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  1. Cable bacteria are multicellular filamentous bacteria that conduct electrons nonlocally between anoxic and oxic sediment regions, creating characteristic electrogenic pH fingerprints. These microbes aggregate in 3D patterns near biogenic structures, and filament fragments are also dispersed throughout deposits. Utilizing pH-sensitive planar optodes to investigate the dynamic response of electrogenic pH fingerprints to sediment reworking, we found that mobile bioturbators like nereid polychaetes (ragworms) can disturb the pH signatures. Sudden sediment disturbance associated with burrows at sub- to multi-centimeter scales eliminates detection of pH signatures. However, electrogenic pH fingerprints can recover in as little as 13 h near abandoned, closed burrows.more »Sequential collapse and regeneration of electrogenic pH fingerprints are associated with occupied and dynamic burrow structures, with the response time positively related to the scale of disturbance. In the case of relatively stable tube structures, built by benthos like spionid polychaetes and extending mm to cm into deposits, the electrogenic pH fingerprint is evident around the subsurface tubes. Cable filaments clearly associate with subsurface regions of enhanced solute exchange (oxidant supply) and relatively stable biogenic structures, including individual tubes and patches of tubes (e.g. made by Sabaco , a bamboo worm). Physically stable environments, favorable redox gradients, and enhanced organic/inorganic substrate availability promote the activity of cable bacteria in the vicinity of tubes and burrows. These findings suggest complex interactions between electrogenic activity fingerprints and species-specific patterns of bioturbation at multiple spatial and temporal scales, and a substantial impact of electrogenic metabolism on subsurface pH and early diagenetic reaction distributions in bioturbated deposits.« less
    Free, publicly-accessible full text available July 8, 2022
  2. Bosansky, B. ; Gonzalez, C. ; Rass, S. ; Sinha, A. (Ed.)
  3. Bosansky, B. ; Gonzalez, C. ; Rass, S. ; Sinha, A. (Ed.)
  4. While the ultimate goal of natural-language based Human-Robot Interaction (HRI) may be free-form, mixed-initiative dialogue,social robots deployed in the near future will likely primarily engage in wakeword-driven interaction, in which users’ commands are prefaced by a wakeword such as “Hey, Robot.” This style of interaction helps to allay user privacy concerns, as the robot’s full speech recognition module need not be employed until the target wakeword is used. Unfortunately, there are a number of concerns in the popular media surrounding this style of interaction, with consumers fearing that it is training users (in particular,children) to be rude towards technology, andmore »by extension, rude towards other humans. In this paper, we present a study that demonstrates how an alternate style of wakeword, i.e., “Excuse me, Robot” may allay this concern, by priming users to phrase commands as Indirect Speech Acts« less
  5. For many applications with limited computation, com- munication, storage and energy resources, there is an im- perative need of computer vision methods that could select an informative subset of the input video for efficient pro- cessing at or near real time. In the literature, there are two relevant groups of approaches: generating a “trailer” for a video or fast-forwarding while watching/processing the video. The first group is supported by video summa- rization techniques, which require processing of the entire video to select an important subset for showing to users. In the second group, current fast-forwarding methods de- pend on eithermore »manual control or automatic adaptation of playback speed, which often do not present an accurate rep- resentation and may still require processing of every frame. In this paper, we introduce FastForwardNet (FFNet), a re- inforcement learning agent that gets inspiration from video summarization and does fast-forwarding differently. It is an online framework that automatically fast-forwards a video and presents a representative subset of frames to users on the fly. It does not require processing the entire video, but just the portion that is selected by the fast-forward agent, which makes the process very computationally efficient. The online nature of our proposed method also enables the users to begin fast-forwarding at any point of the video. Experiments on two real-world datasets demonstrate that our method can provide better representation of the input video (about 6%-20% improvement on coverage of impor- tant frames) with much less processing requirement (more than 80% reduction in the number of frames processed).« less