Arbitrage: the key to pricing options

Arbitrage has become associated in popular attitudes with the most ruthless and profit-driven of human impulses, but the opposite reputation might be more well-deserved. The ability to arbitrage is essential for the efficient operation of markets. An interesting application of the principle of arbitrage arose when it provided the breakthrough insight in economists’ solution to a formerly intractable problem: how to properly price the emergent financial instruments known as options.Arbitrage ; Options (Finance)

Identification and Characterization of Antimicrobial Peptides with Therapeutic Potential

Antimicrobial peptides are key defense molecules adopted by all life forms to prevent infection. They also have other beneficial effects such as boosting immune response, anticancer, and wound healing. The antiviral effects of antimicrobial peptides have laid the foundations for developing new agents to combat seasonal Flu, HIV-1, RSV, Zika, and Ebola. This eBook is constructed to systematically deal with antimicrobial peptides from a variety of natural sources, including fungi, plants, and animals (insects, fish, amphibians, birds, and reptiles). It covers peptide discovery, antimicrobial activity, 3D structure, mechanisms of action and potential applications. Naturally Occurring Antimicrobial Peptides, an eBook published by the journal Pharmaceuticals, provides a helpful introduction to newcomers and refreshes the minds of veterans

Selected Papers from SDEWES 2017: The 12th Conference on Sustainable Development of Energy, Water and Environment Systems

EU energy policy is more and more promoting a resilient, efficient and sustainable energy system. Several agreements have been signed in the last few months that set ambitious goals in terms of energy efficiency and emission reductions and to reduce the energy consumption in buildings. These actions are expected to fulfill the goals negotiated at the Paris Agreement in 2015. The successful development of this ambitious energy policy needs to be supported by scientific knowledge: a huge effort must be made in order to develop more efficient energy conversion technologies based both on renewables and fossil fuels. Similarly, researchers are also expected to work on the integration of conventional and novel systems, also taking into account the needs for the management of the novel energy systems in terms of energy storage and devices management. Therefore, a multi-disciplinary approach is required in order to achieve these goals. To ensure that the scientists belonging to the different disciplines are aware of the scientific progress in the other research areas, specific Conferences are periodically organized. One of the most popular conferences in this area is the Sustainable Development of Energy, Water and Environment Systems (SDEWES) Series Conference. The 12th Sustainable Development of Energy, Water and Environment Systems Conference was recently held in Dubrovnik, Croatia. The present Special Issue of Energies, specifically dedicated to the 12th SDEWES Conference, is focused on five main fields: energy policy and energy efficiency in smart energy systems, polygeneration and district heating, advanced combustion techniques and fuels, biomass and building efficiency

E-Advising: Expanding Advising for Distance LIS Students

Online instruction and programming have expanded the universe of LIS education but have also expanded the needs of online students for assistance navigating institutional structures and requirements. With 24-7 access to coursework, accounts, and the university website, students expect prompt answers to questions through electronic or e-advising. From recruitment to alumni relations, LIS programs and their universities are seeking to expand how they reach distance students in online programs. We will share innovative uses of technology and staffing for e-advising along with what online students have told us in a survey about the kinds of advising they need and expect

Histogram Analysis of ADC in Brain Tumor Patients

At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, post-contrast T1) were registered to the corresponding DTI scan at patient\u27s first visit. Conventionally, hyper-intensity regions on T1-post contrast images are believed to represent the core tumor region while regions highlighted by FLAIR may overestimate tumor size. Thus we annotated tumor regions on the T1-post contrast scans and ADC intensity values for pixels were extracted inside tumor regions as defined on T1-post scans. We fit a mixture Gaussian (MG) model for the extracted pixels using the Expectation-Maximization (EM) algorithm, which produced a set of parameters (mean, various and mixture coefficients) for the MG model. This procedure was performed for each visits resulting in a series of GM parameters. We studied the parameters fitted for ADC and see if they can be used as indicators for tumor progression. Additionally, we studied the ADC characteristics in the peri-tumoral region as identified by hyper-intensity on FLAIR scans. The results show that ADC histogram analysis of the tumor region supports the two compartment model that suggests the low ADC value subregion corresponding to densely packed cancer cell while the higher ADC value region corresponding to a mixture of viable and necrotic cells with superimposed edema. Careful studies of the composition and relative volume of the two compartments in tumor region may provide some insights in the early assessment of tumor response to therapy for recurrence brain cancer patients

A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

On the role of magnetic reconnection in jet/accretion disk systems

The most accepted model for jet production is based on the magneto-centrifugal acceleration out off an accretion disk that surrounds the central source (Blandford & Payne, 1982). This scenario, however, does not explain, e.g., the quasi-periodic ejection phenomena often observed in different astrophysical jet classes. de Gouveia Dal Pino & Lazarian (2005) (hereafter GDPL) have proposed that the large scale superluminal ejections observed in microquasars during radio flare events could be produced by violent magnetic reconnection (MR) episodes. Here, we extend this model to other accretion disk systems, namely: active galactic nuclei (AGNs) and young stellar objects (YSOs), and also discuss its role on jet heating and particle acceleration.Comment: To be published in the IAU Highlights of Astronomy, Volume 15, XXVII IAU General Assembly, August 2009, Ian F. Corbett et al., eds., 201

Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations

Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit user feedback such as user reviews. It however remains under-explored how the extracted aspects can help generate more meaningful recommendations to the users. Meanwhile, existing research on aspect-based recommendations often relies on separate aspect extraction models or assumes the aspects are given, without accounting for the fact the optimal set of aspects could be dependent on the recommendation task at hand. In this work, we propose to combine aspect extraction together with aspect-based recommendations in an end-to-end manner, achieving the two goals together in a single framework. For the aspect extraction component, we leverage the recent advances in large language models and design a new prompt learning mechanism to generate aspects for the end recommendation task. For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model. The recommendation task mediates the learning of the user embeddings and item embeddings, which are used as soft prompts to generate aspects. Therefore, the extracted aspects are personalized and contextualized by the recommendation task. We showcase the effectiveness of our proposed method through extensive experiments on three industrial datasets, where our proposed framework significantly outperforms state-of-the-art baselines in both the personalized aspect extraction and aspect-based recommendation tasks. In particular, we demonstrate that it is necessary and beneficial to combine the learning of aspect extraction and aspect-based recommendation together. We also conduct extensive ablation studies to understand the contribution of each design component in our framework

Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems

Recommending novel content, which expands user horizons by introducing them to new interests, has been shown to improve users' long-term experience on recommendation platforms \cite{chen2021values}. Users however are not constantly looking to explore novel content. It is therefore crucial to understand their novelty-seeking intent and adjust the recommendation policy accordingly. Most existing literature models a user's propensity to choose novel content or to prefer a more diverse set of recommendations at individual interactions. Hierarchical structure, on the other hand, exists in a user's novelty-seeking intent, which is manifested as a static and intrinsic user preference for seeking novelty along with a dynamic session-based propensity. To this end, we propose a novel hierarchical reinforcement learning-based method to model the hierarchical user novelty-seeking intent, and to adapt the recommendation policy accordingly based on the extracted user novelty-seeking propensity. We further incorporate diversity and novelty-related measurement in the reward function of the hierarchical RL (HRL) agent to encourage user exploration \cite{chen2021values}. We demonstrate the benefits of explicitly modeling hierarchical user novelty-seeking intent in recommendations through extensive experiments on simulated and real-world datasets. In particular, we demonstrate that the effectiveness of our proposed hierarchical RL-based method lies in its ability to capture such hierarchically-structured intent. As a result, the proposed HRL model achieves superior performance on several public datasets, compared with state-of-art baselines