Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

Plant growth promotion and Penicillium citrinum

<p>Abstract</p> <p>Background</p> <p>Endophytic fungi are known plant symbionts. They produce a variety of beneficial metabolites for plant growth and survival, as well as defend their hosts from attack of certain pathogens. Coastal dunes are nutrient deficient and offer harsh, saline environment for the existing flora and fauna. Endophytic fungi may play an important role in plant survival by enhancing nutrient uptake and producing growth-promoting metabolites such as gibberellins and auxins. We screened roots of <it>Ixeris repenes </it>(L.) A. Gray, a common dune plant, for the isolation of gibberellin secreting endophytic fungi.</p> <p>Results</p> <p>We isolated 15 endophytic fungi from the roots of <it>Ixeris repenes </it>and screened them for growth promoting secondary metabolites. The fungal isolate IR-3-3 gave maximum plant growth when applied to waito-c rice and <it>Atriplex gemelinii </it>seedlings. Analysis of the culture filtrate of IR-3-3 showed the presence of physiologically active gibberellins, GA₁, GA₃, GA₄and GA₇(1.95 ng/ml, 3.83 ng/ml, 6.03 ng/ml and 2.35 ng/ml, respectively) along with other physiologically inactive GA₅, GA₉, GA₁₂, GA₁₅, GA₁₉, GA₂₀and, GA₂₄. The plant growth promotion and gibberellin producing capacity of IR-3-3 was much higher than the wild type <it>Gibberella fujikuroi</it>, which was taken as control during present study. GA₅, a precursor of bioactive GA₃was reported for the first time in fungi. The fungal isolate IR-3-3 was identified as a new strain of <it>Penicillium citrinum </it>(named as <it>P. citrinum </it>KACC43900) through phylogenetic analysis of 18S rDNA sequence.</p> <p>Conclusion</p> <p>Isolation of new strain of <it>Penicillium citrinum </it>from the sand dune flora is interesting as information on the presence of <it>Pencillium </it>species in coastal sand dunes is limited. The plant growth promoting ability of this fungal strain may help in conservation and revegetation of the rapidly eroding sand dune flora. <it>Penicillium citrinum </it>is already known for producing mycotoxin citrinin and cellulose digesting enzymes like cellulase and endoglucanase, as well as xylulase. Gibberellins producing ability of this fungus and the discovery about the presence of GA₅will open new aspects of research and investigations.</p

Undulatory topographical waves for flow-induced foulant sweeping

Diverse bioinspired antifouling strategies have demonstrated effective fouling-resistant properties with good biocompatibility, sustainability, and long-term activity. However, previous studies on bioinspired antifouling materials have mainly focused on material aspects or static architectures of nature without serious consideration of kinetic topographies or dynamic motion. Here, we propose a magnetically responsive multilayered composite that can generate coordinated, undulatory topographical waves with controlled length and time scales as a new class of dynamic antifouling materials. The undulatory surface waves of the dynamic composite induce local and global vortices near the material surface and thereby sweep away foulants from the surface, fundamentally inhibiting their initial attachment. As a result, the dynamic composite material with undulating topographical waves provides an effective means for efficient suppression of biofilm formation without surface modification with chemical moieties or nanoscale architectures

Functional, Spatial Organization of Posterolateral Cortical Amygdala in the Control of Odor Evoked Behavioral Valence

Posterolateral cortical amygdala (PLCoA) is known to participate in innate behavior response towards odor stimuli. The participation of PLCoA is not restricted to one end of the spectrum: they take part in both aversive and appetitive behaviors that are triggered by exposures to a variety of different odors. By selectively targeting subregions of PLCoA along the anterior-posterior axis and activating the subregions through optogenetic methods, we were able to demonstrate that the posterior subregions incite attraction when activated, while the anterior subregion triggers aversion. Further tests using Arc-CreERT2 mice, which allowed the selective activation of the aversive fox odor, 2,3,5-Trimethyl-3-thiazoline (TMT), the fox odor that is innately aversive to rodents, indicated that the TMT responsive neurons of the posterior PLCoA is, in fact, not sufficient by themselves to cause aversive behavior, unlike its anterior counterpart, which was sufficient for aversion. These data suggest the existence of spatial organization of neurons across the anterior posterior axis within PLCoA tied strongly to the innate behavioral response towards the odor stimulation

Functional, Spatial Organization of Posterolateral Cortical Amygdala in the Control of Odor Evoked Behavioral Valence

Posterolateral cortical amygdala (PLCoA) is known to participate in innate behavior response towards odor stimuli. The participation of PLCoA is not restricted to one end of the spectrum: they take part in both aversive and appetitive behaviors that are triggered by exposures to a variety of different odors. By selectively targeting subregions of PLCoA along the anterior-posterior axis and activating the subregions through optogenetic methods, we were able to demonstrate that the posterior subregions incite attraction when activated, while the anterior subregion triggers aversion. Further tests using Arc-CreERT2 mice, which allowed the selective activation of the aversive fox odor, 2,3,5-Trimethyl-3-thiazoline (TMT), the fox odor that is innately aversive to rodents, indicated that the TMT responsive neurons of the posterior PLCoA is, in fact, not sufficient by themselves to cause aversive behavior, unlike its anterior counterpart, which was sufficient for aversion. These data suggest the existence of spatial organization of neurons across the anterior posterior axis within PLCoA tied strongly to the innate behavioral response towards the odor stimulation

타원체 입자의 원형관 내부 3차원 회전 거동 분석을 위한 홀로그래픽 현미경의 응용

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디지털 홀로그래픽 현미경과 인공지능 기반의 혈구성 질환 진단

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비정형 축구경기 영상의 실시간 분석을 위한 최적화 방법

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Algorithm-Hardware Co-Optimization for Cost-Efficient ML-based ISP Accelerator

In this paper, we present an advanced algorithm-hardware co-optimization method for designing an efficient accelerator architecture for image signal processing (ISP) with deep neural networks (DNNs). Based on the systolic-array structure, for performing the target network model, we newly introduce two evaluation metrics, each of which is dedicated to fairly representing either the processing speed or the energy consumption. Then, the overall evaluation metric is defined to test each systolic array, finding the initial array configuration for the given number of total multipliers. From the initial array, several array-scaling methods are then presented to find the most cost-efficient array structure. In addition, the original ML model is adjusted to further enhance the overall efficiency with subtle quality drops of image outputs. Implementation results in 28nm CMOS technology show that the proposed co-optimization method successfully finds the cost-efficient systolic accelerator architecture for ISP applications, improving the energy efficiency by 51% compared to the straightforward array design.1

홀로그래픽 현미경과 기계학습형 인공 지능을 결합한 말라리아의 자동 진단 기술 개발

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