Information-Theoretically Private Matrix Multiplication From MDS-Coded Storage

We study two problems of private matrix multiplication, over a distributed computing system consisting of a master node, and multiple servers who collectively store a family of public matrices using Maximum-Distance-Separable (MDS) codes. In the first problem of Private and Secure Matrix Multiplication from Colluding servers (MDS-C-PSMM), the master intends to compute the product of its confidential matrix $\mathbf{A}$ with a target matrix stored on the servers, without revealing any information about $\mathbf{A}$ and the index of target matrix to some colluding servers. In the second problem of Fully Private Matrix Multiplication from Colluding servers (MDS-C-FPMM), the matrix $\mathbf{A}$ is also selected from another family of public matrices stored at the servers in MDS form. In this case, the indices of the two target matrices should both be kept private from colluding servers. We develop novel strategies for MDS-C-PSMM and MDS-C-FPMM, which simultaneously guarantee information-theoretic data/index privacy and computation correctness. The key ingredient is a careful design of secret sharings of the matrix $\mathbf{A}$ and the private indices, which are tailored to matrix multiplication task and MDS storage structure, such that the computation results from the servers can be viewed as evaluations of a polynomial at distinct points, from which the intended result can be obtained through polynomial interpolation. We compare the proposed MDS-C-PSMM strategy with a previous MDS-PSMM strategy with a weaker privacy guarantee (non-colluding servers), and demonstrate substantial improvements over the previous strategy in terms of communication and computation performance

Habitat loss alters effects of intransitive higher-order competition on biodiversity: a new metapopulation framework

Recent studies have suggested that intransitive competition, as opposed to hierarchical competition, allow more species to coexist. Furthermore, it is recognized that the prevalent paradigm, which assumes that species interactions are exclusively pairwise, may be insufficient. More importantly, whether and how habitat loss, a key driver of biodiversity loss, can alter these complex competition structures and therefore species coexistence remain unclear. We thus present a new simple yet comprehensive metapopulation framework which can account for any competition pattern and more complex higher-order interactions (HOIs) among species. We find that competitive intransitivity increases community diversity and that HOIs generally enhance this effect. Essentially, intransitivity promotes species richness by preventing the dominance of a few species unlike hierarchical competition, while HOIs facilitate species coexistence through stabilizing community fluctuations. However, variation in species vital rates and habitat loss can weaken or even reverse such higher-order effects, as their interaction can lead to a more rapid decline in competitive intransitivity under HOIs. Thus, it is essential to correctly identify the most appropriate interaction model for a given system before models are used to inform conservation efforts. Overall, our simple model framework provides a more parsimonious explanation for biodiversity maintenance than existing theory

Habitat heterogeneity mediates effects of individual variation on spatial species coexistence

Numerous studies have documented the importance of individual variation (IV) in determining the outcome of competition between species. However, little is known about how the interplay between IV and habitat heterogeneity (i.e. variation and spatial autocorrelation in habitat quality) affects species coexistence at the landscape scale. Here, we incorporate habitat heterogeneity into a competition model with IV, in order to explore the mechanism of spatial species coexistence. We find that individual-level variation and habitat heterogeneity interact to promote species coexistence, more obviously at lower dispersal rates. This is in stark contrast to early non-spatial models, which predicted that IV reinforces competitive hierarchies and therefore speeds up species exclusion. In essence, increasing variation in patch quality and/or spatial habitat autocorrelation moderates differences in the competitive ability of species, thereby allowing species to coexist both locally and globally. Overall, our theoretical study offers a mechanistic explanation for emerging empirical evidence that both habitat heterogeneity and IV promote species coexistence and therefore biodiversity maintenance

Finite element linear static structural analysis and modal analysis for Lunar Lander

Lunar exploration is one of the most important projects in the world. A primary objective of the probe in lunar is to soft-land a manned spacecraft on lunar surface. The soft-landing system is the key composition of the lunar lander. In the overall design of lunar lander, the analysis of touchdown dynamics during landing stage is an important work. In this paper, firstly, based on the mechanical theory, a finite element model for the lunar lander is established. Secondly, the linear static structural analysis under particular conditions is performed to determine the nodal stress and displacement distributions and the modal analysis is conducted to obtain the frequencies and their corresponding vibration shapes. Finally, the weakness parts of the structure and the behavior of the system are obtained by analyzing the simulating results, which are beneficial to the optimizing design for the lunar Lander

O Serviço Social nas Autarquias e a sua Importância para o Desenvolvimento Social Local

Aprofundar o conhecimento do Serviço Social e reflectir sobre a prática profissional do Assistente Social e a criação de políticas sociais com vista ao desenvolvimento social local, nomeadamente, na Câmara Municipal da Batalha, constituíram o objectivo de estudo. A descentralização do Poder Central para o Poder Local, assente na proximidade ao cidadão, mantém-se em discussão na agenda política e ganha maior relevo na conjuntura actual, com a reforma do Poder Local. Contudo, até ao momento, as transferências no âmbito da Acção Social mantêm-se bastante genéricas e sem regulamentação. Por essa razão, o Poder Local intervém na área social sem que essas competências estejam delineadas pela tutela e muitas vezes sem o devido financiamento, deixando aos Executivos Municipais a decisão sobre a criação de políticas sociais. Neste sentido, com o intuito de assegurarem os interesses das suas populações, as Câmaras Municipais implementam medidas sociais de âmbito local, que se revelam mais ou menos intensas, consoante o importância que lhes é dada por cada Executivo, que define as áreas de intervenção prioritárias e quais os recursos disponíveis para investir no domínio social. O Serviço Social revela ser um importante recurso das autarquias na criação das políticas sociais locais, na medida em que o Assistente Social, ao conhecer o território e intervir mais próximo dos cidadãos, pode propor programas de desenvolvimento local, adequados aos interesses da população. No caso particular da Câmara Municipal da Batalha, reflectiu-se sobre a prática da Assistente Social e evocaram-se as políticas sociais por esta planeadas e desenvolvidas, revelando o seu contributo para o desenvolvimento social do concelho. Atestou-se, em género de conclusão, que, apesar do Assistente Social ter um papel cada vez mais preponderante na execução das políticas de desenvolvimento local, a sua prática profissional tem limitações por não ser capaz, por si só, de resolver problemas sociais de génese estrutural, influenciados pela conjuntura nacional e internacional

Local keypoint-based Faster R-CNN

Region-based Convolutional Neural Network (R-CNN) detectors have achieved state-of-the-art results on various challenging benchmarks. Although R-CNN has achieved high detection performance, the research of local information in producing candidates is insufficient. In this paper, we design a Keypoint-based Faster R-CNN (K-Faster) method for object detection. K-Faster incorporates local keypoints in Faster R-CNN to improve the detection performance. In detail, a sparse descriptor, which first detects the points of interest in a given image and then samples a local patch and describes its invariant features, is first employed to produce keypoints. All 2-combinations of the produced keypoints are second selected to generate keypoint anchors, which are helpful for object detection. The heterogeneously distributed anchors are then encoded in feature maps based on their areas and center coordinates. Finally, the keypoint anchors are coupled with the anchors produced by Faster R-CNN, and the coupled anchors are used for Region Proposal Network (RPN) training. Comparison experiments are implemented on PASCAL VOC 07/12 and MS COCO. The experimental results show that our K-Faster approach not only increases the mean Average Precision (mAP) performance but also improves the positioning precision of the detected boxes