cssi/SciSciGPT-SciSciNet huggingface dataset:
TVQA+: Spatio-Temporal Grounding for Video Question Answering

We present the task of Spatio-Temporal Video Question Answering, which requires intelligent systems to simultaneously retrieve relevant moments and detect referenced visual concepts (people and objects) to answer natural language questions about videos. We first augment the TVQA dataset with 310.8K bounding boxes, linking depicted objects to visual concepts in questions and answers. We name this augmented version as TVQA+. We then propose Spatio-Temporal Answerer with Grounded Evidence (STAGE), a unified framework that grounds evidence in both spatial and temporal domains to answer questions about videos. Comprehensive experiments and analyses demonstrate the effectiveness of our framework and how the rich annotations in our TVQA+ dataset can contribute to the question answering task. Moreover, by performing this joint task, our model is able to produce insightful and interpretable spatio-temporal attention visualizations.
Optimal Randomized First-Order Methods for Least-Squares Problems

We provide an exact analysis of a class of randomized algorithms for solving overdetermined least-squares problems. We consider first-order methods, where the gradients are pre-conditioned by an approximation of the Hessian, based on a subspace embedding of the data matrix. This class of algorithms encompasses several randomized methods among the fastest solvers for least-squares problems. We focus on two classical embeddings, namely, Gaussian projections and subsampled randomized Hadamard transforms (SRHT). Our key technical innovation is the derivation of the limiting spectral density of SRHT embeddings. Leveraging this novel result, we derive the family of normalized orthogonal polynomials of the SRHT density and we find the optimal pre-conditioned first-order method along with its rate of convergence. Our analysis of Gaussian embeddings proceeds similarly, and leverages classical random matrix theory results. In particular, we show that for a given sketch size, SRHT embeddings exhibits a faster rate of convergence than Gaussian embeddings. Then, we propose a new algorithm by optimizing the computational complexity over the choice of the sketching dimension. To our knowledge, our resulting algorithm yields the best known complexity for solving least-squares problems with no condition number dependence.
First-principles study of magnetism and electric field effects in 2D systems

This review article provides a bird's-eye view of what first-principles based methods can contribute to next-generation device design and simulation. After a brief overview of methods and capabilities in the area, the authors focus on published work by their group since 2015 and current work on CrI3. The authors introduce both single- and dual-gate models in the framework of density functional theory and the constrained random phase approximation in estimating the Hubbard U for 2D systems vs their 3D counterparts. A wide range of systems, including graphene-based heterogeneous systems, transition metal dichalcogenides, and topological insulators, and a rich array of physical phenomena, including the macroscopic origin of polarization, field effects on magnetic order, interface state resonance induced peak in transmission coefficients, spin filtration, etc., are covered. For CrI3, the authors present their new results on bilayer systems such as the interplay between stacking and magnetic order, pressure dependence, and electric field induced magnetic phase transitions. The authors find that a bare bilayer CrI3, graphene|bilayer CrI3|graphene, hexagonal boron nitride (h-BN)|bilayer CrI3|h-BN, and h-BN|bilayer CrI3|graphene all have a different response at high field, while at small field, the difference is small except for graphene|bilayer CrI3|graphene. The authors conclude with discussion of some ongoing work and work planned in the near future, with the inclusion of further method development and applications.
Natural-abundance 17O NMR spectroscopy of magnetically aligned lipid nanodiscs

Natural-abundance 17O NMR experiments are used to investigate the hydrated water in magnetically aligned synthetic polymer based lipid-nanodiscs. Residual quadrupole couplings (RQCs) measured from the observed five 17O (central and satellite) transitions, and molecular dynamics simulations, are used to probe the ordering of water molecules across the lipid bilayer.
The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery Without Regularization

Low-rank matrix recovery is a fundamental problem in signal processing and machine learning. A recent very popular approach to recovering a low-rank matrix $\mathbf {X}$ is to factorize it as a product of two smaller matrices, i.e., $\mathbf {X}= \mathbf {U}\mathbf {V}^\top$ , and then optimize over $\mathbf {U}, \mathbf {V}$ instead of $\mathbf {X}$ . Despite the resulting non-convexity, recent results have shown that many factorized objective functions actually have benign global geometry—with no spurious local minima and satisfying the so-called strict saddle property—ensuring convergence to a global minimum for many local-search algorithms. Such results hold whenever the original objective function is restricted strongly convex and smooth. However, most of these results actually consider a modified cost function that includes a balancing regularizer. While useful for deriving theory, this balancing regularizer does not appear to be necessary in practice. In this work, we close this theory-practice gap by proving that the unaltered factorized non-convex problem, without the balancing regularizer, also has similar benign global geometry. Moreover, we also extend our theoretical results to the field of distributed optimization.
Compactness and finiteness theorems for rotationally symmetric self shrinkers

In this note, we first show a compactness theorem for rotationally symmetric self shrinkers of entropy less than 2, concluding that there are entropy minimizing self shrinkers diffeomorphic to $$S^1 \times S^{n-1}$$ for each $$n \ge 2$$ in the class of rotationally symmetric self shrinkers. Assuming extra symmetry, namely that the profile curve is convex, we remove the entropy assumption. Supposing the profile curve is additionally reflection symmetric we show there are only finitely many such shrinkers up to rigid motion.
Optomechanical lasers for inertial sensing.

We have developed an inertially sensitive optomechanical laser by combining a vertical-external-cavity surface-emitting laser (VECSEL) with a monolithic fused silica resonator. By placing the external cavity mirror of the VECSEL onto the optomechanical resonator test mass, we create a sensor where external accelerations are directly transcribed onto the lasing frequency. We developed a proof-of-principle laboratory prototype and observe test mass oscillations at the resonance frequency of the sensor through the VECSEL lasing frequency, 4.18±0.03Hz. In addition, we set up an ancillary heterodyne interferometer to track the motion of the mechanical oscillator’s test mass, observing a resonance of 4.194±0.004Hz. The interferometer measurements validate the VECSEL results, confirming the feasibility of using optomechanical lasers for inertial sensing.
Moral Status and the Architects of Principlism

In this article, we discuss Beauchamp and Childress's treatment of the issue of moral status. In particular, we (1) introduce the five different perspectives on moral status that Beauchamp and Childress consider in Principles of Biomedical Ethics and explain their alternative to those perspectives, (2) raise some critical questions about their approach, and (3) offer a different way to think about one of the five theories of moral status (the theory based on human properties) that is more in line with what we believe some of its leading advocates affirm.
Simultaneous max-cut is harder to approximate than max-cut

A systematic study of simultaneous optimization of constraint satisfaction problems was initiated by Bhangale et al. [ICALP, 2015]. The simplest such problem is the simultaneous Max-Cut. Bhangale et al. [SODA, 2018] gave a .878-minimum approximation algorithm for simultaneous Max-Cut which is almost optimal assuming the Unique Games Conjecture (UGC). For single instance Max-Cut, Goemans-Williamson [JACM, 1995] gave an αgw-approximation algorithm where αgw ≈ .87856720... which is optimal assuming the UGC.It was left open whether one can achieve an αgw-minimum approximation algorithm for simultaneous Max-Cut. We answer the question by showing that there exists an absolute constant e0 ≥ 10-5 such that it is NP-hard to get an (αgw - e0)-minimum approximation for simultaneous Max-Cut assuming the Unique Games Conjecture.
Influence of Crystalline and Shape Anisotropy on Electrochromic Modulation in Doped Semiconductor Nanocrystals

Localized surface plasmon resonance (LSPR) modulation appearing in the near-infrared range in doped semiconductor nanocrystals improves electrochromic performance. Although crystalline and shape an...
Simulations of divertor plasmas with inverse sheaths

The effect of strong electron emission from material surfaces has been proposed to form an “inverse sheath”: a region with a positive potential relative to the near-wall plasma which prevents the flow of ions to the wall [M. D. Campanell, “Negative plasma potential relative to electron-emitting surfaces,” Phys. Rev. E. 88, 033103 (2013); M. D. Campanell and M. V. Umansky, “Strongly emitting surfaces unable to float below plasma potential,” Phys. Rev. Lett. 116, 1–5 (2016); M. D. Campanell and G. R. Johnson, “Thermionic cooling of the target plasma to a sub-ev temperature,” Phys. Rev. Lett. 122, 1–5 (2019)]. We assess the viability of this regime in a tokamak device using the 2D edge plasma transport code UEDGE [T. Rognlien et al., “A fully implicit, time dependent 2-D fluid code for modeling tokamak edge plasmas,” J. Nucl. Mater. 196–198, 347–351 (1992)]. Since the UEDGE code does not consider the sheath region directly, we apply boundary conditions at the divertor targets which emulate the physics of both “standard” and “inverse” sheath regimes [R. Masline et al., “Influence of the inverse sheath on divertor plasma performance in tokamak edge plasma simulations,” Contrib. Plasma Phys. 60, e201900097 (2020)]. Using these boundary conditions, we perform scoping studies to assess plasma parameters near the target by varying the density at the core-edge interface. We observe a smooth transition in the resultant profiles of plasma parameters for the standard sheath, and a bifurcation across the simulation set for plasmas with an inverse sheath. The cause of this bifurcation is assessed by performing the parameter scan both with and without impurity radiation; we observe that the bifurcation persists in both cases, indicating that this bifurcation is caused by plasma recombination.
Dynamic Tuning of Near-Field Radiative Thermal Rectification

Taking advantage of phase-transition and reconfigurable metamaterials, dynamic control of nanoscale thermal modulation can be achieved through the near-field radiative thermal rectification devices. In this work, an active-tuning near-field thermal rectifier using reconfigurable phase-transition metamaterials is explored. The rectifier has two terminals separated by vacuum, working under a controllable operational temperature around the critical temperature of the phase-transition material VO2. One of the terminals is a stretchable structure made of PDMS thin film and grating consisting of various types of phase-transition material. The effects of various inclusion forms and all the related geometric parameters are well analyzed. The controllable nanoscale thermal modulation can be achieved and the ultrahigh rectification ratios of 23.7 and 19.8, the highest values ever predicted, can be obtained for two deformation scenarios, respectively. It will shed light on the dynamic tuning of small-scale thermal transport and light manipulation.
CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis

Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes prostate cancer on MRI by capturing the pathology features of cancer. First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features using Common Representation Learning. Second, the model uses the learned correlated MRI features to train a Convolutional Neural Network to localize prostate cancer. The histopathology images are used only in the first step to learn the correlated features. Once learned, these correlated features can be extracted from MRI of new patients (without histopathology or surgery) to localize cancer. We trained and validated our framework on a unique dataset of 75 patients with 806 slices who underwent MRI followed by prostatectomy surgery. We tested our method on an independent test set of 20 prostatectomy patients (139 slices, 24 cancerous lesions, 1.12M pixels) and achieved a per-pixel sensitivity of 0.81, specificity of 0.71, AUC of 0.86 and a per-lesion AUC of \(0.96 \pm 0.07\), outperforming the current state-of-the-art accuracy in predicting prostate cancer using MRI.
An efficient algorithm for Elastic I‐optimal design of generalized linear models

The generalized linear models (GLMs) are widely used in statistical analysis and the related design issues are undoubtedly challenging. The state-of-the-art works mostly apply to design criteria on the estimates of regression coefficients. The prediction accuracy is usually critical in modern decision making and artificial intelligence applications. It is of importance to study optimal designs from the prediction aspects for generalized linear models. In this work, we consider the Elastic I-optimality as a prediction-oriented design criterion for generalized linear models, and develop efficient algorithms for such $\text{EI}$-optimal designs. By investigating theoretical properties for the optimal weights of any set of design points and extending the general equivalence theorem to the $\text{EI}$-optimality for GLMs, the proposed efficient algorithm adequately combines the Fedorov-Wynn algorithm and multiplicative algorithm. It achieves great computational efficiency with guaranteed convergence. Numerical examples are conducted to evaluate the feasibility and computational efficiency of the proposed algorithm.
de Sitter Bubbles and the Swampland

A number of Swampland conjectures and in particular the Trans-Planckian Censorship Conjecture (TCC) suggest that de Sitter space is highly unstable if it exists at all. In this paper we construct effective theories of scalars rolling on potentials which are dual to a chain of short-lived dS spaces decaying from one to the next through a cascade of non-perturbative nucleation of bubbles. We find constraints on the effective potential resulting from various swampland criteria, including TCC, Weak Gravity Conjecture and Distance Conjecture. Surprisingly we find that TCC essentially incorporates all the other ones, and leads to a subclass of possible dual effective potentials. These results marginally rule out emergence of eternal inflation in the dual effective theory. We discuss some cosmological implications of our observations.
Does Sex Modify an Association of Electrophysiological Substrate with Sudden Cardiac Death? The Atherosclerosis Risk in Communities (ARIC) Study.

Background Sex is a well-recognized risk factor for sudden cardiac death (SCD). We hypothesized that sex modifies the association of electrophysiological (EP) substrate with SCD. Objective The purpose of this study was to determine whether there are sex differences in electrocardiographic (ECG) measures and whether sex modifies the association of ECG measures of EP substrate with SCD. Methods Participants from the Atherosclerosis Risk in Communities study with analyzable ECGs (n = 14,725; age 54.2 ± 5.8 years; 55% female; 74% white) were included. EP substrate was characterized by heart rate, QRS, QTc, Cornell voltage, spatial ventricular gradient (SVG), and sum absolute QRST integral (SAI QRST) ECG metrics. Two competing outcomes were adjudicated: SCD and non-SCD. Interaction of ECG metrics with sex was studied in Cox proportional hazards and Fine-Gray competing risk models. Model 1 was adjusted for prevalent cardiovascular disease (CVD) and risk factors. Time-updated model 2 was additionally adjusted for incident nonfatal CVD. Relative hazard ratio (RHR) and relative subhazard ratio with 95% confidence interval (CI) for SCD and non-SCD risk for women relative to men were calculated. Model 1 was adjusted for prevalent CVD and risk factors. Time-updated model 2 was additionally adjusted for incident nonfatal CVD. Results Over median follow-up of 24.4 years, there were 530 SCDs (incidence 1.72; 95% CI 1.58–1.88 per 1000 person-years). Women compared to men experienced a greater risk of SCD associated with Cornell voltage (RHR 1.18; 95% CI 1.06–1.32; P = .003), SAI QRST (RHR 1.16; 95% CI 1.04–1.30; P = .007), and SVG magnitude (RHR 1.24; 95% CI 1.05–1.45; P = .009), independently from incident CVD. Conclusion In women, the global EP substrate is associated with up to 24% greater risk of SCD than in men, suggesting differences in underlying mechanisms and the need for sex-specific SCD risk stratification.
Factorization theorems for relatively prime divisor sums, GCD sums and generalized Ramanujan sums

We build on and generalize recent work on so-termed factorization theorems for Lambert series generating functions. These factorization theorems allow us to express formal generating functions for special sums as invertible matrix transformations involving partition functions. In the Lambert series case, the generating functions at hand enumerate the divisor sum coefficients of $$q^n$$ as $$\sum _{d|n} f(d)$$ for some arithmetic function f. Our new factorization theorems provide analogs to these established expansions generating corresponding sums of the form $$\sum _{d: (d,n)=1} f(d)$$ (type I sums) and the Anderson–Apostol sums $$\sum _{d|(m,n)} f(d) g(n/d)$$ (type II sums) for any arithmetic functions f and g. Our treatment of the type II sums includes a matrix-based factorization method relating the partition function p(n) to arbitrary arithmetic functions f. We conclude the last section of the article by directly expanding new formulas for an arithmetic function g by the type II sums using discrete, and discrete time, Fourier transforms (DFT and DTFT) for functions over inputs of greatest common divisors.
Chernoff-Type Concentration of Empirical Probabilities in Relative Entropy

We study the relative entropy of the empirical probability vector with respect to the true probability vector in multinomial sampling of k categories, which, when multiplied by sample size n, is also the log-likelihood ratio statistic. We generalize a recent result and show that the moment generating function of the statistic is bounded by a polynomial of degree n on the unit interval, uniformly over all true probability vectors. We characterize the family of polynomials indexed by $({k},{n})$ and obtain explicit formulae. Consequently, we develop Chernoff-type tail bounds, including a closed-form version from a large sample expansion of the bound minimizer. Our bound dominates the classic method-of-types bound and is competitive with the state of the art. We demonstrate with an application to estimating the proportion of unseen butterflies.
Scientific Evaluation of Charles Dickens

Abstract I report the results of the test, where the takers had to tell the prose of Charles Dickens from that of Edward Bulwer-Lytton, who is considered by many to be the worst writer in history of letters. The average score is about 50%, which is on the level of random guessing. This suggests that the quality of Dickens’s prose is the same as of that of Bulwer-Lytton.
A low-temperature external cavity diode laser for broad wavelength tuning

We report on the design and characterization of a low-temperature external cavity diode laser (ECDL) system for broad wavelength tuning. The performance achieved with multiple diode models addresses the scarcity of commercial red laser diodes below 633 nm, which is a wavelength range relevant to the spectroscopy of many molecules and ions. Using a combination of multiple-stage thermoelectric cooling and water cooling, the operating temperature of a laser diode is lowered to −64 °C, more than 85 °C below the ambient temperature. The laser system integrates temperature and diffraction grating feedback tunability for coarse and fine wavelength adjustments, respectively. For two different diode models, single-mode operation is achieved with 38 mW output power at 616.8 nm and 69 mW at 622.6 nm, more than 15 nm below their ambient temperature free-running wavelengths. The ECDL design can be used for diodes of any available wavelength, allowing individual diodes to be tuned continuously over tens of nanometers a...
The Umbral Moonshine Module for the Unique Unimodular Niemeier Root System

We use canonically twisted modules for a certain super vertex operator algebra to construct the umbral moonshine module for the unique Niemeier lattice that coincides with its root sublattice. In particular, we give explicit expressions for the vector-valued mock modular forms attached to automorphisms of this lattice by umbral moonshine. We also characterize the vector-valued mock modular forms arising, in which four of Ramanujan’s fifth-order mock theta functions appear as components.
Experimental procedures for precision measurements of the Casimir force with an atomic force microscope

Experimental methods and procedures required for precision measurements of the Casimir force are presented. In particular, the best practices for obtaining stable cantilevers, calibration of the cantilever, correction of thermal and mechanical drift, measuring the contact separation, sphere radius and the roughness are discussed.
Weak antilocalization and universal conductance fluctuations in bismuth telluro-sulfide topological insulators

We report on van der Waals epitaxial growth, materials characterization, and magnetotransport experiments in crystalline nanosheets of Bismuth Telluro-Sulfide (BTS). Highly layered, good-quality crystalline nanosheets of BTS are obtained on SiO2 and muscovite mica. Weak-antilocalization (WAL), electron-electron interaction-driven insulating ground state and universal conductance fluctuations are observed in magnetotransport experiments on BTS devices. Temperature, thickness, and magnetic field dependence of the transport data indicate the presence of two-dimensional surface states along with bulk conduction, in agreement with theoretical models. An extended-WAL model is proposed and utilized in conjunction with a two-channel conduction model to analyze the data, revealing a surface component and evidence of multiple conducting channels. A facile growth method and detailed magnetotransport results indicating BTS as an alternative topological insulator material system are presented.
Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation

Recent work on the lottery ticket hypothesis has produced highly sparse Transformers for NMT while maintaining BLEU. However, it is unclear how such pruning techniques affect a model’s learned representations. By probing Transformers with more and more low-magnitude weights pruned away, we find that complex semantic information is first to be degraded. Analysis of internal activations reveals that higher layers diverge most over the course of pruning, gradually becoming less complex than their dense counterparts. Meanwhile, early layers of sparse models begin to perform more encoding. Attention mechanisms remain remarkably consistent as sparsity increases.
FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

Deep learning has becoming increasingly more popular in recent years, and there are many popular frameworks in the market accordingly, such as Caffe, TensorFlow and Pytorch. All these frameworks natively support CPUs and GPGPUs. However, FPGAs still cannot provide a comprehensive support by these frameworks for deep learning development, especially for the training phase. In this paper, we firstly propose the FeCaffe, i.e. FPGA-enabled Caffe, a hierarchical software and hardware design methodology based on the Caffe, to enable FPGA to support CNN training features. Moreover, we provide some benchmarks of popular CNN networks with FeCaffe, and further analysis in details accordingly. Finally, some optimization directions including FPGA kernel design, system pipeline, network architecture, user case application and heterogeneous platform levels, have been proposed gradually. The result demonstrates the proposed FeCaffe can support almost full features for training and inference respectively with high degree of design flexibility, expansibility and reusability for deep learning development. Compared to prior studies, our architecture can support more network and training settings and current configuration can achieve 6.4x and 8.4x average execution time improvement for forward and backward respectively for LeNet.
Dynamical crossover between the infinite-volume and empty-lattice limits of ultra-cold fermions in 1D optical lattices

Unlike typical condensed-matter systems, ultra-cold atoms loaded into optical lattices allow separate control of both the particle number and system size. As a consequence, there are two distinct "thermodynamic" limits that can be defined for these systems: i) "infinite-volume limit" at constant finite density, and ii) "empty-lattice limit" at constant particle number. To probe the difference between these two limits and their crossover, we consider a partially occupied lattice and study the transport of non-interacting fermions and fermions interacting at the mean-field level into the unoccupied region. In the infinite-volume limit, a finite steady-state current emerges. On the other hand, in the empty-lattice limit there is no finite steady-state current. By changing the initial filling, we find a smooth crossover between the two limits. Our predictions may be verified using available experimental tools and demonstrate a fundamental difference between isolated small systems such as ultra-cold atoms and conventional condensed-matter systems.
A software-based lock-in measurement for student laboratories

A student laboratory experiment is presented that introduces the concept of a lock-in measurement through the exploration of the relationship between the power detected from a modulated light source and the distance between the source and the detector. The lock-in measurement is done in software using time-ordered data for both the reference and the detector signals. The achievable experimental sensitivity is shown to increase by several orders of magnitude over the simple case without using modulation. In addition, the student becomes familiar with selected computational techniques pertinent to large data sets.
Magnetic vortices in the Abelian Higgs model with derivative interactions

We study the properties of a single magnetic vortex and magnetic vortex lattices in a generalization of the Abelian Higgs model containing the simplest derivative interaction that preserves the U(1...
A Global Three Dimensional Radiation Magneto-hydrodynamic Simulation of Super-Eddington Accretion Disks

We study super-Eddington accretion flows onto black holes using a global three-dimensional radiation magneto-hydrodynamical simulation. We solve the time-dependent radiative transfer equation for the specific intensities to accurately calculate the angular distribution of the emitted radiation. Turbulence generated by the magneto-rotational instability provides self-consistent angular momentum transfer. The simulation reaches inflow equilibrium with an accretion rate ∼220 L {sub Edd}/c {sup 2} and forms a radiation-driven outflow along the rotation axis. The mechanical energy flux carried by the outflow is ∼20% of the radiative energy flux. The total mass flux lost in the outflow is about 29% of the net accretion rate. The radiative luminosity of this flow is ∼10 L {sub Edd}. This yields a radiative efficiency ∼4.5%, which is comparable to the value in a standard thin disk model. In our simulation, vertical advection of radiation caused by magnetic buoyancy transports energy faster than photon diffusion, allowing a significant fraction of the photons to escape from the surface of the disk before being advected into the black hole. We contrast our results with the lower radiative efficiencies inferred in most models, such as the slim disk model, which neglect vertical advection. Our inferred radiative efficiencies also exceed publishedmore » results from previous global numerical simulations, which did not attribute a significant role to vertical advection. We briefly discuss the implications for the growth of supermassive black holes in the early universe and describe how these results provided a basis for explaining the spectrum and population statistics of ultraluminous X-ray sources.« less
Connecting Flares and Transient Mass Loss Events in Magnetically Active Stars

We explore the ramification of associating the energetics of extreme magnetic reconnection events with transient mass-loss in a stellar analogy with solar eruptive events. We establish energy partitions relative to the total bolometric radiated flare energy for different observed components of stellar flares and show that there is rough agreement for these values with solar flares. We apply an equipartition between the bolometric radiated flare energy and kinetic energy in an accompanying mass ejection, seen in solar eruptive events and expected from reconnection. This allows an integrated flare rate in a particular waveband to be used to estimate the amount of associated transient mass-loss. This approach is supported by a good correspondence between observational flare signatures on high flaring rate stars and the Sun, which suggests a common physical origin. If the frequent and extreme flares that young solar-like stars and low-mass stars experience are accompanied by transient mass-loss in the form of coronal mass ejections, then the cumulative effect of this mass-loss could be large. We find that for young solar-like stars and active M dwarfs, the total mass lost due to transient magnetic eruptions could have significant impacts on disk evolution, and thus planet formation, and also exoplanet habitability.
HIGH-TEMPERATURE PROCESSING OF SOLIDS THROUGH SOLAR NEBULAR BOW SHOCKS: 3D RADIATION HYDRODYNAMICS SIMULATIONS WITH PARTICLES

A fundamental, unsolved problem in solar system formation is explaining the melting and crystallization of chondrules found in chondritic meteorites. Theoretical models of chondrule melting in nebular shocks have been shown to be consistent with many aspects of thermal histories inferred for chondrules from laboratory experiments; but, the mechanism driving these shocks is unknown. Planetesimals and planetary embryos on eccentric orbits can produce bow shocks as they move supersonically through the disk gas, and are one possible source of chondrule-melting shocks. We investigate chondrule formation in bow shocks around planetoids through three-dimensional radiation hydrodynamics simulations. A new radiation transport algorithm that combines elements of flux-limited diffusion and Monte Carlo methods is used to capture the complexity of radiative transport around bow shocks. An equation of state that includes the rotational, vibrational, and dissociation modes of H2 is also used. Solids are followed directly in the simulations and their thermal histories are recorded. Adiabatic expansion creates rapid cooling of the gas, and tail shocks behind the embryo can cause secondary heating events. Radiative transport is efficient, and bow shocks around planetoids can have luminosities ~few× 10–8 L ☉. While barred and radial chondrule textures could be produced in the radiative shocks explored here, porphyritic chondrules may only be possible in the adiabatic limit. We present a series of predicted cooling curves that merit investigation in laboratory experiments to determine whether the solids produced by bow shocks are represented in the meteoritic record by chondrules or other solids.
Computing Slow Manifolds of Saddle Type

Slow manifolds are important geometric structures in the state spaces of dynamical systems with multiple time scales. This paper introduces an algorithm for computing trajectories on slow man- ifolds that are normally hyperbolic with both stable and unstable fast manifolds. We present two examples of bifurcation problems where these manifolds play a key role and a third example in which saddle-type slow manifolds are part of a traveling wave profile of a partial differential equa- tion. Initial value solvers are incapable of computing trajectories on saddle-type slow manifolds, so the slow manifold of saddle type (SMST) algorithm presented here is formulated as a boundary value method. We take an empirical approach here to assessing the accuracy and effectiveness of the algorithm.
The Sensitivity of Convection Zone Depth to Stellar Abundances: An Absolute Stellar Abundance Scale from Asteroseismology

The base of the convection zone (CZ) is a source of acoustic glitches in the asteroseismic frequency spectra of solar-like oscillators, allowing one to precisely measure the acoustic depth to the feature. We examine the sensitivity of the depth of the CZ to mass, stellar abundances, and input physics, and in particular, the use of a measurement of the acoustic depth to the CZ as an atmosphere-independent, absolute measure of stellar metallicities. We find that for low-mass stars on the main sequence with 0.4 M{sub Sun} {<=} M {<=} 1.6 M{sub Sun }, the acoustic depth to the base of the CZ, normalized by the acoustic depth to the center of the star, {tau}{sub cz,n}, is both a strong function of mass, and varies at the 0.5%-1% per 0.1 dex level in [Z/X], and is therefore also a sensitive probe of the composition. We estimate the theoretical uncertainties in the stellar models and show that combined with reasonable observational uncertainties we can expect to measure the metallicity to within 0.15-0.3 dex for solar-like stars. We discuss the applications of this work to rotational mixing, particularly in the context of the observed mid-F star Li dip, and to distinguishing between differentmore » mixtures of heavy elements.« less
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: (1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; (2) The data, when communicated, are vulnerable to security and privacy issues; (3) The communication of the continuously collected data is not only costly but also energy hungry; (4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.
Probabilistic evolution of stochastic dynamical systems: A meso-scale perspective

Abstract Stochastic dynamical systems arise naturally across nearly all areas of science and engineering. Typically, a dynamical system model is based on some prior knowledge about the underlying dynamics of interest in which probabilistic features are used to quantify and propagate uncertainties associated with the initial conditions, external excitations, etc. From a probabilistic modeling standing point, two broad classes of methods exist, i.e. macro-scale methods and micro-scale methods. Classically, macro-scale methods such as statistical moments-based strategies are usually too coarse to capture the multi-mode shape or tails of a non-Gaussian distribution. Micro-scale methods such as random samples-based approaches, on the other hand, become computationally very challenging in dealing with high-dimensional stochastic systems. In view of these potential limitations, a meso-scale scheme is proposed here that utilizes a meso-scale statistical structure to describe the dynamical evolution from a probabilistic perspective. The significance of this statistical structure is twofold. First, it can be tailored to any arbitrary random space. Second, it not only maintains the probability evolution around sample trajectories but also requires fewer meso-scale components than the micro-scale samples. To demonstrate the efficacy of the proposed meso-scale scheme, a set of examples of increasing complexity are provided. Connections to the benchmark stochastic models as conservative and Markov models along with practical implementation guidelines are presented.
A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards

We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.
Microstructure and rheology of finite inertia neutrally buoyant suspensions

The microstructure and rheological properties of suspensions of neutrally buoyant hard spherical particles in Newtonian fluid under finite inertia conditions are studied using the lattice-Boltzmann method (LBM), which is based on a discrete Boltzmann model for the fluid and Newtonian dynamics for the particles. The suspensions are subjected to simple-shear flow and the properties are studied as a function of Reynolds number and volume fraction, . The inertia is characterized by the particle-scale shear flow Reynolds number , where is the particle radius, is the shear rate and and are the density and viscosity of the fluid, respectively. The influences of inertia and of the volume fraction are investigated for and . The flow-induced microstructure is studied using the pair distribution function . Different stress mechanisms, including those due to surface tractions (stresslet), acceleration and the Reynolds stress due to velocity fluctuations are computed and their influence on the first and second normal stress differences, the particle pressure and the viscosity of the suspensions are detailed. The probability density functions (PDFs) of linear and angular accelerations are also presented.
Fate of heavy quark bound states inside quark-gluon plasma

Transport equations have been applied successfully to describe the quarkonium evolution inside the quark-gluon plasma, which include both plasma screening effects and recombination. We demonstrate how the quarkonium transport equation is derived from QCD by using the open quantum system framework and effective field theory. Weak coupling and Markovian approximations used in the derivation are justified from a separation of scales. By solving the equations numerically, we study the Upsilon production in heavy ion collisions.
Quantum D-modules, Elliptic Braid Groups, and Double Affine Hecke Algebras

We build representations of the elliptic braid group from the data of a quantum D-module M over a ribbon Hopf algebra U. The construction is modelled on, and generalizes, similar constructions by Lyubashenko and Majid (“Invariants of 3-manifolds and projective representations of mapping class groups via quantum groups at roots of unity,” Communications in Mathematical Physics 172 (1995): 467-516; “Braided Groups and Quantum Fourier Transform,” Journal of Algebra 166 (1994): 506-28), and also certain geometric constructions of Calaque, Enriquez, and Etingof (“Universal KZB equations I: The elliptic case,” (2007): preprint arXiv:math/0702670) concerning trigonometric Cherednik algebras. In this context, the former construction is the special case where M is the basic representation, while the latter construction can be recovered as a quasi-classical limit of , as . In the latter case, we produce representations of the double affine Hecke algebra of type , for each n.
Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification Over Encrypted Wi-Fi Traffic

The consumer unmanned aerial vehicle (UAV) market has grown significantly over the past few years. Despite its huge potential in spurring economic growth by supporting various applications, the increase of consumer UAVs poses potential risks to public security and personal privacy. To minimize the risks, efficiently detecting and identifying invading UAVs is in urgent need for both invasion detection and forensics purposes. Aiming to complement the existing physical detection mechanisms, we propose a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use Wi-Fi links for control and video streaming. The proposed framework extracts features derived only from packet size and inter-arrival time of encrypted Wi-Fi traffic, and can efficiently detect UAVs and identify their operation modes. In order to reduce the online identification time, our framework adopts a re-weighted $\ell _{1}$ -norm regularization, which considers the number of samples and computation cost of different features. This framework jointly optimizes feature selection and prediction performance in a unified objective function. To tackle the packet inter-arrival time uncertainty when optimizing the trade-off between the detection accuracy and delay, we utilize maximum likelihood estimation (MLE) method to estimate the packet inter-arrival time. We collect a large number of real-world Wi-Fi data traffic of eight types of consumer UAVs and conduct extensive evaluation on the performance of our proposed method. Evaluation results show that our proposed method can detect and identify tested UAVs within 0.15–0.35s with high accuracy of 85.7–95.2%. The UAV detection range is within the physical sensing range of 70m and 40m in the line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively. The operation mode of UAVs can be identified with high accuracy of 88.5–98.2%.
Clarifying the relationship between efficiency and resonance for flexible inertial swimmers

We study a linear inviscid model of a passively flexible swimmer, calculating its propulsive performance, eigenvalues and eigenfunctions with an eye towards clarifying the relationship between efficiency and resonance. The frequencies of actuation and stiffness ratios we consider span a large range, while the mass ratio is mostly fixed to a low value representative of swimmers. We present results showing how the trailing edge deflection, thrust coefficient, power coefficient and efficiency vary in the stiffness–frequency plane. The trailing edge deflection, thrust coefficient and power coefficient show sharp ridges of resonant behaviour for mid-to-high frequencies and stiffnesses, whereas the efficiency does not show resonant behaviour anywhere. For low frequencies and stiffnesses, the resonant peaks smear together and the efficiency is high. In this region, flutter modes emerge, inducing travelling wave kinematics which make the swimmer more efficient. We also consider the effects of a finite Reynolds number in the form of streamwise drag. The drag adds an offset to the net thrust produced by the swimmer, causing resonant peaks to appear in the efficiency (as observed in experiments in the literature).
Controlled Buckling and Crumpling of Nanoparticle-Coated Droplets

We introduce a new experimental approach to study the structural transitions of large numbers of nanoparticle-coated droplets as their volume is reduced. We use an emulsion system where the dispersed phase is slightly soluble in the continuous phase. By adding a fixed amount of unsaturated continuous phase, the volume of the droplets can be controllably reduced, causing them to buckle or crumple, thereby becoming nonspherical. The resultant morphologies depend both on the extent of volume reduction and the average droplet size. The buckling and crumpling behavior implies that the droplet surfaces are solid.
Randomized resolvent analysis

Randomized numerical algebra is incorporated into resolvent analysis to reduce large-scale resolvent operators to their low-rank approximations. The key to finding the resolvent modes accurately is to weigh the random test matrix using insights from the base flow. Turbulent flow over a NACA0012 airfoil at Re = 23,000 is used to demonstrate significant speedup and memory savings to accurately find principal resolvent modes.
Color-Magnitude Diagrams of Merged Globular Clusters: Metallicity Effects

Mergers of globular clusters (GCs) once associated with dwarf spheroidal (dSph) galaxies have recently been suggested as an explanation for the bimodal horizontal branches (HBs) of some Galactic GCs, most notably NGC 1851, NGC 2808, and NGC 6229. Through analysis of the available color-magnitude diagrams for the GCs in the Fornax and Sagittarius dSPH satellites of the Milky Way, as well as their metallicity distributions, we argue that the merger of two GCs would most likely produce a bimodal distribution in red giant branch (RGB) colors, or at least a significant broadening of the RGB, because of the expected difference in metallicity between the two merging globulars. No GC with a bimodal RGB is currently known, and the tightness of the RGB sequences in the above bimodal HB GCs implies that a merger origin for their HB bimodality is unlikely.