4,468 research outputs found
Privacy Protection Performance of De-identified Face Images with and without Background
Li Meng, 'Privacy Protection Performance of De-identified Face Images with and without Background', paper presented at the 39th International Information and Communication Technology (ICT) Convention. Grand Hotel Adriatic Congress Centre and Admiral Hotel, Opatija, Croatia, May 30 - June 3, 2016.This paper presents an approach to blending a de-identified face region with its original background, for the purpose of completing the process of face de-identification. The re-identification risk of the de-identified FERET face images has been evaluated for the k-Diff-furthest face de-identification method, using several face recognition benchmark methods including PCA, LBP, HOG and LPQ. The experimental results show that the k-Diff-furthest face de-identification delivers high privacy protection within the face region while blending the de-identified face region with its original background may significantly increases the re-identification risk, indicating that de-identification must also be applied to image areas beyond the face region
Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing
Time-of-Use (TOU) electricity pricing provides an opportunity for industrial
users to cut electricity costs. Although many methods for Economic Load
Dispatch (ELD) under TOU pricing in continuous industrial processing have been
proposed, there are still difficulties in batch-type processing since power
load units are not directly adjustable and nonlinearly depend on production
planning and scheduling. In this paper, for hot rolling, a typical batch-type
and energy intensive process in steel industry, a production scheduling
optimization model for ELD is proposed under TOU pricing, in which the
objective is to minimize electricity costs while considering penalties caused
by jumps between adjacent slabs. A NSGA-II based multi-objective production
scheduling algorithm is developed to obtain Pareto-optimal solutions, and then
TOPSIS based multi-criteria decision-making is performed to recommend an
optimal solution to facilitate filed operation. Experimental results and
analyses show that the proposed method cuts electricity costs in production,
especially in case of allowance for penalty score increase in a certain range.
Further analyses show that the proposed method has effect on peak load
regulation of power grid.Comment: 13 pages, 6 figures, 4 table
A Survey of Embodied AI: From Simulators to Research Tasks
There has been an emerging paradigm shift from the era of "internet AI" to
"embodied AI", where AI algorithms and agents no longer learn from datasets of
images, videos or text curated primarily from the internet. Instead, they learn
through interactions with their environments from an egocentric perception
similar to humans. Consequently, there has been substantial growth in the
demand for embodied AI simulators to support various embodied AI research
tasks. This growing interest in embodied AI is beneficial to the greater
pursuit of Artificial General Intelligence (AGI), but there has not been a
contemporary and comprehensive survey of this field. This paper aims to provide
an encyclopedic survey for the field of embodied AI, from its simulators to its
research. By evaluating nine current embodied AI simulators with our proposed
seven features, this paper aims to understand the simulators in their provision
for use in embodied AI research and their limitations. Lastly, this paper
surveys the three main research tasks in embodied AI -- visual exploration,
visual navigation and embodied question answering (QA), covering the
state-of-the-art approaches, evaluation metrics and datasets. Finally, with the
new insights revealed through surveying the field, the paper will provide
suggestions for simulator-for-task selections and recommendations for the
future directions of the field.Comment: Under Review for IEEE TETC
Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine
This paper presents a novel allowable deformation prediction model of surrounding rock based on support vector machine (SVM). The engineering rock mass classification is subdivided based on the national standards Standard for Engineering Classification of Rock Masses in order to get more accurate physicalmechanical parameters. Using the developed parameters, 100 sets of multi-factors and multi-levels orthogonal experiments are designed, which are simulated with two-dimensional numerical models established based on ABAQUS. 100 groups of learning samples and 9 samples of random inspection are obtained. The prediction model has been established from the study of learning samples based on LibSVM. Using this model, 9 samples of random inspection and 9 engineering examples are predicted and the prediction accuracy is good compared with their actual values. It is indicated that this model can meet the initial support design requirements of underground caverns well. The novel model has the advantages of convenience, rapidity, and reliability
Improvements to enhance robustness of third-order scale-independent WENO-Z schemes
Although there are many improvements to WENO3-Z that target the achievement
of optimal order in the occurrence of the first-order critical point (CP1),
they mainly address resolution performance, while the robustness of schemes is
of less concern and lacks understanding accordingly. In light of our analysis
considering the occurrence of critical points within grid intervals, we
theoretically prove that it is impossible for a scale-independent scheme that
has the stencil of WENO3-Z to fulfill the above order achievement, and current
scale-dependent improvements barely fulfill the job when CP1 occurs at the
middle of the grid cell. In order to achieve scale-independent improvements, we
devise new smoothness indicators that increase the error order from 2 to 4 when
CP1 occurs and perform more stably. Meanwhile, we construct a new global
smoothness indicator that increases the error order from 4 to 5 similarly,
through which new nonlinear weights with regard to WENO3-Z are derived and new
scale-independents improvements, namely WENO-ZES2 and -ZES3, are acquired.
Through 1D scalar and Euler tests, as well as 2D computations, in comparison
with typical scale-dependent improvement, the following performances of the
proposed schemes are demonstrated: The schemes can achieve third-order accuracy
at CP1 no matter its location in the stencil, indicate high resolution in
resolving flow subtleties, and manifest strong robustness in hypersonic
simulations (e.g., the accomplishment of computations on hypersonic
half-cylinder flow with Mach numbers reaching 16 and 19, respectively, as well
as essentially non-oscillatory solutions of inviscid sharp double cone flow at
M=9.59), which contrasts the comparative WENO3-Z improvement
Genome-wide DNA methylation and gene expression analyses in monozygotic twins identify potential biomarkers of depression
Depression is currently the leading cause of disability around the world. We conducted an epigenome-wide association study (EWAS) in a sample of 58 depression score-discordant monozygotic twin pairs, aiming to detect specific epigenetic variants potentially related to depression and further integrate with gene expression profile data. Association between the methylation level of each CpG site and depression score was tested by applying a linear mixed effect model. Weighted gene co-expression network analysis (WGCNA) was performed for gene expression data. The association of DNA methylation levels of 66 CpG sites with depression score reached the level of P < 1 x 10(-4). These top CpG sites were located at 34 genes, especially PTPRN2, HES5, GATA2, PRDM7, and KCNIP1. Many ontology enrichments were highlighted, including Notch signaling pathway, Huntington disease, p53 pathway by glucose deprivation, hedgehog signaling pathway, DNA binding, and nucleic acid metabolic process. We detected 19 differentially methylated regions (DMRs), some of which were located at GRIK2, DGKA, and NIPA2. While integrating with gene expression data, HELZ2, PTPRN2, GATA2, and ZNF624 were differentially expressed. In WGCNA, one specific module was positively correlated with depression score (r = 0.62, P = 0.002). Some common genes (including BMP2, PRDM7, KCNIP1, and GRIK2) and enrichment terms (including complement and coagulation cascades pathway, DNA binding, neuron fate specification, glial cell differentiation, and thyroid gland development) were both identified in methylation analysis and WGCNA. Our study identifies specific epigenetic variations which are significantly involved in regions, functional genes, biological function, and pathways that mediate depression disorder.Peer reviewe
The separability of tripartite Gaussian state with amplification and amplitude damping
Tripartite three mode Gaussian state undergoes parametric amplification and
amplitude damping as well as thermal noise is studied. In the case of a state
totally symmetrically interacting with the environment, the time dependent
correlation matrix of the state in evolution is given. The conditions for fully
separability and fully entanglement of the final tripartite three mode Gaussian
state are worked out.Comment: 9 pages, 3 figure
Output entanglement and squeezing of two-mode fields generated by a single atom
A single four-level atom interacting with two-mode cavities is investigated.
Under large detuning condition, we obtain the effective Hamiltonian which is
unitary squeezing operator of two-mode fields. Employing the input-output
theory, we find that the entanglement and squeezing of the output fields can be
achieved. By analyzing the squeezing spectrum, we show that asymmetric detuning
and asymmetric atomic initial state split the squeezing spectrum from one
valley into two minimum values, and appropriate leakage of the cavity is needed
for obtaining output entangled fields
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