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Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data, and then the model suggests parameters that would most likely improve the results. Batch Bayesian optimization reduces the processing time of optimization by parallelizing experiments. However, batch Bayesian optimization cannot be applied if the number of parallelized experiments is limited by the cost or scarcity of equipment; in such cases, sequential methods require an unrealistic amount of time. In this study, we developed pipelining Bayesian optimization (PipeBO) to reduce the processing time of optimization even with a limited number of parallel experiments. PipeBO was inspired by the pipelining of central processing unit architecture, which divides computational tasks into multiple processes. PipeBO was designed to achieve experiment parallelization by overlapping various processes of the experiments. PipeBO uses the results of completed experiments to update the parameters of running parallelized experiments. Using the Black-Box Optimization Benchmarking, which consists of 24 benchmark functions, we compared PipeBO with the sequential Bayesian optimization methods. PipeBO reduced the average processing time of optimization to about 56% for the experiments that consisted of two processes or even less for those with more processes for 20 out of the 24 functions. Overall, PipeBO parallelizes Bayesian optimization in the resource-constrained settings so that efficient optimization can be achieved.

In this paper, we address a nonconvexly constrained nonsmooth optimization problem involving the composition of a weakly convex function and a smooth mapping. To find a stationary point of the target problem, we propose a variable smoothing-type algorithm by combining the ideas of (i) translating the constrained problem into a Euclidean optimization problem with a smooth parametrization of the constraint set; (ii) exploiting a sequence of smoothed surrogate functions, of the cost function, given with the Moreau envelope of a weakly convex function. The proposed algorithm produces a vector sequence by the gradient descent update of a smoothed surrogate function at each iteration. In a case where the proximity operator of the weakly convex function is available, the proposed algorithm does not require any iterative solver for subproblems therein. By leveraging tools in the variational analysis, we show the so-called {\em gradient consistency property}, which is a key ingredient for smoothing-type algorithms, of the smoothed surrogate function used in this paper. Based on the gradient consistency property, we also establish an asymptotic convergence analysis for the proposed algorithm. Numerical experiments demonstrate the efficacy of the proposed algorithm., Comment: 39 pages, 1 figure, 4 tables

The first spectroscopy of $^{52}$K was investigated via in-beam $\gamma$-ray spectroscopy at the RIKEN Radioactive Isotope Beam Factory after one-proton and one-neutron knockout from $^{53}$Ca and $^{53}$K beams impinging on a 15-cm liquid hydrogen target at $\approx$ 230~MeV/nucleon. The energy level scheme of $^{52}$K was built using single $\gamma$ and $\gamma$-$\gamma$ coincidence spectra. The spins and parities of the excited states were established based on momentum distributions of the fragment after the knockout reaction and based on exclusive cross sections. The results were compared to state-of-the-art shell model calculations with the SDPF-Umod interaction and ab initio IMSRG calculations with chiral effective field theory nucleon-nucleon and three-nucleon forces.

WordNet provides a carefully constructed repository of semantic relations, created by specialists. But there is another source of information on semantic relations, the intuition of language users. We present the first systematic study of the degree to which these two sources are aligned. Investigating the cases of misalignment could make proper use of WordNet and facilitate its improvement. Our analysis which uses templates to elicit responses from human participants, reveals a general misalignment of semantic relation knowledge between WordNet and human intuition. Further analyses find a systematic pattern of mismatch among synonymy and taxonomic relations~(hypernymy and hyponymy), together with the fact that WordNet path length does not serve as a reliable indicator of human intuition regarding hypernymy or hyponymy relations.

Recently, much work has concerned itself with the enigma of what exactly PLMs (pretrained language models) learn about different aspects of language, and how they learn it. One stream of this type of research investigates the knowledge that PLMs have about semantic relations. However, many aspects of semantic relations were left unexplored. Only one relation was considered, namely hypernymy. Furthermore, previous work did not measure humans' performance on the same task as that solved by the PLMs. This means that at this point in time, there is only an incomplete view of models' semantic relation knowledge. To address this gap, we introduce a comprehensive evaluation framework covering five relations beyond hypernymy, namely hyponymy, holonymy, meronymy, antonymy, and synonymy. We use six metrics (two newly introduced here) for recently untreated aspects of semantic relation knowledge, namely soundness, completeness, symmetry, asymmetry, prototypicality, and distinguishability and fairly compare humans and models on the same task. Our extensive experiments involve 16 PLMs, eight masked and eight causal language models. Up to now only masked language models had been tested although causal and masked language models treat context differently. Our results reveal a significant knowledge gap between humans and models for almost all semantic relations. Antonymy is the outlier relation where all models perform reasonably well. In general, masked language models perform significantly better than causal language models. Nonetheless, both masked and causal language models are likely to confuse non-antonymy relations with antonymy.

Most real-world datasets consist of a natural hierarchy between classes or an inherent label structure that is either already available or can be constructed cheaply. However, most existing representation learning methods ignore this hierarchy, treating labels as permutation invariant. Recent work [Zeng et al., 2022] proposes using this structured information explicitly, but the use of Euclidean distance may distort the underlying semantic context [Chen et al., 2013]. In this work, motivated by the advantage of hyperbolic spaces in modeling hierarchical relationships, we propose a novel approach HypStructure: a Hyperbolic Structured regularization approach to accurately embed the label hierarchy into the learned representations. HypStructure is a simple-yet-effective regularizer that consists of a hyperbolic tree-based representation loss along with a centering loss, and can be combined with any standard task loss to learn hierarchy-informed features. Extensive experiments on several large-scale vision benchmarks demonstrate the efficacy of HypStructure in reducing distortion and boosting generalization performance especially under low dimensional scenarios. For a better understanding of structured representation, we perform eigenvalue analysis that links the representation geometry to improved Out-of-Distribution (OOD) detection performance seen empirically. The code is available at \url{https://github.com/uiuctml/HypStructure}., Comment: Published as a conference paper at NeurIPS '24, first two authors contributed equally to the work. 40 pages, 23 figures

We discuss the scattering process of a scalar field having a time-dependent mass with another scalar field having a constant mass as a toy model of the scattering problems during preheating after inflation. Despite a general difficulty of analytically solving such models, in our previous work [1], we considered an exactly calculable model of such scattering processes with a time-dependent mass of the form $m^2(t)\supset \mu^4t^2$ and the time-dependence never disappears formally. In this work, we discuss another exactly calculable model with a time-dependent mass that has a spike/peak but asymptotes to a constant, which effectively appears in the preheating model of Higgs inflation with a non-minimal coupling. Thanks to the localized time-dependence of the mass, the daughter particle number density behaves in a physically reasonable way contrary to the one in our previous model due to the infinite time-dependent mass in the asymptotic future. On the other hand, we find that the daughter particle experiences the kinematically forbidden process, which is a non-perturbative phenomenon found in our previous work. As in the previous model, the kinematically forbidden process produces daughter particles exponentially more than the parent particle having the time-dependent mass, which never happens for particle decay processes without time-dependent backgrounds. This result supports the existence of such a non-perturbative particle production process in general time-dependent backgrounds., Comment: 28 pages, 5 figures

We investigate a gravitational model based on local Lorentz invariance and general coordinate invariance. The model incorporates classical scale invariance and the irreversible vierbein postulate, which forbid the introduction of dimensionful parameters and a background spacetime at a particular scale. Through the dynamics of the system, we demonstrate the simultaneous emergence of the Planck mass and a curved spacetime background., Comment: 18 pages, 2 figures

(100), (101), and (111) Au surfaces with chemisorbed oxygen of varying coverages are studied using density and hybrid functional theories and experiments. This clarifies the work function (f), surface structures, stability, and density of states. f of the (100), (101), and (111) Au surfaces calculated using the hybrid functional is 5.06-5.23 eV, and consistent with the reported experimental f of polycrystalline Au films (5.1 +- 0.1 eV). The hybrid functional calculations on f at (100) and (101) AuO surfaces agree with the experimental f. However, in the reported and present experiments, the difference in f between the Au and AuO surfaces (Df) is 0.8-0.9 eV. The reported theoretical Df at (111) surface (3 eV) completely contradicts this value, which has been unresolved. We find that when O atoms are at (111) surface and subsurface, the theoretical Df at (111) agrees with experimental Df (0.8-0.9 eV). This state is considered to correspond to the experiments, because the oxygen-chemisorption is more stable in this state than when O atoms are at (111) surface only. This property is unique to the (111) surface. The prediction of chemisorbed-oxygen-induced disorders that are experimentally observed provides more evidence for the veracity of these calculations., Comment: 23 pages, 12 figures

We report measurements of the absolute branching fractions $\mathcal{B}(B_s^0 \to D_s^{\pm} X)$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X)$, and $\mathcal{B}(B_s^0 \to D^{\pm} X)$, where the latter is measured for the first time. The results are based on a 121.4\,fb$^{-1}$ data sample collected at the $\Upsilon(10860)$ resonance by the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. We reconstruct one $B_s^0$ meson in $e^+e^- \to \Upsilon(10860) \to B_s^{*} \bar{B}_s^{*}$ events and measure yields of $D_s^+$, $D^0$, and $D^+$ mesons in the rest of the event. We obtain $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (68.6 \pm 7.2 \pm 4.0)\%$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (21.5 \pm 6.1 \pm 1.8)\%$, and $\mathcal{B}(B_s^0 \to D^{\pm} X) = (12.6 \pm 4.6 \pm 1.3)\%$, where the first uncertainty is statistical and the second is systematic. Averaging with previous Belle measurements gives $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (63.4 \pm 4.5 \pm 2.2)\%$ and $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (23.9 \pm 4.1 \pm 1.8)\%$. For the $B_s^0$ production fraction at the $\Upsilon(10860)$, we find $f_s = (21.4^{+1.5}_{-1.7})\%$., Comment: 23 pages, 9 figures, submitted to JHEP

In the present study, the nuclear quantum effects (NQEs) on proton diffusivity in oxides were evaluated by molecular dynamics (MD) simulations with the quantum thermal bath (QTB) based on the Langevin dynamics. We employed the proton diffusion in barium zirconate (BaZrO3) with the cubic perovskite structure as the model system, in which protons migrate by rotation around single oxide ions and hopping between adjacent oxide ions. MD simulations with the standard classical thermal bath (CTB) and phonon calculations were also conducted to verify the conventionally used classical harmonic transition state theory (classical h-TST), in which the transition state theory (TST), the harmonic approximation, and the classical approximation are assumed. As a result, the h-TST are reasonable for the proton rotation, while significantly overestimate the activation energy and the pre-exponential factor of the jump frequency for the proton hopping. Furthermore, the classical approximation makes the proton jump frequencies close to linear in the Arrhenius plots, which should actually be nonlinear by the NQEs in the temperature range of 500-2000 K. This suggests the necessity of the treatment beyond the classical h-TST for accurate evaluation of the proton diffusivity in oxides even in the intermediate temperature range (573-873 K).

In this paper, we apply the framework of optimal transport to the formulation of optimal design problems. By considering the Wasserstein space as a set of design variables, we associate each probability measure with a shape configuration of a material in some ways. In particular, we focus on connections between differentials on the Wasserstein space and sensitivities in the standard setting of shape and topology optimization in order to regard the optimization procedure of those problems as gradient flows on the Wasserstein space., Comment: 15 pages

We present measurements of $B \to K{}^{*}(892)\gamma$ decays using $365\,{\rm fb}^{-1}$ of data collected from 2019 to 2022 by the Belle~II experiment at the SuperKEKB asymmetric-energy $e^+e^-$ collider. The data sample contains $(387 \pm 6) \times 10^6$ $B\overline{B}$ events. We measure branching fractions ($\mathcal{B}$) and $C\!P$ asymmetries ($\mathcal{A}_{C\!P}$) for both $B^{0}\to K{}^{*0}\gamma$ and $B^{+}\to K{}^{*+}\gamma$ decays. The difference in $C\!P$ asymmetries ($\Delta \mathcal{A}_{C\!P}$) and the isospin asymmetry ($\Delta_{0+}$) between these neutral and charged channels are also measured. We obtain the following branching fractions and $C\!P$ asymmetries: $\mathcal{B} (B^{0} \to K{}^{*0}\gamma) = (4.14 \pm 0.10 \pm 0.11 ) \times 10^{-5}$, $\mathcal{B} (B^{+} \to K{}^{*+}\gamma) = (4.02 \pm 0.13 \pm 0.13 )\times 10^{-5}$, $\mathcal{A}_{C\!P} (B^{0} \to K{}^{*0}\gamma) = (-3.3 \pm 2.3 \pm 0.4 )\%$, and $\mathcal{A}_{C\!P} (B^{+} \to K{}^{*+}\gamma) = (-0.7 \pm 2.9 \pm 0.6 )\%$. The measured difference in $C\!P$ asymmetries is $\Delta \mathcal{A}_{C\!P} = (+2.6 \pm 3.8 \pm 0.7 )\%$, and the measured isospin asymmetry is $\Delta_{0+} = (+5.0 \pm 2.0 \pm 1.5 )\%$. The first uncertainties listed are statistical and the second are systematic. These results are consistent with world-average values and theory predictions.

In astrophysical environments such as core-collapse supernovae (CCSNe) and binary neutron star mergers (BNSMs), neutrinos potentially experience substantial flavor mixing due to the refractive effects of neutrino self-interactions. Determining the survival probability of neutrinos in asymptotic states is paramount to incorporating flavor conversions' effects in the theoretical modeling of CCSN and BNSM. Some phenomenological schemes have shown good performance in approximating asymptotic states of fast neutrino-flavor conversions (FFCs), known as one of the collective neutrino oscillation modes induced by neutrino self-interactions. However, a recent study showed that they would yield qualitatively different asymptotic states of FFC if the neutrino number is forced to evolve. It is not yet fully understood why the canonical phenomenological models fail to predict asymptotic states. In this paper, we perform detailed investigations through numerical simulations and then provide an intuitive explanation with a quasi-homogeneous analysis. Based on the analysis, we propose a new phenomenological model, in which the quasi-steady evolution of FFCs is analytically determined. The model also allows us to express the convolution term of spatial wave number as a concise form, which corresponds to useful information on analyses for the non-linear feedback from small-scale flavor conversions to large-scale ones. Our model yields excellent agreement with numerical simulations, which lends support to our interpretation.

The performance of unsupervised methods such as clustering depends on the choice of distance metric between features, or ground metric. Commonly, ground metrics are decided with heuristics or learned via supervised algorithms. However, since many datasets are unlabelled, unsupervised ground metric learning approaches have been introduced. One recent, promising option uses Wasserstein singular vectors (WSV), which emerge when computing optimal transport distances between features and samples simultaneously. While WSV is effective, it has complexity $\mathcal{O}(n^5)$, which is prohibitively expensive in some applications. In this work, we propose to augment the WSV method by embedding samples and features on trees, on which we compute the tree-Wasserstein distance (TWD). We demonstrate theoretically and empirically that the algorithm converges to a better approximation of the full WSV approach than the best known alternatives, and does so with $\mathcal{O}(n^3)$ complexity. In addition, we prove that the initial tree structure can be chosen flexibly, since tree geometry does not constrain the richness of the approximation up to the number of edge weights. This proof suggests a fast, recursive algorithm for computing the tree parameter basis set, which we find crucial to realising the efficiency gains at scale. Finally, we employ the tree-WSV algorithm to several single-cell RNA sequencing genomics datasets, demonstrating its scalability and utility for unsupervised cell-type clustering problems. These results poise unsupervised ground metric learning with TWD as a low-rank approximation of WSV with the potential for widespread low-compute application.

We work on the functional renormalization group analysis on a four-fermion model with the CP and P violation in light of nonperturbative exploration of the infrared dynamics of quantum chromodynamics (QCD) arising from the spontaneous CP violation models in a view of the Wilsonian renormalization group. The fixed point structure reveals that in the large-$N_c$ limit, the CP $\bar{\theta}$ parameter is induced and approaches $\pi \cdot (N_f/2)$ (with the number of flavors $N_f$) toward the chiral broken phase due to the criticality and the large anomalous dimensions of the $U(1)$ axial violating four-fermion couplings. This trend seems to be intact even going beyond the large-$N_c$ leading, as long as the infrared dynamics of QCD is governed by the scalar condensate of the quark bilinear as desired. This gives an impact on modeling of the spontaneous CP violation scenarios: the perturbatively irrelevant four-fermion interactions nonperturbatively get relevant in the chiral broken phase, implying that the neutron electric dipole moment becomes too big, unless cancellations due to extra CP and P violating contributions outside of QCD are present at a certain intermediate infrared scale., Comment: 18 pages, 3 figures

We performed shot analyses of X-ray and optical sub-second flares observed during the low/hard state of the 2018 outburst in MAXI J1820$+$070. Optical shots were less spread than X-ray shots. The amplitude of X-ray shots was the highest at the onset of the outburst, and they faded at the transition to the intermediate state. The timescale of shots was $\sim$0.2 s, and we detected the abrupt spectral hardening synchronized with this steep flaring event. The time evolution of optical shots was not similar to that of X-ray shots. These results suggest that accreting gas blobs triggered a series of magnetic reconnections at the hot inner accretion flow in the vicinity of the black hole, which enhanced X-ray emission and generated flaring events. The rapid X-ray spectral hardening would be caused by this kind of magnetic activity. Also, the synchrotron emission not only at the hot flow but also at the jet plasma would contribute to the optical rapid variability. We also found that the low/hard state exhibited six different phases in the hardness-intensity diagram and the correlation plot between the optical flux and the X-ray hardness. The amplitude and duration of X-ray shots varied in synchrony with these phases. This time variation may provide key information about the evolution of the hot flow, the low-temperature outer disk, and the jet-emitting plasma., Comment: 16 pages, 7 figures, accepted for publication in PASJ

Recent active studies have demonstrated that cryptography without one-way functions (OWFs) could be possible in the quantum world. Many fundamental primitives that are natural quantum analogs of OWFs or pseudorandom generators (PRGs) have been introduced, and their mutual relations and applications have been studied. Among them, pseudorandom function-like state generators (PRFSGs) [Ananth, Qian, and Yuen, Crypto 2022] are one of the most important primitives. PRFSGs are a natural quantum analogue of pseudorandom functions (PRFs), and imply many applications such as IND-CPA secret-key encryption (SKE) and EUF-CMA message authentication code (MAC). However, only known constructions of (many-query-secure) PRFSGs are ones from OWFs or pseudorandom unitaries (PRUs). In this paper, we construct classically-accessible adaptive secure PRFSGs in the invertible quantum Haar random oracle (QHRO) model which is introduced in [Chen and Movassagh, Quantum]. The invertible QHRO model is an idealized model where any party can access a public single Haar random unitary and its inverse, which can be considered as a quantum analog of the random oracle model. Our PRFSG constructions resemble the classical Even-Mansour encryption based on a single permutation, and are secure against any unbounded polynomial number of queries to the oracle and construction. To our knowledge, this is the first application in the invertible QHRO model without any assumption or conjecture. The previous best construction in the idealized model is PRFSGs secure up to o({\lambda}/ log {\lambda}) queries in the common Haar state model [Ananth, Gulati, and Lin, TCC 2024].

We determine the $\theta$ dependence of the confinement-deconfinement transition temperature $T_c$ for the 4d SU(2) pure Yang-Mills theory. We perform lattice numerical simulations on three spatial sizes $N_S=24$, $32$, $48$ with a fixed temporal size $N_T=8$. We introduce a non-zero $\theta$-angle by the re-weighting method, which is combined with the sub-volume method to mitigate the sign problem. By taking advantage of the universality in the second order phase transition and the Binder cumulant of the order parameter, the $\theta$-dependence of $T_c$ is determined to be $T_c(\theta)/T_c(0)=1-0.016(3)\,\theta^2+O(\theta^4)$. We point out that the temperature dependence of the topological susceptibility should exhibit a singularity with the exponent for the specific heat., Comment: 1+19 pages, 10 figures

We measure the time-integrated CP asymmetry in $D^{0} \rightarrow K^{0}_{S}K^{0}_{S}$ decays reconstructed in $e^{+}e^{-} \rightarrow c\overline{c}$ events collected by the Belle and Belle II experiments. The corresponding data samples have integrated luminosities of 980 fb$^{-1}$ and 428 fb$^{-1}$, respectively. The $D^{0}$ decays are required to originate from the $D^{*+} \rightarrow D^{0}\pi^{+}$ decay, which determines the charm flavor at production time. A control sample of $D^{0} \rightarrow K^{+}K^{-}$ decays is used to correct for production and detection asymmetries. The result, $(-1.4\pm1.3{\rm(stat)}\pm0.1{\rm (syst)})\%$, is consistent with previous determinations and with CP symmetry., Comment: 10 pages, 3 figures. arXiv admin note: text overlap with arXiv:2410.22961

We perform a model-independent measurement of the $D^0$-$\overline{D}{}^0$ mixing parameters using samples of $e^+e^-$-collision data collected by the Belle and Belle II experiments that have integrated luminosities of $951\ \text{fb}^{-1}$ and $408\ \text{fb}^{-1}$, respectively. Approximately $2.05\times10^6$ neutral $D$ mesons are reconstructed in the $D^0\rightarrow K^0_{S}\pi^+\pi^-$ channel, with the neutral $D$ flavor tagged by the charge of the pion in the $D^{*+}\rightarrow D^0\pi^+$ decay. Assuming charge-parity symmetry, the mixing parameters are measured to be $ x = (4.0\pm1.7\pm0.4)\times 10^{-3} $ and $ y = (2.9\pm1.4\pm0.3)\times 10^{-3}$, where the first uncertainties are statistical and the second systematic. The results are consistent with previous determinations.

At millimeter wavelengths, the atmospheric emission is circularly polarized owing to the Zeeman splitting of molecular oxygen by the Earth's magnetic field. We report a measurement of the signal in the 150 GHz band using 3 years of observations of the \textsc{Polarbear} project. Although the detectors are sensitive to linear polarization, we can measure the circular polarization because a continuously rotating half-wave plate in the optics converts part of circular polarization into linear polarization. The atmospheric circular polarization signal appears as a modulated signal at twice the frequency of rotation of the half-wave plate. We reconstruct the azimuthal gradient of the circular polarization signal and measure the dependencies on the scanning azimuth and the detector bandpass. We compare the signal with a simulation based on atmospheric emission theory, the detector bandpass, and the half-wave plate leakage spectrum model. We find the ratio of the observed azimuthal slope to the simulated slope is $0.92 \pm 0.01\rm{(stat)} \pm 0.07\rm{(sys)}$, which demonstrates that our measurement is consistent with theoretical prediction. This result validates our understanding of the instrument and reinforces the feasibility of measuring the circular polarization using the imperfection of the half-wave plate. Quantifying atmospheric circular polarization is the first step toward conducting a search for cosmological circular polarization at these wavelengths., Comment: 22 pages, 11 figures

Decadal changes in a nearby supernova remnant (SNR) were analyzed using a multiepoch maximum likelihood estimation (MLE) approach. To achieve greater accuracy in capturing the dynamics of SNRs, kinematic features and point-spread function effects were integrated into the MLE framework. Using Cassiopeia A as a representative example, data obtained by the Chandra X-ray Observatory in 2000, 2009, and 2019 were utilized. The proposed multiepoch MLE was qualitatively and quantitatively demonstrated to provide accurate estimates of various motions, including shock waves and faint features, across all regions. To investigate asymmetric structures, such as singular components that deviate from the direction of expansion, the MLE method was extended to combine multiple computational domains and classify kinematic properties using the $k$-means algorithm. This approach allowed for the mapping of different physical states onto the image, and one classified component was suggested to interact with circumstellar material by comparison with infrared observations from the James Webb Space Telescope. Thus, this technique will help quantify the dynamics of SNRs and discover their unique evolution., Comment: Accepted for publication in ApJ. The paper is 20 pages long with 13 figures. Animations for Fig. 8 and Fig. 9, which provide a visual illustration of the results, are available in ApJ

The boolean satisfiability (SAT) problem asks whether there exists an assignment of boolean values to the variables of an arbitrary boolean formula making the formula evaluate to True. It is well-known that all NP-problems can be coded as SAT problems and therefore SAT is important both practically and theoretically. From both of these perspectives, better understanding the patterns and structure implicit in SAT data is of significant value. In this paper, we describe several advances that we believe will help open the door to such understanding: we introduce hardware accelerated algorithms for fast SAT problem generation, a geometric SAT encoding that enables the use of transformer architectures typically applied to vision tasks, and a simple yet effective technique we term head slicing for reducing sequence length representation inside transformer architectures. These advances allow us to scale our approach to SAT problems with thousands of variables and tens of thousands of clauses. We validate our architecture, termed Satisfiability Transformer (SaT), on the SAT prediction task with data from the SAT Competition (SATComp) 2022 problem sets. Prior related work either leveraged a pure machine learning approach, but could not handle SATComp-sized problems, or was hybrid in the sense of integrating a machine learning component in a standard SAT solving tool. Our pure machine learning approach achieves prediction accuracies comparable to recent work, but on problems that are an order of magnitude larger than previously demonstrated. A fundamental aspect of our work concerns the very nature of SAT data and its suitability for training machine learning models. We both describe experimental results that probe the landscape of where SAT data can be successfully used for learning and position these results within the broader context of complexity and learning., Comment: 22 pages

Acceleration of positive muons from thermal energy to $100~$keV has been demonstrated. Thermal muons were generated by resonant multi-photon ionization of muonium atoms emitted from a sheet of laser-ablated aerogel. The thermal muons were first electrostatically accelerated to $5.7~$keV, followed by further acceleration to 100 keV using a radio-frequency quadrupole. The transverse normalized emittance of the accelerated muons in the horizontal and vertical planes were $0.85 \pm 0.25 ~\rm{(stat.)}~^{+0.22}_{-0.13} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad and $0.32\pm 0.03~\rm{(stat.)} ^{+0.05}_{-0.02} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad, respectively. The measured emittance values demonstrated phase space reduction by a factor of $2.0\times 10^2$ (horizontal) and $4.1\times 10^2$ (vertical) allowing good acceleration efficiency. These results pave the way to realize the first-ever muon accelerator for a variety of applications in particle physics, material science, and other fields.

In-context learning (ICL) has emerged as a powerful capability for large language models (LLMs) to adapt to downstream tasks by leveraging a few (demonstration) examples. Despite its effectiveness, the mechanism behind ICL remains underexplored. To better understand how ICL integrates the examples with the knowledge learned by the LLM during pre-training (i.e., pre-training knowledge) and how the examples impact ICL, this paper conducts a theoretical study in binary classification tasks. In particular, we introduce a probabilistic model extending from the Gaussian mixture model to exactly quantify the impact of pre-training knowledge, label frequency, and label noise on the prediction accuracy. Based on our analysis, when the pre-training knowledge contradicts the knowledge in the examples, whether ICL prediction relies more on the pre-training knowledge or the examples depends on the number of examples. In addition, the label frequency and label noise of the examples both affect the accuracy of the ICL prediction, where the minor class has a lower accuracy, and how the label noise impacts the accuracy is determined by the specific noise level of the two classes. Extensive simulations are conducted to verify the correctness of the theoretical results, and real-data experiments also align with the theoretical insights. Our work reveals the role of pre-training knowledge and examples in ICL, offering a deeper understanding of LLMs' behaviors in classification tasks.

Forward-backward stochastic differential equations (FBSDEs) have been generalized by introducing jumps for better capturing random phenomena, while the resulting FBSDEs are far more intricate than the standard one from every perspective. In this work, we establish a forward scheme for potentially high-dimensional FBSDEs with jumps, taking a similar approach to [Bender and Denk, 117 (2007), Stoch. Process. Their Appl., pp.1793-1812], with the aid of machine learning techniques for implementation. The developed forward scheme is built upon a recursive representation that decouples random jumps at every step and converges exponentially fast to the original FBSDE with jumps, often requiring only a few iterations to achieve sufficient accuracy, along with the error bound vanishing for lower jump intensities. The established framework also holds novelty in its neural network-based implementation of a wide class of forward schemes for FBSDEs, notably whether with or without jumps. We provide an extensive collection of numerical results, showcasing the effectiveness of the proposed recursion and its corresponding forward scheme in approximating high-dimensional FBSDEs with jumps (up to 100-dimension) without directly handling the random jumps., Comment: 26 pages, 9 figures, 7 tables

We present a measurement of the branching fraction and time-dependent charge-parity ($CP$) decay-rate asymmetries in $B^0 \to J/\psi \pi^0$ decays. The data sample was collected with the Belle~II detector at the SuperKEKB asymmetric $e^+e^-$ collider in 2019-2022 and contains $(387\pm 6)\times 10^6$ $B\overline{B}$ meson pairs from $\Upsilon(4S)$ decays. We reconstruct $392\pm 24$ signal decays and fit the $CP$ parameters from the distribution of the proper-decay-time difference of the two $B$ mesons. We measure the branching fraction to be $B(B^0 \to J/\psi \pi^0)=(2.02 \pm 0.12 \pm 0.10)\times 10^{-5}$ and the direct and mixing-induced $CP$ asymmetries to be $C_{CP}=0.13 \pm 0.12 \pm 0.03$ and $S_{CP}=-0.88 \pm 0.17 \pm 0.03$, respectively, where the first uncertainties are statistical and the second are systematic. We observe mixing-induced $CP$ violation with a significance of $5.0$ standard deviations for the first time in this mode.

JASMINE is a Japanese planned space mission that aims to reveal the formation history of our Galaxy and discover habitable exoEarths. For these objectives, the JASMINE satellite performs high-precision astrometric observations of the Galactic bulge and high-precision transit monitoring of M-dwarfs in the near-infrared (1.0-1.6 microns in wavelength). For feasibility studies, we develop an image simulation software named JASMINE-imagesim, which produces realistic observation images. This software takes into account various factors such as the optical point spread function (PSF), telescope jitter caused by the satellite's attitude control error (ACE), detector flat patterns, exposure timing differences between detector pixels, and various noise factors. As an example, we report a simulation for the feasibility study of astrometric observations using JASMINE-imagesim. The simulation confirms that the required position measurement accuracy of 4 mas for a single exposure of 12.5-mag objects is achievable if the telescope pointing jitter uniformly dilutes the PSF across all stars in the field of view. On the other hand, the simulation also demonstrates that the combination of realistic pointing jitter and exposure timing differences in the detector can significantly degrade accuracy and prevent achieving the requirement. This means that certain countermeasures against this issue must be developed. This result implies that this kind of simulation is important for mission planning and advanced developments to realize more realistic simulations help us to identify critical issues and also devise effective solutions., Comment: 13 pages, 7 figures, 1 table

To embed structured knowledge within labels into feature representations, prior work (Zeng et al., 2022) proposed to use the Cophenetic Correlation Coefficient (CPCC) as a regularizer during supervised learning. This regularizer calculates pairwise Euclidean distances of class means and aligns them with the corresponding shortest path distances derived from the label hierarchy tree. However, class means may not be good representatives of the class conditional distributions, especially when they are multi-mode in nature. To address this limitation, under the CPCC framework, we propose to use the Earth Mover's Distance (EMD) to measure the pairwise distances among classes in the feature space. We show that our exact EMD method generalizes previous work, and recovers the existing algorithm when class-conditional distributions are Gaussian in the feature space. To further improve the computational efficiency of our method, we introduce the Optimal Transport-CPCC family by exploring four EMD approximation variants. Our most efficient OT-CPCC variant runs in linear time in the size of the dataset, while maintaining competitive performance across datasets and tasks.

In this paper, we generalize the deformations driven by the stress-energy tensor $T$ and investigate their relation to the flow equation for the background metric at the classical level. For a deformation operator $\mathcal O$ as a polynomial function of the stress-energy tensor, we develop a formalism that relates a deformed action to a flow equation for the metric in arbitrary spacetime dimensions. It is shown that in the $T\bar{T}$ deformation and the $\mathcal O(T)=\text{tr}[\textbf{T}]^m$ deformation, the flow equations for the metric allow us to directly obtain exact solutions in closed forms. We also demonstrate the perturbative approach to find the same results. As several applications of the $\mathcal O(T)=\text{tr}[\textbf{T}]^m$ deformation, we discuss the relation between the deformations and gravitational models. Besides, the deformations of Lagrangians for scalar field theories are performed., Comment: 23 pages, no figure, references added and typos corrected

Radio-quiet Seyfert galaxies have been detected in GeV gamma-rays by the Fermi Large Area Telescope (LAT), but the origin of much of this emission is unclear. We consider the nearby example, the Seyfert galaxy GRS 1734-292, which exhibits weak starburst and jet activities that are insufficient to explain the observed gamma-ray flux. With the first detailed multi-wavelength study of this source, we demonstrate that an active galactic nucleus (AGN) disk wind can account for its gamma-ray emission. Using a lepto-hadronic emission model based on a shocked ambient medium and a shocked wind region created by an AGN accretion disk wind, we identify two viable scenarios that are consistent with the Fermi-LAT data and multi-wavelength observations: a hadronic pp-dominated scenario and a leptonic external Compton-dominated scenario. Both of these show that future observations with the Cherenkov Telescope Array (CTA) and the Southern Wide-field Gamma-ray Observatory (SWGO) could detect TeV emission from a disk wind in GRS 1734-292. Such a detection would substantially improve our understanding of cosmic ray acceleration efficiency in AGN disk wind systems, and would establish radio-quiet Seyfert galaxies as cosmic ray accelerators capable of reaching ultra-high energies., Comment: 8 pages, 2 figures, 1 table

Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images. Databases of CT images from multiple manufacturers/scanners, disease status, and patient positioning were used. The segmentation accuracy, and accuracy in estimating the structures volume and density, i.e., mean HU, were evaluated. An approach for segmentation failure detection based on predictive uncertainty was also investigated. The model has shown an overall improvement with respect to all segmentation accuracy and structure volume/density evaluation metrics. The predictive uncertainty yielded large areas under the receiver operating characteristic (AUROC) curves (AUROCs>=.95) in detecting inaccurate and failed segmentations. The high segmentation and muscle volume/density estimation accuracy, along with the high accuracy in failure detection based on the predictive uncertainty, exhibited the model's reliability for analyzing individual MSK structures in large-scale CT databases., Comment: 29 pages, 7+10supp figures, 8 tables

This study focuses on linear partial differential equation (PDE) systems that arise in topology optimization where the thickness of a structure is constrained. The thickness derived from the PDE is a fictitious one, and the key challenge of this work is to verify its equivalence to the intuitive, geometrically defined thickness. The main difficulty lies in that while intuitive thickness is determined solely by the shape, the thickness defined by the PDE depends not only on the shape but also on the entire domain and the diffusion coefficients used in solving the PDE. In this paper, we demonstrate that the thickness of an infinite, straight film as a simple shape with constant thickness is equivalent within a general domain. The proof involves constructing a reference solution within a special domain and evaluating the difference using the maximum (modulus) principle and an interior $H^1$ estimate. Additionally, we provide an estimate of the dependence of thickness on the diffusion coefficient.

We study $d$-wave superconductivity(SC) in the Hubbard model on the isotropic triangular lattice described by the hopping parameter $t$ and on-site Coulomb repulsion $U$ at zero temperature and half-filling using the variational cluster approximation. We found that the $d_{xy}$ SC is the ground state below the Mott insulator phase $U/t \lesssim 6$, and the energy of chiral $d+id$ SC is slightly higher than the $d_{xy}$ SC. The energy difference between the normal and $d_{xy}$ states is about $0.02t \sim 0.06t$ for $U/t \simeq 5$. This result is semi-quantitatively consistent with the SC transition temperature $T_K=3.9$ K of $\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$, where $t$ is estimated to be about $0.06$ eV, and the predicted pairing symmetry $d_{xy}$ agrees with the STM observations. The energy difference between the $d+id$ and $d_{xy}$ is about $0.01t\sim 0.03t$ for $U/t \simeq 5$ so the transition from $d+id$ to $d_{xy}$, or some effects of $d+id$ in $d_{xy}$ phase may be observed in experiments for $\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$., Comment: 6 pages, 5 figures

The interaction of an ensemble of two-level atoms and a quantized electromagnetic field, described by the Dicke Hamiltonian, is an extensively studied problem in quantum optics. However, experimental efforts to explore similar physics in condensed matter typically employ bosonic matter modes (e.g., phonons, magnons, and plasmons) that are describable as simple harmonic oscillators, i.e., an infinite ladder of equally spaced energy levels. Here, we examine ultrastrong coupling between a coherent light mode and an ensemble of paramagnetic spins, a finite-multilevel system, in Gd$_3$Ga$_5$O$_{12}$. The electron paramagnetic resonance of Gd$^{3+}$ ions is tuned by a magnetic field into resonance with a Fabry--P\'erot cavity mode, resulting in the formation of spin--photon hybrid states, or Zeeman polaritons. We observe that the light--matter coupling strength, measured through the vacuum Rabi splitting, decreases with increasing temperature, which can be explained by the temperature-dependent population difference between the lower and higher-energy states, a trait of a finite-level system. This finding demonstrates that a spin--boson system is more compatible with the Dicke model and has advantages over boson--boson systems for pursuing experimental realizations of phenomena predicted for ultrastrongly coupled light--matter hybrids.

We perform the first search for $C\!P$ violation in ${D_{(s)}^{+}\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}}$ decays. We use a combined data set from the Belle and Belle II experiments, which study $e^+e^-$ collisions at center-of-mass energies at or near the $\Upsilon(4S)$ resonance. We use 980 fb$^{-1}$ of data from Belle and 428 fb$^{-1}$ of data from Belle~II. We measure six $C\!P$-violating asymmetries that are based on triple products and quadruple products of the momenta of final-state particles, and also the particles' helicity angles. We obtain a precision at the level of 0.5% for $D^+\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}$ decays, and better than 0.3% for $D^+_{s}\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}$ decays. No evidence of $C\!P$ violation is found. Our results for the triple-product asymmetries are the most precise to date for singly-Cabibbo-suppressed $D^+$ decays. Our results for the other asymmetries are the first such measurements performed for charm decays., Comment: 21 pages, 10 figures

Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these modalities separately. We aim to bridge this divide by integrating images and point clouds on a unified transformer model. This approach integrates the modality-specific properties of images and point clouds and achieves fundamental downstream tasks in image and 3D object recognition on a unified transformer model by learning visual-geometric representations. In this work, we introduce Formula-Supervised Visual-Geometric Pre-training (FSVGP), a novel synthetic pre-training method that automatically generates aligned synthetic images and point clouds from mathematical formulas. Through cross-modality supervision, we enable supervised pre-training between visual and geometric modalities. FSVGP also reduces reliance on real data collection, cross-modality alignment, and human annotation. Our experimental results show that FSVGP pre-trains more effectively than VisualAtom and PC-FractalDB across six tasks: image and 3D object classification, detection, and segmentation. These achievements demonstrate FSVGP's superior generalization in image and 3D object recognition and underscore the potential of synthetic pre-training in visual-geometric representation learning. Our project website is available at https://ryosuke-yamada.github.io/fdsl-fsvgp/., Comment: Accepted to ECCV2024

We reported on the observation of extremely high-density ($>10^{14}$cm$^{-2}$) 2D electron gas in N-polar AlGaN/GaN heterostructures grown on sapphire substrates. Due to introducing the GaN/AlN superlattice (SL) back barrier between the GaN buffer layer and AlGaN barrier layer, we observed a giant enhancement of the 2D electron gas density at the GaN/AlGaN interface from $3\times10^{13}$cm$^{-2}$ (without SL) to $1.4\times10^{14}$cm$^{-2}$ (with SL back barrier) that is only one order of magnitude below the intrinsic crystal limit of $\approx10^{15}$cm$^{-2}$. We found that the changes of 2D electron gas density with SL correlated well with the changes of the wafer warp parameter which suggests that the strains are responsible for the 2D electron gas density enhancement (reduction of the piezoelectric polarization in the GaN channel). Nevertheless, this finding is probably insufficient to fully explain the observed high 2D electron gas density. Simultaneously, the room temperature electron mobility was 169 cm$^2$/Vs, which with the electron density of $1.4\times10^{14}$cm$^{-2}$ gives a low sheet resistance of 264 $\Omega$/sq (one of the lowest reported so far for the N-polar 2D electron gas channel). Finally, the possibility of application of such high-density 2D electron gas with low sheet resistance to transistors, emitters and detectors was discussed., Comment: Version 2.0

Biometric recognition systems, known for their convenience, are widely adopted across various fields. However, their security faces risks depending on the authentication algorithm and deployment environment. Current risk assessment methods faces significant challenges in incorporating the crucial factor of attacker's motivation, leading to incomplete evaluations. This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker's motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases. A survey of 600 Japanese participants demonstrates our method's effectiveness, showing how security measures influence attacker's motivation. This approach helps decision-makers customize biometric systems to enhance security while maintaining usability.

The equilibrium selection problem in the generalized Nash equilibrium problem (GNEP) has recently been studied as an optimization problem, defined over the set of all variational equilibria achievable first through a non-cooperative game among players. However, to make such a selection fairly for all players, we have to rely on an unrealistic assumption, that is, the availability of a reliable center not possible to cause any bias for all players. In this paper, we propose a new equilibrium selection achievable by solving a further GNEP, named the hierarchical Nash equilibrium problem (HNEP), within only the players. The HNEP covers existing optimization-based equilibrium selections as its simplest cases, while the general style of the HNEP can ensure a fair equilibrium selection without assuming any trusted center or randomness. We also propose an iterative algorithm for the HNEP as an application of the hybrid steepest descent method to a variational inequality newly defined over the fixed point set of a quasi-nonexpansive operator. Numerical experiments show the effectiveness of the proposed equilibrium selection via the HNEP.

We propose a proximal variable smoothing algorithm for nonsmooth optimization problem with sum of three functions involving weakly convex composite function. The proposed algorithm is designed as a time-varying forward-backward splitting algorithm with two steps: (i) a time-varying forward step with the gradient of a smoothed surrogate function, designed with the Moreau envelope, of the sum of two functions; (ii) the backward step with a proximity operator of the remaining function. For the proposed algorithm, we present a convergence analysis in terms of a stationary point by using a newly smoothed surrogate stationarity measure. As an application of the target problem, we also present a formulation of multiple-input-multiple-output (MIMO) signal detection with phase-shift keying. Numerical experiments demonstrate the efficacy of the proposed formulation and algorithm., Comment: 5 pages, 3 figures

Recent advances in robotic learning in simulation have shown impressive results in accelerating learning complex manipulation skills. However, the sim-to-real gap, caused by discrepancies between simulation and reality, poses significant challenges for the effective deployment of autonomous surgical systems. We propose a novel approach utilizing image translation models to mitigate domain mismatches and facilitate efficient robot skill learning in a simulated environment. Our method involves the use of contrastive unpaired Image-to-image translation, allowing for the acquisition of embedded representations from these transformed images. Subsequently, these embeddings are used to improve the efficiency of training surgical manipulation models. We conducted experiments to evaluate the performance of our approach, demonstrating that it significantly enhances task success rates and reduces the steps required for task completion compared to traditional methods. The results indicate that our proposed system effectively bridges the sim-to-real gap, providing a robust framework for advancing the autonomy of surgical robots in minimally invasive procedures., Comment: Accepted at 2024 IEEE International Symposium on Micro-NanoMechatronics and Human Science

Exceptional points (EPs) are spectral singularities in non-Hermitian systems where eigenvalues and their corresponding eigenstates coalesce simultaneously. In this study, we calculate scattering poles in an open spherical solid and propose a depth-first search-based method to identify EPs. Using the proposed method, we numerically identify multiple EPs in a parameter space and confirm the simultaneous degeneracy of scattering poles through numerical experiments. The proposed method and findings enable the exploration of applications in practical three-dimension models., Comment: 28 pages, 9 figures

Since the invention of the laser nonlinear effects such as filamentation, Rabi-cycling and collective emission have been explored in the optical regime leading to a wide range of scientific and industrial applications. X-ray free electron lasers (XFELs) have led to the extension of many optical techniques to X-rays for their advantages of angstrom scale spatial resolution and elemental specificity. One such example is XFEL driven population inversion of 1s core hole states resulting in inner-shell K${\alpha}$ (2p to 1s) X-ray lasing in elements ranging from neon to copper, which has been utilized for nonlinear spectroscopy and development of next generation X-ray laser sources. Here we show that strong lasing effects, similar to those observed in the optical regime, can occur at 1.5 to 2.1 angstrom wavelengths during high intensity (> ${10^{19}}$ W/cm${^{2}}$) XFEL driven inner-shell lasing and superfluorescence of copper and manganese. Depending on the temporal substructure of the XFEL pump pulses, the resulting inner-shell X-ray laser pulses can exhibit strong spatial inhomogeneities as well as spectral inhomogeneities and broadening. Through 3D Maxwell Bloch theory we show that the observed spatial inhomogeneities result from X-ray filamentation, and that the spectral broadening is driven by Rabi cycling with sub-femtosecond periods. These findings indicate that we have generated Angstrom-wavelength x-ray pulses (containing ${10^{6}}$ - ${10^{8}}$ photons) in the strong lasing regime, some of them with pulse lengths of less than 100 attoseconds.

3D point clouds enhanced the robot's ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (\eg HyperCD ), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses ({\em weighted CD}) that requires no parameter tuning. To this end, we propose a search scheme, {\em Loss Distillation via Gradient Matching}, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely {\em Landau CD}, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. {\it Our demo code is available at \url{https://github.com/Zhang-VISLab/IROS2024-LossDistillationWeightedCD}.}, Comment: 10 pages, 7 figures, 7 tables, this paper was accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024