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L 98-59 d is a Super-Earth planet orbiting an M-type star. We performed retrievals on the transmission spectrum of L 98-59 d obtained using NIRSpec G395H during a single transit, from JWST Cycle 1 GTO 1224. The wavelength range of this spectrum allows us to detect the presence of several atmospheric species. We found that the spectrum is consistent with a high mean molecular weight atmosphere. The atmospheric spectrum indicates the possible presence of the sulfur-bearing species H$_2$S and SO$_2$, which could hint at active volcanism on this planet if verified by future observations. We also tested for signs of stellar contamination in the spectrum, and found signs of unocculted faculae on the star. The tentative signs of an atmosphere on L 98-59 d presented in this work from just one transit bodes well for possible molecular detections in the future, particularly as it is one of the best targets among small exoplanets for atmospheric characterization using JWST., Comment: Accepted for publication to ApJL

NEMESISPY is a Python package developed to perform parametric atmospheric modelling and radiative transfer calculation for the retrievals of exoplanetary spectra. It is a recent development of the well-established Fortran NEMESIS library (P. G. J. Irwin et al., 2008), which has been applied to the atmospheric retrievals of both solar system planets and exoplanets employing numerous different observing geometries. NEMESISPY can be easily interfaced with Bayesian inference algorithms to retrieve atmospheric properties from spectroscopic observations. Recently, NEMESISPY has been applied to the retrievals of Hubble and Spitzer data of a hot Jupiter (Yang et al., 2023), as well as to JWST/Mid-Infrared Instrument (JWST/MIRI) data of a hot Jupiter (Yang et al., 2024)., Comment: Under review at the Journal of Open Source Software. Project website: https://jingxuan97.github.io/nemesispy/

We present two-dimensional near-infrared temperature maps of the canonical hot Jupiter WASP-43b using a phase-curve observation with JWST NIRSpec/G395H. From the white-light planetary transit, we improve constraints on the planet's orbital parameters and measure a planet-to-star radius ratio of $0.15883^{+0.00056}_{-0.00053}$. Using the white-light phase curve, we measure a longitude of maximum brightness of $6.9^{+0^\circ.5}_{-0^\circ.5}$ east of the substellar point and a phase-curve offset of $10.0^{+0^\circ.8}_{-0^\circ.8}$. We also find an $\approx4\sigma$ detection of a latitudinal hotspot offset of $-13.4^{+3^\circ.2}_{-1^\circ.7}$, the first significant detection of a non-equatorial hotspot in an exoplanet atmosphere. We show that this detection is robust to variations within planetary parameter uncertainties, but only if the transit is used to improve constraints, showing the importance of transit observations to eclipse mapping. Maps retrieved from the NRS1 and NRS2 detectors are similar, with hotspot locations consistent between the two detectors at the $1\sigma$ level. Our JWST data show brighter (hotter) nightsides and a dimmer (colder) dayside at the shorter wavelengths relative to fits to \textit{Spitzer} 3.6 and 4.5 \microns\ phase curves. Through comparison between our phase curves and a set of general circulation models, we find evidence for clouds on the nightside and atmospheric drag or high metallicity reducing the eastward hotspot offset., Comment: 15 pages, 8 figures. In review at ApJ Letters

Spectroscopic phase curves of hot Jupiters measure their emission spectra at multiple orbital phases, thus enabling detailed characterisation of their atmospheres. Precise constraints on the atmospheric composition of these exoplanets offer insights into their formation and evolution. We analyse four phase-resolved emission spectra of the hot Jupiter WASP-43b, generated from a phase curve observed with the MIRI/LRS onboard the JWST, to retrieve its atmospheric properties. Using a parametric 2D temperature model and assuming a chemically homogeneous atmosphere within the observed pressure region, we simultaneously fit the four spectra to constrain the abundances of atmospheric constituents, thereby yielding more precise constraints than previous work that analysed each spectrum independently. Our analysis reveals statistically significant evidence of NH3 (4$\sigma$) in a hot Jupiter's emission spectra for the first time, along with evidence of H2O (6.5$\sigma$), CO (3.1$\sigma$), and a non-detection of CH4. With our abundance constraints, we tentatively estimate the metallicity of WASP-43b at 0.6-6.5$\times$solar and its C/O ratio at 0.6-0.9. Our findings offer vital insights into the atmospheric conditions and formation history of WASP-43b by simultaneously constraining the abundances of carbon, oxygen, and nitrogen-bearing species., Comment: 17 pages, 14 figures, accepted for publication in MNRAS. Comments welcome!

We present eclipse maps of the two-dimensional thermal emission from the dayside of the hot Jupiter WASP-43b, derived from an observation of a phase curve with the JWST MIRI/LRS instrument. The observed eclipse shapes deviate significantly from those expected for a planet emitting uniformly over its surface. We fit a map to this deviation, constructed from spherical harmonics up to order $\ell_{\rm max}=2$, alongside the planetary, orbital, stellar, and systematic parameters. This yields a map with a meridionally-averaged eastward hot-spot shift of $(7.75 \pm 0.36)^{\circ}$, with no significant degeneracy between the map and the additional parameters. We show the latitudinal and longitudinal contributions of the day-side emission structure to the eclipse shape, finding a latitudinal signal of $\sim$200 ppm and a longitudinal signal of $\sim$250 ppm. To investigate the sensitivity of the map to the method, we fix the non-mapping parameters and derive an "eigenmap" fitted with an optimised number of orthogonal phase curves, which yields a similar map to the $\ell_{\rm max}=2$ map. We also fit a map up to $\ell_{\rm max}=3$, which shows a smaller hot-spot shift, with a larger uncertainty. These maps are similar to those produced by atmospheric simulations. We conclude that there is a significant mapping signal which constrains the spherical harmonic components of our model up to $\ell_{\rm max}=2$. Alternative mapping models may derive different structures with smaller-scale features; we suggest that further observations of WASP-43b and other planets will drive the development of more robust methods and more accurate maps., Comment: Accepted for publication in The Astronomical Journal

The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and brown dwarfs. It covers the following data-related topics: molecular and atomic line lists, line profiles, computed cross-sections and opacities, collision-induced absorption and other continuum data, optical properties of aerosols and surfaces, atmospheric chemistry, UV photodissociation and photoabsorption cross-sections, and standards in the description and format of such data. These data aspects are discussed by addressing the following questions for each topic, based on the experience of the "data-provider" and "data-user" communities: (1) what are the types and sources of currently available data, (2) what work is currently in progress, and (3) what are the current and anticipated data needs. We present a GitHub platform for Ariel-related data, with the goal to provide a go-to place for both data-users and data-providers, for the users to make requests for their data needs and for the data-providers to link to their available data. Our aim throughout the paper is to provide practical information on existing sources of data whether in databases, theoretical, or literature sources., Comment: 58 pages, submitted to RAS Techniques and Instruments (RASTI). The authors welcome feedback: corresponding author emails can be found as footnotes on page 2

Intelligent systems are increasingly integral to our daily lives, yet rare safety-critical events present significant latent threats to their practical deployment. Addressing this challenge hinges on accurately predicting the probability of safety-critical events occurring within a given time step from the current state, a metric we define as 'criticality'. The complexity of predicting criticality arises from the extreme data imbalance caused by rare events in high dimensional variables associated with the rare events, a challenge we refer to as the curse of rarity. Existing methods tend to be either overly conservative or prone to overlooking safety-critical events, thus struggling to achieve both high precision and recall rates, which severely limits their applicability. This study endeavors to develop a criticality prediction model that excels in both precision and recall rates for evaluating the criticality of safety-critical autonomous systems. We propose a multi-stage learning framework designed to progressively densify the dataset, mitigating the curse of rarity across stages. To validate our approach, we evaluate it in two cases: lunar lander and bipedal walker scenarios. The results demonstrate that our method surpasses traditional approaches, providing a more accurate and dependable assessment of criticality in intelligent systems.

The assessment of safety performance plays a pivotal role in the development and deployment of connected and automated vehicles (CAVs). A common approach involves designing testing scenarios based on prior knowledge of CAVs (e.g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances. However, substantial differences between CAVs and the prior knowledge can significantly diminish the evaluation efficiency. In response to this issue, existing studies predominantly concentrate on the adaptive design of testing scenarios during the CAV testing process. Yet, these methods have limitations in their applicability to high-dimensional scenarios. To overcome this challenge, we develop an adaptive testing environment that bolsters evaluation robustness by incorporating multiple surrogate models and optimizing the combination coefficients of these surrogate models to enhance evaluation efficiency. We formulate the optimization problem as a regression task utilizing quadratic programming. To efficiently obtain the regression target via reinforcement learning, we propose the dense reinforcement learning method and devise a new adaptive policy with high sample efficiency. Essentially, our approach centers on learning the values of critical scenes displaying substantial surrogate-to-real gaps. The effectiveness of our method is validated in high-dimensional overtaking scenarios, demonstrating that our approach achieves notable evaluation efficiency.

Testing and evaluating the safety performance of autonomous vehicles (AVs) is essential before the large-scale deployment. Practically, the number of testing scenarios permissible for a specific AV is severely limited by tight constraints on testing budgets and time. With the restrictions imposed by strictly restricted numbers of tests, existing testing methods often lead to significant uncertainty or difficulty to quantifying evaluation results. In this paper, we formulate this problem for the first time the "few-shot testing" (FST) problem and propose a systematic framework to address this challenge. To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity. Specifically, under the guidance of better generalization ability of the testing scenario set on AVs, we dynamically adjust this set and the contribution of each testing scenario to the evaluation result based on coverage, leveraging the prior information of surrogate models (SMs). With certain hypotheses on SMs, a theoretical upper bound of evaluation error is established to verify the sufficiency of evaluation accuracy within the given limited number of tests. The experiment results on cut-in scenarios demonstrate a notable reduction in evaluation error and variance of our method compared to conventional testing methods, especially for situations with a strict limit on the number of scenarios.

Spectroscopic phase curves of transiting hot Jupiters are spectral measurements at multiple orbital phases, giving a set of disc-averaged spectra that probe multiple hemispheres. By fitting model phase curves to observations, we can constrain the atmospheric properties of hot Jupiters such as molecular abundance, aerosol distribution and thermal structure, which offer insights into their dynamics, chemistry, and formation. In this work, we propose a novel 2D temperature scheme consisting of a dayside and a nightside to retrieve information from near-infrared phase curves, and apply the scheme to phase curves of WASP-43b observed by HST/WFC3 and Spitzer/IRAC. In our scheme, temperature is constant on isobars on the nightside and varies with cos$^n$(longitude/$\epsilon$) on isobars on the dayside, where $n$ and $\epsilon$ are free parameters. We fit all orbital phases simultaneously using the radiative transfer package NEMESISPY coupled to a Bayesian inference code. We first validate the performance of our retrieval scheme with synthetic phase curves generated from a GCM, and find our 2D scheme can accurately retrieve the latitudinally-averaged thermal structure and constrain the abundance of H$_2$O and CH$_4$. We then apply our 2D scheme to the observed phase curves of WASP-43b and find: (1) the dayside temperature-pressure profiles do not vary strongly with longitude and are non-inverted; (2) the retrieved nightside temperatures are extremely low, suggesting significant nightside cloud coverage; (3) the H$_2$O volume mixing ratio is constrained to $5.6\times10^{-5}$--$4.0\times10^{-4}$, and we retrieve an upper bound for CH$_4$ at $\sim$10$^{-6}$., Comment: 23 pages, 20 figures, 3 tables, accepted for publication in MNRAS

Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances. However, significant differences between CAVs and prior knowledge could severely reduce the evaluation efficiency. Towards addressing this issue, most existing studies focus on the adaptive design of testing scenarios during the CAV testing process, but so far they cannot be applied to high-dimensional scenarios. In this paper, we focus on the adaptive safety performance evaluation by leveraging the testing results, after the CAV testing process. It can significantly improve the evaluation efficiency and be applied to high-dimensional scenarios. Specifically, instead of directly evaluating the unknown quantity (e.g., crash rates) of CAV safety performances, we evaluate the differences between the unknown quantity and known quantity (i.e., control variates). By leveraging the testing results, the control variates could be well designed and optimized such that the differences are close to zero, so the evaluation variance could be dramatically reduced for different CAVs. To handle the high-dimensional scenarios, we propose the sparse control variates method, where the control variates are designed only for the sparse and critical variables of scenarios. According to the number of critical variables in each scenario, the control variates are stratified into strata and optimized within each stratum using multiple linear regression techniques. We justify the proposed method's effectiveness by rigorous theoretical analysis and empirical study of high-dimensional overtaking scenarios.

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times.

In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are closely related, and a joint model benefits both tasks. We refer to our model as DiscProReco, and it first encodes the tokens in each utterance in a conversation with a directed Graph Convolutional Network (GCN). The token states for an utterance are then aggregated to produce a single state for each utterance. The utterance states are then fed into a biaffine classifier to construct a conversational discourse graph. A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer. The joint model is trained and evaluated on a new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) dataset that we annotated with both two types of information. Experimental results on the SPDPR dataset and other benchmarks show that DiscProReco significantly outperforms the state-of-the-art baselines of both tasks., Comment: Accepted by ACL2021

Pronouns are often dropped in Chinese conversations and recovering the dropped pronouns is important for NLP applications such as Machine Translation. Existing approaches usually formulate this as a sequence labeling task of predicting whether there is a dropped pronoun before each token and its type. Each utterance is considered to be a sequence and labeled independently. Although these approaches have shown promise, labeling each utterance independently ignores the dependencies between pronouns in neighboring utterances. Modeling these dependencies is critical to improving the performance of dropped pronoun recovery. In this paper, we present a novel framework that combines the strength of Transformer network with General Conditional Random Fields (GCRF) to model the dependencies between pronouns in neighboring utterances. Results on three Chinese conversation datasets show that the Transformer-GCRF model outperforms the state-of-the-art dropped pronoun recovery models. Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements., Comment: Accept as EMNLP-findings 2020

Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context. Recovering dropped pronouns is essential to applications such as Information Extraction where the referents of these dropped pronouns need to be resolved, or Machine Translation when Chinese is the source language. In this work, we present a novel end-to-end neural network model to recover dropped pronouns in conversational data. Our model is based on a structured attention mechanism that models the referents of dropped pronouns utilizing both sentence-level and word-level information. Results on three different conversational genres show that our approach achieves a significant improvement over the current state of the art., Comment: accepted by NAACL 2019

Next basket recommendation, which aims to predict the next a few items that a user most probably purchases given his historical transactions, plays a vital role in market basket analysis. From the viewpoint of item, an item could be purchased by different users together with different items, for different reasons. Therefore, an ideal recommender system should represent an item considering its transaction contexts. Existing state-of-the-art deep learning methods usually adopt the static item representations, which are invariant among all of the transactions and thus cannot achieve the full potentials of deep learning. Inspired by the pre-trained representations of BERT in natural language processing, we propose to conduct context-aware item representation for next basket recommendation, called Item Encoder Representations from Transformers (IERT). In the offline phase, IERT pre-trains deep item representations conditioning on their transaction contexts. In the online recommendation phase, the pre-trained model is further fine-tuned with an additional output layer. The output contextualized item embeddings are used to capture users' sequential behaviors and general tastes to conduct recommendation. Experimental results on the Ta-Feng data set show that IERT outperforms the state-of-the-art baseline methods, which demonstrated the effectiveness of IERT in next basket representation.