4,344 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems
High-level reasoning can be defined as the capability to generalize over
knowledge acquired via experience, and to exhibit robust behavior in novel
situations. Such form of reasoning is a basic skill in humans, who seamlessly
use it in a broad spectrum of tasks, from language communication to decision
making in complex situations. When it manifests itself in understanding and
manipulating the everyday world of objects and their interactions, we talk
about common sense or commonsense reasoning. State-of-the-art AI systems don't
possess such capability: for instance, Large Language Models have recently
become popular by demonstrating remarkable fluency in conversing with humans,
but they still make trivial mistakes when probed for commonsense competence; on
a different level, performance degradation outside training data prevents
self-driving vehicles to safely adapt to unseen scenarios, a serious and
unsolved problem that limits the adoption of such technology. In this paper we
propose to enable high-level reasoning in AI systems by integrating cognitive
architectures with external neuro-symbolic components. We illustrate a hybrid
framework centered on ACT-R and we discuss the role of generative models in
recent and future applications
Interpretable Neural PDE Solvers using Symbolic Frameworks
Partial differential equations (PDEs) are ubiquitous in the world around us,
modelling phenomena from heat and sound to quantum systems. Recent advances in
deep learning have resulted in the development of powerful neural solvers;
however, while these methods have demonstrated state-of-the-art performance in
both accuracy and computational efficiency, a significant challenge remains in
their interpretability. Most existing methodologies prioritize predictive
accuracy over clarity in the underlying mechanisms driving the model's
decisions. Interpretability is crucial for trustworthiness and broader
applicability, especially in scientific and engineering domains where neural
PDE solvers might see the most impact. In this context, a notable gap in
current research is the integration of symbolic frameworks (such as symbolic
regression) into these solvers. Symbolic frameworks have the potential to
distill complex neural operations into human-readable mathematical expressions,
bridging the divide between black-box predictions and solutions.Comment: Accepted to the NeurIPS 2023 AI for Science Workshop. arXiv admin
note: text overlap with arXiv:2310.1976
Information Processing, Computation and Cognition
Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both ā although others disagree vehemently. Yet different cognitive scientists use ācomputationā and āinformation processingā to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism/computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debatesā empirical aspects
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The genesis and development of mobile learning in Europe
In the past two decades, European researchers have conducted many significant mobile learning projects. The chapter explores how these projects have arisen and what each one has contributed, so as to show the driving forces and outcomes of European innovation in mobile learning. The authors identify context as a central construct in European researchersā conceptualizations of mobile learning and examine theories of learning for the mobile world, based on physical, technological, conceptual, social and temporal mobility. The authors also examine the impacts of mobile learning research on educational practices and the implications for policy. Finally, they suggest future challenges for researchers, developers and policy makers in shaping the future of mobile learning
Monte Carlo studies of the properties of the Majorana quantum error correction code: is self-correction possible during braiding?
The Majorana code is an example of a stabilizer code where the quantum
information is stored in a system supporting well-separated Majorana Bound
States (MBSs). We focus on one-dimensional realizations of the Majorana code,
as well as networks of such structures, and investigate their lifetime when
coupled to a parity-preserving thermal environment. We apply the Davies
prescription, a standard method that describes the basic aspects of a thermal
environment, and derive a master equation in the Born-Markov limit. We first
focus on a single wire with immobile MBSs and perform error correction to
annihilate thermal excitations. In the high-temperature limit, we show both
analytically and numerically that the lifetime of the Majorana qubit grows
logarithmically with the size of the wire. We then study a trijunction with
four MBSs when braiding is executed. We study the occurrence of dangerous error
processes that prevent the lifetime of the Majorana code from growing with the
size of the trijunction. The origin of the dangerous processes is the braiding
itself, which separates pairs of excitations and renders the noise nonlocal;
these processes arise from the basic constraints of moving MBSs in 1D
structures. We confirm our predictions with Monte Carlo simulations in the
low-temperature regime, i.e. the regime of practical relevance. Our results put
a restriction on the degree of self-correction of this particular 1D
topological quantum computing architecture.Comment: Main text: 20 pages, Supplementary Material: 66 pages. Short version:
arXiv:1505.0371
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