Epitaxial ferroelectric hafnia stabilized by symmetry constraints

Ferroelectric memories experienced a revival in the last decade due to the discovery of ferroelectricity in HfO$_2$-based nanometer-thick thin films. These films exhibit exceptional silicon compatibility, overcoming the scaling and integration obstacles that impeded perovskite ferroelectrics' use in high-density integrated circuits. The exact phase responsible for ferroelectricity in hafnia films remains debated with no single factor identified that could stabilize the ferroelectric phase thermodynamically. Here, supported by density functional theory (DFT) high-throughput (HT) calculations that screen a broad range of epitaxial conditions, we demonstrate conclusively that specific epitaxial conditions achievable with common substrates such as yttria-stabilized zirconia (YSZ) and SrTiO$_3$ can favor the polar Pca2$_1$ phase thermodynamically over other polar phases such as R3m and Pmn2$_1$ and nonpolar P2$_1$/c phase. The substrate's symmetry constraint-induced shear strain is crucial for the preference of Pca2$_1$. The strain-stability phase diagrams resolve experiment-theory discrepancies and can guide the improvement of ferroelectric properties of epitaxial hafnia thin films

A Non-topological Extension of Bending-immune Valley Topological Edge States

Breaking parity (P) symmetry in C$_6$ symmetric crystals is a common routine to implement a valley-topological phase. At an interface between two crystals of opposite valley phases, the so-called valley topological edge states emerge, and they have been proven useful for wave transport with robustness against 120$^\circ$ bending and a certain level of disorder. However, whether these attractive transport features are bound with the valley topology or due to topological-irrelevant mechanisms remains unclear. In this letter, we discuss this question by examining transport properties of photonic edge states with varied degrees of the P-breaking that tune the valley topology, and reveal that the edge states preserve their transport robustness insensitive to the topology even when the P-symmetry is recovered. Instead, a unique modal character of the edge states -- with localized momentum hotspots around high-symmetric $K$ ($K'$) points -- is recognized to play the key role, which only concerns the existence of the valleys in the bulk band structures, and has no special requirement on the topology. The "non-topological" notion of valley edge states is introduced to conceptualize this modal character, leading to a coherent understanding of bending immunity in a range of edge modes implemented in C$_3$ symmetric crystals -- such as valley topological edge states, topological edge states of 2D Zak phase, topological-trivial edge states and so on, and to new designs in general rhombic lattices -- with exemplified bending angle as large as 150$^\circ$

Benchmarking the Robustness of Quantized Models

Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities when exposed to various noises in real-world applications. Despite the importance of evaluating the impact of quantization on robustness, existing research on this topic is limited and often disregards established principles of robustness evaluation, resulting in incomplete and inconclusive findings. To address this gap, we thoroughly evaluated the robustness of quantized models against various noises (adversarial attacks, natural corruptions, and systematic noises) on ImageNet. Extensive experiments demonstrate that lower-bit quantization is more resilient to adversarial attacks but is more susceptible to natural corruptions and systematic noises. Notably, our investigation reveals that impulse noise (in natural corruptions) and the nearest neighbor interpolation (in systematic noises) have the most significant impact on quantized models. Our research contributes to advancing the robust quantization of models and their deployment in real-world scenarios.Comment: Workshop at IEEE Conference on Computer Vision and Pattern Recognition 202

Resilient Data Collection in Smart Grid

Sensors and measurement devices are widely deployed in Smart Grid (SG) to monitor the health of the system. However, these devices are subject to damage and attack so that they cannot deliver sensing data to the control center. In tree-based data collection schemes, a relay failure can further lead to unresponsiveness of all the devices in its sub-tree. In this paper, we study the resiliency issue in collecting data from SG measurement devices. We first design a protocol that guarantees successful data collection from all non-faulty devices in a backup-enabled tree structure. Then, we formulate the tree construction problem to optimize data collection time. Since the formulated problem is NP-hard, we propose a heuristic algorithm to solve it. We evaluate our algorithm using a real utility network topology. The experiment results show that our algorithm performs well in large scale networks.CREDCOpe

RobustMQ: Benchmarking Robustness of Quantized Models

Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities when exposed to various noises in real-world applications. Despite the importance of evaluating the impact of quantization on robustness, existing research on this topic is limited and often disregards established principles of robustness evaluation, resulting in incomplete and inconclusive findings. To address this gap, we thoroughly evaluated the robustness of quantized models against various noises (adversarial attacks, natural corruptions, and systematic noises) on ImageNet. The comprehensive evaluation results empirically provide valuable insights into the robustness of quantized models in various scenarios, for example: (1) quantized models exhibit higher adversarial robustness than their floating-point counterparts, but are more vulnerable to natural corruptions and systematic noises; (2) in general, increasing the quantization bit-width results in a decrease in adversarial robustness, an increase in natural robustness, and an increase in systematic robustness; (3) among corruption methods, \textit{impulse noise} and \textit{glass blur} are the most harmful to quantized models, while \textit{brightness} has the least impact; (4) among systematic noises, the \textit{nearest neighbor interpolation} has the highest impact, while bilinear interpolation, cubic interpolation, and area interpolation are the three least harmful. Our research contributes to advancing the robust quantization of models and their deployment in real-world scenarios.Comment: 15 pages, 7 figure

Rotating Objects via In-Hand Pivoting using Vision, Force and Touch

We propose a robotic manipulation system that can pivot objects on a surface using vision, wrist force and tactile sensing. We aim to control the rotation of an object around the grip point of a parallel gripper by allowing rotational slip, while maintaining a desired wrist force profile. Our approach runs an end-effector position controller and a gripper width controller concurrently in a closed loop. The position controller maintains a desired force using vision and wrist force. The gripper controller uses tactile sensing to keep the grip firm enough to prevent translational slip, but loose enough to induce rotational slip. Our sensor-based control approach relies on matching a desired force profile derived from object dimensions and weight and vision-based monitoring of the object pose. The gripper controller uses tactile sensors to detect and prevent translational slip by tightening the grip when needed. Experimental results where the robot was tasked with rotating cuboid objects 90 degrees show that the multi-modal pivoting approach was able to rotate the objects without causing lift or slip, and was more energy-efficient compared to using a single sensor modality and to pick-and-place. While our work demonstrated the benefit of multi-modal sensing for the pivoting task, further work is needed to generalize our approach to any given object.Comment: 8 pages, 8 figures, 2 table

Intrinsic ferroelectric switching in two-dimension α\alpha-In2_2Se3_3

Two-dimensional (2D) ferroelectric semiconductors present opportunities for integrating ferroelectrics into high-density ultrathin nanoelectronics. Among the few synthesized 2D ferroelectrics, $\alpha$-In$_2$Se$_3$, known for its electrically addressable vertical polarization has attracted significant interest. However, the understanding of many fundamental characteristics of this material, such as the existence of spontaneous in-plane polarization and switching mechanisms, remains controversial, marked by conflicting experimental and theoretical results. Here, our combined experimental characterizations with piezoresponse force microscope and symmetry analysis conclusively dismiss previous claims of in-plane ferroelectricity in $\alpha$-In$_2$Se$_3$. The processes of vertical polarization switching in monolayer $\alpha$-In$_2$Se$_3$ are explored with deep-learning-assisted large-scale molecular dynamics simulations, revealing atomistic mechanisms fundamentally different from those of bulk ferroelectrics. Despite lacking in-plane effective polarization, 1D domain walls can be moved by both out-of-plane and in-plane fields, exhibiting unusual avalanche dynamics characterized by abrupt, intermittent moving patterns. The propagating velocity at various temperatures, field orientations, and strengths can be statistically described with a universal creep equation, featuring a dynamical exponent of 2 that is distinct from all known values for elastic interfaces moving in disordered media. This work rectifies a long-held misunderstanding regarding the in-plane ferroelectricity of $\alpha$-In$_2$Se$_3$, and the quantitative characterizations of domain wall velocity will hold broad implications for both the fundamental understanding and technological applications of 2D ferroelectrics.Comment: 30 pages, 6 figure

Genetic insights into the gut microbiota, herpes zoster, and postherpetic neuralgia: a bidirectional two-sample Mendelian randomization study

BackgroundAn increasing amount of evidence suggests that gastrointestinal diseases are risk factors for herpes zoster (HZ) and postherpetic neuralgia (PHN). Among them, the gut microbiota may play a crucial role in this process. Therefore, this study aims to explore the potential causal association between the gut microbiota and HZ and PHN.MethodsBidirectional two-sample Mendelian randomization (MR) analysis was used to detect the causal effect between HZ and PHN and the gut microbiota. Gut microbiota data were derived from the MiBioGen consortium, while HZ and PHN data were obtained from the FinnGen database. We selected single-nucleotide polymorphisms (SNPs) as instrumental variables with a threshold of p < 1 × 10⁻⁵ for the association with the gut microbiota in forward MR analysis and p < 5 × 10⁻8 for the association with HZ or PHN in reverse MR analysis and then removed SNPs in linkage disequilibrium (r2 < 0.001) within a distance of 10,000 kb for both the gut microbiota and HZ and PHN. These SNPs were utilized to assess the causal effect between exposures and outcomes using inverse-variance weighting (IVW), MR–Egger, weighted mean, and weighted median tests.ResultsThe class Deltaproteobacteria, order Desulfovibrionales, family Desulfovibrionaceae, and genus Coprococcus 2 were found to reduce the risk of HZ, while the phylum Cyanobacteria, genus Eubacterium rectale group appeared to increase it. The class Coriobacteriia, order Coriobacteriales, family Coriobacteriaceae, genus Lachnospiraceae NK4A136 and genus Ruminococcaceae UCG011 were found to reduce the risk of PHN, while the genus Candidatus Soleaferrea, genus Eubacterium rectale group, and genus Methanobrevibacter appeared to increase it. Moreover, the onset of HZ was found to increase the level of the genus Eubacterium rectale group. These findings remained robust and unaffected by heterogeneity or horizontal pleiotropy among SNPs in both forward and reverse MR analysis.ConclusionThis MR study provided evidence supporting a potential causal relationship between the gut microbiota and HZ and PHN. Moreover, we found that the causal effect between the gut microbiota and HZ is bidirectional. Further studies are required to clarify the biological mechanisms linking the gut microbiota and these conditions