cssi/SciSciGPT-SciSciNet huggingface dataset:
THE CONORMAL TORUS IS A COMPLETE KNOT INVARIANT

We use microlocal sheaf theory to show that knots can only have Legendrian isotopic conormal tori if they themselves are isotopic or mirror images.
A Performance and Stability Analysis of Low-inertia Power Grids with Stochastic System Inertia

Traditional synchronous generators with rotational inertia are being replaced by low-inertia renewable energy resources (RESs) in many power grids and operational scenarios. Due to emerging market mechanisms, inherent variability of RESs, and existing control schemes, the resulting system inertia levels can not only be low but also markedly time-varying. In this paper, we investigate performance and stability of low-inertia power systems with stochastic system inertia. In particular, we consider system dynamics modeled by a linearized stochastic swing equation, where stochastic system inertia is regarded as multiplicative noise. The H 2 norm is used to quantify the performance of the system in the presence of persistent disturbances or transient faults. The performance metric can be computed by solving a generalized Lyapunov equation, which has fundamentally different characteristics from systems with only additive noise. For grids with uniform inertia and damping parameters, we derive closed-form expressions for the H 2 norm of the proposed stochastic swing equation. The analysis gives insights into how the H 2 norm of the stochastic swing equation depends on 1) network topology; 2) system parameters; and 3) distribution parameters of disturbances. A mean-square stability condition is also derived. Numerical results provide additional insights for performance and stability of the stochastic swing equation.
An econometric perspective on algorithmic subsampling

Data sets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data, and diminishing returns suggest that we may not need terabyte...
Assessment of enzyme active site positioning and tests of catalytic mechanisms through X-ray-derived conformational ensembles.

How enzymes achieve their enormous rate enhancements remains a central question in biology, and our understanding to date has impacted drug development, influenced enzyme design, and deepened our appreciation of evolutionary processes. While enzymes position catalytic and reactant groups in active sites, physics requires that atoms undergo constant motion. Numerous proposals have invoked positioning or motions as central for enzyme function, but a scarcity of experimental data has limited our understanding of positioning and motion, their relative importance, and their changes through the enzyme's reaction cycle. To examine positioning and motions and test catalytic proposals, we collected "room temperature" X-ray crystallography data for Pseudomonas putida ketosteroid isomerase (KSI), and we obtained conformational ensembles for this and a homologous KSI from multiple PDB crystal structures. Ensemble analyses indicated limited change through KSI's reaction cycle. Active site positioning was on the 1- to 1.5-A scale, and was not exceptional compared to noncatalytic groups. The KSI ensembles provided evidence against catalytic proposals invoking oxyanion hole geometric discrimination between the ground state and transition state or highly precise general base positioning. Instead, increasing or decreasing positioning of KSI's general base reduced catalysis, suggesting optimized Angstrom-scale conformational heterogeneity that allows KSI to efficiently catalyze multiple reaction steps. Ensemble analyses of surrounding groups for WT and mutant KSIs provided insights into the forces and interactions that allow and limit active-site motions. Most generally, this ensemble perspective extends traditional structure-function relationships, providing the basis for a new era of "ensemble-function" interrogation of enzymes.
On two lattice points problems about the parabola

We obtain asymptotic formulae with optimal error terms for the number of lattice points under and near a dilation of the standard parabola, the former improving upon an old result of Popov. These r...
Adaptive Huber regression on Markov-dependent data

Abstract High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For the convenience of theoretical analyses, classical methods usually assume independent observations and sub-Gaussian-tailed errors. However, neither of them hold in many real high-dimensional time-series data. Recently (Sun et al., 2019) proposed Adaptive Huber Regression (AHR) to address the issue of heavy-tailed errors. They discover that the robustification parameter of the Huber loss should adapt to the sample size, the dimensionality, and the moments of the heavy-tailed errors. We progress in a vertical direction and justify AHR on dependent observations. Specifically, we consider an important dependence structure — Markov dependence. Our results show that the Markov dependence impacts on the adaption of the robustification parameter and the estimation of regression coefficients in the way that the sample size should be discounted by a factor depending on the spectral gap of the underlying Markov chain.
Impulse Response Data Augmentation and Deep Neural Networks for Blind Room Acoustic Parameter Estimation

The reverberation time (T60) and the direct-to-reverberant ratio (DRR) are commonly used to characterize room acoustic environments. Both parameters can be measured from an acoustic impulse response (AIR) or using blind estimation methods that perform estimation directly from speech. When neural networks are used for blind estimation, however, a large realistic dataset is needed, which is expensive and time consuming to collect. To address this, we propose an AIR augmentation method that can parametrically control the T60 and DRR, allowing us to expand a small dataset of real AIRs into a balanced dataset orders of magnitude larger. Using this method, we train a previously proposed convolutional neural network (CNN) and show we can outperform past single-channel state-of-the-art methods. We then propose a more efficient, straightforward baseline CNN that is 4-5x faster, which provides an additional improvement and is better or comparable to all previously reported single- and multi-channel state-of-the-art methods.
Nearly Optimal Robust Secret Sharing Against Rushing Adversaries

Robust secret sharing is a strengthening of standard secret sharing that allows the shared secret to be recovered even if some of the shares being used in the reconstruction have been adversarially modified. In this work, we study the setting where out of all the n shares, the adversary is allowed to adaptively corrupt and modify up to t shares, where \(n = 2t+1\) (Note that if the adversary is allowed to modify any more shares, then correct reconstruction would be impossible.). Further, we deal with rushing adversaries, meaning that the adversary is allowed to see the honest parties’ shares before modifying its own shares.
The distribution of overlaps between eigenvectors of Ginibre matrices

We study the overlaps between eigenvectors of nonnormal matrices. They quantify the stability of the spectrum, and characterize the joint eigenvalues increments under Dyson-type dynamics. Well known work by Chalker and Mehlig calculated the expectation of these overlaps for complex Ginibre matrices. For the same model, we extend their results by deriving the distribution of diagonal overlaps (the condition numbers), and their correlations. We prove: (i) convergence of condition numbers for bulk eigenvalues to an inverse Gamma distribution; more generally, we decompose the quenched overlap (i.e. conditioned on eigenvalues) as a product of independent random variables; (ii) asymptotic expectation of off-diagonal overlaps, both for microscopic or mesoscopic separation of the corresponding eigenvalues; (iii) decorrelation of condition numbers associated to eigenvalues at mesoscopic distance, at polynomial speed in the dimension; (iv) second moment asymptotics to identify the fluctuations order for off-diagonal overlaps, when the related eigenvalues are separated by any mesoscopic scale; (v) a new formula for the correlation between overlaps for eigenvalues at microscopic distance, both diagonal and off-diagonal. These results imply estimates on the extreme condition numbers, the volume of the pseudospectrum and the diffusive evolution of eigenvalues under Dyson-type dynamics, at equilibrium.
Theoretical Study of Divalent Bis(Pentaisopropylcyclopentadienyl) Actinocenes

The existence of divalent bis(pentaisopropylcyclopentadienyl) actinocene compounds, An(CpiPr5)2 (An = Th, U, Pu, Am, Bk, No, and Lr), is assessed by density functional theory (DFT) calculations with scalar-relativistic small-core pseudopotentials. The calculations predict ground states with significant 6d occupation for Th, U, and Lr, whereas Am, Bk, and No exhibit 5f ground states. A mixed ground state with predominant 5f character is found for Pu. The complexes exhibit a linear coordination geometry and high S10 symmetry except for Pu(CpiPr5)2 and Am(CpiPr5)2, which are found to be bent by 11 and 12°, respectively. Absorption spectra are simulated with time-dependent density functional theory (TD-DFT) and compared to experimental spectra of known tris(C4H4SiMe3) and tris(C5H3(SiMe3)2) compounds [ J. Am. Chem. Soc. 2015 , 137 , 369 - 382 . DOI: 10.1021/ja510831n ] as well as recently synthesized divalent lanthanide analogs Dy(CpiPr5)2 and Tb(CpiPr5)2 [ J. Am. Chem. Soc. , 2019 , 141 , 12967 - 12973 . DOI: 10.1021/jacs.9b05816 ]. Thermodynamic stability is assessed by calculation of adiabatic reduction potentials of the trivalent precursors [An(CpiPr5)2]+, and the feasibility of further reduction to obtain as yet unknown monovalent molecular actinide complexes is discussed.
Feedback control of nonstandard singularly perturbed systems

Feedback control of linear time-invariant singularly perturbed systems of the form x=A/sub 11/x+A/sub 12/z+B/sub 1/u, epsilon z=A/sub 21/x+A/sub 22/z+B/sub 2/u,y=C/sub 1/x+C/sub 2/z Eu, where A/sub 22/ may be singular, is discussed. It is shown that, under stabilizability-detectability assumptions on the slow and fast models, the theory of feedback control of singularly perturbed systems can be extended to the case of singular A/sub 22/. Both state and output feedback results are given. In particular, a parameterization of all compensators that stabilize the system while preserving its two-time-scale structure is given. It is shown that any two-frequency-scale stabilizing compensator can be asymptotically approximated by a compensator designed via a sequential procedure. In this procedure, a fast (high-frequency) compensator is designed first to stabilize the fast model of the system. Then, a strictly proper slow (low-frequency) compensator is designed to stabilize a modified slow model. The overall compensator is taken as a parallel or cascade connection of the slow and fast compensators. The sequential procedure is applied to the design of high-gain feedback compensators for a class of multivariable plants. >
Subnanosecond Fluctuations in Low-Barrier Nanomagnets

Author(s): Kaiser, J; Rustagi, A; Camsari, KY; Sun, JZ; Datta, S; Upadhyaya, P | Abstract: Fast magnetic fluctuations due to thermal torques have useful technological functionality ranging from cryptography to probabilistic computing. The characteristic time of fluctuations in typical uniaxial anisotropy magnets studied so far is bounded from below by the well-known energy relaxation mechanism. This time scales as $\alpha^{-1}$, where $\alpha$ parameterizes the strength of dissipative processes. Here, we theoretically analyze the fluctuating dynamics in easy-plane and antiferromagnetically coupled nanomagnets. We find in such magnets, the dynamics are strongly influenced by fluctuating intrinsic fields, which give rise to an additional dephasing-type mechanism for washing out correlations. In particular, we establish two time scales for characterizing fluctuations (i) the average time for a nanomagnet to reverse|which for the experimentally relevant regime of low damping is governed primarily by dephasing and becomes independent of $\alpha$, (ii) the time scale for memory loss of a single nanomagnet|which scales as $\alpha^{-1/3}$ and is governed by a combination of energy dissipation and dephasing mechanism. For typical experimentally accessible values of intrinsic fields, the resultant thermal-fluctuation rate is increased by multiple orders of magnitude when compared with the bound set solely by the energy relaxation mechanism in uniaxial magnets. This could lead to higher operating speeds of emerging devices exploiting magnetic fluctuations.
Studies on an experimental quartz tube hollow cathode

An experimental study is described in which a quartz tube, hollow cathode was operated in a test fixture allowing the simultaneous measurement of internal cathode pressure, insert temperature profiles, and the emission currents from various cathode components as a function of discharge current and propellant (mercury) mass flow rate for a number of different cathode orifice diameters. Results show that the insert temperature profile is essentially independent of orifice diameter but depends strongly on internal cathode pressure and emission current. The product of internal cathode pressure and insert diameter is shown to be important in determining the emission location and the minimum keeper voltage.
Predicting Second and Third Graders’ Reading Comprehension Gains: Observing Students’ and Classmates Talk During Literacy Instruction Using COLT

This paper introduces a new observation system that is designed to investigate students' and teachers' talk during literacy instruction, Creating Opportunities to Learn from Text (COLT). Using video-recorded observations of 2nd-3rd grade literacy instruction (N=51 classrooms, 337 students, 151 observations), we found that nine types of student talk ranged from using non-verbal gestures to generating new ideas. The more a student talked, the greater were his/her reading comprehension (RC) gains. Classmate talk also predicted RC outcomes (total effect size=0.27). We found that 11 types of teacher talk ranged from asking simple questions to encouraging students' thinking and reasoning. Teacher talk predicted student talk but did not predict students' RC gains directly. Findings highlight the importance of each student's discourse during literacy instruction, how classmates' talk contributes to the learning environments that each student experiences, and how this affects RC gains, with implications for improving the effectiveness of literacy instruction.
The Complex Orthogonal Gelfand–Zeitlin System

In this paper, we use the theory of algebraic groups to prove a number of new and fundamental results about the orthogonal Gelfand-Zeitlin system. We show that the moment map (orthogonal Kostant-Wallach map) is surjective and simplify criteria of Kostant and Wallach for an element to be strongly regular. We further prove the integrability of the orthogonal Gelfand-Zeitlin system on regular adjoint orbits and describe the generic flows of the integrable system. We also study the nilfibre of the moment map and show that in contrast to the general linear case it contains no strongly regular elements. This extends results of Kostant, Wallach, and Colarusso from the general linear case to the orthogonal case.
Spiked sample covariance matrices with possibly multiple bulk components

In this paper, we study the convergent limits and rates of the eigenvalues and eigenvectors for spiked sample covariance matrices whose spectrum can have multiple bulk components. Our model is an e...
DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training

Data parallelism has become the de facto standard for training Deep Neural Network on mul- tiple processing units. In this work we propose DC- S3GD, a decentralized (without Parameter Server) stale-synchronous version of the Delay-Compensated Asynchronous Stochastic Gradient Descent (DC- ASGD) algorithm. In our approach, we allow for the overlap of computation and communication, by averaging in parameter space and compensating the inherent error with a first-order correction of the locally computed gradients. We prove the effectiveness of our approach by training Convolutional Neural Network with large batches and achieving state-of- the-art results.
Specificity in Auxin Responses Is Not Explained by the Promoter Preferences of Activator ARFs.

The plant growth hormone auxin regulates development via a family of transcription factors that share promoter sequence preferences, despite activating different genetic networks.
Universal approximation with quadratic deep networks

Abstract Recently, deep learning has achieved huge successes in many important applications. In our previous studies, we proposed quadratic/second-order neurons and deep quadratic neural networks. In a quadratic neuron, the inner product of a vector of data and the corresponding weights in a conventional neuron is replaced with a quadratic function. The resultant quadratic neuron enjoys an enhanced expressive capability over the conventional neuron. However, how quadratic neurons improve the expressing capability of a deep quadratic network has not been studied up to now, preferably in relation to that of a conventional neural network. Specifically, we ask four basic questions in this paper: (1) for the one-hidden-layer network structure, is there any function that a quadratic network can approximate much more efficiently than a conventional network? (2) for the same multi-layer network structure, is there any function that can be expressed by a quadratic network but cannot be expressed with conventional neurons in the same structure? (3) Does a quadratic network give a new insight into universal approximation? (4) To approximate the same class of functions with the same error bound, could a quantized quadratic network have a lower number of weights than a quantized conventional network? Our main contributions are the four interconnected theorems shedding light upon these four questions and demonstrating the merits of a quadratic network in terms of expressive efficiency, unique capability, compact architecture and computational capacity respectively.
MadMiner: Machine Learning-Based Inference for Particle Physics

Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed. This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper, we introduce MadMiner , a Python module that streamlines the steps involved in this procedure. Wrapping around MadGraph5_aMC and Pythia 8, it supports almost any physics process and model. To aid phenomenological studies, the tool also wraps around Delphes 3, though it is extendable to a full Geant4-based detector simulation. We demonstrate the use of MadMiner in an example analysis of dimension-six operators in ttH production, finding that the new techniques substantially increase the sensitivity to new physics.
A Fused Gaussian Process Model for Very Large Spatial Data

With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This article pro...
Nonparametric false discovery rate control for identifying simultaneous signals

It is frequently of interest to identify simultaneous signals, defined as features that exhibit statistical significance across each of several independent experiments. For example, genes that are consistently differentially expressed across experiments in different animal species can reveal evolutionarily conserved biological mechanisms. However, in some problems the test statistics corresponding to these features can have complicated or unknown null distributions. This paper proposes a novel nonparametric false discovery rate control procedure that can identify simultaneous signals even without knowing these null distributions. The method is shown, theoretically and in simulations, to asymptotically control the false discovery rate. It was also used to identify genes that were both differentially expressed and proximal to differentially accessible chromatin in the brains of mice exposed to a conspecific intruder. The proposed method is available in the R package github.com/sdzhao/ssa.
Impact of Load Models on Power Flow Optimization

Aggregated load models, such as PQ and ZIP, are used to represent the approximated load demand at specific buses in grid simulation and optimization problems. In this paper we examine the impact of model choice on the optimal power flow solution and demonstrate that it is possible for different load models to represent the same amount of real and reactive power at the optimal solution yet correspond to completely different grid operating points. We introduce the metric derived from the maximum power transfer theorem to identify the behavior of an aggregated model in the OPF formulation to indicate its possible limitations. A dataset from the Carnegie Mellon campus is used to characterize three types of load models using a time-series machine learning algorithm, from which the optimal power flow results demonstrate that the choice of load model type has a significant impact on the solution set points. For example, our results show that the PQ load accurately characterizes the CMU data behavior correctly for only 16.7% of the cases.
Frobenius Determinants and Bessel Functions

We study the geometry and partial differential equations arising from the consideration of Frobenius determinants, also called group-determinants. This leads us to address some aspects of twistor theory as well as some extensions of Bessel functions.
Pivot tracing: dynamic causal monitoring for distributed systems

Monitoring and troubleshooting distributed systems are notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today---logs, counters, and metrics---have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This paper presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. Pivot Tracing does not correlate cross-component events using expensive global aggregations, nor does it perform offline analysis. Instead, Pivot Tracing directly correlates events as they happen by piggybacking metadata alongside requests as they execute. This gives Pivot Tracing low runtime overhead---less than 1% for many cross-component monitoring queries.
Advance Market Commitments: Insights from Theory and Experience

Ten years ago, donors committed $1.5 billion to a pilot Advance Market Commitment (AMC) to help purchase pneumococcal vaccine for low-income countries. The AMC aimed to encourage the development of such vaccines, ensure distribution to children in low-income countries, and pilot the AMC mechanism for possible future use. Three vaccines have been developed and more than 150 million children immunized, saving an estimated 700,000 lives. This paper reviews the economic logic behind AMCs, the experience with the pilot, and key issues for future AMCs.
Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutations.

Summary Identifying cancer-relevant mutations in noncoding regions is challenging due to the large numbers of such mutations, their low levels of recurrence, and difficulties in interpreting their functional impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individual’s cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. We conclude that differential ASE is a promising means for discovering gene dysregulation due to cis noncoding mutations.
Rethinking Breast Cancer Surveillance in Women with BRCA-associated Ovarian Cancer in the Post-SOLO Trial Era

5565Background: Patients with BRCA mutations are at increased risk of developing both breast (BC) and epithelial ovarian cancer (EOC). Optimal breast cancer surveillance guidelines for BRCA mutatio...
A Robust Algorithm for Sniffing BLE Long-Lived Connections in Real-Time

Bluetooth Low Energy (BLE) has become an intrinsic wireless technology for the Internet of Things (IoT). With the proliferation of BLE-embedded IoT devices, it is important to study the security and privacy implications of BLE. The forefront attack to BLE devices is the wireless sniffing attack, which would lead to more detrimental threats like jamming, encryption cracking or system penetration. Existing sniffing attacks are based on the correct detection of BLE connection initiation state, but they become ineffective for BLE long-lived connections. In this paper, we focus on the adversary setting with a low-cost single radio and develop a suite of real-time algorithms to determine the key parameters necessary to follow and sniff a BLE connection in the connected state. We implement our algorithms in the open source platform - Ubertooth One and evaluate its performance in terms of sniffing overhead and accuracy. By comparing with state-of- the-art schemes, experimental results show that our sniffer achieves much higher sniffing accuracy (over 80%) and better stability to BLE operational dynamics.
Vitamin D regulation of a SOD1-to-SOD2 antioxidative switch to prevent osteosarcoma transformation

Abstract Superoxide, a form of reactive oxygen species (ROS), is catabolized by superoxide dismutase (SOD) and contributes to carcinogenesis via the oxidative damage it inflicts on cells. The aim of this research was to analyze the potential vitamin D-mediated regulation of the antioxidative “SOD1-to-SOD2 switch” within the human MG-63 osteosarcoma model. For this study, real-time PCR analysis was performed using MG-63 cells exposed to metabolically active 1,25(OH)2D3. Frist, a sustained statistically significant >2-fold suppression of proliferating cell nuclear antigen (PCNA) transcripts was observed after 10nM but not at 100nM of 1,25(OH)2D3 treatment, suggesting a cytostatic effect. In order to assess regulators of mitochondrial oxidative phosphorylation, gene expression of COX2 and COX4l1 of the mitochondrial complex IV and antioxidative enzymes (SOD1, SOD2 and Catalase (CAT)) were monitored. For COX2 and COX4l1, no changes in gene expression were observed. However, a concomitant decrease in CAT and SOD1 mRNA, and increase in SOD2 mRNA after 24 hours of 10nM 1,25(OH)2D3 treatment were observed. A ~8-fold increase in SOD2 mRNA was apparent after 48 hours. The significant increase in SOD2 activity in the presence of vitamin D indicates an antioxidant potential and sensitization of vitamin D during osteosarcoma transformation and mitochondrial detoxification over time.
Environmental Regulation and the Cost of Bank Loans: International Evidence

Abstract Using a sample of 27 countries between 1990 and 2014, we find that banks charge higher interest rates and adjust other contractual features of their loans when lending to firms facing more stringent environmental regulations. Our evidence suggests that lenders’ concerns about the increase in environmental liabilities resulting from regulations is driving the results. Specifically, we show that firms facing such regulations have fewer participants in their loan syndicates, higher bankruptcy risk, and lower credit ratings, despite reducing their leverage. Overall, our results indicate that the observed higher loan spread is the result of environmentally sensitive lending practices by banks.
Nucleolar stress in Drosophila neuroblasts, a model for human ribosomopathies

Different stem cells or progenitor cells display variable threshold requirements for functional ribosomes. This is particularly true for several human ribosomopathies in which select embryonic neural crest cells or adult bone marrow stem cells, but not others, show lethality due to failures in ribosome biogenesis or function (now known as nucleolar stress). To determine if various Drosophila neuroblasts display differential sensitivities to nucleolar stress, we used CRISPR-Cas9 to disrupt the Nopp140 gene that encodes two splice variant ribosome biogenesis factors (RBFs). Disruption of Nopp140 induced nucleolar stress that arrested larvae in the second instar stage. While the majority of larval neuroblasts arrested development, the mushroom body (MB) neuroblasts continued to proliferate as shown by their maintenance of deadpan, a neuroblast-specific transcription factor, and by their continued EdU incorporation. MB neuroblasts in wild-type larvae appeared to contain more fibrillarin and Nopp140 in their nucleoli as compared to other neuroblasts, indicating that MB neuroblasts stockpile RBFs as they proliferate in late embryogenesis while other neuroblasts normally enter quiescence. A greater abundance of Nopp140 encoded by maternal transcripts in Nopp140-/- MB neuroblasts of 1----2-day-old larvae likely rendered these cells more resilient to nucleolar stress.
Image-based phenotyping and genetic analysis of potato skin set and color

ABSTRACT Image-based phenotyping offers new opportunities for fast, objective, and reliable measurement for breeding and genetics research. In the current study, image analysis was used to quantify potato skin color and skin set, which are critical for the marketability of new varieties. A set of 15 red potato varieties and advanced breeding lines was evaluated over two years at a single location, with two harvest times in the second year. After mechanical harvest and grading, 7-8 representative tubers per plot were photographed, and the photos were analyzed with ImageJ to measure skinning (as % surface area) and skin color using the Hue, Chroma and Lightness (HCL) representation. The plot-based heritability was consistently high (> 0.77) across traits and environments; the genetic correlation between environments was also high, ranging from 0.81 to 0.98. Significant increases in Lightness and Chroma, as well as a decrease in skinning, were observed at the late compared to early harvest, while the opposite trends for color were observed after six weeks of storage. The three color traits were unexpectedly collinear in this study, with the first principal component explaining 86% of the variation. This result may reflect the physiology of red color in potato, but the highly selected nature of the 15 genotypes may also be a factor. Image-based phenotyping offers new opportunities to advance genetic gain and understanding for tuber appearance traits that have been difficult to precisely measure in the past.
Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Our knowledge of transcriptional heterogeneities in epithelial stem and progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem and progenitor potential, but a comprehensive dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell state in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses predict a quasi-linear differentiation hierarchy where basal cells progress from Col17a1Hi/Trp63Hi state to early-response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basal-spinous interconversions at multiple basal transcriptional states. Our study provides a systematic view of epidermal cellular dynamics, supporting a revised "hierarchical-lineage" model of homeostasis.
Responsive Fluorophore Aggregation Provides Spectral Contrast for Fluorescence Lifetime Imaging.

Fluorophores experience altered emission lifetimes when incorporated into and liberated from macromolecules or molecular aggregates; this trend suggests the potential for a fluorescent, responsive probe capable of undergoing self-assembly and aggregation and consequently altering the lifetime of its fluorescent moiety to provide contrast between the active and inactive probes. We developed a cyanobenzothioazole-fluorescein conjugate (1), and spectroscopically examined the lifetime changes caused by its reduction-induced aggregation in vitro. A decrease in lifetime was observed for compound 1 in a buffered system activated by the biological reducing agent glutathione, thus suggesting a possible approach for designing responsive self-aggregating lifetime imaging probes.
Scalable Internet video streaming with differential JPEG-2000 video codec and content-aware rate control

A scalable video streaming system that includes video preprocessing, differential JPEG-2000 (DJ2K) video codec, and content-aware rate control is proposed. The proposed streaming solution tackles the problem of maintaining smooth video quality even when there is a significant change in the source content or the transmission bandwidth. The wavelet-based JPEG- 2000 video codec provides temporal, spatial and SNR scalabilities. A rate controller, which is separated from the encoder, can deal with diverse demands of a wide variety of users and various traffic conditions in a heterogeneous network by leveraging offered scalabilities and content- awareness. Simulations are conducted to demonstrate various advantages of the proposed solution.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Energy-Efficient Analog Sensing for Large-Scale and High-Density Persistent Wireless Monitoring

The research challenge of current wireless sensor networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single power charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities is proposed. Sensing is exclusively the responsibility of analog substrate—consisting of low-power, low-cost all-analog sensors—that sits beneath the traditional WSN comprising of digital nodes, which does all the processing of the sensor data received from analog sensors. A low-power and low-cost solution for substrate sensors has been proposed using analog joint source-channel coding (AJSCC) realized via the characteristics of metal–oxide–semiconductor field-effect transistor (MOSFET). Digital nodes (receiver) also estimate the source distribution at the analog sensors (transmitter) using machine learning techniques so as to find the optimal parameters of AJSCC that are communicated back to the analog sensors to adapt their sensing resolution as per the application needs. The proposed techniques have been validated via simulations from MATLAB and LTSpice to show promising performance and indeed prove that our framework can support large-scale high density and persistent WSN deployment.
Quitting in Protest: Presidential Policymaking and Civil Service Response

We formally model the impact of presidential policymaking on the willingness of bureaucrats to exert effort and stay in the government. In the model, centralized policy initiative by the president demotivates policy-oriented bureaucrats and can impel them to quit rather than implicate themselves in presidentially imposed policies they dislike. Those most likely to quit are a range of moderate bureaucrats. More extreme bureaucrats may be willing to wait out an incumbent president in the hope of shaping future policy. As control of the White House alternates between ideologically opposed extreme presidents, policy-minded moderates depart from bureaucratic agencies leaving only policy extremists or poorly performing "slackers." The consequences for policy making are substantial. Despite these adverse consequences, presidents have strong incentives to engage in centralized policymaking.
Continuous Speech Separation: Dataset and Analysis

This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior speech separation studies use pre-segmented audio signals, which are typically generated by mixing speech utterances on computers so that they fully overlap. Also, the separation algorithms have often been evaluated based on signal-based metrics such as signal-to-distortion ratio. However, in natural conversations, speech signals are continuous and contain both overlapped and overlap-free regions. In addition, the signal-based metrics only have weak correlation with automatic speech recognition (ASR) accuracy. Not only does this make it hard to assess the practical relevance of the tested algorithms, it also hinders researchers from developing systems that can be readily applied to real scenarios. In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a continuous audio stream that contains multiple utterances that are partially overlapped by a varying degree. A new real recording dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate conversations and capturing the audio replays with far-field microphones. A Kaldi-based ASR evaluation protocol is established by using a well-trained multi-conditional acoustic model. A recently proposed speaker-independent CSS algorithm is investigated by using LibriCSS. The dataset and evaluation scripts are made available to facilitate the research in this direction1.
A functional non-central limit theorem for multiple-stable processes with long-range dependence

Abstract A functional limit theorem is established for the partial-sum process of a class of stationary sequences which exhibit both heavy tails and long-range dependence. The stationary sequence is constructed using multiple stochastic integrals with heavy-tailed marginal distribution. Furthermore, the multiple stochastic integrals are built upon a large family of dynamical systems that are ergodic and conservative, leading to the long-range dependence phenomenon of the model. The limits constitute a new class of self-similar processes with stationary increments. They are represented by multiple stable integrals, where the integrands involve the local times of intersections of independent stationary stable regenerative sets.
CHARYBDIS: A LISP Program to Display Mathematical Expressions on Typewriter-like Devices

CHARYBDIS (from CHARacter-composed sYmBolic Display) is a LISP program to display mathematical expressions on typewriter-like devices such as line printers, teletypes, and scopes which display lines of characters, as well as typewriters. It was written as part of the output interface for MATHLAB [1].
Real Business Cycle Theory: Wisdom or Whimsy?

Abstract This paper assesses the empirical plausibility of the view that aggregate productivity stocks account for most of the variability in post-World War II US output. We argue that the type of evidence forwarded by proponents of this proposition is too fragile to be believable. The answer could be 70% as Kydland and Prescott (1989) claim, but the data contain almost no evidence against either the view that the answer is really 5% or that the answer is really 200%. Moreover point estimates of the importance of technology shocks are extremely sensitive to small perturbations in the theory. Depending on the sample period investigated, allowing for labor-hoarding behavior in an otherwise standard RBC model reduces the ability of technology shocks to account for aggregate fluctuations by between 30% to 60%.
Value of oak woodlands and open space on private property values in Southern California.

Este articulo analiza hasta que punto el mantenimiento de los bosque de quercineas y pastizales en una zona de rapida urbanizacion en el sur de California tiene influencia en los precios de mercado del bosque nativo y de las viviendas. Este estudio de caso ha sido realizado en la meseta de Santa Rosa en la provincia de Riverside. Las informaciones de los precios de las viviendas y de la tierra se refieren a 4.800 parcelas urbanizadas para viviendas y 8.300 acres de monte abierto de quercineas pertenecientes a propietarios privados. Estos datos se superpusieron con datos espaciales de mapas de vegetacion utilizando sistemas de informacion geografica (SIG). Se calcularon las distancias entre las parcelas y los limites lejanos del monte abierto, los senderos forestales y las superficies de quercineas mas cercanos. Una regresion hedonica muestra que tanto el valor del bosque de quercineas como de las viviendas decrecen, al incrementarse las distancias del monte abierto de quercineas con referencia a las viviendas. Los resultados del modelo se extrapolaron a todas las viviendas y parcelas de tierras en el area de estudio. Una reduccion del 10 por ciento en la distancia de las viviendas a las superficies de quercineas mas cercanas, resulta en un incremento de 4 millones de dolares en el valor total de las viviendas y de 16 millones de dolares en el valor total de la tierra en toda la comunidad. Esto demuestra que los beneficios externos de los pastizales y bosques abiertos de quercineas californianas incrementan simultaneamente los valores de las tierras forestales y viviendas de la comunidad. Existe un valor economico de conservacion de los habitats del bosque mediterraneo californiano. Este prometedor metodo de analisis facilitara en un futuro la caracterizacion de la influencia de diferentes configuraciones de espacios forestales abiertos en los valores de mercados de la tierra y de la vivienda.
Calculation of turbulence-driven secondary motion in ducts with arbitrary cross section

Calculation methods for turbulent duct flows are generalised for ducts with arbitrary cross sections. The irregular physical geometry is transformed into a regular one in computational space, and the flow equations are solved with a finite-volume numerical procedure. The turbulent stresses are calculated with an algebraic stress model derived by simplifying model transport equations for the individual Reynolds stresses. Two variants of such a model are considered in the present study.
Volatile Loss and Retention on Kuiper Belt Objects

Recent discoveries have shown that the very largest Kuiper Belt objects—Eris, 2005 FY9, and Sedna—are coated in methane and may contain other volatile ices as well. New detailed observations show that even within this class of volatile-rich bodies, unexpected differences exist in their surface compositions. 2005 FY9, a body approximately 60% the size of Pluto, with a reflectance spectrum similarly dominated by methane, has a surface depleted in molecular nitrogen by at least an order of magnitude with respect to Pluto. We find that the existence of this new class of volatile-rich objects, the lack of volatiles on most Kuiper Belt objects, and even the otherwise peculiar surface of 2005 FY9 can be explained as a consequence of atmospheric escape of volatile compounds. While previous studies of the surface compositions of objects in the Kuiper Belt have found no explainable patterns, atmospheric escape appears to provide a first-order explanation of the range of surface spectra seen on bodies in the outer solar system.
Referrals in Search Markets

Abstract This paper compares the equilibrium outcomes in search markets with and without referrals. Although it seems clear that consumers would benefit from referrals, it is not at all clear whether firms would unilaterally provide information about competing offers since such information could encourage consumers to purchase the product elsewhere. In a model of a horizontally differentiated product market with sequential consumer search, we show that valuable referrals can arise in the equilibrium: a firm will give referrals to consumers whose ideal product is sufficiently far away from the firm's offering. We allow firms to price-discriminate among consumers, and consumers to misrepresent their tastes. We found that the equilibrium profits tend to be higher in markets with referrals than in markets without. Consumers tend to be better off in the presence of referrals when search costs are not too low, and under a certain parameter range, referrals lead to a Pareto improvement.
International differences in social security and saving

Abstract The U.S. Social Security Administration, in cooperation with similar agencies in other countries, recently developed estimates of social security benefits for twelve major industrial countries. The present paper uses these data to estimate the effects of social security benefits on saving and retirement in an extended life cycle model. The parameter estimates indicate that, with retirement behavior given, social security significantly reduces private saving; an increase of the benefit-to-earnings ratio by 10 percentage points reduces the saving rate by approximately 3 percentage points.
Bargaining, interdependence, and the rationality of fair division

We consider two-person bargaining games with interdependent preferences and bilateral incomplete information. We show that in both the ultimatum game and the two-stage alternating-offers game, our equilibrium predictions are consistent with a number of robust experimental regularities that falsify the standard game-theoretic model: occurrence of disagreements, disadvantageous counteroffers, and outcomes that come close to the equal split of the pie. In the context of infinitehorizon bargaining, the implications of the model pertaining to fair outcomes are even stronger. In particular, the Coase property in our case generates “almost” 50-50 splits of the pie, almost immediately. The present approach thus provides a positive theory for the frequently encountered phenomenon of the 50-50 division of the gains from trade.
The Development and Validation of the Appreciative Intelligence® Scale

While Appreciative Intelligence® – the capacity to reframe and see the potential in any situation and act on it with success – has generated a robust body of literature, scholarly advancement of th...
Orbital Cyst with Ependymal Differentiation Associated with Microphthalmia

Background: Orbital cysts associated with microphthalmia are colobomatous lesions that typically present unilaterally and posterior to the globe. Case Report: A male infant had an orbital cyst associated with microphthalmia located anterior to the globe composed of a neuroglial wall, ependymal-like epithelial lining, with synaptophysin-positive cells resembling the retinal neuronal layer. Conclusion: This orbital cyst may represent a malformation of the eye rather than an encephalocele.