160 research outputs found
Person Search with Natural Language Description
Searching persons in large-scale image databases with the query of natural
language description has important applications in video surveillance. Existing
methods mainly focused on searching persons with image-based or attribute-based
queries, which have major limitations for a practical usage. In this paper, we
study the problem of person search with natural language description. Given the
textual description of a person, the algorithm of the person search is required
to rank all the samples in the person database then retrieve the most relevant
sample corresponding to the queried description. Since there is no person
dataset or benchmark with textual description available, we collect a
large-scale person description dataset with detailed natural language
annotations and person samples from various sources, termed as CUHK Person
Description Dataset (CUHK-PEDES). A wide range of possible models and baselines
have been evaluated and compared on the person search benchmark. An Recurrent
Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to
establish the state-of-the art performance on person search
Comparing Graphs via Persistence Distortion
Metric graphs are ubiquitous in science and engineering. For example, many data are drawn from hidden spaces that are graph-like, such as the cosmic web. A metric graph offers one of the simplest yet still meaningful ways to represent the non-linear structure hidden behind the data. In this paper, we propose a new distance between two finite metric graphs, called the persistence-distortion distance, which draws upon a topological idea. This topological perspective along with the metric space viewpoint provide a new angle to the graph matching problem. Our persistence-distortion distance has two properties not shared by previous methods: First, it is stable against the perturbations of the input graph metrics. Second, it is a continuous distance measure, in the sense that it is defined on an alignment of the underlying spaces of input graphs, instead of merely their nodes. This makes our persistence-distortion distance robust against, for example, different discretizations of the same underlying graph.
Despite considering the input graphs as continuous spaces, that is, taking all points into account, we show that we can compute the persistence-distortion distance in polynomial time. The time complexity for the discrete case where only graph nodes are considered is much faster
SimBa: An Efficient Tool for Approximating Rips-Filtration Persistence via Simplicial Batch-Collapse
In topological data analysis, a point cloud data P extracted from a metric space is often analyzed by computing the persistence diagram or barcodes of a sequence of Rips complexes built on P indexed by a scale parameter. Unfortunately, even for input of moderate size, the size of the Rips complex may become prohibitively large as the scale parameter increases. Starting with the Sparse Rips filtration introduced by Sheehy, some existing methods aim to reduce the size of the complex so as to improve the time efficiency as well. However, as we demonstrate, existing approaches still fall short of scaling well, especially for high dimensional data. In this paper, we investigate the advantages and limitations of existing approaches. Based on insights gained from the experiments, we propose an efficient new algorithm, called SimBa, for approximating the persistent homology of Rips filtrations with quality guarantees. Our new algorithm leverages a batch collapse strategy as well as a new sparse Rips-like filtration. We experiment on a variety of low and high dimensional data sets. We show that our strategy presents a significant size reduction, and our algorithm for approximating Rips filtration persistence is order of magnitude faster than existing methods in practice
Collateral Pathways from the Ventromedial Hypothalamus Mediate Defensive Behaviors
SummaryThe ventromedial hypothalamus (VMH) was thought to be essential for coping with threat, although its circuit mechanism remains unclear. To investigate this, we optogenetically activated steroidogenic factor 1 (SF1)-expressing neurons in the dorsomedial and central parts of the VMH (VMHdm/c), and observed a range of context-dependent somatomotor and autonomic responses resembling animals’ natural defensive behaviors. By activating independent pathways emanating from the VMHdm/c, we demonstrated that VMHdm/c projection to the dorsolateral periaqueductal gray (dlPAG) induces inflexible immobility, while the VMHdm/c to anterior hypothalamic nucleus (AHN) pathway promotes avoidance. Consistent with the behavior changes induced by VMH to AHN pathway activation, direct activation of the AHN elicited avoidance and escape jumping, but not immobility. Retrograde tracing studies revealed that nearly 50% of PAG-projecting VMHdm/c neurons send collateral projection to the AHN and vice versa. Thus, VMHdm/c neurons employ a one-to-many wiring configuration to orchestrate multiple aspects of defensive behaviors
Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters
Single-cell multi-view clustering enables the exploration of cellular
heterogeneity within the same cell from different views. Despite the
development of several multi-view clustering methods, two primary challenges
persist. Firstly, most existing methods treat the information from both
single-cell RNA (scRNA) and single-cell Assay of Transposase Accessible
Chromatin (scATAC) views as equally significant, overlooking the substantial
disparity in data richness between the two views. This oversight frequently
leads to a degradation in overall performance. Additionally, the majority of
clustering methods necessitate manual specification of the number of clusters
by users. However, for biologists dealing with cell data, precisely determining
the number of distinct cell types poses a formidable challenge. To this end, we
introduce scUNC, an innovative multi-view clustering approach tailored for
single-cell data, which seamlessly integrates information from different views
without the need for a predefined number of clusters. The scUNC method
comprises several steps: initially, it employs a cross-view fusion network to
create an effective embedding, which is then utilized to generate initial
clusters via community detection. Subsequently, the clusters are automatically
merged and optimized until no further clusters can be merged. We conducted a
comprehensive evaluation of scUNC using three distinct single-cell datasets.
The results underscored that scUNC outperforms the other baseline methods
Bipartite Consensus for a Class of Nonlinear Multi-agent Systems Under Switching Topologies:A Disturbance Observer-Based Approach
This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output feedback control protocols involving signs of link weights are introduced based on relative output measurements of neighboring agents. In order to estimate the disturbances produced by an exogenous system, a disturbance observer-based approach is developed. Then, sufficient conditions for leader-following bipartite consensus with directed fixed topologies are derived. Furthermore, by assuming that each switching topology contains a directed spanning tree, it is proved that the leader-following bipartite consensus can be realized with the designed output feedback control protocol if the dwell time is larger than a non-negative threshold. Finally, numerical simulations inspired by a real-world DC motors are provided to illustrate the effectiveness of the proposed controllers
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