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Transporting between arbitrary distributions is a fundamental goal in generative modeling. Recently proposed diffusion bridge models provide a potential solution, but they rely on a joint distribution that is difficult to obtain in practice. Furthermore, formulations based on continuous domains limit their applicability to discrete domains such as graphs. To overcome these limitations, we propose Discrete Diffusion Schr\"odinger Bridge Matching (DDSBM), a novel framework that utilizes continuous-time Markov chains to solve the SB problem in a high-dimensional discrete state space. Our approach extends Iterative Markovian Fitting to discrete domains, and we have proved its convergence to the SB. Furthermore, we adapt our framework for the graph transformation and show that our design choice of underlying dynamics characterized by independent modifications of nodes and edges can be interpreted as the entropy-regularized version of optimal transport with a cost function described by the graph edit distance. To demonstrate the effectiveness of our framework, we have applied DDSBM to molecular optimization in the field of chemistry. Experimental results demonstrate that DDSBM effectively optimizes molecules' property-of-interest with minimal graph transformation, successfully retaining other features.

Synthetic aperture radar technology is crucial for high-resolution imaging under various conditions; however, the acquisition of real-world synthetic aperture radar data for deep learning-based automatic target recognition remains challenging due to high costs and data availability issues. To overcome these challenges, synthetic data generated through simulations have been employed, although discrepancies between synthetic and real data can degrade model performance. In this study, we introduce a novel framework, soft segmented randomization, designed to reduce domain discrepancy and improve the generalize ability of synthetic aperture radar automatic target recognition models. The soft segmented randomization framework applies a Gaussian mixture model to segment target and clutter regions softly, introducing randomized variations that align the synthetic data's statistical properties more closely with those of real-world data. Experimental results demonstrate that the proposed soft segmented randomization framework significantly enhances model performance on measured synthetic aperture radar data, making it a promising approach for robust automatic target recognition in scenarios with limited or no access to measured data., Comment: 19 pages, 13 figures

Magnetic susceptibility source separation ($\chi$-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of para- and diamagnetic susceptibility source distributions in the brain. The method utilizes reversible transverse relaxation (R2'=R2*-R2) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for R2 in addition R2*. To address this challenge, we develop a new deep learning network, $\chi$-sepnet, and propose two deep learning-based susceptibility source separation pipelines, $\chi$-sepnet-R2' for inputs with multi-echo GRE and multi-echo spin-echo, and $\chi$-sepnet-R2* for input with multi-echo GRE only. $\chi$-sepnet is trained using multiple head orientation data that provide streaking artifact-free labels, generating high-quality $\chi$-separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source-separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from conventional regularization-based reconstruction methods. In quantitative analysis, $\chi$-sepnet-R2' achieves the best outcomes followed by $\chi$-sepnet-R2*, outperforming the conventional methods. When the lesions of multiple sclerosis patients are assessed, both pipelines report identical lesion characteristics in most lesions ($\chi$para: 99.6% and $\chi$dia: 98.4% out of 250 lesions). The $\chi$-sepnet-R2* pipeline, which only requires multi-echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary., Comment: 33 pages, 12 figures

Regulatory functions are essential in both socioeconomic and biological systems, from corporate managers to regulatory genes in genomes. Regulatory functions come with substantial costs, but are often taken for granted. Here, we empirically examine regulatory costs across diverse systems -- biological organisms (bacteria and eukaryotic genomes), human organizations (companies, federal agencies, universities), and decentralized entities (Wikipedia, cities) -- using scaling analysis. We guide the empirical analysis with a conceptual model, which anticipates the scaling of regulatory costs to shift with the system's internal interaction structure -- well-mixed or modular. We find diverse systems exhibit consistent scaling patterns -- well-mixed systems exhibit superlinear scaling, while modular ones show sublinear or linear scaling. Further, we find that the socioeconomic systems containing more diverse occupational functions tend to have more regulatory costs than expected from their size, confirming the type of interactions also plays a role in regulatory costs. While many socioeconomic systems exhibit efficiencies of scale, regulatory costs in many social systems have grown disproportionally over time. Our finding suggests that the increasing complexity of functions may contribute to this trend. This cross-system comparison offers a framework for understanding regulatory costs and could guide future efforts to identify and mitigate regulatory inefficiencies., Comment: 12 pages, 4 figures

The persistent issue of human bias in recruitment processes poses a formidable challenge to achieving equitable hiring practices, particularly when influenced by demographic characteristics such as gender and race of both interviewers and candidates. Asynchronous Video Interviews (AVIs), powered by Artificial Intelligence (AI), have emerged as innovative tools aimed at streamlining the application screening process while potentially mitigating the impact of such biases. These AI-driven platforms present an opportunity to customize the demographic features of virtual interviewers to align with diverse applicant preferences, promising a more objective and fair evaluation. Despite their growing adoption, the implications of virtual interviewer identities on candidate experiences within AVIs remain underexplored. We aim to address this research and empirical gap in this paper. To this end, we carried out a comprehensive between-subjects study involving 218 participants across six distinct experimental conditions, manipulating the gender and skin color of an AI virtual interviewer agent. Our empirical analysis revealed that while the demographic attributes of the agents did not significantly influence the overall experience of interviewees, variations in the interviewees' demographics significantly altered their perception of the AVI process. Further, we uncovered that the mediating roles of Social Presence and Perception of the virtual interviewer critically affect interviewees' perceptions of fairness (+), privacy (-), and impression management (+).

The study of Khintchin inequalities has a long history in abstract harmonic analysis. While there is almost no possibility of non-trivial Khintchine inequality for central Fourier series on compact connected semisimple Lie groups, we demonstrate a strong contrast within the framework of compact quantum groups. Specifically, we establish a Khintchine inequality with operator coefficients for arbitrary central Fourier series in a large class of non-Kac compact quantum groups. The main examples include the Drinfeld-Jimbo $q$-deformations $G_q$, the free orthogonal quantum groups $O_F^+$, and the quantum automorphism group $G_{aut}(B,\psi)$ with a $\delta$-form $\psi$.

Building on previous works to construct a homogeneous sample of brown dwarfs in binary systems, we investigate microlensing events detected by the Korea Microlensing Telescope Network (KMTNet) survey during the 2022 and 2023 seasons. Given the difficulty in distinguishing brown-dwarf events from those produced by binary lenses with nearly equal-mass components, we analyze all lensing events detected during the seasons that exhibit anomalies characteristic of binary-lens systems. Using the same criteria consistently applied in previous studies, we identify six additional brown dwarf candidates through the analysis of lensing events KMT-2022-BLG-0412, KMT-2022-BLG-2286, KMT-2023-BLG-0201, KMT-2023-BLG-0601, KMT-2023-BLG-1684, and KMT-2023-BLG-1743. An examination of the mass posteriors shows that the median mass of the lens companions ranges from 0.02 $M_\odot$ to 0.05 $M_\odot$, indicating that these companions fall within the brown-dwarf mass range. The mass of the primary lenses ranges from 0.11 $M_\odot$ to 0.68 $M_\odot$, indicating that they are low-mass stars with substantially lower masses compared to the Sun., Comment: 13 pages, 17 figures, 12 tables

Improving the readability of mathematical expressions in text-based document such as subtitle of mathematical video, is an significant task. To achieve this, mathematical expressions should be convert to compiled formulas. For instance, the spoken expression ``x equals minus b plus or minus the square root of b squared minus four a c, all over two a'' from automatic speech recognition is more readily comprehensible when displayed as a compiled formula $x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}$. To convert mathematical spoken sentences to compiled formulas, two processes are required: spoken sentences are converted into LaTeX formulas, and LaTeX formulas are converted into compiled formulas. The latter can be managed by using LaTeX engines. However, there is no way to do the former effectively. Even if we try to solve this using language models, there is no paired data between spoken sentences and LaTeX formulas to train it. In this paper, we introduce MathBridge, the first extensive dataset for translating mathematical spoken sentences into LaTeX formulas. MathBridge comprises approximately 23 million LaTeX formulas paired with the corresponding mathematical spoken sentences. Through comprehensive evaluations, including fine-tuning with proposed data, we discovered that MathBridge significantly enhances the capabilities of pretrained language models for converting to LaTeX formulas from mathematical spoken sentences. Specifically, for the T5-large model, the sacreBLEU score increased from 4.77 to 46.8, demonstrating substantial enhancement., Comment: 9 pages, 6 figures

All human societies present unique narratives that shape their customs and beliefs. Despite cultural differences, some symbolic elements (e.g., heroes and tricksters) are common across many cultures. Here, we reconcile these seemingly contradictory aspects by analyzing mythological themes and traditions at various scales. Our analysis revealed that global mythologies exhibit both geographic and thematic nesting across different scales, manifesting in a layered structure. The largest geographic clusters correspond to the New and Old Worlds, which further divide into smaller bioregions. This hierarchical manifestation closely aligns with historical human migration patterns at a large scale, suggesting that narrative themes were carried through deep history. At smaller scales, the correspondence with bioregions indicates that these themes are locally adapted and diffused into variations across cultures over time. Our approach, which treats myths and traditions as random variables without considering factors like geography, history, or story lineage, suggests that the manifestation of mythology has been well-preserved over time and thus opens exciting research avenues to reconstruct historical patterns and provide insight into human cultural narratives.

Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped region in Fourier space, often necessitate the removal of a vast majority of modes, thereby degrading the quality of the data anisotropically. To address this challenge, we introduce a novel deep generative model based on stochastic interpolants to reconstruct the 21-cm data lost to wedge filtering. Our method leverages the non-Gaussian nature of the 21-cm signal to effectively map wedge-filtered 3D lightcones to samples from the conditional distribution of wedge-recovered lightcones. We demonstrate how our method is able to restore spatial information effectively, considering both varying cosmological initial conditions and astrophysics. Furthermore, we discuss a number of future avenues where this approach could be applied in analyses of the 21-cm signal, potentially offering new opportunities to improve our understanding of the Universe during the epochs of cosmic dawn and reionization., Comment: 12 pages, 5 figures

We investigate microlensing data collected by the Korea Microlensing Telescope Network (KMTNet) survey. Our investigation reveals that the light curves of two lensing events, KMT-2021-BLG-2609 and KMT-2022-BLG-0303, exhibit a similar anomaly, in which short-term positive deviations appear on the sides of the low-magnification lensing light curves. To unravel the nature of these anomalies, we meticulously analyze each of the lensing events. Our investigations reveal that these anomalies stem from a shared channel, wherein the source passed near the planetary caustic induced by a planet with projected separations from the host star exceeding the Einstein radius. We find that interpreting the anomaly of KMT-2021-BLG-2609 is complicated by the "inner--outer" degeneracy, whereas for KMT-2022-BLG-0303, there is no such issue despite similar lens-system configurations. In addition to this degeneracy, interpreting the anomaly in KMT-2021-BLG-2609 involves an additional degeneracy between a pair of solutions, in which the source partially envelops the caustic and the other three solutions in which the source fully envelopes the caustic. As in an earlier case of this so-called von Schlieffen--Cannae degeneracy, the former solutions have substantially higher mass ratio. Through Bayesian analyses conducted based on the measured lensing observables of the event time scale and angular Einstein radius, the host of KMT-2021-BLG-2609L is determined to be a low-mass star with a mass $\sim 0.2~M_\odot$ in terms of a median posterior value, while the planet's mass ranges from approximately 0.032 to 0.112 times that of Jupiter, depending on the solutions. For the planetary system KMT-2022-BLG-0303L, it features a planet with a mass of approximately $0.51~M_{\rm J}$ and a host star with a mass of about $0.37~M_\odot$. In both cases, the lenses are most likely situated in the bulge., Comment: 10 pages, 11 figures, 6 tables

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

Online platforms like Reddit, Wikipedia, and Facebook are integral to modern life, enabling content creation and sharing through posts, comments, and discussions. Despite their virtual and often anonymous nature, these platforms need rules and oversight to maintain a safe and productive environment. As these communities grow, a key question arises: how does the need for regulatory functions scale? Do larger groups require more regulatory actions and oversight per person, or can they manage with less? Our analysis of Reddit's regulatory functions reveals robust scaling relationships across different subreddits, suggesting universal patterns between community size and the amount of regulation needed. We found that the number of comments and moderator actions, such as comment removals, grew faster than the community size, with superlinear exponents of 1.12 and 1.18, respectively. However, bot-based rule enforcement did not keep pace with community growth, exhibiting a slightly sublinear exponent of 0.95. Further analysis of the residuals from these scaling behaviors identified a 'trade-off axis,' where one-way coordination mechanisms (bots and moderators) counteract two-way interactions (comments) and vice versa. Our findings suggest that a more proactive moderation approach, characterized by increased bot activity and moderator comment removals, tends to result in less user engagement under the scaling framework. Understanding these natural scaling patterns and interactions can help platform administrators and policymakers foster healthy online communities while mitigating harmful behaviors such as harassment, doxxing, and misinformation. Without proper regulation, these negative behaviors can proliferate and cause significant damage. Targeted interventions based on these insights are key to ensuring online platforms remain safe and beneficial spaces.

We examine the phase structure of a QCD-like theory at $\bar\theta =\pi$ obtained from supersymmetric $SU(N)$ QCD perturbed by a small amount of supersymmetry breaking via anomaly mediation (AMSB QCD). The spectrum of this theory matches that of QCD at the massless level, though the superpartners are not decoupled. In this theory it is possible to nail down the phase structure at $\bar\theta=\pi$ as a function of the quark masses and the number of flavors $F$. For one flavor we find that there is a critical quark mass, below which CP is unbroken, while above the critical mass CP is spontaneously broken. At the critical mass there is a second-order phase transition along with a massless $\eta'$. We are able to analytically solve for the minima and the critical mass for $N=2,3$ as well as for the large $N$ limit, while for other $N$ one can find numerical results. For two flavors, we find that CP is always broken as long as the quark masses are equal and non-zero, however there is a non-trivial phase boundary for unequal quark masses, which we find numerically. For $F\geq 3$ we obtain an intricate phase boundary which reproduces the various quark mass limits. All our results are in agreement with the predictions for ordinary QCD that were based on anomaly matching arguments for generalized symmetries and the effective chiral Lagrangian. We also briefly comment on the domain wall solutions first discussed by Draper, and are able to present analytic results for the simplest case of $SU(2)$ with one flavor., Comment: 36 pages, 7 figures

Humans share a wide variety of images related to their personal experiences within conversations via instant messaging tools. However, existing works focus on (1) image-sharing behavior in singular sessions, leading to limited long-term social interaction, and (2) a lack of personalized image-sharing behavior. In this work, we introduce Stark, a large-scale long-term multi-modal conversation dataset that covers a wide range of social personas in a multi-modality format, time intervals, and images. To construct Stark automatically, we propose a novel multi-modal contextualization framework, Mcu, that generates long-term multi-modal dialogue distilled from ChatGPT and our proposed Plan-and-Execute image aligner. Using our Stark, we train a multi-modal conversation model, Ultron 7B, which demonstrates impressive visual imagination ability. Furthermore, we demonstrate the effectiveness of our dataset in human evaluation. We make our source code and dataset publicly available., Comment: Project website: https://stark-dataset.github.io

We investigated the nature of the anomalies appearing in four microlensing events KMT-2020-BLG-0757, KMT-2022-BLG-0732, KMT-2022-BLG-1787, and KMT-2022-BLG-1852. The light curves of these events commonly exhibit initial bumps followed by subsequent troughs that extend across a substantial portion of the light curves. We performed thorough modeling of the anomalies to elucidate their characteristics. Despite their prolonged durations, which differ from the usual brief anomalies observed in typical planetary events, our analysis revealed that each anomaly in these events originated from a planetary companion located within the Einstein ring of the primary star. It was found that the initial bump arouse when the source star crossed one of the planetary caustics, while the subsequent trough feature occurred as the source traversed the region of minor image perturbations lying between the pair of planetary caustics. The estimated masses of the host and planet, their mass ratios, and the distance to the discovered planetary systems are $(M_{\rm host}/M_\odot, M_{\rm planet}/M_{\rm J}, q/10^{-3}, \dl/{\rm kpc}) = (0.58^{+0.33}_{-0.30}, 10.71^{+6.17}_{-5.61}, 17.61\pm 2.25,6.67^{+0.93}_{-1.30})$ for KMT-2020-BLG-0757, $(0.53^{+0.31}_{-0.31}, 1.12^{+0.65}_{-0.65}, 2.01 \pm 0.07, 6.66^{+1.19}_{-1.84})$ for KMT-2022-BLG-0732, $(0.42^{+0.32}_{-0.23}, 6.64^{+4.98}_{-3.64}, 15.07\pm 0.86, 7.55^{+0.89}_{-1.30})$ for KMT-2022-BLG-1787, and $(0.32^{+0.34}_{-0.19}, 4.98^{+5.42}_{-2.94}, 8.74\pm 0.49, 6.27^{+0.90}_{-1.15})$ for KMT-2022-BLG-1852. These parameters indicate that all the planets are giants with masses exceeding the mass of Jupiter in our solar system and the hosts are low-mass stars with masses substantially less massive than the Sun., Comment: 10 pages, 12 figures, 7 tables

After the revelation that neural networks tend to produce overconfident predictions, the problem of calibration, which aims to align confidence with accuracy to enhance the reliability of predictions, has gained significant importance. Several solutions based on calibration maps have been proposed to address the problem of recalibrating a trained classifier using additional datasets. In this paper, we offer an algorithm that transforms the weights of the last layer of the classifier, distinct from the calibration-map-based approach. We concentrate on the geometry of the final linear layer, specifically its angular aspect, and adjust the weights of the corresponding layer. We name the method Tilt and Average(\textsc{Tna}), and validate the calibration effect empirically and theoretically. Through this, we demonstrate that our approach, in addition to the existing calibration-map-based techniques, can yield improved calibration performance. Code available : https://github.com/GYYYYYUUUUU/TNA_Angular_Scaling., Comment: 20 pages, 11 figures, to appear in International Conference on Machine Learning (ICML2024)

Crystals with unique ionic arrangements and strong electronic correlations serve as a fertile ground for the emergence of exotic phases, as evidenced by the coexistence of charge density wave (CDW) and superconductivity in vanadium Kagome metals, specifically AV3Sb5 (where A represents K, Rb, or Cs). The formation of a star of David CDW superstructure, resulting from the coordinated displacements of vanadium ions on a corner sharing triangular lattice, has garnered significant attention in efforts to comprehend the influence of electron phonon interaction within this geometrically intricate lattice. However, understanding of the underlying mechanism behind CDW formation, coupled with symmetry protected lattice vibrations, remains elusive. In this study, we employed time resolved X ray scattering experiments utilising an X ray free electron laser. Our findings reveal that the phonon mode associated with the out of plane motion of Cs ions becomes frustrated in the CDW phase. Furthermore, we observed the photoinduced emergence of a metastable CDW phase, facilitated by the alleviation of frustration through nonadiabatic changes in free energy. By elucidating the longstanding puzzle surrounding the intervention of phonons in CDW ordering, this research offers fresh insights into the competition between phonons and periodic lattice distortions, a phenomenon widespread in other correlated quantum materials including layered high Tc superconductors., Comment: Manuscript: 20 pages, 4 figures, SI: 14 pages, 8 figures

Commercially impactful quantum algorithms such as quantum chemistry and Shor's algorithm require a number of qubits and gates far beyond the capacity of any existing quantum processor. Distributed architectures, which scale horizontally by networking modules, provide a route to commercial utility and will eventually surpass the capability of any single quantum computing module. Such processors consume remote entanglement distributed between modules to realize distributed quantum logic. Networked quantum computers will therefore require the capability to rapidly distribute high fidelity entanglement between modules. Here we present preliminary demonstrations of some key distributed quantum computing protocols on silicon T centres in isotopically-enriched silicon. We demonstrate the distribution of entanglement between modules and consume it to apply a teleported gate sequence, establishing a proof-of-concept for T centres as a distributed quantum computing and networking platform., Comment: 14 pages, 13 figures

Understanding transition paths between meta-stable states in molecular systems is fundamental for material design and drug discovery. However, sampling these paths via unbiased molecular dynamics simulations is computationally prohibitive due to the high energy barriers between the meta-stable states. Recent machine learning approaches are often restricted to simple systems or rely on collective variables (CVs) extracted from expensive domain knowledge. In this work, we propose to leverage generative flow networks (GFlowNets) to sample transition paths without relying on CVs. We reformulate the problem as amortized energy-based sampling over transition paths and train a neural bias potential by minimizing the squared log-ratio between the target distribution and the generator, derived from the flow matching objective of GFlowNets. Our evaluation on three proteins (Alanine Dipeptide, Polyproline Helix, and Chignolin) demonstrates that our approach, called TPS-GFN, generates more realistic and diverse transition paths than the previous CV-free machine learning approach., Comment: 8 pages, 5 figures, 2 tables

Advancements in deep generative modeling have changed the paradigm of drug discovery. Among such approaches, target-aware methods that exploit 3D structures of protein pockets were spotlighted for generating ligand molecules with their plausible binding modes. While docking scores superficially assess the quality of generated ligands, closer inspection of the binding structures reveals the inconsistency in local interactions between a pocket and generated ligands. Here, we address the issue by explicitly generating non-covalent interactions (NCIs), which are universal patterns throughout protein-ligand complexes. Our proposed model, NCIDiff, simultaneously denoises NCI types of protein-ligand edges along with a 3D graph of a ligand molecule during the sampling. With the NCI-generating strategy, our model generates ligands with more reliable NCIs, especially outperforming the baseline diffusion-based models. We further adopted inpainting techniques on NCIs to further improve the quality of the generated molecules. Finally, we showcase the applicability of NCIDiff on drug design tasks for real-world settings with specialized objectives by guiding the generation process with desired NCI patterns.

We report a free-floating planet (FFP) candidate identified from the analysis of the microlensing event KMT-2023-BLG-2669. The lensing light curve is characterized by a short duration $(\lesssim 3\,{\rm days})$ and a small amplitude $(\lesssim 0.7\,{\rm mag})$. From the analysis, we find the Einstein timescale of $t_{\rm E} \backsimeq 0.33\,{\rm days}$ and the Einstein radius of $\theta_{\rm E} \backsimeq 4.41\,{\mu}{\rm as}$. These measurements enable us to infer the lens mass as $M = 8\,M_{\oplus} (\pi_{\rm rel} / 0.1\,{\rm mas})^{-1}$, where $\pi_{\rm rel}$ is the relative lens-source parallax. The inference implies that the lens is a sub-Neptune- to Saturn-mass object depending on its unknown distance. This is the ninth isolated planetary-mass microlens with $\theta_{\rm E} < 10\,{\mu}{\rm as}$, which (as shown by \citealt{gould22}) is a useful threshold for a FFP candidate. We conduct extensive searches for possible signals of a host star in the light curve, but find no strong evidence for the host. We investigate the possibility of using late-time high-resolution imaging to probe for possible hosts. In particular, we discuss that for the case of finite-source point-lens FFP candidates, it would be possible to search for very wide separation hosts immediately, although such searches are "high-risk, high-reward"., Comment: 26 pages, 6 figures, accepted in publication in AJ

We investigate the nature of the short-term anomaly that appears in the lensing light curve of KMT-2023-BLG-1866. The anomaly was only partly covered due to its short duration, less than a day, coupled with cloudy weather conditions and restricted nighttime duration. Considering intricacy of interpreting partially covered signals, we thoroughly explore all potential degenerate solutions. Through this process, we identify three planetary scenarios that equally well account for the observed anomaly. These scenarios are characterized by the specific planetary parameters: $(s, q)_{\rm inner} = [0.9740 \pm 0.0083, (2.46 \pm 1.07) \times 10^{-5}]$, $(s, q)_{\rm intermediate} = [0.9779 \pm 0.0017, (1.56 \pm 0.25)\times 10^{-5}]$, and $(s, q)_{\rm outer} = [0.9894 \pm 0.0107, (2.31 \pm 1.29)\times 10^{-5}]$, where $s$ and $q$ denote the projected separation (scaled to the Einstein radius) and mass ratio between the planet and its host, respectively. We identify that the ambiguity between the inner and outer solutions stems from the inner-outer degeneracy, while the similarity between the intermediate solution and the others is due to an accidental degeneracy caused by incomplete anomaly coverage. Through Bayesian analysis utilizing the constraints derived from measured lensing observables and blending flux, our estimation indicates that the lens system comprises a very low-mass planet orbiting an early M-type star situated approximately (6.2 -- 6.5)~kpc from Earth in terms of median posterior values for the different solutions. The median mass of the planet host is in the range of (0.48 -- 0.51)~$M_\odot$, and that of the planet's mass spans a range of (2.6 -- 4.0)~$M_{\rm E}$, varying across different solutions. The detection of KMT-2023-BLG-1866Lb signifies the extension of the lensing surveys to very low-mass planets that have been difficult to be detected from earlier surveys., Comment: 9 pages, 8 figures, 4 tables

It has been previously advocated that the presence of the daily and annual modulations of the axion flux on the Earth's surface may dramatically change the strategy of the axion searches. The arguments were based on the so-called Axion Quark Nugget (AQN) dark matter model which was originally put forward to explain the similarity of the dark and visible cosmological matter densities $\Omega_{\rm dark}\sim \Omega_{\rm visible}$. In this framework, the population of galactic axions with mass $ 10^{-6} {\rm eV}\lesssim m_a\lesssim 10^{-3}{\rm eV}$ and velocity $\langle v_a\rangle\sim 10^{-3} c$ will be accompanied by axions with typical velocities $\langle v_a\rangle\sim 0.6 c$ emitted by AQNs. Furthermore, in this framework, it has also been argued that the AQN-induced axion daily modulation (in contrast with the conventional WIMP paradigm) could be as large as $(10-20)\%$, which represents the main motivation for the present investigation. We argue that the daily modulations along with the broadband detection strategy can be very useful tools for the discovery of such relativistic axions. The data from the CAST-CAPP detector have been used following such arguments. Unfortunately, due to the dependence of the amplifier chain on temperature-dependent gain drifts and other factors, we could not conclusively show the presence or absence of a dark sector-originated daily modulation. However, this proof of principle analysis procedure can serve as a reference for future studies., Comment: 18 pages, 8 figures

Materials potentially hosting Kitaev spin-liquid states are considered crucial for realizing topological quantum computing. However, the intricate nature of spin interactions within these materials complicates the precise measurement of low-energy spin excitations indicative of fractionalized excitations. Using Na$_{2}$Co$_2$TeO$_{6}$ as an example, we study these low-energy spin excitations using the time-resolved resonant elastic x-ray scattering (tr-REXS). Our observations unveil remarkably slow spin dynamics at the magnetic peak, whose recovery timescale is several nanoseconds. This timescale aligns with the extrapolated spin gap of $\sim$ 1 $\mu$eV, obtained by density matrix renormalization group (DMRG) simulations in the thermodynamic limit. The consistency demonstrates the efficacy of tr-REXS in discerning low-energy spin gaps inaccessible to conventional spectroscopic techniques., Comment: 9 pages, 6 figures

The partial transposition from quantum information theory provides a new source to distill the so-called asymptotic freeness without the assumption of classical independence between random matrices. Indeed, a recent paper [MP19] established asymptotic freeness between partial transposes in the bipartite situation. In this paper, we prove almost sure asymptotic freeness in the general multipartite situation and establish a central limit theorem for the partial transposes.

The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known for its quantum-limited noise characteristics, has been incorporated in the detection system to capture the weakly interacting axion signals. However, the performance of the JPA can be influenced by its environment, leading to potential unreliability of a predefined parameter set obtained in a specific laboratory setting. Furthermore, conducting a broadband search requires consecutive characterization of the amplifier across different tuning frequencies. To ensure more reliable measurements, we utilize the Nelder-Mead technique as a numerical search method to dynamically determine the optimal operating conditions. This heuristic search algorithm explores the multidimensional parameter space of the JPA, optimizing critical characteristics such as gain and noise temperature to maximize signal-to-noise ratios for a given experimental setup. Our study presents a comprehensive analysis of the properties of a flux-driven JPA to demonstrate the effectiveness of the algorithm. This approach contributes to ongoing efforts in axion dark matter research by offering an efficient method to enhance axion detection sensitivity through the optimized utilization of JPAs., Comment: 11 pages, 9 figures

A minimal non-thermal dark matter model that can explain both the existence of dark matter and the baryon asymmetry in the universe is studied. It requires two color-triplet, iso-singlet scalars with $\mathcal{O}$(TeV) masses and a singlet Majorana fermion with a mass of $\mathcal{O}$(GeV). The fermion becomes stable and can play the role of the dark matter candidate. We consider the fermion to interact with a top quark via the exchange of QCD-charged scalar fields coupled dominantly to third generation fermions. The signature of a single top quark production associated with a bottom quark and large missing transverse momentum opens up the possibility to search for this type of model at the LHC in a way complementary to existing monotop searches.

We undertake a project to reexamine microlensing data gathered from high-cadence surveys. The aim of the project is to reinvestigate lensing events with light curves exhibiting intricate anomaly features associated with caustics, yet lacking prior proposed models to explain these features. Through detailed reanalyses considering higher-order effects, we identify that accounting for orbital motions of lenses is vital in accurately explaining the anomaly features observed in the light curves of the lensing events OGLE-2018-BLG-0971, MOA-2023-BLG-065, and OGLE-2023-BLG-0136. We estimate the masses and distances to the lenses by conducting Bayesian analyses using the lensing parameters of the newly found lensing solutions. From these analyses, we identify that the lenses of the events OGLE-2018-BLG-0971 and MOA-2023-BLG-065 are binaries composed of M dwarfs, while the lens of OGLE-2023-BLG-0136 is likely to be a binary composed of an early K-dwarf primary and a late M-dwarf companion. For all lensing events, the probability of the lens residing in the bulge is considerably higher than that of it being located in the disk., Comment: 11 pages, 13 figures, 6 tables

Axions are hypothesized particles believed to potentially resolve two major puzzles in modern physics: the strong CP problem and the nature of dark matter. Cavity-based axion haloscopes represent the most sensitive tools for probing their theoretically favored couplings to photons in the microelectronvolt range. However, as the search mass (or frequency) increases, the detection efficiency decreases, largely due to a decrease in cavity volume. Despite the potential of higher-order resonant modes to preserve experimental volume, their practical application in searches has been limited by the challenge of maintaining a high form factor over a reasonably wide search bandwidth. We introduce an innovative tuning method that uses the unique properties of auxetic materials, designed to effectively tune higher modes. This approach was applied to the TM_020 mode for a dark matter axion search exploring a mass range from 21.38 to 21.79 ueV, resulting in the establishment of new exclusion limits for axion-photon coupling greater than approximately 10^-13 GeV^-1. These findings signify a breakthrough, demonstrating that our tuning mechanism facilitates the practical utilization of higher-order modes for cavity haloscope searches., Comment: 7 pages, 5 figures

Optimizing molecules to improve their properties is a fundamental challenge in drug design. For a fine-tuning of molecular properties without losing bio-activity validated in advance, the concept of bioisosterism has emerged. Many in silico methods have been proposed for discovering bioisosteres, but they require expert knowledge for their applications or are restricted to known databases. Here, we introduce DeepBioisostere, a deep generative model to design suitable bioisosteric replacements. Our model allows an end-to-end chemical replacement by intelligently selecting fragments for removal and insertion along with their attachment orientation. Through various scenarios of multiple property control, we showcase the model's capability to modulate specific properties, addressing the challenge in molecular optimization. Our model's innovation lies in its capacity to design a bioisosteric replacement reflecting the compatibility with the surroundings of the modification site, facilitating the control of sophisticated properties like drug-likeness. DeepBioisostere can also provide previously unseen bioisosteric replacements, highlighting its capability for exploring diverse chemical modifications rather than just mining them from known databases. Lastly, we employed DeepBioisostere to improve the sensitivity of a known SARS-CoV-2 main protease inhibitor to the E166V mutant that exhibits drug resistance to the inhibitor, demonstrating its potential application in lead optimization., Comment: 32 pages, 7 figures, and 2 tables for main text

In automotive radar, time-domain thresholding (TD-TH) and time-frequency domain thresholding (TFD-TH) are crucial techniques underpinning numerous interference mitigation methods. Despite their importance, comprehensive evaluations of these methods in dense traffic scenarios with different types of interference are limited. In this study, we segment automotive radar interference into three distinct categories. Utilizing the in-house traffic scenario and automotive radar simulator, we evaluate interference mitigation methods across multiple metrics: probability of detection, signal-to-interference-plus-noise ratio, and phase error involving hundreds of targets and dozens of interfering radars. The numerical results highlight that TFD-TH is more effective than TD-TH, particularly as the density and signal correlation of interfering radars escalate.

We report an extensive high-sensitivity search for axion dark matter above 1\,GHz at the Center for Axion and Precision Physics Research (CAPP). The cavity resonant search, exploiting the coupling between axions and photons, explored the frequency (mass) range of 1.025\,GHz (4.24\,$\mu$eV) to 1.185\,GHz (4.91\,$\mu$eV). We have introduced a number of innovations in this field, demonstrating the practical approach of optimizing all the relevant parameters of axion haloscopes, extending presently available technology. The CAPP 12\,T magnet with an aperture of 320\,mm made of Nb$_3$Sn and NbTi superconductors surrounding a 37-liter ultralight-weight copper cavity is expected to convert DFSZ axions into approximately $10^2$ microwave photons per second. A powerful dilution refrigerator, capable of keeping the core system below 40\,mK, combined with quantum-noise limited readout electronics, achieved a total system noise of about 200\,mK or below, which corresponds to a background of roughly $4\times 10^3$ photons per second within the axion bandwidth. The combination of all those improvements provides unprecedented search performance, imposing the most stringent exclusion limits on axion--photon coupling in this frequency range to date. These results also suggest an experimental capability suitable for highly-sensitive searches for axion dark matter above 1\,GHz., Comment: A detailed axion dark matter article with 27 pages, 22 figures

Light curves of microlensing events occasionally deviate from the smooth and symmetric form of a single-lens single-source event. While most of these anomalous events can be accounted for by employing a binary-lens single-source (2L1S) or a single-lens binary-source (1L2S) framework, it is established that a small fraction of events remain unexplained by either of these interpretations. We carry out a project in which data collected by high-cadence microlensing surveys were reinvestigated with the aim of uncovering the nature of anomalous lensing events with no proposed 2L1S or 1L2S models. From the project, we find that the anomaly appearing in the lensing event OGLE-2023-BLG-0836 cannot be explained by the usual interpretations and conduct a comprehensive analysis of the event. From thorough modeling of the light curve under sophisticated lens-system configurations, we have arrived at the conclusion that a triple-mass lens system is imperative to account for the anomaly features observed in the lensing light curve. From the Bayesian analysis using the measured observables of the event time scale and angular Einstein radius, we determine that the least massive component of the lens has a planetary mass of $4.36^{+2.35}_{-2.18}~M_{\rm J}$. This planet orbits within a stellar binary system composed of two stars with masses $0.71^{+0.38}_{-0.36}~M_\odot$ and $0.56^{+0.30}_{-0.28}~M_\odot$. This lensing event signifies the sixth occurrence of a planetary microlensing system in which a planet belongs to a stellar binary system., Comment: 11 pages, 12 figures

The axion offers a well-motivated solution to two fundamental questions in modern physics: the strong CP problem and the dark matter mystery. Cavity haloscopes, exploiting resonant enhancement of photon signals, provide the most sensitive searches for axion dark matter in the microwave region. However, current experimental sensitivities are limited to the O(10^0) ueV range, while recent theoretical predictions for the axion mass favor up to O(10^2) ueV, suggesting the need of new experimental approaches that are suitable for higher mass regions. CAPP has developed/proposed several haloscopes effective for high-mass axion searches based on new cavity concepts and practical tuning mechanisms. They are characterized by large detection volumes and/or high quality factors at high frequencies, achieved by partitioning a single cavity into multiple cells, exploiting higher-order resonant modes, and constructing dielectric photonic crystal structures. Improving on the dish antenna haloscope scheme, a horn antenna array has also been proposed for volume-efficient broadband search in the THz region. We review these haloscope designs for sensitive search in the high-mass regions and discuss their impacts on future experiments., Comment: 16 pages, 9 figures