Multi-level virtual ring : a foundation network architecture to support peer-to-peer application in wireless sensor network

Two main problems prevent the deployment of peer-to-peer application in a wireless sensor network: the index table, which should be distributed stored rather than uses a central server as the director; the unique node identifier, which cannot use the global addresses. This paper presents a multi-level virtual ring (MVR) structure to solve these two problems.The index table in MVR is distributed stored by using the DHT technique. MVR is constructed decentralized and runs on mobile nodes themselves, requiring no central server or interruption. Naming system in MVR uses natural names rather than global addresses to identify sensor nodes. The MVR can route directly on the name identifiers of the sensor nodes without being aware the location. Some sensor nodes are selected as the backbone nodes by the backbone selection algorithm and are placed on the different levels of the virtual rings. MVR hashes nodes&rsquo; identifiers on the virtual ring, and stores them at the backbone nodes. Furthermore, MVR adopts cross-level routing to improve the routing efficiency.Experiments using ns2 simulator for up to 200 nodes show that the storage and bandwidth requirements of MVR grow slowly with the size of the network. Furthermore, MVR has demonstrated as self-administrating, fault-tolerant, and resilient under the different workloads.<br /

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The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than a classic strategy. Moreover, we leverage a high performance communication scheme for fully exploiting network bandwidth via pipeline broadcast. Overall, the integrated approach achieves substantial energy savings (up to 51.4%) and performance gain (28.6% on average) compared to ScaLAPACK pdgemm() on a cluster with an Ethernet switch, and outperforms ScaLAPACK and DPLASMA pdgemm() respectively by 33.3% and 32.7% on average on a cluster with an Infiniband switch

S-Kcore : a social-aware Kcore decomposition algorithm in pocket switched networks

The key nodes in network play the critical role in system recovery and survival. Many traditional key nodes selection algorithms utilize the characters of the physical topology to find the key nodes. But they can hardly succeed in the mobile ad hoc network due to the mobility nature of the network. In this paper we propose a social-aware Kcore selection algorithm to work in the Pocket Switched Network. The social view of the network suggests the social position of the mobile nodes can help to find the key nodes in the Pocket Switched Network. The S-Kcore selection algorithm is designed to exploit the nodes\u27 social features to improve the performance in data communication. Experiments use the NS2 shows S-Kcore selection algorithm workable in the Pocket Switched Network. Furthermore, with the social behavior information, those key nodes are more suitable to represent and improve the whole network\u27s performance.<br /

Developing rAAV production platform with enhanced productivity, scalability and biosafety

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Consistency Regularization for Generalizable Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variety of real-world scenarios. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. This oversight leads to overfitting issues and constrains the model's generalization ability. In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets. Our method leverages soft pseudo-labels generated from weakly augmented images to supervise strongly augmented images, facilitating the model training process and enhancing the generalization ability of the adapted model. To leverage more potentially useful supervision, we present a sampling-based pseudo-label selection strategy, taking samples with severer domain shift into consideration. Moreover, global-oriented calibration methods are introduced to exploit global class distribution and feature cluster information, further improving the adaptation process. Extensive experiments demonstrate our method achieves state-of-the-art performance on several SFDA benchmarks, and exhibits robustness on unseen testing datasets.Comment: Accepted by ICCV 2023 worksho

Análisis de la gobernanza fronteriza entre los EE. UU. y México desde la perspectiva de la seguridad nacional

Desde la antigüedad hasta el presente, la seguridad fronteriza ha jugado un papel vital en la seguridad nacional, porque la frontera es el límite de un país, conectada a los dos países y también bloqueada a los dos países. Los Estados Unidos y México tienen una frontera de más de tres mil kilómetros, y los problemas que enfrentan también son infinitos, como la droga, las migraciones ilegales y la industrialización fronteriza en México, que también ha afectado seriamente tanto la seguridad fronteriza como la nacional de los dos países. Ellos dos han adoptado muchas políticas para el control fronterizo, pero no hay mucho efecto, por lo que estos problemas han coexistido hasta ahora. En este trabajo se analizan los principales problemas fronterizos entre los EE. UU. y México con el motivo de encontrar sus efectos en la seguridad nacional, estudiando sus historias, características y tendencias para buscar mejores soluciones

Temporal Knowledge Graph Completion: A Survey

Knowledge graph completion (KGC) can predict missing links and is crucial for real-world knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Recently, emerging methods have shown improved predictive results by further incorporating the timestamps of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods can learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the problem definition, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how timestamps of facts are used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC

On exploiting temporal, social, and geographical relationships for data forwarding in Delay Tolerant Networks

Because of unpredictable node mobility and absence of global information in Delay Tolerant Networks (DTNs), effective data forwarding has become a significant challenge in such network. Currently, most of existing data forwarding mechanisms select nodes with high cumulative contact capability as forwarders. However, for the heterogeneity of the transient node contact patterns, these selection approaches may not be the best relay choices within a short time period. This paper proposes an appropriate data forwarding mechanism, which combines time, location, and social characteristics into one coordinate system, to improve the performance of data forwarding in DTNs. The Temporal-Social Relationship and the Temporal-Geographical Relationship reveal the implied connection information among these three factors. This mechanism is formulated and verified in the experimental studies of realistic DTN traces. The empirical results show that our proposed mechanism can achieve better performance compared to the existing schemes with similar forwarding costs (e.g. end-to-end delay and delivery success ratio)