cssi/SciSciGPT-SciSciNet huggingface dataset:
DIAMetrics: Benchmarking Query Engines at Scale

This paper introduces DIAMetrics: a novel framework for end-to-end benchmarking and performance monitoring of query engines. DIAMetrics consists of a number of components supporting tasks such as automated workload summarization, data anonymization, benchmark execution, monitoring, regression identification, and alerting. The architecture of DIAMetrics is highly modular and supports multiple systems by abstracting their implementation details and relying on common canonical formats and pluggable software drivers. The end result is a powerful unified framework that is capable of supporting every aspect of benchmarking production systems and workloads. DIAMetrics has been developed in Google and is being used to benchmark various internal query engines. In this paper, we give an overview of DIAMetrics and discuss its design and implementation. Furthermore, we provide details about its deployment and example use cases. Given the variety of supported systems and use cases within Google, we argue that its core concepts can be used more widely to enable comparative end-to-end benchmarking in other industrial environments.
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries

In this work, we study the problem of answering $k$ queries with $(\epsilon, \delta)$-differential privacy, where each query has sensitivity one. We give an algorithm for this task that achieves an expected $\ell_\infty$ error bound of $O(\frac{1}{\epsilon}\sqrt{k \log \frac{1}{\delta}})$, which is known to be tight (Steinke and Ullman, 2016). A very recent work by Dagan and Kur (2020) provides a similar result, albeit via a completely different approach. One difference between our work and theirs is that our guarantee holds even when $\delta < 2^{-\Omega(k/(\log k)^8)}$ whereas theirs does not apply in this case. On the other hand, the algorithm of Dagan and Kur (2020) has a remarkable advantage that the $\ell_{\infty}$ error bound of $O(\frac{1}{\epsilon}\sqrt{k \log \frac{1}{\delta}})$ holds not only in expectation but always (i.e., with probability one) while we can only get a high probability (or expected) guarantee on the error.
Robust PDF Document Conversion Using Recurrent Neural Networks

The number of published PDF documents in both the academic and commercial world has increased exponentially in recent decades. There is a growing need to make their rich content discoverable to information retrieval tools. Achieving high-quality semantic searches demands that a document's structural components such as title, section headers, paragraphs, (nested) lists, tables and figures (including their captions) are properly identified. Unfortunately, the PDF format is known to not conserve such structural information because it simply represents a document as a stream of low-level printing commands, in which one or more characters are placed in a bounding box with a particular styling. In this paper, we present a novel approach to document structure recovery in PDF using recurrent neural networks to process the low-level PDF data representation directly, instead of relying on a visual re-interpretation of the rendered PDF page, as has been proposed in previous literature. We demonstrate how a sequence of PDF printing commands can be used as input into a neural network and how the network can learn to classify each printing command according to its structural function in the page. This approach has three advantages: First, it can distinguish among more fine-grained labels (typically 10-20 labels as opposed to 1-5 with visual methods), which results in a more accurate and detailed document structure resolution. Second, it can take into account the text flow across pages more naturally compared to visual methods because it can concatenate the printing commands of sequential pages. Last, our proposed method needs less memory and it is computationally less expensive than visual methods. This allows us to deploy such models in production environments at a much lower cost. Through extensive architectural search in combination with advanced feature engineering, we were able to implement a model that yields a weighted average F1 score of 97% across 17 distinct structural labels. The best model we achieved is currently served in production environments on our Corpus Conversion Service (CCS), which was presented at KDD18. This model enhances the capabilities of CCS significantly, as it eliminates the need for human annotated label ground-truth for every unseen document layout. This proved particularly useful when applied to a huge corpus of PDF articles related to COVID-19.
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach

The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems while allowing for underlying complex subsystem level interactions. Particularly, we present the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. We detail a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price (MSRP) level that has the advantage of bypassing uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier adjustments to mark up direct manufacturing costs to MSRP. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers, vehicle classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64 000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.
Fast and Powerful Conditional Randomization Testing via Distillation

We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y is independent of X given Z. The conditional randomization test (CRT) was recently proposed as a way to use distributional information about X|Z to exactly (non-asymptotically) control Type-I error using any test statistic in any dimensionality without assuming anything about Y|(X,Z). This flexibility in principle allows one to derive powerful test statistics from complex prediction algorithms while maintaining statistical validity. Yet the direct use of such advanced test statistics in the CRT is prohibitively computationally expensive, especially with multiple testing, due to the CRT's requirement to recompute the test statistic many times on resampled data. We propose the distilled CRT, a novel approach to using state-of-the-art machine learning algorithms in the CRT while drastically reducing the number of times those algorithms need to be run, thereby taking advantage of their power and the CRT's statistical guarantees without suffering the usual computational expense. In addition to distillation, we propose a number of other tricks like screening and recycling computations to further speed up the CRT without sacrificing its high power and exact validity. Indeed, we show in simulations that all our proposals combined lead to a test that has similar power to the most powerful existing CRT implementations but requires orders of magnitude less computation, making it a practical tool even for large data sets. We demonstrate these benefits on a breast cancer dataset by identifying biomarkers related to cancer stage.
Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs

Deep Neural Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory is physically finite, which limits the image resolutions and batch sizes that could be used in training for better DNN performance. Unlike solutions that require physically upgrade GPUs, the Gradient Check-Pointing(GCP) training trades computation for more memory beyond existing GPU hardware. GCP only stores a subset of intermediate tensors, called Gradient Checkpoints (GCs), during forward. Then during backward, extra local forwards are conducted to compute the missing tensors. The total training memory cost becomes the sum of (1) the memory cost of the gradient checkpoints and (2) the maximum memory cost of local forwards. To achieve maximal memory cut-offs, one needs optimal algorithms to select GCs. Existing GCP approaches rely on either manual input of GCs or heuristics-based GC search on Linear Computation Graphs (LCGs), and cannot apply to Arbitrary Computation Graphs(ACGs). In this paper, we present theories and optimal algorithms on GC selection that, for the first time, are applicable to ACGs and achieve the maximal memory cut-offs. Extensive experiments show that our approach not only outperforms existing approaches (only applicable on LCGs), and is applicable to a vast family of LCG and ACG networks, such as Alexnet, VGG, ResNet, Densenet, Inception Net and highly complicated DNNs by Network Architecture Search. Our work enables GCP training on ACGs, and cuts off up-to 80% of training memory1 with a moderate time overhead (∼ 30%-50%). Codes are available2
Targeted muscle effort distribution with exercise robots: Trajectory and resistance effects.

Abstract The objective of this work is to relate muscle effort distributions to the trajectory and resistance settings of a robotic exercise and rehabilitation machine. Muscular effort distribution, representing the participation of each muscle in the training activity, was measured with electromyography sensors (EMG) and defined as the individual activation divided by the total muscle group activation. A four degrees-of-freedom robot and its impedance control system are used to create advanced exercise protocols whereby the user is asked to follow a path against the machine’s neutral path and resistance. In this work, the robot establishes a zero-effort circular path, and the subject is asked to follow an elliptical trajectory. The control system produces a user-defined stiffness between the deviations from the neutral path and the torque applied by the subject. The trajectory and resistance settings used in the experiments were the orientation of the ellipse and a stiffness parameter. Multiple combinations of these parameters were used to measure their effects on the muscle effort distribution. An artificial neural network (ANN) used part of the data for training the model. Then, the accuracy of the model was evaluated using the rest of the data. The results show how the precision of the model is lost over time. These outcomes show the complexity of the muscle dynamics for long-term estimations suggesting the existence of time-varying dynamics possibly associated with fatigue.
Sales and Markup Dispersion: Theory and Empirics

We characterize the relationship between the distributions of two variables linked by a structural model. We then show that, in models of heterogeneous firms in monopolistic competition, this relationship implies a new demand function that we call “CREMR” (Constant Revenue Elasticity of Marginal Revenue). This demand function is the only one that is consistent with productivity and sales distributions having the same form (whether Pareto, lognormal, or Frechet) in the cross section, and it is necessary and sufficient for Gibrat's Law to hold over time. Among the applications we consider, we use our methodology to characterize misallocation across firms; we derive the distribution of markups implied by any assumptions on demand and productivity; and we show empirically that CREMR‐based markup distributions provide an excellent parsimonious fit to Indian firm‐level data, which in turn allows us to calculate the proportion of firms that are of suboptimal size in the market equilibrium.
Eukaryotic initiation factor EIF-3.G augments mRNA translation efficiency to regulate neuronal activity

The translation initiation complex eIF3 imparts specialized functions to regulate protein expression. However, understanding of eIF3 activities in neurons remains limited despite widespread dysregulation of eIF3 subunits in neurological disorders. Here, we report a selective role of the C. elegans RNA-binding subunit EIF-3.G in shaping the neuronal protein landscape. We identify a missense mutation in the conserved Zinc-Finger (ZF) of EIF-3.G that acts in a gain-of-function manner to dampen neuronal hyperexcitation. Using neuron-type-specific seCLIP, we systematically mapped EIF-3.G-mRNA interactions and identified EIF-3.G occupancy on GC-rich 5'UTRs of a select set of mRNAs enriched in activity-dependent functions. We demonstrate that the ZF mutation in EIF-3.G alters translation in a 5'UTR-dependent manner. Our study reveals an in vivo mechanism for eIF3 in governing neuronal protein levels to control neuronal activity states and offers insights into how eIF3 dysregulation contributes to neurological disorders.
Utility-based pricing and hedging of contingent claims in Almgren-Chriss model with temporary price impact

In this paper, we construct the utility-based optimal hedging strategy for a European-type option in the Almgren-Chriss model with temporary price impact. The main mathematical challenge of this work stems from the degeneracy of the second order terms and the quadratic growth of the first order terms in the associated HJB equation, which makes it difficult to establish sufficient regularity of the value function needed to construct the optimal strategy in a feedback form. By combining the analytic and probabilistic tools for describing the value function and the optimal strategy, we establish the feedback representation of the latter. We use this representation to derive an explicit asymptotic expansion of the utility indifference price of the option, which allows us to quantify the price impact in options' market via the price impact coefficient in the underlying market.
Dense Optic Nerve Head Deformation Estimated using CNN as a Structural Biomarker of Glaucoma Progression

ABSTRACT Purpose To present a new structural biomarker for detecting glaucoma progression based on structural transformation of the optic nerve head (ONH) region. Methods A dense ONH deformation was estimated using deep learning methods namely DDCNet-Multires, FlowNet2, and FlowNet-Correlation, and legacy computational methods namely the topographic change analysis (TCA) and proper orthogonal decomposition (POD) methods using longitudinal confocal scans of the ONH for each study eye. A candidate structural biomarker of glaucoma progression in a study eye was estimated as average magnitude of flow velocities within the ONH region. The biomarker was evaluated using longitudinal confocal scans of 12 laser-treated and 12 contralateral normal eyes of 12 primates from the LSU Experimental Glaucoma Study (LEGS); and 36 progressing eyes and 21 longitudinal normal eyes from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS). Area under the ROC curves (AUC) was used to assess the diagnostic accuracy of the candidate biomarker. Results AUROC (95% CI) for LEGS were: 0.83 (0.79, 0.88) for DDCNet-Multires; 0.83 (0.78, 0.88) for FlowNet2; 0.83 (0.78, 0.88) for FlowNet-Correlation; 0.94 (0.91, 0.97) for POD; and 0.86 (0.82, 0.91) for TCA methods. For DIGS: 0.89 (0.80, 0.97) for DDCNet-Multires; 0.82 (0.71, 0.93) for FlowNet2; 0.93 (0.86, 0.99) for FlowNet-Correlation; 0.86 (0.76, 0.96) for POD; and 0.86 (0.77, 0.95) for TCA methods. Lower diagnostic accuracy of the learning-based methods for LEG study eyes were due to image alignment errors in confocal sequences. Conclusion Deep learning methods trained to estimate generic deformation were able to detect ONH deformation from confocal images and provided a higher diagnostic accuracy when compared to the classical optical flow and legacy biomarkers of glaucoma progression. Because it is difficult to validate the estimates of dense ONH deformation in clinical population, our validation using ONH sequences under controlled experimental conditions confirms the diagnostic accuracy of the biomarkers observed in the clinical population. Performance of these deep learning methods can be further improved by fine-tuning these networks using longitudinal ONH sequences instead of training the network to be a general-purpose deformation estimator.
A graphene edge-mediated quantum gate

We propose a quantum gate architecture that allows for the systematic control of the effective exchange interactions between magnetic impurities embedded in nanoscale graphene flakes connected by a gated bridge. The entanglement between the magnetic moment and the edge states of the fragments is used to electrostatically tune the exchange interaction from ferro to antiferromagnetic by merely changing the bridge's carrier density. By characterizing the effects of size and coupling parameters, we explore different operation regimes of this device by means of exact calculations with the density matrix renormalization group. We analyze the results utilizing a simplified model that accounts for the main many-body mechanisms. Finally, we discuss how to use arrays of these devices to build quantum simulators for quantum many-body Hamiltonians.
Extension of Killing vector fields beyond compact Cauchy horizons

Abstract We prove that any compact Cauchy horizon with constant non-zero surface gravity in a smooth vacuum spacetime is a smooth Killing horizon. The novelty here is that the Killing vector field is shown to exist on both sides of the horizon. This generalises classical results by Moncrief and Isenberg, by dropping the assumption that the metric is analytic. In previous work by Racz and the author, the Killing vector field was constructed on the globally hyperbolic side of the horizon. In this paper, we prove a new unique continuation theorem for wave equations through smooth compact lightlike (characteristic) hypersurfaces which allows us to extend the Killing vector field beyond the horizon. The main ingredient in the proof of this theorem is a novel Carleman type estimate. Using a well-known construction, our result applies in particular to smooth stationary asymptotically flat vacuum black hole spacetimes with event horizons with constant non-zero surface gravity. As a special case, we therefore recover Hawking's local rigidity theorem for such black holes, which was recently proven by Alexakis-Ionescu-Klainerman using a different Carleman type estimate.
COVID-19 multidimensional kaggle literature organization

The unprecedented outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, continues to be a significant worldwide problem. As a result, a surge of new COVID-19 related research has followed suit. The growing number of publications requires document organization methods to identify relevant information. In this paper, we expand upon our previous work with clustering the CORD-19 dataset by applying multi-dimensional analysis methods. Tensor factorization is a powerful unsupervised learning method capable of discovering hidden patterns in a document corpus. We show that a higher-order representation of the corpus allows for the simultaneous grouping of similar articles, relevant journals, authors with similar research interests, and topic keywords. These groupings are identified within and among the latent components extracted via tensor decomposition. We further demonstrate the application of this method with a publicly available interactive visualization of the dataset.
Codes approaching the Shannon limit with polynomial complexity per information bit

We consider codes for channels with extreme noise that emerge in various low-power applications. Simple LDPC codes with parity checks of weight 3 are first studied for any code dimension m → ∞. These codes form modulation schemes: they improve the original channel outputs for any SNR > -6 dB (per information bit) and gain 3 dB over uncoded modulation as SNR grows. However, they also have a floor on the output bit error rate (BER) irrespective of their length. Tight lower and upper bounds, which are virtually identical to simulation results, are then obtained for BER at any SNR. We also study a combined scheme that splits $m$ information bits into $b$ blocks and protects each with some polar code. Decoding moves back and forth between polar and LDPC codes, every time using a polar code of a higher rate. For m → ∞and a sufficiently large parameter b, this design yields a vanishing BER at any SNR above the Shannon limit of -1.59 dB and has complexity order of m log m per information bit.
Concepts-based Accounting Standards

While comparability across firms and consistency over time are generally held to be fundamental goals of financial reporting, I provide an analytic representation of a concept that explains why concepts-based accounting standards cannot assure comparability and why their induced consistency may not always be desirable. While the term, concepts-based accounting standards, has not caught on in the academic and professional literatures, its use here emphasizes the foundational role that language-based concepts play in constructing accounting standards. I appeal to the academic literature in machine learning, neural networks and especially cognitive science – all of which may represent concepts by S-curve (sigmoid) signatures. I then show how S-curves can explain an accounting standard’s (1) precision, (2) comparability across firms, (3) demands placed on judgment, and (4) consistency across time. Accordingly, an S-curve formulation may guide both analytical modelling of accounting standards and add structure to empirical research designs..
Structured Proofs for Adversarial Cyber-Physical Systems

Many cyber-physical systems (CPS) are safety-critical, so it is important to formally verify them, e.g. in formal logics that show a model’s correctness specification always holds. Constructive Differential Game Logic (CdGL) is such a logic for (constructive) hybrid games, including hybrid systems. To overcome undecidability, the user first writes a proof, for which we present a proof-checking tool. We introduce Kaisar, the first language and tool for CdGL proofs, which until now could only be written by hand with a low-level proof calculus. Kaisar’s structured proofs simplify challenging CPS proof tasks, especially by using programming language principles and high-level stateful reasoning. Kaisar exploits CdGL’s constructivity and refinement relations to build proofs around models of game strategies. The evaluation reproduces and extends existing case studies on 1D and 2D driving. Proof metrics are compared and reported experiences are discussed for the original studies and their reproductions.
MUSIQ: Multi-Scale Image Quality Transformer

Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often compromised by the fixed shape constraint in batch training. To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation. To address this, we design a multi-scale image quality Transformer (MUSIQ) to process native resolution images with varying sizes and aspect ratios. With a multi-scale image representation, our proposed method can capture image quality at different granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale embedding is proposed to support the positional embedding in the multi-scale representation. Experimental results verify that our method can achieve state-of-the-art performance on multiple large scale IQA datasets such as PaQ-2-PiQ, SPAQ and KonIQ-10k.
Multimodal Representation Learning via Maximization of Local Mutual Information

We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image. Our method trains image and text encoders by encouraging the resulting representations to exhibit high local mutual information. We make use of recent advances in mutual information estimation with neural network discriminators. We argue that the sum of local mutual information is typically a lower bound on the global mutual information. Our experimental results in the downstream image classification tasks demonstrate the advantages of using local features for image-text representation learning.
Geometry, number theory, and the butterfly spectrum of two-dimensional Bloch electrons.

We take a deeper dive into the geometry and the number theory that underlay the butterfly graphs of the Harper and the generalized Harper models of Bloch electrons in a magnetic field. The root of the number theoretical characteristics of the fractal spectrum is traced to a close relationship between the Farey tree---the hierarchical tree that generates all rationals and the Wannier diagram---a graph that labels all the gaps of the butterfly graph. The resulting Farey-Wannier hierarchical lattice of trapezoids provides a geometrical representation of the nested pattern of butterflies in the butterfly graph. Some features of the energy spectrum, such as absence of some of the Wannier trajectories in the butterfly graph falling outside the number theoretical framework, can be stated as a simple rule of minimal violation of mirror symmetry. In a generalized Harper model, number theoretical framework prevails with the Farey-Wannier hierarchical lattice regrouping to form some hexagonal cells creating different species of butterflies.
Learning Agile Locomotion Skills with a Mentor

Developing agile behaviors for legged robots re-mains a challenging problem. While deep reinforcement learning is a promising approach, learning truly agile behaviors typically requires tedious reward shaping and careful curriculum design. We formulate agile locomotion as a multi-stage learning problem in which a mentor guides the agent throughout the training. The mentor is optimized to place a checkpoint to guide the movement of the robot’s center of mass while the student (i.e. the robot) learns to reach these checkpoints. Once the student can solve the task, we teach the student to perform the task without the mentor. We evaluate our proposed learning system with a simulated quadruped robot on a course consisting of randomly generated gaps and hurdles. Our method significantly outperforms a single-stage RL baseline without a mentor, and the quadruped robot can agilely run and jump across gaps and obstacles. Finally, we present a detailed analysis of the learned behaviors’ feasibility and efficiency.
Gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization

With increasing utilization of comprehensive genomic data to guide clinical care, anticipated to become the standard of care in many clinical settings, the practice of diagnostic medicine is undergoing a notable shift. However, the move from single-gene or panel-based genetic testing to exome and genome sequencing has not been matched by the development of tools to enable diagnosticians to interpret increasingly complex or uncertain genomic findings. Here, we present gene.iobio, a real-time, intuitive and interactive web application for clinically-driven variant interrogation and prioritization. We show gene.iobio is a novel and effective approach that significantly improves upon and reimagines existing methods. In a radical departure from existing methods that present variants and genomic data in text and table formats, gene.iobio provides an interactive, intuitive and visually-driven analysis environment. We demonstrate that adoption of gene.iobio in clinical and research settings empowers clinical care providers to interact directly with patient genomic data both for establishing clinical diagnoses and informing patient care, using sophisticated genomic analyses that previously were only accessible via complex command line tools.
The W. Thurston algorithm applied to real polynomial maps

This note will describe an effective procedure for constructing critically finite real polynomial maps with specified combinatorics.
Off-Policy Risk Assessment in Contextual Bandits

Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data. However, while the bandits literature has adopted a diverse set of objectives, most research on off-policy evaluation to date focuses on the expected reward. In this paper, we introduce Lipschitz risk functionals, a broad class of objectives that subsumes conditional value-at-risk (CVaR), variance, mean-variance, many distorted risks, and CPT risks, among others. We propose Off-Policy Risk Assessment (OPRA), a framework that first estimates a target policy's CDF and then generates plugin estimates for any collection of Lipschitz risks, providing finite sample guarantees that hold simultaneously over the entire class. We instantiate OPRA with both importance sampling and doubly robust estimators. Our primary theoretical contributions are (i) the first uniform concentration inequalities for both CDF estimators in contextual bandits and (ii) error bounds on our Lipschitz risk estimates, which all converge at a rate of $O(1/\sqrt{n})$.
A Quantum-Inspired Classical Solver for Boolean k-Satisfiability Problems

In this paper we detail a classical algorithmic approach to the k-satisfiability (k-SAT) problem that is inspired by the quantum amplitude amplification algorithm. This work falls under the emerging field of quantum-inspired classical algorithms. To propose our modification, we adopt an existing problem model for k-SAT known as Universal SAT (UniSAT), which casts the Boolean satisfiability problem as a non-convex global optimization over a real-valued space. The quantum-inspired modification to UniSAT is to apply a conditioning operation to the objective function that has the effect of "am-plifying" the function value at points corresponding to optimal solutions. We describe the algorithm for achieving this ampli-fication, termed "AmplifySAT," which follows a familiar two-step process of applying an oracle-like operation followed by a reflection about the average. We then discuss opportunities for meaningfully leveraging this processing in a classical digital or analog computing setting, attempting to identify the strengths and limitations of AmplifySAT in the context of existing non-convex optimization strategies like simulated annealing and gradient descent.
Preparing All Students for 21st Century College and Careers

Preparing all students for lifelong success in a rapidly changing global economy requires schools to reconsider both what and how educators teach and students learn in the 21st century. This paper presents examples of curricula, programs, and delivery methods that promote increased learning in core academics, technology, and life and career skills for students with disabilities at the secondary and postsecondary levels. Three initiatives, EnvisionIT (EIT), Ohio’s STEM Ability Alliance (OSAA), and Transition Options in Postsecondary Settings (TOPS), provide models of engendering 21st century skills utilizing 21st century tools that support all students’ transition from high school to college and careers.
Reflections on the LSE Tradition in Econometrics: a Student's Perspective

Since the mid 1960s the LSE tradition, led initially by Denis Sargan and later by David Hendry, has contributed several innovative techniques and modeling strategies to applied econometrics. A key feature of the LSE tradition has been its striving to strike a balance between the theory-oriented perspective of textbook econometrics and the ARIMA data-oriented perspective of time series analysis. The primary aim of this article is to provide a student’s perspective on this tradition. It is argued that its key contributions and its main call to take the data more seriously can be formally justified on sound philosophical grounds and provide a coherent framework for empirical modeling in economics. Its full impact on applied econometrics will take time to unfold, but the pervasiveness of its main message is clear: statistical models that account for the regularities in data can enhance the reliability of inference and policy analysis, and guide the search for better economic theories by demarcating ‘what there is to explain’.
Ordered binding of substrates to yeast aldehyde dehydrogenase.

Yeast aldehyde dehydrogenase catalyzes the pyridine nucleotide-linked oxidation of a broad range of aldehydes to the corresponding acids. The reaction is experimentally irreversible. Initial velocity studies with DPN and two different aldehyde substrates, and equilibrium dialysis experiments with DPN, indicate that the substrates bind in a compulsory order; aldehyde and enzyme interact to make a binary complex which, in turn, forms a ternary complex with DPN. The equilibrium dialysis experiments were complicated by the ubiquitous presence of contaminating aldehydes and were made possible by the inclusion of a DPN-regenerating system. DPNH behaves as a competitive inhibitor of DPN and combines only with the enzyme aldehyde complex. Since neither of the pyridine nucleotides forms a binary complex with enzyme, the data fit a model in which aldehyde binds first while acid dissociates last.
Nonuse of contraception among women at risk of unintended pregnancy in the United States

Abstract Objective This paper seeks to determine factors associated with nonuse of contraception by women at risk of unintended pregnancy in the United States. This nonuse may be associated with about 900,000 unintended births in the US each year. Study Design The 2002 and 2006–2010 National Surveys of Family Growth were combined to yield a nationally representative sample of 9,445 women at risk of unintended pregnancy. Logistic regression analyses identified factors associated with nonuse of contraception. Results This analysis reveals previously undocumented patterns of nonuse: controlling for confounding variables, cohabiting women [adjusted odds ratio (AOR)=2.3, 95% confidence interval (CI)=1.45–3.52] had higher odds of nonuse than married women; women who reported a difficulty getting pregnant (AOR=2.5, 95% CI=2.01–3.01) had higher odds of nonuse than those who did not. Nonuse was also more common among women with a master's degree or more (AOR=1.5, 95% CI=1.11–2.08) compared with those with some college or bachelor's degree, and it was more common among women in their first year after first intercourse than after the first year (AOR 1.6, 95% CI=1.12–2.22). Among women who had a recent unintended birth, the most common reason for not using contraception prior to conception was that she did not think she could get pregnant. Conclusions This study establishes national estimates of reasons for nonuse of contraception and identifies some new subgroups at risk of nonuse. Implications These results may help better understand factors affecting nonuse of contraception and develop strategies for preventing unintended pregnancy in the United States.
A Mandibular Mass With Maxillary Erosion

A woman in her 60s was referred for a large, left-sided, progressively enlarging mandibular mass, present for approximately 7 months. She had no pain or neurosensory disturbances related to the lesion; however, she did have difficulty tolerating a regular diet owing to the bulk of the mass. She had no significant medical history, had quit smoking cigarettes 4 year prior to presentation, but had a 20 pack-year smoking history. Findings from the physical examination were notable for a 4.9-cm exophytic, nodular, slightly mobile mass of the left lower posterior alveolar ridge, involving the buccal and lingual gingiva. Teeth 20 to 24 were mobile. She had no cervical lymphadenopathy. Previous biopsy demonstrated squamous mucosa with ulceration and chronic inflammation. Contrasted computed tomography revealed an exophytic complex mass with areas of calcification and ossification inferiorly and a necrotic, cystic irregularly enhancing portion superiorly (Figure, A-C). An additional biopsy was performed (Figure, D). Themass was excised, and teeth 20 to 26 were removed. The alveolar ridge was recontoured, and the incision was closed primarily. Quiz at jamaotolaryngology.com CT scan, coronal view A CT scan, sagittal view B
Structural and Functional Roles of the Cysteine Residues in the α Subunit of the Escherichia coli Tryptophan Synthetase IV. EFFECT OF INDOLE ON PROTEIN CONFORMATION IN THE SULFHYDRYL REGION AT pH 7.0

Abstract The reaction pattern of the three sulfhydryl groups of the α subunit of the Escherichia coli tryptophan synthetase with N-ethylmaleimide and 5,5'-dithiobis(2-nitrobenzoic acid) (DTNB) at pH 7.0 is profoundly altered in the presence of 10 mm indole. The effect of indole is upon both the rate of reaction, and upon its over-all extent. In the absence of indole, only cysteinyl sulfhydryl groups 80 and 117 of the α subunit are available for reaction with N-ethylmaleimide, but in 10 mm indole all three thiol groups become available for reaction. Similar results were obtained with DTNB (i.e. in the absence of indole a single sulfhydryl group reacts rapidly with DTNB, any further reaction occurring very slowly; in the presence of indole all three sulfhydryl groups react at a slower although uniform rate). The effects of indole in the 7.5 to 10.0 mm concentration range could not be mimicked with 20% sucrose, 20% ethanol, 20% ethylene glycol, or 20% dioxane, solvents known to be effective perturbants of protein tertiary structure. The presence of indole also had no effect on the optical rotatory dispersion of the enzyme, and caused no detectable change in the 16 ± 2% α helical structure found for this protein. Indole 3-glycerol phosphate (InGP) at concentrations twice the enzyme's Km for this substrate in the InGP aldolase reaction catalyzed by the free α subunit, protected all three sulfhydryl groups from reaction with DTNB in the presence of indole. Examination of the indole concentration dependence of its effect on sulfhydryl group reactivity, and determination of its Km in the reverse InGP aldolase reaction (synthesis of InGP from indole in the presence of excess d-glyceraldehyde 3-phosphate) showed that the two processes reached half-saturation at indole concentrations of 3.6 and 4.1 mm, respectively. It is concluded that the effect of indole on sulfhydryl group reactivity reflects enzyme conformational changes related to the mechanism of catalysis of the InGP aldolase reaction by the independent α subunit. Indole-induced conformational changes in α subunit structure may be involved in the regulation and catalysis of the last steps in tryptophan synthesis by the α2β2-tryptophan synthetase complex.
Synthesis and characterization of poly(dimethylsiloxane) based liquid crystals

A novel set of low molecular weight copolymers have been synthesized which were based on the attachment of mesogenic groups to the terminal points of oligomer backbones. The placement of the groups at the end of the chains apparently allowed a second phase to form more easily than if the groups were attached along the backbone as pendant groups. The nature of the coupler or spacer was determined to influence whether the end groups crystallized or formed liquid crystallinity. The observation of liquid crystallinity was found to depend primarily on the relative concentration of mesogen to backbone molecular weight. Depending upon the specific coupler, there existed two critical values of molecular weight between which mesophase behavior was observed. Below a certain value, the end groups were found to crystallize. Above another critical value, there was no measureable second phase. Liquid crystallinity was only observed when the molecular weight was intermediate between the two critical values.
Effect of Low-Intensity Light on Growth Response and Bacteriochlorophyll Concentration in Rhodospirillum rubrum Mutant C

Photosynthesizing cells of Rhodospirillum rubrum mutant C underwent adaptation for growth in low-light conditions. During adaptation there was a transitory increase in the generation time and the bacteriochlorophyll a content of the cells.
Definition of the sequence requirements for binding of the EBNA-1 protein to its palindromic target sites in Epstein-Barr virus DNA

Interaction between the trans-acting DNA-binding protein EBNA-1 and cis-acting sequences in the ori-P region of Epstein-Barr virus DNA is required for maintenance of the viral plasmid state in latently infected B cells and is involved in the regulation of transcription during latency. In the Epstein-Barr virus genome, a total of 26 EBNA-1-binding sites occur within three clustered loci referred to as the family of repeats and dyad symmetry locus of ori-P and the separate BamHI-Q locus. Incubation of a bacterially expressed carboxy-terminal domain of EBNA-1 (28,000-molecular-weight EBNA-1 [28K EBNA-1]) with synthetic monomer and dimer consensus binding sites gave characteristic DNA-protein complexes in a mobility retardation assay. A similar approach with the naturally occurring Q locus confirmed that it contains two distinct but low-affinity binding sites. We then examined the precise sequence requirements for EBNA-1 binding, using a set of 30-base-pair oligonucleotides designed to contain symmetric point mutations within both halves of the palindromic target site. Analysis of all possible single substitutions between positions 1 and 10 in the consensus half-palindrome sequence revealed that positions 9 and 10 did not contribute to EBNA-1 binding and that considerable flexibility could be tolerated at positions 1 and 2. Positions 3 through 8 of the recognition site had the most stringent requirements, with transversions at these positions either reducing or eliminating binding. The relative spacing of the halves of the palindrome was also critical, since the addition or removal of 2 base pairs at the center of the sequence abolished binding. Similar results were obtained when a partially purified preparation of intact Raji EBNA-1 was substituted for the 28K EBNA-1, and the results were further supported by methylation interference studies which indicated contact points between EBNA-1 and the guanine residues at positions -8, -7, and +3 of the binding site. The three naturally occurring EBNA-1-binding loci have previously been shown to differ in their relative affinities for EBNA-1. The present study indicates that the sequence variations occurring within the family of repeats would not affect binding affinity, whereas certain base substitutions within the Q and dyad symmetry sites would be predicted to contribute to the observed lower affinities of these sites. An apparent Kd of 1.5 x 10(-11) M for binding of 28K EBNA-1 to a consensus recognition site was calculated from Scatchard analysis.
Angiosarcomas associated with bone infarcts.

Objective. A bone infarct may occasionally dedifferentiate to osteogenic sarcoma, fibrosarcoma or malignant fibrous histiocytoma. However, the association of an angiosarcoma with a bone infarct is extremely rare. Such an association is presented in three patients. Their clinical course is compared with that of patients with bone infarcts associated with other sarcomas. Design and patients. The three patients were men with a mean age of 43 years. Cases 1 and 3 presented with a pathological fracture at the site of the angiosarcoma. Plain radiography was done in the three patients, computed tomography (CT) was performed in cases 1 and 3 and magnetic resonance imaging (MRI) in case 3. The femur was the site of the three tumors: midshaft in cases 1 and 3 and distal shaft in case 2. On the basis of the radiographic findings, and clinical examination, an open biopsy was performed for the three men, which confirmed the diagnosis of a high-grade angiosarcoma associated with a bone infarct. Results. Case 1 was treated with high-above knee amputation and is still alive after 18 months from the time of operation. Segmental resection of the distal femur with adjuvant chemotherapy and local irradiation was the treatment for case 2, who is still alive with no tumor recurrence on metastatic disease 3 years from the operation. Intramedullary rodding was done for case 3 who died 6 months later. Conclusion. The association of an angiosarcoma with a bone infarct has been established in only five cases. Although the number of such associations is small, it seems that such an association may be prognostically more or less the same as in those cases in which a bone infarct is associated with either osteosarcoma, fibrosarcoma or malignant fibrous histiocytoma, where the survival rate is unfavorable. A cause-and-effect relationship may exist between a bone infarct and subsequent development of a bone sarcoma.
Seafloor geology of the Monterey Bay area continental shelf

Abstract Acoustic swath-mapping of the greater Monterey Bay area continental shelf from Point Ano Nuevo to Point Sur reveals complex patterns of rock outcrops on the shelf, and coarse-sand bodies that occur in distinct depressions on the inner and mid-shelves. Most of the rock outcrops are erosional cuestas of dipping Tertiary rocks that make up the bedrock of the surrounding lands. A mid-shelf mud belt of Holocene sediment buries the Tertiary rocks in a continuous, 6-km-wide zone on the northern Monterey Bay shelf. Rock exposures occur on the inner shelf, near tectonically uplifting highlands, and on the outer shelf, beyond the reach of the mud depositing on the mid-shelf since the Holocene sea-level rise. The sediment-starved shelf off the Monterey Peninsula and south to Point Sur has a very thin cover of Holocene sediment, and bedrock outcrops occur across the whole shelf, with Salinian granite outcrops surrounding the Monterey Peninsula. Coarse-sand deposits occur both bounded within low-relief rippled scour depressions, and in broad sheets in areas like the Sur Platform where fine sediment sources are limited. The greatest concentrations of coarse-sand deposits occur on the southern Monterey Bay shelf and the Sur shelf.
A Game for the Early and Rapid Assimilation of Organic Nomenclature

A method for fast, engaging, and effective coverage of systematic organic nomenclature is described. A one-day, highly structured overview of relevant IUPAC nomenclature is followed by a team-based competitive game, allowing the assimilation of almost two full semesters of nomenclature in just two days. The naming skill is well retained by students and can subsequently be reinforced in lectures and problem sets with minimal use of time.
Endocytosis of cell-cell junctions and spontaneous cell disaggregation in a cultured human ovarian adenocarcinoma (COLO 316)

Abstract Although cultured COLO 316 human ovarian adenocarcinoma cells are joined by extensive tight junctions and numerous desmosomes in confluent monolayers, viable cells may be spontaneously released into the nutrient medium. Intracytoplasmic vesicles containing tight junctional and desmosomal elements were identified in freeze-fracture and thin section preparations of the released cells and some vesicles exhibited structural signs of degradation. Possible mechanisms for tight junctional and desmosomal interiorization and the possible relationship between junctional interiorization and certain malignant behaviors are discussed.