src/hardcoded_data.py

machine_learning_articles = [
    {
        "title": "Data-driven criteria for quantum correlations",
        "abstract": "We build a machine learning model to detect correlations in a three-qubit system using a neural network trained in an unsupervised manner on randomly generated states. The network is forced to recognize separable states, and correlated states are detected as anomalies. Quite surprisingly, we find that the proposed detector performs much better at distinguishing a weaker form of quantum correlations, namely, the quantum discord, than entanglement. In fact, it has a tendency to grossly overestimate the set of entangled states even at the optimal threshold for entanglement detection, while it underestimates the set of discordant states to a much lesser extent. In order to illustrate the nature of states classified as quantum-correlated, we construct a diagram containing various types of states -- entangled, as well as separable, both discordant and non-discordant. We find that the near-zero value of the recognition loss reproduces the shape of the non-discordant separable states with high accuracy, especially considering the non-trivial shape of this set on the diagram. The network architecture is designed carefully: it preserves separability, and its output is equivariant with respect to qubit permutations. We show that the choice of architecture is important to get the highest detection accuracy, much better than for a baseline model that just utilizes a partial trace operation. △ Less\n\nSubmitted 20 July, 2023; originally announced July 2023."
    },
    {
        "title": "PAPR: Proximity Attention Point Rendering",
        "abstract": "Learning accurate and parsimonious point cloud representations of scene surfaces from scratch remains a challenge in 3D representation learning. Existing point-based methods often suffer from the vanishing gradient problem or require a large number of points to accurately model scene geometry and texture. To address these limitations, we propose Proximity Attention Point Rendering (PAPR), a novel method that consists of a point-based scene representation and a differentiable renderer. Our scene representation uses a point cloud where each point is characterized by its spatial position, foreground score, and view-independent feature vector. The renderer selects the relevant points for each ray and produces accurate colours using their associated features. PAPR effectively learns point cloud positions to represent the correct scene geometry, even when the initialization drastically differs from the target geometry. Notably, our method captures fine texture details while using only a parsimonious set of points. We also demonstrate four practical applications of our method: geometry editing, object manipulation, texture transfer, and exposure control. More results and code are available on our project website at https://zvict.github.io/papr/. △ Less\n\nSubmitted 20 July, 2023; originally announced July 2023."
    },
    {
        "title": "Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation",
        "abstract": "It is imperative to ensure the robustness of deep learning models in critical applications such as, healthcare. While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks. We present a 3D frequency domain adversarial attack for volumetric medical image segmentation models and demonstrate its advantages over conventional input or voxel domain attacks. Using our proposed attack, we introduce a novel frequency domain adversarial training approach for optimizing a robust model against voxel and frequency domain attacks. Moreover, we propose frequency consistency loss to regulate our frequency domain adversarial training that achieves a better tradeoff between model's performance on clean and adversarial samples. Code is publicly available at https://github.com/asif-hanif/vafa. △ Less\n\nSubmitted 20 July, 2023; v1 submitted 14 July, 2023; originally announced July 2023."
    },
    {
        "title": "Mathematical Capabilities of ChatGPT",
        "abstract": "We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., the Lean Mathematical Library), current datasets of natural-language mathematics, used to benchmark language models, either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets also test whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT can be used most successfully as a mathematical assistant for querying facts, acting as a mathematical search engine and knowledge base interface. GPT-4 can additionally be used for undergraduate-level mathematics but fails on graduate-level difficulty. Contrary to many positive reports in the media about GPT-4 and ChatGPT's exam-solving abilities (a potential case of selection bias), their overall mathematical performance is well below the level of a graduate student. Hence, if your goal is to use ChatGPT to pass a graduate-level math exam, you would be better off copying from your average peer! △ Less\n\nSubmitted 20 July, 2023; v1 submitted 31 January, 2023; originally announced January 2023."
    }
]

planetary_science_articles = [
    {
        "title": "A Circular Restricted n-body Problem",
        "abstract": "This paper introduces the Circular Restricted n-Body Problem (CRNBP), an extension of the bicircular restricted four-body problem (BCR4BP) designed to describe the dynamics of an n-body system. In the CRNBP, each massive body in the system is constrained to follow a Keplerian motion, similar to the BCR4BP's artificial constraint. The CRNBP is an efficient alternative for trajectory design in multiple-body systems, particularly for outer planetary systems, as it requires integrating only six first-order ordinary differential equations compared to the 6N equations in an ephemerides model. By reproducing complex dynamical behaviors observed in ephemerides n-body problems, we demonstrate the structural stability of the CRNBP. Additionally, we propose a straightforward approach to relate the CRNBP with ephemerides, enabling the exploration of trajectory design possibilities before committing to a dedicated ephemerides analysis. This allows for the identification of general dynamical behaviors and provides valuable insights into the dynamics of multiple body systems. Finally, illustrative examples highlight the richness of trajectories and potential advantages of using the CRNBP for designing complex trajectories in outer planetary systems. The CRNBP proves to be a valuable tool for preliminary trajectory design, facilitating the identification of low-energy trajectories and providing a foundation for further exploration in future dedicated studies. △ Less\n\nSubmitted 20 July, 2023; originally announced July 2023."
    },
    {
        "title": "Science opportunities with solar sailing smallsats",
        "abstract": "Recently, we witnessed how the synergy of small satellite technology and solar sailing propulsion enables new missions. Together, small satellites with lightweight instruments and solar sails offer affordable access to deep regions of the solar system, also making it possible to realize hard-to-reach trajectories that are not constrained to the ecliptic plane. Combining these two technologies can drastically reduce travel times within the solar system, while delivering robust science. With solar sailing propulsion capable of reaching the velocities of ~5-10 AU/yr, missions using a rideshare launch may reach the Jovian system in two years, Saturn in three. The same technologies could allow reaching solar polar orbits in less than two years. Fast, cost-effective, and maneuverable sailcraft that may travel outside the ecliptic plane open new opportunities for affordable solar system exploration, with great promise for heliophysics, planetary science, and astrophysics. Such missions could be modularized to reach different destinations with different sets of instruments. Benefiting from this progress, we present the \"Sundiver\" concept, offering novel possibilities for the science community. We discuss some of the key technologies, the current design of the Sundiver sailcraft vehicle and innovative instruments, along with unique science opportunities that these technologies enable, especially as this exploration paradigm evolves. We formulate policy recommendations to allow national space agencies, industry, and other stakeholders to establish a strong scientific, programmatic, and commercial focus, enrich and deepen the space enterprise and broaden its advocacy base by including the Sundiver paradigm as a part of broader space exploration efforts. △ Less\n\nSubmitted 19 July, 2023; v1 submitted 27 March, 2023; originally announced March 2023."
    },
    {
        "title": "Characterization of the ejecta from NASA/DART impact on Dimorphos: observations and Monte Carlo models",
        "abstract": "The NASA/DART (Double Asteroid Redirection Test) spacecraft successfully crashed on Dimorphos, the secondary component of the binary (65803) Didymos system. Following the impact, a large dust cloud was released, and a long-lasting dust tail was developed. We have extensively monitored the dust tail from the ground and from the Hubble Space Telescope (HST). We provide a characterization of the ejecta dust properties, i.e., particle size distribution and ejection speeds, ejection geometric parameters, and mass, by combining both observational data sets, and by using Monte Carlo models of the observed dust tail. The differential size distribution function that best fits the imaging data was a broken power-law, having a power index of --2.5 for particles of r≤ 3 mm, and of --3.7 for larger particles. The particles range in sizes from 1 μm up to 5 cm. The ejecta is characterized by two components, depending on velocity and ejection direction. The northern component of the double tail, observed since October 8th 2022, might be associated to a secondary ejection event from impacting debris on Didymos, although it is also possible that this feature results from the binary system dynamics alone. The lower limit to the total dust mass ejected is estimated at ∼6×106 kg, half of this mass being ejected to interplanetary space. △ Less\n\nSubmitted 19 July, 2023; originally announced July 2023."
    },
    {
        "title": "Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots",
        "abstract": "The interest in exploring planetary bodies for scientific investigation and in-situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots' inability to traverse steep slopes, unstructured terrain, and loose soil. Additionally, current single-robot approaches only allow a limited exploration speed and a single set of skills. Here, we present a team of legged robots with complementary skills for exploration missions in challenging planetary analog environments. We equipped the robots with an efficient locomotion controller, a mapping pipeline for online and post-mission visualization, instance segmentation to highlight scientific targets, and scientific instruments for remote and in-situ investigation. Furthermore, we integrated a robotic arm on one of the robots to enable high-precision measurements. Legged robots can swiftly navigate representative terrains, such as granular slopes beyond 25 degrees, loose soil, and unstructured terrain, highlighting their advantages compared to wheeled rover systems. We successfully verified the approach in analog deployments at the BeyondGravity ExoMars rover testbed, in a quarry in Switzerland, and at the Space Resources Challenge in Luxembourg. Our results show that a team of legged robots with advanced locomotion, perception, and measurement skills, as well as task-level autonomy, can conduct successful, effective missions in a short time. Our approach enables the scientific exploration of planetary target sites that are currently out of human and robotic reach. △ Less\n\nSubmitted 19 July, 2023; originally announced July 2023."
    }
]

articles = [
    {
        "category": "machine learning",
        "articles": machine_learning_articles
    },
    {
        "category": "planetary science",
        "articles": planetary_science_articles
    },
]

[
    {   "title" : 
        "tldr",
    },
    {   "title" :
        "tldr",
    },
]

# [category1[articles[article1['title1', 'abstract1'], article2['title2', 'abstract2']]], category2[...], ...]

def parse_list_of_lists(input_list, category_names):
    articles_list = []
    
    for idx, item in enumerate(input_list):
        category_name = category_names[idx]
        category_articles = []
        
        for sub_item in item:
            if isinstance(sub_item, list) and len(sub_item) == 2:
                article_title, article_abstract = sub_item
                category_articles.append({"title": article_title, "abstract": article_abstract})
            else:
                raise ValueError("Each sub-item should be a list containing [title, abstract].")
        
        articles_list.append({"category": category_name, "articles": category_articles})
    
    return articles_list


input_list = [
    [["article1 title", "article1 abstract"], ["article2 title", "article2 abstract"]],
    [["article3 title", "article3 abstract"], ["article4 title", "article4 abstract"]]
]

category_names = ["machine learning", "planetary science"]

output_list_of_dicts = parse_list_of_lists(input_list, category_names)
print(output_list_of_dicts)