熱電材料クラスレートにおける調和フォノンと非調和フォノンおよび関連する電子物性)

Tohoku University寺内正己課

Gap Structure of the Overdoped Iron-Pnictide Superconductor Ba(Fe0.942_{0.942}Ni0.058_{0.058})2_{2}As2_{2}: A Low-Temperature Specific-Heat Study

Low-temperature specific heat (SH) is measured on the postannealed Ba(Fe_{1-x}Ni_x)_2As_2 single crystal with x = 0.058 under different magnetic fields. The sample locates on the overdoped sides and the critical transition temperature is determined to be 14.8 K by both the magnetization and SH measurements. A simple and reliable analysis shows that, besides the phonon and normal electronic contributions, a clear T2 termemerges in the low temperature SH data.Our observation is similar to that observed in the Co-doped system in our previous work and is consistent with the theoretical prediction for a superconductor with line nodes in the energy gap.Comment: 5 pages, 4 figure

Natural van der Waals heterostructural single crystals with both magnetic and topological properties

Heterostructures having both magnetism and topology are promising materials for the realization of exotic topological quantum states while challenging in synthesis and engineering. Here, we report natural magnetic van der Waals heterostructures of (MnBi2Te4)m(Bi2Te3)n that exhibit controllable magnetic properties while maintaining their topological surface states. The interlayer antiferromagnetic exchange coupling is gradually weakened as the separation of magnetic layers increases, and an anomalous Hall effect that is well coupled with magnetization and shows ferromagnetic hysteresis was observed below 5 K. The obtained homogeneous heterostructure with atomically sharp interface and intrinsic magnetic properties will be an ideal platform for studying the quantum anomalous Hall effect, axion insulator states, and the topological magnetoelectric effect.Comment: 40 pages, 15 figure

Validating Multimedia Content Moderation Software via Semantic Fusion

The exponential growth of social media platforms, such as Facebook and TikTok, has revolutionized communication and content publication in human society. Users on these platforms can publish multimedia content that delivers information via the combination of text, audio, images, and video. Meanwhile, the multimedia content release facility has been increasingly exploited to propagate toxic content, such as hate speech, malicious advertisements, and pornography. To this end, content moderation software has been widely deployed on these platforms to detect and blocks toxic content. However, due to the complexity of content moderation models and the difficulty of understanding information across multiple modalities, existing content moderation software can fail to detect toxic content, which often leads to extremely negative impacts. We introduce Semantic Fusion, a general, effective methodology for validating multimedia content moderation software. Our key idea is to fuse two or more existing single-modal inputs (e.g., a textual sentence and an image) into a new input that combines the semantics of its ancestors in a novel manner and has toxic nature by construction. This fused input is then used for validating multimedia content moderation software. We realized Semantic Fusion as DUO, a practical content moderation software testing tool. In our evaluation, we employ DUO to test five commercial content moderation software and two state-of-the-art models against three kinds of toxic content. The results show that DUO achieves up to 100% error finding rate (EFR) when testing moderation software. In addition, we leverage the test cases generated by DUO to retrain the two models we explored, which largely improves model robustness while maintaining the accuracy on the original test set.Comment: Accepted by ISSTA 202

Privacy-Preserving Face Recognition Using Random Frequency Components

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.Comment: ICCV 202

Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain

Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the current high accuracy is guaranteed by very deep network structures, facial images often need to be transmitted to third-party servers with high computational power for inference. However, facial images visually reveal the user's identity information. In this process, both untrusted service providers and malicious users can significantly increase the risk of a personal privacy breach. Current privacy-preserving approaches to face recognition are often accompanied by many side effects, such as a significant increase in inference time or a noticeable decrease in recognition accuracy. This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Due to the utilization of differential privacy, it offers a guarantee of privacy in theory. Meanwhile, the loss of accuracy is very slight. This method first converts the original image to the frequency domain and removes the direct component termed DC. Then a privacy budget allocation method can be learned based on the loss of the back-end face recognition network within the differential privacy framework. Finally, it adds the corresponding noise to the frequency domain features. Our method performs very well with several classical face recognition test sets according to the extensive experiments.Comment: ECCV 2022; Code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/dctd

An unexpected twist to the activation of IKKβ:TAK1 primes IKKβ for activation by autophosphorylation

IKKβ {IκB [inhibitor of NF-κB (nuclear factor κB)] kinase β} is required to activate the transcription factor NF-κB, but how IKKβ itself is activated in vivo is still unclear. It was found to require phosphorylation by one or more ‘upstream’ protein kinases in some reports, but by autophosphorylation in others. In the present study, we resolve this contro-versy by demonstrating that the activation of IKKβ induced by IL-1 (interleukin-1) or TNF (tumour necrosis factor) in embryonic fibroblasts, or by ligands that activate Toll-like receptors in macrophages, requires two distinct phosphorylation events: first, the TAK1 [TGFβ (transforming growth factor β)-activated kinase-1]-catalysed phosphorylation of Ser(177) and, secondly, the IKKβ-catalysed autophosphorylation of Ser(181). The phosphorylation of Ser(177) by TAK1 is a priming event required for the subsequent autophosphorylation of Ser(181), which enables IKKβ to phosphorylate exogenous substrates. We also provide genetic evidence which indicates that the IL-1-stimulated, LUBAC (linear ubiquitin chain assembly complex)-catalysed formation of linear ubiquitin chains and their interaction with the NEMO (NF-κB essential modulator) component of the canonical IKK complex permits the TAK1-catalysed priming phosphorylation of IKKβ at Ser(177) and IKKα at Ser(176). These findings may be of general significance for the activation of other protein kinases

Rv1985c, a promising novel antigen for diagnosis of tuberculosis infection from BCG-vaccinated controls

<p>Abstract</p> <p>Background</p> <p>Antigens encoded in the region of difference (RD) of <it>Mycobacterium tuberculosis </it>constitute a potential source of specific antigens for immunodiagnosis. In the present study, recombinant protein Rv1985c from RD2 was cloned, expressed, purified, immunologically characterized and investigated for its potentially diagnostic value for tuberculosis (TB) infection among BCG-vaccinated individuals.</p> <p>Methods</p> <p>T-cell response to Rv1985c was evaluated by IFN-γ ELISPOT in 56 TB patients, 20 latent TB infection (LTBI) and 30 BCG-vaccinated controls in comparison with the commercial T-SPOT. <it>TB </it>kit. Humoral response was evaluated by ELISA in 117 TB patients, 45 LTBI and 67 BCG-vaccinated controls, including all those who had T-cell assay, in comparison with a commercial IgG kit.</p> <p>Results</p> <p>Rv1985c was specifically recognized by cellular and humoral responses from both TB and LTBI groups compared with healthy controls. Rv1985c IgG-ELISA achieved 52% and 62% sensitivity respectively, which outperformed the sensitivity of PATHOZYME-MYCO kit (34%) in detecting active TB (P = 0.011), whereas IFN-γ Rv1985c-ELISPOT achieved 71% and 55% sensitivity in detecting active and LTBI, respectively. Addition of Rv1985c increased sensitivities of ESAT-6, CFP-10 and ESAT-6/CFP-10 combination in detecting TB from 82.1% to 89.2% (P = 0.125), 67.9% to 87.5% (P < 0.001) and 85.7% to 92.9% (P = 0.125), respectively.</p> <p>Conclusions</p> <p>In conclusion, Rv1985c is a novel antigen which can be used to immunologically diagnose TB infection along with other immunodominant antigens among BCG-vaccinated population.</p