex networks in which
information, energy or influence are transmitted. In such directed networks,
analysing flows (and not only the strength of connections) is crucial to reveal
important features of the network that might go undetected if the orientation
of connections is ignored. We showcase here a flow-based approach for community
detection in networks through the study of the network of the most influential
Twitter users during the 2011 riots in England. Firstly, we use directed Markov
Stability to extract descriptions of the network at different levels of
coarseness in terms of interest communities, i.e., groups of nodes within which
flows of information are contained and reinforced. Such interest communities
reveal user groupings according to location, profession, employer, and topic.
The study of flows also allows us to generate an interest distance, which
affords a personalised view of the attention in the network as viewed from the
vantage point of any given user. Secondly, we analyse the profiles of incoming
and outgoing long-range flows with a combined approach of role-based similarity
and the novel relaxed minimum spanning tree algorithm to reveal that the users
in the network can be classified into five roles. These flow roles go beyond
the standard leader/follower dichotomy and differ from classifications based on
regular/structural equivalence. We then show that the interest communities fall
into distinct informational organigrams characterised by a different mix of
user roles reflecting the quality of dialogue within them. Our generic
framework can be used to provide insight into how flows are generated,
distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from:
http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or
http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC
Data-driven intelligent computational design for products: Method, techniques, and applications
Data-driven intelligent computational design (DICD) is a research hotspot
emerged under the context of fast-developing artificial intelligence. It
emphasizes on utilizing deep learning algorithms to extract and represent the
design features hidden in historical or fabricated design process data, and
then learn the combination and mapping patterns of these design features for
the purposes of design solution retrieval, generation, optimization,
evaluation, etc. Due to its capability of automatically and efficiently
generating design solutions and thus supporting human-in-the-loop intelligent
and innovative design activities, DICD has drawn the attentions from both
academic and industrial fields. However, as an emerging research subject, there
are still many unexplored issues that limit the development and application of
DICD, such as specific dataset building, engineering design related feature
engineering, systematic methods and techniques for DICD implementation in the
entire product design process, etc. In this regard, a systematic and operable
road map for DICD implementation from full-process perspective is established,
including a general workflow for DICD project planning, an overall framework
for DICD project implementation, the computing mechanisms for DICD
implementation, key enabling technologies for detailed DICD implementation, and
three application scenarios of DICD. The road map reveals the common mechanisms
and calculation principles of existing DICD researches, and thus it can provide
systematic guidance for the possible DICD applications that have not been
explored
Strong atom-field coupling for Bose-Einstein condensates in an optical cavity on a chip
An optical cavity enhances the interaction between atoms and light, and the
rate of coherent atom-photon coupling can be made larger than all decoherence
rates of the system. For single atoms, this strong coupling regime of cavity
quantum electrodynamics (cQED) has been the subject of spectacular experimental
advances, and great efforts have been made to control the coupling rate by
trapping and cooling the atom towards the motional ground state, which has been
achieved in one dimension so far. For N atoms, the three-dimensional ground
state of motion is routinely achieved in atomic Bose-Einstein condensates
(BECs), but although first experiments combining BECs and optical cavities have
been reported recently, coupling BECs to strong-coupling cavities has remained
an elusive goal. Here we report such an experiment, which is made possible by
combining a new type of fibre-based cavity with atom chip technology. This
allows single-atom cQED experiments with a simplified setup and realizes the
new situation of N atoms in a cavity each of which is identically and strongly
coupled to the cavity mode. Moreover, the BEC can be positioned
deterministically anywhere within the cavity and localized entirely within a
single antinode of the standing-wave cavity field. This gives rise to a
controlled, tunable coupling rate, as we confirm experimentally. We study the
heating rate caused by a cavity transmission measurement as a function of the
coupling rate and find no measurable heating for strongly coupled BECs. The
spectrum of the coupled atoms-cavity system, which we map out over a wide range
of atom numbers and cavity-atom detunings, shows vacuum Rabi splittings
exceeding 20 gigahertz, as well as an unpredicted additional splitting which we
attribute to the atomic hyperfine structure.Comment: 20 pages. Revised version following referees' comments. Detailed
notes adde
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
AI-generated Content for Various Data Modalities: A Survey
AI-generated content (AIGC) methods aim to produce text, images, videos, 3D
assets, and other media using AI algorithms. Due to its wide range of
applications and the demonstrated potential of recent works, AIGC developments
have been attracting lots of attention recently, and AIGC methods have been
developed for various data modalities, such as image, video, text, 3D shape (as
voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human
avatar (body and head), 3D motion, and audio -- each presenting different
characteristics and challenges. Furthermore, there have also been many
significant developments in cross-modality AIGC methods, where generative
methods can receive conditioning input in one modality and produce outputs in
another. Examples include going from various modalities to image, video, 3D
shape, 3D scene, 3D avatar (body and head), 3D motion (skeleton and avatar),
and audio modalities. In this paper, we provide a comprehensive review of AIGC
methods across different data modalities, including both single-modality and
cross-modality methods, highlighting the various challenges, representative
works, and recent technical directions in each setting. We also survey the
representative datasets throughout the modalities, and present comparative
results for various modalities. Moreover, we also discuss the challenges and
potential future research directions
Multi-modal Machine Learning in Engineering Design: A Review and Future Directions
In the rapidly advancing field of multi-modal machine learning (MMML), the
convergence of multiple data modalities has the potential to reshape various
applications. This paper presents a comprehensive overview of the current
state, advancements, and challenges of MMML within the sphere of engineering
design. The review begins with a deep dive into five fundamental concepts of
MMML:multi-modal information representation, fusion, alignment, translation,
and co-learning. Following this, we explore the cutting-edge applications of
MMML, placing a particular emphasis on tasks pertinent to engineering design,
such as cross-modal synthesis, multi-modal prediction, and cross-modal
information retrieval. Through this comprehensive overview, we highlight the
inherent challenges in adopting MMML in engineering design, and proffer
potential directions for future research. To spur on the continued evolution of
MMML in engineering design, we advocate for concentrated efforts to construct
extensive multi-modal design datasets, develop effective data-driven MMML
techniques tailored to design applications, and enhance the scalability and
interpretability of MMML models. MMML models, as the next generation of
intelligent design tools, hold a promising future to impact how products are
designed
- …