Search

The advent of chiplet technology introduces cutting-edge opportunities for constructing highly heterogeneous platforms with specialized accelerators. However, the HPC community currently lacks expertise in hardware development, a gap that must be bridged to leverage these advancements. Additionally, technologies like chiplet is cutting-edge with limited educational resource available. This paper addresses potential hardware specialization direction in HPC and how to cultivate these skills among students and staff, emphasizing the importance of understanding and developing custom hardware (e.g., rapid prototyping and resource estimation). We have been mentoring graduate-level students and new staff in hardware designs in a hands-on manner, encouraging them to utilize modern open-source hardware tools for their designs, which facilitates the sharing of research ideas. Additionally, we provide a summary of theses tools as part of our approach to prototyping and mentoring., Comment: SC24 eduHPC 2 pages

This paper describes speech enhancement for realtime automatic speech recognition (ASR) in real environments. A standard approach to this task is to use neural beamforming that can work efficiently in an online manner. It estimates the masks of clean dry speech from a noisy echoic mixture spectrogram with a deep neural network (DNN) and then computes a enhancement filter used for beamforming. The performance of such a supervised approach, however, is drastically degraded under mismatched conditions. This calls for run-time adaptation of the DNN. Although the ground-truth speech spectrogram required for adaptation is not available at run time, blind dereverberation and separation methods such as weighted prediction error (WPE) and fast multichannel nonnegative matrix factorization (FastMNMF) can be used for generating pseudo groundtruth data from a mixture. Based on this idea, a prior work proposed a dual-process system based on a cascade of WPE and minimum variance distortionless response (MVDR) beamforming asynchronously fine-tuned by block-online FastMNMF. To integrate the dereverberation capability into neural beamforming and make it fine-tunable at run time, we propose to use weighted power minimization distortionless response (WPD) beamforming, a unified version of WPE and minimum power distortionless response (MPDR), whose joint dereverberation and denoising filter is estimated using a DNN. We evaluated the impact of run-time adaptation under various conditions with different numbers of speakers, reverberation times, and signal-to-noise ratios (SNRs)., Comment: Accepted to APSIPA2024

This paper describes sound event localization and detection (SELD) for spatial audio recordings captured by firstorder ambisonics (FOA) microphones. In this task, one may train a deep neural network (DNN) using FOA data annotated with the classes and directions of arrival (DOAs) of sound events. However, the performance of this approach is severely bounded by the amount of annotated data. To overcome this limitation, we propose a novel method of pretraining the feature extraction part of the DNN in a self-supervised manner. We use spatial audio-visual recordings abundantly available as virtual reality contents. Assuming that sound objects are concurrently observed by the FOA microphones and the omni-directional camera, we jointly train audio and visual encoders with contrastive learning such that the audio and visual embeddings of the same recording and DOA are made close. A key feature of our method is that the DOA-wise audio embeddings are jointly extracted from the raw audio data, while the DOA-wise visual embeddings are separately extracted from the local visual crops centered on the corresponding DOA. This encourages the latent features of the audio encoder to represent both the classes and DOAs of sound events. The experiment using the DCASE2022 Task 3 dataset of 20 hours shows non-annotated audio-visual recordings of 100 hours reduced the error score of SELD from 36.4 pts to 34.9 pts., Comment: Accepted to APSIPA2023

Atomic crystals with dislocations deform plastically at low stresses via dislocation glide. Whether dislocation glide occurs in macroscopic frictional granular media has remained unknown. The discrete element method is employed to simulate the structural and mechanical responses of a granular crystal with an edge dislocation. We find that dislocation glide occurs at low interparticle friction, resulting in significantly lower yield stresses than in dislocation-free crystals. Yield stress varies linearly with interparticle friction, attributed to both Peierls stress and frictional effect., Comment: 5 pages, 4 figures

This paper explores how magnetic fields affect the Casimir effect within the context of a simple quasi-1D interacting fermionic system. A novel phenomenon emerges, resulting from the interaction between external magnetic fields and boundary conditions, which alters the ground state in complex ways and leads to first-order phase transitions among various ground states, specifically inhomogeneous solutions with differing node counts. We calculate the Casimir force, which exhibits discontinuities (jumps) at the transition points between the different ground states., Comment: 6 pages, 5 figures

We describe the structure of projective indecomposable modules for the subalgebra consisting of the elements of degree 0 in the hyperalgebra of the $r$-th Frobenius kernel for the algebraic group ${\rm SL}_2(k)$, using the primitive idempotents which were constructed before by the author., Comment: 14 pages

In this paper we construct primitive idempotents of the hyperalgebra for the $r$-th Frobenius kernel of the algebraic group ${\rm SL}(2,k)$., Comment: 31 pages

Intra-operative 2D-3D registration of X-ray images with pre-operatively acquired CT scans is a crucial procedure in orthopedic surgeries. Anatomical landmarks pre-annotated in the CT volume can be detected in X-ray images to establish 2D-3D correspondences, which are then utilized for registration. However, registration often fails in certain view angles due to poor landmark visibility. We propose a novel method to address this issue by detecting arbitrary landmark points in X-ray images. Our approach represents 3D points as distinct subspaces, formed by feature vectors (referred to as ray embeddings) corresponding to intersecting rays. Establishing 2D-3D correspondences then becomes a task of finding ray embeddings that are close to a given subspace, essentially performing an intersection test. Unlike conventional methods for landmark estimation, our approach eliminates the need for manually annotating fixed landmarks. We trained our model using the synthetic images generated from CTPelvic1K CLINIC dataset, which contains 103 CT volumes, and evaluated it on the DeepFluoro dataset, comprising real X-ray images. Experimental results demonstrate the superiority of our method over conventional methods. The code is available at https://github.com/Pragyanstha/rayemb., Comment: Accepted as an oral presentation at ACCV 2024

We demonstrate that a quantum field theory (QFT) in general two-dimensional curved spacetimes can be realized by a system of quantum spins or qubits. We consider a spin-1/2 model on a one-dimensional ring with spatially and temporally varying exchange couplings and magnetic fields. This model reduces to a QFT of Majorana fermions in the continuum limit. From this correspondence, we establish a dictionary for translating between the spacetime-dependent parameters of the spin model and the general metric on which the QFT is defined. After addressing the general case, we consider the Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) metric as a simple example. According to the dictionary, the QFT of Majorana fermions on the FLRW metric corresponds to the Ising model with a time-dependent transverse magnetic field. We demonstrate that the production of Majorana particles in the expanding universe can be simulated with the transverse-field Ising model by increasing the strength of the magnetic field. Furthermore, we examine the Unruh effect through the spin system by using our prescription and show the direct relation between the entanglement (or modular) Hamiltonian in the spin system and the Rindler Hamiltonian. This approach provides an experimentally viable system for probing various phenomena in QFT within curved spacetime, while also opening the door to uncovering nontrivial phenomena in spin systems inspired by curved spacetime physics. It offers fresh perspectives on both QFT in curved spacetimes and quantum many-body spin systems, revealing profound connections between these fields., Comment: 15 pages, 3 figures

The construction of highly coherent x-ray sources has enabled new research opportunities across the scientific landscape. The maximum raw data rate per beamline now exceeds 40 GB/s, posing unprecedented challenges for the online processing and offline storage of the big data. Such challenge is particularly prominent for x-ray photon correlation spectroscopy (XPCS), where real time analyses require simultaneous calculation on all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The lossy compression reduces the computational time by a factor of 10,000, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

The Leggett-Garg inequality (LGI) serves as a criterion to determine the adherence of macroscopic system dynamics to macrorealism, as postulated by Leggett and Garg. A violation of this inequality implies either the absence of a realistic description of the system or the impracticality of noninvasive measurements. In this work, we investigate the violation of the LGI for the system of bosons in a double-well potential. Specifically, we explore the violation of the LGI in the dynamics of bosons in a double-well potential in the Bose-Einstein-Condensation (BEC) regime, where the system can be considered as two weakly coupled Bose condensates, and in the single-particle regime to establish the conditions under which the violation of the LGI occurs. Our analysis reveals that the LGI is violated due to Josephson oscillations, while it remains unviolated in the strong coupling regime, attributed to the self-trapping phenomena. Notably, we observe that the violation of the LGI becomes increasingly significant as the particle number increases. These findings provide valuable insights into the macrorealistic behavior of Bose condensates and highlight the effect of measurements on the dynamics of a macroscopic system., Comment: 14 pages, 10 figures

We introduce a novel laser-scanning optical microscopy technique that employs optical-frequency-comb (OFC) lasers. This method facilitates multimodal spectroscopic imaging by analyzing interferograms produced via a dual-comb spectroscopic approach. Such interferograms capture comprehensive light information, including amplitude, phase, polarization, frequency, and time of flight information, enabling multimodal imaging from a single measurement. We demonstrate the potential of this technique across several spectroscopic imaging applications., Comment: 11 pages, 8 figures

We numerically examine binary-sized granular mixtures confined between two parallel walls subjected to vertical vibration using the discrete element method. For a size ratio of $3$ between large and small particles, we study the structure of large particles in moderately dense regimes where the combined two-dimensional packing fractions of both particle sizes exceed $1$. When the fraction of small particles is small, segregation of the large particles occurs. In contrast, as the fraction of small particles increases, an effective repulsion between the large particles emerges over distances greater than the large particle diameter, suppressing their segregation. The emergence of reduction in segregation is confirmed for another size ratio, vibrational acceleration, system size, and for a case of bidisperse size distribution. Additionally, at the size ratio of $3$, the effective repulsion induces a hexagonal phase of the large particles at packing fractions lower than in mono-component systems. This work will provide a fresh insight into granular physics, prompting further experimental and theoretical study., Comment: 28 pages, 20 figures

Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. These lossy compressors are designed with distinct compression models and design principles, such that each of them features particular pros and cons. In this paper we provide a comprehensive survey of emerging error-bounded lossy compression techniques for different use cases each involving big data to process. The key contribution is fourfold. (1) We summarize an insightful taxonomy of lossy compression into 6 classic compression models. (2) We provide a comprehensive survey of 10+ commonly used compression components/modules used in error-bounded lossy compressors. (3) We provide a comprehensive survey of 10+ state-of-the-art error-bounded lossy compressors as well as how they combine the various compression modules in their designs. (4) We provide a comprehensive survey of the lossy compression for 10+ modern scientific applications and use-cases. We believe this survey is useful to multiple communities including scientific applications, high-performance computing, lossy compression, and big data., Comment: submitted to ACM Computing journal, requited to be 35 pages including references

We numerically study the flow of dense granular materials between parallel plates driven by an external force. The granular materials form a jammed solid-like state when the external force is below a critical force, while they flow like fluids above the critical force. The transition is characterized by the mass flux. The critical force depends on the average packing fraction and the distance between the plates. The scaling laws for the critical force and the mass flux are predicted theoretically based on a continuum model. They are numerically verified.

We investigate spatially inhomogeneous solutions in a top-down holographic model: the D3/D7 model which provides a holographic description of the chiral phase transition for a finite external magnetic field, chemical potential, and temperature. We numerically find a domain wall (or kink) solution in the three dimensional space, which incorporates between the chiral symmetry broken phase at the spatial infinity, under the homogeneous sources. Along with the inhomogeneity of the chiral condensate, the charge density is also spatially modulated. The modulated charge density and finite magnetic field lead to the chiral edge current close to the domain wall. We explore the dependences of those profiles on the chemical potential and temperature near the first and second order phase transition points. Our results indicate that the inhomogeneous solutions we found are in good agreement with those obtained by the Ginzburg--Landau theory in the vicinity of the transition points., Comment: 30 pages, 20 figures; v2: version published in JHEP

An aqueous sodium chloride solution was injected at a controlled rate into a granular bed in a quasi-two-dimensional cell. The granular bed was made of dried, highly water-absorbent gel particles whose swelling rate was controlled by the salinity of the injected fluid. At a high salinity level (low swelling rate), high injection rate, and short timescale, the injected fluid percolated between the gel particles in an isotropic manner. Meanwhile, at a low salinity level (high swelling rate), low injection rate, and long timescale, the gel particles clogged the flow path, resulting in anisotropic branch-like structures of the injected fluid front. The transition of the injection pattern could be understood based on the ratio of the characteristic timescales of swelling and injection. Moreover, the clogged pattern showed an oscillatory pressure drop whose amplitude was increased with higher salinity. Such an oscillatory behavior observed in an injection process in a swelling gel particle may be relevant in geological situation; i.e., such as fluid migration underground., Comment: 6 pages, 4 figures

We construct an efficient momentum space approach to the superconductivity in quasiperiodic multilayer systems. To this end, we extend the Brillouin zone (BZ) folding method to the superconducting (SC) phases by formulating the gap equation in the momentum space representation with the BZ folding. In this picture, we find that the SC order parameters generally exhibit finite momenta, which corresponds to the spatial fluctuation of the SC order in quasiperiodic multilayers. We apply the BZ folding method to a one dimensional model of quasiperiodic superconductors and demonstrate its numerical efficiency compared to the conventional method based on the real space representation with a large system size., Comment: 11 pages, 8 figures

We give generating sets of the Jacobson radical of the hyperalgebra of the $r$-th Frobenius kernel of the algebraic group ${\rm SL}_2$ over an algebraically closed field of characteristic $p>0$. This result generalizes earlier work by Wong for $r=1$ and odd $p$., Comment: 35 pages. To appear in Journal of Computational Algebra

In the hyperalgebra of the $r$-th Frobenius kernel of a universal Chevalley group over a field of characteristic $p>0$, we study some subsets and the subalgebras generated by them and give some results. We are particularly interested in the case that $p$ is 'very small' and the Dynkin diagram is not simply-laced., Comment: 21 pages

The recent trend toward deep learning has led to the development of a variety of highly innovative AI accelerator architectures. One such architecture, the Cerebras Wafer-Scale Engine 2 (WSE-2), features 40 GB of on-chip SRAM, making it a potentially attractive platform for latency- or bandwidth-bound HPC simulation workloads. In this study, we examine the feasibility of performing continuous energy Monte Carlo (MC) particle transport on the WSE-2 by porting a key kernel from the MC transport algorithm to Cerebras's CSL programming model. New algorithms for minimizing communication costs and for handling load balancing are developed and tested. The WSE-2 is found to run 130 times faster than a highly optimized CUDA version of the kernel run on an NVIDIA A100 GPU -- significantly outpacing the expected performance increase given the difference in transistor counts between the architectures.

In the hyperalgebra $\mathcal{U}_r$ of the $r$-th Frobenius kernel $({\rm SL}_2)_r$ of the algebraic group ${\rm SL}_2$, we construct a basis of the $\mathcal{U}_r$-module generated by a certain element which was given by the author before. As its applications, we also prove some results on the $\mathcal{U}_r$-modules and the algebra $\mathcal{U}_r$., Comment: 30 pages

Let $G$ be a simply connected and simple algebraic group defined and split over a finite prime field $\mathbb{F}_p$ of $p$ elements. In this paper, using an $\mathbb{F}_p$-linear map splitting Frobenius endomorphism on a hyperalgebra relative to $G$, we obtain some $\mathbb{F}_p$-linear isomorphisms induced by multiplication in the hyperalgebra., Comment: 28 pages

This paper describes an efficient unsupervised learning method for a neural source separation model that utilizes a probabilistic generative model of observed multichannel mixtures proposed for blind source separation (BSS). For this purpose, amortized variational inference (AVI) has been used for directly solving the inverse problem of BSS with full-rank spatial covariance analysis (FCA). Although this unsupervised technique called neural FCA is in principle free from the domain mismatch problem, it is computationally demanding due to the full rankness of the spatial model in exchange for robustness against relatively short reverberations. To reduce the model complexity without sacrificing performance, we propose neural FastFCA based on the jointly-diagonalizable yet full-rank spatial model. Our neural separation model introduced for AVI alternately performs neural network blocks and single steps of an efficient iterative algorithm called iterative source steering. This alternating architecture enables the separation model to quickly separate the mixture spectrogram by leveraging both the deep neural network and the multichannel optimization algorithm. The training objective with AVI is derived to maximize the marginalized likelihood of the observed mixtures. The experiment using mixture signals of two to four sound sources shows that neural FastFCA outperforms conventional BSS methods and reduces the computational time to about 2% of that for the neural FCA., Comment: 5 pages, 2 figures, accepted to EUSIPCO 2023

We address the problem of accurately interpolating measured anechoic steering vectors with a deep learning framework called the neural field. This task plays a pivotal role in reducing the resource-intensive measurements required for precise sound source separation and localization, essential as the front-end of speech recognition. Classical approaches to interpolation rely on linear weighting of nearby measurements in space on a fixed, discrete set of frequencies. Drawing inspiration from the success of neural fields for novel view synthesis in computer vision, we introduce the neural steerer, a continuous complex-valued function that takes both frequency and direction as input and produces the corresponding steering vector. Importantly, it incorporates inter-channel phase difference information and a regularization term enforcing filter causality, essential for accurate steering vector modeling. Our experiments, conducted using a dataset of real measured steering vectors, demonstrate the effectiveness of our resolution-free model in interpolating such measurements., Comment: Camera ready version for HSCMA 24 at ICASSP 24

This study explores the impact of gender differences in preferences and productivity in home production on the time allocation in married couples, particularly in relation to childcare responsibilities. Using aggregated data from Japan, we estimate a life-cycle model that tracks the development of a child from infancy to adulthood by extending the work of Blundell et al. (2018). Our findings reveal a decrease in maternal earnings following childbirth, aligning with the empirical evidence of the child penalty. However, the model's projections and the actual data diverge significantly during the phase of maternal earnings recovery, showing a discrepancy of approximately 50%, the size of which implies an involuntary reduction in the wife's market work earnings., Comment: 29 pages

Graph states are entangled states useful for several quantum information processing tasks such as measurement-based quantum computation and quantum metrology. As the size of graph states realized in experiments increases, it becomes more essential to devise efficient methods estimating the fidelity between the ideal graph state and an experimentally-realized actual state. Any efficient fidelity-estimation method, in general, must use multiple experimental settings, i.e., needs to switch between at least two measurements. Recently, it has been shown that a single measurement is sufficient if the noise can be modeled as the phase-flip error. Since the bit-flip error should also occur in several experiments, it is desired to extend this simplest method to noise models that include phase and bit-flip errors. However, it seems to be nontrivial because their result strongly depends on properties of the phase-flip error. In this paper, by analyzing effects of the bit-flip error on stabilizer operators of graph states, we achieve the extension to the depolarizing noise, which is a major noise model including phase and bit-flip errors. We also numerically evaluate our simplest method for noise models interpolating between the phase-flip and depolarizing noises., Comment: 10 pages, 6 figures

In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity, or accurately filtered in real-time, thereby necessitating low-latency processing. Deep neural networks, effective in other filtering tasks, have not been widely employed in such data acquisition systems, due to design and deployment difficulties. We present an open source, lightweight, compiler framework, without any proprietary dependencies, OpenHLS, based on high-level synthesis techniques, for translating high-level representations of deep neural networks to low-level representations, suitable for deployment to near-sensor devices such as field-programmable gate arrays. We evaluate OpenHLS on various workloads and present a case-study implementation of a deep neural network for Bragg peak detection in the context of high-energy diffraction microscopy. We show OpenHLS is able to produce an implementation of the network with a throughput 4.8 $\mu$s/sample, which is approximately a 4$\times$ improvement over the existing implementation

We have performed intensive follow-up observations of a Type IIn/Ia-CSM SN (SN IIn/Ia-CSM), 2020uem, with photometry, spectroscopy, and polarimetry. In this paper, we report on the results of our observations focusing on optical/near-infrared (NIR) photometry and spectroscopy. The maximum V-band magnitude of SN 2020uem is over $-19.5$ mag. The light curves decline slowly with a rate of $\sim 0.75 {\rm ~mag}/100 {\rm ~days}$. In the late phase ($\gtrsim 300$ days), the light curves show accelerated decay ($\sim 1.2 {\rm ~mag}/100 {\rm ~days}$). The optical spectra show prominent hydrogen emission lines and broad features possibly associated with Fe-peak elements. In addition, the $\rm H\alpha$ profile exhibits a narrow P-Cygni profile with the absorption minimum of $\sim 100 {\rm ~km~s^{-1}}$. SN 2020uem shows a higher $\rm H\alpha/H\beta$ ratio ($\sim 7$) than those of SNe IIn, which suggests a denser CSM. The NIR spectrum shows the Paschen and Brackett series with continuum excess in the H and Ks bands. We conclude that the NIR excess emission originates from newly-formed carbon dust. The dust mass ($M_{\rm d}$) and temperature ($T_{\rm d}$) are derived to be $(M_{\rm d}, T_{\rm d}) \sim (4-7 \times 10^{-5} {\rm ~M_{\odot}}, 1500-1600 {\rm ~K})$. We discuss the differences and similarities between the observational properties of SNe IIn/Ia-CSM and those of other SNe Ia and interacting SNe. In particular, spectral features around $\sim 4650$ {\text \AA} and $\sim 5900$ {\text \AA} of SNe IIn/Ia-CSM are more suppressed than those of SNe Ia; these lines are possibly contributed, at least partly, by \ion{Mg}{1}] and \ion{Na}{1}, and may be suppressed by high ionization behind the reverse shock caused by the massive CSM., Comment: 22 pages, 15 figures, 3 tables. Accepted for publication in ApJ

We develop the rheology of a dilute granular gas mixture. Motivated by the interaction of charged granular particles, we assume that the grains interact via a square shoulder and well potential. Employing kinetic theory, we compute the temperature and the shear viscosity as functions of the shear rate. Numerical simulations confirm our results above the critical shear rate. At a shear rate below a critical value, clustering of the particles occurs., Comment: 12 pages, 14 figures

Quasiperiodic systems show a universal gap structure due to quasiperiodicity which is analogous to gap openings at the Brillouin zone boundary in periodic systems. The integrated density of states (IDoS) below those energy gaps are characterized by a few integers, which is known as the ``gap labeling theorem'' (GLT) for quasiperiodic systems. In this study, focusing on multilayer thin film systems such as twisted bilayer graphene and stacked transition metal dichalcogenides, we extend the GLT for multilayer systems of arbitrary dimensions and number of layers, using an approach based on the algebra called ``a noncommutative torus''. We find that the energy gaps and the associated IDoS are generally characterized by $_{DN}C_D$ integer labels in $N$ layer systems in the $D$ dimensions, when the effect of the interlayer coupling can be approximated by a quasiperiodic intralayer coupling for each layer. We demonstrate that the generalized GLT holds for quasiperiodic 1D tight binding models by numerical simulations., Comment: 12 pages, 4 figures

Exquisite sensitivities are a prominent advantage of quantum sensors. Ramsey sequences allow precise measurement of direct current fields, while Hahn-echo-like sequences measure alternating current fields. However, the latter are restrained for use with high-frequency fields (above approximately $1$ kHz) due to finite coherence times, leaving less-sensitive noncoherent methods for the low-frequency range. In this paper, we propose to bridge the gap with a fitting-based algorithm with a frequency-independent sensitivity to coherently measure low-frequency fields. As the algorithm benefits from coherence-based measurements, its demonstration with a single nitrogen-vacancy center gives a sensitivity of $9.4$ nT Hz$^{-0.5}$ for frequencies below about $0.6$ kHz down to near-constant fields. To inspect the potential in various scenarios, we apply the algorithm at a background field of tens of nTs, and we measure low-frequency signals via synchronization., Comment: 12 pages, 5 figures; supp 14 pages, 9 figures

We investigate the time evolution of the second-order R\'enyi entropy (RE) for bosons in a one-dimensional optical lattice following a sudden quench of the hopping amplitude $J$. Specifically, we examine systems that are quenched into the strongly correlated Mott-insulating (MI) regime with $J/U\ll 1$ ($U$ denotes the strength of the on-site repulsive interaction) from the MI limit with $J=0$. In this regime, the low-energy excited states can be effectively described by fermionic quasiparticles known as doublons and holons. They are excited in entangled pairs through the quench dynamics. By developing an effective theory, we derive a direct relation between the RE and correlation functions associated with doublons and holons. This relation allows us to analytically calculate the RE and obtain a physical picture for the RE, both in the ground state and during time evolution through the quench dynamics, in terms of doublon holon pairs. In particular, we show that the RE is proportional to the population of doublon-holon pairs that span the boundary of the subsystem. Our quasiparticle picture introduces some remarkable features that are absent in previous studies on the dynamics of entanglement entropy in free-fermion models. It provides with valuable insights into the dynamics of entanglement entropy in strongly-correlated systems., Comment: 14 pages, 8 figures

With more commercial constellations planned, the number of Low Earth Orbit (LEO) objects is set to TRIPLE in two years. The growth in LEO objects directly increases the probability of unintentional collisions between objects due to accumulating space debris. Effective space traffic management needs accurate knowledge of the variability in upper atmosphere densities. Data assimilative modeling, where physics-based models are informed by measurements, supplies the best capability today for specifying and predicting space weather. The foundation for this modeling comes from the SET High Accuracy Satellite Drag Model (HASDM) density database. We report on studies to understand the variabilities in HASDM. We identify two thermospheric features from the SET HASDM density database. First, we have confirmed that the time scale is very rapid (1-hour) for molecular conduction above 200 km to transfer energy vertically in the thermosphere. This results couples with a longer timescale for conduction in the 100-200 km region where it takes up to 2 days for energy to transition across that region via molecular conduction. We now have an excellent picture of the timescales of energy change throughout the thermosphere. Second, the SET HASDM density data display a common range of variability despite the level of daily averaged geomagnetic activity as represented by Ap. During higher levels of daily averaged geomagnetic activity, the density mean and median values increase at all altitude levels. However, the relative range of variability is consistent from one daily average of Ap to the next. The reason is likely to be that the underlying pre-storm density of the thermosphere is determined by the solar EUV and FUV irradiances that create a thermal foundation of the upper atmosphere. The daily averaged geomagnetic activity is a perturbation upon that foundation.

We numerically investigate the mechanical and geometrical properties of dense wet granular particles with irreversible attractive interaction. The shear modulus exhibits two inflection points as the packing fraction increases, and the bulk modulus shows a non-monotonic behavior. The coordination number also exhibits two inflection points. The peak position in the pair correlation function shifts to a lower value due to attractive interaction. The Voronoi tessellation of the particle configuration reveals that the probability density function for the volume of the Voronoi cell broadens as the packing fraction approaches the jamming point., Comment: 9 pages, 12 figures

The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 provides species density, mass density, and temperature estimates as function of location and space weather conditions. MSIS models have long been a popular choice of atmosphere model in the research and operations community alike, but - like many models - does not provide uncertainty estimates. In this work, we develop an exospheric temperature model based in machine learning (ML) that can be used with NRLMSIS 2.0 to calibrate it relative to high-fidelity satellite density estimates. Instead of providing point estimates, our model (called MSIS-UQ) outputs a distribution which is assessed using a metric called the calibration error score. We show that MSIS-UQ debiases NRLMSIS 2.0 resulting in reduced differences between model and satellite density of 25% and is 11% closer to satellite density than the Space Force's High Accuracy Satellite Drag Model. We also show the model's uncertainty estimation capabilities by generating altitude profiles for species density, mass density, and temperature. This explicitly demonstrates how exospheric temperature probabilities affect density and temperature profiles within NRLMSIS 2.0. Another study displays improved post-storm overcooling capabilities relative to NRLMSIS 2.0 alone, enhancing the phenomena that it can capture.

In many cases beam coupling impedances or wake fields are calculated with computer simulator such as CST Studio Suite, GdfidL Electromagnetic Field Simulator and so on. But evaluation with the stretched-wire method is still very useful by its flexibility to the change of the device-under-test configuration, speed to get results and, more than anything, its accessibility on the real devices. One of the drawbacks in the practical procedure, difficulty of Thru-Reflect-Line calibration, is overcome using the calibration method recently introduced in high frequency vector network analyzers, "2-X THRU de-embedding". Here the procedure is explained in detail. The method has been successfully applied to RF cavity and FX kicker measurement in the J-PARC MR., Comment: 4 pages, 11 figures, IPAC2022 (13th International Particle Accelerator Conference)

Today, most X-ray pixel detectors used at light sources transmit raw pixel data off the detector ASIC. With the availability of more advanced ASIC technology nodes for scientific application, more digital functionality from the computing domains (e.g., compression) can be integrated directly into a detector ASIC to increase data velocity. In this paper, we describe a lightweight, user-configurable detector ASIC digital architecture with on-chip compression which can be implemented in \SI{130}{\nm} technologies in a reasonable area on the ASIC periphery. In addition, we present a design to efficiently handle the variable data from the stream of parallel compressors. The architecture includes user-selectable lossy and lossless compression blocks. The impact of lossy compression algorithms is evaluated on simulated and experimental X-ray ptychography datasets. This architecture is a practical approach to increase pixel detector frame rates towards the continuous \SI{1}{\MHz} regime for not only coherent imaging techniques such as ptychography, but also for other diffraction techniques at X-ray light sources.

The search for Population III (Pop III) stars has fascinated and eluded astrophysicists for decades. One promising place for capturing evidence of their presence must be high-redshift objects; signatures should be recorded in their characteristic chemical abundances. We deduce the Fe and Mg abundances of the broad-line region (BLR) from the intensities of ultraviolet Mg II and Fe II emission lines in the near-infrared spectrum of UKIDSS Large Area Survey (ULAS) J1342+0928 at $z = 7.54$, by advancing our novel flux-to-abundance conversion method developed for $z\sim 1$ quasars. We find that the BLR of this quasar is extremely enriched, by a factor of 20 relative to the solar Fe abundance, together with a very low Mg/Fe abundance ratio: $[\mathrm{Fe/H}]=+1.36\pm0.19$ and $[\mathrm{Mg/Fe}]=-1.11\pm0.12$, only 700 million years after the Big Bang. We conclude that such an unusual abundance feature cannot be explained by the standard view of chemical evolution that considers only the contributions from canonical supernovae. While there remains uncertainty in the high-mass end of the Pop III IMF, here we propose that the larger amount of iron in ULAS J1342+0928 was supplied by a pair-instability supernova (PISN) caused by the explosion of a massive Pop III star in the high-mass end of the possible range of 150-300 $M_\odot$. Chemical-evolution models based on initial PISN enrichment well explain the trend in [Fe/Mg]-$z$ all the way from $z < 3$ to $z = 7.54$. We predict that stars with very low [Mg/Fe] at all metallicities are hidden in the Galaxy, and they will be efficiently discovered by ongoing new-generation photometric surveys., Comment: 17 pages, 4 figures, 3 tables. Accepted for publication in ApJ

This paper describes a practical dual-process speech enhancement system that adapts environment-sensitive frame-online beamforming (front-end) with help from environment-free block-online source separation (back-end). To use minimum variance distortionless response (MVDR) beamforming, one may train a deep neural network (DNN) that estimates time-frequency masks used for computing the covariance matrices of sources (speech and noise). Backpropagation-based run-time adaptation of the DNN was proposed for dealing with the mismatched training-test conditions. Instead, one may try to directly estimate the source covariance matrices with a state-of-the-art blind source separation method called fast multichannel non-negative matrix factorization (FastMNMF). In practice, however, neither the DNN nor the FastMNMF can be updated in a frame-online manner due to its computationally-expensive iterative nature. Our DNN-free system leverages the posteriors of the latest source spectrograms given by block-online FastMNMF to derive the current source covariance matrices for frame-online beamforming. The evaluation shows that our frame-online system can quickly respond to scene changes caused by interfering speaker movements and outperformed an existing block-online system with DNN-based beamforming by 5.0 points in terms of the word error rate., Comment: IWAENC 2022

We investigate the time-dependent R\'{e}nyi entanglement entropy after a quantum quench starting from the Mott-insulating and charge-density-wave states in a one-dimensional free boson system. The second R\'{e}nyi entanglement entropy is found to be the negative of the logarithm of the permanent of a matrix consisting of time-dependent single-particle correlation functions. From this relation and a permanent inequality, we obtain rigorous conditions for satisfying the volume-law entanglement growth. We also succeed in calculating the time evolution of the R\'{e}nyi entanglement entropy in unprecedentedly large systems by brute-force computations of the permanent. We discuss possible applications of our findings to the real-time dynamics of noninteracting bosonic systems., Comment: 15 pages, 8 figures. The authors Daichi Kagamihara and Ryui Kaneko contributed equally to this work

This paper describes the practical response- and performance-aware development of online speech enhancement for an augmented reality (AR) headset that helps a user understand conversations made in real noisy echoic environments (e.g., cocktail party). One may use a state-of-the-art blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) that works well in various environments thanks to its unsupervised nature. Its heavy computational cost, however, prevents its application to real-time processing. In contrast, a supervised beamforming method that uses a deep neural network (DNN) for estimating spatial information of speech and noise readily fits real-time processing, but suffers from drastic performance degradation in mismatched conditions. Given such complementary characteristics, we propose a dual-process robust online speech enhancement method based on DNN-based beamforming with FastMNMF-guided adaptation. FastMNMF (back end) is performed in a mini-batch style and the noisy and enhanced speech pairs are used together with the original parallel training data for updating the direction-aware DNN (front end) with backpropagation at a computationally-allowable interval. This method is used with a blind dereverberation method called weighted prediction error (WPE) for transcribing the noisy reverberant speech of a speaker, which can be detected from video or selected by a user's hand gesture or eye gaze, in a streaming manner and spatially showing the transcriptions with an AR technique. Our experiment showed that the word error rate was improved by more than 10 points with the run-time adaptation using only twelve minutes of observation., Comment: IEEE/RSJ IROS 2022

This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments. A major approach that has actively been studied in simulated environments is to sequentially perform speech enhancement and automatic speech recognition (ASR) based on deep neural networks (DNNs) trained in a supervised manner. In our task, however, such a pretrained system fails to work due to the mismatch between the training and test conditions and the head movements of the user. To enhance only the utterances of a target speaker, we use beamforming based on a DNN-based speech mask estimator that can adaptively extract the speech components corresponding to a head-relative particular direction. We propose a semi-supervised adaptation method that jointly updates the mask estimator and the ASR model at run-time using clean speech signals with ground-truth transcriptions and noisy speech signals with highly-confident estimated transcriptions. Comparative experiments using the state-of-the-art distant speech recognition system show that the proposed method significantly improves the ASR performance., Comment: INTERSPEECH 2022

Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related to the ground-truth dataset used for fitting. A benefit to ML over parametric models is that there are no predefined basis functions limiting the phenomena that can be modeled. In this work, we develop ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched dataset of outputs from the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer-derived density dataset from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post-storm cooling in the middle-thermosphere. We find that both NRLMSIS 2.0 and JB2008-ML do not account for post-storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g. the 2003 Halloween storms). Conversely, HASDM-ML and CHAMP-ML do show evidence of post-storm cooling indicating that this phenomenon is present in the original datasets. Results show that density reductions up to 40% can occur 1--3 days post-storm depending on location and the strength of the storm.

Context: A rich population of low-mass brown dwarfs and isolated planetary mass objects has been reported recently in the Upper Scorpius and Ophiuchus star forming complex. Aims: We investigate the membership, nature and properties of 17 of these isolated planetary mass candidates using low-resolution near-infrared spectra. Methods: We investigate the membership by looking for evidences of youth using four diagnostics: the slope of the continuum between the J and Ks band, the Hcont and TLI-g gravity sensitive indices, and by comparing the spectra to young and field (old) M and L-dwarf standards. Results: All the targets but one are confirmed as young ultracool objects, with spectral types between L0 and L6 and masses in the range 0.004-0.013 M according to evolutionary models. The status of the last target is unclear at this point. Conclusions: Only one possible contaminant has been identified among the 17 targets, suggesting that the contamination level of the original sample must be lower than 6%, Comment: Accepted on 2022 June 2. Pending language revision

We theoretically study the entropy production and the work extracted from a system connected to two reservoirs by periodic modulations of the electrochemical potentials of the reservoirs and the parameter of a system Hamiltonian under isothermal conditions. We find that the modulation of the parameters can drive a geometric state, which is away from a nonequilibrium steady state. Using this property, we construct a demon in which the entropy production during the first one-cycle is negative such that we can extract the work if we start from the nonequilibrium steady state without parameter modulations. We use the Anderson model to implement the demon in a realistic situation., Comment: 16 pages, 16 figures, v6: revised version

In order to take full advantage of the U.S. Department of Energy's billion-dollar investments into the next-generation research infrastructure (e.g., exascale, light sources, colliders), advances are required not only in detector technology but also in computing and specifically AI. Let us consider an example from X-ray science. Nanoscale X-ray imaging is a crucial tool to enable a wide range of scientific explorations from materials science and biology to mechanical and civil engineering. The next-generation light sources will increase the X-ray beam brightness and coherent flux by 100 to 1,000 times. In order to image larger samples, the continuous frame rate of pixel array detectors must be increased, approaching 1 MHz, which requires several Tbps (aggregated) to transfer pixel data out to a data acquisition system. Using 65-nm CMOS technology, an optimistic raw data rate off such a chip is about 100-200 Gbps. However, a continuous 1 MHz detector with only $256 \times 256$ pixels at 16-bit resolution, for example, will require 1,000 Gbps (i.e., 1 Tbps) bandwidth off the chip! It is impractical to have multiple high-speed transceivers running in parallel to provide such bandwidth and represents the first data bottleneck. New approaches are necessary to reduce the data size by performing data compression or AI-based feature extraction directly inside a detector silicon chip in a streaming manner before sending it off-chip., Comment: White paper for AI@DOE Roundtable, December 8-9, 2021 (virtual). arXiv admin note: text overlap with arXiv:2110.07828

Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation). Such degradation is possible in real-world use cases due to the diversity of sample characteristics. That is, a set of samples is a mixture of critical ones which should not be missed and less important ones. Therefore, we cannot understand the performance by accuracy alone. While existing research aims to prevent a model degradation, insights into the related methods are needed to grasp their benefits and limitations. In this talk, we will present implications derived from a comparison of methods for reducing degradation. Especially, we formulated use cases for industrial settings in terms of arrangements of a data set. The results imply that a practitioner should care about better method continuously considering dataset availability and life cycle of an AI system because of a trade-off between accuracy and preventing degradation.

This paper describes heavy-tailed extensions of a state-of-the-art versatile blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) from a unified point of view. The common way of deriving such an extension is to replace the multivariate complex Gaussian distribution in the likelihood function with its heavy-tailed generalization, e.g., the multivariate complex Student's t and leptokurtic generalized Gaussian distributions, and tailor-make the corresponding parameter optimization algorithm. Using a wider class of heavy-tailed distributions called a Gaussian scale mixture (GSM), i.e., a mixture of Gaussian distributions whose variances are perturbed by positive random scalars called impulse variables, we propose GSM-FastMNMF and develop an expectationmaximization algorithm that works even when the probability density function of the impulse variables have no analytical expressions. We show that existing heavy-tailed FastMNMF extensions are instances of GSM-FastMNMF and derive a new instance based on the generalized hyperbolic distribution that include the normal-inverse Gaussian, Student's t, and Gaussian distributions as the special cases. Our experiments show that the normalinverse Gaussian FastMNMF outperforms the state-of-the-art FastMNMF extensions and ILRMA model in speech enhancement and separation in terms of the signal-to-distortion ratio.

The primordial He abundance $Y_\mathrm{P}$ is a powerful probe of cosmology. Currently, $Y_\mathrm{P}$ is best determined by observations of metal-poor galaxies, while there are only a few known local extremely metal-poor ($<0.1 Z_\odot$) galaxies (EMPGs) having reliable He/H measurements with HeI$\lambda$10830 near-infrared (NIR) emission. Here we present deep Subaru NIR spectroscopy for 10 EMPGs. Combining the existing optical data, He/H values of 5 out of the 10 EMPGs are reliably derived by the Markov chain Monte Carlo algorithm. Adding the existing 3 EMPGs and 51 moderately metal-poor ($0.1-0.4 Z_\odot$) galaxies with reliable He/H estimates, we obtain $Y_\mathrm{P}=0.2370^{+0.0034}_{-0.0033}$ by linear regression in the $\mathrm{(He/H)}-\mathrm{(O/H)}$ plane, where we increase the number of EMPGs from 3 to 8 anchoring He/H of the most metal-poor gas in galaxies. Although our $Y_\mathrm{P}$ measurement and previous measurements are consistent, our result is slightly ($\sim 1\sigma$) smaller due to our EMPGs. With our $Y_\mathrm{P}$ and the existing primordial deuterium $D_\mathrm{P}$ measurement, we constrain the effective number of neutrino species $N_\mathrm{eff}$ and the baryon-to-photon ratio $\eta$ showing $\gtrsim 1-2\sigma$ tensions with the Standard Model and Planck Collaboration et al. (2020). Motivated by the tensions, we allow the degeneracy parameter of electron-neutrino $\xi_e$ to vary as well as $N_\mathrm{eff}$ and $\eta$. We obtain $\xi_e = 0.05^{+0.03}_{-0.02}$, $N_\mathrm{eff}=3.11^{+0.34}_{-0.31}$, and $\eta\times10^{10}=6.08^{+0.06}_{-0.06}$ from the $Y_\mathrm{P}$ and $D_\mathrm{P}$ measurements with a prior of $\eta$ taken from Planck Collaboration et al. (2020). Our constraints suggest a lepton asymmetry and allow for a high value of $N_\mathrm{eff}$ within the $1\sigma$ level, which could mitigate the Hubble tension., Comment: 22 pages, 9 figures, accepted for publication in ApJ