Search

In this brief, we describe the distribution of college prep course offerings across Texas and determine which school characteristics are associated with higher and lower numbers of course offerings. We also examine how college prep course offerings are related to student-level college prep course-taking and, in turn, how course-taking behaviors are related to long-term educational outcomes. This investigation of the distribution of college prep course offerings across Texas high schools reveals that more college prep courses are offered at larger, urban and suburban schools with higher levels of academic achievement and lower levels of economic disadvantage. Offering more college prep courses is associated with higher levels of course-taking, which, in turn, is associated with improved chances of completing a postsecondary credential, particularly for lower-achieving students.

This research brief is Part III of a four-part study of decentralization in Houston Independent School District (HISD). This part examines the impact of decentralization on student outcomes. In hopes of improving student performance, HISD implemented decentralization throughout the 1990s (see Part I of this project for a detailed discussion of the reforms--ED598239). In addition to giving principals more autonomy to develop staffing plans, class schedules, and teaching practices to cater to their students' unique needs, the reforms culminated with a change to the district's funding model from a full-time equivalency (FTE) model to a per unit allocation (PUA) model in the 1999-2000 school year. The PUA model placed more control of a school's budget in the hands of its principal. The goal of this study is to determine if the decentralization reforms, particularly the changeover to a PUA model, improved student achievement. The primary objective of this brief is to provide evidence on the link between HISD's decentralization reforms and student achievement. Towards that end, Texas Assessment for Academic Skills (TAAS) pass rates were computed for all campuses in the state of Texas between 1996-97 and 2001-02--three years prior to and three years after HISD adopted the PUA-based funding model. To estimate the effect of decentralization, this study compared the change in pass rates (across all grades and all subjects) in HISD schools before and after decentralization was fully implemented in the district to a matched set of schools from across the state that did not experience decentralization over the same time period. In addition to examining the overall relationship between decentralization and achievement, this study also attends to differences in the effect of decentralization by student-subgroup (black, Hispanic, and economically disadvantaged students), school level (elementary vs. middle vs. high) and school size (small schools). [Title varies: "Decentralization Reform in HISD, Part III: Evidence on the Relationship between Decentralization and Achievement."]

A cyclic proof system allows us to perform inductive reasoning without explicit inductions. We propose a cyclic proof system for HFLN, which is a higher-order predicate logic with natural numbers and alternating fixed-points. Ours is the first cyclic proof system for a higher-order logic, to our knowledge. Due to the presence of higher-order predicates and alternating fixed-points, our cyclic proof system requires a more delicate global condition on cyclic proofs than the original system of Brotherston and Simpson. We prove the decidability of checking the global condition and soundness of this system, and also prove a restricted form of standard completeness for an infinitary variant of our cyclic proof system. A potential application of our cyclic proof system is semi-automated verification of higher-order programs, based on Kobayashi et al.'s recent work on reductions from program verification to HFLN validity checking., Comment: 27 pages

The black-box nature of deep learning models prevents them from being completely trusted in domains like biomedicine. Most explainability techniques do not capture the concept-based reasoning that human beings follow. In this work, we attempt to understand the behavior of trained models that perform image processing tasks in the medical domain by building a graphical representation of the concepts they learn. Extracting such a graphical representation of the model's behavior on an abstract, higher conceptual level would unravel the learnings of these models and would help us to evaluate the steps taken by the model for predictions. We show the application of our proposed implementation on two biomedical problems - brain tumor segmentation and fundus image classification. We provide an alternative graphical representation of the model by formulating a concept level graph as discussed above, which makes the problem of intervention to find active inference trails more tractable. Understanding these trails would provide an understanding of the hierarchy of the decision-making process followed by the model. [As well as overall nature of model]. Our framework is available at https://github.com/koriavinash1/BioExp

We give a definition of quarternion Lie algebra and of the quarternification of a complex Lie algebra. By our definition gl(n,H), sl(n,H), so*(2n) and sp(n) are quarternifications of gl(n,C), sl(n,C), so(n,C) and u(n) respectively. Then we shall prove that a simple Lie algebra admits the quarternification. For the proof we follow the well known argument due to Harich-Chandra, Chevalley and Serre to construct the simple Lie algebra from its corresponding root system. The root space decomposition of this quarternion Lie algebra will be given. Each root space of a fundamental root is complex 2-dimensional.

Magnetic Resonance (MR) Imaging and Computed Tomography (CT) are the primary diagnostic imaging modalities quite frequently used for surgical planning and analysis. A general problem with medical imaging is that the acquisition process is quite expensive and time-consuming. Deep learning techniques like generative adversarial networks (GANs) can help us to leverage the possibility of an image to image translation between multiple imaging modalities, which in turn helps in saving time and cost. These techniques will help to conduct surgical planning under CT with the feedback of MRI information. While previous studies have shown paired and unpaired image synthesis from MR to CT, image synthesis from CT to MR still remains a challenge, since it involves the addition of extra tissue information. In this manuscript, we have implemented two different variations of Generative Adversarial Networks exploiting the cycling consistency and structural similarity between both CT and MR image modalities on a pelvis dataset, thus facilitating a bidirectional exchange of content and style between these image modalities. The proposed GANs translate the input medical images by different mechanisms, and hence generated images not only appears realistic but also performs well across various comparison metrics, and these images have also been cross verified with a radiologist. The radiologist verification has shown that slight variations in generated MR and CT images may not be exactly the same as their true counterpart but it can be used for medical purposes., Comment: 9 pages, 4 figures

Singlet fission is a photoconversion process that generates a doubly excited, maximally spin entangled pair state. This state has applications to quantum information and computing that are only beginning to be realized. In this article, we construct and analyze a spin-exciton hamiltonian to describe the dynamics of the two-triplet state. We find the selection rules that connect the doubly excited, spin-singlet state to the manifold of quintet states and comment on the mechanism and conditions for the transition into formally independent triplets. For adjacent dimers that are oriented and immobilized in an inert host, singlet fission can be strongly state-selective. We make predictions for electron paramagnetic resonance experiments and re-assign some transitions from recent literature. Our results give conditions for which magnetic resonance pulses can drive transitions between optically polarized magnetic sublevels of the two-exciton states, making it possible to realize quantum gates at room temperature in these systems., Comment: 17 pages, 5 figures

Understanding how racial information impacts human decision making in online systems is critical in today's world. Prior work revealed that race information of criminal defendants, when presented as a text field, had no significant impact on users' judgements of recidivism. We replicated and extended this work to explore how and when race information influences users' judgements, with respect to the saliency of presentation. Our results showed that adding photos to the race labels had a significant impact on recidivism predictions for users who identified as female, but not for those who identified as male. The race of the defendant also impacted these results, with black defendants being less likely to be predicted to recidivate compared to white defendants. These results have strong implications for how system-designers choose to display race information, and cautions researchers to be aware of gender and race effects when using Amazon Mechanical Turk workers., Comment: This paper has been accepted for publication at CHI2020

In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modifications on U-Nets and other popular CNN architectures. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs.

We provide a theoretical framework for quantifying the expected level of synchronization in a network of noisy oscillators. Through linearization around the synchronized state, we derive the following quantities as functions of the eigenvalues and eigenfunctions of the network Laplacian using a standard technique for dealing with multivariate Ornstein-Uhlenbeck processes: the magnitude of the fluctuations around a synchronized state and the disturbance coefficients $\alpha_i$ that represent how strongly node $i$ disturbs the synchronization. With this approach, we can quantify the effect of individual nodes and links on synchronization. Our theory can thus be utilized to find the optimal network structure for accomplishing the best synchronization. Furthermore, when the noise levels of the oscillators are heterogeneous, we can also find optimal oscillator configurations, i.e., where to place oscillators in a given network depending on their noise levels. We apply our theory to several example networks to elucidate optimal network structures and oscillator configurations., Comment: To appear in Chaos

Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential cancer cases, an automated solution as an aid to histopathologists is highly desirable. In the recent past, deep learning-based techniques have provided state of the art results in a wide variety of image analysis tasks, including analysis of digitized slides. However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis. We propose a deep learning-based framework for histopathology tissue analysis. We demonstrate the generalizability of our framework, including training and inference, on several open-source datasets, which include CAMELYON (breast cancer metastases), DigestPath (colon cancer), and PAIP (liver cancer) datasets. We discuss multiple types of uncertainties pertaining to data and model, namely aleatoric and epistemic, respectively. Simultaneously, we demonstrate our model generalization across different data distribution by evaluating some samples on TCGA data. On CAMELYON16 test data (n=139) for the task of lesion detection, the FROC score achieved was 0.86 and in the CAMELYON17 test-data (n=500) for the task of pN-staging the Cohen's kappa score achieved was 0.9090 (third in the open leaderboard). On DigestPath test data (n=212) for the task of tumor segmentation, a Dice score of 0.782 was achieved (fourth in the challenge). On PAIP test data (n=40) for the task of viable tumor segmentation, a Jaccard Index of 0.75 (third in the challenge) was achieved, and for viable tumor burden, a score of 0.633 was achieved (second in the challenge). Our entire framework and related documentation are freely available at GitHub and PyPi.