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This paper proposes a method for unsupervised anomalous sound detection (UASD) and captioning the reason for detection. While there is a method that captions the difference between given normal and anomalous sound pairs, it is assumed to be trained and used separately from the UASD model. Therefore, the obtained caption can be irrelevant to the differences that the UASD model captured. In addition, it requires many caption labels representing differences between anomalous and normal sounds for model training. The proposed method employs a retrieval-augmented approach for captioning of anomalous sounds. Difference captioning in the embedding space output by the pre-trained CLAP (contrastive language-audio pre-training) model makes the anomalous sound detection results consistent with the captions and does not require training. Experiments based on subjective evaluation and a sample-wise analysis of the output captions demonstrate the effectiveness of the proposed method.

Gamma rays consist of high-energy photons that selectively interact with nuclei, induce and mediate nuclear reactions and elementary particle interactions, and exceed x-rays in penetrating power and thus are indispensable for analysis and modification of dense or compressed object interior. Yet, the available gamma sources lack power and brightness which, if available, would revolutionize science and technology. The predicted laser-driven Gamma Flash (GF) would be the highest-power and the brightest terrestrial gamma source with a 30-40% laser-to-gamma energy conversion. It is based on inverse Compton scattering in a laser-solid interaction at irradiance typically above $10^{23}W/cm^2$. GF is one of the motivating goals for the most advanced laser facilities. However, till now GF remains overshadowed by simultaneously generated low-brightness Bremsstrahlung. Here we experimentally differentiate these two mechanisms and demonstrate a GF dominant regime producing several times the number of Bremsstrahlung photons. We found steep GF yield growth with the laser power and irradiance. Simulations revealed a Terawatt GF with nanometre source and an attosecond pulse train with a record brightness of $~10^{22}photons/mm^2mrad^2s0.1\%BW$ at up to tens of MeV. The small source size and high brightness paves the way towards spatially coherent gamma rays. At high photon energies, our regime is comparable in brightness to astrophysical Gamma Ray Bursts. We anticipate that the gamma ray source based on our findings will facilitate a breakthrough in research on future inertial fusion energy by enabling high-spatial-resolution time-resolved radiography of fuel mixing instabilities in extremely compressed targets. Such a new compact bright ultrafast gamma source could facilitate significant advances in time-resolved nuclear physics, homeland security, and nuclear waste management and non-proliferation., Comment: 36 pages, 11 figures

One promising way to enhance superconductivity is to have coexisting wide and incipient narrow bands, where the Fermi level intersecting the wide band lies just above the narrow band, by which finite-energy spin fluctuations act as glue to mediate pair scattering. As an extreme case of the narrow band dispersion, we investigate spin-fluctuation-mediated superconductivity in two-dimensional Hubbard models with an incipient flat band. For all of the systems investigated in this study, the Kagome, Lieb, and bilayer square lattices with a flat band, we find that spin-singlet pairing superconductivity is enhanced when the flat band is nearly fully filled, due to the interband pair scattering even when the flat band becomes dispersive by correlation effects. Among these models, enhancement of superconductivity is weak in the Lieb lattice, possibly because the density of states of the wide band goes to zero at the Dirac point where the flat and wide bands intersect. Also, when the electron density is smaller so that the flat band approaches half filling, ferromagnetic spin fluctuations and spin-triplet pairing arises, which does not develop strongly compared to the case of the spin-singlet pairing for the incipient band situation., Comment: 9 pages, 6 figures

In this paper, we propose a novel adaptive Control Barrier Function (CBF) based controller for nonlinear systems with complex, time-varying input constraints. Conventional CBF approaches often struggle with feasibility issues and stringent assumptions when addressing input constraints. Unlike these methods, our approach converts the input-constraint problem into an output-constraint CBF design. This transformation simplifies the Quadratic Programming (QP) formulation and enhances compatibility with the CBF framework. We design an adaptive CBF-based controller to manage the mismatched uncertainties introduced by this transformation. Our method systematically addresses the challenges of complex, time-varying, and state-dependent input constraints. The efficacy of the proposed approach is validated using numerical examples., Comment: 8 pages, 2 figures

A minor improvement is made to the calculation of the inhomogeneity term. The new calculation gives better agreement with the observations of Daoud et al. and Cheng-Graessley-Melnichenko., Comment: 6 pages, 7 figures

Transition metal dichalcogenides (TMDs) exhibit unique properties and potential applications when reduced to one-dimensional (1D) nanoribbons (NRs), owing to quantum confinement and high edge densities. However, effective growth methods for self-aligned TMD NRs are still lacking. We demonstrate a versatile approach for the lattice-guided growth of dense, aligned TMD NR arrays via chemical vapor deposition (CVD) on anisotropic sapphire substrates, without the need for tailored surface steps. This method enables the synthesis of NRs with widths below 10 nm, with their longitudinal axis parallel to the zigzag direction. The growth is influenced by both the substrate and CVD temperature, indicating the role of anisotropic precursor diffusion and substrate interaction. The 1D nature of the NRs was asserted by the observation of Coulomb blockade at low temperature. Pronounced catalytic activity was observed at the edges of the NRs, indicating their promise for efficient catalysis., Comment: 39 pages, 24 figures

Pressure-sensitive paint (PSP) was applied to the surface of a NACA0012 airfoil to investigate pressure fluctuations associated with trailing edge (TE) noise under low-velocity flow conditions. The primary focus is to assess the feasibility of employing laser pulses exposed at the airfoil surface to mitigate TE noise. However, the weak pressure fluctuations accompanying TE noise pose a challenge, as they are overshadowed by image sensor noise in high-speed cameras capturing PSP emission changes. A novel time-resolved phase-locking technique was introduced to address this issue, utilizing the signal from a semiconductor pressure transducer at the trailing edge as a phase-lock trigger source. By repetitively conducting phase-locked measurements (1150 times), time series ensemble-averaged data based on PSP emission images were obtained, enabling the capture of these subtle pressure fluctuations. Quantitatively, fluctuations with a dominant frequency of 679 Hz and an amplitude of 50 Pa are resolved within an accuracy of about 15 Pa, achieved at a recording rate of 19.2 kHz. Both the suppression and subsequent redevelopment of the pressure field with the TE noise offer valuable insights into the dynamics of TE noise and open avenues for targeted noise reduction strategies in aerodynamic applications.

Physics-informed Neural Networks (PINNs) is a method for numerical simulation that incorporates a loss function corresponding to the governing equations into a neural network. While PINNs have been explored for their utility in inverse analysis, their application in acoustic analysis remains limited. This study presents a method to identify loss parameters in acoustic tubes using PINNs. We categorized the loss parameters into two groups: one dependent on the tube's diameter and another constant, independent of it. The latter were set as the trainable parameters of the neural network. The problem of identifying the loss parameter was formulated as an optimization problem, with the physical properties being determined through this process. The neural network architecture employed was based on our previously proposed ResoNet, which is designed for analyzing acoustic resonance. The efficacy of the proposed method is assessed through both forward and inverse analysis, specifically through the identification of loss parameters. The findings demonstrate that it is feasible to accurately identify parameters that significantly impact the sound field under analysis. By merely altering the governing equations in the loss function, this method could be adapted to various sound fields, suggesting its potential for broad application., Comment: 10 pages, 7 figures, The following article has been submitted to Mechanical Engineering Journal. After it is published, it will be found at https://www.jstage.jst.go.jp/browse/mej

This study proposes introducing convex optimization to find initial perturbations of atmospheric models for realizing specified changes in subsequent forecasts. In the proposed method, we formulate and solve an inverse problem to find effective perturbations in atmospheric variables so that controlled variables satisfy specified changes at a specified time. The proposed method first constructs a sensitivity matrix of controlled variables, such as accumulated precipitation, to the initial atmospheric variables, such as temperature and humidity, through sensitivity analysis using numerical weather prediction (NWP) models. The sensitivity matrix is used to solve the inverse problem as convex optimization, in which a global optimal solution can be found computationally efficiently. The proposed method was validated through a benchmark warm bubble experiment using an NWP model. The experiments showed that identified perturbation successfully realized specified spatial distributions of accumulated precipitation. These results demonstrated the possibility of controlling the real atmosphere by solving inverse problems and adding small perturbations to atmospheric states., Comment: license changed to CC BY 4.0; revised to improve readability; some figures in Appendix omitted to improve conciseness

GitGuardian monitored secrets exposure in public GitHub repositories and reported developers leaked over 12 million secrets (database and other credentials) in 2023, indicating a 113% surge from 2021. Despite the availability of secret detection tools, developers ignore the tools' reported warnings because of false positives (25%-99%). However, each secret protects assets of different values accessible through asset identifiers (a DNS name and a public or private IP address). The asset information for a secret can aid developers in filtering false positives and prioritizing secret removal from the source code. However, existing secret detection tools do not provide the asset information, thus presenting difficulty to developers in filtering secrets only by looking at the secret value or finding the assets manually for each reported secret. The goal of our study is to aid software practitioners in prioritizing secrets removal by providing the assets information protected by the secrets through our novel static analysis tool. We present AssetHarvester, a static analysis tool to detect secret-asset pairs in a repository. Since the location of the asset can be distant from where the secret is defined, we investigated secret-asset co-location patterns and found four patterns. To identify the secret-asset pairs of the four patterns, we utilized three approaches (pattern matching, data flow analysis, and fast-approximation heuristics). We curated a benchmark of 1,791 secret-asset pairs of four database types extracted from 188 public GitHub repositories to evaluate the performance of AssetHarvester. AssetHarvester demonstrates precision of (97%), recall (90%), and F1-score (94%) in detecting secret-asset pairs. Our findings indicate that data flow analysis employed in AssetHarvester detects secret-asset pairs with 0% false positives and aids in improving the recall of secret detection tools.

To achieve atomic-level characterization of the diamond (001) surface, persistent efforts have been made over the past few decades. The motivation behind the pursuit extends beyond investigating surface defects and adsorbates; it also involves unraveling the mystery of the smooth growth of diamond. However, the inherently low conductivity and the short C-C bonds render atomic resolution imaging exceptionally challenging. Here, we successfully overcame these challenges by employing non-contact atomic force microscopy with reactive Si tips. Atomic resolution imaging was achieved even at room temperature. With density-functional-theory calculations, we clarified that the critical factors for atomic resolution are in the formation of tilted C-Si bonds between scanning probes and surfaces, along with reordering of the surface C-C dimers. Implications of the findings extend beyond the realm of surface characterization. The present atomic-resolution microscopies drive future advancements in diamond technologies by providing avenues for identifying dopants and constructing artificial nanostructures.

A multi-agent system comprises numerous agents that autonomously make decisions to collectively accomplish tasks, drawing significant attention for their wide-ranging applications. Within this context, formation control emerges as a prominent task, wherein agents collaboratively shape and maneuver while preserving formation integrity. Our focus centers on cyclic pursuit, a method facilitating the formation of circles, ellipses, and figure-eights under the assumption that agents can only perceive the relative positions of those preceding them. However, this method's scope has been restricted to these specific shapes, leaving the feasibility of forming other shapes uncertain. In response, our study proposes a novel method based on cyclic pursuit capable of forming a broader array of shapes, enabling agents to individually shape while pursuing preceding agents, thereby extending the repertoire of achievable formations. We present two scenarios concerning the information available to agents and devise formation control methods tailored to each scenario. Through extensive simulations, we demonstrate the efficacy of our proposed method in forming multiple shapes, including those represented as Fourier series, thereby underscoring the versatility and effectiveness of our approach.

Navigating multi-robot systems in complex terrains has always been a challenging task. This is due to the inherent limitations of traditional robots in collision avoidance, adaptation to unknown environments, and sustained energy efficiency. In order to overcome these limitations, this research proposes a solution by integrating living insects with miniature electronic controllers to enable robotic-like programmable control, and proposing a novel control algorithm for swarming. Although these creatures, called cyborg insects, have the ability to instinctively avoid collisions with neighbors and obstacles while adapting to complex terrains, there is a lack of literature on the control of multi-cyborg systems. This research gap is due to the difficulty in coordinating the movements of a cyborg system under the presence of insects' inherent individual variability in their reactions to control input. In response to this issue, we propose a novel swarm navigation algorithm addressing these challenges. The effectiveness of the algorithm is demonstrated through an experimental validation in which a cyborg swarm was successfully navigated through an unknown sandy field with obstacles and hills. This research contributes to the domain of swarm robotics and showcases the potential of integrating biological organisms with robotics and control theory to create more intelligent autonomous systems with real-world applications.

On the basis of the thermodynamic theory of the excluded volume effects, we show that the size exponent varies abruptly, depending on the change of the segment concentration. For linear polymers, the exponent changes discontinuously from $\nu=3/5$ for the isolated system ($\bar{\phi}=0$) in good solvents to $\nu=1/2$ in the finite concentration ($0<\bar{\phi}\le1$), while for branched polymers having $\nu_{0}=1/4$, the corresponding exponent varies from $\nu=1/2$ ($\bar{\phi}=0$) to $\nu\cong 1/3$ ($0<\bar{\phi}\le1$)., Comment: 10 pages, 10 figures