cssi/SciSciGPT-SciSciNet huggingface dataset:
The extragalactic nature of H0323 + 022

H 0323 + 22, if galactic, is unprecedented in its radio and X-ray properties. A deep R-band image of the source shows it to be spatially extended, with significant light detectable 10 arcsec from the bright nucleus, thereby implying, in the event that it represents an underlying galaxy, that its distance is in the 0.5-1.5 Gpc range and that its radio, optical, and X-ray luminosities are typical of other BL Lac objects. The G star-like absorption spectrum, however, then remains to be explained. Because the two closest bright optical objects also exhibit G star spectra, it is suggested that the Doxsey et al. (1983) G spectra are due to misidentification of the object. H 0323 + 22 is both extragalactic and probably similar to other X-ray selected BL Lac objects.
Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide

Nitrogen dioxide (NO2) is a primary constituent of traffic-related air pollution and has well-established harmful environmental and human-health impacts. Knowledge of the spatiotemporal distribution of NO2 is critical for exposure and risk assessment. A common approach for assessing air pollution exposure is linear regression involving spatially referenced covariates, known as land-use regression (LUR). We develop a scalable approach for simultaneous variable selection and estimation of LUR models with spatiotemporally correlated errors, by combining a general-Vecchia Gaussian-process approximation with a penalty on the LUR coefficients. In comparison to existing methods using simulated data, our approach resulted in higher model-selection specificity and sensitivity and in better prediction in terms of calibration and sharpness, for a wide range of relevant settings. In our spatiotemporal analysis of daily, US-wide, ground-level NO2 data, our approach was more accurate, and produced a sparser and more interpretable model. Our daily predictions elucidate spatiotemporal patterns of NO2 concentrations across the United States, including significant variations between cities and intra-urban variation. Thus, our predictions will be useful for epidemiological and risk-assessment studies seeking daily, national-scale predictions, and they can be used in acute-outcome health-risk assessments.
Automatic assessment of human gastric motility and emptying from dynamic 3D magnetic resonance imaging.

BACKGROUND Time-sequenced magnetic resonance imaging (MRI) of the stomach is an emerging technique for non-invasive assessment of gastric emptying and motility. However, an automated and systematic image processing pipeline for analyzing dynamic 3D (ie, 4D) gastric MRI data has not been established. This study uses an MRI protocol for imaging the stomach with high spatiotemporal resolution and provides a pipeline for assessing gastric emptying and motility. METHODS Diet contrast-enhanced MRI images were acquired from seventeen healthy humans after they consumed a naturalistic contrast meal. An automated image processing pipeline was developed to correct for respiratory motion, to segment and compartmentalize the lumen-enhanced stomach, to quantify total gastric and compartmental emptying, and to compute and visualize gastric motility on the luminal surface of the stomach. KEY RESULTS The gastric segmentation reached an accuracy of 91.10 ± 0.43% with the Type-I error and Type-II error being 0.11 ± 0.01% and 0.22 ± 0.01%, respectively. Gastric volume decreased 34.64 ± 2.8% over 1 h where the emptying followed a linear-exponential pattern. The gastric motility showed peristaltic patterns with a median = 4 wave fronts (range 3-6) and a mean frequency of 3.09 ± 0.07 cycles per minute. Further, the contractile amplitude was stronger in the antrum than in the corpus (antrum vs. corpus: 5.18 ± 0.24 vs. 3.30 ± 0.16 mm; p < 0.001). CONCLUSIONS & INFERENCES Our analysis pipeline can process dynamic 3D MRI images and produce personalized profiles of gastric motility and emptying. It will facilitate the application of MRI for monitoring gastric dynamics in research and clinical settings.
A multi-channel approach for automatic microseismic event association using RANSAC-based Arrival Time Event Clustering (RATEC)

Abstract In the presence of background noise, arrival times picked from a surface microseismic data set usually include a number of false picks that can lead to uncertainty in location estimation. To eliminate false picks and improve the accuracy of location estimates, we develop an association algorithm termed RANSAC-based Arrival Time Event Clustering (RATEC) that clusters picked arrival times into event groups based on random sampling and fitting moveout curves that approximate hyperbolas. Arrival times far from the fitted hyperbolas are classified as false picks and removed from the data set prior to location estimation. Simulations of synthetic data for a 1-D linear array show that RATEC is robust under different noise conditions and generally applicable to various types of subsurface structures. By generalizing the underlying moveout model, RATEC is extended to the case of a 2-D surface monitoring array. The effectiveness of event location for the 2-D case is demonstrated using a data set collected by the 5200-element dense Long Beach array. The obtained results suggest that RATEC is effective in removing false picks and hence can be used for phase association before location estimates.
Game Changer: Can Modifications to Audit Firm Communication Improve Auditors’ Actions in Response to Heightened Fraud Risk?

Regulators express concern over auditors’ failure to respond to fraud risks. Audit firms communicate the importance of remaining skeptical and alert for fraud, but busy auditors give these messages insufficient attention. Building on psychology theory, we develop an innovative intervention designed to improve audit firm communication by incorporating game-like elements. We expect game-like elements to pique auditors’ interest, deepen their cognitive processing, and enhance their awareness of important fraud concepts, making them more alert for fraud when they return to their work. We experimentally demonstrate that the intervention improves auditors’ awareness of important fraud concepts, and these benefits persist to improve auditors’ fraud detection actions. Importantly, auditors receiving communication that simulates current practice fail to respond to heightened fraud risk, confirming regulators’ concerns. In additional analyses, a model supports our intervention promoting deeper processing of the communication, enabling auditors’ subsequent recognition of heightened fraud risk and effective actions. Thus, our results contribute to theory and practice.
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets

Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users --- not just the majority group --- and they also need to be fair in how they divide exposure among the items. These fairness considerations can partially be met by adding diversity to the rankings, as done in several recent works. However, we show in this paper that user fairness, item fairness and diversity are fundamentally different concepts. In particular, we find that algorithms that consider only one of the three desiderata can fail to satisfy and even harm the other two. To overcome this shortcoming, we present the first ranking algorithm that explicitly enforces all three desiderata. The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally. From its solution, a ranking policy can be derived via a novel Birkhoff-von Neumann decomposition algorithm that optimizes diversity. Beyond the theoretical analysis, we investigate empirically on a new benchmark dataset how effectively the proposed ranking algorithm can control user fairness, item fairness and diversity, as well as the trade-offs between them.
Achievable rate-region for 3—User Classical-Quantum Interference Channel using Structured Codes

We consider the problem of characterizing an inner bound to the capacity region of a 3—user classical-quantum interference channel (3—CQIC). The best known coding scheme for communicating over CQICs is based on unstructured random codes and employs the techniques of message splitting and superposition coding. For classical 3—user interference channels (ICs), it has been proven that coding techniques based on coset codes - codes possessing algebraic closure properties - strictly outperform all coding techniques based on unstructured codes. In this work, we develop analogous techniques based on coset codes for 3to1—CQICs - a subclass of 3—user CQICs. We analyze its performance and derive a new inner bound to the capacity region of 3to1—CQICs that subsume the current known largest and strictly enlarges the same for identified examples.
Turbulent conductivity measurements in a spherical liquid sodium flow.

We report the first measurement of a reduction in the conductivity of liquid sodium due to turbulence in a spherical flow (the $\ensuremath{\beta}$ effect). The sodium is contained in a 0.15 m diameter sphere, typical flow speeds are about 1 m/s, and magnetic Reynold's numbers range from 1 to 8. We find a reduction from the molecular value of the conductivity of about $4%$. Results are in rough agreement with simple predictions from mean-field electrodynamics.
Graph Based Answer Set Programming Solver Systems.

Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm for knowledge representation and solving combinatorial problems. Computing the answer set of an ASP program is NP-hard in general, and researchers have been investing significant effort to speed it up. The majority of current ASP solvers employ SAT solver-like technology to find these answer sets. As a result, justification for why a literal is in the answer set is hard to produce. There are dependency graph based approaches to find answer sets, but due to the representational limitations of dependency graphs, such approaches are limited. This paper proposes a novel dependency graph-based approach for finding answer sets in which conjunction of goals is explicitly represented as a node which allows arbitrary answer set programs to be uniformly represented. Our representation preserves causal relationships allowing for justification for each literal in the answer set to be elegantly found. In this paper, we explore two different approaches based on the graph representation: bottom-up and top-down. The bottom-up approach finds models by assigning truth values along with the topological order, while the top-down approach generates models starting from the constraints.
Neural Plasticity Networks

Neural plasticity is an important functionality of human brain, in which number of neurons and synapses can shrink or expand in response to stimuli throughout the span of life. We model this dynamic learning process as an $L_{0}$ - norm regularized binary optimization problem, in which each unit of a neural network (e.g., weight, neuron or channel, etc.) is attached with a stochastic binary gate, whose parameters determine the level of activity of a unit in the network. At the beginning, only a small portion of binary gates (therefore the corresponding neurons) are activated, while the remaining neurons are in a hibernation mode. As the learning proceeds, some neurons might be activated or deactivated if doing so can be justified by the cost-benefit tradeoff measured by the $L_{0}$ -norm regularized objective. As the training gets mature, the probability of transition between activation and deactivation will diminish until a final hardening stage. We demonstrate that all of these learning dynamics can be modulated by a single parameter $k$ seamlessly. Our neural plasticity network (NPN) can prune or expand a network depending on the initial capacity of network provided by the user; it also unifies dropout (when $k=0$ ), traditional training of DNNs (when $k=\infty$ ) and interpolates between these two. To the best of our knowledge, this is the first learning framework that unifies network sparsification and network expansion in an end-to-end training pipeline. Extensive experiments on synthetic dataset and multiple image classification benchmarks demonstrate the superior performance of NPN. We show that both network sparsification and network expansion can yield compact models of similar architectures, while retaining competitive accuracies of the original networks. 11Our source code is available at https://github.com/leo-yangli/npns.
Continuous real-time detection of quasiparticle trapping in aluminum nanobridge Josephson junctions

Nonequilibrium quasiparticles are ubiquitous in superconducting electronics. These quasiparticles can trap in the internal Andreev bound states of a phase-biased Josephson junction, providing a mechanism for studying their presence and behavior. We characterize a quasiparticle trapping detector device based on a two junction aluminum nanobridge superconducting quantum interference device incorporated into a transmission line resonator. When the device is flux-biased, distinct resonant frequencies develop depending on the trapped quasiparticle number. We demonstrate continuous detection of up to 3 trapped quasiparticles, with detection of a trapped quasiparticle with a signal-to-noise ratio of 27 in 5 μs. We describe initial measurements of quasiparticle behavior and discuss the possible optimization and application of such detector devices.
An Autonomous Driving Framework for Long-Term Decision-Making and Short-Term Trajectory Planning on Frenet Space

In this paper, we present a hierarchical framework for decision-making and planning on highway driving tasks. We utilized intelligent driving models (IDM and MOBIL) to generate long-term decisions based on the traffic situation flowing around the ego. The decisions both maximize ego performance while respecting other vehicles' objectives. Short-term trajectory optimization is performed on the Frenet space to make the calculations invariant to the road's three-dimensional curvatures. A novel obstacle avoidance approach is introduced on the Frenet frame for the moving obstacles. The optimization explores the driving corridors to generate spatiotemporal polynomial trajectories to navigate through the traffic safely and obey the BP commands. The framework also introduces a heuristic supervisor that identifies unexpected situations and recalculates each module in case of a potential emergency. Experiments in CARLA simulation have shown the potential and the scalability of the framework in implementing various driving styles that match human behavior.
Quantized Gromov-Wasserstein

The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to allow for the comparison of probability distributions defined on different metric spaces. Scalable computation of GW distances and associated matchings on graphs and point clouds have recently been made possible by state-of-the-art algorithms such as S-GWL and MREC. Each of these algorithmic breakthroughs relies on decomposing the underlying spaces into parts and performing matchings on these parts, adding recursion as needed. While very successful in practice, theoretical guarantees on such methods are limited. Inspired by recent advances in the theory of quantization for metric measure spaces, we define Quantized Gromov Wasserstein (qGW): a metric that treats parts as fundamental objects and fits into a hierarchy of theoretical upper bounds for the GW problem. This formulation motivates a new algorithm for approximating optimal GW matchings which yields algorithmic speedups and reductions in memory complexity. Consequently, we are able to go beyond outperforming state-of-the-art and apply GW matching at scales that are an order of magnitude larger than in the existing literature, including datasets containing over 1M points.
Doubly transitive lines I: Higman pairs and roux

Abstract We study lines through the origin of finite-dimensional complex vector spaces that enjoy a doubly transitive automorphism group. In doing so, we make fundamental connections with both discrete geometry and algebraic combinatorics. In particular, we show that doubly transitive lines are necessarily optimal packings in complex projective space, and we introduce a fruitful generalization of regular abelian distance-regular antipodal covers of the complete graph.
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

Transformer architecture has emerged to be successful in a number of natural language processing tasks. However, its applications to medical vision remain largely unexplored. In this study, we present UTNet, a simple yet powerful hybrid Transformer architecture that integrates self-attention into a convolutional neural network for enhancing medical image segmentation. UTNet applies self-attention modules in both encoder and decoder for capturing long-range dependency at different scales with minimal overhead. To this end, we propose an efficient self-attention mechanism along with relative position encoding that reduces the complexity of self-attention operation significantly from \(O(n^2)\) to approximate O(n). A new self-attention decoder is also proposed to recover fine-grained details from the skipped connections in the encoder. Our approach addresses the dilemma that Transformer requires huge amounts of data to learn vision inductive bias. Our hybrid layer design allows the initialization of Transformer into convolutional networks without a need of pre-training. We have evaluated UTNet on the multi-label, multi-vendor cardiac magnetic resonance imaging cohort. UTNet demonstrates superior segmentation performance and robustness against the state-of-the-art approaches, holding the promise to generalize well on other medical image segmentations.
On the Barashenkov-Bogdan-Zhanlav solitons and their stability

Abstract The Barashenkov-Bogdan-Zhanlav solitons u ± for the forced NLS/Lugiato-Lefever model on the line are considered. While the instability of u + was established in the original paper, [3], the analogous question for u − was only considered heuristically and numerically. We rigorously analyze the stability of u − in the various regime of the parameters. In particular, we show that u − is spectrally stable for small pump strength h . Moreover, u − remains spectrally stable until a pair of neutral eigenvalues of negative Krein signature hits another pair of eigenvalues, which has emanated from the edge of the continuous spectrum, [1 , 2 , 3] . After the collision, an instability is conjectured and numerically observed in previous works, [3] .
POSITIVITY DETERMINES THE QUANTUM COHOMOLOGY OF GRASSMANNIANS

We prove that if X is a Grassmannian of type A, then the Schubert basis of the (small) quantum cohomology ring QH(X) is the only homogeneous deformation of the Schubert basis of the ordinary cohomology ring of X that multiplies with non-negative structure constants. This implies that the (three point, genus zero) Gromov-Witten invariants of X are uniquely determined by Witten's presentation of QH(X) and the fact that they are non-negative. We conjecture that the same is true for any flag variety X = G/P of simply laced Lie type. For the variety GL(n)/B of complete flags, this conjecture is equivalent to Fomin, Gelfand, and Postnikov's conjecture that the quantum Schubert polynomials of type A are uniquely determined by positivity properties. Our proof for Grassmannians answers a question of Fulton.
Legged Robot State Estimation in Slippery Environments Using Invariant Extended Kalman Filter with Velocity Update

This paper proposes a state estimator for legged robots operating in slippery environments. An Invariant Extended Kalman Filter (InEKF) is implemented to fuse inertial and velocity measurements from a tracking camera and leg kinematic constraints. The misalignment between the camera and the robot-frame is also modeled thus enabling auto-calibration of camera pose. The leg kinematics based velocity measurement is formulated as a right-invariant observation. Nonlinear observability analysis shows that other than the rotation around the gravity vector and the absolute position, all states are observable except for some singular cases. Discrete observability analysis demonstrates that our filter is consistent with the underlying nonlinear system. An online noise parameter tuning method is developed to adapt to the highly time-varying camera measurement noise. The proposed method is experimentally validated on a Cassie bipedal robot walking over slippery terrain. A video for the experiment can be found at https://youtu.be/VIqJL0cUr7s.
Spin-polarized fermions with p-wave interactions

We have created quantum degenerate Fermi gases of $^{6}\mathrm{Li}$ atoms at unprecedented high densities exceeding $1/{\ensuremath{\lambda}\phantom{\rule{-0.5em}{0ex}}\ensuremath{-}}^{3}$ (where $\ensuremath{\lambda}$ is the wavelength of resonant light). In this regime, new optical properties are predicted. Spin-polarized Fermi gases are usually regarded as noninteracting due to the weakness of $p$-wave interactions. Here we study the properties of ``$p$-wave matter,'' where elastic and inelastic $p$-wave collisions determine the dynamics of the system. We characterize thermalization, evaporative cooling, and inelastic two-body and three-body collisions. $P$-wave dipolar relaxation creates a metastable mixture of the lowest and highest hyperfine states.
Improved Regularization and Robustness for Fine-tuning in Neural Networks

A widely used algorithm for transfer learning is fine-tuning, where a pre-trained model is fine-tuned on a target task with a small amount of labeled data. When the capacity of the pre-trained model is much larger than the size of the target data set, fine-tuning is prone to overfitting and "memorizing" the training labels. Hence, an important question is to regularize fine-tuning and ensure its robustness to noise. To address this question, we begin by analyzing the generalization properties of fine-tuning. We present a PAC-Bayes generalization bound that depends on the distance traveled in each layer during fine-tuning and the noise stability of the fine-tuned model. We empirically measure these quantities. Based on the analysis, we propose regularized self-labeling -- the interpolation between regularization and self-labeling methods, including (i) layer-wise regularization to constrain the distance traveled in each layer; (ii) self label-correction and label-reweighting to correct mislabeled data points (that the model is confident) and reweight less confident data points. We validate our approach on an extensive collection of image and text data sets using multiple pre-trained model architectures. Our approach improves baseline methods by 1.76% (on average) for seven image classification tasks and 0.75% for a few-shot classification task. When the target data set includes noisy labels, our approach outperforms baseline methods by 3.56% on average in two noisy settings.
Improved bounds for the sunflower lemma

A sunflower with $r$ petals is a collection of $r$ sets so that the intersection of each pair is equal to the intersection of all of them. Erdős and Rado proved the sunflower lemma: for any fixed $r$, any family of sets of size $w$, with at least about $w^w$ sets, must contain a sunflower with $r$ petals. The famous sunflower conjecture states that the bound on the number of sets can be improved to $c^w$ for some constant $c$. In this paper, we improve the bound to about $(\log w)^w$. In fact, we prove the result for a robust notion of sunflowers, for which the bound we obtain is sharp up to lower order terms.
Surrogate Regret Bounds for Polyhedral Losses

Surrogate risk minimization is an ubiquitous paradigm in supervised machine learning, wherein a target problem is solved by minimizing a surrogate loss on a dataset. Surrogate regret bounds, also called excess risk bounds, are a common tool to prove generalization rates for surrogate risk minimization. While surrogate regret bounds have been developed for certain classes of loss functions, such as proper losses, general results are relatively sparse. We provide two general results. The first gives a linear surrogate regret bound for any polyhedral (piecewise-linear and convex) surrogate, meaning that surrogate generalization rates translate directly to target rates. The second shows that for sufficiently non-polyhedral surrogates, the regret bound is a square root, meaning fast surrogate generalization rates translate to slow rates for the target. Together, these results suggest polyhedral surrogates are optimal in many cases.
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks.

Machine Learning (ML) models are susceptible to evasion attacks. Evasion accuracy is typically assessed using aggregate evasion rate, and it is an open question whether aggregate evasion rate enables feature-level diagnosis on the effect of adversarial perturbations on evasive predictions. In this paper, we introduce a novel framework that harnesses explainable ML methods to guide high-fidelity assessment of ML evasion attacks. Our framework enables explanation-guided correlation analysis between pre-evasion perturbations and post-evasion explanations. Towards systematic assessment of ML evasion attacks, we propose and evaluate a novel suite of model-agnostic metrics for sample-level and dataset-level correlation analysis. Using malware and image classifiers, we conduct comprehensive evaluations across diverse model architectures and complementary feature representations. Our explanation-guided correlation analysis reveals correlation gaps between adversarial samples and the corresponding perturbations performed on them. Using a case study on explanation-guided evasion, we show the broader usage of our methodology for assessing robustness of ML models .
Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization

An autotuning is an approach that explores a search space of possible implementations/configurations of a kernel or an application by selecting and evaluating a subset of implementations/configurations on a target platform and/or use models to identify a high performance implementation/configuration. In this paper, we develop an autotuning framework that leverages Bayesian optimization to explore the parameter space search. We select six of the most complex benchmarks from the application domains of the PolyBench benchmarks (syr2k, 3mm, heat-3d, lu, covariance, and Floyd-Warshall) and apply the newly developed LLVM Clang/Polly loop optimization pragmas to the benchmarks to optimize them. We then use the autotuning framework to optimize the pragma parameters to improve their performance. The experimental results show that our autotuning approach outperforms the other compiling methods to provide the smallest execution time for the benchmarks syr2k, 3mm, heat-3d, lu, and covariance with two large datasets in 200 code evaluations for effectively searching the parameter spaces with up to 170,368 different configurations. We compare four different supervised learning methods within Bayesian optimization and evaluate their effectiveness. We find that the Floyd-Warshall benchmark did not benefit from autotuning because Polly uses heuristics to optimize the benchmark to make it run much slower. To cope with this issue, we provide some compiler option solutions to improve the performance.
Research data infrastructure for high-throughput experimental materials science

Summary The High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov ) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.
Pressure-Induced Insulator–Metal Transition in Two-Dimensional Mott Insulator NiPS3

The pressure-induced insulator to metal transition (IMT) of layered magnetic nickel phosphorous tri-sulfide NiPS3 was studied in-situ under quasi-uniaxial conditions by means of electrical resistance (R) and X-ray diffraction (XRD) measurements. This sluggish transition is shown to occur at 35 GPa. Transport measurements show no evidence of superconductivity to the lowest measured temperature (~ 2 K). The structure results presented here differ from earlier in-situ work that subjected the sample to a different pressure state, suggesting that in NiPS3 the phase stability fields are highly dependent on strain. It is suggested that careful control of the strain is essential when studying the electronic and magnetic properties of layered van der Waals solids.
Millimeter-wave monolithic GaAs IMPATT VCO

A monolithic voltage-controlled oscillator (VCO) has been constructed using a GaAs double-drift Read IMPATT as the active element and a similar diode biased below breakdown as the varactor. The chip produced 120-mW peak power over an electronically controlled tuning range between 47 and 48 GHz. A computer analysis based on characterized circuit parameters has been used to predict the performance of the chip. >
Bacterial keratitis: similar bacterial and clinical outcomes in female versus male New Zealand White rabbits infected with Serratia marcescens.

PURPOSE Females and males respond differently to a number of systemic viral infections. Differences between females and males with respect to the severity of keratitis caused by Gram-negative bacteria such as Serratia marcescens are less well established. METHODS In this study we injected female and male New Zealand White rabbit corneas with a keratitis isolate of S. marcescens and evaluated the eyes after 48 hours for a number of clinical and microbiological parameters. RESULTS No statistical differences in bacterial burden and corneal scores were recorded between female and male rabbits although there was a non-significant trend toward a higher frequency of female rabbits demonstrating hypopyons. CONCLUSIONS This data suggests that for experimental bacterial keratitis studies involving Gram-negative rods, a single sex or mixed group of rabbit is sufficient for evaluating pathology and bacterial burdens. This will reduce the number of animals used for subsequent studies.
Studies on the Pseudouridinuria of Chronic Lymphocytic Leukemia

This paper reports a group of investigations attempting to explain the elevated pseudouridine excretion in the presence of normal uric acid excretion which occurs in chronic lymphocytic leukemia (CLL). Studies with isotopically labeled uric acid and adenine indicated that diversion of body purines from uric acid does not occur. Similarly the chemical analysis of the white cells in CLL did not reveal any excess content of pyrimidines, nor did the turnover of the ribonucleotides show any selective retention of purines as compared with pyrimidines. Studies with isotopically labeled orotic acid, however, indicated on overproduction of other pyrimidines as well as pseudouridine. Though no explanation has yet been found for the selective pyrimidine overproduction seen in CLL, a major portion of these pyrimidines appear to be derived from rapidly renewing sources.
Introduction to Visual Basic for Applications

Visual Basic for Applications (VBA) is a programming language environment that allows Microsoft Excel users to automate tasks, create their own functions, perform complex calculations, and interact with spreadsheets. Despite some important limitations, VBA adds useful capabilities to spreadsheet modeling and is a good tool to know for finance professionals for whom Microsoft Excel is the platform of choice. Keywords: programming language; Microsoft Excel; automate; spreadsheets; Subroutines; functions; FunctionName(list of inputs); Return value; variableName; variableType; condition; input dialog box; the number the user entered; the result obtained; debugging
Sensory neural networks and their adaptations

The signal processing principles utilized in sensory neural systems such as the retina and optic lobes of insects are studied with a view to deriving principles for design of analog integrated circuit peripheral (early) processing systems. The basic peripheral processing principles in biological visual systems are gain adaptation and lateral inhibition. The utility, both in biological and artificial neural networks, of nonlinear lateral inhibition is examined. Because lateral inhibition involves only near neighbor interactions between cells, optoelectronic implementations in the form of monolithically integrated photodetector arrays are particularly attractive. These have wide practical applicability for machine vision, optical scanning, and image enhancement purposes. Such implementations are completely analog and can be designed to require only one transistor per unilateral interconnection. >
The Effect of Dıfferent Sulfuric Acid Concentrations on Seed Germınatıon of Carob (Ceratonia siliqua L.)

The carob tree has been grown well on poor, sandy, calcareous and limestone soils. It is drought resistance, the plant is especially useful for areas where irrigation is impractical or rainfall is unreliable. It is one of the favorite trees planted in the arid and semiarid regions of the southern Mediterranean basin. Propagation of carob is a major factor to achieve carob cultivation. Therefore, the studies on modern methods of propagation and nursery management are a very critical subject. Carob can be propagated vegatatively, but vegetative propagation by cuttings has not been useful. Grafting is generally used commercially. For grafting, first of all we must obtained seedlings. Seedlings. Carob seeds are very recalcitrant. Therefore it is necessary to scarify or stratify; both applications have demonstrated faster germination. The objective of this study was to evaluate different sulfuric acid concentration on seed germination. Seeds, obtained from wild types of carob were used as the experimental material. In this research, scarified carob seeds were treated with different sulfuric acid concentrations (Control, 10, 20, 40, 60, 80 and 98%) for 30 minutes in petri dishes at 26 oC and placed in the plant growth chamber. The results of this study indicated that the lowest germination rate was 22.92% in the control treatment followed by 23.61% by soaking the seeds in 60% H2SO4. The highest germination rate (93.06%) was obtained with the treatment of 98% H2SO4 for 30 minutes.
Generalizing the four gamete condition and splits equivalence theorem: perfect phylogeny on three state characters

We study the three state perfect phylogeny problem and show that there is a three state perfect phylogeny for a set of input sequences if and only if there is a perfect phylogeny for every subset of three characters. In establishing these results, we prove fundamental structural features of the perfect phylogeny problem on three state characters and completely characterize the obstruction sets that must occur in input sequences that do not have a perfect phylogeny. We also give a proof for a stated lower bound involved in the conjectured generalization of our main result to any number of states.
Carbon nanotubes as non-contact optical strain sensors in smart skins

Progress is reported in the development of a novel non-contact strain measurement technology in which the sensors are single-walled carbon nanotubes. This approach exploits the characteristic short-wave infrared fluorescence signatures of semiconducting single-walled carbon nanotubes and the systematic shifts of their fluorescence wavelengths when the nanotubes are axially strained. A strain-sensing smart skin is prepared by coating the surface to be monitored with a thin film of a composite containing well-dispersed single-walled carbon nanotubes embedded in a urethane varnish host. Strain in the surface transfers through the host polymer to the embedded nanotubes. The nanotube strains are then quantitatively monitored by exciting the region of interest with a visible laser beam and capturing and analyzing the resulting single-walled carbon nanotube fluorescence spectrum. High-quality spectra are shown for strain-sensing smart skin films in which the nanotubes were pre-processed by selective extraction w...
The Dangers of Exchange‐Rate Pegging in Emerging‐Market Countries

This paper examines the question of whether pegging exchange rates is a good strategy for emerging-market countries. Although pegging the exchange rate provides a nominal anchor for emerging-market countries that can help them to control inflation, the analysis in this paper does not provide support for this strategy for the conduct of monetary policy. First there are the usual criticisms of exchange-rate pegging, that it entails the loss of an independent monetary policy, exposes the country to the transmission of shocks from the anchor country, increases the likelihood of speculative attacks and potentially weakens the accountability of policymakers to pursue anti-inflationary policies. However, most damaging to the case for exchange-rate pegging in emerging-market countries is that it can increase financial fragility and heighten the potential for financial crises. Because of the devastating effects on the economy that financial crises can bring, an exchange-rate peg is a very dangerous strategy for controlling inflation in emerging-market countries. Instead, this paper suggests that a strategy with a greater likelihood of success involves the granting of independence to the central bank and the adoption of inflation targeting.
High-Field Solution NMR Spectroscopy as a Tool for Assessing Protein Interactions with Small Molecule Ligands

The ability of a small molecule to bind and modify the activity of a protein target at a specific site greatly impacts the success of drugs in the pharmaceutical industry. One of the most important tools for evaluating these interactions has been high-field solution nuclear magnetic resonance (NMR) because of its unique ability to examine even weak protein-drug interactions at high resolution. NMR can be used to evaluate the structural, thermodynamic and kinetic aspects of a binding reaction. The basis of NMR screening experiments is that binding causes a perturbation in the physical properties of both molecules. Unique properties of small and macromolecules allow selective detection of either the protein target or ligand, even in a mixture of compounds. This review outlines current methodologies for assessing protein-ligand interactions from the perspectives of the protein target and ligand and delineates the fundamental principles for understanding NMR approaches in drug research. Advances in instrumentation, pulse sequences, isotopic labeling strategies, and the development of competition experiments support the study of higher molecular weight protein targets, facilitate higher-throughput and expand the range of binding affinities that can be evaluated, enhancing the utility of NMR for identifying and characterizing potential therapeutics to druggable protein targets.
Atomic force microscopic study of polymeric film growth in copper electroplating bath with benzotriazole

Abstract Non-contact mode atomic force microscopy is used to investigate the protective film formed by benzotriazole (BTAH) on a Cu(1 1 1) surface in acidic solution under potential control. The amount of film growth as determined by AFM correlates well with that determined electrochemically. The AFM images indicate that the Cu-BTA film is highly heterogeneous relating to the amount of BTA available at the surface. Less concentrated BTA solution yields less uniform films. After the reduction of the film, the Cu surface roughness increases slightly due to the restricted diffusion of Cu species through the Cu-BTA film during Cu reduction.
Infrared and Raman results for the β - and β ′ ′ -gallate superionic conductors

Infrared and Raman measurements are reported for the lattice vibration modes of the fast ion conductors $M$ $\ensuremath{\beta}$ gallate and $M$ ${\ensuremath{\beta}}^{\ensuremath{'}\ensuremath{'}}$ gallate where $M$ is Na, K, Rb, Ag, or Tl, and are compared with similar measurements for $\ensuremath{\beta}$-aluminate. The transverse- and longitudinal-optic modes are obtained from the infrared reflectivity. The exclusion principle states that for crystals with a center of inversion the infrared and Raman modes transform as different irreducible representations and hence have different frequencies. By comparing the experimental results for a given $M$, it can be seen that the exclusion principle is obeyed for the high-frequency modes for both the gallates and the aluminates. However, for the lowest-frequency modes the gallates still appear to obey the exclusion principle, but the aluminates do not. The corresponding frequencies in the gallates and aluminates are compared with comments on the lowest-frequency modes. The complete polarized Raman spectra for one K $\ensuremath{\beta}$-gallate crystal was measured. The results compare favorably with the published results for Na $\ensuremath{\beta}$-aluminate.
Low-level image processing and edge enhancement using a self-organizing neural network

A self-organizing artificial neural network has been described to enhance and restore gray-level images for applications in low-level image processing. The image is described by a set of interconnected neurons with their values equal to the gray-level values of corresponding pixels. The first-order and second-order contrast links are defined among the neurons which are analyzed for a change in their values in the adaptive constrained environment. Each selected neuron is analyzed only once per iteration, in which its value may be readjusted by incrementing or decrementing the current value. As a result, at the end of each iteration the image data is reorganized. The structure and algorithm of the proposed neural network are presented along with various experimental results showing the capability of such a network to restore and enhance the gray-level images
Reduction of reradiated field in equatorial plane of parasitic antenna

A suitable impedance connected at the center of a parasitic dipole or the base of a monopole antenna can reduce to zero the reradiated field in the equatorial plane. The two-term theory provides a simple formula for the determination of the required impedance for dipole elements up to 1.25 wavelengths long or monopoles up to 0.625\lambda . Examples and tables are given.
Information Search: External Secondary Information for Strategic Marketing Planning in Nonprofit Organizations

SUMMARY This article deals with the need for external secondary data for marketing planning, and the sources of such information; information available at no cost or for a nominal charge. It describes the types of information an organization might want with regard to its various Publics, its competitors, and macro-environmental forces which might affect its success. Information sources are listed and described under one of two categories, those available on the Internet and those available through traditional libranies.
The temperature dependence of the thermal conductivity of narrow fractions of ozeksuat oil

The theory of corresponding states can be used to investigate the thermal conductivity of narrow fractions of oil.
Dative questions: A study in the relation of acceptability to grammaticality of an english sentence type

Abstract Generative grammarians have contended that English sentences of the type Who(m) did you give the book? (what are here called ‘dative questions’) are ungrammatical. The incorporation of the necessary restrictions in the grammar of English to account for this, however, requires a weakening of linguistic theory. It would be desirable, therefore, to account for the restriction within performance theory, as has been proposed by Jackendoff and Culicover (1971) . Their particular account is shown here to be inadequate. In the course of trying to devise a better account, we found, by two different questionnaire-type experiments, that some English speakers, all from metropolitan New York City, accept dative questions. On the basis of this finding, we theorize that the observed variation in acceptability of dative questions is best accounted for by differences in the perceptual strategies for determining the grammatical relations in perceived clauses that different populations of English listeners use. There are thus no dialect differences, strictly speaking, for dative questions; they are all grammatical for all English speakers.
Double jeopardy: Suicide and malpractice

Psychiatric malpractice is not a common occurrence. However, the suicide of a patient under treatment is an event that may lead to legal action. The author details the basic facts and ideas necessary to an understanding of this phenomenon. The plaintiff must ultimately prove that there was a dereliction of professional duty that led directly to the death. Several cases from the legal literature are presented to illustrate how judges evaluate this type of claim. Recommendations designed to minimize the risk of being found negligent are offered, and include detailing the specifics of suicide precautions, careful documentation of decisions to grant patients increased freedom, consultation from supervisors or colleagues, and outreach to survivors. Suicide is not always preventable, but following appropriate standards of care is likely to convince the court that no liability need be attached to the patient's demise.
Measurement of the second-order nonlinear susceptibility of proton-exchanged LiNbO 3

We have measured the d33 nonlinear coefficient in proton-exchanged LiNbO3 by using angle-dependent reflected second-harmonic generation and observed a reduction to ≤1% of the bulk LiNbO3 value.
Regulation of calmodulin-binding myosins

Abstract Myosins constitute a diverse superfamily of actin-based mechanoenzymes that are involved in many essential cellular motilities. In addition to conventional muscle myosin II, ten other classes o f unconventional myosins are known. Many unconventional myosins bind multiple calmodulin light chains and Ca 2+ , which can dramatically alter their mechanochemical and enzymatic activity. Calmodulin-binding myosins can also be regulated by phospholipid binding, phosphorylation of the heavy chain and actin-binding proteins. The molecular details linking unconventional-myosin regulation and function are just beginning to emerge.