Faithful reasoning in medical vision-language models (VLMs) requires not only accurate predictions but also transparent alignment between textual rationales and visual evidence. While Chain-of-Thought (CoT) prompting has shown promise in medical visual question answering (VQA), no large-scale expert-level dataset has captured stepwise reasoning with precise visual grounding. We introduce S-Chain, the first large-scale dataset of 12,000 expert-annotated medical images with bounding boxes and structured visual CoT (SV-CoT), explicitly linking visual regions to reasoning steps. The dataset further supports 16 languages, totaling over 700k VQA pairs for broad multilingual applicability. Using S-Chain, we benchmark state-of-the-art medical VLMs (ExGra-Med, LLaVA-Med) and general-purpose VLMs (Qwen2.5-VL, InternVL2.5), showing that SV-CoT supervision significantly improves interpretability, grounding fidelity, and robustness. Beyond benchmarking, we study its synergy with retrieval-augmented generation, revealing how domain knowledge and visual grounding interact during autoregressive reasoning. Finally, we propose a new mechanism that strengthens the alignment between visual evidence and reasoning, improving both reliability and efficiency. S-Chain establishes a new benchmark for grounded medical reasoning and paves the way toward more trustworthy and explainable medical VLMs.

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: this https URL

Lead halide perovskites have catalyzed the rise of main-group metal halide materials as promising candidates for next-generation optoelectronics, including solar cells, light-emitting diodes, lasers, sensors, and photocatalysts. Among these, effi-cient light-emission arises from self-trapped excitons, wherein excited states induce transient lattice distortions that localize excitons. However, the complex interplay of factors, such as lattice distortions, lattice softness, and electron-phonon cou-pling dynamics, obscures the direct structure-property relationships complicating the targeted material design. In this study, we advance the understanding of self-trapped exciton (STE)-based emission in hybrid antimony and bismuth halides, em-phasizing the interplay of structural and electronic factors that enhance white-light emission. We systematically vary com-position, anion dimensionality, connectivity, and the organic cation and find that the presence of Bi/Sb and Cl in edge-sharing anion motifs promotes white-light emission and optimal electron-phonon coupling. Chlorides outperform bromides, and organic cations, such as CMA and BZA, only subtly influence optical behavior by altering lattice dynamics and rigidity, resulting in tunable emission characteristics without compromising STEs. This work deepens the understanding of the emis-sion mechanisms in hybrid halide perovskites and establishes guiding principles for tailoring optoelectronic properties, paving the way for advanced materials with enhanced white-light emission for next-generation optoelectronic applications.

Based on $10.64~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data taken at center-of-mass energies between 4.237 and 4.699 GeV with the BESIII detector, we study the leptonic $D^+_s$ decays using the $e^+e^-\to D^{*+}_{s} D^{*-}_{s}$ process. The branching fractions of $D_s^+\to\ell^+\nu_{\ell}\,(\ell=\mu,\tau)$ are measured to be $\mathcal{B}(D_s^+\to\mu^+\nu_\mu)=(0.547\pm0.026_{\rm stat}\pm0.016_{\rm syst})\%$ and $\mathcal{B}(D_s^+\to\tau^+\nu_\tau)=(5.60\pm0.16_{\rm stat}\pm0.20_{\rm syst})\%$, respectively. The product of the decay constant and Cabibbo-Kobayashi-Maskawa matrix element $|V_{cs}|$ is determined to be $f_{D_s^+}|V_{cs}|=(246.5\pm5.9_{\rm stat}\pm3.6_{\rm syst}\pm0.5_{\rm input})_{\mu\nu}~\mathrm{MeV}$ and $f_{D_s^+}|V_{cs}|=(252.7\pm3.6_{\rm stat}\pm4.5_{\rm syst}\pm0.6_{\rm input}))_{\tau \nu}~\mathrm{MeV}$, respectively. Taking the value of $|V_{cs}|$ from a global fit in the Standard Model, we obtain ${f_{D^+_s}}=(252.8\pm6.0_{\rm stat}\pm3.7_{\rm syst}\pm0.6_{\rm input})_{\mu\nu}$ MeV and ${f_{D^+_s}}=(259.2\pm3.6_{\rm stat}\pm4.5_{\rm syst}\pm0.6_{\rm input})_{\tau \nu}$ MeV, respectively. Conversely, taking the value for $f_{D_s^+}$ from the latest lattice quantum chromodynamics calculation, we obtain $|V_{cs}| =(0.986\pm0.023_{\rm stat}\pm0.014_{\rm syst}\pm0.003_{\rm input})_{\mu\nu}$ and $|V_{cs}| = (1.011\pm0.014_{\rm stat}\pm0.018_{\rm syst}\pm0.003_{\rm input})_{\tau \nu}$, respectively.

Data-hungry neuro-AI modelling requires ever larger neuroimaging datasets. CNeuroMod-THINGS meets this need by capturing neural representations for a wide set of semantic concepts using well-characterized images in a new densely-sampled, large-scale fMRI dataset. Importantly, CNeuroMod-THINGS exploits synergies between two existing projects: the THINGS initiative (THINGS) and the Courtois Project on Neural Modelling (CNeuroMod). THINGS has developed a common set of thoroughly annotated images broadly sampling natural and man-made objects which is used to acquire a growing collection of large-scale multimodal neural responses. Meanwhile, CNeuroMod is acquiring hundreds of hours of fMRI data from a core set of participants during controlled and naturalistic tasks, including visual tasks like movie watching and videogame playing. For CNeuroMod-THINGS, four CNeuroMod participants each completed 33-36 sessions of a continuous recognition paradigm using approximately 4000 images from the THINGS stimulus set spanning 720 categories. We report behavioural and neuroimaging metrics that showcase the quality of the data. By bridging together large existing resources, CNeuroMod-THINGS expands our capacity to model broad slices of the human visual experience.

The development of AI for mental health is hindered by a lack of authentic therapy dialogues, due to strict privacy regulations and the fact that clinical sessions were historically rarely recorded. We present an LLM-driven pipeline that generates synthetic counseling dialogues based on structured client profiles and psychological questionnaires. Grounded on the principles of Cognitive Behavioral Therapy (CBT), our method creates synthetic therapeutic conversations for clinical disorders such as anxiety and depression. Our framework, SQPsych (Structured Questionnaire-based Psychotherapy), converts structured psychological input into natural language dialogues through therapist-client simulations. Due to data governance policies and privacy restrictions prohibiting the transmission of clinical questionnaire data to third-party services, previous methodologies relying on proprietary models are infeasible in our setting. We address this limitation by generating a high-quality corpus using open-weight LLMs, validated through human expert evaluation and LLM-based assessments. Our SQPsychLLM models fine-tuned on SQPsychConv achieve strong performance on counseling benchmarks, surpassing baselines in key therapeutic skills. Our findings highlight the potential of synthetic data to enable scalable, data-secure, and clinically informed AI for mental health support. We will release our code, models, and corpus at this https URL

By analyzing $6.32~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected at the center-of-mass energies between 4.178 and 4.226\,GeV with the BESIII detector, we determine the branching fraction of the leptonic decay $D_s^+\to\tau^+\nu_\tau$ with $\tau^+\to\pi^+\pi^0\bar \nu_\tau$, to be $\mathcal{B}_{D_s^+\to\tau^+\nu_\tau}=(5.29\pm0.25_{\rm stat}\pm0.20_{\rm syst})\%$. We estimate the product of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cs}|$ and the $D_s^+$ decay constant $f_{D^+_s}$ to be $f_{D_s^+}|V_{cs}|=(244.8\pm5.8_{\rm stat}\pm4.8_{\rm syst})~\mathrm{MeV}$ using the known values of the $\tau^+$ and $D_s^+$ masses as well as the $D_s^+$ lifetime, together with our branching fraction measurement. Combining with the value of $|V_{cs}|$ obtained from a global fit in the standard model and $f_{D_s^+}$ from lattice quantum chromodynamics, we obtain $f_{D_s^+}=(251.6\pm5.9_{\rm stat}\pm4.9_{\rm syst})$\,MeV and $|V_{cs}| = 0.980\pm0.023_{\rm stat}\pm0.019_{\rm syst}$.

Using about 23 $\mathrm{fb^{-1}}$ of data collected with the BESIII detector operating at the BEPCII storage ring, a precise measurement of the $e^{+}e^{-} \rightarrow \pi^{+}\pi^{-}J/\psi$ Born cross section is performed at center-of-mass energies from 3.7730 to 4.7008 GeV. Two structures, identified as the $Y(4220)$ and the $Y(4320)$ states, are observed in the energy-dependent cross section with a significance larger than $10\sigma$. The masses and widths of the two structures are determined to be ($M, \Gamma$) = ($4221.4\pm1.5\pm2.0$ MeV/$c^{2}$, $41.8\pm2.9\pm2.7$ MeV) and ($M, \Gamma$) = ($4298\pm12\pm26$ MeV/$c^{2}$, $127\pm17\pm10$ MeV), respectively. A small enhancement around 4.5 GeV with a significance about $3\sigma$, compatible with the $\psi(4415)$, might also indicate the presence of an additional resonance in the spectrum. The inclusion of this additional contribution in the fit to the cross section affects the resonance parameters of the $Y(4320)$ state.

This paper presents a computational method for generating virtual 3D morphologies of functional materials using low-parametric stochastic geometry models, i.e., digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, that can be deployed for virtual materials testing by means of spatially resolved numerical simulations of macroscopic properties. Generative adversarial networks (GANs) have gained popularity for calibrating models to generate realistic 3D morphologies. However, GANs often comprise of numerous uninterpretable parameters make systematic variation of morphologies for virtual materials testing challenging. In contrast, low-parametric stochastic geometry models (e.g., based on Gaussian random fields) enable targeted variation but may struggle to mimic complex morphologies. Combining GANs with advanced stochastic geometry models (e.g., excursion sets of more general random fields) addresses these limitations, allowing model calibration solely from 2D image data. This approach is demonstrated by generating a digital twin of all-solid-state battery (ASSB) cathodes. Since the digital twins are parametric, they support systematic exploration of structural scenarios and their macroscopic properties. The proposed method facilitates simulation studies for optimizing 3D morphologies, benefiting not only ASSB cathodes but also other materials with similar structures.

The spin and parity of the $Z_c(3900)^\pm$ state are determined to be $J^P=1^+$ with a statistical significance larger than $7\sigma$ over other quantum numbers in a partial wave analysis of the process $e^+e^-\to \pi^+\pi^-J/\psi$. We use a data sample of 1.92 fb$^{-1}$ accumulated at $\sqrt{s}=4.23$ and 4.26 GeV with the BESIII experiment. When parameterizing the $Z_c(3900)^\pm$ with a Flatte-like formula, we determine its pole mass $M_\textrm{pole}=(3881.2\pm4.2_\textrm{stat}\pm52.7_\textrm{syst})\textrm{MeV}/c^2$ and pole width $\Gamma_\textrm{pole}=(51.8\pm4.6_\textrm{stat}\pm36.0_\textrm{syst})\textrm{MeV}$. We also measure cross sections for the process $e^+e^-\to Z_c(3900)^+\pi^-+c.c.\to J/\psi\pi^+\pi^-$ and determine an upper limit at the 90\% confidence level for the process $e^+e^-\to Z_c(4020)^+\pi^-+c.c.\to J/\psi\pi^+\pi^-$.

Using $e^+e^-$ collision data collected with the BESIII detector operating at the Beijing Electron Positron Collider, the cross section of $e^+e^-\to \pi^+\pi^- h_c$ is measured at 59 points with center-of-mass energy $\sqrt{s}$ ranging from $4.009$ to $4.950~\mathrm{GeV}$ with a total integrated luminosity of $22.2~\mathrm{fb}^{-1}$. The cross section between $4.3$ and $4.45~\mathrm{GeV}$ exhibits a plateau-like shape and drops sharply around $4.5~\mathrm{GeV}$, which cannot be described by two resonances only. Three coherent Breit-Wigner functions are used to parameterize the $\sqrt{s}$-dependent cross section line shape. The masses and widths are determined to be $M_1=(4223.6_{-3.7-2.9}^{+3.6+2.6})~\mathrm{MeV}/c^2$, $\Gamma_1=(58.5_{-11.4-6.5}^{+10.8+6.7})~\mathrm{MeV}$, $M_2=(4327.4_{-18.8-9.3}^{+20.1+10.7})~\mathrm{MeV}/c^2$, $\Gamma_2=(244.1_{-27.1-18.3}^{+34.0+24.2})~\mathrm{MeV}$, and $M_3=(4467.4_{-5.4-2.7}^{+7.2+3.2})~\mathrm{MeV}/c^2$, $\Gamma_3=(62.8_{-14.4-7.0}^{+19.2+9.9})~\mathrm{MeV}$. The first uncertainties are statistical and the second are systematic. The inclusion of the relatively narrower third component proves crucial for reproducing the drop at around 4.5~GeV. The statistical significance of the three-resonance assumption over the two-resonance assumption is greater than $5\sigma$.

Charged lepton flavor violation is forbidden in the Standard Model but possible in several new physics scenarios. In many of these models, the radiative decays $\tau^{\pm}\rightarrow\ell^{\pm}\gamma$ ($\ell=e,\mu$) are predicted to have a sizeable probability, making them particularly interesting channels to search at various experiments. An updated search via $\tau^{\pm}\rightarrow\ell^{\pm}\gamma$ using full data of the Belle experiment, corresponding to an integrated luminosity of 988 fb$^{-1}$, is reported for charged lepton flavor violation. No significant excess over background predictions from the Standard Model is observed, and the upper limits on the branching fractions, $\mathcal{B}(\tau^{\pm}\rightarrow \mu^{\pm}\gamma)$ $\leq$ $4.2\times10^{-8}$ and $\mathcal{B}(\tau^{\pm}\rightarrow e^{\pm}\gamma)$ $\leq$ $5.6\times10^{-8}$, are set at 90\% confidence level.

Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup's probability of success. Nowadays, investors can use not only various fundamental information about a startup (e.g., the age of the startup, the number of founders, and the business sector) but also textual description of a startup's innovation and business model, which is widely available through online venture capital (VC) platforms such as Crunchbase. To support the decision-making of investors, we develop a machine learning approach with the aim of locating successful startups on VC platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Thereby, we assess to what extent self-descriptions on VC platforms are predictive of startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power. Our work provides a decision support tool for investors to find profitable investment opportunities.

The phase structure of QCD remains an open fundamental problem of standard model physics. In particular at finite density, our knowledge is limited. Yet, numerous model studies point towards a rich and complex phase diagram at large density. Functional methods like the functional renormalization group and Dyson-Schwinger equations offer a way to study hot and dense QCD matter directly from first principles. I will discuss the phase structure of QCD and its experimental signatures through the lens of these methods.

Using data samples of $(10087\pm 44)\times10^{6}$ $J/\psi$ events and $(2712.4\pm 14.3)\times10^{6}$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider, we search for the CP violating decays $J/\psi\rightarrow K^{0}_{S}K^{0}_{S}$ and $\psi(3686)\rightarrow K^{0}_{S}K^{0}_{S}$. No significant signals are observed over the expected background yields. The upper limits on their branching fractions are set as \mathcal{B}(J/\psi\rightarrow K^{0}_{S}K^{0}_{S}) <4.7\times 10^{-9} and \mathcal{B}(\psi(3686)\rightarrow K^{0}_{S}K^{0}_{S}) <1.1\times 10^{-8} at the 90% confidence level. These results improve the previous limits by a factor of three for $J/\psi\rightarrow K^{0}_{S} K^{0}_{S}$ and two orders of magnitude for $\psi(3686)\rightarrow K^{0}_{S} K^{0}_{S}$.

We perform a study of the $X(3872)$ lineshape using the data samples of $e^+e^-\to\gamma X(3872)$, $X(3872)\to D^0\bar{D}^0 \pi^0$ and $\pi^+\pi^- J/\psi$ collected with the BESIII detector. The effects of the coupled-channels and the off-shell $D^{*0}$ are included in the parameterization of the lineshape. The lineshape mass parameter is obtained to be $M_{X}=(3871.63\pm 0.13^{+0.06}_{-0.05})$ MeV. Two poles are found on the first and second Riemann sheets corresponding to the $D^{*0}\bar{D}^0$ branch cut. The pole location on the first sheet is much closer to the $D^{*0}\bar{D}^0$ threshold than the other, and is determined to be $7.04\pm0.15^{+0.07}_{-0.08}$ MeV above the $D^0\bar{D}^0\pi^0$ threshold with an imaginary part $-0.19\pm0.08^{+0.14}_{-0.19}$ MeV.

We report a measurement of the cross section for the process $e^+e^-\to\pi^+\pi^-J/\psi$ around the $X(3872)$ mass in search for the direct formation of $e^+e^-\to X(3872)$ through the two-photon fusion process. No enhancement of the cross section is observed at the $X(3872)$ peak and an upper limit on the product of electronic width and branching fraction of $X(3872)\to\pi^+\pi^-J/\psi$ is determined to be \Gamma_{ee}\times\mathcal{B}(X(3872)\to\pi^+\pi^-J/\psi)<7.5\times10^{-3}\,\text{eV} at $90\,\%$ confidence level under an assumption of total width of $1.19\pm0.21$ MeV. This is an improvement of a factor of about $17$ compared to the previous limit. Furthermore, using the latest result of $\mathcal{B}(X(3872)\to\pi^+\pi^-J/\psi)$, an upper limit on the electronic width $\Gamma_{ee}$ of $X(3872)$ is obtained to be <0.32\,\text{eV} at the $90\,\%$ confidence level.

We present the first experimental search for the rare charm decay $D^{0} \to \pi^{0} \nu \bar{\nu}$. It is based on an $e^+e^-$ collision sample consisting of $10.6\times10^{6}$ pairs of $D^0\bar{D}^0$ mesons collected by the BESIII detector at $\sqrt{s}$=3.773 GeV, corresponding to an integrated luminosity of 2.93~fb$^{-1}$. A data-driven method is used to ensure the reliability of the background modeling. No significant $D^{0} \to \pi^{0} \nu \bar{\nu}$ signal is observed in data and an upper limit of the branching fraction is set to be $2.1\times 10^{-4}$ at the 90$\%$ confidence level. This is the first experimental constraint on charmed-hadron decays into dineutrino final states.

Duncan Dowson, whom this issue commemorates, was a world leader in the field of biotribology, with prolific contributions both in fluid-based and boundary lubrication of biological tissues, in particular articular cartilage, a central issue in biotribology due to its importance for joint homeostasis. Here we explore further the issue of cartilage boundary lubrication, which has been attributed to phospholipid (PL)-exposing layers at the cartilage surface in part. A surface force balance (SFB) with unique sensitivity is used to investigate the normal and frictional interactions of the boundary layers formed by PLs extracted from osteoarthritic (OA) human synovial fluid (hSF). Our results reveal that vesicles of the OA-hSF lipids rupture spontaneously to form bilayers on the mica substrate (which, like the in-vivo articular cartilage surface in synovial joints, is negatively-charged) which then undergo hemifusion at quite low pressures in the SFB, attributed to the large heterogeneity of the hSF lipids. Nanometric friction measurements reveal friction coefficients mu = ca. 0.03 across the hemi-fused bilayer of these lipids, indicating residual hydration lubrication at the lipid-headgroup vs. substrate interface. Addition of calcium ions causes an increase in friction to mu = ca. 0.2, attributed either to calcium-bridging attraction of lipid headgroups to the negatively-charged substrate, or a shift of the slip plane to the more dissipative hydrophobic-tail vs. hydrophobic-tail interface. Our results suggest that the heterogeneity and composition of the OA-hSF lipids may be associated with higher friction at the cartilage boundary layers, and thus a connection with greater wear and degradation, due to hemifusion of the exposed lipid bilayers.

The cross section of the process $e^+e^-\to \pi^+D^0D^{*-}$ for center-of-mass energies from 4.05 to 4.60~GeV is measured precisely using data samples collected with the BESIII detector operating at the BEPCII storage ring. Two enhancements are clearly visible in the cross section around 4.23 and 4.40~GeV. Using several models to describe the dressed cross section yields stable parameters for the first enhancement, which has a mass of $4228.6 \pm 4.1 \pm 6.3 \un{MeV}/c^2$and a width of$77.0 \pm 6.8 \pm 6.3 \un{MeV}$, where the first uncertainties are statistical and the second ones are systematic. Our resonant mass is consistent with previous observations of the $Y(4220)$ state and the theoretical prediction of a $D\bar{D}_1(2420)$ molecule. This result is the first observation of $Y(4220)$ associated with an open-charm final state. Fits with three resonance functions with additional $Y(4260)$, $Y(4320)$, $Y(4360)$, $\psi(4415)$, or a new resonance, do not show significant contributions from either of these resonances. The second enhancement is not from a single known resonance. It could contain contributions from $\psi(4415)$ and other resonances, and a detailed amplitude analysis is required to better understand this enhancement.