Magnetic and thermal properties of metallic antiferromagnetic materials

Metallic antiferromagnetic materials are of great potential in spintronics due to their insensitivity to external fields and faster dynamics compared to typical ferromagnetic materials. Although they have these advantages, studies of their order parameter is difficult to perform because of the lack of net magnetization. The linear magneto-optic Kerr effect (MOKE) is often used to probe magnetism of ferromagnetic materials, but MOKE cannot be applied to collinear antiferromagnets due to the cancellation of sublattice magnetization. Magneto-optic constants that are quadratic in magnetization, however, provide an approach for studying antiferromagnets on picosecond timescales. I combined transient measurements of linear birefringence and optical reflectivity to study the optical response of Fe2As to small ultrafast temperature excursions. We performed temperature-dependent pumpprobe measurements on crystallographically isotropic (001) and anisotropic (010) faces of Fe2As bulk crystals and found that the largest optical signals arise from changes in the index of refraction along the z-axis, perpendicular to the Neel vector. Both real and imaginary parts of the transient optical birefringence signal approximately follow the temperature dependence of the magnetic heat capacity, as expected if the changes in dielectric function are dominated by contributions of exchange interactions to the dielectric function. In spintronic devices, it is essential to determine the dynamics of magnetic precession, the frequency of spin waves, the thermal stability of magnetic domains, and the efficiency. Thus magnetocrystalline anisotropy is a fundamental property of antiferromagnetic materials. Torque magnetometry measurements of Fe2As were performed. We reported that the four-fold magnetocrystalline anisotropy K22 in the (001)-plane of Fe2As is K22 = −150 kJ/m^3 at T = 4 K, much smaller than the perpendicular magnetic anisotropy of ferromagnetic materials structure widely used in spintronics device. K22 is strongly temperature dependent and close to zero at T > 150 K. The anisotropy K1 in the (010) plane is too large to be measured by torque magnetometry and we determine K1 = −830 kJ/m^3 using first-principles density functional theory. Our simulations show that the contribution to the anisotropy from classical magnetic dipole-dipole interactions is comparable to the contribution from spin-orbit coupling. The calculated four-fold anisotropy in the (001) plane K22 ranges from −290 to 280 kJ/m^3, the same order of magnitude as the measured value. We used K1 from theory to predict the frequency and polarization of the lowest frequency antiferromagnetic resonance mode and find that the mode is linearly polarized in the (001)-plane with frequency 670 GHz. As we observed The field-dependent domain distribution and quadratic magnetization can potentially be measured with optical technique. We set up a static system for imaging in-plane magnetic domains. To test this system, I measured the quadratic MOKE coefficient of ferromagnetic cobalt and YIG thin films, and the field-dependent quadratic magneto-optical signal of Fe2As. The noise floor of the mapping system is determined to be ∼ 10^−

Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems

Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavior of ML systems, especially for different corner cases, is of growing importance. In this paper, we propose a generic framework for evaluating security and robustness of ML systems using different real-world safety properties. We further design, implement and evaluate VeriVis, a scalable methodology that can verify a diverse set of safety properties for state-of-the-art computer vision systems with only blackbox access. VeriVis leverage different input space reduction techniques for efficient verification of different safety properties. VeriVis is able to find thousands of safety violations in fifteen state-of-the-art computer vision systems including ten Deep Neural Networks (DNNs) such as Inception-v3 and Nvidia's Dave self-driving system with thousands of neurons as well as five commercial third-party vision APIs including Google vision and Clarifai for twelve different safety properties. Furthermore, VeriVis can successfully verify local safety properties, on average, for around 31.7% of the test images. VeriVis finds up to 64.8x more violations than existing gradient-based methods that, unlike VeriVis, cannot ensure non-existence of any violations. Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60.2%.Comment: 16 pages, 11 tables, 11 figure

Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)

It is beneficial to develop an efficient machine-learning based method for addition using embedded hexadecimal digits. Through a comparison between human-developed machine learning model and models sampled through Neural Architecture Search (NAS) we determine an efficient approach to solve this problem with a final testing loss of 0.2937 for a human-developed model

CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation

Audio-visual video segmentation~(AVVS) aims to generate pixel-level maps of sound-producing objects within image frames and ensure the maps faithfully adhere to the given audio, such as identifying and segmenting a singing person in a video. However, existing methods exhibit two limitations: 1) they address video temporal features and audio-visual interactive features separately, disregarding the inherent spatial-temporal dependence of combined audio and video, and 2) they inadequately introduce audio constraints and object-level information during the decoding stage, resulting in segmentation outcomes that fail to comply with audio directives. To tackle these issues, we propose a decoupled audio-video transformer that combines audio and video features from their respective temporal and spatial dimensions, capturing their combined dependence. To optimize memory consumption, we design a block, which, when stacked, enables capturing audio-visual fine-grained combinatorial-dependence in a memory-efficient manner. Additionally, we introduce audio-constrained queries during the decoding phase. These queries contain rich object-level information, ensuring the decoded mask adheres to the sounds. Experimental results confirm our approach's effectiveness, with our framework achieving a new SOTA performance on all three datasets using two backbones. The code is available at \url{https://github.com/aspirinone/CATR.github.io}Comment: accepted by ACM MM 202