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The newly accessible mid-infrared (MIR) window offered by the James Webb Space Telescope (JWST) for exoplanet imaging is expected to provide valuable information to characterize their atmospheres. In particular, coronagraphs on board the JWST Mid-InfraRed instrument (MIRI) are capable of imaging the coldest directly imaged giant planets at the wavelengths where they emit most of their flux. The MIRI coronagraphs have been specially designed to detect the NH3 absorption around 10.5 microns, which has been predicted by atmospheric models. We aim to assess the presence of NH3 while refining the atmospheric parameters of one of the coldest companions detected by directly imaging GJ 504 b. Its mass is still a matter of debate and depending on the host star age estimate, the companion could either be placed in the brown dwarf regime or in the young Jovian planet regime. We present an analysis of MIRI coronagraphic observations of the GJ 504 system. We took advantage of previous observations of reference stars to build a library of images and to perform a more efficient subtraction of the stellar diffraction pattern. We detected the presence of NH3 at 12.5 sigma in the atmosphere, in line with atmospheric model expectations for a planetary-mass object and observed in brown dwarfs within a similar temperature range. The best-fit model with Exo-REM provides updated values of its atmospheric parameters, yielding a temperature of Teff = 512 K and radius of R = 1.08 RJup. These observations demonstrate the capability of MIRI coronagraphs to detect NH3 and to provide the first MIR observations of one of the coldest directly imaged companions. Overall, NH3 is a key molecule for characterizing the atmospheres of cold planets, offering valuable insights into their surface gravity. These observations provide valuable information for spectroscopic observations planned with JWST., Comment: Accepted to A&A

Complete characterization of a multimode optical process has paved the way for understanding complex optical phenomena, leading to the development of novel optical technologies. Until now, however, characterizations have mainly focused on a linear-optical process, despite the plethora of multimode nonlinear-optical processes crucial for photonic technologies. Here we report the experimental characterization of multimode nonlinear-optical quantum processes by obtaining the full information needed to describe them while satisfying the necessary physical condition. Specifically, to characterize a second-order nonlinear-optical process of parametric downconversion, we determine the amplification and noise matrices of multimode field quadratures. The full information allows us to factorize the multimode process, leading to the identification of eigenquadratures and their associated amplification and noise properties. Moreover, we demonstrate the broad applicability of our method by characterizing various nonlinear-optical quantum processes, including cluster state generation, mode-dependent loss with nonlinear interaction, and a quantum noise channel. Our method, by providing a versatile technique for characterizing a nonlinear-optical process, will be beneficial for developing scalable photonic technologies.

Superconductors at temperatures below the critical temperature $T_c$ can be modeled as a mixture of Fermi and Bose gases, where the Fermi gas consists of conduction electrons and the Bose gas comprises Cooper pairs. This simple model enables the computation of the temperature dependence of $2 r(T) / N$, where $N$ is the total number of conduction electrons and $r(T)$ is the number of Cooper pairs at temperature $T$. Analyzing $2 r(T) / N$ across various superconductors may provide significant insights into the mechanisms behind high-temperature superconductivity, especially regarding coherence in Cooper pairs., Comment: 8 pges, 2 pdf figures

Light-emitting electrochemical cells (LECs) are promising candidates for fully solution-processed lighting applications because they can comprise a single active-material layer and air-stable electrodes. While their performance is often claimed to be independent of the electrode material selection due to the in-situ formation of electric double layers (EDLs), we demonstrate conceptually and experimentally that this understanding needs to be modified. Specifically, the exciton generation zone is observed to be affected by the electrode work function. We rationalize this finding by proposing that the ion concentration in the injection-facilitating EDLs depends on the offset between the electrode work function and the respective semiconductor orbital, which in turn influences the number of ions available for electrochemical doping and hence shifts the exciton generation zone. Further, we investigate the effects of the electrode selection on exciton losses to surface plasmon polaritons and discuss the impact of cavity effects on the exciton density. We conclude by showing that the measured electrode-dependent LEC luminance transients can be replicated by an optical model that considers these electrode-dependent effects to calculate the attained light outcoupling of the LEC stack. As such, our findings provide rational design criteria considering the electrode materials, the active-material thickness, and its composition in concert to achieve optimum LEC performance.

For over 25 years, the DAMA/LIBRA collaboration has claimed to observe an annual modulation signal, suggesting the existence of dark matter interactions. However, no other experiments have replicated their result using different detector materials. To address this puzzle, the COSINE-100 collaboration conducted a model-independent test using 106 kg of sodium iodide as detectors, the same target material as DAMA/LIBRA. Analyzing data collected over 6.4 years, with improved energy calibration and time-dependent background description, we found no evidence of an annual modulation signal, challenging the DAMA/LIBRA result with a confidence level greater than 3$\sigma$. This finding represents a significant step toward resolving the long-standing debate surrounding DAMA/LIBRA's dark matter claim, indicating that the observed modulation is unlikely to be caused by dark matter interactions.

LiDAR panoptic segmentation, which jointly performs instance and semantic segmentation for things and stuff classes, plays a fundamental role in LiDAR perception tasks. While most existing methods explicitly separate these two segmentation tasks and utilize different branches (i.e., semantic and instance branches), some recent methods have embraced the query-based paradigm to unify LiDAR panoptic segmentation. However, the distinct spatial distribution and inherent characteristics of objects(things) and their surroundings(stuff) in 3D scenes lead to challenges, including the mutual competition of things/stuff and the ambiguity of classification/segmentation. In this paper, we propose decoupling things/stuff queries according to their intrinsic properties for individual decoding and disentangling classification/segmentation to mitigate ambiguity. To this end, we propose a novel framework dubbed DQFormer to implement semantic and instance segmentation in a unified workflow. Specifically, we design a decoupled query generator to propose informative queries with semantics by localizing things/stuff positions and fusing multi-level BEV embeddings. Moreover, a query-oriented mask decoder is introduced to decode corresponding segmentation masks by performing masked cross-attention between queries and mask embeddings. Finally, the decoded masks are combined with the semantics of the queries to produce panoptic results. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our DQFormer framework., Comment: 13 pages, 10 figures

COSINE-100 is a direct dark matter search experiment, with the primary goal of testing the annual modulation signal observed by DAMA/LIBRA, using the same target material, NaI(Tl). In previous analyses, we achieved the same 1 keV energy threshold used in the DAMA/LIBRA's analysis that reported an annual modulation signal with 11.6$\sigma$ significance. In this article, we report an improved analysis that lowered the threshold to 0.7 keV, thanks to the application of Multi-Layer Perception network and a new likelihood parameter with waveforms in the frequency domain. The lower threshold would enable a better comparison of COSINE-100 with new DAMA results with a 0.75 keV threshold and account for differences in quenching factors. Furthermore the lower threshold can enhance COSINE-100's sensitivity to sub-GeV dark matter searches.

COSINE-100 aims to conclusively test the claimed dark matter annual modulation signal detected by DAMA/LIBRA collaboration. DAMA/LIBRA has released updated analysis results by lowering the energy threshold to 0.75 keV through various upgrades. They have consistently claimed to have observed the annual modulation. In COSINE-100, it is crucial to lower the energy threshold for a direct comparison with DAMA/LIBRA, which also enhances the sensitivity of the search for low-mass dark matter, enabling COSINE-100 to explore this area. Therefore, it is essential to have a precise and quantitative understanding of the background spectrum across all energy ranges. This study expands the background modeling from 0.7 to 4000 keV using 2.82 years of COSINE-100 data. The modeling has been improved to describe the background spectrum across all energy ranges accurately. Assessments of the background spectrum are presented, considering the nonproportionality of NaI(Tl) crystals at both low and high energies and the characteristic X-rays produced by the interaction of external backgrounds with materials such as copper. Additionally, constraints on the fit parameters obtained from the alpha spectrum modeling fit are integrated into this model. These improvements are detailed in the paper.

The AMoRE collaboration searches for neutrinoless double beta decay of $^{100}$Mo using molybdate scintillating crystals via low temperature thermal calorimetric detection. The early phases of the experiment, AMoRE-pilot and AMoRE-I, have demonstrated competitive discovery potential. Presently, the AMoRE-II experiment, featuring a large detector array with about 90 kg of $^{100}$Mo isotope, is under construction.This paper discusses the baseline design and characterization of the lithium molybdate cryogenic calorimeters to be used in the AMoRE-II detector modules. The results from prototype setups that incorporate new housing structures and two different crystal masses (316 g and 517 - 521 g), operated at 10 mK temperature, show energy resolutions (FWHM) of 7.55 - 8.82 keV at the 2.615 MeV $^{208}$Tl $\gamma$ line, and effective light detection of 0.79 - 0.96 keV/MeV. The simultaneous heat and light detection enables clear separation of alpha particles with a discrimination power of 12.37 - 19.50 at the energy region around $^6$Li(n, $\alpha$)$^3$H with Q-value = 4.785 MeV. Promising detector performances were demonstrated at temperatures as high as 30 mK, which relaxes the temperature constraints for operating the large AMoRE-II array.

Machine learning (ML) has demonstrated significant promise in accelerating drug design. Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model predicting the target property of interest. The model predictions are used to determine which designs to evaluate in the lab, and the model is updated on the new measurements to inform the next cycle of decisions. A key challenge is that the experimental feedback from each cycle inspires changes in the candidate proposal or experimental protocol for the next cycle, which lead to distribution shifts. To promote robustness to these shifts, we must account for them explicitly in the model training. We apply domain generalization (DG) methods to classify the stability of interactions between an antibody and antigen across five domains defined by design cycles. Our results suggest that foundational models and ensembling improve predictive performance on out-of-distribution domains. We publicly release our codebase extending the DG benchmark ``DomainBed,'' and the associated dataset of antibody sequences and structures emulating distribution shifts across design cycles.

AMoRE searches for the signature of neutrinoless double beta decay of $^{100}$Mo with a 100 kg sample of enriched $^{100}$Mo. Scintillating molybdate crystals coupled with a metallic magnetic calorimeter operate at milli-Kelvin temperatures to measure the energy of electrons emitted in the decay. As a demonstration of the full-scale AMoRE, we conducted AMoRE-I, a pre-experiment with 18 molybdate crystals, at the Yangyang Underground Laboratory for over two years. The exposure was 8.02 kg$\cdot$year (or 3.89 kg$_{\mathrm{^{100}Mo}}\cdot$year) and the total background rate near the Q-value was 0.025 $\pm$ 0.002 counts/keV/kg/year. We observed no indication of $0\nu\beta\beta$ decay and report a new lower limit of the half-life of $^{100}$Mo $0\nu\beta\beta$ decay as $ T^{0\nu}_{1/2}>3.0\times10^{24}~\mathrm{years}$ at 90\% confidence level. The effective Majorana mass limit range is $m_{\beta\beta}<$(210--610) meV using nuclear matrix elements estimated in the framework of different models, including the recent shell model calculations., Comment: 8 pages, 5 figures

AMoRE-II aims to search for neutrinoless double beta decay with an array of 423 Li$_2$$^{100}$MoO$_4$ crystals operating in the cryogenic system as the main phase of the Advanced Molybdenum-based Rare process Experiment (AMoRE). AMoRE has been planned to operate in three phases: AMoRE-pilot, AMoRE-I, and AMoRE-II. AMoRE-II is currently being installed at the Yemi Underground Laboratory, located approximately 1000 meters deep in Jeongseon, Korea. The goal of AMoRE-II is to reach up to $T^{0\nu\beta\beta}_{1/2}$ $\sim$ 6 $\times$ 10$^{26}$ years, corresponding to an effective Majorana mass of 15 - 29 meV, covering all the inverted mass hierarchy regions. To achieve this, the background level of the experimental configurations and possible background sources of gamma and beta events should be well understood. We have intensively performed Monte Carlo simulations using the GEANT4 toolkit in all the experimental configurations with potential sources. We report the estimated background level that meets the 10$^{-4}$counts/(keV$\cdot$kg$\cdot$yr) requirement for AMoRE-II in the region of interest (ROI) and show the projected half-life sensitivity based on the simulation study.

When the interaction Hamiltonian is $\widehat{H}_I = \kappa \left(\hat{x}_3 \hat{p}_1 + \hat{p}_3 \hat{p_2} \right)$ for large $\kappa$ and the initial quantum state is $\Psi_{in} (x_1, x_2, x_3) = \prod_{j=1}^3 \phi_j (x_j)$, the joint measurement of complementary variables $x_3$ and $p_3$ at $t = 1 / \kappa$ induces the uncertainty $\Delta_{\hat{x}_1} (t = 1 / \kappa) \Delta_{ \hat{x}_2} (t = 1 / \kappa) \geq \frac{1}{2} (\Delta_{\hat{x}_1} \Delta_{\hat{p}_1} + \Delta_{\hat{x}_2} \Delta_{\hat{p}_2} ) + \Delta_{\hat{x}_3} \Delta_{\hat{p}_3}$ , where the standard deviations in the right hand are the deviations at $t=0$. This is a generalized version of the well-known Arthurs-Keller inequality $\Delta_{\hat{x}_1} (t = 1 / \kappa) \Delta_{ \hat{x}_2} (t = 1 / \kappa) \geq 1$ arising when all $\phi_j (x_j)$ are the minimal uncertainty Gaussian states. If the initial probe state is entangled, it is shown that the generalized Arthurs-Kelly inequality can be violated. We show the violation explicitly by introducing a special example., Comment: 14 pages, 2 figures

This study presents a model of automatic speech recognition (ASR) designed to diagnose pronunciation issues in children with speech sound disorders (SSDs) to replace manual transcriptions in clinical procedures. Since ASR models trained for general purposes primarily predict input speech into real words, employing a well-known high-performance ASR model for evaluating pronunciation in children with SSDs is impractical. We fine-tuned the wav2vec 2.0 XLS-R model to recognize speech as pronounced rather than as existing words. The model was fine-tuned with a speech dataset from 137 children with inadequate speech production pronouncing 73 Korean words selected for actual clinical diagnosis. The model's predictions of the pronunciations of the words matched the human annotations with about 90% accuracy. While the model still requires improvement in recognizing unclear pronunciation, this study demonstrates that ASR models can streamline complex pronunciation error diagnostic procedures in clinical fields., Comment: 12 pages, 2 figures

We report a study on the background of the Advanced Molybdenum-Based Rare process Experiment (AMoRE), a search for neutrinoless double beta decay (\znbb) of $^{100}$Mo. The pilot stage of the experiment was conducted using $\sim$1.9 kg of \CAMOO~ crystals at the Yangyang Underground Laboratory, South Korea, from 2015 to 2018. We compared the measured $\beta/\gamma$ energy spectra in three experimental configurations with the results of Monte Carlo simulations and identified the background sources in each configuration. We replaced several detector components and enhanced the neutron shielding to lower the background level between configurations. A limit on the half-life of $0\nu\beta\beta$ decay of $^{100}$Mo was found at $T_{1/2}^{0\nu} \ge 3.0\times 10^{23}$ years at 90\% confidence level, based on the measured background and its modeling. Further reduction of the background rate in the AMoRE-I and AMoRE-II are discussed.