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Title: Some Heuristics for the Homological Simpli cation Problem
In this paper, we consider heuristic approaches for solving the homological simpli cation problem. While NP-Hard in general, we propose an algorithm that in practice signi cantly reduces topological noise from large datasets, such as those from medical or biological imaging  more » « less
Award ID(s):
1638507 1759796
NSF-PAR ID:
10095830
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
CCCG
Issue:
August
Page Range / eLocation ID:
8-10
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Abstract

    We present a phylogenetic analysis of spiders using a dataset of 932 spider species, representing 115 families (only the family Synaphridae is unrepresented), 700 known genera, and additional representatives of 26 unidentified or undescribed genera. Eleven genera of the orders Amblypygi, Palpigradi, Schizomida and Uropygi are included as outgroups. The dataset includes six markers from the mitochondrial (12S, 16S,COI) and nuclear (histone H3, 18S, 28S) genomes, and was analysed by multiple methods, including constrained analyses using a highly supported backbone tree from transcriptomic data. We recover most of the higher‐level structure of the spider tree with good support, including Mesothelae, Opisthothelae, Mygalomorphae and Araneomorphae. Several of our analyses recover Hypochilidae and Filistatidae as sister groups, as suggested by previous transcriptomic analyses. The Synspermiata are robustly supported, and the families Trogloraptoridae and Caponiidae are found as sister to the Dysderoidea. Our results support the Lost Tracheae clade, including Pholcidae, Tetrablemmidae, Diguetidae, Plectreuridae and the family Pacullidae (restored status) separate from Tetrablemmidae. The Scytodoidea include Ochyroceratidae along with Sicariidae, Scytodidae, Drymusidae and Periegopidae; our results are inconclusive about the separation of these last two families. We did not recover monophyletic Austrochiloidea and Leptonetidae, but our data suggest that both groups are more closely related to the Cylindrical Gland Spigot clade rather than to Synspermiata. Palpimanoidea is not recovered by our analyses, but also not strongly contradicted. We find support for Entelegynae and Oecobioidea (Oecobiidae plus Hersiliidae), and ambiguous placement of cribellate orb‐weavers, compatible with their non‐monophyly. Nicodamoidea (Nicodamidae plus Megadictynidae) and Araneoidea composition and relationships are consistent with recent analyses. We did not obtain resolution for the titanoecoids (Titanoecidae and Phyxelididae), but the Retrolateral Tibial Apophysis clade is well supported. Penestomidae, and probably Homalonychidae, are part of Zodarioidea, although the latter family was set apart by recent transcriptomic analyses. Our data support a large group that we call the marronoid clade (including the families Amaurobiidae, Desidae, Dictynidae, Hahniidae, Stiphidiidae, Agelenidae and Toxopidae). The circumscription of most marronoid families is redefined here. Amaurobiidae include the Amaurobiinae and provisionally Macrobuninae. We transfer Malenellinae (Malenella, from Anyphaenidae), Chummidae (Chumma) (new syn.) and Tasmarubriinae (Tasmarubrius,TasmabrochusandTeeatta, from Amphinectidae) to Macrobuninae. Cybaeidae are redefined to includeCalymmaria,Cryphoeca,EthobuellaandWillisius(transferred from Hahniidae), andBlabommaandYorima(transferred from Dictynidae). Cycloctenidae are redefined to includeOrepukia(transferred from Agelenidae) andPakehaandParavoca(transferred from Amaurobiidae). Desidae are redefined to include five subfamilies: Amphinectinae, withAmphinecta,Mamoea,Maniho,ParamamoeaandRangitata(transferred from Amphinectidae); Ischaleinae, withBakalaandManjala(transferred from Amaurobiidae) andIschalea(transferred from Stiphidiidae); Metaltellinae, withAustmusia,Buyina,Calacadia,Cunnawarra,Jalkaraburra,Keera,Magua,Metaltella,PenaoolaandQuemusia; Porteriinae (new rank), withBaiami,Cambridgea,CorasoidesandNanocambridgea(transferred from Stiphidiidae); and Desinae, withDesis, and provisionallyPoaka(transferred from Amaurobiidae) andBarahna(transferred from Stiphidiidae).Argyronetais transferred from Cybaeidae to Dictynidae.Cicurinais transferred from Dictynidae to Hahniidae. The generaNeoramia(from Agelenidae) andAorangia,MarplesiaandNeolana(from Amphinectidae) are transferred to Stiphidiidae. The family Toxopidae (restored status) includes two subfamilies: Myroinae, withGasparia,Gohia,Hulua,Neomyro,Myro,OmmatauxesisandOtagoa(transferred from Desidae); and Toxopinae, withMidgeeandJamara, formerly Midgeeinae,new syn.(transferred from Amaurobiidae) andHapona,Laestrygones,Lamina,ToxopsandToxopsoides(transferred from Desidae). We obtain a monophyletic Oval Calamistrum clade and Dionycha; Sparassidae, however, are not dionychans, but probably the sister group of those two clades. The composition of the Oval Calamistrum clade is confirmed (including Zoropsidae, Udubidae, Ctenidae, Oxyopidae, Senoculidae, Pisauridae, Trechaleidae, Lycosidae, Psechridae and Thomisidae), affirming previous findings on the uncertain relationships of the “ctenids”AncylometesandCupiennius, although a core group of Ctenidae are well supported. Our data were ambiguous as to the monophyly of Oxyopidae. In Dionycha, we found a first split of core Prodidomidae, excluding the Australian Molycriinae, which fall distantly from core prodidomids, among gnaphosoids. The rest of the dionychans form two main groups, Dionycha part A and part B. The former includes much of the Oblique Median Tapetum clade (Trochanteriidae, Gnaphosidae, Gallieniellidae, Phrurolithidae, Trachelidae, Gnaphosidae, Ammoxenidae, Lamponidae and the Molycriinae), and also Anyphaenidae and Clubionidae.Orthobulais transferred from Phrurolithidae to Trachelidae. Our data did not allow for complete resolution for the gnaphosoid families. Dionycha part B includes the families Salticidae, Eutichuridae, Miturgidae, Philodromidae, Viridasiidae, Selenopidae, Corinnidae and Xenoctenidae(new fam., includingXenoctenus,ParavulsorandOdo, transferred from Miturgidae, as well asIncasoctenusfrom Ctenidae). We confirm the inclusion ofZora(formerly Zoridae) within Miturgidae.

     
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  2. null (Ed.)
    The marine-based West Antarctic Ice Sheet (WAIS) is currently retreating due to shifting wind-driven oceanic currents that transport warm waters toward the ice margin, resulting in ice shelf thinning and accelerated mass loss of the WAIS. Previous results from geologic drilling on Antarctica’s continental margins show significant variability in marine-based ice sheet extent during the late Neogene and Quaternary. Numerical models indicate a fundamental role for oceanic heat in controlling this variability over at least the past 20 My. Although evidence for past ice sheet variability has been collected in marginal settings, sedimentologic sequences from the outer continental shelf are required to evaluate the extent of past ice sheet variability and the associated oceanic forcings and feedbacks. International Ocean Discovery Program Expedition 374 drilled a latitudinal and depth transect of five drill sites from the outer continental shelf to rise in the eastern Ross Sea to resolve the relationship between climatic and oceanic change and WAIS evolution through the Neogene and Quaternary. This location was selected because numerical ice sheet models indicate that this sector of Antarctica is highly sensitive to changes in ocean heat flux. The expedition was designed for optimal data-model integration and will enable an improved understanding of the sensitivity of Antarctic Ice Sheet (AIS) mass balance during warmer-than-present climates (e.g., the Pleistocene “super interglacials,” the mid-Pliocene, and the late early to middle Miocene). The principal goals of Expedition 374 were to • Evaluate the contribution of West Antarctica to far-field ice volume and sea level estimates; • Reconstruct ice-proximal atmospheric and oceanic temperatures to identify past polar amplification and assess its forcings and feedbacks; • Assess the role of oceanic forcing (e.g., sea level and temperature) on AIS stability/instability; • Identify the sensitivity of the AIS to Earth’s orbital configuration under a variety of climate boundary conditions; and • Reconstruct eastern Ross Sea paleobathymetry to examine relationships between seafloor geometry, ice sheet stability/instability, and global climate. To achieve these objectives, we will • Use data and models to reconcile intervals of maximum Neogene and Quaternary Antarctic ice advance with far-field records of eustatic sea level change; • Reconstruct past changes in oceanic and atmospheric temperatures using a multiproxy approach; • Reconstruct Neogene and Quaternary sea ice margin fluctuations in datable marine continental slope and rise records and correlate these records to existing inner continental shelf records; • Examine relationships among WAIS stability/instability, Earth’s orbital configuration, oceanic temperature and circulation, and atmospheric pCO2; and • Constrain the timing of Ross Sea continental shelf overdeepening and assess its impact on Neogene and Quaternary ice dynamics. Expedition 374 was carried out from January to March 2018, departing from Lyttelton, New Zealand. We recovered 1292.70 m of high-quality cores from five sites spanning the early Miocene to late Quaternary. Three sites were cored on the continental shelf (Sites U1521, U1522, and U1523). At Site U1521, we cored a 650 m thick sequence of interbedded diamictite, mudstone, and diatomite, penetrating the Ross Sea seismic Unconformity RSU4. The depositional reconstructions of past glacial and open-marine conditions at this site will provide unprecedented insight into environmental change on the Antarctic continental shelf during the early and middle Miocene. At Site U1522, we cored a discontinuous upper Miocene to Pleistocene sequence of glacial and glaciomarine strata from the outer shelf, with the primary objective to penetrate and date seismic Unconformity RSU3, which is interpreted to represent the first major continental shelf–wide expansion and coalescing of marine-based ice streams from both East and West Antarctica. At Site U1523, we cored a sediment drift located beneath the westerly flowing Antarctic Slope Current (ASC). Cores from this site will provide a record of the changing vigor of the ASC through time. Such a reconstruction will enable testing of the hypothesis that changes in the vigor of the ASC represent a key control on regulating heat flux onto the continental shelf, resulting in the ASC playing a fundamental role in ice sheet mass balance. We also cored two sites on the continental slope and rise. At Site U1524, we cored a Plio–Pleistocene sedimentary sequence on the continental rise on the levee of the Hillary Canyon, which is one of the largest conduits of Antarctic Bottom Water delivery from the Antarctic continental shelf into the abyssal ocean. Drilling at Site U1524 was intended to penetrate into middle Miocene and older strata but was initially interrupted by drifting sea ice that forced us to abandon coring in Hole U1524A at 399.5 m drilling depth below seafloor (DSF). We moved to a nearby alternate site on the continental slope (U1525) to core a single hole with a record complementary to the upper part of the section recovered at Site U1524. We returned to Site U1524 3 days later, after the sea ice cleared. We then cored Hole U1524C with the rotary core barrel with the intention of reaching the target depth of 1000 m DSF. However, we were forced to terminate Hole U1524C at 441.9 m DSF due to a mechanical failure with the vessel that resulted in termination of all drilling operations and a return to Lyttelton 16 days earlier than scheduled. The loss of 39% of our operational days significantly impacted our ability to achieve all Expedition 374 objectives as originally planned. In particular, we were not able to obtain the deeper time record of the middle Miocene on the continental rise or abyssal sequences that would have provided a continuous and contemporaneous archive to the high-quality (but discontinuous) record from Site U1521 on the continental shelf. The mechanical failure also meant we could not recover sediment cores from proposed Site RSCR-19A, which was targeted to obtain a high-fidelity, continuous record of upper Neogene and Quaternary pelagic/hemipelagic sedimentation. Despite our failure to recover a shelf-to-rise transect for the Miocene, a continental shelf-to-rise transect for the Pliocene to Pleistocene interval is possible through comparison of the high-quality records from Site U1522 with those from Site U1525 and legacy cores from the Antarctic Geological Drilling Project (ANDRILL). 
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  3. Obeid, Iyad Selesnick (Ed.)
    Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 
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  4. null (Ed.)
    The primary objectives of International Ocean Discovery Program (IODP) Expedition 367/368 to the northern South China Sea (SCS) margin were to (1) examine its history of continental breakup and (2) compare it with other nonvolcanic or magma-poor rifted margins with the broader goal of testing models for continental breakup. A secondary objective was to further our understanding of the paleoceanographic and environmental development of the SCS and southeast Asia during the Cenozoic. Four primary sites were selected for the overall program: one in the outer margin high (OMH) and three seaward of the OMH on distinct, margin-parallel basement ridges. These three ridges are informally labeled A, B, and C and are located in the continent–ocean transition (COT) zone ranging from the OMH to the interpreted steady-state oceanic crust (Ridge C) of the SCS. The main scientific objectives include the following: Determining the nature of the basement in crustal units across the COT of the SCS that are critical to constrain style of rifting, Constraining the time interval from initial crustal extension and plate rupture to the initial generation of igneous ocean crust, Constraining vertical crustal movements during breakup, and Examining the nature of igneous activity from rifting to seafloor spreading. In addition, the sediment cores from the drill sites targeting primarily tectonic and basement objectives will provide information on the Cenozoic regional environmental development of the Southeast Asia margin. Site U1499 on Ridge A and Site U1500 on Ridge B were drilled during Expedition 367. Expedition 368 was planned to drill at two primary sites (U1501 and U1503) at the OMH and Ridge C, respectively, but based on drilling results from Expedition 367, Expedition 368 chose to insert an alternate site on Ridge A (Site U1502). In addition, Expedition 368 added two more sites on the OMH (Sites U1504 and U1505). Expedition 367/368 completed operations at six of the seven sites (U1499–U1502, U1504, and U1505). Site U1503, however, was not completed beyond casing without coring to 990 m because of mechanical problems with the drilling equipment that prevented the expedition, after 25 May 2017, from operating with a drill string longer than 3400 m. New alternate Site U1504, proposed during Expedition 367, met this condition. Original Site U1505 also met the operational constraints of the 3400 m drill string (total) and was an alternate site for the already-drilled Site U1501. At Site U1499, we cored to 1081.8 m in 22.1 days with 52% recovery and then logged downhole data from 655 to 1020 m. In 31 days at Site U1500, we penetrated to 1529 m, cored a total of 1012.8 m with 37% recovery, and collected log data from 842 to 1133 m. At Site U1501, we cored to 697.1 m in 9.4 days with 78.5% recovery. We also drilled ahead for 433.5 m in Hole U1501D and then logged downhole data from 78.3 to 399.3 m. In 19.3 days at Site U1502, we penetrated 1679.0 m in Holes U1502A (758 m) and U1502B (921 m), set 723.7 m of casing and cored a total of 576.3 m with 53.5% recovery, and collected downhole log data from 785.3 to 875.3 m and seismic data through the 10¾ inch casing. At Site U1503, we penetrated 995.1 m and set 991.5 m of 10¾ inch casing, but no cores were taken because of a mechanical problem with the drawworks. At Site U1504, we took 40 rotary core barrel (RCB) cores over two holes. The cored interval between both holes was 277.3 m with 26.8% recovery. An 88.2 m interval was drilled in Hole U1504B. At Site U1505, we cored 668.0 m with 101.1% recovery. Logging data was collected from 80.1 to 341.2 m. Operations at this site covered 6.1 days. Except for Sites U1503 and U1505, all sites were drilled to acoustic basement. A total of 6.65 days were lost due to mechanical breakdown or waiting on spare supplies for repair of drilling equipment, but drilling options were severely limited from 25 May to the end of the expedition by the defective drawworks limiting deployment of drill string longer than 3400 m. At Site U1499, coring ~200 m into the interpreted acoustic basement sampled sedimentary rocks, possibly including early Miocene chalks underlain by Oligocene polymict breccias and poorly cemented gravels of unknown age comprising sandstone pebbles and cobbles. Preliminary structural and lithologic analysis suggests that the gravels might be early to late synrift sediment. At Site U1500, the main seismic reflector corresponds to the top of a basalt sequence at ~1379.1 m. We cored 149.90 m into this volcanic package and recovered 114.92 m (77%) of sparsely to moderately plagioclase-phyric basalt comprising numerous lava flows, including pillow lavas with glass, chilled margins, altered veins, hyaloclastites, and minor sediment. Preliminary geochemical analyses indicate that the basalt is tholeiitic. Sampling of the Pleistocene to lower Miocene sedimentary section at Sites U1499 and U1500 was not continuous for two reasons. First, there was extremely poor recovery in substantial intervals interpreted to be poorly lithified sands, possibly turbidites. Second, we chose to drill down without coring in some sections at Site U1500 to ensure sufficient time to achieve this site’s high-priority deep drilling objectives. The upper Miocene basin sequence, which consists of interbedded claystone, siltstone, and sandstone can be correlated between the two sites by seismic stratigraphic mapping and biostratigraphy. At Site U1501 on the OMH, coring ~45 m into the acoustic basement sampled prerift(?) deposits comprising sandstone to conglomerate of presumed Mesozoic age. These deposits are overlain by siliciclastic synrift sediments of Eocene to Oligocene age followed by primarily carbonaceous postrift sediments of early Miocene to Pleistocene age. Site U1502 on Ridge A was cased to 723.7 m. No coring was attempted shallower than 380 m to save operational time and because of low expectations for core recovery in the upper Plio–Pleistocene sequence. At this site, we recovered 180 m of hydrothermally altered brecciated basalts comprising sheet and pillow lavas below deep-marine sediments of Oligocene to late Miocene age. At Site U1503 on Ridge C, 991.5 m of casing was installed in preparation for the planned deep drilling to ~1800 m. No coring was performed due to mechanical failures, and the site was abandoned without further activity except for installation of a reentry cone. Coring at Site U1504 on the OMH, located ~45 km east of Site U1501, recovered mostly foliated, greenschist facies metamorphic rocks below late Eocene(?) carbonate rocks (partly reef debris) and early Miocene to Pleistocene sediments. At Site U1505, we cored to 480.15 m through Pleistocene to late Oligocene mainly carbonaceous ooze followed at depth by early Oligocene siliciclastic sediments. Efforts were made at every drill site to correlate the core with the seismic data and seismic stratigraphic unconformities interpreted in the Eocene to Plio–Pleistocene sedimentary sequence prior to drilling. The predrilling interpretation of ages of these unconformities was in general confirmed by drilling results, although some nontrivial corrections can be expected from detailed postexpedition work on integrating seismic stratigraphic interpretations with detailed bio- and lithostratigraphy. As a result of the limited length of drill string that could be deployed during the later part of Expedition 368, the secondary expedition objectives addressing the environmental history of the SCS and Southeast Asia received more focus than originally planned, allowing Site U1505 (alternate to Site U1501) to be included. Despite this change in focus, Expedition 367/368 provided solid evidence for a process of breakup that included vigorous synrift magmatism as opposed to the often-favored interpretation of the SCS margin as a magma-starved margin or a margin possibly overprinted at a much later stage by plume-related magmatism. In this broader perspective, Expedition 367/368 accomplished a fundamental objective of the two-expedition science program. 
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    The marine-based West Antarctic Ice Sheet (WAIS) is currently locally retreating because of shifting wind-driven oceanic currents that transport warm waters toward the ice margin, resulting in ice shelf thinning and accelerated mass loss. Previous results from geologic drilling on Antarctica’s continental margins show significant variability in ice sheet extent during the late Neogene and Quaternary. Climate and ice sheet models indicate a fundamental role for oceanic heat in controlling ice sheet variability over at least the past 20 My. Although evidence for past ice sheet variability is available from ice-proximal marine settings, sedimentary sequences from the continental shelf and rise are required to evaluate the extent of past ice sheet variability and the associated forcings and feedbacks. International Ocean Discovery Program Expedition 374 drilled a latitudinal and depth transect of five sites from the outer continental shelf to rise in the central Ross Sea to resolve Neogene and Quaternary relationships between climatic and oceanic change and WAIS evolution. The Ross Sea was targeted because numerical ice sheet models indicate that this sector of Antarctica responds sensitively to changes in ocean heat flux. Expedition 374 was designed for optimal data-model integration to enable an improved understanding of Antarctic Ice Sheet (AIS) mass balance during warmer-than-present climates (e.g., the Pleistocene “super interglacials,” the mid-Pliocene, and the Miocene Climatic Optimum). The principal goals of Expedition 374 were to: 1. Evaluate the contribution of West Antarctica to far-field ice volume and sea level estimates; 2. Reconstruct ice-proximal oceanic and atmospheric temperatures to quantify past polar amplification; 3. Assess the role of oceanic forcing (e.g., temperature and sea level) on AIS variability; 4. Identify the sensitivity of the AIS to Earth’s orbital configuration under a variety of climate boundary conditions; and 5. Reconstruct Ross Sea paleobathymetry to examine relationships between seafloor geometry, ice sheet variability, and global climate. To achieve these objectives, postcruise studies will: 1. Use data and models to reconcile intervals of maximum Neogene and Quaternary ice advance and retreat with far-field records of eustatic sea level; 2. Reconstruct past changes in oceanic and atmospheric temperatures using a multiproxy approach; 3. Reconstruct Neogene and Quaternary sea ice margin fluctuations and correlate these records to existing inner continental shelf records; 4. Examine relationships among WAIS variability, Earth’s orbital configuration, oceanic temperature and circulation, and atmospheric pCO2; and 5. Constrain the timing of Ross Sea continental shelf overdeepening and assess its impact on Neogene and Quaternary ice dynamics. Expedition 374 departed from Lyttelton, New Zealand, in January 2018 and returned in March 2018. We recovered 1292.70 m of high-quality core from five sites spanning the early Miocene to late Quaternary. Three sites were cored on the continental shelf (Sites U1521, U1522, and U1523). At Site U1521, we cored a 650 m thick sequence of interbedded diamictite and diatom-rich mudstone penetrating seismic Ross Sea Unconformity 4 (RSU4). The depositional reconstructions of past glacial and open-marine conditions at this site will provide unprecedented insight into environmental change on the Antarctic continental shelf during the late early and middle Miocene. At Site U1522, we cored a discontinuous late Miocene to Pleistocene sequence of glacial and glaciomarine strata from the outer shelf with the primary objective of penetrating and dating RSU3, which is interpreted to reflect the first continental shelf–wide expansion of East and West Antarctic ice streams. Site U1523, located on the outer continental shelf, targeted a sediment drift beneath the westward-flowing Antarctic Slope Current (ASC) to test the hypothesis that changes in ASC vigor regulate ocean heat flux onto the continental shelf and thus ice sheet mass balance. We also cored two sites on the continental rise and slope. At Site U1524, we recovered a Plio–Pleistocene sedimentary sequence from the levee of the Hillary Canyon, one of the largest conduits of Antarctic Bottom Water from the continental shelf to the abyssal ocean. Site U1524 was designed to penetrate into middle Miocene and older strata, but coring was initially interrupted by drifting sea ice that forced us to abandon coring in Hole U1524A at 399.5 m drilling depth below seafloor (DSF). We moved to a nearby alternate site on the continental slope (Site U1525) to core a single hole designed to complement the record at Site U1524. We returned to Site U1524 after the sea ice cleared and cored Hole U1524C with the rotary core barrel system with the intention of reaching the target depth of 1000 m DSF. However, we were forced to terminate Hole U1524C at 441.9 m DSF because of a mechanical failure with the vessel that resulted in termination of all drilling operations and forced us to return to Lyttelton 16 days earlier than scheduled. The loss of 39% of our operational days significantly impacted our ability to achieve all Expedition 374 objectives. In particular, we were not able to recover continuous middle Miocene sequences from the continental rise designed to complement the discontinuous record from continental shelf Site U1521. The mechanical failure also meant we could not recover cores from proposed Site RSCR-19A, which was targeted to obtain a high-fidelity, continuous record of upper Neogene and Quaternary pelagic/hemipelagic sedimentation. Despite our failure to recover a continental shelf-to-rise Miocene transect, records from Sites U1522, U1524, and U1525 and legacy cores from the Antarctic Geological Drilling Project (ANDRILL) can be integrated to develop a shelf-to-rise Plio–Pleistocene transect. 
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