cssi/SciSciGPT-SciSciNet huggingface dataset:
An Online Learning Approach to Optimizing Time-Varying Costs of AoI

We consider systems that require timely monitoring of sources over a communication network, where the cost of delayed information is unknown, time-varying and possibly adversarial. For the single source monitoring problem, we design algorithms that achieve sublinear regret compared to the best fixed policy in hindsight. For the multiple source scheduling problem, we design a new online learning algorithm called Follow the Perturbed Whittle Leader and show that it has low regret compared to the best fixed scheduling policy in hindsight, while remaining computationally feasible. The algorithm and its regret analysis are novel and of independent interest to the study of online restless multi-armed bandit problems. We further design algorithms that achieve sublinear regret compared to the best dynamic policy when the environment is slowly varying. Finally, we apply our algorithms to a mobility tracking problem. We consider non-stationary and adversarial mobility models and illustrate the performance benefit of using our online learning algorithms compared to an oblivious scheduling policy.
Question Generation for Adaptive Education

Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.
Distributional Impacts of Retail Vaccine Availability

As countries transition from facing COVID-19 vaccine supply shortfalls to requiring novel strategies to facilitate vaccination, modern retail chains—often designed and located to target particular demographic groups—are a potential vaccine delivery vehicle. Using geospatial data, we quantify the proximity to vaccines created by a U.S. federal program that distributes vaccines to commercial retail pharmacies. We then quantify the impact of a proposal to provide vaccines at Dollar General, a low-priced general merchandise retailer. We show that adding Dollar General to the federal program would substantially decrease the distance to vaccine sites for low-income and minority U.S. households.
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

Sub-seasonal forecasting (SSF) focuses on predicting key variables such as temperature and precipitation on the 2-week to 2-month time scale. Skillful SSF would have immense societal value in such areas as agricultural productivity, water resource management, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction, and is still a largely understudied problem. In this paper, we carefully investigate 10 Machine Learning (ML) approaches to sub-seasonal temperature forecasting over the contiguous U.S. on the SSF dataset we collect, including a variety of climate variables from the atmosphere, ocean, and land. Because of the complicated atmosphere-land-ocean couplings and the limited amount of good quality observational data, SSF imposes a great challenge for ML despite the recent advances in various domains. Our results indicate that suitable ML models, e.g., XGBoost, to some extent, capture the predictability on sub-seasonal time scales and can outperform the climatological baselines, while Deep Learning (DL) models barely manage to match the best results with carefully designed architecture. Besides, our analysis and exploration provide insights on important aspects to improve the quality of sub-seasonal forecasts, e.g., feature representation and model architecture. The SSF dataset and code are released with this paper for use by the broader research community.
A deeper dive: A mixed methods approach to risk tolerance

Most risk tolerance studies are quantitative, even though many factors that may affect the manifestation of risk tolerance are qualitative. This study employed a mixed-methods approach to investigate how individuals consider risk tolerance as it relates to their financial situation. Fuzzy-trace theory, a psycholinguistic theory of risk processing rooted in prospect theory that is becoming increasingly popular in the medical field, guided the study. Quantitative results indicate that stated versus revealed measures of risk tolerance are not consistent for most people. However, higher risk literacy increases the likelihood of consistency. Qualitative results reveal that individuals perceive risk tolerance through various lenses, including knowledge, values, emotions, and personal experience.
IrEne: Interpretable Energy Prediction for Transformers

Existing software-based energy measurements of NLP models are not accurate because they do not consider the complex interactions between energy consumption and model execution. We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models. IrEne constructs a model tree graph that breaks down the NLP model into modules that are further broken down into low-level machine learning (ML) primitives. IrEne predicts the inference energy consumption of the ML primitives as a function of generalizable features and fine-grained runtime resource usage. IrEne then aggregates these low-level predictions recursively to predict the energy of each module and finally of the entire model. Experiments across multiple Transformer models show IrEne predicts inference energy consumption of transformer models with an error of under 7% compared to the ground truth. In contrast, existing energy models see an error of over 50%. We also show how IrEne can be used to conduct energy bottleneck analysis and to easily evaluate the energy impact of different architectural choices. We release the code and data at https://github.com/StonyBrookNLP/irene.
Numerical approaches for calculating the low-field dc Hall coefficient of the doped Hubbard model

Using determinant quantum Monte Carlo, we compare three methods of evaluating the dc Hall coefficient ${R}_{H}$ of the Hubbard model: the direct measurement of the off-diagonal current-current correlator ${\ensuremath{\chi}}_{xy}$ in a system coupled to a finite magnetic field (FF), ${\ensuremath{\chi}}_{xy}^{\text{FF}}$; the three-current linear response to an infinitesimal field as measured in the zero-field (ZF) Hubbard Hamiltonian, ${\ensuremath{\chi}}_{xy}^{\text{ZF}}$; and the leading order of the recurrent expansion ${R}_{H}^{(0)}$ in terms of thermodynamic susceptibilities. The two quantities ${\ensuremath{\chi}}_{xy}^{\text{FF}}$ and ${\ensuremath{\chi}}_{xy}^{\text{ZF}}$ can be compared directly in imaginary time. Proxies for ${R}_{H}$ constructed from the three-current correlator ${\ensuremath{\chi}}_{xy}^{\text{ZF}}$ can be determined under different simplifying assumptions and compared with ${R}_{H}^{(0)}$. We find these different quantities to be consistent with one another, validating previous conclusions about the close correspondence between Fermi surface topology and the sign of ${R}_{H}$, even for strongly correlated systems. These various quantities also provide a useful set of numerical tools for testing theoretical predictions about the full behavior of the Hall conductivity for strong correlations.
Liquidity and price pressure in the corporate bond market: evidence from mega-bonds

Abstract Larger bonds offer greater liquidity, which should reduce their yields. A simple way for firms to reduce financing costs is to sell bonds with large face values. We find that mega-bonds are more liquid than smaller bonds. However, offering yield spreads on mega-bonds are not lower and are higher than spreads of bonds issued by similar companies. The discount applied to large new issues is consistent with price pressure effects that are also present in the secondary market prices of the issuing firm's existing bonds. Our results suggest a hidden cost to issuing very liquid bonds.
Some finiteness results for algebraic groups and unramified cohomology over higher-dimensional fields

Abstract We formulate and analyze several finiteness conjectures for linear algebraic groups over higher-dimensional fields. In fact, we prove all of these conjectures for algebraic tori as well as in some other situations. This work relies in an essential way on several finiteness results for unramified cohomology.
Limit Theorems for Random Non-uniformly Expanding or Hyperbolic Maps with Exponential Tails

We prove a Berry–Esseen theorem, a local central limit theorem and (local) large and (global) moderate deviations principles for i.i.d. (uniformly) random non-uniformly expanding or hyperbolic maps with exponential first return times. Using existing results the problem is reduced to certain random (Young) tower extensions, which is the main focus of this paper. On the random towers we will obtain our results using contraction properties of random complex equivariant cones with respect to the complex Hilbert projective metric.
Synthesized difference in differences

We consider estimating the conditional average treatment effect for everyone by eliminating confounding and selection bias. Unfortunately, randomized clinical trials (RCTs) eliminate confounding but impose strict exclusion criteria that prevent sampling of the entire clinical population. Observational datasets are more inclusive but suffer from confounding. We therefore analyze RCT and observational data simultaneously in order to extract the strengths of each. Our solution builds upon Difference in Differences (DD), an algorithm that eliminates confounding from observational data by comparing outcomes before and after treatment administration. DD requires a parallel slopes assumption that may not apply in practice when confounding shifts across time. We instead propose Synthesized Difference in Differences (SDD) that infers the correct (possibly non-parallel) slopes by linearly adjusting a conditional version of DD using additional RCT data. The algorithm achieves state of the art performance across multiple synthetic and real datasets even when the RCT excludes the majority of patients.
MIMHD: accurate and efficient hyperdimensional inference using multi-bit in-memory computing

Hyperdimensional Computing (HDC) is an emerging computational framework that mimics important brain functions by operating over high-dimensional vectors, called hypervectors (HVs). In-memory computing implementations of HDC are desirable since they can significantly reduce data transfer overheads. All existing in-memory HDC platforms consider binary HVs where each dimension is represented with a single bit. However, utilizing multi-bit HVs allows HDC to achieve acceptable accuracies in lower dimensions which in turn leads to higher energy efficiencies. Thus, we propose a highly accurate and efficient multi-bit in-memory HDC inference platform called MIMHD. MIMHD supports multi-bit operations using ferroelectric field-effect transistor (FeFET) crossbar arrays for multiply-and-add and FeFET multi-bit content-addressable memories for associative search. We also introduce a novel hardware-aware retraining framework (HWART) that trains the HDC model to learn to work with MIMHD. For six popular datasets and 4000 dimension HVs, MIMHD using 3-bit (2-bit) precision HVs achieves (i) average accuracies of 92.6% (88.9%) which is 8.5% (4.8%) higher than binary implementations; (ii) 84.1x (78.6x) energy improvement over a GPU, and (iii) 38.4x (34.3x) speedup over a GPU, respectively. The 3-bit MIMHD is 4.3x and 13x faster and more energy-efficient than binary HDC accelerators while achieving similar accuracies.
Bilateral Market for Distribution-level Coordination of Flexible Resources using Volttron

Increasing penetrations of distributed energy resources (DERs) and responsive loads (RLs) in the electric power distribution systems calls for a mechanism for joint supply-demand coordination. Recently, several transactive/bilateral co-ordination mechanisms have been proposed for the distribution-level coordination of flexible resources. Implementing a transactive market coordination approach requires a secure, reliable, and computationally efficient multi-agent platform. An example of such a platform is VOLTTRON, developed by the Pacific Northwest National Laboratories (PNNL). The VOLTTRON platform allows the market actors to exchange information and execute proper control actions in a decentralized way. This paper aims to provide a proof-of-concept of the transactive market coordination approach via a small-scale demonstration on the VOLTTRON platform. The steps needed to implement the proposed market architecture using virtual machines and VOLTTRON are thoroughly described, and illustrative examples are provided to show the market-clearing process for different scenarios.
A Competition of Critics in Human Decision-Making

Recent experiments and theories of human decision-making suggest positive and negative errors are processed and encoded differently by serotonin and dopamine, with serotonin possibly serving to oppose dopamine and protect against risky decisions. We introduce a temporal difference (TD) model of human decision-making to account for these features. Our model involves two critics, an optimistic learning system and a pessimistic learning system, whose predictions are integrated in time to control how potential decisions compete to be selected. Our model predicts that human decision-making can be decomposed along two dimensions: the degree to which the individual is sensitive to (1) risk and (2) uncertainty. In addition, we demonstrate that the model can learn about the mean and standard deviation of rewards, and provide information about reaction time despite not modeling these variables directly. Lastly, we simulate a recent experiment to show how updates of the two learning systems could relate to dopamine and serotonin transients, thereby providing a mathematical formalism to serotonin’s hypothesized role as an opponent to dopamine. This new model should be useful for future experiments on human decision-making.
Information content differentiates enhancers from silencers in mouse photoreceptors.

Different cell types are established by activating and repressing the activity of specific sets of genes, a process controlled by proteins called transcription factors. Transcription factors work by recognizing and binding short stretches of DNA in parts of the genome called cis-regulatory sequences. A cis-regulatory sequence that increases the activity of a gene when bound by transcription factors is called an enhancer, while a sequence that causes a decrease in gene activity is called a silencer. To establish a cell type, a particular transcription factor will act on both enhancers and silencers that control the activity of different genes. For example, the transcription factor cone-rod homeobox (CRX) is critical for specifying different types of cells in the retina, and it acts on both enhancers and silencers. In rod photoreceptors, CRX activates rod genes by binding their enhancers, while repressing cone photoreceptor genes by binding their silencers. However, CRX always recognizes and binds to the same DNA sequence, known as its binding site, making it unclear why some cis-regulatory sequences bound to CRX act as silencers, while others act as enhancers. Friedman et al. sought to understand how enhancers and silencers, both bound by CRX, can have different effects on the genes they control. Since both enhancers and silencers contain CRX binding sites, the difference between the two must lie in the sequence of the DNA surrounding these binding sites. Using retinas that have been explanted from mice and kept alive in the laboratory, Friedman et al. tested the activity of thousands of CRX-binding sequences from the mouse genome. This showed that both enhancers and silencers have more copies of CRX-binding sites than sequences of the genome that are inactive. Additionally, the results revealed that enhancers have a diverse collection of binding sites for other transcription factors, while silencers do not. Friedman et al. developed a new metric they called information content, which captures the diverse combinations of different transcription binding sites that cis-regulatory sequences can have. Using this metric, Friedman et al. showed that it is possible to distinguish enhancers from silencers based on their information content. It is critical to understand how the DNA sequences of cis-regulatory regions determine their activity, because mutations in these regions of the genome can cause disease. However, since every person has thousands of benign mutations in cis-regulatory sequences, it is a challenge to identify specific disease-causing mutations, which are relatively rare. One long-term goal of models of enhancers and silencers, such as Friedman et al.’s information content model, is to understand how mutations can affect cis-regulatory sequences, and, in some cases, lead to disease.
K-stability of Fano varieties: an algebro-geometric approach

We give a survey of the recent progress on the study of K-stability of Fano varieties by an algebro-geometric approach.
A Study of Neural Training with Iterative Non-Gradient Methods

In this work we demonstrate provable guarantees on the training of depth-2 neural networks in new regimes than previously explored. (1) First we give a simple stochastic algorithm that can train a ReLU gate in the realizable setting in linear timewhile using significantly milder conditions on the data distribution than previous results. Leveraging some additional distributional assumptions we also show approximate recovery of the true label generating parameters when training a ReLU gate while a probabilistic adversary is allowed to corrupt the true labels of the training data.Our guarantee on recovering the true weight degrades gracefully with increasing probability of attack and its nearlyoptimal in the worst case. Additionally our analysis allows for mini-batching and computes how the convergence time scales with the mini-batch size. (2) Secondly, we exhibit a non-gradient iterative algorithm “Neuro-Tron” which gives a first-of-its-kind poly-time approximate solving of a neural regression (here in the infinity-norm) problem at finitenet widths and for non-realizable data.
Pseudogene-Mediated Gene Conversion After CRISPR-Cas9 Editing Demonstrated by Partial CD33 Conversion with SIGLEC22P

Although gene editing workflows typically consider the possibility of off-target editing, pseudogene-directed homology repair has not, to our knowledge, been reported previously. Here, we employed a CRISPR-Cas9 strategy for targeted excision of exon 2 in CD33 in U937 human monocyte cell line. Candidate clonal cell lines were screened by using a clinically relevant antibody known to label the IgV domain encoded by exon 2 (P67.6, gemtuzumab). In addition to the anticipated deletion of exon 2, we also found unexpected P67.6-negative cell lines, which had apparently retained CD33 exon 2. Sequencing revealed that these lines underwent gene conversion from the nearby SIGLEC22P pseudogene during homology repair that resulted in three missense mutations relative to CD33. Ectopic expression studies confirmed that the P67.6 epitope is dependent upon these amino acids. In summation, we report that pseudogene-directed homology repair can lead to aberrant CRISPR gene editing.
Differential impacts of contact tracing and lockdowns on outbreak size in COVID-19 model applied to China

Abstract The COVID-19 pandemic has led to widespread attention given to the notions of “flattening the curve” during lockdowns, and successful contact tracing programs suppressing outbreaks. However a more nuanced picture of these interventions’ effects on epidemic trajectories is necessary. By mathematical modeling each as reactive quarantine measures, dependent on current infection rates, with different mechanisms of action, we analytically derive distinct nonlinear effects of these interventions on final and peak outbreak size. We simultaneously fit the model to provincial reported case and aggregated quarantined contact data from China. Lockdowns compressed the outbreak in China inversely proportional to population quarantine rates, revealing their critical dependence on timing. Contact tracing had significantly less impact on final outbreak size, but did lead to peak size reduction. Our analysis suggests that altering the cumulative cases in a rapidly spreading outbreak requires sustained interventions that decrease the reproduction number close to one, otherwise some type of swift lockdown measure may be needed.
Active Domain Adaptation via Clustering Uncertainty-Weighted Embeddings

Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-effective it is desirable to select a maximally-informative subset via active learning (AL). We study the problem of AL under a domain shift, called Active Domain Adaptation (Active DA). We empirically demonstrate how existing AL approaches based solely on model uncertainty or diversity sampling are suboptimal for Active DA. Our algorithm, Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings (ADA-CLUE), i) identifies target instances for labeling that are both uncertain under the model and diverse in feature space, and ii) leverages the available source and target data for adaptation by optimizing a semi-supervised adversarial entropy loss that is complementary to our active sampling objective. On standard image classification-based domain adaptation benchmarks, ADA-CLUE consistently outperforms competing active adaptation, active learning, and domain adaptation methods across domain shifts of varying severity.
Choosing news topics to explain stock market returns

We analyze methods for selecting topics in news articles to explain stock returns. We find, through empirical and theoretical results, that supervised Latent Dirichlet Allocation (sLDA) implemented through Gibbs sampling in a stochastic EM algorithm will often overfit returns to the detriment of the topic model. We obtain better out-of-sample performance through a random search of plain LDA models. A branching procedure that reinforces effective topic assignments often performs best. We test these methods on an archive of over 90,000 news articles about S&P 500 firms.
Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform

Fast and accurate simulation of imaging through atmospheric turbulence is essential for developing turbulence mitigation algorithms. Recognizing the limitations of previous approaches, we introduce a new concept known as the phase-to-space (P2S) transform to significantly speed up the simulation. P2S is build upon three ideas: (1) reformulating the spatially varying convolution as a set of invariant convolutions with basis functions, (2) learning the basis function via the known turbulence statistics models, (3) implementing the P2S transform via a light-weight network that directly convert the phase representation to spatial representation. The new simulator offers 300x -- 1000x speed up compared to the mainstream split-step simulators while preserving the essential turbulence statistics.
Batched Thompson Sampling

We introduce a novel anytime Batched Thompson sampling policy for multi-armed bandits where the agent observes the rewards of her actions and adjusts her policy only at the end of a small number of batches. We show that this policy simultaneously achieves a problem dependent regret of order $O(\log(T))$ and a minimax regret of order $O(\sqrt{T\log(T)})$ while the number of batches can be bounded by $O(\log(T))$ independent of the problem instance over a time horizon $T$. We also show that in expectation the number of batches used by our policy can be bounded by an instance dependent bound of order $O(\log\log(T))$. These results indicate that Thompson sampling maintains the same performance in this batched setting as in the case when instantaneous feedback is available after each action, while requiring minimal feedback. These results also indicate that Thompson sampling performs competitively with recently proposed algorithms tailored for the batched setting. These algorithms optimize the batch structure for a given time horizon $T$ and prioritize exploration in the beginning of the experiment to eliminate suboptimal actions. We show that Thompson sampling combined with an adaptive batching strategy can achieve a similar performance without knowing the time horizon $T$ of the problem and without having to carefully optimize the batch structure to achieve a target regret bound (i.e. problem dependent vs minimax regret) for a given $T$.
Two-Stage Clustering of Human Preferences for Action Prediction in Assembly Tasks

To effectively assist human workers in assembly tasks a robot must proactively offer support by inferring their preferences in sequencing the task actions. Previous work has focused on learning the dominant preferences of human workers for simple tasks largely based on their intended goal. However, people may have preferences at different resolutions: they may share the same high-level preference for the order of the sub-tasks but differ in the sequence of individual actions. We propose a two-stage approach for learning and inferring the preferences of human operators based on the sequence of sub-tasks and actions. We conduct an IKEA assembly study and demonstrate how our approach is able to learn the dominant preferences in a complex task. We show that our approach improves the prediction of human actions through cross-validation. Lastly we show that our two-stage approach improves the efficiency of task execution in an online experiment and demonstrate its applicability in a real-world robot-assisted IKEA assembly.
Effects of Family Income and Life Cycle Stages On Financial Asset Ownership

02881. Phone: (401) 874-4036. Fax: (401) 874-2581. E-mail: xiao@uriacc.uri.edu Acknowledgments: Helpful comments from Suzanne Lindamood, Cara Miller, Carole Prather, Carmen Steggell, session participants of the 1995 AFCPE meeting, and three anonymous referees on the earlier versions of the paper are highly appreciated. This study is supported by Certified Financial Planner Board of Standards and University of Rhode Island Research Council.
Yersinia pseudotuberculosis YopE prevents uptake by M cells and instigates M cell extrusion in human ileal enteroid-derived monolayers.

Many pathogens use M cells to access the underlying Peyer’s patches and spread to systemic sites via the lymph as demonstrated by ligated loop murine intestinal models. However, the study of intera...
Power analysis of embedded software: a first step towards software power minimization

Embedded computer systems are characterized by the presence of a dedicated processor and the software that runs on it. Power constraints are increasingly becoming the critical component of the design specification of these systems. At present, however, power analysis tools can only be applied at the lower levels of the design-the circuit or gate level. It is either impractical or impossible to use the lower level tools to estimate the power cost of the software component of the system. This paper describes the first systematic attempt to model this power cost. A power analysis technique is developed that has been applied to two commercial microprocessors-Intel 486DX2 and Fujitsu SPARClite 934. This technique can be employed to evaluate the power cost of embedded software. This can help in verifying if a design meets its specified power constraints. Further, it can also be used to search the design space in software power optimization. Examples with power reduction of up to 40%, obtained by rewriting code using the information provided by the instruction level power model, illustrate the potential of this idea. >
Adversarial Examples in Multi-Layer Random ReLU Networks

We consider the phenomenon of adversarial examples in ReLU networks with independent gaussian parameters. For networks of constant depth and with a large range of widths (for instance, it suffices if the width of each layer is polynomial in that of any other layer), small perturbations of input vectors lead to large changes of outputs. This generalizes results of Daniely and Schacham (2020) for networks of rapidly decreasing width and of Bubeck et al (2021) for two-layer networks. The proof shows that adversarial examples arise in these networks because the functions that they compute are very close to linear. Bottleneck layers in the network play a key role: the minimal width up to some point in the network determines scales and sensitivities of mappings computed up to that point. The main result is for networks with constant depth, but we also show that some constraint on depth is necessary for a result of this kind, because there are suitably deep networks that, with constant probability, compute a function that is close to constant.
Self-Optimizing Loop Sifting and Majorization for 3D Reconstruction

Visual simultaneous localization and mapping (vSLAM) and 3D reconstruction methods have gone through impressive progress. These methods are very promising for autonomous vehicle and consumer robot applications because they can map large-scale environments such as cities and indoor environments without the need for much human effort. However, when it comes to loop detection and optimization, there is still room for improvement. vSLAM systems tend to add the loops very conservatively to reduce the severe influence of the false loops. These conservative checks usually lead to correct loops rejected, thus decrease performance. In this paper, an algorithm that can sift and majorize loop detections is proposed. Our proposed algorithm can compare the usefulness and effectiveness of different loops with the dense map posterior (DMP) metric. The algorithm tests and decides the acceptance of each loop without a single user-defined threshold. Thus it is adaptive to different data conditions. The proposed method is general and agnostic to sensor type (as long as depth or LiDAR reading presents), loop detection, and optimization methods. Neither does it require a specific type of SLAM system. Thus it has great potential to be applied to various application scenarios. Experiments are conducted on public datasets. Results show that the proposed method outperforms state-of-the-art methods.
A millimeter-wave integrated-circuit antenna based on the Fresnel zone plate

A new type of millimeter-wave integrated-circuit antenna is based on a quasi-optical design is investigated. It consists of a Fresnel zone plate on one side of a dielectric substrate and a resonant strip dipole antenna at the focus of the zone plate on the opposite side of the substrate. All of the components are made using simple integrated-circuit fabrication techniques: thin-film metal depositions on planar dielectric substrates. Another features of this design is the short focal length of the zone plate; the focal length/diameter (f/d) for the zone plates studied ranges from 0.1 to 0.5. The antennas described are for a frequency of 230 GHz ( lambda /sub 0/=1.3 mm); however, the design is easily scaled to other millimeter-wave or submillimeter-wave frequencies. Measured results are reported for four different focal length zone plates. Moderate gains, above 20 dB, are obtained. A theory is developed which predicts the on-axis gain, beamwidth, and sidelobe levels. Design graphs are given to aid in the selection of the geometrical parameters to achieve a desired gain from the integrated-circuit zone-plane antenna. >
Unconditional Fock state generation using arbitrarily weak photonic nonlinearities

We present a mechanism that harnesses extremely weak Kerr-type nonlinearities in a single driven cavity to deterministically generate single-photon Fock states and more general photon-blockaded sta...
Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization.

Summary Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as a simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We apply our method first to a subset of the UK Biobank consisting of blood traits and inflammatory disease, and then to a broader set of 411 heritable phenotypes. We detect many effects with strong literature support, as well as numerous behavioral effects that appear to stem from physician advice given to people at high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis in ever-growing databases of genotypes and phenotypes.
Alteration of in House Wastewater Flow with Low Flush Toilet Fixtures and Graywater Recycle

Clean water has been regarded as an unlimited resource for many years, and little consideration has been given to the cautious, conservative use of water. However, the problems and impacts associated with water consumption are stimulating interest in water conservation. Alleviating overloads to water supply and sewage treatment facilities, reducing operational requirements and costs, as well as rendering innovative technologies more feasible are being recognized as potential benefits of reducing water demands and wastewater loads.
Viruses of Entamoeba histolytica. II. Morphogenesis of the polyhedral particle (ABRM 2 leads to HK-9) leads to HB-301 and the filamentous agent (ABRM) 2 leads to HK-9

The intracellular development of two morphologically different amoebal viruses has been studied by electron microscopy. One is a polyhedral agent which was observed as early as 24 hr after infection in the perinuclear cytoplasm. Subsequently, cell lysis occurred and particles were found in large number bound to membranes of disrupted amoebae. Other particles were found in phagocytic vacuoles suggesting a possible portal of entry into amoebae. The other virus is a filamentous particle which is first seen in small clusters in the nucleus after 24 hr of infection. The number of particles increases such that by 72 hr massive whorls of particles occupy a substantial part of the nucleus. After rupture of the nuclear membrane, clusters of filaments are widely dispersed throughout the cytoplasm. Still later, the cytoplasmic membrane disintegrates and clusters of filaments are found extracellularly, but free of cell membranes. The morphology of these agents is discussed in comparison with a variety of plant, animal, and bacterial viruses.
Solution of problems in dynamic elasticity via a mixed least squares method

Purpose – The purpose of this paper is to present an original mixed least squares method for solving problems in dynamic elasticity. Design/methodology/approach – The proposed approach involves two different types of unknowns: velocities and stresses. The approximate solution to the dynamic elasticity equations is obtained via a minimization of a least squares functional, consisting of two terms: a term, which includes the squared residual of a weak form of the time rate of the constitutive relationships, expressed in terms of velocities and stresses, and a term, which depends on the squared residual of the equations of motion. At each time step the functional is minimized with respect to the velocities and stresses, which for the purpose of this study, are approximated by equal order piece-wise linear polynomial functions. The time discretization is based upon the backward Euler scheme. The displacements are computed from the obtained velocities in terms of a finite difference interpolation. The proposed...
Adaptive optical coherence tomography using photorefractive quantum wells

Summary form only given. In this paper, we report the first demonstration of adaptive OCT using a photorefractive quantum well (PRQW) as an adaptive beam combiner. The coherent light is detected as an adaptive homodyne signal. Depth information is retrieved by using a short-coherence light source and scanning a delay line. The system operates at quadrature for maximum homodyne detection (determined solely by choosing center wavelength), requiring no active feedback. This scheme is robust and insensitive to mechanical vibrations and laser speckle.
Abstract 18201: The Association between HMG-CoA Reductase Inhibitor Use and Neurological Outcome Following Cardiac Arrest in Patients Treated with Therapeutic Hypothermia

Introduction: HMG-CoA reductase inhibitors (statins) are known to reduce both inflammation and oxidative stress - key components within the pathophysiologic cascade of anoxic brain injury and reper...
Localization of Ca2+ Channel Subtypes on Rat Spinal Motor Neurons, Interneurons, and Nerve Terminals

Ca2+ channels in distinct subcellular compartments of neurons mediate voltage-dependent Ca2+ influx, which integrates synaptic responses, regulates gene expression, and initiates synaptic transmission. Antibodies that specifically recognize the α1 subunits of class A, B, C, D, and E Ca2+ channels have been used to investigate the localization of these voltage-gated ion channels on spinal motor neurons, interneurons, and nerve terminals of the adult rat. Class A P/Q-type Ca2+ channels were present mainly in a punctate pattern in nerve terminals located along the cell bodies and dendrites of motor neurons. Both smooth and punctate staining patterns were observed over the surface of the cell bodies and dendrites with antibodies to class B N-type Ca2+channels, indicating the presence of these channels in the cell surface membrane and in nerve terminals. Class C and D L-type and class E R-type Ca2+ channels were distributed mainly over the cell soma and proximal dendrites. Class A P/Q-type Ca2+ channels were present predominantly in the presynaptic terminals of motor neurons at the neuromuscular junction. Occasional nerve terminals innervating skeletal muscles from the hindlimb were labeled with antibodies against class B N-type Ca2+ channels. Staining of the dorsal laminae of the rat spinal cord revealed a complementary distribution of class A and class B Ca2+ channels in nerve terminals in the deeper versus the superficial laminae. Many of the nerve terminals immunoreactive for class B N-type Ca2+ channels also contained substance P, an important neuropeptide in pain pathways, suggesting that N-type Ca2+ channels are predominant at synapses that carry nociceptive information into the spinal cord.
A Case of Subcutaneous Seeding of Hepatocellular Carcinoma After Fine Needle Aspiration Biopsy

Cancer spread along the needle track following fine needle aspiration biopsy is said to be a rare complication. The authors report a case of subcutaneous implantation of hepatocellular carcinoma following ultrasono-guided fine needle aspiration biopsy. The patient, a 67-year-old Korean male was found to have a large hepatocellular carcinoma diagnosed by fine needle aspiration biopsy. Four months later, the patient felt two subcutaneous growing lumps at the previous aspiration site. The authors confirmed them histologically 11 months after aspiration.
Chemical Characterization of a New Surface Antigenic Polysaccharide from a Mutant of Staphylococcus aureus

A mutant of Staphylococcus aureus H was isolated by virtue of its inability to agglutinate with antibodies against teichoic acid of S. aureus. Immunological studies revealed that the mutant, S. aureus T, possessed a new surface antigen in addition to having the antigenic determinant of the wild-type strain, the ribitol teichoic acid. The presence of this additional surface component rendered strain T resistant to staphylococcal typing phages, presumably by masking the phage-receptor sites. The polymer was separated from teichoic acid by chromatography on diethylaminoethyl cellulose and was shown to be composed of two amino sugars, N-acetyl-d-fucosamine and N-acetyl-d-mannosamin uronic acid.
Censorship and Cultural Sensibility: The Regulation of Language in Tudor-Stuart England

Introduction Chapter 1. "That Great and Immoderate Liberty of Lying" Chapter 2. The Index and the English: Two Traditions of Early Modern Censorship Chapter 3. Roman Law Chapter 4. The Christian Transmission of Roman Law Iniuria Chapter 5. The Law of All Civility Chapter 6. Defendants' Rights and Poetic Justice Chapter 7. Hermeneutics, History, and the Delegitimation of Censorship Chapter 8. Intent Chapter 9. Ideological Censorship Notes Bibliography Index Acknowledgments
The effect of spatial aperture variations on the thermal performance of discretely fractured geothermal reservoirs

The effect of spatial aperture variations on the thermal performance of discretely fractured geothermal reservoirs was investigated using finite element method solutions to the convective heat transfer in the fracture coupled with a boundary integral description of conductive heat transfer in the rock. The dipolar flow was generated by a source of fluid volume at an injection well and a sink at a production well within a circular fracture. The statistics of the thermal performance of an ensemble of fracture realizations was evaluated. Fractures with self-affine aperture fields where long range correlations dominant over short range correlations were generated. The results showed that spatial aperture variations most frequently lead to diminished thermal performance by creating flow channeling that reduces the heat transfer area. Enhanced thermal performance occurred in the less common cases when the aperture was small in the region between the wellbores, causing fluid flow to sweep out greater areas of the fracture and extract heat from a larger area. The standard deviation of the apertures had the largest influence on the thermal performance, while the spatial correlation of the aperture played a secondary role. Larger values of the standard deviation led to more adverse thermal performance. For the range of standard deviations investigated, the fraction of fractures exhibiting enhanced thermal performance compared to the base case of no aperture variations ranged from 34 to 49 %. The degradation of thermal performance due to aperture variations was largest when the well bore spacing was a larger fraction of the fracture diameter. Reservoirs consisting of two non-intersecting fractures connected to the same injection and production wells were also modeled. The uneven split of the flow between the reservoirs in this case caused a further deterioration of thermal performance compared with the single fracture reservoirs. However, flow control was able to overcome nearly all of the additional loss of thermal performance for the multifracture reservoir.
Ultrastructural analysis of the endothelial : pericyte relationship in diabetic cutaneous vessels

The microvessels in the buttock skin of 15 patients with long-standing juvenile diabetes were studied both by electron microscopy and three-dimensional (3D) computer reconstruction of a prototypical diabetic postcapillary venule. Endothelial cell gaps were found in postcapillary venules and capillaries, but only in association with an increased deposition of basement membrane-like material in the vascular wall. In parallel with the increased amounts of deposited basement membrane-like material, the space between pericytes and endothelial cells was wider and the cytoplasmic processes that formed the contact points between them were longer and thinner than normal. Pericytes, devoid of any cytoplasmic contacts with the underlying endothelial cells, were observed as isolated cells within the outer third of the vascular wall in markedly thickened vessels. These observations offer an explanation for the known increased vascular permeability of diabetic vessels, and suggest a possible explanation for the development of diabetic retinopathy with aneurysm formation.
Performance evaluation of on-chip register and cache organizations

Chip area is a critical resource in the design of VLSI processors. There are many different alternative designs that could fill this chip area. This paper compares several different local memory organizations applicable for single-chip processors. Several cache types—instruction, data, split, unified, stack, top-of-stack—are considered. These are compared to multiple register set architectures to which various caches can also be added. The performance metric of interest is effective access time, since a wide variety of register and cache organizations are used. A model for access time and a model for chip area required for each organization form the basis for comparison. Extensive simulations of several register-memory organizations are presented. Address traces from a VAX-11/780 running systems programs were used in the simulation.
Nano-optomechanical circuits on silicon substrates

We present our most recent progresses in developing nano-optomechanical circuits on silicon substrates. Through active coupling of NEMS with high Q cavities, we demonstrate further scaling of NEMS in size, sensitivity, frequency and damping rate.
The Waning of American Apartheid

Racial residential segregation has a long and persistent history in the United States. Data from the most recent decade give hope that housing patterns and racial attitudes are moving—albeit slowly...