Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new model for each task. Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks. These methods achieve surprisingly good performance and are shown to be more stable than their corresponding fully fine-tuned counterparts. However, such kind of methods is still not well understood. Some natural questions arise: How does the parameter sparsity lead to promising performance? Why is the model more stable than the fully fine-tuned models? How to choose the tunable parameters? In this paper, we first categorize the existing methods into random approaches, rule-based approaches, and projection-based approaches based on how they choose which parameters to tune. Then, we show that all of the methods are actually sparse fine-tuned models and conduct a novel theoretical analysis of them. We indicate that the sparsity is actually imposing a regularization on the original model by controlling the upper bound of the stability. Such stability leads to better generalization capability which has been empirically observed in a lot of recent research works. On the other hand, the random and rule-based methods do not utilize task-specific data information while the projection-based approaches suffer from the projection discontinuity problem. To solve these problems, we propose a novel Second-order Approximation Method (SAM) which approximates the original problem with an analytically solvable optimization function. The tunable parameters are determined by directly optimizing the approximation function. We conduct extensive experiments on several tasks. The experimental results show that our proposed SAM model outperforms many strong baseline models and it also verifies our theoretical analysis.

On the Effectiveness of Parameter-Efficient Fine-Tuning

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Strawberry (Fragaria × ananassa) is the most cultivated horticultural crop among the small berries due to its rich, healthy components. The major barrier in strawberry cultivation is caused by gray mold fungus (Botrytis cinerea Pers.) that causes an extensive loss in production. The predominant use of copper-based fungicides for controlling the gray mold fungus has been toxic to the plants, and fungal strains were developing resistance. Therefore, the present work aimed to evaluate and identify an alternative approach for controlling the gray mold fungus by screening the strawberry germplasm for resistance. Developing new cultivars requires the effective identification of resistant strawberry genotypes; hence, the current study was aimed at the evaluation of strawberry genetic resources under lab conditions by establishing an invitro leaf disc assay. The leaf discs from six different strawberry varieties were screened on 0.6% agar media and infected with actively growing gray mold fungus mycelial discs and incubated in the growth chambers maintained at 25˚C with 80% of humidity for two days under dark and 5 days under 16/8 hours of light/dark conditions. The fungal infection injury rate was measured at 7 days after infection. We identified two highly resistant, two moderate resistant, and two sensitive genotypes among the six varieties screened. The fungal infection was successful on leaf discs and the resistant genotypes showed less necrotic lesions, and sensitive genotypes showed more lesions. This assay provided a simple and rapid method to identify the strawberry genotype's resistance to gray mold fungus and potential donor genotypes.

Evaluation of Strawberry Germplasm for Gray Mold Fugal Resistance

Blueberries (Vaccinium corymbosum L.) are a very important crop because of their unique health benefits. The major hurdle for growing blueberries is the pH in the soil, as they need acidic soil. Hence improving the pH level tolerance in the blueberries provides a viable option for the growers to cultivate in a wide range of soil types and allow them to grow in several regions of the world. With this, our objective of the study was to screen the blueberry germplasm variation for high pH levels and identifying the tolerant lines. We used an in-vitro plant twig assay to screen the population using phosphate buffer solutions containing several pH gradients ranging from 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, and 7.0. In the assay, we used 6 different varieties of blueberries for testing the variation for pH level. Among six varieties, we screened, identified one highly resistant, 4 moderately resistant genotypes, and one sensitive genotype to the high pH. The resistance lines maintained a stay green nature at the high pH level, but the sensitive lines turned to brownish and chlorophyll degradation was observed. Developing new cultivars for high pH tolerance requires the effective identification of tolerant blueberry genotypes; therefore, the current approach was aimed at evaluating blueberry genetic resources under laboratory conditions by establishing an invitro plant twig assay. This assay provided an easy and time-consuming method to identify the blueberry genotypes tolerant to high pH and a better genetic source for developing the pH tolerant varieties.

Screening of Blueberry Varieties for pH Variation

Leaf shape is genetically determined, characteristic to each plant species (heterochrony), and changes developmentally (heteroblasty) and seasonally (environmental plasticity). Aside from being a fascinating biological phenomenon, leaf shape is also important from the perspective of agricultural and environmental sustainability, as it influences the efficiency of light harvesting, evapotranspiration and thermal regulation in plants. This study examines the genetic variation of leaf shape in an F1 hybrid population from a cross between two North American grapevine species Vitis rupestris and Vitis riparia. We collected leaf shape data from three different positions in the shoot. The analysis of the data will allow us to assess the phenotypic variation due to heterochrony, heteroblasty and environmental plasticity. The data presented here examine the range of two key phenotypic features, leaf size and leaf blade width) in F1 progeny. The range of variation in these two phenotypes from the parent phenotypes demonstrate transgressional segregation. These traits may be exploited by agricultural sustainability to breed specific plants with agronomically optimal morphology.

Transgressive Segregation of Leaf Shape in Grapevine

Eastern black Walnut (Juglans nigra), endemic to the Eastern US is highly sought after for its timber products and uniquely flavored nuts. The University of Missouri houses a significant Black Walnut repository at the Horticulture and Agroforestry Research Center in New Franklin, MO. The cultivars in this repository are used to support a breeding program focused on economic traits related to nut production (nutmeat mass, overall nut quality, precociousness, and yield). In 1996, four ramets of 67 cultivars were each grafted and subsequently planted in an experimental orchard for further evaluation. Beginning in 2001 and ending in 2015, each tree was evaluated for eleven phenological and tens nut quality/yield characteristics. The economically relevant traits nut weight, percent kernel, and precociousness (total nuts produced age 6 to 10) ranged from 8.8 to 43.0 g, 5.3 to 39.3 percent, and 16 to 1522 total nut, respectively. Nut weight and percent kernel were negatively correlated (r=-0.60), while precociousness correlated positively (r=0.38) with percent kernel and negatively (r=-0.32) with nut weight. Preconsciousness also correlated negatively with first (r=-0.39) and last (r=-0.30) pistillate emergence. Furthermore, principal components and biplot analyses revealed high variation of the evaluated cultivars leading to the prioritization of certain breeding parents over others.

Elias Bunting is currently an undergraduate senior in the College of Agriculture Food and Natural Resources at the University of Missouri. Elias’ research interest lies in determining the drivers of genotypic and phenotypic traits in eastern American black walnut. In his research, he has worked to determine how to leverage these traits for improved walnut breeding programs.

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On the Effectiveness of Parameter-Efficient Fine-Tuning

Extent, Impacts and Prospects of genetically engineered crops in Africa

Screening of Potetial Sunflower Genotypes in Response to Water Stress at Early Growth stage

Overexpression of GhKTI12 Enhances Salinity and Drought Stress Tolerance in Nicotiana tabacum

Evaluation of Strawberry Germplasm for Gray Mold Fugal Resistance

Screening of Blueberry Varieties for pH Variation

Transgressive Segregation of Leaf Shape in Grapevine

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