207,293 research outputs found
An optimization approach to study the phase changing behavior of multi-component mixtures
The appropriate design, construction, and operation of carbon capture and
storage (CCS) and enhanced oil recovery (EOR) processes require a deep
understanding of the resulting phases behavior in hydrocarbons-CO_2
multi-component mixtures under reservoir conditions. To model this behavior a
nonlinear system consists of the equation of states and some mixing rules (for
each component) needed to be solved simultaneously. The mixing usually requires
to model the binary interaction between the components of the mixture. This
work employs optimization techniques to enhance the predictions of such model
by optimizing the binary interaction parameters. The results show that the
optimized parameters, although obtained mathematically, are in physical ranges
and can reproduce successfully the experimental observations, specially for the
multi-component hydrocarbons systems containing Carbon dioxide at reservoir
temperatures and pressuresComment: To be published in Cadernos do IME - S\'erie Matem\'atica da UER
Connected Component Algorithm for Gestures Recognition
This paper presents head and hand gestures recognition system for Human Computer Interaction (HCI). Head and Hand gestures are an important modality for human computer interaction. Vision based recognition system can give computers the capability of understanding and responding to the hand and head gestures. The aim of this paper is the proposal of real time vision system for its application within a multimedia interaction environment. This recognition system consists of four modules, i.e. capturing the image, image extraction, pattern matching and command determination. If hand and head gestures are shown in front of the camera, hardware will perform respective action. Gestures are matched with the stored database of gestures using pattern matching. Corresponding to matched gesture, the hardware is moved in left, right, forward and backward directions. An algorithm for optimizing connected component in gesture recognition is proposed, which makes use of segmentation in two images. Connected component algorithm scans an image and group its pixels into component based on pixel connectivity i.e. all pixels in connected component share similar pixel intensity values and are in some way connected with each other. Once all groups have been determined, each pixel is labeled with a color according to component it was assigned to
How to Retrain Recommender System? A Sequential Meta-Learning Method
Practical recommender systems need be periodically retrained to refresh the
model with new interaction data. To pursue high model fidelity, it is usually
desirable to retrain the model on both historical and new data, since it can
account for both long-term and short-term user preference. However, a full
model retraining could be very time-consuming and memory-costly, especially
when the scale of historical data is large. In this work, we study the model
retraining mechanism for recommender systems, a topic of high practical values
but has been relatively little explored in the research community.
Our first belief is that retraining the model on historical data is
unnecessary, since the model has been trained on it before. Nevertheless,
normal training on new data only may easily cause overfitting and forgetting
issues, since the new data is of a smaller scale and contains fewer information
on long-term user preference. To address this dilemma, we propose a new
training method, aiming to abandon the historical data during retraining
through learning to transfer the past training experience. Specifically, we
design a neural network-based transfer component, which transforms the old
model to a new model that is tailored for future recommendations. To learn the
transfer component well, we optimize the "future performance" -- i.e., the
recommendation accuracy evaluated in the next time period. Our Sequential
Meta-Learning(SML) method offers a general training paradigm that is applicable
to any differentiable model. We demonstrate SML on matrix factorization and
conduct experiments on two real-world datasets. Empirical results show that SML
not only achieves significant speed-up, but also outperforms the full model
retraining in recommendation accuracy, validating the effectiveness of our
proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202
Laser induced fluorescence for axion dark matter detection: a feasibility study in YLiF:Er
We present a detection scheme to search for QCD axion dark matter, that is
based on a direct interaction between axions and electrons explicitly predicted
by DFSZ axion models. The local axion dark matter field shall drive transitions
between Zeeman-split atomic levels separated by the axion rest mass energy . Axion-related excitations are then detected with an upconversion scheme
involving a pump laser that converts the absorbed axion energy (
hundreds of eV) to visible or infrared photons, where single photon
detection is an established technique. The proposed scheme involves rare-earth
ions doped into solid-state crystalline materials, and the optical transitions
take place between energy levels of electron configuration. Beyond
discussing theoretical aspects and requirements to achieve a cosmologically
relevant sensitivity, especially in terms of spectroscopic material properties,
we experimentally investigate backgrounds due to the pump laser at temperatures
in the range K. Our results rule out excitation of the upper Zeeman
component of the ground state by laser-related heating effects, and are of some
help in optimizing activated material parameters to suppress the
multiphonon-assisted Stokes fluorescence.Comment: 8 pages, 5 figure
Hikester - the event management application
Today social networks and services are one of the most important part of our
everyday life. Most of the daily activities, such as communicating with
friends, reading news or dating is usually done using social networks. However,
there are activities for which social networks do not yet provide adequate
support. This paper focuses on event management and introduces "Hikester". The
main objective of this service is to provide users with the possibility to
create any event they desire and to invite other users. "Hikester" supports the
creation and management of events like attendance of football matches, quest
rooms, shared train rides or visit of museums in foreign countries. Here we
discuss the project architecture as well as the detailed implementation of the
system components: the recommender system, the spam recognition service and the
parameters optimizer
BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
Stability of Soft Quasicrystals in a Coupled-Mode Swift-Hohenberg Model for Three-Component Systems
In this article, we discuss the stability of soft quasicrystalline phases in
a coupled-mode Swift-Hohenberg model for three-component systems, where the
characteristic length scales are governed by the positive-definite gradient
terms. Classic two-mode approximation method and direct numerical minimization
are applied to the model. In the latter approach, we apply the projection
method to deal with the potentially quasiperiodic ground states. A variable
cell method of optimizing the shape and size of higher-dimensional periodic
cell is developed to minimize the free energy with respect to the order
parameters. Based on the developed numerical methods, we rediscover decagonal
and dodecagonal quasicrystalline phases, and find diverse periodic phases and
complex modulated phases. Furthermore, phase diagrams are obtained in various
phase spaces by comparing the free energies of different candidate structures.
It does show not only the important roles of system parameters, but also the
effect of optimizing computational domain. In particular, the optimization of
computational cell allows us to capture the ground states and phase behavior
with higher fidelity. We also make some discussions on our results and show the
potential of applying our numerical methods to a larger class of mean-field
free energy functionals.Comment: 26 pages, 13 figures; accepted by Communications in Computational
Physic
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