cssi/SciSciGPT-SciSciNet huggingface dataset:
Debundling accountability: prosecutor and public defender elections in Florida

I explore the impact of public defender and prosecutor elections using caseload data from Florida. While most states within the U.S. use popular elections to select and retain prosecutors, public defenders are typically appointed positions. Florida is novel in that for both positions popular, partisan elections are used to select the office's leader. I first document important distortions in pre-trial case handling. A public defender re-election is associated with an increase in the proportion of cases resolved via plea bargaining, while prosecutor re-elections are associated with less plea bargaining. At the trial phase, I present evidence that public defender re-elections are associated with a decrease in the proportion of jury trials that result in a conviction, while a prosecutor re-election coincides with an increase in the conviction rate. The results are consistent with voters holding both of these elected officials accountable for doing their job. Public defenders are able to obtain plea bargains at a higher rate and secure acquittals for their clients when up for re-election. Prosecutors do not plea bargain as much and win at trial when up for re-election.
Simulation-Based Analysis of COVID-19 Spread Through Classroom Transmission on a University Campus

Airborne transmission is now believed to be the primary way that COVID-19 spreads. We study the airborne transmission risk associated with holding in-person classes on university campuses. We utilize a model for airborne transmission risk in an enclosed room that considers the air change rate for the room, mask efficiency, initial infection probability of the occupants, and also the activity level of the occupants. We introduce, and use for our evaluations, a metric $R_0^{eff}$ that represents the ratio of new infections that occur over a week due to classroom interactions to the number of infected individuals at the beginning of the week. This can be seen as a surrogate for the well-known R 0 reproductive number metric, but limited in scope to classroom interactions and calculated on a weekly basis. The simulations take into account the possibility of repeated in-classroom interactions between students throughout the week. We presented model predictions were generated using Fall 2019 and Fall 2020 course registration data at a large US university, allowing us to evaluate the difference in transmission risk between in-person and hybrid programs. We quantify the impact of parameters such as reduced occupancy levels and mask efficacy. Our simulations indicate that universal mask usage results in an approximately 3.6× reduction in new infections through classroom interactions. Moving 90% of the classes online leads to about 18× reduction in new cases. Reducing class occupancy to 20%, by having hybrid classes, results in an approximately 2.15 − 2.3× further reduction in new infections.
Causal Indicators for Assessing the Truthfulness of Child Speech in Forensic Interviews.

Abstract When interviewing a child who may have witnessed a crime, the interviewer must ask carefully directed questions in order to elicit a truthful statement from the child. The presented work uses Granger causal analysis to examine and represent child–interviewer interaction dynamics over such an interview. Our work demonstrates that Granger Causal analysis of psycholinguistic and acoustic signals from speech yields significant predictors of whether a child is telling the truth, as well as whether a child will disclose witnessing a transgression later in the interview. By incorporating cross-modal Granger causal features extracted from audio and transcripts of forensic interviews, we are able to substantially outperform conventional deception detection methods and a number of simulated baselines. Our results suggest that a child’s use of concreteness and imageability in their language are strong psycholinguistic indicators of truth-telling and that the coordination of child and interviewer speech signals is much more informative than the specific language used throughout the interview.
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning

Off-policy policy evaluation (OPE) is the problem of estimating the online performance of a policy using only pre-collected historical data generated by another policy. Given the increasing interest in deploying learning-based methods for safety-critical applications, many recent OPE methods have recently been proposed. Due to disparate experimental conditions from recent literature, the relative performance of current OPE methods is not well understood. In this work, we present the first comprehensive empirical analysis of a broad suite of OPE methods. Based on thousands of experiments and detailed empirical analyses, we offer a summarized set of guidelines for effectively using OPE in practice, and suggest directions for future research.
SeReMAS: Self-Resilient Mobile Autonomous Systems Through Predictive Edge Computing

Edge computing enables Mobile Autonomous Systems (MASs) to execute continuous streams of heavy-duty mission-critical processing tasks, such as real-time obstacle detection and navigation. However, in practical applications, erratic patterns in channel quality, network load, and edge server load can interrupt the task flow’s execution, which necessarily leads to severe disruption of the system’s key operations. Existing work has mostly tackled the problem with reactive approaches, which cannot guarantee task-level reliability. Conversely, in this paper we focus on learning-based predictive edge computing to achieve self-resilient task offloading. By conducting a preliminary experimental evaluation, we show that there is no dominant feature that can predict the edge-MAS system reliability, which calls for an ensemble and selection of weaker features. To tackle the complexity of the problem, we propose SeReMAS, a data-driven optimization framework. We first mathematically formulate a Redundant Task Offloading Problem (RTOP), where a MAS may connect to multiple edge servers for redundancy, and needs to select which server(s) to transmit its computing tasks in order to maximize the probability of task execution while minimizing channel and edge resource utilization. We then create a predictor based on Deep Reinforcement Learning (DRL), which produces the optimum task assignment based on application-, network- and telemetry-based features. We prototype SeReMAS on a testbed composed by a Tarot650 quadcopter drone, mounting a PixHawk flight controller, a Jetson Nano board, and three 802.11n WiFi interfaces. We extensively evaluate SeReMAS by considering an application where one drone offloads high-resolution images for real-time analysis to three edge servers on the ground. Experimental results show that SeReMAS improves the task execution probability by 17% with respect to existing reactive-based approaches. To allow full reproducibility of results, we share the dataset and code with the research community.
A Compression-Compilation Framework for On-mobile Real-time BERT Applications.

Transformer-based deep learning models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model to meet both resource and real-time specifications of mobile devices. Our framework applies a compiler-aware neural architecture optimization method (CANAO), which can generate the optimal compressed model that balances both accuracy and latency. We are able to achieve up to 7.8x speedup compared with TensorFlow-Lite with only minor accuracy loss. We present two types of BERT applications on mobile devices: Question Answering (QA) and Text Generation. Both can be executed in real-time with latency as low as 45ms. Videos for demonstrating the framework can be found on this https URL
Tensors in computations

The notion of a tensor captures three great ideas: equivariance, multilinearity, separability. But trying to be three things at once makes the notion difficult to understand. We will explain tensors in an accessible and elementary way through the lens of linear algebra and numerical linear algebra, elucidated with examples from computational and applied mathematics.
Pharmacokinetic Investigation of Commercially Available Edible Marijuana Products in Humans: Potential Influence of Body Composition and Influence on Glucose Control

The purpose of the study was to describe and compare the pharmacokinetics of five commercial edible marijuana products, determine the influence of body composition on pharmacokinetics, and, in light of epidemiology suggesting marijuana may offer diabetes protection, explore the influence of edible marijuana on glucose tolerance. Seven regular users of marijuana self-administered five edible products in a randomized crossover design; each product contained 10 mg of delta-9-tetrahydrocannabinol (THC). Thirty minutes following marijuana ingestion, participants imbibed a 75 g glucose beverage. Time-to-peak plasma THC concentration ranged between 35 and 90 min; maximal plasma THC concentration (Cmax) ranged between 3.2 and 5.5 ng/mL. Differences between products in plasma THC concentration during the first 20-30 min were detected (p = 0.019). Relations were identified between body composition and pharmacokinetic parameters for some products; however, none of these body composition characteristics were consistently related to pharmacokinetics across all five of the products. Edible marijuana had no effect on oral glucose tolerance compared with a marijuana-free control (Matsuda Index; p > 0.395). Commercially available edible marijuana products evoke different plasma THC concentrations shortly after ingestion, but do not appear to influence acute glucose regulation. These data may allow recreational marijuana users to make informed decisions pertaining to rates of edible marijuana ingestion and avoid overdose.
On the degeneracy of spin ice graphs, and its estimate via the Bethe permanent

The concept of spin ice can be extended to a general graph. We study the degeneracy of spin ice graph on arbitrary interaction structures via graph theory. We map spin ice graphs to the Ising model...
Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach

In order to create user-centric and personalized privacy management tools, the underlying models must account for individual users’ privacy expectations, preferences, and their ability to control their information sharing activities. Existing studies of users’ privacy behavior modeling attempt to frame the problem from a request’s perspective, which lack the crucial involvement of the information owner, resulting in limited or no control of policy management. Moreover, very few of them take into the consideration the aspect of correctness, explainability, usability, and acceptance of the methodologies for each user of the system. In this paper, we present a methodology to formally model, validate, and verify personalized privacy disclosure behavior based on the analysis of the user’s situational decision-making process. We use a model checking tool named UPPAAL to represent users’ self-reported privacy disclosure behavior by an extended form of finite state automata (FSA), and perform reachability analysis for the verification of privacy properties through computation tree logic (CTL) formulas. We also describe the practical use cases of the methodology depicting the potential of formal technique towards the design and development of user-centric behavioral modeling. This paper, through extensive amounts of experimental outcomes, contributes several insights to the area of formal methods and user-tailored privacy behavior modeling.
Posterior consistency of semi-supervised regression on graphs

Graph-based semi-supervised regression (SSR) involves estimating the value of a function on a weighted graph from its values (labels) on a small subset of the vertices; it can be formulated as a Bayesian inverse problem. This paper is concerned with the consistency of SSR in the context of classification, in the setting where the labels have small noise and the underlying graph weighting is consistent with well-clustered vertices. We present a Bayesian formulation of SSR in which the weighted graph defines a Gaussian prior, using a graph Laplacian, and the labeled data defines a likelihood. We analyze the rate of contraction of the posterior measure around the ground truth in terms of parameters that quantify the small label error and inherent clustering in the graph. We obtain bounds on the rates of contraction and illustrate their sharpness through numerical experiments. The analysis also gives insight into the choice of hyperparameters that enter the definition of the prior.
reciprocalspaceship: a Python library for crystallographic data analysis.

Crystallography uses the diffraction of X-rays, electrons or neutrons by crystals to provide invaluable data on the atomic structure of matter, from single atoms to ribosomes. Much of crystallography's success is due to the software packages developed to enable automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges – many existing programs are closed source, sparsely documented, or challenging to integrate with modern libraries for scientific computing and machine learning. Described here is reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely used pandas library with built-in methods for handling space groups, unit cells and symmetry-based operations. As is illustrated, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development and release of new methods.
Communicating Inferred Goals With Passive Augmented Reality and Active Haptic Feedback

Robots learn as they interact with humans. Consider a human teleoperating an assistive robot arm: as the human guides and corrects the arm's motion, the robot gathers information about the human's desired task. But how does the human know what their robot has inferred? Today's approaches often focus on conveying intent: for instance, using legible motions or gestures to indicate what the robot is planning. However, closing the loop on robot inference requires more than just revealing the robot's current policy: the robot should also display the alternatives it thinks are likely, and prompt the human teacher when additional guidance is necessary. In this letter we propose a multimodal approach for communicating robot inference that combines both passive and active feedback. Specifically, we leverage information-rich augmented reality to passively visualize what the robot has inferred, and attention-grabbing haptic wristbands to actively prompt and direct the human's teaching. We apply our system to shared autonomy tasks where the robot must infer the human's goal in real-time. Within this context, we integrate passive and active modalities into a single algorithmic framework that determines when and which type of feedback to provide. Combining both passive and active feedback experimentally outperforms single modality baselines; during an in-person user study, we demonstrate that our integrated approach increases how efficiently humans teach the robot while simultaneously decreasing the amount of time humans spend interacting with the robot. Videos here: https://youtu.be/swq_u4iIP-g
Hardness results for Multimarginal Optimal Transport problems

Abstract Multimarginal Optimal Transport ( MOT ) is the problem of linear programming over joint probability distributions with fixed marginals. A key issue in many applications is the complexity of solving MOT : the linear program has exponential size in the number of marginals k and their support sizes n . A recent line of work has shown that MOT is poly ( n , k ) -time solvable for certain families of costs that have poly ( n , k ) -size implicit representations. However, it is unclear what further families of costs this line of algorithmic research can encompass. In order to understand these fundamental limitations, this paper initiates the study of intractability results for MOT . Our main technical contribution is developing a toolkit for proving NP -hardness and inapproximability results for MOT problems. This toolkit reduces proving intractability of MOT problems to proving intractability of more amenable discrete optimization problems. We demonstrate this toolkit by using it to establish the intractability of a number of MOT problems studied in the literature that have resisted previous algorithmic efforts. For instance, we provide evidence that repulsive costs make MOT intractable by showing that several such problems of interest are NP -hard to solve—even approximately.
Network Pruning via Annealing and Direct Sparsity Control

Artificial neural networks (ANNs) especially deep convolutional neural networks are very popular these days and have been proved to successfully offer quite reliable solutions to many vision problems. However, the use of deep neural networks is widely impeded by their intensive computational and memory cost. In this paper, we propose a novel efficient network pruning framework that is suitable for both non-structured and structured channel-level pruning. Our proposed method tightens a sparsity constraint by gradually removing network parameters or filter channels based on a criterion and a schedule. The attractive fact that the network size keeps dropping throughout the iterations makes it suitable for the pruning of any untrained or pre-trained network. Because our method uses a $L_{0}$ constraint instead of the $L_{1}$ penalty, it does not introduce any bias in the training parameters or filter channels. Furthermore, the $L_{0}$ constraint makes it easy to directly specify the desired sparsity level during the network pruning process. Finally, experimental validation on extensive synthetic and real vision datasets show that the proposed method obtains better or competitive performance compared to other states of art network pruning methods.
Enriching Load Data Using Micro-PMUs and Smart Meters

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high- and low-resolution data sources, i.e., micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. We have verified the proposed approach using real high- and low-resolution load data.
RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation

Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when targets are of irregular shape or difficult to distinguish from background or neighboring objects. Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management. However, tree crowns in remote sensing imagery are often difficult to label and annotate due to irregular shape, overlapping canopies, shadowing, and indistinct edges. There are also multiple approaches to annotation in this field (e.g., rectangular boxes vs. convex polygons) that further contribute to annotation imprecision. However, current evaluation methods do not account for this uncertainty in annotations, and quantitative metrics for evaluation can vary across multiple annotators. In this article, we address these limitations by developing an adaptation of the Rand index (RI) for weakly labeled crown delineation that we call RandCrowns (RC). Our new RC evaluation metric provides a method to appropriately evaluate delineated tree crowns while taking into account imprecision in the ground-truth delineations. The RC metric reformulates the RI by adjusting the areas over which each term of the index is computed to account for uncertain and imprecise object delineation labels. Quantitative comparisons to the commonly used intersection over union method show a decrease in the variance generated by differences among multiple annotators. Combined with qualitative examples, our results suggest that the RC metric is more robust for scoring target delineations in the presence of uncertainty and imprecision in annotations that are inherent to tree crown delineation.
Towards a Compulsory Computing Curriculum at Primary and Lower-Secondary Schools: The Case of Czechia

A new national curriculum in informatics for primary and lower secondary education has been in force in the Czech Republic since September 2021. This curriculum introduces a compulsory subject, which systematically focuses on computing from the age of 9. This paper shows the path we have taken, the visions and principles on which the reform has been built, and the milestones that have lined the way from a user-led approach to computers to development of computational thinking and understanding the world of computers as a major educational objective.
The Pareto Frontier of model selection for general Contextual Bandits

Recent progress in model selection raises the question of the fundamental limits of these techniques. Under specific scrutiny has been model selection for general contextual bandits with nested policy classes, resulting in a COLT2020 open problem. It asks whether it is possible to obtain simultaneously the optimal single algorithm guarantees over all policies in a nested sequence of policy classes, or if otherwise this is possible for a trade-off $\alpha\in[\frac{1}{2},1)$ between complexity term and time: $\ln(|\Pi_m|)^{1-\alpha}T^\alpha$. We give a disappointing answer to this question. Even in the purely stochastic regime, the desired results are unobtainable. We present a Pareto frontier of up to logarithmic factors matching upper and lower bounds, thereby proving that an increase in the complexity term $\ln(|\Pi_m|)$ independent of $T$ is unavoidable for general policy classes. As a side result, we also resolve a COLT2016 open problem concerning second-order bounds in full-information games.
Quantum Gravity Microstates from Fredholm Determinants.

Various two dimensional quantum gravity theories of Jackiw-Teitelboim (JT) form have descriptions as random matrix models. Such models, treated nonperturbatively, can give an explicit and tractable description of the underlying ``microstate'' degrees of freedom, which play a prominent role in regimes where the smooth geometrical picture of the physics is inadequate. This is shown using a natural tool, a Fredholm determinant $\mathrm{det}(\mathbf{1}\ensuremath{-}\mathbf{K})$, which can be defined explicitly for a wide variety of JT gravity theories. To illustrate the methods, the statistics of the first several energy levels of a nonperturbative definition of JT gravity are constructed explicitly using numerical methods, and the full quenched free energy ${F}_{Q}(T)$ of the system is computed for the first time. These results are also of relevance to quantum properties of black holes in higher dimensions.
Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs.

Abstract Background Chest radiographs (CXR) are frequently used as a screening tool for patients with suspected COVID-19 infection pending reverse transcriptase polymerase chain reaction (RT-PCR) results, despite recommendations against this. We evaluated radiologist performance for COVID-19 diagnosis on CXR at the time of patient presentation in the Emergency Department (ED). Materials and methods We extracted RT-PCR results, clinical history, and CXRs of all patients from a single institution between March and June 2020. 984 RT-PCR positive and 1043 RT-PCR negative radiographs were reviewed by 10 emergency radiologists from 4 academic centers. 100 cases were read by all radiologists and 1927 cases by 2 radiologists. Each radiologist chose the single best label per case: Normal, COVID-19, Other – Infectious, Other – Noninfectious, Non-diagnostic, and Endotracheal Tube. Cases labeled with endotracheal tube (246) or non-diagnostic (54) were excluded. Remaining cases were analyzed for label distribution, clinical history, and inter-reader agreement. Results 1727 radiographs (732 RT-PCR positive, 995 RT-PCR negative) were included from 1594 patients (51.2% male, 48.8% female, age 59 ± 19 years). For 89 cases read by all readers, there was poor agreement for RT-PCR positive (Fleiss Score 0.36) and negative (Fleiss Score 0.46) exams. Agreement between two readers on 1638 cases was 54.2% (373/688) for RT-PCR positive cases and 71.4% (679/950) for negative cases. Agreement was highest for RT-PCR negative cases labeled as Normal (50.4%, n = 479). Reader performance did not improve with clinical history or time between CXR and RT-PCR result. Conclusion At the time of presentation to the emergency department, emergency radiologist performance is non-specific for diagnosing COVID-19.
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data

Unsupervised Data Augmentation (UDA) is a semisupervised technique that applies a consistency loss to penalize differences between a model’s predictions on (a) observed (unlabeled) examples; and (b) corresponding ‘noised’ examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over which design decisions are necessary and how to extend the method to sequence labeling tasks. In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP. Notably, although prior work has emphasized the use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these more complex perturbation models. Furthermore, we find that applying UDA’s consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting. In short, UDA need not be unsupervised to realize much of its noted benefits, and does not require complex data augmentation to be effective.
Inverse-Weighted Survival Games

Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other criteria, such as binary classification losses at a chosen set of time horizons, e.g. Brier score (BS) and Bernoulli log likelihood (BLL). Models trained with maximum likelihood may have poor BS or BLL since maximum likelihood does not directly optimize these criteria. Directly optimizing criteria like BS requires inverse-weighting by the censoring distribution, estimation of which itself also requires inverse-weighted by the failure distribution. But neither are known. To resolve this dilemma, we introduce Inverse-Weighted Survival Games to train both failure and censoring models with respect to criteria such as BS or BLL. In these games, objectives for each model are built from re-weighted estimates featuring the other model, where the re-weighting model is held fixed during training. When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point. This means models in the game do not leave the correct distributions once reached. We construct one case where this stationary point is unique. We show that these games optimize BS on simulations and then apply these principles on real world cancer and critically-ill patient data.
Planck Spectroscopy

We developed and experimentally demonstrated a minimalistic spectroscopic technique that does not require wavelength-selective components such as prisms, gratings, or interferometers—instead using the temperature and wavelength dependence of Planck’s law of thermal emission.
Batch effects in population genomic studies with low-coverage whole genome sequencing data: causes, detection, and mitigation.

Over the past few decades, there has been an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining datasets to achieve unprecedented sample sizes, spatial coverage, or temporal replication in population genomic studies. However, a common concern is that non-biological differences between datasets may generate patterns of variation in the data that can confound real biological patterns, a problem known as batch effects. In this paper, we compare two batches of low-coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a "batch-effect-naive" bioinformatic pipeline, batch effects systematically biased our genetic diversity estimates, population structure inference, and selection scans. We then demonstrate that these batch effects resulted from multiple technical differences between our datasets, including the sequencing chemistry (four-channel vs. two-channel), sequencing run, read type (single end vs. paired end), read length (125bp vs. 150bp), DNA degradation level (degraded vs. well preserved), and sequencing depth (0.8x vs. 0.3x on average). Lastly, we illustrate that a set of simple bioinformatic strategies (such as different read trimming and SNP filtering), can be used to detect batch effects in our data and substantially mitigate their impact. We conclude that combining datasets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.
Engineered Allosteric Activation of Kinases in Living Cells

Studies of cellular and tissue dynamics benefit greatly from tools that can control protein activity with specificity and precise timing in living systems. Here, we describe a new approach to confer allosteric regulation specifically on the catalytic activity of kinases. A highly conserved portion of the kinase catalytic domain is modified with a small protein insert that inactivates the catalytic activity of the kinase, but does not affect other protein interactions. Catalytic activity is restored upon addition of rapamycin or its non-immunosuppressive analogs. We demonstrate the approach by specifically activating focal adhesion kinase (FAK) within minutes in living cells, thereby demonstrating a novel role for FAK in regulation of membrane dynamics. Molecular modeling and mutagenesis indicate that the protein insert reduces activity by increasing the flexibility of the catalytic domain. Drug binding restores activity by increasing rigidity of a specific region in the kinase that is critical for function. We further demonstrate the wide applicability of our approach by successful regulation of Src and p38 kinases in living cells.
Quantum Algorithms for Open Lattice Field Theory

Certain aspects of some unitary quantum systems are well-described by evolution via a non-Hermitian effective Hamiltonian, as in the Wigner-Weisskopf theory for spontaneous decay. Conversely, any non-Hermitian Hamiltonian evolution can be accommodated in a corresponding unitary system + environment model via a generalization of Wigner-Weisskopf theory. This demonstrates the physical relevance of novel features such as exceptional points in quantum dynamics, and opens up avenues for studying many body systems in the complex plane of coupling constants. In the case of lattice field theory, sparsity lends these channels the promise of efficient simulation on standardized quantum hardware. We thus consider quantum operations that correspond to Suzuki-Lee-Trotter approximation of lattice field theories undergoing non-Hermitian time evolution, with potential applicability to studies of spin or gauge models at finite chemical potential, with topological terms, to quantum phase transitions - a range of models with sign problems. We develop non-Hermitian quantum circuits and explore their promise on a benchmark, the quantum one-dimensional Ising model with complex longitudinal magnetic field, showing that observables can probe the Lee-Yang edge singularity. The development of attractors past critical points in the space of complex couplings indicates a potential for study on near-term noisy hardware.
Nuclear magnetic resonance in study of active sites of pyridoxal-dependent enzymes.

Publisher Summary This chapter examines the active sites of pyridoxal-dependent enzymes using nuclear magnetic resonance (NMR). NMR measurements on the 31 P of the coenzyme have been extended to bound coenzyme analogs. Phosphate ions, which bind tightly in the coenzyme site and act as weakly bound inhibitors in the substrate site, can also be observed using NMR. The isomer 31 P accounts for 100% of the phosphorus present in enzymes but the sensitivity is low. A large number of 31 P NMR measurements have been made on various aspartate aminotransferases and other pyridoxal 5'-phosphate (PLP)-dependent enzymes. The 19 F nucleus is useful for NMR spectroscopy because of its high sensitivity and the small van der Waals radius of the atom. The latter allows for its incorporation in place of hydrogen in many places in substrates, inhibitors, coenzymes, and even in protein side chains. The high sensitivity allows use of lower concentrations than are needed for some other nuclei.
Purchasing or Borrowing : Making Interlibrary Loan Decisions That Enhance Patron Satisfaction

Abstract The Interlibrary Loan Department is authorized to make immediate purchasing decisions for inexpensive items to speed delivery of requested materials to patrons and to enhance patron satisfaction. Criteria for purchasing, rather than borrowing materials for patrons through interlibrary loan are outlined. The practices and procedures of several other institutions about buying rather than borrowing in the interlibrary loan environment are reviewed. Copyright guidelines are another factor that affects decisions about borrowing or purchasing articles.
Compressive imaging of broadband single-photon sources with superconducting detectors

We experimentally characterize the spatio-spectral properties of photon pairs generated by spontaneous parametric down-conversion in a Type-0 PPKTP waveguide using compressive imaging techniques combined superconducting single photon detectors.
Experimental characterization of the reflectance of 60-degree waveguide bends in photonic crystal waveguides

Photonic crystal waveguide bends were fabricated and characterized. Good agreement was obtained between the numerical predictions of the transmission spectra and the experimental observations.
Fluorescence lifetime of a molecule near a diffuse surface: Coupling to surface polaritons

For surfaces in which the electron density does not drop abruptly from its bulk value to zero, the spectrum of surface polaritons includes, besides the usual plasmon branch, additional branches, referred to as higher multipoles. The complete spectrum of polaritons is given for a simple two-step model of a surface. The coupling of these modes to an excited fluorescent molecule located near the surface is discussed. The change in the width of the molecular transition, due to decay via the surface polariton channels, should reflect the structure in the density of surface polariton states.
Enhanced luminescence from AlxGa1-xN/AlyGa1-yN quantum wells grown by gas source molecular beam epitaxy with ammonia

We report the structural and optical properties of Al x Ga 1-x N/Al y Ga 1-y N quantum wells (QWs) structures grown by gas source molecular beam epitaxy with ammonia on sapphire (0001) substrates. QWs structures consist of five pairs of Al y Ga 1-y N, 0.3 x Ga 1-x N, 0.55<x<1, barriers (nominally 5 nm thick). All the structures were completed with a 10 nm thick cap layer of AlN. We observed a significant enhancement in the cathodoluminescence intensities and longer photoluminescence lifetimes for QW structures grown in the 3D mode, as confirmed by spotty reflection high energy electron diffraction patterns. These effects are attributed to the formation of AlGaN quantum dots in the well materials.
PLASMIN(OGEN) CARBOHYDRATE CHAINS MEDIATE BINDING TO DIPEPTIDYL PEPTIDASE IV (CD 26) IN RHEUMATOID ARTHRITIS HUMAN SYNOVIAL FIBROBLASTS

Summary Objective: To assess the reactivity between dipeptidyl peptidase IV (DPP IV) and the α2,3-linked sialic acid of the plasminogen (Pg)Thr 345 O-linked carbohydrate chain as the mechanism enabling plasmin (Pm) to induce intracellular Ca 2+ via DPP IV on rheumatoid synovial fibroblasts, and identify the DPP IV region responsible for this interaction. Methods: Cytosolic Ca 2+ mobilization in rheumatoid synovial fibroblasts was assayed by Digital Imaging Microscopy (DIM). DPP IV was purified by affinity chromatography on an immobilized polysaccharide structurally analogous to the last two residues of the Pg 2 carbohydrate chain. Binding of Pg to DPP IV and identification of the DPP IV reactive site were determined by an enzyme-linked immunosorbent assay (ELISA) and inhibition of Pm-induced intracellular Ca 2+ mobilization in these cells by peptides comprising three regions of DPP IV primary structure. Results: Cytosolic Ca 2+ mobilization induced by Pm on rheumatoid synovial fibroblasts is inhibited by L-lactose, a sugar that interferes with sialic acid binding to lectins. Asialo Pg which binds and can be converted into Pm on the surface of these cells is not able to induce intracellular Ca 2+ mobilization. A peptide comprising the DPP IV primary sequence L 313 QWLRRI inhibits both Pg binding to DPP IV and Pm-induced intracellular Ca 2+ mobilization on these cells. Conclusion: The intracellular Ca 2+ mobilization resulting from the reaction between Pg/Pm and DPP IV is mediated by a lectin-like region in DPP IV. This region is structurally analogous to the sequence (QxW) 3 , previously identified as a carbohydrate-binding region in several lectin families.
The Coulomb correction term in proton absorbing optical potentials

Abstract The results of phenomenological analyses of nucleon elastic scattering by 40Ca are analyzed, and the Coulomb correction term of the absorbing part of the optical model potential is empirically extracted. The results show general agreement with the theoretical calculations of Jeukenne, Lejeune and Mahaux.
SOLVENT EXTRACTION BY DIALKYL-SUBSTITUTED DIPHOSPHONIC ACIDS IN A DEPOLYMERIZING DILUENT. II. Fe(III) AND ACTINIDE IONS

In this work, the extraction of Am(III), U(VI), Th(IV) and Fe(III) by P,P′-di(2-ethylhexyl) methylene- (H2DEH[MDP]), ethylene- (H2DEH[EDP]), and butylene- (H2DEH[BuDP]) diphosphonic acids dissolved in the monomerizing diluent 1-decanol has been investigated. A comparison of the acid dependencies with those observed in o-xylene has revealed a number of novel features which have been ascribed to extraction of metal nitrates through a solvation mechanism made possible by facile transfer of nitrate ions into the 1-decanol phase. The use of 1-decanol as the diluent gives rise to strong suppression of metal extraction, which increases along the series Fe(III)<U(VI)<Am(III)<Th(IV) and H2DEH[MDP]<H2DEH[EDP]≤H2DEH[BuDP]. The extractant dependencies of U(VI) and Th(IV) have slopes equal to 2 with all extractants. Those for Am(III) and Fe(III) exhibit variable slopes with limiting values of 1 at the lowest and 3 at the highest extractant concentrations. From the combined information provided by the acid and extracta...
Thermobaculum terrenum gen. nov., sp. nov.: a non-phototrophic gram-positive thermophile representing an environmental clone group related to the Chloroflexi (green non-sulfur bacteria) and Thermomicrobia

A novel bacterium was cultivated from an extreme thermal soil in Yellowstone National Park, Wyoming, USA, that at the time of sampling had a pH of 3.9 and a temperature range of 65–92 °C. This organism was found to be an obligate aerobic, non-spore-forming rod, and formed pink-colored colonies. Phylogenetic analysis of the 16S rRNA gene sequence placed this organism in a clade composed entirely of environmental clones most closely related to the phyla Chloroflexi and Thermomicrobia. This bacterium stained gram-positive, contained a novel fatty-acid profile, had cell wall muramic acid content similar to that of Bacillus subtilis (significantly greater than Escherichia coli), and failed to display a lipopolysaccharide profile in SDS-polyacrylamide gels that would be indicative of a gram-negative cell wall structure. Ultrastructure examinations with transmission electron microscopy showed a thick cell wall (approximately 34 nm wide) external to a cytoplasmic membrane. The organism was not motile under the culture conditions used, and electron microscopic examination showed no evidence of flagella. Genomic G+C content was 56.4 mol%, and growth was optimal at 67 °C and at a pH of 7.0. This organism was able to grow heterotrophically on various carbon compounds, would use only oxygen as an electron acceptor, and its growth was not affected by light. A new species of a novel genus is proposed, with YNP1T (T=type strain) being Thermobaculum terrenum gen. nov., sp. nov. (16S rDNA gene GenBank accession AF391972). This bacterium has been deposited in the American Type Culture Collection (ATCC BAA-798) and the University of Oregon Culture Collection of Microorganisms from Extreme Environments (CCMEE 7001).
Feature based object decomposition for finite element meshing

Performing a finite element analysis requires overlaying an object with a mesh of varying density based on the expected stress levels within the part. Attempts have been made in the past to automat the finite element meshing procedure. The method presented here is “intelligent” in the sense that it examines the complete part for potential stress gradients and decomposes the part into hexahedral regions according to the geometry gradients in the part. High geometry gradients are regions of high curvature, especially edges. The algorithm segregates high gradient features into isolation volumes. It then continues to decompose each isolation volume dependent on the particular geometry contained in the feature. The result is a set of hexahedral bricks suitable for passing to an automatic meshing routine.
QUICK: a tool for graphical user-interface construction by non-programmers

The last decade has placed the superiority of graphical user interfaces over traditional text-based approaches beyond dispute. In almost all contexts, users have found graphical interfaces easier to learn, faster to use, and less error-prone. However, it has been shown that the creation of powerful graphical interfaces takes up to 80% of the time required to develop an application. In our work, we seek to extend the benefits of graphical interaction to the next layer of computer user—the interface designer. Our work in this area is distinguished from other efforts by two important differences. First, while other efforts focus primarily on the design of “standard” user interfaces, our approach emphasizes the creation of unique and innovative interfaces by supporting, among other things, arbitrary user-designed graphical representations, direct specification of animation, and digitized sound. Second, our goal is to cater to the nonprogrammer. Thus, we address a challenging trade-off: maximizing power and flexibility in an extremely simple environment. We explore the utility of the prototype object-oriented paradigm, a high-level userinterface language, and a direct-manipulation programming environment in this context.
Heterogeneous immunoassays using magnetic beads on a digital microfluidic platform

A digital microfluidic platform for performing heterogeneous sandwich immunoassays based on efficient handling of magnetic beads is presented in this paper. This approach is based on manipulation of discrete droplets of samples and reagents using electrowetting without the need for channels where the droplets are free to move laterally. Droplet-based manipulation of magnetic beads therefore does not suffer from clogging of channels. Immunoassays on a digital microfluidic platform require the following basic operations: bead attraction, bead washing, bead retention, and bead resuspension. Several parameters such as magnetic field strength, pull force, position, and buffer composition were studied for effective bead operations. Dilution-based washing of magnetic beads was demonstrated by immobilizing the magnetic beads using a permanent magnet and splitting the excess supernatant using electrowetting. Almost 100% bead retention was achieved after 7776-fold dilution-based washing of the supernatant. Efficient resuspension of magnetic beads was achieved by transporting a droplet with magnetic beads across five electrodes on the platform and exploiting the flow patterns within the droplet to resuspend the beads. All the magnetic-bead droplet operations were integrated together to generate standard curves for sandwich heterogeneous immunoassays on human insulin and interleukin-6 (IL-6) with a total time to result of 7 min for each assay.
Blowup with Small BV Data in Hyperbolic Conservation Laws

We construct weak solutions of 3×3 conservation laws which blow up in finite time. The system is strictly hyperbolic at every state in the solution, and the data can be chosen to have arbitrarily small total variation. This is thus an example where Glimm's existence theorem fails to apply, and it implies the necessity of uniform hyperbolicity in Glimm's theorem. Because our system is very simple, we can carry out explicit calculations and understand the global geometry of wave curves.
Hard problems for simple logic programs

A number of optimizations have been proposed for Datalog programs involving a single intensional predicate (“single-IDB programs”). Examples include the detection of commutativity and separability ([Naug88],[RSUV89], [Ioan89a]) in linear logic programs, and the detection of ZYT-linearizability ([ZYT88], [RSUV89], [Sara89], [Sara90]) in nonlinear programs. We show that the natural generalizations of the commutativity and ZYT-linearizability problems (respectively, the sequencability and base-case linearizability problems) are undecidable. Our constructions involve the simulation of context-free grammars using single-IDB programs that have a bounded number of initialisation rules. The constructions may be used to show that containment (or equivalence) is undecidable for such programs, even if the programs are linear, or if each program contains a single recursive rule. These results tighten those of [Shmu87] and [Abit89].