cssi/SciSciGPT-SciSciNet huggingface dataset:
Dietary nicotine intake and risk of Parkinson disease: a prospective study

Background Tobacco use was observed to be associated with a lower risk of Parkinson disease (PD) in previous epidemiologic studies, with nicotine as a potential candidate. The association between dietary nicotine and PD risk has, however, not been examined in prospective studies yet. Objectives We aimed to examine prospectively the association between dietary nicotine intake and subsequent PD risk among never-smokers. Methods The current study was based on never-smoker participants from 2 large prospective cohorts: the Nurses' Health Study (n = 31,615) and the Health Professionals Follow-up Study (n = 19,523). The studies contained information on dietary nicotine intake from 1986 from validated FFQs. Dietary nicotine intake was calculated based on consumption of peppers, tomatoes, processed tomatoes, potatoes, and tea. Incident cases of PD were identified via questionnaires and subsequently confirmed by reviewing medical records. We used Cox proportional hazard models to calculate cohort-specific HRs, and used fixed-effects models to calculate the pooled HR. Results During 26 y of follow-up, we identified 601 incident PD cases (296 women and 305 men). After adjusting for potential covariates, the pooled HR for the highest compared with the lowest quintile of dietary nicotine intake was 0.70 (95% CI: 0.51, 0.94). The significant inverse association was, however, only observed in women (adjusted HR: 0.64; 95% CI: 0.42, 0.96), not in men (adjusted HR: 0.77; 95% CI: 0.50, 1.20). Further adjusting for environmental tobacco smoke exposure, family history of PD, and use of ibuprofen generated similar significant results in women. Consistently, greater consumption of peppers was associated with lower risk of PD (adjusted HR for ≥5 times/wk compared with ≤3 times/mo: 0.49; 95% CI: 0.25, 0.94) in women but not in men (adjusted HR: 1.04; 95% CI: 0.57, 1.90). Conclusions Women with greater dietary nicotine intake had a lower risk of PD than those with lower intake.
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first √T-type regret guarantee for this problem, under the feedback model where collisions are announced to the colliding players. Such a bound was not known even for the simpler stochastic version. We also prove the first sublinear regret guarantee for the feedback model where collision information is not available, namely T1−1 2m where m is the number of players.
Streaming Verification for Graph Problems: Optimal Tradeoffs and Nonlinear Sketches.

We study graph computations in an enhanced data streaming setting, where a space-bounded client reading the edge stream of a massive graph may delegate some of its work to a cloud service. We seek algorithms that allow the client to verify a purported proof sent by the cloud service that the work done in the cloud is correct. A line of work starting with Chakrabarti et al. (ICALP 2009) has provided such algorithms, which we call schemes, for several statistical and graph-theoretic problems, many of which exhibit a tradeoff between the length of the proof and the space used by the streaming verifier. This work designs new schemes for a number of basic graph problems---including triangle counting, maximum matching, topological sorting, and single-source shortest paths---where past work had either failed to obtain smooth tradeoffs between these two key complexity measures or only obtained suboptimal tradeoffs. Our key innovation is having the verifier compute certain nonlinear sketches of the input stream, leading to either new or improved tradeoffs. In many cases, our schemes in fact provide optimal tradeoffs up to logarithmic factors. Specifically, for most graph problems that we study, it is known that the product of the verifier's space cost $v$ and the proof length $h$ must be at least $\Omega(n^2)$ for $n$-vertex graphs. However, matching upper bounds are only known for a handful of settings of $h$ and $v$ on the curve $h \cdot v=\tilde{\Theta}(n^2)$. For example, for counting triangles and maximum matching, schemes with costs lying on this curve are only known for $(h=\tilde{O}(n^2), v=\tilde{O}(1))$, $(h=\tilde{O}(n), v=\tilde{O}(n))$, and the trivial $(h=\tilde{O}(1), v=\tilde{O}(n^2))$. A major message of this work is that by exploiting nonlinear sketches, a significant ``portion'' of costs on the tradeoff curve $h \cdot v = n^2$ can be achieved.
Analytical Performance Modeling of NoCs under Priority Arbitration and Bursty Traffic

Networks-on-Chip (NoCs) used in commercial many-core processors typically incorporate priority arbitration. Moreover, they experience bursty traffic due to application workloads. However, most state-of-the-art NoC analytical performance analysis techniques assume fair arbitration and simple traffic models. To address these limitations, we propose an analytical modeling technique for priority-aware NoCs under bursty traffic. Experimental evaluations with synthetic and bursty traffic show that the proposed approach has less than 10% modeling error with respect to cycle-accurate NoC simulator.
Di-Higgs Production in SUSY models at the LHC

We study the modification to di-Higgs production via gluon fusion within the context of the minimal supersymmetric standard model and the next-to-minimal supersymmetric standard model in the parameter space allowed by current experimental and theoretical constraints, and also relevant to the Large Hadron Collider (LHC) experiments in the near future. The calculation is based on the analytical expression of the leading-order Feynman amplitudes (which includes both quark and squark loops). We separate the di-Higgs production cross section into resonant, non-resonant, and interference parts, in order to better understand the mechanisms that are responsible for the modification to di-Higgs production rate in different regions of the allowed parameter space. We also investigate the sensitivity of high-luminosity LHC (HL-LHC) to the di-Higgs production in these low-energy supersymmetry models. Furthermore, we examine the complementarity between di-Higgs searches and direct searches for BSM particles and precision Higgs couplings measurements at the HL-LHC. We found that the di-Higgs production cross section can be enhanced significantly through resonant production. In the region where the resonant production cross section is small, di-Higgs production receives a moderate enhancement due to the modifications in the Higgs couplings. In addition, there is a strong correlation between di-Higgs production and single Higgs production, and di-Higgs production is not as sensitive as single Higgs production at the HL-LHC.
Bernstein-Sato theory for arbitrary ideals in positive characteristic

Mustaţa defined Bernstein-Sato polynomials in prime characteristic for principal ideals and proved that the roots of these polynomials are related to the $F$-jumping numbers of the ideal. This approach was later refined by Bitoun. Here we generalize these techniques to develop analogous notions for the case of arbitrary ideals and prove that these have similar connections to $F$-jumping numbers.
Increased FOXL2 expression alters uterine structures and functions

The transcription factor forkhead box L2 (FOXL2) regulates sex differentiation and reproductive function. Elevated levels of this transcription factor have been observed in the diseases of the uterus, such as endometriosis. However, the impact of elevated FOXL2 expression on uterine physiology remains unknown. In order to determine the consequences of altered FOXL2 in the female reproductive axis, we generated mice with over-expression of FOXL2 (FOXL2OE) by crossing Foxl2LsL/+ with the Progesterone receptor Pgrcre model. FOXL2OE uterus showed severe morphological abnormality including abnormal epithelial stratification, blunted adenogenesis, increased endometrial fibrosis, and disrupted myometrial morphology. In contrast, increasing FOXL2 levels specifically in uterine epithelium by crossing the Foxl2LsL/+ with the lactoferrin Ltficre mice resulted in the eFOXL2OE mice with uterine epithelial stratification but without defects in endometrial fibrosis and adenogenesis, demonstrating a role of the endometrial stroma in the uterine abnormalities of the FOXL2OE mice. Transcriptomic analysis of 12 weeks old Pgrcre and FOXL2OE uterus at diestrus stage showed multiple signaling pathways related with cellular matrix, wnt/β-catenin, and altered cell cycle. Furthermore, we found FOXL2OE mice were sterile. The infertility was caused in part by a disruption of the hypophyseal ovarian axis resulting in an anovulatory phenotype. The FOXL2OE mice failed to show decidual responses during artificial decidualization in ovariectomized mice demonstrating the uterine contribution to the infertility phenotype. These data support that aberrantly increased FOXL2 expressions in the female reproductive tract can disrupt ovarian and uterine functions.
In Search of "Hepatic Factor": Lack of Evidence for ALK1 Ligands BMP9 and BMP10.

Abstract In children with single ventricle physiology, the Glenn procedure is performed to redirect venous return from the superior vena cava directly to the pulmonary arteries and route venous return from the inferior vena cava exclusively to the systemic circulation. Although this surgery successfully palliates the hemodynamic stress experienced by the single ventricle, patients frequently develop pulmonary arteriovenous malformations (PAVMs). Interestingly, PAVMs may regress upon rerouting of hepatic venous effluent to the pulmonary vasculature, suggesting the presence of a circulating “hepatic factor” that is required to prevent PAVMs. Here, we test the hypothesis that hepatic factor is bone morphogenetic protein 9 (BMP9) and/or BMP10. These circulating ligands are produced by the liver and activate endothelial endoglin (ENG)/ALK1 signaling, and mutations in ENG and ALK1 cause hereditary hemorrhagic telangiectasia, a genetic disease associated with AVM development. However, we found no within-subject variation in BMP9, BMP10, or BMP9/10 plasma concentrations when sampled from five cardiovascular sites, failing to support the idea that the Glenn would limit access of these ligands to the lung vasculature. Unexpectedly, however, we found a significant decrease in all three ligand concentrations in Glenn cases versus controls. Our findings suggest that BMP9/BMP10/ENG/ALK1 signaling may be decreased in the Glenn vasculature but fail to implicate these ligands as hepatic factor.
Semi-supervised logistic learning based on exponential tilt mixture models

Consider semi-supervised learning for classification, where both labeled and unlabeled data are available for training. The goal is to exploit both datasets to achieve higher prediction accuracy than just using labeled data alone. We develop a semi-supervised logistic learning method based on exponential tilt mixture models, by extending a statistical equivalence between logistic regression and exponential tilt modeling. We study maximum nonparametric likelihood estimation and derive novel objective functions which are shown to be Fisher consistent. We also propose regularized estimation and construct simple and highly interpretable EM algorithms. Finally, we present numerical results which demonstrate the advantage of the proposed methods compared with existing methods.
Overexpression of Mdm36 reveals Num1 foci that mediate dynein-dependent microtubule sliding in budding yeast.

Current model for spindle positioning requires attachment of the microtubule (MT) motor cytoplasmic dynein to the cell cortex, where it generates pulling force on astral MTs to effect spindle displacement. How dynein is anchored by cortical attachment machinery to generate large spindle-pulling forces remains unclear. Here, we show that cortical clustering of Num1, the yeast dynein attachment molecule, is limited by its assembly factor Mdm36. Overexpression of Mdm36 results in an overall enhancement of Num1 clustering but reveals a population of dim Num1 clusters that mediate dynein-anchoring at the cell cortex. Direct imaging shows that bud-localized, dim Num1 clusters containing only ∼6 copies of Num1 molecules mediate dynein-dependent spindle pulling via lateral MT sliding mechanism. Mutations affecting Num1 clustering interfere with mitochondrial tethering but not dynein-based spindle-pulling function of Num1. We propose that formation of small ensembles of attachment molecules is sufficient for dynein anchorage and cortical generation of large spindle-pulling force.
Non-tracial Free Graph von Neumann Algebras

Given a finite, directed, connected graph $$\Gamma $$ equipped with a weighting $$\mu $$ on its edges, we provide a construction of a von Neumann algebra equipped with a faithful, normal, positive linear functional $$(\mathcal {M}(\Gamma ,\mu ),\varphi )$$ . When the weighting $$\mu $$ is instead on the vertices of $$\Gamma $$ , the first author showed the isomorphism class of $$(\mathcal {M}(\Gamma ,\mu ),\varphi )$$ depends only on the data $$(\Gamma ,\mu )$$ and is an interpolated free group factor equipped with a scaling of its unique trace (possibly direct sum copies of $$\mathbb {C}$$ ). Moreover, the free dimension of the interpolated free group factor is easily computed from $$\mu $$ . In this paper, we show for a weighting $$\mu $$ on the edges of $$\Gamma $$ that the isomorphism class of $$(\mathcal {M}(\Gamma ,\mu ),\varphi )$$ depends only on the data $$(\Gamma ,\mu )$$ , and is either as in the vertex weighting case or is a free Araki–Woods factor equipped with a scaling of its free quasi-free state (possibly direct sum copies of $$\mathbb {C}$$ ). The latter occurs when the subgroup of $${\mathbb {R}}^+$$ generated by $$\mu (e_1)\cdots \mu (e_n)$$ for loops $$e_1\cdots e_n$$ in $$\Gamma $$ is non-trivial, and in this case the point spectrum of the free quasi-free state will be precisely this subgroup. As an application, we give the isomorphism type of some infinite index subfactors considered previously by Jones and Penneys.
EL-shelling on comodernistic lattices

Abstract We prove the equivalence of EL-shellability and the existence of recursive atom ordering independent of roots. We show that a comodernistic lattice, as defined by Schweig and Woodroofe, admits a recursive atom ordering independent of roots, therefore is EL-shellable. We also present and discuss a simpler EL-shelling on one of the most important classes of comodernistic lattice, the order congruence lattices.
The microRNA miR-133b functions to slow Duchenne muscular dystrophy pathogenesis.

Key points Impairment of muscle biogenesis contributes to the progression of Duchenne muscular dystrophy (DMD). As a muscle enriched microRNA that has been implicated in muscle biogenesis, the role of miR-133b in DMD remains unknown. To assess miR-133b function in DMD-affected skeletal muscles, we genetically ablated miR-133b in the mdx mouse model of DMD. We show that deletion of miR-133b exacerbates the dystrophic phenotype of DMD-afflicted skeletal muscle by dysregulating muscle stem cells involved in muscle biogenesis, in addition to affecting signalling pathways related to inflammation and fibrosis. Our results provide evidence that miR-133b may underlie DMD pathology by affecting the proliferation and differentiation of muscle stem cells. Abstract Duchenne muscular dystrophy (DMD) is characterized by progressive skeletal muscle degeneration. No treatments are currently available to prevent the disease. While the muscle enriched microRNA miR-133b has been implicated in muscle biogenesis, its role in DMD remains unknown. To assess miR-133b function in DMD-affected skeletal muscles, we genetically ablated miR-133b in the mdx mouse model of DMD. In the absence of miR-133b, the tibialis anterior muscle of P30 mdx mice is smaller in size and exhibits a thickened interstitial space containing more mononucleated cells. Additional analysis revealed that miR-133b deletion influences muscle fibre regeneration, satellite cell proliferation and differentiation, and induces widespread transcriptomic changes in mdx muscle. These include known miR-133b targets as well as genes involved in cell proliferation and fibrosis. Altogether, our data demonstrate that skeletal muscles utilize miR-133b to mitigate the deleterious effects of DMD.
On Feedback Control in Kelly Betting: An Approximation Approach

In this paper, we consider a simple discrete-time optimal betting problem using the celebrated Kelly criterion, which calls for maximization of the expected logarithmic growth of wealth. While the classical Kelly betting problem can be solved via standard concave programming technique, an alternative but attractive approach is to invoke a Taylor-based approximation, which recasts the problem into quadratic programming and obtain the closed-form approximate solution. The focal point of this paper is to fill some voids in the existing results by providing some interesting properties when such an approximate solution is used. Specifically, the best achievable betting performance, positivity of expected cumulative gain or loss and its associated variance, expected growth property, variance of logarithmic growth, and results related to the socalled survivability (no bankruptcy) are provided.
Hardware Memory Management for Future Mobile Hybrid Memory Systems

The current mobile applications have rapidly growing memory footprints, posing a great challenge for memory system design. Insufficient DRAM main memory will incur frequent data swaps between memory and storage, a process that hurts performance, consumes energy, and deteriorates the write endurance of typical flash storage devices. Alternately, a larger DRAM has higher leakage power and drains the battery faster. Furthermore, DRAM scaling trends make further growth of DRAM in the mobile space prohibitive due to cost. Emerging nonvolatile memory (NVM) has the potential to alleviate these issues due to its higher capacity per cost than DRAM and minimal static power. Recently, a wide spectrum of NVM technologies, including phase-change memories (PCMs), memristor, and 3-D XPoint has emerged. Despite the mentioned advantages, NVM has longer access latency compared to DRAM and NVM writes can incur higher latencies and wear costs. Therefore, the integration of these new memory technologies in the memory hierarchy requires a fundamental rearchitecting of traditional system designs. In this work, we propose a hardware-accelerated memory manager (HMMU) that addresses in a flat address space, with a small partition of the DRAM reserved for subpage block-level management. We design a set of data placement and data migration policies within this memory manager such that we may exploit the advantages of each memory technology. By augmenting the system with this HMMU, we reduce the overall memory latency while also reducing writes to the NVM. The experimental results show that our design achieves a 39% reduction in energy consumption with only a 12% performance degradation versus an all-DRAM baseline that is likely untenable in the future.
Middle-mile Network Optimization in Rural Wireless Meshes

The status quo of limited broadband connectivity in rural areas motivates the need for fielding alternatives such as long-distance wireless mesh networks. A key aspect of fielding wireless meshes cost-effectively is planning how to connect the last-mile networks to the core network service providers (i.e., the network between the edge access terminals and the landline / optical fiber terminals) with minimal infrastructure cost and throughput constraints. This so-called middle-mile network optimization, which includes topology construction, tower height assignment, antenna and orientation selection, as well as transmit power assignment, is known to be a computationally hard problem. In this paper, we provide the first polynomial time approximation solution for a generalized version of the middle-mile network optimization problem, wherein point-to-point (i.e., WiFi p2p) links are deployed to bridge last-mile networks. Our solution has a cost performance ratio of O(ln|A| + |B|/|A| + |A|+|B|/ Γ), where A and B respectively denote the number of terminals and nonterminals and Γ is the ratio of link capacity terminal demand. Furthermore, our solution extends to hybrid networks, i.e., point-to-multipoint (i.e., WiFi p2mp) or omnidirectional (i.e., TV White Space) can serve as hyperlinks in addition to point-to-point links, to further reduce the cost of wireless links. We provide a complementary heuristic for our middle-mile network optimization solution that adds hyperlinks if and only if they reduce the cost.
Spike-FlowNet: Event-Based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks

Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based cameras suffer critically. This is attributed to their high temporal resolution, high dynamic range, and low-power consumption. However, conventional computer vision methods as well as deep Analog Neural Networks (ANNs) are not suited to work well with the asynchronous and discrete nature of event camera outputs. Spiking Neural Networks (SNNs) serve as ideal paradigms to handle event camera outputs, but deep SNNs suffer in terms of performance due to the spike vanishing phenomenon. To overcome these issues, we present Spike-FlowNet, a deep hybrid neural network architecture integrating SNNs and ANNs for efficiently estimating optical flow from sparse event camera outputs without sacrificing the performance. The network is end-to-end trained with self-supervised learning on Multi-Vehicle Stereo Event Camera (MVSEC) dataset. Spike-FlowNet outperforms its corresponding ANN-based method in terms of the optical flow prediction capability while providing significant computational efficiency.
Identification of ubiquitin Ser57 kinases regulating the oxidative stress response in yeast

Ubiquitination regulates many different cellular processes, including protein quality control, membrane trafficking, and stress responses. The diversity of ubiquitin functions in the cell is partly due to its ability to form chains with distinct linkages that can alter the fate of substrate proteins in unique ways. The complexity of the ubiquitin code is further enhanced by post-translational modifications on ubiquitin itself, the biological functions of which are not well understood. Here, we present genetic and biochemical evidence that serine 57 (Ser57) phosphorylation of ubiquitin functions in stress responses in Saccharomyces cerevisiae, including the oxidative stress response. We also identify and characterize the first known Ser57 ubiquitin kinases in yeast and human cells, and we report that two Ser57 ubiquitin kinases regulate the oxidative stress response in yeast. These studies implicate ubiquitin phosphorylation at the Ser57 position as an important modifier of ubiquitin function, particularly in response to proteotoxic stress.
Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory (DFT) and molecular dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the phase-field method (PFM) and calculation of phase diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist, and Bayesian methodologies. Challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.
Active learning of deep surrogates for PDEs: application to metasurface design

Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components. However, the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables. For photonic-device models, we find that this training becomes especially challenging as design regions grow larger than the optical wavelength. We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples. Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve, and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.
Genome Wide Epistasis Study of On-Statin Cardiovascular Events with Iterative Feature Reduction and Selection.

Predicting risk for major adverse cardiovascular events (MACE) is an evidence-based practice that incorporates lifestyle, history, and other risk factors. Statins reduce risk for MACE by decreasing lipids, but it is difficult to stratify risk following initiation of a statin. Genetic risk determinants for on-statin MACE are low-effect size and impossible to generalize. Our objective was to determine high-level epistatic risk factors for on-statin MACE with GWAS-scale data. Controlled-access data for 5890 subjects taking a statin collected from Vanderbilt University Medical Center’s BioVU were obtained from dbGaP. We used Random Forest Iterative Feature Reduction and Selection (RF-IFRS) to select highly informative genetic and environmental features from a GWAS-scale dataset of patients taking statin medications. Variant-pairs were distilled into overlapping networks and assembled into individual decision trees to provide an interpretable set of variants and associated risk. 1718 cases who suffered MACE and 4172 controls were obtained from dbGaP. Pathway analysis showed that variants in genes related to vasculogenesis (FDR = 0.024), angiogenesis (FDR = 0.019), and carotid artery disease (FDR = 0.034) were related to risk for on-statin MACE. We identified six gene-variant networks that predicted odds of on-statin MACE. The most elevated risk was found in a small subset of patients carrying variants in COL4A2, TMEM178B, SZT2, and TBXAS1 (OR = 4.53, p < 0.001). The RF-IFRS method is a viable method for interpreting complex “black-box” findings from machine-learning. In this study, it identified epistatic networks that could be applied to risk estimation for on-statin MACE. Further study will seek to replicate these findings in other populations.
Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression

We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a geostationary operational environmental satellite (GOES) class. When we additionally consider non-flaring regions, we find an increased average error of approximately three-fourths a GOES class. We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem.
Chiral Restoration in the Early Universe: Pion Halo in the Sky

vanishing above Tc, is usually taken to be the signal for the onset of chiral symmetry restoration at high T. But is it the old T = 0 chiral symmetry that is ‘restored’? In this talk, I report on the spacetime quantization of the BPFTW effective action for quarks in a hot environ. The fermion propagator is known to give a pseudo-Lorentz invariant particle pole as well as new spacelike cuts. Our quantization shows that the spacelike cuts directly lead to a thermal vacuum that is a generalized NJL state, with a curious 90° phase. This 90° is responsible for vanishing at high T. The thermal vacuum is invariant under a new chiral charge, but continues to break the old zero temperature chirality. Our quantization suggests a new class of order parameters that probe the physics of these spacelike cuts. In usual scenario, the pion dissociates in the early alphabet soup. With this new understanding of the thermal vacuum, the pion remains a Nambu-Goldstone particle at high T, and will not dissociate. It propagates at the speed of light but with a halo.
Relative Afferent Pupillary Defect Screening Through Transfer Learning

Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on relative afferent pupillary defect (RAPD), which is based on the difference between the reactions of the eyes when they are exposed to light stimuli. Incumbent RAPD assessment methods are based on subjective practices that can lead to unreliable measurements. To eliminate subjectivity and obtain reliable measurements, we introduced an automated framework to detect RAPD. For validation, we conducted a clinical study with lab-on-a-headset , which can perform automated light reflex test. In addition to benchmarking handcrafted algorithms, we proposed a transfer learning-based approach that transformed a deep learning-based generic object recognition algorithm into a pupil detector. Based on the conducted experiments, proposed algorithm RAPDNet can achieve a sensitivity and a specificity of 90.6% over 64 test cases in a balanced set, which corresponds to an AUC of 0.929 in ROC analysis. According to our benchmark with three handcrafted algorithms and nine performance metrics, RAPDNet outperforms all other algorithms in every performance category.
A Bayesian feature allocation model for tumor heterogeneity

We develop a feature allocation model for inference on genetic tumor variation using next-generation sequencing data. Specifically, we record single nucleotide variants (SNVs) based on short reads mapped to human reference genome and characterize tumor heterogeneity by latent haplotypes defined as a scaffold of SNVs on the same homologous genome. For multiple samples from a single tumor, assuming that each sample is composed of some sample-specific proportions of these haplotypes, we then fit the observed variant allele fractions of SNVs for each sample and estimate the proportions of haplotypes. Varying proportions of haplotypes across samples is evidence of tumor heterogeneity since it implies varying composition of cell subpopulations. Taking a Bayesian perspective, we proceed with a prior probability model for all relevant unknown quantities, including, in particular, a prior probability model on the binary indicators that characterize the latent haplotypes. Such prior models are known as feature allocation models. Specifically, we define a simplified version of the Indian buffet process, one of the most traditional feature allocation models. The proposed model allows overlapping clustering of SNVs in defining latent haplotypes, which reflects the evolutionary process of subclonal expansion in tumor samples.
Survival trees for left-truncated and right-censored data, with application to time-varying covariate data

Tree methods (recursive partitioning) are a popular class of nonparametric methods for analyzing data. One extension of the basic tree methodology is the survival tree, which applies recursive partitioning to censored survival data. There are several existing survival tree methods in the literature, which are mainly designed for right-censored data. We propose two new survival trees for left-truncated and right-censored (LTRC) data, which can be seen as a generalization of the traditional survival tree for right-censored data. Further, we show that such trees can be used to analyze survival data with time-varying covariates, essentially building a time-varying covariates survival tree. Implementation of the methods is easy, and simulations and real data analysis results show that the proposed methods work well for LTRC data and survival data with time-varying covariates, respectively.
Reduced-Order Quasilinear Model of Ocean Boundary-Layer Turbulence

AbstractThe combined effectiveness of model reduction and the quasilinear approximation for the reproduction of the low-order statistics of oceanic surface boundary layer turbulence is investigated...
Double Semions in Arbitrary Dimension

We present a generalization of the double semion topological quantum field theory to higher dimensions, as a theory of \({d-1}\) dimensional surfaces in a d dimensional ambient space. We construct a local Hamiltonian that is a sum of commuting projectors and analyze the excitations and the ground state degeneracy. Defining a consistent set of local rules requires the sign structure of the ground state wavefunction to depend not just on the number of disconnected surfaces, but also upon their higher Betti numbers through the semicharacteristic. For odd d the theory is related to the toric code by a local unitary transformation, but for even d the dimension of the space of zero energy ground states is in general different from the toric code and for even \({d > 2}\) it is also in general different from that of the twisted \({Z_2}\) Dijkgraaf–Witten model.
Evidence of Fast Pebble Growth Near Condensation Fronts in the HL Tau Protoplanetary Disk

Water and simple organic molecular ices dominate the mass of solid materials available for planetesimal and planet formation beyond the water snow line. Here we analyze ALMA long baseline 2.9, 1.3 and 0.87 mm continuum images of the young star HL Tau, and suggest that the emission dips observed are due to rapid pebble growth around the condensation fronts of abundant volatile species. Specifically, we show that the prominent innermost dip at 13 AU is spatially resolved in the 0.87 mm image, and its center radius is coincident with the expected mid-plane condensation front of water ice. In addition, two other prominent dips, at distances of 32 and 63 AU, cover the mid-plane condensation fronts of pure ammonia or ammonia hydrates and clathrate hydrates (especially with CO and N_2) formed from amorphous water ice. The spectral index map of HL Tau between 1.3 and 0.87 mm shows that the flux ratios inside the dips are statistically larger than those of nearby regions in the disk. This variation can be explained by a model with two dust populations, where most of the solid mass resides in a component that has grown to decimeter size scales inside the dips. Such growth is in accord with recent numerical simulations of volatile condensation, dust coagulation, and settling.
Relative Entropy in Biological Systems

In this paper we review various information-theoretic characterizations of the approach to equilibrium in biological systems. The replicator equation, evolutionary game theory, Markov processes and chemical reaction networks all describe the dynamics of a population or probability distribution. Under suitable assumptions, the distribution will approach an equilibrium with the passage of time. Relative entropy—that is, the Kullback–Leibler divergence, or various generalizations of this—provides a quantitative measure of how far from equilibrium the system is. We explain various theorems that give conditions under which relative entropy is nonincreasing. In biochemical applications these results can be seen as versions of the Second Law of Thermodynamics, stating that free energy can never increase with the passage of time. In ecological applications, they make precise the notion that a population gains information from its environment as it approaches equilibrium.
Prospects for Characterizing Host Stars of the Planetary System Detections Predicted for the Korean Microlensing Telescope Network

I investigate the possibility of constraining the flux of the lens (i.e., host star) for the types of planetary systems the Korean Microlensing Telescope Network is predicted to find. I examine the potential to obtain lens flux measurements by (1) imaging the lens once it is spatially resolved from the source, (2) measuring the elongation of the point-spread function of the microlensing target (lens+source) when the lens and source are still unresolved, and (3) taking prompt follow-up photometry. In each case I simulate the observing programs for a representative example of current ground-based adaptive optics (AO) facilities (specifically NACO on the Very Large Telescope), future ground-based AO facilities (GMTIFS on the Giant Magellan Telescope, GMT), and future space telescopes (NIRCAM on the James Webb Space Telescope, JWST). Given the predicted distribution of relative lens-source proper motions, I find that the lens flux could be measured to a precision of for 60% of planet detections ≥5 yr after each microlensing event for a simulated observing program using GMT, which images resolved lenses. NIRCAM on JWST would be able to carry out equivalently high-precision measurements for ~28% of events Δt = 10 yr after each event by imaging resolved lenses. I also explore the effects various blend components would have on the mass derived from prompt follow-up photometry, including companions to the lens, companions to the source, and unassociated interloping stars. I find that undetected blend stars would cause catastrophic failures (i.e., >50% fractional uncertainty in the inferred lens mass) for (16 · f bin)% of planet detections, where f bin is the binary fraction, with the majority of these failures occurring for host stars with mass 0.3 M ☉.
An XMM-Newton Survey of the Soft X-ray Background. I. The O VII and O VIII Lines Between l = 120° and l = 240°

We present measurements of the soft X-ray background (SXRB) O VII and O VIII intensity between l = 120° and l = 240°, the first results of a survey of the SXRB using archival XMM-Newton observations. We do not restrict ourselves to blank-sky observations, but instead use as many observations as possible, removing bright or extended sources by hand if necessary. In an attempt to minimize contamination from near-Earth solar wind charge exchange (SWCX) emission, we remove times of high solar wind proton flux from the data. Without this filtering we are able to extract measurements from 586 XMM-Newton observations. With this filtering, ~1/2 of the observations are rendered unusable, and we are able to extract measurements from 303 observations. The oxygen intensities are typically ~0.5-10 photons cm–2 s–1 sr–1 (line units, L.U.) for O VII and ~0-5 L.U. for O VIII. The proton flux filtering does not systematically reduce the oxygen intensities measured from a given observation. However, the filtering does preferentially remove the observations with higher oxygen intensities. Our data set includes 69 directions with multiple observations, whose oxygen intensity variations can be used to constrain SWCX models. One observation exhibits an O VII enhancement of ~25 L.U. over two other observations of the same direction, although most SWCX enhancements are 4 L.U. for O VII and 2 L.U. for O VIII. We find no clear tendency for the O VII centroid to shift toward the forbidden line energy in observations with bright SWCX enhancements. There is also no universal association between enhanced SWCX emission and increased solar wind flux or the closeness of the sightline to the sub-solar region of the magnetosheath. After removing observations likely to be contaminated by heliospheric SWCX emission, we use our results to examine the Galactic halo. There is some scatter in the halo intensity about the predictions of a simple plane-parallel model, indicating a patchiness to the halo emission. The O VII/O VIII intensity ratio implies a halo temperature of ~2.0-2.5 × 106 K, in good agreement with previous studies.
A Search for Exotrojans in Transiting Exoplanetary systems

We present a search for Trojan companions to 25 transiting exoplanets. We use the technique of Ford & Gaudi, in which a difference is sought between the observed transit time and the transit time that is calculated by fitting a two-body Keplerian orbit to the radial-velocity data. This technique is sensitive to the imbalance of mass at the L4/L5 points of the planet-star orbit. No companions were detected. The median 2σ upper limit is 60 M ⊕ , and the most constraining limit is 2.5 M ⊕ for the case of GJ 436.
Non-axisymmetric Aberration Patterns from Wide-field Telescopes Using Spin-weighted Zernike Polynomials

If the optical system of a telescope is perturbed from rotational symmetry, the Zernike wavefront aberration coefficients describing that system can be expressed as a function of position in the focal plane using spin-weighted Zernike polynomials. Methodologies are presented to derive these polynomials to arbitrary order. This methodology is applied to aberration patterns produced by a misaligned Ritchey–Chretien telescope and to distortion patterns at the focal plane of the DESI optical corrector, where it is shown to provide a more efficient description of distortion than conventional expansions.
Warped Higgsless Models with IR--Brane Kinetic Terms

We examine a warped higgsless SU(2)L × SU(2)R × U(1)B−L model in 5-d with IR(TeV)-brane kinetic terms. It is shown that adding a brane term for the U(1)B−L gauge field does not affect the scale ( ~ 2–3 TeV) where perturbative unitarity in WL+WL−→WL+WL− is violated. This term could, however, enhance the agreement of the model with the precision electroweak data. In contrast, the inclusion of a kinetic term corresponding to the SU(2)D custodial symmetry of the theory delays the unitarity violation in WL± scattering to energy scales of ~ 6–7 TeV for a significant fraction of the parameter space. This is about a factor of 4 improvement compared to the corresponding scale of unitarity violation in the Standard Model without a Higgs. We also show that null searches for extra gauge bosons at the Tevatron and for contact interactions at LEP II place non-trivial bounds on the size of the IR-brane terms.
The Kerr metric

This review describes the events leading up to the discovery of the Kerr metric in 1963 and the enormous impact the discovery has had in the subsequent 50 years. The review discusses the Penrose process, the four laws of black hole mechanics, uniqueness of the solution, and the no-hair theorems. It also includes Kerr perturbation theory and its application to black hole stability and quasi-normal modes. The Kerr metric's importance in the astrophysics of quasars and accreting stellar-mass black hole systems is detailed. A theme of the review is the 'miraculous' nature of the solution, both in describing in a simple analytic formula the most general rotating black hole, and in having unexpected mathematical properties that make many calculations tractable. Also included is a pedagogical derivation of the solution suitable for a first course in general relativity.
Why is understanding glassy polymer mechanics so difficult

In this Perspective, I describe recent work on systems in which the traditional distinctions between (i) unentangled ver- sus well-entangled systems and (ii) melts versus glasses seem least useful, and argue for the broader use in glassy polymer mechanics of two more dichotomies: systems which possess (iii) unary versus binary and (iv) cooperative versus noncooperative relaxation dynamics. I discuss the applicability of (iii-iv) to under- standing the functional form of strain hardening. Results from molecular dynamics simulations show that the ''dramatic'' hard- ening observed in densely entangled systems is associated with a crossover from unary, noncooperative to binary, cooperative relaxation as strain increases; chains stretch between entangle- ment points, altering the character of local plasticity. Promising approaches for future research along these lines are discussed. © 2011 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 49: 979-984, 2011
Mean-field inference methods for neural networks

Machine learning algorithms relying on deep neural networks recently allowed a great leap forward in artificial intelligence. Despite the popularity of their applications, the efficiency of these algorithms remains largely unexplained from a theoretical point of view. The mathematical description of learning problems involves very large collections of interacting random variables, difficult to handle analytically as well as numerically. This complexity is precisely the object of study of statistical physics. Its mission, originally pointed towards natural systems, is to understand how macroscopic behaviors arise from microscopic laws. Mean-field methods are one type of approximation strategy developed in this view. We review a selection of classical mean-field methods and recent progress relevant for inference in neural networks. In particular, we remind the principles of derivations of high-temperature expansions, the replica method and message passing algorithms, highlighting their equivalences and complementarities. We also provide references for past and current directions of research on neural networks relying on mean-field methods.
A LEPTONIC–HADRONIC MODEL FOR THE AFTERGLOW OF GAMMA-RAY BURST 090510

We model multiwavelength afterglow data from the short gamma-ray burst (GRB) 090510 using a combined leptonic-hadronic model of synchrotron radiation from an adiabatic blast wave. High-energy, 100 MeV, emission in our model is dominated by proton-synchrotron radiation, while electron-synchrotron radiation dominates in the X-ray and ultraviolet wavelengths. The collimation-corrected GRB energy, depending on the jet-break time, in this model could be as low as 3 × 1051 erg but two orders of magnitude larger than the absolute γ-ray energy. We also calculated the opacities for electron-positron pair production by γ-rays and found that TeV γ-rays from proton-synchrotron radiation can escape the blast wave at early time, and their detection can provide evidence of a hadronic emission component dominating at high energies.