cssi/SciSciGPT-SciSciNet huggingface dataset:
African Development and the Marginalisation of Domestic Capitalists

The revival of industrial policy discussions has operated in parallel to reports of increasing domestic wealth accumulation across Africa. Regional and continent-wide industrialisation has begun to be rhetorically linked to discussions of regional common markets and through the African Continental Free Trade Area. Yet, there is barely any mention of integrating African capital into the African industrial policy agenda. Instead, the re-imagination of industrial policy relies on foreign investors, particularly the relocation of Chinese industry to various parts of the continent. This paper has two core objectives. Firstly, to explain why the study of African capitalists – popular in the 1980s and 1990s – has remained relatively dormant since then. Dominant narratives – through neopatrimonalism and dependency-inspired arguments – have been pessimistic about the potential of African capitalists to deliver structural transformation. Gradually, these narratives, alongside intellectual trends within mainstream social science and African studies, have discouraged the study of politics of state–business relations in Africa. Yet African capitalists have become increasingly prominent in popular culture. Many of the wealthiest and most prominent capitalists have emerged through owning diversified business groups across the continent. Secondly, this paper argues that more attention should be dedicated to the study of the politics of the emergence and sustenance of African diversified business groups (DBGs). To achieve this goal, a fluid categorisation of DBGs is introduced, building on Ben Ross Schneider’s previous work. Through three country case studies – Rwanda, Kenya and Tanzania – this paper highlights how a range of DBGs are emerging across three different political contexts.
Unsupervised 3D Pose Estimation With Geometric Self-Supervision

We present an unsupervised learning approach to re- cover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi- view image data, 3D skeletons, correspondences between 2D-3D points, or use previously learned 3D priors during training. A lifting network accepts 2D landmarks as inputs and generates a corresponding 3D skeleton estimate. Dur- ing training, the recovered 3D skeleton is reprojected on random camera viewpoints to generate new ‘synthetic’ 2D poses. By lifting the synthetic 2D poses back to 3D and re-projecting them in the original camera view, we can de- fine self-consistency loss both in 3D and in 2D. The training can thus be self supervised by exploiting the geometric self- consistency of the lift-reproject-lift process. We show that self-consistency alone is not sufficient to generate realistic skeletons, however adding a 2D pose discriminator enables the lifter to output valid 3D poses. Additionally, to learn from 2D poses ‘in the wild’, we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data. This improves results and demonstrates the useful- ness of 2D pose data for unsupervised 3D lifting. Results on Human3.6M dataset for 3D human pose estimation demon- strate that our approach improves upon the previous un- supervised methods by 30% and outperforms many weakly supervised approaches that explicitly use 3D data.
Uniform Substitution At One Fell Swoop

Uniform substitution of function, predicate, program or game symbols is the core operation in parsimonious provers for hybrid systems and hybrid games. By postponing soundness-critical admissibility checks, this paper introduces a uniform substitution mechanism that proceeds in a linear pass homomorphically along the formula. Soundness is recovered using a simple variable condition at the replacements performed by the substitution. The setting in this paper is that of differential hybrid games, in which discrete, continuous, and adversarial dynamics interact in differential game logic Open image in new window . This paper proves soundness and completeness of one-pass uniform substitutions for Open image in new window .
Robust Microfabrication of Highly Parallelized Three-Dimensional Microfluidics on Silicon

We present a new, robust three dimensional microfabrication method for highly parallel microfluidics, to improve the throughput of on-chip material synthesis by allowing parallel and simultaneous operation of many replicate devices on a single chip. Recently, parallelized microfluidic chips fabricated in Silicon and glass have been developed to increase the throughput of microfluidic materials synthesis to an industrially relevant scale. These parallelized microfluidic chips require large arrays (>10,000) of Through Silicon Vias (TSVs) to deliver fluid from delivery channels to the parallelized devices. Ideally, these TSVs should have a small footprint to allow a high density of features to be packed into a single chip, have channels on both sides of the wafer, and at the same time minimize debris generation and wafer warping to enable permanent bonding of the device to glass. Because of these requirements and challenges, previous approaches cannot be easily applied to produce three dimensional microfluidic chips with a large array of TSVs. To address these issues, in this paper we report a fabrication strategy for the robust fabrication of three-dimensional Silicon microfluidic chips consisting of a dense array of TSVs, designed specifically for highly parallelized microfluidics. In particular, we have developed a two-layer TSV design that allows small diameter vias (d 50,000 (d = 15 µm) TSVs to be fabricated on a single 4” wafer, using only conventional semiconductor fabrication equipment, with 100% yield (M = 16 chips) compared to 30% using previous approaches. We demonstrated the utility of these fabrication strategies by developing a chip that incorporates 20,160 flow focusing droplet generators onto a single 4” Silicon wafer, representing a 100% increase in the total number of droplet generators than previously reported. To demonstrate the utility of this chip for generating pharmaceutical microparticle formulations, we generated 5–9 µm polycaprolactone particles with a CV 1 trillion particles/hour).
A Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles

Rule-based (RB) energy management strategies are widely used in hybrid-electric vehicles because they are easy to implement and can be used without prior knowledge about future trips. In the literature, parameters used in RB methods are tuned and designed using known driving cycles. Although promising results have been demonstrated, it is difficult to apply such cycle-specific methods on real trips of last-mile delivery vehicles that have significant trip-to-trip differences in distance and energy intensity. In this paper, a reinforcement learning method and a RB strategy is used to improve the fuel economy of an in-use extended range electric vehicle (EREV) used in a last-mile package delivery application. An intelligent agent is trained on historical trips of a single delivery vehicle to tune a parameter in the engine-generator control logic during the trip using real-time information. The method is demonstrated on actual historical delivery trips in a simulation environment. An average of 19.5% in fuel efficiency improvement in miles per gallon gasoline equivalent is achieved on 44 test trips with a distance range of 31 miles to 54 miles not used for training, demonstrating promise to generalize the method. The presented framework is extendable to other RB methods and EREV applications like transit buses and commuter vehicles where similar trips are frequently repeated day-to-day.
Sparse High-Dimensional Isotonic Regression

We consider the problem of estimating an unknown coordinate-wise monotone function given noisy measurements, known as the isotonic regression problem. Often, only a small subset of the features affects the output. This motivates the sparse isotonic regression setting, which we consider here. We provide an upper bound on the expected VC entropy of the space of sparse coordinate-wise monotone functions, and identify the regime of statistical consistency of our estimator. We also propose a linear program to recover the active coordinates, and provide theoretical recovery guarantees. We close with experiments on cancer classification, and show that our method significantly outperforms several standard methods.
An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Models

Speaker-independent speech recognition systems trained with data from many users are generally robust against speaker variability and work well for a large population of speakers. However, these systems do not always generalize well for users with very different speech characteristics. This issue can be addressed by building personalized systems that are designed to work well for each specific user. In this paper, we investigate the idea of securely training personalized end-to-end speech recognition models on mobile devices so that user data and models never leave the device and are never stored on a server. We study how the mobile training environment impacts performance by simulating on-device data consumption. We conduct experiments using data collected from speech impaired users for personalization. Our results show that personalization achieved 63.7\% relative word error rate reduction when trained in a server environment and 58.1% in a mobile environment. Moving to on-device personalization resulted in 18.7% performance degradation, in exchange for improved scalability and data privacy. To train the model on device, we split the gradient computation into two and achieved 45% memory reduction at the expense of 42% increase in training time.
On the Hardness and Inapproximability of Recognizing Wheeler Graphs

In recent years several compressed indexes based on variants of the Burrows-Wheeler transformation have been introduced. Some of these are used to index structures far more complex than a single string, as was originally done with the FM-index [Ferragina and Manzini, J. ACM 2005]. As such, there has been an increasing effort to better understand under which conditions such an indexing scheme is possible. This has led to the introduction of Wheeler graphs [Gagie et al., Theor. Comput. Sci., 2017]. Gagie et al. showed that de Bruijn graphs, generalized compressed suffix arrays, and several other BWT related structures can be represented as Wheeler graphs, and that Wheeler graphs can be indexed in a way which is space efficient. Hence, being able to recognize whether a given graph is a Wheeler graph, or being able to approximate a given graph by a Wheeler graph, could have numerous applications in indexing. Here we resolve the open question of whether there exists an efficient algorithm for recognizing if a given graph is a Wheeler graph. We present: - The problem of recognizing whether a given graph G=(V,E) is a Wheeler graph is NP-complete for any edge label alphabet of size sigma >= 2, even when G is a DAG. This holds even on a restricted, subset of graphs called d-NFA's for d >= 5. This is in contrast to recent results demonstrating the problem can be solved in polynomial time for d-NFA's where d <= 2. We also show the recognition problem can be solved in linear time for sigma =1; - There exists an 2^{e log sigma + O(n + e)} time exact algorithm where n = |V| and e = |E|. This algorithm relies on graph isomorphism being computable in strictly sub-exponential time; - We define an optimization variant of the problem called Wheeler Graph Violation, abbreviated WGV, where the aim is to remove the minimum number of edges in order to obtain a Wheeler graph. We show WGV is APX-hard, even when G is a DAG, implying there exists a constant C >= 1 for which there is no C-approximation algorithm (unless P = NP). Also, conditioned on the Unique Games Conjecture, for all C >= 1, it is NP-hard to find a C-approximation; - We define the Wheeler Subgraph problem, abbreviated WS, where the aim is to find the largest subgraph which is a Wheeler Graph (the dual of the WGV). In contrast to WGV, we prove that the WS problem is in APX for sigma=O(1); The above findings suggest that most problems under this theme are computationally difficult. However, we identify a class of graphs for which the recognition problem is polynomial time solvable, raising the open question of which parameters determine this problem's difficulty.
L1EM: a tool for accurate locus specific LINE-1 RNA quantification

Motivation LINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE-1 elements are repetitive, it is difficult to quantify LINE-1 RNA at specific loci and to separate transcripts with protein coding capability from other sources of LINE-1 RNA. Results We provide a tool, L1EM that uses the expectation maximization algorithm to quantify LINE-1 RNA at each genomic locus, separating transcripts that are capable of generating retrotransposition from those that are not. We show the accuracy of L1EM on simulated data and against long read sequencing from HEK cells. Availability and implementation L1EM is written in python. The source code along with the necessary annotations are available at https://github.com/FenyoLab/L1EM and distributed under GPLv3. Supplementary information Supplementary data are available at Bioinformatics online.
Infant Contact-less Non-Nutritive Sucking Pattern Quantification via Facial Gesture Analysis.

Non-nutritive sucking (NNS) is defined as the sucking action that occurs when a finger, pacifier, or other object is placed in the baby's mouth, but there is no nutrient delivered. In addition to providing a sense of safety, NNS even can be regarded as an indicator of infant's central nervous system development. The rich data, such as sucking frequency, the number of cycles, and their amplitude during baby's non-nutritive sucking is important clue for judging the brain development of infants or preterm infants. Nowadays most researchers are collecting NNS data by using some contact devices such as pressure transducers. However, such invasive contact will have a direct impact on the baby's natural sucking behavior, resulting in significant distortion in the collected data. Therefore, we propose a novel contact-less NNS data acquisition and quantification scheme, which leverages the facial landmarks tracking technology to extract the movement signals of baby's jaw from recorded baby's sucking video. Since completion of the sucking action requires a large amount of synchronous coordination and neural integration of the facial muscles and the cranial nerves, the facial muscle movement signals accompanying baby's sucking pacifier can indirectly replace the NNS signal. We have evaluated our method on videos collected from several infants during their NNS behaviors and we have achieved the quantified NNS patterns closely comparable to results from visual inspection as well as contact-based sensor readings.
A System-level Behavioral Detection Framework for Compromised CPS Devices: Smart-Grid Case

Cyber-Physical Systems (CPS) play a significant role in our critical infrastructure networks from power-distribution to utility networks. The emerging smart-grid concept is a compelling critical CPS infrastructure that relies on two-way communications between smart devices to increase efficiency, enhance reliability, and reduce costs. However, compromised devices in the smart grid poses several security challenges. Consequences of propagating fake data or stealing sensitive smart grid information via compromised devices are costly. Hence, early behavioral detection of compromised devices is critical for protecting the smart grid’s components and data. To address these concerns, in this article, we introduce a novel and configurable system-level framework to identify compromised smart grid devices. The framework combines system and function call tracing techniques with signal processing and statistical analysis to detect compromised devices based on their behavioral characteristics. We measure the efficacy of our framework with a realistic smart grid substation testbed that includes both resource-limited and resource-rich devices. In total, using our framework, we analyze six different types of compromised device scenarios with different resources and attack payloads. To the best of our knowledge, the proposed framework is the first in detecting compromised CPS smart grid devices with system and function-level call tracing techniques. The experimental results reveal an excellent rate for the detection of compromised devices. Specifically, performance metrics include accuracy values between 95% and 99% for the different attack scenarios. Finally, the performance analysis demonstrates that the use of the proposed framework has minimal overhead on the smart grid devices’ computing resources.
Parameter estimation and uncertainty quantification for systems biology models

Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quantification before reliable predictions become possible. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.
Hot gas in galaxy clusters: Theory and simulations

The Sunyaev-Zeldovich Effect (SZE) detectable in galaxy clusters has emerged as a powerful new probe of the low-to-intermediate redshift universe (see articles by Birkin-shaw and Rephaeli in this volume, as well the review by Carlstrom et al. [1]). Within the prevailing theory of cosmological structure formation, galaxy clusters form in rare, massive peaks of the cosmic density field. Because of natural biasing, such regions get a “head start” on structure formation on all scales smaller than the cluster scale. As a consequence, galaxy clusters at the present epoch contain the oldest objects in the universe in an evolutionary sense [2]. This makes galaxy clusters intrinsically interesting as astrophysical objects, worthy of study observationally, theoretically, and computationally.
The Impact of SDG 3 on Health Priorities in Kenya

In September 2015, the United Nations General Assembly adopted the 2030 Agenda for Sustainable Development, which includes 17 Sustainable Development Goals (SDGs). Like the previous Millennium Deve...
Data-Centric Mixed-Variable Bayesian Optimization for Materials Design

Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify "lack of data" uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.
Evolution of the Informational Complexity of Contemporary Western Music

We measure the complexity of songs in the Million Song Dataset (MSD) in terms of pitch, timbre, loudness, and rhythm to investigate their evolution from 1960 to 2010. By comparing the Billboard Hot 100 with random samples, we find that the complexity of popular songs tends to be more narrowly distributed around the mean, supporting the idea of an inverted U-shaped relationship between complexity and hedonistic value. We then examine the temporal evolution of complexity, reporting consistent changes across decades, such as a decrease in average loudness complexity since the 1960s, and an increase in timbre complexity overall but not for popular songs. We also show, in contrast to claims that popular songs sound more alike over time, that they are not more similar than they were 50 years ago in terms of pitch or rhythm, although similarity in timbre shows distinctive patterns across eras and similarity in loudness has been increasing. Finally, we show that musical genres can be differentiated by their distinctive complexity profiles.
Estimating the anomaly base rate

Abstract The anomaly zoo has caused many to question whether researchers are using the right tests of statistical significance. But even if researchers are using the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors (i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly, the “anomaly base rate”). We propose a way to estimate it by combining two key insights: Empirical Bayes methods capture the implicit process by which researchers form priors about the likelihood that a new variable is a tradable anomaly based on their past experience, and under certain conditions, a one-to-one mapping exists between these prior beliefs and the best-fit tuning parameter in a penalized regression. The anomaly base rate varies substantially over time, and we study trading-strategy performance to verify our estimation results.
Matchings in k‐partite k‐uniform hypergraphs

For $k\ge 3$ and $\epsilon>0$, let $H$ be a $k$-partite $k$-graph with parts $V_1,\dots, V_k$ each of size $n$, where $n$ is sufficiently large. Assume that for each $i\in [k]$, every $(k-1)$-set in $\prod_{j\in [k]\setminus \{i\}} V_i$ lies in at least $a_i$ edges, and $a_1\ge a_2\ge \cdots \ge a_k$. We show that if $a_1, a_2\ge \epsilon n$, then $H$ contains a matching of size $\min\{n-1, \sum_{i\in [k]}a_i\}$. In particular, $H$ contains a matching of size $n-1$ if each crossing $(k-1)$-set lies in at least $\lceil n/k \rceil$ edges, or each crossing $(k-1)$-set lies in at least $\lfloor n/k \rfloor$ edges and $n\equiv 1\bmod k$. This special case answers a question of Rodl and Rucinski and was independently obtained by Lu, Wang, and Yu. The proof of Lu, Wang, and Yu closely follows the approach of Han [Combin. Probab. Comput. 24 (2015), 723--732] by using the absorbing method and considering an extremal case. In contrast, our result is more general and its proof is thus more involved: it uses a more complex absorbing method and deals with two extremal cases.
Robust Convergence Analysis of Three-Operator Splitting

Operator splitting methods solve composite optimization problems by breaking them into smaller sub-problems that can be solved sequentially or in parallel. In this paper, we propose a unified framework for certifying both linear and sublinear convergence rates for three-operator splitting (TOS) method under a variety of assumptions about the objective function. By viewing the algorithm as a dynamical system with feedback uncertainty (the oracle model), we leverage robust control theory to analyze the worst-case performance of the algorithm using matrix inequalities. We then show how these matrix inequalities can be used to verify sublinear/linear convergence of the TOS algorithm and guide the search for selecting the parameters of the algorithm (both symbolically and numerically) for optimal worst-case performance. We illustrate our results numerically by solving an input-constrained optimal control problem.
Projections onto the Set of Feasible Inputs and the Set of Feasible Solutions

We study the projection onto the set of feasible inputs and the set of feasible solutions of a polynomial optimisation problem (POP). Our motivation is increasing the robustness of solvers for POP: Without a priori guarantees of feasibility of a particular instance, one should like to perform the projection onto the set of feasible inputs prior to running a solver. Without a certificate of optimality, one should like to project the output of the solver onto the set of feasible solutions subsequently. We study the computational complexity, formulations, and convexifications of the projections. Our results are illustrated on IEEE test cases of Alternating Current Optimal Power Flow (ACOPF) problem.
Importance of self-interaction-error removal in density functional calculations on water cluster anions

Accurate description of the excess charge in water cluster anions is challenging for standard semi-local and (global) hybrid density functional approximations (DFAs). Using the recent unitary invariant implementation of the Perdew-Zunger self-interaction correction (SIC) method using Fermi-Lowdin orbitals, we assess the effect of self-interaction error on the vertical detachment energies of water cluster anions with the local spin density approximation (LSDA), Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation, and the strongly constrained and appropriately normed (SCAN) meta-GGA functionals. Our results show that for the relative energies of isomers with respect to reference CCSD(T) values, the uncorrected SCAN functional has the smallest deviation of 21 meV, better than that for the MP2 method. The performance of SIC-SCAN is comparable to that of MP2 and is better than SIC-LSDA and SIC-PBE, but it reverses the ordering of the two lowest isomers for water hexamer anions. Removing self interaction error (SIE) corrects the tendency of LSDA, PBE, and SCAN to over-bind the extra electron. The vertical detachment energies (VDEs) of water cluster anions, obtained from the total energy differences of corresponding anion and neutral clusters, are significantly improved by removing self-interaction and are better than the hybrid B3LYP functional, but fall short of MP2 accuracy. Removing SIE results in substantial improvement in the position of the eigenvalue of the extra electron. The negative of the highest occupied eigenvalue after SIC provides an excellent approximation to the VDE, especially for SIC-PBE where the mean absolute error with respect to CCSD(T) is only 17 meV, the best among all approximations compared in this work.
A regularized spatial market segmentation method with Dirichlet process—Gaussian mixture prior

Abstract Spatially referenced data are increasingly available thanks to the development of modern GPS technology. They also provide rich opportunities for spatial analytics in the field of marketing science. Our main interest is to propose a new efficient statistical framework to conduct spatial segmentation analysis for restaurants located in a metropolitan area in the U.S. The spatial segmentation problem poses important statistical challenges: selecting the optimal number of underlying structures of market segments, capturing complex and flexible spatial structures, and resolving any possible small sample size issue which can be typical in latent class analysis. Existing approaches try to tackle these issues in heuristic ways or seem silent on them. To overcome these challenges, we propose a new statistical framework based on regularized spatial mixture regressions with Dirichlet process integrating ridge or lasso regularization. Our simulation study demonstrates that the proposed models successfully recover the underlying spatial clustering structures and outperforms two existing benchmark models. In the empirical analysis using online customer satisfaction data from the Yelp, our model provides interesting insights on segment-level key drivers of customer satisfaction and interpretable relationships between regional demographics and restaurants’ characteristics.
Poisson polynomial identities II

We study Poisson polynomial identities for the symmetric Poisson algebra of a Lie algebra and for the graded Poisson algebra associated to a ring of differential operators. Connections are made among degrees of identities, coadjoint orbits and Krull dimension.
Robin Spectral Rigidity of the Ellipse

In this paper, we investigate $$C^1$$ isospectral deformations of the ellipse with Robin boundary conditions, allowing both the Robin function and domain to deform simultaneously. We prove that if the deformations preserve the reflectional symmetries of the ellipse, then the first variation of both the domain and Robin function must vanish. If the deformation is in fact smooth, reparametrizing allows us to show that the first variation actually vanishes to infinite order. In particular, there exist no such analytic isospectral deformations. The key ingredients are a version of Hadamard’s variational formula for variable Robin boundary conditions and an oscillatory integral representation of the wave trace variation which uses action angle coordinates for the billiard map. For the latter, we in fact construct an explicit parametrix for the wave propagator in the interior, microlocally near geodesic loops.
On the moduli spaces of metrics with nonnegative sectional curvature

The Kreck–Stolz $$s$$ invariant is used to distinguish connected components of the moduli space of positive scalar curvature metrics. We use a formula of Kreck and Stolz to calculate the $$s$$ invariant for metrics on $$S^n$$ bundles with nonnegative sectional curvature. We then apply it to show that the moduli spaces of metrics with nonnegative sectional curvature on certain 7-manifolds have infinitely many path components. These include certain positively curved Eschenburg and Aloff–Wallach spaces.
Measure Twice: Promise of Liquid Biopsy in Pediatric High-Grade Gliomas.

Abstract Purpose To review and critique the current state of liquid biopsy in pHGG. Materials and Methods Published literature was reviewed for articles related to liquid biopsy in pediatric glioma and adult glioma with a focus on high-grade gliomas. Results This review discusses the current state of liquid biomarkers of pHGG and their potential applications for liquid biopsy development. Conclusions While nascent, the progress toward identifying circulating analytes of pHGG primes the field of neuro-oncoogy for liquid biopsy development.
Analgesia use among 984 women with preeclampsia: A retrospective observational single-center study.

Abstract Study objective To determine the incidence of platelet count Design Retrospective analysis of all women with preeclampsia who presented to the labor floor during calendar years 2012–2015. Setting Academic medical center in New York City, United States. Patients Nine hundred and eighty four women with preeclampsia. Measurements Data collected included all platelet counts recorded for a period of 72 h prior to delivery. Demographic data, laboratory data including complete blood count with platelets, blood urea nitrogen (BUN) Creatinine (CR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), International Normalization Ratio (INR), and fibrinogen level, placement of neuraxial anesthesia and presence of epidural hematoma were also recorded. Main results We identified 984 women with preeclampsia who had a median presenting platelet count of 195,000 mm−3 [151,000–233,000] and a final platelet count of 193,000 mm−3 [147,000–233,000]. Platelets did not decrease significantly over the 72 h with a median change of 0 [−8–5.0] and a median % change of 0 [−4.1–2.6]. 64 (6.5%) patients had a platelet count Conclusions These findings suggest that the incidence of thrombocytopenia in women with preeclampsia, especially platelet count
Intestinal Transport of Bile Acids

The sections in this article are: 1 Rate Determinants of Intestinal Bile Acid Transport 1.1 Passive Absorption from Monomer Solutions 1.2 Passive Absorption from Micellar Solutions 1.3 Carrier-Mediated Absorption 2 Mechanisms of Cellular Bile Acid Transport 2.1 General Characteristics 2.2 Major Site for Bile Acid Absorption 2.3 Structure-Activity Relationships 2.4 Membrane Site for Active Transport 2.5 Sodium—Bile Acid Cotransport System 2.6 Cellular Metabolism and Exit of Bile Acids 3 Ontogenesis of Bile Acid Transport 4 Summary
Deep Image Blending

Image composition is an important operation to create visual content. Among image composition tasks, image blending aims to seamlessly blend an object from a source image onto a target image with lightly mask adjustment. A popular approach is Poisson image blending [23], which enforces the gradient domain smoothness in the composite image. However, this approach only considers the boundary pixels of target image, and thus can not adapt to texture of target background image. In addition, the colors of the target image often seep through the original source object too much causing a significant loss of content of the source object. We propose a Poisson blending loss that achieves the same purpose of Poisson image blending. In addition, we jointly optimize the proposed Poisson blending loss as well as the style and content loss computed from a deep network, and reconstruct the blending region by iteratively updating the pixels using the L-BFGS solver. In the blending image, we not only smooth out gradient domain of the blending boundary but also add consistent texture into the blending region. User studies show that our method outperforms strong baselines as well as state-of-the-art approaches when placing objects onto both paintings and real-world images. Code is available at: https://github.com/owenzlz/DeepImageBlending
A Bayesian nonparametric model for zero-inflated outcomes: Prediction, clustering, and causal estimation

Researchers are often interested in predicting outcomes, detecting distinct subgroups of their data, or estimating causal treatment effects. Pathological data distributions that exhibit skewness and zero-inflation complicate these tasks-requiring highly flexible, data-adaptive modeling. In this paper, we present a multipurpose Bayesian nonparametric model for continuous, zero-inflated outcomes that simultaneously predicts structural zeros, captures skewness, and clusters patients with similar joint data distributions. The flexibility of our approach yields predictions that capture the joint data distribution better than commonly used zero-inflated methods. Moreover, we demonstrate that our model can be coherently incorporated into a standardization procedure for computing causal effect estimates that are robust to such data pathologies. Uncertainty at all levels of this model flow through to the causal effect estimates of interest-allowing easy point estimation, interval estimation, and posterior predictive checks verifying positivity, a required causal identification assumption. Our simulation results show point estimates to have low bias and interval estimates to have close to nominal coverage under complicated data settings. Under simpler settings, these results hold while incurring lower efficiency loss than comparator methods. We use our proposed method to analyze zero-inflated inpatient medical costs among endometrial cancer patients receiving either chemotherapy or radiation therapy in the SEER-Medicare database.
Maternal and perinatal outcomes in programmed versus natural vitrified-warmed blastocyst transfer cycles

Abstract Research question Do maternal and perinatal outcomes differ between natural and programmed frozen embryo transfer (FET) cycles? Design Retrospective cohort study at a university-affiliated fertility centre including 775 patients who underwent programmed or natural FET cycles resulting in a singleton live birth using blastocysts vitrified between 2013 and 2018. Results A total of 384 natural and 391 programmed FET singleton pregnancies were analysed. Programmed FET resulted in higher overall maternal complications (32.2% [126/391] versus 18.8% [72/384]; P Conclusion Vitrified–warmed blastocyst transfer in a programmed cycle resulted in a twofold higher probability of HDP compared with transfer in a natural cycle. Natural FET cycle should, therefore, be recommended as first line for all eligible patients undergoing FET to reduce the risk of HDP.
On Adaptive Linear-Quadratic Regulators

Abstract Performance of adaptive control policies is assessed through the regret with respect to the optimal regulator, which reflects the increase in the operating cost due to uncertainty about the dynamics parameters. However, available results in the literature do not provide a quantitative characterization of the effect of the unknown parameters on the regret. Further, there are problems regarding the efficient implementation of some of the existing adaptive policies. Finally, results regarding the accuracy with which the system’s parameters are identified are scarce and rather incomplete. This study aims to comprehensively address these three issues. First, by introducing a novel decomposition of adaptive policies, we establish a sharp expression for the regret of an arbitrary policy in terms of the deviations from the optimal regulator. Second, we show that adaptive policies based on slight modifications of the Certainty Equivalence scheme are efficient. Specifically, we establish a regret of (nearly) square-root rate for two families of randomized adaptive policies. The presented regret bounds are obtained by using anti-concentration results on the random matrices employed for randomizing the estimates of the unknown parameters. Moreover, we study the minimal additional information on dynamics matrices that using them the regret will become of logarithmic order. Finally, the rates at which the unknown parameters of the system are being identified are presented.
Assessment of the Clinical Utility of Plasma Metagenomic Next-Generation Sequencing in a Pediatric Hospital Population

ABSTRACT Metagenomic next-generation sequencing (mNGS) of plasma cell-free DNA (cfDNA) is commercially available, but its role in the workup of infectious diseases is unclear. To understand the clinical utility of plasma mNGS, we retrospectively reviewed patients tested at a pediatric institution over 2 years to evaluate the clinical relevance of the organism(s) identified and the impact on antimicrobial management. We also investigated the effect of pretest antimicrobials and interpretation of molecules of microbial cfDNA per microliter (MPM) of plasma. Twenty-nine of 59 (49%) mNGS tests detected organism(s), and 28/51 (55%) organisms detected were clinically relevant. The median MPM of clinically relevant organisms was 1,533, versus 221 for irrelevant organisms (P = 0.01). mNGS test positive and negative percent agreements were 53% and 79%, respectively, and 50% of negative mNGS tests were true negatives. Fourteen percent of tests impacted clinical management by changing antimicrobial therapy. Immunocompromised status was the only patient characteristic that trended toward a significant clinical impact (P = 0.056). No patients with culture-negative endocarditis had organisms identified by mNGS. There were no significant differences in antimicrobial duration retest between tests with clinically relevant organism(s) and those that returned negative, nor were the MPMs different between pretreated and untreated organisms, suggesting that 10 days of antimicrobial therapy as observed in this cohort did not sterilize testing; however, no pretreated organisms identified resulted in a new diagnosis impacting clinical management. Plasma mNGS demonstrated higher utility for immunocompromised patients, but given the detection of many clinically irrelevant organisms (45%), cautious interpretation and infectious diseases consultation are prudent.
Credible communication in dynastic government

Abstract This paper studies information disclosure in a model of dynastic government. When information about past policy choices comes exclusively from the reports of previous administrations, each administration has an incentive to choose its (suboptimal) one-shot expenditure policy, and then misrepresent its choice to its successor. Consequently, it has been suggested that “horizontal accountability,” i.e., a system of governance where auditing functions lie outside the executive branch, can ensure credible disclosure of a government's activities. This paper suggests a cautious approach to that view. The baseline model examines the reporting incentives of an external auditor who can independently verify the information each period. Even with auditing, credible disclosure is shown to be problematic. Various extensions to this baseline model are examined. In one extension, “liberal” (i.e., those prefering larger government expenditures) and “conservative” (those prefering smaller expenditures) regimes and auditors evolve over time. It is shown that “conservative” (“liberal”) auditors are not credible when the current regime is also “conservative” (“liberal”). Moreover, because information transmission stops when the auditor's and the regime's biases coincide, effective deterrents even in the “good” periods (when the auditor's and the administration's biases differ) are difficult to construct. In all periods the equilibrium requirement of auditor neutrality constrains the dynamic incentives for efficient policy choices. These constraints are shown to bind away from optimal policies in standard constructions of equilibrium. Various ways in which auditing protocols can overcome these problems are discussed.
Understanding preferences for income redistribution

Abstract Recent research suggests that income redistribution preferences vary across identity groups. We employ statistical learning methods that emphasize pattern recognition; classification and regression trees (CART™) and random forests (RandomForests™), to uncover what these groups are. Using data from the General Social Survey, we find that, out of a large set of identity markers, only race, gender, age, and socioeconomic class are important classifiers for income redistribution preferences. Further, the uncovered identity groupings are characterized by complex patterns of interaction amongst these salient classifiers. We explore the extent to which existing theories of income redistribution can explain our results, but conclude that current approaches do not fully explain the findings.
Tariffs and Growth in Late Nineteenth Century America

Were high import tariffs somehow related to the strong U.S. economic growth during the late nineteenth century? This paper examines this frequently mentioned but controversial question and investigates the channels by which tariffs could have promoted growth during this period. The paper shows that: (i) late nineteenth century growth hinged more on population expansion and capital accumulation than on productivity growth; (ii) tariffs may have discouraged capital accumulation by raising the price of imported capital goods; (iii) productivity growth was most rapid in non-traded sectors (such as utilities and services) whose performance was not directly related to the tariff.
Semantic Adversarial Deep Learning

Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences.
Application of conformal symmetry to quantum electrodynamics

Conformal symmetry is used to determine the position-space vertex function ${\ensuremath{\Gamma}}_{\ensuremath{\lambda}}(x,y;z)$ of massless quantum electrodynamics in terms of the current-current correlation function ${\ensuremath{\pi}}_{\ensuremath{\mu}\ensuremath{\nu}}(x,y)$ and coefficients ${E}_{1}$ and ${E}_{2}$ appearing in the Wilson expansion of the product $\overline{\ensuremath{\psi}}(y)\ensuremath{\psi}(x)$ of Fermi fields. This relationship holds order by order in perturbation theory for graphs containing a single electron line, evaluated in a special position-dependent gauge. The relationship provides an explicit and perhaps simplified method for computing the vacuum-polarization amplitude ${F}_{1}(\ensuremath{\alpha})$ and is used to analyze the behavior of single-electron-loop amplitudes in quantum electrodynamics for values of the coupling constant $\ensuremath{\alpha}$ near a zero of ${F}_{1}(\ensuremath{\alpha})$.
Dynamic Properties of Supercooled Chlorinated Biphenyls

A study of the dynamics of a series of biphenyl compounds having varying chlorine levels was carried out. Increasing the chlorine content increases the glass transition temperature and makes the dynamics substantially more sensitive to density changes. Nonetheless, in the vicinity of their respective glass transitions, the different liquids display very similar extents of dynamic correlation and dynamic heterogeneity. The slight narrowing of the relaxation peak with increasing chlorine follows the general trend of the effect of increasing molecular polarity. This relationship between the peak breadth and dipole moment was reproduced in molecular dynamics simulations of a simplified model of the Aroclor molecule.
Cryptococcus neoformans evades pulmonary immunity by modulating xylose precursor transport

ABSTRACT Cryptococcus neoformans is a fungal pathogen that kills almost 200,000 people each year and is distinguished by abundant and unique surface glycan structures that are rich in xylose. A mutant strain of C. neoformans that cannot transport xylose precursors into the secretory compartment is severely attenuated in virulence in mice yet surprisingly is not cleared. We found that this strain failed to induce the nonprotective T helper cell type 2 (Th2) responses characteristic of wild-type infection, instead promoting sustained interleukin 12p40 (IL-12p40) induction and increased IL-17A (IL-17) production. It also stimulated dendritic cells to release high levels of proinflammatory cytokines, a behavior we linked to xylose expression. We further discovered that inducible bronchus-associated lymphoid tissue (iBALT) forms in response to infection with either wild-type cryptococci or the mutant strain with reduced surface xylose; although iBALT formation is slowed in the latter case, the tissue is better organized. Finally, our temporal studies suggest that lymphoid structures in the lung restrict the spread of mutant fungi for at least 18 weeks after infection, which is in contrast to ineffective control of the pathogen after infection with wild-type cells. These studies demonstrate the role of xylose in modulation of host response to a fungal pathogen and show that cryptococcal infection triggers iBALT formation.
Creation of a primary care personal asthma action plan

National guidelines recommend that all asthmatics are offered a personal action plan, writes Rachel Looms