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"Richter, David"
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Reading the eighteenth-century novel
\"This book about reading the English novel during the \"long eighteenth century,\" a stretch of time that, in the generally accepted ways of breaking up British literary history into discrete periods for university courses, begins some time after the Restoration of King Charles II in 1660 and ends around 1830, before the reign of Queen Victoria. At the beginning of this period, the novel can hardly be said to exist, and writing prose fiction is a mildly disreputable literary activity. Around 1720, Daniel Defoe's fictional autobiographies spark continuations and imitations, and in the 1740s, with Samuel Richardson and Henry Fielding's novels begin what is perceived as \"a new kind of writing.\" By the end of the period, with Jane Austen and Walter Scott, the novel has not only come into existence, it has developed into a more-or-less respectable genre, and in fact publishers have begun to issue series of novels (edited by Walter Scott and by Anna Barbauld, among others) that establish for that time, if not necessarily for ours, a canon of the English novel. With the decline of the English drama and the almost complete eclipse of the epic, the novel has become by default the serious literary long form, on its way to becoming by the mid-nineteenth century, with Dickens, Thackeray, and Eliot, the pre-eminent genre of literature. This chapter will consider how and why the novel came to be when it did\"-- Provided by publisher.
Book
Assessing the performance of domain-specific models for plant leaf disease classification: a comprehensive benchmark of transfer-learning on open datasets
Agriculture and its yields are indispensable to human life all over the planet. It is an essential part of many countries’ economies and without it the world’s population can not be fed. As such, guaranteeing harvest with minimal loss is a primary objective. One factor that heavily contributes to loss in crop harvesting are plant diseases, which often affect crops and their leaves. A plant’s leaf often carries symptoms that indicate whether or not a plant is infected, but traditional manual approaches to identifying these symptoms are tedious and laborious. Additionally the process of manually spotting diseases can be rather slow in a field where urgency and fast identification are very important. The sooner a disease gets identified, the sooner countermeasures can be carried out. To improve both the accuracy with which diseases can be recognized, as well as increasing the speed at which this can be carried out, deep learning methods have proven useful. Recently the field of plant disease recognition has seen a big uptick in the application of various convolutional neural network (CNN) models for the automatic classification of diseases. There exist many different highly-capable models at this time. There also exists a range of plant leaf disease classification image datasets containing different plants and diseases. However, there seems to be no consensus on which model is best suited to handle this task and the same can be said for the datasets. To the best of our knowledge, prior work has used a wide range of different models with different datasets in the way of feasibility studies, but without comprehensively identifying which models are best used in this field. In this work we test a large number of state-of-the-art CNN models on a wide range of openly available datasets to asses their performance and to identify models that are best suited for this field, in order to be able to built better models, and even new foundation models, based on these findings. 23 models have been tested on 18 datasets, both using transfer-learning and transfer-learning with additional fine-tuning added, for five iterations each. Transfer-learning allows models to utilize knowledge obtained from other previous tasks to be used for new tasks, reducing training time and lowering the need for training data. The experiments result in a total of 4140 having been trained for this work. All results will be compared and contextualized in order to find the best models architecture for plant leaf disease classification as well as assessing which datasets are well suited for this task.
Journal Article
The town
The story of a pioneer family and the transition they have to make as urban areas begin to spread in the 1800s.
Book
HCLmNet: A unified hybrid continual learning strategy multimodal network for lung cancer survival prediction
Lung cancer survival prediction remains one of the most challenging tasks in modern healthcare, as accurate and adaptive prediction models are essential for improving patient outcomes. However, the continuous inflow of new patient data in hospital environments demands models that can update incrementally without losing prior knowledge a challenge known as catastrophic forgetting. This problem is compounded by the complexity of multimodal data integration, which combines heterogeneous sources such as CT and PET imaging, genomic (DNA) sequences, and clinical records. Traditional deep learning (DL) models, especially CNN-based systems, often fail to capture subtle patterns such as ground-glass opacities or multi-lesion tumors and cannot effectively adapt to new data streams. To overcome these challenges, this study proposes HCLmNet, a Hybrid Continual Learning (CL) Multimodal Network that integrates Elastic Weight Consolidation (EWC) with three complementary replay-based modules: Experience Replay (ER), Instance-Level Correlation Replay (EICR), and Class-Level Correlation Replay (ECCR). ER stabilizes learning through selective sample replay; EICR preserves fine-grained inter-instance relationships across modalities; and ECCR employs triplet-based contrastive learning to maintain class-level correlations. The architecture incorporates a Swin Transformer (SwinT) for extracting critical imaging features, XLNet for modeling DNA patterns, and a Fully Connected Network (FCN) for processing temporal clinical data. A cross-attention fusion layer integrates these modalities, while an FCN and Cox Proportional Hazards (CoxPH) model produce final 5-year survival predictions. Experimental results on multimodal lung cancer datasets show that traditional models such as CoxPH and DeepSurv achieved Concordance Index (C-index) scores of 0.65 and 0.70, respectively. The base multimodal model without CL achieves a C-index of 0.76 and a Mean Absolute Error (MAE) of 189 days. In contrast, the proposed HCLmNet, equipped with CL mechanisms, reaches a C-index of 0.84, representing a 7.7% improvement over the best baseline. Furthermore, the model reduces the MAE from 252 and 189 days to 140 days and minimizes catastrophic forgetting to 0.08. These improvements stem from the synergistic integration of the ER, EICR, and ECCR CL modules, which enable the model to retain prior knowledge while effectively adapting to new data. Overall, HCLmNet demonstrates superior stability, adaptability, and interpretability for lung cancer survival prediction in dynamic clinical environments.
Journal Article
The Oxford illustrated history of the Third Reich
At age thirty in 1919, Adolf Hitler had no accomplishments. He was a rootless loner, a corporal in a shattered army, without money or prospects. A little more than twenty years later, in autumn 1941, he directed his dynamic forces against the Soviet Union, and in December, the Germans were at the gates of Moscow and Leningrad. At that moment, Hitler appeared - however briefly - to be the most powerful ruler on the planet. Given this dramatic turn of events, it is little wonder that since 1945 generations of historians keep trying to explain how it all happened. This richly illustrated history provides a readable and fresh approach to the complex history of the Third Reich, from the coming to power of the Nazis in 1933 to the final collapse in 1945. Using photographs, paintings, propaganda images, and a host of other such materials from a wide range of sources, including official documents, cinema, and the photography of contemporary amateurs, foreigners, and the Allied armies, it distills our ideas about the period and provides a balanced and accessible account of the whole era.
Book
PLDC‐Net: A Domain‐Specific Base Model for Plant Leaf Disease Classification Domain Adaptation Tasks
Plant diseases are the cause of heavy losses of crop production and, therefore, a big contributor to food shortages. Identifying these diseases as early as possible is important to limit the negative effects that these diseases have on the yields, as slow response time will lead to the spread of diseases and further loss. Traditionally, trained staff will go into the fields, multiple times during the growth period, and inspect the plants in samples through field disease monitoring. These traditional processes are time‐consuming and costly, and can be error‐prone, if the staff is not properly educated or if the staff simply makes mistakes due to oversight, for example. To aid farmers with the process of correctly identifying diseases, artificial intelligence deep learning methods have been employed in recent years. However, to train such deep learning models, one needs to obtain sufficiently large and high‐quality datasets and a model architecture that is capable of extracting relevant features to accurately classify the plant leaves. Datasets are still a limitation in the field of plant leaf disease classification. As such, domain adaptation methods such as transfer learning are often employed to overcome this data shortage. However, in current research, these domain adaptation methods almost exclusively rely on ImageNet as the pretraining dataset, a dataset that is domain unrelated to plant leaf disease detection, and models are often left unmodified and un‐optimized as a result. In this work, we propose the pretraining of an improved attention‐based and SiLU‐activated DenseNet201 architecture called PLDC‐Net that is pretrained on a large‐scale plant leaf disease dataset constructed by the authors to create a domain‐specific base model for better domain adaptation to new plants and diseases, validating the improved results through transfer learning, fine‐tuning, one‐shot learning, and few‐shot learning. PLDC‐Net has managed up to just over 24% improvements in F1‐Score over the baseline in domain adaptation results.
Journal Article
Risk Preference
Psychology offers conceptual and analytic tools that can advance the discussion on the nature of risk preference and its measurement in the behavioral sciences. We discuss the revealed and stated preference measurement traditions, which have coexisted in both psychology and economics in the study of risk preferences, and explore issues of temporal stability, convergent validity, and predictive validity with regard to measurement of risk preferences. As for temporal stability, do risk preference as a psychological trait show a degree of stability over time that approximates what has been established for other major traits, such as intelligence, or, alternatively, are they more similar in stability to transitory psychological states, such as emotional states? Convergent validity refers to the degree to which different measures of a psychological construct capture a common underlying characteristic or trait. Do measures of risk preference all capture a unitary psychological trait that is indicative of risky behavior across various domains, or do they capture various traits that independently contribute to risky behavior in specific areas of life, such as financial, health, and recreational domains? Predictive validity refers to the extent to which a psychological trait has power in forecasting behavior. Intelligence and major personality traits have been shown to predict important life outcomes, such as academic and professional achievement, which suggests there could be studies of the short- and long-term outcomes of risk preference— something lacking in current psychological (and economic) research. We discuss the current empirical knowledge on risk preferences in light of these considerations.
Journal Article
A Lagrangian Cloud Model for the Study of Marine Fog
A large-eddy simulation model is coupled with a Lagrangian cloud model to study marine fog. In this model, aerosols and droplets are treated from a Lagrangian frame of reference, in contrast to the traditional bulk and bin microphysical models. Droplet growth via condensation is governed by Köhler theory and environmental conditions local to the droplet. Coupling to the vapour and temperature fields of the flow ensures mass, momentum, and energy conservation between the air and droplet phases. Based on the recent C-FOG field campaign, a simulation is performed which highlights the benefits and potential of this type of model. By initializing the simulation with the measured aerosol size distribution and making assumptions about the chemical composition of the multiple peaks, the simulations provide a clear explanation for the observed bimodal droplet distribution during C-FOG: high supersaturation levels cause condensational growth of nearly all coarse-mode aerosols (presumed to be composed of marine salt), as well as a large number of accumulation model aerosols (presumed to be of continental origin with a lower hygroscopicity). The largest peak in the resulting droplet distribution is created from coarse-mode aerosols with high hygroscopicity, while the secondary peak is only possible due to the limited impact of the largest peak on saturation levels inside the fog. Thus, for the simulated levels of supersaturation, it is the limited number of coarse-mode aerosols which is responsible for the emergence of a larger second peak.
Journal Article
Strategies for combating antibiotic resistance in bacterial biofilms
Biofilms, which are complexes of microorganisms that adhere to surfaces and secrete protective extracellular matrices, wield substantial influence across diverse domains such as medicine, industry, and environmental science. Despite ongoing challenges posed by biofilms in clinical medicine, research in this field remains dynamic and indeterminate. This article provides a contemporary assessment of biofilms and their treatment, with a focus on recent advances, to chronicle the evolving landscape of biofilm research.
Journal Article