370 research outputs found
A Systematic Survey on Deep Generative Models for Graph Generation
Graphs are important data representations for describing objects and their
relationships, which appear in a wide diversity of real-world scenarios. As one
of a critical problem in this area, graph generation considers learning the
distributions of given graphs and generating more novel graphs. Owing to its
wide range of applications, generative models for graphs have a rich history,
which, however, are traditionally hand-crafted and only capable of modeling a
few statistical properties of graphs. Recent advances in deep generative models
for graph generation is an important step towards improving the fidelity of
generated graphs and paves the way for new kinds of applications. This article
provides an extensive overview of the literature in the field of deep
generative models for the graph generation. Firstly, the formal definition of
deep generative models for the graph generation as well as preliminary
knowledge is provided. Secondly, two taxonomies of deep generative models for
unconditional, and conditional graph generation respectively are proposed; the
existing works of each are compared and analyzed. After that, an overview of
the evaluation metrics in this specific domain is provided. Finally, the
applications that deep graph generation enables are summarized and five
promising future research directions are highlighted
Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing
Active inference is a normative framework for explaining behaviour under the free energy principle—a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy—a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error—plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference
Preventing Catastrophic Cyber–Physical Attacks on the Global Maritime Transportation System:A Case Study of Hybrid Maritime Security in the Straits of Malacca and Singapore
This paper examines hybrid threats to maritime transportation systems and their governance responses; focusing on the congested Straits of Malacca and Singapore (SOMS) as an illustrative case study. The methodology combines secondary sources with primary data from 42 expert interviews, a 28 respondent survey, and two maritime security roundtables. Key findings were that ships’ critical systems are increasingly interconnected, yet aging IT infrastructure and minimal cybersecurity awareness among crews heighten risks. Meanwhile, regional terrorist groups have previously targeted shipping and shown considerable skill in exploiting online tools, aligning with broader calls for jihadist violence. Furthermore, opportunistic piracy persists in the SOMS with the potential to disrupt shipping. Experts confirmed that maritime cybersecurity lags behind other critical infrastructure sectors and needs updated governance. Initial International Maritime Organization (IMO) guidelines lack specificity but revisions and updated IMO guidance are in process, while Port state implementation of maritime cybersecurity standards varies. Crucially, information sharing remains inadequate, even as recorded attacks increase. Findings underscore that although major hybrid incidents have not occurred, simulations and threat actors’ capabilities demonstrate potential for catastrophic collisions or cascading disruption in congested waterways. Mitigating factors like redundancy and crew training are deficient currently. Some alignment between SOMS states on maritime security cooperation exists, but not on cyber threats specifically. Key recommendations include an anonymous cyber attack reporting system, reinforced training and shipboard systems, and consolidated regional frameworks. Until these priorities are addressed, the analysis concludes that hybrid vulnerabilities in this vital global chokepoint remain a serious concern
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformation
We study the problem of few-shot physically-aware articulated mesh
generation. By observing an articulated object dataset containing only a few
examples, we wish to learn a model that can generate diverse meshes with high
visual fidelity and physical validity. Previous mesh generative models either
have difficulties in depicting a diverse data space from only a few examples or
fail to ensure physical validity of their samples. Regarding the above
challenges, we propose two key innovations, including 1) a hierarchical mesh
deformation-based generative model based upon the divide-and-conquer philosophy
to alleviate the few-shot challenge by borrowing transferrable deformation
patterns from large scale rigid meshes and 2) a physics-aware deformation
correction scheme to encourage physically plausible generations. We conduct
extensive experiments on 6 articulated categories to demonstrate the
superiority of our method in generating articulated meshes with better
diversity, higher visual fidelity, and better physical validity over previous
methods in the few-shot setting. Further, we validate solid contributions of
our two innovations in the ablation study. Project page with code is available
at https://meowuu7.github.io/few-arti-obj-gen.Comment: ICCV 2023. Project Page: https://meowuu7.github.io/few-arti-obj-ge
On looking into words (and beyond): Structures, Relations, Analyses
On Looking into Words is a wide-ranging volume spanning current research into word structure and morphology, with a focus on historical linguistics and linguistic theory. The papers are offered as a tribute to Stephen R. Anderson, the Dorothy R. Diebold Professor of Linguistics at Yale, who is retiring at the end of the 2016-2017 academic year. The contributors are friends, colleagues, and former students of Professor Anderson, all important contributors to linguistics in their own right. As is typical for such volumes, the contributions span a variety of topics relating to the interests of the honorand. In this case, the central contributions that Anderson has made to so many areas of linguistics and cognitive science, drawing on synchronic and diachronic phenomena in diverse linguistic systems, are represented through the papers in the volume.
The 26 papers that constitute this volume are unified by their discussion of the interplay between synchrony and diachrony, theory and empirical results, and the role of diachronic evidence in understanding the nature of language. Central concerns of the volume include morphological gaps, learnability, increases and declines in productivity, and the interaction of different components of the grammar. The papers deal with a range of linked synchronic and diachronic topics in phonology, morphology, and syntax (in particular, cliticization), and their implications for linguistic theory
On looking into words (and beyond): Structures, Relations, Analyses
On Looking into Words is a wide-ranging volume spanning current research into word structure and morphology, with a focus on historical linguistics and linguistic theory. The papers are offered as a tribute to Stephen R. Anderson, the Dorothy R. Diebold Professor of Linguistics at Yale, who is retiring at the end of the 2016-2017 academic year. The contributors are friends, colleagues, and former students of Professor Anderson, all important contributors to linguistics in their own right. As is typical for such volumes, the contributions span a variety of topics relating to the interests of the honorand. In this case, the central contributions that Anderson has made to so many areas of linguistics and cognitive science, drawing on synchronic and diachronic phenomena in diverse linguistic systems, are represented through the papers in the volume.
The 26 papers that constitute this volume are unified by their discussion of the interplay between synchrony and diachrony, theory and empirical results, and the role of diachronic evidence in understanding the nature of language. Central concerns of the volume include morphological gaps, learnability, increases and declines in productivity, and the interaction of different components of the grammar. The papers deal with a range of linked synchronic and diachronic topics in phonology, morphology, and syntax (in particular, cliticization), and their implications for linguistic theory
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