85 research outputs found
Robust Perception through Equivariance
Deep networks for computer vision are not reliable when they encounter
adversarial examples. In this paper, we introduce a framework that uses the
dense intrinsic constraints in natural images to robustify inference. By
introducing constraints at inference time, we can shift the burden of
robustness from training to the inference algorithm, thereby allowing the model
to adjust dynamically to each individual image's unique and potentially novel
characteristics at inference time. Among different constraints, we find that
equivariance-based constraints are most effective, because they allow dense
constraints in the feature space without overly constraining the representation
at a fine-grained level. Our theoretical results validate the importance of
having such dense constraints at inference time. Our empirical experiments show
that restoring feature equivariance at inference time defends against
worst-case adversarial perturbations. The method obtains improved adversarial
robustness on four datasets (ImageNet, Cityscapes, PASCAL VOC, and MS-COCO) on
image recognition, semantic segmentation, and instance segmentation tasks.
Project page is available at equi4robust.cs.columbia.edu
Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
Large-scale first principles molecular dynamics are crucial for simulating
complex processes in chemical, biomedical, and materials sciences. However, the
unfavorable time complexity with respect to system sizes leads to prohibitive
computational costs when the simulation contains over a few hundred atoms in
practice. We present an In-Distribution substructure Embedding Active Learner
(IDEAL) to enable efficient simulation of large complex systems with quantum
accuracy by maintaining a machine learning force field (MLFF) as an accurate
surrogate to the first principles methods. By extracting high-uncertainty
substructures into low-uncertainty atom environments, the active learner is
allowed to concentrate on and learn from small substructures of interest rather
than carrying out intractable quantum chemical computations on large
structures. IDEAL is benchmarked on various systems and shows sub-linear
complexity, accelerating the simulation thousands of times compared with
conventional active learning and millions of times compared with pure first
principles simulations. To demonstrate the capability of IDEAL in practical
applications, we simulated a polycrystalline lithium system composed of one
million atoms and the full ammonia formation process in a Haber-Bosch reaction
on a 3-nm Iridium nanoparticle catalyst on a computing node comprising one
single A100 GPU and 24 CPU cores
Effect of Exogenous Phosphate on the Lability and Phytoavailability of Arsenic in Soils
The effect of exogenous phosphate (P, 200 mg·kg-1 soil) on the lability and phyto-availability of arsenic (As) was studied using the diffusive gradients in thin films (DGT) technique. Lettuce were grown on the As-amended soils following the stabilization of soil labile As after 90 day’s incubation. Phosphate (P) application generally facilitated plant growth except one grown on P-sufficient soil. Soil labile As concentration increased in all the soils after P application due to a competition effect. Plant As concentration increased in red soils collected from Hunan Province, while decreases were observed in the other soils. Even though, an overall trend of decrease was obtained in As phytoavailability along with the increase of DGT-measured soil labile P/As molar ratio. The functional equation between P/As and As phytoavailability provided a critical value of 1.7, which could be used as a guidance for rational P fertilization, thus avoiding overfertilization
Subject-independent EEG classification based on a hybrid neural network
A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI
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Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network
Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties
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