cssi/SciSciGPT-SciSciNet huggingface dataset:
Reduced-Order Estimation of the Uniform Completely Connected Homogeneous Influence Model (UCC-HIM)

The influence model (IM) is a discrete-time stochastic automaton that captures spatiotemporal network dynamics. It constitutes a reduced-order representation of networked Markov chains and has found broad stochastic network applications. Parameter estimation from observation data is critical for utilizing IM in real applications. The master Markov chain approach used in the literature incurs significant computational cost. In this paper, we develop an efficient estimation algorithm for a special class of IM, named the uniform completely connected homogeneous influence model (UCC-HIM), through exploiting its special network topology. Specially, we introduce a reduced-order Markov chain representation for the UCC-HIM, analyze its relationship with the master Markov chain, based on which an efficient estimation algorithm is developed. Two simulation studies verify the accuracy and computation reduction of the proposed estimation approach. Stochastic automaton, spatiotemporal processes, influence model, parameter estimation, reduced-order analysis
Senior Leaders and Those Most Responsible at the Extraordinary Chambers in the Courts of Cambodia

This essay explores the way in which the Extraordinary Chambers in the Courts of Cambodia (ECCC) has interpreted its personal jurisdiction, particularly the language in its constitutive documents referring to senior leaders and those most responsible. It also compares the approach adopted by the ECCC to the approach ultimately adopted by the Special Court for Sierra Leone (SCSL), which has very similar language in its constitutive documents.
Illuminating Invisible Grain Boundaries in Coalesced Single-Orientation WS2 Monolayer Films.

Engineering atomic-scale defects is crucial for realizing wafer-scale, single-crystalline transition metal dichalcogenide monolayers for electronic devices. However, connecting atomic-scale defects to larger morphologies poses a significant challenge. Using electron microscopy and ReaxFF reactive force field-based molecular dynamics simulations, we provide insights into WS2 crystal growth mechanisms, providing a direct link between synthetic conditions and microstructure. Dark-field TEM imaging of coalesced monolayer WS2 films illuminates defect arrays that atomic-resolution STEM imaging identifies as translational grain boundaries. Electron diffraction and high-resolution imaging reveal that the films have nearly a single orientation with imperfectly stitched domains that tilt out-of-plane when released from the substrate. Imaging and ReaxFF simulations uncover two types of translational mismatch, and we discuss their origin related to relatively fast growth rates. Statistical analysis of >1300 facets demonstrates that microstructural features are constructed from nanometer-scale building blocks, describing the system across sub-Angstrom to multimicrometer length scales.
Dependence-robust inference using resampled statistics

We develop inference procedures robust to general forms of weak dependence. The procedures use test statistics constructed by resampling data in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well-known forms of weak dependence and justifies the claim of dependence-robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities.
Computing Matrix Squareroot via Non Convex Local Search

We consider the problem of computing the squareroot of a positive semidefinite (PSD) matrix. Several fast algorithms (some based on eigenvalue decomposition and some based on Taylor expansion) are known to solve this problem. In this paper, we propose another way to solve this problem: a natural algorithm performing gradient descent on a non-convex formulation of the matrix squareroot problem. We show that on an $n\times n$ input PSD matrix ${M}$, if the initial point is well conditioned, then the algorithm finds an $\epsilon$-accurate solution in $O\left(\kappa^{3/2} \log \frac{\left\|{M}\right\|_F}{\epsilon}\right)$ iterations, where $\kappa$ is the condition number of $M$. Each iteration involves three matrix multiplications (and does not use either matrix inversions or solutions of linear system), giving a total run time of $O\left(n^{\omega}\kappa^{3/2}\log\frac{\left\|{M}\right\|_F}{\epsilon}\right)$, where $\omega$ is the matrix multiplication exponent. Furthermore we show that our algorithm is robust to errors in each iteration. We also show a lower bound of $\Omega(\kappa)$ iterations for our algorithm demonstrating that the dependence of our result on $\kappa$ is necessary. Existing analyses of similar algorithms (e.g., Newton's method) require commutativity of the input matrix with each iterate of the algorithm which is ensured by choosing the starting iterate carefully. Our analysis, on the other hand, is much more general and does not require each iterate to commute with the input matrix. Consequently, our result guarantees convergence from a wide range of starting points. More generally, our result demonstrates that non-convex optimization can be a viable approach to obtaining fast and robust algorithms. Our argument is quite general and we believe it will find application in designing such algorithms for other problems in numerical linear algebra.
Graph signal recovery using restricted Boltzmann machines

Abstract Given: (a) clean training dataset, (b) trained graph machine learning pipeline, and (c) noisy test dataset; how do we make the pipeline robust to such noise? In an attempt to answer this question, we propose a modification to the pipeline that exploits both the content addressability of a restricted Boltzmann machine and the message passing capabilities of a graph neural network. We investigate which of the hidden layers of the pipeline is best suited for our proposed modification. By establishing that a decrease in mutual information indicates an increase in prediction accuracy which is in turn driven by progressive geometric clustering of the samples belonging to the same class, we attempt to explain the behaviour exhibited by the modified pipeline. Empirical results demonstrate that our approach makes the pipeline robust to some real-world noisy scenarios in citation datasets.
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices.

The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51x faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.
Macroprudential regulations and systemic risk: Does the one-size-fits-all approach work?

Abstract This study empirically investigates whether systemic risk varies among countries with different income levels in response to macroprudential policy instruments (MPIs). The results suggest a negative association between overall MPIs and systemic risk using a sample of 68 countries covering the period between 2000 and 2017. However, not all instruments show intended stability benefits, especially for low and lower-middle-income economies. A comparative analysis reveals that upper-middle-income and high-income countries do receive stability benefits from MPIs. However, low and lower-middle-income economies show unintended instability costs in connection with MPIs, suggesting that a one-size-fits-all approach to macroprudential regulations is not beneficial. Low and lower-middle-income countries should carefully ascertain the choice, implementation, and monitoring of macroprudential policies.
Improving Network Slimming With Nonconvex Regularization

Convolutional neural networks (CNNs) have developed to become powerful models for various computer vision tasks ranging from object detection to semantic segmentation. However, most of the state-of-the-art CNNs cannot be deployed directly on edge devices such as smartphones and drones, which need low latency under limited power and memory bandwidth. One popular, straightforward approach to compressing CNNs is network slimming, which imposes $\ell _{1}$ regularization on the channel-associated scaling factors via the batch normalization layers during training. Network slimming thereby identifies insignificant channels that can be pruned for inference. In this paper, we propose replacing the $\ell _{1}$ penalty with an alternative nonconvex, sparsity-inducing penalty in order to yield a more compressed and/or accurate CNN architecture. We investigate $\ell _{p} (0 , transformed $\ell _{1}$ ( $\text{T}\ell _{1}$ ), minimax concave penalty (MCP), and smoothly clipped absolute deviation (SCAD) due to their recent successes and popularity in solving sparse optimization problems, such as compressed sensing and variable selection. We demonstrate the effectiveness of network slimming with nonconvex penalties on three neural network architectures – VGG-19, DenseNet-40, and ResNet-164 – on standard image classification datasets. Based on the numerical experiments, $\text{T}\ell _{1}$ preserves model accuracy against channel pruning, $\ell _{1/2, 3/4}$ yield better compressed models with similar accuracies after retraining as $\ell _{1}$ , and MCP and SCAD provide more accurate models after retraining with similar compression as $\ell _{1}$ . Network slimming with $\text{T}\ell _{1}$ regularization also outperforms the latest Bayesian modification of network slimming in compressing a CNN architecture in terms of memory storage while preserving its model accuracy after channel pruning.
Development of a yeast-based assay for bioavailable phosphorous

ABSTRACT Preventing eutrophication of inland freshwater ecosystems requires quantifying the phosphorous (P) content of the streams and rivers that feed them. Typical methods for measuring P assess soluble reactive P (SRP) or total P (TP) and require expensive analytical techniques that produce hazardous waste. Here we present a novel method for measuring the more relevant bioavailable P (BAP); this assay utilizes the growth of familiar baker’s yeast, avoids production of hazardous waste, and reduces cost relative to measurements of SRP and TP. The yeast BAP (yBAP) assay takes advantage of the observation that yeast density at saturating growth increases linearly with provided P. We show that this relationship can be used to measure P in freshwater in concentration ranges relevant to eutrophication. In addition, we measured yBAP in water containing known amount of fertilizer and in samples from agricultural waterways. We observed that the majority of yBAP values were between those obtained from standard SRP and TP measurements, demonstrating that the assay is compatible with real-world settings. The cost-effective and nonhazardous nature of the yeast-based assay suggests that it could have utility in a range of settings, offering added insight to identify water systems at risk of eutrophication from excess phosphorus.
Backward-Wave Oscillator with Distributed Power Extraction Operating at an Exceptional Point of Degeneracy

We propose a new and efficient degenerate synchronous regime for backward wave oscillators (BWO) based on an exceptional point of degeneracy (EPD) in the RF-electron beam interactive system. Compared to conventional BWOs, we introduce distributed power extraction all the way along the BWO structure. The EPD is obtained in the interactive RF-beam system by the simultaneous presence of distributed gain (due to the electron beam) and power extraction. Current PIC simulation results show that at a guiding magnetic field of 2.6 T, electron beam of 600 kV and 1740 A, an output power of 0.5 GW is extracted with power conversion efficiency of 47% and oscillation frequency of 9.7 GHz. This paper shows the feasibility of this new concept, and performance could be further improved.
A data harmonization pipeline to leverage external controls and boost power in GWAS.

The use of external controls in genome-wide association study (GWAS) can significantly increase the size and diversity of the control sample, enabling high-resolution ancestry matching and enhancing the power to detect association signals. However, the aggregation of controls from multiple sources is challenging due to batch effects, difficulty in identifying genotyping errors, and the use of different genotyping platforms. These obstacles have impeded the use of external controls in GWAS and can lead to spurious results if not carefully addressed. We propose a unified data harmonization pipeline that includes an iterative approach to quality control (QC) and imputation, implemented before and after merging cohorts and arrays. We apply this harmonization pipeline to aggregate 27 517 European control samples from 16 collections within dbGaP. We leverage these harmonized controls to conduct a GWAS of Crohn's disease. We demonstrate a boost in power over using the cohort samples alone, and that our procedure results in summary statistics free of any significant batch effects. This harmonization pipeline for aggregating genotype data from multiple sources can also serve other applications where individual level genotypes, rather than summary statistics, are required.
Extracting Concepts for Precision Oncology from the Biomedical Literature

This paper describes an initial dataset and automatic natural language processing (NLP) method for extracting concepts related to precision oncology from biomedical research articles. We extract five concept types: Cancer, Mutation, Population, Treatment, Outcome. A corpus of 250 biomedical abstracts were annotated with these concepts following standard double-annotation procedures. We then experiment with BERT-based models for concept extraction. The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and an F1 of 67.1. Finally, we propose additional directions for research for improving extraction performance and utilizing the NLP system in downstream precision oncology applications.
Identifying Women with Mammographically-Occult Breast Cancer Leveraging GAN-Simulated Mammograms.

Our objective is to show the feasibility of using simulated mammograms to detect mammographically-occult (MO) cancer in women with dense breasts and a normal screening mammogram who could be triaged for additional screening with magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional Generative Adversarial Network (CGAN) to simulate a mammogram with normal appearance using the opposite mammogram as the condition. We used a Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN, we used screening mammograms of 1366 women. For MO cancer detection, we used screening mammograms of 333 women (97 MO cancer) with dense breasts. We simulated the right mammogram for normal controls and the cancer side for MO cancer cases. We created two RCDT images, one from a real mammogram pair and another from a real-simulated mammogram pair. We finetuned a VGG16 on resulting RCDT images to classify the women with MO cancer. We compared the classification performance of the CNN trained on fused RCDT images, CNNFused to that of trained only on real RCDT images, CNNReal, and to that of trained only on simulated RCDT images, CNNSimulated. The test AUC for CNNFused was 0.77 with a 95% confidence interval (95CI) of [0.71, 0.83], which was statistically better (p-value < 0.02) than the CNNReal AUC of 0.70 with a 95CI of [0.64, 0.77] and CNNSimulated AUC of 0.68 with a 95CI of [0.62, 0.75]. It showed that CGAN simulated mammograms can help MO cancer detection.
Is Executive Function The Universal Acid

This essay responds to Hirstein, Sifferd and Fagan’s book, Responsible Brains (MIT Press, 2018), which claims that executive function is the guiding mechanism that supports both responsible agency and the necessity for some excuses. In contrast, I suggest that executive function is not the universal acid and the neuroscience at present contributes almost nothing to the necessary psychological level of explanation and analysis. To the extent neuroscience can be useful, it is virtually entirely dependent on well-validated psychology to correlate with the neuroscientific variables under investigation. The essay considers what executive function is and what the neuroscience adds to our understanding of it. Then it addresses moral and legal responsibility generally, and specific doctrines. Executive function is seldom found to be the most perspicuous approach to any of the general or specific moral and legal questions.
The search for failed supernovae with the Large Binocular Telescope: N6946-BH1, still no star

We present new Large Binocular Telescope, Hubble Space Telescope, and Spitzer Space Telescope data for the failed supernova candidate N6946-BH1. We also report an unsuccessful attempt to detect the candidate with Chandra. The ~300,000 Lsun red supergiant progenitor underwent an outburst in 2009 and has since disappeared in the optical. In the LBT data from May 2008 through October 2019, the upper limit on any increase in the R-band luminosity of the source is 2000 Lsun. HST and Spitzer observations show that the source continued to fade in the near-IR and mid-IR, fading by approximately a factor of 2 between October 2015 and September 2017 to 2900 Lsun at H band (F160W). Models of the spectral energy distribution are inconsistent with a surviving star obscured either by an ongoing wind or dust formed in the transient. The disappearance of N6946-BH1 remains consistent with a failed supernova.
IQ Photonic Receiver for Coherent Imaging with a Scalable Aperture

Silicon photonics (SiP) integrated coherent image sensors offer higher sensitivity and improved range-resolution-product compared to direct detection image sensors such as CCD and CMOS devices. Previous generation of SiP coherent imagers suffer from relative optical phase fluctuations between the signal and reference paths, which results in random phase and amplitude fluctuations in the output signal. This limitation negatively impacts the SNR and signal acquisition times. Here we present a coherent imager system that suppresses the optical carrier signal and removes non-idealities from the relative optical path using a photonic in-phase (I) and quadrature (Q) receiver via a 90∘ hybrid detector. Furthermore, we incorporate row-column read-out and row-column addressing schemes to address the electro-optical interconnect density challenge. Our novel row-column read-out architecture for the sensor array requires only 2N interconnects for N2 sensors. An 8×8 IQ sensor array is presented as a proof-of-concept demonstration with 1.2×10-5 resolution over range accuracy. Free-space FMCW ranging with 250 resolution at 1 distance has been demonstrated using this sensor array.
The role of nostalgia in retro sewing

This article explores the role of nostalgia as a motivation for retro sewing and the ways in which nostalgia shapes the practices of retro sewing. Retro sewers circumvent typical models of clothing...
Bayesian sparse mediation analysis with targeted penalization of natural indirect effects

Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high‐dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high‐dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure‐mediator effect and mediator‐outcome effect with either (a) a four‐component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modelling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four‐component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in‐depth analysis of two ongoing epidemiologic studies: the Multi‐Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms.
Bargaining with voluntary transmission of private information: An experimental analysis of final offer arbitration

Abstract In Final Offer Arbitration, both parties submit a proposal to an arbitrator. In the event the parties cannot reach a negotiated settlement, the arbitrator chooses the submitted proposal that is closest to her preferred settlement. In such a model, asymmetric information can lead to costly bargaining failure. Voluntary disclosure of private information has the potential to eliminate disputes which would otherwise arise. However, the timing of this information transmission is crucial, because information can be used by the previously uninformed party to submit a superior proposal to the arbitrator. Thus, there is an incentive to conceal information if it is to be revealed prior to the submission of proposals to the arbitrator. However, information transmission should occur if it takes place after the submission of these proposals. In this paper, we test this theory experimentally and find individuals with a strong case reveal their private information in 40% of negotiations when revelation comes prior to the submission of proposals and in 60% of negotiations when revelation comes after the submission of these proposals. Moreover, information disclosure by a party with favorable information increases the rate of settlement as predicted by the theory.
Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems

The ubiquitous use of IoT and machine learning applications is creating large amounts of data that require accurate and real-time processing. Although edge-based smart data processing can be enabled by deploying pretrained models, the energy and memory constraints of edge devices necessitate distributed deep learning between the edge and the cloud for complex data. In this paper, we propose a distributed system to exploit both the edge and the cloud for training and inference. We propose a new architecture, MEANet, with a main block, an extension block, and an adaptive block for the edge. The inference process can terminate at either the main block, the extension block, or the cloud. MEANet is trained to categorize inputs into easy/hard/complex classes. The main block identifies instances of easy/hard classes and classifies easy classes with high confidence. Only data with high probabilities of belonging to hard classes would be sent to the extension block for prediction. Further, only if the neural network at the edge shows low confidence in the prediction, the instance is considered complex and sent to the cloud for further processing. The training technique lends to the majority of inference on edge devices while going to the cloud only for a small set of complex jobs. The performance of the proposed system is evaluated via extensive experiments using modified models of ResNets and MobileNetV2 on CIFAR-100 and ImageNet datasets. The results show that the proposed distributed model has improved accuracy and lower energy consumption compared to standard models, indicating its capacity to adapt.
Design of Maximum-Gain Dielectric Lens Antenna via Phase Center Analysis

In this work, a method is presented to maximize the gain of millimeter-wave (mm-wave) lens antennas using phase center analysis. This analysis is required when the underlying radiator antenna is not a point source, especially for the cases of arrays. Commonly, for designing a lens antenna, the lens is simply positioned on top of antenna radiator, which is may not always provide the optimum gain and aperture efficiency. A solution is proposed, where the lens will be placed at a distance calculated using the phase center analysis to produce the maximum gain from the lens-antenna systems. A mm-wave microstrip antenna array is designed, and the proposed method is applied for the gain enhancement. Simulation results show that the proposed scheme obtains around 25% gain enhancement compared to the traditional method.
Measuring Wikipedia Article Quality in One Dimension by Extending ORES with Ordinal Regression

Organizing complex peer production projects and advancing scientific knowledge of open collaboration each depend on the ability to measure quality. Wikipedia community members and academic researchers have used article quality ratings for purposes like tracking knowledge gaps and studying how political polarization shapes collaboration. Even so, measuring quality presents many methodological challenges. The most widely used systems use quality assesements on discrete ordinal scales, but such labels can be inconvenient for statistics and machine learning. Prior work handles this by assuming that different levels of quality are “evenly spaced” from one another. This assumption runs counter to intuitions about degrees of effort needed to raise Wikipedia articles to different quality levels. I describe a technique extending the Wikimedia Foundations’ ORES article quality model to address these limitations. My method uses weighted ordinal regression models to construct one-dimensional continuous measures of quality. While scores from my technique and from prior approaches are correlated, my approach improves accuracy for research datasets and provides evidence that the “evenly spaced” assumption is unfounded in practice on English Wikipedia. I conclude with recommendations for using quality scores in future research and include the full code, data, and models.
A Comparison Between Joint Space and Task Space Mappings for Dynamic Teleoperation of an Anthropomorphic Robotic Arm in Reaction Tests

Teleoperation—i.e., controlling a robot with human motion—proves promising in enabling a humanoid robot to move as dynamically as a human. But how to map human motion to a humanoid robot matters because a human and a humanoid robot rarely have identical topologies and dimensions. This work presents an experimental study that utilizes reaction tests to compare joint space and task space mappings for dynamic teleoperation of an anthropomorphic robotic arm that possesses human-level dynamic motion capabilities. The experimental results suggest that the robot achieved similar and, in some cases, human-level dynamic performances with both mappings for the six participating human subjects. All subjects became proficient at teleoperating the robot with both mappings after practice, despite that the subjects and the robot differed in size and link length ratio and that the teleoperation required the subjects to move unintuitively. Yet, most subjects developed their teleoperation proficiencies more quickly with task space mapping than with joint space mapping after similar amounts of practice. This study also indicates the potential values of three-dimensional task space mapping, a teleoperation training simulator, and force feedback to the human pilot for intuitive and dynamic teleoperation of a humanoid robot’s arms.
Towards Synthetic Multivariate Time Series Generation for Flare Forecasting.

One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest resulting in an extreme imbalance in the data. There have been many methods introduced in the literature for overcoming this issue; simple data manipulation through undersampling and oversampling, utilizing cost-sensitive learning algorithms, or by generating synthetic data points following the distribution of the existing data. While synthetic data generation has recently received a great deal of attention, there are real challenges involved in doing so for high-dimensional data such as multivariate time series. In this study, we explore the usefulness of the conditional generative adversarial network (CGAN) as a means to perform data-informed oversampling in order to balance a large dataset of multivariate time series. We utilize a flare forecasting benchmark dataset, named SWAN-SF, and design two verification methods to both quantitatively and qualitatively evaluate the similarity between the generated minority and the ground-truth samples. We further assess the quality of the generated samples by training a classical, supervised machine learning algorithm on synthetic data, and testing the trained model on the unseen, real data. The results show that the classifier trained on the data augmented with the synthetic multivariate time series achieves a significant improvement compared with the case where no augmentation is used. The popular flare forecasting evaluation metrics, TSS and HSS, report 20-fold and 5-fold improvements, respectively, indicating the remarkable statistical similarities, and the usefulness of CGAN-based data generation for complicated tasks such as flare forecasting.
The Fundamental Theorem of Natural Selection

Suppose we have n different types of self-replicating entity, with the population Pi of the ith type changing at a rate equal to Pi times the fitness fi of that type. Suppose the fitness fi is any continuous function of all the populations P1,…,Pn. Let pi be the fraction of replicators that are of the ith type. Then p=(p1,…,pn) is a time-dependent probability distribution, and we prove that its speed as measured by the Fisher information metric equals the variance in fitness. In rough terms, this says that the speed at which information is updated through natural selection equals the variance in fitness. This result can be seen as a modified version of Fisher’s fundamental theorem of natural selection. We compare it to Fisher’s original result as interpreted by Price, Ewens and Edwards.
Supercurrent transmission through Ni/Ru/Ni synthetic antiferromagnets

Josephson junctions containing ferromagnetic materials are generating interest for use in superconducting electronics and cryogenic memory. Optimizing the performance of such junctions is an ongoing effort, requiring exploration of a broad range of magnetic systems. Here, we study supercurrent transmission through Ni/Ru/Ni synthetic antiferromagnets with the idea that their magnetic properties may be superior to those of isolated Ni layers. We find that the decay of the supercurrent as a function of Ni thickness is very slow with a decay length of 7.5 ± 0.8 nm. We also characterize the magnetic properties of the synthetic antiferromagnets as a function of Ni and Ru thicknesses.
Thermoelectric Precession in Turbulent Magnetoconvection

We present laboratory measurements of the interaction between thermoelectric currents and turbulent magnetoconvection. In a cylindrical volume of liquid gallium heated from below and cooled from above and subject to a vertical magnetic field, it is found that the large-scale circulation (LSC) can undergo a slow axial precession. Our experiments demonstrate that this LSC precession occurs only when electrically conducting boundary conditions are employed, and that the precession direction reverses when the axial magnetic field direction is flipped. A thermoelectric magnetoconvection (TEMC) model is developed that successfully predicts the zeroth-order magnetoprecession dynamics. Our TEMC magnetoprecession model hinges on thermoelectric current loops at the top and bottom boundaries, which create Lorentz forces that generate horizontal torques on the overturning large-scale circulatory flow. The thermoelectric torques in our model act to drive a precessional motion of the LSC. This model yields precession frequency predictions that are in good agreement with the experimental observations. We postulate that thermoelectric effects in convective flows, long argued to be relevant in liquid metal heat transfer and mixing processes, may also have applications in planetary interior magnetohydrodynamics.
Single-Shot Compression for Hypothesis Testing

Enhanced processing power in the cloud allows constrained devices to offload costly computations: for instance, complex data analytics tasks can be computed by remote servers. Remote execution calls for a new compression paradigm that optimizes performance on the analytics task within a rate constraint, instead of the traditional rate-distortion framework which focuses on source reconstruction. This paper considers a simple binary hypothesis testing scenario where the resource constrained client (transmitter) performs fixed-length single-shot compression on data sampled from one of two distributions; the server (receiver) performs a hypothesis test on multiple received samples to determine the correct source distribution. To this end, the task-aware compression problem is formulated as finding the optimal source coder that maximizes the asymptotic error performance of the hypothesis test on the server side under a rate constraint. A new source coding strategy based on a greedy optimization procedure is proposed and it is shown that that the proposed compression scheme outperforms universal fixed-length single-shot coding scheme for a range of rate constraints.
Recent advances in high density area array interconnect bonding for 3D integration

The demand for more complex and multifunctional microsystems with enhanced performance characteristics for military applications is driving the electronics industry toward the use of best-of-breed materials and device technologies. Threedimensional (3-D) integration provides a way to build complex microsystems through bonding and interconnection of individually optimized device layers without compromising system performance and fabrication yield. Bonding of device layers can be achieved through polymer bonding or metal-metal interconnect bonding with a number of metalmetal systems. RTI has been investigating and characterizing Cu-Cu and Cu/Sn-Cu processes for high density area array imaging applications, demonstrating high yield bonding between sub-15 μm pads on large area array configurations. This paper will review recent advances in the development of high yield, large area array metal-metal interconnects which enable 3-D integration of heterogeneous materials (e.g. HgCdTe with silicon) and heterogeneous fabrication processes (e.g. infrared emitters or microbolometers with ICs) for imaging and scene projector applications.
Sparsity-based multi-target localization exploiting multi-frequency coprime array

In this paper, a novel sparsity-based multi-target localization approach is proposed by exploiting a coprime array operated with multiple narrowband signals of distinct but closely separated carrier frequencies. The cross-covariance matrix is formulated between baseband array data corresponding to different sensing frequencies to generate virtual difference coarrays, which enable direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In addition, the use of well designed multi-frequency signals unwraps the propagation phase information, thereby enabling unambiguous estimation of the target ranges. The DOA and range estimations are cast as a sparse reconstruction problem and are solved using the complex mulititask Bayesian compressive sensing (CMT-BCS) technique. The effectiveness of the proposed technique is verified through simulation results.
Numerical Singularity Analysis of Feedback-Controlled Chemical Reactors

A numerical singularity analysis technique for the classification of the different static bifurcation behaviors of a nonlinear system is presented. The utility of such an analysis method is demonstrated through an application to a feedback-controlled tubular chemical reactor. The results include an illustration of the increase in complexity of the multiplicity patterns introduced by the feedback controller and a definition of the region in parameter space where the stabilized state is globally unique.
How the Internet of Things will improve reliability tracking

Collecting good, useable reliability data has been a challenge for many companies in the past. Usage statistics are usually not reported by customers, and service/return forms are often not filled out completely. Often, companies do not have control over their products once they are sold in the marketplace and therefore cannot adequately monitor the use of their fielded units. The Internet of Things (IoT) is going to change that. IoT will revolutionize the way we monitor reliability by providing a real-time view of the field performance of a product or a group of products [1]. By connecting products to the internet, they are able to communicate sensor readings which can include operating time and failure modes. IoT can overcome current limitations for data collection, allowing companies to monitor and track products for failure and performance in the field.
Metaphors for today’s leadership: VUCA world, millennial and “Cloud Leaders”

Purpose – The purpose of this paper is to evidence the scenarios any leader is currently facing in front of three specific situations: a volatile, uncertain, complex and ambiguous world (VUCA); a generation that is changing the way to form relationships, work and knowledge transfer;and the possibility for a “Cloud Leadership” to overcome today’s reality of constant change, redirection, new frontiers and formatting. Design/methodology/approach – This paper seeks one theoretical entailment, so that the world today presented by Johansen (2012) from four perspectives needs to be considered from the perspective of leadership. Findings – The paper views leadership as “a Cloud.” It brings new insights to a social and organizational analysis of leaders today. The metaphorical language is creative in the formative accompaniment of the Millennials, it provides and clarifies the orientation in all areas where they interact. Research limitations/implications – Leaders leading Millennials face challenges with specific...
Global Aspects of Ergodic Group Actions

The subject of this book is the study of ergodic, measure preserving actions of countable discrete groups on standard probability spaces. It explores a direction that emphasizes a global point of view, concentrating on the structure of the space of measure preserving actions of a given group and its associated cocycle spaces. These are equipped with canonical topological actions that give rise to the usual concepts of conjugacy of actions and cohomology of cocycles. Structural properties of discrete groups such as amenability, Kazhdan's property (T) and the Haagerup Approximation Property play a significant role in this theory as they have important connections to the global structure of these spaces. One of the main topics discussed in this book is the analysis of the complexity of the classification problems of conjugacy and orbit equivalence of actions, as well as of cohomology of cocycles. This involves ideas from topological dynamics, descriptive set theory, harmonic analysis, and the theory of unitary group representations. Also included in this title is a study of properties of the automorphism group of a standard probability space and some of its important subgroups, such as the full and automorphism groups of measure preserving equivalence relations and connections with the theory of costs. The book contains nine appendices that present necessary background material in functional analysis, measure theory, and group representations, thus making the book accessible to a wider audience.
Purification and characterization of yeast orotidine 5'-monophosphate decarboxylase overexpressed from plasmid PGU2

Orotidine 5'-monophosphate decarboxylase (ODCase) has been overexpressed in yeast 15C cells transformed with a plasmid carrying the URA3 gene that encodes ODCase. Twenty g of cells having ODCase activity equal to 30 mg of pure enzyme per liter of cell culture were obtained after 9 h of galactose induction. To remove yeast proteases, a 60-90% ammonium sulfate fractionation step plus the addition of EDTA as an inhibitor of metallopeptidases was necessary. The purification protocol yielded ODCase that was protease-free and stable to storage at 4 degrees C for 16 months. The pure enzyme had a specific activity of 40 units/mg in 50 mM phosphate buffer, pH 6, and could be stored at -20 degrees C in 20% glycerol with retention of full activity for more than 2 years. The enzyme had a Km for orotidine 5'-monophosphate of 0.7 microM at pH 6 and 25 degrees C. The molecular weight of the plasmid-derived ODCase monomer determined by electrophoresis on denaturing polyacrylamide gels was 29,500. ODCase sedimented through sucrose density gradients as a monomer of about 30 kDa at low protein concentration and in the absence of ligands that bind at the catalytic site. An increase in the sedimentation rate could be induced by increasing the ODCase concentration or by adding ligands that are competitive inhibitors. ODCase sedimented in a single band typical of a protein of 46 kDa at the highest protein concentration studied or in the presence of 50 mM phosphate or 933 microM substrate (orotidine 5'-monophosphate) or product (UMP). A dimer sedimenting as a protein of about 64 kDa occurred in the presence of 50 microM 6-azauridine 5'-monophosphate or 2 microM 1-(5'-phospho-beta-D-ribofuranosyl) barbituric acid, competitive inhibitors of ODCase. These results resemble the ligand-induced subunit association of the ODCase domain of bifunctional UMP synthase and support the use of yeast ODCase as a model for ODCases from other species.
Effects of Electrical Loads on 42V Automotive Power Systems

Demands for higher fuel economy, performance, reliability, convenience, as well as reduced emissions push the automotive industry to seek electrification of ancillaries and engine augmentations. In cars of the future, throttle actuation, steering, anti-lock braking, rear-wheel steering, active suspension and ride-height adjustment, air-conditioning, and electrically heated catalyst will all benefit from the electrical power system. Therefore, a higher system voltage, such as the proposed 42V, is necessary to handle these new introduced loads. In this paper, an overview of the systems that will benefit from the 42V bus is presented. Effects of the new introduced electrical loads on the electrical power systems of conventional cars are described. Dynamic characteristics of each load for a typical drive cycle are defined. In addition, system level issues and vehicle performances such as fuel economy are addressed.
Exposure to Reduced Sulfur Gases Impairs Neurobehavioral Function

ABSTRACTBackground.In the 19th century, deaths from acute exposure to hydrogen sulfide (H2S) portended permanent brain injury from nonlethal doses. The neurobehavioral effects of H2S exposures lasting from moments to years were compared in 16 subjects, 2 years to 22 years afterward.Methods.Neurophys
[1]Chemical mismatch cleavage

Publisher Summary Mutation detection and analysis have become important technical steps in correlating candidate genes with genetic diseases. Chemical mismatch cleavage is a powerful method for scanning large regions of sequence, yet it has been relatively underutilized. The method was developed by Cotton and coworkers and the reagents and conditions used in Maxam–Gilbert DNA sequencing for the preferential cleavage of mismatched bases in heteroduplex DNA were systematically optimized. The reactions exploit the susceptibility of mismatched T and C residues in heteroduplex DNA to modification by osmium tetroxide and hydroxylamine, respectively, followed by piperidine cleavage at the site of the modified base. The chemical cleavage protocol has been streamlined so that all of the chemical modification and cleavage reactions can be run in a single day and the samples run on a sequencing gel the following day. Hydroxylamine, osmium tetroxide, and piperidine are all hazardous materials, and hence should be handled carefully to ensure safety. In the process, heteroduplexes are formed by combining probe with an excess of target, heat denaturing, and then reannealing. Siliconized 0.7-ml microfuge tubes are recommended for the chemical cleavage reactions. Frameshift mutations, either small deletions or insertions, are the most easily detected because they produce strong signals with both the hydroxylamine and OsO 4 reactions; in addition, when the results of the cleavage of coding and noncoding strands are compared, the corresponding sizes of the cleavage fragments should add up to that of the full-length probe. Whether the alteration is an insertion or a deletion cannot easily be determined by this method.
Remarks on ``Adiabatic stabilization: Observation of the surviving population''

Questions are raised about certain experimental and theoretical claims that atoms may be stabilized into their bound states, and prevented from achieving full ionization, by the application of adiabatic, ultraintense, high-frequency laser pulses. It is pointed out that those authors have used the weak-field concepts of cross section and ionization rate in an ultra intense field regime where they have no physical significance.
Instability Effects in Thermal Fatigue

This paper considers shape changes which develop progressively in hourglass specimens subjected to fully reversed cyclic plastic strain applied in a variety of mechanical and thermal waveforms. While the strain at the minimum diameter is controlled, off-center locations subjected to cyclic plastic strain undergo ratcheting. Two major off-center shape changes have been observed: barreling and necking. The paper discusses the reasons for these changes.
High-efficiency tandem perovskite solar cells

A method to cost-effectively upgrade the performance of an established small-bandgap solar technology is to deposit a large-bandgap polycrystalline semiconductor on top to make a tandem solar cell. Metal-halide perovskites have recently been demonstrated as large-bandgap semiconductors that perform well even as a defective and polycrystalline material. We review the initial experimental and modeling work performed on these tandems. We also discuss in-depth the challenges of perovskite-based tandems and the innovations needed from the solar research community to propel perovskite-based tandems into the high-efficiency (>25%) regime and reach commercial competitiveness.