Online Open-set Semi-supervised Object Detection via Semi-supervised Outlier Filtering

Open-set semi-supervised object detection (OSSOD) methods aim to utilize practical unlabeled datasets with out-of-distribution (OOD) instances for object detection. The main challenge in OSSOD is distinguishing and filtering the OOD instances from the in-distribution (ID) instances during pseudo-labeling. The previous method uses an offline OOD detection network trained only with labeled data for solving this problem. However, the scarcity of available data limits the potential for improvement. Meanwhile, training separately leads to low efficiency. To alleviate the above issues, this paper proposes a novel end-to-end online framework that improves performance and efficiency by mining more valuable instances from unlabeled data. Specifically, we first propose a semi-supervised OOD detection strategy to mine valuable ID and OOD instances in unlabeled datasets for training. Then, we constitute an online end-to-end trainable OSSOD framework by integrating the OOD detection head into the object detector, making it jointly trainable with the original detection task. Our experimental results show that our method works well on several benchmarks, including the partially labeled COCO dataset with open-set classes and the fully labeled COCO dataset with the additional large-scale open-set unlabeled dataset, OpenImages. Compared with previous OSSOD methods, our approach achieves the best performance on COCO with OpenImages by +0.94 mAP, reaching 44.07 mAP

Magnetic Borophenes from an Evolutionary Search

A computational methodology based on ab initio evolutionary algorithms and spin-polarized density functional theory was developed to predict two-dimensional magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A metastable borophene with nonzero thickness is an antiferromagnetic semiconductor from first-principles calculations, and can be further tuned into a half-metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for magnetism, but also result in an out-of-plane negative Poisson\u27s ratio under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties simultaneously

The linear and nonlinear Jaynes-Cummings model for the multiphoton transition

With the Jaynes-Cummings model, we have studied the atom and light field quantum entanglement of multiphoton transition, and researched the effect of initial state superposition coefficient $C_{1}$, the transition photon number $N$, the quantum discord $\delta$ and the nonlinear coefficient $\chi$ on the quantum entanglement degrees. We have given the quantum entanglement degrees curves with time evolution, and obtained some results, which should have been used in quantum computing and quantum information.Comment: arXiv admin note: text overlap with arXiv:1404.0821, arXiv:1205.0979 by other author

Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning

Recent progress in interactive zero-knowledge (ZK) proofs has improved the efficiency of proving large-scale computations significantly. Nevertheless, real-life applications (e.g., in the context of private inference using deep neural networks) often involve highly complex computations, and existing ZK protocols lack the expressiveness and scalability to prove results about such computations efficiently. In this paper, we design, develop, and evaluate a ZK system (Mystique) that allows for efficient conversions between arithmetic and Boolean values, between publicly committed and privately authenticated values, and between fixed-point and floating-point numbers. Targeting large-scale neural-network inference, we also present an improved ZK protocol for matrix multiplication that yields a 7× improvement compared to the state-of-the-art. Finally, we incorporate Mystique in Rosetta, a TensorFlow-based privacy-preserving framework. Mystique is able to prove correctness of an inference on a private image using a committed (private) ResNet-101 model in 28 minutes, and can do the same task when the model is public in 5 minutes, with only a 0.02% decrease in accuracy compared to a non-ZK execution when testing on the CIFAR-10 dataset. Our system is the first to support ZK proofs about neural-network models with over 100 layers with virtually no loss of accuracy

AntMan: Interactive Zero-Knowledge Proofs with Sublinear Communication

Recent works on interactive zero-knowledge (ZK) protocols provide a new paradigm with high efficiency and scalability. However, these protocols suffer from high communication overhead, often linear to the circuit size. In this paper, we proposed two new ZK protocols with communication sublinear to the circuit size, while maintaining a similar level of computational efficiency. -- We designed a ZK protocol that can prove $B$ executions of any circuit $C$ in communication $O(B + |C|)$ field elements (with free addition gates), while the best prior work requires a communication of $O(B|C|)$ field elements. Our protocol is enabled by a new tool called as information-theoretic polynomial authentication code, which may be of independent interest. -- We developed an optimized implementation of this protocol which shows high practicality. For example, with $B=2048$, $|C|=2^{20}$, and under 50 Mbps bandwidth and 16 threads, QuickSilver, a state-of-the-art ZK protocol based on vector oblivious linear evaluation (VOLE), can only prove $0.78$ million MULT gates per second (mgps) and send one field element per gate; our protocol can prove $14$ mgps ($18\times$ improvement) and send $0.0064$ field elements per gate ($156\times$ improvement) under the same hardware configuration. -- Extending the above idea, we constructed a ZK protocol that can prove a single execution of any circuit $C$ in communication $O(|C|^{3/4})$. This is the first ZK protocol with sublinear communication for an arbitrary circuit in the VOLE-based ZK family

Helium-bearing superconductor at high pressure

Helium (He) is the most inert noble gas at ambient conditions. It adopts a hexagonal close packed structure (P63/mmc) and remains in the insulating phase up to 32 TPa. In contrast, lithium (Li) is one of the most reactive metals at zero pressure, while its cubic high-pressure phase (Fd-3m) is a weak metallic electride above 475 GPa. Strikingly, a stable compound of Li5He2 (R-3m) was formed by mixing Fd-3m Li with P63/mmc He above 700 GPa. The presence of helium promotes the lattice transformation from Fd-3m Li to Pm-3m Li, and tuns the three-dimensional distributed interstitial electrons into the mixture of zero- and two-dimensional anionic electrons. This significantly increases the degree of metallization at the Fermi level, consequently, the coupling of conductive anionic electrons with the Li-dominated vibrations is the key factor to the formation of superconducting electride Li5He2 with a transition temperature up to 26 K, dynamically stable to pressures down to 210 GPa.Comment: 5 pages, 3 figure