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Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose a novel artificial intelligence (AI) simulator, Conditional Generative Modeling for Ground Motion (CGM-GM), to synthesize high-frequency and spatially continuous earthquake ground motion waveforms. CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, learning complex wave physics and Earth heterogeneities, without explicit physics constraints. This is achieved through a probabilistic autoencoder that captures latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. CGM-GM demonstrates a strong potential for outperforming a state-of-the-art non-ergodic empirical ground motion model and shows great promise in seismology and beyond.

In this paper, we rethink sparse lexical representations for image retrieval. By utilizing multi-modal large language models (M-LLMs) that support visual prompting, we can extract image features and convert them into textual data, enabling us to utilize efficient sparse retrieval algorithms employed in natural language processing for image retrieval tasks. To assist the LLM in extracting image features, we apply data augmentation techniques for key expansion and analyze the impact with a metric for relevance between images and textual data. We empirically show the superior precision and recall performance of our image retrieval method compared to conventional vision-language model-based methods on the MS-COCO, PASCAL VOC, and NUS-WIDE datasets in a keyword-based image retrieval scenario, where keywords serve as search queries. We also demonstrate that the retrieval performance can be improved by iteratively incorporating keywords into search queries., Comment: Accepted to ECCV 2024 Workshops: 2nd Workshop on Traditional Computer Vision in the Age of Deep Learning (TradiCV)

Recently, representation learning with contrastive learning algorithms has been successfully applied to challenging unlabeled datasets. However, these methods are unable to distinguish important features from unimportant ones under simply unsupervised settings, and definitions of importance vary according to the type of downstream task or analysis goal, such as the identification of objects or backgrounds. In this paper, we focus on unsupervised image clustering as the downstream task and propose a representation learning method that enhances features critical to the clustering task. We extend a clustering-friendly contrastive learning method and incorporate a contrastive analysis approach, which utilizes a reference dataset to separate important features from unimportant ones, into the design of loss functions. Conducting an experimental evaluation of image clustering for three datasets with characteristic backgrounds, we show that for all datasets, our method achieves higher clustering scores compared with conventional contrastive analysis and deep clustering methods., Comment: 12 pages, 6 figures, 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2024)

Spoken dialogue plays a crucial role in human-AI interactions, necessitating dialogue-oriented spoken language models (SLMs). To develop versatile SLMs, large-scale and diverse speech datasets are essential. Additionally, to ensure hiqh-quality speech generation, the data must be spontaneous like in-wild data and must be acoustically clean with noise removed. Despite the critical need, no open-source corpus meeting all these criteria has been available. This study addresses this gap by constructing and releasing a large-scale spoken dialogue corpus, named Japanese Corpus for Human-AI Talks (J-CHAT), which is publicly accessible. Furthermore, this paper presents a language-independent method for corpus construction and describes experiments on dialogue generation using SLMs trained on J-CHAT. Experimental results indicate that the collected data from multiple domains by our method improve the naturalness and meaningfulness of dialogue generation., Comment: 8 pages, 6 figures

The Simons Observatory (SO) is a ground-based cosmic microwave background experiment currently being deployed to Cerro Toco in the Atacama Desert of Chile. The initial deployment of SO, consisting of three 0.46m-diameter small-aperture telescopes and one 6m-primary large-aperture telescope, will field over 60,000 transition-edge sensors that will observe at frequencies between 30 GHz and 280 GHz. SO will read out its detectors using Superconducting Quantum Interference Device (SQUID) microwave-frequency multiplexing $\mu$mux, a form of frequency division multiplexing where an RF-SQUID couples each TES bolometer to a superconducting resonator tuned to a unique frequency. Resonator frequencies are spaced roughly every 2 MHz between 4 and 6 GHz, allowing for multiplexing factors on the order of 1000. One challenge of $\mu$mux is matching each tracked resonator with its corresponding physical detector. Variations in resonator fabrication, and frequency shifts between cooldowns caused by trapped flux can cause the measured resonance frequencies to deviate significantly from their designed values. In this study, we introduce a method for pairing measured and designed resonators by constructing a bipartite graph based on the two resonator sets, and assigning edge weights based on measured resonator and detector properties such as resonance frequency, detector pointing, and assigned bias lines. Finding the minimum-cost matching for a given set of edge weights is a well-studied problem that can be solved very quickly, and this matching tells us the best assignment of measured resonators to designed detectors for our input parameters. We will present results based on the first on-sky measurements from SAT1, the first SO MF small-aperture telescope.

A novel information entropy of turbulence systems with multiple field quantities is formulated. Inspired by quantum mechanics, the von Neumann entropy (vNE) and the entanglement entropy(EE) are derived from a density matrix for the turbulence state in terms of the multi-field singular value decomposition (MFSVD). Applying the information-theoretic entropy analyses to spatio-temporal dynamics in turbulent plasmas with phase-transition-like behavior, we discover a new nontrivial transition threshold regarding the vNE, which significantly deviates from the transition threshold of the field energy considered in the conventional approaches. These findings provide us with new classifications of the turbulence state in terms of combined energy and information. It is also shown that the EE for nonlinear interactions in turbulence simultaneously describes not only the information for the strength of nonlinear mode couplings but also the direction of net energy transfer., Comment: 13 page, 5 figures

We propose a scar model to reproduce how double-strand breaks occur in the telomeric DNA damaged by the effect of the $\beta$-decay of tritium to helium. In this scar model, the two hydrogens bonded to the 5$^\prime$ carbon connecting the pent saccharides and phosphate are removed. Molecular dynamics simulations using the reactive force field are carried out for 10 cases for the telomeric DNA consisting of 16 base pairs (32 nucleotides). It results in double-strand breaks (DSBs) being observed for structures with more than 24 scars. For 16 scar cases, only single-strand breaks (SSB) are observed. Moreover, in the case of $\left\{16, 0 \right\}$ and $\left\{ 0, 16 \right\},$ where only one of the strands had scars, SSB occurs only in the scarred strand. Secondly, in the $\left\{16, 8 \right\}$ and $\left\{ 8, 16 \right\}$ cases, DSBs occurs. Therefore, we conclude that the following conditions are necessary for DSBs:(i) Scars must occur on both the L and R strands. (ii) A large number of scars (24 or more) must occur in close proximity to each other.

Coupon allocation drives customer purchases and boosts revenue. However, it presents a fundamental trade-off between exploiting the current optimal policy to maximize immediate revenue and exploring alternative policies to collect data for future policy improvement via off-policy evaluation (OPE). While online A/B testing can validate new policies, it risks compromising short-term revenue. Conversely, relying solely on an exploitative policy hinders the ability to reliably estimate and enhance future policies. To balance this trade-off, we propose a novel approach that combines a model-based revenue maximization policy and a randomized exploration policy for data collection. Our framework enables flexibly adjusting the mixture ratio between these two policies to optimize the balance between short-term revenue and future policy improvement. We formulate the problem of determining the optimal mixture ratio between a model-based revenue maximization policy and a randomized exploration policy for data collection. We empirically verified the effectiveness of the proposed mixed policy using both synthetic and real-world data. Our main contributions are: (1) Demonstrating a mixed policy combining deterministic and probabilistic policies, flexibly adjusting the data collection vs. revenue trade-off. (2) Formulating the optimal mixture ratio problem as multi-objective optimization, enabling quantitative evaluation of this trade-off. By optimizing the mixture ratio, businesses can maximize revenue while ensuring reliable future OPE and policy improvement. This framework is applicable in any context where the exploration-exploitation trade-off is relevant.

The impact of quantization on the overall performance of deep learning models is a well-studied problem. However, understanding and mitigating its effects on a more fine-grained level is still lacking, especially for harder tasks such as object detection with both classification and regression objectives. This work defines the performance for a subset of task-critical categories, i.e. the critical-category performance, as a crucial yet largely overlooked fine-grained objective for detection tasks. We analyze the impact of quantization at the category-level granularity, and propose methods to improve performance for the critical categories. Specifically, we find that certain critical categories have a higher sensitivity to quantization, and are prone to overfitting after quantization-aware training (QAT). To explain this, we provide theoretical and empirical links between their performance gaps and the corresponding loss landscapes with the Fisher information framework. Using this evidence, we apply a Fisher-aware mixed-precision quantization scheme, and a Fisher-trace regularization for the QAT on the critical-category loss landscape. The proposed methods improve critical-category metrics of the quantized transformer-based DETR detectors. They are even more significant in case of larger models and higher number of classes where the overfitting becomes more severe. For example, our methods lead to 10.4% and 14.5% mAP gains for, correspondingly, 4-bit DETR-R50 and Deformable DETR on the most impacted critical classes in the COCO Panoptic dataset., Comment: Poster presentation at the 2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024)

Large earthquakes can be destructive and quickly wreak havoc on a landscape. To mitigate immediate threats, early warning systems have been developed to alert residents, emergency responders, and critical infrastructure operators seconds to a minute before seismic waves arrive. These warnings provide time to take precautions and prevent damage. The success of these systems relies on fast, accurate predictions of ground motion intensities, which is challenging due to the complex physics of earthquakes, wave propagation, and their intricate spatial and temporal interactions. To improve early warning, we propose a novel AI-enabled framework, WaveCastNet, for forecasting ground motions from large earthquakes. WaveCastNet integrates a novel convolutional Long Expressive Memory (ConvLEM) model into a sequence to sequence (seq2seq) forecasting framework to model long-term dependencies and multi-scale patterns in both space and time. WaveCastNet, which shares weights across spatial and temporal dimensions, requires fewer parameters compared to more resource-intensive models like transformers and thus, in turn, reduces inference times. Importantly, WaveCastNet also generalizes better than transformer-based models to different seismic scenarios, including to more rare and critical situations with higher magnitude earthquakes. Our results using simulated data from the San Francisco Bay Area demonstrate the capability to rapidly predict the intensity and timing of destructive ground motions. Importantly, our proposed approach does not require estimating earthquake magnitudes and epicenters, which are prone to errors using conventional approaches; nor does it require empirical ground motion models, which fail to capture strongly heterogeneous wave propagation effects.

The Simons Observatory (SO) is a Cosmic Microwave Background experiment located in the Atacama Desert in Chile. SO consists of three small aperture telescopes (SATs) and one large aperture telescope (LAT) with a total of 60,000 detectors in six frequency bands. As an observatory, SO encompasses hundreds of hardware components simultaneously running at different readout rates, all separate from its 60,000 detectors on-sky and their metadata. We provide an overview of commissioning SO's data acquisition software system for SAT-MF1, the first SAT deployed to the Atacama site. Additionally, we share insights from deploying data access software for all four telescopes, detailing how performance limitations affected data loading and quality investigations, which led to site-compatible software improvements., Comment: 15 pages, 9 figures, 3 tables. Presented at SPIE Astronomical Telescopes + Instrumentation 2024 Second submission: fix typos

The Simons Observatory (SO) is a cosmic microwave background (CMB) observatory consisting of three small aperture telescopes and one large aperture telescope. SO is located in the Atacama Desert in Chile at an elevation of 5180m. Distributed among the four telescopes are over 60,000 transition-edge sensor (TES) bolometers across six spectral bands centered between 27 and 280 GHz. A large collection of ancillary hardware devices which produce lower rate `housekeeping' data are used to support the detector data collection. We developed a distributed control system, which we call the observatory control system (ocs), to coordinate data collection among all systems within the observatory. ocs is a core component of the deployed site software, interfacing with all on-site hardware. Alongside ocs we utilize a combination of internally and externally developed open source projects to enable remote monitoring, data management, observation coordination, and data processing. Deployment of a majority of the software is done using Docker containers. The deployment of software packages is partially done via automated Ansible scripts, utilizing a GitOps based approach for updating infrastructure on site. We describe an overview of the software and computing systems deployed within SO, including how those systems are deployed and interact with each other. We also discuss the timing distribution system and its configuration as well as lessons learned during the deployment process and where we plan to make future improvements., Comment: 19 pages, 4 figures

Normalizing flow-based generative models have been widely used in applications where the exact density estimation is of major importance. Recent research proposes numerous methods to improve their expressivity. However, conditioning on a context is largely overlooked area in the bijective flow research. Conventional conditioning with the vector concatenation is limited to only a few flow types. More importantly, this approach cannot support a practical setup where a set of context-conditioned (specialist) models are trained with the fixed pretrained general-knowledge (generalist) model. We propose ContextFlow++ approach to overcome these limitations using an additive conditioning with explicit generalist-specialist knowledge decoupling. Furthermore, we support discrete contexts by the proposed mixed-variable architecture with context encoders. Particularly, our context encoder for discrete variables is a surjective flow from which the context-conditioned continuous variables are sampled. Our experiments on rotated MNIST-R, corrupted CIFAR-10C, real-world ATM predictive maintenance and SMAP unsupervised anomaly detection benchmarks show that the proposed ContextFlow++ offers faster stable training and achieves higher performance metrics. Our code is publicly available at https://github.com/gudovskiy/contextflow., Comment: Accepted to UAI 2024. Preprint

The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment that includes small-aperture telescopes (SATs) observing from an altitude of 5,200 m in the Atacama Desert in Chile. The SO SATs will cover six spectral bands between 27 and 280 GHz to search for primordial B-modes to a sensitivity of $\sigma(r)=0.002$, with quantified systematic errors well below this value. Each SAT is a self-contained cryogenic telescope with a 35$^\circ$ field of view, 42 cm diameter optical aperture, 40 K half-wave plate, 1 K refractive optics, and $<0.1$ K focal plane that holds $>12,000$ TES detectors. We describe the nominal design of the SATs and present details about the integration and testing for one operating at 93 and 145 GHz.

Recovering quantum information from a noisy quantum system is one of the central challenges in quantum information science and fundamental physics. The key to this goal is explicitly constructing a decoder. In this paper, we provide two explicit decoding quantum circuits that are both capable of recovering quantum information when a decoupling condition is satisfied, i.e., when quantum information is in principle recoverable. The decoders are constructed by using the fixed-point amplitude amplification algorithm based on the quantum singular value transformation, which significantly extends an approach by Yoshida and Kitaev in a specific noise model to general situations. We also show that the proposed decoding circuits reduce the computational cost compared to a previously known explicit decoder. Our constructions not only show an intriguing intersection between decoders and quantum algorithms but also reveal the power of an algorithmic approach to recovering quantum information., Comment: 23 pages, 12 figures, 2 tables

We present Ir $4f$ core-level hard-x-ray photoemission spectroscopy (HAXPES) experiments conducted across antiferromagnetic (AFM) ordering transition in Ruddlesden-Popper iridates Sr$_2$IrO$_4$ and Sr$_3$Ir$_2$O$_7$. The Ir $4f$ spectra exhibit distinct changes between the AFM and paramagnetic (PM) phases, with the spectral difference $I_\text{PM}-I_\text{AFM}$ showing a contrasting behavior in the two compounds. By employing computational simulations using the local-density approximation combined with the dynamical mean-field theory method, we elucidate that $I_\text{PM}-I_\text{AFM}$ primary reflects the Slater or Mott-Hubbard character of the AFM insulating state rather than material specific details. This sensitivity to fine low-energy electronic structure arises from the dependence of charge-transfer responses to the sudden creation of a localized core hole on both metal-insulator transitions and long-range AFM ordering. Our result broadens the applications of core-level HAXPES as a tool for characterization of electronic structure., Comment: Main: 7 pages, 3 figures ; SM: 4 pages, 4 figures

We present UTDUSS, the UTokyo-SaruLab system submitted to Interspeech2024 Speech Processing Using Discrete Speech Unit Challenge. The challenge focuses on using discrete speech unit learned from large speech corpora for some tasks. We submitted our UTDUSS system to two text-to-speech tracks: Vocoder and Acoustic+Vocoder. Our system incorporates neural audio codec (NAC) pre-trained on only speech corpora, which makes the learned codec represent rich acoustic features that are necessary for high-fidelity speech reconstruction. For the acoustic+vocoder track, we trained an acoustic model based on Transformer encoder-decoder that predicted the pre-trained NAC tokens from text input. We describe our strategies to build these models, such as data selection, downsampling, and hyper-parameter tuning. Our system ranked in second and first for the Vocoder and Acoustic+Vocoder tracks, respectively., Comment: 5 pages, 3 figures

In text-to-speech synthesis, the ability to control voice characteristics is vital for various applications. By leveraging thriving text prompt-based generation techniques, it should be possible to enhance the nuanced control of voice characteristics. While previous research has explored the prompt-based manipulation of voice characteristics, most studies have used pre-recorded speech, which limits the diversity of voice characteristics available. Thus, we aim to address this gap by creating a novel corpus and developing a model for prompt-based manipulation of voice characteristics in text-to-speech synthesis, facilitating a broader range of voice characteristics. Specifically, we propose a method to build a sizable corpus pairing voice characteristics descriptions with corresponding speech samples. This involves automatically gathering voice-related speech data from the Internet, ensuring its quality, and manually annotating it using crowdsourcing. We implement this method with Japanese language data and analyze the results to validate its effectiveness. Subsequently, we propose a construction method of the model to retrieve speech from voice characteristics descriptions based on a contrastive learning method. We train the model using not only conservative contrastive learning but also feature prediction learning to predict quantitative speech features corresponding to voice characteristics. We evaluate the model performance via experiments with the corpus we constructed above., Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing. arXiv admin note: text overlap with arXiv:2309.13509

The $d^5$ electron configurations under the crystal field, spin-orbit coupling, and Coulomb interaction give rise to a plethora of profound ground states. Ruthenium perovskite oxides exhibit a number of unconventional properties yet the Ru$^{4+}$ state ($4d^4$) is usually stable in these materials. In this regard, Ru$^{3+}$ ions in perovskite materials are expected to be a mesmerising playground of $4d^5$ electron configurations. Here, we report measurements of x-ray photoemission spectroscopy on recently synthesized perovskite ruthenium oxide thin films, LaRuO$_3$ and NdRuO$_3$, whose valence state of the ruthenium ions is trivalent. We discuss correlation and spin-orbit effects from the valence-band spectra, in particular an additional peak structure around 3-5 eV, reminiscent of the so-called 3 eV peak observed in Sr$_2$RuO$_4$. Moreover, we find that the core-level spectra of these materials are quantitatively different from those in other ruthenates which possess Ru$^{4+}$ ions, e.g., SrRuO$_3$. We therefore argue that the core level spectra of LaRuO$_3$ and NdRuO$_3$ are peculiar to the Ru$^{3+}$ states., Comment: 5 pages, 4 figures

There has been a growing interest in non-Hermitian physics. One of its main goals is to engineer dissipation and to explore ensuing functionality. In magnonics, the effect of dissipation due to local damping on magnon transport has been explored. However, the effects of non-local damping on the magnonic analog of the Josephson effect remain missing, despite that non-local damping is inevitable and has been playing a central role in magnonics. Here, we uncover theoretically that a surprisingly rich dynamics can emerge in magnetic junctions due to intrinsic non-local damping, using analytical and numerical methods. In particular, under microwave pumping, we show that coherent spin precession in the right and left insulating ferromagnet (FM) of the junction becomes synchronized by non-local damping and thereby a magnonic analog of the $\varphi$ Josephson junction emerges, where $\varphi$ stands here for the relative precession phase of right and left FM in the stationary limit. Remarkably, $\varphi$ decreases monotonically from $ \pi$ to $\pi/2$ as the magnon-magnon interaction, arising from spin anisotropies, increases. Moreover, we also find a magnonic diode effect giving rise to rectification of magnon currents. Our predictions are readily testable with current device and measurement technologies at room temperatures., Comment: 5+7 pages, 3+3 figures

While branching network structures abound in nature, their objective analysis is more difficult than expected because existing quantitative methods often rely on the subjective judgment of branch structures. This problem is particularly pronounced when dealing with images comprising discrete particles. Here we propose an objective framework for quantitative analysis of branching networks by introducing the mathematical definitions for internal and external structures based on topological data analysis, specifically, persistent homology. We compare persistence diagrams constructed from images with and without plots on the convex hull. The unchanged points in the two diagrams are the internal structures and the difference between the two diagrams is the external structures. We construct a mathematical theory for our method and show that the internal structures have a monotonicity relationship with respect to the plots on the convex hull, while the external structures do not. This is the phenomenon related to the resolution of the image. Our method can be applied to a wide range of branch structures in biology, enabling objective analysis of numbers, spatial distributions, sizes, and more. Additionally, our method has the potential to be combined with other tools in topological data analysis, such as the generalized persistence landscape.

The delay Lotka-Volterra equation is a delay-differential extension of the well known Lotka-Volterra equation, and is known to have N-soliton solutions. In this paper, Backlund transformations, Lax pairs and infinite conserved quantities of the delay Lotka-Volterra equation and its discrete analogue are constructed. The conserved quantities of the delay Lotka-Volterra equation turn out to be complicated and described by using the time-ordered product of linear operators., Comment: 11 pages

We present an evaluation of 32-bit POSIT arithmetic through its implementation as accelerators on FPGAs and GPUs. POSIT, a floating-point number format, adaptively changes the size of its fractional part. We developed hardware designs for FPGAs and software for GPUs to accelerate linear algebra operations using Posit(32,2) arithmetic. Our FPGA- and GPU-based accelerators in Posit(32,2) arithmetic significantly accelerated the Cholesky and LU decomposition algorithms for dense matrices. In terms of numerical accuracy, Posit(32,2) arithmetic is approximately 0.5 - 1.0 digits more accurate than the standard 32-bit format, especially when the norm of the elements of the input matrix is close to 1. Evaluating power consumption, we observed that the power efficiency of the accelerators ranged between 0.043 - 0.076 Gflops/watts for the LU decomposition in Posit(32,2) arithmetic. The power efficiency of the latest GPUs as accelerators of Posit(32,2) arithmetic is better than that of the evaluated FPGA chip., Comment: 11 pages, 8 figures; Published in HPCAsia '24: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region

We study the interplay between the Kondo and superconducting (SC) proximity effects, taking place in a triangular triple quantum dot (TTQD) connected to one normal and two SC leads. This system shows various quantum phases. Without the SC leads, the lowest two states that belong to the different spin sectors, $S=0$ and $S=1/2$, become energetically very close to each other near half filling. The singlet one is a Kondo-screened state by conduction electrons from the normal lead, and the doublet one is a resonating valence bond state with unpaired free spin which remains unscreened. Furthermore, when one additional electron enters the TTQD, the ground state becomes a doublet in which the $S=1$ local moment due to the Nagaoka ferromagnetism is partially screened by conduction electrons.The Cooper pairs penetrating into the TTQD from the SC leads reconstruct the wavefunctions and vary the phase boundaries between these quantum states in the parameter space. We calculate ground-state phase diagrams using the numerical renormalization group, and show that the SC proximity effect induces a reentrant transition in-between the three- and four-electron fillings., Comment: 7 pages, 3 figures

Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks. However, the conventional finetuning process with randomly sampled data points results in diminished training efficiency. To address this drawback, we propose a novel approach, Vision-language Collaborative Active Finetuning (VeCAF). With the emerging availability of labels and natural language annotations of images through web-scale crawling or controlled generation, VeCAF makes use of these information to perform parametric data selection for PVM finetuning. VeCAF incorporates the finetuning objective to select significant data points that effectively guide the PVM towards faster convergence to meet the performance goal. This process is assisted by the inherent semantic richness of the text embedding space which we use to augment image features. Furthermore, the flexibility of text-domain augmentation allows VeCAF to handle out-of-distribution scenarios without external data. Extensive experiments show the leading performance and high computational efficiency of VeCAF that is superior to baselines in both in-distribution and out-of-distribution image classification tasks. On ImageNet, VeCAF uses up to 3.3x less training batches to reach the target performance compared to full finetuning, and achieves an accuracy improvement of 2.7% over the state-of-the-art active finetuning method with the same number of batches., Comment: 13 pages

The physics of driven collective modes in quantum materials underpin a number of striking non-equilibrium functional responses, which include enhanced magnetism, ferroelectricity and superconductivity. However, the coherent coupling between multiple modes at once are difficult to capture by single-pump probe (one-dimensional) spectroscopy, and often remain poorly understood. One example is phonon-mediated amplification of Josephson plasmons in YBa$_2$Cu$_3$O$_{6+x}$, in which at least three normal modes of the solid are coherently mixed as a source of enhanced superconductivity. Here, we go beyond previous pump-probe experiments in this system and acquire two-dimensional frequency maps using pairs of mutually delayed, carrier envelope phase stable mid-infrared pump pulses, combined with measurements of the time-modulated second-order nonlinear optical susceptibility. We find that the driven zone-center phonons amplify coherent pairs of opposite-momentum Josephson plasma polaritons, generating a squeezed state of interlayer phase fluctuations. The squeezed state is a potentially important ingredient in the microscopic physics of photo-induced superconductivity in this and other materials., Comment: 20 pages, 7 figures

The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adverse weather effects. However, the conventional MoE architecture with parallel Feed Forward Network (FFN) experts leads to significant parameter and computational overheads that hinder its efficient deployment. In addition, the naive MoE linear router is suboptimal in assigning task-specific features to multiple experts which limits its further scalability. In this work, we propose an efficient MoE architecture with weight sharing across the experts. Inspired by the idea of linear feature modulation (FM), our architecture implicitly instantiates multiple experts via learnable activation modulations on a single shared expert block. The proposed Feature Modulated Expert (FME) serves as a building block for the novel Mixture-of-Feature-Modulation-Experts (MoFME) architecture, which can scale up the number of experts with low overhead. We further propose an Uncertainty-aware Router (UaR) to assign task-specific features to different FM modules with well-calibrated weights. This enables MoFME to effectively learn diverse expert functions for multiple tasks. The conducted experiments on the multi-deweather task show that our MoFME outperforms the baselines in the image restoration quality by 0.1-0.2 dB and achieves SOTA-compatible performance while saving more than 72% of parameters and 39% inference time over the conventional MoE counterpart. Experiments on the downstream segmentation and classification tasks further demonstrate the generalizability of MoFME to real open-world applications., Comment: aaai2024

Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce uncalibrated closed-set predictions for OOD data. A more robust classifiers with the uncertainty estimation typically require a potentially unavailable OOD dataset for outlier exposure training, or a considerable amount of additional memory and compute to build ensemble models. In this work, we improve on uncertainty estimation without extra OOD data or additional inference costs using an alternative Split-Ensemble method. Specifically, we propose a novel subtask-splitting ensemble training objective, where a common multiclass classification task is split into several complementary subtasks. Then, each subtask's training data can be considered as OOD to the other subtasks. Diverse submodels can therefore be trained on each subtask with OOD-aware objectives. The subtask-splitting objective enables us to share low-level features across submodels to avoid parameter and computational overheads. In particular, we build a tree-like Split-Ensemble architecture by performing iterative splitting and pruning from a shared backbone model, where each branch serves as a submodel corresponding to a subtask. This leads to improved accuracy and uncertainty estimation across submodels under a fixed ensemble computation budget. Empirical study with ResNet-18 backbone shows Split-Ensemble, without additional computation cost, improves accuracy over a single model by 0.8%, 1.8%, and 25.5% on CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively. OOD detection for the same backbone and in-distribution datasets surpasses a single model baseline by, correspondingly, 2.2%, 8.1%, and 29.6% mean AUROC., Comment: ICML2024. Project website is available at https://antonioo-c.github.io/projects/split-ensemble

In the context of avatar-mediated communication, it is crucial for the face-to-face interlocutor to sense the operator's presence and emotions via the avatar. Although androids resembling humans have been developed to convey presence through appearance and movement, few studies have prioritized deepening the communication experience for both operator and interlocutor using android robot as an avatar. Addressing this gap, we introduce the ``Cybernetic Avatar `Yui','' featuring a human-like head unit with 28 degrees of freedom, capable of expressing gaze, facial emotions, and speech-related mouth movements. Through an eye-tracking unit in a Head-Mounted Display (HMD) and degrees of freedom on both eyes of Yui, operators can control the avatar's gaze naturally. Additionally, microphones embedded in Yui's ears allow operators to hear surrounding sounds in three dimensions, enabling them to discern the direction of calls based solely on auditory information. An HMD's face-tracking unit synchronizes the avatar's facial movements with those of the operator. This immersive interface, coupled with Yui's human-like appearance, enables real-time emotion transmission and communication, enhancing the sense of presence for both parties. Our experiments demonstrate Yui's facial expression capabilities, and validate the system's efficacy through teleoperation trials, suggesting potential advancements in avatar technology., Comment: 13pages, 19 figures

We consider solving linear optimization (LO) problems with uncertain objective coefficients. For such problems, we often employ robust optimization (RO) approaches by introducing an uncertainty set for the unknown coefficients. Typical RO approaches require observations or prior knowledge of the unknown coefficient to define an appropriate uncertainty set. However, such information may not always be available in practice. In this study, we propose a novel uncertainty set for robust linear optimization (RLO) problems without prior knowledge of the unknown coefficients. Instead, we assume to have data of known constraint parameters and corresponding optimal solutions. Specifically, we derive an explicit form of the uncertainty set as a polytope by applying techniques of inverse optimization (IO). We prove that the RLO problem with the proposed uncertainty set can be equivalently reformulated as an LO problem. Numerical experiments show that the RO approach with the proposed uncertainty set outperforms classical IO in terms of performance stability., Comment: 6 pages, 1 figure, To appear in Proceedings of International Conference on Operations Research 2023

Off-Policy Evaluation (OPE) aims to assess the effectiveness of counterfactual policies using only offline logged data and is often used to identify the top-k promising policies for deployment in online A/B tests. Existing evaluation metrics for OPE estimators primarily focus on the "accuracy" of OPE or that of downstream policy selection, neglecting risk-return tradeoff in the subsequent online policy deployment. To address this issue, we draw inspiration from portfolio evaluation in finance and develop a new metric, called SharpeRatio@k, which measures the risk-return tradeoff of policy portfolios formed by an OPE estimator under varying online evaluation budgets (k). We validate our metric in two example scenarios, demonstrating its ability to effectively distinguish between low-risk and high-risk estimators and to accurately identify the most efficient one. Efficiency of an estimator is characterized by its capability to form the most advantageous policy portfolios, maximizing returns while minimizing risks during online deployment, a nuance that existing metrics typically overlook. To facilitate a quick, accurate, and consistent evaluation of OPE via SharpeRatio@k, we have also integrated this metric into an open-source software, SCOPE-RL (https://github.com/hakuhodo-technologies/scope-rl). Employing SharpeRatio@k and SCOPE-RL, we conduct comprehensive benchmarking experiments on various estimators and RL tasks, focusing on their risk-return tradeoff. These experiments offer several interesting directions and suggestions for future OPE research., Comment: ICLR2024

This paper introduces SCOPE-RL, a comprehensive open-source Python software designed for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection (OPS). Unlike most existing libraries that focus solely on either policy learning or evaluation, SCOPE-RL seamlessly integrates these two key aspects, facilitating flexible and complete implementations of both offline RL and OPE processes. SCOPE-RL put particular emphasis on its OPE modules, offering a range of OPE estimators and robust evaluation-of-OPE protocols. This approach enables more in-depth and reliable OPE compared to other packages. For instance, SCOPE-RL enhances OPE by estimating the entire reward distribution under a policy rather than its mere point-wise expected value. Additionally, SCOPE-RL provides a more thorough evaluation-of-OPE by presenting the risk-return tradeoff in OPE results, extending beyond mere accuracy evaluations in existing OPE literature. SCOPE-RL is designed with user accessibility in mind. Its user-friendly APIs, comprehensive documentation, and a variety of easy-to-follow examples assist researchers and practitioners in efficiently implementing and experimenting with various offline RL methods and OPE estimators, tailored to their specific problem contexts. The documentation of SCOPE-RL is available at https://scope-rl.readthedocs.io/en/latest/., Comment: preprint, open-source software: https://github.com/hakuhodo-technologies/scope-rl

Signatures of photo-induced superconductivity have been reported in cuprate materials subjected to a coherent phonon drive. A 'cold' superfluid was extracted from the transient Terahertz conductivity and was seen to coexist with 'hot' uncondensed quasi-particles, a hallmark of a driven-dissipative system of which the interplay between coherent and incoherent responses are not well understood. Here, time resolved spontaneous Raman scattering was used to probe the lattice temperature in the photo-induced superconducting state of YBa2Cu3O6.48. An increase in lattice temperature of approximately 80 K was observed by measuring the time dependent Raman scattering intensity of an undriven 'spectator' phonon mode. This is to be compared with an estimated increase in quasi-particle temperatures of nearly 200 K. These temperature changes provide quantitative information on the nature of the driven state and its decay, and may provide a strategy to optimize this effect., Comment: 14 pages, 4 figures

In text-to-speech, controlling voice characteristics is important in achieving various-purpose speech synthesis. Considering the success of text-conditioned generation, such as text-to-image, free-form text instruction should be useful for intuitive and complicated control of voice characteristics. A sufficiently large corpus of high-quality and diverse voice samples with corresponding free-form descriptions can advance such control research. However, neither an open corpus nor a scalable method is currently available. To this end, we develop Coco-Nut, a new corpus including diverse Japanese utterances, along with text transcriptions and free-form voice characteristics descriptions. Our methodology to construct this corpus consists of 1) automatic collection of voice-related audio data from the Internet, 2) quality assurance, and 3) manual annotation using crowdsourcing. Additionally, we benchmark our corpus on the prompt embedding model trained by contrastive speech-text learning., Comment: Submitted to ASRU2023

This paper proposes a novel approach to generating omni-directional images from a single snapshot picture. The previous method has relied on the generative adversarial networks based on convolutional neural networks (CNN). Although this method has successfully generated omni-directional images, CNN has two drawbacks for this task. First, since a convolutional layer only processes a local area, it is difficult to propagate the information of an input snapshot picture embedded in the center of the omni-directional image to the edges of the image. Thus, the omni-directional images created by the CNN-based generator tend to have less diversity at the edges of the generated images, creating similar scene images. Second, the CNN-based model requires large video memory in graphics processing units due to the nature of the deep structure in CNN since shallow-layer networks only receives signals from a limited range of the receptive field. To solve these problems, MLPMixer-based method was proposed in this paper. The MLPMixer has been proposed as an alternative to the self-attention in the transformer, which captures long-range dependencies and contextual information. This enables to propagate information efficiently in the omni-directional image generation task. As a result, competitive performance has been achieved with reduced memory consumption and computational cost, in addition to increasing diversity of the generated omni-directional images., Comment: This is a pre-print of an article in ACPR2023. The proceedings will be published in Lecture Notes in Computer Science (LNCS). The code is available at https://github.com/islab-sophia/odigen-mlpmixer

For cosmic microwave background (CMB) polarization observations, calibration of detector polarization angles is essential. We have developed a fully remote controlled calibration system with a sparse wire grid that reflects linearly polarized light along the wire direction. The new feature is a remote-controlled system for regular calibration, which has not been possible in sparse wire grid calibrators in past experiments. The remote control can be achieved by two electric linear actuators that load or unload the sparse wire grid into a position centered on the optical axis of a telescope between the calibration time and CMB observation. Furthermore, the sparse wire grid can be rotated by a motor. A rotary encoder and a gravity sensor are installed on the sparse wire grid to monitor the wire direction. They allow us to achieve detector angle calibration with expected systematic error of $0.08^{\circ}$. The calibration system will be installed in small-aperture telescopes at Simons Observatory.

A delay analogue of the box and ball system (BBS) is presented. This new soliton cellular automaton is constructed by the ultra-discretization of the delay discrete Lotka-Volterra equation, which is an integrable delay analogue of the discrete Lotka-Volterra equation. Soliton patterns generated by this delay BBS are classified into normal solitons and abnormal solitons. Normal solitons have a clear relationship to the solitons of the BBS with K kinds of balls. On the other hand, abnormal solitons show various types of novel soliton patterns, which have not been observed in almost all known BBSs. We obtain them by numerical experiments, and then construct {\tau}-functions of them analytically in 1-soliton cases.

Dark photons have been considered potential candidates for dark matter. The dark photon dark matter (DPDM) has a mass and interacts with electromagnetic fields via kinetic mixing with a coupling constant of $\chi$. Thus, DPDMs are converted into ordinary photons at metal surfaces. Using a millimeter-wave receiver set in a radioshielding box, we performed experiments to detect the conversion photons from the DPDM in the frequency range 10--18 GHz, which corresponds to a mass range 41--74 $\mu\mathrm{eV}$. We found no conversion photon signal in this range and set the upper limits to $\chi < (0.5\text{--}3.9) \times 10^{-10}$ at a 95% confidence level., Comment: 8 pages, 14 figures

Uniaxial pressure provides an efficient approach to control the competition between charge density waves (CDWs) and superconductivity in underdoped YBa$_{2}$Cu$_{3}$O$_{\rm{y}}$. It can enhance the correlation volume of ubiquitous short-range 2D CDW correlations, and induces a long-range 3D CDW otherwise only accessible at large magnetic fields. Here, we use x-ray diffraction to study the strain and doping evolution of these CDWs. No signatures of discommensurations nor pair density waves are observed in the investigated strain-temperature parameter space, but direct evidence for a form of competition between 2D and 3D CDWs is uncovered. We show that the interplay between the 3D CDW, the 2D CDWs and superconductivity is qualitatively well described by including strain effects in simulations of a nonlinear sigma model of competing superconducting and CDW orders. From a broader perspective, our results underscore the potential of strain tuning as a powerful tool for probing and manipulating competing orders in quantum materials., Comment: I. Vinograd and S. M. Souliou contributed equally to this work

We report the ground-state symmetry of the Ce $4f$ states in the heavy fermion superconductor CeNi$_{2}$Ge$_{2}$, yielding anisotropic $cf$-hybridization between the Ce $4f$ states and conducting electrons. By analyzing linear dichroism in soft x-ray absorption and core-level hard x-ray photoemission spectra, the $4f$ symmetry is determined as $\Sigma$-type $\Gamma_{7}$, promoting predominant hybridization with the conducting electrons originating from the Ge site. The band structures probed by the soft x-ray angle-resolved photoemission indicates that the Ge $4p$ components contribute to the band renormalization through the anisotropic hybridization effects, suggesting that the control of the electronic structures of Ge orbital gives an impact to achieve the exotic phenomena in CeNi$_{2}$Ge$_{2}$.

Understanding the mechanism of ferromagnetism in ferromagnetic topological insulators (TIs) is a key to realize exotic time-reversal-symmetry-broken quantum phases. However, electronic states relevant to the ferromagnetism are highly controversial. Here we report angle-resolved photoemission spectroscopy on (CrxSb1-x)2Te3 thin films, high-Curie-temperature (T_C) ferromagnetic TIs, spanning the non-doped (T_C=0 K) to highly-doped (T_C=192 K) region. We found that, upon Cr doping to Sb2Te3, the bulk valence-band valley exhibits filling-in behavior while retaining band inversion, leading to the formation of a nearly-flat band in high-T_C regime and evolution from a six-petal flower to a Star-of-David Fermi surface. Despite the weakening of spin-orbit coupling with Cr doping, the Dirac-cone state persists up to the highest-T_C sample, and shows a clear magnetic-gap opening below TC accompanied with an unexpected band shift, signifying its strong coupling with spontaneous ferromagnetism. The present result lays the foundation for understanding the interplay between band topology and ferromagnetism in TIs., Comment: 32 pages, 9 figures, 1 table

In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data.

General Matrix Multiplication (GEMM) is a fundamental operation widely used in scientific computations. Its performance and accuracy significantly impact the performance and accuracy of applications that depend on it. One such application is semidefinite programming (SDP), and it often requires binary128 or higher precision arithmetic to solve problems involving SDP stably. However, only some processors support binary128 arithmetic, which makes SDP solvers generally slow. In this study, we focused on accelerating GEMM with binary128 arithmetic on field-programmable gate arrays (FPGAs) to enable the flexible design of accelerators for the desired computations. Our binary128 GEMM designs on a recent high-performance FPGA achieved approximately 90GFlops, 147x faster than the computation executed on a recent CPU with 20 threads for large matrices. Using our binary128 GEMM design on the FPGA, we successfully accelerated two numerical applications: LU decomposition and SDP problems, for the first time., Comment: 12 pages, 8 figures

In this study, we investigate the bulk electronic structure of Pb-substituted LaO$_{0.5}$F$_{0.5}$BiS$_2$ single crystals, using two types of hard-x-ray spectroscopy. High-energy-resolution fluorescence-detected x-ray absorption spectroscopy revealed a spectral change at low temperatures. Using density functional theory (DFT) simulations, we find that the temperature-induced change originates from a structural phase transition, similar to the pressure-induced transition in LaO$_{0.5}$F$_{0.5}$BiS$_2$. This finding suggests that the mechanism of bulk superconductivity induced by Pb substitution is the same as that under high pressure. Furthermore, a novel low-valence state with a mixture of divalent and trivalent Bi ions is discovered using hard x-ray photoemission spectroscopy with the aid of DFT calculations., Comment: 8 pages, 6 figures, 1 table

This article presents the "PubChemQC B3LYP/6-31G*//PM6" dataset, containing electronic properties of 85,938,443 molecules. It includes orbitals, orbital energies, total energies, dipole moments, and other relevant properties. The dataset encompasses a wide range of molecules, from essential compounds to biomolecules up to 1000 molecular weight, covering 94.0% of the original PubChem Compound catalog (as of August 29, 2016). The electronic properties were calculated using the B3LYP/6-31G* and PM6 methods. The dataset is available in three formats: (i) GAMESS quantum chemistry program files, (ii) selected JSON output files, and (iii) a PostgreSQL database, enabling researchers to query molecular properties. Five sub-datasets offer more specific data. The first two subsets include molecules with C, H, O, and N, under 300 and 500 molecular weight respectively. The third and fourth subsets contain C, H, N, O, P, S, F, and Cl, under 300 and 500 molecular weight respectively. The fifth subset includes C, H, N, O, P, S, F, Cl, Na, K, Mg, and Ca, under 500 molecular weight. Coefficients of determination ranged from 0.892 (CHON500) to 0.803 (whole) for the HOMO-LUMO energy gap. These findings represent extensive investigations and can be utilized for drug discovery, material science, and other applications. The datasets are available under the Creative Commons Attribution 4.0 International license at https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.

There has been a growing interest in non-Hermitian quantum mechanics. The key concepts of quantum mechanics are quantum fluctuations. Quantum fluctuations of quantum fields confined in a finite-size system induce the zero-point energy shift. This quantum phenomenon, the Casimir effect, is one of the most striking phenomena of quantum mechanics in the sense that there are no classical analogs and has been attracting much attention beyond the hierarchy of energy scales, ranging from elementary particle physics to condensed matter physics, together with photonics. However, the non-Hermitian extension of the Casimir effect and the application to spintronics have not yet been investigated enough, although exploring energy sources and developing energy-efficient nanodevices are its central issues. Here we fill this gap. By developing a magnonic analog of the Casimir effect into non-Hermitian systems, we show that this non-Hermitian Casimir effect of magnons is enhanced as the Gilbert damping constant (i.e., the energy dissipation rate) increases. When the damping constant exceeds a critical value, the non-Hermitian Casimir effect of magnons exhibits an oscillating behavior, including a beating one, as a function of the film thickness and is characterized by the exceptional point. Our result suggests that energy dissipation serves as a key ingredient of Casimir engineering., Comment: 7+3 pages, 2+2 figures, 0+1 table

Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is not robust in practical data setups with distributional shifts and out-of-distribution (OOD) classes. As a result, the predicted probabilities can be very imprecise when used as confidence scores at test time. To address this, we propose a generative model for concurrent in-distribution misclassification (IDM) and OOD detection that relies on a normalizing flow framework. The proposed flow-based detector with an energy-based inputs (FlowEneDet) can extend previously deployed segmentation models without their time-consuming retraining. Our FlowEneDet results in a low-complexity architecture with marginal increase in the memory footprint. FlowEneDet achieves promising results on Cityscapes, Cityscapes-C, FishyScapes and SegmentMeIfYouCan benchmarks in IDM/OOD detection when applied to pretrained DeepLabV3+ and SegFormer semantic segmentation models., Comment: Accepted to UAI 2023. Preprint

Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. Currently, an efficient alternative to computing ground-state 3D molecular geometries from 2D graphs is lacking. Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs. To this end, we develop an equilibrium message passing neural network (EMPNN) to better capture ground-state geometries from molecular graphs. To provide a testbed for 3D molecular geometry analysis, we develop a benchmark that includes a large-scale molecular geometry dataset, data splits, and evaluation protocols. Experimental results show that EMPNN can efficiently predict more accurate ground-state 3D geometries than RDKit and other deep learning methods. Results also show that the proposed framework outperforms self-supervised learning methods on property prediction tasks.

Information scrambling refers to the unitary dynamics that quickly spreads and encodes localized quantum information over an entire many-body system and makes the information accessible from any small subsystem. While information scrambling is the key to understanding complex quantum many-body dynamics and is well-understood in random unitary models, it has been hardly explored in Hamiltonian systems. In this Letter, we investigate the information recovery in various time-independent Hamiltonian systems, including chaotic spin chains and Sachdev-Ye-Kitaev (SYK) models. We show that information recovery is possible in certain, but not all, chaotic models, which highlights the difference between information recovery and quantum chaos based on the energy spectrum or the out-of-time-ordered correlators. We also show that information recovery probes transitions caused by the change of information-theoretic features of the dynamics., Comment: 8 pages, 6 figures, Supplemental Materials (13 pages, 11 figures). Substantial revision, including correction of the code and normalization for spin models, and estimation of lower bounds on the recovery error

Recent work [M. J. Gullans et al., Physical Review X, 11(3):031066 (2021)] has shown that quantum error correcting codes defined by random Clifford encoding circuits can achieve a non-zero encoding rate in correcting errors even if the random circuits on $n$ qubits, embedded in one spatial dimension (1D), have a logarithmic depth $d=\mathcal{O}(\log{n})$. However, this was demonstrated only for a simple erasure noise model. In this work, we discover that this desired property indeed holds for the conventional Pauli noise model. Specifically, we numerically demonstrate that the hashing bound, i.e., a rate known to be achieved with $d=\mathcal{O}(n)$-depth random encoding circuits, can be attained even when the circuit depth is restricted to $d=\mathcal{O}(\log n)$ in 1D for depolarizing noise of various strengths. This analysis is made possible with our development of a tensor-network maximum-likelihood decoding algorithm that works efficiently for $\log$-depth encoding circuits in 1D.