Copyright and Fair Use Resources for Undergraduates: A Content Analysis of Academic Libraries' Websites

Using content analysis, this study examines what information on copyright and fair use academic libraries offer users through their websites, with a particular focus on undergraduate information needs. This was done by analyzing the websites of thirty-three academic libraries, selected from the list of the member institutions of the Association of Research Libraries. The results of this exploratory study show that while most libraries provide general educational information on copyright, they do not fully explain all aspects of copyright and fair use. Particularly lacking are resources that help undergraduate creators understand what they can and cannot do under fair use. For academic libraries to support undergraduates in becoming effective information users and creators, they should offer resources that fully educate undergraduate students on their rights and responsibilities as both creators and consumers of information

An Interactive, Mobile-Based Tool for Personal Social Network Data Collection and Visualization Among a Geographically Isolated and Socioeconomically Disadvantaged Population: Early-Stage Feasibility Study with Qualitative User Feedback

Background: Personal social networks have a profound impact on our health, yet collecting personal network data for use in health communication, behavior change, or translation and dissemination interventions has proved challenging. Recent advances in social network data collection software have reduced the burden of network studies on researchers and respondents alike, yet little testing has occurred to discover whether these methods are: (1) acceptable to a variety of target populations, including those who may have limited experience with technology or limited literacy; and (2) practical in the field, specifically in areas that are geographically and technologically disconnected, such as rural Appalachian Kentucky. Objective: We explored the early-stage feasibility (Acceptability, Demand, Implementation, and Practicality) of using innovative, interactive, tablet-based network data collection and visualization software (OpenEddi) in field collection of personal network data in Appalachian Kentucky. Methods: A total of 168 rural Appalachian women who had previously participated in a study on the use of a self-collected vaginal swab (SCVS) for human papillomavirus testing were recruited by community-based nurse interviewers between September 2013 and August 2014. Participants completed egocentric network surveys via OpenEddi, which captured social and communication network influences on participation in, and recruitment to, the SCVS study. After study completion, we conducted a qualitative group interview with four nurse interviewers and two participants in the network study. Using this qualitative data, and quantitative data from the network study, we applied guidelines from Bowen et al to assess feasibility in four areas of early-stage development of OpenEddi: Acceptability, Demand, Implementation, and Practicality. Basic descriptive network statistics (size, edges, density) were analyzed using RStudio. Results: OpenEddi was perceived as fun, novel, and superior to other data collection methods or tools. Respondents enjoyed the social network survey component, and visualizing social networks produced thoughtful responses from participants about leveraging or changing network content and structure for specific health-promoting purposes. Areas for improved literacy and functionality of the tool were identified. However, technical issues led to substantial (50%) data loss, limiting the success of its implementation from a researcher\u27s perspective, and hindering practicality in the field. Conclusions: OpenEddi is a promising data collection tool for use in geographically isolated and socioeconomically disadvantaged populations. Future development will mitigate technical problems, improve usability and literacy, and test new methods of data collection. These changes will support goals for use of this tool in the delivery of network-based health communication and social support interventions to socioeconomically disadvantaged populations

Exonuclease activity and P nucleotide addition in the generation of the expressed immunoglobulin repertoire

BACKGROUND: Immunoglobulin rearrangement involves random and imprecise processes that act to both create and constrain diversity. Two such processes are the loss of nucleotides through the action of unknown exonuclease(s) and the addition of P nucleotides. The study of such processes has been compromised by difficulties in reliably aligning immunoglobulin genes and in the partitioning of nucleotides between segment ends, and between N and P nucleotides. RESULTS: A dataset of 294 human IgM sequences was created and partitioned with the aid of a probabilistic model. Non-random removal of nucleotides is seen between the three IGH gene types with the IGHV gene averaging removals of 1.2 nucleotides compared to 4.7 for the other gene ends (p < 0.001). Individual IGHV, IGHD and IGHJ gene subgroups also display statistical differences in the level of nucleotide loss. For example, within the IGHJ group, IGHJ3 has average removals of 1.3 nucleotides compared to 6.4 nucleotides for IGHJ6 genes (p < 0.002). Analysis of putative P nucleotides within the IgM and pooled datasets revealed only a single putative P nucleotide motif (GTT at the 3' D-REGION end) to occur at a frequency significantly higher then would be expected from random N nucleotide addition. CONCLUSIONS: The loss of nucleotides due to the action of exonucleases is not random, but is influenced by the nucleotide composition of the genes. P nucleotides do not make a significant contribution to diversity of immunoglobulin sequences. Although palindromic sequences are present in 10% of immunologlobulin rearrangements, most of the 'palindromic' nucleotides are likely to have been inserted into the junction during the process of N nucleotide addition. P nucleotides can only be stated with confidence to contribute to diversity of less than 1% of sequences. Any attempt to identify P nucleotides in immunoglobulins is therefore likely to introduce errors into the partitioning of such sequences

Web-based Elicitation of Human Perception on mixup Data

Synthetic data is proliferating on the web and powering many advances in machine learning. However, it is not always clear if synthetic labels are perceptually sensible to humans. The web provides us with a platform to take a step towards addressing this question through online elicitation. We design a series of elicitation interfaces, which we release as \texttt{HILL MixE Suite}, and recruit 159 participants, to provide perceptual judgments over the kinds of synthetic data constructed during \textit{mixup} training: a powerful regularizer shown to improve model robustness, generalization, and calibration. We find that human perception does not consistently align with the labels traditionally used for synthetic points and begin to demonstrate the applicability of these findings to potentially increase the reliability of downstream models. We release all elicited judgments in a new data hub we call \texttt{H-Mix}

Learning to Receive Help: Intervention-Aware Concept Embedding Models

Concept Bottleneck Models (CBMs) tackle the opacity of neural architectures by constructing and explaining their predictions using a set of high-level concepts. A special property of these models is that they permit concept interventions, wherein users can correct mispredicted concepts and thus improve the model's performance. Recent work, however, has shown that intervention efficacy can be highly dependent on the order in which concepts are intervened on and on the model's architecture and training hyperparameters. We argue that this is rooted in a CBM's lack of train-time incentives for the model to be appropriately receptive to concept interventions. To address this, we propose Intervention-aware Concept Embedding models (IntCEMs), a novel CBM-based architecture and training paradigm that improves a model's receptiveness to test-time interventions. Our model learns a concept intervention policy in an end-to-end fashion from where it can sample meaningful intervention trajectories at train-time. This conditions IntCEMs to effectively select and receive concept interventions when deployed at test-time. Our experiments show that IntCEMs significantly outperform state-of-the-art concept-interpretable models when provided with test-time concept interventions, demonstrating the effectiveness of our approach.Comment: Accepted as a spotlight at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

Variation in the cortical area map of C57BL/6J and DBA/2J inbred mice predicts strain identity

BACKGROUND: Recent discoveries suggest that arealization of the mammalian cortical sheet develops in a manner consonant with principles established for embryonic patterning of the body. Signaling centers release morphogens that determine regional growth and tissue identity by regulating regional expression of transcription factors. Research on mouse cortex has identified several candidate morphogens that affect anteroposterior or mediolateral cortical regionalization as well as mitogenesis. Inbred strains of laboratory mice can be exploited to study cortical area map formation if there are significant phenotypic differences with which to correlate gene polymorphism or expression data. Here we describe differences in the cortical area map of two commonly used inbred strains of laboratory mice, C57BL/6J and DBA/2J. Complete cortical hemispheres from adult mice were dissected and stained for the cytochrome oxidase enzyme in order to measure histochemically defined cortical areas. RESULTS: C57BL/6J has the larger neocortex, relatively larger primary visual cortex (V1), but relatively smaller posterior medial barrel subfield of the primary somatosensory cortex (PMBSF). The sample of C57BL/6J and DBA/2J mice can be discriminated with 90% accuracy on the basis of these three size dimensions. CONCLUSION: C57BL/6J and DBA/2J have markedly different cortical area maps, suggesting that inbred strains harbor enough phenotypic variation to encourage a forward genetic approach to understanding cortical development, complementing other approaches

Learning Personalized Decision Support Policies

Individual human decision-makers may benefit from different forms of support to improve decision outcomes. However, a key question is which form of support will lead to accurate decisions at a low cost. In this work, we propose learning a decision support policy that, for a given input, chooses which form of support, if any, to provide. We consider decision-makers for whom we have no prior information and formalize learning their respective policies as a multi-objective optimization problem that trades off accuracy and cost. Using techniques from stochastic contextual bandits, we propose $\texttt{THREAD}$, an online algorithm to personalize a decision support policy for each decision-maker, and devise a hyper-parameter tuning strategy to identify a cost-performance trade-off using simulated human behavior. We provide computational experiments to demonstrate the benefits of $\texttt{THREAD}$ compared to offline baselines. We then introduce $\texttt{Modiste}$, an interactive tool that provides $\texttt{THREAD}$ with an interface. We conduct human subject experiments to show how $\texttt{Modiste}$ learns policies personalized to each decision-maker and discuss the nuances of learning decision support policies online for real users.Comment: Working pape