The effect of fast and regeneration in light versus dark on regulation in the hydra-algal symbiosis

Green hydra are able to regenerate tentacles after fast durations which cause brown, i.e., asymbiotic, hydra to fail completely, but the presence of endosymbiotic algae does not always enhance regeneration in fasted hydra. Green hydra whose nutritional state falls below some threshold, exhibit a light induced inhibition of regeneration. That is, hydra, fasted in the light, then randomly assigned to light or dark after decapitation, regenerate better in the dark. This effect of light does not appear to be present either in brown hydra or in normally green hydra from which the algae were removed. In a large strain of Chlorohydra viridissima, after fasts of intermediate duration (10 and 15 days), this light induced inhibition of regeneration is associated with an increase in the number of algae per gastric cell in regenerating hydra relative to non-regenerating controls

Energetics in Daphnia Pulex Populations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/119066/1/ecy1959402232.pd

A possible initial condition for red tides on the coast of Florida

It seems likely that the occurrence of a discrete mass of water, with a salinity lower than that of normal Gulf of Mexico surface water, is a necessary prerequisite for the occurrence of red tide off the Florida Coast

The size of water masses containing plankton blooms

If a phytoplankton population is assumed to be increasing logarithmically in a mass of water surrounded by water which is unsuitable for the survival of the population, it can be shown that there is a minimum critical size for the water mass below which no increase in concentration of phytoplankton can occur...

Non-Line-of-Sight Passive Acoustic Localization Around Corners

Non-line-of-sight (NLoS) imaging is an important challenge in many fields ranging from autonomous vehicles and smart cities to defense applications. Several recent works in optics and acoustics tackle the challenge of imaging targets hidden from view (e.g. placed around a corner) by measuring time-of-flight (ToF) information using active SONAR/LiDAR techniques, effectively mapping the Green functions (impulse responses) from several sources to an array of detectors. Here, leveraging passive correlations-based imaging techniques, we study the possibility of acoustic NLoS target localization around a corner without the use of controlled active sources. We demonstrate localization and tracking of a human subject hidden around the corner in a reverberating room, using Green functions retrieved from correlations of broadband noise in multiple detectors. Our results demonstrate that the controlled active sources can be replaced by passive detectors as long as a sufficiently broadband noise is present in the scene.Comment: 6 pages, 3 figure

Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans

The recently introduced Controlled Text Reduction (CTR) task isolates the text generation step within typical summarization-style tasks. It does so by challenging models to generate coherent text conforming to pre-selected content within the input text (``highlights''). This framing enables increased modularity in summarization-like tasks, allowing to couple a single CTR model with various content-selection setups and modules. However, there are currently no reliable CTR models, while the performance of the existing baseline for the task is mediocre, falling short of practical utility. Here, we address this gap by introducing a high-quality, open-source CTR model that tackles two prior key limitations: inadequate enforcement of the content-preservation constraint, and suboptimal silver training data. Addressing these, we amplify the content-preservation constraint in both training, via RL, and inference, via a controlled decoding strategy. Further, we substantially improve the silver training data quality via GPT-4 distillation. Overall, pairing the distilled dataset with the highlight-adherence strategies yields marked gains over the current baseline, of up to 30 ROUGE-L points, providing a reliable CTR model for downstream use.Comment: EMNLP 2023, finding

The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models

Large language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses. A primary issue arising in this context is the management of (un)answerable queries by LLMs, which often results in hallucinatory behavior due to overconfidence. In this paper, we explore the behavior of LLMs when presented with (un)answerable queries. We ask: do models represent the fact that the question is (un)answerable when generating a hallucinatory answer? Our results show strong indications that such models encode the answerability of an input query, with the representation of the first decoded token often being a strong indicator. These findings shed new light on the spatial organization within the latent representations of LLMs, unveiling previously unexplored facets of these models. Moreover, they pave the way for the development of improved decoding techniques with better adherence to factual generation, particularly in scenarios where query (un)answerability is a concern.Comment: EMNLP 202

SummHelper: Collaborative Human-Computer Summarization

Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process. In this paper, we introduce SummHelper, a 2-phase summarization assistant designed to foster human-machine collaboration. The initial phase involves content selection, where the system recommends potential content, allowing users to accept, modify, or introduce additional selections. The subsequent phase, content consolidation, involves SummHelper generating a coherent summary from these selections, which users can then refine using visual mappings between the summary and the source text. Small-scale user studies reveal the effectiveness of our application, with participants being especially appreciative of the balance between automated guidance and opportunities for personal input.Comment: Demo pape

Geoglobus acetivorans sp. nov., an iron(III)-reducing archaeon from a deep-sea hydrothermal vent

En libre-accès sur Archimer : http://archimer.ifremer.fr/doc/2009/publication-6977.pdfInternational audienceA hyperthermophilic, anaerobic, dissimilatory Fe(III)-reducing, facultatively chemolithoautotrophic archaeon (strain SBH6(T)) was isolated from a hydrothermal sample collected from the deepest of the known World Ocean hydrothermal fields, Ashadze field (1 degrees 58' 21'' N 4 degrees 51' 47'' W) on the Mid-Atlantic Ridge, at a depth of 4100 m. The strain was enriched using acetate as the electron donor and Fe(III) oxide as the electron acceptor. Cells of strain SBH6(T) were irregular cocci, 0.3-0.5 mum in diameter. The temperature range for growth was 50-85 degrees C, with an optimum at 81 degrees C. The pH range for growth was 5.0-7.5, with an optimum at pH 6.8. Growth of SBH6(T) was observed at NaCl concentrations ranging from 1 to 6 % (w/v) with an optimum at 2.5 % (w/v). The isolate utilized acetate, formate, pyruvate, fumarate, malate, propionate, butyrate, succinate, glycerol, stearate, palmitate, peptone and yeast extract as electron donors for Fe(III) reduction. It was also capable of growth with H(2) as the sole electron donor, CO(2) as a carbon source and Fe(III) as an electron acceptor without the need for organic substances. Fe(III) [in the form of poorly crystalline Fe(III) oxide or Fe(III) citrate] was the only electron acceptor that supported growth. 16S rRNA gene sequence analysis revealed that the closest relative of the isolated organism was Geoglobus ahangari 234(T) (97.0 %). On the basis of its physiological properties and phylogenetic analyses, the isolate is considered to represent a novel species, for which the name Geoglobus acetivorans sp. nov. is proposed. The type strain is SBH6(T) (=DSM 21716(T) =VKM B-2522(T))