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1. Velocities of an Erupting Filament

   https://doi.org/10.3847/1538-4357/ac3a04  

   Wang, Shuo ; Jenkins, Jack M. ; Muglach, Karin ; Martinez Pillet, Valentin ; Beck, Christian ; Long, David M. ; Choudhary, Debi Prasad ; McAteer, James ( February 2022 , The Astrophysical Journal)

   Solar filaments exist as stable structures for extended periods of time before many of them form the core of a coronal mass ejection (CME). We examine the properties of an erupting filament on 2017 May 29–30 with high-resolution Hei10830 Å and Hαspectra from the Dunn Solar Telescope, full-disk Dopplergrams of Hei10830 Å from the Chromospheric Telescope, and EUV and coronograph data from SDO and STEREO. Pre-eruption line-of-sight velocities from an inversion of Heiwith the HAZEL code exhibit coherent patches of 5 Mm extent that indicate counter-streaming and/or buoyant behavior. During the eruption, individual, aligned threads appear in the Heivelocity maps. The distribution of velocities evolves from Gaussian to strongly asymmetric. The maximal optical depth of Hei10830 Å decreased fromτ= 1.75 to 0.25, the temperature increased by 13 kK, and the average speed and width of the filament increased from 0 to 25 km s⁻¹and 10 to 20 Mm, respectively. All data sources agree that the filament rose with an exponential acceleration reaching 7.4 m s⁻²that increased to a final velocity of 430 km s⁻¹at 22:24 UT; a CME was associated with this filament eruption. The properties during the eruption favor a kink/torus instability, which requires the existence of a flux rope. We conclude that full-disk chromospheric Dopplergrams can be used to trace the initial phase of on-disk filament eruptions in real time, which might potentially be useful for modeling the source of any subsequent CMEs.

    

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2. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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3. Speed and Acceleration of Coronal Mass Ejections Associated with Sustained Gamma-Ray Emission Events Observed by Fermi/LAT

   https://doi.org/10.3847/1538-4357/ace627  

   Mäkelä, Pertti ; Gopalswamy, Nat ; Akiyama, Sachiko ; Xie, Hong ; Yashiro, Seiji ( August 2023 , The Astrophysical Journal)

   The sustained gamma-ray emission (SGRE) from the Sun is a prolonged enhancement of >100 MeV gamma-ray emission that extends beyond the flare impulsive phase. The origin of the >300 MeV protons resulting in SGRE is debated, with both flares and shocks driven by coronal mass ejections (CMEs) being the suggested sites of proton acceleration. We compared the near-Sun acceleration and space speed of CMEs with “Prompt” and “Delayed” (SGRE) gamma-ray components. We found that “Delayed”-component-associated CMEs have higher initial accelerations and space speeds than “Prompt Only”-component-associated CMEs. We selected halo CMEs (HCMEs) associated with type II radio bursts (shock-driving HCMEs) and compared the average acceleration and space speed between HCME populations with or without SGRE events, major solar energetic particle (SEP) events, metric, or decameter-hectometric (DH) type II radio bursts. We found that the SGRE-producing HCMEs associated with a DH type II radio burst and/or a major SEP event have higher space speeds and especially initial accelerations than those without an SGRE event. We estimated the radial distances and speeds of the CME-driven shocks at the end time of the 2012 January 23 and March 7 SGRE events using white-light images of STEREO Heliospheric Imagers and radio dynamic spectra of Wind WAVES. The shocks were at the radial distances of 0.6–0.8 au and their speeds were high enough (≈975 km s⁻¹and ≈750 km s⁻¹, respectively) for high-energy particle acceleration. Therefore, we conclude that our findings support the CME-driven shock as the source of >300 MeV protons.

    

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4. Constraining a Companion of the Galactic Center Black Hole Sgr A*

   https://doi.org/10.3847/1538-4357/ad09b3  

   Will, Clifford M. ; Naoz, Smadar ; Hees, Aurélien ; Tucker, Alexandria ; Zhang, Eric ; Do, Tuan ; Ghez, Andrea ( December 2023 , The Astrophysical Journal)

   We use 23 yr of astrometric and radial velocity data on the orbit of the star S0-2 to constrain a hypothetical intermediate-mass black hole orbiting the massive black hole Sgr A* at the Galactic center. The data place upper limits on variations of the orientation of the stellar orbit at levels between 0.°02 and 0.°07 per year. We use a combination of analytic estimates and full numerical integrations of the orbit of S0-2 in the presence of a black hole binary. For a companion intermediate-mass black hole outside the orbit of S0-2 (1020 au), we find that a companion black hole with massm_cbetween 10³and 10⁵M_⊙is excluded, with a boundary behaving as$a_c\sim m_c^{1/3}$. For a companion witha_c< 1020 au, a black hole with mass between 10³and 10⁵M_⊙is excluded, with$a_c\sim m_c^{-1/2}$. These bounds arise from quadrupolar perturbations of the orbit of S0-2. Significantly stronger bounds on an inner companion arise from the fact that the location of S0-2 is measured relative to the bright emission of Sgr A* and that separation is perturbed by the “wobble” of Sgr A* about the center of mass between it and the companion. The result is a set of bounds as small as 400M_⊙at 200 au; the numerical simulations suggest a bound from these effects varying as$a_c\sim m_c^{-1}$. We compare and contrast our results with those from a recent analysis by the GRAVITY collaboration.

    

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5. Performance Optimization and Guidance of a Low-Altitude Skid-to-Turn Vehicle. Part I: Performance Optimization

   https://doi.org/10.2514/6.2019-0354  

   Dennis, Miriam ; Rao, Anil V. ( January 2019 , 2019 AIAA Guidance, Navigation, and Control Conference, San Diego)

   The problem of air-to-surface trajectory optimization for a low-altitude skid-to-turn vehicle is considered. The objective is for the vehicle to move level at a low altitude for as long as possible and perform a rapid bunt (negative sensed-acceleration load) maneuver near the final time in order to attain terminal target conditions. The vehicle is modeled as a point mass in motion over a flat Earth, and the vehicle is controlled using thrust magnitude, angle of attack, and sideslip angle. The trajectory optimization problem is posed as a two-phase optimal control problem using a weighted objective function. The work described in this paper is the first part of a two-part sequence on trajectory optimization and guidance of a skid-to-turn vehicle. In both cases, the objective is to minimize the time taken by the vehicle to complete a bunt maneuver subject to the following constraints: dynamic, boundary, state, path, and interior-point event constraints. In the first part of this two-part study, the performance of thevehicle is assessed. In particular, the key features of the optimal reference trajectories and controls are provided. The results of this study identify that as greater weight is placed on minimizing the height of the bunt maneuver or as the maximum altitude constraint is raised, the time of the bunt maneuver decreases and the time of the problem solution increases. Also, the results of this study identify that as the allowable crossrange of the vehicle is reduced, the time and height of the bunt maneuver increases and the time of the problem solution decrease 

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