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We investigated the temperature (T) dependence of the irreversibility field Hirr(T) in high-critical-temperature cuprate Bi2+xSr2-xCa1-yYyCu2O8+d (Bi-2212) single crystals over a wide range of hole doping level (p). Hirr(T) was evaluated by measuring the magnetization hysteresis loop. The value of Hirr(T) extrapolated to T = 0 K [Hirr(0)], is either equal to or sets the lower boundary for the upper critical field at T = 0 K [Hc2(0)]. Tc shows a parabolic p-dependence (peak at p = 0.16), whereas Hirr(0) increases monotonically with p by approximately one order of magnitude, from 19 T for the most underdoped sample (p = 0.065, Tc = 24 K) to 209 T for the most overdoped sample (p = 0.200, Tc = 75 K). The present results qualitatively agree with Hc2(0) values evaluated from the specific heat measurements. The observed p-dependence of Hirr(0) in Bi-2212 is distinct from those in YBa2Cu3O7-d and HgBa2CuO6+d, in which a pronounced dip structure appears in the underdoped region. Considering that the dip structures observed in these two systems are likely associated with the formation of competing orders (most likely field-induced charge orders), the present results indicate that the influence of the competing order in Bi-2212 is less prominent than that in the other two systems., Comment: 32 pages, 9 figures, 1 table

This paper presents an estimation method for time-varying graph signals among multiple sub-networks. In many sensor networks, signals observed are associated with nodes (i.e., sensors), and edges of the network represent the inter-node connectivity. For a large sensor network, measuring signal values at all nodes over time requires huge resources, particularly in terms of energy consumption. To alleviate the issue, we consider a scenario that a sub-network, i.e., cluster, from the whole network is extracted and an intra-cluster analysis is performed based on the statistics in the cluster. The statistics are then utilized to estimate signal values in another cluster. This leads to the requirement for transferring a set of parameters of the sub-network to the others, while the numbers of nodes in the clusters are typically different. In this paper, we propose a cooperative Kalman filter between two sub-networks. The proposed method alternately estimates signals in time between two sub-networks. We formulate a state-space model in the source cluster and transfer it to the target cluster on the basis of optimal transport. In the signal estimation experiments of synthetic and real-world signals, we validate the effectiveness of the proposed method., Comment: 9 pages, 9 figures

Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map simply to ones in a similarity-transformed positive graph Laplacian, thus enabling reuse of well-studied spectral filters designed for positive graphs. We propose a fast method to learn a balanced signed graph Laplacian directly from data. Specifically, for each node $i$, to determine its polarity $\beta_i \in \{-1,1\}$ and edge weights $\{w_{i,j}\}_{j=1}^N$, we extend a sparse inverse covariance formulation based on linear programming (LP) called CLIME, by adding linear constraints to enforce ``consistent" signs of edge weights $\{w_{i,j}\}_{j=1}^N$ with the polarities of connected nodes -- i.e., positive/negative edges connect nodes of same/opposing polarities. For each LP, we adapt projections on convex set (POCS) to determine a suitable CLIME parameter $\rho > 0$ that guarantees LP feasibility. We solve the resulting LP via an off-the-shelf LP solver in $\mathcal{O}(N^{2.055})$. Experiments on synthetic and real-world datasets show that our balanced graph learning method outperforms competing methods and enables the use of spectral filters and graph convolutional networks (GCNs) designed for positive graphs on signed graphs., Comment: 5 pages, 1 figure. Submitted to ICASSP 2025

The present study aims to detect helium-3 in nickel-based metal nano-composites doped with zirconia, which exhibited anomalous heat generation when exposed to hydrogen gas at approximately 450{\deg}C. Two complementary analytical techniques were employed: Nuclear Reaction Analysis (NRA) utilizing 1.4 MeV deuteron beams from a tandem accelerator, and Thermal Desorption Spectrometry (TDS) using a quadrupole mass spectrometer. Both methods successfully detected helium-3 in the samples. Given the extreme rarity of this isotope, its presence strongly suggests the occurrence of nuclear reactions within the nickel-containing materials. These findings lend support to the 4H/TSC (4 Hydrogen/Tetrahedral Symmetric Condensate) model, which uniquely predicts helium-3 as one of the primary reaction products., Comment: 18 pages, 7 figures. This is the version of the article before peer review, as submitted by an author to Japanese Journal of Applied Physics

This paper presents a novel single-channel decomposition approach to facilitate the decomposition of electroencephalography (EEG) signals recorded with limited channels. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain--computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.

This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required. To ensure their responsible use and implementation, both developers and users of research incorporating these tools, should familiarize themselves with their current limitations, including the issues related to scientific validity, risk of bias, and consider equity and ethical implications. We also surveyed the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools., Comment: 24 pages, 1 figure, 1 table, 2 boxes, 103 references

While data qubits with a long coherence time are essential for the storage of quantum information, ancilla qubits are pivotal in quantum error correction (QEC) for fault-tolerant quantum computing. The recent development of optical tweezer arrays, such as the preparation of large-scale qubit arrays and high-fidelity gate operations, offers the potential for realizing QEC protocols, and one of the important next challenges is to control and detect ancilla qubits while minimizing atom loss and crosstalk. Here, we present the realization of a hybrid system consisting of a dual-isotope ytterbium (Yb) atom array, in which we can utilize a nuclear spin qubit of fermionic ${}^{171}\mathrm{Yb}$ as a data qubit and an optical clock qubit of bosonic ${}^{174}\mathrm{Yb}$ as an ancilla qubit with a capacity of non-destructive qubit readout. We evaluate the crosstalk between qubits regarding the impact on the coherence of the nuclear spin qubits from the imaging light for ${}^{174}\mathrm{Yb}$. The Hahn-echo sequence with a 399 nm probe and 556 nm cooling beams for ${}^{174}\mathrm{Yb}$, we observe 99.1(1.8) % coherence retained under 20 ms exposure, yielding an imaging fidelity of 0.9992 and a survival probability of 0.988. The Ramsey sequence with a 556 nm probe beam shows negligible influence on the coherence, suggesting the potential future improvement of low cross-talk measurements. This result highlights the potential of the hybrid-Yb atom array for ancilla-qubit-based QEC protocols.

We study theoretically the feasibility of the semi-exclusive $^{12}$C($p,dp$)$X$ reaction for the observation of $\eta^\prime$ mesic nuclei using the transport model JAM. The semi-exclusive measurements of the ($p,d$) reaction with protons from $\eta^\prime$ non-mesonic two-body absorption ($\eta^\prime NN \to NN$) are found to be critically important for the observation of the $\eta^\prime$ bound states. The Green's function method is used to calculate the momentum spectrum of forward going deuterons corresponding to the excitation energy spectrum of the $\eta^\prime \otimes {}^{11}$C system in the semi-exclusive measurement. The semi-exclusive measurements are considered to be important in general for the $\eta^\prime$ mesic nucleus formation., Comment: 7 pages, 2 figures, 1 table

This paper introduces the physics-inspired synthesized underwater image dataset (PHISWID), a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis. Deep learning approaches to underwater image enhancement typically demand extensive datasets, yet acquiring paired clean and degraded underwater ones poses significant challenges. While several underwater image datasets have been proposed using physics-based synthesis, a publicly accessible collection has been lacking. Additionally, most underwater image synthesis approaches do not intend to reproduce atmospheric scenes, resulting in incomplete enhancement. PHISWID addresses this gap by offering a set of paired ground-truth (atmospheric) and synthetically degraded underwater images, showcasing not only color degradation but also the often-neglected effects of marine snow, a composite of organic matter and sand particles that considerably impairs underwater image clarity. The dataset applies these degradations to atmospheric RGB-D images, enhancing the dataset's realism and applicability. PHISWID is particularly valuable for training deep neural networks in a supervised learning setting and for objectively assessing image quality in benchmark analyses. Our results reveal that even a basic U-Net architecture, when trained with PHISWID, substantially outperforms existing methods in underwater image enhancement. We intend to release PHISWID publicly, contributing a significant resource to the advancement of underwater imaging technology.

We consider an integro-differential equation model for traffic flow which is an extension of the Burgers equation model. To discuss the model, we first examine general settings for integrable integro-differential equations and find that they are obtained through a simple residue formula from integrable eqations in a complex domain. As demonstration of the efficiency of this approach, we list several integrable equations including a difference equation with double singular integral and an equation with elliptic singular integral. Then, we discuss the traffic model with singular integral and show that the model exhibits interaction between free flow region and congested region depending on the parameter of non-locality.

This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations of graphs and are used in various applications in signal processing, machine learning, and data mining. In many problems of interest, these adjacency matrices can be large, so efficient compression methods are crucial. In this paper, we propose a lossy compression of weighted adjacency matrices, where the binary adjacency information is encoded losslessly (so the topological information of the graph is preserved) while the edge weights are compressed lossily. For the edge weight compression, the target graph is converted into a line graph, whose nodes correspond to the edges of the original graph, and where the original edge weights are regarded as a graph signal on the line graph. We then transform the edge weights on the line graph with a graph filter bank for sparse representation. Experiments on synthetic data validate the effectiveness of the proposed method by comparing it with existing lossy matrix compression methods.

We present a comparative analysis of 1$s$ and 2$p$ core-level hard X-ray photoelectron spectroscopy (HAXPES) spectra in metallic VO$_2$ and CrO$_2$. Even though the V 1$s$ and 2$p$ spectra in VO$_2$ display similar line shapes except the absence or presence of a spin-orbit coupling splitting, the Cr 1$s$ and 2$p$ spectra exhibit distinct main-line shapes. The experimental HAXPES spectra are analyzed by the Anderson impurity model based on the density functional theory + dynamical mean-field theory and a conventional MO$_6$ cluster model. We elucidate the complex interplay between formation of the intra-atomic multiplet and charge transfer effect on the chemical bonding followed by the 1$s$ and 2$p$ core electron excitations. We demonstrate the advantage of the 1$s$ excitations to the routinely-employed 2$p$ excitations for distinguishing between metal-ligand and metal-metal charge transfer contributions in early 3$d$ transition-metal oxides.

This work presents a framework for a robot with a multi-fingered hand to freely utilize daily tools, including functional parts like buttons and triggers. An approach heatmap is generated by selecting a functional finger, indicating optimal palm positions on the object's surface that enable the functional finger to contact the tool's functional part. Once the palm position is identified through the heatmap, achieving the functional grasp becomes a straightforward process where the fingers stably grasp the object with low-dimensional inputs using the eigengrasp. As our approach does not need human demonstrations, it can easily adapt to various sizes and designs, extending its applicability to different objects. In our approach, we use directional manipulability to obtain the approach heatmap. In addition, we add two kinds of energy functions, i.e., palm energy and functional energy functions, to realize the eigengrasp. Using this method, each robotic gripper can autonomously identify its optimal workspace for functional grasping, extending its applicability to non-anthropomorphic robotic hands. We show that several daily tools like spray, drill, and remotes can be efficiently used by not only an anthropomorphic Shadow hand but also a non-anthropomorphic Barrett hand., Comment: 8 pages, 7 figures

Recent numerical studies suggest that magnetic fields play an important role in primordial star formation in the early universe. However, the detailed evolution of the magnetic field in the collapse phase still has uncertainties because of the complicated physics associated with turbulence in a collapsing magnetized system. Here, we perform a suite of numerical MHD simulations that follow the collapse of magnetized, turbulent primordial gas clouds to investigate the evolution of the magnetic field associated with the turbulence, assuming a polytropic equation of state with exponent $\gamma_{\rm eff}$ and with various numerical resolutions. In addition, we generalize the analytic theory of magnetic field growth/saturation so that it can deal with various exponents $\gamma_{\rm eff}$ and turbulence energy spectra. We find that the numerical results are well reproduced by the theory for various $\gamma_{\rm eff}$ through the collapse phase during the formation of the first stars. The magnetic field is eventually amplified by a factor of $10^{12}$ -- $10^{15}$ due to kinematic and non-linear turbulent dynamo effects and reaches 3% -- 100% of the equipartition level, depending on $\gamma_{\rm eff}$. We also find that the transition between the kinematic and non-linear stages can be analytically estimated. These results indicate that the strong magnetic field accompanied by supersonic turbulence is a general property and suggest that it can play a crucial role in the formation of the first stars., Comment: 15 pages, 8 figures, 1 tables, accepted for publication in ApJ