Randomized Structural Sparsity based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multi-center Reproducibility Study

In this paper, we focus on how to locate the relevant or discriminative brain regions related with external stimulus or certain mental decease, which is also called support identification, based on the neuroimaging data. The main difficulty lies in the extremely high dimensional voxel space and relatively few training samples, easily resulting in an unstable brain region discovery (or called feature selection in context of pattern recognition). When the training samples are from different centers and have betweencenter variations, it will be even harder to obtain a reliable and consistent result. Corresponding, we revisit our recently proposed algorithm based on stability selection and structural sparsity. It is applied to the multi-center MRI data analysis for the first time. A consistent and stable result is achieved across different centers despite the between-center data variation while many other state-of-the-art methods such as two sample t-test fail. Moreover, we have empirically showed that the performance of this algorithm is robust and insensitive to several of its key parameters. In addition, the support identification results on both functional MRI and structural MRI are interpretable and can be the potential biomarkers.Comment: arXiv admin note: text overlap with arXiv:1410.465

Powering One-shot Topological NAS with Stabilized Share-parameter Proxy

One-shot NAS method has attracted much interest from the research community due to its remarkable training efficiency and capacity to discover high performance models. However, the search spaces of previous one-shot based works usually relied on hand-craft design and were short for flexibility on the network topology. In this work, we try to enhance the one-shot NAS by exploring high-performing network architectures in our large-scale Topology Augmented Search Space (i.e., over 3.4*10^10 different topological structures). Specifically, the difficulties for architecture searching in such a complex space has been eliminated by the proposed stabilized share-parameter proxy, which employs Stochastic Gradient Langevin Dynamics to enable fast shared parameter sampling, so as to achieve stabilized measurement of architecture performance even in search space with complex topological structures. The proposed method, namely Stablized Topological Neural Architecture Search (ST-NAS), achieves state-of-the-art performance under Multiply-Adds (MAdds) constraint on ImageNet. Our lite model ST-NAS-A achieves 76.4% top-1 accuracy with only 326M MAdds. Our moderate model ST-NAS-B achieves 77.9% top-1 accuracy just required 503M MAdds. Both of our models offer superior performances in comparison to other concurrent works on one-shot NAS

High oxygen pressure floating zone growth and crystal structure of the layered nickelates R4_4Ni3_3O10_{10} (R=La, Pr)

Single crystals of the metallic Ruddlesden-Popper trilayer nickelates R$_4$Ni$_3$O$_{10}$ (R=La, Pr) were successfully grown using an optical-image floating zone furnace under oxygen pressure (pO$_2$) of 20 bar for La$_4$Ni$_3$O$_{10}$ and 140 bar for Pr$_4$Ni$_3$O$_{10}$. A combination of synchrotron and laboratory x-ray single crystal diffraction, high-resolution synchrotron x-ray powder diffraction and measurements of physical properties revealed that R$_4$Ni$_3$O$_{10}$ (R=La, Pr) crystallizes in the monoclinic $P$2$_1$/$a$ (Z=2) space group at room temperature, and that a metastable orthorhombic phase ($Bmab$) can be trapped by post-growth rapid cooling. Both La$_4$Ni$_3$O$_{10}$ and Pr$_4$Ni$_3$O$_{10}$ crystals undergo a metal-to-metal transition (MMT) below room temperature. In the case of Pr$_4$Ni$_3$O$_{10}$, the MMT is found at ~157.6 K. For La$_4$Ni$_3$O$_{10}$, the MMT depends on the lattice symmetry: 147.5 K for $Bmab$ vs. 138.6 K for $P$2$_1$/$a$. Lattice anomalies were found at the MMT that, when considered together with the pronounced dependence of the transition temperature on subtle structural differences between $Bmab$ and $P$2$_1$/$a$ phases, demonstrates a not insignificant coupling between electronic and lattice degrees of freedom in these trilayer nickelates.Comment: 21 pages, 8 figures, 3 table

HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems. Despite that the convolutional neural networks are remarkable in learning discriminative features from images, the learning of comprehensive features of pedestrians for fine-grained tasks remains an open problem. In this study, we propose a new attention-based deep neural network, named as HydraPlus-Net (HP-net), that multi-directionally feeds the multi-level attention maps to different feature layers. The attentive deep features learned from the proposed HP-net bring unique advantages: (1) the model is capable of capturing multiple attentions from low-level to semantic-level, and (2) it explores the multi-scale selectiveness of attentive features to enrich the final feature representations for a pedestrian image. We demonstrate the effectiveness and generality of the proposed HP-net for pedestrian analysis on two tasks, i.e. pedestrian attribute recognition and person re-identification. Intensive experimental results have been provided to prove that the HP-net outperforms the state-of-the-art methods on various datasets.Comment: Accepted by ICCV 201

Randomized Structural Sparsity via Constrained Block Subsampling for Improved Sensitivity of Discriminative Voxel Identification

In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers. The main difficulty in doing this is an extremely high dimensional voxel space and few training samples, resulting in unreliable feature selection. In order to deal with the difficulty, stability selection has received a great deal of attention lately, especially due to its finite sample control of false discoveries and transparent principle for choosing a proper amount of regularization. However, it fails to make explicit use of the correlation property or structural information of these discriminative features and leads to large false negative rates. In other words, many relevant but probably correlated discriminative voxels are missed. Thus, we propose a new variant on stability selection "randomized structural sparsity", which incorporates the idea of structural sparsity. Numerical experiments demonstrate that our method can be superior in controlling for false negatives while also keeping the control of false positives inherited from stability selection

PV-NAS: Practical Neural Architecture Search for Video Recognition

Recently, deep learning has been utilized to solve video recognition problem due to its prominent representation ability. Deep neural networks for video tasks is highly customized and the design of such networks requires domain experts and costly trial and error tests. Recent advance in network architecture search has boosted the image recognition performance in a large margin. However, automatic designing of video recognition network is less explored. In this study, we propose a practical solution, namely Practical Video Neural Architecture Search (PV-NAS).Our PV-NAS can efficiently search across tremendous large scale of architectures in a novel spatial-temporal network search space using the gradient based search methods. To avoid sticking into sub-optimal solutions, we propose a novel learning rate scheduler to encourage sufficient network diversity of the searched models. Extensive empirical evaluations show that the proposed PV-NAS achieves state-of-the-art performance with much fewer computational resources. 1) Within light-weight models, our PV-NAS-L achieves 78.7% and 62.5% Top-1 accuracy on Kinetics-400 and Something-Something V2, which are better than previous state-of-the-art methods (i.e., TSM) with a large margin (4.6% and 3.4% on each dataset, respectively), and 2) among median-weight models, our PV-NAS-M achieves the best performance (also a new record)in the Something-Something V2 dataset

Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning via Trust Region Decomposition

Non-stationarity is one thorny issue in multi-agent reinforcement learning, which is caused by the policy changes of agents during the learning procedure. Current works to solve this problem have their own limitations in effectiveness and scalability, such as centralized critic and decentralized actor (CCDA), population-based self-play, modeling of others and etc. In this paper, we novelly introduce a $\delta$-stationarity measurement to explicitly model the stationarity of a policy sequence, which is theoretically proved to be proportional to the joint policy divergence. However, simple policy factorization like mean-field approximation will mislead to larger policy divergence, which can be considered as trust region decomposition dilemma. We model the joint policy as a general Markov random field and propose a trust region decomposition network based on message passing to estimate the joint policy divergence more accurately. The Multi-Agent Mirror descent policy algorithm with Trust region decomposition, called MAMT, is established with the purpose to satisfy $\delta$-stationarity. MAMT can adjust the trust region of the local policies adaptively in an end-to-end manner, thereby approximately constraining the divergence of joint policy to alleviate the non-stationary problem. Our method can bring noticeable and stable performance improvement compared with baselines in coordination tasks of different complexity.Comment: 32 pages, 23 figure

Video Generation from Single Semantic Label Map

This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process. Different from typical end-to-end approaches, which model both scene content and dynamics in a single step, we propose to decompose this difficult task into two sub-problems. As current image generation methods do better than video generation in terms of detail, we synthesize high quality content by only generating the first frame. Then we animate the scene based on its semantic meaning to obtain the temporally coherent video, giving us excellent results overall. We employ a cVAE for predicting optical flow as a beneficial intermediate step to generate a video sequence conditioned on the initial single frame. A semantic label map is integrated into the flow prediction module to achieve major improvements in the image-to-video generation process. Extensive experiments on the Cityscapes dataset show that our method outperforms all competing methods.Comment: Paper accepted at CVPR 2019. Source code and models available at https://github.com/junting/seg2vid/tree/maste

CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval

Text-image cross-modal retrieval is a challenging task in the field of language and vision. Most previous approaches independently embed images and sentences into a joint embedding space and compare their similarities. However, previous approaches rarely explore the interactions between images and sentences before calculating similarities in the joint space. Intuitively, when matching between images and sentences, human beings would alternatively attend to regions in images and words in sentences, and select the most salient information considering the interaction between both modalities. In this paper, we propose Cross-modal Adaptive Message Passing (CAMP), which adaptively controls the information flow for message passing across modalities. Our approach not only takes comprehensive and fine-grained cross-modal interactions into account, but also properly handles negative pairs and irrelevant information with an adaptive gating scheme. Moreover, instead of conventional joint embedding approaches for text-image matching, we infer the matching score based on the fused features, and propose a hardest negative binary cross-entropy loss for training. Results on COCO and Flickr30k significantly surpass state-of-the-art methods, demonstrating the effectiveness of our approach.Comment: Accepted by ICCV 201

Stacked Charge Stripes in Quasi-Two_Dimensional Trilayer Nickelate La4Ni3O8

The quasi-two-dimensional nickelate La4Ni3O8 (La-438) is an anion deficient n=3 Ruddlesden-Popper (R-P) phase that consists of trilayer networks of square planar Ni ions, formally assigned as Ni1+ and Ni2+ in a 2:1 ratio. While previous studies on polycrystalline samples have identified a 105 K phase transition with a pronounced electronic and magnetic response but weak lattice character, no consensus on the origin of this transition has been reached. Here we show using synchrotron x-ray diffraction on high-pO2 floating-zone grown single crystals that this transition is driven by a real space ordering of charge into a quasi-2D charge stripe ground state. The charge stripe superlattice propagation vector, q=(2/3, 0, 1), corresponds with that found in the related 1/3-hole doped single layer Ruddlesden-Popper nickelate, La5/3Sr1/3NiO4 (LSNO-1/3, Ni2.33+) with orientation at 45-degrees to the Ni-O bonds. Like LSNO-1/3, the charge stripes in La-438 are weakly correlated along the c axis to form a staggered ABAB stacking that minimizes the Coulomb repulsion among the stripes. Surprisingly, however, we find that the charge stripes within each trilayer of La-438 are stacked in phase from one layer to the next, at odds with any simple Coulomb repulsion argument