Search

A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

Protoclusters are high-$z$ overdense regions that will evolve into clusters of galaxies by $z=0$, making them ideal for studying galaxy evolution expected to be accelerated by environmental effects. However, it has been challenging to identify protoclusters beyond $z=3$ only by photometry due to large redshift uncertainties, hindering statistical study. To tackle the issue, we develop a new deep-learning-based protocluster detection model, PCFNet, which considers a protocluster as a point cloud. To detect protoclusters at $z\sim4$ using only optical broad-band photometry, we train and evaluate PCFNet with mock $g$-dropout galaxies based on the N-body simulation with the semi-analytic model. We use the sky distribution, $i$-band magnitude, $(g-i)$ color, and the redshift probability density function surrounding a target galaxy on the sky. PCFNet achieves to detect five times more protocluster member candidates while maintaining high purity (recall $=7.5\pm0.2$%, precision $=44\pm1$%) than conventional methods. Moreover, PCFNet is able to detect more progenitors ($M_\mathrm{halo}^{z=0}=10^{14-14.5}\,M_\odot$) that are less massive than supermassive clusters like the Coma cluster. We apply PCFNet to the observational photometric dataset of the HSC-SSP Deep/UltraDeep layer ($\sim17\mathrm{\,deg^2}$) and detect $121$ protocluster candidates at $z\sim4$. We find the rest-UV luminosities of our protocluster member candidates are brighter than those of field galaxies, which is consistent with previous studies. Additionally, the quenching of satellite galaxies depends on both the core galaxy's halo mass at $z\sim4$ and accumulated mass until $z=0$ in the simulation. PCFNet is very flexible and can find protoclusters at other redshifts or in future extensive surveys by Euclid, LSST, and Roman., Comment: 22 pages, 16 figures, 5 tables, Accepted in ApJ

This paper proposes a foundation model called "CLaSP" that can search time series signals using natural language that describes the characteristics of the signals as queries. Previous efforts to represent time series signal data in natural language have had challenges in designing a conventional class of time series signal characteristics, formulating their quantification, and creating a dictionary of synonyms. To overcome these limitations, the proposed method introduces a neural network based on contrastive learning. This network is first trained using the datasets TRUCE and SUSHI, which consist of time series signals and their corresponding natural language descriptions. Previous studies have proposed vocabularies that data analysts use to describe signal characteristics, and SUSHI was designed to cover these terms. We believe that a neural network trained on these datasets will enable data analysts to search using natural language vocabulary. Furthermore, our method does not require a dictionary of predefined synonyms, and it leverages common sense knowledge embedded in a large-scale language model (LLM). Experimental results demonstrate that CLaSP enables natural language search of time series signal data and can accurately learn the points at which signal data changes.

The rapid development of modern artificial intelligence (AI) systems has created an urgent need for their scientific quantification. While their fluency across a variety of domains is impressive, modern AI systems fall short on tests requiring symbolic processing and abstraction - a glaring limitation given the necessity for interpretable and reliable technology. Despite a surge of reasoning benchmarks emerging from the academic community, no comprehensive and theoretically-motivated framework exists to quantify reasoning (and more generally, symbolic ability) in AI systems. Here, we adopt a framework from computational complexity theory to explicitly quantify symbolic generalization: algebraic circuit complexity. Many symbolic reasoning problems can be recast as algebraic expressions. Thus, algebraic circuit complexity theory - the study of algebraic expressions as circuit models (i.e., directed acyclic graphs) - is a natural framework to study the complexity of symbolic computation. The tools of algebraic circuit complexity enable the study of generalization by defining benchmarks in terms of their complexity-theoretic properties (i.e., the difficulty of a problem). Moreover, algebraic circuits are generic mathematical objects; for a given algebraic circuit, an arbitrarily large number of samples can be generated for a specific circuit, making it an optimal testbed for the data-hungry machine learning algorithms that are used today. Here, we adopt tools from algebraic circuit complexity theory, apply it to formalize a science of symbolic generalization, and address key theoretical and empirical challenges for its successful application to AI science and its impact on the broader community.

In recent years, thermal feedback has emerged as a significant sensory modality in virtual reality. However, the concept of conveying the sensation of thermal movement remains largely unexplored. We propose HeatFlicker, a virtual campfire device that recreates the flickering of fire by using a thermal illusion of moving heat identified in preliminary experiments. This device creates the illusion of heat moving from a fixed heat source. In our demonstration, we provide a novel thermal experience by simulating the flickering of a real fire., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

Materials with J-shaped stress-strain behavior under uniaxial stretching, where strength increases as deformation progresses, have been developed through various materials designs. On the other hand, polymer materials that progressively stiffen under bending remain unrealized. To address this gap, this study drew inspiration from membrane tensegrity structures, which achieve structural stability by balancing compressive forces in rods and tensile forces in membrane. Notably, some of these structures exhibit increased stiffness under bending. Using a multipolymer patterning technique, we developed a polymer film exhibiting membrane tensegrity-like properties that stiffens under bending. This effect results from membrane tension generated by rod protrusions and an increase in second moment of area at regions with maximum curvature.

We propose an image-adaptive object detection method for adverse weather conditions such as fog and low-light. Our framework employs differentiable preprocessing filters to perform image enhancement suitable for later-stage object detections. Our framework introduces two differentiable filters: a B\'ezier curve-based pixel-wise (BPW) filter and a kernel-based local (KBL) filter. These filters unify the functions of classical image processing filters and improve performance of object detection. We also propose a domain-agnostic data augmentation strategy using the BPW filter. Our method does not require data-specific customization of the filter combinations, parameter ranges, and data augmentation. We evaluate our proposed approach, called Enhanced Robustness by Unified Image Processing (ERUP)-YOLO, by applying it to the YOLOv3 detector. Experiments on adverse weather datasets demonstrate that our proposed filters match or exceed the expressiveness of conventional methods and our ERUP-YOLO achieved superior performance in a wide range of adverse weather conditions, including fog and low-light conditions., Comment: Accepted to WACV 2025

This paper introduces the risk-sensitive control as inference (RCaI) that extends CaI by using R\'{e}nyi divergence variational inference. RCaI is shown to be equivalent to log-probability regularized risk-sensitive control, which is an extension of the maximum entropy (MaxEnt) control. We also prove that the risk-sensitive optimal policy can be obtained by solving a soft Bellman equation, which reveals several equivalences between RCaI, MaxEnt control, the optimal posterior for CaI, and linearly-solvable control. Moreover, based on RCaI, we derive the risk-sensitive reinforcement learning (RL) methods: the policy gradient and the soft actor-critic. As the risk-sensitivity parameter vanishes, we recover the risk-neutral CaI and RL, which means that RCaI is a unifying framework. Furthermore, we give another risk-sensitive generalization of the MaxEnt control using R\'{e}nyi entropy regularization. We show that in both of our extensions, the optimal policies have the same structure even though the derivations are very different., Comment: Accepted at NeurIPS 2024

Working memory is a central cognitive ability crucial for intelligent decision-making. Recent experimental and computational work studying working memory has primarily used categorical (i.e., one-hot) inputs, rather than ecologically relevant, multidimensional naturalistic ones. Moreover, studies have primarily investigated working memory during single or few cognitive tasks. As a result, an understanding of how naturalistic object information is maintained in working memory in neural networks is still lacking. To bridge this gap, we developed sensory-cognitive models, comprising a convolutional neural network (CNN) coupled with a recurrent neural network (RNN), and trained them on nine distinct N-back tasks using naturalistic stimuli. By examining the RNN's latent space, we found that: (1) Multi-task RNNs represent both task-relevant and irrelevant information simultaneously while performing tasks; (2) The latent subspaces used to maintain specific object properties in vanilla RNNs are largely shared across tasks, but highly task-specific in gated RNNs such as GRU and LSTM; (3) Surprisingly, RNNs embed objects in new representational spaces in which individual object features are less orthogonalized relative to the perceptual space; (4) The transformation of working memory encodings (i.e., embedding of visual inputs in the RNN latent space) into memory was shared across stimuli, yet the transformations governing the retention of a memory in the face of incoming distractor stimuli were distinct across time. Our findings indicate that goal-driven RNNs employ chronological memory subspaces to track information over short time spans, enabling testable predictions with neural data.

We extend the previously published model that distinguishes between the diffusive motion of diamagnetic muons and the dynamics of ions around the muon in matter, and propose a generalized model for {\sl paramagnetic muons} (Mu$^0$s, bound states of a muon and an unpaired electron) observed in non-metallic host materials. The new model distinguishes among the independent motion of unpaired electron associated with Mu$^0$, the self-diffusive motion of Mu$^0$ as single atomic entity, and that of the ions surrounding Mu$^0$, where the muon spin relaxation is induced by dynamical fluctuations of the hyperfine (HF) field exerted from the unpaired electron (e.g., due to spin/charge exchange reaction) and/or that of the nuclear hyperfine (NHF) interactions between the unpaired electron and the surrounding ions. We have applied this model to the muonated radicals (Mu$^0$s in a polaron state) in conducting polymers, and examined the validity of the interpretations claimed in the earlier literature that the spin relaxation is induced by quasi-one dimensional motion of the unpaired electron. The result suggests that experimental support for such a claim is still inadequate and needs to be reexamined, including the possibility of other origins for the fluctuations. It is expected that our model will prove a useful guide for $\mu$SR studies of various local dynamics involving paramagnetic muon states., Comment: 11 pages, 12 figures

Thermodynamic force and irreversible current are the foundational concepts of classical nonequilibrium thermodynamics. Entropy production rate is provided by their product in classical systems, ranging from mesoscopic to macroscopic systems. However, there is no complete quantum extension of such a structure that respects quantum mechanics. In this paper, we propose anti-Hermitian operators that represent currents and forces accompanied by a gradient structure in open quantum systems described by the quantum master equation. We prove that the entropy production rate is given by the product of the force and current operators, which extends the canonical expression of the entropy production rate in the classical systems. The framework constitutes a comprehensive analogy with the nonequilibrium thermodynamics of discrete classical systems. We also show that the structure leads to the extensions of some results in stochastic thermodynamics: the geometric housekeeping-excess decomposition of entropy production and thermodynamic trade-off relations such as the thermodynamic uncertainty relation and the dissipation-time uncertainty relation. In discussing the trade-off relations, we will introduce a measure of fluctuation, which we term the quantum diffusivity., Comment: 24 pages, 5 figures

Measurements of the response function of the PIENU NaI(T$\ell$) and CsI crystal calorimeter using a monochromatic 70 MeV/c positron beam at various incidence angles are described. The experimental setup and relevant physical processes involved were simulated using Geant4 to reproduce positron energy spectra. Monte Carlo simulations were compared with experimental data across ten calorimeter-beam angles and showed good agreement. This allowed the validation of simulation parameters that were essential for precise measurements of pion decays., Comment: 7 pages, 9 figures

The evolution of galaxy sizes in different wavelengths provides unique insights on galaxy build-up across cosmic epochs. Such measurements can now finally be done at $z>3$ thanks to the exquisite spatial resolution and multi-wavelength capability of the JWST. With the public data from the CEERS, PRIMER-UDS, and PRIMER-COSMOS surveys, we measure the sizes of $\sim 3500$ star-forming galaxies at $3 \leqslant z<9$, in 7 NIRCam bands using the multi-wavelength model fitting code GalfitM. The size-mass relation is measured in four redshift bins, across all NIRCam bands. We find that, the slope and intrinsic scatter of the rest-optical size-mass relation are constant across this redshift range and consistent with previous HST-based studies at low-z. When comparing the relations across different wavelengths, the average rest-optical and rest-UV relations are consistent with each other up to $z=6$, but the intrinsic scatter is largest in rest-UV wavelengths compared to rest-optical and redder bands. This behaviour is independent of redshift and we speculate that it is driven by bursty star-formation in $z>4$ galaxies. Additionally, for $3\leqslant z<4$ star-forming galaxies at $\rm M_* > 10^{10} M_{\odot}$, we find smaller rest-$\rm 1\rm\,\mu m$ sizes in comparison to rest-optical (and rest-UV) sizes, suggestive of colour gradients. When comparing to simulations, we find agreement over $\rm M_* \approx 10^{9} - 10^{10} M_{\odot}$ but beyond this mass, the observed size-mass relation is significantly steeper. Our results show the power of JWST/NIRCam to provide new constraints on galaxy formation models., Comment: 14 pages, 9 figures, submitted to A&A

Standard approaches to quantum computing require significant overhead to correct for errors. The hardware size for conventional quantum processors in solids often increases linearly with the number of physical qubits, such as for transmon qubits in superconducting circuits or electron spin qubits in quantum dot arrays. While photonic circuits based on flying qubits do not suffer from decoherence or lack of potential scalability, they have encountered significant challenges to overcome photon loss in long delay circuits. Here, we propose an alternative approach that utilizes flying electronic wave packets propagating in solid-state quantum semiconductor circuits. Using a novel time-bin architecture for the electronic wave packets, hardware requirements are drastically reduced because qubits can be created on-demand and manipulated with a common hardware element, unlike the localized approach of wiring each qubit individually. The electronic Coulomb interaction enables reliable coupling and readout of qubits. Improving upon previous devices, we realize electronic interference at the level of a single quantized mode that can be used for manipulation of electronic wavepackets. This important landmark lays the foundation for fault-tolerant quantum computing with a compact and scalable architecture based on electron interferometry in semiconductors.

Recently, the navigation of mobile robots in unknown environments has become a particularly significant research topic. Previous studies have primarily employed real-time environmental mapping using cameras and LiDAR, along with self-localization and path generation based on those maps. Additionally, there is research on Sim-to-Real transfer, where robots acquire behaviors through pre-trained reinforcement learning and apply these learned actions in real-world navigation. However, strictly the observe action and modelling of unknown environments that change unpredictably over time with accuracy and precision is an extremely complex endeavor. This study proposes a simple navigation algorithm for traversing unknown environments by utilizes the number of swarm robots. The proposed algorithm assumes that the robot has only the simple function of sensing the direction of the goal and the relative positions of the surrounding robots. The robots can navigate an unknown environment by simply continuing towards the goal while bypassing surrounding robots. The method does not need to sense the environment, determine whether they or other robots are stuck, or do the complicated inter-robot communication. We mathematically validate the proposed navigation algorithm, present numerical simulations based on the potential field method, and conduct experimental demonstrations using developed robots based on the sound fields for navigation., Comment: 11 pages, 22 figures

For a given knot $K$ and $w>0$, we construct infinitely many mutually distinct hyperbolic knots $\{K_i\}$ such that the $P$-satellites of $K$ and $K_i$ have the same HOMFLY polynomial up to given $z$-degrees, for all braided patterns $P$ with winding number less than or equal to $w$., Comment: 5 pages, no figures

We present the analysis of three intermediate-mass quiescent galaxies (QGs) with stellar masses of $\sim10^{10}M_{\rm \odot}$ at redshifts $z\sim 3 - 4$ using NIRSpec low-resolution spectroscopy. Utilising the SED fitting code BAGPIPES, we confirm these target galaxies are consistent with quiescent population, with their specific star formation rates (sSFR) falling below 2-dex the star-forming main sequence at the same redshifts. Additionally, we identify these QGs to be less massive than those discovered in previous works, particularly prior to the JWST era. Two of our target galaxies exhibit the potentially-blended H${\alpha}$+[NII] emission line within their spectra with $S/N>5$. We discuss whether this feature comes from an Active Galactic Nucleus (AGN) or star formation although future high-resolution spectroscopy is required to reach a conclusion. One of the target galaxies is covered by JWST/NIRCam imaging of the PRIMER survey. Using the 2D profile fitting code Galfit, we examine its morphology, revealing a disc-like profile with a S\'{e}rsic index of $n=1.1 \pm 0.1$. On the size-mass relation, we find a potential distinction between less-massive ($\log_{10}{(M_*/M_\odot)}<10.3$) and massive ($\log_{10}{(M_*/M_\odot)}>10.3$) QGs in their evolutionary pathways. The derived quenching timescales for our targets are less than 1 Gyr. This may result from these galaxies being quenched by AGN feedback, supporting the AGN scenario of the emission line features., Comment: MNRAS accepted

Mpemba effects (MPEs), where a hotter system cools faster than a colder one, present intriguing anomalies in relaxation processes. Despite their universal observation and significant fundamental and practical implications, a comprehensive theoretical understanding based on microscopic properties remains elusive. In this Letter, we introduce two universal frameworks for classical systems to address this gap. Firstly, we reveal that MPEs, traditionally defined by macroscopic temperature comparisons, can be understood through microstate comparisons. This insight offers a straightforward and universal microscopic perspective on MPEs, relevant for experiments and numerical simulations to identify their microscopic origins. Secondly, we establish a "no-Mpemba theorem," a rigorous sufficient condition for the absence of MPEs, thereby identifying specific classes of systems devoid of these effects. Our findings are exemplified using ferromagnetic Ising models and one-dimensional multiparticle systems, demonstrating the practical applicability of our theoretical advancements., Comment: 7 pages, 2 figures (main text) + 10 pages, 4 figures (supplemental material). Minor updates

Quantum signal processing (QSP) and quantum singular value transformation (QSVT) have provided a unified framework for understanding many quantum algorithms, including factorization, matrix inversion, and Hamiltonian simulation. As a multivariable version of QSP, multivariable quantum signal processing (M-QSP) is proposed. M-QSP interleaves signal operators corresponding to each variable with signal processing operators, which provides an efficient means to perform multivariable polynomial transformations. However, the necessary and sufficient condition for what types of polynomials can be constructed by M-QSP is unknown. In this paper, we propose a classical algorithm to determine whether a given pair of multivariable Laurent polynomials can be implemented by M-QSP, which returns True or False. As one of the most important properties of this algorithm, it returning True is the necessary and sufficient condition. The proposed classical algorithm runs in polynomial time in the number of variables and signal operators. Our algorithm also provides a constructive method to select the necessary parameters for implementing M-QSP. These findings offer valuable insights for identifying practical applications of M-QSP., Comment: 20 pages, 2 figures

Uncertainty of environments has long been a difficult characteristic to handle, when performing real-world robot tasks. This is because the uncertainty produces unexpected observations that cannot be covered by manual scripting. Learning based robot controlling methods are a promising approach for generating flexible motions against unknown situations, but still tend to suffer under uncertainty due to its deterministic nature. In order to adaptively perform the target task under such conditions, the robot control model must be able to accurately understand the possible uncertainty, and to exploratively derive the optimal action that minimizes such uncertainty. This paper extended an existing predictive learning based robot control method, which employ foresight prediction using dynamic internal simulation. The foresight module refines the model's hidden states by sampling multiple possible futures and replace with the one that led to the lower future uncertainty. The adaptiveness of the model was evaluated on a door opening task. The door can be opened either by pushing, pulling, or sliding, but robot cannot visually distinguish which way, and is required to adapt on the fly. The results showed that the proposed model adaptively diverged its motion through interaction with the door, whereas conventional methods failed to stably diverge. The models were analyzed on Lyapunov exponents of RNN hidden states which reflect the possible divergence at each time step during task execution. The result indicated that the foresight module biased the model to consider future consequences, which lead to embedding uncertainties at the policy of the robot controller, rather than the resultant observation. This is beneficial for implementing adaptive behaviors, which indices derivation of diverse motion during exploration.

Major retinal layer segmentation methods from OCT images assume that the retina is flattened in advance, and thus cannot always deal with retinas that have changes in retinal structure due to ophthalmopathy and/or curvature due to myopia. To eliminate the use of flattening in retinal layer segmentation for practicality of such methods, we propose novel data augmentation methods for OCT images. Formula-driven data augmentation (FDDA) emulates a variety of retinal structures by vertically shifting each column of the OCT images according to a given mathematical formula. We also propose partial retinal layer copying (PRLC) that copies a part of the retinal layers and pastes it into a region outside the retinal layers. Through experiments using the OCT MS and Healthy Control dataset and the Duke Cyst DME dataset, we demonstrate that the use of FDDA and PRLC makes it possible to detect the boundaries of retinal layers without flattening even retinal layer segmentation methods that assume flattening of the retina., Comment: The 11th OMIA Workshop on MICCAI 2024

We prove Shafarevich-type arithmetic finiteness results for Mukai $n$-folds of genus 7 when $n=3,9,$ and $10$. Especially, for Mukai $3$-folds of genus $7$, which was one of the missing cases in the recent work of Javanpeykar--Loughran and Licht, we prove the cohomological generalization of the Shafarevich conjecture. Additionally, we show that the cohomological good reduction criteria do not hold for Mukai $3$-folds of genus $7$. Furthermore, we demonstrate that the Shafarevich conjecture fails when $n=6$. On the other hand, we show that there exists a Mukai $n$-fold of genus $7$ over $\mathbb{Z}$ if and only if $5\leq n \leq 10$. While the cases for $5 \leq n \leq 9$ provide the first non-trivial examples of smooth projective varieties over $\mathbb{Z}$, the non-existence for $n=4$ extends Fontaine and Abrashkin's results., Comment: 42 pages

Multibiometrics, which uses multiple biometric traits to improve recognition performance instead of using only one biometric trait to authenticate individuals, has been investigated. Previous studies have combined individually acquired biometric traits or have not fully considered the convenience of the system.Focusing on a single face image, we propose a novel multibiometric method that combines five biometric traits, i.e., face, iris, periocular, nose, eyebrow, that can be extracted from a single face image. The proposed method does not sacrifice the convenience of biometrics since only a single face image is used as input.Through a variety of experiments using the CASIA Iris Distance database, we demonstrate the effectiveness of the proposed multibiometrics method., Comment: APSIPA ASC 2024

Distributionally robust optimal control (DROC) is gaining interest. This study presents a reformulation method for discrete DROC (DDROC) problems to design optimal control policies under a worst-case distributional uncertainty. The reformulation of DDROC problems impacts both the utility of tractable improvements in continuous DROC problems and the inherent discretization modeling of DROC problems. DROC is believed to have tractability issues; namely, infinite inequalities emerge over the distribution space. Therefore, investigating tractable reformulation methods for these DROC problems is crucial. One such method utilizes the strong dualities of the worst-case expectations. However, previous studies demonstrated that certain non-trivial inequalities remain after the reformulation. To enhance the tractability of DDROC, the proposed method reformulates DDROC problems into one-layer smooth convex programming with only a few trivial inequalities. The proposed method is applied to a DDROC version of a patrol-agent design problem., Comment: 7 pages, 1 figure, This work has been submitted to the IEEE for possible publication

Due to scarcity of time-series data annotated with descriptive texts, training a model to generate descriptive texts for time-series data is challenging. In this study, we propose a method to systematically generate domain-independent descriptive texts from time-series data. We identify two distinct approaches for creating pairs of time-series data and descriptive texts: the forward approach and the backward approach. By implementing the novel backward approach, we create the Temporal Automated Captions for Observations (TACO) dataset. Experimental results demonstrate that a contrastive learning based model trained using the TACO dataset is capable of generating descriptive texts for time-series data in novel domains.

We characterize $B$-valued semi-circular system in terms of $B$-valued free probabilistic analogue of Poincar\'{e} inequality. This is a $B$-valued generalization of Biane's theorem \cite[Theorem 5.1]{b03}. Moreover, we prove that Voiculescu's conjecture on $B$-valued free Poincar\'{e} inequality in \cite{aim06} is not in the affirmative as it is., Comment: 28 pages; any comments are welcome!

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

This study introduces a novel theoretical framework for analyzing heteroscedastic Gaussian processes (HGPs) that identify unknown systems in a data-driven manner. Although HGPs effectively address the heteroscedasticity of noise in complex training datasets, calculating the exact posterior distributions of the HGPs is challenging, as these distributions are no longer multivariate normal. This study derives the exact means, variances, and cumulative distributions of the posterior distributions. Furthermore, the derived theoretical findings are applied to a chance-constrained tracking controller. After an HGP identifies an unknown disturbance in a plant system, the controller can handle chance constraints regarding the system despite the presence of the disturbance., Comment: This work has been submitted to the IEEE for possible publication

This letter presents contraction analysis of continuation method for suboptimal model predictive control. A contraction metric is proposed that reflects hierarchical dynamics inherent in the continuation method and, thus, leads us to a pair of tractable matrix inequalities characterizing contraction of closed-loop system controlled by the continuation method. A numerical example verifies our contraction analysis and demonstrates the tractability of the presented matrix inequalities., Comment: Submitted to IEEE Control Systems Letters (L-CSS)

An adaptive controller design for cases with disturbances is critical in practical applications for preventing unexpected control performance degradation and instability. Recently, adaptive control systems with relaxed persistent excitation (PE) conditions have been proposed to solve this problem; however, most discussions have focused on continuous-time systems. In this study, we propose a novel adaptive control method for discrete-time systems with disturbances that combines directional forgetting and concurrent learning. The proposed method does not require the PE condition, information on disturbances, unknown parameters, or matching conditions, and it guarantees exponential uniform ultimate unbounded (UUB). It was also theoretically demonstrated that the upper bound of the UUB can be designed based on the forgetting factor, which is a design parameter. Numerical simulation results illustrate the effectiveness of the proposed method., Comment: 6 pages, 1 figure

Demographic bias is one of the major challenges for face recognition systems. The majority of existing studies on demographic biases are heavily dependent on specific demographic groups or demographic classifier, making it difficult to address performance for unrecognised groups. This paper introduces ``LabellessFace'', a novel framework that improves demographic bias in face recognition without requiring demographic group labeling typically required for fairness considerations. We propose a novel fairness enhancement metric called the class favoritism level, which assesses the extent of favoritism towards specific classes across the dataset. Leveraging this metric, we introduce the fair class margin penalty, an extension of existing margin-based metric learning. This method dynamically adjusts learning parameters based on class favoritism levels, promoting fairness across all attributes. By treating each class as an individual in facial recognition systems, we facilitate learning that minimizes biases in authentication accuracy among individuals. Comprehensive experiments have demonstrated that our proposed method is effective for enhancing fairness while maintaining authentication accuracy.

Understanding the rapid formation of supermassive black holes (SMBHs) in the early universe requires an understanding of how stellar mass grows in the host galaxies. Here, we perform an analysis of rest-frame optical spectra and imaging from JWST of two quasar host galaxies at z>6 which exhibit Balmer absorption lines. These features in the stellar continuum indicate a lack of young stars, similar to low-redshift post-starburst galaxies whose star formation was recently quenched. We find that the stellar mass (log(M_* / M_sun) > 10.6) of each quasar host grew in a starburst episode at redshift 7 or 8. One of the targets exhibits little ongoing star formation, as evidenced by the photometric signature of the Balmer break and a lack of spatially resolved H-alpha emission, placing it well below the star formation main sequence at z = 6. The other galaxy is transitioning to a quiescent phase; together, the two galaxies represent the most distant massive post-starburst galaxies known. The maturity of these two galaxies is further supported by the stellar velocity dispersions of their host galaxies, placing them slightly above the upper end of the local M_BH - sigma_* relation. The properties of our two post-starburst galaxies, each hosting an active SMBH with log(M_BH / M_sun) > 9, suggests that black holes played a major role in shaping the formation of the first massive galaxies in the Universe., Comment: 24 pages, 7 figures, submitted to a Nature journal

Here we publish three years of data for the UCNtau experiment performed at the Los Alamos Ultra Cold Neutron Facility at the Los Alamos Neutron Science Center. These data are in addition to our previously published data. Our goals in this paper are to better understand and quantify systematic uncertainties and to improve the lifetime statistical precision. We report a measured value for these runs from 2020-2022 for the neutron lifetime of 877.94+/-0.37 s; when all the data from UCNtau are averaged we report an updated value for the lifetime of 877.82+/-0.22 (statistical)+0.20-0.17 (systematic) s. We utilized improved monitor detectors, reduced our correction due to UCN upscattering on ambient gas, and employed four different main UCN detector geometries both to reduce the correction required for rate dependence and explore potential contributions due to phase space evolution.

The in vivo circadian clock in single cyanobacteria is studied here by time-lapse fluorescence microscopy when the temperature is lowered below 25{\deg}C . We first disentangle the circadian clock behavior from the bacterial cold shock response by identifying a sequence of "death steps" based on cellular indicators. By analyzing only "alive" tracks, we show that the dynamic response of individual oscillatory tracks to a step-down temperature signal is described by a simple Stuart-Landau oscillator model. The same dynamical analysis applied to in vitro data (KaiC phosphorylation level following a temperature step-down) allows for extracting and comparing both clock's responses to a temperature step down. It appears, therefore, that both oscillators go through a similar supercritical Hopf bifurcation. Finally, to quantitatively describe the temperature dependence of the resulting in vivo and in vitro Stuart-Landau parameters $\mu(T)$ and $\omega_c(T)$, we propose two simplified analytical models: temperature-dependent positive feedback or time-delayed negative feedback that is temperature compensated. Our results provide strong constraints for future models and emphasize the importance of studying transitory regimes along temperature effects in circadian systems.

Chiral superconductivity exhibits the formation of novel electron pairs that breaks the time-reversal symmetry and has been actively studied in various quantum materials in recent years. However, despite its potential to provide definitive information, effects of disorder in the crystal structure on the chiral superconductivity has not yet been clarified, and therefore the investigation using a solid-solution system is desirable. We report muon-spin-relaxation (muSR) results of layered pnictide BaPtAs_1-x_Sb_x_ with a honeycomb network composed of Pt and (As, Sb). We observed an increase of the zero-field muon-spin relaxation rate in the superconducting (SC) state at the Sb end of x=1.0, suggesting the occurrence of spontaneous magnetic field due to the time-reversal symmetry breaking in the SC state. On the other hand, spontaneous magnetic field was almost and completely suppressed for the As-Sb mixed samples of x=0.9 and 0.2, respectively, suggesting that the time-reversal symmetry breaking SC state in x=1.0 is sensitive to disorder. The magnetic penetration depth estimated from transverse-field muSR measurements at x=1.0 and 0.2 behaved like weak-coupling s-wave superconductivity. These seemingly incompatible zero-field and transverse-field muSR results of BaPtAs_1-x_Sb_x_ with x=1.0 could be understood in terms of chiral d-wave superconductivity with point nodes on the three-dimensional Fermi surface., Comment: 6 pages, 3 figures, 1 table

We prove the uniqueness in determining a spatially varying zeroth-order coefficient of a one-dimensional time-fractional diffusion equation by initial value and Cauchy data at one end point of the spatial interval.

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

Most exoplanets with a few Earth radii are more inflated than bare-rock planets with the same mass, indicating a substantial volatile amount. Neither the origin of the volatiles nor the planet's bulk composition can be constrained from the mass-radius relation alone, and the spectral characterization of their atmospheres is needed to solve this degeneracy. Previous studies showed that chemical interaction between accreted volatile and possible molten rocky surface (i.e., magma ocean) can greatly affects the atmospheric composition. However, a variety in the atmospheric compositions of such planets with different properties remains elusive. In this work, we examine the dependence of atmospheric H, O, and C on planetary parameters (atmospheric thickness, planetary mass, equilibrium temperature, and magma properties such as redox state) assuming nebula gas accretion on an Earth-like core, using an atmosphere-magma chemical equilibrium model. Consistent with previous work, we show that atmospheric $\rm H_{2}O$ fraction on a fully molten rocky interior with an Earth-like redox state is on the order of $10^{-2}$-$10^{-1}$ regardless of other planetary parameters. Despite the solubility difference between H- and C-bearing species, C/H increases only a few times above the nebula value except for atmospheric pressure $\lesssim$1000 bar and $\rm H_{2}O$ fraction $\gtrsim$10\%. This results in a negative O/H-C/O trend and depleted C/O below one-tenth of the nebula gas value under an oxidized atmosphere, which could provide a piece of evidence of rocky interior and endogenic water. We also highlight the importance of constraints on the high-pressure material properties for interpreting the magma-atmospheric interaction of inflated planets., Comment: 30 pages, 15 figures

High-precision displacement control for water-hydraulic artificial muscles is a challenging issue due to its strong hysteresis characteristics that is hard to be modelled precisely, and many control methods have been proposed. Recently, data-driven control methods have attracted much attention because they do not explicitly use mathematical models, making design much easier. In our previous work, we proposed fictitious reference iterative tuning (FRIT)-based model predictive control (FMPC), which combines data-driven and model-based methods for the muscle and showed its effectiveness because it can consider input constraints as well. However, the problem in which control performance strongly depends on prior input-output data remains still unsolved. Adaptive FRIT based on directional forgetting has also been proposed; however, it is difficult to achieve the desired transient performance because it cannot consider input constraints and there are no design parameters that directly determine the control performance, such as MPC. In this study, we propose a novel data-driven adaptive model matching-based controller that combines these methods. Experimental results show that the proposed method could significantly improve the control performance and achieve high robustness against inappropriate initial experimental data , while considering the input constraints in the design phase., Comment: 14 pages, 17 figures

Learning from label proportions (LLP) is a kind of weakly supervised learning that trains an instance-level classifier from label proportions of bags, which consist of sets of instances without using instance labels. A challenge in LLP arises when the number of instances in a bag (bag size) is numerous, making the traditional LLP methods difficult due to GPU memory limitations. This study aims to develop an LLP method capable of learning from bags with large sizes. In our method, smaller bags (mini-bags) are generated by sampling instances from large-sized bags (original bags), and these mini-bags are used in place of the original bags. However, the proportion of a mini-bag is unknown and differs from that of the original bag, leading to overfitting. To address this issue, we propose a perturbation method for the proportion labels of sampled mini-bags to mitigate overfitting to noisy label proportions. This perturbation is added based on the multivariate hypergeometric distribution, which is statistically modeled. Additionally, loss weighting is implemented to reduce the negative impact of proportions sampled from the tail of the distribution. Experimental results demonstrate that the proportion label perturbation and loss weighting achieve classification accuracy comparable to that obtained without sampling. Our codes are available at https://github.com/stainlessnight/LLP-LargeBags., Comment: Accepted at ECAI2024

Computer-generated holography (CGH) is a promising technology for augmented reality displays, such as head-mounted or head-up displays. However, its high computational demand makes it impractical for implementation. Recent efforts to integrate neural networks into CGH have successfully accelerated computing speed, demonstrating the potential to overcome the trade-off between computational cost and image quality. Nevertheless, deploying neural network-based CGH algorithms on computationally limited embedded systems requires more efficient models with lower computational cost, memory footprint, and power consumption. In this study, we developed a lightweight model for complex hologram generation by introducing neural network quantization. Specifically, we built a model based on tensor holography and quantized it from 32-bit floating-point precision (FP32) to 8-bit integer precision (INT8). Our performance evaluation shows that the proposed INT8 model achieves hologram quality comparable to that of the FP32 model while reducing the model size by approximately 70% and increasing the speed fourfold. Additionally, we implemented the INT8 model on a system-on-module to demonstrate its deployability on embedded platforms and high power efficiency., Comment: 11 pages, 4 figures

We study the ODE/IM correspondence between two-dimensional $WA_{r}$/$WD_{r}$-type conformal field theories and the higher-order ordinary differential equations (ODEs) obtained from the affine Toda field theories associated with $A_r^{(1)}/D_r^{(1)}$-type affine Lie algebras. We calculate the period integrals of the WKB solution to the ODE along the Pochhammer contour, where the WKB expansions correspond to the classical conserved currents of the Drinfeld-Sokolov integrable hierarchies. We also compute the integrals of motion for $WA_{r}$($WD_{r}$) algebras on a cylinder. Their eigenvalues on the vacuum state are confirmed to agree with the period integrals up to the sixth order. These results generalize the ODE/IM correspondence to higher-order ODEs and can be used to predict higher-order integrals of motion., Comment: 1+47 pages, 1 figure