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Emergent collective modes in lattices give birth to many intriguing physical phenomena in condensed matter physics. Among these collective modes, large-area modes typically feature small-level spacings, while a mode with stable frequency tends to be spatially tightly confined. Here, we theoretically propose and experimentally demonstrate a spectral-isolated photonic topological corner mode with a tunable mode area and stable frequency in a two-dimensional photonic crystal. This mode emerges from hybridizing the large-area homogeneous mode and in-gap topological corner modes. Remarkably, this large-area homogeneous mode possesses unique chirality and has a tunable mode area under the change of the mass term of the inner topological non-trivial lattice. We experimentally observe such topological large-area corner modes(TLCM) in a 2D photonic system and demonstrate the robustness by introducing disorders in the structure. Our findings have propelled the forefront of higher-order topology research, transitioning it from single-lattice systems to multi-lattice systems. They may support promising potential applications, particularly in vertical-cavity surface-emitting lasers., Comment: 5 pages, 4 figures

In this work, we present a computational framework for coupled electro-chemo-(nonlinear) mechanics at the particle scale for solid-state batteries. The framework accounts for interfacial fracture between the active particles and solid electrolyte due to intercalation stresses. We extend discontinuous finite element methods for a sharp interface treatment of discontinuities in concentrations, fluxes, electric fields and in displacements, the latter arising from active particle-solid electrolyte interface fracture. We model the degradation in the charge transfer process that results from the loss of contact due to fracture at the electrolyte-active particle interfaces. Additionally, we account for the stress-dependent kinetics that can influence the charge transfer reactions and solid state diffusion. The discontinuous finite element approach does not require a conformal mesh. This offers the flexibility to construct arbitrary particle shapes and geometries that are based on design, or are obtained from microscopy images. The finite element mesh, however, can remain Cartesian, and independent of the particle geometries. We demonstrate this computational framework on micro-structures that are representative of solid-sate batteries with single and multiple anode and cathode particles.

As more data-intensive tasks with large footprints are deployed in virtual machines (VMs), huge pages are widely used to eliminate the increasing address translation overhead. However, once the huge page mapping is established, all the base page regions in the huge page share a single extended page table (EPT) entry, so that the hypervisor loses awareness of accesses to base page regions. None of the state-of-the-art solutions can obtain access information at base page granularity for huge pages. We observe that this can lead to incorrect decisions by the hypervisor, such as incorrect data placement in a tiered memory system and unshared base page regions when sharing pages. This paper proposes FHPM, a fine-grained huge page management for virtualization without hardware and guest OS modification. FHPM can identify access information at base page granularity, and dynamically promote and demote pages. A key insight of FHPM is to redirect the EPT huge page directory entries (PDEs) to new companion pages so that the MMU can track access information within huge pages. Then, FHPM can promote and demote pages according to the current hot page pressure to balance address translation overhead and memory usage. At the same time, FHPM proposes a VM-friendly page splitting and collapsing mechanism to avoid extra VM-exits. In combination, FHPM minimizes the monitoring and management overhead and ensures that the hypervisor gets fine-grained VM memory accesses to make the proper decision. We apply FHPM to improve tiered memory management (FHPM-TMM) and to promote page sharing (FHPM-Share). FHPM-TMM achieves a performance improvement of up to 33% and 61% over the pure huge page and base page management. FHPM-Share can save 41% more memory than Ingens, a state-of-the-art page sharing solution, with comparable performance.

Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important for machine learning safety. While a number of methods have been proposed by prior work, they often underestimate the actual error, sometimes by a large margin, which greatly impacts their applicability to real tasks. In this work, we identify pseudo-label shift, or the difference between the predicted and true OOD label distributions, as a key indicator to this underestimation. Based on this observation, we introduce a novel method for estimating model performance by leveraging optimal transport theory, Confidence Optimal Transport (COT), and show that it provably provides more robust error estimates in the presence of pseudo-label shift. Additionally, we introduce an empirically-motivated variant of COT, Confidence Optimal Transport with Thresholding (COTT), which applies thresholding to the individual transport costs and further improves the accuracy of COT's error estimates. We evaluate COT and COTT on a variety of standard benchmarks that induce various types of distribution shift -- synthetic, novel subpopulation, and natural -- and show that our approaches significantly outperform existing state-of-the-art methods with an up to 3x lower prediction error., Comment: NeurIPS 2023

Topological phases based on tight-binding models have been extensively studied in recent decades. By mimicking the linear combination of atomic orbitals in tight-binding models based on the evanescent couplings between resonators in classical waves, numerous experimental demonstrations of topological phases have been successfully conducted. However, in dielectric photonic crystals, the Mie resonances' states decay too slowly as $1/r$ when $r$ $\to$ $\infty$, leading to intrinsically different physical properties between tight-binding models and dielectric photonic crystals. Here, we propose a confined Mie resonance photonic crystal by embedding perfect electric conductors in between dielectric rods, leading to a perfectly matched band structure as the tight-binding models with nearest-neighbour couplings. As a consequence, disentangled band structure spanned by higher atomic orbitals is observed. Moreover, we also achieve a three-dimensional photonic crystal with a complete photonic bandgap and third-order topology based on our design. Our implementation provides a versatile platform for studying exotic higher-orbital bands and achieving tight-binding-like 3D topological photonic crystals., Comment: 6 pages, 4 figures

Targeting signaling pathways that drive cancer cell migration or proliferation is a common therapeutic approach. A popular experimental technique, the scratch assay, measures the migration and proliferation-driven cell monolayer formation. Scratch assay analyses do not differentiate between migration and proliferation effects and do not attempt to measure dynamic effects. To improve upon these methods, we combine high-throughput scratch assays, continuous video microscopy, and variational system identification (VSI) to infer partial differential equation (PDE) models of cell migration and proliferation. We capture the evolution of cell density fields over time using live cell microscopy and automated image processing. We employ VSI techniques to identify cell density dynamics modeled with first-order kinetics of advection-diffusion-reaction systems. We present a comparison of our methods to results obtained using traditional inference approaches on previously analyzed 1-dimensional scratch assay data. We demonstrate the application of this pipeline on high throughput 2-dimensional scratch assays and find that decreasing serum levels can decrease random cell migration by approximately 20%. Our integrated experimental and computational pipeline can be adapted for automatically quantifying the effect of biological perturbations on cell migration and proliferation in various cell lines.

Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops. Being able to estimate a model's performance on test data is important in practice as it indicates when to trust to model's decisions. We present a simple yet effective method to predict a model's performance on an unknown distribution without any addition annotation. Our approach is rooted in the Optimal Transport theory, viewing test samples' output softmax scores from deep neural networks as empirical samples from an unknown distribution. We show that our method, Confidence Optimal Transport (COT), provides robust estimates of a model's performance on a target domain. Despite its simplicity, our method achieves state-of-the-art results on three benchmark datasets and outperforms existing methods by a large margin.

The building block of in-memory computing with spintronic devices is mainly based on the magnetic tunnel junction with perpendicular interfacial anisotropy (p-MTJ). The resulting asymmetric write and read-out operations impose challenges in downscaling and direct cascadability of p-MTJ devices. Here, we propose that a new symmetric write and read-out mechanism can be realized in perpendicular-anisotropy spin-orbit (PASO) quantum materials based on Fe3GeTe2 and WTe2. We demonstrate that field-free and deterministic reversal of the perpendicular magnetization can be achieved by employing unconventional charge to z-spin conversion. The resulting magnetic state can be readily probed with its intrinsic inverse process, i.e., z-spin to charge conversion. Using the PASO quantum material as a fundamental building block, we implement the functionally complete set of logic-in-memory operations and a more complex nonvolatile half-adder logic function. Our work highlights the potential of PASO quantum materials for the development of scalable energy-efficient and ultrafast spintronic computing., Comment: Accepted by Science Advances

The memory demand of virtual machines (VMs) is increasing, while DRAM has limited capacity and high power consumption. Non-volatile memory (NVM) is an alternative to DRAM, but it has high latency and low bandwidth. We observe that the VM with heterogeneous memory may incur up to a $1.5\times$ slowdown compared to a DRAM VM, if not managed well. However, none of the state-of-the-art heterogeneous memory management designs are customized for virtualization on a real system. In this paper, we propose HMM-V, a Heterogeneous Memory Management system for Virtualization. HMM-V automatically determines page hotness and migrates pages between DRAM and NVM to achieve performance close to the DRAM system. First, HMM-V tracks memory accesses through page table manipulation, but reduces the cost by leveraging Intel page-modification logging (PML) and a multi-level queue. Second, HMM-V quantifies the ``temperature'' of page and determines the hot set with bucket-sorting. HMM-V then efficiently migrates pages with minimal access pause and handles dirty pages with the assistance of PML. Finally, HMM-V provides pooling management to balance precious DRAM across multiple VMs to maximize utilization and overall performance. HMM-V is implemented on a real system with Intel Optane DC persistent memory. The four-VM co-running results show that HMM-V outperforms NUMA balancing and hardware management (Intel Optane memory mode) by $51\%$ and $31\%$, respectively.

Seeking the criterion for diagnosing topological phases in real materials has been one of the major tasks in topological physics. Currently, bulk-boundary correspondence based on spectral measurements of in gap topological boundary states and the fractional corner anomaly derived from the measurement of the fractional spectral charge are two main approaches to characterize topologically insulating phases. However, these two methods require a complete band-gap with either in-gap states or strict spatial symmetry of the overall sample which significantly limits their applications to more generalized cases. Here we propose and demonstrate an approach to link the non-trivial hierarchical bulk topology to the multidimensional partition of local-density of states (LDOS) respectively, denoted as the bulk-LDOS correspondence. Specifically, in a finite-size topologically nontrivial photonic crystal, we observe that the distribution of LDOS is divided into three partitioned regions of the sample - the two-dimensional interior bulk area (avoiding edge and corner areas), one-dimensional edge region (avoiding the corner area), and zero-dimensional corner sites. In contrast, the LDOS is distributed across the entire two-dimensional bulk area across the whole spectrum for the topologically trivial cases. Moreover, we present the universality of this criterion by validating this correspondence in both a higher-order topological insulator without a complete band gap and with disorders. Our findings provide a general way to distinguish topological insulators and unveil the unexplored features of topological directional band-gap materials without in-gap states.

The best arm identification problem in the multi-armed bandit setting is an excellent model of many real-world decision-making problems, yet it fails to capture the fact that in the real-world, safety constraints often must be met while learning. In this work we study the question of best-arm identification in safety-critical settings, where the goal of the agent is to find the best safe option out of many, while exploring in a way that guarantees certain, initially unknown safety constraints are met. We first analyze this problem in the setting where the reward and safety constraint takes a linear structure, and show nearly matching upper and lower bounds. We then analyze a much more general version of the problem where we only assume the reward and safety constraint can be modeled by monotonic functions, and propose an algorithm in this setting which is guaranteed to learn safely. We conclude with experimental results demonstrating the effectiveness of our approaches in scenarios such as safely identifying the best drug out of many in order to treat an illness.

In this paper, we introduce a multi-armed bandit problem termed max-min grouped bandits, in which the arms are arranged in possibly-overlapping groups, and the goal is to find the group whose worst arm has the highest mean reward. This problem is of interest in applications such as recommendation systems and resource allocation, and is also closely related to widely-studied robust optimization problems. We present two algorithms based successive elimination and robust optimization, and derive upper bounds on the number of samples to guarantee finding a max-min optimal or near-optimal group, as well as an algorithm-independent lower bound. We discuss the degree of tightness of our bounds in various cases of interest, and the difficulties in deriving uniformly tight bounds., Comment: AAAI 2022 paper + technical appendix (supplementary material), single-column format

We present an approach to studying and predicting the spatio-temporal progression of infectious diseases. We treat the problem by adopting a partial differential equation (PDE) version of the Susceptible, Infected, Recovered, Deceased (SIRD) compartmental model of epidemiology, which is achieved by replacing compartmental populations by their densities. Building on our recent work (Computational Mechanics, 66, 1177, 2020), we replace our earlier use of global polynomial basis functions with those having local support, as epitomized in the finite element method, for the spatial representation of the SIRD parameters. The time dependence is treated by inferring constant parameters over time intervals that coincide with the time step in semi-discrete numerical implementations. In combination, this amounts to a scheme of field inversion of the SIRD parameters over each time step. Applied to data over ten months of 2020 for the pandemic in the US state of Michigan and to all of Mexico, our system inference via field inversion infers spatio-temporally varying PDE SIRD parameters that replicate the progression of the pandemic with high accuracy. It also produces accurate predictions, when compared against data, for a three week period into 2021. Of note is the insight that is suggested on the spatio-temporal variation of infection, recovery and death rates, as well as patterns of the population's mobility revealed by diffusivities of the compartments.

Defect engineering plays an important role in tailoring the electronic transport properties of van der Waals materials. However, it is usually achieved through tuning the type and concentration of defects, rather than dynamically reconfiguring their spatial distribution. Here, we report temperature-sensitive spatial redistribution of defects in PdSe2 thin flakes through scanning tunneling microscopy (STM). We observe that the spatial distribution of Se vacancies in PdSe2 flakes exhibits a strong anisotropic characteristic at 80 K, and that this orientation-dependent feature is weakened when temperature is raised. Moreover, we carry out transport measurements on PdSe2 thin flakes and show that the anisotropic features of carrier mobility and phase coherent length are also sensitive to temperature. Combining with theoretical analysis, we conclude that temperature-driven defect spatial redistribution could interpret the temperature-sensitive electrical transport behaviors in PdSe2 thin flakes. Our work highlights that engineering spatial distribution of defects in the van der Waals materials, which has been overlooked before, may open up a new avenue to tailor the physical properties of materials and explore new device functionalities., Comment: 24 pages, 12 figures, accepted by Physical Review Materials

Production software oftentimes suffers from the issue of performance inefficiencies caused by inappropriate use of data structures, programming abstractions, and conservative compiler optimizations. It is desirable to avoid unnecessary memory operations. However, existing works often use a whole-program fine-grained monitoring method with incredibly high overhead. To this end, we propose a learning-aided approach to identify unnecessary memory operations intelligently with low overhead. By applying several prevalent graph neural network models to extract program semantics with respect to program structure, execution order and dynamic states, we present a novel, hybrid program embedding approach so that to derive unnecessary memory operations through the embedding. We train our model with tens of thousands of samples acquired from a set of real-world benchmarks. Results show that our model achieves 90% of accuracy and incurs only around a half of time overhead of the state-of-art tool., Comment: 9 pages, 6 figures, 3 tables

We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al., Comp. Meth. App. Mech. Eng., 356, 44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.

Versatile quantum modes emerge for plasmon describing the collective oscillations of free electrons in metallic nanoparticles when the particle sizes are greatly reduced. Rather than traditional nanoscale study, the understanding of quantum plasmon desires extremal atomic control of the nanoparticles, calling for size dependent plasmon measurement over a series of nanoparticles with atomically adjustable atom number over several orders of magnitude. Here we report the N dependent plasmonic evolution of atomically size selected gold particles with N= 100 70000 using electron energy loss (EEL) spectroscopy in a scanning transmission electron microscope. The EEL mapping assigns a feature at 2.7 eV as the bulk plasmon and another at 2.4 eV as surface plasmon, which evolution reveals three regimes. When N decreases from 70000 to 887, the bulk plasmon stays unchanged while the surface plasmon exhibits a slight red shift from 2.4 to 2.3 eV. It can be understood by the dominance of classical plasmon physics and electron boundary scattering induced retardation. When N further decreases from 887 to 300, the bulk plasmon disappears totally and the surface plasmon shows a steady blueshift, which indicates that the quantum confinement emerges and modifies the intraband transition. When N 100 300, the plasmon is split to three fine features, which is attributed to superimposed single electron transitions between the quantized molecular like energy level by the time dependent density functional theory calculations. The surface plasmon's excitation ratio has a scaling law with an exponential dependence on N ( N^0.669), essentially the square of the radius. A unified evolution picture from the classical to quantum, molecular plasmon is thus demonstrated.

Early processing of visual information takes place in the human retina. Mimicking neurobiological structures and functionalities of the retina provide a promising pathway to achieving vision sensor with highly efficient image processing. Here, we demonstrate a prototype vision sensor that operates via the gate-tunable positive and negative photoresponses of the van der Waals (vdW) vertical heterostructures. The sensor emulates not only the neurobiological functionalities of bipolar cells and photoreceptors but also the unique synaptic connectivity between bipolar cells and photoreceptors. By tuning gate voltage for each pixel, we achieve reconfigurable vision sensor for simultaneously image sensing and processing. Furthermore, our prototype vision sensor itself can be trained to classify the input images, via updating the gate voltages applied individually to each pixel in the sensor. Our work indicates that vdW vertical heterostructures offer a promising platform for the development of neural network vision sensor., Comment: 20 pages, 4 figures

Band topology and related spin (or pseudo-spin) physics of photons provide us with a new dimension for manipulating light, which is potentially useful for information communication and data storage. Especially, the quantum spin Hall effect of light, where electromagnetic waves propagate along surfaces of samples with strong spin-momentum locking, paves the way for achieving topologically protected photonic spin transport. Recently, the conventional bulk-edge correspondence of the band topology has been extended to higher-order cases that enables the explorations of topological states with codimensions larger than 1 such as hinge and corner states. Here, for the first time, we demonstrate a higherorder quantum spin Hall effect of light by utilizing an all-dielectric C6v-symmetric photonic crystal. We observe corner states with opposite pseudospin polarizations at different corners owing to nontrivial higher-order topology and finite spin-spin coupling. By applying the spin-polarized excitation sources, we can selectively excite the corner states at different spatial positions through spin-momentum-locked decaying edge states, resembling the quantum spin Hall effect in a higher-order manner. Our work which breaks the barriers between the spin photonics and higher-order topology opens the frontiers for studying lower-dimensional spinful classical surface waves and supports explorations in robust communications., Comment: 24 pages, 4 figures

We present two approaches to system identification, i.e. the identification of partial differential equations (PDEs) from measurement data. The first is a regression-based Variational System Identification procedure that is advantageous in not requiring repeated forward model solves and has good scalability to large number of differential operators. However it has strict data type requirements needing the ability to directly represent the operators through the available data. The second is a Bayesian inference framework highly valuable for providing uncertainty quantification, and flexible for accommodating sparse and noisy data that may also be indirect quantities of interest. However, it also requires repeated forward solutions of the PDE models which is expensive and hinders scalability. We provide illustrations of results on a model problem for pattern formation dynamics, and discuss merits of the presented methods.