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Multi-Channel Imaging (MCI) contains an array of challenges for encoding useful feature representations not present in traditional images. For example, images from two different satellites may both contain RGB channels, but the remaining channels can be different for each imaging source. Thus, MCI models must support a variety of channel configurations at test time. Recent work has extended traditional visual encoders for MCI, such as Vision Transformers (ViT), by supplementing pixel information with an encoding representing the channel configuration. However, these methods treat each channel equally, i.e., they do not consider the unique properties of each channel type, which can result in needless and potentially harmful redundancies in the learned features. For example, if RGB channels are always present, the other channels can focus on extracting information that cannot be captured by the RGB channels. To this end, we propose DiChaViT, which aims to enhance the diversity in the learned features of MCI-ViT models. This is achieved through a novel channel sampling strategy that encourages the selection of more distinct channel sets for training. Additionally, we employ regularization and initialization techniques to increase the likelihood that new information is learned from each channel. Many of our improvements are architecture agnostic and could be incorporated into new architectures as they are developed. Experiments on both satellite and cell microscopy datasets, CHAMMI, JUMP-CP, and So2Sat, report DiChaViT yields a 1.5-5.0% gain over the state-of-the-art.

While demands for change and accountability for harmful AI consequences mount, foreseeing the downstream effects of deploying AI systems remains a challenging task. We developed AHA! (Anticipating Harms of AI), a generative framework to assist AI practitioners and decision-makers in anticipating potential harms and unintended consequences of AI systems prior to development or deployment. Given an AI deployment scenario, AHA! generates descriptions of possible harms for different stakeholders. To do so, AHA! systematically considers the interplay between common problematic AI behaviors as well as their potential impacts on different stakeholders, and narrates these conditions through vignettes. These vignettes are then filled in with descriptions of possible harms by prompting crowd workers and large language models. By examining 4113 harms surfaced by AHA! for five different AI deployment scenarios, we found that AHA! generates meaningful examples of harms, with different problematic AI behaviors resulting in different types of harms. Prompting both crowds and a large language model with the vignettes resulted in more diverse examples of harms than those generated by either the crowd or the model alone. To gauge AHA!'s potential practical utility, we also conducted semi-structured interviews with responsible AI professionals (N=9). Participants found AHA!'s systematic approach to surfacing harms important for ethical reflection and discovered meaningful stakeholders and harms they believed they would not have thought of otherwise. Participants, however, differed in their opinions about whether AHA! should be used upfront or as a secondary-check and noted that AHA! may shift harm anticipation from an ideation problem to a potentially demanding review problem. Drawing on our results, we discuss design implications of building tools to help practitioners envision possible harms.

Noisy labels can impair model performance, making the study of learning with noisy labels an important topic. Two conventional approaches are noise modeling and noise detection. However, these two methods are typically studied independently, and there has been limited work on their collaboration. In this work, we explore the integration of these two approaches, proposing an interconnected structure with three crucial blocks: noise modeling, source knowledge identification, and enhanced noise detection using noise source-knowledge-integration methods. This collaboration structure offers advantages such as discriminating hard negatives and preserving genuinely clean labels that might be suspiciously noisy. Our experiments on four datasets, featuring three types of noise and different combinations of each block, demonstrate the efficacy of these components' collaboration. Our collaborative structure methods achieve up to a 10% increase in top-1 classification accuracy in synthesized noise datasets and 3-5% in real-world noisy datasets. The results also suggest that these components make distinct contributions to overall performance across various noise scenarios. These findings provide valuable insights for designing noisy label learning methods customized for specific noise scenarios in the future. Our code is accessible to the public.

Most deep neural networks are trained under fixed network architectures and require retraining when the architecture changes. If expanding the network's size is needed, it is necessary to retrain from scratch, which is expensive. To avoid this, one can grow from a small network by adding random weights over time to gradually achieve the target network size. However, this naive approach falls short in practice as it brings too much noise to the growing process. Prior work tackled this issue by leveraging the already learned weights and training data for generating new weights through conducting a computationally expensive analysis step. In this paper, we introduce MixtureGrowth, a new approach to growing networks that circumvents the initialization overhead in prior work. Before growing, each layer in our model is generated with a linear combination of parameter templates. Newly grown layer weights are generated by using a new linear combination of existing templates for a layer. On one hand, these templates are already trained for the task, providing a strong initialization. On the other, the new coefficients provide flexibility for the added layer weights to learn something new. We show that our approach boosts top-1 accuracy over the state-of-the-art by 2-2.5% on CIFAR-100 and ImageNet datasets, while achieving comparable performance with fewer FLOPs to a larger network trained from scratch. Code is available at https://github.com/chaudatascience/mixturegrowth., Comment: Accepted at IEEE Winter Conference on Applications of Computer Vision (WACV) 2024

Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, they offer users minimal control over the formatting and specificity of resulting topics. To tackle these issues, we introduce TopicGPT, a prompt-based framework that uses large language models (LLMs) to uncover latent topics in a text collection. TopicGPT produces topics that align better with human categorizations compared to competing methods: it achieves a harmonic mean purity of 0.74 against human-annotated Wikipedia topics compared to 0.64 for the strongest baseline. Its topics are also interpretable, dispensing with ambiguous bags of words in favor of topics with natural language labels and associated free-form descriptions. Moreover, the framework is highly adaptable, allowing users to specify constraints and modify topics without the need for model retraining. By streamlining access to high-quality and interpretable topics, TopicGPT represents a compelling, human-centered approach to topic modeling., Comment: Accepted to NAACL 2024 (Main conference)

Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on instruments and experimental goals. Yet, there has not been a systemic attempt to create and evaluate neural networks that are invariant to the number and type of channels. As a result, trained models remain specific to individual studies and are hardly reusable for other microscopy settings. In this paper, we present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework. In addition, we adapted several existing techniques to create channel-adaptive models and compared their performance on this benchmark to fixed-channel, baseline models. We find that channel-adaptive models can generalize better to out-of-domain tasks and can be computationally efficient. We contribute a curated dataset (https://doi.org/10.5281/zenodo.7988357) and an evaluation API (https://github.com/broadinstitute/MorphEm.git) to facilitate objective comparisons in future research and applications., Comment: Accepted at NeurIPS Track on Datasets and Benchmarks, 2023

This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box proposals to their closest text label. However, this method has a critical issue: many low-quality boxes, such as over- and under-covered-object boxes, have the same similarity score as high-quality boxes since CLIP is not trained on exact object location information. To address this issue, we propose a novel method, LP-OVOD, that discards low-quality boxes by training a sigmoid linear classifier on pseudo labels retrieved from the top relevant region proposals to the novel text. Experimental results on COCO affirm the superior performance of our approach over the state of the art, achieving $\textbf{40.5}$ in $\text{AP}_{novel}$ using ResNet50 as the backbone and without external datasets or knowing novel classes during training. Our code will be available at https://github.com/VinAIResearch/LP-OVOD.

Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary. In this paper, we introduce a communication regime named CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. Specifically, we remove the token sampling step from LLMs and let them communicate their beliefs across the vocabulary through the expectation of the raw transformer output embeddings. Remarkably, by deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights, outperforming the state-of-the-art LLM debate methods using natural language by 0.5-5.0% across five reasoning tasks and multiple open-source LLMs of varying sizes. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs. We anticipate that CIPHER will inspire further exploration for the design of interactions within LLM agent systems, offering a new direction that could significantly influence future developments in the field., Comment: Accepted to ICLR 2024

Prosocial behavior, helping others in need in particular, occurs preferentially in response to the perceived distress of one's own group members or ingroup. To investigate the development of ingroup bias, neural activity during a helping test was analyzed in adolescent and adult rats. Although adults selectively released trapped ingroup members, adolescent rats helped both ingroup and outgroup members, suggesting that ingroup bias emerges in adulthood. Analysis of brain-wide neural activity, indexed by expression of the early-immediate gene c-Fos, revealed increased activity for ingroup members across a broad set of regions previously associated with empathy. Adolescents showed reduced hippocampal and insular activity and increased orbitofrontal cortex activity compared to adults. Non-helper adolescents demonstrated increased amygdala connectivity. These findings demonstrate that biases for group-dependent prosocial behavior develop with age in rats and suggest that specific brain regions contribute to prosocial selectivity, pointing to possible targets for the functional modulation of ingroup bias.

Prosocial behavior, helping others in need in particular, occurs preferentially in response to the perceived distress of one's own group members or ingroup. To investigate the development of ingroup bias, neural activity during a helping test was analyzed in adolescent and adult rats. Although adults selectively released trapped ingroup members, adolescent rats helped both ingroup and outgroup members, suggesting that ingroup bias emerges in adulthood. Analysis of brain-wide neural activity, indexed by expression of the early-immediate gene c-Fos, revealed increased activity for ingroup members across a broad set of regions previously associated with empathy. Adolescents showed reduced hippocampal and insular activity and increased orbitofrontal cortex activity compared to adults. Non-helper adolescents demonstrated increased amygdala connectivity. These findings demonstrate that biases for group-dependent prosocial behavior develop with age in rats and suggest that specific brain regions contribute to prosocial selectivity, pointing to possible targets for the functional modulation of ingroup bias.

Background and objectivesHealth care organizations track patient experience data, identify areas of improvement, monitor provider performance, and assist providers in improving their interactions with patients. Some practices use one-on-one provider counseling ("shadow coaching") to identify and modify provider behaviors. A recent evaluation of a large shadow coaching program found statistically significant improvements in coached providers' patient experience scores immediately after being coached. This study aimed to examine the content of the recommendations given to those providers aimed at improving provider-patient interactions, characterize these recommendations, and examine their actionability.MethodsProviders at a large, urban federally qualified health center were selected for coaching based on Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) patient experience scores (92 of 320 providers), shadowed by a trained peer coach for a half to full day and received recommendations on how to improve interactions with their patients. We coded 1082 recommendations found in the 92 coaching reports.ResultsReports contained an average of 12 recommendations. About half encouraged consistency of existing behaviors and half encouraged new behaviors. Most recommendations related to behaviors of the provider rather than support staff and targeted actions within the examination room rather than other spaces (eg, waiting room). The most common recommendations mapped to behavioral aspects of provider communication. Most recommendations targeted verbal rather than nonverbal communication behaviors. Most recommendations were actionable (ie, specific, descriptive), with recommendations that encouraged new behaviors being more actionable than those that encouraged existing actions.ConclusionsPatient experience surveys are effective at identifying where improvement is needed but are not always informative enough to instruct providers on how to

One of the most fundamental graph problems is finding a shortest path from a source to a target node. While in its basic forms the problem has been studied extensively and efficient algorithms are known, it becomes significantly harder as soon as parts of the graph are susceptible to failure. Although one can recompute a shortest replacement path after every outage, this is rather inefficient both in time and/or storage. One way to overcome this problem is to shift computational burden from the queries into a pre-processing step, where a data structure is computed that allows for fast querying of replacement paths, typically referred to as a Distance Sensitivity Oracle (DSO). While DSOs have been extensively studied in the theoretical computer science community, to the best of our knowledge this is the first work to construct DSOs using deep learning techniques. We show how to use deep learning to utilize a combinatorial structure of replacement paths. More specifically, we utilize the combinatorial structure of replacement paths as a concatenation of shortest paths and use deep learning to find the pivot nodes for stitching shortest paths into replacement paths., Comment: arXiv admin note: text overlap with arXiv:2007.11495 by other authors

Persistence diagrams (PDs), often characterized as sets of death and birth of homology class, have been known for providing a topological representation of a graph structure, which is often useful in machine learning tasks. Prior works rely on a single graph signature to construct PDs. In this paper, we explore the use of a family of multi-scale graph signatures to enhance the robustness of topological features. We propose a deep learning architecture to handle this set input. Experiments on benchmark graph classification datasets demonstrate that our proposed architecture outperforms other persistent homology-based methods and achieves competitive performance compared to state-of-the-art methods using graph neural networks. In addition, our approach can be easily applied to large size of input graphs as it does not suffer from limited scalability which can be an issue for graph kernel methods.

We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. To address this challenging problem, we introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: Counting-DETR. The former is aimed at generating pseudo ground-truth bounding boxes to train the latter. The latter leverages the pseudo ground-truth provided by the former but takes the necessary steps to account for the imperfection of pseudo ground-truth. To validate the performance of our method on the new task, we introduce two new datasets named FSCD-147 and FSCD-LVIS. Both datasets contain images with complex scenes, multiple object classes per image, and a huge variation in object shapes, sizes, and appearance. Our proposed approach outperforms very strong baselines adapted from few-shot object counting and few-shot object detection with a large margin in both counting and detection metrics. The code and models are available at https://github.com/VinAIResearch/Counting-DETR., Comment: Accepted to ECCV 2022; The first two authors contribute equally

Crises such as natural disasters, global pandemics, and social unrest continuously threaten our world and emotionally affect millions of people worldwide in distinct ways. Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about the emotional states of the population as well as provide emotional support to those who need such support. We present CovidEmo, ~3K English tweets labeled with emotions and temporally distributed across 18 months. Our analyses reveal the emotional toll caused by COVID-19, and changes of the social narrative and associated emotions over time. Motivated by the time-sensitive nature of crises and the cost of large-scale annotation efforts, we examine how well large pre-trained language models generalize across domains and timeline in the task of perceived emotion prediction in the context of COVID-19. Our analyses suggest that cross-domain information transfers occur, yet there are still significant gaps. We propose semi-supervised learning as a way to bridge this gap, obtaining significantly better performance using unlabeled data from the target domain., Comment: LREC 2022

Background and objectivesDelivering care as a patient-centered medical home (PCMH) is being widely adopted across the United States by primary care practices to better meet patient needs. A key PCMH element is measuring patient experience for practice improvement. The National Committee for Quality Assurance (NCQA) PCMH recognition program requires practices to both measure patient experience and engage in continuous practice/quality improvement to attain PCMH recognition and then throughout full PCMH transformation. The NCQA recommends but does not require that practices administer the Consumer Assessment of Healthcare Providers and Systems (CAHPS) clinician and group patient experience survey (CG-CAHPS) plus 14 CAHPS PCMH items, known as the CAHPS PCMH survey. We examine aspects of patient experience measured by practices with a varying number of years on their journey of PCMH transformation.MethodsWe randomly selected practices from the 2008-2017 NCQA directory of practices that had applied for PCMH recognition based on region, physician count, number of years and level of PCMH recognition, and use of the CG-CAHPS PCMH survey. We collected characteristics of the practices from practice leader(s) knowledgeable about the practice's PCMH history and patient experience data. We confirmed the patient experience surveys used during their PCMH history and requested copies of their non-CAHPS survey(s). For practices not administering the recommended CG-CAHPS survey (53/105 practices), we obtained and coded the content of their non-CAHPS surveys (68%; 36/53). We mapped the patient experience domains and specific measures to the CG-CAHPS survey (versions 2.0 and 3.0), CAHPS PCMH item set (versions 2.0 and 3.0), and the available CG-CAHPS supplemental items.ResultsWhether or not practices administered the CG-CAHPS items, most of them addressed topics contained in the CG-CAHPS survey such as Access to care, Provider communication, Office staff helpfulness/courteousness, Care

Background and objectivesDelivering care as a patient-centered medical home (PCMH) is being widely adopted across the United States by primary care practices to better meet patient needs. A key PCMH element is measuring patient experience for practice improvement. The National Committee for Quality Assurance (NCQA) PCMH recognition program requires practices to both measure patient experience and engage in continuous practice/quality improvement to attain PCMH recognition and then throughout full PCMH transformation. The NCQA recommends but does not require that practices administer the Consumer Assessment of Healthcare Providers and Systems (CAHPS) clinician and group patient experience survey (CG-CAHPS) plus 14 CAHPS PCMH items, known as the CAHPS PCMH survey. We examine aspects of patient experience measured by practices with a varying number of years on their journey of PCMH transformation.MethodsWe randomly selected practices from the 2008-2017 NCQA directory of practices that had applied for PCMH recognition based on region, physician count, number of years and level of PCMH recognition, and use of the CG-CAHPS PCMH survey. We collected characteristics of the practices from practice leader(s) knowledgeable about the practice's PCMH history and patient experience data. We confirmed the patient experience surveys used during their PCMH history and requested copies of their non-CAHPS survey(s). For practices not administering the recommended CG-CAHPS survey (53/105 practices), we obtained and coded the content of their non-CAHPS surveys (68%; 36/53). We mapped the patient experience domains and specific measures to the CG-CAHPS survey (versions 2.0 and 3.0), CAHPS PCMH item set (versions 2.0 and 3.0), and the available CG-CAHPS supplemental items.ResultsWhether or not practices administered the CG-CAHPS items, most of them addressed topics contained in the CG-CAHPS survey such as Access to care, Provider communication, Office staff helpfulness/courteousness, Care

Objectives: While there are characteristics of professionalism that seem to be innate in some people, we believe that these same characteristics can be taught and developed. Our objectives included: 1. Defining Professionalism, and the associated principles, as they relate to the General Surgery Residency Program at the University of Manitoba. 2. Adapting/developing a tool that can measure these defined principles. 3. Developing and implementing a 6-month Professionalism education program within the General Surgery Residency core curriculum at the University of Manitoba. 4. Measuring changes in self-perceptions of professionalism following the introduction of the professionalism curriculum. Methods: 1. A previously validated questionnaire detailing self-perceptions of professionalism was administered to all General Surgery residents at the University of Manitoba. 2. A six-month, five-part professionalism education program was implemented (the intervention): • A ‘Defining Professionalism’ session was held • A Code of Professionalism was developed from the ‘Defining Professionalism’ session • Crucial Conversations was administered • Residents took part in a Professionalism Journal Club • Residents participated in a cinemeducation session 3. Post intervention, the same previously validated questionnaire was administered to each General Surgery resident who consented to participate in the study. There was an additional online survey which asked participants to detail a situation in which they had used principles or strategies learned in the training program to achieve a breakthrough in professionalism. 4. Dr. Kelleher completed follow-up interviews with participants, on a volunteer basis. The questions detailed participants’ definitions of professionalism and whether residents thought professionalism can be taught. Results: With the administration of a Professionalism Education Program (PEP), self-perceptions of professionalism showed significant improvement in the are

Objectives: While there are characteristics of professionalism that seem to be innate in some people, we believe that these same characteristics can be taught and developed. Our objectives included: 1. Defining Professionalism, and the associated principles, as they relate to the General Surgery Residency Program at the University of Manitoba. 2. Adapting/developing a tool that can measure these defined principles. 3. Developing and implementing a 6-month Professionalism education program within the General Surgery Residency core curriculum at the University of Manitoba. 4. Measuring changes in self-perceptions of professionalism following the introduction of the professionalism curriculum. Methods: 1. A previously validated questionnaire detailing self-perceptions of professionalism was administered to all General Surgery residents at the University of Manitoba. 2. A six-month, five-part professionalism education program was implemented (the intervention): • A ‘Defining Professionalism’ session was held • A Code of Professionalism was developed from the ‘Defining Professionalism’ session • Crucial Conversations was administered • Residents took part in a Professionalism Journal Club • Residents participated in a cinemeducation session 3. Post intervention, the same previously validated questionnaire was administered to each General Surgery resident who consented to participate in the study. There was an additional online survey which asked participants to detail a situation in which they had used principles or strategies learned in the training program to achieve a breakthrough in professionalism. 4. Dr. Kelleher completed follow-up interviews with participants, on a volunteer basis. The questions detailed participants’ definitions of professionalism and whether residents thought professionalism can be taught. Results: With the administration of a Professionalism Education Program (PEP), self-perceptions of professionalism showed significant improvement in the are

The road is vital for many aspects of life, and road maintenance is crucial for human safety. One of the critical tasks to allow timely repair of road damages is to quickly and efficiently detect and classify them. This work details the strategies and experiments evaluated for these tasks. Specifically, we evaluate Detectron2's implementation of Faster R-CNN using different base models and configurations. We also experiment with these approaches using the Global Road Damage Detection Challenge 2020, A Track in the IEEE Big Data 2020 Big Data Cup Challenge dataset. The results show that the X101-FPN base model for Faster R-CNN with Detectron2's default configurations are efficient and general enough to be transferable to different countries in this challenge. This approach results in F1 scores of 51.0% and 51.4% for the test1 and test2 sets of the challenge, respectively. Though the visualizations show good prediction results, the F1 scores are low. Therefore, we also evaluate the prediction results against the existing annotations and discover some discrepancies. Thus, we also suggest strategies to improve the labeling process for this dataset., Comment: Under review for Global Road Damage Detection Challenge 2020, A Track in the IEEE Big Data 2020 Big Data Cup Challenge

Objectives: While there are characteristics of professionalism that seem to be innate in some people, we believe that these same characteristics can be taught and developed. Our objectives included: 1. Defining Professionalism, and the associated principles, as they relate to the General Surgery Residency Program at the University of Manitoba. 2. Adapting/developing a tool that can measure these defined principles. 3. Developing and implementing a 6-month Professionalism education program within the General Surgery Residency core curriculum at the University of Manitoba. 4. Measuring changes in self-perceptions of professionalism following the introduction of the professionalism curriculum. Methods: 1. A previously validated questionnaire detailing self-perceptions of professionalism was administered to all General Surgery residents at the University of Manitoba. 2. A six-month, five-part professionalism education program was implemented (the intervention): • A ‘Defining Professionalism’ session was held • A Code of Professionalism was developed from the ‘Defining Professionalism’ session • Crucial Conversations was administered • Residents took part in a Professionalism Journal Club • Residents participated in a cinemeducation session 3. Post intervention, the same previously validated questionnaire was administered to each General Surgery resident who consented to participate in the study. There was an additional online survey which asked participants to detail a situation in which they had used principles or strategies learned in the training program to achieve a breakthrough in professionalism. 4. Dr. Kelleher completed follow-up interviews with participants, on a volunteer basis. The questions detailed participants’ definitions of professionalism and whether residents thought professionalism can be taught. Results: With the administration of a Professionalism Education Program (PEP), self-perceptions of professionalism showed significant improvement in the are

Objectives: While there are characteristics of professionalism that seem to be innate in some people, we believe that these same characteristics can be taught and developed. Our objectives included: 1. Defining Professionalism, and the associated principles, as they relate to the General Surgery Residency Program at the University of Manitoba. 2. Adapting/developing a tool that can measure these defined principles. 3. Developing and implementing a 6-month Professionalism education program within the General Surgery Residency core curriculum at the University of Manitoba. 4. Measuring changes in self-perceptions of professionalism following the introduction of the professionalism curriculum. Methods: 1. A previously validated questionnaire detailing self-perceptions of professionalism was administered to all General Surgery residents at the University of Manitoba. 2. A six-month, five-part professionalism education program was implemented (the intervention): • A ‘Defining Professionalism’ session was held • A Code of Professionalism was developed from the ‘Defining Professionalism’ session • Crucial Conversations was administered • Residents took part in a Professionalism Journal Club • Residents participated in a cinemeducation session 3. Post intervention, the same previously validated questionnaire was administered to each General Surgery resident who consented to participate in the study. There was an additional online survey which asked participants to detail a situation in which they had used principles or strategies learned in the training program to achieve a breakthrough in professionalism. 4. Dr. Kelleher completed follow-up interviews with participants, on a volunteer basis. The questions detailed participants’ definitions of professionalism and whether residents thought professionalism can be taught. Results: With the administration of a Professionalism Education Program (PEP), self-perceptions of professionalism showed significant improvement in the are

An oral history with Chau Huyen Pham, born in Saigon, Vietnam on September 17, 1983. She discusses her childhood memories of South Vietnam and her experiences in school and in her community. She also discusses how her family left Vietnam by purchasing plane tickets from a private loan through a non-profit organization. Her flight took a total of two days where she stopped in Thailand, Japan, San Francisco, and finally arriving in LAX. She currently lives in Garden Grove, California., Recorded Digitally

An oral history with Chau Huyen Pham, born in Saigon, Vietnam on September 17, 1983. She discusses her childhood memories of South Vietnam and her experiences in school and in her community. She also discusses how her family left Vietnam by purchasing plane tickets from a private loan through a non-profit organization. Her flight took a total of two days where she stopped in Thailand, Japan, San Francisco, and finally arriving in LAX. She currently lives in Garden Grove, California., Recorded Digitally

Transit-Oriented Development ("TOD") has been used as a smart growth catalyst for renewal, maximizing accessibility and opportunity. However, these new investments can raise rapid appreciation in property and housing costs, spurring the possibility for gentrification in low-income neighborhoods. Many TODs fail to integrate this new mixed-use development with the context, potentially obliterating existing communities. The use of form-based codes in designing TODs can preserve the social infrastructure that makes up the soul of the community. Third Ward, Houston, TX will serve as a case study addressing how a TOD can help revitalize a vernacular neighborhood by revealing its own identity and "sense of place" against the pressures of gentrification.

Purpose To compare urgency symptoms in women with interstitial cystitis/bladder pain syndrome (IC/BPS) and overactive bladder (OAB). Materials and Methods Women with diagnoses of IC/BPS (n???=???194) and OAB (n???=???85) were recruited from the clinical practices of Urologists (n???=???8) and Gynecologists (n???=???16) with recognized expertise in the diagnosis and management of these conditions. Subjects completed a comprehensive telephone survey about their current symptoms. The questionnaire included 11 questions about urinary urgency. Responses were compared between the two groups. Results Urgency was commonly reported as a symptom by women with both conditions (81% IC/BPS and 91% OAB). Compared with IC/BPS, urgency in OAB more often resulted in leakage, and was perceived to be more of a problem. In IC/BPS, the urgency was primarily reported as due to pain, pressure, or discomfort, while in OAB the urgency was more commonly due to fear of leakage. However, approximately 40% of women with OAB also report urgency due to pain, pressure, or discomfort. Similar proportions of both groups (???60%) indicated that the urgency occurred ???suddenly??? instead of more gradually over a period of minutes or hours. Conclusions Urgency symptoms differed in women diagnosed with IC/BPS versus those diagnosed with OAB, but there was significant overlap. This suggests that ???urgency??? is not a well-defined and commonly understood symptom that can be utilized to clearly discriminate between IC/BPS and OAB. These findings reinforce the clinical observation that it is often challenging to differentiate between these two conditions. Neurourol. Urodynam. 30:402???405, 2011. ?? 2010 Wiley-Liss, Inc.

BackgroundWhile falls and urinary incontinence are prevalent among older patients, who sometimes rely on proxies to provide their health information, the validity of proxy reports of concern about falls and urinary incontinence remains unknown.MethodsTelephone interviews with 43 consecutive patients with falls or fear of falling and/or bothersome urinary incontinence and their proxies chosen by patients as most knowledgeable about their health. The questionnaire included items derived from the Medical Outcomes Study Short Form 12 (SF-12), a scale assessing concerns about urinary incontinence (UI), and a measure of fear of falling, the Falls Efficacy Scale (FES). Scores were estimated using items asking the proxy perspective (6 items from the SF-12, 10 items from a UI scale, and all 10 FES items). Proxy and patient scores were compared using intraclass correlation coefficients (ICC, one-way model). Variables associated with absolute agreement between patients and proxies were explored.ResultsPatients had a mean age of 81 years (range 75-93) and 67% were female while proxies had a mean age of 70 (range 42-87) and 49% were female. ICCs were 0.63 for the SF-12, 0.52 for the UI scale, and 0.29 for the FES. Proxies tended to understate patients' general health and incontinence concern, but overstate patients' concern about falling. Proxies who lived with patients and those who more often see patients more closely reflected patient FES scores compared to those who lived apart or those who saw patients less often. Internal consistency reliability of proxy responses was 0.62 for the SF-12, 0.86 for the I-QOL, and 0.93 for the FES. In addition, construct validity of the proxy FES scale was supported by greater proxy-perceived fear of falling for patients who received medical care after a fall during the past 12 months (p < .05).ConclusionCaution should be exercised when using proxies as a source of information about older patients' health perceptions. Questions asking abou

BackgroundWhile falls and urinary incontinence are prevalent among older patients, who sometimes rely on proxies to provide their health information, the validity of proxy reports of concern about falls and urinary incontinence remains unknown.MethodsTelephone interviews with 43 consecutive patients with falls or fear of falling and/or bothersome urinary incontinence and their proxies chosen by patients as most knowledgeable about their health. The questionnaire included items derived from the Medical Outcomes Study Short Form 12 (SF-12), a scale assessing concerns about urinary incontinence (UI), and a measure of fear of falling, the Falls Efficacy Scale (FES). Scores were estimated using items asking the proxy perspective (6 items from the SF-12, 10 items from a UI scale, and all 10 FES items). Proxy and patient scores were compared using intraclass correlation coefficients (ICC, one-way model). Variables associated with absolute agreement between patients and proxies were explored.ResultsPatients had a mean age of 81 years (range 75-93) and 67% were female while proxies had a mean age of 70 (range 42-87) and 49% were female. ICCs were 0.63 for the SF-12, 0.52 for the UI scale, and 0.29 for the FES. Proxies tended to understate patients' general health and incontinence concern, but overstate patients' concern about falling. Proxies who lived with patients and those who more often see patients more closely reflected patient FES scores compared to those who lived apart or those who saw patients less often. Internal consistency reliability of proxy responses was 0.62 for the SF-12, 0.86 for the I-QOL, and 0.93 for the FES. In addition, construct validity of the proxy FES scale was supported by greater proxy-perceived fear of falling for patients who received medical care after a fall during the past 12 months (p < .05).ConclusionCaution should be exercised when using proxies as a source of information about older patients' health perceptions. Questions asking abou

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth