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Parameter-Efficient Fine-Tuning (PEFT) of text-to-image models has become an increasingly popular technique with many applications. Among the various PEFT methods, Low-Rank Adaptation (LoRA) and its variants have gained significant attention due to their effectiveness, enabling users to fine-tune models with limited computational resources. However, the approximation gap between the low-rank assumption and desired fine-tuning weights prevents the simultaneous acquisition of ultra-parameter-efficiency and better performance. To reduce this gap and further improve the power of LoRA, we propose a new PEFT method that combines two classes of adaptations, namely, transform and residual adaptations. In specific, we first apply a full-rank and dense transform to the pre-trained weight. This learnable transform is expected to align the pre-trained weight as closely as possible to the desired weight, thereby reducing the rank of the residual weight. Then, the residual part can be effectively approximated by more compact and parameter-efficient structures, with a smaller approximation error. To achieve ultra-parameter-efficiency in practice, we design highly flexible and effective tensor decompositions for both the transform and residual adaptations. Additionally, popular PEFT methods such as DoRA can be summarized under this transform plus residual adaptation scheme. Experiments are conducted on fine-tuning Stable Diffusion models in subject-driven and controllable generation. The results manifest that our method can achieve better performances and parameter efficiency compared to LoRA and several baselines.

Background: A muonic atom, composed of a negative muon and an atomic nucleus, undergoes two primary decay processes: muon-electron decay and muon nuclear capture. The branching ratio between these two processes can be determined from the measured lifetime of the muonic atom. While past researches have examined the general trend of muon capture rates across different nuclei, experimental and theoretical investigations into the isotope dependence of the lifetime remain limited. Purpose: The present study aims to measure the lifetimes of the muonic atom of isotopically enriched silicon isotopes. Methods: The experiment was conducted at the muon facility in the Material and Life Science Facility (MLF), J-PARC. A muon beam was stopped in various target materials, including isotopically enriched $^{28,29,30}$Si. The lifetimes of the muonic atoms were measured by detecting decay electrons using a $\mu$SR spectrometer. The decay spectra were analyzed by fitting with multi-exponential functions to account for contributions from the target nucleus and surrounding materials. Results: For the first time, the lifetimes of the muonic atom of isotopically enriched $^{28,29,30}$Si were measured. Additionally, seven other targets were studied to validate the experimental method and analysis procedure. The results were compared with theoretical models such as Primakoff, Goulard-Primakoff, and the recently-developed microscopic and evaporation model (MEM). Conclusions: The Primakoff and Goulard-Primakoff formulas, while reproducing certain aspects of the isotope dependence, require further refinement. The comparison with MEM calculations constrains the axial vector ($g_A$) and induced pseudoscalar ($g_P$) coupling constants. The present experiment establishes a method for measuring the lifetimes of the muonic atom and will contribute to future systematic investigations., Comment: 10 pages, 6 figures, 2 tables

The extremely luminous infrared galaxy (ELIRG), WISE J090924.01+000211.1 (hereafter; WISE J0909+0002, $z=1.87$) is an extraordinary object with a quasar aspect. This study performs monitoring observations of WISE J0909+0002 with the 105 cm Murikabushi telescope, Okayama and Akeno 50 cm telescopes/MITSuME ($g'$, $R_{\rm c}$, and $I_{\rm c}$ bands), and the SaCRA 55 cm telescope/MuSaSHI ($r$, $i$, and $z$ bands). We obtain the following results by combining the UV/optical light curves of the CRTS, Pan-STARRS, and ZTF archive data, and our observational data: (1) the light curves of WISE J0909+0002 present quasi-periodic (sinusoidal) oscillations with the rest-frame period of $\sim$ 660$-$689 day; (2) the structure functions of WISE J0909+0002 do not show a damped random walk (DRW) trend; (3) the mock DRW light curves present periodic-like trend on rare occasions in 10000 simulations; (4) the relativistic boost scenario is favored, since the relation between variability amplitude and power-law slope ratio is consistent with the theoretical prediction of this scenario, and a substantial parameter space exists between the inclination angles and the black hole mass; (5) the circumbinary disk model is difficult to explain the spectral energy distribution of our target; (6) the significant radio flux density of WISE J0909+0002 is not detected from the VLA FIRST Survey, thus the radio jet precession scenario is ruled out. From our results, the Doppler boost scenario is likely as a cause of the periodic variability, consequently the quasi-periodic oscillations in WISE J0909+0002 is possibly interpreted by a supermassive blackhole binary. Additional observations to investigate the continuity of the periodic trend would bring new insights into mechanisms of the quasi-periodic oscillations and/or ELIRGs., Comment: 19 pages, 11 figures, published by publication in PASJ

We prove that the category of finitely generated graded modules over the quiver Hecke algebra of arbitrary type admits numerous stratifications in the sense of Kleshchev. A direct consequence is that the full subcategory corresponding to the quantum unipotent subgroup associated with any Weyl group element is an affine highest weight category. Our results significantly generalize earlier works by Kato, Brundan, Kleshchev, McNamara and Muth. The key ingredient is a realization of standard modules via determinantial modules. We utilize the technique of R-matrices to study these standard modules., Comment: 61 pages. The sections following Section 6.5 in the previous version have been deleted due to deficiencies. v3: minor revision

To understand the evolution of global accretion disk structure in the ``rebrightening'' phase of MAXI J1820$+$070, we perform a comprehensive analysis of its near infrared/optical/UV to X-ray spectral energy distribution (SED) utilizing data obtained by OISTER, Las Cumbres Observatory (LCO), Swift, NICER, and NuSTAR in 2019. Optical spectra observed with Seimei telescope in 2019 and 2020 are also analyzed. On the basis of the optical and X-ray light curves and their flux ratios, we divide the whole phase into 3 periods, Periods I (flux rise), II (decay), and III (dim). In the first 2 periods, the source stayed in the low/hard state (LHS), where the X-ray (0.3--30 keV) and optical/UV SED can be both fitted with power-law models. We interpret that the X-ray emission arises from hot corona via Comptonization, whereas the optical/UV flux is dominated by synchrotron radiation from the jets, with a partial contribution from the irradiated disk. The optical/UV power-law component smoothly connects to a simultaneous radio flux, supporting its jet origin. Balmer line profiles in the optical spectra indicate that the inner radius of an irradiated disk slightly decreased from $\sim 2\times 10^5 r_{\rm g}$ (Period I) to $\sim 1\times 10^5 r_{\rm g}$ (Period II), where $r_{\rm g}$ is the gravitational radius, implying a change of the hot corona geometry. In Period III, the SED can be reproduced by an advection-dominated accretion flow and jet emission. However, the double-peaked H$\alpha$ emission line indicates that a cool disk remained at large radii., Comment: 18 pages, 9 figures, Published in PASJ

We report the results of quasi-simultaneous multiwavelength (near-infrared, optical, UV, and X-ray) observations of the Galactic X-ray black hole binary MAXI J1820+070 performed in 2019 May 10-13, $\sim 60$ days after the onset of the first rebrightening phase. It showed a much larger optical-to-X-ray luminosity ratio ($\sim 8$) than in the initial outburst epoch. The primary components of the spectral energy distribution (SED) can be best interpreted by radiatively inefficient accretion flow (RIAF) spectrum showing a luminosity peak in the optical band. By comparison with theoretical calculations, we estimate the mass accretion rate to be $\dot{M}/(8 L_{\rm Edd}/c^2) \sim 10^{-3}$, where $c$ is the light speed and $L_{\rm Edd}$ is the Eddington luminosity. In addition to the RIAF emission, a blue power-law component is detected in the optical-UV SED, which is most likely synchrotron radiation from the jet. The optical spectrum taken at the Seimei telescope shows a weak and narrow H$\alpha$ emission line, whose emitting region is constrained to be $\gtrsim 2 \times 10^{4}$ times the gravitational radius. We suggest that the entire disk structure cannot be described by a single RIAF solution but cooler material responsible for the H$\alpha$ emission must exist at the outermost region., Comment: 11 pages, 5 figures, Published in PASJ in 2022

Blind inverse problems, where both the target data and forward operator are unknown, are crucial to many computer vision applications. Existing methods often depend on restrictive assumptions such as additional training, operator linearity, or narrow image distributions, thus limiting their generalizability. In this work, we present LADiBI, a training-free framework that uses large-scale text-to-image diffusion models to solve blind inverse problems with minimal assumptions. By leveraging natural language prompts, LADiBI jointly models priors for both the target image and operator, allowing for flexible adaptation across a variety of tasks. Additionally, we propose a novel posterior sampling approach that combines effective operator initialization with iterative refinement, enabling LADiBI to operate without predefined operator forms. Our experiments show that LADiBI is capable of solving a broad range of image restoration tasks, including both linear and nonlinear problems, on diverse target image distributions.

We introduce a novel interpretable Neural Network (NN) model designed to perform precision bulk reconstruction under the AdS/CFT correspondence. According to the correspondence, a specific condensed matter system on a ring is holographically equivalent to a gravitational system on a bulk disk, through which tabletop quantum gravity experiments may be possible as reported in arXiv:2211.13863. The purpose of this paper is to reconstruct a higher-dimensional gravity metric from the condensed matter system data via machine learning using the NN. Our machine reads spatially and temporarily inhomogeneous linear response data of the condensed matter system, and incorporates a novel layer that implements the Runge-Kutta method to achieve better numerical control. We confirm that our machine can let a higher-dimensional gravity metric be automatically emergent as its interpretable weights, using a linear response of the condensed matter system as data, through supervised machine learning. The developed method could serve as a foundation for generic bulk reconstruction, i.e., a practical solution to the AdS/CFT correspondence, and would be implemented in future tabletop quantum gravity experiments., Comment: 24 pages, 10 figures

Topological solitons, which are stable, localized solutions of nonlinear differential equations, are crucial in various fields of physics and mathematics, including particle physics and cosmology. However, solving these solitons presents significant challenges due to the complexity of the underlying equations and the computational resources required for accurate solutions. To address this, we have developed a novel method using neural network (NN) to efficiently solve solitons. A similar NN approach is Physics-Informed Neural Networks (PINN). In a comparative analysis between our method and PINN, we find that our method achieves shorter computation times while maintaining the same level of accuracy. This advancement in computational efficiency not only overcomes current limitations but also opens new avenues for studying topological solitons and their dynamical behavior., Comment: 12 pages, 4 figures

Selective state space models (SSMs) represented by Mamba have demonstrated their computational efficiency and promising outcomes in various tasks, including automatic speech recognition (ASR). Mamba has been applied to ASR task with the attention-based encoder-decoder framework, where the cross-attention mechanism between encoder and decoder remains. This paper explores the capability of Mamba as the decoder-only architecture in ASR task. Our MAmba-based DEcoder-ONly approach (MADEON) consists of a single decoder that takes speech tokens as a condition and predicts text tokens in an autoregressive manner. To enhance MADEON, we further propose speech prefixing that performs bidirectional processing on speech tokens, which enriches the contextual information in the hidden states. Our experiments show that MADEON significantly outperforms a non-selective SSM. The combination of speech prefixing and the recently proposed Mamba-2 yields comparable performance to Transformer-based models on large datasets., Comment: Accepted to SLT 2024

This paper reports a practical articulated wheeled in-pipe inspection robot "AIRo-7.1" which is waterproof and dustproof, and can adapt to 3 to 4 in inner diameters. The joint torque can be adjusted by a PWM open-loop control. The middle joint angle can be controlled by a position feedback control system while the other two joints are bent by torsional springs. Thanks to this simple and high-density design, not only downsizing of the robot but also wide range of the adaptive inner diameter were achieved. However, the relationship between the actual middle joint torque value and the PWM duty ratio should be pre-known because the reducer used in AIRo-7.1 was designed by ourselves. Therefore, preliminary experiments were conducted to clarify the relationship between them. To examine the adaptive movement, experiments in both 3 in and 4 in pipes with vertical, bend, and diameter change sections. Finally, field experiment was also conducted. From the results, high adaptability to different inner diameters of pipes and slippery environments were confirmed although waterproof and dustproof were not perfectly working., Comment: The Twenty-Ninth International Symposium on Artificial Life and Robotics 2024 (AROB 29th 2024), The Ninth International Symposium on BioComplexity 2024 (ISBC 9th 2024), The Seventh International Symposium on Swarm Behavior and Bio-Inspired Robotics 2024 (SWARM 7th 2024) B-Con Plaza, Beppu, Japan and ONLINE, January 24-26, 2024

Latent diffusion models have enabled continuous-state diffusion models to handle a variety of datasets, including categorical data. However, most methods rely on fixed pretrained embeddings, limiting the benefits of joint training with the diffusion model. While jointly learning the embedding (via reconstruction loss) and the latent diffusion model (via score matching loss) could enhance performance, our analysis shows that end-to-end training risks embedding collapse, degrading generation quality. To address this issue, we introduce CATDM, a continuous diffusion framework within the embedding space that stabilizes training. We propose a novel objective combining the joint embedding-diffusion variational lower bound with a Consistency-Matching (CM) regularizer, alongside a shifted cosine noise schedule and random dropping strategy. The CM regularizer ensures the recovery of the true data distribution. Experiments on benchmarks show that CATDM mitigates embedding collapse, yielding superior results on FFHQ, LSUN Churches, and LSUN Bedrooms. In particular, CATDM achieves an FID of 6.81 on ImageNet $256\times256$ with 50 steps. It outperforms non-autoregressive models in machine translation and is on a par with previous methods in text generation.

Deep learning can give a significant impact on physics performance of electron-positron Higgs factories such as ILC and FCCee. We are working on two topics on event reconstruction to apply deep learning. The first is jet flavor tagging, in which we apply particle transformer to ILD full simulation to obtain jet flavor, including strange tagging. The second is particle flow, which clusters calorimeter hits and assigns tracks to them to improve jet energy resolution. We modified the algorithm developed in context of CMS HGCAL based on GravNet and Object Condensation techniques and add a track-cluster assignment function into the network. The overview and performance of these algorithms are described., Comment: 6 pages, 3 figures, Submitted to Proc. 42nd International Conference on High Energy Physics (ICHEP2024), July 2024, Prague

Recent literature has effectively utilized diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discrete latent codes have shown strong performance, particularly in modalities suited for discrete compressed representations, such as image and motion generation. However, their discrete and non-differentiable nature has limited their application to inverse problems formulated in continuous spaces. This paper presents a novel method for addressing linear inverse problems by leveraging image-generation models based on discrete diffusion as priors. We overcome these limitations by approximating the true posterior distribution with a variational distribution constructed from categorical distributions and continuous relaxation techniques. Furthermore, we employ a star-shaped noise process to mitigate the drawbacks of traditional discrete diffusion models with absorbing states, demonstrating that our method performs comparably to continuous diffusion techniques. To the best of our knowledge, this is the first approach to use discrete diffusion model-based priors for solving image inverse problems.

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

Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human feedback methods usually rely on predefined heuristic reward functions or pretrained reward models built on large-scale datasets, limiting their applicability to scenarios where collecting such data is costly or difficult. To effectively and efficiently utilize human feedback, we develop a framework, HERO, which leverages online human feedback collected on the fly during model learning. Specifically, HERO features two key mechanisms: (1) Feedback-Aligned Representation Learning, an online training method that captures human feedback and provides informative learning signals for fine-tuning, and (2) Feedback-Guided Image Generation, which involves generating images from SD's refined initialization samples, enabling faster convergence towards the evaluator's intent. We demonstrate that HERO is 4x more efficient in online feedback for body part anomaly correction compared to the best existing method. Additionally, experiments show that HERO can effectively handle tasks like reasoning, counting, personalization, and reducing NSFW content with only 0.5K online feedback.

Multi-threaded programs are expected to improve responsiveness and conserve resources by dividing an application process into multiple threads for concurrent processing. However, due to scheduling and the interaction of multiple threads, their runtime behavior is more complex than that of single-threaded programs, making which makes debugging difficult unless the concepts specific to multi-threaded programs and the execution order of instructions can be understood. In this paper, we propose a learning tool for multi-threaded programs using trace tables.

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

In this paper, we consider a resolvent problem arising from the free boundary value problem for the compressible fluid model of Korteweg type, which is called as the Navier-Stokes-Korteweg system, with surface tension in the half-space. The Navier-Stokes-Korteweg system is known as a diffuse interface model for liquid-vapor two-phase flows. Our purpose is to show the $\mathcal R$-boundedness for the solution operator families of the resolvent problem, which gives us the maximal regularity estimates in the $L_p$-in-time and $L_q$-in-space setting by applying the Weis's operator valued Fourier multiplier theorem., Comment: 36 pages

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present a detailed study of the MASTER OT J030227.28+191754.5 outburst in 2021-2022, reaching an amplitude of 10.2 mag and a duration of 60 d. The detections of (1) the double-peaked optical emission lines, and (2) the early and ordinary superhumps, established that MASTER OT J030227.28+191754.5 is an extremely energetic WZ Sge-type dwarf nova (DN). Based on the superhump observations, we obtained its orbital period and mass ratio as 0.05986(1) d and 0.063(1), respectively. These are within a typical range of low-mass-ratio DNe. According to the binary parameters derived based on the thermal-tidal instability model, our analyses showed that (1) the standard disk model requires an accretion rate $\simeq$ 10$^{20}$ g s$^{-1}$ to explain its peak optical luminosity and (2) large mass was stored in the disk at the outburst onset. These cannot be explained solely by the impact of its massive ($\gtrsim$ 1.15 M$_\odot$) primary white dwarf implied by Kimura et al. (2023). Instead, we propose that the probable origin of this enormously energetic DN outburst is the even lower quiescence viscosity than other WZ Sge-type DNe. This discussion is qualitatively valid for most possible binary parameter spaces unless the inclination is low ($\lesssim 40^\circ$) enough for the disk to be bright explaining the outburst amplitude. Such low inclinations, however, would not allow detectable amplitude of early superhumps in the current thermal-tidal instability model. The optical spectra at outburst maximum showed the strong emission lines of Balmer, He I, and He II series whose core is narrower than $\sim 800$ km s$^{-1}$. Considering its binary parameters, a Keplerian disk cannot explain this narrow component, but the presumable origin is disk winds., Comment: 23 pages, 13 figures, 2 tables. Accepted by PASJ. Part of the online supplemental information is included

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

We study the extension of the chromo-natural inflation model by incorporating nonminimal coupling between the axion field and gravity. Nonminimal coupling is introduced so that it enhances friction in the axion's equation of motion and thus supports slow-roll inflation. This enhanced friction effectively delays the activation of the gauge field, thereby preventing the overproduction of gravitational waves in the CMB scale. We extend previous results by describing the nonminimal coupling in a general and unifying way utilizing Horndeski gravity. This allows us to explore systematically and comprehensively possible enhanced friction models of chromo-natural inflation consistent with observations. We find a novel enhanced friction model that shows better agreement (within 1$\sigma$) with CMB measurements than the previous nonminimally coupled chromo-natural inflation model. The gravitational-wave spectrum starts to rise at some wavenumber due to retarded activation of the gauge field in the late stage of inflation. We show how one can identify the wavenumber at which this occurs based on the background evolution and present a universal analytic formula for the gravitational-wave spectrum that can be used for any enhanced friction model of chromo-natural inflation., Comment: 22 pages, 7 figures

Hydrocarbon dust is one of the dominant components of interstellar dust, which mainly consists of polycyclic aromatic hydrocarbons and aliphatic hydrocarbons. While hydrocarbon dust is thought to be processed in interstellar radiation fields or shocks, detailed processing mechanisms are not completely understood yet. We investigate the processing of hydrocarbon dust by analyzing the relation between the luminosities emitted by hydrocarbon dust and the total infrared luminosities $(L_{\mathrm{IR}})$ for 138 star-forming galaxies at redshift $z\ <\ 0.3$. Using near-infrared 2.5-5 $\mathrm{\mu m}$ spectra obtained with AKARI, we derived the luminosities of the aromatic hydrocarbon feature at 3.3 $\mathrm{\mu m}$ ($L_\mathrm{aromatic}$) and the aliphatic hydrocarbon feature at 3.4-3.6 $\mathrm{\mu m}$ ($L_\mathrm{aliphatic}$). We also derived $L_\mathrm{IR}$ and the radiation field strength by modeling the spectral energy distributions of the 138 galaxies with AKARI, WISE and IRAS photometry data. We find that galaxies with higher $L_\mathrm{IR}$ tend to exhibit lower $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratios. Furthermore, we find that there is an anti-correlation between $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratios and the radiation field strength, and also that the galaxies with low $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratios are dominated by merger galaxies. These results support that hydrocarbon dust is processed through photodissociation in strong radiation fields and/or shocks during merging processes of galaxies; the $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratio is likely to decrease in such harsh interstellar conditions since the aliphatic bonds are known to be chemically weaker than the aromatic bonds., Comment: 10 pages, 11 figures, accepted for publication in PASJ

The PHENIX experiment measured the centrality dependence of two-pion Bose-Einstein correlation functions in $\sqrt{s_{_{NN}}}=200$~GeV Au$+$Au collisions at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The data are well represented by L\'evy-stable source distributions. The extracted source parameters are the correlation-strength parameter $\lambda$, the L\'evy index of stability $\alpha$, and the L\'evy-scale parameter $R$ as a function of transverse mass $m_T$ and centrality. The $\lambda(m_T)$ parameter is constant at larger values of $m_T$, but decreases as $m_T$ decreases. The L\'evy scale parameter $R(m_T)$ decreases with $m_T$ and exhibits proportionality to the length scale of the nuclear overlap region. The L\'evy exponent $\alpha(m_T)$ is independent of $m_T$ within uncertainties in each investigated centrality bin, but shows a clear centrality dependence. At all centralities, the L\'evy exponent $\alpha$ is significantly different from that of Gaussian ($\alpha=2$) or Cauchy ($\alpha=1$) source distributions. Comparisons to the predictions of Monte-Carlo simulations of resonance-decay chains show that in all but the most peripheral centrality class (50%-60%), the obtained results are inconsistent with the measurements, unless a significant reduction of the in-medium mass of the $\eta'$ meson is included. In each centrality class, the best value of the in-medium $\eta'$ mass is compared to the mass of the $\eta$ meson, as well as to several theoretical predictions that consider restoration of $U_A(1)$ symmetry in hot hadronic matter., Comment: 401 authors from 75 institutions, 23 pages, 15 figures, 2 tables. v2 is version accepted for publication by Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

With the development of speech synthesis, recent research has focused on challenging tasks, such as speaker generation and emotion intensity control. Attribute interpolation is a common approach to these tasks. However, most previous methods for attribute interpolation require specific modules or training methods. We propose an attribute interpolation method in speech synthesis by model merging. Model merging is a method that creates new parameters by only averaging the parameters of base models. The merged model can generate an output with an intermediate feature of the base models. This method is easily applicable without specific modules or training methods, as it uses only existing trained base models. We merged two text-to-speech models to achieve attribute interpolation and evaluated its performance on speaker generation and emotion intensity control tasks. As a result, our proposed method achieved smooth attribute interpolation while keeping the linguistic content in both tasks., Comment: Accepted by INTERSPEECH 2024

To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. The performance of models trained using only standard spatial and energy and charged track position information from an event was compared to models trained using timing information available from AHCAL, which is expected to improve sensitivity to shower development and, therefore, aid in clustering. Both simulation and experimental data were used to train and test the models and their performances were compared. The best-performing neural network achieved significantly superior event reconstruction when timing information was utilised in training for the case where the charged hadron had more energy than the neutral one, motivating temporally sensitive calorimeters. All models under test were observed to tend to allocate energy deposited by the more energetic of the two showers to the less energetic one. Similar shower reconstruction performance was observed for a model trained on simulation and applied to data and a model trained and applied to data.

In this paper, we consider the model describing viscous incompressible liquid crystal flows, called the Beris-Edwards model, in the half-space.This model is a coupled system by the Navier-Stokes equations with the evolution equation of the director fields $Q$. The purpose of this paper is to prove that the linearized problem has a unique solution satisfying the maximal $L^p$ -$L^q$ regularity estimates, which is essential for the study of quasi-linear parabolic or parabolic-hyperbolic equations. Our method relies on the $\mathcal R$-boundedness of the solution operator families to the resolvent problem in order to apply operator-valued Fourier multiplier theorems. Consequently, we also have the local well-posedness for the Beris-Edwards model with small initial data., Comment: 56 pages

In modern recommender systems, especially in e-commerce, predicting multiple targets such as click-through rate (CTR) and post-view conversion rate (CTCVR) is common. Multi-task recommender systems are increasingly popular in both research and practice, as they leverage shared knowledge across diverse business scenarios to enhance performance. However, emerging real-world scenarios and data privacy concerns complicate the development of a unified multi-task recommendation model. In this paper, we propose PF-MSMTrec, a novel framework for personalized federated multi-scenario multi-task recommendation. In this framework, each scenario is assigned to a dedicated client utilizing the Multi-gate Mixture-of-Experts (MMoE) structure. To address the unique challenges of multiple optimization conflicts, we introduce a bottom-up joint learning mechanism. First, we design a parameter template to decouple the expert network parameters, distinguishing scenario-specific parameters as shared knowledge for federated parameter aggregation. Second, we implement personalized federated learning for each expert network during a federated communication round, using three modules: federated batch normalization, conflict coordination, and personalized aggregation. Finally, we conduct an additional round of personalized federated parameter aggregation on the task tower network to obtain prediction results for multiple tasks. Extensive experiments on two public datasets demonstrate that our proposed method outperforms state-of-the-art approaches. The source code and datasets will be released as open-source for public access.

This paper explores the capability of Mamba, a recently proposed architecture based on state space models (SSMs), as a competitive alternative to Transformer-based models. In the speech domain, well-designed Transformer-based models, such as the Conformer and E-Branchformer, have become the de facto standards. Extensive evaluations have demonstrated the effectiveness of these Transformer-based models across a wide range of speech tasks. In contrast, the evaluation of SSMs has been limited to a few tasks, such as automatic speech recognition (ASR) and speech synthesis. In this paper, we compared Mamba with state-of-the-art Transformer variants for various speech applications, including ASR, text-to-speech, spoken language understanding, and speech summarization. Experimental evaluations revealed that Mamba achieves comparable or better performance than Transformer-based models, and demonstrated its efficiency in long-form speech processing., Comment: Accepted at Interspeech 2024

High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 535 authors from 84 institutions, 12 pages, 8 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

This study developed a new current-readout technique capable of handling measurements with high count rates reaching 1 Gcps. By directly capturing the output current of a photomultiplier as a digitized waveform, we estimated event rates, overcoming the limitations imposed by pulse pileup constraints and deadtimes. This innovative method was applied to a muon spin rotation/relaxation/resonance experiment at the Japan Proton Accelerator Research Complex, demonstrating its anticipated performance. Furthermore, we explored methods for estimating statistical uncertainty and investigated potential applications in analog-logic OR/AND gates. Overall, our findings reveal that the developed technique opens up avenues for the development of future non-binary logic circuits operating based on n-adic numbers.

In motion generation, controllability as well as generation quality and speed is becoming more and more important. There are various motion editing tasks, such as in-betweening, upper body editing, and path-following, but existing methods perform motion editing with a data-space diffusion model, which is slow in inference compared to a latent diffusion model. In this paper, we propose MoLA, which provides fast and high-quality motion generation and also can deal with multiple editing tasks in a single framework. For high-quality and fast generation, we employ a variational autoencoder and latent diffusion model, and improve the performance with adversarial training. In addition, we apply a training-free guided generation framework to achieve various editing tasks with motion control inputs. We quantitatively show the effectiveness of adversarial learning in text-to-motion generation, and demonstrate the applicability of our editing framework to multiple editing tasks in the motion domain., Comment: 12 pages, 6 figures