cssi/SciSciGPT-SciSciNet huggingface dataset:
The Fluid Mechanics of Gravitational Structure Formation

The standard model for gravitational structure formation in astrophysics, astronomy, and cosmology is questioned. Cold dark matter (CDM) hierarchical clustering cosmology neglects particle collisions, viscosity, turbulence and diffusion and makes predictions in conflict with observations. From Jeans 1902 and CDMHC, the non-baryonic dark matter NBDM forms small clumps during the plasma epoch after the big bang that ``cluster'' into larger clumps. CDM halo clusters collect the baryonic matter (H and He) by gravity so that after 300 Myr of ``dark ages'', huge, explosive (Population III) first stars appear, and then galaxies and galaxy clusters. Contrary to CDMHC cosmology, ``hydro-gravitational-dynamics'' HGD cosmology suggests the diffusive NBDM material cannot clump and the clumps cannot cluster. From HGD, the big bang results from an exothermic turbulent instability at Planck scales (10^{-35} m). Turbulent stresses cause an inflation of space and fossil density turbulence remnants that trigger gravitational instability at protosupercluster masses (10^{46} kg) in the H-He plasma. These fragment along plasma turbulence vortex lines to form protogalaxy masses (10^{42} kg) just before the transition to gas. The gas has x10^{-13} smaller viscosity, so it fragments at planetary and globular-star-cluster masses (10^{25} and 10^{36} kg) to form the baryonic dark matter (BDM). Observations from the Hubble Space Telescope show protogalaxies (PGs) in linear clusters reflecting their likely fragmentation on plasma vortex lines. From merging BDM planets, these PGs gently form small stars in globular clusters <1 Myr after the big bang without the dark ages, superstars, or reionization of CDM cosmology.
Persistent Cohomology for Data With Multicomponent Heterogeneous Information

Persistent homology is a powerful tool for characterizing the topology of a data set at various geometric scales. When applied to the description of molecular structures, persistent homology can capture the multiscale geometric features and reveal certain interaction patterns in terms of topological invariants. However, in addition to the geometric information, there is a wide variety of nongeometric information of molecular structures, such as element types, atomic partial charges, atomic pairwise interactions, and electrostatic potential functions, that is not described by persistent homology. Although element-specific homology and electrostatic persistent homology can encode some nongeometric information into geometry based topological invariants, it is desirable to have a mathematical paradigm to systematically embed both geometric and nongeometric information, i.e., multicomponent heterogeneous information, into unified topological representations. To this end, we propose a persistent cohomology based framework for the enriched representation of data. In our framework, nongeometric information can either be distributed globally or reside locally on the datasets in the geometric sense and can be properly defined on topological spaces, i.e., simplicial complexes. Using the proposed persistent cohomology based framework, enriched barcodes are extracted from datasets to represent heterogeneous information. We consider a variety of datasets to validate the present formulation and illustrate the usefulness of the proposed method based on persistent cohomology. It is found that the proposed framework outperforms or at least matches the state-of-the-art methods in the protein-ligand binding affinity prediction from massive biomolecular datasets without resorting to any deep learning formulation.
The Competitive Struggle to Win Tournaments: The Allies’ Race to Capture Adolf Hitler’s Eagle’s Nest

Tournament theory posits that there are situations where winning matters a lot and, as a result, agent rewards are not proportional to performance. According to tournament theorists, the large pay differentials that exist between organizational levels are intended to motivate agents to exert greater effort in an attempt to win the prize. Although a large corpus of literature on tournaments has emerged over time, little is known about the social dynamics involved in tournaments. This article addresses this gap through a historical narrative concerning how Allied forces in World War II competed to capture Adolf Hitler’s famed Bavarian reception house, known to the world as the Eagle’s Nest.
Searching for TMD-Factorization Breaking in p + p and p + A Collisions: Color Interactions in QCD

Dihadron and isolated direct photon-hadron angular correlations have been measured in $p$+$p$ and $p$+A collisions to investigate possible effects from transverse-momentum-dependent factorization breaking due to color exchange between partons involved in the hard scattering and the proton remnants. The correlations are sensitive to nonperturbative initial-state and final-state transverse momentum $k_T$ and $j_T$ in the azimuthal nearly back-to-back region $\Delta \phi \sim \pi$. In this region, transverse-momentum-dependent evolution can be studied when several different hard scales are measured. To have sensitivity to small transverse momentum scales, nonperturbative momentum widths of \pout, the out-of-plane transverse momentum component perpendicular to the trigger particle, are measured. To quantify the magnitude of any transverse-momentum-dependent factorization breaking effects, calculations will need to be performed for comparison.
Visualization of spatial gene expression in plants by modified RNAscope fluorescent in situ hybridization.

In situ analysis of biomarkers such as DNA, RNA and proteins are important for research and diagnostic purposes. At the RNA level, plant gene expression studies rely on qPCR, RNAseq and probe-based in situ hybridization (ISH). However, for ISH experiments poor stability of RNA and RNA based probes commonly results in poor detection or poor reproducibility. Recently, the development and availability of the RNAscope RNA-ISH method addressed these problems by novel signal amplification and background suppression. This method is capable of simultaneous detection of multiple target RNAs down to the single molecule level in individual cells, allowing researchers to study spatio-temporal patterning of gene expression. However, this method has not been optimized thus poorly utilized for plant specific gene expression studies which would allow for fluorescent multiplex detection. Here we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley. We have shown the spatial distribution pattern of HvGAPDH and the low expressed disease resistance gene Rpg1 in leaf tissue sections of barley and discuss precautions that should be followed during image analysis. We have shown the ubiquitous HvGAPH and predominantly stomatal guard cell associated subsidiary cell expressed Rpg1 expression pattern in barley leaf sections and described the improve RNAscope methodology suitable for plant tissues using confocal laser microscope. By addressing the problems in the sample collection and incorporating additional sample backing steps we have significantly reduced the section detachment and experiment failure problems. Further, by reducing the time of protease treatment, we minimized the sample disintegration due to over digestion of barley tissues. RNAscope multiplex fluorescent RNA-ISH detection is well described and adapted for animal tissue samples, however due to morphological and structural differences in the plant tissues the standard protocol is deficient and required optimization. Utilizing barley specific HvGAPDH and Rpg1 RNA probes we report an optimized method which can be used for RNAscope detection to determine the spatial expression and semi-quantification of target RNAs. This optimized method will be immensely useful in other plant species such as the widely utilized Arabidopsis.
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming to collect. Thus, a crucial research question is how to obtain supervision in a cost-effective way. In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models. An entity trigger is defined as a group of words in a sentence that helps to explain why humans would recognize an entity in the sentence. We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our proposed model, Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging. Our framework is significantly more cost-effective than the traditional neural NER frameworks. Experiments show that using only 20% of the trigger-annotated sentences results in a comparable performance as using 70% of conventional annotated sentences.
Recurrent Pheochromocytoma in an Elderly Patient

Pheochromocytomas are rare neuroendocrine tumors that can affect people of all ages and are commonly diagnosed in the 4th and 5th decades of life. Familial pheochromocytomas are diagnosed mostly between the 2nd and 3rd decades of life. They can be benign or metastatic and often present as isolated tumors or along with other neuroendocrine syndromes. We present a case of an elderly man who underwent laparoscopic adrenalectomy for pheochromocytoma at the age of 60 years but developed recurrence of metastatic pheochromocytoma after ten years. We also conducted a literature review to understand the epidemiology and presentation of the tumor and to emphasize that there should be a low threshold of suspicion for timely diagnosis and management of recurrent pheochromocytoma.
An improved approximation algorithm for TSP in the half integral case

We design a 1.49993-approximation algorithm for the metric traveling salesperson problem (TSP) for instances in which an optimal solution to the subtour linear programming relaxation is half-integral. These instances received significant attention over the last decade due to a conjecture of Schalekamp, Williamson and van Zuylen stating that half-integral LP solutions have the largest integrality gap over all fractional solutions. So, if the conjecture of Schalekamp et al. holds true, our result shows that the integrality gap of the subtour polytope is bounded away from 3/2.
Sheltering in Place and Domestic Violence: Evidence from Calls for Service during COVID-19

The COVID-19 pandemic has led to an economic slowdown as more people practice social distancing and shelter at home. The increase in family isolation, unemployment, and economic stress has the potential to increase domestic violence. We document the pandemic's impact on police calls for service for domestic violence. The pandemic increased domestic violence calls by 7.5% during March through May of 2020, with effects concentrated during the first five weeks after social distancing began. The increase in reported domestic violence incidents began before official stay-at-home orders were mandated. It is not driven by any particular demographic group but does appear to be driven by households without a previous history of domestic violence.
A Non-PCP Approach to Succinct Quantum-Safe Zero-Knowledge

Today’s most compact zero-knowledge arguments are based on the hardness of the discrete logarithm problem and related classical assumptions. If one is interested in quantum-safe solutions, then all of the known techniques stem from the PCP-based framework of Kilian (STOC 92) which can be instantiated based on the hardness of any collision-resistant hash function. Both approaches produce asymptotically logarithmic sized arguments but, by exploiting extra algebraic structure, the discrete logarithm arguments are a few orders of magnitude more compact in practice than the generic constructions.
"Bring Your Own Greedy"+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack.

The problem of selecting a small-size representative summary of a large dataset is a cornerstone of machine learning, optimization and data science. Motivated by applications to recommendation systems and other scenarios with query-limited access to vast amounts of data, we propose a new rigorous algorithmic framework for a standard formulation of this problem as a submodular maximization subject to a linear (knapsack) constraint. Our framework is based on augmenting all partial Greedy solutions with the best additional item. It can be instantiated with negligible overhead in any model of computation, which allows the classic \greedy algorithm and its variants to be implemented. We give such instantiations in the offline (Greedy+Max), multi-pass streaming (Sieve+Max) and distributed (Distributed+Max) settings. Our algorithms give ($1/2-\epsilon$)-approximation with most other key parameters of interest being near-optimal. Our analysis is based on a new set of first-order linear differential inequalities and their robust approximate versions. Experiments on typical datasets (movie recommendations, influence maximization) confirm scalability and high quality of solutions obtained via our framework. Instance-specific approximations are typically in the 0.6-0.7 range and frequently beat even the $(1-1/e) \approx 0.63$ worst-case barrier for polynomial-time algorithms.
Faster Deterministic All Pairs Shortest Paths in Congest Model

We present a new deterministic algorithm for distributed weighted all pairs shortest paths (APSP) in both undirected and directed graphs. Our algorithm runs in ~O(n4/3 ) rounds in the Congest models on graphs with arbitrary edge weights, and it improves on the previous ~O(n3/2) bound of Agarwal et al. [ARKP18]. The main components of our new algorithm are a new faster technique for constructing blocker set deterministically and a new pipelined method for deterministically propagating distance values from source nodes to the blocker set nodes in the network. Both of these techniques have potential applications to other distributed algorithms. Our new deterministic algorithm for computing blocker set adapts the NC approximate hypergraph set cover algorithm in Berger et al. [BRS94] to the distributed construction of a blocker set. It follows the two-step process of first designing a randomized algorithm that uses only pairwise independence, and then derandomizes this algorithm using a sample space of linear size. This algorithm runs in almost the same number of rounds as the initial step in our APSP algorithm that computes h-hops shortest paths. This result significantly improves on the deterministic blocker set algorithms in [ARKP18, AR19] by removing an additional n ·|Q| term in the round bound, where Q is the blocker set. The other new component in our APSP algorithm is a deterministic pipelined approach to propagate distance values from source nodes to blocker nodes. We use a simple natural round-robin method for this step, and we show using a suitable progress measure that it achieve the ~O(n4/3) bound on the number of rounds. It appears that the standard deterministic methods for efficiently broadcasting multiple values, and for sending or receiving messages using the routing schedule in an undirected APSP algorithm [HPDG+19,LSP19] do not apply to this setting.
Has mortality risen disproportionately for the least educated

Abstract We examine whether the least educated population groups experienced the worst mortality trends at the beginning of the 21st century by measuring changes in mortality across education quartiles. We document sharply differing gender patterns. Among women, mortality trends improved fairly monotonically with education. Conversely, male trends for the lowest three education quartiles were often similar. For both sexes, the gap in mortality between the top 25 percent and the bottom 75 percent is growing. However, there are many groups for whom these patterns are reversed – with better experiences for the less educated – or where the differences are statistically indistinguishable.
Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant.

Background In solid organ transplantation, donor-derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient from donor cells. Methods Whole-exome sequencing of donor and recipient DNA and single-cell RNA sequencing (scRNA-seq) of five human kidney transplant biopsy cores distinguished immune cell contributions from both participants. DNA-sequence comparisons used single nucleotide variants (SNVs) identified in the exome sequences across all samples. Results Analysis of expressed SNVs in the scRNA-seq data set distinguished recipient versus donor origin for all 81,139 cells examined. The leukocyte donor/recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages displayed inflammatory activation whereas donor macrophages demonstrated antigen presentation and complement signaling. Recipient-origin T cells expressed cytotoxic and proinflammatory genes consistent with an effector cell phenotype, whereas donor-origin T cells appeared quiescent, expressing oxidative phosphorylation genes. Finally, both donor and recipient T cell clones within the rejecting kidney suggested lymphoid aggregation. The results indicate that donor-origin macrophages and T cells have distinct transcriptional profiles compared with their recipient counterparts, and that donor macrophages can persist for years post-transplantation. Conclusions Analysis of single nucleotide variants and their expression in single cells provides a powerful novel approach to accurately define leukocyte chimerism in a complex organ such as a transplanted kidney, coupled with the ability to examine transcriptional profiles at single-cell resolution.
Integrating Sub-6 GHz and Millimeter Wave to Combat Blockage: Delay-Optimal Scheduling

Millimeter wave (mmWave) technologies have the potential to achieve very high data rates, but suffer from intermittent connectivity. In this paper, we provision an architecture to integrate sub-6 GHz and mmWave technologies, where we incorporate the sub-6 GHz interface as a fallback data transfer mechanism to combat blockage and intermittent connectivity of the mmWave communications. To this end, we investigate the problem of scheduling data packets across the mmWave and sub-6 GHz interfaces such that the average delay of system is minimized. This problem can be formulated as Markov Decision Process. We first investigate the problem of discounted delay minimization, and prove that the optimal policy is of the threshold-type, i.e., data packets should always be routed to the mmWave interface as long as the number of packets in the system is smaller than a threshold. Then, we show that the results of the discounted delay problem hold for the average delay problem as well. Through numerical results, we demonstrate that under heavy traffic, integrating sub-6 GHz with mmWave can reduce the average delay by up to 70%. Further, our scheduling policy substantially reduces the delay over the celebrated MaxWeight policy.
TENSOR BASIS GAUSSIAN PROCESS MODELS OF HYPERELASTIC MATERIALS

In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this approach that embeds rotational invariance of the stress-stretch constitutive relation in the GPR representation. This approach requires fewer training examples and achieves higher accuracy while maintaining invariance to rotations exactly. Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential. Although the error of this model for predicting the stress tensor is higher, the strain-energy density is recovered with high accuracy from limited training data. The approaches presented here are examples of physics-informed machine learning. They go beyond purely data-driven approaches by embedding the physical system constraints directly into the Gaussian process representation of materials models.
Dyson Brownian motion for general $$\beta $$ and potential at the edge

In this paper, we compare the solutions of the Dyson Brownian motion for general $$\beta $$ and potential V and the associated McKean–Vlasov equation near the edge. Under suitable conditions on the initial data and the potential V, we obtain optimal rigidity estimates of particle locations near the edge after a short time $$t={{\,\mathrm{o}\,}}(1)$$ . Our argument uses the method of characteristics along with a careful estimate involving an equation of the edge. With the rigidity estimates as an input, we prove a central limit theorem for mesoscopic statistics near the edge, which, as far as we know, has been done for the first time in this paper. Additionally, combining our results with Landon and Yau (Edge statistics of Dyson Brownian motion. arXiv:1712.03881 , 2017), we give a proof of the local ergodicity of the Dyson Brownian motion for general $$\beta $$ and potential at the edge, i.e., we show the distribution of extreme particles converges to the Tracy–Widom $$\beta $$ distribution in a short time.
The Long Term Impacts of Grants on Poverty: 9-year Evidence From Uganda's Youth Opportunities Program

In 2008, Uganda gave 400 USD per person to thousands of young people to help them start skilled trades, work more, and raise incomes. Four years on, an experimental evaluation found grants raised work by 17 percent and earnings by 38 percent (Blattman, Fiala, Martinez 2014). After nine years, we find these gains have dissipated. Grantees' investment leveled off; controls eventually increased their incomes through business and casual labor; and so both groups converged in employment, earnings, and consumption levels. We see little effect on mortality, fertility, or family health and education. However, grants had lasting impacts on durable asset stocks and skilled work.
Topology of the U12-U6atac snRNA Complex of the Minor Spliceosome and Binding by NTC-Related Protein RBM22.

Splicing of precursor messenger RNA is catalyzed by the spliceosome, a dynamic ribonucleoprotein assembly including five small nuclear (sn)RNAs and >100 proteins. RNA components catalyze the two tr...
The investment portfolio of the Swiss National Bank and its carbon footprint

This paper details the nature of the equity holdings of the Swiss National Bank (SNB) and estimates its carbon footprint. By analysing SNB holdings in the 100 most polluting companies in the world,...
Computation of Helicopter Rotor Acoustics in Forward Flight

This paper presents a new method for computing acoustic signals from helicopter rotors in forward flight. The aerodynamic and acoustic solutions in the near field are computed with a finite-difference solver for the Euler equations. A nonrotating cylindrical Kirchhoff surface is then placed around the entire rotor system. This Kirchhoff surface moves subsonically with the rotor in forward flight. The finite-difference solution is interpolated onto this cylindrical surface at each time step and a Kirchhoff integration is used to carry the acoustic signal to the far field. Computed values for high-speed impulsive noise show excellent agreement with model-rotor and flight-test experimental data. Results from the new method offer high accuracy with reasonable computer resource requirements.
Single cell transcriptome research in human placenta

Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.
Anomalous Motion Detection On Highway Using Deep Learning

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset the Highway Traffic Anomaly (HTA) dataset– for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.
Essential commutants on strongly pseudo-convex domains

Abstract Consider a bounded strongly pseudo-convex domain Ω with smooth boundary in C n . Let T be the Toeplitz algebra on the Bergman space L a 2 ( Ω ) . That is, T is the C ⁎ -algebra generated by the Toeplitz operators { T f : f ∈ L ∞ ( Ω ) } . Extending the work [27] , [28] in the special case of the unit ball, we show that on any such Ω, T and { T f : f ∈ VO bdd } + K are essential commutants of each other, where K is the collection of compact operators on L a 2 ( Ω ) . On a general Ω considered in this paper, the proofs require many new ideas and techniques. These same techniques also enable us to show that for A ∈ T , if 〈 A k z , k z 〉 → 0 as z → ∂ Ω , then A is a compact operator.
TRAF-like Proteins Regulate Cellular Survival in the Planarian Schmidtea mediterranea.

Summary Tissue homeostasis relies on the timely renewal of cells that have been damaged or have surpassed their biological age. Nonetheless, the underlying molecular mechanism coordinating tissue renewal is unknown. The planarian Schmidtea mediterranea harbors a large population of stem cells that continuously divide to support the restoration of tissues throughout the body. Here, we identify that TNF Receptor Associated Factors (TRAFs) play critical roles in cellular survival during tissue repair in S. mediterranea. Disruption with RNA-interference of TRAF signaling results in rapid morphological defects and lethality within two weeks. The TRAF phenotype is accompanied by an increased number of mitoses and cell death. Our results also reveal TRAF signaling is required for proper regeneration of the nervous system. Taken together, we find functional conservation of TRAF-like proteins in S. mediterranea as they act as crucial regulators of cellular survival during tissue homeostasis and regeneration.
Tracking sex: The implications of widespread sexual data leakage and tracking on porn websites:

This article explores tracking and privacy risks on pornography websites. Our analysis of 22,484 pornography websites indicated that 93% leak user data to a third-party. Tracking on these sites is ...
Multistage distributionally robust mixed-integer programming with decision-dependent moment-based ambiguity sets

We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages. We first consider two ambiguity sets defined by decision-dependent bounds on the first and second moments of uncertain parameters and by mean and covariance matrix that exactly match decision-dependent empirical ones, respectively. For both sets, we show that the subproblem in each stage can be recast as a mixed-integer linear program (MILP). Moreover, we extend the general moment-based ambiguity set in Delage and Ye (Oper Res 58(3):595–612, 2010) to the multistage decision-dependent setting, and derive mixed-integer semidefinite programming (MISDP) reformulations of stage-wise subproblems. We develop methods for attaining lower and upper bounds of the optimal objective value of the multistage MISDPs, and approximate them using a series of MILPs. We deploy the Stochastic Dual Dynamic integer Programming (SDDiP) method for solving the problem under the three ambiguity sets with risk-neutral or risk-averse objective functions, and conduct numerical studies on multistage facility-location instances having diverse sizes under different parameter and uncertainty settings. Our results show that the SDDiP quickly finds optimal solutions for moderate-sized instances under the first two ambiguity sets, and also finds good approximate bounds for the multistage MISDPs derived under the third ambiguity set. We also demonstrate the efficacy of incorporating decision-dependent distributional ambiguity in multistage decision-making processes.
Bootstrap Cross-Validation Improves Model Selection in Pharmacometrics

Cross-validation assesses the predictive ability of a model, allowing one to rank models accordingly. Although the nonparametric bootstrap is almost always used to assess the variability of a param...
Photoinduced Dirac semimetal in ZrTe5

Novel phases of matter with unique properties that emerge from quantum and topological protection present an important thrust of modern research. Of particular interest is to engineer these phases on demand using ultrafast external stimuli, such as photoexcitation, which offers prospects of their integration into future devices compatible with optical communication and information technology. Here, we use MeV Ultrafast Electron Diffraction (UED) to show how a transient three-dimensional (3D) Dirac semimetal state can be induced by a femtosecond laser pulse in a topological insulator ZrTe5. We observe marked changes in Bragg diffraction, which are characteristic of bond distortions in the photoinduced state. Using the atomic positions refined from the UED, we perform density functional theory (DFT) analysis of the electronic band structure. Our results reveal that the equilibrium state of ZrTe5 is a topological insulator with a small band gap of ~ 25 meV, consistent with angle-resolved photoemission (ARPES) experiments. However, the gap is closed in the presence of strong spin-orbit coupling (SOC) in the photoinduced transient state, where massless Dirac fermions emerge in the chiral band structure. The time scale of the relaxation dynamics to the transient Dirac semimetal state is remarkably long, τ ~ 160 ps, which is two orders of magnitude longer than the conventional phonon-driven structural relaxation. The long relaxation is consistent with the vanishing density of states in Dirac spectrum and slow spin-repolarization of the SOC-controlled band structure accompanying the emergence of Dirac fermions. The photon-induced changes in local atomic bond length of ZrTe5 reveal electronic topological phase transition from weak topological insulator to Dirac semimetal.
Sampling-based Motion Planning via Control Barrier Functions

Robot motion planning is central to real-world autonomous applications, such as self-driving cars, persistence surveillance, and robotic arm manipulation. One challenge in motion planning is generating control signals for nonlinear systems that result in obstacle free paths through dynamic environments. In this paper, we propose Control Barrier Function guided Rapidly-exploring Random Trees (CBF-RRT), a sampling-based motion planning algorithm for continuoustime nonlinear systems in dynamic environments. The algorithm focuses on two objectives: efficiently generating feasible controls that steer the system toward a goal region, and handling environments with dynamical obstacles in continuous time. We formulate the control synthesis problem as a Quadratic Program (QP) that enforces Control Barrier Function (CBF) constraints to achieve obstacle avoidance. Additionally, CBF-RRT does not require nearest neighbor or explicit collision checks during sampling.
A new underground laboratory in the USA for a neutrino factory detector and other scientific projects

Abstract A neutrino factory storage ring can provide beams to various locations. We discuss the ICANOE detector (at LNGS) at one such site. We then describe the prospects for the use of the underground location at Carlsbad, NM for a neutrino factory detector. A brief discussion is given of a simple magnetized Fe detector of 10–50 kT for this site.
Modulations of viscous fluid conduit periodic waves

Modulated periodic interfacial waves along a conduit of viscous liquid are explored using nonlinear wave modulation theory and numerical methods. Large-amplitude periodic-wave modulation (Whitham) theory does not require integrability of the underlying model equation, yet often either integrable equations are studied or the full extent of Whitham theory is not developed. Periodic wave solutions of the nonlinear, dispersive, non-integrable conduit equation are characterized by their wavenumber and amplitude. In the weakly nonlinear regime, both the defocusing and focusing variants of the nonlinear Schrodinger (NLS) equation are derived, depending on the carrier wavenumber. Dark and bright envelope solitons are found to persist in long-time numerical solutions of the conduit equation, providing numerical evidence for the existence of strongly nonlinear, large-amplitude envelope solitons. Due to non-convex dispersion, modulational instability for periodic waves above a critical wavenumber is predicted and observed. In the large-amplitude regime, structural properties of the Whitham modulation equations are computed, including strict hyperbolicity, genuine nonlinearity and linear degeneracy. Bifurcating from the NLS critical wavenumber at zero amplitude is an amplitude-dependent elliptic region for the Whitham equations within which a maximally unstable periodic wave is identified. The viscous fluid conduit system is a mathematically tractable, experimentally viable model system for wide-ranging nonlinear, dispersive wave dynamics.
The non-equilibrium attractor for kinetic theory in relaxation time approximation

I demonstrate that the concept of a non-equilibrium attractor can be extended beyond the lowest-order moments typically considered in hydrodynamic treatments. Using a previously obtained exact solution to the relaxation-time approximation Boltzmann equation for a transversally homogeneous and boost-invariant system subject to Bjorken flow, I derive an equation obeyed by all moments of the one-particle distribution function. Using numerical solutions, I show that, similar to the pressure anisotropy, all moments of the distribution function exhibit attractor-like behavior wherein all initial conditions converge to a universal solution after a short time with the exception of moments which are sensitive to modes with zero longitudinal momentum and high transverse momentum. In addition, I compute the exact solution for the distribution function itself on very fine lattices in momentum space and demonstrate that (a) an attractor for the full distribution function exists and (b) solutions with generic initial conditions relax to this solution, first at low momentum and later at high momentum.
Dopant Solubility, and Charge Compensation in La-doped SrSnO3 Films

We investigate lanthanum (La) as an n-type dopant in the strain-stabilized tetragonal phase of SrSnO3 grown on GdScO3 (110) using a radical-based hybrid molecular beam epitaxy approach. Fully coherent, epitaxial films with an atomically smooth film surface were obtained irrespective of doping density. By combining secondary ion mass spectroscopy and Hall measurements, we demonstrate that each La atom contributes one free electron to the film, confirming that it occupies the Sr site in SrSnO3 and that it is completely activated. Carrier density exceeding 1 × 1020 cm−3 was achieved in La-doped SrSnO3 films, which is in excellent agreement with the dopant-solubility limit predicted by density functional theory calculations. A record-high room-temperature mobility of 70 cm2 V−1 s−1 at 1 × 1020 cm−3 was obtained in a 12 nm La-doped SrSnO3 film, making this the thinnest perovskite oxide semiconductor with electron mobility exceeding 25 cm2 V−1 s−1 at room temperature. We discuss the structure-dopant-transport property relationships, providing essential knowledge for the design of electronic devices using these materials.
AN EXTENSION OF THE ATHENA++ CODE FRAMEWORK FOR GRMHD BASED ON ADVANCED RIEMANN SOLVERS AND STAGGERED-MESH CONSTRAINED TRANSPORT

We present a new general relativistic magnetohydrodynamics (GRMHD) code integrated into the Athena++ framework. Improving upon the techniques used in most GRMHD codes, ours allows the use of advanced, less diffusive Riemann solvers, in particular HLLC and HLLD. We also employ a staggered-mesh constrained transport algorithm suited for curvilinear coordinate systems in order to maintain the divergence-free constraint of the magnetic field. Our code is designed to work with arbitrary stationary spacetimes in one, two, or three dimensions, and we demonstrate its reliability through a number of tests. We also report on its promising performance and scalability.
Estimation of Apollo Lunar Dust Transport using Optical Extinction Measurements

A technique to estimate mass erosion rate of surface soil during landing of the Apollo Lunar Module (LM) and total mass ejected due to the rocket plume interaction is proposed and tested. The erosion rate is proportional to the product of the second moment of the lofted particle size distribution N(D), and third moment of the normalized soil size distribution S(D), divided by the integral of S(D)⋅D2/v(D), where D is particle diameter and v(D) is the vertical component of particle velocity. The second moment of N(D) is estimated by optical extinction analysis of the Apollo cockpit video. Because of the similarity between mass erosion rate of soil as measured by optical extinction and rainfall rate as measured by radar reflectivity, traditional NWS radar/rainfall correlation methodology can be applied to the lunar soil case where various S(D) models are assumed corresponding to specific lunar sites.
Minimal distances between SCFTs

We study lower bounds on the minimal distance in theory space between four-dimensional superconformal field theories (SCFTs) connected via broad classes of renormalization group (RG) flows preserving various amounts of supersymmetry (SUSY). For $ \mathcal{N} $ = 1 RG flows, the ultraviolet (UV) and infrared (IR) endpoints of the flow can be parametrically close. On the other hand, for RG flows emanating from a maximally supersymmetric SCFT, the distance to the IR theory cannot be arbitrarily small regardless of the amount of (non-trivial) SUSY preserved along the flow. The case of RG flows from $ \mathcal{N} $ =2 UV SCFTs is more subtle. We argue that for RG flows preserving the full $ \mathcal{N} $ =2 SUSY, there are various obstructions to finding examples with parametrically close UV and IR endpoints. Under reasonable assumptions, these obstructions include: unitarity, known bounds on the c central charge derived from associativity of the operator product expansion, and the central charge bounds of Hofman and Maldacena. On the other hand, for RG flows that break $ \mathcal{N} $ = 2 → $ \mathcal{N} $ = 1, it is possible to find IR fixed points that are parametrically close to the UV ones. In this case, we argue that if the UV SCFT possesses a single stress tensor, then such RG flows excite of order all the degrees of freedom of the UV theory. Furthermore, if the UV theory has some flavor symmetry, we argue that the UV central charges should not be too large relative to certain parameters in the theory.
Domain reduction techniques for global NLP and MINLP optimization

Optimization solvers routinely utilize presolve techniques, including model simplification, reformulation and domain reduction techniques. Domain reduction techniques are especially important in speeding up convergence to the global optimum for challenging nonconvex nonlinear programming (NLP) and mixed-integer nonlinear programming (MINLP) optimization problems. In this work, we survey the various techniques used for domain reduction of NLP and MINLP optimization problems. We also present a computational analysis of the impact of these techniques on the performance of various widely available global solvers on a collection of 1740 test problems.
The role of host phenology for parasite transmission.

Phenology is a fundamental determinant of species distributions, abundances, and interactions. In host–parasite interactions, host phenology can affect parasite fitness due to the temporal constraints it imposes on host contact rates. However, it remains unclear how parasite transmission is shaped by the wide range of phenological patterns observed in nature. We develop a mathematical model of the Lyme disease system to study the consequences of differential tick developmental-stage phenology for the transmission of B. burgdorferi. Incorporating seasonal tick activity can increase B. burgdorferi fitness compared to continuous tick activity but can also prevent transmission completely. B. burgdorferi fitness is greatest when the activity period of the infectious nymphal stage slightly precedes the larval activity period. Surprisingly, B. burgdorferi is eradicated if the larval activity period begins long after the end of nymphal activity due to a feedback with mouse population dynamics. These results highlight the importance of phenology, a common driver of species interactions, for the fitness of a parasite.
Competition of Pairing and Nematicity in the Two-Dimensional Electron Gas

Due to its extremely rich phase diagram, the two-dimensional electron gas exposed to perpendicular magnetic fields has been the subject of intense and sustained study. One particularly interesting ...
Modulation of the turbulence regeneration cycle by inertial particles in planar Couette flow

Two-way coupled direct numerical simulations are used to investigate the effects of inertial particles on self-sustained, turbulent coherent structures (i.e. the so-called regeneration cycle) in plane Couette flow at low Reynolds number just above the onset of transition. Tests show two limiting behaviours with increasing particle inertia, similar to the results from previous linear stability analyses: low-inertia particles trigger the laminar-to-turbulent instability whereas high-inertia particles tend to stabilize turbulence due to the extra dissipation induced by particle–fluid coupling. Furthermore, it is found that the streamwise coupling between phases is the dominant factor in damping of the turbulence and is highly related to the spatial distribution of the particles. The presence of particles in different turbulent coherent structures (large-scale vortices or large-scale streaks) determines the turbulent kinetic energy of particulate phase, which is related to the particle response time scaled by the turnover time of large-scale vortices. By quantitatively investigating the periodic character of the whole regeneration cycle and the phase difference between linked sub-steps, we show that the presence of inertial particles does not alter the periodic nature of the cycle or the relative length of each of the sub-steps. Instead, high-inertia particles greatly weaken the large-scale vortices as well as the streamwise vorticity stretching and lift-up effects, thereby suppressing the fluctuating amplitude of the large-scale streaks. The primary influence of low-inertia particles, however, is to strengthen the large-scale vortices, which fosters the cycle and ultimately reduces the critical Reynolds number.
A Bayesian approach to the g-formula:

Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin's original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal effects in small samples or when data are sparse. We demonstrate an approach to estimate the effect of environmental tobacco smoke on body mass index among children aged 4-9 years who were enrolled in a longitudinal birth cohort in New York, USA. We provide an algorithm and supply SAS and Stan code that can be adopted to implement this computational approach more generally.
Norms on the cohomology of hyperbolic 3-manifolds

We study the relationship between two norms on the first cohomology of a hyperbolic 3-manifold: the purely topological Thurston norm and the more geometric harmonic norm. Refining recent results of Bergeron, Sengun, and Venkatesh as well as older work of Kronheimer and Mrowka, we show that these norms are roughly proportional with explicit constants depending only on the volume and injectivity radius of the hyperbolic 3-manifold itself. Moreover, we give families of examples showing that some (but not all) qualitative aspects of our estimates are sharp. Finally, we exhibit closed hyperbolic 3-manifolds where the Thurston norm grows exponentially in terms of the volume and yet there is a uniform lower bound on the injectivity radius.
Nonlinear force-free configurations in cylindrical geometry

We find a new family of solutions for force-free magnetic structures in cylindrical geometry. These solutions have radial power-law dependence and are periodic but non-harmonic in the azimuthal direction; they generalize the vacuum -independent potential fields to current-carrying configurations.
Dynamical RG and Critical Phenomena in de Sitter Space

Perturbative quantum field theory in de Sitter space is known to give rise to a variety of contributions that grow with time (secular terms). Despite significant progress, a complete understanding of the physical origin of this growth remains an outstanding problem. In this paper, we will study the origin this secular growth in de Sitter space for interacting theories that are near attractive conformal fixed points. We show that the secular terms are determined by the anomalous dimensions of the same theory in flat space and can be re-summed using the dynamical renormalization group. This behavior is mandatory at the conformal fixed point but we show that it holds away from the fixed point as well. We analyze this problem in general using conformal perturbation theory and study conformally coupled scalar fields in four and 4 − ϵ dimensions as examples.
Machine learning for imaging Cherenkov detectors

Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at present we are witnessing the onset of new highly efficient and fast applications. This paper focuses on novel directions with applications to Cherenkov detectors. In particular, recent advances on detector design and calibration, as well as particle identification are presented.