cssi/SciSciGPT-SciSciNet huggingface dataset:
Development of Ratiometric Bioluminescent Sensors for in Vivo Detection of Bacterial Signaling

Second messenger signaling networks allow cells to sense and adapt to changing environmental conditions. In bacteria, the nearly ubiquitous second messenger molecule cyclic di-GMP coordinates diverse processes such as motility, biofilm formation, and virulence. In bacterial pathogens, these signaling networks allow the bacteria to survive changing environmental conditions that are experienced during infection of a mammalian host. While studies have examined the effects of cyclic di-GMP levels on virulence in these pathogens, it has not been possible to visualize cyclic di-GMP levels in real time during the stages of host infection. Toward this goal, we generate the first ratiometric, chemiluminescent biosensor scaffold that selectively responds to c-di-GMP. By engineering the biosensor scaffold, a suite of Venus-YcgR-NLuc (VYN) biosensors is generated that provide extremely high sensitivity (KD < 300 pM) and large changes in the bioluminescence resonance energy transfer (BRET) signal (up to 109%). As a proof-of-concept that VYN biosensors can image cyclic di-GMP in tissues, we show that the VYN biosensors function in the context of a tissue phantom model, with only ∼103-104 biosensor-expressing E. coli cells required for the measurement. Furthermore, we utilize the biosensor in vitro to assess changes in cyclic di-GMP in V. cholerae grown with different inputs found in the host environment. The VYN sensors developed here can serve as robust in vitro diagnostic tools for high throughput screening, as well as genetically encodable tools for monitoring the dynamics of c-di-GMP in live cells, and lay the groundwork for live cell imaging of c-di-GMP dynamics in bacteria within tissues and other complex environments.
A 6-nm ultra-photostable DNA FluoroCube for fluorescence imaging

Photobleaching limits extended imaging of fluorescent biological samples. We developed DNA-based 'FluoroCubes' that are similar in size to the green fluorescent protein, have single-point attachment to proteins, have a ~54-fold higher photobleaching lifetime and emit ~43-fold more photons than single organic dyes. We demonstrate that DNA FluoroCubes provide outstanding tools for single-molecule imaging, allowing the tracking of single motor proteins for >800 steps with nanometer precision.
To make or to serve-industrial specialization drivers of foreign subsidiaries in service and manufacturing sectors across agglomerations in a transition economy

This paper examines the dynamics of the location patterns of the foreign direct investment(FDI)subsidiaries in manufacturing and service sectors across regions in a developing economy before and after the accession of the European Union in 2004. The data at the NUTS level 2 on regions in Poland is analyzed employing the revealed location advantage (RLA) index for industrial specialization (RLAis) of the regions. The study tests the concentration patterns of industrial sectors along with the industrial specialization level of regions’ participating foreign enterprises across all regions of Poland. The findings allow answering for the presented hypothesis testing the level of industrial specialization across regions and its impact on narrowing industrial specialization, wages and the level of R&D investment. The results determine that the location of foreign firms in service-related industries is concentrating mostly in the largest agglomerations with the presence of large markets and universities. While, also, several regions seem to be able to narrow their manufacturing specializations, they generally locate at, or near, the legacy centers of those industrial sectors. This provides answers for, and confirms the raised hypothesis and generally follows the existing theoretical path of the developed economies. The study, also, shows that Polish regions attracting a higher number of foreign service-related investments also seem to increase their industrial specialization in select manufacturing sectors that can increasingly attract R&D investment.
Image Privacy Prediction Using Deep Neural Networks

Images today are increasingly shared online on social networking sites such as Facebook, Flickr, and Instagram. Image sharing occurs not only within a group of friends but also more and more outside a user’s social circles for purposes of social discovery. Despite that current social networking sites allow users to change their privacy preferences, this is often a cumbersome task for the vast majority of users on the Web, who face difficulties in assigning and managing privacy settings. When these privacy settings are used inappropriately, online image sharing can potentially lead to unwanted disclosures and privacy violations. Thus, automatically predicting images’ privacy to warn users about private or sensitive content before uploading these images on social networking sites has become a necessity in our current interconnected world.In this article, we explore learning models to automatically predict appropriate images’ privacy as private or public using carefully identified image-specific features. We study deep visual semantic features that are derived from various layers of Convolutional Neural Networks (CNNs) as well as textual features such as user tags and deep tags generated from deep CNNs. Particularly, we extract deep (visual and tag) features from four pre-trained CNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and ResNet, and compare their performance for image privacy prediction. The results of our experiments obtained on a Flickr dataset of 32,000 images show that ResNet yeilds the best results for this task among all four networks. We also fine-tune the pre-trained CNN architectures on our privacy dataset and compare their performance with the models trained on pre-trained features. The results show that even though the overall performance obtained using the fine-tuned networks is comparable to that of pre-trained networks, the fine-tuned networks provide an improved performance for the private class. The results also show that the learning models trained on features extracted from ResNet outperform the state-of-the-art models for image privacy prediction. We further investigate the combination of user tags and deep tags derived from CNN architectures using two settings: (1) Support Vector Machines trained on the bag-of-tags features and (2) text-based CNN. We compare these models with the models trained on ResNet visual features and show that, even though the models trained on the visual features perform better than those trained on the tag features, the combination of deep visual features with image tags shows improvements in performance over the individual feature sets. We also compare our models with prior privacy prediction approaches and show that for private class, we achieve an improvement of ≈ 10% over prior CNN-based privacy prediction approaches. Our code, features, and the dataset used in experiments are available at https://github.com/ashwinitonge/deepprivate.git.
Detection of Item Preknowledge Using Response Times.

Benefiting from item preknowledge is a major type of fraudulent behavior during educational assessments. This article suggests a new statistic that can be used for detecting the examinees who may h...
Spectral Bayesian Estimation for General Stochastic Hybrid Systems

Abstract General Stochastic Hybrid Systems (GSHS) have been formulated to represent various types of uncertainties in hybrid dynamical systems. In this paper, we propose computational techniques for Bayesian estimation of GSHS. In particular, the Fokker–Planck equation that describes the evolution of uncertainty distributions along GSHS is solved by spectral techniques, where an arbitrary form of probability density of the hybrid state is represented by a mixture of Fourier series. The method is based on splitting the Fokker–Planck equation represented by an integro-partial differential equation into the partial differentiation part for continuous diffusion and the integral part for discrete transition, and integrating the solution of each part. The propagated density function is used with a likelihood function in the Bayes’ formula to estimate the hybrid state for given sensor measurements. The unique feature of the proposed technique is that the probability density describing complete stochastic properties of the hybrid state is constructed without relying on the common Gaussian assumption, in contrast to other methods that compute certain expected values such as mean or variance. We apply the proposed method to the estimation of two examples: the bouncing ball model and the Dubins vehicle model. We show that the proposed technique yields propagated densities consistent with Monte Carlo simulations, more accurate estimates over the Gaussian based approach and some computational benefits over a particle filter.
Underused Methods in Developmental Science to Inform Policy and Practice

This is the peer reviewed version of the article, which has been published in final form at the above citation. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Chapter 14 – Advanced Mathematics

The chapter covers six basic topics: it starts with some of the built-in statistical and set operations in MATLAB, then curve fitting, complex numbers, solving systems of linear algebraic equations, and integration and differentiation in calculus. Some matrix operations, and matrix properties, particularly for square matrices, are introduced. Matrix solutions using the Gauss-Jordan and Gauss-Jordan elimination methods are described. Finally, some of the symbolic math toolbox functions are shown, including those that solve equations.
Analysis of computational codon usage models and their association with translationally slow codons

Improved computational modeling of protein translation rates, including better prediction of where translational slowdowns along an mRNA sequence may occur, is critical for understanding co-translational folding. Because codons within a synonymous codon group are translated at different rates, many computational translation models rely on analyzing synonymous codons. Some models rely on genome-wide codon usage bias (CUB), believing that globally rare and common codons are the most informative of slow and fast translation, respectively. Others use the CUB observed only in highly expressed genes, which should be under selective pressure to be translated efficiently (and whose CUB may therefore be more indicative of translation rates). No prior work has analyzed these models for their ability to predict translational slowdowns. Here, we evaluate five models for their association with slowly translated positions as denoted by two independent ribosome footprint (RFP) count experiments from S. cerevisiae, because RFP data is often considered as a "ground truth" for translation rates across mRNA sequences. We show that all five considered models strongly associate with the RFP data and therefore have potential for estimating translational slowdowns. However, we also show that there is a weak correlation between RFP counts for the same genes originating from independent experiments, even when their experimental conditions are similar. This raises concerns about the efficacy of using current RFP experimental data for estimating translation rates and highlights a potential advantage of using computational models to understand translation rates instead.
Uniform Geometrical Theory of Diffraction

Keller's geometrical theory of diffraction (GTD) [1,2] constitutes a major breakthrough for solving problems of electromagnetic (EM) radiation and scattering at high frequencies. The GTD can also be applied to solving acoustic and elastic wave problems; however, only the EM case is discussed here. Recently, the development of fast solvers for signifi-cantly increasing the efficiency of numerical methods in solving large problems has met with some success. However, for truly large problems, asymptotic high-frequency methods in general, and especially ray methods such as the GTD and its uniform version, still remain the most useful analysis tools. Keywords: Keller's geometrical theory of diffraction; electromagnetic radiation; geometrical optics; uniform theory of diffraction; diffracted ray field
Financial Development, Growth, and the Distribution of Income

A paradigm is presented in which both the extent of financial intermediation and the rate of economic growth are endogenously determined. Financial intermediation promotes growth because it allows a higher rate of return to be earned on capital, and growth in turn provides the means to implement costly financial structures. Thus financial intermediation and economic growth are inextricably linked in accord with the Goldsmith-McKinnon-Shaw view on economic development. The model also generates a development cycle reminiscent of the Kuznet hypothesis. In particular, in the transition from a primitive slow-growing economy to a developed fast-growing one, a nation passes through a stage in which the distribution of wealth across the rich and poor widens.
Auctions Versus Negotiations in Procurement: An Empirical Analysis

Should the buyer of a customized good use competitive bidding or negotiation to select a contractor? To shed light on this question, we offer a framework that compares auctions with negotiations. We then examine a comprehensive data set of private sector building contracts awarded in Northern California during the years 1995-2000. The analysis suggests a number of possible limitations to the use of auctions.Auctions may perform poorly when projects are complex,contractual design is incomplete and there are few available bidders.Furthermore,auctions may still be communication between buyers and sellers,preventing the buyer from utilizing the contractor’s expertise when designing the project. Some implications of these results for procurement in the public sector are discussed.
Insulin-like Growth Factors

The insulin-like growth factor (IGF) system includes two peptide ligands, IGF-I and IGF-II; their receptors, the IGF-I receptor and the mannose-6-phosphate receptor, and six IGF-binding proteins. This family of growth factors plays fundamental roles in normal physiology and in neoplastic and metabolic disease states. Circulating IGFs are produced primarily in the liver but are also produced locally, resulting in both endocrine and autocrine/paracrine actions. Because of complexity in location and modes of regulation, previously unknown and important functions for the IGFs are rapidly being discovered.
Social connections and cultural heterogeneity

This paper proposes an evolutionary model in which the assortativity level of matching is endogenously determined. We consider a population consisting of two cultural groups. Each group has a leader who aims to maximize the group’s average fitness by exerting costly effort to enhance either within-group or cross-group social connections. Within-group social connections increase the assortativity level of matching and promote segregation, while cross-group social connections decrease the assortativity level of matching and encourage integration. We find that the endogenous process by which the assortativity level is determined can generate an intermediate level of assortativity that is neither complete segregation nor maximal integration and lead to a rich set of cultural phenomena including cultural heterogeneity, which cannot be accounted for in many cases if the assortativity level is given exogenously.
Co-twin Control Methods†

The co-twin control method is an elegant research design that uses differences within twin pairs to examine the association between a putative environmental risk factor and an outcome variable. The design is extremely efficient for examining risk factor–outcome associations compared to unmatched designs. This is especially true when using monozygotic (MZ) twins who share 100% of their genetic material. Co-twin control studies can be used in both the experimental and observational settings. Statistical analysis for continuously distributed outcome variables focuses on the within-pair mean difference, while for dichotomous outcomes, the matched pair odds ratio is used. Whenever possible, it is informative to examine differences in the relationship between the risk factor–outcome association by zygosity; findings that are only present in dizygotic (DZ) pairs suggest that the observed association may be due to genetic influence on both the risk factor and outcome. We present an illustration of the analysis of a co-twin control study, looking at the association between military service in Vietnam and posttraumatic stress disorder, with data from the Vietnam Era Twin Registry. A wide range of new regression modeling methods, which account for nonindependent observations, can be readily adapted for co-twin control studies. Keywords: matched pair; confounding; odds ratio; within-pair difference
Electromagnetic Subsurface Remote Sensing

Subsurface electromagnetic (EM) methods are applied to obtain underground information that is not available from surface observations. Subsurface EM methods include a variety of techniques depending on the application, surveying method, system, and interpretation procedure, and thus a “best” method simply does not exist. One of the most challenging parts of subsurface EM methods is interpretation of the data. Data interpretation is also a critical factor in evaluating the effectiveness of the system. In the following sections, we will use several typical applications to outline the basic principles of subsurface EM methods. Physical insight is emphasized rather than rigorous mathematical analysis. Keywords: electromagnetic; remote sensing; subsurface eletromagnetic methods; borehole; ground penetrating radar; magnetotelluric
Tropomyosin 1 genetically constrains in vitro hematopoiesis

Identifying causal variants and genes from human genetic studies of hematopoietic traits is important to enumerate basic regulatory mechanisms underlying these traits, and could ultimately augment translational efforts to generate platelets and/or red blood cells in vitro. To identify putative causal genes from these data, we performed computational modeling using available genome-wide association datasets for platelet and red blood cell traits. Our model identified a joint collection of genomic features enriched at established trait associations and plausible candidate variants. Additional studies associating variation at these loci with change in gene expression highlighted Tropomyosin 1 (TPM1) among our top-ranked candidate genes. CRISPR/Cas9-mediated TPM1 knockout in human induced pluripotent stem cells (iPSCs) enhanced hematopoietic progenitor development, increasing total megakaryocyte and erythroid cell yields. Our findings may help explain human genetic associations and identify a novel genetic strategy to enhance in vitro hematopoiesis. A similar trait-specific gene prioritization strategy could be employed to help streamline functional validation experiments for virtually any human trait.
Braid Group Representations from Twisted Tensor Products of Algebras

We unify and generalize several approaches to constructing braid group representations from finite groups, using iterated twisted tensor products. We provide some general characterizations and classification of these representations, focusing on the size of their images, which are typically finite groups. The well-studied Gaussian representations associated with metaplectic modular categories can be understood in this framework, and we give some new examples to illustrate their ubiquity. Our results suggest a relationship between the braiding on the G-gaugings of a pointed modular category $${\mathcal {C}}(A,Q)$$ and that of $${\mathcal {C}}(A,Q)$$ itself.
Linking Lysosomal Enzyme Targeting Genes and Energy Metabolism with Altered Gray Matter Volume in Children with Persistent Stuttering

Developmental stuttering is a childhood onset neurodevelopmental disorder with an unclear etiology. Subtle changes in brain structure and function are present in both children and adults who stutter. It is a highly heritable disorder, and 12-20% of stuttering cases may carry a mutation in one of four genes involved in intracellular trafficking. To better understand the relationship between genetics and neuroanatomical changes, we used gene expression data from the Allen Institute for Brain Science and voxel-based morphometry to investigate the spatial correspondence between gene expression patterns and differences in gray matter volume between children with persistent stuttering (n = 26, and 87 scans) and their fluent peers (n = 44, and 139 scans). We found that the expression patterns of two stuttering-related genes (GNPTG and NAGPA) from the Allen Institute data exhibited a strong positive spatial correlation with the magnitude of between-group gray matter volume differences. Additional gene set enrichment analyses revealed that genes whose expression was highly correlated with the gray matter volume differences were enriched for glycolysis and oxidative metabolism in mitochondria. Because our current study did not examine the participants' genomes, these results cannot establish the direct association between genetic mutations and gray matter volume differences in stuttering. However, our results support further study of the involvement of lysosomal enzyme targeting genes, as well as energy metabolism in stuttering. Future studies assessing variations of these genes in the participants' genomes may lead to increased understanding of the biological mechanisms of the observed spatial relationship between gene expression and gray matter volume.
Two-stage athermal solidification of semiflexible polymers and fibers

We study how solidification of model freely rotating polymers under athermal quasistatic compression varies with their bond angle θ0. All systems undergo two discrete, first-order-like transitions: entanglement at ϕ = ϕE(θ0) followed by jamming at ϕ = ϕJ(θ0) ≃ (4/3 ± 1/12)ϕE(θ0). For ϕ < ϕE(θ0), systems are in a “gas” phase wherein all chains remain free to translate and reorient. For ϕE(θ0) ≤ ϕ ≤ ϕJ(θ0), systems are in a liquid-like phase wherein chains are entangled. In this phase, chains' rigid-body-like motion is blocked, yet they can still locally relax via dihedral rotations, and hence energy and pressure remain extremely small. The ability of dihedral relaxation mechanisms to accommodate further compression becomes exhausted, and systems rigidify, at ϕJ(θ0). At and slightly above ϕJ, the bulk moduli increase linearly with the pressure P rather than jumping discontinuously, indicating these systems solidify via rigidity percolation. The character of the energy and pressure increases above ϕJ(θ0) can be characterized via chains' effective aspect ratio αeff. Large-αeff (small-θ0) systems' jamming is bending-dominated and is similar to that observed in systems composed of straight fibers. Small-αeff (large-θ0) systems' jamming is dominated by the degree to which individual chains' dihedrals can collapse into compact, tetrahedron-like structures. For intermediate θ0, chains remain in highly disordered globule-like configurations throughout the compression process; jamming occurs when entangled globules can no longer even locally relax away from one another.
Extensions of the Dynamic Programming Framework: Battery Scheduling, Demand Charges, and Renewable Integration

We consider a general class of dynamic programming (DP) problems with nonseparable objective functions. We show that for any problem in this class, there exists an augmented-state DP problem that satisfies the principle of optimality and the solutions to which yield solutions to the original problem. Furthermore, we identify a subclass of DP problems with naturally forward separable objective functions for which this state-augmentation scheme is tractable. We extend this framework to stochastic DP problems, proposing a suitable definition of the principle of optimality. We then apply the resulting algorithms to the problem of optimal battery scheduling with demand charges using a data-based stochastic model for electricity usage and solar generation by the consumer.
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatme...
Integral points on varieties defined by matrix factorization into elementary matrices

Abstract Let K be a number field and S be a finite set of valuations of K containing the archimedean valuations. Let O be the ring of S-integers. For A ∈ S L 2 ( O ) and k ≥ 1 , we define matrix-factorization varieties V k ( A ) over O which parametrize factoring A into a product of k elementary matrices beginning with lower triangular; the equations defining V k ( A ) are written in terms of Euler's continuant polynomials. We show that the V k ( A ) are rational ( k − 3 ) -folds with an inductive fibration structure. We combine this geometric structure with arithmetic results to study the Zariski closure of the O -points of V k ( A ) . We prove that for k ≥ 4 the O -points on V k ( A ) are Zariski dense if V k ( A ) ( O ) ≠ ∅ assuming the group of units O × is infinite. This can then be combined with results on factoring into elementary matrices for S L 2 ( O ) . One result is that for k ≥ 9 the O -points on V k ( A ) are Zariski dense if O × is infinite.
A Survey of Unsupervised Deep Domain Adaptation

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.
Role of fluctuations in fluid mechanics and dendritic solidification

Abstract Many recent advances have occurred in our understanding of the role of fluctuations in fluid mechanics and dendritic solidification. Our purpose here is to present a review of some of these. We shall argue that if one understands completely the simple diffusion-limited aggregation (DLA) model or the closely related dielectric breakdown model (DBM), one understands the role of fluctuations in a range of fluid mechanical systems, as well as in dendritic solidification. The detailed descriptions of some such systems requires suitably chosen variants, such as DBM with anisotropy and noise reduction.
From Hierarchical to Relative Hyperbolicity

We provide a simple, combinatorial criteria for a hierarchically hyperbolic space to be relatively hyperbolic by proving a new formulation of relative hyperbolicity in terms of hierarchy structures. In the case of clean hierarchically hyperbolic groups, this criteria characterizes relative hyperbolicity. We apply our criteria to graphs associated to surfaces and prove that the separating curve graph of a surface is relatively hyperbolic when the surface has zero or two punctures. We also recover a celebrated theorem of Brock and Masur on the relative hyperbolicity of the Weil-Petersson metric on Teichmuller space for surfaces with complexity three.
NC Algorithms for Popular Matchings in One-Sided Preference Systems and Related Problems

The popular matching problem is of matching a set of applicants to a set of posts, where each applicant has a preference list, ranking a non-empty subset of posts in the order of preference, possibly with ties. A matching M is popular if there is no other matching M′ such that more applicants prefer M′ to M. We give the first NC algorithm to solve the popular matching problem without ties. We also give an NC algorithm that solves the maximum-cardinality popular matching problem. No NC or RNC algorithms were known for the matching problem in preference systems prior to this work. Moreover, we give an NC algorithm for a weaker version of the stable matching problem, that is, the problem of finding the "next" stable matching given a stable matching.
An Optimal Control Derivation of Nonlinear Smoothing Equations

The purpose of this paper is to review and highlight some connections between the problem of nonlinear smoothing and optimal control of the Liouville equation. The latter has been an active area of recent research interest owing to work in mean-field games and optimal transportation theory. The nonlinear smoothing problem is considered here for continuous-time Markov processes. The observation process is modeled as a nonlinear function of a hidden state with an additive Gaussian measurement noise. A variational formulation is described based upon the relative entropy formula introduced by Newton and Mitter. The resulting optimal control problem is formulated on the space of probability distributions. The Hamilton’s equation of the optimal control are related to the Zakai equation of nonlinear smoothing via the log transformation. The overall procedure is shown to generalize the classical Mortensen’s minimum energy estimator for the linear Gaussian problem.
Fusobacterium nucleatum host-cell binding and invasion induces IL-8 and CXCL1 secretion that drives colorectal cancer cell migration.

Fusobacterium nucleatum is implicated in accelerating colorectal cancer (CRC) and is found within metastatic CRC cells in patient biopsies. Here, we found that bacterial invasion of CRC cells and cocultured immune cells induced a differential cytokine secretion that may contribute to CRC metastasis. We used a modified galactose kinase markerless gene deletion approach and found that F. nucleatum invaded cultured HCT116 CRC cells through the bacterial surface adhesin Fap2. In turn, Fap2-dependent invasion induced the secretion of the proinflammatory cytokines IL-8 and CXCL1, which are associated with CRC progression and promoted HCT116 cell migration. Conditioned medium from F. nucleatum-infected HCT116 cells caused naive cells to migrate, which was blocked by depleting CXCL1 and IL-8 from the conditioned medium. Cytokine secretion from HCT116 cells and cellular migration were attenuated by inhibiting F. nucleatum host-cell binding and entry using galactose sugars, l-arginine, neutralizing membrane protein antibodies, or fap2 deletion. F. nucleatum also induces the mobilization of immune cells in the tumor microenvironment. However, in neutrophils and macrophages, the bacterial-induced secretion of cytokines was Fap2 independent. Thus, our findings show that F. nucleatum both directly and indirectly modulates immune and cancer cell signaling and migration. Because increased IL-8 and CXCL1 production in tumors is associated with increased metastatic potential and cell seeding, poor prognosis, and enhanced recruitment of tumor-associated macrophages and fibroblasts, we propose that inhibition of host-cell binding and invasion, potentially through vaccination or novel galactoside compounds, could be an effective strategy for reducing F. nucleatum-associated CRC metastasis.
An Interactive Control Approach to 3D Shape Reconstruction

The ability to accurately reconstruct the 3D facets of a scene is one of the key problems in robotic vision. However, even with recent advances with machine learning, there is no high-fidelity universal 3D reconstruction method for this optimization problem as schemes often cater to specific image modalities and are often biased by scene abnormalities. Simply put, there always remains an "information" gap due to the dynamic nature of real-world scenarios. To this end, we demonstrate a feedback control framework which invokes operator inputs (also prone to errors) in order to augment existing reconstruction schemes. For proof-of-concept, we choose a classical region-based stereoscopic reconstruction approach and show how an ill-posed model can be augmented with operator input to be much more robust to scene artifacts. We provide necessary conditions for stability via Lyapunov analysis and perhaps more importantly, we show that the stability depends on a notion of absolute curvature. Mathematically, this aligns with previous work that has shown Ricci curvature as proxy for functional robustness of dynamical networked systems. We conclude with results that show how our method can improve standalone reconstruction schemes.
Epidemiology of COVID-19 and Predictors of Recovery in the Republic of Korea.

Background The recent COVID-19 pandemic has emerged as a threat to global health. Though current evidence on the epidemiology of the disease is emerging, very little is known about the predictors of recovery. Objectives To describe the epidemiology of confirmed COVID-19 patients in the Republic of Korea and identify predictors of recovery. Materials and methods Using publicly available data for confirmed COVID-19 cases from the Korea Centers for Disease Control and Prevention from January 20, 2020, to April 30, 2020, we undertook descriptive analyses of cases stratified by sex, age group, place of exposure, date of confirmation, and province. Correlation was tested among all predictors (sex, age group, place of exposure, and province) with Pearson's correlation coefficient. Associations between recovery from COVID-19 and predictors were estimated using a multivariable logistic regression model. Results Majority of the confirmed cases were females (56%), 20-29 age group (24.3%), and primarily from three provinces-Gyeongsangbuk-do (36.9%), Gyeonggi-do (20.5%), and Seoul (17.1%). The case fatality ratio was 2.1%, and 41.6% cases recovered. Older patients, patients from provinces such as Daegu, Gyeonggi-do, Gyeongsangbuk-do, Jeju-do, Jeollabuk-do, and Jeollanam-do, and those contracting the disease from healthcare settings had lower recovery. Conclusions Our study adds to the very limited evidence base on potential predictors of recovery among confirmed COVID-19 cases. We call additional research to explore the predictors of recovery and support development of policies to protect the vulnerable patient groups.
COVID-19 Pandemic: Can Boosting Immune Responses by Maintaining Adequate Nutritional Balance Reduce Viral Insults?

The impact of nutrition on immunity is an intense area of research. Malnutrition is linked to a higher risk of microbial infections, while severe infections usually lead to a nutritional imbalance in affected patients. The nutritional status has an impact on the severity of the ongoing SARS-CoV-2 infection. Studies conducted on small number of patients have shown the benefits of maintaining optimal Vitamin (B12 and D) and mineral (zinc and magnesium) balance in reducing the intensity of COVID-19. Although consuming a balanced diet with a healthy lifestyle is always desirable, the importance of such practice is even more meaningful during the COVID-19 pandemic. Keeping an optimal balance of vitamins and minerals through healthy dietary habits helps to maintain a robust immune system that is essential to combat invading microbial pathogens, including SARS-CoV-2.
Sustainable innovation and the triple bottom-line: a market-based capabilities and stakeholder perspective

Firms are creating sustainable innovations in response to customers and other stakeholders. Yet little is known about the capabilities required, as well as whether these innovations produce the tou...
A Graph-constrained Changepoint Detection Approach for ECG Segmentation

Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with different features; however, their performance highly depends on applying preprocessing steps which makes them unreliable in realtime data analysis of ambulatory care settings and remote monitoring systems, where the collected data is highly noisy. Moreover, some issues still remain with the current algorithms in regard to the diverse morphological categories for the ECG signal and their high computation cost. In this paper, we introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of Rpeak positions without employing any preprocessing step. The proposed model guarantees to compute the globally optimal changepoint detection solution. It is also generic in nature and can be applied to other time-series biomedical signals. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55 which are comparable to other state-of-the-art approaches.1 2
Settling the relationship between Wilber's bounds for dynamic optimality

In FOCS 1986, Wilber proposed two combinatorial lower bounds on the operational cost of any binary search tree (BST) for a given access sequence $X \in [n]^m$. Both bounds play a central role in the ongoing pursuit of the dynamic optimality conjecture (Sleator and Tarjan, 1985), but their relationship remained unknown for more than three decades. We show that Wilber's Funnel bound dominates his Alternation bound for all $X$, and give a tight $\Theta(\lg\lg n)$ separation for some $X$, answering Wilber's conjecture and an open problem of Iacono, Demaine et. al. The main ingredient of the proof is a new "symmetric" characterization of Wilber's Funnel bound, which proves that it is invariant under rotations of $X$. We use this characterization to provide initial indication that the Funnel bound matches the Independent Rectangle bound (Demaine et al., 2009), by proving that when the Funnel bound is constant, $\mathsf{IRB}_{\diagup\hspace{-.6em}\square}$ is linear. To the best of our knowledge, our results provide the first progress on Wilber's conjecture that the Funnel bound is dynamically optimal (1986).
Temporal Memory With Magnetic Racetracks

Race logic is a relative timing code that represents information in a wavefront of digital edges on a set of wires in order to accelerate dynamic programming and machine learning algorithms. Skyrmions, bubbles, and domain walls are mobile magnetic configurations (solitons) with applications for Boolean data storage. We propose to use current-induced displacement of these solitons on magnetic racetracks as a native temporal memory for race logic computing. Locally synchronized racetracks can spatially store relative timings of digital edges and provide nondestructive readout. The linear kinematics of skyrmion motion, the tunability and low-voltage asynchronous operation of the proposed device, and the elimination of any need for constant skyrmion nucleation and annihilation make these magnetic racetracks a natural memory for low-power, high-throughput race logic applications.
A Quantitative Analytical Model for Predicting and Optimizing the Rate Performance of Battery Cells

Summary The prediction of the performance of battery cells is usually accomplished by computationally expensive numerical simulations. Here, we present a simple analytical model as an efficient alternative to predict the rate capability of battery cells limited by electrolyte transport without the need to fit parameters to simulations. It exhibits very good agreement with simulations over a wide range of discharge rate and electrode thickness and offers a speedup of >105 times. The optimal electrode properties predicted by the model differ
Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores.

Bipolar disorder (BD) has high clinical heterogeneity, frequent psychiatric comorbidities, and elevated suicide risk. To determine genetic differences between common clinical sub-phenotypes of BD, we performed a systematic polygenic risk score (PRS) analysis using multiple PRSs from a range of psychiatric, personality, and lifestyle traits to dissect differences in BD sub-phenotypes in two BD cohorts: the Mayo Clinic BD Biobank (N = 968) and Genetic Association Information Network (N = 1001). Participants were assessed for history of psychosis, early-onset BD, rapid cycling (defined as four or more episodes in a year), and suicide attempts using questionnaires and the Structured Clinical Interview for DSM-IV. In a combined sample of 1969 bipolar cases (45.5% male), those with psychosis had higher PRS for SCZ (OR = 1.3 per S.D.; p = 3e-5) but lower PRSs for anhedonia (OR = 0.87; p = 0.003) and BMI (OR = 0.87; p = 0.003). Rapid cycling cases had higher PRS for ADHD (OR = 1.23; p = 7e-5) and MDD (OR = 1.23; p = 4e-5) and lower BD PRS (OR = 0.8; p = 0.004). Cases with a suicide attempt had higher PRS for MDD (OR = 1.26; p = 1e-6) and anhedonia (OR = 1.22; p = 2e-5) as well as lower PRS for educational attainment (OR = 0.87; p = 0.003). The observed novel PRS associations with sub-phenotypes align with clinical observations such as rapid cycling BD patients having a greater lifetime prevalence of ADHD. Our findings confirm that genetic heterogeneity contributes to clinical heterogeneity of BD and consideration of genetic contribution to psychopathologic components of psychiatric disorders may improve genetic prediction of complex psychiatric disorders.
Improving Blood Vessel Tortuosity Measurements via Highly Sampled Numerical Integration of the Frenet-Serret Equations

Measures of vascular tortuosity—how curved and twisted a vessel is—are associated with a variety of vascular diseases. Consequently, measurements of vessel tortuosity that are accurate and comparable across modality, resolution, and size are greatly needed. Yet in practice, precise and consistent measurements are problematic—mismeasurements, inability to calculate, or contradictory and inconsistent measurements occur within and across studies. Here, we present a new method of measuring vessel tortuosity that ensures improved accuracy. Our method relies on numerical integration of the Frenet-Serret equations. By reconstructing the three-dimensional vessel coordinates from tortuosity measurements, we explain how to identify and use a minimally-sufficient sampling rate based on vessel radius while avoiding errors associated with oversampling and overfitting. Our work identifies a key failing in current practices of filtering asymptotic measurements and highlights inconsistencies and redundancies between existing tortuosity metrics. We demonstrate our method by applying it to manually constructed vessel phantoms with known measures of tortuousity, and 9,000 vessels from medical image data spanning human cerebral, coronary, and pulmonary vascular trees, and the carotid, abdominal, renal, and iliac arteries.
Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses.

A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj . In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.
The Busy Beaver Frontier

The Busy Beaver function, with its incomprehensibly rapid growth, has captivated generations of computer scientists, mathematicians, and hobbyists. In this survey, I o er a personal view of the BB function 58 years after its introduction, emphasizing lesser-known insights, recent progress, and especially favorite open problems. Examples of such problems include: when does the BB function rst exceed the Ackermann function? Is the value of BB(20) independent of set theory? Can we prove that BB(n + 1) > 2BB(n) for large enough n? Given BB(n), how many advice bits are needed to compute BB(n + 1)? Do all Busy Beavers halt on all inputs, not just the 0 input? Is it decidable, given n, whether BB(n) is even or odd?
The Battle for (Pre-)Modernity: Medieval Festivals in Contemporary Russia

In May 2016 a medieval Russian “knight” lanced a drone out of the skies above Lipetsk. The assailant was part of the Rusborg Historical Festival, one of hundreds of medieval re‐enactment events that have sprung up across Russia since 2000 and have gained widespread popularity in recent years. This article considers these festivals as part of a larger trend in neo-medievalism that has come to occupy a surprisingly prominent place in contemporary Russian culture. Examining this trend in historical reconstruction, this article demonstrates how it is motivated by contemporary concerns about globalization and modernity and Russia’s place in the modern world. Though these issues are not explicitly discussed at medieval festivals, this article suggests that such events build towards an “affective public sphere,” that is, a space of public experience centered on aesthetics and affect rather than rational discourse. The immersive imaginary environments provided at these festivals encourage participants to explore the anxieties, nostalgias, and hopes evoked by contemporary life through emotional, affective experience, rather than rational political debate. Keywords: neomedievalism, affect studies, public sphere, festivals, Times and Epochs