cssi/SciSciGPT-SciSciNet huggingface dataset:
An exponential lower bound for the degrees of invariants of cubic forms and tensor actions

Abstract Using the Grosshans Principle, we develop a method for proving lower bounds for the maximal degree of a system of generators of an invariant ring. This method also gives lower bounds for the maximal degree of a set of invariants that define Hilbert's null cone. We consider two actions: The first is the action of SL ( V ) on S 3 ( V ) ⊕ 4 , the space of 4-tuples of cubic forms, and the second is the action of SL ( V ) × SL ( W ) × SL ( Z ) on the tensor space ( V ⊗ W ⊗ Z ) ⊕ 9 . In both these cases, we prove an exponential lower degree bound for a system of invariants that generate the invariant ring or that define the null cone.
Women and Labor Market Changes in the Global Economy: Growth Helps, Inequalities Hurt and Public Policy Matters

This report examines the level and changes in female and male participation rates, employment segregation, and female wages relative to male wages across the world economy. It funds sufficient evidence to support the view that labor markets in developing countries are transformed relatively quickly in the sense that gender differentials in employment and pay are narrowing much faster than they did in industrialized countries. However, the report evaluates the inefficiencies arising from persisting gender differentials in the labor market and finds them to be potentially significant. The estimates also indicate that the resulting deadweight losses are borne primarily by women while men gain mainly in relative terms-there are no real winners from discrimination. The paper concludes that growth benefits women at large, inequalities can have significantly adverse effects on welfare, and market-based development alone can be a weak instrument doe reducing inequality between the sexes. To break the vicious cycle of women's low initial human capital endowments and inferior labor market outcomes compared to men's, the report proposes greater access to education and training for girls and women, enforceable equal pay and equal employment opportunities legislation, a taxation and benefits structure that treats reproduction as an economic activity and women as equal partners within households, and a better accounting of women's work to include invisible production.
On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution

The stability of spike deconvolution, which aims at recovering point sources from their convolution with a point spread function (PSF), is known to be related to the separation between those sources. When the observations are noisy, it is critical to ensure support stability , where the deconvolution does not lead to spurious, or oppositely, missing estimates of the point sources. In this paper, we study the resolution limit of stably recovering the support of two closely located point sources using the Beurling-LASSO estimator, which is a convex optimization approach based on total variation regularization. We establish a sufficient separation criterion between the sources, depending only on the PSF, above which the Beurling-LASSO estimator is guaranteed to return a stable estimate of the point sources, with the same number of estimated elements as that of the ground truth. Our result highlights the impact of PSF on the resolution limit in the noisy setting, which was not evident in previous studies of the noiseless setting. Towards the end, we show that the same resolution limit applies to resolving two close-located sources in conjunction of other well-separated sources.
Empirical Tests of Impacts of Rationing: The Case of Poland in Transition

Abstract This study tests hypothesis derived from the theory of rationing using data for Polish households during the transition. The demand system consists of six commodity groups, and virtual prices are derived for the rationed goods. The empirical results are consistent with the theory: larger own-price elasticities for non-rationed goods after the reform, increased complementarity and decreased substitutability. The results also show that ignoring rationing overstates the welfare decrease during transition.
Mid-P Values†

Generally, hypothesis testing is conducted by way of a comparison of the value of a test statistic that summarizes the extent to which the data deviate from the null hypothesis with a reference distribution. The P value is the tail area of reference distribution relative to the value of the test statistic. Often, the reference distribution will be discrete, and when it is, the test will be conservative, as only certain P values can be attained. This conservatism will result in a loss of power. One approach to dealing with this is the mid-P value, which essentially credits back half of the lost power. Keywords: distribution function; exact test; nonparametric test; ranks
Early Short-Course Corticosteroids in Hospitalized Patients With COVID-19.

BACKGROUND: There is no proven antiviral or immunomodulatory therapy for coronavirus disease 2019 (COVID-19). The disease progression associated with the proinflammatory host response prompted us to examine the role of early corticosteroid therapy in patients with moderate to severe COVID-19. METHODS: We conducted a single pretest, single posttest quasi-experiment in a multicenter health system in Michigan from 12 March to 27 March 2020. Adult patients with confirmed moderate to severe COVID were included. A protocol was implemented on 20 March 2020 using early, short-course, methylprednisolone 0.5 to 1 mg/kg/day divided in 2 intravenous doses for 3 days. Outcomes of standard of care (SOC) and early corticosteroid groups were evaluated, with a primary composite endpoint of escalation of care from ward to intensive care unit (ICU), new requirement for mechanical ventilation, and mortality. All patients had at least 14 days of follow-up. RESULTS: We analyzed 213 eligible subjects, 81 (38%) and 132 (62%) in SOC and early corticosteroid groups, respectively. The composite endpoint occurred at a significantly lower rate in the early corticosteroid group (34.9% vs 54.3%, P = .005). This treatment effect was observed within each individual component of the composite endpoint. Significant reduction in median hospital length of stay was also observed in the early corticosteroid group (5 vs 8 days, P < .001). Multivariate regression analysis demonstrated an independent reduction in the composite endpoint at 14-days controlling for other factors (adjusted odds ratio: 0.41; 95% confidence interval, .22 - .77). CONCLUSIONS: An early short course of methylprednisolone in patients with moderate to severe COVID-19 reduced escalation of care and improved clinical outcomes. CLINICAL TRIALS REGISTRATION: NCT04374071.
A Rapidly Evolving Polybasic Motif Modulates Bacterial Detection by Guanylate Binding Proteins

Cell-autonomous immunity relies on the rapid detection of invasive pathogens by host proteins. Guanylate binding proteins (GBPs) have emerged as key mediators of vertebrate immune defense through their ability to recognize a diverse array of intracellular pathogens and pathogen-containing cellular compartments. Human and mouse GBPs have been shown to target distinct groups of microbes, although the molecular determinants of pathogen specificity remain unclear. We show that rapid diversification of a C-terminal polybasic motif (PBM) in primate GBPs controls recognition of the model cytosolic bacterial pathogen Shigella flexneri By swapping this membrane-binding motif between primate GBP orthologs, we found that the ability to target S. flexneri has been enhanced and lost in specific lineages of New World primates. Single substitutions in rapidly evolving sites of the GBP1 PBM are sufficient to abolish or restore bacterial detection abilities, illustrating a role for epistasis in the evolution of pathogen recognition. We further demonstrate that the squirrel monkey GBP2 C-terminal domain recently gained the ability to target S. flexneri through a stepwise process of convergent evolution. These findings reveal a mechanism by which accelerated evolution of a PBM shifts GBP target specificity and aid in resolving the molecular basis of GBP function in cell-autonomous immune defense.IMPORTANCE Many infectious diseases are caused by microbes that enter and survive within host cells. Guanylate binding proteins (GBPs) are a group of immune proteins which recognize and inhibit a variety of intracellular pathogenic microbes. We discovered that a short sequence within GBPs required for the detection of bacteria, the polybasic motif (PBM), has been rapidly evolving between primate species. By swapping PBMs between primate GBP1 genes, we were able to show that specific sequences can both reduce and improve the ability of GBP1 to target intracellular bacteria. We also show that the ability to envelop bacteria has independently evolved in GBP2 of South American monkeys. Taking the results together, this report illustrates how primate GBPs have adapted to defend against infectious pathogens.
Femtoscopically Probing the Freeze-out Configuration in Heavy Ion Collisions

Two-particle femtoscopy reveals the space-time substructure of the freeze-out configuration from heavy ion collisions. Detailed fingerprints of bulk collectivity are evident in space-momentum correlations, which have been systematically measured as a function of particle type, three-momentum, and collision conditions. A clear scenario, dominated by hydrodynamic-type flow emerges. Reproducing the strength and features of the femtoscopic signals in models involves important physical quantities like the Equation of State, as well as less fundamental technical details. An interesting approximate "factorization" in the measured systematics suggests that the overall physical freeze-out scale is set by final state chemistry, but the kinematic substructure is largely universal. Referring to previous results from hadron and lepton collisions, we point to the importance of determining whether these "universal" trends persist from the largest to the smallest systems. We review theoretical expectations for heavy ion femtoscopy at the LHC, and point to directions needing further theory and experimental work at RHIC and the LHC.
A game-theoretic proof of Shelah's theorem on labeled trees

We give a new proof of a theorem of Shelah which states that for every family of labeled trees, if the cardinality $\kappa$ of the family is much larger (in the sense of large cardinals) than the cardinality $\lambda$ of the set of labels, more precisely if the partition relation $\kappa \to (\omega)^{\mathord{<}\omega}_\lambda$ holds, then there is a homomorphism from one labeled tree in the family to another. Our proof uses a characterization of such homomorphisms in terms of games.
CSAI: Open-Source Cellular Radio Access Network Security Analysis Instrument

This paper presents our methodology and software toolbox that allows analyzing the radio access network security of laboratory and commercial 4G and future 5G cellular networks. We leverage a free open-source software suite that implements the LTE UE and eNB enabling real-time signaling using software radio peripherals. We modify the UE software processing stack to act as an LTE packet collection and examination tool. This is possible because of the openness of the 3GPP specifications. Hence, we are able to receive and decode LTE downlink messages for the purpose of analyzing potential security problems of the standard. This paper shows how to rapidly prototype LTE tools and build a software-defined radio access network (RAN) analysis instrument for research and education. Using the Cellular Security Analysis Instrument (CSAI), a researcher can analyze broadcast and paging messages of cellular networks. CSAI is also able to test networks to aid in the identification of vulnerabilities and verify functionality post-remediation. Additionally, we found that it can crash a software eNB which motivates equivalent analyses of commercial network equipment and its robustness against denial of service attacks.
DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring.

During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. “co-binding changes”) can affect the co-regulating associations between TFs (i.e. “rewiring the co-regulator network”). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. To address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.
Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant

Ion mobility brings an additional dimension of separation to LC–MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis.
A MULTI-FIDELITY NEURAL NETWORK SURROGATE SAMPLING METHOD FOR UNCERTAINTY QUANTIFICATION

We propose a multi-fidelity neural network surrogate sampling method for the uncertainty quantification of physical/biological systems described by ordinary or partial differential equations. We first generate a set of low/high-fidelity data by low/high-fidelity computational models, e.g. using coarser/finer discretizations of the governing differential equations. We then construct a two-level neural network, where a large set of low-fidelity data are utilized in order to accelerate the construction of a high-fidelity surrogate model with a small set of high-fidelity data. We then embed the constructed high-fidelity surrogate model in the framework of Monte Carlo sampling. The proposed algorithm combines the approximation power of neural networks with the advantages of Monte Carlo sampling within a multi-fidelity framework. We present two numerical examples to demonstrate the accuracy and efficiency of the proposed method. We show that dramatic savings in computational cost may be achieved when the output predictions are desired to be accurate within small tolerances.
B-Cell Maturation Antigen (BCMA) as a Target for New Drug Development in Relapsed and/or Refractory Multiple Myeloma.

During the past two decades there has been a major shift in the choice of agents to treat multiple myeloma, whether newly diagnosed or in the relapsed/refractory stage. The introduction of new drug classes, such as proteasome inhibitors, immunomodulators, and anti-CD38 and anti-SLAMF7 monoclonal antibodies, coupled with autologous stem cell transplantation, has approximately doubled the disease’s five-year survival rate. However, this positive news is tempered by the realization that these measures are not curative and patients eventually relapse and/or become resistant to the drug’s effects. Thus, there is a need to discover newer myeloma-driving molecular markers and develop innovative drugs designed to precisely regulate the actions of such putative targets. B cell maturation antigen (BCMA), which is found almost exclusively on the surfaces of malignant plasma cells to the exclusion of other cell types, including their normal counterparts, has emerged as a specific target of interest in this regard. Immunotherapeutic agents have been at the forefront of research designed to block BCMA activity. These agents encompass monoclonal antibodies, such as the drug conjugate belantamab mafodotin; bispecific T-cell engager strategies exemplified by AMG 420; and chimeric antigen receptor (CAR) T-cell therapeutics that include idecabtagene vicleucel (bb2121) and JNJ-68284528.
A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity.

There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Becaus...
Soft subdivision motion planning for complex planar robots

Abstract The design and implementation of theoretically-sound robot motion planning algorithms is challenging. Within the framework of resolution-exact algorithms, it is possible to exploit soft predicates for collision detection. The design of soft predicates is a balancing act between their implementability and their accuracy/effectivity. In this paper, we focus on the class of planar polygonal rigid robots with arbitrarily complex geometry. We exploit the remarkable decomposability property of soft collision-detection predicates of such robots. We introduce a general technique to produce such a decomposition. If the robot is an m-gon, the complexity of this approach scales linearly in m. This contrasts with the O ( m 3 ) complexity known for exact planners. It follows that we can now routinely produce soft predicates for any rigid polygonal robot. This results in resolution-exact planners for such robots within the general Soft Subdivision Search (SSS) framework. This is a significant advancement in the theory of sound and complete planners for planar robots. We implemented such decomposed predicates in our open-source Core Library . The experiments show that our algorithms are effective, perform in real time on non-trivial environments, and can outperform many sampling-based methods.
Survey on Individual Differences in Visualization

Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one-size-fits-all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence-based reviews, and spur future inquiry.
Optimal Safe Controller Synthesis: A Density Function Approach

This paper considers the synthesis of optimal safe controllers based on density functions. We present an algorithm for robust constrained optimal control synthesis using the duality relationship between the density function and the value function. The density function follows the Liouville equation and is the dual of the value function, which satisfies Bellman’s optimality principle. Thanks to density functions, constraints over the distribution of states, such as safety constraints, can be posed straightforwardly in an optimal control problem. The constrained optimal control problem is then solved with a primal-dual algorithm. This formulation is extended to the case with external disturbances, and we show that the robust constrained optimal control can be solved with a modified primal-dual algorithm. We apply this formulation to the problem of finding the optimal safe controller that minimizes the cumulative intervention. An adaptive cruise control (ACC) example is used to demonstrate the efficacy of the proposed, wherein we compare the result of the density function approach with the conventional control barrier function (CBF) method.
Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

COVID-19 (coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the pathophysiology of this virus is complex and largely unknown, we employed a network-biology-fueled approach and integrated transcriptome data pertaining to lung epithelial cells with human interactome to generate Calu-3-specific human-SARS-CoV-2 interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 targets central nodes of the host-viral network, which participate in core functional pathways. Network centrality analyses discover 33 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation, and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host-modifying responses and cytokine storm. Overall, our network-centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into the pathogenicity of SARS-CoV-2 that may help foster effective therapeutic design.
AGI and the Knight-Darwin Law: Why Idealized AGI Reproduction Requires Collaboration

Can an AGI create a more intelligent AGI? Under idealized assumptions, for a certain theoretical type of intelligence, our answer is: “Not without outside help”. This is a paper on the mathematical structure of AGI populations when parent AGIs create child AGIs. We argue that such populations satisfy a certain biological law. Motivated by observations of sexual reproduction in seemingly-asexual species, the Knight-Darwin Law states that it is impossible for one organism to asexually produce another, which asexually produces another, and so on forever: that any sequence of organisms (each one a child of the previous) must contain occasional multi-parent organisms, or must terminate. By proving that a certain measure (arguably an intelligence measure) decreases when an idealized parent AGI single-handedly creates a child AGI, we argue that a similar Law holds for AGIs.
Urinary metals and incident diabetes in midlife women: Study of Women's Health Across the Nation (SWAN).

Introduction Environmental exposure to metals may play a role in the pathogenesis of diabetes; however, evidence from human studies is limited. We prospectively evaluated the associations of 20 urinary metal concentrations and their mixtures with incident diabetes in the Study of Women’s Health Across the Nation, a multisite, multiethnic cohort study of midlife women. Research design and methods The sample included 1237 white, black, Chinese and Japanese-American women, aged 45–56 years, free of diabetes at baseline (1999–2000) who were followed through 2016. Concentrations of 20 metals (arsenic, barium, beryllium, cadmium, cobalt, chromium, cesium, copper, mercury, manganese, molybdenum, nickel, lead, antimony, tin, thallium, uranium, vanadium, tungsten and zinc) were measured in urine specimens at baseline. Incident diabetes was identified annually by fasting glucose ≥126 mg/dL, self-reported doctor-diagnosed diabetes, or self-reported use of antidiabetic medications. A non-parametric clustering method, k-means clustering, was used to identify subgroups with different exposure patterns to metal mixtures. Results After multivariable adjustment, the HR (95% CI) for diabetes associated with each doubling increase in urinary metal concentrations was 1.19 (1.10 to 1.30) for arsenic and 1.20 (1.05 to 1.37) for lead, in Cox proportional hazards models after controlling for multiple comparisons. A doubling in urinary excretion of zinc was associated with higher risk of diabetes (adjusted HR 1.31, 95% CI 1.11 to 1.53). Two distinct exposure patterns to metal mixtures—‘high’ versus ‘low’—were identified. Participants assigned to the ‘high’ pattern had higher overall concentrations of all metals compared with those classified into the ‘low’ pattern. Adjusted HR for diabetes associated with ‘high’ pattern compared with ‘low’ was 1.42 (1.08 to 1.87). Conclusions Higher urinary concentrations of arsenic and lead, increased urinary excretion of zinc, as well as higher overall exposure to metal mixtures were associated with elevated risk of diabetes. Future studies should further investigate the underlying mechanisms.
Weighted estimates for the Bergman projection on the Hartogs triangle

Abstract We apply modern techniques of dyadic harmonic analysis to obtain sharp estimates for the Bergman projection in weighted Bergman spaces. Our main theorem focuses on the Bergman projection on the Hartogs triangle. The estimates of the operator norm are in terms of a Bekolle-Bonami type constant. As an application of the results obtained, we give, for example, an upper bound for the L p norm of the Bergman projection on the generalized Hartogs triangle H m / n in C 2 .
Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer's disease.

Alzheimer's disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human AD cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies and quantitative trait loci to further characterize the genetic architecture of AD. We perform co-expression network analysis across more than 1200 human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of AD gene co-expression changes and estimate cell-type proportion changes in human AD by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of AD are enriched in a microglial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).
Noradrenergic circuits in the forebrain control affective responses to novelty.

In rodents, exposure to novel environments elicits initial anxiety-like behavior (neophobia) followed by intense exploration (neophilia) that gradually subsides as the environment becomes familiar. Thus, innate novelty-induced behaviors are useful indices of anxiety and motivation in animal models of psychiatric disease. Noradrenergic neurons are activated by novelty and implicated in exploratory and anxiety-like responses, but the role of norepinephrine (NE) in neophobia has not been clearly delineated. We sought to define the role of central NE transmission in neophilic and neophobic behaviors. We assessed dopamine β-hydroxylase knockout (Dbh −/−) mice lacking NE and their NE-competent (Dbh +/−) littermate controls in neophilic (novelty-induced locomotion; NIL) and neophobic (novelty-suppressed feeding; NSF) behavioral tests with subsequent quantification of brain-wide c-fos induction. We complimented the gene knockout approach with pharmacological interventions. Dbh −/− mice exhibited blunted locomotor responses in the NIL task and completely lacked neophobia in the NSF test. Neophobia was rescued in Dbh −/− mice by acute pharmacological restoration of central NE with the synthetic precursor l-3,4-dihydroxyphenylserine (DOPS), and attenuated in control mice by the inhibitory α2-adrenergic autoreceptor agonist guanfacine. Following either NSF or NIL, Dbh −/− mice demonstrated reduced c-fos in the anterior cingulate cortex, medial septum, ventral hippocampus, bed nucleus of the stria terminalis, and basolateral amygdala. These findings indicate that central NE signaling is required for the expression of both neophilic and neophobic behaviors. Further, we describe a putative noradrenergic novelty network as a potential therapeutic target for treating anxiety and substance abuse disorders.
Ultrasound Monitoring of Descending Aortic Aneurysms and Dissections in Mice.

Abscisic acid (ABA) and gibberellin (GA) are two antagonistic phytohormones that regulate seed germination in response to biotic and abiotic environmental stresses. We demonstrate here that MOTHER OF FT AND TFL1 (MFT), which encodes a phosphatidylethanolamine-binding protein, regulates seed germination via the ABA and GA signaling pathways in Arabidopsis thaliana. MFT is specifically induced in the radical-hypocotyl transition zone of the embryo in response to ABA, and mft loss-of-function mutants show hypersensitivity to ABA in seed germination. In germinating seeds, MFT expression is directly regulated by ABA-INSENSITIVE3 (ABI3) and ABI5, two key transcription factors in ABA signaling pathway. MFT is also upregulated by DELLA proteins in the GA signaling pathway. MFT in turn provides negative feedback regulation of ABA signaling by directly repressing ABI5. We conclude that during seed germination, MFT promotes embryo growth by constituting a negative feedback loop in the ABA signaling pathway.
On the high–low method for NLS on the hyperbolic space

In this paper, we first prove that the cubic, defocusing nonlinear Schrodinger equation on the two dimensional hyperbolic space with radial initial data in Hs(H2) is globally well-posed and scatters when s > 3/4. Then, we extend the result to nonlinearities of order p > 3. The result is proved by extending the high–low method of Bourgain in the hyperbolic setting and by using a Morawetz type estimate proved by Staffilani and Ionescu.
LFTag: A Scalable Visual Fiducial System with Low Spatial Frequency

Visual fiducial systems are a key component of many robotics and AR/VR applications for 6-DOF monocular relative pose estimation and target identification. This paper presents LFTag, a visual fiducial system based on topological detection and relative position data encoding which optimizes data density within spatial frequency constraints. The marker is constructed to resolve rotational ambiguity, which combined with the robust geometric and topological false positive rejection, allows all marker bits to be used for data.When compared to existing state-of-the-art square binary markers (AprilTag) and topological markers (TopoTag) in simulation, the proposed fiducial system (LFTag) offers significant advances in dictionary size and range. LFTag 3×3 achieves 546 times the dictionary size of AprilTag 25h9 and LFTag 4×4 achieves 126 thousand times the dictionary size of AprilTag 41h12 while simultaneously achieving longer detection range. LFTag 3×3 also achieves more than twice the detection range of TopoTag 4×4 at the same dictionary size.
Topology optimization under uncertainty using a stochastic gradient-based approach

Topology optimization under uncertainty (TOuU) often defines objectives and constraints by statistical moments of geometric and physical quantities of interest. Most traditional TOuU methods use gradient-based optimization algorithms and rely on accurate estimates of the statistical moments and their gradients, e.g., via adjoint calculations. When the number of uncertain inputs is large or the quantities of interest exhibit large variability, a large number of adjoint (and/or forward) solves may be required to ensure the accuracy of these gradients. The optimization procedure itself often requires a large number of iterations, which may render TOuU computationally expensive, if not infeasible. To tackle this difficulty, we here propose an optimization approach that generates a stochastic approximation of the objective, constraints, and their gradients via a small number of adjoint (and/or forward) solves, per optimization iteration. A statistically independent (stochastic) approximation of these quantities is generated at each optimization iteration. The total cost of this approach is only a small factor larger than that of the corresponding deterministic topology optimization problem. We incorporate the stochastic approximation of objective, constraints, and their design sensitivities into two classes of optimization algorithms. First, we investigate the stochastic gradient descent (SGD) method and a number of its variants, which have been successfully applied to large-scale optimization problems for machine learning. Second, we study the use of the proposed stochastic approximation approach within conventional nonlinear programming methods, focusing on the globally convergent method of moving asymptotes (GCMMA). The performance of these algorithms is investigated with structural design optimization problems utilizing a solid isotropic material with penalization (SIMP), as well as an explicit level set method. These investigations, conducted on both two- and three-dimensional structures, illustrate the efficacy of the proposed stochastic gradient approach for TOuU applications.
LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY

We present a general numerical approach for constructing governing equations for unknown dynamical systems when only data on a subset of the state variables are available. The unknown equations for these observed variables are thus a reduced system of the complete set of state variables. Reduced systems possess memory integrals, based on the well known Mori-Zwanzig (MZ) formulism. Our numerical strategy to recover the reduced system starts by formulating a discrete approximation of the memory integral in the MZ formulation. The resulting unknown approximate MZ equations are of finite dimensional, in the sense that a finite number of past history data are involved. We then present a deep neural network structure that directly incorporates the history terms to produce memory in the network. The approach is suitable for any practical systems with finite memory length. We then use a set of numerical examples to demonstrate the effectiveness of our method.
Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory

Here we present a multi-level discrete-state nonvolatile photonic memory based on an ultra-compact ( $ ) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. Emulating an opportunely trained $100\times 100$ fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 92% inference accuracy and robustness towards noise when performing predictions of unseen data.
Subinvariance in Leibniz algebras

Abstract Leibniz algebras are certain generalizations of Lie algebras. Motivated by the concept of subinvariance in group theory, Schenkman studied properties of subinvariant subalgebras of a Lie algebra. In this paper we define subinvariant subalgebras of Leibniz algebras and study their properties. It is shown that the signature results on subinvariance in Lie algebras have analogs for Leibniz algebras.
Low-Cost Fiducial-based 6-Axis Force-Torque Sensor

Commercial six-axis force-torque sensors suffer from being some combination of expensive, fragile, and hard-touse. We propose a new fiducial-based design which addresses all three points. The sensor uses an inexpensive webcam and can be fabricated using a consumer-grade 3D printer. Open-source software is used to estimate the 3D pose of the fiducials on the sensor, which is then used to calculate the applied force-torque. A browser-based (installation free) interface demonstrates ease-of-use. The sensor is very light and can be dropped or thrown with little concern. We characterize our prototype in dynamic conditions under compound loading, finding a mean R2 of over 0.99 for the F x , F y , M x , and M y axes, and over 0.87 and 0.90 for the F z and M z axes respectively. The open source design files allow the sensor to be adapted for diverse applications ranging from robot fingers to human-computer interfaces, while the sdesign principle allows for quick changes with minimal technical expertise. This approach promises to bring six-axis force-torque sensing to new applications where the precision, cost, and fragility of traditional strain-gauge based sensors are not appropriate. The open-source sensor design can be viewed at http://sites.google.com/view/fiducialforcesensor.
Risk Assessment and Planning with Bidirectional Reachability for Autonomous Driving

Risk assessment to quantify the danger associated with taking a certain action is critical to navigating safely through crowded urban environments during autonomous driving. Risk assessment and subsequent planning is usually done by first tracking and predicting trajectories of other agents, such as vehicles and pedestrians, and then choosing an action to avoid future collisions. However, few existing risk assessment algorithms handle occlusion and other sensory limitations effectively. One either assesses the risk in the worst-case scenario and thus makes the ego vehicle overly conservative, or predicts as many hidden agents as possible and thus makes the computation intensive. This paper explores the possibility of efficient risk assessment under occlusion via both forward and backward reachability. The proposed algorithm can not only identify the location of risk-inducing factors, but can also be used during motion planning. The proposed method is evaluated on various four-way highly occluded intersections with up to five other vehicles in the scene. Compared with other risk assessment algorithms, the proposed method shows better efficiency, meaning that the ego vehicle reaches the goal at a higher speed. In addition, it also lowers the median collision rate by 7.5× when compared to state of the art techniques.
The DNA damage-sensing NER repair factor XPC-RAD23B does not recognize bulky DNA lesions with a missing nucleotide opposite the lesion

The Nucleotide Excision Repair (NER) mechanism removes a wide spectrum of structurally different lesions that critically depend on the binding of the DNA damage sensing NER factor XPC-RAD23B (XPC) to the lesions. The bulky mutagenic benzo[a]pyrene diol epoxide metabolite-derived cis- and trans-B[a]P-dG lesions (G*) adopt base-displaced intercalative (cis) or minor groove (trans) conformations in fully paired DNA duplexes with the canonical C opposite G* (G*:C duplexes). While XPC has a high affinity for binding to these DNA lesions in fully complementary double-stranded DNA, we show here that deleting only the C in the complementary strand opposite the lesion G* embedded in 50-mer duplexes, fully abrogates XPC binding. Accurate values of XPC dissociation constants (KD) were determined by employing an excess of unmodified DNA as a competitor; this approach eliminated the binding and accumulation of multiple XPC molecules to the same DNA duplexes, a phenomenon that prevented the accurate estimation of XPC binding affinities in previous studies. Surprisingly, a detailed comparison of XPC dissociation constants KD of unmodified and lesion-containing G*:Del complexes, showed that the KD values were -2.5-3.6 times greater in the case of G*:Del than in the unmodified G:Del and fully base-paired G:C duplexes. The origins of this unexpected XPC lesion avoidance effect is attributed to the intercalation of the bulky, planar B[a]P aromatic ring system between adjacent DNA bases that thermodynamically stabilize the G*:Del duplexes. The strong lesion-base stacking interactions associated with the absence of the partner base, prevent the DNA structural distortions needed for the binding of the BHD2 and BHD3 β-hairpins of XPC to the deletion duplexes, thus accounting for the loss of XPC binding and the known NER-resistance of G*:Del duplexes.
Weak maximum principle for biharmonic equations in quasiconvex Lipschitz domains

Abstract In dimension two or three, the weak maximum principle for biharmonic equation is valid in any bounded Lipschitz domains. In higher dimensions (greater than three), it was only known that the weak maximum principle holds in convex domains or C 1 domains, and may fail in general Lipschitz domains. In this paper, we prove the weak maximum principle in higher dimensions in quasiconvex Lipschitz domains, which is a sharp condition in some sense and recovers both convex and C 1 domains.
How Pervasive Is Earnings Management? Evidence from a Structural Model

Although researchers often view earnings management as being widespread, measuring the cost and level of earnings management is a nontrivial task. We derive a measure of earnings management cost an...
Efficient Minimum Word Error Rate Training of RNN-Transducer for End-to-End Speech Recognition.

In this work, we propose a novel and efficient minimum word error rate (MWER) training method for RNN-Transducer (RNN-T). Unlike previous work on this topic, which performs on-the-fly limited-size beam-search decoding and generates alignment scores for expected edit-distance computation, in our proposed method, we re-calculate and sum scores of all the possible alignments for each hypothesis in N-best lists. The hypothesis probability scores and back-propagated gradients are calculated efficiently using the forward-backward algorithm. Moreover, the proposed method allows us to decouple the decoding and training processes, and thus we can perform offline parallel-decoding and MWER training for each subset iteratively. Experimental results show that this proposed semi-on-the-fly method can speed up the on-the-fly method by 6 times and result in a similar WER improvement (3.6%) over a baseline RNN-T model. The proposed MWER training can also effectively reduce high-deletion errors (9.2% WER-reduction) introduced by RNN-T models when EOS is added for endpointer. Further improvement can be achieved if we use a proposed RNN-T rescoring method to re-rank hypotheses and use external RNN-LM to perform additional rescoring. The best system achieves a 5% relative improvement on an English test-set of real far-field recordings and a 11.6% WER reduction on music-domain utterances.
Symplectic toric stratified spaces with isolated singularities

The goal of this paper is to classify symplectic toric stratified spaces with isolated singularities. This extends a result of Burns, Guillemin, and Lerman which carries out this classification in the compact connected case. In making this classification, it is necessary to classify symplectic toric cones. Via a well-known equivalence between symplectic toric cones and contact toric manifolds, this allows for the classification of contact toric manifolds as well, extending Lerman's classification of compact connected contact toric manifolds.
Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares

In this paper, the problem of target localization in the presence of outlying sensors is tackled. This problem is important in practice because in many real-world applications the sensors might report irrelevant data unintentionally or maliciously. The problem is formulated by applying robust statistics techniques on squared range measurements and two different approaches to solve the problem are proposed. The first approach is computationally efficient; however, only the objective convergence is guaranteed theoretically. On the other hand, the whole-sequence convergence of the second approach is established. To enjoy the benefit of both approaches, they are integrated to develop a hybrid algorithm that offers computational efficiency and theoretical guarantees.The algorithms are evaluated for different simulated and real-world scenarios. The numerical results show that the proposed methods meet the Cr'amer-Rao lower bound (CRLB) for a sufficiently large number of measurements. When the number of the measurements is small, the proposed position estimator does not achieve CRLB though it still outperforms several existing localization methods.
Magnetism in amorphous carbon

We investigate reported ferromagnetism in a new form of amorphous carbon. We use spin constrained first-principles simulations to obtain amorphous carbon structures with the desired magnetization. We show the existence of $s{p}^{2}$-like threefold coordinated carbon atoms plays an important role in causing magnetism in amorphous carbon. We predict detailed geometries of threefold carbon atoms that induce the magnetic order in amorphous carbon.
Forecasting the Cost of U.S. Health Care in 2040

Abstract One of the most important debates among health economists in rich nations is whether advances in biotechnology will spare their health care systems from a financial crisis. We must consider that prevalence rates of chronic diseases declined during the twentieth century and that this rate of decline has accelerated. However, health care costs may continue to increase even as the age of onset of chronic diseases is delayed, because the proportion of a cohort living to late ages will increase. The accelerating decline in the prevalence of chronic diseases during the course of the twentieth century supports the proposition that increases in life expectancy during the twenty-first century will be fairly large, but the effect on health care in the U.S. will be modest. The income elasticity for health services is calculated at 1.6, meaning that income expenditures on health care in the U.S. are likely to rise from a current level of about 15 percent to about 29 percent of GDP in 2040.