Text-to-image person re-identification (TIReID) aims to retrieve semantically related images from a large gallery given a text query. Most existing TIReID methods train the model on an ideal assumption that positive (negative) samples are semantically correlated (uncorrelated) on the visual and textual modal. However, we observe that incorrect annotation is unavoidable and ambiguous textual description is often taken as the input in practice, which leads to false negatives that ruin the feature aligning between modals for deviated identification. This work presents a novel False Negative Mitigation (FNM) method by identifying potential false negatives through distribution differences and mitigating them with dedicated loss. Specifically, the false negative mitigation loss is designed to adaptively adjust the optimization margin for potential false negatives in the latent space. Moreover, a Dual-Level Feature Representation method is introduced to leverage both global and local features during the mitigation, whereas a Momentum Contractive (MoC) module is plugged to enrich the training data for accurate similarity distribution estimation. We conduct extensive experiments on three public benchmark datasets and the results demonstrate that our FNM method achieves State-of-The-Art (SoTA) performance at all evaluation metrics.

Text-Image Person Re-identification (TIReID) aims to retrieve the relevant pedestrian images according to the given textual query. Recent methods typically achieve this goal through image-text contrastive learning, which assumes that only paired images and texts from the same pedestrian are considered positive samples. However, we observe that there exist negative samples, termed false negatives, that are highly semantically related to the anchor in practice. Training with these false negatives may adversely affect feature representation learning and semantic alignment between modalities. This work proposed a false negative detection and suppression (FNDS) method to mitigate their adverse impact. Our FNCD consists of a False Negative Consensus Detection (FNCD) mechanism and an Adaptive False Negative Suppression (AFNS) method. FNCD combines dual-grained detection to consensually identify potential false negatives, while AFNS assigns adaptive weights to the false negative similarities for more robust suppression. Extensive experiments conducted on three public benchmark datasets demonstrate the effectiveness of the proposed method.

As graph size (numbers of vertices and edges) is increasing from billions to trillions, efficient graph processing requires exascale computing clusters, which consist of hundreds of thousands of nodes connected via hierarchical networks with multiple levels of communication domains, e.g., multilevel triangle communication domains. While the computation of traversal-centric graph algorithms is relatively simple (e.g., status check), communication is the bottleneck due to the transfer of numerous small messages among hierarchical triangle communication domains.

Graph algorithms, such as Breadth First Search (BFS), Single Source Shortest Path (SSSP), PageRank (PR), and Connected Components (CC), are increasingly important in big data processing and analytics. As graph scales (numbers of vertices and edges) have increased from billions to trillions, Supercomputers have huge numbers (up to hundreds of thousands) of computing nodes (CNs) that can provide ultra-high aggregate computing power and memory capacity, thus being particularly suitable for processing extreme-scale graphs with trillions of vertices and edges. However, existing cluster-based graph-parallel systems perform poorly when deployed on supercomputers, since their partitioning methods overlook the hierarchical nature of supercomputer networks and incur prohibitive communication storm. This paper presents XTree, an efficient traversal-based partitioning method for minimizing communication overhead of graph processing on supercomputers. We observe that supercomputers' huge numbers of CNs are usually organized into hierarchical communication domains, which can be modeled as a domain tree where communication in lower-level domains is significantly faster than that in higher-level ones. Therefore, the key idea of XTree's partitioning is to exploit hierarchical locality by viewing the graph as a BFS tree and leveraging the topology knowledge to map the graph's BFS tree onto the domain tree, We evaluate the effectiveness of XTree by running various graph algorithms, on both real-world big graphs and synthetic trillion-scale graphs. XTree substantially reduces communication overhead and achieves orders of magnitude speedup against the Graph500 reference implementations with the state-of-the-art 2D-decomposition partitioning.

BACKGROUND: Building biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regulatory relationships are complex and often nonlinear or non-monotonic, which makes inference using linear models challenging. RESULTS: In this paper, we propose a multi-layer perceptron-based differential equation method, which operates by training a fully connected neural network (NN) to simulate the transcription rate of genes in traditional differential equations. We verify whether the regulatory network constructed by the NN method can continue to achieve the expected biological function by verifying the degree of overlap between the regulatory network discovered by NN and the regulatory network constructed by the Hill function. And we validate our approach by adapting to noise signals, regulator knockout, and constructing large-scale gene regulatory networks using link-knockout techniques. We apply a real dataset (the mesoderm inducer Xenopus Brachyury expression) to construct the core topology of the gene regulatory network and find that Xbra is only strongly expressed at moderate levels of activin signaling. CONCLUSION: We have demonstrated from the results that this method has the ability to identify the underlying network topology and functional mechanisms, and can also be applied to larger and more complex gene network topologies.

As the era of exascale supercomputing is coming, it is vital for next-generation supercomputers to find appropriate applications with high social and economic benefit. In recent years, it has been widely accepted that extremely-large graph computation is a promising killer application for supercomputing. Although Tianhe series supercomputers are leading in the world-wide competition of supercomputing (ranked No. 1 in the Top500 list for six times), previously they had been inefficient in graph computation according to the Graph500 list. This is mainly because the previous graph processing system cannot leverage the advanced hardware features of Tianhe supercomputers. To address the problem, in this paper we present our integrated optimizations for improving the graph computation performance on our next-generation Tianhe supercomputing system, mainly including sorting with buffering for heavy vertices, vectorized searching with SVE (Scalable Vector Extension) on matrix2000+ CPUs, and group communication on the proprietary interconnection network. Performance evaluation on a subset of the Tianhe supercomputer (with 512 nodes and 196,608 cores) shows that our customized graph processing system effectively improves the graph search performance and achieves the BFS performance of 2131.98 GTEPS.