Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy

Social networks have been used to understand how information flows through an organization as well as identifying individuals that appear to have control over this information flow. Such individuals are identified as being central nodes in a graph representation of the social network and have high "betweenness" values. Rather than looking at graphs derived from email, on-line forums, or telephone connections, we consider sequences of bipartite graphs that represent face-to-face meetings between individuals, and define a new metric to identify the information elite individuals. We show that, in our simulations, individuals that attend many meetings with many different people do not always have high betweenness values, even though they seem to be the ones that control the information flow.Singapore-MIT Alliance (SMA

Micropillar compression deformation of single crystals of Fe₃Ge with the L1₂ structure

The plastic deformation behavior of single crystals of Fe₃Ge with the L1₂ structure has been investigated at room temperature as a function of crystal orientation by micropillar compression tests. In addition to slip on (010), slip on (111) is observed to occur in Fe₃Ge for the first time. The CRSS (critical resolved shear stress) for (111)[10overline{1}] slip, estimated by extrapolating the size-dependent strength variation to the ‘bulk’ size, is ~240 MPa, which is almost 6 times that (~40 MPa) for (010)[10overline{1}] slip similarly estimated. The dissociation scheme for the superlattice dislocation with b=[10overline{1}] is confirmed to be of the APB (anti-phase boundary)-type both on (010) and on (111), in contrast to the previous prediction for the SISF (superlattice intrinsic stacking fault) scheme on (111) because of the expected APB instability. While superlattice dislocations do not have any preferential directions to align when gliding on (010) (indicative of low frictional stress at room temperature), the alignment of superlattice dislocations along their screw orientation is observed when gliding on (111). This is proved to be due to thermally-activated cross-slip to form Kear-Wilsdorf locks, indicative of the occurrence of yield stress anomaly that is observed in many other L12 compounds such as Ni₃Al. Some important deformation characteristics expected to occur in Fe₃Ge (such as the absence of SISF-couple dissociation and the occurrence of yield stress anomaly) will be discussed in the light of the experimental results obtained (APB energies on (111) and (010) and CRSS values for slip on (111) and (010))

ヒトSPCA1aのクライオ電子顕微鏡解析によるゴルジ体へのCa2+/Mn2+輸送機構の解明

Tohoku University稲葉謙次課

Neural Vector Fields: Generalizing Distance Vector Fields by Codebooks and Zero-Curl Regularization

Recent neural networks based surface reconstruction can be roughly divided into two categories, one warping templates explicitly and the other representing 3D surfaces implicitly. To enjoy the advantages of both, we propose a novel 3D representation, Neural Vector Fields (NVF), which adopts the explicit learning process to manipulate meshes and implicit unsigned distance function (UDF) representation to break the barriers in resolution and topology. This is achieved by directly predicting the displacements from surface queries and modeling shapes as Vector Fields, rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods do. In this way, our approach is capable of encoding both the distance and the direction fields so that the calculation of direction fields is differentiation-free, circumventing the non-trivial surface extraction step. Furthermore, building upon NVFs, we propose to incorporate two types of shape codebooks, \ie, NVFs (Lite or Ultra), to promote cross-category reconstruction through encoding cross-object priors. Moreover, we propose a new regularization based on analyzing the zero-curl property of NVFs, and implement this through the fully differentiable framework of our NVF (ultra). We evaluate both NVFs on four surface reconstruction scenarios, including watertight vs non-watertight shapes, category-agnostic reconstruction vs category-unseen reconstruction, category-specific, and cross-domain reconstruction

Neural Vector Fields: Implicit Representation by Explicit Learning

Deep neural networks (DNNs) are widely applied for nowadays 3D surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp templates explicitly by moving vertices or represent 3D surfaces implicitly as signed or unsigned distance functions. Taking advantage of both advanced explicit learning process and powerful representation ability of implicit functions, we propose a novel 3D representation method, Neural Vector Fields (NVF). It not only adopts the explicit learning process to manipulate meshes directly, but also leverages the implicit representation of unsigned distance functions (UDFs) to break the barriers in resolution and topology. Specifically, our method first predicts the displacements from queries towards the surface and models the shapes as \textit{Vector Fields}. Rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods, the produced vector fields encode the distance and direction fields both and mitigate the ambiguity at "ridge" points, such that the calculation of direction fields is straightforward and differentiation-free. The differentiation-free characteristic enables us to further learn a shape codebook via Vector Quantization, which encodes the cross-object priors, accelerates the training procedure, and boosts model generalization on cross-category reconstruction. The extensive experiments on surface reconstruction benchmarks indicate that our method outperforms those state-of-the-art methods in different evaluation scenarios including watertight vs non-watertight shapes, category-specific vs category-agnostic reconstruction, category-unseen reconstruction, and cross-domain reconstruction. Our code is released at https://github.com/Wi-sc/NVF.Comment: Accepted by CVPR2023. Video: https://www.youtube.com/watch?v=GMXKoJfmHr