Understanding Exoplanet Atmospheres

The study of exoplanet atmospheres is a blossoming field. Over the past two decades, dozens of hot gas giant atmospheres have been observed using a variety of techniques with both space and ground telescopes, revealing the presence of water, the ubiquity of clouds, the presence of equatorial jets, the existence of photoevaporation, and much more. For the far more abundant planets smaller than ~3 R⊕, which potentially have a wide variety of exotic atmospheric compositions, observations are more challenging. We are just beginning to characterize their atmospheres. This thesis consists of 6 papers on the topic of exoplanet atmospheres. In paper 1, we use Spitzer observations to make rudimentary 1D maps of two hot giant planets, allowing us to infer atmospheric circulation properties and compare them to models and to similar observations of other giant planets. In paper 2, we present PLATON, a fast, open source, easy to use, and easy to understand Python package that calculates transmission spectra for exoplanets and retrieves atmospheric characteristics based on observed spectra. PLATON supports the most common atmospheric parameters, in addition to less commonly included features such as a Mie scattering cloud model and unocculted starspot corrections. In paper 3, we add significant improvements to PLATON, including updated molecular opacities and emission spectra capability. In addition, we perform the most comprehensive retrieval on published HST and Spitzer transmission and emission spectra of the archetypal hot Jupiter HD 189733b, finding that they are well-matched by a moderately metal-enhanced atmosphere with a solar C/O ratio where the terminator is dominated by extended nm-sized hazes. Papers 4-6 cover mass loss from sub-Neptunes. In paper 4, we present a tight upper limit on the amount of escaping helium from the the archetypal super Earth 55 Cnc e, suggesting that it has no primordial (H/He) atmosphere. In paper 5, we obtain the first detection of an escaping atmosphere from a young mini Neptune by measuring Lyα absorption from HD 63433c. We do not detect absorption from the inner planet, suggesting that the inner planet may have lost its primordial atmosphere while the outer one has not. In paper 6, we detect escaping helium from a young mini Neptune for the first time. The inferred mass loss rate is high enough to strip a significant portion of the atmosphere within the planet's lifetime; combined with the previous paper, these observations support the canonical explanation of mini Neptunes as rocky planets with a substantial primordial H/He atmosphere and validate models predicting that mini Neptunes can transform into super Earths.</p

Evaluating Membership Inference Through Adversarial Robustness

The usage of deep learning is being escalated in many applications. Due to its outstanding performance, it is being used in a variety of security and privacy-sensitive areas in addition to conventional applications. One of the key aspects of deep learning efficacy is to have abundant data. This trait leads to the usage of data which can be highly sensitive and private, which in turn causes wariness with regard to deep learning in the general public. Membership inference attacks are considered lethal as they can be used to figure out whether a piece of data belongs to the training dataset or not. This can be problematic with regards to leakage of training data information and its characteristics. To highlight the significance of these types of attacks, we propose an enhanced methodology for membership inference attacks based on adversarial robustness, by adjusting the directions of adversarial perturbations through label smoothing under a white-box setting. We evaluate our proposed method on three datasets: Fashion-MNIST, CIFAR-10, and CIFAR-100. Our experimental results reveal that the performance of our method surpasses that of the existing adversarial robustness-based method when attacking normally trained models. Additionally, through comparing our technique with the state-of-the-art metric-based membership inference methods, our proposed method also shows better performance when attacking adversarially trained models. The code for reproducing the results of this work is available at \url{https://github.com/plll4zzx/Evaluating-Membership-Inference-Through-Adversarial-Robustness}.Comment: Accepted by The Computer Journal. Pre-print versio

Masked Language Model Based Textual Adversarial Example Detection

Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown that adversarial examples tend to deviate from the underlying data manifold of normal examples, whereas pre-trained masked language models can fit the manifold of normal NLP data. To explore how to use the masked language model in adversarial detection, we propose a novel textual adversarial example detection method, namely Masked Language Model-based Detection (MLMD), which can produce clearly distinguishable signals between normal examples and adversarial examples by exploring the changes in manifolds induced by the masked language model. MLMD features a plug and play usage (i.e., no need to retrain the victim model) for adversarial defense and it is agnostic to classification tasks, victim model's architectures, and to-be-defended attack methods. We evaluate MLMD on various benchmark textual datasets, widely studied machine learning models, and state-of-the-art (SOTA) adversarial attacks (in total $3*4*4 = 48$ settings). Experimental results show that MLMD can achieve strong performance, with detection accuracy up to 0.984, 0.967, and 0.901 on AG-NEWS, IMDB, and SST-2 datasets, respectively. Additionally, MLMD is superior, or at least comparable to, the SOTA detection defenses in detection accuracy and F1 score. Among many defenses based on the off-manifold assumption of adversarial examples, this work offers a new angle for capturing the manifold change. The code for this work is openly accessible at \url{https://github.com/mlmddetection/MLMDdetection}.Comment: 13 pages,3 figure

Cloning and characterization of two subunits of calcineurin cDNA in naked carp (Gymnocypris przewalskii) from Lake Qinghai, China

The naked carp (Gymnocypris przewalskii), a native teleost, plays an important role in maintenance of the ecological balance in the system of Lake Qinghai (altitude, 3.2 km) on the Qinghai-Tibet Plateau in China. Calcineurin (CN) is the only member of the serine/threonine phosphatase family that can be activated by both Ca2+ and calmodulin (CaM) and involved in many important physiological processes such as salt tolerance/adaption. In this report, cDNAs of CN catalytic subunit paralogue isoforms: GpCAα (GenBank accession no.JQ407043), GpCAγ (GenBank accession no. JQ407043), and CN regulatory subunit (GpCB) (GenBank accession no. JQ410473), were isolated from Gymnocypris przewalskii and their expression patterns in embryos developmentwere characterized. Gene expression profile demonstrated that GpCA and GpCB mRNA was distributed ubiquitously in all embryonic stages and showed decline until final stage of development. Immunohistologicalanalysis revealed CN localization in different tissues including kidney, heart, brain, spermary, and gill. Collectively, these results provide molecular basis and clues to further understand the role of CN during embryos development and its function in tissues for the adaptation mechanism of naked carp