cssi/SciSciGPT-SciSciNet huggingface dataset:
Randomized Greedy Sensor Selection: Leveraging Weak Submodularity

We study the problem of estimating a random process from the observations collected by a network of sensors that operate under resource constraints. When the dynamics of the process and sensor observations are described by a state-space model and the resource are unlimited, the conventional Kalman filter provides the minimum mean square error (MMSE) estimates. However, at any given time, restrictions on the available communications bandwidth and computational capabilities and/or power impose a limitation on the number of network nodes, whose observations can be used to compute the estimates. We formulate the problem of selecting the most informative subset of the sensors as a combinatorial problem of maximizing a monotone set function under a uniform matroid constraint. For the MMSE estimation criterion, we show that the maximum elementwise curvature of the objective function satisfies a certain upper-bound constraint and is, therefore, weak submodular. Building upon the work of Mirzasoleiman et al. on submodular maximization, we develop an efficient randomized greedy algorithm for sensor selection and establish guarantees on the estimator's performance in this setting. Extensive simulation results demonstrate the efficacy of the randomized greedy algorithm compared to state-of-the-art greedy and semidefinite programming relaxation methods.
Robust and High-Performance Electrodes via Crumpled Au-CNT Forests for Stretchable Supercapacitors

Summary Stretchable supercapacitors based on vertically aligned nanotubes or nanowires have attracted considerable attention because of their improved robustness and electrochemical performance under large and repeated deformations. Here, we demonstrate a robust and high-performance stretchable electrode based on crumpled Au-coated carbon nanotube forest (Au-CNT forest). Experimental measurements show that the resistance of the Au-CNT forest electrode is around one order magnitude lower than that of a pure CNT forest electrode. The biaxially crumpled Au-CNT forest electrode demonstrates nearly identical electrochemical performance at different measured charge/discharge rates under different strain conditions (i.e., from 0% to 800% area strain). The as-prepared symmetric supercapacitor demonstrates a maximum specific capacitance of ∼6 mF cm−2 at the current density of 40 mA cm−2 under large strains, exhibiting superior mechanical and electrochemical stability. This research presents a facile process to fabricate highly stretchable supercapacitors based on vertically aligned nanotubes or nanowires for achieving exceptional and robust electrochemical performance.
Distributions of centrality on networks

Abstract We provide a framework for determining agents' centralities in a broad family of random networks. Current understanding of network centrality is largely restricted to deterministic settings, but practitioners frequently use random network models. Our main theorems show that on large random networks, centrality measures are close to their expected values with high probability. We illustrate the economic consequences via three applications: (1) In network formation models with community structure, we show network segregation and differences in community size produce inequality. Benefits from peer effects accrue disproportionately to bigger and better-connected communities. (2) When link probabilities depend on spatial structure, we compute and compare the centralities of agents in different locations. (3) In models where connections depend on several independent characteristics, we can determine centralities ‘characteristic-by-characteristic’. The basic techniques from these applications, which use the main theorems to reduce questions about random networks to deterministic calculations, extend to many network games.
IroT/MavN is a Legionella Transmembrane Fe(II) Transporter: Metal Selectivity and Translocation Kinetics Revealed by In‐vitro Real‐time Transport

In intravacuolar pathogens, iron is essential for growth and virulence. In Legionella pneumophila, a putative transmembrane protein inserted on the surface of the host pathogen-containing vacuole, IroT/MavN, facilitates intravacuolar iron acquisition from the host by an unknown mechanism, bypassing the problem of Fe(III) insolubility and mobilization. We developed a platform for purification and reconstitution of IroT in artificial lipid bilayer vesicles (proteoliposomes). By encapsulating the fluorescent reporter probe Fluozin-3, we reveal, by real-time metal transport assays, that IroT is a high-affinity iron transporter selective for Fe(II) over other essential transition metals. Mutational analysis reveals important residues in the transmembrane helices, soluble domains, and loops important for substrate recognition and translocation. The work establishes the substrate transport properties in a novel transporter family important for iron acquisition at the host-pathogen intravacuolar interface and provides chemical tools for a comparative investigation of the translocation properties in other iron transporter families.
Variational multiscale modeling with discretely divergence-free subscales

Abstract We introduce a residual-based stabilized formulation for incompressible Navier–Stokes flow that maintains discrete (and, for divergence-conforming methods, strong) mass conservation for inf–sup stable spaces with H 1 -conforming pressure approximation, while providing optimal convergence in the diffusive regime, robustness in the advective regime, and energetic stability. The method is formally derived using the variational multiscale (VMS) concept, but with a discrete fine-scale pressure field which is solved for alongside the coarse-scale unknowns such that the coarse and fine scale velocities separately satisfy discrete mass conservation. We show energetic stability for the full Navier–Stokes problem, and we prove convergence and robustness for a linearized model (Oseen flow), under the assumption of a divergence-conforming discretization. Numerical results indicate that all properties extend to the fully nonlinear case and that the proposed formulation can serve to model unresolved turbulence.
Chapter 6 – Double-Flash Steam Power Plants

This chapter discusses the various process and methodologies involved in the designing of double-flash steam power plants. Many aspects of a double-flash plant are similar to a single-flash plant. The double-flash steam plant is an improvement on the single-flash design as it can produce 15–25% more power output for the same geothermal fluid conditions. The addition of the second-flash process increases the number of possible arrangements in the double-flash plants. The process for the double-flash plant is illustrated in a temperature entropy diagram based on the thermodynamics of the conversion process. At the point of analysis, the mass flow through the turbine must be increased because the low-pressure steam from the flasher is admitted to the steam path, and joins the partially expanded high-pressure steam. The optimization process is more complicated because of the extra degree of freedom in the choice of operating parameters. The potential problems associated with the dissolution of silica from waste brines leaving the power plants are examined in the chapter. In the matter of water pollution, the waste brine from a double-flash plant will carry more highly concentrated contaminants than single-flash plants.
Natural orifice translumenal endoscopic surgery (NOTES)

Natural orifice translumenal endoscopic surgery (NOTES) : , Natural orifice translumenal endoscopic surgery (NOTES) : , کتابخانه دیجیتال جندی شاپور اهواز
Capital Controls, The Dual Exchange Rate, and Devaluation

Abstract This paper re-examines the effect of devaluation under capital-account restrictions, adding to traditional formulations the seemingly minor (but realistic) assumption that central-bank reserves earn interest. In an intertemporal model, devaluation is no longer neutral in the long run as it is in the literature on the monetary approach to the balance of payments. Furthermore, the economy may possess multiple stationary states, some of them unstable. The analysis confirms, however, that even large devaluations must improve the balance of payments. A by-product of the model is a pricing formula for the financial exchange rate in a dual exchange-rate system. That formula is consistent with recent consumption-based models of asset pricing.
Contribution of International Development Activities to Comprehensive Internationalization of U.S. Public Universities.

Globalization creates increasing interdependence between countries’ economic, political, and social processes leading to rise of global challenges and opportunities. Developed countries became important players in tackling the world's biggest issues. Governments traditionally turn to higher education institutions for their teaching, discovery, and outreach expertise to address global challanges. U.S. universities engage in a variety of international activities. However, role of international development activities of U.S. colleges and universities in their comprehensive internationalization has been lacking sufficient attention of scholars. This article investigated contribution of international development activities to comprehensive internationalization of U.S. public higher education institutions through analysis of qualitative data. The findings of the study demonstrate the contribution of international development activities to comprehensive internationalization efforts of public universities through advancement of their education, research, and service missions.
Estimating probabilities in recommendation systems

Summary. Recommendation systems are emerging as an important business application with significant economic impact. Currently popular systems include Amazon's book recommendations, Netflix's movie recommendations and Pandora's music recommendations. We address the problem of estimating probabilities associated with recommendation system data by using non-parametric kernel smoothing. In our estimation we interpret missing items as randomly censored observations of preference relations and obtain efficient computation schemes by using combinatorial properties of generating functions. We demonstrate our approach with several case-studies involving real world movie recommendation data. The results are comparable with state of the art techniques while also providing probabilistic preference estimates outside the scope of traditional recommender systems.
6 – Rational Expressions

Publisher Summary This chapter discusses rational expressions. Rational expressions are to polynomials what fractions are to integers. Multiplying (or dividing) the numerator and denominator of a rational expression by the same nonzero quantity always yields an equivalent rational expression. The fundamental property can be used to reduce rational expressions to lowest terms. To reduce a rational expression to lowest terms, each expression is factored completely and then both the numerator and denominator are divided by any factors they have in common. Factoring is the single most important tool used in working with rational expressions. It is not necessary to show the distributive property when adding rational expressions. Rational expressions cannot be combined by addition unless they have the same denominator. Because of this property, it must be made sure in all addition problems involving rational expressions that all the expressions have the same denominator. The least common denominator (LCD) for a set of denominators is the simplest quantity that is exactly divisible by all the denominators. The main idea in adding fractions is to write each fraction again with the LCD for a denominator, and the same process can be used to add rational expressions.
AMTRAN: Implications of an Interactive Mathematical Computer System for an Educational Institution

The advent of computing in a remote-terminal time-sharing context permits the user to interact more directly with the computer in attacking his problem. Moreover, certain standard problems of numerical analysis (e.g., least-squares approximations, locating zeroes of functions, etc.) arise in science and engineering with sufficient frequency to suggest, in view of remote-terminal capability, the development of interactive computer systems to aid in the application of mathematical analysis. Such a system is AMTRAN (for Automatic Mathematical TRANslation), being developed at NASA's Marshall Space Flight Center under the direction of Dr. Robert N. Seitz[1].
Magnetic Resonance Imaging of the Genitourinary Tract

Magnetic resonance imaging, in general, is in a state of evolution. This article demonstrates its potential use in the diagnosis of genitourinary pathology. Controlled studies will be required, however, in order to determine the place of this imaging modality in the vast array of imaging options currently available to the radiologist and urologic surgeon. It is expected that, in the not too distant future, the indications for magnetic resonance imaging in the diagnosis of genitourinary disorders will become clear.
Bernstein–Sato varieties and annihilation of powers

Given a complex germ $f$ near the point $\mathfrak{x}$ of the complex manifold $X$, equipped with a factorization $f = f_{1} \cdots f_{r}$, we consider the $\mathscr{D}_{X,\mathfrak{x}}[s_{1}, \dots, s_{r}]$-module generated by $ F^{S} := f_{1}^{s_{1}} \cdots f_{r}^{s_{r}}$. We show for a large class of germs that the annihilator of $F^{S}$ is generated by derivations and this property does not depend on the chosen factorization of $f$. We further study the relationship between the Bernstein-Sato variety attached to $F$ and the cohomology support loci of $f$, via the $\mathscr{D}_{X,\mathfrak{x}}$-map $\nabla_{A}$. This is related to multiplication by $f$ on certain quotient modules. We show that for our class of divisors the injectivity of $\nabla_{A}$ implies its surjectivity. Restricting to reduced, free divisors, we also show the reverse, using the theory of Lie-Rinehart algebras. In particular, we analyze the dual of $\nabla_{A}$ using techniques pioneered by Narvaez-Macarro. As an application of our results we establish a conjecture of Budur in the tame case: if $\text{V}(f)$ is a central, essential, indecomposable, and tame hyperplane arrangement, then the Bernstein-Sato variety associated to $F$ contains a certain hyperplane. By the work of Budur, this verifies the Topological Mulivariable Strong Monodromy Conjecture for tame arrangements. Finally, in the reduced and free case, we characterize local systems outside the cohomology support loci of $f$ near $\mathfrak{x}$ in terms of the simplicity of modules derived from $F^{S}.$
Quasisymmetric functions from combinatorial Hopf monoids and Ehrhart Theory

We investigate quasisymmetric functions coming from combinatorial Hopf monoids. We show that these invariants arise naturally in Ehrhart theory, and that some of their specializations are Hilbert functions for relative simplicial complexes. This class of complexes, called forbidden composition complexes, also forms a Hopf monoid, thus demonstrating a link between Hopf algebras, Ehrhart theory, and commutative algebra. We also study various specializations of quasisymmetric functions.
Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms

Differential privacy (DP) provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data. We address the pro...
Fluorescence Labeling of Circulating Tumor Cells with a Folate Receptor-Targeted Molecular Probe for Diffuse In Vivo Flow Cytometry.

We recently developed a new instrument called “diffuse in vivo flow cytometry” (DiFC) for enumeration of rare fluorescently labeled circulating tumor cells (CTCs) in small animals without drawing blood samples. Until now, we have used cell lines that express fluorescent proteins or were pre-labeled with a fluorescent dye ex vivo. In this work, we investigated the use of a folate receptor (FR)-targeted fluorescence molecular probe for in vivo labeling of FR+ CTCs for DiFC. We used EC-17, a FITC-folic acid conjugate that has been used in clinical trials for fluorescence-guided surgery. We studied the affinity of EC-17 for FR+ L1210A and KB cancer cells. We also tested FR− MM.1S cells. We tested the labeling specificity in cells in culture in vitro and in whole blood. We also studied the detectability of labeled cells in mice in vivo with DiFC. EC-17 showed a high affinity for FR+ L1210A and KB cells in vitro. In whole blood, 85.4 % of L1210A and 80.9 % of KB cells were labeled above non-specific background with EC-17, and negligible binding to FR− MM.1S cells was observed. In addition, EC-17-labeled CTCs were readily detectable in circulation in mice with DiFC. This work demonstrates the feasibility of labeling CTCs with a cell-surface receptor-targeted probe for DiFC, greatly expanding the potential utility of the method for pre-clinical animal models. Because DiFC uses diffuse light, this method could be also used to enumerate CTCs in larger animal models and potentially even in humans.
Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards

Throughout a conversation, participants make choices that can orient the flow of the interaction. Such choices are particularly salient in the consequential domain of crisis counseling, where a difficulty for counselors is balancing between two key objectives: advancing the conversation towards a resolution, and empathetically addressing the crisis situation. In this work, we develop an unsupervised methodology to quantify how counselors manage this balance. Our main intuition is that if an utterance can only receive a narrow range of appropriate replies, then its likely aim is to advance the conversation forwards, towards a target within that range. Likewise, an utterance that can only appropriately follow a narrow range of possible utterances is likely aimed backwards at addressing a specific situation within that range. By applying this intuition, we can map each utterance to a continuous orientation axis that captures the degree to which it is intended to direct the flow of the conversation forwards or backwards. This unsupervised method allows us to characterize counselor behaviors in a large dataset of crisis counseling conversations, where we show that known counseling strategies intuitively align with this axis. We also illustrate how our measure can be indicative of a conversation’s progress, as well as its effectiveness.
Dataless Model Selection With the Deep Frame Potential

Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency. Standard approaches rely on ad-hoc engineering or computationally expensive validation on a specific dataset. We instead attempt to quantify networks by their intrinsic capacity for unique and robust representations, enabling efficient architecture comparisons without requiring any data. Building upon theoretical connections between deep learning and sparse approximation, we propose the deep frame potential: a measure of coherence that is approximately related to representation stability but has minimizers that depend only on network structure. This provides a framework for jointly quantifying the contributions of architectural hyper-parameters such as depth, width, and skip connections. We validate its use as a criterion for model selection and demonstrate correlation with generalization error on a variety of common residual and densely connected network architectures.
Strength from Weakness: Fast Learning Using Weak Supervision

We study generalization properties of weakly supervised learning. That is, learning where only a few "strong" labels (the actual target of our prediction) are present but many more "weak" labels are available. In particular, we show that having access to weak labels can significantly accelerate the learning rate for the strong task to the fast rate of $\mathcal{O}(\nicefrac1n)$, where $n$ denotes the number of strongly labeled data points. This acceleration can happen even if by itself the strongly labeled data admits only the slower $\mathcal{O}(\nicefrac{1}{\sqrt{n}})$ rate. The actual acceleration depends continuously on the number of weak labels available, and on the relation between the two tasks. Our theoretical results are reflected empirically across a range of tasks and illustrate how weak labels speed up learning on the strong task.
Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection

Complex systems, represented as dynamic networks, comprise of components that influence each other via direct and/or indirect interactions. Recent research has shown the importance of using Higher-Order Networks (HONs) for modeling and analyzing such complex systems, as the typical Markovian assumption in developing the First Order Network (FON) can be limiting. This higher-order network representation not only creates a more accurate representation of the underlying complex system, but also leads to more accurate network analysis. In this paper, we first present a scalable and accurate model, BuildHON+, for higher-order network representation of data derived from a complex system with various orders of dependencies. Then, we show that this higher-order network representation modeled by BuildHON+ is significantly more accurate in identifying anomalies than FON, demonstrating a need for the higher-order network representation and modeling of complex systems for deriving meaningful conclusions.
TaqMan quantitative real-time PCR for detecting Avipoxvirus DNA in various sample types from hummingbirds

Background Avian pox is a viral disease documented in a wide range of bird species. Disease-related detrimental effects can cause dyspnea and dysphagia, and birds with high metabolic requirements, such as hummingbirds, are thus especially vulnerable to the pathogen. Hummingbirds have a strong presence in California, especially in urban environments. However, little is understood regarding the impact of pox virus on hummingbird populations. Currently, diagnosing a pox infection relies on obtaining a tissue biopsy, which poses significant risks to birds and challenges in the field. Understanding the ecology of hummingbird pox viral infections could be advanced by a minimally invasive ante-mortem diagnostic method. Our aim was to address whether pox infections can be diagnosed using integumentary system samples besides tissue biopsies. To meet this goal, we tested multiple integumentary sample types using a quantitative real-time PCR assay. A secondary study goal was to determine which sample types (ranging from minimally to highly invasive sampling) were optimal for identifying infected birds. Methodology and principal findings Pox-like lesion tissue, pectoral muscle, feathers, toenail clippings, blood, and swabs (both pox-like lesion tissue and non pox-like lesion tissue) were taken from live birds and carcasses of two species of hummingbirds found in California. To maximize successful diagnosis, especially for samples with low viral load, a real-time quantitative PCR assay was developed for detecting the hummingbird-specific Avipoxvirus 4b core protein gene. Avipoxvirus DNA was successfully amplified from all sample types obtained from 27 individuals. These results were compared to those of conventional PCR and comparisons were also made among sample types, utilizing lesion tissue samples as the gold standard. Conclusions and significance Hummingbird avian pox can be diagnosed without relying on tissue biopsies. We identify that feather samples, of which contour feathers yielded the best results, can be used for diagnosing infected birds, thus reducing sampling risk. In sum, the real-time PCR assay detected viral DNA in various integumentary system sample types and will be useful in future studies of hummingbird disease ecology.
WHAT IS A REALIST THEORY OF LAW

This essay offers a programmatic statement for a realist theory of law. Although I have been influenced by (and written about) the work of earlier American, Scandinavian, Italian and other legal realists, this is not an essay about what others have thought. This is an essay about what I take realism about law to mean and what its theoretical commitments are; I shall use other realists to sometimes illustrate the distinctive positions of a realist theory of law, but will make clear where I depart from them. A realist theory of law involves both a “realist” and a “naturalistic” perspective on law. Let me explain how I understand these perspectives.
Deontological Ethics By Monotonicity Shape Constraints

We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding shape constraints that constrain the model to respond only positively to relevant inputs. We analyze the relationship between these deontological constraints that act on individuals and the consequentialist group-based fairness goals of one-sided statistical parity and equal opportunity. This strategy works with sensitive attributes that are Boolean or real-valued such as income and age, and can help produce more responsible and trustworthy AI.
Islands in asymptotically flat 2D gravity

The large-N limit of asymptotically flat two-dimensional dilaton gravity coupled to N free matter fields provides a useful toy model for semiclassical black holes and the information paradox. Analyses of the asymptotic information flux as given by the entanglement entropy show that it follows the Hawking curve, indicating that information is destroyed in these models. Recently, motivated by developments in AdS/CFT, a semiclassical island rule for entropy has been proposed. We define and compute the island rule entropy for black hole formation and evaporation in the large-N RST model of dilaton gravity and show that, in contrast, it follows the unitary Page curve. The relation of these two observations, and interesting properties of the dilaton gravity island rule, are discussed.
A dining philosophers algorithm with polynomial response time

Presents an efficient distributed online algorithm for scheduling jobs that are created dynamically, subject to resource constraints that require that certain pairs of jobs not run concurrently. The focus is on the response time of the system to each job, i.e. the length of the time interval that starts when the job is created or assigned to a processor and ends at the instant the execution of the job begins. The goal is to provide guarantees on the response time to each job j in terms of the density of arrivals of jobs that conflict with j. The model is completely asynchronous and includes various resource allocation problems that have been studied extensively, including the dining philosophers problem and its generalizations to arbitrary networks. In these versions of the problem, the resource requirements of each new job j determines an upper bound delta /sub j/ on the number of jobs that can exist concurrently in the system and conflict with j. Given such upper bounds, no scheduling algorithm can guarantee a response time better than delta /sub j/ times the maximum execution or message transmission time. A simple algorithm that guarantees response time that is essentially polynomial in delta /sub j/ is presented. It is based on the notion of a distribution queue and has a compact implementation. >
Stance Prediction for Contemporary Issues: Data and Experiments.

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.
MANAGING FOR RATINGS: REAL EFFECTS OF A CORPORATE RATINGS CRITERIA CHANGE

We exploit a change in criteria by Standard and Poor's to examine the real effects of an exogenous credit ratings change on corporate policies. We use a criteria recalibration by Standard and Poor's, unrelated to firms' fundamentals, as a quasi-natural experiment to analyze the impact of a ratings upgrade on issuance activity, investment rate, cash holdings, and payout policy of companies. Our findings suggest that upgraded firms subsequently issue more debt relative to equity, increase their investment rate, and decreasing their cash holdings. We find limited evidence that firms increase their payout to shareholders. In addition we find upgraded firms issue public bonds at lower yields. Our results support the view that credit ratings have a real effect on corporations.
Achieving Performance and Safety in Large Scale Systems with Saturation using a Nonlinear System Level Synthesis Approach

We present a novel class of nonlinear controllers that allows interpolation among differently behaving linear controllers as a case study for recently proposed Linear and Nonlinear System Level Synthesis framework. The structure of the nonlinear controller allows for simultaneously satisfying performance and safety objectives defined for small- and large-disturbance regimes. The proposed controller is distributed, handles delays, sparse actuation, and localizes disturbances. We show our nonlinear controller always outperforms its linear counterpart for constrained LQR problems. We further demonstrate the anti-windup property of the proposed control strategy for saturated systems via simulation.
Quantitative fluxomics of circulating metabolites

Summary Mammalian organs are nourished by nutrients carried by the blood circulation. These nutrients originate from diet and internal stores, and can undergo various interconversions before their eventual use as tissue fuel. Here we develop isotope tracing, mass spectrometry, and mathematical analysis methods to determine the direct sources of circulating nutrients, their interconversion rates, and eventual tissue-specific contributions to TCA cycle metabolism. Experiments with fifteen nutrient tracers enabled extensive accounting for both circulatory metabolic cycles and tissue TCA inputs, across fed and fasted mice on either high-carbohydrate or ketogenic diet. We find that a majority of circulating carbon flux is carried by two major cycles: glucose-lactate and triglyceride-glycerol-fatty acid. Futile cycling through these pathways is prominent when dietary content of the associated nutrients is low, rendering internal metabolic activity robust to food choice. The presented in vivo flux quantification methods are broadly applicable to different physiological and disease states.
Negotiation and Dispute Prevention in Global Cooperative Institutions: International Community Interests, IUU Fishing, and the Biodiversity Beyond National Jurisdiction Negotiation

In areas beyond national jurisdiction communitarian interests are vulnerable to a weak legal order, evidenced by the prevalence of IUU fishing. The negotiation of an agreement for conservation and sustainable use of marine biodiversity in areas beyond national jurisdiction (BBNJ) and the still-evolving jurisprudence for erga omnes obligations present opportunities to strengthen them through global cooperative institutions by empowering states and non-state actors. BBNJ bodies designed to lead effective cooperation with other regimes should be considered strengthening, not undermining, to the legal order of the ocean, as economic interests emphasized in other instruments may be enhanced in the long-term by the BBNJ agreement’s planning, transparency, capacity building and technology transfer provisions. International courts have found that individual states have standing to represent community interests; non-state actors could also be recognized to have standing to access judicial fora when existential interests of humanity are at stake.
Measuring Pain in Sickle Cell Disease using Clinical Text

Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans. Complications such as pain, stroke, and organ failure occur in SCD as malformed, sickled red blood cells passing through small blood vessels get trapped. Particularly, acute pain is known to be the primary symptom of SCD. The insidious and subjective nature of SCD pain leads to challenges in pain assessment among Medical Practitioners (MPs). Thus, accurate identification of markers of pain in patients with SCD is crucial for pain management. Classifying clinical notes of patients with SCD based on their pain level enables MPs to give appropriate treatment. We propose a binary classification model to predict pain relevance of clinical notes and a multiclass classification model to predict pain level. While our four binary machine learning (ML) classifiers are comparable in their performance, Decision Trees had the best performance for the multiclass classification task achieving 0.70 in F-measure. Our results show the potential clinical text analysis and machine learning offer to pain management in sickle cell patients.
Potential Biases Arising from Epidemic Dynamics in Observational Seroprotection Studies.

The extent and duration of immunity following SARS-CoV-2 infection are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods to alleviate biases in these studies is important for informing their design and analysis. Confounding by individual-level risk factors in observational studies like these is relatively well appreciated. Here, we show how geographic structure and the underlying, natural dynamics of epidemics can also induce noncausal associations. We take the approach of simulating serologic studies in the context of an uncontrolled or a controlled epidemic, under different assumptions about whether prior infection does or does not protect an individual against subsequent infection, and using various designs and analytic approaches to analyze the simulated data. We find that in studies assessing whether seropositivity confers protection against future infection, comparing seropositive individuals to seronegative individuals with similar time-dependent patterns of exposure to infection, by stratifying or matching on geographic location and time of enrollment, is essential to prevent bias.
Classification accuracy and efficiency of writing screening using automated essay scoring.

Abstract The present study leveraged advances in automated essay scoring (AES) technology to explore a proof of concept for a writing screener using the Project Essay Grade (PEG) program. First, the study investigated the extent to which an AES-scored multi-prompt writing screener accurately classified students as at risk of failing a Common Core-aligned English language arts state test. Second, the study explored whether a similar level of classification accuracy could be achieved with a more efficient form of the AES-screener with fewer writing prompts. Third, the classification accuracy of the AES-scored screeners was compared to that of screeners scored for word count. Students in Grades 3–5 (n = 185, 167, and 187, respectively) composed six essays in response to multiple writing-prompt screeners on six different randomly assigned topics, consisting of two essays in each of three different genres (narrative, informative, and persuasive). Receiver operating characteristic (ROC) curve analysis was used to assess classification accuracy and to identify multiple cut scores with associated sensitivity and specificity values, and positive and negative posttest probabilities. Results indicated that the AES-scored multi-prompt screener and screeners with fewer prompts yield acceptable classification accuracy, are efficient, and are more accurate than screeners scored for word count. Overall, results illustrate the viability of writing screening using AES.
Cyber-Physical Microgrids: Toward Future Resilient Communities

Microgrids can be isolated from large-scale power transmission/distribution systems (macrogrids) to deliver energy to their local communities using local energy resources and distribution systems when power outages occur in the macrogrids. In such situations, microgrids could be considered the last available resource to provide energy to critical infrastructure.
MEMS-actuated metasurface Alvarez lens.

Miniature lenses with a tunable focus are essential components for many modern applications involving compact optical systems. While several tunable lenses have been reported with various tuning mechanisms, they often face challenges with respect to power consumption, tuning speed, fabrication cost, or production scalability. In this work, we have adapted the mechanism of an Alvarez lens – a varifocal composite lens in which lateral shifts of two optical elements with cubic phase surfaces give rise to a change in the optical power – to construct a miniature, microelectromechanical system (MEMS)-actuated metasurface Alvarez lens. Implementation based on an electrostatic MEMS generates fast and controllable actuation with low power consumption. The utilization of metasurfaces – ultrathin and subwavelength-patterned diffractive optics – as optical elements greatly reduces the device volume compared to systems using conventional freeform lenses. The entire MEMS Alvarez metalens is fully compatible with modern semiconductor fabrication technologies, granting it the potential to be mass-produced at a low unit cost. In the reported prototype operating at 1550 nm wavelength, a total uniaxial displacement of 6.3 µm was achieved in the Alvarez metalens with a direct-current (DC) voltage application up to 20 V, which modulated the focal position within a total tuning range of 68 µm, producing more than an order of magnitude change in the focal length and a 1460-diopter change in the optical power. The MEMS Alvarez metalens has a robust design that can potentially generate a much larger tuning range without substantially increasing the device volume or energy consumption, making it desirable for a wide range of imaging and display applications. Researchers in the United States have developed a miniature lens which can change focal length quickly and with low power usage. The device is based on the Alvarez lens design in which the movement of two optical elements changes the optical power. A normal Alvarez lens uses elements with matching curved surfaces, but Zheyi Han and her co-authors at the University of Washington adjusted the design to use metasurface optics instead. Metasurfaces provide the same effect as normal lens elements but are flat and thin, making it easier to incorporate them in miniature devices. The team used a MEMS system to move the metasurface elements, enabling them to achieve a 1460-diopter change with the application of under 20V. This approach will be useful in fabricating tunable lenses for ultra-compact uses, such as endoscopy and mobile devices.
Star topology increases ballistic resistance in thin polymer films

Abstract Polymeric films with greater impact and ballistic resistance are highly desired for numerous applications, but molecular configurations that best address this need remain subject to debate. We study the resistance to ballistic impact of thin polymer films using coarse-grained molecular dynamics simulations, investigating melts of linear polymer chains and star polymers with varying number ( 2 ≤ f ≤ 16 ) and degree of polymerization ( 10 ≤ M ≤ 50 ) of the arms. We show that increasing the number of arms f or the length of the arms M both result in greater specific penetration energy within the parameter ranges studied. Greater interpenetration of chains in stars with larger f allows energy to be dissipated predominantly through rearrangement of the stars internally, rather than chain sliding. During film deformation, stars with large f show higher energy absorption rates soon after contact with the projectile, whereas stars with larger M have a delayed response where dissipation arises primarily from chain sliding, which results in significant back face deformation. Our results suggest that stars may be advantageous for tuning energy dissipation mechanisms of ultra-thin films. These findings set the stage for a topology-based strategy for the design of impact-resistant polymer films.
Localized dynamics following a quantum quench in a non-integrable system: an example on the sawtooth ladder

Motivated by the recent discovery of ergodicity breaking in geometrically frustrated systems, we study the quench dynamics of interacting hardcore bosons on a sawtooth ladder. We identify a set of initial states for which this system exhibits characteristic signatures of localization like initial state memory retention and slow growth of entanglement entropy for a wide parameter regime. Remarkably, this localization persists even when the many-body spectrum is thermalizing. We argue that the localized dynamics originates from an interaction induced quantum interference. Our results show that the sawtooth ladder can be a fertile platform for realizing non-equilibrium quantum states of matter.
Riemannian metrics on the moduli space of GHMC anti-de Sitter structures

We first extend the construction of the pressure metric to the deformation space of globally hyperbolic maximal Cauchy-compact anti-de Sitter structures. We show that, in contrast with the case of the Hitchin components, the pressure metric is degenerate and we characterize its degenerate locus. We then introduce a nowhere degenerate Riemannian metric adapting the work of Qiongling Li on the $$\mathrm {SL}(3,\mathbb {R})$$ -Hitchin component to this moduli space. We prove that the Fuchsian locus is a totally geodesic copy of Teichmuller space endowed with a multiple of the Weil–Petersson metric.
Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson's correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.
Design and performance of an in-vacuum, magnetic field mapping system for the Muon g-2 experiment

The E989 Muon g-2 experiment at Fermilab aims to measure the anomalous magnetic moment, a$_\mu$, of the muon with a precision of 140 parts-per-billion. This requires a precise measurement of both the anomalous spin precession frequency, $\omega_a$, and the average magnetic field in terms of the equivalent, free proton Larmor frequency, $\omega_p$. The measurement of $\omega_p$ with a total systematic uncertainty of 70 parts-per-billion involves a combination of various NMR probes. There are 378 probes in fixed locations constantly monitoring field drifts. A water-based probe provides the calibration in terms of $\omega_p$. A crucial element for the multi-step measurement of $\omega_p$ is the regular mapping of the magnetic field over the muon storage region. The former E821 experiment at Brookhaven employed an in-vacuum field mapping system equipped with 17 NMR probes, which was developed by the University of Heidelberg. We have refurbished and upgraded this system with new probes and electronics. The upgrades include a new communication scheme incorporating time-division multiplexing to separate the important NMR reference clock from the data communication. The addition digitization of the NMR signals replaced the hardware-implemented zero-crossing counting of the E821 system. The digitized signals offer new capabilities in the NMR frequency analysis and its related systematic uncertainties. While the mechanical systems that move the field mapper around the ring have been mostly refurbished, the motion control system was completely replaced with a custom-built electronics centered around a commercial Galil motion controller. Both the field mapping NMR system and its motion control were successfully commissioned at Fermilab and have been in reliable operation during the first data taking periods. This article provides details of the upgrades of the field mapper and its performance.
Similarity Between Points in Metric Measure Spaces

This paper is about similarity between objects that can be represented as points in metric measure spaces. A metric measure space is a metric space that is also equipped with a measure. For example, a network with distances between its nodes and weights assigned to its nodes is a metric measure space. Given points x and y in different metric measure spaces or in the same space, how similar are they? A well known approach is to consider x and y similar if their neighborhoods are similar. For metric measure spaces, similarity between neighborhoods is well captured by the Gromov-Hausdorff-Prokhorov distance, but it is \(\mathsf {NP}\)-hard to compute this distance even in quite simple cases. We propose a tractable alternative: the radial distribution distance between the neighborhoods of x and y. The similarity measure based on the radial distribution distance is coarser than the similarity based on the Gromov-Hausdorff-Prokhorov distance but much easier to compute.