Mercy Amid Terror? The Role of Amnesties during Russia's Civil War

ArticleThis is the author accepted manuscript. The final version is available from MHRA via the DOI in this record.Russia's Civil War was a period of escalating violence as the Bolsheviks struggled to retain power, yet it was also a period of numerous amnesties. This article analyses the nature and impact of these amnesties, and explores their value to the Bolsheviks. These amnesties were not a sign of mercy; they never admitted mistakes or granted innocence, but excused or underplayed crimes and their significance. Instead, amnesties had a range of practical and political functions for the state, not least of which was to act as a ‘safety valve’ to release burgeoning pressures on the fledgling justice system and tensions between state and society.The majority of the research for this article was funded by a research fellowship from The Leverhulme Trust and I am very grateful for their support. Additional research emerged from a related project funded by the British Academy to whom I am also very grateful

Approaches to decision-making among late-stage melanoma patients: a multifactorial investigation.

PurposeThe treatment decisions of melanoma patients are poorly understood. Most research on cancer patient decision-making focuses on limited components of specific treatment decisions. This study aimed to holistically characterize late-stage melanoma patients' approaches to treatment decision-making in order to advance understanding of patient influences and supports.Methods(1) Exploratory analysis of longitudinal qualitative data to identify themes that characterize patient decision-making. (2) Pattern analysis of decision-making themes using an innovative method for visualizing qualitative data: a hierarchically-clustered heatmap. Participants were 13 advanced melanoma patients at a large academic medical center.ResultsExploratory analysis revealed eight themes. Heatmap analysis indicated two broad types of patient decision-makers. "Reliant outsiders" relied on providers for medical information, demonstrated low involvement in decision-making, showed a low or later-in-care interest in clinical trials, and expressed altruistic motives. "Active insiders" accessed substantial medical information and expertise in their networks, consulted with other doctors, showed early and substantial interest in trials, demonstrated high involvement in decision-making, and employed multiple decision-making strategies.ConclusionWe identified and characterized two distinct approaches to decision-making among patients with late-stage melanoma. These differences spanned a wide range of factors (e.g., behaviors, resources, motivations). Enhanced understanding of patients as decision-makers and the factors that shape their decision-making may help providers to better support patient understanding, improve patient-provider communication, and support shared decision-making

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure

Exploring Deep Space: Learning Personalized Ranking in a Semantic Space

Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of information. To recommend items we propose to first learn a user-independent high-dimensional semantic space in which items are positioned according to their substitutability, and then learn a user-specific transformation function to transform this space into a ranking according to the user's past preferences. An advantage of the proposed architecture is that it can be used to effectively recommend items using either content that describes the items or user-item ratings. We show that this approach significantly outperforms state-of-the-art recommender systems on the MovieLens 1M dataset.Comment: 6 pages, RecSys 2016 RSDL worksho

The preparation of possible prodrugs for cancer chemotherapy

This thesis concerns the design and preparation of compounds which are relevant to a new strategy for the selective chemotherapeutic treatment of tumour cells. These bridged polycyclic compounds are prepared by Diels-Alder chemistry involving cyclic dienes. The bridge is to be cleaved at the cancer site by tumour-selective chemistry acting on an appropriate trigger. This activation of a low toxicity 'prodrug' results in the formation of a planar, aromatic structure which is a characteristic of known anti-cancer drugs (intercalating agents). The crucial step in the organic synthesis of these potential prodrugs is a DielsAlder reaction involving two classes of diene, 1-(methylthio)isobenzofurans and pyranones, with various dienophiles. Examination of this step started with the reaction of 1,1-bis(methylthio)ethene with various pyranones. Stable adducts were isolated from its reaction with methyl coumalate and a benzopyranone; but only substituted carbazoles were observed from the reaction with pyrano[3,4-b]indol-3-ones. An investigation into the thermal stability of the isolated adducts resulted in the observation of an unusual [1,]-methylthio migration. In comparison to 1, 1 bis(methylthio)ethene, 1-(methylthio)-1-(p-tolylsulfonyl)ethene was observed to have lower reactivity and regioselectivity upon reaction with electron-deficient pyranones. The resulting adducts were unstable due to the facile elimination of p-toluenesulfinic acid and only aromatic products were isolated. Diels-Alder adducts were isolated from the reaction of 1-(methylthio)-1-(p-tolylsulfonyl)ethene with 1-(methylthio)isobenzofurans, but they were of low stability and of mixed regio- and stereo- chemistries. The reaction of arynes with 1-(methylthio)isobenzofurans was also investigated. The Diels-Alder reaction between 3,4-didehydropyridine and a protected 1-(methylthio)isobenzofuran resulted in the preparation of a precursor of a tricyclic hetero-aromatic compound, namely the known biologically active compound, 2-azaanthraquinone

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.Comment: 8 page

Beyond Exploratory: A Tailored Framework for Assessing Rigor in Qualitative Health Services Research

Objective: To propose a framework for assessing the rigor of qualitative research that identifies and distinguishes between the diverse objectives of qualitative studies currently used in patient-centered outcomes and health services research (PCOR and HSR). Study Design: Narrative review of published literature discussing qualitative guidelines and standards in peer-reviewed journals and national funding organizations that support PCOR and HSR. Principal Findings: We identify and distinguish three objectives of current qualitative studies in PCOR and HSR: exploratory, descriptive, and comparative. For each objective, we propose methodological standards that can be used to assess and improve rigor across all study phases—from design to reporting. Similar to quantitative studies, we argue that standards for qualitative rigor differ, appropriately, for studies with different objectives and should be evaluated as such. Conclusions: Distinguishing between different objectives of qualitative HSR improves the ability to appreciate variation in qualitative studies as well as appropriately evaluate the rigor and success of studies in meeting their own objectives. Researchers, funders, and journal editors should consider how adopting the criteria for assessing qualitative rigor outlined here may advance the rigor and potential impact of qualitative research in patient-centered outcomes and health services research