cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal Scheduling of Water Distribution Systems

With dynamic electricity pricing, the operation of water distribution systems (WDS) is expected to become more variable. The pumps moving water from reservoirs to tanks and consumers can serve as energy storage alternatives if properly operated. Nevertheless, optimal WDS scheduling is challenged by the hydraulic law, according to which the pressure along a pipe drops proportionally to its squared water flow (WF). The optimal water flow (OWF) task is formulated here as a mixed-integer nonconvex problem incorporating flow and pressure constraints, critical for the operation of fixed-speed pumps, tanks, reservoirs, and pipes. The hydraulic constraints of the OWF problem are subsequently relaxed to second-order cone constraints. To restore feasibility of the original nonconvex constraints, a penalty term is appended to the objective of the relaxed OWF. The modified problem can be solved as a mixed-integer second-order cone program, which is analytically shown to yield WDS-feasible minimizers under certain sufficient conditions. Under these conditions, by suitably weighting the penalty term, the minimizers of the relaxed problem can attain arbitrarily small optimality gaps, thus providing OWF solutions. Numerical tests using real-world demands and prices on benchmark WDS demonstrate the relaxation to be exact even for setups where the sufficient conditions are not met.
Toda maps, cocycles, and canonical systems

I present a discussion of the hierarchy of Toda flows that gives center stage to the associated cocycles and the maps they induce on the $m$ functions. In the second part, these ideas are then applied to canonical systems; an important feature of this discussion will be my proposal that the role of the shift on Jacobi matrices should now be taken over by the more general class of twisted shifts.
Extracorporeal life support in pediatric trauma: a systematic review

Introduction Extracorporeal membrane oxygenation (ECMO) was once thought to be contraindicated in trauma patients, however ECMO is now used in adult patients with post-traumatic acute respiratory distress syndrome (ARDS) and multisystem trauma. Despite acceptance as a therapy for the severely injured adult, there is a paucity of evidence supporting ECMO use in pediatric trauma patients. Methods An electronic literature search of PubMed, MEDLINE, and the Cochrane Database of Collected Reviews from 1972 to 2018 was performed. Included studies reported on ECMO use after trauma in patients ≤18 years of age and reported outcome data. The Institute of Health Economics quality appraisal tool for case series was used to assess study quality. Results From 745 studies, four met inclusion criteria, reporting on 58 pediatric trauma patients. The age range was Conclusion ECMO may be a therapeutic option in critically ill pediatric trauma patients. Consideration should be made for the expansion of ECMO utilization in pediatric trauma patients including its application for pediatric patients at adult trauma centers with ECMO capabilities.
Daytime colour preference in Drosophila depends on the circadian clock and TRP channels

Light discrimination according to colour can confer survival advantages by guiding animals towards food and shelter and away from potentially harmful situations1,2. Such colour-dependent behaviour can be learned or innate. Data on innate colour preference in mammals remain controversial3 and there are limited data for simpler organisms4-7. Here we show that, when given a choice among blue, green and dim light, fruit flies exhibit an unexpectedly complex pattern of colour preference that changes according to the time of day. Flies show a strong preference for green in the early morning and late afternoon, a reduced green preference at midday and a robust avoidance of blue throughout the day. Genetic manipulations reveal that the peaks in green preference require rhodopsin-based visual photoreceptors and are controlled by the circadian clock. The midday reduction in green preference in favour of dim light depends on the transient receptor potential (TRP) channels dTRPA1 and Pyrexia, and is also timed by the clock. By contrast, avoidance of blue light is primarily mediated by multidendritic neurons, requires rhodopsin 7 and the TRP channel Painless, and is independent of the clock. Our findings show that several TRP channels are involved in colour-driven behaviour in Drosophila, and reveal distinct pathways of innate colour preference that coordinate the behavioural dynamics of flies in ambient light.
Single-trial neural dynamics are dominated by richly varied movements

When experts are immersed in a task, do their brains prioritize task-related activity? Most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. We wondered whether task-performing animals explore a broader movement landscape and how this impacts neural activity. We characterized movements using video and other sensors and measured neural activity using widefield and two-photon imaging. Cortex-wide activity was dominated by movements, especially uninstructed movements not required for the task. Some uninstructed movements were aligned to trial events. Accounting for them revealed that neurons with similar trial-averaged activity often reflected utterly different combinations of cognitive and movement variables. Other movements occurred idiosyncratically, accounting for trial-by-trial fluctuations that are often considered 'noise'. This held true throughout task-learning and for extracellular Neuropixels recordings that included subcortical areas. Our observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity.
In-advance path reservation for file transfers in e-science applications

We develop several multipath reservation algorithms for in-advance scheduling of single and multiple file transfers in connection-oriented optical networks. These algorithms consider the jobs one at a time or in a batch. The latter can be potentially useful to minimize the resource conflict between multiple consecutive requests. Extensive simulations using both real world networks and random topologies show that the greedy strategy, which process requests one at a time, can perform comparable to batch scheduling and is significantly better in terms of computational time requirement. Further, this strategy can be extended to reduce the path switching overheads.
Manufacturability Oriented Model Correction and Build Direction Optimization for Additive Manufacturing

We introduce a method to analyze and modify a shape to make it manufacturable for a given additive manufacturing (AM) process. Different AM technologies, process parameters or materials introduce geometric constraints on what is manufacturable or not. Given an input 3D model and minimum printable feature size dictated by the manufacturing process characteristics and parameters, our algorithm generates a corrected geometry that is printable with the intended AM process. A key issue in model correction for manufacturability is the identification of critical features that are affected by the printing process. To address this challenge, we propose a topology aware approach to construct the allowable space for a print head to traverse during the 3D printing process. Combined with our build orientation optimization algorithm, the amount of modifications performed on the shape is kept at minimum while providing an accurate approximation of the as-manufactured part. We demonstrate our method on a variety of 3D models and validate it by 3D printing the results.
Capacity and Energy-Efficiency of Delayed Access Scheme for Small Cell Networks

Data applications may typically tolerate a moderate delay before packet transmission between user equipment (UE) and cell begins. This delay can be taken advantage to reduce the communication distance, improve coverage probability, and increase overall energy-efficiency of the small cell network. To demonstrate such merits, we suggest a simple access scheme and analyze the distribution of coverage probability and throughput as a function of delay and transmit distance. Sufficient number of small base stations (SBSs) handle the peak traffic load. To improve energy-efficiency of the network, a number of SBSs are switched off at low traffic periods. Energy-efficiency can be further improved by turning all of the SBSs on and off, rather than selecting a subset and leaving them off. By doing so, coverage probability and bit-rate can be improved by delaying their transmissions and waiting for a closer SBS to become available. Results show that by turning SBSs on and off continuously and taking advantage of initial delay to connect a SBS yield an order of magnitude improvement in energy-efficiency, improves the coverage probability significantly at low signal to interference and noise (SINR) regime.
Distributed Data Summarization in Well-Connected Networks

We study distributed algorithms for some fundamental problems in data summarization. Given a communication graph $G$ of $n$ nodes each of which may hold a value initially, we focus on computing $\sum_{i=1}^N g(f_i)$, where $f_i$ is the number of occurrences of value $i$ and $g$ is some fixed function. This includes important statistics such as the number of distinct elements, frequency moments, and the empirical entropy of the data. In the CONGEST model, a simple adaptation from streaming lower bounds shows that it requires $\tilde{\Omega}(D+ n)$ rounds, where $D$ is the diameter of the graph, to compute some of these statistics exactly. However, these lower bounds do not hold for graphs that are well-connected. We give an algorithm that computes $\sum_{i=1}^{N} g(f_i)$ exactly in $\tau_G \cdot 2^{O(\sqrt{\log n})}$ rounds where $\tau_G$ is the mixing time of $G$. This also has applications in computing the top $k$ most frequent elements. We demonstrate that there is a high similarity between the GOSSIP model and the CONGEST model in well-connected graphs. In particular, we show that each round of the GOSSIP model can be simulated almost-perfectly in $\tilde{O}(\tau_G $ rounds of the CONGEST model. To this end, we develop a new algorithm for the GOSSIP model that $1\pm \epsilon$ approximates the $p$-th frequency moment $F_p = \sum_{i=1}^N f_i^p$ in $\tilde{O}(\epsilon^{-2} n^{1-k/p})$ rounds, for $p \geq2$, when the number of distinct elements $F_0$ is at most $O\left(n^{1/(k-1)}\right)$. This result can be translated back to the CONGEST model with a factor $\tilde{O}(\tau_G)$ blow-up in the number of rounds.
Successive approximation for H/sub /spl infin//

This paper gives three new algorithms for the H/sub /spl infin// control problem based on the idea of successive approximation. Unlike the generally used solution procedure based on the central Riccati equation, the proposed algorithms use a gain-updating scheme which has the advantage of connecting any stabilizing gain to the H/sub /spl infin// optimal gain. Various properties of the updating scheme are proved. In particular, it is shown under mild conditions that the existence, stabilizability and monotonicity of the solutions of the Riccati equation at each step are naturally guaranteed. The algorithms only involve an H/sub /spl infin// norm evaluation problem at each step and are proved to be convergent to the central solution for the given parameter of H/sub /spl infin// norm bound. These algorithms are particularly effective when the system order is large. A numerical example is given to illustrate the proposed algorithms.
Buy rough, sell smooth

Recent work has documented roughness in the time series of stock market volatility and investigated its implications for option pricing. We study a strategy for trading stocks based on measures of ...
TSM: Temporal Shift Module for Efficient Video Understanding

The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance. Specifically, it can achieve the performance of 3D CNN but maintain 2D CNN’s complexity. TSM shifts part of the channels along the temporal dimension; thus facilitate information exchanged among neighboring frames. It can be inserted into 2D CNNs to achieve temporal modeling at zero computation and zero parameters. We also extended TSM to online setting, which enables real-time low-latency online video recognition and video object detection. TSM is accurate and efficient: it ranks the first place on the Something-Something leaderboard upon publication; on Jetson Nano and Galaxy Note8, it achieves a low latency of 13ms and 35ms for online video recognition. The code is available at: https://github. com/mit-han-lab/temporal-shift-module.
Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning

Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores’ spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a ``manifold" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.
When Your Robot Breaks: Active Learning During Plant Failure

Detecting and adapting to catastrophic failures in robotic systems requires a robot to learn its new dynamics quickly and safely to best accomplish its goals. To address this challenging problem, we propose probabilistically-safe, online learning techniques to infer the altered dynamics of a robot at the moment a failure (e.g., physical damage) occurs. We combine model predictive control and active learning within a chance-constrained optimization framework to safely and efficiently learn the new plant model of the robot. We leverage a neural network for function approximation in learning the latent dynamics of the robot under failure conditions. Our framework generalizes to various damage conditions while being computationally light-weight to advance real-time deployment. We empirically validate within a virtual environment that we can regain control of a severely damaged aircraft in seconds and require only 0.1 seconds to find safe, information-rich trajectories, outperforming state-of-the-art approaches.
The rodent hippocampus as a bilateral structure: A review of hemispheric lateralization

The left and right rodent hippocampi exhibit striking lateralization in some of the very neural substrates considered to be critical for hippocampal cognitive function. Despite this, there is an overwhelming lack of consideration for hemispheric differences in studies of the rodent hippocampus. Asymmetries identified so far suggest that a bilateral model of the hippocampus will be essential for an understanding of this brain region, and perhaps of the brain more widely. Although hypotheses have been proposed to explain how the left and right hippocampi contribute to behavior and cognition, these hypotheses have either been refuted by more recent studies or have been limited in the scope of data they explain. Here, I will first review data on human and rodent hippocampal lateralization. The implications of these data suggest that considering the hippocampus as a bilateral structure with functional lateralization will be critical moving forward in understanding the function and mechanisms of this brain region. In exploring these implications, I will then propose a hypothesis of the hippocampus as a bilateral structure. This discrete-continuous hypothesis proposes that the left and right hippocampi contribute to spatial memory and navigation in a complementary manner. Specifically, the left hemisphere stores spatial information as discrete, salient locations, and the right hemisphere represents space continuously, contributing to route computation and flexible spatial navigation. Consideration of hippocampal lateralization in designing future studies may provide insight into the function of the hippocampus and resolve debates concerning its function.
Exploration of Cell Development Pathways through High-Dimensional Single Cell Analysis in Trajectory Space

High-dimensional single cell profiling coupled with computational modeling is emerging as a powerful tool to elucidate developmental programs directing cell lineages. We introduce tSpace, an algorithm based on the concept of "trajectory space", in which cells are defined by their distance along nearest neighbor pathways to every other cell in a population. Graphical mapping of cells in trajectory space allows unsupervised reconstruction and exploration of complex developmental sequences. Applied to flow and mass cytometry data, the method faithfully reconstructs thymic T cell development and reveals development and trafficking regulation of tonsillar B cells. Applied to the single cell transcriptome of mouse intestine and C. elegans, the method recapitulates development from intestinal stem cells to specialized epithelial phenotypes more faithfully than existing algorithms and orders C. elegans cells concordantly to the associated embryonic time. tSpace profiling of complex populations is well suited for hypothesis generation in developing cell systems.
Characteristics of Ice Cloud-Precipitation of Warm Season Mesoscale Convective Systems over the Great Plains

AbstractIn this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. T...
Gene Expression in Patient-Derived Neural Progenitors Implicates WNT5A Signaling in the Etiology of Schizophrenia.

Abstract Background Genome-wide association studies of schizophrenia have demonstrated that variations in noncoding regions are responsible for most of the common variation heritability of the disease. It is hypothesized that these risk variants alter gene expression. Therefore, studying alterations in gene expression in schizophrenia may provide a direct approach to understanding the etiology of the disease. In this study we use cultured neural progenitor cells derived from olfactory neuroepithelium (CNON cells) as a genetically unaltered cellular model to elucidate the neurodevelopmental aspects of schizophrenia. Methods We performed a gene expression study using RNA sequencing of CNON cells from 111 control subjects and 144 individuals with schizophrenia. Differentially expressed genes were identified with DESeq2 software, using covariates to correct for sex, age, library batches, and 1 surrogate variable component. Results A total of 80 genes were differentially expressed (false discovery rate Conclusions CNON cells are a good model of the neurodevelopmental aspects of schizophrenia and can be used to elucidate the etiology of the disorder.
Innovative Accounting Interviewing: A Comparison of Real and Virtual Accounting Interviewers

Recent technological advances make it possible to create automated, virtual interviewers, called embodied conversational agents (ECAs). We study how an ECA compares to a human interviewer in three ...
Ptychographic phase retrieval by proximal algorithms

We derive a set of ptychography phase-retrieval iterative engines based on proximal algorithms originally developed in convex optimization theory, and discuss their connections with existing ones. The use of proximal operator creates a simple frame work that allows us to incorporate the effect of noise from a maximum-likelihood principle. We focus on three particular algorithms, namely proximal minimization, alternating direction method of multiplier and accelerated proximal gradient, and benckmark their performance with numerical simulations and experimental x-ray data. Among them, accelerated proximal gradient shows superior performance in terms of both accuracy and convergence rate for a noisy dataset.
The value of using early-career earnings data in the College Scorecard to guide college choices

Abstract Policymakers are increasingly including early-career earnings data in consumer-facing college search tools to help students and families make more informed postsecondary education decisions. We offer new evidence on the degree to which existing college-specific earnings data equip consumers with useful information by documenting the level of selection bias in the earnings metrics reported in the U.S. Department of Education's College Scorecard. Given growing interest in reporting earnings by college and major, we focus on the degree to which earnings differences across four-year colleges and universities can be explained by differences in major composition across institutions. We estimate that more than 70% of the variation in median earnings across institutions is explained by observable factors, and accounting for differences in major composition explains 20–30% of the variation in earnings over and above institutional selectivity and student composition. We also identify large variations in the distribution of earnings within colleges; as a result, comparisons of early-career earnings can be extremely sensitive to whether the median, 25th, or 75th percentiles are presented. Taken together, our findings indicate that consumers can easily draw misleading conclusions about institutional quality when using publicly available earnings data to compare institutions.
The Gravitational Wave Signature of Core-Collapse Supernovae

We review the ensemble of anticipated gravitational-wave (GW) emission processes in stellar core collapse and postbounce core-collapse supernova evolution. We discuss recent progress in the modeling of these processes and summarize most recent GW signal estimates. In addition, we present new results on the GW emission from postbounce convective overturn and protoneutron star g-mode pulsations based on axisymmetric radiation-hydrodynamic calculations. Galactic core-collapse supernovae are very rare events, but within 3–5 Mpc from Earth, the rate jumps to 1 in ~2 years. Using the set of currently available theoretical gravitational waveforms, we compute upper-limit optimal signal-to-noise ratios based on current and advanced LIGO/GEO600/VIRGO noise curves for the recent SN 2008bk which exploded at ~3.9 Mpc. While initial LIGOs cannot detect GWs emitted by core-collapse events at such a distance, we find that advanced LIGO-class detectors could put significant upper limits on the GW emission strength for such events. We study the potential occurrence of the various GW emission processes in particular supernova explosion scenarios and argue that the GW signatures of neutrino-driven, magneto-rotational, and acoustically-driven core-collapse SNe may be mutually exclusive. We suggest that even initial LIGOs could distinguish these explosion mechanisms based on the detection (or non-detection) of GWs from a galactic core-collapse supernova.
Local Smart Inverter Control to Mitigate the Effects of Photovoltaic (PV) Generation Variability

The rapidly transforming electric power distribution system due to the integration of distributed energy resources (DERs) specifically roof-top photovoltaic (PV) generation may lead to significant operational challenges. In this paper, we address the challenges related to variable power generation profile from PV resources leading to voltage fluctuations at the secondary feeder level. The objective is to develop local smart inverter control methods to reduce the voltage fluctuations at distribution buses using reactive power support. Towards this goal, an approach based on the power flow measurements of the lines connected to the PV generation buses and the local PV generation measurements is proposed to obtain the required reactive power support to mitigate voltage fluctuations. The proposed method is validated using two three-phase unbalanced test systems: IEEE-123 bus and the modified R3-12.47-2 taxonomy feeder with 329-buses. It is shown that the proposed local control approach is effective in mitigating voltage violations resulting from large changes in active power injections of the PV generators and in reducing overall voltage fluctuations due to PV generation variability. Further, it is validated that the proposed local control approach results in a better performance with regard to decreasing the voltage fluctuations compared to a standard Thevenin impedance-based method.
Testosterone secretion varies in a sex- and stage-specific manner: Insights on the regulation of competitive traits from a sex-role reversed species.

Abstract Testosterone (T) mediates a variety of traits that function in competition for mates, including territorial aggression, ornaments, armaments, and gametogenesis. The link between T and mating competition has been studied mainly in males, but females also face selection pressures to compete for mates. Sex-role reversed species, in which females are the more competitive sex, provide a unique perspective on the role of T in promoting competitive traits. Here, we examine patterns of T secretion in sex-role reversed northern jacanas (Jacana spinosa) during breeding, when females are fertile and males are either seeking copulations or conducting parental care. We measured baseline levels of T in circulation along with a suite of behavioral and morphological traits putatively involved in mating competition. We evaluated hypotheses that levels of T track gonadal sex and parental role, and we begin to investigate whether T and competitive traits co-vary in a sex- and stage- specific manner. Although females had higher expression of competitive traits than males at either breeding stage, we found that females and incubating males had similar levels of T secretion, which were lower than those observed in copulating males. T was correlated with wing spur length in females and testes mass in copulating males, but was otherwise uncorrelated with other competitive traits. These findings suggest that levels of T in circulation alone do not predict variation in competitive traits across levels of analysis, including gonadal sex and parental role. Instead, our findings coupled with prior research indicate that selection for female mating competition and male care may generate different physiological regulation of competitive traits.
Multiprojective witness sets and a trace test

In the field of numerical algebraic geometry, positive-dimensional solution sets of systems of polynomial equations are described by witness sets. In this paper, we define multiprojective witness sets which encode the multidegree information of an irreducible multiprojective variety. Our main results generalize the regeneration solving procedure, a trace test, and numerical irreducible decomposition to the multiprojective case. Examples are included to demonstrate this new approach.
Impacts of climate change on wind resources over North America based on NA-CORDEX

Abstract The continuing growth in energy demand has promoted wind energy development worldwide. As anthropogenic greenhouse gas emissions will continue to modify the atmospheric circulation and global climate, assessing the impacts of climate change on wind energy is necessary. This study uses high-resolution simulations in the North America downscaling project (NA-CORDEX) to investigate the future change in wind resources over North America. Wind power is projected to decrease over large areas of the western US and East Coast throughout the year. The central US, with the greatest potential for wind energy development, will experience stronger wind variability and significantly increased wind power during some seasons. Wind power shows a significant increase (up to 20%) in the Southern Plains during spring and summer. The Northern Plains and the Midwest may experience slightly increased wind power (within 10%) during winter and spring. The seasonal and spatial patterns in the wind power change are relatively robust among the different emission scenarios, different future periods, and simulations with different spatial resolutions. This study outlines the regions that may benefit from or be negatively impacted by global climate change. Therefore, climate effects should be considered in the future build-out of wind energy resources.
Analysis of a Stochastic Model for Coordinated Platooning of Heavy-duty Vehicles

Platooning of heavy-duty vehicles (HDVs) is a key component of smart and connected highways and is expected to bring remarkable fuel savings and emission reduction. In this paper, we study the coordination of HDV platooning on a highway section. We model the arrival of HDVs as a Poisson process. Multiple HDVs are merged into one platoon if their headways are below a given threshold. The merging is done by accelerating the following vehicles to catch up with the leading ones. We characterize the following random variables: (i) platoon size, (ii) headway between platoons, and (iii) travel time increment due to platoon formation. We formulate and solve an optimization problem to determine the headway threshold for platooning that leads to minimal cost (time plus fuel). We also compare our results with that from Simulation of Urban MObility (SUMO).
Optimal reporter placement in sparsely measured genetic networks using the Koopman operator

Optimal sensor placement is an important yet unsolved problem in control theory. In biological organisms, genetic activity is often highly nonlinear, making it difficult to design libraries of promoters to act as reporters of the cell state. We make use of the Koopman observability gramian to develop an algorithm for optimal sensor (or reporter) placement for discrete time nonlinear dynamical systems to ease the difficulty of design of the promoter library. This ease is enabled due to the fact that the Koopman operator represents the evolution of a nonlinear system linearly by lifting the states to an infinite-dimensional space of observables. The Koopman framework ideally demands high temporal resolution, but data in biology are often sampled sparsely in time. Therefore we compute what we call the temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator, the latter of which is identified from the sparse data. The optimal placement of sensors then corresponds to maximizing the observability of the fine-grained system. We demonstrate the algorithm on a simulation example of a circadian oscillator.
Preparative scale mass spectrometry: A brief history of the calutron

Calutrons were developed in the laboratory of E. O. Lawrence at the University of California at Berkeley. They were a modification of the cyclotrons he had invented and used in his Noble Prize winning investigations of the atomic nucleus. At the time their construction was undertaken, calutrons represented the only certain means of preparing enriched uranium isotopes for the construction of a fission bomb. The effort was successful enough that every atom of the 42 kg of 235U used in the first uranium bomb had passed through at least one stage of calutron separation. At peak production, the first stage separators, α tanks, yielded an aggregate 258-g/d 235U enriched to about 10 at. % from its natural abundance level of 0. 72 at. %. The second stage separators, β tanks, used the 10 at. % material as feedstock and produced a total 204-g/d 235U enriched to at least 80 at. %. The latter, weapons grade, material was used in fission bombs. Under typical operating conditions, each α tank operated at a uranium beam intensity at the collectors of approximately 20 mA and each β tank at a beam intensity of approximately 215 mA at the collectors. Bulk separation of isotopes for bomb production ceased in 1945. Since that time calutrons have been used to separate stable isotopes, but on a more limited scale than wartime weapons production. Stable isotope separations since 1960 have taken place using one modified beta tank.
Remote sensing of angular scattering effect of aerosols in a North American megacity

Abstract The angle-dependent scattering effect of aerosols in the atmosphere not only influences climate through radiative forcing effects but also impacts trace gas remote sensing by modifying the path of radiation through the atmosphere. The aerosol phase function, which characterizes the angular signature of scattering, has been continuously monitored from ground-based and space-borne observations. However, the range of scattering angles these instruments can sample is very limited. Here, we report multi-year measurements from a mountain-top remote sensing instrument: the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS), which overlooks the Los Angeles megacity. The observational geometries of CLARS-FTS provide a wide range of scattering angles, from about 20° (forward) to about 140° (backward), which is larger than the range provided by any existing aerosol remote sensing instrument. We then quantify the aerosol angular scattering effect using the O2 ratio, which is the ratio of retrieved O2 Slant Column Density (SCD) to geometric O2 SCD. The O2 ratio quantifies the light path modification due to aerosol scattering, with a value of 1 representing an aerosol-free scenario. The lower the O2 ratio value than 1, the stronger the aerosol loading. CLARS-FTS measurements are highly sensitive to the angular scattering effect of aerosols in the Los Angeles (LA) urban atmosphere, due to the long light path going through the boundary layer and the wide range of observational angles. The differences in aerosol scattering between different surface reflection points targeted by CLARS-FTS can be explained by differences in their angular scattering geometries. The correlation between measurements at different targets can be used to quantify the strength of the angular dependence of the aerosol phase function. Applying the correlation technique to CLARS-FTS measurements, we find that, from 2011 to 2018, there is no significant trend in the aerosol phase function in the LA megacity. Overall, this study provides a practical observing strategy for quantifying the angular dependence of aerosol scattering in urban atmospheres that could potentially contribute towards improved greenhouse gas remote sensing in megacities.
Evaluation of low-Q 2 fits to ep and ed elastic scattering data

Abstract We examined several low- Q 2 elastic ep and ed scattering data sets using various models to extract the proton and deuteron rms radii and developed a comprehensive algorithm for estimating the systematic bias of each extracted radius. In each case, we chose the model and upper bound for Q 2 that minimized the combination of statistical uncertainty and bias. We attribute the discrepancy between small (≈0.84 fm) and large (≈0.88 fm) proton radius extractions to the absence of data that can accurately isolate the linear and quadratic contributions to the form factor at low- Q 2 . In light of this ambiguity, we estimated the model-dependence of each extracted radius by studying the distribution of many possible fits. The resulting radii are 0.842(4) fm for the proton and 2.092(19) fm for the deuteron. These results indicate radii consistent with those determined from muonic hydrogen and deuterium measurements.
Metric Representations of Networks: A Uniqueness Result

In this paper, we consider the problem of projecting networks onto metric spaces. Networks are structures that encode relationships between pairs of elements or nodes. However, these relationships can be independent of each other, and need not be defined for every pair of nodes. This is in contrast to a metric space, which requires that a distance between every pair of elements in the space be defined. To understand how to project networks onto metric spaces, we take an axiomatic approach: we first state two axioms for projective maps from the set of all networks to the set of finite metric spaces, then show that only one projection satisfies these requirements. The developed technique is shown to be an effective method for finding approximate solutions to combinatorial optimization problems. Finally, we illustrate the use of metric trees for efficient search in projected networks.
Structured reviews for data and knowledge-driven research.

Hypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists. To better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read-write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/. Availability and implementation Database URL: http://ngly1graph.org/. Network data files are at: https://github.com/SuLab/ngly1-graph and source code at: https://github.com/SuLab/bioknowledge-reviewer. Contact asu@scripps.edu.
Development of a real-time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications.

Purpose An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique. Methods We installed a Bluetooth sensor network in a three-floor clinic building to track patients, staff, and devices. The Bluetooth sensors measured the strength of the signal broadcasted from Bluetooth tags, which was fed into a deep neural network to calculate the location of the tags. The proposed deep neural network consists of a long short-term memory (LSTM) network and a deep classifier for tracking moving objects. Additionally, a spatial-temporal constraint algorithm was implemented to further increase the accuracy and stability of the results. To train the neural network, we divided the building into 115 zones and collected training data in each zone. We further augmented the training data to generate cross-zone trajectories, mimicking the real-world scenarios. We tuned the parameters for the proposed neural network to achieve relatively good accuracy. Results The proposed deep neural network achieved an overall accuracy of about 97% for tracking objects in each individual zone in the whole three-floor building, 1.5% higher than the baseline neural network that was proposed in an earlier paper, when using 10 s of signals. The accuracy increased with the density of Bluetooth sensors. For tracking moving objects, the proposed neural network achieved stable and accurate results. When latency is less of a concern, we eliminated the effect of latency from the accuracy and gained an accuracy of 100% for our testing trajectories, significantly improved from the baseline method. Conclusions The proposed deep neural network composed of a LSTM, a deep classifier and a posterior constraint algorithm significantly improved the accuracy and stability of RTLS for tracking moving objects.
Interval debulking surgery is not worth the wait: a National Cancer Database study comparing primary cytoreductive surgery versus neoadjuvant chemotherapy.

Objective In previous studies, neoadjuvant chemotherapy followed by interval debulking surgery was not inferior to primary cytoreductive surgery as initial treatment for advanced epithelial ovarian cancer. Our study aimed to compare surgical and survival outcomes between the two treatments in a large national database. Methods Data were extracted from the National Cancer Database from January 2004 to December 2015. Patients with FIGO (International Federation of Gynecologists and Obstetricians) stage III-IV epithelial ovarian cancer and known sequence of treatment were included: primary cytoreductive (surgery=26 717 and neoadjuvant chemotherapy=9885). Tubal and primary peritoneal cancer diagnostic codes were not included. Residual disease after treatment was defined based on recorded data: R0 defined as microscopic or no residual disease; R1 defined as macroscopic residual disease. Multivariate Cox proportional HR was used for survival analysis. Multivariate logistic regression analysis was utilized to compare mortality between groups. Outcomes were adjusted for significant covariates. Validation was performed using propensity score matching of significant covariates. Results A total of 36 602 patients were included in the analysis. Patients who underwent primary cytoreductive surgery had better survival than those treated with neoadjuvant chemotherapy followed by interval surgery, after adjusting for age, co-morbidities, stage, and residual disease (p Conclusions Most patients with advanced epithelial ovarian cancer may benefit from primary cytoreductive surgery. Patients treated with neoadjuvant chemotherapy should be those with co-morbidities unfit for surgery.
A Cascade Model of Wave Turbulence with Applications to Surface Gravity and Capillary Waves

A heuristic approach to the derivation of power spectra of wave motion is described and applied to capillary waves. The case of gravity waves studied earlier is briefly reviewed. In contract to the previous studies, the nonlinearity of the wave motion is not required to be small, and the mean number of resonantly interacting wave harmonics is not limited to a smallest possible number (which is 4 for gravity waves on a deep fluid and 3 for capillary waves). The main external parameter of the problem is the input flux Q of the wave energy related to the mean wind velocity. Depending on its value, wave spectra take various forms---from that corresponding to the weak-turbulence limit to that corresponding to the saturated (Phillips') wave spectra...
The moderating influence of firm market power on the transaction cost economics model: An empirical test in a forward channel integration context

Transaction cost economics (TCE) has guided a variety of research on governance in the strategic management literature. An important question arises, however, as to whether the TCE framework is equally appropriate for all types of firms in all business settings. In this paper, we argue that TCE is not and suggest that firms with high market power may be able to lower transaction costs under high asset specificity and uncertainty in nonintegrated distribution channels, avoiding the need to utilize highly integrated channels as a result. We test our hypotheses with data collected from 40 manufacturers of electronic and telecommunications products in 109 product-markets in the United States. The results support our hypothesis that transaction cost factors are better at explaining forward channel integration for firms with low market power than for firms with high market power. Our results indicate that the basic TCE framework must be supplemented by the market power construct to adequately explain forward channel integration decisions. Copyright © 2007 John Wiley & Sons, Ltd.
Protonation of polyaniline with lightly sulfonated polystyrene

Protonation of polyaniline base with lightly sulfonated polystyrene in polar solvents such as dimethyl sulfoxide (DMSO) and N-methyl pyrrolidone (NMP) was investigated by UV-Vis absorption spectroscopy. The isosbestic point clearly shows that quinoid unit and semiquinoid unit are in equilibrium and is a function of the sulfonic acid concentration. The protonation of polyaniline is retarded in NMP compared to DMSO due to prevalent hydrogen bonding.
Chapter 6 – Rural Dispensing

This chapter begins by looking briefly at the history of rural dispensing. It moves on to the current rules, sets out which areas may be affected and what their characteristics are. The main section deals with the procedure for applications to dispense in rural areas by doctors and by pharmacists. Finally, it looks at how some patients may obtain their medicines from a doctor regardless of where they live.