489 research outputs found
Numerical Simulations of Spread Characteristics of Toxic Cyanide in the Danjiangkou Reservoir in China under the Effects of Dam Cooperation
Many accidents of releasing toxic pollutants into surface water happen each year in the world. It is believed that dam cooperation can affect flow field in reservoir and then can be applied to avoiding and reducing spread speed of toxic pollutants to drinking water intake mouth. However, few studies investigated the effects of dam cooperation on the spread characteristics of toxic pollutants in reservoir, especially the source reservoir for water diversion with more than one dam. The Danjiangkou Reservoir is the source reservoir of the China’ South-to-North Water Diversion Middle Route Project. The human activities are active within this reservoir basin and cyanide-releasing accident once happened in upstream inflow. In order to simulate the spread characteristics of cyanide in the reservoir in the condition of dam cooperation, a three-dimensional water quality model based on the Environmental Fluid Dynamics Code (EFDC) has been built and put into practice. The results indicated that cooperation of two dams of the Danjiangkou Reservoir could be applied to avoiding and reducing the spread speed of toxic cyanide in the reservoir directing to the water intake mouth for water diversions
Online Ridesharing with Meeting Points [Technical Report]
Nowadays, ridesharing becomes a popular commuting mode. Dynamically arriving
riders post their origins and destinations, then the platform assigns drivers
to serve them. In ridesharing, different groups of riders can be served by one
driver if their trips can share common routes. Recently, many ridesharing
companies (e.g., Didi and Uber) further propose a new mode, namely "ridesharing
with meeting points". Specifically, with a short walking distance but less
payment, riders can be picked up and dropped off around their origins and
destinations, respectively. In addition, meeting points enables more flexible
routing for drivers, which can potentially improve the global profit of the
system. In this paper, we first formally define the Meeting-Point-based Online
Ridesharing Problem (MORP). We prove that MORP is NP-hard and there is no
polynomial-time deterministic algorithm with a constant competitive ratio for
it. We notice that a structure of vertex set, -skip cover, fits well to the
MORP. -skip cover tends to find the vertices (meeting points) that are
convenient for riders and drivers to come and go. With meeting points, MORP
tends to serve more riders with these convenient vertices. Based on the idea,
we introduce a convenience-based meeting point candidates selection algorithm.
We further propose a hierarchical meeting-point oriented graph (HMPO graph),
which ranks vertices for assignment effectiveness and constructs -skip cover
to accelerate the whole assignment process. Finally, we utilize the merits of
-skip cover points for ridesharing and propose a novel algorithm, namely
SMDB, to solve MORP. Extensive experiments on real and synthetic datasets
validate the effectiveness and efficiency of our algorithms.Comment: 18 page
Double Graphs Regularized Multi-view Subspace Clustering
Recent years have witnessed a growing academic interest in multi-view
subspace clustering. In this paper, we propose a novel Double Graphs
Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to
harness both global and local structural information of multi-view data in a
unified framework. Specifically, DGRMSC firstly learns a latent representation
to exploit the global complementary information of multiple views. Based on the
learned latent representation, we learn a self-representation to explore its
global cluster structure. Further, Double Graphs Regularization (DGR) is
performed on both latent representation and self-representation to take
advantage of their local manifold structures simultaneously. Then, we design an
iterative algorithm to solve the optimization problem effectively. Extensive
experimental results on real-world datasets demonstrate the effectiveness of
the proposed method
SNP@Evolution: a hierarchical database of positive selection on the human genome
<p>Abstract</p> <p>Background</p> <p>Positive selection is a driving force that has shaped the modern human. Recent developments in high throughput technologies and corresponding statistics tools have made it possible to conduct whole genome surveys at a population scale, and a variety of measurements, such as heterozygosity (HET), <it>F</it><sub><it>ST</it></sub>, and Tajima's D, have been applied to multiple datasets to identify signals of positive selection. However, great effort has been required to combine various types of data from individual sources, and incompatibility among datasets has been a common problem. SNP@Evolution, a new database which integrates multiple datasets, will greatly assist future work in this area.</p> <p>Description</p> <p>As part of our research scanning for evolutionary signals in HapMap Phase II and Phase III datasets, we built SNP@Evolution as a multi-aspect database focused on positive selection. Among its many features, SNP@Evolution provides computed <it>F</it><sub><it>ST </it></sub>and HET of all HapMap SNPs, 5+ HapMap SNPs per qualified gene, and all autosome regions detected from whole genome window scanning. In an attempt to capture multiple selection signals across the genome, selection-signal enrichment strength (E<sub>S</sub>) values of HET, <it>F</it><sub><it>ST</it></sub>, and <it>P</it>-values of iHS of most annotated genes have been calculated and integrated within one frame for users to search for outliers. Genes with significant E<sub>S </sub>or <it>P</it>-values (with thresholds of 0.95 and 0.05, respectively) have been highlighted in color. Low diversity chromosome regions have been detected by sliding a 100 kb window in a 10 kb step. To allow this information to be easily disseminated, a graphical user interface (GBrowser) was constructed with the Generic Model Organism Database toolkit.</p> <p>Conclusion</p> <p>Available at <url>http://bighapmap.big.ac.cn</url>, SNP@Evolution is a hierarchical database focused on positive selection of the human genome. Based on HapMap Phase II and III data, SNP@Evolution includes 3,619,226/1,389,498 SNPs with their computed HET and <it>F</it><sub><it>ST</it></sub>, as well as qualified genes of 21,859/21,099 with E<sub>S </sub>values of HET and <it>F</it><sub><it>ST</it></sub>. In at least one HapMap population group, window scanning for selection signals has resulted in 1,606/10,138 large low HET regions. Among Phase II and III geographical groups, 660 and 464 regions show strong differentiation.</p
Robust and Efficient Network Reconstruction in Complex System via Adaptive Signal Lasso
Network reconstruction is important to the understanding and control of
collective dynamics in complex systems. Most real networks exhibit sparsely
connected properties, and the connection parameter is a signal (0 or 1).
Well-known shrinkage methods such as lasso or compressed sensing (CS) to
recover structures of complex networks cannot suitably reveal such a property;
therefore, the signal lasso method was proposed recently to solve the network
reconstruction problem and was found to outperform lasso and CS methods.
However, signal lasso suffers the problem that the estimated coefficients that
fall between 0 and 1 cannot be successfully selected to the correct class. We
propose a new method, adaptive signal lasso, to estimate the signal parameter
and uncover the topology of complex networks with a small number of
observations. The proposed method has three advantages: (1) It can effectively
uncover the network topology with high accuracy and is capable of completely
shrinking the signal parameter to either 0 or 1, which eliminates the
unclassified portion in network reconstruction; (2) The method performs well in
scenarios of both sparse and dense signals and is robust to noise
contamination; (3) The method only needs to select one tuning parameter versus
two in signal lasso, which greatly reduces the computational cost and is easy
to apply. The theoretical properties of this method are studied, and numerical
simulations from linear regression, evolutionary games, and Kuramoto models are
explored. The method is illustrated with real-world examples from a human
behavioral experiment and a world trade web.Comment: 15 pages, 8 figures, 4 table
Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors
We propose a new method for learning a generalized animatable neural human
representation from a sparse set of multi-view imagery of multiple persons. The
learned representation can be used to synthesize novel view images of an
arbitrary person from a sparse set of cameras, and further animate them with
the user's pose control. While existing methods can either generalize to new
persons or synthesize animations with user control, none of them can achieve
both at the same time. We attribute this accomplishment to the employment of a
3D proxy for a shared multi-person human model, and further the warping of the
spaces of different poses to a shared canonical pose space, in which we learn a
neural field and predict the person- and pose-dependent deformations, as well
as appearance with the features extracted from input images. To cope with the
complexity of the large variations in body shapes, poses, and clothing
deformations, we design our neural human model with disentangled geometry and
appearance. Furthermore, we utilize the image features both at the spatial
point and on the surface points of the 3D proxy for predicting person- and
pose-dependent properties. Experiments show that our method significantly
outperforms the state-of-the-arts on both tasks. The video and code are
available at https://talegqz.github.io/neural_novel_actor
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