cssi/SciSciGPT-SciSciNet huggingface dataset:
Mathematical analysis on out-of-sample extensions

Let $X=\mathbf{X}\cup\mathbf{Z}$ be a data set in $\mathbb{R}^D$, where $\mathbf{X}$ is the training set and $\mathbf{Z}$ is the test one. Many unsupervised learning algorithms based on kernel methods have been developed to provide dimensionality reduction (DR) embedding for a given training set $\Phi: \mathbf{X} \to \mathbb{R}^d$ ( $d\ll D$) that maps the high-dimensional data $\mathbf{X}$ to its low-dimensional feature representation $\mathbf{Y}=\Phi(\mathbf{X})$. However, these algorithms do not straightforwardly produce DR of the test set $\mathbf{Z}$. An out-of-sample extension method provides DR of $\mathbf{Z}$ using an extension of the existent embedding $\Phi$, instead of re-computing the DR embedding for the whole set $X$. Among various out-of-sample DR extension methods, those based on Nystrom approximation are very attractive. Many papers have developed such out-of-extension algorithms and shown their validity by numerical experiments. However, the mathematical theory for the DR extension still need further consideration. Utilizing the reproducing kernel Hilbert space (RKHS) theory, this paper develops a preliminary mathematical analysis on the out-of-sample DR extension operators. It treats an out-of-sample DR extension operator as an extension of the identity on the RKHS defined on $\mathbf{X}$. Then the Nystrom-type DR extension turns out to be an orthogonal projection. In the paper, we also present the conditions for the exact DR extension and give the estimate for the error of the extension.
Thermo-fluid Topology Optimization and Experimental Study of Conformal Cooling Channels for 3D Printed Plastic Injection Molds

Abstract With the advent of additive manufacturing, innovative design methods, such as network-based techniques, and structural topology optimization have been used to generate complex and highly efficient cooling systems in recent years. However, methods that incorporate coupled thermal and fluid analysis remain scarce. In this paper, a coupled thermal-fluid topology optimization algorithm is introduced for the design of conformal cooling channels. The problem is formulated based on a coupling of Navier- Stokes equations and convection-diffusion equation. The problem is solved by gradient-based optimization after analytical sensitivity derived using adjoint method. With this method, the channel position problem is replaced to a material distribution problem. The material distribution directly depends on the effect of flow resistance, heat conduction, natural and forced convection. The algorithm leads to a two-dimensional conceptual design having optimal heat transfer and balanced flow, which is further transformed into three-dimensional cooling channel design. Here, a comprehensive study is presented, starting from design, simulation, 3D printing process and experimental testing of an injection mold with conformal cooling channels in industrial production environment. A traditional mold model is provided by an industrial collaborator. To enhance the overall thermo-fluid performance of the mold and improve final product quality, a redesign of this mold core is done with conformal cooling channels inside. The final design is 3D printed in pre-alloyed tool-steel powder Maraging Steel using Truprint 3000 metal 3D printing machine. The printed core required some heat treatment and finishing processes and added features to be incorporated to make it production ready. Once all the preparation was complete, the core was tested experimentally in a multicavity injection molding machine in real industrial environment at our industrial partner’s production facility. This paper describes all the steps starting from design, analysis, die 3D printing and finally ending at final experimental testing, as well as recommendations for tool designer and injection molding industry to implement additive manufacturing for their benefit. This paper is not just focused on a specific aspect such as design, simulation or manufacturing, but rather a comprehensive paper presenting a case study on implementation of topology optimization and additive manufacturing in real life industrial production scenario.
Constructing deep neural networks by Bayesian network structure learning

We introduce a principled approach for unsupervised structure learning of deep neural networks. We propose a new interpretation for depth and inter-layer connectivity where conditional independencies in the input distribution are encoded hierarchically in the network structure. Thus, the depth of the network is determined inherently. The proposed method casts the problem of neural network structure learning as a problem of Bayesian network structure learning. Then, instead of directly learning the discriminative structure, it learns a generative graph, constructs its stochastic inverse, and then constructs a discriminative graph. We prove that conditional-dependency relations among the latent variables in the generative graph are preserved in the class-conditional discriminative graph. We demonstrate on image classification benchmarks that the deepest layers (convolutional and dense) of common networks can be replaced by significantly smaller learned structures, while maintaining classification accuracy—state-of-the-art on tested benchmarks. Our structure learning algorithm requires a small computational cost and runs efficiently on a standard desktop CPU.
Classification of real rational knots of low degree in the 3-sphere

In this paper, we classify, up to rigid isotopy, real rational knots of degrees less than or equal to 6 in a real quadric homeomorphic to the 3-sphere. We also study their connections with rigid isotopy classes of real rational knots of low degree in ℝℙ3 and classify real rational curves of degree 6 in the 3-sphere with exactly one ordinary double point.
The Self-Linking Number in Planar Open Book Decompositions

We construct a Seifert surface for a given null-homologous transverse link in a contact manifold that is compatible with a planar open book decomposition, then obtain a formula of the self-linking number. It extends Bennequin's self-linking number formula for braids in the standard contact 3-sphere.
Brill–Noether theory of maximally symmetric graphs

Abstract We analyze the Brill–Noether theory of trivalent graphs and multigraphs having largest possible automorphism group in a fixed genus. For trivalent multigraphs with loops of genus at least 3, we show that there exists a graph with maximal automorphism group which is Brill–Noether special. We prove similar results for multigraphs without loops of genus at least 6, as well as simple graphs of genus at least 7. This analysis yields counterexamples, in any sufficiently large genus, to a conjecture of Caporaso.
An experimental platform for multi-spacecraft phase-array communications

The emergence of small satellites and CubeSats for interplanetary exploration will mean hundreds if not thousands of spacecraft exploring every corner of the solar-system. Current methods for communication and tracking of deep space probes use ground based systems such as the Deep Space Network (DSN). However, the increased communication demand will require radically new methods to ease communication congestion. Networks of communication relay satellites located at strategic locations such as geostationary orbit and Lagrange points are potential solutions. Instead of one large communication relay satellite, we could have scores of small satellites that utilize phase arrays to effectively operate as one large satellite. Excess payload capacity on rockets can be used to warehouse more small satellites in the communication network. The advantage of this network is that even if one or a few of the satellites are damaged or destroyed, the network still operates but with degraded performance. The satellite network would operate in a distributed architecture and some satellites maybe dynamically repurposed to split and communicate with multiple targets at once. The potential for this alternate communication architecture is significant, but this requires development of satellite formation flying and networking technologies. Our research has found neural-network control approaches such as the Artificial Neural Tissue can be effectively used to control multirobot/multi-spacecraft systems and can produce human competitive controllers. We have been developing a laboratory experiment platform called Athena to develop critical spacecraft control algorithms and cognitive communication methods. We briefly report on the development of the platform and our plans to gain insight into communication phase arrays for space.
Consensus with linear objective maps

We consider in this paper a weighted consensus problem over directed graphs, where the agents interact in a decentralized way, with the resulting dynamics formulated in continuous time. We introduce a linear function, called the objective map, that defines the desired final state as a convex combination of the initial states of the agents. We provide a complete answer to the question of whether there is a decentralized consensus dynamics over a given digraph which converges to the final state specified by an objective map. In particular, we characterize not only the set of objective maps that are feasible for a given digraph, but also the consensus dynamics that implements the objective map.
An Efficient System for Subgraph Discovery

Subgraph discovery in a data graph (finding subsets of vertices and edges satisfying a user-specified criteria) is an essential and general graph analytics operation with a wide spectrum of applications. We present Nuri, a general subgraph discovery system that allows users to succinctly specify subgraphs of interest and criteria for ranking them. Given such specifications, Nuri efficiently finds the k most relevant subgraphs. It prioritizes (i.e., expands earlier than others) subgraphs that are more likely to expand into the desired subgraphs (prioritized subgraph expansion) and proactively discards irrelevant subgraphs from which the desired subgraphs cannot be constructed (pruning). Nuri can also efficiently store and retrieve a large number of subgraphs on disk without being limited by the size of main memory. We demonstrate using both real and synthetic datasets that Nuri only on a single core outperforms the closest alternative distributed system running on 40 cores by more than 2 orders of magnitude for clique discovery and 1 order of magnitude for subgraph isomorphism and pattern mining.
Large time behavior of the zero dispersion limit of the fifth order KdV equation

We study the zero dispersion limit of the fifth order KdV equations when time is sufficiently large. In general, the weak limit may be described by an arbitrary odd number of hyperbolic equations. Unlike the KdV case, these are non-strictly hyperbolic equations. However, we show that the weak limit is governed by three hyperbolic equations in a domain in the space-time for all times bigger than a large time. Outside this domain, the weak limit satisfies a single hyperbolic equation.
Privately Learning High-Dimensional Distributions

We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and learning a product distribution over the Boolean hypercube in total variation distance. The sample complexity of our algorithms nearly matches the sample complexity of the optimal non-private learners for these tasks in a wide range of parameters, showing that privacy comes essentially for free for these problems. In particular, in contrast to previous approaches, our algorithm for learning Gaussians does not require strong a priori bounds on the range of the parameters. Our algorithms introduce a novel technical approach to reducing the sensitivity of the estimation procedure that we call recursive private preconditioning.
Pareto price discrimination

Abstract As the collection of consumer data becomes more common, online merchants are better equipped to price discriminate now more than ever before. While standard first-degree price discrimination benefits merchants and harms consumers relative to uniform pricing, I derive an alternative first-degree pricing strategy that achieves efficiency and Pareto improves upon the uniform pricing equilibrium. Furthermore, I show that price ceilings can enforce these Pareto price discrimination strategies, making Pareto price discrimination a viable option for merchants and policy makers.
A note on Alexander polynomials of 2-bridge links

A formula for the Alexander polynomial of a 2-bridge knot or link given by Hartley and also by Minkus has a beautiful interpretation as a walk on the integers. We extend this to the 2-variable Alex...
Cosmic rays and magnetic fields in the core and halo of the starburst M82: implications for galactic wind physics

Cosmic rays (CRs) and magnetic fields may be dynamically important in driving large-scale galactic outflows from rapidly star-forming galaxies. We construct two-dimensional axisymmetric models of the local starburst and super-wind galaxy M82 using the CR propagation code GALPROP. Using prescribed gas density and magnetic field distributions, wind profiles, CR injection rates, and stellar radiation fields, we simultaneously fit both the integrated gamma-ray emission and the spatially-resolved multi-frequency radio emission extended along M82's minor axis. We explore the resulting constraints on the gas density, magnetic field strength, CR energy density, and the assumed CR advection profile. In accord with earlier one-zone studies, we generically find low central CR pressures, strong secondary electron/positron production, and an important role for relativistic bremsstrahlung losses in shaping the synchrotron spectrum. We find that the relatively low central CR density produces CR pressure gradients that are weak compared to gravity, strongly limiting the role of CRs in driving M82's fast and mass-loaded galactic outflow. Our models require strong magnetic fields and advection speeds of order ~1000 km/s on kpc scales along the minor axis in order to reproduce the extended radio emission. Degeneracies between the controlling physical parameters of the model and caveats to these findings are discussed.
Random Gossip Processes in Smartphone Peer-to-Peer Networks

In this paper, we study random gossip processes in communication models that describe the peer-to-peer networking functionality included in standard smartphone operating systems. Random gossip processes spread information through the basic mechanism of randomly selecting neighbors for connections. These processes are well-understood in standard peer-to-peer network models, but little is known about their behavior in models that abstract the smartphone peer-to-peer setting. With this in mind, we begin by studying a simple random gossip process in the synchronous mobile telephone model (the most common abstraction used to study smartphone peer-to-peer systems). By introducing a new analysis technique, we prove that this simple process is actually more efficient than the best-known gossip algorithm in the mobile telephone model, which required complicated coordination among the nodes in the network. We then introduce a novel variation of the mobile telephone model that removes the synchronized round assumption, shrinking the gap between theory and practice. We prove that simple random gossip processes still converge in this setting and that information spreading still improves along with graph connectivity. This new model and the tools we introduce provide a solid foundation for the further theoretical analysis of algorithms meant to be deployed on real smartphone peer-to-peer networks. More generally, our results in this paper imply that simple random information spreading processes should be expected to perform well in this emerging new pseer-to-peer setting.
Bisectors and Pinned Distances

We prove, under suitable conditions, a lower bound on the number of pinned distances determined by small subsets of two-dimensional vector spaces over fields. For finite subsets of the Euclidean plane we prove an upper bound for their bisector energy.
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis

Linear dimensionality reduction methods are commonly used to extract low-dimensional structure from high-dimensional data. However, popular methods disregard temporal structure, rendering them prone to extracting noise rather than meaningful dynamics when applied to time series data. At the same time, many successful unsupervised learning methods for temporal, sequential and spatial data extract features which are predictive of their surrounding context. Combining these approaches, we introduce Dynamical Components Analysis (DCA), a linear dimensionality reduction method which discovers a subspace of high-dimensional time series data with maximal predictive information, defined as the mutual information between the past and future. We test DCA on synthetic examples and demonstrate its superior ability to extract dynamical structure compared to commonly used linear methods. We also apply DCA to several real-world datasets, showing that the dimensions extracted by DCA are more useful than those extracted by other methods for predicting future states and decoding auxiliary variables. Overall, DCA robustly extracts dynamical structure in noisy, high-dimensional data while retaining the computational efficiency and geometric interpretability of linear dimensionality reduction methods.
A Mean-Field Team Approach to Minimize the Spread of Infection in a Network

In this paper, a stochastic dynamic control strategy is presented to prevent the spread of an infection over a homogeneous network. The infectious process is persistent, i.e., it continues to contaminate the network once it is established. It is assumed that there is a finite set of network management options available such as degrees of nodes and promotional plans to minimize the number of infected nodes while taking the implementation cost into account. The network is modeled by an exchangeable controlled Markov chain, whose transition probability matrices depend on three parameters: the selected network management option, the state of the infectious process, and the empirical distribution of infected nodes (with not necessarily a linear dependence). Borrowing some techniques from mean-field team theory the optimal strategy is obtained for any finite number of nodes using dynamic programming decomposition and the convolution of some binomial probability mass functions. For infinite-population networks, the optimal solution is described by a Bellman equation. It is shown that the infinite-population strategy is a meaningful sub-optimal solution for finite-population networks if a certain condition holds. The theoretical results are verified by an example of rumor control in social networks.
Simultaneous confidence corridors for mean functions in functional data analysis of imaging data

Motivated by recent work involving the analysis of biomedical imaging data, we present a novel procedure for constructing simultaneous confidence corridors for the mean of imaging data. We propose to use flexible bivariate splines over triangulations to handle an irregular domain of the images that is common in brain imaging studies and in other biomedical imaging applications. The proposed spline estimators of the mean functions are shown to be consistent and asymptotically normal under some regularity conditions. We also provide a computationally efficient estimator of the covariance function and derive its uniform consistency. The procedure is also extended to the two-sample case in which we focus on comparing the mean functions from two populations of imaging data. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyze brain positron emission tomography data in two different studies. One data set used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Hyperbolic topology and bounded locally homeomorphic quasiregular mappings in 3-space

We use our new type of bounded locally homeomorphic quasiregular mappings in the unit 3-ball to address long standing problems for such mappings, including the Vuorinen injectivity problem. The construction of such mappings comes from our construction of non-trivial compact 4-dimensional cobordisms M with symmetric boundary components and whose interiors have complete 4-dimensional real hyperbolic structures. Such bounded locally homeomorphic quasiregular mappings are defined in the unit 3-ball B3 ⊂ ℝ3 as mappings equivariant with the standard conformal action of uniform hyperbolic lattices Γ ⊂ Isom H3 in the unit 3-ball and with its discrete representation G = ρ(Γ) ⊂ Isom H4. Here, G is the fundamental group of our non-trivial hyperbolic 4-cobordism M = (H4 ∪ Ω(G))/G, and the kernel of the homomorphism ρ: Γ → G is a free group F3 on three generators.
Neurodevelopmental wiring deficits in the Ts65Dn mouse model of Down syndrome.

Abstract Down syndrome is the most common genetic cause of intellectual disability and occurs due to the trisomy of human chromosome 21. Adolescent and adult brains from humans with Down syndrome exhibit various neurological phenotypes including a reduction in the size of the corpus callosum, hippocampal commissure and anterior commissure. However, it is unclear when and how these interhemispheric connectivity defects arise. Using the Ts65Dn mouse model of Down syndrome, we examined interhemispheric connectivity in postnatal day 0 (P0) Ts65Dn mouse brains. We find that there is no change in the volume of the corpus callosum or anterior commissure in P0 Ts65Dn mice. However, the volume of the hippocampal commissure is significantly reduced in P0 Ts65Dn mice, and this may contribute to the impaired learning and memory phenotype of this disorder. Interhemispheric connectivity defects that arise during development may be due to disrupted axon growth. In line with this, we find that developing hippocampal neurons display reduced axon length in vitro, as compared to neurons from their euploid littermates. This study is the first to report the presence of defective interhemispheric connectivity at the time of birth in Ts65Dn mice, providing evidence that early therapeutic intervention may be an effective time window for the treatment of Down syndrome.
Candidate Gene Networks for Acylsugar Metabolism and Plant Defense in Wild Tomato Solanum pennellii

Many solanaceous plants secrete acylsugars, which are branched-chain and straight-chain fatty acids esterified to Glu or Suc. These compounds have important roles in plant defense and potential commercial applications. However, several acylsugar metabolic genes remain unidentified, and little is known about regulation of this pathway. Comparative transcriptomics between low- and high-acylsugar-producing accessions of Solanum pennellii revealed that expression levels of known and novel candidate genes (putatively encoding beta-ketoacyl-(acyl-carrier-protein) synthases, peroxisomal acyl-activating enzymes, ATP binding cassette (ABC) transporters, and central carbon metabolic proteins) were positively correlated with acylsugar accumulation, except two genes previously reported to be involved in acylglucose biosynthesis. Genes putatively encoding oxylipin metabolic proteins, subtilisin-like proteases, and other antimicrobial defense proteins were upregulated in low-acylsugar-producing accessions. Transcriptome analysis after biochemical inhibition of biosynthesis of branched-chain amino acids (precursors to branched-chain fatty acids) by imazapyr showed concentration-dependent downregulation of known and most acylsugar candidate genes, but not defense genes. Weighted gene correlation network analysis identified separate coexpressed gene networks for acylsugar metabolism (including six transcription factor genes and flavonoid metabolic genes) and plant defense (including genes putatively encoding NB-ARC and leucine-rich repeat sequences, protein kinases and defense signaling proteins, and previously mentioned defense proteins). Additionally, virus-induced gene silencing of two trichomes preferentially expressed candidate genes for straight-chain fatty acid biosynthesis confirmed their role in acylsugar metabolism.
Crowdsourcing and Crowdfunding in the Manufacturing and Services Sectors

In the last few years, we have seen the emergence of two new ways in which firms interact with outside stakeholders, namely crowdsourcing and crowdfunding service providers. In this article, we def...
Clustering Activity–Travel Behavior Time Series using Topological Data Analysis

Over the last few years, traffic data have been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new divide and combine-based approach to do K-means clustering on activity–travel behavior time series using features that are derived using tools in time series analysis and topological data analysis. Our approach facilitates a case study, where each individual’s daily activity–travel behavior is characterized as a categorical time series consisting of three different levels. Clustering data from five waves of the National Household Travel Survey ranging from 1990 to 2017 suggests that activity–travel patterns of individuals over the last 3 decades can be grouped into three clusters. Results also provide evidence in support of recent claims about differences in activity–travel patterns of different survey cohorts. The proposed method is generally applicable and is not limited only to activity–travel behavior analysis in transportation studies. Driving behavior, travel mode choice, household vehicle ownership, when being characterized as categorical time series, can all be analyzed using the proposed method.
Effect of Middle-Ear Pathology on High-Frequency Ear Canal Reflectance Measurements in the Frequency and Time Domains

The effects of middle-ear pathology on wideband acoustic immittance and reflectance at frequencies above 6–8 kHz have not been documented, nor has the effect of such pathologies on the time-domain reflectance. We describe an approach that utilizes sound frequencies as high as 20 kHz and quantifies reflectance in both the frequency and time domains. Experiments were performed with fresh normal human temporal bones before and after simulating various middle-ear pathologies, including malleus fixation, stapes fixation, and disarticulation. In addition to experimental data, computational modeling was used to obtain fitted parameter values of middle-ear elements that vary systematically due to the simulated pathologies and thus may have diagnostic implications. Our results demonstrate that the time-domain reflectance, which requires acoustic measurements at high frequencies, varies with middle-ear condition. Furthermore, the extended bandwidth frequency-domain reflectance data was used to estimate parameters in a simple model of the ear canal and middle ear that separates three major conductive pathologies from each other and from the normal state.
A free boundary problem associated with the isoperimetric inequality

This paper proves a 30-year-old conjecture that disks and annuli are the only domains where analytic content—the uniform distance from $$\bar z$$ to analytic functions—achieves its lower bound. This problem is closely related to several well-known free boundary problems, in particular, Serrin’s problem about laminary flow of incompressible viscous fluid for multiply-connected domains, and Garabedian’s problem on the shape of electrified droplets. Some further ramifications and open questions, including extensions to higher dimensions, are also discussed.
Arestov’s theorems on Bernstein’s inequality

Abstract Following Arestov’s Λ -method we give a simple, elementary, and at least partially new proof of Arestov’s famous extension of Bernstein’s inequality in L p to all p ≥ 0 . Our crucial observation is that Boyd’s approach to prove Mahler’s inequality for algebraic polynomials P n ∈ P n c can be extended to all trigonometric polynomials T n ∈ F n c .
Functional coupling between NMDA receptors and SK channels in rat hypothalamic magnocellular neurons: altered mechanisms during heart failure

Key points Glutamatergic NMDA receptors (NMDARs) and small conductance Ca2+ -activated K+ (SK) channels are critical synaptic and intrinsic mechanisms, respectively, that regulate the activity of hypothalamic magnocellular neurosecretory neurons (MNNs). In this work, we investigated whether NMDARs and SK channels in MNNs are functionally coupled, and whether an altered coupling may contribute to exacerbated neuronal activity in this condition. We report that NMDARs and SK channels form a functional Ca2+ -dependent negative feedback loop that restrains the excitatory effect on membrane potential and firing activity evoked by NMDAR activation. The negative feedback loop between NMDARs and SK channels was blunted or absent in MNNs of heart failure (HF) rats. These results help us better understand how synaptic and intrinsic mechanisms regulate hypothalamic neuronal activity, as well as how changes in the interaction among these disparate mechanisms contribute to altered neuronal activity during prevalent neurogenic cardiovascular diseases. Abstract Glutamatergic NMDA receptors (NMDARs) and small conductance Ca2+ -activated K+ (SK) channels are critical synaptic and intrinsic mechanisms, respectively, that regulate the activity of hypothalamic magnocellular neurosecretory neurons (MNNs), both under physiological and pathological states, such as lactation and heart failure (HF). However, whether NMDARs and SK channels in MNNs are functionally coupled, and whether changes in this coupling contribute to exacerbated neuronal activity during HF is at present unknown. In the present study, we addressed these questions using patch-clamp electrophysiology and confocal Ca2+ imaging in a rat model of ischaemic HF. We found that in MNNs of sham rats, blockade of SK channels with apamin (200 nM) significantly increased the magnitude of an NMDAR-evoked current (INMDA ). We also observed that blockade of SK channels potentiated NMDAR-evoked firing, and abolished spike frequency adaptation in MNNs from sham, but not HF rats. Importantly, a larger INMDA -ΔCa2+ response was observed under basal conditions in HF compared to sham rats. Finally, we found that dialysing recorded cells with the Ca2+ chelator BAPTA (10 mM) increased the magnitude of INMDA in MNNs from both sham and HF rats, and occluded the effects of apamin in the former. Together our studies demonstrate that in MNNs, NMDARs and SK channels are functionally coupled, forming a local negative feedback loop that restrains the excitatory effect evoked by NMDAR activation. Moreover, our studies also support a blunted NMDAR-SK channel coupling in MNNs of HF rats, establishing it as a pathophysiological mechanism contributing to exacerbated hypothalamic neuronal activity during this prevalent neurogenic cardiovascular disease.
Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction.

Natural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These representations include the EQ (Entity-Quality) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically-rich format, as well as numerical vector representations generated using Natural Language Processing (NLP) methods (such as the bag-of-words approach and document embedding). We compared resulting phenotype similarity measures to those derived from manually curated data to determine the performance of each method. Computationally derived EQ and vector representations were comparably successful in recapitulating biological truth to representations created through manual EQ statement curation. Moreover, NLP methods for generating vector representations of phenotypes are scalable to large quantities of text because they require no human input. These results indicate that it is now possible to computationally and automatically produce and populate large-scale information resources that enable researchers to query phenotypic descriptions directly.
On sparsity of the solution to a random quadratic optimization problem

The standard quadratic optimization problem (StQP), i.e. the problem of minimizing a quadratic form $$\mathbf{x}^TQ\mathbf{x}$$ on the standard simplex $$\{\mathbf{x}\ge \mathbf{0}: \mathbf{x}^T\mathbf{e}=1\}$$ , is studied. The StQP arises in numerous applications, and it is known to be NP-hard. Chen, Peng and Zhang showed that almost certainly the StQP with a large random matrix $$Q=Q^T$$ , $$Q_{i,j},\, (i\le j)$$ being independent and identically concave-distributed, attains its minimum at a point $$\mathbf{x}$$ with support size $$|\{j: x_j>0\}|$$ bounded in probability. Later Chen and Peng proved that for $$Q=(M+M^T)/2$$ , with $$M_{i,j}$$ i.i.d. normal, the likely support size is at most 2. In this paper we show that the likely support size is poly-logarithmic in n, the problem size, for a considerably broader class of the distributions. Unlike the cited papers, the mild constraints are put on the asymptotic behavior of the distribution at a single left endpoint of its support, rather than on the distribution’s shape elsewhere. It also covers the distributions with the left endpoint $$-\infty $$ , provided that the distribution of $$Q_{i,j},\, (i\le j)$$ (of $$M_{i,j}$$ , if $$Q=(M+M^T)/2$$ resp.) has a (super/sub) exponentially narrow left tail.
Adaptation within embryonic and neonatal heart environment reveals alternative fates for adult c-kit+ cardiac interstitial cells.

Cardiac interstitial cells (CICs) perform essential roles in myocardial biology through preservation of homeostasis as well as response to injury or stress. Studies of murine CIC biology reveal remarkable plasticity in terms of transcriptional reprogramming and ploidy state with important implications for function. Despite over a decade of characterization and in vivo utilization of adult c-Kit+ CIC (cCIC), adaptability and functional responses upon delivery to adult mammalian hearts remain poorly understood. Limitations of characterizing cCIC biology following in vitro expansion and adoptive transfer into the adult heart were circumvented by delivery of the donated cells into early cardiogenic environments of embryonic, fetal, and early postnatal developing hearts. These three developmental stages were permissive for retention and persistence, enabling phenotypic evaluation of in vitro expanded cCICs after delivery as well as tissue response following introduction to the host environment. Embryonic blastocyst environment prompted cCIC integration into trophectoderm as well as persistence in amniochorionic membrane. Delivery to fetal myocardium yielded cCIC perivascular localization with fibroblast-like phenotype, similar to cCICs introduced to postnatal P3 heart with persistent cell cycle activity for up to 4 weeks. Fibroblast-like phenotype of exogenously transferred cCICs in fetal and postnatal cardiogenic environments is consistent with inability to contribute directly toward cardiogenesis and lack of functional integration with host myocardium. In contrast, cCICs incorporation into extra-embryonic membranes is consistent with fate of polyploid cells in blastocysts. These findings provide insight into cCIC biology, their inherent predisposition toward fibroblast fates in cardiogenic environments, and remarkable participation in extra-embryonic tissue formation.
Polynomial matrix completion for missing data imputation and transductive learning

This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimension. Specifically, we assume that the columns of a matrix are generated by polynomials acting on a low-dimensional intrinsic variable, and wish to recover the missing entries under this assumption. We show that we can identify the complete matrix of minimum intrinsic dimension by minimizing the rank of the matrix in a high dimensional feature space. We develop a new formulation of the resulting problem using the kernel trick together with a new relaxation of the rank objective, and propose an efficient optimization method. We also show how to use our methods to complete data drawn from multiple nonlinear manifolds. Comparative studies on synthetic data, subspace clustering with missing data, motion capture data recovery, and transductive learning verify the superiority of our methods over the state-of-the-art.
Using Stakeholder Input to Guide Cultural and Contextual Adaptations for a Universal School-Based Intervention

Strong student–teacher relationships foster students’ social, emotional, and academic development, particularly for students from marginalized racial/ethnic groups. The current study gathered input from teachers, school and district administrators, and education researchers (N = 22) regarding strategies that can help teachers build relationships with high school students and to build such relationships in an equitable manner. Participants completed quantitative ratings, open-ended survey questions, and then participated in a series of focus groups. Descriptive analyses of quantitative ratings and content analysis of qualitative data examined teachers’ perceptions of the value of relationships generally and equity in relationships in particular. Analyses also examined barriers to relationship building and potential strategies to address barriers. Results suggest that teachers see relationships with students as important, but they vary in the amount of time and effort they invest in relationships. Teachers may not have the requisite training or skills, particularly when dealing with students with behavioral difficulties and/or individualized needs. Some teachers have professional identities that center around their content area and may not conceptualize building relationships with students as part of their role. Finally, structural features of secondary schools and a lack of leadership support were seen as barriers to relationship building. Participants had a number of concrete suggestions for successful school-wide efforts to enhance student–teacher relationships. With regard to equity, participants did not perceive that teachers take an equity lens to relationship building. Attitudinal, systemic, and skills-related barriers to relationships with racial/ethnic minority students were named, and strategies suggested included elevating student voice. The paper concludes with a discussion of implications for future research and practice.
SimBSI: An open-source Simulink library for developing closed-loop brain signal interfaces in animals and humans.

Objective: A promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans is still lacking. The objective of this paper is to develop such a unifying neurotechnological software framework. Approach: Here we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem: 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform. Main results: We use several examples to demonstrate the capabilities of the library, ranging from simple signal processing, to online EEG source imaging, cognitive task design, and closed-loop neuromodulation. We further demonstrate the simplicity of developing a sophisticated experimental environment for rodents within this environment. Significance: With the SimBSI library we hope to aid BCI practitioners of dissimilar backgrounds in the development of, much needed, single and cross-species closed-loop neuroscientific experiments. These experiments may provide the necessary mechanistic data for BCIs to become effective therapeutic tools.
Frequency Observations and Statistic Analysis of Worldwide Main Power Grids Using FNET/GridEye

With the increasing renewable energy sources, concerns about how renewable energy sources impact frequency have risen. There are few reports regarding power frequency status in worldwide main power grids and what are differences of frequency status between power grids in mainland and island. FNET/GridEye, a wide-area measurement system collecting frequency and phase angle data at the distribution level, provides an opportunity to observe and study the power frequency in different power grids over the world. In this paper, 13 different power grids, spreading at different mainland and islands over the world, are observed and compared. A more detail statistical analysis was conducted for typical power grids in three different places, e.g., U.S Eastern Interconnection (EI), Egypt, and Japan. The probability functions of frequency based on the measured data are calculated. The distributions of frequency in different power grids fall into two categories, e.g., single-peak distribution and multi-peak distribution. Furthermore, a meaningful insight that the single-peak distributions of the frequency almost follow the normal distribution is found. The frequency observations and statistic analysis of worldwide main power grids using FNET/GridEye could help the power system operators understand the frequency statistical characteristic more deeply.
On the regime of localized excitations for disordered oscillator systems

We study quantum oscillator lattice systems with disorder, in arbitrary dimension, requiring only partial localization of the associated effective one-particle Hamiltonian. This leads to a many-body localized regime of excited states with arbitrarily large energy density. We prove zero-velocity Lieb–Robinson bounds for the dynamics of Weyl operators as well as for position and momentum operators restricted to this regime. Dynamical localization is also shown in the form of quasi-locality of the time evolution of local Weyl operators and through exponential clustering of the dynamic correlations of states with localized excitations.
Global flows with invariant measures for the inviscid modified sqg equations

We consider the family known as modified or generalized surface quasi-geostrophic equations (mSQG) consisting of the classical inviscid surface quasi-geostrophic (SQG) equation together with a family of regularized active scalars given by introducing a smoothing operator of nonzero but possibly arbitrarily small degree. This family naturally interpolates between the 2D Euler equation and the SQG equation. For this family of equations we construct an invariant measure on a rough $$L^2$$ -based Sobolev space and establish the existence of solutions of arbitrarily large lifespan for initial data in a set of full measure in the rough Sobolev space.
Comparison Between the f -Electron and Conduction-Electron Density of States in the Falicov-Kimball Model at Low Temperature

The spinless Falicov-Kimball model is one of the simplest models of many-body physics. While the conduction-electron density of states is temperature independent in the normal state, the f -electron density of states is strongly temperature dependent—it has an orthogonality catastrophe singularity in the metallic phase and is gapped in the insulating phase. The question we address here is whether the spectral gap is the same for both electron species as T → 0. We find strong evidence to indicate that the answer is affirmative.
Leveraging digital rock physics workflows in unconventional petrophysics: A review of opportunities, challenges, and benchmarking

Abstract Digital rock physics (DRP), via both direct numerical simulation and pore-network modeling, holds great promise in terms of probing such pore-scale controls on transport, particularly with multiphase flow and sensitivity analysis of time-intensive measurements such as relative permeability. However, despite advances in micro-computed tomography (microCT) and scanning electron microscopy (SEM) techniques, obtaining cost-effective representative elementary volumes (REV) at sufficient resolution that capture dual-scale porosity and surface textures remains a formidable challenge in establishing digital rock physics as a predictive toolset. Furthermore, implementers are faced with several options of numerical solvers such as finite element method, lattice Boltzmann method, and mass balance-based pore-network modeling. This paper reviews the current status of establishing an REV and upscaling techniques for DRP in tight and/or diagenetically-altered rocks, highlighting successful and unsuccessful pore-to-core data benchmarking examples by the authors and the greater literature in terms of static and dynamic properties. The review finds that performing DRP on a single image modality is not sufficient, even for many conventional rocks, and that it is crucial to interface with experimental data, be it core analysis deliverables or subpore-scale and Darcy-scale microfluidics. In unconventional rocks, the majority of work does not leverage mesoscale simulations, instead zooming in to a discrete pore-scale scenario that is often not benchmarked with SCAL data. Even when a simulation domain is benchmarked, the matching of a discrete case with a multivariable situation is non-unique. Benchmarking with dynamic or pseudo-dynamic core data such as MICP and single phase perm will greatly help reduce variables. Finally, this paper offers a technical roadmap for the robust application of unconventional DRP for the petrophysical and general subsurface community.
Graph based analysis for gene segment organization In a scrambled genome.

Abstract DNA recombinant processes can involve gene segments that overlap or interleave with gene segments of another gene. Such gene segment appearances relative to each other are called here gene segment organization. We use graphs to represent the gene segment organization in a chromosome locus. Vertices of the graph represent contigs resulting after the recombination and the edges represent the gene segment organization prior to rearrangement. To each graph we associate a vector whose entries correspond to graph properties, and consider this vector as a point in a higher dimensional Euclidean space such that cluster formations and analysis can be performed with a hierarchical clustering method. The analysis is applied to a recently sequenced model organism Oxytricha trifallax, a species of ciliate with highly scrambled genome that undergoes massive rearrangement process after conjugation. The analysis shows some emerging star-like graph structures indicating that segments of a single gene can interleave, or even contain all of the segments from fifteen or more other genes in between its segments. We also observe that as many as six genes can have their segments mutually interleaving or overlapping.