cssi/SciSciGPT-SciSciNet huggingface dataset:
Impact of International Student Enrollment on US Gross Domestic Product

The goal of this paper is to analyze the impact of international student enrollments on the US real gross domestic product. It is hypothesized that an increase in international student enrollment tends to have a positive impact on US real gross domestic product. It is also hypothesized that an increase in economic impact of international students has a positive impact on the US real gross domestic product. A regression model is formulated that consists of percentage increase in real gross domestic product (economic growth rate) as the dependent variable. The independent variables used in the regression model are annual percentage increase in international student enrollments in the US and the annual percentage increase in economic impact of the international students in the US. Data is collected from 2009 to 2015 from the Bureau of Economic Analysis and NAFSA. It is important to analyze the rationale as to why highly skilled immigration is vital for promotion of entrepreneurship and innovation in the US economy. The current administration is trying to undermine the positive impact of highly skilled immigration on the US economy. The issue is critically important in the prevailing political climate in the US economy. An attempt is also made to formulate strategies that would help in mitigating negative outcomes such as potential declines in revenues for US universities as well as decline in research and grant funding that would arise due to decrease in international student enrolments.
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

Training machine learning models that are robust against adversarial inputs poses seemingly insurmountable challenges. To better understand adversarial robustness, we consider the underlying problem of learning robust representations. We develop a notion of representation vulnerability that captures the maximum change of mutual information between the input and output distributions, under the worst-case input perturbation. Then, we prove a theorem that establishes a lower bound on the minimum adversarial risk that can be achieved for any downstream classifier based on its representation vulnerability. We propose an unsupervised learning method for obtaining intrinsically robust representations by maximizing the worst-case mutual information between the input and output distributions. Experiments on downstream classification tasks support the robustness of the representations found using unsupervised learning with our training principle.
Reinit\(^{++}\): Evaluating the Performance of Global-Restart Recovery Methods for MPI Fault Tolerance

Scaling supercomputers comes with an increase in failure rates due to the increasing number of hardware components. In standard practice, applications are made resilient through checkpointing data and restarting execution after a failure occurs to resume from the latest checkpoint. However, re-deploying an application incurs overhead by tearing down and re-instating execution, and possibly limiting checkpointing retrieval from slow permanent storage.
How Can We Accelerate Progress Towards Human-like Linguistic Generalization?

This position paper describes and critiques the Pretraining-Agnostic Identically Distributed (PAID) evaluation paradigm, which has become a central tool for measuring progress in natural language understanding. This paradigm consists of three stages: (1) pre-training of a word prediction model on a corpus of arbitrary size; (2) fine-tuning (transfer learning) on a training set representing a classification task; (3) evaluation on a test set drawn from the same distribution as that training set. This paradigm favors simple, low-bias architectures, which, first, can be scaled to process vast amounts of data, and second, can capture the fine-grained statistical properties of a particular data set, regardless of whether those properties are likely to generalize to examples of the task outside the data set. This contrasts with humans, who learn language from several orders of magnitude less data than the systems favored by this evaluation paradigm, and generalize to new tasks in a consistent way. We advocate for supplementing or replacing PAID with paradigms that reward architectures that generalize as quickly and robustly as humans.
Weighted Sobolev $L^{p}$ estimates for homotopy operators on strictly pseudoconvex domains with $C^{2}$ boundary

We derive estimates in a weighted Sobolev space $$W^{k,p}_{\beta }(D)$$ for a homotopy operator on a bounded strictly pseudoconvex domain D of $$C^2$$ boundary in $${\mathbb {C}}^n$$ . As a result, we show that given any $$2n< p < \infty $$ , $$k > 1$$ , $$q \ge 1$$ , and a $$\overline{\partial }$$ -closed (0, q) form $$\varphi $$ of class $$W^{k,p}(D)$$ , there exists a solution u to $$\overline{\partial } u = \varphi $$ , such that $$u \in W^{k,p}_{\frac{1}{2} - \epsilon }(D)$$ for any $$\epsilon > 0$$ . If $$k=1$$ , then we can take p to be any value between 1 and $$\infty $$ . In other words, the solution gains almost $$\frac{1}{2}$$ -derivative in a suitable sense.
Sparse and Low-Rank Tensor Estimation via Cubic Sketchings

In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is developed based on sparse tensor decomposition and thresholded gradient descent, which ensures exact recovery in the noiseless case and stable recovery in the noisy case with high probability. The non-asymptotic analysis sheds light on an interplay between optimization error and statistical error. The proposed procedure is shown to be rate-optimal under certain conditions. As a technical by-product, novel high-order concentration inequalities are derived for studying high-moment sub-Gaussian tensors. An interesting tensor formulation illustrates the potential application to high-order interaction pursuit in high-dimensional linear regression.
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks

This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and gossip communications. The algorithms can also employ acceleration on the computation and communications. We provide a unified analysis of their convergence rate, measured in terms of the Bregman distance associated to the saddle point reformation of the distributed optimization problem. When acceleration is employed, the rate is shown to be optimal, in the sense that it matches (under the proposed metric) existing complexity lower bounds of distributed algorithms applicable to such a class of problem and using only gradient information and gossip communications. Preliminary numerical results on distributed least-square regression problems show that the proposed algorithm compares favorably on existing distributed schemes.
Using Phase Data From MR Temperature Imaging to Visualize Anatomy During MRI-Guided Focused Ultrasound Neurosurgery

Neurosurgery targets in the thalamus can be challenging to identify during transcranial MRI-guided focused ultrasound (MRgFUS) thermal ablation due to poor image quality. They also neighbor structures that can result in side effects if damaged. Here we demonstrate that the phase data obtained during MRgFUS for MR temperature imaging (MRTI) contains anatomic information that could be useful in guiding this procedure. This approach was evaluated in 68 thalamotomies for essential tremor (ET). We found that we could readily visualize the red nucleus and subthalamic nucleus, and those nuclei were consistently aligned with the sonication target coordinates. We also could consistently visualize the internal capsule, which needs to be protected from thermal damage to prevent side effects. Preliminary results from four pallidotomies in Parkinson’s disease patients suggest that this approach might also be useful in visualizing the optic tract in addition to the internal capsule. Overall, this approach can visualize anatomic landmarks that may be useful to refine atlas-based targeting for MRgFUS. Since the same data is used for MRTI and anatomic visualization, there are no errors induced by registration to previously obtained planning images or image distortion, and no additional time is needed.
Public Perceptions of Artificial Intelligence and Robotics in Medicine.

Objective: To understand better the public perception and comprehension of medical technology such as artificial intelligence (AI) and robotic surgery. In addition to this, to identify sensitivity ...
Deficient uracil base excision repair leads to persistent dUMP in HIV proviruses during infection of monocytes and macrophages

Non-dividing cells of the myeloid lineage such as monocytes and macrophages are target cells of HIV that have low dNTP pool concentrations and elevated levels of dUTP, which leads to frequent incorporation of dUMP opposite to A during reverse transcription ("uracilation"). One factor determining the fate of dUMP in proviral DNA is the host cell uracil base excision repair (UBER) system. Here we explore the relative UBER capacity of monocytes (MC) and monocyte-derived macrophages (MDM) and the fate of integrated uracilated viruses in both cell types to understand the implications of viral dUMP on HIV diversification and infectivity. We find that the kinetics for MC infection is compatible with their lifetime in vivo and their near absence of hUNG2 activity is consistent with the retention of viral dUMP at high levels at least until differentiation into macrophages, where UBER becomes possible. Overexpression of human uracil DNA glycosylase in MDM prior to infection resulted in rapid removal of dUMP from HIV cDNA and near complete depletion of dUMP-containing viral copies. This finding establishes that the low hUNG2 expression level in these cells limits UBER but that hUNG2 is restrictive against uracilated viruses. In contrast, overexpression of hUNG2 after viral integration did not accelerate the excision of uracils, suggesting that they may poorly accessible in the context of chromatin. We found that viral DNA molecules with incorporated dUMP contained unique (+) strand transversion mutations that were not observed when dUMP was absent (G→T, T→A, T→G, A→C). These observations and other considerations suggest that dUMP introduces errors predominantly during (-) strand synthesis when the template is RNA. Overall, the likelihood of producing a functional virus from in vitro infection of MC is about 50-fold and 300-fold reduced as compared to MDM and activated T cells. The results implicate viral dUMP incorporation in MC and MDM as a potential viral diversification and restriction pathway during human HIV infection.
SIS Epidemic Model under Mobility on Multi-layer Networks

We study the influence of heterogeneous mobility patterns in a population on the SIS epidemic model. In particular, we consider a patchy environment in which each patch comprises individuals belonging the different classes, e.g., individuals in different socio-economic strata. We model the mobility of individuals of each class across different patches through an associated Continuous Time Markov Chain (CTMC). The topology of these multiple CTMCs constitute the multi-layer network of mobility. At each time, individuals move in the multilayer network of spatially-distributed patches according to their CTMC and subsequently interact with the local individuals in the patch according to an SIS epidemic model. We derive a deterministic continuum limit model describing these mobility-epidemic interactions. We establish the existence of a Disease-Free Equilibrium (DFE) and an Endemic Equilibrium (EE) under different parameter regimes and establish their (almost) global asymptotic stability using Lyapunov techniques. We derive simple sufficient conditions that highlight the influence of the multi-layer network on the stability of DFE. Finally, we numerically illustrate that the derived model provides a good approximation to the stochastic model with a finite population and also demonstrate the influence of the multi-layer network structure on the transient performance.
The Incapacitated Surrogate: What is the Consultation-Liaison Psychiatrist's Role?

Background When a patient is found to be lacking capacity to make a medical decision, the medical team is advised to turn to the patient's most appropriate surrogate decision maker (hereafter, surrogate) to make a choice on behalf of the patient. The assumption made by the medical team is that the surrogate will have the capacity to make appropriate medical decisions on behalf of the patient. At times though, the capacity of the surrogate himself may be called into question, leading to uncertainty in terms of how best to proceed in the care of the patient. Consultation-liaison psychiatrists are commonly consulted to assess a patient's capacity to make a particular medical decision, but their role assisting in cases of incapacitated surrogates is less clear. Objective In this article, we summarize the existing literature and current state laws regarding incapacitated surrogates and propose guidelines for the role the consultation-liaison psychiatrist can take when the surrogate may be incapacitated.
Topology-Aware Cooperative Data Protection in Blockchain-Based Decentralized Storage Networks

The continuous rise of the blockchain technology is moving various information systems towards decentralization. Blockchain-based decentralized storage networks (DSNs) offer significantly higher privacy and lower costs to customers compared with centralized cloud storage associated with specific vendors. Coding is required to retrieve data stored on failing components. While coding solutions for centralized storage have been intensely studied, those for DSNs have not yet been discussed. In this paper, we propose a coding scheme where each node receives extra protection through cooperation with nodes in its neighborhood in a heterogeneous DSN with any given topology. Our scheme can achieve faster recovery speed compared with existing network coding methods, and can correct more erasure patterns compared with our previous work.
A General Superapproximation Result

A general superapproximation result is derived in this paper which is useful for the local/interior error analysis of finite element methods.
Bayesian system ID: optimal management of parameter, model, and measurement uncertainty

System identification of dynamical systems is often posed as a least squares minimization problem. The aim of these optimization problems is typically to learn either propagators or the underlying vector fields from trajectories of data. In this paper, we study a first principles derivation of appropriate objective formulations for system identification based on probabilistic principles. We compare the resulting inference objective to those used by emerging data-driven methods based on dynamic mode decomposition (DMD) and system identification of nonlinear dynamics (SINDy). We show that these and related least squares formulations are specific cases of a more general objective function. We also show that the more general objective function yields more robust and reliable recovery in the presence of sparse data and noisy measurements. We attribute this success to an explicit accounting of imperfect model forms, parameter uncertainty, and measurement uncertainty. We study the computational complexity of an approximate marginal Markov Chain Monte Carlo method to solve the resulting inference problem and numerically compare our results on a number of canonical systems: linear pendulum, nonlinear pendulum, the Van der Pol oscillator, the Lorenz system, and a reaction–diffusion system. The results of these comparisons show that in cases where DMD and SINDy excel, the Bayesian approach performs equally well, and in cases where DMD and SINDy fail to produce reasonable results, the Bayesian approach remains robust and can still deliver reliable results.
Relation algebras of Sugihara, Belnap, Meyer, and Church

Abstract Algebras introduced by, or attributed to, Sugihara, Belnap, Meyer, and Church are representable as algebras of binary relations with set-theoretically defined operations. They are definitional reducts or subreducts of proper relation algebras. The representability of Sugihara matrices yields sound and complete set-theoretical semantics for R -mingle.
The Human Fragile X Mental Retardation Protein Inhibits the Elongation Step of Translation through Its RGG and C-Terminal Domains.

Fragile X mental retardation protein (FMRP) is an RNA-binding protein that regulates the translation of numerous mRNAs in neurons. The precise mechanism of translational regulation by FMRP is unkno...
Tools of Control? Comparing Congressional and Presidential Performance Management Reforms

Presidents are claimed to have a stronger interest in an effective bureaucracy than Congress, because they must be responsive to the public as a whole rather than narrow interests. We examine this claim in the context of multiple waves of US performance management reforms: the Government Performance and Results Act of 1993, the Program Assessment Rating Tool (2002-2008) and the GPRA Modernization Act of 2010. Using four waves of federal employee surveys spanning 17 years, we measure reform success as employees‟ purposeful use of performance data as a result of being exposed to routines embedded in the reforms. We find that the legislative-led GPRAMA is associated with more purposeful data use on aggregate, while the PART executive reform succumbed to a partisan pattern of implementation. Statutory reforms are less likely to be experienced as ideological tools than executive branch reforms used by the President to impose control over agencies.
Sufficiently Accurate Model Learning

Modeling how a robot interacts with the environment around it is an important prerequisite for designing control and planning algorithms. In fact, the performance of controllers and planners is highly dependent on the quality of the model. One popular approach is to learn data driven models in order to compensate for inaccurate physical measurements and to adapt to systems that evolve over time. In this paper, we investigate a method to regularize model learning techniques to provide better error characteristics for traditional control and planning algorithms. This work proposes learning "Sufficiently Accurate" models of dynamics using a primal-dual method that can explicitly enforce constraints on the error in pre-defined parts of the state-space. The result of this method is that the error characteristics of the learned model is more predictable and can be better utilized by planning and control algorithms. The characteristics of Sufficiently Accurate models are analyzed through experiments on a simulated ball paddle system.
Irregularity of Distribution in Wasserstein Distance

We study the non-uniformity of probability measures on the interval and circle. On the interval, we identify the Wasserstein-p distance with the classical $$L^p$$ -discrepancy. We thereby derive sharp estimates in Wasserstein distances for the irregularity of distribution of sequences on the interval and circle. Furthermore, we prove an $$L^p$$ -adapted Erdős–Turan inequality, and use it to extend a well-known bound of Polya and Vinogradov on the equidistribution of quadratic residues in finite fields.
On critical elliptic problems with singular Trudinger–Moser nonlinearities

Abstract We establish some existence results for a class of critical elliptic problems with singular exponential nonlinearities. We do not assume any global sign conditions on the nonlinearity, which makes our results new even in the nonsingular case.
Risk of Adverse Outcomes in Hospitalized Patients with Autoimmune Disease and COVID-19: A Matched Cohort Study from New York City.

Objective To examine the impact of autoimmune disease on the composite outcome of intensive care unit admission, intubation, or death, from COVID-19 in hospitalized patients. Methods Retrospective cohort study of 186 patients hospitalized with COVID-19 between March 1st–April 15th, 2020 at NewYork-Presbyterian Hospital/Columbia University Irving Medical Center. The cohort included 62 patients with autoimmune disease and 124 age- and sex-matched controls. The primary outcome was a composite of intensive care unit admission, intubation, and death, with secondary outcome assessing time to in-hospital death. Baseline demographics, comorbidities, medications, vital signs, and laboratory values were collected. Conditional logistic regression and Cox proportional hazards regression were used to assess the association between autoimmune disease and clinical outcomes. Results Patients with autoimmune disease were more likely to have at least one comorbidity (25.8% vs. 12.9%, p=0.03), take chronic immunosuppressive medications (66.1% vs. 4.0%, p Conclusion Among patients hospitalized with COVID-19, individuals with autoimmune disease did not have an increased risk of a composite outcome of intensive care unit admission, intubation, or death.
Understanding Cultural Persistence and Change

We examine a determinant of cultural persistence that has emerged from a class of models in evolutionary anthropology: the similarity of the environment across generations. Within these models, when the environment is more stable across generations, the traits that have evolved up to the previous generation are more likely to be suitable for the current generation. In equilibrium, a greater value is placed on tradition and there is greater cultural persistence. We test this hypothesis by measuring the variability of climatic measures across 20-year generations from 500 to 1900. Employing a variety of tests that use different samples and empirical strategies, we find that populations with ancestors who lived in environments with more cross-generational instability place less importance on maintaining tradition today and exhibit less cultural persistence.
Learning during the COVID-19 pandemic: It is not who you teach, but how you teach

Abstract We use unique data from seven intermediate economics courses taught at four R1 institutions to examine the effects of the COVID-19 pandemic on student learning. Because the same assessments of course knowledge mastery were administered across semesters, we can cleanly infer the impact of the unanticipated switch to remote teaching in Spring 2020. During the pandemic, total assessment scores declined by 0.2 standard deviations on average. However, we find substantial heterogeneity in learning outcomes across courses. Course instructors were surveyed about their pedagogy practices and our analysis suggests that prior online teaching experience and teaching methods that encouraged active engagement, such as the use of small group activities and projects, played an important role in mitigating this negative effect. In contrast, we find that student characteristics, including gender, race, and first-generation status, had no significant association with the decline in student performance in the pandemic semester.
Cluster-based information retrieval by using (K-means)-hierarchical parallel genetic algorithms approach

Cluster-based information retrieval is one of the information retrieval (IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. In this paper, we proposed a (K-means)-hierarchical parallel genetic algorithms approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (K-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.
Behavioral and brain signatures of substance use vulnerability in childhood.

Abstract The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [rp]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (rp: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.
Graph-based analysis of nonreciprocity in coupled-mode systems

In this work we derive the general conditions for obtaining nonreciprocity in multi-mode parametrically-coupled systems. The results can be applied to a broad variety of optical, microwave, and hybrid systems including recent electro- and opto-mechanical devices. In deriving these results, we use a graph-based methodology to derive the scattering matrix. This approach naturally expresses the terms in the scattering coefficients as separate graphs corresponding to distinct coupling paths between modes such that it is evident that nonreciprocity arises as a consequence of multi-path interference and dissipation in key ancillary modes. These concepts facilitate the construction of new devices in which several other characteristics might also be simultaneously optimized. As an example, we synthesize a novel three-mode unilateral amplifier design by use of graphs. Finally, we analyze the isolation generated in a common parametric multi-mode system, the dc-SQUID.
Global weak solutions for generalized SQG in bounded domains

We prove the existence of global L2 weak solutions for a family of generalized inviscid surface quasigeostrophic (SQG) equations in bounded domains of ℝ2. In these equations, the active scalar is transported by a velocity field which is determined by the scalar through a more singular nonlocal operator compared to the SQG equation. The result is obtained by establishing appropriate commutator representations for the weak formulation together with good bounds for them in bounded domains.
Fluctuation spectra of tilted and intersecting D-branes from the Born-Infeld action

Abstract We consider the spectra of excitations around diagonal and intersecting D-brane configurations on tori. These configurations are described by constant curvature connections in a dual gauge theory description. The low-energy string fluctuation spectrum is reproduced exactly by the gauge theory in the case of vanishing field strength; however, this correspondence breaks down for fixed non-zero field strength. We show that in many cases the full Born-Infeld action correctly captures the low-energy spectrum in the case of non-vanishing field strength. This gives a field theory description of the low-energy physics of systems of diagonally wound branes and branes at angles as considered by Berkooz, Douglas and Leigh. This description extends naturally to non-supersymmetric configurations, where the tachyonic instability associated with brane-antibrane systems appears as an instability around a saddle point solution of the corresponding Yang-Mills/Born-Infeld theory. In some cases, the field theory description requires a non-abelian generalization of the Born-Infeld action. We follow Tseytlin's recent proposal for formulating such an action. In the case of intersecting branes, the non-abelian Bom-Infeld theory produces a transcendental relation which comes tantalizingly close to reproducing the correct spectrum; however, a discrepancy remains which indicates that a further clarification of the non-abelian Born-Infeld action may be necessary.
On the estimation of the Mori-Zwanzig memory integral

We develop a thorough mathematical analysis to deduce conditions for the accuracy and convergence of different approximations of the memory integral in the Mori-Zwanzig (MZ) equation. In particular, we derive error bounds and sufficient convergence conditions for short-memory approximations, the t-model, and hierarchical (finite-memory) approximations. In addition, we derive useful upper bounds for the MZ memory integral, which allow us to estimate a priori the contribution of the MZ memory to the dynamics. Such upper bounds are easily computable for systems with finite-rank projections. Numerical examples are presented and discussed for linear and nonlinear dynamical systems evolving from random initial states.We develop a thorough mathematical analysis to deduce conditions for the accuracy and convergence of different approximations of the memory integral in the Mori-Zwanzig (MZ) equation. In particular, we derive error bounds and sufficient convergence conditions for short-memory approximations, the t-model, and hierarchical (finite-memory) approximations. In addition, we derive useful upper bounds for the MZ memory integral, which allow us to estimate a priori the contribution of the MZ memory to the dynamics. Such upper bounds are easily computable for systems with finite-rank projections. Numerical examples are presented and discussed for linear and nonlinear dynamical systems evolving from random initial states.
Hybrid Open-Loop Closed-Loop Control of Coupled Human–Robot Balance During Assisted Stance Transition With Extra Robotic Legs

A new approach to the human–robot shared control of the extra robotic legs (XRL) wearable augmentation system is presented. The XRL system consists of two extra legs that bear the entirety of its backpack payload, as well as some of the human operator's weight. The XRL system must support its own balance and assist the operator stably while allowing them to move in selected directions. In some directions of the task space the XRL must constrain the human motion with position feedback for balance, while in other directions the XRL must have no position feedback, so that the human can move freely. Here, we present hybrid open-loop/closed-loop control architecture for mixing the two control modes in a systematic manner. The system is reduced to individual joint feedback control that is simple to implement and reliable against failure. The method is applied to the XRL system that assists a human in conducting a nuclear waste decommissioning task. A prototype XRL system has been developed and demonstrated with a simulated human performing the transition from standing to crawling and back again while coupled to the prototype XRL system.
Vortices and Dynamics in Trapped Bose-Einstein Condensates

I review the basic physics of ultracold dilute trapped atomic gases, with emphasis on Bose-Einstein condensation and quantized vortices. The hydrodynamic form of the Gross-Pitaevskii equation (a nonlinear Schrodinger equation) illuminates the role of the density and the quantum-mechanical phase. One unique feature of these experimental systems is the opportunity to study the dynamics of vortices in real time, in contrast to typical experiments on superfluid 4He. I discuss three specific examples (precession of single vortices, motion of vortex dipoles, and Tkachenko oscillations of a vortex array). Other unusual features include the study of quantum turbulence and the behavior for rapid rotation, when the vortices form dense regular arrays. Ultimately, the system is predicted to make a quantum phase transition to various highly correlated many-body states (analogous to bosonic quantum Hall states) that are not superfluid and do not have condensate wave functions. At present, this transition remains elusive. Conceivably, laser-induced synthetic vector potentials can serve to reach this intriguing phase transition.
On the electrostatic equilibrium of charges and cavities in a conductor

We consider a charged conductor of arbitrary shape, in electrostatic equilibrium, with one or more cavities inside it, and with fixed charges placed outside the conductors and inside the cavities. The field inside a particular cavity is then only due to charges within that cavity itself and to the surface charge induced on the surface of the same cavity. A similar statement holds for the exterior of the conductor. Although this is an elementary property of conductors, it is not a trivial statement, as explained in this article. Undergraduate texts in electrodynamics do not discuss at length or provide a complete argument for an important problem such as this. Two simple and complete proofs are provided in this note with the help of the standard electrostatic uniqueness theorems.
Calibration-Free Positioning Technique Using Wi-Fi Ranging and Built-In Sensors of Mobile Devices

As positioning solutions integrate multiple components to improve accuracy, the number of parameters that require calibration has increased. This article studies a calibration-free positioning technique using Wi-Fi ranging and pedestrian dead reckoning (PDR), where every parameter in the system is optimized in real time. This significantly decreases the time and effort required to perform manual calibration procedures and enables the positioning solution to achieve robust performance in various situations. Additionally, this article studies an efficient way of performing irregular Wi-Fi ranging procedures to improve battery life and network performance of mobile devices. The positioning performance of the proposed method was verified using a real-time Android application on several mobile devices under a large indoor office environment. Without any calibration, the proposed method achieved up to 1.38-m average positioning accuracy for received signal strength (RSS)-based ranging scenarios, which differs only by 30 cm from the benchmark assuming perfect calibration. In addition, the proposed method achieved up to 1.04-m accuracy for round trip time (RTT)-based ranging scenarios with a 40-MHz bandwidth configuration, which differs only by 10 cm from the benchmark.
Blow-up criteria for the Navier–Stokes equations in non-endpoint critical Besov spaces

We obtain an improved blow-up criterion for solutions of the Navier–Stokes equations in critical Besov spaces. If a mild solution u has maximal existence time T ∗ < ∞ , then the non-endpoint critical Besov norms must become infinite at the blow-up time: lim t ↑ T ∗ ∥ u ( ⋅ , t ) ∥ Ḃ p , q − 1 + 3 ∕ p ( ℝ 3 ) = ∞ , 3 < p , q < ∞ . In particular, we introduce a priori estimates for the solution based on elementary splittings of initial data in critical Besov spaces and energy methods. These estimates allow us to rescale around a potential singularity and apply backward uniqueness arguments. The proof does not use profile decomposition.
Conformal bootstrap for percolation and polymers

The conformal bootstrap is applied to percolation and dilute self-avoiding polymers, two theories with Virasoro central charge $c=0$ in two dimensions. In both cases we propose a spectrum of operators motivated by Virasoro symmetry which is devoid of a stress energy tensor as an approximate means of enforcing $c=0$. Percolation is treated in $2\leq D \leq 6$ dimensions, and the self-avoiding walk in $2 \leq D \leq 4$.
Basins of Attraction for Cascading Maps

We study a finite uni-directional array of "cascading" or "threshold coupled" chaotic maps. We describe some of the attractors for such systems and prove general results about their basins of attraction. In particular, we show that the basins of attraction have infinitely many path components. We show that these components always accumulate at the corners of the domain of the system. For all threshold parameters above a certain value, we show that they accumulate at a Cantor set in the interior of the domain. For certain ranges of the threshold, we prove that the system has many attractors.
Solar Cycle Related Changes at the Base of the Convection Zone

The frequencies of solar oscillations are known to change with solar activity. We use principal component analysis to examine these changes with high precision. In addition to the well-documented changes in solar normal mode oscillations with activity as a function of frequency, which originate in the surface layers of the Sun, we find a small but statistically significant change in frequencies with an origin at and below the base of the convection zone. We find that at r = (0.712+ 0.0097−0.0029) R☉, the change in sound speed is δ c2/c2 = (7.23 ± 2.08) × 10−5 between high and low activity. This change is very tightly correlated with solar activity. In addition, we use the splitting coefficients to examine the latitudinal structure of these changes. We find changes in sound speed correlated with surface activity for r 0.9 R☉.
Bailing out the Milky Way: variation in the properties of massive dwarfs among galaxy-sized systems

Recent kinematical constraints on the internal densities of the Milky Way's dwarf satellites have revealed a discrepancy with the subhalo populations of simulated Galaxy-scale halos in the standard cold dark matter model of hierarchical structure formation. In particular, the Via Lactea II and Aquarius simulations both have large subhalos with internal densities that are larger than the constraints inferred for any Milky Way dwarf satellites. This has been dubbed the ``too big to fail'' problem, with reference to the improbability of large and invisible companions existing in the Galactic environment. In this paper, we argue that both the Milky Way observations and simulated subhalos are consistent with the predictions of the standard model for structure formation. Specifically, we show that there is significant variation in the properties of subhalos among distinct host halos of fixed mass and suggest that this can reasonably account for the deficit of dense satellites in the Milky Way. We exploit well-tested analytic techniques to predict the properties in a large sample of distinct host halos with a variety of masses spanning the range expected of the Galactic halo. Such techniques render the problem of estimating the variance in subhalo properties computationally feasible. The analytic model produces subhalo populations consistent with both Via Lactea II and Aquarius, and our results suggest that natural variation in subhalo properties suffices to explain the discrepancy between Milky Way satellite kinematics and these numerical simulations. At least ~ 10% of Milky Way-sized halos host subhalo populations for which there is no ``too big to fail'' problem, even when the host halo mass is as large as Mhost = 1012.2 h−1 M⊙. Follow-up studies consisting of high-resolution simulations of a large number of Milky Way-sized hosts are necessary to confirm our predictions. In the absence of such efforts, the ``too big to fail'' problem does not appear to be a significant challenge to the standard model of cold dark matter halos undergoing hierarchical formation.
Road Network Reconstruction from satellite images with Machine Learning Supported by Topological Methods

Automatic Extraction of road network from satellite images is a goal that can benefit and even enable new technologies. Methods that combine machine learning (ML) and computer vision have been proposed in recent years which make the task semi-automatic by requiring the user to provide curated training samples. The process can be fully automatized if training samples can be produced algorithmically. In this work, we develop such a technique by infusing a persistence-guided discrete Morse based graph reconstruction algorithm into ML framework. We elucidate our contributions in two phases. First, in a semi-automatic framework, we combine a discrete-Morse based graph reconstruction algorithm with an existing CNN framework to segment input satellite images. We show that this leads to reconstructions with better connectivity and less noise. Next, in a fully automatic framework, we leverage the power of the discrete-Morse based graph reconstruction algorithm to train a CNN from a collection of images without labelled data and use the same algorithm to produce the final output from the segmented images created by the trained CNN. We apply the discrete-Morse based graph reconstruction algorithm iteratively to improve the accuracy of the CNN. We show experimental results on datasets from SpaceNet Challenge. Full version of the paper appears in [8].
Quasars with super-metal-rich emission-line regions

We study the degree of chemical enrichment in the broad emission line regions (BELRs) of two QSOs with unusually strong nitrogen emission lines. The N V λ1240/C IV λ1549 intensity ratio is often used as a metallicity indicator for QSOs. The validity of this approach can be tested by studying objects in which the N IV] and N III] lines, in addition to N V, are unusually strong and easily measurable. If all of these ionization states of nitrogen point to the same metallicity, it implies that the large N V strengths observed in most QSOs are not due to some peculiarity of the N V λ1240 line. This test had previously been applied to Q0353-383, a QSO long known to have extremely strong N III] and N IV] lines, with the result supporting high metallicity in that object. Here we make the same check in two other QSOs with very strong nitrogen lines, as a step toward using such QSOs to better probe the early chemical enrichment histories of their host galaxies. J1254+0241 has a metallicity of about 10 times solar, with good agreement between the abundance results from different line ratios. J1546+5253 has a more moderate metallicity, about 5 times solar, but the abundances determined from different line ratios show a much wider scatter than they do for J1254+0241 or Q0353-383. This QSO also has an unusual low-ionization emission line spectrum similar to some low-ionization BAL QSOs and to the unusual AGN I Zw 1. We attribute the peculiarities in its spectrum to some combination of unusual structure and/or unusual physical conditions in its BELR. Our results further affirm the validity of the N V/C IV ratio as an abundance indicator in QSOs.
Signatures of Hot Molecular Hydrogen Absorption from Protoplanetary Disks. I. Non-thermal Populations

The environment around protoplanetary disks (PPDs) regulates processes which drive the chemical and structural evolution of circumstellar material. We perform a detailed empirical survey of warm molecular hydrogen (H$_2$) absorption observed against H I-Ly$\alpha$ (Ly$\alpha$: $\lambda$ 1215.67 A) emission profiles for 22 PPDs, using archival Hubble Space Telescope (HST) ultraviolet (UV) spectra from the Cosmic Origins Spectrograph (COS) and the Space Telescope Imaging Spectrograph (STIS) to identify H$_2$ absorption signatures and quantify the column densities of H$_2$ ground states in each sightline. We compare thermal equilibrium models of H$_2$ to the observed H$_2$ rovibrational level distributions. We find that, for the majority of targets, there is a clear deviation in high energy states (T$_{exc}$ $\gtrsim$ 20,000 K) away from thermal equilibrium populations (T(H$_2$) $\gtrsim$ 3500 K). We create a metric to estimate the total column density of non-thermal H$_2$ (N(H$_2$)$_{nLTE}$) and find that the total column densities of thermal (N(H$_2$)) and N(H$_2$)$_{nLTE}$ correlate for transition disks and targets with detectable C IV-pumped H$_2$ fluorescence. We compare N(H$_2$) and N(H$_2$)$_{nLTE}$ to circumstellar observables and find that N(H$_2$)$_{nLTE}$ correlates with X-ray and FUV luminosities, but no correlations are observed with the luminosities of discrete emission features (e.g., Ly$\alpha$, C IV). Additionally, N(H$_2$) and N(H$_2$)$_{nLTE}$ are too low to account for the H$_2$ fluorescence observed in PPDs, so we speculate that this H$_2$ may instead be associated with a diffuse, hot, atomic halo surrounding the planet-forming disk. We create a simple photon-pumping model for each target to test this hypothesis and find that Ly$\alpha$ efficiently pumps H$_2$ levels with T$_{exc}$ $\geq$ 10,000 K out of thermal equilibrium.
The Search for Electroweakinos

In this review, we consider a general theoretical framework for fermionic color-singlet states—including a singlet, a doublet, and a triplet under the Standard Model SU(2)L gauge symmetry, correspo...
Sparse Array Beamforming Design for Wideband Signal Models

We develop sparse array receive beamformer design methods achieving maximum signal-to-interference plus noise ratio for wideband sources and jammers. Both tapped delay line filtering and the DFT realizations to wideband array processing are considered. The array sparsity stems from the limited number of available radio frequency (RF) transmission chains that switch between the sensors, thereby configuring different arrays at different times. The sparse array configuration design problem is formulated as a quadratically constraint quadratic program and solved by using semidefinite relaxation. A computationally viable approach through successive convex relaxation is also pursued. In order to realize an implementable design, in presence of missing autocorrelation lags, we propose parameter-free block Toeplitz matrix completion to estimate the received data correlation matrix across the entire array aperture. It is shown that the optimum wideband sparse array effectively utilizes the array aperture and provides considerable performance improvement over suboptimal array topologies.