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GraphChallenge.org triangle counting performance [e-print]

Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of preparsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, Ttri, is a strong function of the number of edges in the graph, Ne, which improved significantly from 2017 (Ttri \approx (Ne/10^8)^4=3) to 2018 (Ttri \approx Ne/10^9) and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs
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Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems...

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GraphChallenge.org sparse deep neural network performance [e-print]

Summary

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems. The sparse DNN challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment. In 2019 several sparse DNN challenge submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art sparse DNN execution time, TDNN, is a strong function of the number of DNN operations performed, Nop. The sparse DNN challenge provides a clear picture of current sparse DNN systems and underscores the need for new innovations to achieve high performance on very large sparse DNNs.
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Summary

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective...

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COVID-19: famotidine, histamine, mast cells, and mechanisms [eprint]

Summary

SARS-CoV-2 infection is required for COVID-19, but many signs and symptoms of COVID-19 differ from common acute viral diseases. Currently, there are no pre- or post-exposure prophylactic COVID-19 medical countermeasures. Clinical data suggest that famotidine may mitigate COVID-19 disease, but both mechanism of action and rationale for dose selection remain obscure. We explore several plausible avenues of activity including antiviral and host-mediated actions. We propose that the principal famotidine mechanism of action for COVID-19 involves on-target histamine receptor H2 activity, and that development of clinical COVID-19 involves dysfunctional mast cell activation and histamine release.
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Summary

SARS-CoV-2 infection is required for COVID-19, but many signs and symptoms of COVID-19 differ from common acute viral diseases. Currently, there are no pre- or post-exposure prophylactic COVID-19 medical countermeasures. Clinical data suggest that famotidine may mitigate COVID-19 disease, but both mechanism of action and rationale for dose selection remain...

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75,000,000,000 streaming inserts/second using hierarchical hypersparse GraphBLAS matrices

Summary

The SuiteSparse GraphBLAS C-library implements high performance hypersparse matrices with bindings to a variety of languages (Python, Julia, and Matlab/Octave). GraphBLAS provides a lightweight in-memory database implementation of hypersparse matrices that are ideal for analyzing many types of network data, while providing rigorous mathematical guarantees, such as linearity. Streaming updates of hypersparse matrices put enormous pressure on the memory hierarchy. This work benchmarks an implementation of hierarchical hypersparse matrices that reduces memory pressure and dramatically increases the update rate into a hypersparse matrices. The parameters of hierarchical hypersparse matrices rely on controlling the number of entries in each level in the hierarchy before an update is cascaded. The parameters are easily tunable to achieve optimal performance for a variety of applications. Hierarchical hypersparse matrices achieve over 1,000,000 updates per second in a single instance. Scaling to 31,000 instances of hierarchical hypersparse matrices arrays on 1,100 server nodes on the MIT SuperCloud achieved a sustained update rate of 75,000,000,000 updates per second. This capability allows the MIT SuperCloud to analyze extremely large streaming network data sets.
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Summary

The SuiteSparse GraphBLAS C-library implements high performance hypersparse matrices with bindings to a variety of languages (Python, Julia, and Matlab/Octave). GraphBLAS provides a lightweight in-memory database implementation of hypersparse matrices that are ideal for analyzing many types of network data, while providing rigorous mathematical guarantees, such as linearity. Streaming updates...

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The 2017 Buffalo Area Icing and Radar Study (BAIRS II)

Published in:
MIT Lincoln Laboratory Report ATC-447

Summary

The second Buffalo Area Icing and Radar Study (BAIRS II) was conducted during the winter of 2017. The BAIRS II partnership between Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL), the National Research Council of Canada (NRC), and Environment and Climate Change Canada (ECCC) was sponsored by the Federal Aviation Administration (FAA). It is a follow-up to the similarly sponsored partnership of the original BAIRS conducted in the winter of 2013. The original BAIRS provided in situ verification and validation of icing and hydrometeors, respectively, within the radar domain in support of a hydrometeor-classification-based automated icing hazard algorithm. The BAIRS II motivation was to: --Collect additional in situ verification and validation data, --Probe further dual polarimetric radar features associated with icing hazard, --Provide foundations for additions to the icing hazard algorithm beyond hydrometeor classifications, and --Further characterize observable microphysical conditions in terms of S-band dual polarimetric radar data. With BAIRS II, the dual polarimetric capability is provided by multiple Next Generation Weather Radar (NEXRAD) S-band radars in New York State, and the verification of the icing hazard with microphysical and hydrometeor characterizations is provided by NRC's Convair-580 instrumented research plane during five icing missions covering about 21 mission hours. The ability to reliably interpret the NEXRAD dual polarization radar-sensed thermodynamic phase of the hydrometeors (solid, liquid, mix) in the context of cloud microphysics and precipitation physics makes it possible to assess the icing hazard potential to aviation. The challenges faced are the undetectable nature of supercooled cloud droplets (for Sband) and the isotropic nature of Supercooled Large Drops (SLD). The BAIRS II mission strategy pursued was to study and probe radar-identifiable, strongly anisotropic crystal targets (dendrites and needles) with which supercooled water (and water saturated conditions) are physically linked as a means for dual polarimetric detection of icing hazard. BAIRS II employed superior optical array probes along with state and microphysical instrumentation; and, using again NEXRAD-feature-guided flight paths, was able to make advances from the original BAIRS helpful to the icing algorithm development. The key findings that are given thorough treatment in this report are: --Identification of the radar-detectable "crystal sandwich" structure from two anisotropic crystal types stratified by in situ air temperature in association with varying levels of supercooled water --with layer thicknesses observed to 2 km, --over hundred-kilometer scales matched with the mesoscale surveillance of the NEXRAD radars, --Development and application of a multi-sensor cloud phase algorithm to distinguish between liquid phase, mixed phase, and glaciated (no icing) conditions for purposes of a "truth" database and improved analysis in BAIRS II, --Development of concatenated hydrometeor size distributions to examine the in situ growth of both liquid and solid hydrometeors over a broad size spectrum; used, in part, to demonstrate differences between maritime and continental conditions, and --The Icing Hazard Levels (IHL) algorithm’s verification in icing conditions is consistent with previous work and, new, is documented to perform well when indicating "glaciated" (no icing) conditions.
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Summary

The second Buffalo Area Icing and Radar Study (BAIRS II) was conducted during the winter of 2017. The BAIRS II partnership between Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL), the National Research Council of Canada (NRC), and Environment and Climate Change Canada (ECCC) was sponsored by the Federal Aviation...

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Kawasaki disease, multisystem inflammatory syndrome in children: antibody-induced mast cell activation hypothesis

Published in:
J Pediatrics & Pediatr Med. 2020; 4(2): 1-7

Summary

Multisystem Inflammatory Syndrome in Children (MIS-C) is appearing in infants, children, and young adults in association with COVID-19 (coronavirus disease 2019) infections of SARS-CoV-2. Kawasaki Disease (KD) is one of the most common vasculitides of childhood. KD presents with similar symptoms to MIS-C especially in severe forms such as Kawasaki Disease Shock Syndrome (KDSS). The observed symptoms for MIS-C and KD are consistent with Mast Cell Activation Syndrome (MCAS) characterized by inflammatory molecules released from activated mast cells. Based on the associations of KD with multiple viral and bacterial pathogens, we put forward the hypothesis that KD and MIS-C result from antibody activation of mast cells by Fc receptor-bound pathogen antibodies causing a hyperinflammatory response upon second pathogen exposure. Within this hypothesis, MIS-C may be atypical KD or a KD-like disease associated with SARS-CoV-2. We extend the mast cell hypothesis that increased histamine levels are inducing contraction of effector cells with impeded blood flow through cardiac capillaries. In some patients, pressure from impeded blood flow, within cardiac capillaries, may result in increased coronary artery blood pressure leading to aneurysms, a well-known complication in KD.
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Summary

Multisystem Inflammatory Syndrome in Children (MIS-C) is appearing in infants, children, and young adults in association with COVID-19 (coronavirus disease 2019) infections of SARS-CoV-2. Kawasaki Disease (KD) is one of the most common vasculitides of childhood. KD presents with similar symptoms to MIS-C especially in severe forms such as Kawasaki...

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Medical countermeasures analysis of 2019-nCoV and vaccine risks for antibody-dependent enhancement (ADE)

Published in:
https://www.preprints.org/manuscript/202003.0138/v1

Summary

Background: In 80% of patients, COVID-19 presents as mild disease. 20% of cases develop severe (13%) or critical (6%) illness. More severe forms of COVID-19 present as clinical severe acute respiratory syndrome, but include a T-predominant lymphopenia, high circulating levels of proinflammatory cytokines and chemokines, accumulation of neutrophils and macrophages in lungs, and immune dysregulation including immunosuppression. Methods: All major SARS-CoV-2 proteins were characterized using an amino acid residue variation analysis method. Results predict that most SARS-CoV-2 proteins are evolutionary constrained, with the exception of the spike (S) protein extended outer surface. Results were interpreted based on known SARS-like coronavirus virology and pathophysiology, with a focus on medical countermeasure development implications. Findings: Non-neutralizing antibodies to variable S domains may enable an alternative infection pathway via Fc receptor-mediated uptake. This may be a gating event for the immune response dysregulation observed in more severe COVID-19 disease. Prior studies involving vaccine candidates for FCoV SARS-CoV-1 and Middle East Respiratory Syndrome coronavirus (MERS-CoV) demonstrate vaccination-induced antibody-dependent enhancement of disease (ADE), including infection of phagocytic antigen presenting cells (APC). T effector cells are believed to play an important role in controlling coronavirus infection; pan-T depletion is present in severe COVID-19 disease and may be accelerated by APC infection. Sequence and structural conservation of S motifs suggests that SARS and MERS vaccine ADE risks may foreshadow SARS-CoV-2 S-based vaccine risks. Autophagy inhibitors may reduce APC infection and T-cell depletion. Amino acid residue variation analysis identifies multiple constrained domains suitable as T cell vaccine targets. Evolutionary constraints on proven antiviral drug targets present in SARS-CoV-1 and SARS-CoV-2 may reduce risk of developing antiviral drug escape mutants. Interpretation: Safety testing of COVID-19 S protein-based B cell vaccines in animal models is strongly encouraged prior to clinical trials to reduce risk of ADE upon virus exposure.
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Summary

Background: In 80% of patients, COVID-19 presents as mild disease. 20% of cases develop severe (13%) or critical (6%) illness. More severe forms of COVID-19 present as clinical severe acute respiratory syndrome, but include a T-predominant lymphopenia, high circulating levels of proinflammatory cytokines and chemokines, accumulation of neutrophils and macrophages...

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Automated discovery of cross-plane event-based vulnerabilities in software-defined networking

Summary

Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers' functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the control plane towards unwanted states that bypass network security restrictions (i.e., cross-plane attacks). Unfortunately, because of the complex interplay among controllers, apps, and data plane inputs, at present it is difficult to systematically identify and analyze these cross-plane vulnerabilities. We present EVENTSCOPE, a vulnerability detection tool that automatically analyzes SDN control plane event usage, discovers candidate vulnerabilities based on missing event-handling routines, and validates vulnerabilities based on data plane effects. To accurately detect missing event handlers without ground truth or developer aid, we cluster apps according to similar event usage and mark inconsistencies as candidates. We create an event flow graph to observe a global view of events and control flows within the control plane and use it to validate vulnerabilities that affect the data plane. We applied EVENTSCOPE to the ONOS SDN controller and uncovered 14 new vulnerabilities.
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Summary

Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers' functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the...

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Wind information requirements for NextGen applications phase 7 report

Summary

This report details the Required Time of Arrival (RTA) performance of B757 aircraft arriving at various meter fixes across a range of altitudes from 33,000' down to 3,000' above ground level (AGL). The system tested demonstrated less than ±10 second arrival error in at least 95% of flights at meter fixes down to 7,000' AGL regardless of the forecast quality provided. Below 7,000' AGL, RTA performance significantly degraded demonstrating around 80% compliance under the best forecast and operating conditions. This report also provides a comprehensive lexicon of aviation and air traffic control related "wind" terms.
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Summary

This report details the Required Time of Arrival (RTA) performance of B757 aircraft arriving at various meter fixes across a range of altitudes from 33,000' down to 3,000' above ground level (AGL). The system tested demonstrated less than ±10 second arrival error in at least 95% of flights at meter...

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AI data wrangling with associative arrays [e-print]

Published in:
Submitted to Northeast Database Day, NEDB 2020, https://arxiv.org/abs/2001.06731

Summary

The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical representations of these data enables data translation and analysis optimization within and across steps. Associative array algebra provides a mathematical foundation that naturally describes the tabular structures and set mathematics that are the basis of databases. Likewise, the matrix operations and corresponding inference/training calculations used by neural networks are also well described by associative arrays. More surprisingly, a general denormalized form of hierarchical formats, such as XML and JSON, can be readily constructed. Finally, pivot tables, which are among the most widely used data analysis tools, naturally emerge from associative array constructors. A common foundation in associative arrays provides interoperability guarantees, proving that their operations are linear systems with rigorous mathematical properties, such as, associativity, commutativity, and distributivity that are critical to reordering optimizations.
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Summary

The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical...

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