cssi/SciSciGPT-SciSciNet huggingface dataset:
An explicit semi-Lagrangian, spectral method for solution of Lagrangian transport equations in Eulerian-Lagrangian formulations

Abstract An explicit high-order semi-Lagrangian method is developed for the solution of Lagrangian transport equations in Eulerian-Lagrangian formulations. The method is consistent with an explicit, discontinuous spectral element method (DSEM) discretization of the Eulerian formulation. The semi-Lagrangian method seeds particles at Gauss quadrature collocation nodes within a spectral element. The particles are integrated explicitly in time and form the nodal basis for an advected interpolant. This interpolant is mapped back in a semi-Lagrangian fashion to the Gauss quadrature points through a least squares fit using constraints for element boundary values and optional constraints for mass and energy preservation. The stable explicit time step of the DSEM solver is sufficiently small to prevent particles seeded at the Gauss quadrature points from leaving the element’s bounds. The semi-Lagrangian method is hence local and parallel and does not have the grid complexity, and parallelization challenges of the commonly used Lagrangian particle solvers in particle-mesh methods for solution of Eulerian-Lagrangian formulations. Numerical tests in one and two dimensions for linear and non-linear advection show that the method converges exponentially. The use of mass and energy constraints can improve accuracy depending on the order of accuracy of the time integrator.
The impact of Hurricane Michael on longleaf pine habitats in Florida

Global biodiversity hotspots (GBHs) are increasingly vulnerable to human stressors such as anthropogenic climate change, which will alter the ecology of these habitats, even where protected. The longleaf pine (Pinus palustris) ecosystem (LPE) of the North American Coastal Plain is a GBH where disturbances are integral for ecosystem maintenance. However, stronger storms due to climate change may be outside their historical norm. In this study, we estimate the extent of Florida LPE that was directly affected by Hurricane Michael in 2018, an unprecedented Category 5 storm. We then leveraged a unique data set in a Before-After study of four sites within this region. We used variable-area transects and generalized linear mixed-effects models to estimate tree densities and logistic regression to estimate mortality by size class. We found at least 28% of the global total remaining extent of LPE was affected in Florida alone. Mortality was highest in medium sized trees (30–45 cm dbh) and ranged from 4.6–15.4% at sites further from the storm center, but increased to 87.8% near the storm center. As the frequency and intensity of extreme events increases, management plans to mitigate climate change need to account for large-scale stochastic mortality events to preserve critical habitats.
A versatile, multi-laser twin-microscope system for light-sheet imaging

Light-sheet microscopy offers faster imaging and reduced phototoxicity in comparison to conventional point-scanning microscopy, making it a preferred technique for imaging biological dynamics for durations of hours or days. Such extended imaging sessions pose a challenge, as it reduces the number of specimens that can be imaged in a given day. Here, we present a versatile light-sheet imaging instrument that combines two independently controlled microscope-twins, built so that they can share an ultrafast near-infrared laser and a bank of continuous-wave visible lasers, increasing the throughput and decreasing the cost. To permit a wide variety of specimens to be imaged, each microscope-twin provides flexible imaging parameters, including (i) operation in one-photon and/or two-photon excitation modes, (ii) delivery of one to three light-sheets via a trio of orthogonal excitation arms, (iii) sub-micron to micron imaging resolution, (iv) multicolor compatibility, and (v) upright (with provision for inverted) detection geometry. We offer a detailed description of the twin-microscope design to aid instrument builders who wish to construct and use similar systems. We demonstrate the instrument's versatility for biological investigation by performing fast imaging of the beating heart in an intact zebrafish embryo, deep imaging of thick patient-derived tumor organoids, and gentle whole-brain imaging of neural activity in behaving larval zebrafish.
Ownership: A Perennial Prize or a Fading Goal? A Curation, Framework, and Agenda for Future Research

For many, it is difficult to imagine that sharing used clothes with strangers and/or buying low quality, temporary garments could ever be preferable to owning our own. However, new findings from the Journal of Consumer Research suggest there may be contexts in which, and consumers for whom, this may in fact be true. We suggest that, given this work, consumer researchers are uniquely situated to explore whether we have indeed reached the proposed, “End of Ownership” (Perzanowski and Schultz 2017), as it has been known. If so, we must ask what may be gained as the desire for ownership wanes, in addition to what marketers, consumers and society might stand to lose in its absence.
Non-uniform distribution of myosin-mediated forces governs red blood cell membrane curvature through tension modulation

The biconcave disk shape of the mammalian red blood cell (RBC) is unique to the RBC and is vital for its circulatory function. Due to the absence of a transcellular cytoskeleton, RBC shape is determined by the membrane skeleton, a network of actin filaments cross-linked by spectrin and attached to membrane proteins. While the physical properties of a uniformly distributed actin network interacting with the lipid bilayer membrane have been assumed to control RBC shape, recent experiments reveal that RBC biconcave shape also depends on the contractile activity of nonmuscle myosin IIA (NMIIA) motor proteins. Here, we use the classical Helfrich-Canham model for the RBC membrane to test the role of heterogeneous force distributions along the membrane and mimic the contractile activity of sparsely distributed NMIIA filaments. By incorporating this additional contribution to the Helfrich-Canham energy, we find that the RBC biconcave shape depends on the ratio of forces per unit volume in the dimple and rim regions of the RBC. Experimental measurements of NMIIA densities at the dimple and rim validate our prediction that (a) membrane forces must be non-uniform along the RBC membrane and (b) the force density must be larger in the dimple than the rim to produce the observed membrane curvatures. Furthermore, we predict that RBC membrane tension and the orientation of the applied forces play important roles in regulating this force-shape landscape. Our findings of heterogeneous force distributions on the plasma membrane for RBC shape maintenance may also have implications for shape maintenance in different cell types.
Hypocalcemia with Immune Checkpoint Inhibitors: The Disparity among Various Reports.

Immune-related adverse events affecting parathyroid function are rarely reported with immune checkpoint inhibitors (ICPIs). Activating calcium-sensing receptor antibodies causing autoimmune hypoparathyroidism with nivolumab was recently reported. KEYNOTE-189 and CHECKMATE-067 trials reported a 21-29% hypocalcemia event rate, but the etiology of hypocalcemia was not reported. A chart review was performed to study patients receiving ICPI from 2015 to 2018 at multiple sites affiliated with Saint Vincent Hospital. The study population was divided into two groups based on the presence or absence of calcium altering conditions or medications. True hypocalcemia incidence was calculated after correcting calcium for albumin from the initiation of ICPI to their last follow-up. Group 1 (n = 83) includes patients with no calcium altering conditions or medications. Group 2 (n = 98) includes patients on calcium supplements (n = 17), vitamin D (n = 44), bisphosphonates (n = 24), >stage IIIB chronic kidney disease (CKD) (n = 5), and bone metastasis (n = 38). Hypocalcemia events in Group 1 vs. Group 2 were 8.4% and 19.3%, respectively. Our entire study demonstrated 26.8% vs. 1.1% of Grade I vs. II hypocalcemia events. However, after correcting the calcium for albumin, hypocalcemia incidence was 0.56% (n = 1). No further workup was done to investigate the etiology as that patient passed away. Our data suggest that the true hypocalcemia incidence after using albumin-corrected calcium values is very low in patients receiving IPCI, even in the presence of calcium altering factors. The percentage of patients with hypocalcemia is much higher and similar to the KEYNOTE-189 and CHECKMATE-067 trials when serum calcium values without albumin correction are used. Thus, the higher reported incidence of hypocalcemia in these trials is likely due to the reporting of serum calcium without albumin correction.
Decision Trees for Decision-Making under the Predict-then-Optimize Framework

We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A natural loss function in this framework is to measure the suboptimality of the decisions induced by the predicted input parameters, as opposed to measuring loss using input parameter prediction error. This natural loss function is known in the literature as the Smart Predict-then-Optimize (SPO) loss, and we propose a tractable methodology called SPO Trees (SPOTs) for training decision trees under this loss. SPOTs benefit from the interpretability of decision trees, providing an interpretable segmentation of contextual features into groups with distinct optimal solutions to the optimization problem of interest. We conduct several numerical experiments on synthetic and real data including the prediction of travel times for shortest path problems and predicting click probabilities for news article recommendation. We demonstrate on these datasets that SPOTs simultaneously provide higher quality decisions and significantly lower model complexity than other machine learning approaches (e.g., CART) trained to minimize prediction error.
Learning From Synthetic Animals

Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use synthetic images and ground truth generated from CAD animal models to address this challenge. To bridge the domain gap between real and synthetic images, we propose a novel consistency-constrained semi-supervised learning method (CC-SSL). Our method leverages both spatial and temporal consistencies, to bootstrap weak models trained on synthetic data with unlabeled real images. We demonstrate the effectiveness of our method on highly deformable animals, such as horses and tigers. Without using any real image label, our method allows for accurate keypoint prediction on real images. Moreover, we quantitatively show that models using synthetic data achieve better generalization performance than models trained on real images across different domains in the Visual Domain Adaptation Challenge dataset. Our synthetic dataset contains 10+ animals with diverse poses and rich ground truth, which enables us to use the multi-task learning strategy to further boost models' performance.
Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York.

Abstract Purpose New York State (NYS) is an epicenter of the SARS-CoV-2 pandemic in the United States. Reliable estimates of cumulative incidence in the population are critical to tracking the extent of transmission and informing policies. Methods We conducted a statewide seroprevalence study in a 15,101 patron convenience sample at 99 grocery stores in 26 counties throughout NYS. SARS-CoV-2 cumulative incidence was estimated from antibody reactivity by first poststratification weighting and then adjusting by antibody test characteristics. The percent diagnosed was estimated by dividing the number of diagnoses by the number of estimated infection-experienced adults. Results Based on 1887 of 15,101 (12.5%) reactive results, estimated cumulative incidence through March 29 was 14.0% (95% confidence interval [CI]: 13.3%–14.7%), corresponding to 2,139,300 (95% CI: 2,035,800–2,242,800) infection-experienced adults. Cumulative incidence was highest in New York City 22.7% (95% CI: 21.5%–24.0%) and higher among Hispanic/Latino (29.2%), non-Hispanic black/African American (20.2%), and non-Hispanic Asian (12.4%) than non-Hispanic white adults (8.1%, P Conclusions From the largest U.S. serosurvey to date, we estimated >2 million adult New York residents were infected through late March, with substantial disparities, although cumulative incidence remained less than herd immunity thresholds. Monitoring, testing, and contact tracing remain essential public health strategies.
Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model

Online social media platforms are turning into the prime source of news and narratives about worldwide events. However, a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. Our topic model identifies topics' recurrence over time with a varying time resolution. This framework not only captures the topic distributions from the data, but also approximates the user activity fluctuations over time. Furthermore, we define significance-dispersity trade-off (SDT) as a comparison measure to identify the topic with the highest lifetime attractiveness in a timestamped corpus. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria. Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data.
Delay-compensated control of sandwiched ODE–PDE–ODE hyperbolic systems for oil drilling and disaster relief

Abstract Motivated by engineering applications of subsea installation by deepwater construction vessels in oil drilling, and of aid delivery by unmanned aerial vehicles in disaster relief, we develop output-feedback boundary control of heterodirectional coupled hyperbolic PDEs sandwiched between two ODEs, where the measurement is the output state of one ODE and suffers a time delay. After rewriting the time-delay dynamics as a transport PDE of which the left boundary connects with the sandwiched system, a state observer is built to estimate the states of the overall system of ODE-heterodirectional coupled hyperbolic PDEs–ODE–transport PDE using the right boundary state of the last transport PDE. An observer-based output-feedback controller acting at the first ODE is designed to stabilize the overall system using backstepping transformations and frequency-domain designs. The exponential stability results of the closed-loop system, boundedness and exponential convergence of the control input are proved. The obtained theoretical result is applied to control of a deepwater oil drilling construction vessel as a simulation case, where the simulation results show the proposed control design reduces cable oscillations and places the oil drilling equipment to be installed in the target area on the sea floor. Performance deterioration under extreme and unmodeled disturbances is also illustrated.
A neural correlate of visual discomfort from flicker

The theory of "visual stress" holds that visual discomfort results from overactivation of the visual cortex. Despite general acceptance, there is a paucity of empirical data that confirm this relationship, particularly for discomfort from visual flicker. We examined the association between neural response and visual discomfort using flickering light of different temporal frequencies that separately targeted the LMS, L-M, and S postreceptoral channels. Given prior work that has shown larger cortical responses to flickering light in people with migraine, we examined 10 headache-free people and 10 migraineurs with visual aura. The stimulus was a uniform field, 50 degrees in diameter, that modulated with high-contrast flicker between 1.625 and 30 Hz. We asked subjects to rate their visual discomfort while we recorded steady-state visually evoked potentials (ssVEPs) from early visual cortex. The peak temporal sensitivity ssVEP amplitude varied by postreceptoral channel and was consistent with the known properties of these visual channels. There was a direct, linear relationship between the amplitude of neural response to a stimulus and the degree of visual discomfort it evoked. No substantive differences between the migraine and control groups were found. These data link increased visual cortical activation with the experience of visual discomfort.
Detection of base analogs incorporated during DNA replication by nanopore sequencing

DNA synthesis is a fundamental requirement for cell proliferation and DNA repair, but no single method can identify the location, direction and speed of replication forks with high resolution. Mammalian cells have the ability to incorporate thymidine analogs along with the natural A, T, G and C bases during DNA synthesis, which allows for labeling of replicating or repaired DNA. Here, we demonstrate the use of the Oxford Nanopore Technologies MinION to detect 11 different thymidine analogs including CldU, BrdU, IdU as well as EdU alone or coupled to Biotin and other bulky adducts in synthetic DNA templates. We also show that the large adduct Biotin can be distinguished from the smaller analog IdU, which opens the possibility of using analog combinations to identify the location and direction of DNA synthesis. Furthermore, we detect IdU label on single DNA molecules in the genome of mouse pluripotent stem cells and using CRISPR/Cas9-mediated enrichment, determine replication rates using newly synthesized DNA strands in human mitochondrial DNA. We conclude that this novel method, termed Replipore sequencing, has the potential for on target examination of DNA replication in a wide range of biological contexts.
Are financing constraints binding for investment? Evidence from a natural experiment

Abstract This paper shows that the availability of cash flows dominates the effects of cost of capital for investment at the firm level. Using an exogenous tax reform in Canada as a quasi-natural experiment, I find that a temporary and unexpected increase in the cost of capital for firms with low availability of retained earnings has no effect on investment of those firms. A subsequent direct increase in the availability of cash flows has large effects on investment. This suggests that internal financing constraints are binding for firms, as they prefer to use low cost retained earnings to finance their investment.
Bile acid composition regulates the manganese transporter Slc30a10 in intestine

Bile acids (BAs) comprise heterogenous amphipathic cholesterol-derived molecules that carry out physicochemical and signaling functions. A major site of BA action is the terminal ileum, where enterocytes actively reuptake BAs and express high levels of BA-sensitive nuclear receptors. BA pool size and composition are affected by changes in metabolic health, and vice versa. One of several factors that differentiate BAs is the presence of a hydroxyl group on C12 of the steroid ring. 12α-Hydroxylated BAs (12HBAs) are altered in multiple disease settings, but the consequences of 12HBA abundance are incompletely understood. We employed mouse primary ileum organoids to investigate the transcriptional effects of varying 12HBA abundance in BA pools. We identified Slc30a10 as one of the top genes differentially induced by BA pools with varying 12HBA abundance. SLC30A10 is a manganese efflux transporter critical for whole-body manganese excretion. We found that BA pools, especially those low in 12HBAs, induce cellular manganese efflux and that Slc30a10 induction by BA pools is driven primarily by lithocholic acid signaling via the vitamin D receptor. Administration of lithocholic acid or a vitamin D receptor agonist resulted in increased Slc30a10 expression in mouse ileum epithelia. These data demonstrate a previously unknown role for BAs in intestinal control of manganese homeostasis.
Thermodynamic modeling of high-grade metabasites: a case study using the Tso Morari UHP eclogite

Thermodynamic modeling is an important technique to simulate the evolution of metamorphic rocks, particularly the poorly preserved prograde metamorphic reactions. The development of new thermodynamic modeling techniques and availability of updated thermodynamic databases and activity–composition (a–X) relations, call for an evaluation of best practices for modeling pressure–temperature (P–T) paths of metabasites. In this paper, eclogite from the Tso Morari UHP terrane, NW India, is used as a representative metabasite to directly compare the outputs (pseudosections and P–T paths) generated from recent versions of the widely used THERMOCALC and Theriak-Domino programs. We also evaluate the impact of using the most updated thermodynamic database (ds 62, Holland and Powell in J Metamorph Geol 29(3):333–383, 10.1111/j.1525-1314.2010.00923.x, 2011) relative to an older version (ds 55, Holland and Powell in J Metamorph Geol 16(3):309–343, 10.1111/j.1525-1314.1998.00140.x, 1998), and the effect of the user’s choice of mineral a–X relations while considering the effect of garnet fractionation on the rock’s effective bulk composition. The following modeling protocols were assessed: (1) TC33; THERMOCALC version 3.33 with database ds 55 and garnet a–X relations of White et al. (J Metamorph Geol 25(5):511–527, 10.1111/j.1525-1314.2007.00711.x, 2007); (2) TC47; THERMOCALC version 3.47 with database ds 62 and garnet a–X relations of White et al. (J Metamorph Geol 32(3):261–286, 10.1111/jmg.12071, 2014a); (3) TDG; Theriak-Domino with database ds 62 and garnet a–X relations of White et al. (2014a), and (4) TDW; Theriak-Domino with database ds 62 and garnet a–X relations of White et al. (2007). TC47 and TDG modeling yield a similar peak metamorphic P–T of 34 ± 1.5 kbar at 544 ± 15 °C and 551 ± 12 °C, respectively. The results are 5–8 kbar higher in pressure than that determined from TC33 modeling (26 ± 1 kbar at 565 ± 8 °C), and TDW modeling (28.5 ± 1.5 kbar at 563 ± 13 °C). Results indicate that all four modeling protocols generally provide consistent metamorphic phase relations and thermodynamic simulations regarding fractionation of the bulk composition and prograde metamorphism within uncertainty. In all model calculations, the initial bulk composition measured by XRF does not represent the effective bulk composition at the time of garnet nucleation. The choice of garnet a–X relations can affect predictions of peak pressure, regardless of program choice. This study illustrates the importance of careful consideration of which a–X relations one chooses, as well as the need for comparison between modeling predictions and evidence from the geochemistry and petrography of the rock(s) themselves.
A Semi-Definite Programming Approach to Robust Adaptive MPC under State Dependent Uncertainty

We propose an Adaptive MPC framework for uncertain linear systems to achieve robust satisfaction of state and input constraints. The uncertainty in the system is assumed additive, state dependent, and globally Lipschitz with a known Lipschitz constant. We use a non-parametric technique for online identification of the system uncertainty by approximating its graph via envelopes defined by quadratic constraints. At any given time, by solving a set of convex optimization problems, the MPC controller guarantees robust constraint satisfaction for the closed-loop system for all possible values of system uncertainty modeled by the envelope. The uncertainty envelope is refined with data using Set Membership Methods. We highlight the efficacy of the proposed framework via a detailed numerical example.
Varying random coefficient models

Abstract This paper analyzes unobserved heterogeneity when observed characteristics are modeled nonlinearly. The proposed model builds on varying random coefficients (VRC) that are determined by nonlinear functions of observed regressors and additively separable unobservables. This paper proposes a novel estimator of the VRC density based on weighted sieve minimum distance. The main example of sieve bases are Hermite functions which yield a numerically stable estimation procedure. This paper shows inference results that go beyond what has been shown in ordinary RC models. We provide in each case rates of convergence and also establish pointwise limit theory of linear functionals, where a prominent example is the density of potential outcomes. In addition, a multiplier bootstrap procedure is proposed to construct uniform confidence bands. A Monte Carlo study examines finite sample properties of the estimator and shows that it performs well even when the regressors associated to RC are far from being heavy tailed. Finally, the methodology is applied to analyze heterogeneity in income elasticity of demand for housing.
Fast tetrahedral meshing in the wild

We propose a new tetrahedral meshing method, fTetWild, to convert triangle soups into high-quality tetrahedral meshes. Our method builds on the TetWild algorithm, replacing the rational triangle insertion with a new incremental approach to construct and optimize the output mesh, interleaving triangle insertion and mesh optimization. Our approach makes it possible to maintain a valid floating-point tetrahedral mesh at all algorithmic stages, eliminating the need for costly constructions with rational numbers used by TetWild, while maintaining full robustness and similar output quality. This allows us to improve on TetWild in two ways. First, our algorithm is significantly faster, with running time comparable to less robust Delaunay-based tetrahedralization algorithms. Second, our algorithm is guaranteed to produce a valid tetrahedral mesh with floating-point vertex coordinates, while TetWild produces a valid mesh with rational coordinates which is not guaranteed to be valid after floating-point conversion. As a trade-off, our algorithm no longer guarantees that all input triangles are present in the output mesh, but in practice, as confirmed by our tests on the Thingi10k dataset, the algorithm always succeeds in inserting all input triangles.
Unique Structural Features of Mammalian NEIL2 DNA Glycosylase Prime Its Activity for Diverse DNA Substrates and Environments.

Summary Oxidative damage on DNA arising from both endogenous and exogenous sources can result in base modifications that promote errors in replication as well as generating sites of base loss (abasic sites) that present unique challenges to maintaining genomic integrity. These lesions are excised by DNA glycosylases in the first step of the base excision repair pathway. Here we present the first crystal structure of a NEIL2 glycosylase, an enzyme active on cytosine oxidation products and abasic sites. The structure reveals an unusual “open” conformation not seen in NEIL1 or NEIL3 orthologs. NEIL2 is predicted to adopt a “closed” conformation when bound to its substrate. Combined crystallographic and solution-scattering studies show the enzyme to be conformationally dynamic in a manner distinct among the NEIL glycosylases and provide insight into the unique substrate preference of this enzyme. In addition, we characterized three cancer variants of human NEIL2, namely S140N, G230W, and G303R.
Frequency Stability Using MPC-Based Inverter Power Control in Low-Inertia Power Systems

The electrical grid is evolving from a network consisting of mostly synchronous machines to a mixture of synchronous machines, and inverter-based resources such as wind, solar, and energy storage. This transformation has led to a decrease in mechanical inertia, which necessitate a need for the new resources to provide frequency responses through their inverter interfaces. In this paper we proposed a new strategy based on model predictive control to determine the optimal active-power set-point for inverters in the event of a disturbance in the system. Our framework explicitly takes the hard constraints in power, and energy into account, and we show that it is robust to measurement noise, limited communications, and delay by using an observer to estimate the model mismatches in real-time. We demonstrate the proposed controller significantly outperforms an optimally tuned virtual synchronous machine on a standard 39-bus system under a number of scenarios. In turn, this implies optimized inverter-based resources can provide better frequency responses compared to conventional synchronous machines.
On clique immersions in line graphs

Abstract We prove that if L ( G ) immerses K t then L ( m G ) immerses K m t , where m G is the graph obtained from G by replacing each edge in G with a parallel edge of multiplicity m . This implies that when G is a simple graph, L ( m G ) satisfies a conjecture of Abu-Khzam and Langston. We also show that when G is a line graph, G has a K t -immersion iff G has a K t -minor whenever t ≤ 4 , but this equivalence fails in both directions when t ≥ 5 .
Machine learning based refined differential gene expression analysis of pediatric sepsis

Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.
Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata

We apply two evolutionary search algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that can perform computational tasks requiring global coordination. In particular, we compare search efficiency for PSO and GAs applied to both the density classification problem and to the novel generation of ”chaotic” CA. Our work furthermore introduces a new variant of PSO, the Binary Global-Local PSO (BGL-PSO).
Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling

Automated identification and classification of ion solvation sites in diverse chemical systems will improve the understanding and design of polymer electrolytes for battery applications. We introduce a machine learning approach to classify and characterize ion solvation environments based on feature vectors extracted from all-atom simulations. This approach is demonstrated in poly(3,4-propylenedioxythiophene), which is a promising candidate polymer binder for Li-ion batteries. In the dry polymer, four distinct Li⁺ solvation environments are identified close to the backbone of the polymer. Upon swelling of the polymer with propylene carbonate solvent, the nature of Li⁺ solvation changes dramatically, featuring a rapid diversification of solvation environments. This application of machine learning can be generalized to other polymer condensed-phase systems to elucidate the molecular mechanisms underlying ion solvation.
The Capacity of Private Information Retrieval From Uncoded Storage Constrained Databases

Private information retrieval (PIR) allows a user to retrieve a desired message from a set of databases without revealing the identity of the desired message. The replicated database scenario, where $N$ databases store each of the $K$ messages was considered by Sun and Jafar, and the optimal download cost was characterized as $\left ({1+ \frac {1}{N}+ \frac {1}{N^{2}}+ \cdots + \frac {1}{N^{K-1}}}\right)$ . In this work, we consider the problem of PIR from uncoded storage constrained databases. Each database has a storage capacity of $\mu KL$ bits, where $L$ is the size of each message in bits, and $\mu \in [{1/N, 1}]$ is the normalized storage. The novel aspect of this work is to characterize the optimum download cost of PIR from uncoded storage constrained databases for any “normalized storage” value in the range $\mu \in [{1/N, 1}]$ . In particular, for any $(N,K)$ , we show that the optimal trade-off between normalized storage, $\mu $ , and the download cost, $D(\mu)$ , is a piece-wise linear function given by the lower convex hull of the $N$ pairs $\left ({\frac {t}{N}, \left ({1+ \frac {1}{t}+ \frac {1}{t^{2}}+ \cdots + \frac {1}{t^{K-1}}}\right)}\right)$ for $t=1,2,\ldots, N$ . To prove this result, we first present a storage constrained PIR scheme for any $(N,K)$ . Next, we obtain a general lower bound on the download cost for PIR, which is valid for any arbitrary storage architecture. The uncoded storage assumption is then applied which allows us to express the lower bound as a linear program (LP). Finally, we solve the LP to obtain tight lower bounds on the download cost for different regimes of storage, which match the proposed storage constrained PIR scheme.
Evaluating Strategies to Reduce Arsenic Poisoning in South Asia: A View from the Social Sciences

The World Health Organization has labeled the problem of arsenic contamination of groundwater in South Asia as “the largest mass poisoning in human history.” Various technical solutions to the prob...
The sparse variance contamination model

We consider a Gaussian contamination (i.e., mixture) model where the contamination manifests itself as a change in variance. We study this model in various asymptotic regimes, in parallel with the ...
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping

This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories. We demonstrate the effectiveness of our mapping and collision checking algorithms for autonomous navigation of an Ackermann-drive robot in unknown environments.
Spectral analysis modal methods (SAMMs) using non-time-resolved PIV

We present spectral analysis modal methods (SAMMs) to perform POD in the frequency domain using non-time-resolved particle image velocity (PIV) data combined with unsteady surface pressure measurements. In particular, time-resolved unsteady surface pressure measurements are synchronized with non-time-resolved planar PIV measurements acquired at 15 Hz in a Mach 0.6 cavity flow. Leveraging the spectral linear stochastic estimation (LSE) method of Tinney et al. (Exp Fluids 41:763–775, 2006), we first estimate the cross-correlations between the velocity field and the unsteady pressure sensors via sequential time shifts, followed by a Fast Fourier transform to obtain the pressure–velocity cross spectral density matrix. This leads to a linear multiple-input/multiple-output (MIMO) model that determines the optimal transfer functions between the input cavity wall pressure and the output velocity field. Two variants of SAMMs are developed and applied. The first, termed “SAMM-SPOD”, combines the MIMO model with the SPOD algorithm of Towne et al. (J Fluid Mech, https://doi.org/10.1017/jfm.2018.283, 2018). The second, called “SAMM-RR”, adds independent sources and uses a sorted eigendecomposition of the input pressure cross-spectral matrix to enable an efficient reduced-rank eigendecomposition of the velocity cross-spectral matrix. In both cases, the resulting rank-1 POD eigenvalues associated with the Rossiter frequencies exhibit very good agreement with those obtained using independent time-resolved PIV measurements. The results demonstrate that SAMMs provide a methodology to perform space-time POD without requiring a high-speed PIV system, while avoiding potential pitfalls associated with traditional time-domain LSE.
Multidataset Independent Subspace Analysis With Application to Multimodal Fusion

Unsupervised latent variable models—blind source separation (BSS) especially—enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled approach to multidataset combination that takes advantage of multidimensional subspace structures. In turn, we extend BSS models to capture the underlying modes of shared and unique variability across and within datasets. Our approach leverages joint information from heterogeneous datasets in a flexible and synergistic fashion. We call this method multidataset independent subspace analysis (MISA). Methodological innovations exploiting the Kotz distribution for subspace modeling, in conjunction with a novel combinatorial optimization for evasion of local minima, enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for multimodal information fusion, including sample-poor regimes ( $N = 600$ ) and low signal-to-noise ratio, promoting novel applications in both unimodal and multimodal brain imaging data.
Neutrosophic Modal Logic

We introduce now for the first time the neutrosophic modal logic. The Neutrosophic Modal Logic includes the neutrosophic operators that express the modalities. It is an extension of neutrosophic predicate logic and of neutrosophic propositional logic. Applications of neutrosophic modal logic are to neutrosophic modal metaphysics. Similarly to classical modal logic, there is a plethora of neutrosophic modal logics. Neutrosophic modal logics is governed by a set of neutrosophic axioms and neutrosophic rules.
MutSignatures: an R package for extraction and analysis of cancer mutational signatures

Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment.
EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology

Considerable morphological phenotyping studies in nephrology have emerged in the past few years, aiming to discover hidden regularities between clinical and imaging phenotypes. Such studies have been largely enabled by deep learning based image analysis to extract sparsely located targeting objects (e.g., glomeruli) on high-resolution whole slide images (WSI). However, such methods need to be trained using labor-intensive high-quality annotations, ideally labeled by pathologists. Inspired by the recent “human-in-the-loop” strategy, we developed EasierPath, an open-source tool to integrate human physicians and deep learning algorithms for efficient large-scale pathological image quantification as a loop. Using EasierPath, physicians are able to (1) optimize the recall and precision of deep learning object detection outcomes adaptively, (2) seamlessly support deep learning outcomes refining using either our EasierPath or prevalent ImageScope software without changing physician’s user habit, and (3) manage and phenotype each object with user-defined classes. As a user case of EasierPath, we present the procedure of curating large-scale glomeruli in an efficient human-in-the-loop fashion (with two loops). From the experiments, the EasierPath saved 57% of the annotation efforts to curate 8,833 glomeruli during the second loop. Meanwhile, the average precision of glomerular detection was leveraged from 0.504 to 0.620. The EasierPath software has been released as open-source to enable the large-scale glomerular prototyping. The code can be found in https://github.com/yuankaihuo/EasierPath.
Cardinal-Recognizing Infinite Time Turing Machines

We introduce a model of infinitary computation which enhances the infinite time Turing machine model slightly but in a natural way by giving the machines the capability of detecting cardinal stages of computation. The computational strength with respect to ITTMs is determined to be precisely that of the strong halting problem and the nature of the new characteristic ordinals (clockable, writable, etc.) is explored.
On the Abelian fivefolds attached to cubic surfaces

To a family of smooth projective cubic surfaces one can canonically associate a family of abelian fivefolds. In characteristic zero, we calculate the Hodge groups of the abelian varieties which arise in this way. In arbitrary characteristic we calculate the monodromy group of the universal family of abelian varieties, and thus show that the Galois group of the 27 lines on a general cubic surface in positive characteristic is as large as possible.
Photometric Asymmetry Between Clockwise and Counterclockwise Spiral Galaxies in SDSS

While galaxies with clockwise and counterclockwise handedness are visually different, they are expected to be symmetric in all of their other characteristics. Previous experiments using both manual analysis and machine vision have shown that the handedness of Sloan Digital Sky Survey galaxies can be predicted with accuracy significantly higher than mere chance using its photometric data alone. However, some of these previous experiments were based on manually classified galaxies, and the results may therefore be subjected to bias originated from the human perception. This paper describes an experiment based on a set of 162,514 galaxies classified automatically to clockwise and counterclockwise spiral galaxies, showing that the source of the asymmetry in Sloan Digital Sky Survey (SDSS) database is not the human perception bias. The results are compared to two smaller datasets, and confirm the observation that the handedness of SDSS galaxies can be predicted by their photometry. The experiment also shows statistically significant differences in the measured magnitude of SDSS galaxies, according which galaxies with clockwise patterns are brighter than galaxies with counterclockwise patterns. The magnitude of that difference changes across RA ranges, and exhibits a strong correlation with the cosine of the right ascension.
Leveraging quantum annealing for large MIMO processing in centralized radio access networks

User demand for increasing amounts of wireless capacity continues to outpace supply, and so to meet this demand, significant progress has been made in new MIMO wireless physical layer techniques. Higher-performance systems now remain impractical largely only because their algorithms are extremely computationally demanding. For optimal performance, an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user is often required. The base station's computational capacity is thus becoming one of the key limiting factors on wireless capacity. QuAMax is the first large MIMO centralized radio access network design to address this issue by leveraging quantum annealing on the problem. We have implemented QuAMax on the 2,031 qubit D-Wave 2000Q quantum annealer, the state-of-the-art in the field. Our experimental results evaluate that implementation on real and synthetic MIMO channel traces, showing that 10 µs of compute time on the 2000Q can enable 48 user, 48 AP antenna BPSK communication at 20 dB SNR with a bit error rate of 10-6 and a 1,500 byte frame error rate of 10-4.
Hydrodynamical equation for electron swarms

We study the long-time behaviour of light particles, for example an electron swarm in which Coulomb interactions are unimportant, subjected to an external field and elastic collisions with an inert neutral gas. The time evolution of the velocity and position distribution function is described by a linear Boltzmann equation (LBE). The small ratio of electron to neutral masses, ∈, makes the energy transfer between them very inefficient. We show that, under suitable scalings, the LBE reduces, in the limit ∈ → 0, to a formally exact equation for the speed (energy) and position distribution of the electrons that contains mixed spatial and speed derivatives. When the system is spatially homogeneous, this equation reduces to and thus justifies, for small-enough ∈, the commonly used 'two-term' approximation.
Topological pressure of simultaneous level sets

Multifractal analysis studies level sets of asymptotically defined quantities in a topological dynamical system. We consider the topological pressure function on such level sets, relating it both to the pressure on the entire phase space and to a conditional variational principle. We use this to recover information on the topological entropy and Hausdorff dimension of the level sets.Our approach is thermodynamic in nature, requiring only existence and uniqueness of equilibrium states for a dense subspace of potential functions. Using an idea of Hofbauer, we obtain results for all continuous potentials by approximating them with functions from this subspace.This technique allows us to extend a number of previous multifractal results from the C1+e case to the C1 case. We consider ergodic ratios Sn/Snψ where the function ψ need not be uniformly positive, which lets us study dimension spectra for non-uniformly expanding maps. Our results also cover coarse spectra and level sets corresponding to more general limiting behaviour.