cssi/SciSciGPT-SciSciNet huggingface dataset:
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport

Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structural summary of the prior one. With a long history in scientific computing, many coarsening strategies were developed based on mathematically driven heuristics. Recently, resurgent interests exist in deep learning to design hierarchical methods learnable through differentiable parameterization. These approaches are paired with downstream tasks for supervised learning. In practice, however, supervised signals (e.g., labels) are scarce and are often laborious to obtain. In this work, we propose an unsupervised approach, coined OTCoarsening, with the use of optimal transport. Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs. We demonstrate that the proposed approach produces meaningful coarse graphs and yields competitive performance compared with supervised methods for graph classification and regression.
An Automated Method for Determining the Flutter Velocity and the Matched Point

A new method is developed for the automated determination of flutter solutions and the matchedpoint flutter density. The method employs a Laguerre iteration technique and closed-form analytical expressions to compute the first and second derivatives of damping with respect to inverse of reduced frequency and of flutter velocity with respect to square root of inverse of air density. The mathematical development of the method is discussed, and the results of applications of the method to an all-movable control surface and transport-type wing are presented. The efficiency and accuracy are compared to the traditional damping-velocity graphical method. The results indicate that the new technique is accurate and fast and is well suited for use in automated flutter design. The use of the method in aeroelastic optimization and potential application to thep-fe method are discussed.
Engineering Sketch Generation for Computer-Aided Design

Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects. In this paper we tackle the problem of learning based engineering sketch generation as a first step towards synthesis and composition of parametric CAD models. We propose two generative models, CurveGen and TurtleGen, for engineering sketch generation. Both models generate curve primitives without the need for a sketch constraint solver and explicitly consider topology for downstream use with constraints and 3D CAD modeling operations. We find in our perceptual evaluation using human subjects that both CurveGen and TurtleGen produce more realistic engineering sketches when compared with the current state-of-the-art for engineering sketch generation.
Future Avenues in the Study of Islamic Law

In his essay, Najam Haider calls for “more cohesive and integrated scholarly engagement with the pre-modern Islamicate world.” To that end, the author urges scholars to creatively engage and treat legal texts as valuable sources for understanding the social and political predicates of Islamic societies. For example, tracing the creation and migration of legal texts across regions can yield valuable insights into multiple ideas and ideologies across the pre-modern Islamic world, as a part of a larger intertextual world where scholars study all actors in Islamic history as interacting with, complementing, and arguing against one another.
In-Flight Actuator Failure Recovery of a Hexrotor Via Multiple Models and Extended High-Gain Observers

We study an in-flight actuator failure recovery problem for a hexrotor UAV. The hexrotor may experience external disturbances and modeling error, which are accounted for in the control design and distinguished from an actuator failure. A failure of any one actuator occurs during flight and must be identified quickly and accurately. This is achieved through the use of a multiple-model, multiple extended high-gain observer (EHGO) based output feedback control strategy. The family of EHGOs are responsible for estimating states, disturbances, and are used to select the appropriate model based on the system dynamics after a failure has occurred. The proposed method is theoretically analyzed and validated through simulations and experiments.
Strong uniform consistency with rates for kernel density estimators with general kernels on manifolds

We provide a strong uniform consistency result with the convergence rate for the kernel density estimation on Riemannian manifolds with Riemann integrable kernels (in the ambient Euclidean space). We also provide a strong uniform consistency result for the kernel density estimation on Riemannian manifolds with Lebesgue integrable kernels. The kernels considered in this paper are different from the kernels in the Vapnik-Chervonenkis class that are frequently considered in statistics society. We illustrate the difference when we apply them to estimate probability density function. We also provide the necessary and sufficient condition for a kernel to be Riemann integrable on a submanifold in the Euclidean space.
Random forest model identifies serve strength as a key predictor of tennis match outcome

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80% accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.
Secretaries with Advice

The secretary problem is probably the purest model of decision making under uncertainty. In this paper we ask which advice can we give the algorithm to improve its success probability? We propose a general model that unifies a broad range of problems: from the classic secretary problem with no advice, to the variant where the quality of a secretary is drawn from a known distribution and the algorithm learns each candidate's quality on arrival, to more modern versions of advice in the form of samples, to an ML-inspired model where a classifier gives us noisy signal about whether or not the current secretary is the best on the market. Our main technique is a factor revealing LP that captures all of the problems above. We use this LP formulation to gain structural insight into the optimal policy. Using tools from linear programming, we present a tight analysis of optimal algorithms for secretaries with samples, optimal algorithms when secretaries' qualities are drawn from a known distribution, and a new noisy binary advice model.
Testing galaxy formation and dark matter with low surface brightness galaxies

Abstract Galaxies are the basic structural element of the universe; galaxy formation theory seeks to explain how these structures came to be. I trace some of the foundational ideas in galaxy formation, with emphasis on the need for non-baryonic cold dark matter. Many elements of early theory did not survive contact with observations of low surface brightness galaxies, leading to the need for auxiliary hypotheses like feedback. The failure points often trace to the surprising predictive successes of an alternative to dark matter, the Modified Newtonian Dynamics (MOND). While dark matter models are flexible in accommodating observations, they do not provide the predictive capacity of MOND. If the universe is made of cold dark matter, why does MOND get any predictions right?
Utilizing multimodal imaging to visualize potential mechanism for sudden death in epilepsy.

Abstract Sudden death in epilepsy or SUDEP is a fatal condition that accounts for more than 4000 deaths each year. Limited clinical and preclinical data on sudden death suggest critical contributions from autonomic, cardiac, and respiratory pathways. A potential mechanism for such sudden and severe cardiorespiratory dysregulation may be linked to acid reflux-induced laryngospasm. Here, we expand on our previous investigations and utilize a novel multimodal approach to provide visual evidence of acid reflux-initiated cardiorespiratory distress and subsequent sudden death in seizing rats. We used systemic kainic acid to acutely induce seizure activity in Long Evans rats, under urethane anesthesia. We recorded electroencephalography (EEG), electrocardiography (ECG), chest plethysmography, and esophageal pH signals through a multimodal recording platform, during simultaneous fast MRI scans of the rat stomach and esophagus. MRI images, in correlation with electrophysiology data were used to identify seizure progression, stomach acid movement up the esophagus, cardiorespiratory changes, and sudden death. In all cases of sudden death, esophageal pH recordings alongside MRI images visualized stomach acid movement up the esophagus. Severe cardiac (ST segment elevation), respiratory (intermittent apnea) and brain activity (EEG narrowing due to hypoxia) changes were observed only after acid reached larynx, which strongly suggested onset of laryngospasm following acid reflux. The complementary information coming from electrophysiology and fast MRI scans provided insight into the mechanism of esophageal reflux, laryngospasm, obstructive apnea, and subsequent sudden death in seizing animals. The results carry clinical significance as it outlines a potential mechanism that may be relevant to SUDEP in humans.
A Learning-based Optimal Market Bidding Strategy for Price-Maker Energy Storage

Load serving entities with storage units reach sizes and performances that can significantly impact clearing prices in electricity markets. Nevertheless, price endogeneity is rarely considered in storage bidding strategies and modeling the electricity market is a challenging task. Meanwhile, model-free reinforcement learning such as the Actor-Critic are becoming increasingly popular for designing energy system controllers. Yet implementation frequently requires lengthy, data-intense, and unsafe trial-and-error training. To fill these gaps, we implement an online Supervised Actor-Critic (SAC) algorithm, supervised with a model-based controller - Model Predictive Control (MPC). The energy storage agent is trained with this algorithm to optimally bid while learning and adjusting to its impact on the market clearing prices. We compare the supervised Actor-Critic algorithm with the MPC algorithm as a supervisor, finding that the former reaps higher profits via learning. Our contribution, thus, is an online and safe SAC algorithm that outperforms the current model-based state-of-the-art.
Cops, robbers, and burning bridges

We consider a variant of Cops and Robbers wherein each edge traversed by the robber is deleted from the graph. The focus is on determining the minimum number of cops needed to capture a robber on a graph $G$, called the {\em bridge-burning cop number} of $G$ and denoted $c_b(G)$. We determine $c_b(G)$ exactly for several elementary classes of graphs and give a polynomial-time algorithm to compute $c_b(T)$ when $T$ is a tree. We also study two-dimensional square grids and tori, as well as hypercubes, and we give bounds on the capture time of a graph (the minimum number of rounds needed for a single cop to capture a robber on $G$, provided that $c_b(G) = 1$).
Adversarial machine learning

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning - the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques. © 2011 ACM.
Triple Hill’s Vortex Synthetic Eddy Method

The generation of initial or inflow synthetic turbulent velocity or scalar fields reproducing statistical characteristics of realistic turbulence is still a challenge. The synthetic eddy method, previously introduced in the context of inflow conditions for large eddy simulations, is based on the assumption that turbulence can be regarded as a superposition of coherent structures. In this paper, a new type of synthetic eddy method is proposed, where the fundamental eddy is constructed by superposing three Hill’s vortices, with their axes orthogonal to each other. A distribution of Hill’s vortices is used to synthesize an anisotropic turbulent velocity field that satisfies the incompressibility condition and match a given Reynolds stress tensor. The amplitudes of the three vortices that form the fundamental eddy are calculated from known Reynolds stress profiles through a transformation from the physical reference frame to the principal-axis reference frame. In this way, divergence-free anisotropic turbulent velocity fields are obtained that can reproduce a given Reynolds stress tensor. The model was tested on both isotropic and anisotropic turbulent velocity fields, in the framework of grid turbulence decay and turbulent channel flow, respectively. The transition from artificial to realistic turbulence in the proximity to the inflow boundary was found to be small in all test cases that were considered.
Monitoring reactivation of latent HIV by label-free gradient light interference microscopy

Summary Human immunodeficiency virus (HIV) can infect cells and take a quiescent and nonexpressive state called latency. In this study, we report insights provided by label-free, gradient light interference microscopy (GLIM) about the changes in dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that the mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for latent cells that reactivate than those of the cells that did not reactivate. Cells with mean dry mass and diameter less than approximately 10 pg and 8 μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than approximately 28-30 pg and mean diameter greater than 11–12 μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells.
Emergent Prosociality in Multi-Agent Games Through Gifting

Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game. However, state of the art reinforcement learning algorithms often suffer from converging to socially less desirable equilibria when multiple equilibria exist. Previous works address this challenge with explicit reward shaping, which requires the strong assumption that agents can be forced to be prosocial. We propose using a less restrictive peer-rewarding mechanism, gifting, that guides the agents toward more socially desirable equilibria while allowing agents to remain selfish and decentralized. Gifting allows each agent to give some of their reward to other agents. We employ a theoretical framework that captures the benefit of gifting in converging to the prosocial equilibrium by characterizing the equilibria's basins of attraction in a dynamical system. With gifting, we demonstrate increased convergence of high risk, general-sum coordination games to the prosocial equilibrium both via numerical analysis and experiments.
Improving Robustness of Neural Dialog Systems in a Data-Efficient Way with Turn Dropout

Neural network-based dialog models often lack robustness to anomalous, out-of-domain (OOD) user input which leads to unexpected dialog behavior and thus considerably limits such models' usage in mission-critical production environments. The problem is especially relevant in the setting of dialog system bootstrapping with limited training data and no access to OOD examples. In this paper, we explore the problem of robustness of such systems to anomalous input and the associated to it trade-off in accuracies on seen and unseen data. We present a new dataset for studying the robustness of dialog systems to OOD input, which is bAbI Dialog Task 6 augmented with OOD content in a controlled way. We then present turn dropout, a simple yet efficient negative sampling-based technique for improving robustness of neural dialog models. We demonstrate its effectiveness applied to Hybrid Code Network-family models (HCNs) which reach state-of-the-art results on our OOD-augmented dataset as well as the original one. Specifically, an HCN trained with turn dropout achieves state-of-the-art performance of more than 75% per-utterance accuracy on the augmented dataset's OOD turns and 74% F1-score as an OOD detector. Furthermore, we introduce a Variational HCN enhanced with turn dropout which achieves more than 56.5% accuracy on the original bAbI Task 6 dataset, thus outperforming the initially reported HCN's result.
Language Transformations in the Classroom

Language transformations are algorithms that take a language specification in input, and return the language specification modified. Language transformations are useful for automatically adding features such as subtyping to programming languages (PLs), and for automatically deriving abstract machines. In this paper, we set forth the thesis that teaching programming languages features with the help of language transformations, in addition to the planned material, can be beneficial for students to help them deepen their understanding of the features being taught. We have conducted a study on integrating language transformations into an undergraduate PL course. We describe our study, the material that we have taught, and the exam submitted to students, and we present the results from this study. Although we refrain from drawing general conclusions on the effectiveness of language transformations, this paper offers encouraging data. We also offer this paper to inspire similar studies.
Sketching Semidefinite Programs for Faster Clustering

Many clustering problems can be solved using semidefinite programming. Theoretical results in this vein frequently consider data with a planted clustering and a notion of signal strength such that the semidefinite program exactly recovers the planted clustering when the signal strength is sufficiently large. In practice, semidefinite programs are notoriously slow, and so speedups are welcome. In this paper, we show how to sketch a popular semidefinite relaxation of a graph clustering problem known as minimum bisection, and our analysis supports a meta-claim that the clustering task is less computationally burdensome when there is more signal.
A note on mock automorphic forms and the BPS index

We discuss mock automorphic forms from the point of view of representation theory, that is, mock automorphic forms obtained from weak harmonic Maas forms giving rise to nontrivial $$(\mathfrak g,K)$$ -cohomology. We consider the possibility of replacing the ‘holomorphic’ condition with a “cohomological” one when generalizing to general reductive groups. Such a candidate for replacement allows for growing Fourier coefficients, in contrast to automorphic forms under the Miatello-Wallach conjecture. In the second part, we provide an overview of the connection with BPS black hole counts as a physical motivation for studying mock automorphic forms.
The year 2000: A food industry forecast

This study forecasts future trends and likely developments in the food industry and serves as a planning tool for manufacturers, retailers, and others interested in food system evolution. A Delphi study is used to forecast the future of the food system in four major areas: supermarket sales distribution, technological developments, operational developments, and structural developments. The study indicates a growth in the sales share of fresh, branded foods and an increased use of technology in customer service. © 1992 John Wiley & Sons, Inc.
Brown-Halmos characterization of multi-Toeplitz operators associated with noncommutative poly-hyperballs

We obtain a noncommutative multivariable analogue of Louhichi and Olofsson characterization of Toeplitz operators with harmonic symbols on the weighted Bergman space $A_m({\bf D})$, as well as Eschmeier and Langendorfer extension to the unit ball of ${\bf C}^n$. All our results are proved in the more general setting of noncommutative poly-hyperballs ${\bf D_n^m}(H)$, ${\bf n,m}\in {\bf N}^k$, and are used to characterize the bounded free $k$-pluriharmonic functions with operator coefficients on poly-hyperballs and to solve the associated Dirichlet extension problem. In particular, the results hold for the reproducing kernel Hilbert space with kernel $$ \kappa_{\bf m}(z,w):=\prod_{i=1}^k \frac{1}{(1-\bar z_i w_i)^{m_i}},\qquad z,w\in {\bf D}^k, $$ where $m_i\geq 1$. This includes the Hardy space, the Bergman space, and the weighted Bergman space over the polydisk.
Reciprocity and Unveiling in Two-Sided Reputation Systems: Evidence from an Experiment on Airbnb

Reputation systems are used by nearly every digital marketplace, but designs vary and the effects of these designs are not well understood. We use a large-scale experiment on Airbnb to study the ca...
Pediatric Drug Allergies: Updates on Beta-Lactam, Nonsteroidal Anti-Inflammatory Drug, and Chemotherapeutic Reactions.

Adverse drug reactions are frequently reported in pediatric patients. In this review article, the authors discuss pediatric drug allergies with emphasis on the most common culprits, beta-lactam antibiotics and non-steroidal anti-inflammatory drugs. The authors also discuss reactions to non-beta-lactam antibiotics and chemotherapeutics. Skin testing has not yet been validated for many drugs, although notable exceptions include penicillin and carboplatin. The gold standard for diagnosis in most cases remains drug challenge, and the need for penicillin skin testing prior to oral provocation challenge has been questioned in recent studies. Successful desensitizations have also been reported with several drugs.
Exploiting Locality in Scalable Ordered Maps

We present the skip vector, a novel highperformance concurrent data structure based on the skip list. The key innovation in the skip vector is to flatten the index and data layers of the skip list into vectors. This increases spatial locality, reduces synchronization overhead, and avoids much of the costly pointer chasing that skip lists incur. We evaluate a skip vector implementation in C++. Our implementation coordinates interactions among threads by utilizing optimistic traversal with sequence locks. To ensure memory safety, it employs hazard pointers; this leads to tight bounds on wasted space, but due to the skip vector design, does not lead to high overhead. Performance of the skip vector for small data set sizes is higher than for a comparable skip list, and as the amount of data increases, the benefits of the skip vector over a skip list increase.
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models

Abstract Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. It highlights the accuracy-fairness tradeoff instead of the single dimensional focus on prediction accuracy in the contexts of deep neural network (DNN) and discrete choice models (DCM). We first operationalize computational fairness by equality of opportunity, then differentiate between the bias inherent in data and the bias introduced by modeling. The models inheriting the inherent biases can risk perpetuating the existing inequality in the data structure, and the biases in modeling can further exacerbate it. We then demonstrate the prediction disparities in travel behavior modeling using the 2017 National Household Travel Survey (NHTS) and the 2018–2019 My Daily Travel Survey in Chicago. Empirically, DNN and DCM reveal consistent prediction disparities across multiple social groups: both over-predict the false negative rate of frequent driving for the ethnic minorities, the low-income and the disabled populations, and falsely predict a higher travel burden of the socially disadvantaged groups and the rural populations than reality. Comparing DNN with DCM, we find that DNN can outperform DCM in prediction disparities because of DNN’s smaller misspecification error. To mitigate prediction disparities, this study introduces an absolute correlation regularization method, which is evaluated with synthetic and real-world data. The results demonstrate the prevalence of prediction disparities in travel behavior modeling, and the disparities still persist regarding a variety of model specifics such as the number of DNN layers, batch size and weight initialization. Since these prediction disparities can exacerbate social inequity if prediction results without fairness adjustment are used for transportation policy making, we advocate for careful consideration of the fairness problem in travel behavior modeling, and the use of bias mitigation algorithms for fair transport decisions.
Perigenual and Subgenual Anterior Cingulate Afferents Converge on Common Pyramidal Cells in Amygdala Subregions of the Macaque

The subgenual (sgACC) and pregenual (pgACC) anterior cingulate are important afferents of the amygdala, with different cytoarchitecture, connectivity, and function. The sgACC is associated with arousal mechanisms linked to salient cues, while the pgACC is engaged in conflict decision-making, including in social contexts. After placing same-size, small volume tracer injections into sgACC and pgACC of the same hemisphere in male Macaques, we examined anterogradely labeled fiber distribution to understand how these different functional systems communicate in the main amygdala nuclei at both mesocopic and cellular levels. The sgACC has broad-based termination patterns. In contrast, the pgACC has a more restricted pattern which was always nested in sgACC terminals. Terminal overlap occurred in subregions of the accessory basal and basal nuclei, which we termed 9hotspots9. In triple-labeling confocal studies, the majority of randomly selected CAMKIIα (+) cells (putative amygdala glutamatergic neurons) in 9hotspots9 received dual contacts from the sgACC and pgACC. The ratio of dual contacts occurred over a surprisingly narrow range, suggesting a consistent, tight balance of afferent contacts on postsynaptic neurons. Large boutons, which are associated with greater synaptic strength, were approximately 3 times more frequent on sgACC versus pgACC axon terminals in 9hotspots9, consistent with a fast ‘driver’ function. Together, the results reveal a nested interaction in which pgACC (9conflict/social monitoring’) terminals converge with the broader sgACC (9salience9) terminals at both the mesoscopic and cellular level. The pre-synaptic organization in 9hotspots9 suggest that shifts in arousal states can rapidly, and flexibly influence decision-making functions in the amygdala. SIGNIFICANCE STATEMENT The subgenual (sgACC) and perigenual cingulate (pgACC) have distinct structural and functional characteristics and are important afferent modulators of the amygdala. The sgACC is critical for arousal, while the pgACC mediates conflict-monitoring, including in social contexts. Using dual tracer injections in the same monkey, we found that sgACC inputs broadly project in the main amygdala nuclei, whereas pgACC inputs were more restricted and nested in zones containing sgACC terminals (‘hotspots’). The majority of CAMKIIα + (excitatory) amygdala neurons in 9hotspots9 received converging contacts, which were tightly balanced. pgACC and sgACC afferent streams are therefore highly interdependent in these specific amygdala subregions, permitting 9internal arousal9 states to rapidly shape responses of amygdala neurons involved in conflict and social monitoring networks.
Gaussian process regression constrained by boundary value problems

Abstract We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and boundary conditions, but for which only scattered observations of the source term are available. Scattered observations of the solution may also be used in the regression. The framework combines co-kriging with the linear transformation of a Gaussian process together with the use of kernels given by spectral expansions in eigenfunctions of the boundary value problem. Thus, it benefits from a reduced-rank property of covariance matrices. We demonstrate that the resulting framework yields more accurate and stable solution inference as compared to physics-informed Gaussian process regression without boundary condition constraints.
A magnetic levitation based low-gravity simulator with an unprecedented large functional volume.

Low-gravity environment can have a profound impact on the behaviors of biological systems, the dynamics of fluids, and the growth of materials. Systematic research on the effects of gravity is crucial for advancing our knowledge and for the success of space missions. Due to the high cost and the limitations in the payload size and mass in typical spaceflight missions, ground-based low-gravity simulators have become indispensable for preparing spaceflight experiments and for serving as stand-alone research platforms. Among various simulator systems, the magnetic levitation-based simulator (MLS) has received long-lasting interest due to its easily adjustable gravity and practically unlimited operation time. However, a recognized issue with MLSs is their highly non-uniform force field. For a solenoid MLS, the functional volume V1%, where the net force results in an acceleration <1% of the Earth’s gravity g, is typically a few microliters (μL) or less. In this work, we report an innovative MLS design that integrates a superconducting magnet with a gradient-field Maxwell coil. Through an optimization analysis, we show that an unprecedented V1% of over 4000 μL can be achieved in a compact coil with a diameter of 8 cm. We also discuss how such an MLS can be made using existing high-Tc-superconducting materials. When the current in this MLS is reduced to emulate the gravity on Mars (gM = 0.38g), a functional volume where the gravity varies within a few percent of gM can exceed 20,000 μL. Our design may break new ground for future low-gravity research.
Decoherent quench dynamics across quantum phase transitions

We present a formulation for investigating quench dynamics across quantum phase transitions in the presence of decoherence. We formulate decoherent dynamics induced by continuous quantum non-demolition measurements of the instantaneous Hamiltonian. We generalize the well-studied universal Kibble-Zurek behavior for linear temporal drive across the critical point. We identify a strong decoherence regime wherein the decoherence time is shorter than the standard correlation time, which varies as the inverse gap above the groundstate. In this regime, we find that the freeze-out time $\bar{t}\sim\tau^{{2\nu z}/({1+2\nu z})}$ for when the system falls out of equilibrium and the associated freeze-out length $\bar{\xi}\sim\tau^{\nu/({1+2\nu z})}$ show power-law scaling with respect to the quench rate $1/\tau$, where the exponents depend on the correlation length exponent $\nu$ and the dynamical exponent $z$ associated with the transition. The universal exponents differ from those of standard Kibble-Zurek scaling. We explicitly demonstrate this scaling behavior in the instance of a topological transition in a Chern insulator system. We show that the freeze-out time scale can be probed from the relaxation of the Hall conductivity. Furthermore, on introducing disorder to break translational invariance, we demonstrate how quenching results in regions of imbalanced excitation density characterized by an emergent length scale which also shows universal scaling. We perform numerical simulations to confirm our analytical predictions and corroborate the scaling arguments that we postulate as universal to a host of systems.
Money is money: The economic impact of BerkShares

Abstract Community currencies are a popular way localities around the world pursue a sustainable economy. However, no studies look at the economic impact of community currencies using modern causal inference methods. We use the synthetic control method to examine the short and long-term effects of the popular community currency BerkShares on the number of businesses, unemployment rate, and per capita income in the county in which it circulates. The results suggest that BerkShares has not had a discernible impact either directly on businesses or indirectly on local economic conditions. Additionally, BerkShares has not impacted business dynamics in a measurable way. Results from the synthetic control method suggest that establishment births, deaths, expansions, and contractions figures following BerkShares are no different than what they would have been without the currency. Given its lack of discernible impact, the results serve as a caution for localities considering community currencies as an economic development strategy.
Pain, suffering, and jury awards: A study of the cost of wrongful convictions

This paper estimates the cost of wrongful convictions and the time spent in prison based on a cross sectional regression analysis of awards and settlements for individuals who were wrongfully convicted and incarcerated for crimes they did not commit. Key variables of interest are number of days spent in prison, days on probation, and demographics of wrongfully convicted and their families. The sample consists of all known individuals (n=514) who successfully sued for damages and received payment through either trial or settlement between 1992 and 2019. Wrongful convictions cause significant tangible and intangible harm to individuals and their families which can be monetized using ex post jury awards. The average “cost” of a wrongful conviction is estimated to be $6.1 million, or $1,334 per day of incarceration. The marginal cost decreases over time: initial incarceration is valued at over $50,000 for the first day; year one is valued at $1.5 million ($4,000/day), while the marginal cost of the 10th year is estimated to be approximately $350,000 ($950/day). Wrongfully convicting and incarcerating one individual is about as costly as the harm caused by a career criminal over their lifetime. Future research might focus on society’s ex ante willingness-to-pay to avoid wrongful convictions. This study provides policymakers with a tool to value increased expenditures for DNA testing, indigent defense counsel, and other innovations designed to reduce wrongful convictions.
Strongly coupled QFT dynamics via TQFT coupling

We consider a class of quantum field theories and quantum mechanics, which we couple to ℤN topological QFTs, in order to classify non-perturbative effects in the original theory. The ℤN TQFT structure arises naturally from turning on a classical background field for a ℤN 0- or 1-form global symmetry. In SU(N) Yang-Mills theory coupled to ℤN TQFT, the non-perturbative expansion parameter is exp[−SI/N] = exp[−8π2/g2N] both in the semi-classical weak coupling domain and strong coupling domain, corresponding to a fractional topological charge configurations. To classify the non-perturbative effects in original SU(N) theory, we must use PSU(N) bundle and lift configurations (critical points at infinity) for which there is no obstruction back to SU(N). These provide a refinement of instanton sums: integer topological charge, but crucially fractional action configurations contribute, providing a TQFT protected generalization of resurgent semi-classical expansion to strong coupling. Monopole-instantons (or fractional instantons) on T3 × $$ {S}_L^1 $$ can be interpreted as tunneling events in the ’t Hooft flux background in the PSU(N) bundle. The construction provides a new perspective to the strong coupling regime of QFTs and resolves a number of old standing issues, especially, fixes the conflicts between the large-N and instanton analysis. We derive the mass gap at θ = 0 and gaplessness at θ = π in $$ \mathbbm{CP} $$ 1 model, and mass gap for arbitrary θ in $$ \mathbbm{CP} $$ N−1, N ≥ 3 on ℝ2.
African Slavery and Spanish Empire: Imperial Imaginings and Bourbon Reform in Eighteenth-Century Cuba and Beyond

This article traces a philosophical shift that opened the door to a new departure in eighteenth-century Spanish empire: a newly emerging sense that the slave trade and African slavery were essential to the wealth of nations. Contextualizing this ideological reconfiguration within mid-eighteenth century debates, this article draws upon the works of political economists and royal councilors in Madrid and puts them in conversation with the words and actions of individuals in and from Cuba, including people of African descent themselves. Because of the central place of the island in eighteenth-century imperial rivalry and reform, as well as its particular demographic situation, Cuba served as a catalyst for these debates about the place of African slavery and the transatlantic slave trade in Spanish empire. Ultimately, between the mid-eighteenth century and the turn of the nineteenth, this new mode of thought would lead to dramatic transformations in the institution of racial slavery and Spanish imperial political economy.
Chapter 2 Flow of non-newtonian fluids in porous media

Abstract Flow of non-Newtonian fluids through porous media occurs in many subsurface systems and has found applications in certain technological areas. Previous studies of the flow of fluids through porous media were focusing for the most part on Newtonian fluids. Since the 1950s, the flow of non-Newtonian fluids through porous media has received a significant amount of attention because of its important industrial applications, and considerable progress has been made. However, our understanding of non-Newtonian flow in porous media is very limited when compared with that of Newtonian flow. This work presents a comprehensive theoretical study of single and multiple phase flow of non-Newtonian fluids through porous media. The emphasis in this study is in obtaining some physical insights into the flow of power-law and Bingham fluids. Therefore, this work is divided into three parts: (1) review of the laboratory and theoretical research on non-Newtonian flow, (2) development of new numerical and analytical solutions, (3) theoretical studies of transient flow of non-Newtonian fluids in porous media, and (4) demonstration of applying a new method of well test analysis and displacement efficiency evaluation to field problems.
Estimation of the Mean and the Autocovariance Function

If {X t } is a real-valued stationary process, then from a second-order point of view it is characterized by its mean μ and its autocovariance function γ(•). The estimation of μ, γ(•) and the autocorrelation function ρ(•) = γ(•)/γ(0) from observations of X 1, ..., X n , therefore plays a crucial role in problems of inference and in particular in the problem of constructing an appropriate model for the data. In this chapter we consider several estimators which will be used and examine some of their properties.
High‐frequency internal waves near the Luzon Strait observed by underwater gliders

[1] Tidal flow through the Luzon Strait produces large internal waves that propagate westward into the South China Sea and eastward into the Pacific. Underwater gliders gathered sustained observations of internal waves during seven overlapping missions from April 2007 through July 2008. A particular focus is the high-frequency internal waves, where the operational definition of high involves periods shorter than a glider profile taking 3–6 h. Internal wave vertical velocity is estimated from measurements of pressure and glider orientation through two methods: (1) use of a model of glider flight balancing buoyancy and drag along the glider path and (2) high-pass filtering of the observed glider vertical velocity. By combining high-frequency vertical velocities from glider flight with low-frequency estimates from isopycnal depth variations between dives, a spectrum covering five decades of frequency is constructed. A map of the standard deviation of vertical velocity over the survey area shows a decay from the Luzon Strait into the Pacific. The growth of high-frequency vertical velocity with propagation into the South China Sea is observed through two 2-week time series stations. The largest observed vertical velocities are greater than 0.2 m s−1 and are associated with displacements approaching 200 m. The high-frequency waves are observed at regular intervals of 1 day as they ride on diurnal tidal internal waves generated in the Strait.
Homotopy Methods in Control System Design and Analysis

Recent technologies have led to stringent control system requirements. This has increased the importance and complexity of the analysis and design of control systems, which often require the solution of systems of nonlinear equations of high order. Some challenging computational problems in control design include model order reduction, high dimensional Riccati equations, fixed-structure optimization, robust analysis and feedback synthesis, sensor/actuator placement, and simultaneous controller/structure design. This paper describes these problems, and the directions in which globally convergent homotopy methods must be extented in order to be applicable to computational problems in control. By way of illustration, a computationally effective probability-one homotopy algorithm is presented for the optimal projection formulation of the reduced order model problem, together with some numerical results.
The Spectacular City: Violence and Performance in Urban Bolivia

The Spectacular City: Violence and Performance in Urban Bolivia. Daniel M. Goldstein. Durham, NC: Duke University Press, 2004. 275 pp.
GRAPHICAL SOLUTION OF MULTISTAGE FLUIDIZED BED HEAT EXCHANGER

The design of multistage fluidized beds for heat exchange necessitates the solution of the mass and energy balance equations combined with the equilibrium relations for each stage. This paper prese...
Using capacity utilization to adjust ROI performance drivers

As this article shows, sellers and buyers of business divisions that operate below capacity may systematically underestimate the value of the divisions unless they first remove the effects of idle capacity from the analysis. © 2006 Wiley Periodicals, Inc.
Nuclear export, enlightened

The physicist Richard Feynman once quipped that the key to solving or making significant progress on a scientific problem was to “find the open channel.” For the cell biological problem of nucleus-to-cytoplasm transport, the literal open channels have long been known: the nuclear pore complexes (NPCs). However, in the metaphorical sense as well, recent work has significantly “opened” the NPC channel as regards the biophysics of the engagement and transit of outward-bound cargo. In this issue of PNAS, Siebrasse et al. (1) track single molecules of a native messenger RNA and provide details on the approach of these transcripts to the NPCs, the probability and persistence of engagement, and the channel transit times. This impressive study was enabled by the use of an iconic system for tracking specific transcripts in the nucleus of a living cell, together with a recently developed innovation in the method of microscopy used.
Deoxyribonucleic acid synthesis in Escherichia coli infected with some deoxyribonucleic acid polymerase-less mutants of bacteriophage T4.

Abstract Data are presented to show that when E. coli B cells are infected by amber mutants whose mutations are located in gene 43 of bacteriophage T4, the usual phage-induced DNA polymerase (DNA nucleotidyltransferase, E.C. 2.7.7.7) activity does not appear. Instead, DNA polymerase activity decreases in extracts of E. coli B infected with these mutants, as compared to the activity present in extracts of uninfected cells. These data indicate that gene 43 of bacteriophage T4 is either the structural gene for DNA polymerase or possibly a regulator gene. The host DNA polymerase cannot overcome the deficiency of phage-induced DNA polymerase in E. coli B infected with gene 43 amber mutants. The host enzyme does not catalyze the synthesis of detectable amounts of DNA in the infected cells even though it can catalyze T4-DNA replication in vitro. One gene 3 mutant,amN101does induce a small amount of DNA synthesis in E. coli B cells after a considerable lag, but this mutant appears to be an atypical amber mutant since it replicates poorly in the usual permissive host, E. coli CR63. This limited ability of am N101 to replicate in E. coli CR63 is accompanied by defective DNA synthesis and control of early enzyme synthesis as is observed with other amber mutants in E. coli B, the nonpermissive host.
Left Ventricular Function in Acute Myocardial Infarction and its Clinical Significance

Investigations on left ventricular function in patients with acute myocardial infarction and the relatonship to clinical findings have shown: (1) limitations in the use and interpretation of central venous pressure; (2) pulmonary artery end-diastolic pressure accurately reflects left ventricular filling pressure in the absence of pulmonary vascular or mitral valve disease; (3) left ventricular filling pressure is frequently elevated in mild or clinical uncomplicated infarction; (4) left ventricular function frequently improves during the immediate as well as late convalescent period; (5) the hemodynamic and clinical evaluations may frequently be at variance; (6) a left ventricular gallop is usually associated with an abnormally elevated left ventricular filling pressure; (7) anterior infarctions present greater depression of left ventricular function than inferior infarctions; and (8) monitoring of hemodynamics can be useful in following the changes in left ventricular function and the response to therapy in patients with heart failure and cardiogenic shock.