1,210 research outputs found
Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction
Quality of Service (QoS) prediction is an essential task in recommendation
systems, where accurately predicting unknown QoS values can improve user
satisfaction. However, existing QoS prediction techniques may perform poorly in
the presence of noise data, such as fake location information or virtual
gateways. In this paper, we propose the Probabilistic Deep Supervision Network
(PDS-Net), a novel framework for QoS prediction that addresses this issue.
PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate
layers and learns probability spaces for both known features and true labels.
Moreover, PDS-Net employs a condition-based multitasking loss function to
identify objects with noise data and applies supervision directly to deep
features sampled from the probability space by optimizing the Kullback-Leibler
distance between the probability space of these objects and the real-label
probability space. Thus, PDS-Net effectively reduces errors resulting from the
propagation of corrupted data, leading to more accurate QoS predictions.
Experimental evaluations on two real-world QoS datasets demonstrate that the
proposed PDS-Net outperforms state-of-the-art baselines, validating the
effectiveness of our approach
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
Proceedings of Mathsport international 2017 conference
Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017.
MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet.
Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports
Systems biology approaches to a rational drug discovery paradigm
The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/openurl/content.php?genre=article&doi=10.2174/1568026615666150826114524.Prathipati P., Mizuguchi K.. Systems biology approaches to a rational drug discovery paradigm. Current Topics in Medicinal Chemistry, 16, 9, 1009. https://doi.org/10.2174/1568026615666150826114524
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