Polarized neutron reflectometry (PNR) is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge to parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from PNR data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator (TI)-ferromagnetic insulator heterostructure Bi$_2$Se$_3$/EuS, exhibiting proximity magnetism, in good agreement with the results of conventional fitting. We further analyze a more challenging PNR profile of the TI-antiferromagnet heterostructure (Bi,Sb)$_2$Te$_3$/Cr$_2$O$_3$, and identify possible interfacial proximity magnetism in this material. We anticipate the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.
Following the discovery of a new family of kagome prototypical materials with structure AV$_3$Sb$_5$ (A = K, Rb, Cs), there has been heightened interest in studying correlation-driven electronic phenomena in these kagome lattice systems. The study of these materials has gone beyond magneto-transport measurements to reveal exciting features such as Dirac bands, anomalous Hall effect, bulk superconductivity with $T_c$ $sim$ 0.9 K-2.5 K, and the observation of charge density wave instabilities which suggests an intertwining of topological physics and new quantum orders. Moreover, very recent works on numerous types of experiments have appeared further examining the unconventional superconductivity and the exotic electronic states found within these kagome materials. Theories on the strong interactions that play a role in these systems have been proposed to shed light on the nature of these topological charge density waves. In this brief review, we summarize these recent experimental findings and theoretical proposals to connect them with the concepts of topological physics and strongly-correlated electron systems.
Carbonyl sulfide (OCS) is an abundant sulfur (S)-bearing species in the interstellar medium. It is present not only in the gas phase, but also on interstellar grains as as solid; therefore, OCS very likely undergoes physico-chemical processes on icy surfaces at very low temperatures. The present study experimentally and computationally investigates the reaction of solid OCS with hydrogen (H) atoms on amorphous solid water at low temperatures. The results show that H addition to OCS proceeds via quantum tunneling, and further H addition leads to the formation of carbon monoxide (CO), hydrogen sulfide (H2S), formaldehyde (H2CO), methanol (CH3OH) and thioformic acid (HC(O)SH). These experimental results are explained by our quantum chemical calculations, which demonstrate that the initial H addition to the S atom of OCS is the most predominant, leading to the formation of OCS-H radicals. Once the formed OCS-H radical is stabilized on ices, further H addtion to the S atom yields CO and H2S, while that to the C atom yields HC(O)SH. We have also confirmed, in a separate experiment, the HCOSH formation by the HCO reactions with the SH radicals. The present results would have an important implication for the recent detection of HC(O)SH toward H+0.693-0.027.
In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset as a new benchmark corpus built based on a strict annotation schemes for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese aspect based sentiment task. Our experiments show that our approach achieves the best performances with the F1-score of 84.48% for the aspect task and 63.06% for the sentiment task, which performs several conventional machine learning and deep learning systems. Last but not least, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in future.
Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To overcome this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural network (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16x2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity.
Reconfigurable intelligent surfaces (RISs) are considered as potential technologies for the upcoming sixth-generation (6G) wireless communication system. Various benefits brought by deploying one or multiple RISs include increased spectrum and energy efficiency, enhanced connectivity, extended communication coverage, reduced complexity at transceivers, and even improved localization accuracy. However, to unleash their full potential, fundamentals related to RISs, ranging from physical-layer (PHY) modelling to RIS phase control, need to be addressed thoroughly. In this paper, we provide an overview of some timely research problems related to the RIS technology, i.e., PHY modelling (including also physics), channel estimation, potential RIS architectures, and RIS phase control (via both model-based and data-driven approaches), along with recent numerical results. We envision that more efforts will be devoted towards intelligent wireless environments, enabled by RISs.
In real-world video surveillance applications, person re-identification (ReID) suffers from the effects of occlusions and detection errors. Despite recent advances, occlusions continue to corrupt the features extracted by state-of-art CNN backbones, and thereby deteriorate the accuracy of ReID systems. To address this issue, methods in the literature use an additional costly process such as pose estimation, where pose maps provide supervision to exclude occluded regions. In contrast, we introduce a novel Holistic Guidance (HG) method that relies only on person identity labels, and on the distribution of pairwise matching distances of datasets to alleviate the problem of occlusion, without requiring additional supervision. Hence, our proposed student-teacher framework is trained to address the occlusion problem by matching the distributions of between- and within-class distances (DCDs) of occluded samples with that of holistic (non-occluded) samples, thereby using the latter as a soft labeled reference to learn well separated DCDs. This approach is supported by our empirical study where the distribution of between- and within-class distances between images have more overlap in occluded than holistic datasets. In particular, features extracted from both datasets are jointly learned using the student model to produce an attention map that allows separating visible regions from occluded ones. In addition to this, a joint generative-discriminative backbone is trained with a denoising autoencoder, allowing the system to self-recover from occlusions. Extensive experiments on several challenging public datasets indicate that the proposed approach can outperform state-of-the-art methods on both occluded and holistic datasets
Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions. While the problem of single-target domain adaptation (STDA) for object detection has recently received much attention, multi-target domain adaptation (MTDA) remains largely unexplored, despite its practical relevance in several real-world applications, such as multi-camera video surveillance. Compared to the STDA problem that may involve large domain shifts between complex source and target distributions, MTDA faces additional challenges, most notably the computational requirements and catastrophic forgetting of previously-learned targets, which can depend on the order of target adaptations. STDA for detection can be applied to MTDA by adapting one model per target, or one common model with a mixture of data from target domains. However, these approaches are either costly or inaccurate. The only state-of-art MTDA method specialized for detection learns targets incrementally, one target at a time, and mitigates the loss of knowledge by using a duplicated detection model for knowledge distillation, which is computationally expensive and does not scale well to many domains. In this paper, we introduce an efficient approach for incremental learning that generalizes well to multiple target domains. Our MTDA approach is more suitable for real-world applications since it allows updating the detection model incrementally, without storing data from previous-learned target domains, nor retraining when a new target domain becomes available. Our proposed method, MTDA-DTM, achieved the highest level of detection accuracy compared against state-of-the-art approaches on several MTDA detection benchmarks and Wildtrack, a benchmark for multi-camera pedestrian detection.
Reconfigurable intelligent surfaces (RISs) have emerged as a cost- and energy-efficient technology that can customize and program the physical propagation environment by reflecting radio waves in preferred directions. However, the purely passive reflection of RISs not only limits the end-to-end channel beamforming gains, but also hinders the acquisition of accurate channel state information for the phase control at RISs. In this paper, we provide an overview of a hybrid relay-reflecting intelligent surface (HR-RIS) architecture, in which only a few elements are active and connected to power amplifiers and radio frequency chains. The introduction of a small number of active elements enables a remarkable system performance improvement which can also compensate for losses due to hardware impairments such as the deployment of limited-resolution phase shifters. Particularly, the active processing facilitates efficient channel estimation and localization at HR-RISs. We present two practical architectures for HR-RISs, namely, fixed and dynamic HR-RISs, and discuss their applications to beamforming, channel estimation, and localization. The benefits, key challenges, and future research directions for HR-RIS-aided communications are also highlighted. Numerical results for an exemplary deployment scenario show that HR-RISs with only four active elements can attain up to 42.8 percent and 41.8 percent improvement in spectral efficiency and energy efficiency, respectively, compared with conventional RISs.
The interplay between strong electron correlation and band topology is at the forefront of condensed matter research. As a direct consequence of correlation, magnetism enriches topological phases and also has promising functional applications. However, the influence of topology on magnetism remains unclear, and the main research effort has been limited to ground state magnetic orders. Here we report a novel order above the magnetic transition temperature in magnetic Weyl semimetal (WSM) CeAlGe. Such order shows a number of anomalies in electrical and thermal transport, and neutron scattering measurements. We attribute this order to the coupling of Weyl fermions and magnetic fluctuations originating from a three-dimensional Seiberg-Witten monopole, which qualitatively agrees well with the observations. Our work reveals a prominent role topology may play in tailoring electron correlation beyond ground state ordering, and offers a new avenue to investigate emergent electronic properties in magnetic topological materials.
