cssi/SciSciGPT-SciSciNet huggingface dataset:
GLaMST: Grow Lineages along Minimum Spanning Tree for B Cell Receptor Sequencing Data

B cell affinity maturation enables B cells to generate high-affinity antibodies. This process involves somatic hypermutation of B cell immunoglobulin receptor (BCR) genes and selection by their ability to bind antigens. Lineage trees are used to describe this microevolution of B cell immunoglobulin genes. In a lineage tree, each node is one BCR sequence that mutated from the germinal center and each directed edge represents a single base mutation, insertion or deletion. In BCR sequencing data, the observed data only contains a subset of BCR sequences in this microevolution process. Therefore, reconstructing the lineage tree from experimental data requires algorithms to build the tree based on partially observed tree nodes. We developed a new algorithm named Grow Lineages along Minimum Spanning Tree (GLaMST), which efficiently reconstruct the lineage tree given observed BCR sequences that correspond to a subset of the tree nodes. Through comparison using simulated and real data, GLaMST outperforms existing algorithms in simulations with high rates of mutation, insertion and deletion, and generates lineage trees with smaller size and closer to ground truth according to tree features that highly correlated with selection pressure. GLaMST outperforms state-of-art in reconstruction of the BCR lineage tree in both efficiency and accuracy. Integrating it into existing BCR sequencing analysis frameworks can significant improve lineage tree reconstruction aspect of the analysis.
Valence quark model from lattice QCD

We study the valence approximation in lattice QCD of hadrons where the cloud quark and antiquark are deleted by truncating the backward time propagation (Z graphs) in the connected insertion. Whereas the sea quarks are eliminated in the disconnected insertions. It is shown in this valence approximation that the ratios of isovector to isoscalar matrix elements (e.g. F A/DA and F S/DS ratios) in the nucleon reproduce the SU(6) quark model predications in a lattice QCD calculation. We also calculate how the hadron masses are affected. It is found that, within statistical errors, the nucleon and the Δ become degenerate in the quark mass range we have studied which ranges from the unambiguously to about 3 times the strange. The π and ρ also become degenerate in this range.
Zika virus-based immunotherapy enhances long-term survival of rodents with brain tumors through upregulation of memory T-cells.

Zika virus (ZIKV) exhibits a tropism for brain tumor cells and has been used as an oncolytic virus to target brain tumors in mice with modest effects on extending median survival. Recent studies have highlighted the potential for combining virotherapy and immunotherapy to target cancer. We postulated that ZIKV could be used as an adjuvant to enhance the long-term survival of mice with malignant glioblastoma and generate memory T-cells capable of providing long-term immunity against cancer remission. To test this hypothesis mice bearing malignant intracranial GL261 tumors were subcutaneously vaccinated with irradiated GL261 cells previously infected with the ZIKV. Mice also received intracranial injections of live ZIKV, irradiation attenuated ZIKV, or irradiated GL261 cells previously infected with ZIKV. Long-term survivors were rechallenged with a second intracranial tumor to examine their immune response and look for the establishment of protective memory T-cells. Mice with subcutaneous vaccination plus intracranial irradiation attenuated ZIKV or intracranial irradiated GL261 cells previously infected with ZIKV exhibited the greatest extensions to overall survival. Flow cytometry analysis of immune cells within the brains of long-term surviving mice after tumor rechallenge revealed an increase in the number of T-cells, including CD4+ and tissue-resident effector/ effector memory CD4+ T-cells, in comparison to long-term survivors that were mock-rechallenged, and in comparison to naive untreated mice challenged with intracranial gliomas. These results suggest that ZIKV can serve as an adjuvant to subcutaneous tumor vaccines that enhance long-term survival and generate protective tissue-resident memory CD4+ T-cells.
SwarmMesh: A Distributed Data Structure for Cooperative Multi-Robot Applications

We present an approach to the distributed storage of data across a swarm of mobile robots that forms a shared global memory. We assume that external storage infrastructure is absent, and that each robot is capable of devoting a quota of memory and bandwidth to distributed storage. Our approach is motivated by the insight that in many applications data is collected at the periphery of a swarm topology, but the periphery also happens to be the most dangerous location for storing data, especially in exploration missions. Our approach is designed to promote data storage in the locations in the swarm that best suit a specific feature of interest in the data, while accounting for the constantly changing topology due to individual motion. We analyze two possible features of interest: the data type and the data item position in the environment. We assess the performance of our approach in a large set of simulated experiments. The evaluation shows that our approach is capable of storing quantities of data that exceed the memory of individual robots, while maintaining near-perfect data retention in high-load conditions.
Pixelhop++: A Small Successive-Subspace-Learning-Based (Ssl-Based) Model For Image Classification

The successive subspace learning (SSL) principle was developed and used to design an interpretable learning model, known as the PixelHop method, for image classification in our prior work. Here, we propose an improved PixelHop method and call it Pixel- Hop++. First, to make the PixelHop model size smaller, we decouple ajoint spatial-spectral input tensor to multiple spatial tensors (one for each spectral component) under the spatial-spectral separability assumption and perform the Saab transform in a channel-wise manner, called the channel-wise (c/w) Saab transform. Second, by performing this operation from one hop to another successively, we construct a channel-decomposed feature tree whose leaf nodes contain features of one dimension (1D). Third, these 1D features are ranked according to their cross-entropy values, which allows us to select a subset of discriminant features for image classification. In Pixel- Hop++, one can control the learning model size of fine-granularity, offering a flexible tradeoff between the model size and the classification performance. We demonstrate the flexibility of PixelHop++ on MNIST, Fashion MNIST, and CIFAR-10 three datasets.
Two-photon X-ray ghost microscope.

This article presents a non-classical imaging mechanism that produces a diffraction-limited and magnified ghost image of the internal structure of an object through the measurement of intensity fluctuation correlation formed by two-photon interference. In principle, the lensless X-ray ghost imaging mechanism may achieve a spatial resolution determined by the wavelength and the angular diameter of the X-ray source, ∼λ/Δθs, with possible reduction caused by additional optics. In addition, it has the ability to image select “slices” deep within an object, which can be used for constructing 3D view of its internal structure.
Bismuth nanoparticles obtained by a facile synthesis method exhibit antimicrobial activity against Staphylococcus aureus and Candida albicans.

Bismuth compounds are known for their activity against multiple microorganisms; yet, the antibiotic properties of bismuth nanoparticles (BiNPs) remain poorly explored. The objective of this work is to further the research of BiNPs for nanomedicine-related applications. Stable Polyvinylpyrrolidone (PVP)-coated BiNPs were produced by a chemical reduction process, in less than 30 min. We produced stable, small, spheroid PVP-coated BiNPs with a crystalline organization. The PVP-BiNPs showed potent antibacterial activity against the pathogenic bacterium Staphylococcus aureus and antifungal activity against the opportunistic pathogenic yeast Candida albicans, both under planktonic and biofilm growing conditions. Our results indicate that BiNPs represent promising antimicrobial nanomaterials, and this facile synthetic method may allow for further investigation of their activity against a variety of pathogenic microorganisms.
Faster Johnson–Lindenstrauss transforms via Kronecker products

The Kronecker product is an important matrix operation with a wide range of applications in supporting fast linear transforms, including signal processing, graph theory, quantum computing and deep learning. In this work, we introduce a generalization of the fast Johnson-Lindenstrauss projection for embedding vectors with Kronecker product structure, the Kronecker fast Johnson-Lindenstrauss transform (KFJLT). The KFJLT reduces the embedding cost to an exponential factor of the standard fast Johnson-Lindenstrauss transform (FJLT)'s cost when applied to vectors with Kronecker structure, by avoiding explicitly forming the full Kronecker products. We prove that this computational gain comes with only a small price in embedding power: given $N = \prod_{k=1}^d n_k$, consider a finite set of $p$ points in a tensor product of $d$ constituent Euclidean spaces $\bigotimes_{k=d}^{1}\mathbb{R}^{n_k} \subset \mathbb{R}^{N}$. With high probability, a random KFJLT matrix of dimension $N \times m$ embeds the set of points up to multiplicative distortion $(1\pm \varepsilon)$ provided by $m \gtrsim \varepsilon^{-2} \cdot \log^{2d - 1} (p) \cdot \log N$. We conclude by describing a direct application of the KFJLT to the efficient solution of large-scale Kronecker-structured least squares problems for fitting the CP tensor decomposition.
XFlow: An algorithm for extracting ion chromatograms

Mass spectrometry is a fundamental tool for modern proteomics. The increasing availability of mass spectrometry data paired with the increasing sensitivity and fidelity of the instruments necessitates new and more potent analytical methods. To that end, we have created and present XFlow, a feature detection algorithm for extracting ion chromatograms from MS1 LC-MS data. XFlow is a parameter-free procedurally agnostic feature detection algorithm that utilizes the latent properties of ion chromatograms to resolve them from the surrounding noise present in MS1 data. XFlow is designed to function on either profile or centroided data across different resolutions and instruments. This broad applicability lends XFlow strong utility as a one-size-fits-all method for MS1 analysis or target acquisition for MS2. XFlow is written in Java and packaged with JS-MS, an open-source mass spectrometry analysis toolkit.
SLE loop measures

We use Minkowski content (i.e., natural parametrization) of SLE to construct several types of $$\hbox {SLE}_\kappa $$ loop measures for $$\kappa \in (0,8)$$ . First, we construct rooted $$\hbox {SLE}_\kappa $$ loop measures in the Riemann sphere $$\widehat{\mathbb {C}}$$ , which satisfy Mobius covariance, conformal Markov property, reversibility, and space-time homogeneity, when the loop is parametrized by its $$(1+\frac{\kappa }{8})$$ -dimensional Minkowski content. Second, by integrating rooted $$\hbox {SLE}_\kappa $$ loop measures, we construct the unrooted $$\hbox {SLE}_\kappa $$ loop measure in $$\widehat{\mathbb {C}}$$ , which satisfies Mobius invariance and reversibility. Third, we use Brownian loop measures to extend the rooted and unrooted $$\hbox {SLE}_\kappa $$ loop measures from $$\widehat{\mathbb {C}}$$ to subdomains of $$\widehat{\mathbb {C}}$$ , which respectively satisfy conformal covariance and conformal invariance, and then further use the conformal invariance to extend unrooted $$\hbox {SLE}_\kappa $$ loop measures to some Riemann surfaces. Finally, using a similar approach, we construct $$\hbox {SLE}_\kappa $$ bubble measures in simply/multiply connected domains rooted at a boundary point. The space-time homogeneity of rooted $$\hbox {SLE}_\kappa $$ loop measures in $$\widehat{\mathbb {C}}$$ confirms a conjecture by Greg Lawler on the existence of such measures.
Evaluation of supercritical cryogen storage and transfer systems for future NASA missions

Conceptual designs of Space Transportation Vehicles (STV), and their orbital servicing facilities, that utilize supercritical, single phase, cryogenic propellant were established and compared with conventional subcritical, two phases, STV concepts. The analytical study was motivated by the desire to avoid fluid management problems associated with the storage, acquisition and transfer of subcritical liquid oxygen and hydrogen propellants in the low gravity environment of space. Although feasible, the supercritical concepts suffer from STV weight penalties and propellant resupply system power requirements which make the concepts impractical.
Association of poor housing conditions with COVID-19 incidence and mortality across US counties.

Objective Poor housing conditions have been linked with worse health outcomes and infectious disease spread. Since the relationship of poor housing conditions with incidence and mortality of COVID-19 is unknown, we investigated the association between poor housing condition and COVID-19 incidence and mortality in US counties. Methods We conducted cross-sectional analysis of county-level data from the US Centers for Disease Control, US Census Bureau and John Hopkins Coronavirus Resource Center for 3135 US counties. The exposure of interest was percentage of households with poor housing conditions (one or greater of: overcrowding, high housing cost, incomplete kitchen facilities, or incomplete plumbing facilities). Outcomes were incidence rate ratios (IRR) and mortality rate ratios (MRR) of COVID-19 across US counties through 4/21/2020. Multilevel generalized linear modeling (with total population of each county as a denominator) was utilized to estimate relative risk of incidence and mortality related to poor housing conditions with adjustment for population density and county characteristics including demographics, income, education, prevalence of medical comorbidities, access to healthcare insurance and emergency rooms, and state-level COVID-19 test density. We report incidence rate ratios (IRRs) and mortality ratios (MRRs) for a 5% increase in prevalence in households with poor housing conditions. Results Across 3135 US counties, the mean percentage of households with poor housing conditions was 14.2% (range 2.7% to 60.2%). On April 21st, the mean (SD) number of cases and deaths of COVID-19 were 255.68 (2877.03) cases and 13.90 (272.22) deaths per county, respectively. In the adjusted models standardized by county population, with each 5% increase in percent households with poor housing conditions, there was a 50% higher risk of COVID-19 incidence (IRR 1.50, 95% CI: 1.38–1.62) and a 42% higher risk of COVID-19 mortality (MRR 1.42, 95% CI: 1.25–1.61). Results remained similar using earlier timepoints (3/31/2020 and 4/10/2020). Conclusions and relevance Counties with a higher percentage of households with poor housing had higher incidence of, and mortality associated with, COVID-19. These findings suggest targeted health policies to support individuals living in poor housing conditions should be considered in further efforts to mitigate adverse outcomes associated with COVID-19.
Brain2Char: a deep architecture for decoding text from brain recordings

Objective Decoding language representations directly from the brain can enable new Brain-Computer Interfaces (BCI) for high bandwidth human-human and human-machine communication. Clinically, such technologies can restore communication in people with neurological conditions affecting their ability to speak. Approach In this study, we propose a novel deep network architecture Brain2Char, for directly decoding text (specifically character sequences) from direct brain recordings (called Electrocorticography, ECoG). Brain2Char framework combines state-of-the-art deep learning modules --- 3D Inception layers for multiband spatiotemporal feature extraction from neural data and bidirectional recurrent layers, dilated convolution layers followed by language model weighted beam search to decode character sequences, optimizing a connectionist temporal classification (CTC) loss. Additionally, given the highly non-linear transformations that underlie the conversion of cortical function to character sequences, we perform regularizations on the network's latent representations motivated by insights into cortical encoding of speech production and artifactual aspects specific to ECoG data acquisition. To do this, we impose auxiliary losses on latent representations for articulatory movements, speech acoustics and session specific non-linearities. Main results In 3 (out of 4) participants reported here, Brain2Char achieves 10.6%, 8.5% and 7.0%Word Error Rates (WER) respectively on vocabulary sizes ranging from 1200 to 1900 words. Significance These results establish a new end-to-end approach on decoding text from brain signals and demonstrate the potential of Brain2Char as a high-performance communication BCI.
FAST COMMUNITY DETECTION BY SCORE

Consider a network where the nodes split into $K$ different communities. The community labels for the nodes are unknown and it is of major interest to estimate them (i.e., community detection). Degree Corrected Block Model (DCBM) is a popular network model. How to detect communities with the DCBM is an interesting problem, where the main challenge lies in the degree heterogeneity. We propose a new approach to community detection which we call the Spectral Clustering On Ratios-of-Eigenvectors (SCORE). Compared to classical spectral methods, the main innovation is to use the entry-wise ratios between the first leading eigenvector and each of the other leading eigenvectors for clustering. Let $A$ be the adjacency matrix of the network. We first obtain the $K$ leading eigenvectors of $A$, say, $\hat{\eta}_{1},\ldots,\hat{\eta}_{K}$, and let $\hat{R}$ be the $n\times(K-1)$ matrix such that $\hat{R}(i,k)=\hat{\eta}_{k+1}(i)/\hat{\eta}_{1}(i)$, $1\leq i\leq n$, $1\leq k\leq K-1$. We then use $\hat{R}$ for clustering by applying the $k$-means method. The central surprise is, the effect of degree heterogeneity is largely ancillary, and can be effectively removed by taking entry-wise ratios between $\hat{\eta}_{k+1}$ and $\hat{\eta}_{1}$, $1\leq k\leq K-1$. The method is successfully applied to the web blogs data and the karate club data, with error rates of $58/1222$ and $1/34$, respectively. These results are more satisfactory than those by the classical spectral methods. Additionally, compared to modularity methods, SCORE is easier to implement, computationally faster, and also has smaller error rates. We develop a theoretic framework where we show that under mild conditions, the SCORE stably yields consistent community detection. In the core of the analysis is the recent development on Random Matrix Theory (RMT), where the matrix-form Bernstein inequality is especially helpful.
Partial resolution by toroidal blow-ups

We give an alternate proof of a theorem of Tevelev about improving a nontoroidal ideal sheaf by a sequence of toroidal blow-ups.
The many faces of far-from-equilibrium thermodynamics: Deterministic chaos, randomness, or emergent order?

Far-from-equilibrium systems are ubiquitous in nature. They are also rich in terms of diversity and complexity. Therefore, it is an intellectual challenge to be able to understand the physics of far-from-equilibrium phenomena. In this article, we revisit a standard tabletop experiment, the Rayleigh–Benard convection, to explore some fundamental questions and present a new perspective from a first-principles point of view. We address how nonequilibrium fluctuations differ from equilibrium fluctuations, how emergence of order out of equilibrium breaks symmetries in the system, and how free energy of a system gets locally bifurcated to operate a Carnot-like engine to maintain order. The exploration and investigation of these nontrivial questions are the focus of this article.
Entanglement-Assisted Capacities of Compound Quantum Channels

We study universal quantum codes for entanglement-assisted quantum communication over compound quantum channels. In this setting, sender and receiver do not know the specific channel that will be used for communication, but only know the set that the channel is selected from. We investigate different variations of the problem: uninformed users, informed receiver, informed sender, and feedback assistance. We derive single-letter formulas for all corresponding channel capacities. Our proofs are based on one-shot decoupling bounds and properties of smooth entropies.
Engineering first-order quantum phase transitions for weak signal detection

The quantum critical detector (QCD), recently introduced for weak signal amplification [L.-P. Yang and Z. Jacob, Opt. Express 27, 10482 (2019)], functions by exploiting high sensitivity near the phase transition point of first-order quantum phase transitions (QPTs). We contrast the behavior of the first-order and the second-order quantum phase transitions in the detector. We find that the giant sensitivity, which can be utilized for quantum amplification, only exists in the first-order QPTs. We define two new magnetic order parameters to quantitatively characterize the first-order QPT of the interacting spins in the detector. We also introduce the Husimi Q-functions as a powerful tool to show the fundamental change in the ground-state wave function of the detector during the QPTs, especially the intrinsic dynamical change within the detector during a quantum critical amplification. We explicitly show the high figures of merit of the QCD via the quantum gain and the signal-to-quantum noise ratio. Specifically, we predict the existence of a universal first-order QPT in the interacting-spin system resulting from two competing ferromagnetic orders. Our results motivate new designs of weak signal detectors by engineering first-order QPTs, which are of fundamental significance in the search for new particles, quantum metrology, and information science.The quantum critical detector (QCD), recently introduced for weak signal amplification [L.-P. Yang and Z. Jacob, Opt. Express 27, 10482 (2019)], functions by exploiting high sensitivity near the phase transition point of first-order quantum phase transitions (QPTs). We contrast the behavior of the first-order and the second-order quantum phase transitions in the detector. We find that the giant sensitivity, which can be utilized for quantum amplification, only exists in the first-order QPTs. We define two new magnetic order parameters to quantitatively characterize the first-order QPT of the interacting spins in the detector. We also introduce the Husimi Q-functions as a powerful tool to show the fundamental change in the ground-state wave function of the detector during the QPTs, especially the intrinsic dynamical change within the detector during a quantum critical amplification. We explicitly show the high figures of merit of the QCD via the quantum gain and the signal-to-quantum noise ratio. Specifi...
Assessing Astrophysical Uncertainties in Direct Detection with Galaxy Simulations

We study the local dark matter velocity distribution in simulated Milky Way-mass galaxies, generated at high resolution with both dark matter and baryons. We find that the dark matter in the Solar neighborhood is influenced appreciably by the inclusion of baryons, increasing the speed of dark matter particles compared to dark matter-only simulations. The gravitational potential due to the presence of a baryonic disk increases the amount of high velocity dark matter, resulting in velocity distributions which are more similar to the Maxwellian Standard Halo Model than predicted from dark matter-only simulations. Further, the velocity structures present in baryonic simulations possess a greater diversity than expected from dark matter-only simulation. We show the impact on the direct detection experiments LUX, DAMA/Libra, and CoGeNT using our simulated velocity distributions, and explore how resolution and halo mass within the Milky Way's estimated mass range impact the results. A Maxwellian fit to the velocity distribution tends to overpredict the amount of dark matter in the high velocity tail, even with baryons, and thus leads to overly optimistic direct detection bounds on models which are dependent on this region of phase space for an experimental signal. Our work further demonstrates that it is critical to transform simulated velocity distributions to the lab frame of reference, due to the fact that velocity structure in the Solar neighborhood appears when baryons are included. There is more velocity structure present when baryons are included than in dark matter-only simulations. Even when baryons are included, the importance of the velocity structure is not as apparent in the Galactic frame of reference as in the Earth frame.
Affine dual equivalence and $k$-Schur functions

The k-Schur functions were first introduced by Lapointe, Lascoux and Morse [18] in the hopes of refining the expansion of Macdonald polynomials into Schur functions. Recently, an alternative definition for k-Schur functions was given by Lam, Lapointe, Morse, and Shimozono [17] as the weighted generating function of starred strong tableaux which correspond with labeled saturated chains in the Bruhat order on the affine symmetric group modulo the symmetric group. This definition has been shown to correspond to the Schubert basis for the affine Grassmannian of type A [15] and at t = 1 it is equivalent to the k-tableaux characterization of Lapointe and Morse [22]. In this paper, we extend Haiman’s dual equivalence relation on standard Young tableaux [12] to all starred strong tableaux. The elementary equivalence relations can be interpreted as labeled edges in a graph which share many of the properties of Assaf’s dual equivalence graphs. These graphs display much of the complexity of working with k-Schur functions and the interval structure on Sn/Sn. We introduce the notions of flattening and squashing skew starred strong tableaux in analogy with jeu de taquin slides in order to give a method to find all isomorphism types for affine dual equivalence graphs of rank 4. Finally, we state some open problems on other ways to generalize dual equivalence.
Topological implications of the total generalized electron-flow magnetic helicity invariant in electron magnetohydrodynamics

Topological implications of the total generalized electron-flow magnetic helicity He in electron magnetohydrodynamics(EMHD) are explored. The invariance of He is shown to imply the invariance of the sum of the linkage of the magnetic field lines, the linkage of electron-flow vorticity field lines and the mutual linkage among these two sets of field lines. This result appears to support a change in the magnetic field topology and hence pave the way for magnetic reconnection in EMHD via a change in the concomitant electron-flow vorticity topology.
Tutorial: opto-mechanical cooling by the back-action of cavity photons

We present a simple classical analysis of light interacting with a Fabry-Perot cavity consisting of a fixed (dielectric) front mirror and a vibrating rear mirror. In the adiabatic approximation, the returning light exhibits sideband symmetry, which will go away once the photon lifetime becomes comparable to or longer than the oscillation period of the rear mirror. When the oscillation period is short compared to the cavity photon lifetime, one must approach the problem differently, treating the vibrating mirror as a scatterer which sends a fraction of the incident light into sideband frequencies. With proper detuning, the cavity’s internal radiation pressure could either dampen or amplify the vibrations of the mirror; the former is the physical principle behind opto-mechanical cooling by the back-action of cavity photons.
Spectrum-Revealing Cholesky Factorization for Kernel Methods

Kernel methods represent some of the most popular machine learning tools for data analysis. Since exact kernel methods can be prohibitively expensive for large problems, reliable low-rank matrix approximations and high-performance implementations have become indispensable for practical applications of kernel methods. In this work, we introduce spectrum-revealing Cholesky factorization, a reliable low-rank matrix factorization, for kernel matrix approximation. We also develop an efficient and effective randomized algorithm for computing this factorization. Our numerical experiments demonstrate that this algorithm is as effective as other Cholesky factorization based kernel methods on machine learning problems, but significantly faster.
Uniform Lipschitz Estimates in Bumpy Half-Spaces

This paper is devoted to the proof of uniform Holder and Lipschitz estimates close to oscillating boundaries, for divergence form elliptic systems with periodically oscillating coefficients. Our main point is that no structure is assumed on the oscillations of the boundary. In particular, those oscillations are neither periodic, nor quasiperiodic, nor stationary ergodic. We investigate the consequences of our estimates on the large scales of Green and Poisson kernels. Our work opens the door to the use of potential theoretic methods in problems concerned with oscillating boundaries, which is an area of active research.
THE GEODESIC RULE FOR HIGHER CODIMENSIONAL GLOBAL DEFECTS

We generalize the geodesic rule to the case of formation of higher codimensional global defects. Relying on energetic arguments, we argue that, for such defects, the geometric structures of interest are the totally geodesic submanifolds. On the other hand, stochastic arguments lead to a diffusion equation approach, from which the geodesic rule is deduced. It turns out that the most appropriate geometric structure that one should consider is the convex hull of the values of the order parameter on the causal volumes whose collision gives rise to the defect. We explain why these two approaches lead to similar results when calculating the density of global defects by using a theorem of Cheeger and Gromoll. We present a computation of the probability of formation of strings/vortices in the case of a system, such as nematic liquid crystals, whose vacuum is ℝP2.
Chiral kinetic theory and anomalous hydrodynamics in even spacetime dimensions

We apply chiral kinetic theory to a gas of weakly interacting Weyl fermions coupled to electromagnetism in (2N + 1) + 1 spacetime dimensions to obtain the 'Gibbs free energy current' from which all equilibrium finite temperature anomalous contributions, such as the chiral magnetic and vortical currents, can be derived. Our results agree with those derived previously using thermodynamic constraints.
Rayleigh scattering in an optical nanofiber as a probe of higher-order mode propagation

Optical nanofibers (ONFs) provide a rich platform for exploring atomic and optical phenomena even when they support only a single spatial mode. Nanofibers supporting higher-order modes (HOMs) provide additional degrees of freedom to enable complex evanescent field profiles for interaction with the surrounding medium, but local control of these profiles requires nondestructive evaluation of the propagating fields. Here, we use Rayleigh scattering for rapid measurement of the propagation of light in few-mode ONFs. Imaging the Rayleigh scattered light provides direct visualization of the spatial evolution of propagating fields throughout the entire fiber, including the transition from core–cladding guidance to cladding–air guidance. We resolve the interference between HOMs to determine local beat lengths and modal content along the fiber, and show that the modal superposition in the waist can be systematically controlled by adjusting the input superposition. With this diagnostic we can measure variations in the radius of the fiber waist to below 3 nm in situ using purely optical means. This nondestructive technique also provides useful insight into light propagation in ONFs.
MASS–RADIUS RELATION FOR ROCKY PLANETS BASED ON PREM

Several small dense exoplanets are now known, inviting comparisons to Earth and Venus. Such comparisons require translating their masses and sizes to composition models of evolved multi-layer-interior planets. Such theoretical models rely on our understanding of the Earth's interior, as well as independently derived equations of state (EOS), but have so far not involved direct extrapolations from Earth's seismic model -PREM. In order to facilitate more detailed compositional comparisons between small exoplanets and the Earth, we derive here a semi-empirical mass-radius relation for two-layer rocky planets based on PREM: ${\frac{R}{R_\oplus}} = (1.07-0.21\cdot \text{CMF})\cdot (\frac{M}{M_\oplus})^{1/3.7}$, where CMF stands for Core Mass Fraction. It is applicable to 1$\sim$8 M$_{\oplus}$ and CMF of 0.0$\sim$0.4. Applying this formula to Earth and Venus and several known small exoplanets with radii and masses measured to better than $\sim$30\% precision gives a CMF fit of $0.26\pm0.07$.
Comments on the Neyman–Fisher Controversy and Its Consequences

The Neyman-Fisher controversy considered here originated with the 1935 presentation of Jerzy Neyman's Statistical Problems in Agricultural Experimentation to the Royal Statistical Society. Neyman asserted that the standard ANOVA F-test for randomized complete block designs is valid, whereas the analogous test for Latin squares is invalid in the sense of detecting differentiation among the treatments, when none existed on average,more often than desired (i.e., having a higher Type I error than advertised). However, Neyman's expressions for the expected mean residual sum of squares, for both designs, are generally incorrect. Furthermore, Neyman's belief that the Type I error (when testing the null hypothesis of zero average treatment effects) is higher than desired, whenever the expected mean treatment sum of squares is greater than the expected mean residual sum of squares, is generally incorrect. Simple examples show that, without further assumptions on the potential outcomes, one cannot determine the Type I error of the F-test from expected sums of squares. Ultimately, we believe that the Neyman- Fisher controversy had a deleterious impact on the development of statistics, with a major consequence being that potential outcomes were ignored in favor of linear models and classical statistical procedures that are imprecise without applied contexts.
Observation of the pressure effect in simulations of droplets splashing on a dry surface

At atmospheric pressure, a droplet impacting on a surface produces a splash. Reducing the ambient pressure suppresses this splash. The pressure effect is not well understood and this is the first study to present an in-depth comparison between various theoretical splashing models and simulations.
Relative energy gap for harmonic maps of Riemann surfaces into real analytic Riemannian manifolds

We extend the well-known Sacks-Uhlenbeck energy gap result (1981) for harmonic maps from closed Riemann surfaces into closed Riemannian manifolds from the case of maps with small energy (thus near a constant map), to the case of harmonic maps with high absolute energy but small energy relative to a reference harmonic map.
Noise suppression in inverse weak value-based phase detection

We examine the effect of different sources of technical noise on inverse weak value-based precision phase measurements. We find that this type of measurement is similarly robust to technical noise as related experiments in the weak value regime. In particular, the measurements considered here are robust to additive Gaussian white noise and angular jitter noise commonly encountered in optical experiments. Additionally, we show the same techniques used for precision phase measurement can be used with the same technical advantages for optical frequency measurements.
Inference of Causal Information Flow in Collective Animal Behavior

Understanding and even defining what constitutes animal interactions remains a challenging problem. Correlational tools may be inappropriate for detecting communication between a set of many agents exhibiting nonlinear behavior. A different approach is to define coordinated motions in terms of an information theoretic channel of direct causal information flow. In this work, we consider time series data obtained by an experimental protocol of optical tracking of the insect species Chironomus riparius . The data constitute reconstructed 3-D spatial trajectories of the insects’ flight trajectories and kinematics. We present an application of the optimal causation entropy (oCSE) principle to identify direct causal relationships or information channels among the insects. The collection of channels inferred by oCSE describes a network of information flow within the swarm. We find that information channels with a long spatial range are more common than expected under the assumption that causal information flows should be spatially localized. The tools developed herein are general and applicable to the inference and study of intercommunication networks in a wide variety of natural settings.
Multilayer Brain Networks

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure–function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.
QCD interference effects of heavy particles below threshold

Abstract We consider how two classes of heavy particles: extra vector-like families, and strongly interacting superpartners, manifest themselves below threshold, by interference of virtual loops with normal QCD processes. Quantitative estimates are presented.
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance

If the same neural network architecture is trained multiple times on the same dataset, will it make similar linguistic generalizations across runs? To study this question, we fine-tuned 100 instances of BERT on the Multi-genre Natural Language Inference (MNLI) dataset and evaluated them on the HANS dataset, which evaluates syntactic generalization in natural language inference. On the MNLI development set, the behavior of all instances was remarkably consistent, with accuracy ranging between 83.6% and 84.8%. In stark contrast, the same models varied widely in their generalization performance. For example, on the simple case of subject-object swap (e.g., determining that “the doctor visited the lawyer” does not entail “the lawyer visited the doctor”), accuracy ranged from 0.0% to 66.2%. Such variation is likely due to the presence of many local minima in the loss surface that are equally attractive to a low-bias learner such as a neural network; decreasing the variability may therefore require models with stronger inductive biases.
Local structure analysis in ab initio liquid water

Within the framework of density functional theory, the inclusion of exact exchange and non-local van der Waals/dispersion (vdW) interactions is crucial for predicting a microscopic structure of ambient liquid water that quantitatively agrees with experiment. In this work, we have used the local structure index (LSI) order parameter to analyse the local structure in such highly accurate ab initio liquid water. At ambient conditions, the LSI probability distribution, P(I ), was unimodal with most water molecules characterised by more disordered high-density-like local environments. With thermal excitations removed, the resultant bimodal P(I ) in the inherent potential energy surface (IPES) exhibited a 3:1 ratio between high-density- and low-density-like molecules, with the latter forming small connected clusters amid the predominant population. By considering the spatial correlations and hydrogen bond network topologies among water molecules with the same LSI identities, we demonstrate that the signatures o...
Elliptic fibrations on a generic Jacobian Kummer surface

We describe all the elliptic fibrations with section on the Kummer surface X of the Jacobian of a very general curve C of genus 2 over an algebraically closed field of characteristic 0, modulo the automorphism group of X and the symmetric group on the Weierstrass points of C. In particular, we compute elliptic parameters and Weierstrass equations for the 25 different fibrations and analyze the reducible fibers and Mordell-Weil lattices. This answers completely a question posed by Kuwata and Shioda in 2008.
Improved opacity expansion for medium-induced parton splitting

Abstract We revisit the calculation of the medium-induced gluon radiative spectrum and propose a novel expansion scheme that encompasses the two known analytic limits: i) the high frequency regime dominated by a single hard scattering that corresponds to the leading order in the standard opacity expansion, ii) the low frequency regime that is dominated by multiple soft scatterings. Our approach is based on expanding around the harmonic oscillator instead of vacuum in the leading logarithmic approximation. We compute the first two orders in this improved opacity expansion and show that they account for the aforementioned limits.
A comparative study of structural similarity and regularization for joint inverse problems governed by PDEs

Joint inversion refers to the simultaneous inference of multiple parameter fields from observations of systems governed by single or multiple forward models. In many cases these parameter fields reflect different attributes of a single medium and are thus spatially correlated or structurally similar. By imposing prior information on their spatial correlations via a joint regularization term, we seek to improve the reconstruction of the parameter fields relative to inversion for each field independently. One of the main challenges is to devise a joint regularization functional that conveys the spatial correlations or structural similarity between the fields while at the same time permitting scalable and efficient solvers for the joint inverse problem. We describe several joint regularizations that are motivated by these goals: a cross-gradient and a normalized cross-gradient structural similarity term, the vectorial total variation, and a joint regularization based on the nuclear norm of the gradients. Based on numerical results from three classes of inverse problems with piecewise-homogeneous parameter fields, we conclude that the vectorial total variation functional is preferable to the other methods considered. Besides resulting in good reconstructions in all experiments, it allows for scalable, efficient solvers for joint inverse problems governed by PDE forward models.
Spinneret: Aiding Creative Ideation through Non-Obvious Concept Associations

Mind mapping is a popular way to explore a design space in creative thinking exercises, allowing users to form associations between concepts. Yet, most existing digital tools for mind mapping focus on authoring and organization, with little support for addressing the challenges of mind mapping such as stagnation and design fixation. We present Spinneret, a functional approach to aid mind mapping by providing suggestions based on a knowledge graph. Spinneret uses biased random walks to explore the knowledge graph in the neighborhood of an existing concept node in the mind map, and provides "suggestions" for the user to add to the mind map. A comparative study with a baseline mind-mapping tool reveals that participants created more diverse and distinct concepts with Spinneret, and reported that the suggestions inspired them to think of ideas they would otherwise not have explored.
Supernova constraints on higher-dimensional cosmology with a phantom field

We use observational data on the magnitude-redshift relation for Type Ia supernovae (SNeIa) together with constraints on the ages of the oldest stars to rule out a higher-dimensional extension of General Relativity with a negative kinetic energy scalar field. This theory is of considerable physical interest because it produces accelerated expansion at both early and late times with a single new field, as in quintessential inflation scenarios. It is also of mathematical interest because it is characterized by an analytic expression for the macroscopic scale factor a(t). We show that cosmological solutions of this theory can be usefully parametrized by a single quantity, the lookback time τtr corresponding to the transition from deceleration to acceleration. Supernovae data from the recently released Supernova Cosmology Project (SCP) Union 2.1 compilation single out a narrow range of values for τtr. In the context of the theory, however, these same values of τtr imply that the universe is much older than th...
Theory of plasmonic waves on a chain of metallic nanoparticles in a liquid crystalline host

Using the quasistatic approximation, we calculate the dispersion relations for plasmonic waves along a chain of metallic nanoparticles when the host is a nematic or a cholesteric liquid crystal (NLC or CLC). If the director of the NLC is perpendicular to the chain, the doubly degenerate transverse (T) branches split into two linearly polarized branches. For a CLC with a twist axis parallel to the chain, the two T branches again split, but are no longer linearly polarized. We illustrate these results numerically by calculating the dispersion relations for Drude metal particles in either an NLC or a CLC.
Unexpected local minima in the width complexes for knots

In [9], Schultens defines the width complex for a knot in order to understand the different positions a knot can occupy in S 3 and the isotopies between these positions. She poses several questions about these width complexes; in particular, she asks whether the width complex for a knot can have local minima that are not global minima. In this paper, we find an embedding of the unknot 01 that is a local minimum but not a global minimum in the width complex for 01 , resolving a question of Scharlemann. We use this embedding to exhibit for any knot K infinitely many distinct local minima that are not global minima of the width complex for K .
Diophantine properties of continued fractions on the Heisenberg group

We provide several results on the Diophantine properties of continued fractions on the Heisenberg group, many of which are analogous to classical results for real continued fractions. In particular, we show an analog of Khinchin’s theorem and we also show that convergents are the best rational approximations up to a constant factor of the denominator.