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The theory of jet emitting disks (JEDs) provides a mathematical framework for a self-consistent treatment of steady-state accretion and ejection. A large-scale vertical magnetic field threads the accretion disk where magnetic turbulence occurs in a strongly magnetized plasma. A fraction of mass leaves the disk and feeds the two laminar super-Alf\'enic jets. In previous treatments of JEDs, the disk turbulence has been considered to provide only anomalous transport coefficients, namely magnetic diffusivities and viscosity. However, 3D numerical experiments show that turbulent magnetic pressure also sets in. We included this additional pressure term using a prescription that is consistent with the latest 3D global (and local) simulations. We then solved the complete system of self-similar magnetohydrodynamic (MHD) equations, accounting for all dynamical terms. The disk becomes puffier and less electrically conductive, causing radial and toroidal electric currents to flow at the disk surface. Field lines within the disk become straighter, with their bending and shearing occurring mainly at the surface. Accretion remains supersonic, but becomes faster at the disk surface. Large values of both turbulent pressure and magnetic diffusivities allow powerful jets to be driven, and their combined effects have a constructive influence. Nevertheless, cold outflows do not seem to be able to reproduce mass-loss rates as large as those observed in numerical simulations. Our results are a major upgrade of the JED theory, allowing a direct comparison with full 3D global numerical simulations. We argue that JEDs provide a state-of-the-art mathematical description of the disk configurations observed in numerical simulations, commonly referred to as magnetically arrested disks (MADs). However, further efforts from both theoretical and numerical perspectives are needed to firmly establish this point., Comment: 18 pages, 14 figures

Text-to-image diffusion models sometimes depict blended concepts in the generated images. One promising use case of this effect would be the nonword-to-image generation task which attempts to generate images intuitively imaginable from a non-existing word (nonword). To realize nonword-to-image generation, an existing study focused on associating nonwords with similar-sounding words. Since each nonword can have multiple similar-sounding words, generating images containing their blended concepts would increase intuitiveness, facilitating creative activities and promoting computational psycholinguistics. Nevertheless, no existing study has quantitatively evaluated this effect in either diffusion models or the nonword-to-image generation paradigm. Therefore, this paper first analyzes the conceptual blending in a pretrained diffusion model, Stable Diffusion. The analysis reveals that a high percentage of generated images depict blended concepts when inputting an embedding interpolating between the text embeddings of two text prompts referring to different concepts. Next, this paper explores the best text embedding space conversion method of an existing nonword-to-image generation framework to ensure both the occurrence of conceptual blending and image generation quality. We compare the conventional direct prediction approach with the proposed method that combines $k$-nearest neighbor search and linear regression. Evaluation reveals that the enhanced accuracy of the embedding space conversion by the proposed method improves the image generation quality, while the emergence of conceptual blending could be attributed mainly to the specific dimensions of the high-dimensional text embedding space., Comment: Paper accepted at ACM MM 2024 (doi: 10.1145/3664647.3681202) with supplementary materials concatenated

Mouse and human brains have different functions that depend on their neuronal networks. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. These structural features allow mouse neurons to be integrated in the limited space of the brain, though thin neurites should suppress distal connections according to cable theory. We implemented this mouse-mimetic constraint in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, and birds. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the four datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks., Comment: 41 pages, 4 figures

The tasks of lexical complexity prediction (LCP) and complex word identification (CWI) commonly presuppose that difficult to understand words are shared by the target population. Meanwhile, personalization methods have also been proposed to adapt models to individual needs. We verify that a recent Japanese LCP dataset is representative of its target population by partially replicating the annotation. By another reannotation we show that native Chinese speakers perceive the complexity differently due to Sino-Japanese vocabulary. To explore the possibilities of personalization, we compare competitive baselines trained on the group mean ratings and individual ratings in terms of performance for an individual. We show that the model trained on a group mean performs similarly to an individual model in the CWI task, while achieving good LCP performance for an individual is difficult. We also experiment with adapting a finetuned BERT model, which results only in marginal improvements across all settings., Comment: Accepted to TSAR 2024

Multi-label multi-view action recognition aims to recognize multiple concurrent or sequential actions from untrimmed videos captured by multiple cameras. Existing work has focused on multi-view action recognition in a narrow area with strong labels available, where the onset and offset of each action are labeled at the frame-level. This study focuses on real-world scenarios where cameras are distributed to capture a wide-range area with only weak labels available at the video-level. We propose the method named Multi-view Action Selection Learning (MultiASL), which leverages action selection learning to enhance view fusion by selecting the most useful information from different viewpoints. The proposed method includes a Multi-view Spatial-Temporal Transformer video encoder to extract spatial and temporal features from multi-viewpoint videos. Action Selection Learning is employed at the frame-level, using pseudo ground-truth obtained from weak labels at the video-level, to identify the most relevant frames for action recognition. Experiments in a real-world office environment using the MM-Office dataset demonstrate the superior performance of the proposed method compared to existing methods. The source code is available at https://github.com/thanhhff/MultiASL/., Comment: ACM Multimedia Asia 2024

Collapsars - rapidly rotating stellar cores that form black holes (BHs) - can power gamma-ray bursts (GRBs) and are proposed to be key contributors to the production of heavy elements in the Universe via the rapid neutron capture process ($r$-process). Previous neutrino-transport collapsar simulations have been unable to unbind neutron-rich material from the disk. However, these simulations have not included magnetic fields or the BH, both of which are essential for launching mass outflows. We present $\nu$H-AMR, a novel neutrino-transport general relativistic magnetohydrodynamic ($\nu$GRMHD) code, which we use to perform the first 3D $\nu$GRMHD collapsar simulations. We find a self-consistent formation of a disk with initially weak magnetic flux, resulting in a low accretion speed and leaving sufficient time for the disk to neutronize. However, once substantial magnetic flux accumulates near the BH, it becomes dynamically important, leading to a magnetically arrested disk that unbinds some of the neutron-rich material. The strong flux also accelerates the accretion speed, preventing further disk neutronization. The neutron-rich disk ejecta collides with the infalling stellar gas, generating a shocked cocoon with an electron fraction, $Y_\text{e}\gtrsim0.2$. Continuous mixing between the cocoon and neutron-poor stellar gas incrementally raises the outflow $Y_\text{e}$, but the final $r$-process yield is determined earlier at the point of neutron capture freeze-out. Our models require extreme magnetic fluxes and mass accretion rates to eject neutron-rich material ($Y_\text{e}\lesssim0.3$), implying very high $r$-process ejecta masses $M_\text{ej}\lesssim{}M_\odot$. Future work will explore under what conditions more typical collapsar engines become $r$-process factories., Comment: 13 pages, 5 figures, 1 table (plus 7 pages and 7 figures in Appendix) uses aastex62.cls. Submitted to ApJ, comments welcome. Movies available at https://www.youtube.com/playlist?list=PLVsp-mE5pJ_UBsLD1LU1ZZCzPoLgo6wUP

We introduce homological measurement, a framework for measuring the logical Pauli operators encoded in CSS stabilizer codes. The framework is based on the algebraic description of such codes as chain complexes. Protocols such as lattice surgery some of its recent generalizations are shown to be special cases of homological measurement. Using this framework, we develop a specific protocol called edge expanded homological measurement for fault-tolerant measurement of arbitrary logical Pauli operators of general qLDPC codes, requiring a number of ancillary qubits growing only linearly with the weight of the logical operator measured, and guaranteed that the distance of the code is preserved. We further benchmark our protocol numerically in a photonic architecture based on GKP qubits, showing that the logical error rate of various codes are on par with other methods requiring more ancilla qubits., Comment: 20 pages, 5 figures, 5 tables. Version 2 fixes a mistake in section 3.4 and briefly addresses related recent work

In recent years, there has been an increasing demand for underwater cameras that monitor the condition of offshore structures and check the number of individuals in aqua culture environments with long-period observation. One of the significant issues with this observation is that biofouling sticks to the aperture and lens densely and prevents cameras from capturing clear images. This study examines an underwater camera that applies material technologies with high inherent resistance to biofouling and computer vision technologies based on image reconstruction by deep learning to lens-less cameras. For this purpose, our prototype camera uses a coded aperture with 1k rectangular shape pinholes in a thin metal plate, such as copper, which hinder the growth of biofouling and keep the surface clean. Although images taken by lens-less cameras are usually not well formed due to lack of the traditional glass-based lens, a deep learning approach using ViT (Vision Transformer) has recently demonstrated reconstructing original photo images well and our study shows that using gated MLP (Multilayer Perceptron) also yields good results. On the other hand, a certain degree of thickness for bio-repellence materials is required to exhibit their effect the thickness of aperture is necessary to use apertures sufficiently thinner than the size of the pinholes to avoid unintentional reflection and absorption on the sidewalls. Therefore, we prepared a sufficiently thin plate for image reconstruction and now currently we conduct tests of the lens-less camera of the bio-repellence aperture with actual seawater environments to determine whether it can sufficiently demonstrate the biofouling effect compared with usual camera with only waterproof., Comment: 9 pages, 8 figures, Ocean Optics 2024

Quasars are powered by supermassive black hole (SMBH) accretion disks, yet standard disk models are inconsistent with many quasar observations. Recently, Hopkins et al. (2024) simulated the formation of a quasar disk feeding a SMBH of mass $M=1.3\times10^7\,M_\odot$ in a host galaxy that evolved from cosmological initial conditions. The disk had surprisingly strong toroidal magnetic fields that supported it vertically from gravity and powered fast accretion. What radiation and feedback can such a system produce? To answer this, we must follow the gas to the event horizon. For this, we interpolated the accretion system onto the grid of the general-relativistic radiation magnetohydrodynamics code H-AMR and performed 3D simulations with BH spins $a=0$ and $a=0.9375$. This remapping generates spurious magnetic monopoles, which we erase using a novel divergence cleaning approach. Despite the toroidal magnetic field's dominance at large radii, vertical magnetic flux builds up at the event horizon. This causes a magnetic state transition within the inner $200$ gravitational radii of the disk, where net vertical magnetic flux begins dominating the accretion flow. This powers strong winds and, if the BH spins, relativistic jets that can spin-down the BH within $5-10\,{\rm Myrs}$. Sometimes, vertical magnetic fields of opposite sign reach the BH, causing polarity inversion events that briefly destroy the jets and, possibly, the X-ray corona. The disk powers accretion at rates $5\times$ the Eddington limit, which can double the BH mass in $5-10\,{\rm Myrs}$. When $a=0.9375$ ($a=0$), the energy in outflows and radiation equals about $60\%$ ($10\%$) and $100\%$ ($3\%$) of the accreted rest mass energy, respectively. Much of the light escapes in cool, extended $\gtrsim1300\,{\rm au}$ photospheres, consistent with quasar microlensing and the ``big blue bump'' seen in spectral energy distributions., Comment: 33 pages, 18 figures. Submitted to ApJ. Comments welcome!

Do improvements in Artificial Intelligence (AI) benefit workers? We study how AI capabilities influence labor income in a competitive economy where production requires multidimensional knowledge, and firms organize production by matching humans and AI-powered machines in hierarchies designed to use knowledge efficiently. We show that advancements in AI in dimensions where machines underperform humans decrease total labor income, while advancements in dimensions where machines outperform humans increase it. Hence, if AI initially underperforms humans in all dimensions and improves gradually, total labor income initially declines before rising. We also characterize the AI that maximizes labor income. When humans are sufficiently weak in all knowledge dimensions, labor income is maximized when AI is as good as possible in all dimensions. Otherwise, labor income is maximized when AI simultaneously performs as poorly as possible in the dimensions where humans are relatively strong and as well as possible in the dimensions where humans are relatively weak. Our results suggest that choosing the direction of AI development can create significant divisions between the interests of labor and capital.

In this paper, we consider multi-dimensional birth and death chains and continuous time quantum walks (CTQW) related to them. For CTQW related to our forms of multi-dimensional birth and death chains, we obtain the time scaled independence between multiple dimensions about the transition probability of CTQW. By using this feature, we analyze CTQW on the path graph, which is related to 1-dimensional Ehrenfest model. We also have a random variable which is related to our models and converges to the standard Gaussian distribution., Comment: 11pages

The grammatical knowledge of language models (LMs) is often measured using a benchmark of linguistic minimal pairs, where LMs are presented with a pair of acceptable and unacceptable sentences and required to judge which is acceptable. The existing dominant approach, however, naively calculates and compares the probabilities of paired sentences using LMs. Additionally, large language models (LLMs) have yet to be thoroughly examined in this field. We thus investigate how to make the most of LLMs' grammatical knowledge to comprehensively evaluate it. Through extensive experiments of nine judgment methods in English and Chinese, we demonstrate that a probability readout method, in-template LP, and a prompting-based method, Yes/No probability computing, achieve particularly high performance, surpassing the conventional approach. Our analysis reveals their different strengths, e.g., Yes/No probability computing is robust against token-length bias, suggesting that they harness different aspects of LLMs' grammatical knowledge. Consequently, we recommend using diverse judgment methods to evaluate LLMs comprehensively.

We propose a general approach to quantitatively assessing the risk and vulnerability of artificial intelligence (AI) systems to biased decisions. The guiding principle of the proposed approach is that any AI algorithm must outperform a random guesser. This may appear trivial, but empirical results from a simplistic sequential decision-making scenario involving roulette games show that sophisticated AI-based approaches often underperform the random guesser by a significant margin. We highlight that modern recommender systems may exhibit a similar tendency to favor overly low-risk options. We argue that this "random guesser test" can serve as a useful tool for evaluating the utility of AI actions, and also points towards increasing exploration as a potential improvement to such systems., Comment: Accepted into AIBS 2024: The First Workshop on AI Behavioral Science, 5 pages, 4 figures

The $\gamma$ decays of the Isovector Giant Dipole Resonance (GDR) of the deformed nucleus $^{154}$Sm from $2^+_1$-Smekal-Raman and elastic scattering were measured using linearly polarized, quasi-monochromatic photon beams. The two scattering processes were disentangled through their distinct angular distributions. Their branching ratio and cross sections were determined at six excitation energies covering the $^{154}$Sm GDR. Both agree with the predictions of the geometrical model for the GDR and establish $\gamma$ decay as an observable sensitive to the structure of the resonance. Consequently, the data place strong constraints on the nuclear shape, including the degree of triaxiality. The derived $^{154}$Sm shape parameters $\beta=0.2926(26)$ and $\gamma=5.0(14)$ agree well with other measurements and recent Monte Carlo Shell-Model calculations., Comment: 7 pages, 3 figures, 3 tables

Aims: We consider cold self-similar magnetohydrodynamic (MHD) disc wind solutions to describe jets that are launched from the circumcompanion accretion discs in post-AGB binaries. Resulting predictions are matched to observations for five different post-AGB binaries. This both tests the physical validity of the MHD disc wind paradigm and reveals the accretion disc properties. Results: Many of the time-series' properties are reproduced well by the models, though systematic mismatches, such as overestimated rotation, remain. Four targets imply accretion discs that reach close to the secondary's stellar surface, while one is fitted with an unrealistically large inner radius of about 20 stellar radii. Some fits imply inner disc temperatures over 10 000 K, seemingly discrepant with a previous observational estimate from H band interferometry. This estimate is, however, shown to be biased. Fitted mass-accretion rates range from about 10^-6 to 10^-3 solar masses per year. Relative to the jets launched from young stellar objects (YSOs), all targets prefer winds with higher ejection efficiencies, lower magnetizations and thicker discs. Conclusions: Our models show that current cold MHD disc wind solutions can explain many of the jet-related Balmer alpha features seen in post-AGB binaries, though systematic discrepancies remain. This includes, but is not limited to, overestimated rotation and underestimated post-AGB circumbinary disc lifetimes. The consideration of thicker discs and the inclusion of irradiation from the post-AGB primary, leading to warm magnetothermal wind launching, might alleviate these., Comment: Accepted for publication in Astronomy & Astrophysics. 21 pages, 9 figures. Final language edits applied

Non-Gaussian quantum gates are essential components for optical quantum information processing. However, the efficient implementation of practically important multi-mode higher-order non-Gaussian gates has not been comprehensively studied. We propose a measurement-based method to directly implement general, multi-mode, and higher-order non-Gaussian gates using only fixed non-Gaussian ancillary states and adaptive linear optics. Compared to existing methods, our method allows for a more resource-efficient and experimentally feasible implementation of multi-mode gates that are important for various applications in optical quantum technology, such as the two-mode cubic quantum non-demolition gate or the three-mode continuous-variable Toffoli gate, and their higher-order extensions. Our results will expedite the progress toward fault-tolerant universal quantum computing with light., Comment: 20 pages, 8 figures

This paper focuses on tracking birds that appear small in a panoramic video. When the size of the tracked object is small in the image (small object tracking) and move quickly, object detection and association suffers. To address these problems, we propose Adaptive Slicing Aided Hyper Inference (Adaptive SAHI), which reduces the candidate regions to apply detection, and Detection History-aware Similarity Criterion (DHSC), which accurately associates objects in consecutive frames based on the detection history. Experiments on the NUBird2022 dataset verifies the effectiveness of the proposed method by showing improvements in both accuracy and speed., Comment: 6 pages, 6 figures

Image understanding is a foundational task in computer vision, with recent applications emerging in soccer posture analysis. However, existing publicly available datasets lack comprehensive information, notably in the form of posture sequences and 2D pose annotations. Moreover, current analysis models often rely on interpretable linear models (e.g., PCA and regression), limiting their capacity to capture non-linear spatiotemporal relationships in complex and diverse scenarios. To address these gaps, we introduce the 3D Shot Posture (3DSP) dataset in soccer broadcast videos, which represents the most extensive sports image dataset with 2D pose annotations to our knowledge. Additionally, we present the 3DSP-GRAE (Graph Recurrent AutoEncoder) model, a non-linear approach for embedding pose sequences. Furthermore, we propose AutoSoccerPose, a pipeline aimed at semi-automating 2D and 3D pose estimation and posture analysis. While achieving full automation proved challenging, we provide a foundational baseline, extending its utility beyond the scope of annotated data. We validate AutoSoccerPose on SoccerNet and 3DSP datasets, and present posture analysis results based on 3DSP. The dataset, code, and models are available at: https://github.com/calvinyeungck/3D-Shot-Posture-Dataset.

We propose a new positional encoding method for a neural network architecture called the Transformer. Unlike the standard sinusoidal positional encoding, our approach is based on solid mathematical grounds and has a guarantee of not losing information about the positional order of the input sequence. We show that the new encoding approach systematically improves the prediction performance in the time-series classification task., Comment: arXiv admin note: text overlap with arXiv:2305.17149

Continuous-variable (CV) quantum information processing is a promising candidate for large-scale fault-tolerant quantum computation. However, analysis of CV quantum process relies mostly on direct computation of the evolution of operators in the Heisenberg picture, and the features of CV space has yet to be thoroughly investigated in an intuitive manner. One key ingredient for further exploration of CV quantum computing is the construction of a computational model that brings visual intuition and new tools for analysis. In this paper, we delve into a graphical computational model, inspired by a similar model for qubit-based systems called the ZX calculus, that enables the representation of arbitrary CV quantum process as a simple directed graph. We demonstrate the utility of our model as a graphical tool to comprehend CV processes intuitively by showing how equivalences between two distinct quantum processes can be proven as a sequence of diagrammatic transformations in certain cases. We also examine possible applications of our model, such as measurement-based quantum computing, characterization of Gaussian and non-Gaussian processes, and circuit optimization., Comment: 34 pages, 3 figures

Open-vocabulary Temporal Action Detection (Open-vocab TAD) is an advanced video analysis approach that expands Closed-vocabulary Temporal Action Detection (Closed-vocab TAD) capabilities. Closed-vocab TAD is typically confined to localizing and classifying actions based on a predefined set of categories. In contrast, Open-vocab TAD goes further and is not limited to these predefined categories. This is particularly useful in real-world scenarios where the variety of actions in videos can be vast and not always predictable. The prevalent methods in Open-vocab TAD typically employ a 2-stage approach, which involves generating action proposals and then identifying those actions. However, errors made during the first stage can adversely affect the subsequent action identification accuracy. Additionally, existing studies face challenges in handling actions of different durations owing to the use of fixed temporal processing methods. Therefore, we propose a 1-stage approach consisting of two primary modules: Multi-scale Video Analysis (MVA) and Video-Text Alignment (VTA). The MVA module captures actions at varying temporal resolutions, overcoming the challenge of detecting actions with diverse durations. The VTA module leverages the synergy between visual and textual modalities to precisely align video segments with corresponding action labels, a critical step for accurate action identification in Open-vocab scenarios. Evaluations on widely recognized datasets THUMOS14 and ActivityNet-1.3, showed that the proposed method achieved superior results compared to the other methods in both Open-vocab and Closed-vocab settings. This serves as a strong demonstration of the effectiveness of the proposed method in the TAD task., Comment: The 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024)

Along the northern coast of Kyushu Island, significant storm surges were unlikely to occur because the strong wind does not blow directly to the coast when typhoons passes. However, during Typhoon Maysak, various areas along the coast experienced flooding due to the storm surges. Additionally, inundation occurred when the typhoon was more than 600 km away from the coast. In this study, we classified the past typhoons into northeastward-moving, northward-moving and direct-passing overhead types and analyzed the storm surge using observational data and numerical simulations. Regarding northeastward-moving types, Hakata Bay located in the coast experienced two surge peaks. The first peak was induced by the inverted barometer effect and the stagnation of seawater in the Tsushima Strait. The second peak occurred because of the 10-hour oscillation and Ekman transport in the Tsushima Strait. For northward-moving types, Ekman transport was further intensified, resulting in a high storm surge that lasted for more than 10 hours. Regarding directly passing overhead types, one or two peaks occurred in a short period during the closest approach. The first peak was caused by the inverted barometer effect and Ekman transport, whereas the second peak was caused by the 2-hour harbor oscillation in Hakata Bay., Comment: 33 pages, 25 figures

This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generation Blockchain platforms. First, we expand the existing framework for collaborative dictionary learning (CollabDict), which has previously been limited to Gaussian mixture models, by incorporating deep variational autoencoders (VAEs) into the framework, with a particular focus on anomaly detection. We demonstrate that the VAE-based anomaly score function shares the same mathematical structure as the non-deep model, and provide comprehensive qualitative comparison. Second, considering the widespread use of "pre-trained models," we provide a mathematical analysis on data privacy leakage when models trained with CollabDict are shared externally. We show that the CollabDict approach, when applied to Gaussian mixtures, adheres to a Renyi differential privacy criterion. Additionally, we propose a practical metric for monitoring internal privacy breaches during the learning process., Comment: To appear in Proceeding of 2023 International workshop Blockchain Kaigi (BCK 23), JPS Conference Proceedings, 2024

Observation of blue-shifted X-ray absorption lines indicates the presence of wind from the accretion disk in X-ray binaries. Magnetohydrodynamic (MHD) driving is one of the possible wind launching mechanisms. Recent theoretical development makes magnetic accretion-ejection self-similar solutions much more generalized, and wind can be launched even at much lower magnetization compared to equipartition value, which was the only possibility beforehand. Here, we model the transmitted spectra through MHD driven photoionized wind - models which have different values of magnetizations. We investigate the possibility of detecting absorption lines by the upcoming instruments XRISM and Athena. Attempts are made to find the robustness of the method of fitting asymmetric line profiles by multiple Gaussians. We use photoionization code XSTAR to simulate the transmitted model spectra. Fake observed spectra are finally produced by convolving model spectra with instruments' responses. Since the line asymmetries are apparent in the convolved spectra as well, this can be used as an observable diagnostic to fit for, in future XRISM and Athena spectra. We demonstrate some amount of rigor in assessing the equivalent widths of the major absorption lines, including the Fe XXVI Ly$\alpha$ doublets which can be clearly distinguished in the superior quality, future high resolution spectra. Disk magnetization becomes another crucial MHD variable that can significantly alter the absorption line profiles. Low magnetization pure MHD outflow models are dense enough to be observed by the existing or upcoming instruments. Thus these models become simpler alternatives to MHD-thermal models. Fitting with multiple Gaussians is a promising method to handle asymmetric line profiles, as well as the Fe XXVI Ly$\alpha$ doublets., Comment: 17 pages (including 4 pages of Appendix), 13 figures, Accepted for publication in A&A

We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experiencing homelessness in New York transition to permanent housing and Leket -- the national food bank of Israel that rescues food from farms and elsewhere to feed the hungry. Specifically, we developed an estimation and optimization approach for partially-observed episodic restless bandits under $k$-step transitions. The results show that our Thompson sampling with Markov chain recovery (via Stein variational gradient descent) algorithm significantly outperforms baselines for the problems of both organizations. We carried out this work in a prospective manner with the express goal of devising a flexible-enough but also useful-enough solution that can help overcome a lack of sustainable impact in data science for social good.

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical space, digital twins, autonomous driving, smart cities, and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters using the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or needing multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems., Comment: 47 pages, 23 figures

Entanglement is a fundamental resource of various optical quantum-information-processing (QIP) applications. Towards high-speed QIP system, entanglement should be encoded in short wavepackets. We report real-time observation of ultrafast optical Einstein-Podolsky-Rosen (EPR) correlation at a picosecond timescale in a continuous-wave (CW) system. Optical phase-sensitive amplification using 6-THz-bandwidth waveguide-optical-parametric amplifier enhances the effective efficiency of 70-GHz-bandwidth homodyne detectors, mainly used in 5th-generation telecommunication, enabling its use in real-time quantum-state measurement. While power measurement using frequency scanning, i.e., optical spectrum analyzer, is not performed in real-time, our observation is demonstrated through real-time amplitude measurement and can be directly employed in QIP applications. Observed EPR states show quantum correlation of 4.5 dB below shotnoise level encoded in wavepackets with 40-ps period, equivalent to 25-GHz repetition -- ${10^3}$ times faster than previous entanglement observation in CW system. The quantum correlation of 4.5 dB is already sufficient for several QIP applications, and our system can be readily extended to large-scale entanglement. Moreover, our scheme has high compatibility with optical communication technology such as wavelength-division multiplexing, and femtosecond-timescale observation is also feasible. Our demonstration is paradigm shift in accelerating accessible quantum correlation, the foundational resource of all quantum applications, from the nanosecond to picosecond timescale, enabling ultra-fast optical QIP., Comment: 14 pages, 4 figures

Quantum information processors greatly benefit from high clock frequency to fully harnessing the quantum advantages before they get washed out by the decoherence. In this pursuit, all-optical systems offer unique advantages due to their inherent 100 THz carrier frequency, permitting one to develop THz clock frequency processors. In practice, the bandwidth of the quantum light sources and the measurement devices has been limited to the MHz range and the generation rate of nonclassical states to kHz order -- a tiny fraction of what can be achieved. In this work, we go beyond this limitation by utilizing optical parametric amplifier (OPA) as a squeezed-light source and optical phase-sensitive amplifiers (PSA) to realize high-rate generation of broadband non-Gaussian states and their quantum tomography. Our state generation and measurement system consists of a 6-THz squeezed-light source, a 6-THz PSA, and a 66-GHz homodyne detector. With this system, we have successfully demonstrated non-Gaussian state generation at a 0.9 MHz rate -- almost three orders of magnitude higher than the current state-of-the-art experiments -- with a sub-nanosecond wave packet using continuous-wave laser. The performance is constrained only by the superconducting detector's jitter which currently limits the usable bandwidth of the squeezed light to 1 GHz, rather than the optical and electronic systems. Therefore, if we can overcome the limitation of the timing jitter of superconducting detector, non-Gaussian state generation and detection at GHz rate, or even THz rate, for optical quantum processors might be possible with OPAs., Comment: 17 pages, 5 figures

I examine the implications of cross-market mergers of suppliers to intermediaries that bundle products for consumers. These mergers are controversial. Some argue that suppliers' products will be substitutes for intermediaries, despite not being substitutes for consumers. Others contend that because bundling makes products complements for consumers, products must be complements for intermediaries. I contribute to this debate by showing that two products can be complements for consumers but substitutes for intermediaries when the products serve a similar role in attracting consumers to purchase the bundle. This result leads to new recommendations and helps explain why cross-market hospital mergers raise prices.

Large-scale vision language models (LVLMs) are language models that are capable of processing images and text inputs by a single model. This paper explores the use of LVLMs to generate review texts for images. The ability of LVLMs to review images is not fully understood, highlighting the need for a methodical evaluation of their review abilities. Unlike image captions, review texts can be written from various perspectives such as image composition and exposure. This diversity of review perspectives makes it difficult to uniquely determine a single correct review for an image. To address this challenge, we introduce an evaluation method based on rank correlation analysis, in which review texts are ranked by humans and LVLMs, then, measures the correlation between these rankings. We further validate this approach by creating a benchmark dataset aimed at assessing the image review ability of recent LVLMs. Our experiments with the dataset reveal that LVLMs, particularly those with proven superiority in other evaluative contexts, excel at distinguishing between high-quality and substandard image reviews., Comment: 9pages, under reviewing

Producing a polarized lanthanum (La) target with high polarization and long relaxation time is crucial for realizing time-reversal violation experiments using polarized neutron beams. We use a LaAlO3 crystal doped with a small amount of Nd3+ ions for the polarized lanthanum target. Optimizing the amount of Nd3+ ions is considerably important because the achievable polarization and relaxation time strongly depend on this amount. We established a fundamental method to grow single crystals of Nd3+:LaAlO3 using an optical floating zone method that employs halogen lamps and evaluated the crystals with the dynamic nuclear polarization (DNP) method for polarizing nuclear spins. Two crystal samples were grown by ourselves and evaluated with the DNP at 1.3 K and 2.3 T for the first time except for the target materials of protons. The enhancement of NMR signals for 139La and 27Al was successfully observed, and the enhancement factors were eventually 3.5+-0.3 and 13+-3 for the samples with Nd3+ ions of 0.05 and 0.01 mol%, respectively. These enhancement factors correspond to absolute vector polarizations of 0.27+-0.02% (Nd 0.05 mol%) and 1.4+-0.3% (Nd 0.01 mol%). Although the obtained polarizations are still low, they are acceptable as a first step. The combination scheme of the crystal growth and evaluation of the crystals is found to be effectively applicable for optimizing the amount of Nd3+ ions for improving the performance of the polarized target., Comment: 18 pages, 7 figures, Resubmitted to Review of Scientific Instruments

Stabilizer codes are the most widely studied class of quantum error-correcting codes and form the basis of most proposals for a fault-tolerant quantum computer. A stabilizer code is defined by a set of parity-check operators, which are measured in order to infer information about errors that may have occurred. In typical settings, measuring these operators is itself a noisy process and the noise strength scales with the number of qubits involved in a given parity check, or its weight. Hastings proposed a method for reducing the weights of the parity checks of a stabilizer code, though it has previously only been studied in the asymptotic regime. Here, we instead focus on the regime of small-to-medium size codes suitable for quantum computing hardware. We provide both a fully explicit description of Hastings's method and propose a substantially simplified weight reduction method that is applicable to the class of quantum product codes. Our simplified method allows us to reduce the check weights of hypergraph and lifted product codes to at most six, while preserving the number of logical qubits and at least retaining (in fact often increasing) the code distance. The price we pay is an increase in the number of physical qubits by a constant factor, but we find that our method is much more efficient than Hastings's method in this regard. We benchmark the performance of our codes in a photonic quantum computing architecture based on GKP qubits and passive linear optics, finding that our weight reduction method substantially improves code performance.

We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level causal structures in an unsupervised manner. Instance-level causality identifies causal relationships among individual events, providing more fine-grained information for decision-making. Existing work in the literature either requires strong assumptions, such as linearity in the intensity function, or heuristically defined model parameters that do not necessarily meet the requirements of Granger causality. We propose Instance-wise Self-Attentive Hawkes Processes (ISAHP), a novel deep learning framework that can directly infer the Granger causality at the event instance level. ISAHP is the first neural point process model that meets the requirements of Granger causality. It leverages the self-attention mechanism of the transformer to align with the principles of Granger causality. We empirically demonstrate that ISAHP is capable of discovering complex instance-level causal structures that cannot be handled by classical models. We also show that ISAHP achieves state-of-the-art performance in proxy tasks involving type-level causal discovery and instance-level event type prediction.

Deep reinforcement learning (RL) has been shown to be effective in producing approximate solutions to some vehicle routing problems (VRPs), especially when using policies generated by encoder-decoder attention mechanisms. While these techniques have been quite successful for relatively simple problem instances, there are still under-researched and highly complex VRP variants for which no effective RL method has been demonstrated. In this work we focus on one such VRP variant, which contains multiple trucks and multi-leg routing requirements. In these problems, demand is required to move along sequences of nodes, instead of just from a start node to an end node. With the goal of making deep RL a viable strategy for real-world industrial-scale supply chain logistics, we develop new extensions to existing encoder-decoder attention models which allow them to handle multiple trucks and multi-leg routing requirements. Our models have the advantage that they can be trained for a small number of trucks and nodes, and then embedded into a large supply chain to yield solutions for larger numbers of trucks and nodes. We test our approach on a real supply chain environment arising in the operations of Japanese automotive parts manufacturer Aisin Corporation, and find that our algorithm outperforms Aisin's previous best solution., Comment: 13 pages, 4 figures

The nuclear two-photon or double-gamma ($2\gamma$) decay is a second-order electromagnetic process whereby a nucleus in an excited state emits two gamma rays simultaneously. To be able to directly measure the $2\gamma$ decay rate in the low-energy regime below the electron-positron pair-creation threshold, we combined the isochronous mode of a storage ring with Schottky resonant cavities. The newly developed technique can be applied to isomers with excitation energies down to $\sim100$\,keV and half-lives as short as $\sim10$\,ms. The half-life for the $2\gamma$ decay of the first-excited $0^+$ state in bare $^{72}\mathrm{Ge}$ ions was determined to be $23.9\left(6\right)$\,ms, which strongly deviates from expectations., Comment: Submitted to Physical Review Letters, 8 pages, 3 figures, 2 tables, 7 equations

Neutron transmission experiments can offer a new type of highly sensitive search for time-reversal invariance violating (TRIV) effects in nucleon-nucleon interactions via the same enhancement mechanism observed for large parity violating (PV) effects in neutron-induced compound nuclear processes. In these compound processes, the TRIV cross-section is given as the product of the PV cross-section, a spin-factor $\kappa$, and a ratio of TRIV and PV matrix elements. We determined $\kappa$ to be $0.59\pm0.05$ for $^{139}$La+$n$ using both $(n, \gamma)$ spectroscopy and ($\vec{n}+^{139}\vec{\rm La}$) transmission. This result quantifies for the first time the high sensitivity of the $^{139}$La 0.75~eV $p$-wave resonance in a future search for P-odd/T-odd interactions in ($\vec{n}+^{139}\vec{\rm La}$) forward transmission.

The rise of Artificial Intelligence (AI) has the potential to fundamentally reshape the knowledge economy by solving problems at scale. This paper introduces a framework to study this transformation, incorporating AI into an economy where humans form hierarchical firms: Less knowledgeable individuals become "workers" solving routine problems, while more knowledgeable individuals become "solvers," assisting workers with exceptional problems. We model AI as a technology that transforms computing power into "AI agents," which can either operate autonomously (as co-workers or solvers/co-pilots) or non-autonomously (only as co-pilots). We show that basic autonomous AI displaces humans towards specialized problem solving, leading to smaller, less productive, and less decentralized firms. In contrast, advanced autonomous AI reallocates humans to routine work, resulting in larger, more productive, and more decentralized firms. While autonomous AI primarily benefits the most knowledgeable individuals, non-autonomous AI disproportionately benefits the least knowledgeable. However, autonomous AI achieves higher overall output. These findings reconcile seemingly contradictory empirical evidence and reveal key tradeoffs involved in regulating AI autonomy.

The race to develop image generation models is intensifying, with a rapid increase in the number of text-to-image models available. This is coupled with growing public awareness of these technologies. Though other generative AI models--notably, large language models--have received recent critical attention for the social and other non-technical issues they raise, there has been relatively little comparable examination of image generation models. This paper reports on a novel, comprehensive categorization of the social issues associated with image generation models. At the intersection of machine learning and the social sciences, we report the results of a survey of the literature, identifying seven issue clusters arising from image generation models: data issues, intellectual property, bias, privacy, and the impacts on the informational, cultural, and natural environments. We situate these social issues in the model life cycle, to aid in considering where potential issues arise, and mitigation may be needed. We then compare these issue clusters with what has been reported for large language models. Ultimately, we argue that the risks posed by image generation models are comparable in severity to the risks posed by large language models, and that the social impact of image generation models must be urgently considered.

Accurate atmospheric 3D wind observations are a high priority in the science community. To address this requirement and to support researchers' needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019-August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. Recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC's capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture, Comment: 20 pages, 13 figures, 6 tables

Disk-fed accretion onto neutron stars can power a wide range of astrophysical sources ranging from X-ray binaries, to accretion powered millisecond pulsars, ultra-luminous X-ray sources, and gamma-ray bursts. A crucial parameter controlling the gas-magnetosphere interaction is the strength of the stellar dipole. In addition, coherent X-ray pulsations in many neutron star systems indicate that the star's dipole moment is oblique relative to its rotation axis. Therefore, it is critical to systematically explore the 2D parameter space of the star's magnetic field strength and obliquity, which is what this work does, for the first time, in the framework of 3D general-relativistic magnetohydrodynamics. If the accretion disk carries its own vertical magnetic field, this introduces an additional factor: the relative polarity of the disk and stellar magnetic fields. We find that depending on the strength of the stellar dipole and the star-disk relative polarity, the neutron star's jet power can either increase or decrease with increasing obliquity. For weak dipole strength (equivalently, high accretion rate), the parallel polarity results in a positive correlation between jet power and obliquity, whereas the anti-parallel orientation displays the opposite trend. For stronger dipoles, the relative polarity effect disappears, and jet power always decreases with increasing obliquity. The influence of the relative polarity gradually disappears as obliquity increases. Highly oblique pulsars tend to have an increased magnetospheric radius, a lower mass accretion rate, and enter the propeller regime at lower magnetic moments than aligned stars., Comment: 10 pages, 5 figures, submitted to ApJL

In this paper, we consider a spectral analysis of the Correlated Random Walk (CRW) on the path. We apply an analytical method for the Quantum Walk to CRW. For the isospectral coin cases, we obtain all of the eigenvalues and the corresponding eigenvectors of the time evolution operator of CRW, and also obtain the limiting distribution., Comment: 14 pages, 2 figures

Jetted astrophysical phenomena with black hole (BH) engines, including binary mergers, jetted tidal disruption events, and X-ray binaries, require a large-scale vertical magnetic field for efficient jet formation. However, a dynamo mechanism that could generate these crucial large-scale magnetic fields has not been identified and characterized. We have employed 3D global general relativistic magnetohydrodynamical (MHD) simulations of accretion disks to quantify, for the first time, a dynamo mechanism that generates large-scale magnetic fields. This dynamo mechanism primarily arises from the nonlinear evolution of the magnetorotational instability (MRI). In this mechanism, large non-axisymmetric MRI-amplified shearing wave modes, mediated by the axisymmetric azimuthal magnetic field, generate and sustain the large-scale vertical magnetic field through their nonlinear interactions. We identify the advection of magnetic loops as a crucial feature, transporting the large-scale vertical magnetic field from the outer regions to the inner regions of the accretion disk. This leads to a larger characteristic size of the, now advected, magnetic field when compared to the local disk height. We characterize the complete dynamo mechanism with two timescales: one for the local magnetic field generation, $t_{\rm g}$, and one for the large-scale scale advection, $t_{\rm adv}$. Whereas the dynamo we describe is nonlinear, we explore the potential of linear mean field models to replicate its core features. Our findings indicate that traditional $\alpha$-dynamo models, often computed in stratified shearing box simulations, are inadequate and that the effective large-scale dynamics is better described by the shear current effects or stochastic $\alpha$-dynamos., Comment: To be submitted to MNRAS. Comments welcomed!

Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation.

The conventional accretion disk lore is that magnetized turbulence is the principal angular momentum transport process that drives accretion. However, when dynamically important large-scale magnetic fields thread an accretion disk, they can produce mass and angular momentum outflows, known as winds, that also drive accretion. Yet, the relative importance of turbulent and wind-driven angular momentum transport is still poorly understood. To probe this question, we analyze a long-duration ($1.2 \times 10^5 r_{\rm g}/c$) simulation of a rapidly rotating ($a=0.9$) black hole feeding from a thick ($H/r\sim0.3$), adiabatic, magnetically arrested disk (MAD), whose dynamically important magnetic field regulates mass inflow and drives both uncollimated and collimated outflows (i.e., winds and jets, respectively). By carefully disentangling the various angular momentum transport processes within the system, we demonstrate the novel result that disk winds and disk turbulence both extract roughly equal amounts of angular momentum from the disk. We find cumulative angular momentum and mass accretion outflow rates of $\dot{L}\propto r^{0.9}$ and $\dot{M}\propto r^{0.4}$, respectively. This result suggests that understanding both turbulent and laminar stresses is key to understanding the evolution of systems where geometrically thick MADs can occur, such as the hard state of X-ray binaries, low-luminosity active galactic nuclei, some tidal disruption events, and possibly gamma-ray bursts., Comment: 12 pages, 6 figures

Spinning supermassive black holes (BHs) in active galactic nuclei (AGN) magnetically launch relativistic collimated outflows, or jets. Without angular momentum supply, such jets are thought to perish within $3$ orders of magnitude in distance from the BH, well before reaching kpc-scales. We study the survival of such jets at the largest scale separation to date, via 3D general relativistic magnetohydrodynamic simulations of rapidly spinning BHs immersed into uniform zero-angular-momentum gas threaded by weak vertical magnetic field. We place the gas outside the BH sphere of influence, or the Bondi radius, chosen much larger than the BH gravitational radius, $R_\text{B}=10^3R_\text{g}$. The BH develops dynamically-important large-scale magnetic fields, forms a magnetically-arrested disk (MAD), and launches relativistic jets that propagate well outside $R_\text{B}$ and suppress BH accretion to $1.5\%$ of the Bondi rate, $\dot{M}_\text{B}$. Thus, low-angular-momentum accretion in the MAD state can form large-scale jets in Fanaroff-Riley (FR) type I and II galaxies. Subsequently, the disk shrinks and exits the MAD state: barely a disk (BAD), it rapidly precesses, whips the jets around, globally destroys them, and lets $5-10\%$ of $\dot{M}_\text{B}$ reach the BH. Thereafter, the disk starts rocking back and forth by angles $90-180^\circ$: the rocking accretion disk (RAD) launches weak intermittent jets that spread their energy over a large area and suppress BH accretion to $\lesssim 2 \% ~ \dot{M}_\text{B}$. Because BAD and RAD states tangle up the jets and destroy them well inside $R_\text{B}$, they are promising candidates for the more abundant, but less luminous, class of FR0 galaxies.

Despite the remarkable progress in natural language understanding with pretrained Transformers, neural language models often do not handle commonsense knowledge well. Toward commonsense-aware models, there have been attempts to obtain knowledge, ranging from automatic acquisition to crowdsourcing. However, it is difficult to obtain a high-quality knowledge base at a low cost, especially from scratch. In this paper, we propose PHALM, a method of building a knowledge graph from scratch, by prompting both crowdworkers and a large language model (LLM). We used this method to build a Japanese event knowledge graph and trained Japanese commonsense generation models. Experimental results revealed the acceptability of the built graph and inferences generated by the trained models. We also report the difference in prompting humans and an LLM. Our code, data, and models are available at github.com/nlp-waseda/comet-atomic-ja.