356 research outputs found
Conjugate Gradient Algorithm for the Symmetric Arrowhead Solution of Matrix Equation AXB=C
Based on the conjugate gradient (CG) algorithm, the constrained matrix equation AXB=C and the associate optimal approximation problem are considered for the symmetric arrowhead matrix solutions in the premise of consistency. The convergence results of the method are presented. At last, a numerical example is given to illustrate the efficiency of this method
Preparation of a low viscosity urethane-based composite for improved dental restoratives
Several new urethane-based dimethacrylates were synthesized, characterized and used to formulate the resin composites. Compressive strength (CS) was used as a screen tool to evaluate the mechanical property of the formed composites. Flexural strength, diametral tensile strength, water sorption, degree of conversion and shrinkage of the composites were also evaluated. The results show that most of the synthesized urethane-based dimethacrylates were solid, which are not suitable to dental filling restorations. However, it was found that liquid urethane-based dimethacrylates could be derivatized using asymmetrical methacrylate synthesis. Not only the newly synthesized urethane-based dimethacrylates showed lower viscosity values but also their constructed composites exhibited higher mechanical strengths. Without triethyleneglycol dimethacrylate (TEGDMA) addition, the new urethane-constructed composites showed significantly lower water sorption and shrinkage
Disorder induced field effect transistor in bilayer and trilayer graphene
We propose use of disorder to produce a field effect transistor (FET) in
biased bilayer and trilayer graphene. Modulation of the bias voltage can
produce large variations in the conductance when the disorder's effects are
confined to only one of the graphene layers. This effect is based on the bias
voltage's ability to select which of the graphene layers carries current, and
is not tied to the presence of a gap in the density of states. In particular,
we demonstrate this effect in models of gapless ABA-stacked trilayer graphene,
gapped ABC-stacked trilayer graphene, and gapped bilayer graphene.Comment: 21 pages, 7 figure
Deconfounded Causal Collaborative Filtering
Recommender systems may be confounded by various types of confounding factors
(also called confounders) that may lead to inaccurate recommendations and
sacrificed recommendation performance. Current approaches to solving the
problem usually design each specific model for each specific confounder.
However, real-world systems may include a huge number of confounders and thus
designing each specific model for each specific confounder is unrealistic. More
importantly, except for those "explicit confounders" that researchers can
manually identify and process such as item's position in the ranking list,
there are also many "latent confounders" that are beyond the imagination of
researchers. For example, users' rating on a song may depend on their current
mood or the current weather, and users' preference on ice creams may depend on
the air temperature. Such latent confounders may be unobservable in the
recorded training data. To solve the problem, we propose a deconfounded causal
collaborative filtering model. We first frame user behaviors with unobserved
confounders into a causal graph, and then we design a front-door adjustment
model carefully fused with machine learning to deconfound the influence of
unobserved confounders. The proposed model is able to handle both global
confounders and personalized confounders. Experiments on real-world e-commerce
datasets show that our method is able to deconfound unobserved confounders to
achieve better recommendation performance.Comment: 9 pages, 5 figures; comments and suggestions are highly appreciate
OpenAGI: When LLM Meets Domain Experts
Human intelligence has the remarkable ability to assemble basic skills into
complex ones so as to solve complex tasks. This ability is equally important
for Artificial Intelligence (AI), and thus, we assert that in addition to the
development of large, comprehensive intelligent models, it is equally crucial
to equip such models with the capability to harness various domain-specific
expert models for complex task-solving in the pursuit of Artificial General
Intelligence (AGI). Recent developments in Large Language Models (LLMs) have
demonstrated remarkable learning and reasoning abilities, making them promising
as a controller to select, synthesize, and execute external models to solve
complex tasks. In this project, we develop OpenAGI, an open-source AGI research
platform, specifically designed to offer complex, multi-step tasks and
accompanied by task-specific datasets, evaluation metrics, and a diverse range
of extensible models. OpenAGI formulates complex tasks as natural language
queries, serving as input to the LLM. The LLM subsequently selects,
synthesizes, and executes models provided by OpenAGI to address the task.
Furthermore, we propose a Reinforcement Learning from Task Feedback (RLTF)
mechanism, which uses the task-solving result as feedback to improve the LLM's
task-solving ability. Thus, the LLM is responsible for synthesizing various
external models for solving complex tasks, while RLTF provides feedback to
improve its task-solving ability, enabling a feedback loop for self-improving
AI. We believe that the paradigm of LLMs operating various expert models for
complex task-solving is a promising approach towards AGI. To facilitate the
community's long-term improvement and evaluation of AGI's ability, we
open-source the code, benchmark, and evaluation methods of the OpenAGI project
at https://github.com/agiresearch/OpenAGI.Comment: 18 pages, 6 figures, 7 table
SAP: An IoT Application Module Placement Strategy Based on Simulated Annealing Algorithm in Edge-Cloud Computing
The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption
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