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Most reported negative linear compressibility (NLC) materials exhibit either a small NLC over a large pressure range or a high NLC over a very small pressure range. Here, we report the remarkable discovery of giant NLC in the low-temperature form of CuCN (LT-CuCN) over an unusually large pressure range. High-pressure XRD studies on LT-CuCN observed the NLC of -20.5 TPa-1 along the a-axis at zero pressure, and the ambient orthorhombic phase remained stable up to 9.8 GPa. Pressure and temperature-dependent Raman studies identified the phonon vibrations responsible for NLC and negative thermal expansion (NTE).

The spatio-temporal complexity of video data presents significant challenges in tasks such as compression, generation, and inpainting. We present four key contributions to address the challenges of spatiotemporal video processing. First, we introduce the 3D Mobile Inverted Vector-Quantization Variational Autoencoder (3D-MBQ-VAE), which combines Variational Autoencoders (VAEs) with masked token modeling to enhance spatiotemporal video compression. The model achieves superior temporal consistency and state-of-the-art (SOTA) reconstruction quality by employing a novel training strategy with full frame masking. Second, we present MotionAura, a text-to-video generation framework that utilizes vector-quantized diffusion models to discretize the latent space and capture complex motion dynamics, producing temporally coherent videos aligned with text prompts. Third, we propose a spectral transformer-based denoising network that processes video data in the frequency domain using the Fourier Transform. This method effectively captures global context and long-range dependencies for high-quality video generation and denoising. Lastly, we introduce a downstream task of Sketch Guided Video Inpainting. This task leverages Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. Our models achieve SOTA performance on a range of benchmarks. Our work offers robust frameworks for spatiotemporal modeling and user-driven video content manipulation. We will release the code, datasets, and models in open-source., Comment: Under submission at a conference

The field of bio-inspired soft grippers has emerged as a transformative area of research with profound implications for biomedical applications. This book chapter provides a comprehensive overview of the principles, developments, challenges, and prospects of soft grippers that draw inspiration from biological systems. Bio-inspired soft grippers have gained prominence due to their unique characteristics, including compliance, adaptability, and biocompatibility. They have revolutionized the way we approach biomedical tasks, offering safer interactions with delicate tissues and enabling complex operations that were once inconceivable with rigid tools. The chapter delves into the fundamental importance of soft grippers in biomedical contexts. It outlines their significance in surgeries, diagnostics, tissue engineering, and various medical interventions. Soft grippers have the capacity to mimic the intricate movements of biological organisms, facilitating minimally invasive procedures and enhancing patient outcomes. A historical perspective traces the evolution of soft grippers in biomedical research, highlighting key milestones and breakthroughs. From early attempts to emulate the dexterity of octopus tentacles to the latest advancements in soft lithography and biomaterials, the journey has been marked by ingenuity and collaboration across multiple disciplines. Motivations for adopting soft grippers in biomedical applications are explored, emphasizing their ability to reduce invasiveness, increase precision, and provide adaptability to complex anatomical structures. The requirements and challenges in designing grippers fit for medical contexts are outlined, encompassing biocompatibility, sterilization, control, and integration.

Almost in every heavily computation-dependent application, from 6G communication systems to autonomous driving platforms, a large portion of computing should be near to the client side. Edge computing (AI at Edge) in mobile devices is one of the optimized approaches for addressing this requirement. Therefore, in this work, the possibilities and challenges of implementing a low-latency and power-optimized smart mobile system are examined. Utilizing Field Programmable Gate Array (FPGA) based solutions at the edge will lead to bandwidth-optimized designs and as a consequence can boost the computational effectiveness at a system-level deadline. Moreover, various performance aspects and implementation feasibilities of Neural Networks (NNs) on both embedded FPGA edge devices (using Xilinx Multiprocessor System on Chip (MPSoC)) and Cloud are discussed throughout this research. The main goal of this work is to demonstrate a hybrid system that uses the deep learning programmable engine developed by Xilinx Inc. as the main component of the hardware accelerator. Then based on this design, an efficient system for mobile edge computing is represented by utilizing an embedded solution., Comment: 25. ITG Fachtagung Mobilkommunikation

We theoretically investigate the transport signature of single and multiple Floquet Majorana end modes~(FMEMs), appearing in an experimentally feasible setup with Rashba nanowire~(NW) placed in closed proximity to a conventional $s$-wave superconductor, in the presence of an external Zeeman field. Periodic drive causes the anomalous $\pi$-modes to emerge in addition to the regular $0$-modes in the driven system where the former does not exhibit any static analog. For single $0$- and/or $\pi$-FMEM, differential conductance exhibits a quantized value of $2e^{2}/h$ while we consider the sum over all the photon sectors, supporting Floquet sum rule. We examine the stability of this summed conductance against random onsite disorder. We further investigate the summed conductance in several cases hosting multiple~(more than one) $0$- or $\pi$-modes at the end of the NW. In these cases, we obtain quantized values of $n\times 2e^{2}/h$ of summed conductance with $n$ being the number of modes~($0$ / $\pi$) located at one end of NW. We repeat our analysis for another experimentally realizable model system known as helical Shiba chain. Moreover, we corroborate our results by computing the differential conductance for FMEMs using non-equilibrium Green's function method. Our work opens up the possibility of studying the transport signatures of FMEMs in these realistic models., Comment: 20 Pages, 12 PDF Figures, Comments are welcome

n this study, we utilize a rigorous ab initio-based finite momentum Bethe-Salpeter equation to investigate the photoluminescence emission in two-dimensional hexagonal tungsten carbide (h-WC). This thermodynamically stable monolayer exhibits an indirect optical gap, resulting in phonon-assisted emission. We observe that light absorption is a direct process centered around the direct quasiparticle gap, while light emission is indirect and requires modes between $\Gamma$-$M$ in the phonon dispersion. The emission lines feature prominent phonon replicas at cryogenic temperatures, particularly near-infrared wavelengths (1.09 and 1.17 eV), and we observe exciton thermalization with the crystal beyond 25 K. Additionally, non-radiative recombination is a remarkably fast process, occurring at order of a few femtoseconds (4.8 fs at 0 K and 2.8 fs at 300 K) compared to radiative recombination (2.3 ps at 0 K and 214 ns at 300 K). These optical characteristics of 2D h-WC may facilitate the promise of photon-emitter devices for near-infrared signal communication.

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.