Argument Mining with Structured SVMs and RNNs

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.Comment: Accepted for publication at ACL 2017. 11 pages, 5 figures. Code at https://github.com/vene/marseille and data at http://joonsuk.org

Microbial electrocatalysis with Geobacter sulfurreducens biofilm on stainless steel cathodes

Stainless steel and graphite electrodes were individually addressed and polarized at−0.60V vs. Ag/AgCl in reactors filled with a growth medium that contained 25mM fumarate as the electron acceptor and no electron donor, in order to force the microbial cells to use the electrode as electron source. When the reactor was inoculated with Geobacter sulfurreducens, the current increased and stabilized at average values around 0.75Am−2 for graphite and 20.5Am−2 for stainless steel. Cyclic voltammetry performed at the end of the experiment indicated that the reduction started at around −0.30V vs. Ag/AgCl on stainless steel. Removing the biofilm formed on the electrode surface made the current totally disappear, confirming that the G.sulfurreducens biofilm was fully responsible for the electrocatalysis of fumarate reduction. Similar current densities were recorded when the electrodes were polarized after being kept in open circuit for several days. The reasons for the bacteria presence and survival on non-connected stainless steel coupons were discussed. Chronoamperometry experiments performed at different potential values suggested that the biofilm-driven catalysis was controlled by electrochemical kinetics. The high current density obtained, quite close to the redox potential of the fumarate/succinate couple, presents stainless steel as a remarkable material to support biocathodes

Sensory Processing, Chronic Pain, and Recovery from Substance Use

Background: Those with chronic pain have worse drug and alcohol treatment outcomes and higher rates of relapse compared to those without pain or with reduced pain (Ilgen et al., 2020). Methods: A descriptive study was employed with the aim of determining what are the sensory processing pattens of those with co-morbid chronic pain and in recovery for substance use. All participants completed the Adolescent and Adult Sensory Profile, the Brief Pain Inventory, and the Personal Recovery Outcome Measure (PROM). Results: From the preliminary findings, individuals with co-morbid chronic pain and substance use may have atypical sensory processing in areas of sensation sensitive, sensation avoiding, and low registration, compared to the normative sample. Individuals within this clinical setting in general were not receiving non-pharmacological treatment for their pain. With pain fluctuating between mild (2.2/10) and severe (6.0/10) throughout the day. Results of the PROM were an average of 21/30. Item 21 states “I can manage stress”. From these findings a 5-week sensory based OT Group was implemented at a PHP/IOP treatment center focusing on self-management, health-management, and stress reduction strategies to improve overall occupational performance. Conclusion: Individuals with co-morbid chronic pain and substance use may have sensory processing patterns that are impacting function and recovery. This highlights the potential value of further research and consideration in clinical practice of these unique patterns and how they may be impacting recovery and long-term sobriety.https://soar.usa.edu/otdcapstonesspring2021/1004/thumbnail.jp

Hybrid Communication Protocols and Control Algorithms for NextGen Aircraft Arrivals

Capacity constraints imposed by current air traffic management technologies and protocols could severely limit the performance of the Next Generation Air Transportation System (NextGen). A fundamental design decision in the development of this system is the level of decentralization that balances system safety and efficiency. A new surveillance technology called automatic dependent surveillance-broadcast (ADS-B) can be potentially used to shift air traffic control to a more distributed architecture; however, channel variations and interference with existing secondary radar replies can affect ADS-B systems. This paper presents a framework for managing arrivals at an airport by using a hybrid centralized/distributed algorithm for communication and control. The algorithm combines the centralized control that is used in congested regions with the distributed control that is used in lower traffic density regions. The hybrid algorithm is evaluated through realistic simulations of operations around a major airport. The proposed strategy is shown to significantly improve air traffic control performance under various operating conditions by adapting to the underlying communication, navigation, and surveillance systems. The performance of the proposed strategy is found to be comparable to fully centralized strategies, despite requiring significantly less ground infrastructure.National Science Foundation (U.S.) (Grant CNS-931843)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N0014-08-0696)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-12-1-0609)United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Grant FA9550-10-1-0567