cssi/SciSciGPT-SciSciNet huggingface dataset:
Moving outside the lab: Markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump

Abstract Markerless motion capture using deep learning approaches have potential to revolutionize the field of biomechanics by allowing researchers to collect data outside of the laboratory environment, yet there remain questions regarding the accuracy and ease of use of these approaches. The purpose of this study was to apply a markerless motion capture approach to extract lower limb angles in the sagittal plane during the vertical jump and to evaluate agreement between the custom trained model and gold standard motion capture. We performed this study using a large open source data set (N = 84) that included synchronized commercial video and gold standard motion capture. We split these data into a training set for model development (n = 69) and test set to evaluate capture performance relative to gold standard motion capture using coefficient of multiple correlations (CMC) (n = 15). We found very strong agreement between the custom trained markerless approach and marker-based motion capture within the test set across the entire movement (CMC > 0.991, RMSE
Gamma Calculus Beyond Villani and Explicit Convergence Estimates for Langevin Dynamics with Singular Potentials

We apply Gamma calculus to the hypoelliptic and non-symmetric setting of Langevin dynamics under general conditions on the potential. This extension allows us to provide explicit estimates on the convergence rate (which is exponential) to equilibrium for the dynamics in a weighted $$H^1(\mu )$$ sense, $$\mu $$ denoting the unique invariant probability measure of the system. The general result holds for singular potentials, such as the well-known Lennard–Jones interaction and confining well, and it is applied in such a case to estimate the rate of convergence when the number of particles N in the system is large.
PARTICLE PHYSICS IN THE SKY: THE CURRENT STATUS OF INFLATION

I discuss the current status of inflationary cosmology in light of the recent WMAP 3-year data release. The basic predictions of inflation are all supported by the data. Inflation also makes predictions which are have not been well tested by current data but can be by future experiments, most notably a deviation from a scale-invariant power spectrum and the production of primordial gravitational waves. A scale-invariant spectrum is disfavored by current data, but not conclusively. Tensor modes are currently poorly constrained, and slow-roll inflation does not make an unambiguous prediction of the expected amplitude of primordial gravitational waves. A tensor/scalar ratio of r ≃ 0.01 is within reach of near-future measurements.
A tetrachotomy for expansions of the real ordered additive group

Let $$\mathcal {R}$$ be an expansion of the ordered real additive group. When $$\mathcal {R}$$ is o-minimal, it is known that either $$\mathcal {R}$$ defines an ordered field isomorphic to $$(\mathbb {R},<,+,\cdot )$$ on some open subinterval $$I\subseteq \mathbb {R}$$ , or $$\mathcal {R}$$ is a reduct of an ordered vector space. We say $$\mathcal {R}$$ is field-type if it satisfies the former condition. In this paper, we prove a more general result for arbitrary expansions of $$(\mathbb {R},<,+)$$ . In particular, we show that for expansions that do not define dense $$\omega $$ -orders (we call these type A expansions), an appropriate version of Zilber’s principle holds. Among other things we conclude that in a type A expansion that is not field-type, every continuous definable function $$[0,1]^m \rightarrow \mathbb {R}^n$$ is locally affine outside a nowhere dense set.
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks

We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves state-of-the-art results, outperforming recent neural network-based techniques and widely used Poisson Surface Reconstruction (which, as we demonstrate, can also be viewed as a type of kernel method). Because our approach is based on a simple kernel formulation, it is easy to analyze and can be accelerated by general techniques designed for kernel-based learning. We provide explicit analytical expressions for our kernel and argue that our formulation can be seen as a generalization of cubic spline interpolation to higher dimensions. In particular, the RKHS norm associated with Neural Splines biases toward smooth interpolants.
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks

Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine learning approaches ...
Metabolic depression in sea urchin barrens associated with food deprivation

The proliferation of sea urchins can decimate macroalgal forests in coastal ecosystems, leading to persistent barren seascapes. While kelp forests are among the most productive ecosystems on the planet, productivity in these urchin barrens is dramatically reduced. Moreover, urchins inhabiting these food-depauperate barrens face starvation and many survive in these barrens for years or decades. Urchins in barrens can persist by eating food subsidies from drift algae, pelagic salps, tubeworms, as well as encrusting and filamentous algae, microbial mats, and slow-growing species resistant to herbivory. Despite both food from endogenous production and exogenous subsidies, many urchins in barrens likely experience prolonged food deprivation. This resource limitation may create a trade-off between reproduction and survival; for example, fecundity of purple sea urchins (Strongylocentrotus purpuratus) is 99.9% lower in barrens. Despite food constraints, red sea urchins (Mesocentrotus franciscanus), the dominant urchin species at our study sites, can live in excess of 100 years and barrens in Haida Gwaii, British Columbia (BC), Canada, have persisted for at least 143 years. While these phenomena are widespread and well documented, the bioenergetic adaptations that allow urchins to persist in these food-depauperate barrens remain poorly understood. To quantify habitat-specific differences in metabolic rates and energy reserves (as measured by gonadal mass), we conducted respirometry on and measured gonadal mass in M. franciscanus at three locations in BC inside and outside of adjacent kelp forest and barrens habitat. Here we demonstrate that M. franciscanus in barrens versus kelp forests have substantially lower energy reserves and, importantly, also exhibit dramatic reductions in size-specific resting metabolic rates (RMR), even after standardizing by metabolically active body mass. On average, gonadal mass was 44.6% lower and RMR scaled to metabolically active body mass was 40% lower in barrens urchins than in kelp forest urchins. Such a shift in metabolic rate may provide a mechanism that facilitates barren state stability over long time scales as M. franciscanus can lower energetic demands while they wait for small pulses of food, scrape by on low-productivity resources, and suppress recruitment of macroalgae for months, years, or decades.
Infinity: A Scalable Infrastructure for In-Network Applications

Network programmability is an area of research both defined by its potential and its current limitations. While programmable hardware enables customization of device operation, tailoring processing to finely tuned objectives, limited resources stifle much of the capability and scalability desired for future technologies. Current solutions to overcome these limitations simply shift the problem, temporarily offloading memory needs or processing to other systems while incurring both round-trip time and complexity costs. To overcome these unnecessary costs, we introduce Infinity, a resource disaggregation method to move processing to capable devices while continuing to forward as the original owner. By forwarding both the processing need and associated data simultaneously we are able to scale operation with minimal overhead and delay, improving both capability and performance objectives for in-network processing.
Sequential Decomposition of Graphon Mean Field Games

In this paper, we present a sequential decomposition algorithm to compute graphon mean-field equillibrium (GMFE) of dynamic graphon mean-field games (GMFGs). We consider a large population of players sequentially making strategic decisions where the actions of each player affect their neighbors which is captured in a graph, generated by a known graphon. Each player observes a private state and also a common information as a graphon mean-field population state which represents the empirical networked distribution of other players' types. We consider non-stationary population state dynamics and present a novel backward recursive algorithm to compute GMFE that depend on both, a player's private type, and the current (dynamic) population state determined through the graphon. Each step in this algorithm consists of solving a fixed-point equation. We provide conditions on model parameters for which there exists such a GMFE. Using this algorithm, we obtain the GMFE for a specific security setup in cyber physical systems for different graphons that capture the interactions between the nodes in the system.
A universal reduced glass transition temperature for liquids

Data on the dependence of the glass transition temperature on the molecular structure for low-molecular-weight liquids are analyzed in order to determine whether Boyer's reduced glass transition temperature (1952) is a universal constant as proposed. It is shown that the Boyer ratio varies widely depending on the chemical nature of the molecule. It is pointed out that a characteristic temperature ratio, defined by the ratio of the sum of the melting temperature and the boiling temperature to the sum of the glass transition temperature and the boiling temperature, is a universal constant independent of the molecular structure of the liquid. The average value of the ratio obtained from data for 65 liquids is 1.15.
The minimum wage and annual earnings inequality

Abstract We estimate the impact of the minimum wage on U.S. male annual earnings inequality, using administrative Social Security earnings records from 1981–2015. The minimum wage reduces inequality in the bottom quartile of the earnings distribution, and especially in the bottom decile.
Faster Algorithms for Bounded Tree Edit Distance.

Tree edit distance is a well-studied measure of dissimilarity between rooted trees with node labels. It can be computed in O(n³) time [Demaine, Mozes, Rossman, and Weimann, ICALP 2007], and fine-grained hardness results suggest that the weighted version of this problem cannot be solved in truly subcubic time unless the APSP conjecture is false [Bringmann, Gawrychowski, Mozes, and Weimann, SODA 2018]. We consider the unweighted version of tree edit distance, where every insertion, deletion, or relabeling operation has unit cost. Given a parameter k as an upper bound on the distance, the previous fastest algorithm for this problem runs in O(nk³) time [Touzet, CPM 2005], which improves upon the cubic-time algorithm for k≪ n^{2/3}. In this paper, we give a faster algorithm taking O(nk² log n) time, improving both of the previous results for almost the full range of log n ≪ k≪ n/√{log n}.
Meta-analytic connectivity modelling of deception-related brain regions

Brain-based deception research began only two decades ago and has since included a wide variety of contexts and response modalities for deception paradigms. Investigations of this sort serve to better our neuroscientific and legal knowledge of the ways in which individuals deceive others. To this end, we conducted activation likelihood estimation (ALE) and meta-analytic connectivity modelling (MACM) using BrainMap software to examine 45 task-based fMRI brain activation studies on deception. An activation likelihood estimation comparing activations during deceptive versus honest behavior revealed 7 significant peak activation clusters (bilateral insula, left superior frontal gyrus, bilateral supramarginal gyrus, and bilateral medial frontal gyrus). Meta-analytic connectivity modelling revealed an interconnected network amongst the 7 regions comprising both unidirectional and bidirectional connections. Together with subsequent behavioral and paradigm decoding, these findings implicate the supramarginal gyrus as a key component for the sociocognitive process of deception.
RegTech and Predictive Lawmaking: Closing the RegLag Between Prospective Regulated Activity and Regulation

Regulation chronically suffers significant delay starting at the detectable initiation of a “regulable activity” and culminating at effective regulatory response. Regulator reaction is impeded by various impediments: (i) confusion in optimal level, form and choice of regulatory agency, (ii) political resistance to creating new regulatory agencies, (iii) lack of statutory authorization to address particular novel problems, (iv) jurisdictional competition among regulators, (v) Congressional disinclination to regulate given political conditions, and (vi) a lack of expertise, both substantive and procedurally, to deploy successful counter-measures. Delay is rooted in several stubborn institutions, including libertarian ideals permeating both the U.S. legal system and the polity, constitutional constraints on exercise of governmental powers, chronic resource constraints including underfunding, and agency technical incapacities. Therefore, regulatory prospecting to identify regulable activity often lags the suspicion of future regulable activity or its first discernable appearance. This article develops the regulatory lag theory (RegLag), reviews regulatory technologies (RegTech) that show promise in narrowing the RegLag gap, and proposes programs to improve regulatory agency clairvoyance to more aggressively adapt to changing regulable activities, such as by using anticipatory approaches that show promise to narrow RegLag delays.
Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries

Information is crucial to the function of a democratic society where well-informed citizens can make rational political decisions. While in the past political entities primarily utilized newspapers and later radio and television to inform the public, the political arena has transformed into a more complex structure with the rise of the Internet and online social media. Now, more than ever, people express themselves online while mainstream news agencies attempt to utilize the power of the Internet to spread their articles as much as possible. To grasp the political coexistence of mainstream media and online social media, in this paper, we analyze these two sources of information in the context of the U.S. 2020 presidential election. In particular, we collected data during the 2020 Democratic Party presidential primaries pertaining to the candidates, and, by analyzing this data, we highlight similarities and differences between these two main types of sources, detect the potential impact they have on each other, and understand how this impact relationship can change over time.
Condor-G: A Computation Management Agent for Multi-Institutional Grids

In recent years, there has been a dramatic increase in the number of available computing and storage resources. Yet few tools exist that allow these resources to be exploited effectively in an aggregated form. We present the Condor-G system, which leverages software from Globus and Condor to enable users to harness multi-domain resources as if they all belong to one personal domain. We describe the structure of Condor-G and how it handles job management, resource selection, security, and fault tolerance. We also present results from application experiments with the Condor-G system. We assert that Condor-G can serve as a general-purpose interface to Grid resources, for use by both end users and higher-level program development tools.
Stability of a numerical scheme for methane transport in hydrate zone under equilibrium and non-equilibrium conditions

In this paper we carry out numerical analysis for a family of simplified gas transport models with hydrate formation and dissociation in subsurface, in equilibrium and non-equilibrium conditions. These models are adequate for simulation of hydrate phase change at basin and at shorter time scales, but the analysis does not account directly for the related effects of evolving hydraulic properties. To our knowledge this is the first analysis of such a model. It is carried out for the transport steps while keeping the pressure solution fixed. We frame the transport model as conservation law with a non-smooth space-dependent flux function; the kinetic model approximates this equilibrium. We prove weak stability of the upwind scheme applied to the regularized conservation law. We illustrate the model, confirm convergence with numerical simulations, and illustrate its use for some relevant equilibrium and non-equilibrium scenarios.
Probabilistic Group Testing with a Linear Number of Tests

In probabilistic nonadaptive group testing (PGT), we aim to characterize the number of pooled tests necessary to identify a random k-sparse vector of defectives with high probability. Recent work has shown that $n$ tests are necessary when $k$ = $w$ ( $n$ / log n). It is also known that O (k log n) tests are necessary and sufficient in other regimes. This leaves open the important sparsity regime where the probability of a defective item is~ 1/ log $n$ (or $k$ = 8 ( $n$ / log n)) where the number of tests required is linear in n. In this work we aim to exactly characterize the number of tests in this sparsity regime. In particular, we seek to determine the number of defectives λ ( $a$ ) $n$ / log $n$ that can be identified if the number of tests is an. In the process, we give upper and lower bounds on the exact point at which individual testing becomes suboptimal, and the use of a carefully constructed pooled test design is beneficial.
Unifying AoI Minimization and Remote Estimation--Optimal Sensor/Controller Coordination With Random Two-Way Delay

The ubiquitous usage of communication networks in modern sensing and control applications has kindled new interests on the timing-based coordination between sensors and controllers, i.e., how to use the "waiting time" to improve the system performance. Contrary to the common belief that a zero-wait policy is always optimal, Sun et al. showed that a controller can strictly improve the data freshness, the so-called Age-of-Information (AoI), by postponing transmission in order to lengthen the duration of staying in a good state. The optimal waiting policy for the sensor side was later characterized in the context of remote estimation. Instead of focusing on the sensor and controller sides separately, this work develops the optimal joint sensor/controller waiting policy in a Wiener-process system. The results can be viewed as strict generalization of the above two important results in the sense that not only do we consider joint sensor/controller designs (as opposed to sensor-only or controller-only schemes), but we also assume random delay in both the forward and feedback directions (as opposed to random delay in only one direction). In addition to provable optimality, extensive simulation is used to verify the performance of the proposed scheme in various settings.
Distinguishability and "which pathway" information in multidimensional interferometric spectroscopy with a single entangled photon-pair.

Correlated photons inspire abundance of metrology-related platforms, which benefit from quantum (anti-) correlations and outperform their classical counterparts. While these mainly focus on entangl...
AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-Ray

Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision. However, most existing deep learning models only look at the entire X-ray image for classification, failing to utilize important anatomical information. In this paper, we propose a novel multi-label chest X-ray classification model that accurately classifies the image finding and also localizes the findings to their correct anatomical regions. Specifically, our model consists of two modules, the detection module and the anatomical dependency module. The latter utilizes graph convolutional networks, which enable our model to learn not only the label dependency but also the relationship between the anatomical regions in the chest X-ray. We further utilize a method to efficiently create an adjacency matrix for the anatomical regions using the correlation of the label across the different regions. Detailed experiments and analysis of our results show the effectiveness of our method when compared to the current state-of-the-art multi-label chest X-ray image classification methods while also providing accurate location information.
Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis.

Measuring the acoustic characteristics of a space is often done by capturing its impulse response (IR), a representation of how a full-range stimulus sound excites it. This work generates an IR from a single image, which can then be applied to other signals using convolution, simulating the reverberant characteristics of the space shown in the image. Recording these IRs is both time-intensive and expensive, and often infeasible for inaccessible locations. We use an end-to-end neural network architecture to generate plausible audio impulse responses from single images of acoustic environments. We evaluate our method both by comparisons to ground truth data and by human expert evaluation. We demonstrate our approach by generating plausible impulse responses from diverse settings and formats including well known places, musical halls, rooms in paintings, images from animations and computer games, synthetic environments generated from text, panoramic images, and video conference backgrounds.
Globally local: Hyper-local modeling for accurate forecast of COVID-19.

Abstract Importance: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it. Objective: This paper’s primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels. Design: We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast. Results: The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective — county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves. Conclusions and Relevance: Accurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity.
GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger

Vision-based tactile sensors have the potential to provide important contact geometry to localize the objective with visual occlusion. However, it is challenging to measure high-resolution 3D contact geometry for a compact robot finger, to simultaneously meet optical and mechanical constraints. In this work, we present the GelSight Wedge sensor, which is optimized to have a compact shape for robot fingers, while achieving high-resolution 3D reconstruction. We evaluate the 3D reconstruction under different lighting configurations, and extend the method from 3 lights to 1 or 2 lights. We demonstrate the flexibility of the design by shrinking the sensor to the size of a human finger for fine manipulation tasks. We also show the effectiveness and potential of the reconstructed 3D geometry for pose tracking in the 3D space.
PSLA: Improving Audio Tagging With Pretraining, Sampling, Labeling, and Aggregation

Audio tagging is an active research area and has a wide range of applications. Since the release of AudioSet, great progress has been made in advancing model performance, which mostly comes from the development of novel model architectures and attention modules. However, we find that appropriate training techniques are equally important for building audio tagging models with AudioSet, but have not received the attention they deserve. To fill the gap, in this work, we present PSLA, a collection of model agnostic training techniques that can noticeably boost the model accuracy including ImageNet pretraining, balanced sampling, data augmentation, label enhancement, model aggregation. While many of these techniques have been previously explored, we conduct a thorough investigation on their design choices and combine them together. By training an EfficientNet with pretraining, balanced sampling, data augmentation, and model aggregation, we obtain a single model (with 13.6 M parameters) and an ensemble model that achieve mean average precision (mAP) scores of 0.444 and 0.474 on AudioSet, respectively, outperforming the previous best system of 0.439 with 81 M parameters. In addition, our model also achieves a new state-of-the-art mAP of 0.567 on FSD50K. We also investigate the impact of label enhancement on the model performance.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpor...
A hybrid six-dimensional muon cooling channel using gas filled rf cavities

An alternative cooling approach to prevent rf breakdown in magnetic fields is described that simultaneously reduces all six phase-space dimensions of a muon beam. In this process, cooling is accomplished by reducing the beam momentum through ionization energy loss in discrete absorbers and replenishing the momentum loss only in the longitudinal direction through gas-filled rf cavities. The advantage of gas filled cavities is that they can run at high gradients in magnetic fields without breakdown. With this approach, we show that our channel can achieve a decrease of the 6-dimensional phase-space volume by several orders of magnitude. With the aid of numerical simulations, we demonstrate that the transmission of our proposed channel is comparable to that of an equivalent channel with vacuum rf cavities. Finally, we discuss the sensitivity of the channel performance to the choice of gas and operating pressure.
Suppression of the optical linewidth and spin decoherence of a quantum spin center in a p–n diode

We present a quantitative theory of the suppression of the optical linewidth due to charge fluctuation noise in a p–n diode. We connect the local electric field with the voltage across the diode, allowing for the identification of the defect depth from the experimental threshold voltage. Furthermore, we show that an accurate description of the decoherence of such spin centers requires a complete spin–1 formalism that yields a bi-exponential decoherence process, and predict how reduced charge fluctuation noise suppresses the spin center's decoherence rate. The material is based on work supported by the U.S. Department of Energy, Office of Basic Energy Sciences, under Award Number DE-SC0019250. undefined
The complex history of genome duplication and hybridization in North American gray treefrogs.

Polyploid speciation has played an important role in evolutionary history across the tree of life, yet there remain large gaps in our understanding of how polyploid species form and persist. While systematic studies have been conducted in numerous polyploid complexes, recent advances in sequencing technology have demonstrated that conclusions from data-limited studies may be spurious and misleading. The North American gray treefrog complex, consisting of the diploid Hyla chrysoscelis and the tetraploid Hyla versicolor, has long been used as a model system in a variety of biological fields, yet all taxonomic studies to date were conducted with only a few loci from nuclear and mitochondrial genomes. Here, we utilized anchored hybrid enrichment and high-throughput sequencing to capture hundreds of loci along with whole mitochondrial genomes to investigate the evolutionary history of this complex. We used several phylogenetic and population genetic methods, including coalescent simulations and testing of polyploid speciation models with Approximate Bayesian Computation (ABC), to determine that H. versicolor was most likely formed via autopolyploidization from a now extinct lineage of H. chrysoscelis. We also uncovered evidence of significant hybridization between diploids and tetraploids where they co-occur, and show that historical hybridization between these groups led to the re-formation of distinct polyploid lineages following the initial whole genome duplication event. Our study indicates that a wide variety of methods and explicit model testing of polyploid histories can greatly facilitate efforts to uncover the evolutionary history of polyploid complexes.
Constraints on the Occurrence of ‘Oumuamua-Like Objects

At present, there exists no consensus in the astronomical community regarding either the bulk composition or the formation mechanism for the interstellar object 1I/2017 U1 ('Oumuamua). With the goal of assessing the merits of the various scenarios that have been suggested to explain 'Oumuamua's appearance and observed properties, we report a number of new analyses and provide an up-to-date review of the current hypotheses. We consider the interpretations that can reconcile 'Oumuamua's observed non-Keplerian trajectory with the non-detection of traditional cometary volatiles. We examine the ability of these proposed formation pathways to populate the galaxy with sufficient interstellar objects such that the detection of 'Oumuamua by Pan-STARRS would be statistically-favored. We consider two exotic ices, hydrogen and nitrogen, showing that the frigid temperature requirement for the former and the necessary formation efficiency of the latter pose serious difficulties for these interpretations. Via order-of-magnitude arguments and hydrodynamical cratering simulations, we show that impacts on extrasolar Kuiper Belt analogues are not expected to generate N2 ice fragments as large as 'Oumuamua. In addition, we discuss observational tests to confirm the presence of these ices in future interstellar objects. Next, we examine the explanations that attribute 'Oumuamua's properties to other compositions: ultra-porous dust aggregates and thin membranes powered by solar radiation pressure, among others. While none of these hypotheses are perfectly satisfactory, we make predictions that will be testable by the Vera Rubin Observatory to resolve the tension introduced by 'Oumuamua.
Numerical viscosity solutions to Hamilton-Jacobi equations via a Carleman estimate and the convexification method

Abstract We propose a globally convergent numerical method, called the convexification, to numerically compute the viscosity solution to first-order Hamilton-Jacobi equations through the vanishing viscosity process where the viscosity parameter is a fixed small number. By convexification, we mean that we employ a suitable Carleman weight function to convexify the cost functional defined directly from the form of the Hamilton-Jacobi equation under consideration. The strict convexity of this functional is rigorously proved using a new Carleman estimate. We also prove that the unique minimizer of the this strictly convex functional can be reached by the gradient descent method. Moreover, we show that the minimizer well approximates the viscosity solution of the Hamilton-Jacobi equation as the noise contained in the boundary data tends to zero. Some interesting numerical illustrations are presented.
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient

We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addition, it can scale to very large problems because its core operation is matrix-vector multiplications. PDLP is derived by applying the primal-dual hybrid gradient (PDHG) method, popularized by Chambolle and Pock (2011), to a saddle-point formulation of LP. PDLP enhances PDHG for LP by combining several new techniques with older tricks from the literature; the enhancements include diagonal preconditioning, presolving, adaptive step sizes, and adaptive restarting. PDLP improves the state of the art for first-order methods applied to LP. We compare PDLP with SCS, an ADMM-based solver, on a set of 383 LP instances derived from MIPLIB 2017. With a target of $10^{-8}$ relative accuracy and 1 hour time limit, PDLP achieves a 6.3x reduction in the geometric mean of solve times and a 4.6x reduction in the number of instances unsolved (from 227 to 49). Furthermore, we highlight standard benchmark instances and a large-scale application (PageRank) where our open-source prototype of PDLP, written in Julia, outperforms a commercial LP solver.
Gelfand–Tsetlin bases of representations for super Yangian and quantum affine superalgebra

We give explicit actions of Drinfeld generators on Gelfand–Tsetlin bases of super Yangian modules associated with skew Young diagrams. In particular, we give another proof that these representations are irreducible. We study irreducible tame $$\mathrm {Y}(\mathfrak {gl}_{1|1})$$ -modules and show that a finite-dimensional irreducible $$\mathrm {Y}(\mathfrak {gl}_{1|1})$$ -module is tame if and only if it is thin. We also give the analogous statements for quantum affine superalgebra of type A.
Distance-Independent Entanglement Generation in a Quantum Network using Space-Time Multiplexed Greenberger–Horne–Zeilinger (GHZ) Measurements

In a quantum network that successfully creates links—shared Bell states between neighboring repeater nodes—with probability p in each time slot, and performs Bell State Measurements at nodes with success probability q < 1, the end-to-end entanglement generation rate drops exponentially with the distance between consumers, despite multi-path routing. If repeaters can perform multi-qubit projective measurements in the GHZ basis that succeed with probability q, the rate does not change with distance in a certain (p,q) region, but decays exponentially outside. This region where the distance-independent rate occurs is the super-critical region of a new percolation problem. We extend this GHZ protocol to incorporate a time-multiplexing blocklength k, the number of time slots over which a repeater can mix-and-match successful links to perform fusion on. As k increases, the super-critical region expands. For a given (p,q), the entanglement rate initially increases with k, and once inside the super-critical region for a high enough k, it decays as 1/k GHZ states per time slot. When memory coherence time exponentially distributed with mean μ is incorporated, it is seen that increasing k does not indefinitely increase the super-critical region; it has a hard μ-dependent limit. Finally, we find that incorporating space-division multiplexing, i.e., running the above protocol independently in up to d disconnected network regions, where d is the network’s node degree, one can go beyond the 1 GHZ state per time slot rate that the above randomized local-link-state protocol cannot surpass. As (p,q) increases, one can approach the ultimate min-cut entanglement-generation capacity of d GHZ states per slot.
Differential influences of achievement approach goals and intrinsic/extrinsic motivation on help-seeking in e-learning

Considering the importance yet paucity of help-seeking in e-learning, the present study investigated the motivational antecedents of help-seeking among online college students. We explored and compared the influences of achievement approach goals from the old and new achievement motivation models (Elliot & McGregor, 2001; Elliot, Murayama, & Pekrun, 2011) on online students’ help-seeking through intrinsic/extrinsic motivation. Path analyses were used to test two models of help-seeking among college students from four online educational psychology classes (N = 93) based on the two models of achievement goals. Our results showed that the new 3 × 2 model was a better fit than the old 2 × 2 model, suggesting that the achievement approach goals of the new model differ from those of the old model conceptually as Elliot, Murayama, and Pekrun (2011) posited. Second, our results revealed both unexpected direct and indirect positive influence of performance- and other-approach goals on online students’ help-seeking behaviour through extrinsic motivation. Third, while mastery-approach goals indirectly predicted help-seeking through intrinsic motivation, self- and task-approach predicted help-seeking in a dramatically different manner. Self-approach goals displayed indirect influence on help-seeking through intrinsic motivation similar to mastery-approach, yet task-approach displayed a negative direct influence on help-seeking. These results suggested the potential positive impact of self-approach and the detrimental influence of task-approach goals on help-seeking in e-learning environment. Conceptual issues and pedagogical implications for online instructions are discussed. https://doi.org/10.34105/j.kmel.2013.05.011
Phase diagram of the bosonic Kondo-Hubbard model

We study a bosonic version of the Kondo lattice model with an onsite repulsion in the conduction band, implemented with alkali-metal atoms in two bands of an optical lattice. Using both weak- and strong-coupling perturbation theory, we find that at unit filling of the conduction bosons the superfluid-to-Mott-insulator transition should be accompanied by a magnetic transition from a ferromagnet (in the superfluid) to a paramagnet (in the Mott insulator). Furthermore, an analytic treatment of Gutzwiller mean-field theory reveals that quantum spin fluctuations induced by the Kondo exchange cause the otherwise continuous superfluid-to-Mott-insulator phase transition to be first order. We show that lattice separability imposes a serious constraint on proposals to exploit excited bands for quantum simulations, and discuss a way to overcome this constraint in the context of our model by using an experimentally realized nonseparable lattice. A method to probe the first-order nature of the transition based on collapses and revivals of the matter-wave field is also discussed.
The Thermal Performance of a 1.5 MVA HTS Generator

A 1.5‐MVA high temperature superconducting ( HTS ) generator of novel design has been designed, built and successfully tested by the General Electric Company. The 1.5‐ MVA generator has served as the engineering prototype for a much larger 100‐MVA beta unit now under design.The HTS coil in the 1.5 ‐ MVA demonstrator is designed to operate in the range of 20–40 K and is cooled with a closed‐cycle helium refrigeration system employing GM type cryocoolers. This paper will discuss the calculation of the thermal loads to the rotor from all anticipated sources. These sources include conduction losses through the coil suspension system, radiative heat loads to the cold‐system components, residual gas conduction losses, helium‐transfer coupling losses and lead losses. These predicted losses were compared to those measured during actual electrical testing of the rotor at 3600 RPM in order to validate the predictive calculations employed for the 100 MVA machine.
Conversion of carbamoyl phosphate to hydroxyurea: An Assay for Carbamoylphosphate Synthetase

Abstract A specific colorimetric method for determination of hydroxyurea is described. Using this method it was shown that carbamoyl phosphate or cyanate (a decomposition product of carbamoyl phosphate) and hydroxylamine react to form hydroxyurea. Hydroxylamine was employed to trap carbamoyl phosphate produced by carbamoylphosphate synthetase. A radio chemical assay for activity of carbamoylphosphate synthetase gives results in agreement with the method based on coupling the formation of carbamoyl phosphate with ornithine transcarbamoylase. This new assay is sensitive, rapid, and reproducible, and does not require supplementary enzymes or their substrates in the incubation medium.
What features of visually guided arm movements are encoded in the simple spike discharge of cerebellar Purkinje cells

Publisher Summary The cerebellar cortex and nuclei play a major role in the production of smooth, coordinated movements. There is a host of hypotheses on the cerebellum's role in motor behavior. Testing the validity of any hypothesis of cerebellar function requires defining the kinematic or dynamic parameters encoded or represented in the discharge of cerebellar neurons. This step is crucial to the understanding of the controlled variables and the information processed by the cerebellum. This conceptual approach forms the basis for this chapter and examines what is currently known about the encoding of limb movement parameters in the cerebellum, including some of the recent observations. Emphasis is placed on the simple spike discharge of Purkinje cells. Purkinje cells, as well as dentate and interposed neurons, are reported to possess directionally sensitive discharge in single joint and multijoint movements. To provide a more complete description of the encoding of kinematics, the discharge properties of Purkinje cells for reaching movements throughout the horizontal workspace are evaluated. One salient finding is that the simple spike discharge of Purkinje cells is spatially tuned. Each cell's simple spike discharge defines a movement field.
Scheduling of a benchmark flexible manufacturing cell using the Lagrangian relaxation technique

With some modifications, the Lagrangian relaxation technique is applied to a benchmark flexible manufacturing cell (FMC) producing turbocharger parts. Extensive testing shows that reasonable results are obtained with relatively small computation times. Analysis indicates that this technique is most effective in reducing the root-mean-square tardiness of multioperational jobs. >
A novel RF vertical MOSFET for pulsed applications [UHF amplifier]

This paper discusses the results of a high breakdown voltage vertical DMOS RF amplifier that employs power trench technology/spl trade/ to achieve high pulse RF peak power. The RF device can achieve a power of 80 W at an operating voltage of 26 V and a 5% duty cycle.
Effect of Dihydrostreptomycin on Phagocytosis of Mouse-Peritoneal Macrophages In Vitro

The effect of the antibiotic dihydrostreptomycin on the phagocytic and bactericidal ability of peritoneal macrophages obtained from mice has been investigated. In subliminal concentrations which did not influence the bacterial growth, the drug caused macrophages to ingest and kill bacteria (Escherichia coli) at a higher rate than did macrophages without antibiotic. The differences for phagocytosis and intracellular killing of E. coli with and without a subliminal amount of dihydrostreptomycin were statistically significant. Macrophages pretreated with the antibiotic did not demonstrate any enhancement of phagocytosis.
Renin activity in dog brain: enzymological similarity to cathepsin D

The distribution and biochemical properties of the renin activity present in the dog brain were compared with those of the lysosomal enzyme cathepsin D. Renin and cathepsin activity were present in all brain regions studied, in association with high angiotensinase activity. Brain renin activity was partially purified by ammonium sulfate fractionation and Sephadex gel filtration, resulting in the removal of angiotensinase activity. The specific brain renin activity increased approximately one hundred times during this procedure; cathepsin D activity accompanied the brain renin activity throughout the purification and showed a similar increase in specific activity. The renin and cathepsin activity in the partially purified preparation behaved identically during isoelectric focusing. The partially purified renin and cathepsin activity exhibited saturation kinetics with their respective substrates and were without activity above pH 6.0. Both enzyme activities were irreversibly inhibited by the pepsin inhibitor pepstatin, in nanomolar concentrations. These data, in conjunction with the literature concerning brain cathepsin, suggest that the renin activity in brain is due to cathepsin D, and that this renin activity exhibited by cathepsin D may be of limited significance under physiological conditions.
Bivariate Conditional Sampling of Moisture Flux over a Tropical Ocean

Abstract New applications of conditional sampling using the bivariate joint frequency distribution (JFD) and conditional mean distribution (CMD) are introduced to analyze time series of water vapor flux obtained from aircraft gust-probe vertical velocity and water vapor data collected during free convective conditions (zi/L≈−46) in the Atlantic trade winds. The JFD can provide estimates of fractional time as well as mean and variance estimates for the four ways vertical velocity/moisture fluctuations can combine (up-moist, up-dry, down-moist, down-dry). These combinations were interpreted using a subcloud layer model consisting of upward and downward moving convective cells and a mixing zone between them. The data suggest detrainment from the up-moving cell becomes the predominant mixing process above 0.08zi. The fractional time and mean values were used to parameterize the moisture flux. Three parameterization methods are introduced; two estimated the moisture flux to within 10% while a third, simpler ve...
Quarks of Almost Any Spin

Quark-model predictions are investigated for quarks of any half-integral spin. While quarks of spin \textonehalf{} must obey effective Bose statistics if they are to be in space-symmetric states, we show that quarks of spin greater than \textonehalf{} can obey either Fermi or Bose statistics. The generalization to quarks of any half-integral spin leads to essentially the same conclusions we found previously for spin-$\frac{3}{2}$ Fermi quarks. The quark-model predictions so far tested depend very little on the spin or statistics of quarks, but a measurement of the magnetic moment of the ${\ensuremath{\Xi}}^{\ensuremath{-}}$ or ${\ensuremath{\Omega}}^{\ensuremath{-}}$ hyperon would test the spin and statistics of quarks.
On the first occurrence of certain patterns of quadratic residues and non-residues

Effective upper bounds are obtained for the first occurrence of certain mixed patterns of quadratic residues and non-residues using the character sum estimates of D. A. Burgess and a proof of a conjecture of E. Lehmer.
Low light-level two-photon absorption using tapered optical fibers in rubidium vapor

We describe an experimental observation of two-photon absorption with ultralow power levels of less than 150 nanoWatts. The experiment involves the use of sub-wavelength diameter tapered optical fibers suspended in rubidium vapor.