Estimation and Visualization of Digital Library Content Similarities

We present a semantic similarity-based recommender service. Our experimental application and validation domain consists of K-12 engineering learning resources. Given a learning resource, we must determine which educational standards it addresses and vice versa, find resources that align with a given standard. One approach to this problem suggests transitively inferring standard alignment from the semantic similarity of other, previously aligned resources. We investigate a bigram-based similarity estimator and a Sammon map-based user interface for visualizing the resulting similarity space. Validation was performed using resources in TeachEngineering.org, a K-12 STEM digital library. Target classifications were derived from author-generated tables of content for these resources. Testing shows good performance of the similarity measure, both in its correspondence to the collection’s table of contents and in the form of a two-dimensional Sammon map. The results provide evidence for the feasibility and practicality of using automated similarity measures in standards alignment and similar problems

Properties of the Molecular Cores of Low Luminosity Objects

We present a survey toward 16 Low Luminosity Objects (LLOs with an internal luminosity, Lint, lower than 0.2 Lsun) with N2H+ (1-0), N2H+ (3-2), N2D+ (3-2), HCO+ (3-2) and HCN (3-2) using the Arizona Radio Observatory Kitt Peak 12m Telescope and Submillimeter Telescope. Our goal is to probe the nature of these faint protostars which are believed to be either very low mass or extremely young protostars. We find that the N2D+/N2H+ column density ratios of LLOs are similar to those of typical starless cores and Class 0 objects. The N2D+/N2H+ column density ratios are relatively high (> 0.05) for LLOs with kinetic temperatures less than 10 K in our sample. The distribution of N2H+ (1-0) line widths spreads between that of starless cores and young Class 0 objects. If we use the line width as a dynamic evolutionary indicator, LLOs are likely young Class 0 protostellar sources. We further use the optically thick tracers, HCO+ (3-2) and HCN (3-2), to probe the infall signatures of our targets. We derive the asymmetry parameters from both lines and estimate the infall velocities by fitting the HCO+ (3-2) spectra with two-layer models. As a result, we identify eight infall candidates based on the infall velocities and seven candidates have infall signatures supported by asymmetry parameters from at least one of HCO+ (3-2) and HCN (3-2).Comment: 15 pages, 8 figures, accepted to Ap

Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits

Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit trade-off in linear bandits problems as well as generalized linear bandits problems. We develop novel index policies that we prove achieve order-optimality, and show that they achieve empirical performance competitive with the state-of-the-art benchmark methods in extensive experiments. The new policies achieve this with low computation time per pull for linear bandits, and thereby resulting in both favorable regret as well as computational efficiency

Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs

We consider the infinite-horizon linear Markov Decision Processes (MDPs), where the transition probabilities of the dynamic model can be linearly parameterized with the help of a predefined low-dimensional feature mapping. While the existing regression-based approaches have been theoretically shown to achieve nearly-optimal regret, they are computationally rather inefficient due to the need for a large number of optimization runs in each time step, especially when the state and action spaces are large. To address this issue, we propose to solve linear MDPs through the lens of Value-Biased Maximum Likelihood Estimation (VBMLE), which is a classic model-based exploration principle in the adaptive control literature for resolving the well-known closed-loop identification problem of Maximum Likelihood Estimation. We formally show that (i) VBMLE enjoys $\widetilde{O}(d\sqrt{T})$ regret, where $T$ is the time horizon and $d$ is the dimension of the model parameter, and (ii) VBMLE is computationally more efficient as it only requires solving one optimization problem in each time step. In our regret analysis, we offer a generic convergence result of MLE in linear MDPs through a novel supermartingale construct and uncover an interesting connection between linear MDPs and online learning, which could be of independent interest. Finally, the simulation results show that VBMLE significantly outperforms the benchmark method in terms of both empirical regret and computation time

Using a Q Matrix to Assess Students’ Latent Skills in an Online Course

Online teaching and learning has become an increasingly important aspect of the educational mission of universities. In person, teachers have time-tested tools for assessing student ability, including a wealth of verbal and nonverbal communication. The online format provides a wealth of data, and promises—but may not yet deliver—useful tools for this sort of just-in-time assessment. Publisher homework websites and quizzes inside a learning management system like Canvas can theoretically provide up-to-the-minute performance data including scores, use of help features, access of resources, and more. Our setting (teaching introductory online quantitative classes in the College of Business at a large research university) makes these innovations particularly appealing. Publishers have correctly identified our interest in “knowing” our students better via their online performance, but we have not yet seen an off-the-shelf solution that gets at our need: the ability to quickly and effectively react to student data in real time. In this paper, we discuss a portion of our research conducted in an online quantitative methods class, a 200-level undergraduate course in the College of Business. This research included constructing a Q Matrix as part of a Cognitive Diagnosis Model for our quantitative methods class. A Q Matrix is a mathematical tool that creates a linkage between underlying concept development and students’ performance on test items. In order to create assessments of learning which are based on student responses to questions, we must first investigate whether these questions are actually testing the foundational concepts we wish to evaluate. The Q Matrix offers a more holistic view of student achievement, and allows better insight (in terms of specificity regarding particular skills and concepts) into student growth and accomplishment than traditional item response methods. Q Matrix analysis requires serious attention to questions about how students are learning material and what underlying skills are being assessed by test questions. The research is based on two main theoretical foundations: Item Response Theory and Cognitive Diagnosis Models

γ\gamma-SUP: A clustering algorithm for cryo-electron microscopy images of asymmetric particles

Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful tool for obtaining three-dimensional (3D) structures of biological macromolecules in native states. A minimum cryo-EM image data set for deriving a meaningful reconstruction is comprised of thousands of randomly orientated projections of identical particles photographed with a small number of electrons. The computation of 3D structure from 2D projections requires clustering, which aims to enhance the signal to noise ratio in each view by grouping similarly oriented images. Nevertheless, the prevailing clustering techniques are often compromised by three characteristics of cryo-EM data: high noise content, high dimensionality and large number of clusters. Moreover, since clustering requires registering images of similar orientation into the same pixel coordinates by 2D alignment, it is desired that the clustering algorithm can label misaligned images as outliers. Herein, we introduce a clustering algorithm $\gamma$-SUP to model the data with a $q$-Gaussian mixture and adopt the minimum $\gamma$-divergence for estimation, and then use a self-updating procedure to obtain the numerical solution. We apply $\gamma$-SUP to the cryo-EM images of two benchmark macromolecules, RNA polymerase II and ribosome. In the former case, simulated images were chosen to decouple clustering from alignment to demonstrate $\gamma$-SUP is more robust to misalignment outliers than the existing clustering methods used in the cryo-EM community. In the latter case, the clustering of real cryo-EM data by our $\gamma$-SUP method eliminates noise in many views to reveal true structure features of ribosome at the projection level.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS680 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cytomegalovirus enteritis in immunocompetent patients: Report of two cases diagnosed using single-balloon enteroscopy

SummaryCytomegalovirus (CMV) infection of the gastrointestinal tract involves mostly the colon and rectum and mainly develops in immunocompromised patients. CMV infection in the small intestines has rarely been reported in immunocompetent patients. We report two cases of CMV enteritis that developed in immunocompetent patients and involved the ileum and jejunum, respectively. Both of them were diagnosed with single-balloon enteroscopy (SBE) and further confirmed with histopathology. The first case is a 71-year-old woman with a presentation of obscure gastrointestinal bleeding and severe anemia. Neither esophagogastroduodenoscopy nor colonoscopy identified any active bleeding. SBE and biopsy disclosed multiple scattered ulcers in the distal ileum and histopathology confirmed CMV ileitis. The hemorrhage subsided after conservative medical treatment. The second case is a 59-year-old woman with a presentation of progressive abdominal pain. SBE showed diffuse irregularly-shaped ulcers located from the upper to middle jejunum, and CMV jejunitis was confirmed with endoscopic biopsy and histopathological examination. Antiviral therapy was prescribed and her abdominal pain improved gradually. We discuss the clinical manifestations and management strategies of CMV infection that develops in the small intestines of immunocompetent patients. In addition, we highlight the endoscopic characteristics of CMV enteritis and the clinical utilities of SBE in the evaluation of patients with suspected CMV infection of the small intestines

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost ({\em e.g.}, running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at https://github.com/pingyang1117/SMILEtrack_Officia

Investigation of Hepatoprotective Activity of Induced Pluripotent Stem Cells in the Mouse Model of Liver Injury

To date liver transplantation is the only effective treatment for end-stage liver diseases. Considering the potential of pluripotency and differentiation into tridermal lineages, induced pluripotent stem cells (iPSCs) may serve as an alternative of cell-based therapy. Herein, we investigated the effect of iPSC transplantation on thioacetamide- (TAA-) induced acute/fulminant hepatic failure (AHF) in mice. Firstly, we demonstrated that iPSCs had the capacity to differentiate into hepatocyte-like cells (iPSC-Heps) that expressed various hepatic markers, including albumin, α-fetoprotein, and hepatocyte nuclear factor-3β, and exhibited biological functions. Intravenous transplantation of iPSCs effectively reduced the hepatic necrotic area, improved liver functions and motor activity, and rescued TAA-treated mice from lethal AHF. 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate cell labeling revealed that iPSCs potentially mobilized to the damaged liver area. Taken together, iPSCs can effectively rescue experimental AHF and represent a potentially favorable cell source of cell-based therapy

Delayed Suspicion, Treatment and Isolation of Tuberculosis Patients in Pulmonology/Infectious Diseases and Non-Pulmonology/Infectious Diseases Wards

Background/PurposeDelayed diagnosis and isolation increases the risk of nosocomial transmission of tuberculosis (TB). To assess the risk of delayed management of TB, we analyzed the risk factors of prolonged delay in isolation of smear-positive TB patients in pulmonology/infectious diseases and other wards in a tertiary teaching hospital.MethodsWe enrolled smear-positive TB patients aged > 16 years with delayed respiratory isolation following hospitalization. Medical records were reviewed retrospectively. Time intervals between admission, order of sputum acid-fast staining, initiation of anti-tuberculous treatment and isolation were compared between pulmonology/infectious diseases wards (PIWs) and other wards. Risk factors were analyzed in patients with prolonged isolation delay of > 7 days in individual groups.ResultsIsolation was delayed in 191 (73.7%) of 259 hospitalized smear-positive TB patients. Median suspicion, treatment and isolation delays were 0, 3 and 4 days in PIWs and 1, 5 and 7 days in other wards. For patients admitted to non-PIWs, atypical chest radiographs, symptoms without dyspnea or not being admitted from the emergency department (ED) were risk factors for prolonged isolation delay exceeding 7 days. The only risk factor for delayed isolation in patients admitted to PIWs was age ≥ 70 years.ConclusionDelays in suspicion, treatment and isolation of TB patients were longer in non-PIWs. Clinicians should be alert to those admitted to non-PIWs with atypical chest radiographs, atypical symptoms, or not admitted from the ED