cssi/SciSciGPT-SciSciNet huggingface dataset:
THE KEPLER DICHOTOMY IN PLANETARY DISKS: LINKING KEPLER OBSERVABLES TO SIMULATIONS OF LATE-STAGE PLANET FORMATION

NASA's Kepler Mission uncovered a wealth of planetary systems, many with planets on short-period orbits. These short-period systems reside around 50% of Sun-like stars and are similarly prevalent around M dwarfs. Their formation and subsequent evolution is the subject of active debate. In this paper, we simulate late-stage, in situ planet formation across a grid of planetesimal disks with varying surface density profiles and total mass. We compare simulation results with observable characteristics of the Kepler sample. We identify mixture models with different primordial planetesimal disk properties that self-consistently recover the multiplicity, radius, period and period ratio, and duration ratio distributions of the Kepler planets. We draw three main conclusions. (1) We favor a "frozen-in" narrative for systems of short-period planets, in which they are stable over long timescales, as opposed to metastable. (2) The "Kepler dichotomy," an observed phenomenon of the Kepler sample wherein the architectures of planetary systems appear to either vary significantly or have multiple modes, can naturally be explained by formation within planetesimal disks with varying surface density profiles. Finally, (3) we quantify the nature of the "Kepler dichotomy" for both GK stars and M dwarfs, and find that it varies with stellar type. While the mode of planet formation that accounts for high multiplicity systems occurs in 24% ± 7% of planetary systems orbiting GK stars, it occurs in 63% ± 16% of planetary systems orbiting M dwarfs.
Anomalous feedback and negative domain wall resistance

Magnetic induction can be regarded as a negative feedback effect, where the motive-force opposes the change of magnetic flux that generates the motive-force. In artificial electromagnetics emerging from spintronics, however, this is not necessarily the case. By studying the current-induced domain wall dynamics in a cylindrical nanowire, we show that the spin motive-force exerting on electrons can either oppose or support the applied current that drives the domain wall. The switching into the anomalous feedback regime occurs when the strength of the dissipative torque β is about twice the value of the Gilbert damping constant α. The anomalous feedback manifests as a negative domain wall resistance, which has an analogy with the water turbine.
Quantum Zeno effect as a topological phase transition in full counting statistics and spin noise spectroscopy

When the interaction of a quantum system with a detector changes from weak to strong-coupling limits, the system experiences a transition from the regime with quantum-mechanical coherent oscillations to the regime with a frozen dynamics. In addition to this quantum Zeno transition, we show that the full counting statistics of detector signal events experiences a topological phase transition at the boundary between two phases at intermediate coupling of a quantum system to the detector. We demonstrate that this transition belongs to the class of topological phase transitions that can be classified by elements of the braid group. We predict that this transition can be explored experimentally by means of the optical spin noise spectroscopy.
Geometric-phase-effect tunnel-splitting oscillations in single-molecule magnets with fourth-order anisotropy induced by orthorhombic distortion

We analyze the interference between tunneling paths that occurs for a spin system with both fourth-order and second-order transverse anisotropy. Using an instanton approach, we find that as the strength of the second-order transverse anisotropy is increased, the tunnel splitting is modulated, with zeros occurring periodically. This effect results from the interference of four tunneling paths connecting easy-axis spin orientations and occurs in the absence of any magnetic field.
GEOMETRIC VARIATIONS OF THE BOLTZMANN ENTROPY

We perform a calculation of the first and second order infinitesimal variations, with respect to energy, of the Boltzmann entropy of constant energy hypersurfaces of a system with a finite number of degrees of freedom. We comment on the stability interpretation of the second variation in this framework.
Bridge number and integral Dehn surgery

R and give a lower bound on the bridge number of K n with respect to Heegaard splittings of M; as a function of n, the genus of the splitting, and the catching surface Q. As a result, the bridge number of K n tends to infinity with n. In application, we look at a family of knotsfK n g found by Teragaito that live in a small Seifert fiber space M and where each K n admits a Dehn surgery giving S 3 . We show that the bridge number of K n with respect to any genus-2 Heegaard splitting of M tends to infinity with n. This contrasts with other work of the authors as well as with the conjectured picture for knots in lens spaces that admit Dehn surgeries giving S 3 .
Hunting for primordial non-Gaussianity in the cosmic microwave background

Since the first limit on the (local) primordial non-Gaussianity parameter, fNL, was obtained from the Cosmic Background Explorer (COBE) data in 2002, observations of the cosmic microwave background (CMB) have been playing a central role in constraining the amplitudes of various forms of non-Gaussianity in primordial fluctuations. The current 68% limit from the 7-year data of the Wilkinson Microwave Anisotropy Probe (WMAP) is fNL = 32 ? 21, and the Planck satellite is expected to reduce the uncertainty by a factor of 4 in a few years from now. If fNL 1 is found by Planck with high statistical significance, all single-field models of inflation would be ruled out. Moreover, if the Planck satellite finds fNL ~ 30, then it would be able to test a broad class of multi-field models using the 4-point function (trispectrum) test of ?NL ? (6fNL/5)2. In this paper, we review the methods (optimal estimator), results (WMAP 7-year) and challenges (secondary anisotropy, second-order effect and foreground) of measuring primordial non-Gaussianity from the CMB data, present a science case for the trispectrum and conclude with future prospects.
Nilsequences, null-sequences, and multiple correlation sequences

A (d-parameter) basic nilsequence is a sequence of the form ψ(n) = f(ax), n ∈ Z, where x is a point of a compact nilmanifold X, a is a translation on X, and f ∈ C(X); a nilsequence is a uniform limit of basic nilsequences. If X = G/Γ be a compact nilmanifold, Y is a subnilmanifold of X, (g(n))n∈Zd is a polynomial sequence in G, and f ∈ C(X), we show that the sequence φ(n) = ∫ g(n)Y f is the sum of a basic nilsequence and a sequence that converges to zero in uniform density (a null-sequence). We also show that an integral of a family of nilsequences is a nilsequence plus a null-sequence. We deduce that for any invertible finite measure preserving system (W,B, μ, T ), polynomials p1, . . . , pk:Z d −→ Z, and sets A1, . . . , Ak ∈ B, the sequence φ(n) = μ(T 1A1 ∩ . . .∩T kAk), n ∈ Z , is the sum of a nilsequence and a null-sequence.
Neural Twins Talk

Inspired by how the human brain employs more neural pathways when increasing the focus on a subject, we introduce a novel twin cascaded attention model that outperforms a state-of-the-art image captioning model that was originally implemented using one channel of attention for the visual grounding task. Visual grounding ensures the existence of words in the caption sentence that are grounded into a particular region in the input image. After a deep learning model is trained on visual grounding task, the model employs the learned patterns regarding the visual grounding and the order of objects in the caption sentences, when generating captions. We report the results of our experiments in three image captioning tasks on the COCO dataset. The results are reported using standard image captioning metrics to show the improvements achieved by our model over the previous image captioning model. The results gathered from our experiments suggest that employing more parallel attention pathways in a deep neural network leads to higher performance. Our implementation of NTT is publicly available at: https://github.com/zanyarz/NeuralTwinsTalk.
IDENTIFICATION OF OUTFLOWS AND CANDIDATE DUAL ACTIVE GALACTIC NUCLEI IN SDSS QUASARS AT z = 0.8-1.6

We present a sample of 131 quasars from the Sloan Digital Sky Survey at redshifts 0.8 < z < 1.6 with double peaks in either of the high-ionization narrow emission lines [Ne V] λ3426 or [Ne III] λ3869. These sources were selected with the intention of identifying high-redshift analogs of the z < 0.8 active galactic nuclei (AGNs) with double-peaked [O III] λ5007 lines, which might represent AGN outflows or dual AGNs. Lines of high ionization potential are believed to originate in the inner, highly photoionized portion of the narrow line region, and we exploit this assumption to investigate the possible kinematic origins of the double-peaked lines. For comparison, we measure the [Ne V] λ3426 and [Ne III] λ3869 double peaks in low-redshift (z < 0.8) [O III]-selected sources. We find that [Ne V] λ3426 and [Ne III] λ3869 show a correlation between line splitting and line width similar to that of [O III] λ5007 in other studies, and the velocity splittings are correlated with the quasar Eddington ratio. These results suggest an outflow origin for at least a subset of the double peaks, allowing us to study the high-ionization gas kinematics around quasars. However, we find that a non-negligible fraction of our sample show no evidence for an ionization stratification. For these sources, the outflow scenario is less compelling, leaving the dual AGN scenario as a viable possibility. Finally, we find that our sample shows an anti-correlation between the velocity-offset ratio and luminosity ratio of the components, which is a potential dynamical argument for the presence of dual AGNs. Therefore, this study serves as a first attempt at extending the selection of candidate dual AGNs to higher redshifts.
On Estimation of Contamination from Hydrogen Cyanide in Carbon Monoxide Line-intensity Mapping

Line-intensity mapping surveys probe large-scale structure through spatial variations in molecular line emission from a population of unresolved cosmological sources. Future such surveys of carbon monoxide line emission, specifically the CO(1-0) line, face potential contamination from a disjointed population of sources emitting in a hydrogen cyanide emission line, HCN(1-0). This paper explores the potential range of the strength of HCN emission and its effect on the CO auto power spectrum, using simulations with an empirical model of the CO/HCN–halo connection. We find that effects on the observed CO power spectrum depend on modeling assumptions but are very small for our fiducial model, which is based on current understanding of the galaxy–halo connection. Given the fiducial model, we expect the bias in overall CO detection significance due to HCN to be less than 1%.
Deceleration of Alpha Particles in the Solar Wind by Instabilities and the Rotational Force: Implications for Heating, Azimuthal Flow, and the Parker Spiral Magnetic Field

Protons and alpha particles in the fast solar wind are only weakly collisional and exhibit a number of nonequilibrium features, including relative drifts between particle species. Two non-collisional mechanisms have been proposed for limiting differential flow between alpha particles and protons: plasma instabilities and the rotational force. Both mechanisms decelerate the alpha particles. In this paper, we derive an analytic expression for the rate Qflow at which energy is released by alpha-particle deceleration, accounting for azimuthal flow and conservation of total momentum. We show that instabilities control the deceleration of alpha particles at r rcrit , where r 2.5 AU crit  in the fast solar wind in the ecliptic plane. We find that Qflow is positive at r rcrit < and Q 0 flow = at r rcrit  , consistent with the previous finding that the rotational force does not lead to a release of energy. We compare the value of Qflow at r rcrit < with empirical heating rates for protons and alpha particles, denoted Qp and Qa, deduced from in situ measurements of fast-wind streams from the Helios and Ulysses spacecraft. We find that Qflow exceeds Qa at r 1A U < , and that Q Q flow p decreases with increasing distance from the Sun from a value of about one at r = 0.29–0.42 AU to about 1/4 at 1 AU. We conclude that the continuous energy input from alpha-particle deceleration at r rcrit < makes an important contribution to the heating of the fast solar wind. We also discuss the implications of the alpha-particle drift for the azimuthal flow velocities of the ions and for the Parker spiral magnetic field.
The Borg Cube Simulation: Cosmological Hydrodynamics with CRK-SPH

A challenging requirement posed by next-generation observations is a firm theoretical grasp of the impact of baryons on structure formation. Cosmological hydrodynamic simulations modeling gas physics are vital in this regard. A high degree of modeling flexibility exists in this space making it important to explore a range of methods in order to gauge the accuracy of simulation predictions. We present results from the first cosmological simulation using Conservative Reproducing Kernel Smoothed Particle Hydrodynamics (CRK-SPH). We employ two simulations: one evolved purely under gravity and the other with non-radiative hydrodynamics. Each contains 2x2304^3 cold dark matter plus baryon particles in an 800 Mpc/h box. We compare statistics to previous non-radiative simulations including power spectra, mass functions, baryon fractions, and concentration. We find self-similar radial profiles of gas temperature, entropy, and pressure and show that a simple analytic model recovers these results to better than 40% over two orders of magnitude in mass. We quantify the level of non-thermal pressure support in halos and demonstrate that hydrostatic mass estimates are biased low by 24% (10%) for halos of mass 10^15 (10^13) Msun/h. We compute angular power spectra for the thermal and kinematic Sunyaev-Zel'dovich effects and find good agreement with the low-l Planck measurements. Finally, artificial scattering between particles of unequal mass is shown to have a large impact on the gravity-only run and we highlight the importance of better understanding this issue in hydrodynamic applications. This is the first in a simulation campaign using CRK-SPH with future work including subresolution gas treatments.
Network Positions in Active Learning Environments in Physics.

Social network analysis on students in different active-learning introductory physics courses shows that in all courses there is a late-term tendency of students to form coherent subgroups that communicate actively among themselves but have few interposition links.
Bounding the greedy strategy in finite-horizon string optimization

We consider an optimization problem where the decision variable is a string of bounded length. For some time there has been an interest in bounding the performance of the greedy strategy for this problem. Here, we provide weakened sufficient conditions for the greedy strategy to be bounded by a factor of (1 − (1 − 1/K)K), where K is the optimization horizon length. Specifically, we introduce the notions of K-submodularity and K-GO-concavity, which together are sufficient for this bound to hold. By introducing a notion of curvature η ∈ (0; 1), we prove an even tighter bound with the factor (1/η)(1−e−η). Finally, we illustrate the strength of our results by considering two example applications. We show that our results provide weaker conditions on parameter values in these applications than in previous results.
Powers of the Vandermonde determinant, Schur functions and recursive formulas

The decomposition of an even power of the Vandermonde determinant in terms of the basis of Schur functions matches the decomposition of the Laughlin wavefunction as a linear combination of Slater wavefunctions and thus contributes to the understanding of the quantum Hall effect. We investigate several combinatorial properties of the coefficients in the decomposition. In particular, we give recursive formulas for the coefficient of the Schur function sμ in the decomposition of an even power of the Vandermonde determinant in n + 1 variables in terms of the coefficient of the Schur function sλ in the decomposition of the same even power of the Vandermonde determinant in n variables if the Young diagram of μ is obtained from the Young diagram of λ by adding a tetris type shape to the top or to the left.
Killing tensors and symmetries

A new method is presented for finding Killing tensors in spacetimes with symmetries. The method is used to find all the Killing tensors of Melvin's magnetic universe and the Schwarzschild vacuum. We show that they are all trivial. The method requires less computation than solving the full Killing tensor equations directly, and it can be used even when the spacetime is not algebraically special.
Optical and electronic properties of doped p -type CuI: Explanation of transparent conductivity from first principles

We report the properties of the reported transparent conductor CuI, including the effect of heavy $p$-type doping. The results, based on first-principles calculations, include an analysis of the electronic structure and calculations of optical and dielectric properties. We find that the origin of the favorable transparent conducting behavior lies in the absence in the visible of strong interband transitions between deeper valence bands and states at the valence-band maximum that become empty with $p$-type doping. Instead, strong interband transitions to the valence-band maximum are concentrated in the infrared with energies below 1.3 eV. This is contrast to the valence bands of many wide-band-gap materials. Turning to the mobility, we find that the states at the valence-band maximum are relatively dispersive. This originates from their antibonding Cu $d$--I $p$ character. We find a modest enhancement of the Born effective charges relative to nominal values, leading to a dielectric constant $\ensuremath{\varepsilon}(0)=6.3$. This is sufficiently large to reduce ionized impurity scattering, leading to the expectation that the properties of CuI can still be significantly improved through sample quality.
Algebraic error estimates for the stochastic homogenization of uniformly parabolic equations

We establish an algebraic error estimate for the stochastic homogenization of fully nonlinear, uniformly parabolic equations in stationary ergodic spatiotemporal media. The approach is similar to that of Armstrong and Smart in the study of quantitative stochastic homogenization of uniformly elliptic equations.
Theory of electron–phonon–dislon interacting system—toward a quantized theory of dislocations

MIT S3TEC Energy Research Frontier Center of the Department of Energy (United States. Department of Energy. Office of Basic Energy Sciences r award No. DE-SC0001299/DEFG02-09ER46577)
A hybrid memory kernel approach for condensed phase non-adiabatic dynamics.

The spin-boson model is a simplified Hamiltonian often used to study non-adiabatic dynamics in large condensed phase systems, even though it has not been solved in a fully analytic fashion. Herein, we present an exact analytic expression for the dynamics of the spin-boson model in the infinitely slow-bath limit and generalize it to approximate dynamics for faster baths. We achieve the latter by developing a hybrid approach that combines the exact slow-bath result with the popular non-interacting blip approximation (NIBA) method to generate a memory kernel that is formally exact to second-order in the diabatic coupling but also contains higher-order contributions approximated from the second-order term alone. This kernel has the same computational complexity as the NIBA, but is found to yield dramatically superior dynamics in regimes where the NIBA breaks down—such as systems with large diabatic coupling or energy bias. This indicates that this hybrid approach could be used to cheaply incorporate higher-order effects into second-order methods and could potentially be generalized to develop alternate kernel resummation schemes.
Assembling a ring-shaped crystal in a microfabricated surface ion trap

Using an ensemble of ions to register one qubit each, the so-called trapped ion quantum computer could serve as a universal computing machine, with the advantages in speed and power that a quantum device could bring. However, actually making a suitably large trap is far from trivial. The authors microfabricate a ring-shaped ion trap and demonstrate its storage of 400 evenly spaced calcium ions. Its circularly symmetric trapping potential furthermore enables fundamental experiments on e.g. simulated Hawking radiation or the Aharonov-Bohm effect.
Spacetime foam: from entropy and holography to infinite statistics and nonlocality

Due to quantum fluctuations, spacetime is foamy on small scales. The degree of foaminess is found to be consistent with holography, a principle prefigured in the physics of black hole entropy. It has bearing on the ultimate accuracies of clocks and measurements and the physics of quantum computation. Consistent with existing archived data on active galactic nuclei from the Hubble Space Telescope, the application of the holographic spacetime foam model to cosmology requires the existence of dark energy which, we argue, is composed of an enormous number of inert “particles” of extremely long wavelength. We suggest that these “particles” obey infinite statistics in which all representations of the particle permutation group can occur, and that the nonlocality present in systems obeying infinite statistics may be related to the nonlocality present in holographic theories. We also propose to detect spacetime foam by looking for halos in the images of distant quasars, and argue that it does not modify the GZK cutoff in the ultra-high energy cosmic ray spectrum and its contributions to time-offlight differences of high energy gamma rays from distant GRB are too small to be detectable.
On the Origin of Planetary Nebula K648 in Globular Cluster M15

We examine two scenarios for formation of the planetary nebula K648: a prompt scenario in which the planetary nebula is ejected and formed immediately after a helium shell flash and a delayed scenario in which a third dredge-up occurs and the envelope is ejected during the following interpulse phase. We present models of both scenarios and find that each can produce K648-like systems. We suggest that the prompt scenario is more favorable but cannot rule out the delayed scenario.
Logarithmic intertwining operators and genus-one correlation functions

This is the first of two papers in which we study the modular invariance of pseudotraces of logarithmic intertwining operators. We construct and study genus-one correlation functions for logarithmic intertwining operators among generalized modules over a positive-energy and C2-cofinite vertex operator algebra V. We consider grading-restricted generalized V-modules which admit a right action of some associative algebra P, and intertwining operators among such modules which commute with the action of P (P-intertwining operators). We obtain duality properties, i.e. suitable associativity and commutativity properties, for P-intertwining operators. Using pseudotraces introduced by Miyamoto and studied by Arike, we define formal q-traces of products of P-intertwining operators, and obtain certain identities for these formal series. This allows us to show that the formal q-traces satisfy a system of differential equations with regular singular points, and therefore are absolutely convergent in a suitable region and can be extended to yield multivalued analytic functions, called genus-one correlation functions. Furthermore, we show that the space of solutions of these differential equations is invariant under the action of the modular group.
Active Cryovolcanism on Europa

Evidence for plumes of water on Europa has previously been found using the Hubble Space Telescope using two different observing techniques. Roth et al. found line emission from the dissociation products of water. Sparks et al. found evidence for off-limb continuum absorption as Europa transited Jupiter. Here, we present a new transit observation of Europa that shows a second event at the same location as a previous plume candidate from Sparks et al., raising the possibility of a consistently active source of erupting material on Europa. This conclusion is bolstered by comparison with a nighttime thermal image from the Galileo Photopolarimeter-Radiometer that shows a thermal anomaly at the same location, within the uncertainties. The anomaly has the highest observed brightness temperature on the Europa nightside. If heat flow from a subsurface liquid water reservoir causes the thermal anomaly, its depth is ≈1.8–2 km, under simple modeling assumptions, consistent with scenarios in which a liquid water reservoir has formed within a thick ice shell. Models that favor thin regions within the ice shell that connect directly to the ocean, however, cannot be excluded, nor modifications to surface thermal inertia by subsurface activity. Alternatively, vapor deposition surrounding an active vent could increase the thermal inertia of the surface and cause the thermal anomaly. This candidate plume region may offer a promising location for an initial characterization of Europa's internal water and ice and for seeking evidence of Europa's habitability.
How to Light it Up: Simulating Ram-pressure Stripped X-ray Bright Tails

Some tails of ram-pressure stripped galaxies are detected in H I, some in H{alpha}, and some in X-ray (but never in all three so far). We use numerical simulations to probe the conditions for the production of X-ray bright tails, demonstrating that the primary requirement is a high-pressure intracluster medium (ICM). This is because the stripped tail is mostly in pressure equilibrium with the ICM, but mixing leaves it with densities and temperatures intermediate between the cold gas in the disk and the hot ICM. Given a high enough ICM pressure, this mixed gas lies in the X-ray bright region of the phase diagram. We compare the simulations to observations of the ram-pressure stripped tail of ESO 137-001, showing excellent agreement in the total measured X-ray and H{alpha} emission and non-flaring morphology of the tail, and consistent H I measurements. Using these comparisons, we constrain the level of mixing and efficiency of heat conduction in the ICM.
Spin-orbit torque and spin pumping in YIG/Pt with interfacial insertion layers

We experimentally investigate spin-orbit torque and spin pumping in Y3Fe5O12 (YIG)/Pt bilayers with ultrathin insertion layers at the interface. An insertion layer of Cu suppresses both spin-orbit torque and spin pumping, whereas an insertion layer of Ni80Fe20 (permalloy, Py) enhances them in a quantitatively consistent manner with the reciprocity of the two spin transmission processes. However, we observe a large enhancement of Gilbert damping with the insertion of Py that cannot be accounted for solely by spin pumping, suggesting significant spin-memory loss due to the interfacial magnetic layer. Our findings indicate that the magnetization at the YIG-metal interface strongly influences the transmission and depolarization of pure spin current.
Near-Optimal Phase Retrieval of Sparse Vectors

In many areas of imaging science, it is difficult to measure the phase of linear measurements. As such, one often wishes to reconstruct a signal from intensity measurements, that is, perform phase retrieval. In several applications the signal in question is believed to be sparse. In this paper, we use ideas from the recently developed polarization method for phase retrieval and provide an algorithm that is guaranteed to recover a sparse signal from a number of phaseless linear measurements that scales linearly with the sparsity of the signal (up to logarithmic factors). This is particularly remarkable since it is known that a certain popular class of convex methods is not able to perform recovery unless the number of measurements scales with the square of the sparsity of the signal. This is a shorter version of a more complete publication that will appear elsewhere. 1
Sowing Black Hole Seeds: Direct Collapse Black Hole Formation with Realistic Lyman–Werner Radiation in Cosmological Simulations

We study the birth of supermassive black holes from the direct collapse process and characterize the sites where these black hole seeds form. In the pre-reionization epoch, molecular hydrogen (H$_2$) is an efficient coolant, causing gas to fragment and form Population III stars, but Lyman-Werner radiation can suppress H$_2$ formation and allow gas to collapse directly into a massive black hole. The critical flux required to inhibit H$_2$ formation, $J_{\rm crit}$, is hotly debated, largely due to the uncertainties in the source radiation spectrum, H$_2$ self-shielding, and collisional dissociation rates. Here, we test the power of the direct collapse model in a self-consistent, time-dependant, non-uniform Lyman-Werner radiation field -- the first time such has been done in a cosmological volume -- using an updated version of the SPH+N-body tree code Gasoline with H$_2$ non-equilibrium abundance tracking, H$_2$ cooling, and a modern SPH implementation. We vary $J_{\rm crit} $ from $30$ to $10^3$ in units of $J_{21}$ to study how this parameter impacts the number of seed black holes and the type of galaxies which host them. We focus on black hole formation as a function of environment, halo mass, metallicity, and proximity of the Lyman-Werner source. Massive black hole seeds form more abundantly with lower $J_{\rm crit}$ thresholds, but regardless of $J_{\rm crit}$, these seeds typically form in halos that have recently begun star formation. Our results do not confirm the proposed atomic cooling halo pair scenario; rather black hole seeds predominantly form in low-metallicity pockets of halos which already host star formation.
Dissipation-induced topological transitions in continuous Weyl materials

This paper investigates the impact of dissipation on continuous topological Weyl materials, comparing two possible realizations of a Weyl-point dispersion based on breaking time-reversal symmetry or inversion symmetry.
Coding Schemes for Securing Cyber-Physical Systems Against Stealthy Data Injection Attacks

This paper considers a method of coding the sensor outputs in order to detect stealthy false data injection attacks. An intelligent attacker can design a sequence of data injection to sensors and actuators that pass the state estimator and statistical fault detector, based on knowledge of the system parameters. To stay undetected, the injected data should increase the state estimation errors while keep the estimation residues small. We employ a coding matrix to change the original sensor outputs to increase the estimation residues under intelligent data injection attacks. This is a low-cost method compared with encryption schemes over all sensor measurements in communication networks. We show the conditions of a feasible coding matrix under the assumption that the attacker does not have knowledge of the exact coding matrix. An algorithm is developed to compute a feasible coding matrix, and, we show that in general, multiple feasible coding matrices exist. To defend against attackers who estimates the coding matrix via sensor and actuator measurements, time-varying coding matrices are designed according to the detection requirements. A heuristic algorithm to decide the time length of updating a coding matrix is then proposed.
Crowdsampling the Plenoptic Function

Many popular tourist landmarks are captured in a multitude of online, public photos. These photos represent a sparse and unstructured sampling of the plenoptic function for a particular scene. In this paper, we present a new approach to novel view synthesis under time-varying illumination from such data. Our approach builds on the recent multi-plane image (MPI) format for representing local light fields under fixed viewing conditions. We introduce a new DeepMPI representation, motivated by observations on the sparsity structure of the plenoptic function, that allows for real-time synthesis of photorealistic views that are continuous in both space and across changes in lighting. Our method can synthesize the same compelling parallax and view-dependent effects as previous MPI methods, while simultaneously interpolating along changes in reflectance and illumination with time. We show how to learn a model of these effects in an unsupervised way from an unstructured collection of photos without temporal registration, demonstrating significant improvements over recent work in neural rendering. More information can be found at crowdsampling.io.
Identification of biological correlates associated with respiratory failure in COVID-19.

Coronavirus disease 2019 (COVID-19) is a global public health concern. Recently, a genome-wide association study (GWAS) was performed with participants recruited from Italy and Spain by an international consortium group. Summary GWAS statistics for 1610 patients with COVID-19 respiratory failure and 2205 controls were downloaded. In the current study, we analyzed the summary statistics with the information of loci and p-values for 8,582,968 single-nucleotide polymorphisms (SNPs), using gene ontology analysis to determine the top biological processes implicated in respiratory failure in COVID-19 patients. We considered the top 708 SNPs, using a p-value cutoff of 5 × 10− 5, which were mapped to the nearest genes, leading to 144 unique genes. The list of genes was input into a curated database to conduct gene ontology and protein-protein interaction (PPI) analyses. The top ranked biological processes were wound healing, epithelial structure maintenance, muscle system processes, and cardiac-relevant biological processes with a false discovery rate < 0.05. In the PPI analysis, the largest connected network consisted of 8 genes. Through a literature search, 7 out of the 8 gene products were found to be implicated in both pulmonary and cardiac diseases. Gene ontology and PPI analyses identified cardio-pulmonary processes that may partially explain the risk of respiratory failure in COVID-19 patients.
On Borel Anosov representations in even dimensions

We prove that a word hyperbolic group which admits a $P_{2q+1}$-Anosov representation into $\mathsf{PGL}(4q+2, \mathbb{R})$ contains a finite-index subgroup which is either free or a surface group. As a consequence, we give an affirmative answer to Sambarino's question for Borel Anosov representations into $\mathsf{SL}(4q+2,\mathbb{R})$.
Pilot Study of Electronic Nicotine Delivery Systems (ENDS) Cessation Methods.

Currently, 7.6% of the U.S. young adults aged 18–24 years old use e-cigarettes. This study piloted three methods of Electronic Nicotine Delivery Systems (ENDS) cessation by measuring cessation rates, motivational techniques that contributed to cessation success, and participants’ changes after decreasing vape use. Participants were randomized into three study arms (nicotine replacement therapy (NRT) + behavioral support, vape-taper + behavioral support, self-guided) in a 1:1:1 ratio. All participants were invited to attend nine in-person or phone appointments over the 6-month study period. At 12 weeks, 3 of 7 (42.9%) participants in the NRT + behavioral support arm, 6 of 8 (75%) vape-taper + behavioral support arm, and 7 of 9 (77.8%) self-guided arm self-reported being vape-free and nicotine-free. At 6 months, 3 of 7 (42.9%) participants in the NRT + behavioral support arm, 6 of 8 (75%) vape-taper + behavioral support arm, and 4 of 9 (44.4%) self-guided arm self-reported being vape-free and nicotine-free. A challenge to quitting and remain quit is social pressures, but participants identified self-control and establishing new habits to be the best methods to overcome the desire to vape. Participants who received behavioral support and a vape-taper plan from pharmacists were more likely to be vape-free and nicotine-free at 6 months.
Towards the classification of rank-r $$ \mathcal{N} $$ = 2 SCFTs. Part I. Twisted partition function and central charge formulae

We derive explicit formulae to compute the a and c central charges of four dimensional $$ \mathcal{N} $$ = 2 superconformal field theories (SCFTs) directly from Coulomb branch related quantities. The formulae apply at arbitrary rank. We also discover general properties of the low-energy limit behavior of the flavor symmetry of $$ \mathcal{N} $$ = 2 SCFTs which culminate with our $$ \mathcal{N} $$ = 2 UV-IR simple flavor condition. This is done by determining precisely the relation between the integrand of the partition function of the topologically twisted version of the 4d $$ \mathcal{N} $$ = 2 SCFTs and the singular locus of their Coulomb branches. The techniques developed here are extensively applied to many rank-2 SCFTs, including new ones, in a companion paper. This manuscript is dedicated to the memory of Rayshard Brooks, George Floyd, Breonna Taylor and the countless black lives taken by US police forces and still awaiting justice. Our hearts are with our colleagues of color who suffer daily the consequences of this racist world.
Scaled Atomic-Layer-Deposited Indium Oxide Nanometer Transistors With Maximum Drain Current Exceeding 2 A/mm at Drain Voltage of 0.7 V

In this work, we demonstrate scaled back-end-of-line (BEOL) compatible indium oxide (In2O3) transistors by atomic layer deposition (ALD) with channel thickness (Tch) of 1.0-1.5 nm, channel length (Lch) down to 40 nm, and equivalent oxide thickness (EOT) of 2.1 nm, with record high drain current of 2.0 A/mm at VDS of 0.7 V among all oxide semiconductors. Enhancement-mode In2O3 transistors with ID over 1.0 A/mm at VDS of 1 V are also achieved by controlling the channel thickness down to 1.0 nm at atomic layer scale. Such high current density in a relatively low mobility amorphous oxide semiconductor is understood by the formation of high density 2D channel beyond $4\times 10^{13}$ /cm2 at HfO2/In2O3 oxide/oxide interface.
Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.
Strongly deleterious mutations are a primary determinant of extinction risk due to inbreeding depression

Human-driven habitat fragmentation and loss have led to a proliferation of small and isolated plant and animal populations with high risk of extinction. One of the main threats to extinction in these populations is inbreeding depression, which is primarily caused by recessive deleterious mutations becoming homozygous due to inbreeding. The typical approach for managing these populations is to maintain high genetic diversity, increasingly by translocating individuals from large populations to initiate a "genetic rescue." However, the limitations of this approach have recently been highlighted by the demise of the gray wolf population on Isle Royale, which declined to the brink of extinction soon after the arrival of a migrant from the large mainland wolf population. Here, we use a novel population genetic simulation framework to investigate the role of genetic diversity, deleterious variation, and demographic history in mediating extinction risk due to inbreeding depression in small populations. We show that, under realistic models of dominance, large populations harbor high levels of recessive strongly deleterious variation due to these mutations being hidden from selection in the heterozygous state. As a result, when large populations contract, they experience a substantially elevated risk of extinction after these strongly deleterious mutations are exposed by inbreeding. Moreover, we demonstrate that, although genetic rescue is broadly effective as a means to reduce extinction risk, its effectiveness can be greatly increased by drawing migrants from small or moderate-sized source populations rather than large source populations due to smaller populations harboring lower levels of recessive strongly deleterious variation. Our findings challenge the traditional conservation paradigm that focuses on maximizing genetic diversity in small populations in favor of a view that emphasizes minimizing strongly deleterious variation. These insights have important implications for managing small and isolated populations in the increasingly fragmented landscape of the Anthropocene.
Long-Term Effect Estimation with Surrogate Representation

There are many scenarios where short- and long-term causal effects of an intervention are different. For example, low-quality ads may increase short-term ad clicks but decrease the long-term revenue via reduced clicks. This work, therefore, studies the the problem of long-term effect where the outcome of primary interest, orprimary outcome, takes months or even years to accumulate. The observational study of long-term effect presents unique challenges. First, the confounding bias causes large estimation error and variance, which can further accumulate towards the prediction of primary outcomes. Second, short-term outcomes are often directly used as the proxy of the primary outcome, i.e., thesurrogate. Nevertheless, this method entails the strong surrogacy assumption that is often impractical. To tackle these challenges, we propose to build connections between long-term causal inference and sequential models in machine learning. This enables us to learnsurrogate representations that account for thetemporal unconfoundedness and circumvent the stringent surrogacy assumption by conditioning on the inferred time-varying confounders. Experimental results show that the proposed framework outperforms the state-of-the-art.
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images.

Abstract Background and Objective Non-invasively predicting the risk of cancer metastasis before surgery can play an essential role in determining which patients can benefit from neoadjuvant chemotherapy. This study aims to investigate and test the advantages of applying a random projection algorithm to develop and optimize a radiomics-based machine learning model to predict peritoneal metastasis in gastric cancer patients using a small and imbalanced computed tomography (CT) image dataset. Methods A retrospective dataset involving CT images acquired from 159 patients is assembled, including 121 and 38 cases with and without peritoneal metastasis, respectively. A computer-aided detection scheme is first applied to segment primary gastric tumor volumes and initially compute 315 image features. Then, five gradients boosting machine (GBM) models embedded with five feature selection methods (including random projection algorithm, principal component analysis, least absolute shrinkage, and selection operator, maximum relevance and minimum redundancy, and recursive feature elimination) along with a synthetic minority oversampling technique, are built to predict the risk of peritoneal metastasis. All GBM models are trained and tested using a leave-one-case-out cross-validation method. Results Results show that the GBM model embedded with a random projection algorithm yields a significantly higher prediction accuracy (71.2%) than the other four GBM models (p Conclusions This study demonstrates that CT images of the primary gastric tumors contain discriminatory information to predict the risk of peritoneal metastasis, and a random projection algorithm is a promising method to generate optimal feature vector, improving the performance of machine learning based prediction models.
H-phase precipitation and its effects on martensitic transformation in NiTi-Hf high-temperature shape memory alloys

Abstract Precipitate microstructure in the B2 parent phase is known to have profound impacts on the properties of NiTi-based high temperature shape memory alloys (HTSMAs), including the martensitic transformation (MT) start temperature M s , temperature- and stress-hysteresis, work output, dimensional stability and functional fatigue resistance. In order to understand the underlying mechanisms and hence to optimize aging heat treatments to achieve desired properties, we systematically investigate both the mechanical and chemical effects associated with nanoscale coherent precipitates on the behavior of MT. Using NiTi-Hf HTSMA as an example, we first study the equilibrium shape and stress and strain fields of an H-phase precipitate as a function of its size. We then determine quantitatively the elastic interaction energy between a precipitate and a nucleating martensitic particle consisting of either a single variant or multiple self-accommodating variants. In the meantime, we calculate the variation of concentration field around an H-phase precipitate during its growth. Finally, we quantify and compare the effects of the spatially inhomogeneous stress and concentration fields around an H-phase precipitate on M s . The results indicate that the former is the dominant factor for long aging times while latter is the dominant factor for short aging times. Since the model predicts M s as a function of aging temperature and time, it can aid the design of aging heat treatment schedule to achieve desired M s .
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?

Convolutional neural networks often dominate fully-connected counterparts in generalization performance, especially on image classification tasks. This is often explained in terms of \textquotedblleft better inductive bias.\textquotedblright\ However, this has not been made mathematically rigorous, and the hurdle is that the fully connected net can always simulate the convolutional net (for a fixed task). Thus the training algorithm plays a role. The current work describes a natural task on which a provable sample complexity gap can be shown, for standard training algorithms. We construct a single natural distribution on Rd×{±1} on which any orthogonal-invariant algorithm (i.e. fully-connected networks trained with most gradient-based methods from gaussian initialization) requires Ω(d2) samples to generalize while O(1) samples suffice for convolutional architectures. Furthermore, we demonstrate a single target function, learning which on all possible distributions leads to an O(1) vs Ω(d2/e) gap. The proof relies on the fact that SGD on fully-connected network is orthogonal equivariant. Similar results are achieved for l2 regression and adaptive training algorithms, e.g. Adam and AdaGrad, which are only permutation equivariant.
Q-Systems and Extensions of Completely Unitary Vertex Operator Algebras

We relate extensions of completely unitary VOAs and (commutative) Q-systems. As an application, we show that any unitary extension of a completely unitary VOA is completely unitary.