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We investigate the effect of three-dimensionality on the synchronization characteristics of the wake behind an oscillating circular cylinder at Re = 300. Cylinder oscillations in rotation, transverse translation, and streamwise translation are considered. We utilize phase-reduction analysis, which quantifies the phase-sensitivity function of periodic flows, to examine the synchronization properties. Here, we present an ensemble-based framework for phase-reduction analysis to handle three-dimensional wakes that are not perfectly time-periodic. Based on the phase-sensitivity functions, synchronizability to three types of cylinder oscillations is evaluated. In spite of similar trends, we find that phase-sensitivity functions involving three-dimensional wakes are lower in magnitude compared to those of two-dimensional wakes, which leads to narrower conditions for synchronization to weak cylinder oscillations. We unveil that the difference between the phase-sensitivity functions of two- and three-dimensional flows is strongly correlated to the amplitude variation of the three-dimensional flow by the cylinder motions. This finding reveals that the cylinder motion modifies the three-dimensionality of the wake as well as the phase of vortex shedding, which leads to reduced phase modulation. The synchronization conditions of three-dimensional wakes, predicted by phase-reduction analysis, agree with the identification by parametric studies using direct numerical simulations for forced oscillations with small amplitudes. This study presents the potential capability of phase-reduction to study synchronization characteristics of complex flows., Comment: 21 pages, 15 figures

Log-Structured Merge (LSM) tree-based Key-Value Stores (KVSs) are widely adopted for their high performance in write-intensive environments, but they often face performance degradation due to write stalls during compaction. Prior solutions, such as regulating I/O traffic or using multiple compaction threads, can cause unexpected drops in throughput or increase host CPU usage, while hardware-based approaches using FPGA, GPU, and DPU aimed at reducing compaction duration introduce additional hardware costs. In this study, we propose KVACCEL, a novel hardware-software co-design framework that eliminates write stalls by leveraging a dual-interface SSD. KVACCEL allocates logical NAND flash space to support both block and key-value interfaces, using the key-value interface as a temporary write buffer during write stalls. This strategy significantly reduces write stalls, optimizes resource usage, and ensures consistency between the host and device by implementing an in-device LSM-based write buffer with an iterator-based range scan mechanism. Our extensive evaluation shows that for write-intensive workloads, KVACCEL outperforms ADOC by up to 1.17x in terms of throughput and performance-to-CPU-utilization efficiency. For mixed read-write workloads, both demonstrate comparable performance., Comment: 11 pages, 14 figures

Beam shaping in nanophotonic systems remains a challenge due to the reliance on complex heuristic optimization procedures. In this work, we experimentally demonstrate a novel approach to topological beam shaping using Jackiw-Rebbi states in metasurfaces. By fabricating thin-film dielectric structures with engineered Dirac-mass distributions, we create domain walls that allow precise control over beam profiles. We observe the emergence of Jackiw-Rebbi states and confirm their localized characteristics. Notably, we achieve a flat-top beam profile by carefully tailoring the Dirac mass distribution, highlighting the potential of this method for customized beam shaping. This experimental realization establishes our approach as a new mechanism for beam control, rooted in topological physics, and offers an efficient strategy for nanophotonic design.

The difficulty of acquiring abundant, high-quality data, especially in multi-lingual contexts, has sparked interest in addressing low-resource scenarios. Moreover, current literature rely on fixed expressions from language IDs, which results in the inadequate learning of language representations, and the failure to generate speech in unseen languages. To address these challenges, we propose a novel method that directly extracts linguistic features from audio input while effectively filtering out miscellaneous acoustic information including speaker-specific attributes like timbre. Subjective and objective evaluations affirm the effectiveness of our approach for multi-lingual text-to-speech, and highlight its superiority in low-resource transfer learning for previously unseen language., Comment: EMNLP 2024 Findings

Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs., Comment: 14 pages, 8 figures

The dynamical Franz-Keldysh effect, indicative of the transient light-matter interaction regime between quantum and classical realms, is widely recognized as an essential signature in wide bandgap condensed matter systems such as dielectrics. In this study, we applied the time-resolved transient absorption spectroscopy to investigate ultrafast optical responses in graphene, a zero-bandgap system. We observed in the gate-tuned graphene that the massless Dirac materials notably enhance intraband light-driven transitions, significantly leading to the giant dynamical Franz-Keldysh effect compared to the massive Dirac materials, a wide bandgap system. In addition, employing the angle-resolved spectroscopy, it is found that the unique polarimetry orientation, i.e., perpendicular polarizations for the pump and the probe, further pronounces the optical spectra to exhibit the complete fishbone structure, reflecting the quantum pseudospin nature of Dirac cones. Our findings expand the establishment of emergent transient spectroscopy frameworks into not only zero-bandgap systems but also pseudospin-mediated quantum phenomena, moving beyond dielectrics., Comment: 7 pages, 5 figures

To model complex turbulent flow and heat transfer phenomena, this study aims to analyze and develop a reduced modeling approach based on artificial neural network (ANN) and wrapper methods. This approach has an advantage over other methods such as the correlation-based filter method in terms of removing redundant or irrelevant parameters even under non-linearity among them. As a downside, the overfitting and randomness of ANN training may produce inconsistent subsets over selection trials especially in a higher physical dimension. This study analyzes a few existing ANN-based wrapper methods and develops a revised one based on the gradient-based subset selection indices to minimize the loss in the total derivative or the directional consistency at each elimination step. To examine parameter reduction performance and consistency-over-trials, we apply these methods to a manufactured subset selection problem, modeling of the bubble size in a turbulent bubbly flow, and modeling of the spatially varying turbulent Prandtl number in a duct flow. It is found that the gradient-based subset selection to minimize the total derivative loss results in improved consistency-over-trials compared to the other ANN-based wrapper methods, while removing unnecessary parameters successfully. For the reduced turbulent Prandtl number model, the gradient-based subset selection improves the prediction in the validation case over the other methods. Also, the reduced parameter subsets show a slight increase in the training speed compared to the others., Comment: 24 pages, 26 figures

The data movement in large-scale computing facilities (from compute nodes to data nodes) is categorized as one of the major contributors to high cost and energy utilization. To tackle it, in-storage processing (ISP) within storage devices, such as Solid-State Drives (SSDs), has been explored actively. The introduction of computational storage drives (CSDs) enabled ISP within the same form factor as regular SSDs and made it easy to replace SSDs within traditional compute nodes. With CSDs, host systems can offload various operations such as search, filter, and count. However, commercialized CSDs have different hardware resources and performance characteristics. Thus, it requires careful consideration of hardware, performance, and workload characteristics for building a CSD-based storage system within a compute node. Therefore, storage architects are hesitant to build a storage system based on CSDs as there are no tools to determine the benefits of CSD-based compute nodes to meet the performance requirements compared to traditional nodes based on SSDs. In this work, we proposed an analytical model-based storage capacity planner called CSDPlan for system architects to build performance-effective CSD-based compute nodes. Our model takes into account the performance characteristics of the host system, targeted workloads, and hardware and performance characteristics of CSDs to be deployed and provides optimal configuration based on the number of CSDs for a compute node. Furthermore, CSDPlan estimates and reduces the total cost of ownership (TCO) for building a CSD-based compute node. To evaluate the efficacy of CSDPlan, we selected two commercially available CSDs and 4 representative big data analysis workloads.

We applied the time-resolved attosecond transient absorption spectroscopy to systematically investigate ultrafast optical responses of condensed matter systems. Under an intense pump pulse, absorption spectra indicate that the non-interacting electrons of band insulators produce a field-induced redshift, known as the dynamical Franz-Keldysh effect, as commonly expected. In contrast to the band insulators, in Mott insulators, unconventional spectra are observed which do not fully reflect the dynamical Franz-Keldysh effect. While it still exhibits the fishbone-like structures mimicking the dynamical Franz-Keldysh effect, the spectra show a negative difference absorption below the band edge, rendering a blueshift. In addition, the decomposed difference absorption reveals the creation and the annihilation of double occupancy mainly contribute to the negative signal, implying that the unconventional spectra are purely driven by the electron-correlations. These demonstrations of unconventional responses would guide us to the correlation-embedded attosecond electron dynamics in condensed matter systems., Comment: 7 pages, 8 figures

Under strong laser fields, electrons in solids radiate high-harmonic fields by travelling through quantum pathways in Bloch bands in the sub-laser-cycle timescales. Understanding these pathways in the momentum space through the high-harmonic radiation can enable an all-optical ultrafast probe to observe coherent lightwave-driven processes and measure electronic structures as recently demonstrated for semiconductors. However, such demonstration has been largely limited for semimetals because the absence of the bandgap hinders an experimental characterization of the exact pathways. In this study, by combining electrostatic control of chemical potentials with HHG measurement, we resolve quantum pathways of massless Dirac fermions in graphene under strong laser fields. Electrical modulation of HHG reveals quantum interference between the multi-photon interband excitation channels. As the light-matter interaction deviates beyond the perturbative regime, elliptically polarized laser fields efficiently drive massless Dirac fermions via an intricate coupling between the interband and intraband transitions, which is corroborated by our theoretical calculations. Our findings pave the way for strong-laser-field tomography of Dirac electrons in various quantum semimetals and their ultrafast electronics with a gate control., Comment: 33 pages, 4 figures, Soonyoung Cha and Minjeong Kim contributed equally to this work, To whom correspondence should be addressed: jonghwankim@postech

Hexagonal boron nitride (h-BN) is a key ingredient for various two-dimensional (2D) van der Waals heterostructure devices, but the exact role of h-BN encapsulation in relation to the internal defects of 2D semiconductors remains unclear. Here, we report that h-BN encapsulation greatly removes the defect-related gap states by stabilizing the chemisorbed oxygen molecules onto the defects of monolayer WS2 crystals. Electron energy loss spectroscopy (EELS) combined with theoretical analysis clearly confirms that the oxygen molecules are chemisorbed onto the defects of WS2 crystals and are fixated by h-BN encapsulation, with excluding a possibility of oxygen molecules trapped in bubbles or wrinkles formed at the interface between WS2 and h-BN. Optical spectroscopic studies show that h-BN encapsulation prevents the desorption of oxygen molecules over various excitation and ambient conditions, resulting in a greatly lowered and stabilized free electron density in monolayer WS2 crystals. This suppresses the exciton annihilation processes by two orders of magnitude compared to that of bare WS2. Furthermore, the valley polarization becomes robust against the various excitation and ambient conditions in the h-BN encapsulated WS2 crystals.

This paper proposes Bayesian optimization augmented factoring self-scheduling (BO FSS), a new parallel loop scheduling strategy. BO FSS is an automatic tuning variant of the factoring self-scheduling (FSS) algorithm and is based on Bayesian optimization (BO), a black-box optimization algorithm. Its core idea is to automatically tune the internal parameter of FSS by solving an optimization problem using BO. The tuning procedure only requires online execution time measurement of the target loop. In order to apply BO, we model the execution time using two Gaussian process (GP) probabilistic machine learning models. Notably, we propose a locality-aware GP model, which assumes that the temporal locality effect resembles an exponentially decreasing function. By accurately modeling the temporal locality effect, our locality-aware GP model accelerates the convergence of BO. We implemented BO FSS on the GCC implementation of the OpenMP standard and evaluated its performance against other scheduling algorithms. Also, to quantify our method's performance variation on different workloads, or workload-robustness in our terms, we measure the minimax regret. According to the minimax regret, BO FSS shows more consistent performance than other algorithms. Within the considered workloads, BO FSS improves the execution time of FSS by as much as 22% and 5% on average., Comment: Part of TPDS Special Section on Parallel and Distributed Computing Techniques for AI, ML, and DL