Topological Kondo Insulators

This article reviews recent theoretical and experimental work on a new class of topological material - topological Kondo insulators, which develop through the interplay of strong correlations and spin-orbit interactions. The history of Kondo insulators is reviewed along with the theoretical models used to describe these heavy fermion compounds. The Fu-Kane method of topological classification of insulators is used to show that hybridization between the conduction electrons and localized f-electrons in these systems gives rise to interaction-induced topological insulating behavior. Finally, some recent experimental results are discussed, which appear to confirm the theoretical prediction of the topological insulating behavior in Samarium hexaboride, where the long-standing puzzle of the residual low-temperature conductivity has been shown to originate from robust surface states.Comment: Accepted as an article in the Annual Review of Condensed Matter Physics, Volume 7 (2016

Sparse density estimator with tunable kernels

A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators

Dielectric Loss Tangent Extraction using Two Single-Ended Striplines of Different Width

Frequency-dependent electrical properties of dielectric materials are one of the most important factors for high-speed signal integrity design. Recently a method of accurately measuring the dielectric loss tangent ($\tan \delta$) of differential lines was proposed. By taking into account the ratio between the differential and common mode per-unit-length resistances the surface roughness contribution to the total loss is eliminated and dielectric parameters can be determined. In this article a similar method is applied to a combination of two single-ended lines. To evaluate the accuracy of the extraction, the impact of the de-embedding errors was investigated, which allows to optimize the test PCB design. The extraction method was validated in measurement using a PCB with several two-width pairs of strip lines. The extracted loss tangent of several optimal two width pairs of single-ended lines is validated by the SPDR measurements

Hybrid Retrieval-Augmented Generation for Real-time Composition Assistance

Retrieval augmented models show promise in enhancing traditional language models by improving their contextual understanding, integrating private data, and reducing hallucination. However, the processing time required for retrieval augmented large language models poses a challenge when applying them to tasks that require real-time responses, such as composition assistance. To overcome this limitation, we propose the Hybrid Retrieval-Augmented Generation (HybridRAG) framework that leverages a hybrid setting that combines both client and cloud models. HybridRAG incorporates retrieval-augmented memory generated asynchronously by a Large Language Model (LLM) in the cloud. By integrating this retrieval augmented memory, the client model acquires the capability to generate highly effective responses, benefiting from the LLM's capabilities. Furthermore, through asynchronous memory integration, the client model is capable of delivering real-time responses to user requests without the need to wait for memory synchronization from the cloud. Our experiments on Wikitext and Pile subsets show that HybridRAG achieves lower latency than a cloud-based retrieval-augmented LLM, while outperforming client-only models in utility

Unlocking Spatial Comprehension in Text-to-Image Diffusion Models

We propose CompFuser, an image generation pipeline that enhances spatial comprehension and attribute assignment in text-to-image generative models. Our pipeline enables the interpretation of instructions defining spatial relationships between objects in a scene, such as `An image of a gray cat on the left of an orange dog', and generate corresponding images. This is especially important in order to provide more control to the user. CompFuser overcomes the limitation of existing text-to-image diffusion models by decoding the generation of multiple objects into iterative steps: first generating a single object and then editing the image by placing additional objects in their designated positions. To create training data for spatial comprehension and attribute assignment we introduce a synthetic data generation process, that leverages a frozen large language model and a frozen layout-based diffusion model for object placement. We compare our approach to strong baselines and show that our model outperforms state-of-the-art image generation models in spatial comprehension and attribute assignment, despite being 3x to 5x smaller in parameters

Event-based pedestrian detection using dynamic vision sensors

Pedestrian detection has attracted great research attention in video surveillance, traffic statistics, and especially in autonomous driving. To date, almost all pedestrian detection solutions are derived from conventional framed-based image sensors with limited reaction speed and high data redundancy. Dynamic vision sensor (DVS), which is inspired by biological retinas, efficiently captures the visual information with sparse, asynchronous events rather than dense, synchronous frames. It can eliminate redundant data transmission and avoid motion blur or data leakage in high-speed imaging applications. However, it is usually impractical to directly apply the event streams to conventional object detection algorithms. For this issue, we first propose a novel event-to-frame conversion method by integrating the inherent characteristics of events more efficiently. Moreover, we design an improved feature extraction network that can reuse intermediate features to further reduce the computational effort. We evaluate the performance of our proposed method on a custom dataset containing multiple real-world pedestrian scenes. The results indicate that our proposed method raised its pedestrian detection accuracy by about 5.6–10.8%, and its detection speed is nearly 20% faster than previously reported methods. Furthermore, it can achieve a processing speed of about 26 FPS and an AP of 87.43% when implanted on a single CPU so that it fully meets the requirement of real-time detection

Comparative investigations of the crystal structure and photoluminescence property of eulytite-type Ba3Eu(PO4)3 and Sr3Eu(PO4)3

In this study, the Ba3Eu(PO4)3 and Sr3Eu(PO4)3 compounds were synthesized and the crystal structures were determined for the first time by Rietveld refinement using powder X-ray diffraction (XRD) patterns. Ba3Eu-(PO4)3 crystallizes in cubic space group I4¯3d, with cell parameters of a = 10.47996(9) Å, V = 1151.01(3) Å3 and Z = 4; Ba2+ and Eu3+ occupy the same site with partial occupancies of 3/4 and 1/4, respectively. Besides, in this structure, there exists two distorted kinds of the PO4 polyhedra orientation. Sr3Eu(PO4)3 is isostructural to Ba3Eu(PO4)3 and has much smaller cell parameters of a = 10.1203(2) Å, V = 1036.52(5) Å3. The bandgaps of Ba3Eu(PO4)3 and Sr3Eu(PO4)3 are determined to be 4.091 eV and 3.987 eV, respectively, based on the UV–Vis diffuse reflectance spectra. The photoluminescence measurements reveal that, upon 396 nm n-UV light excitation, Ba3Eu(PO4)3 and Sr3Eu(PO4)3 exhibit orange-red emission with two main peaks at 596 nm and prevailing 613 nm, corresponding to the 5D0 → 7F1 and 5D0 → 7F2 transitions of Eu3+, respectively. The dynamic disordering in the crystal structures contributes to the broadening of the luminescence spectra. The electronic structure of the hosphates was calculated by the first-principles method. The analysis elucidats that the band structures are mainly governed by the orbits of phosphorus, oxygen and europium, and the sharp peaks of the europium f-orbit occur at the top of the valence bands

A Segmentation Strategy for Structures with Common Mode Coupling

The level of electromagnetic coupling to electronic devices can vary widely from one device to another. When considering the induced voltage from an incoming plane wave on printed circuit boards (PCBs) and their attached cable harnesses, there is significant variety in the configuration of the devices that could be seen. This encourages the use of segmentation, so that the components of these devices (PCBs, connectors, and harnesses) can be modeled separately to alleviate simulation burden. This allows for a more flexible model and a \u27toolbox\u27 to construct devices with. The goal of this work is to use segmentation to model the external electromagnetic radiation from these devices. The radiation pattern and reciprocity theory can later be used to calculate the voltage coupled from an incident plane wave. Most realistic devices exhibit strong common mode (or antenna mode) coupling that cannot be ignored during segmentation. When segmenting such structures, a multi-modal approach is needed to incorporate coupling from both the common (CM) and differential (DM) modes and to allow these currents to flow properly between the blocks. This work introduces the concept by segmenting a simple dipole, which requires the common mode only, and then applies the complete methodology to a more complicated structure that requires the incorporation of both modes