With ChatGPT under the spotlight, utilizing large language models (LLMs) to assist academic writing has drawn a significant amount of debate in the community. In this paper, we aim to present a comprehensive study of the detectability of ChatGPT-generated content within the academic literature, particularly focusing on the abstracts of scientific papers, to offer holistic support for the future development of LLM applications and policies in academia. Specifically, we first present GPABench2, a benchmarking dataset of over 2.8 million comparative samples of human-written, GPT-written, GPT-completed, and GPT-polished abstracts of scientific writing in computer science, physics, and humanities and social sciences. Second, we explore the methodology for detecting ChatGPT content. We start by examining the unsatisfactory performance of existing ChatGPT detecting tools and the challenges faced by human evaluators (including more than 240 researchers or students). We then test the hand-crafted linguistic features models as a baseline and develop a deep neural framework named CheckGPT to better capture the subtle and deep semantic and linguistic patterns in ChatGPT written literature. Last, we conduct comprehensive experiments to validate the proposed CheckGPT framework in each benchmarking task over different disciplines. To evaluate the detectability of ChatGPT content, we conduct extensive experiments on the transferability, prompt engineering, and robustness of CheckGPT.

LTT-9779 b is an ultra-hot Neptune (Rp ~ 4.7 Re, Mp ~ 29 Me) orbiting its Sun-like host star in just 19 hours, placing it deep within the "hot Neptune desert," where Neptunian planets are seldom found. We present new JWST NIRSpec G395H phase-curve observations that probe its atmospheric composition in unprecedented detail. At near-infrared wavelengths, which penetrate the high-altitude clouds inferred from previous NIRISS/SOSS spectra, thermal emission reveals a carbon-rich atmosphere with opacity dominated by carbon monoxide (CO) and carbon dioxide (CO2). Both species are detected at all orbital phases, with retrieved mixing ratios of 10^-1 for CO and 10^-4 for CO2, indicating a globally well-mixed reservoir of carbon-bearing gases. We also moderately detect water vapor (H2O) and tentatively detect sulfur dioxide (SO2), providing insight into its chemistry and possible photochemical production under intense stellar irradiation. From these detections we infer a carbon-to-oxygen ratio near unity (C/O ~ 1) and a metallicity exceeding 500X Solar, consistent with equilibrium chemistry predictions for high-temperature atmospheres. This enrichment raises the mean molecular weight, reducing atmospheric escape, and likely helps LTT-9779 b retain a substantial atmosphere despite extreme irradiation. Our findings show that LTT-9779 b survives where few planets can, maintaining a carbon-rich atmosphere in a region where hot Neptune-class worlds are expected to evaporate. This makes LTT-9779 b a valuable laboratory for studying atmospheric escape and chemical processes under extreme conditions, offering new insight into the survival of planets in the hot Neptune desert.

Time series forecasting underpins vital decision-making across various sectors, yet raw predictions from sophisticated models often harbor systematic errors and biases. We examine the Forecast-Then-Optimize (FTO) framework, pioneering its systematic synopsis. Unlike conventional Predict-Then-Optimize (PTO) methods, FTO explicitly refines forecasts through optimization techniques such as ensemble methods, meta-learners, and uncertainty adjustments. Furthermore, deep learning and large language models have established superiority over traditional parametric forecasting models for most enterprise applications. This paper surveys significant advancements from 2016 to 2025, analyzing mainstream deep learning FTO architectures. Focusing on real-world applications in operations management, we demonstrate FTO's crucial role in enhancing predictive accuracy, robustness, and decision efficacy. Our study establishes foundational guidelines for future forecasting methodologies, bridging theory and operational practicality.

Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photography devices or created by human artists (i.e., real images). While the outstanding performance of such generative models is generally well received, security concerns arise. For instance, such image generators could be used to facilitate fraud or scam schemes, generate and spread misinformation, or produce fabricated artworks. In this paper, we present a systematic attempt at understanding and detecting AI-generated images (AI-art) in adversarial scenarios. First, we collect and share a dataset of real images and their corresponding artificial counterparts generated by four popular AI image generators. The dataset, named ARIA, contains over 140K images in five categories: artworks (painting), social media images, news photos, disaster scenes, and anime pictures. This dataset can be used as a foundation to support future research on adversarial AI-art. Next, we present a user study that employs the ARIA dataset to evaluate if real-world users can distinguish with or without reference images. In a benchmarking study, we further evaluate if state-of-the-art open-source and commercial AI image detectors can effectively identify the images in the ARIA dataset. Finally, we present a ResNet-50 classifier and evaluate its accuracy and transferability on the ARIA dataset.

NbOI$_2$ has recently emerged as a new van der Waals material combining semiconducting behavior with intrinsic in plane ferroelectricity and pronounced transport and optical anisotropy. However, its photocarrier dynamics remain largely unexplored. Here we report transient absorption spectroscopy of NbOI$_2$ using femtosecond pump probe reflectance measurements. A pronounced transient absorption feature is observed near the 2.34 eV excitonic resonance, arising from photocarrier induced excitonic energy shifts and saturation. The decay dynamics reveal an exciton lifetime of several tens of picoseconds and show density-dependent behavior consistent with exciton exciton annihilation, yielding an annihilation coefficient of 0.09 cm$^2$ s$^{-1}$, which is comparable to that in monolayer transition metal dichalcogenides. Polarization resolved measurements further reveal a pronounced in-plane anisotropy in the transient response that follows the linear absorption anisotropy. These findings provide fundamental insight into photocarrier dynamics in NbOI$_2$ and establish key parameters for understanding and exploiting its optoelectronic behavior.

This work presents single-differential electron-neutrino charged-current cross sections on argon measured using the MicroBooNE detector at the Fermi National Accelerator Laboratory. The analysis uses data recorded when the Neutrinos at the Main Injector beam was operating in both neutrino and antineutrino modes, with exposures of $2 \times 10^{20}$ and $5 \times 10^{20}$ protons on target, respectively. A selection algorithm targeting electron-neutrino charged-current interactions with at least one proton, one electron, and no pions in the final topology is used to measure differential cross sections as a function of outgoing electron energy, total visible energy, and opening angle between the electron and the most energetic proton. The interaction rate as a function of proton multiplicity is also reported. The total cross section is measured as [4.1 $\pm$ 0.4 (stat.) $\pm$ 1.2 (syst.)]$\,$$\times \,10^{-39} \mathrm{cm}^{2}/\,\mathrm{nucleon}$. The unfolded cross-section measurements are compared to predictions from neutrino event generators commonly employed in the field. Good agreement is seen across all variables within uncertainties.

A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust performance against diverse channel conditions, they also have a major drawback: they require processing high-dimensional features, which can be prohibitive for mobile applications. In this work, we design a positioning neural network (P-NN) that substantially reduces the complexity of deep learning-based WP through carefully crafted minimum description features. Our feature selection is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We also develop a novel methodology for adaptively selecting the size of feature space, which optimizes over balancing the expected amount of useful information and classification capability, quantified using information-theoretic measures on the signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP).

Tabular learning transforms raw features into optimized spaces for downstream tasks, but its effectiveness deteriorates under distribution shifts between training and testing data. We formalize this challenge as the Distribution Shift Tabular Learning (DSTL) problem and propose a novel Shift-Aware Feature Transformation (SAFT) framework to address it. SAFT reframes tabular learning from a discrete search task into a continuous representation-generation paradigm, enabling differentiable optimization over transformed feature sets. SAFT integrates three mechanisms to ensure robustness: (i) shift-resistant representation via embedding decorrelation and sample reweighting, (ii) flatness-aware generation through suboptimal embedding averaging, and (iii) normalization-based alignment between training and test distributions. Extensive experiments show that SAFT consistently outperforms prior tabular learning methods in terms of robustness, effectiveness, and generalization ability under diverse real-world distribution shifts.

We report a new measurement of flux-integrated differential cross sections for charged-current (CC) muon neutrino interactions with argon nuclei that produce no final state pions $(\nu_\mu\mathrm{CC}0\pi)$. These interactions are of particular importance as a topologically defined signal dominated by quasielastic-like interactions. This measurement was performed with the MicroBooNE liquid argon time projection chamber detector located at the Fermilab Booster Neutrino Beam (BNB), and uses an exposure of $1.3\times10^{21}$ protons on target collected between 2015 and 2020. The results are presented in terms of single and double-differential cross sections as a function of the final state muon momentum and angle. The data are compared with widely-used neutrino event generators. We find good agreement with the single-differential measurements, while only a subset of generators are also able to adequately describe the data in double-differential distributions. This work facilitates comparison with Cherenkov detector measurements, including those located at the BNB.

Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-centric perspective, improving data quality is essential for enhancing model performance in tabular data-driven applications. This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces. Feature selection aims to identify and retain the most informative attributes, while feature generation constructs new features to better capture complex data patterns. We systematically review existing generative methods for tabular data engineering, analyzing their latest advancements, real-world applications, and respective strengths and limitations. This survey emphasizes how RL-based and generative techniques contribute to the automation and intelligence of feature engineering. Finally, we summarize the existing challenges and discuss future research directions, aiming to provide insights that drive continued innovation in this field.

We propose and study a system of Schrödinger's problems and functional equations in probability theory. More precisely, we consider a system of variational problems of relative entropies for probability measures with given two endpoint marginals, which can be defined inductively. We also consider an inductively defined system of functional equations, which are Euler's equations for our variational problems. These are generalizations of Schrödinger's problem and functional equation in probability theory. We prove the existence and uniqueness of solutions to our functional equations up to a multiplicative function, from which we show the existence and uniqueness of a minimizer of our variational problem. Our problem gives an approach for a stochastic optimal transport analog of the Knothe--Rosenblatt rearrangement from a variational problem point of view.

We present a simple direct discretization for functionals used in the variational mesh generation and adaptation. Meshing functionals are discretized on simplicial meshes and the Jacobian matrix of the continuous coordinate transformation is approximated by the Jacobian matrices of affine mappings between elements. The advantage of this direct geometric discretization is that it preserves the basic geometric structure of the continuous functional, which is useful in preventing strong decoupling or loss of integral constraints satisfied by the functional. Moreover, the discretized functional is a function of the coordinates of mesh vertices and its derivatives have a simple analytical form, which allows a simple implementation of variational mesh generation and adaptation on computer. Since the variational mesh adaptation is the base for a number of adaptive moving mesh and mesh smoothing methods, the result in this work can be used to develop simple implementations of those methods. Numerical examples are given.

LTT-9779 b is an ultra-hot Neptune (Rp ~ 4.7 Re, Mp ~ 29 Me) orbiting its Sun-like host star in just 19 hours, placing it deep within the "hot Neptune desert," where Neptunian planets are seldom found. We present new JWST NIRSpec G395H phase-curve observations that probe its atmospheric composition in unprecedented detail. At near-infrared wavelengths, which penetrate the high-altitude clouds inferred from previous NIRISS/SOSS spectra, thermal emission reveals a carbon-rich atmosphere with opacity dominated by carbon monoxide (CO) and carbon dioxide (CO2). Both species are detected at all orbital phases, with retrieved mixing ratios of 10^-1 for CO and 10^-4 for CO2, indicating a globally well-mixed reservoir of carbon-bearing gases. We also moderately detect water vapor (H2O) and tentatively detect sulfur dioxide (SO2), providing insight into its chemistry and possible photochemical production under intense stellar irradiation. From these detections we infer a carbon-to-oxygen ratio near unity (C/O ~ 1) and a metallicity exceeding 500X Solar, consistent with equilibrium chemistry predictions for high-temperature atmospheres. This enrichment raises the mean molecular weight, reducing atmospheric escape, and likely helps LTT-9779 b retain a substantial atmosphere despite extreme irradiation. Our findings show that LTT-9779 b survives where few planets can, maintaining a carbon-rich atmosphere in a region where hot Neptune-class worlds are expected to evaporate. This makes LTT-9779 b a valuable laboratory for studying atmospheric escape and chemical processes under extreme conditions, offering new insight into the survival of planets in the hot Neptune desert.

NbOI$_2$ has recently emerged as a new van der Waals material combining semiconducting behavior with intrinsic in plane ferroelectricity and pronounced transport and optical anisotropy. However, its photocarrier dynamics remain largely unexplored. Here we report transient absorption spectroscopy of NbOI$_2$ using femtosecond pump probe reflectance measurements. A pronounced transient absorption feature is observed near the 2.34 eV excitonic resonance, arising from photocarrier induced excitonic energy shifts and saturation. The decay dynamics reveal an exciton lifetime of several tens of picoseconds and show density-dependent behavior consistent with exciton exciton annihilation, yielding an annihilation coefficient of 0.09 cm$^2$ s$^{-1}$, which is comparable to that in monolayer transition metal dichalcogenides. Polarization resolved measurements further reveal a pronounced in-plane anisotropy in the transient response that follows the linear absorption anisotropy. These findings provide fundamental insight into photocarrier dynamics in NbOI$_2$ and establish key parameters for understanding and exploiting its optoelectronic behavior.

This note represents an attempt to gather the input related to light-by-light scattering ($\gamma\gamma$) cross-section measurements at LHC with the aim of checking the consistency with different standard model predictions. For the first time, we also consider the contribution from the $\eta_b(1S)$ meson production to the diphoton invariant mass distribution, by calculating its inclusive photoproduction cross-section. Using a simplified set of assumptions, we find a result of $115\pm 19\,\,\text{nb}$, consistent with standard model predictions within two standard deviations. Although an improved determination of the integrated fiducial $\textrm{PbPb}\,(\gamma\gamma)\to \textrm{Pb}^{(\ast)}\textrm{+}\textrm{Pb}^{(\ast)}\,\gamma\gamma$ cross-section by approximately 10\% could be potentially achieved relative to current measurements, further improvements are expected with the inclusion of existing or forthcoming LHC nuclear data.

The goal of this whitepaper is to give a comprehensive overview of the rich field of forward physics. We discuss the occurrences of BFKL resummation effects in special final states, such as Mueller-Navelet jets, jet gap jets, and heavy quarkonium production. It further addresses TMD factorization at low x and the manifestation of a semi-hard saturation scale in (generalized) TMD PDFs. More theoretical aspects of low x physics, probes of the quark gluon plasma, as well as the possibility to use photon-hadron collisions at the LHC to constrain hadronic structure at low x, and the resulting complementarity between LHC and the EIC are also presented. We also briefly discuss diffraction at colliders as well as the possibility to explore further the electroweak theory in central exclusive events using the LHC as a photon-photon collider.

Lithium-ion batteries are the enabling power source for transportation electrification. However, in real-world applications, they remain vulnerable to internal short circuits (ISCs) and the consequential risk of thermal runaway (TR). Toward addressing the challenge of ISCs and TR, we undertake a systematic study that extends from dynamic modeling to fault detection in this paper. First, we develop {\em BattBee}, the first equivalent circuit model to specifically describe the onset of ISCs and the evolution of subsequently induced TR. Drawing upon electrochemical modeling, the model can simulate ISCs at different severity levels and predict their impact on the initiation and progression of TR events. With the physics-inspired design, this model offers strong physical interpretability and predictive accuracy, while maintaining structural simplicity to allow fast computation. Then, building upon the BattBee model, we develop fault detection observers and derive detection criteria together with decision-making logics to identify the occurrence and emergence of ISC and TR events. This detection approach is principled in design and fast in computation, lending itself to practical applications. Validation based on simulations and experimental data demonstrates the effectiveness of both the BattBee model and the ISC/TR detection approach. The research outcomes underscore this study's potential for real-world battery safety risk management.

The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences. To this end, we first propose an efficient approach to convert the RNA sequences into images characterizing their base-pairing probability. As a result, classifying RNA sequences is converted to an image classification problem that can be efficiently solved by available CNN-based classification models. The paper also considers the folding potential of the ncRNAs in addition to their primary sequence. Based on the proposed approach, a benchmark image classification dataset is generated from the RFAM database of ncRNA sequences. In addition, three classical CNN models have been implemented and compared to demonstrate the superior performance and efficiency of the proposed approach. Extensive experimental results show the great potential of using deep learning approaches for RNA classification.

The moving mesh PDE (MMPDE) method for variational mesh generation and adaptation is studied theoretically at the discrete level, in particular the nonsingularity of the obtained meshes. Meshing functionals are discretized geometrically and the MMPDE is formulated as a modified gradient system of the corresponding discrete functionals for the location of mesh vertices. It is shown that if the meshing functional satisfies a coercivity condition, then the mesh of the semi-discrete MMPDE is nonsingular for all time if it is nonsingular initially. Moreover, the altitudes and volumes of its elements are bounded below by positive numbers depending only on the number of elements, the metric tensor, and the initial mesh. Furthermore, the value of the discrete meshing functional is convergent as time increases, which can be used as a stopping criterion in computation. Finally, the mesh trajectory has limiting meshes which are critical points of the discrete functional. The convergence of the mesh trajectory can be guaranteed when a stronger condition is placed on the meshing functional. Two meshing functionals based on alignment and equidistribution are known to satisfy the coercivity condition. The results also hold for fully discrete systems of the MMPDE provided that the time step is sufficiently small and a numerical scheme preserving the property of monotonically decreasing energy is used for the temporal discretization of the semi-discrete MMPDE. Numerical examples are presented.

The graph edit distance (GED) is a well-established distance measure widely used in many applications. However, existing methods for the GED computation suffer from several drawbacks including oversized search space, huge memory consumption, and lots of expensive backtracking. In this paper, we present BSS_GED, a novel vertex-based mapping method for the GED computation. First, we create a small search space by reducing the number of invalid and redundant mappings involved in the GED computation. Then, we utilize beam-stack search combined with two heuristics to efficiently compute GED, achieving a flexible trade-off between available memory and expensive backtracking. Extensive experiments demonstrate that BSS GED is highly efficient for the GED computation on sparse as well as dense graphs and outperforms the state-of-the-art GED methods. In addition, we also apply BSS_GED to the graph similarity search problem and the practical results confirm its efficiency.