cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimization Based Motion Planning for Multi-Limbed Vertical Climbing Robots

Motion planning trajectories for a multi-limbed robot to climb up walls requires a unique combination of constraints on torque, contact force, and posture. This paper focuses on motion planning for one particular setup wherein a six-legged robot braces itself between two vertical walls and climbs vertically with end effectors that only use friction. Instead of motion planning with a single nonlinear programming (NLP) solver, we decoupled the problem into two parts with distinct physical meaning: torso postures and contact forces. The first part can be formulated as either a mixed-integer convex programming (MICP) or NLP problem, while the second part is formulated as a series of standard convex optimization problems. Variants of the two wall climbing problem e.g., obstacle avoidance, uneven surfaces, and angled walls, help verify the proposed method in simulation and experimentation.
Global solutions to a structure acoustic interaction model with nonlinear sources

Abstract This article focuses on a structural acoustic interaction system consisting of a semilinear wave equation defined on a smooth bounded domain Ω ⊂ R 3 which is strongly coupled with a Berger plate equation acting only on a flat part of the boundary of Ω. In particular, the source terms acting on the wave and plate equations are allowed to have arbitrary growth order. We employ a standard Galerkin approximation scheme to establish a rigorous proof of the existence of local weak solutions. In addition, under some conditions on the parameters in the system, we prove such solutions exist globally in time and depend continuously on the initial data.
Subtour elimination constraints imply a matrix-tree theorem SDP constraint for the TSP

Abstract De Klerk et al., (2008) give a semidefinite programming constraint for the Traveling Salesman Problem (TSP) based on the matrix-tree theorem. This constraint says that the aggregate weight of all spanning trees in a solution to a TSP relaxation is at least that of a cycle graph. In this note, we show that the semidefinite constraint holds for any weighted 2-edge-connected graph and, in particular, is implied by the subtour elimination constraints.
Potent CRISPR-Cas9 inhibitors from Staphylococcus genomes

Anti-CRISPRs (Acrs) are small proteins that inhibit the RNA-guided DNA targeting activity of CRISPR-Cas enzymes. Encoded by bacteriophage and phage-derived bacterial genes, Acrs prevent CRISPR-mediated inhibition of phage infection and can also block CRISPR-Cas-mediated genome editing in eukaryotic cells. To identify Acrs capable of inhibiting Staphylococcus aureus Cas9 (SauCas9), an alternative to the most commonly used genome editing protein Streptococcus pyogenes Cas9 (SpyCas9), we used both self-targeting CRISPR screening and guilt-by-association genomic search strategies. Here we describe three potent inhibitors of SauCas9 that we name AcrIIA13, AcrIIA14, and AcrIIA15. These inhibitors share a conserved N-terminal sequence that is dispensable for DNA cleavage inhibition and have divergent C termini that are required in each case for inhibition of SauCas9-catalyzed DNA cleavage. In human cells, we observe robust inhibition of SauCas9-induced genome editing by AcrIIA13 and moderate inhibition by AcrIIA14 and AcrIIA15. We also find that the conserved N-terminal domain of AcrIIA13-AcrIIA15 binds to an inverted repeat sequence in the promoter of these Acr genes, consistent with its predicted helix-turn-helix DNA binding structure. These data demonstrate an effective strategy for Acr discovery and establish AcrIIA13-AcrIIA15 as unique bifunctional inhibitors of SauCas9.
On non-unique solutions in mean field games

The theory of mean field games is a tool to understand noncooperative dynamic stochastic games with a large number of players. Much of the theory has evolved under conditions ensuring uniqueness of the mean field game Nash equilibrium. However, in some situations, typically involving symmetry breaking, non-uniqueness of solutions is an essential feature. To investigate the nature of non-unique solutions, this paper focuses on the technically simple setting where players have one of two states, with continuous time dynamics, and the game is symmetric in the players, and players are restricted to using Markov strategies. All the mean field game Nash equilibria are identified for a symmetric follow the crowd game. Such equilibria correspond to symmetric ϵ-Nash Markov equilibria for N players with ϵ converging to zero as N goes to infinity.In contrast to the mean field game, there is a unique Nash equilibrium for finite N. It is shown that fluid limits arising from the Nash equilibria for finite N as N goes to infinity are mean field game Nash equilibria, and evidence is given supporting the conjecture that such limits, among all mean field game Nash equilibria, are the ones that are stable fixed points of the mean field best response mapping.
Libraries Full Circle: The Cross Section of Community, the Public Sphere, and Third Place

The purpose of this article is to anchor public libraries in their conversational history, and to connect the dots between the characteristics of contemporary libraries and the parallel life cycle ...
Implementation of a Renal Precision Medicine Program: Clinician Attitudes and Acceptance

A precision health initiative was implemented across a multi-hospital health system, wherein a panel of genetic variants was tested and utilized in the clinical care of chronic kidney disease (CKD) patients. Pharmacogenomic predictors of antihypertensive response and genomic predictors of CKD were provided to clinicians caring for nephrology patients. To assess clinician knowledge, attitudes, and willingness to act on genetic testing results, a Likert-scale survey was sent to and self-administered by these nephrology providers (N = 76). Most respondents agreed that utilizing pharmacogenomic-guided antihypertensive prescribing is valuable (4.0 ± 0.7 on a scale of 1 to 5, where 5 indicates strong agreement). However, the respondents also expressed reluctance to use genetic testing for CKD risk stratification due to a perceived lack of supporting evidence (3.2 ± 0.9). Exploratory sub-group analyses associated this reluctance with negative responses to both knowledge and attitude discipline questions, thus suggesting reduced exposure to and comfort with genetic information. Given the evolving nature of genomic implementation in clinical care, further education is warranted to help overcome these perception barriers.
The cyclic executive model and Ada

Periodic processes are major parts of many real-time embedded computer applications. The programming language Ada permits programming simple periodic processes, but it has some serious limitations; producing Ada programs with real-time performance comparable to those produced to date using traditional cyclic executives requires resorting to techniques that are specific to one machine or compiler. We present and evaluate the cyclic executive model for controlling periodic processes. The features and limitations of Ada for programming cyclic executive software are discussed and demonstrated, and some practical techniques for circumventing Ada limitations are described.
Transparency and Negotiated Prices: The Value of Information in Hospital-Supplier Bargaining

Using data on hospitals’ purchases across a large number of important product categories, we find that access to information on purchasing by peer hospitals leads to reductions in the prices hospit...
Persistification of Robotic Tasks

In this article, we propose a control framework that enables robots to execute tasks persistently, i.e., over time horizons much longer than robots’ battery life. This is achieved by ensuring that the energy stored in the batteries of the robots is never depleted. This condition is framed as a set invariance constraint in an optimization problem whose objective is that to minimize the difference between the robots’ control inputs and nominal control inputs corresponding to the task that is to be executed. We refer to this process as the persistification of a robotic task. Forward invariance of subsets of the state space of the robots is turned into a control input constraint by using control barrier functions. The solution to the formulated optimization problem with energy constraints ensures that the robotic task is persistent. To illustrate the operation of the proposed framework, we consider two tasks whose persistent execution is particularly relevant: environment exploration and environment surveillance. We show the persistification of these two tasks both in simulation and on a team of wheeled mobile robots on the Robotarium.
On Distributed Quantization for Classification

We consider the problem of distributed feature quantization, where the goal is to enable a pretrained classifier at a central node to carry out its classification on features that are gathered from distributed nodes through communication constrained channels. We propose the design of distributed quantization schemes specifically tailored to the classification task: unlike quantization schemes that help the central node reconstruct the original signal as accurately as possible, our focus is not reconstruction accuracy, but instead correct classification. Our work does not make any a priori distributional assumptions on the data, but instead uses training data for the quantizer design. Our main contributions include: we prove NP-hardness of finding optimal quantizers in the general case; we design an optimal scheme for a special case; we propose quantization algorithms, that leverage discrete neural representations and training data, and can be designed in polynomial-time for any number of features, any number of classes, and arbitrary division of features across the distributed nodes. We find that tailoring the quantizers to the classification task can offer significant savings: as compared to alternatives, we can achieve more than a factor of two reduction in terms of the number of bits communicated, for the same classification accuracy.
On Global-in-x Stability of Blasius Profiles

We characterize the well known self-similar Blasius profiles, $$[{\bar{u}}, {\bar{v}}]$$, as downstream attractors to solutions [u, v] to the 2D, stationary Prandtl system. It was established in Serrin (Proc R Soc Lond A 299:491–507, 1967) using maximum principle techniques that $$\Vert u - {\bar{u}}\Vert _{L^\infty _y} \rightarrow 0$$ as $$x \rightarrow \infty $$. In the case of localized data near Blasius, this paper provides an energy based proof of asymptotic stability. Central to our analysis is a new weighted “quotient estimate” which couples with a higher order, nonlinear energy cascade. Similar quotient estimates have played a crucial role in establishing the validity of the inviscid Prandtl layer expansion in Guo and Iyer (Validity of steady Prandtl layer expansions. arXiv:1805.05891 2018).
AITuning: Machine Learning-Based Tuning Tool for Run-Time Communication Libraries.

In this work, we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly effective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get better performance in a parallel application can be expressed as a game: Find the right combination/path that provides the best reward. Even though AITuning has been designed to be utilized with different run-time libraries, we focused this work on applying it to the OpenCoarrays run-time communication library, built on top of MPI-3. This work not only shows the potential of using a reinforcement learning algorithm for tuning communication libraries, but also demonstrates how the MPI Tool Information Interface, introduced by the MPI-3 standard, can be used effectively by run-time libraries to improve the performance without human intervention.
Pricing to Market, Staggered Contracts, and Real Exchange Rate Persistence

Abstract This paper explores an explanation for the high degree of persistence and volatility observed in real exchange rate data. In particular, it considers a class of preferences that are translogin form, which exhibit the property that the elasticity of demand is not constant. This property is shown to be important for generating pricing-to-market behavior in price-setting firms and for helping staggered contracts to generate endogenous persistence. The paper finds that translog preferences generate significantly greater persistence in the real exchange rate than does the standard CES specification. While the model cannot fully reproduce the high level of persistence observed in the data under plausible parameter values, it can reproduce the level of volatility.
Optimal Multiperiod Pricing with Service Guarantees

We study the multiperiod pricing problem of a service firm with capacity levels that vary over time. Customers are heterogeneous in their arrival and departure periods as well as valuations, and are fully strategic with respect to their purchasing decisions. The firm's problem is to set a sequence of prices that maximizes its revenue while guaranteeing service to all paying customers. We provide a dynamic programming based algorithm that computes the optimal sequence of prices for this problem in polynomial time. We show that due to the presence of strategic customers, available service capacity at a time period may bind the price offered at another time period. This phenomenon leads the firm to utilize the same price in multiple periods, in effect limiting the number of different prices that the service firm utilizes in optimal price policies. Also, when customers become more strategic patient for service, the firm offers higher prices. This may lead to the underutilization of capacity, lower revenues, and reduced customer welfare. We observe that the firm can combat this problem if it has an ability, beyond posted prices, to direct customers to different service periods. This paper was accepted by Dimitris Bertsimas, optimization.
Who Should Govern Congress? Access to Power and the Salary Grab of 1873

�We examine the politics of the “Salary Grab” of 1873, legislation that increased congressional salaries retroactively by 50 percent. A group of New England and Midwestern elites opposed the Salary Grab, along with congressional franking and patronage-based civil service appointments, as part of a reform effort to reshape “who should govern Congress.” Our analyses of congressional voting confirm the existence of this nonparty elite coalition. Although these elites lost many legislative battles in the short run, their efforts kept reform on the legislative agenda throughout the late nineteenth century and ultimately set the stage for the Progressive movement in the early twentieth century.
The Implementation of APL\360

APL\360 is an experimental, conversational System/360 implementation of APL, the Iverson language. It provides fast response and efficient execution to a large number of typewriter terminals. With 40 to 50 terminals connected and in normal use, each with a block of storage (called a workspace) allocated, reaction time (defined as the time from completion of an input message until the user's program begins execution) is typically 0.2 to 0.5 second. At the terminal this is manifested by nearly instantaneous response to a trivial request. Under these conditions, the CPU is executing user programs about 75% of the time, while supervisor overhead and I/O waiting time amount to less than 5%. The APL processor is interpretive; however, because of the efficiencies afforded by array operations, program execution is often one-tenth to one-fifth as fast as compiled code. APL\360 is currently running on a System/360 Model 50 with 262,144 bytes of core storage, a 2314 Direct Access Storage Facility, and two 2702 Transmission Control Units to which IBM 1050 and 2741 Communication Terminals are connected via telephone lines.
Choosing a Lesser Evil: Partisanship, Labor, and Corporate Taxation under Globalization

Leftist governments tend to tax corporations heavily. However, they are unable to do so all the time. In this study, I posit that whether leftist governments can enact this preferred tax policy is ...
Confinement, reduced entanglement, and spin-glass order in a random quantum spin-ice model

We study an effective spin model derived perturbatively from a random transverse-field Ising model on the pyrochlore lattice. The model consists of spin configurations on the pyrochlore lattice, restricted to the spin-ice subspace, with spins interacting with random Ising exchange couplings as well as ring exchanges along the hexagons of the lattice. This model is studied by exact diagonalization up to $N=64$ site systems. We calculate spin-glass correlation functions and local entanglement entropy ${S}_{T}$ between spins in a single tetrahedron and the rest of the system. We find that the model undergoes two phase transitions. At weak randomness the model is in a quantum spin-ice phase where ${S}_{T}=ln6$. Increasing randomness, at low transverse fields, first leads to a frozen phase, with long-range spin-glass order and ${S}_{T}=ln2$ corresponding to the Cat states associated with Ising order. Further increase in randomness leads to a random resonating-hexagon phase with a frozen backbone of spins and a broad distribution of entanglement entropies. The implications of these studies for non-Kramers rare-earth pyrochlores are discussed.
Image segmentation using simple Markov field models

Abstract By modelling a picture as a two-state Markov field, MAP estimation techniques are used to develop suboptimal but computationally tractable binary segmentation algorithms. The algorithms are shown to perform well at low signal-to-noise ratios, and analytical procedures are developed for estimating the Markov field transition probabilities. In addition, extensions of this approach to the multispectral and multiregion cases are discussed.
Testing a fine is a price in the lab

Abstract Recent research in experimental law and economics shows that the imposition of a fine, intended to deter some harmful behavior, may crowd out moral motivation: the behavior occurs more frequently even though a payment is charged to discourage it. In A Fine is a Price, Gneezy and Rustichini (2000) suggest that the payment may induce subjects to frame the intended prohibition as a permission in exchange for a price. Obviously, the effect if confirmed may impact almost any form of public regulation or contract drafting. May fines charged for plastic bags in fact increase usage? May penalty clauses in contracts increase the likelihood of breach? Our study tests this theory, distinguishing it from six alternative crowding-out mechanisms and from confounds in Gneezy and Rustichini’s original day-care study. In our experiment, subjects are offered a contract to perform a real effort task. Subjects are paid upon the acceptance of the contract. If they breach the contract, they keep their payment, but burden their partner with an additional workload. We vary our treatments by specifying different amounts that subject need to pay if they do not perform: T1 specifies no payment, T2 stipulates a low fine disproportional to the extra workload imposed, and T3 stipulates a proportional fine; the payments go to the contractual partner. We find the same mean performance level across all three treatments. However, splitting the sample according to the subjects’ social value orientation reveals a strong crowding-out effect in the pro-social group and an offsetting price effect for the pro-self players. Our last treatment, T4, aims to identify the mechanism behind this crowding out effect. In this treatment, the payment is made to the experimenter, instead of the contractual partner as under T2 and T3. In the absence of a transfer between the parties, T4 forces subjects to view the fine as a penalty, instead of an exchange. Results show that T4 crowds in the pro-social subjects’ compliance, providing causal evidence for the Fine-is-a-Price hypothesis.
Aging interacts with tumor biology to produce major changes in the immune tumor microenvironment

Advanced age is strongly correlated with both increased cancer incidence and general immune decline. The immune tumor microenvironment (ITME) has been established as an important prognostic of both therapeutic efficacy and overall patient survival. Thus, age-related immune decline is an important consideration for the treatment of a large subset of cancer patients. Current studies of aging-related immune alterations are predominantly performed on non-cancerous tissue, requiring additional study into the effects of age on tumor immune infiltration. We leverage large scale transcriptional data sets from The Cancer Genome Atlas and the Genotype-Tissue Expression project to distinguish normal age-related immune alterations from age-related changes in tumor immune infiltration. We demonstrate that while there is overlap between the normal immune aging phenotype and that of the ITME, there are several changes in immune cell abundance that are specific to the ITME, particularly in T cell, NK cell, and Macrophage populations. These results suggest that aged immune cells are more susceptible to tumor suppression of cytotoxic immune cell infiltration and activity than normal tissues, which creates an unfavorable ITME in older patients in excess of normal immune decline with age and may inform the application of existing and emerging immunotherapies for this large population of patients. We additionally identify that age-related increases in tumor mutational burden are associated with decreased DNA methylation and increased expression of the immune checkpoint genes PDL1, CD80, and LAG3 which may have implications for therapeutic application of immune checkpoint blockade in older patients.
Task-based hybrid shared control for training through forceful interaction:

Despite the fact that robotic platforms can provide both consistent practice and objective assessments of users over the course of their training, there are relatively few instances where physical ...
A single cell transcriptional atlas of early synovial joint development.

Synovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 7,329 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization, and in vitro characterization of prospectively isolated populations, we have identified the transcriptional profiles of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.
SmartExchange: trading higher-cost memory storage/access for lower-cost computation

We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep neural networks (DNNs). We develop a novel algorithm to enforce a specially favorable DNN weight structure, where each layerwise weight matrix can be stored as the product of a small basis matrix and a large sparse coefficient matrix whose non-zero elements are all power-of-2. To our best knowledge, this algorithm is the first formulation that integrates three mainstream model compression ideas: sparsification or pruning, decomposition, and quantization, into one unified framework. The resulting sparse and readily-quantized DNN thus enjoys greatly reduced energy consumption in data movement as well as weight storage. On top of that, we further design a dedicated accelerator to fully utilize the SmartExchange-enforced weights to improve both energy efficiency and latency performance. Extensive experiments show that 1) on the algorithm level, SmartExchange outperforms state-of-the-art compression techniques, including merely sparsification or pruning, decomposition, and quantization, in various ablation studies based on nine models and four datasets; and 2) on the hardware level, SmartExchange can boost the energy efficiency by up to $6.7 \times$ and reduce the latency by up to $19.2 \times$ over four state-of-the-art DNN accelerators, when benchmarked on seven DNN models (including four standard DNNs, two compact DNN models, and one segmentation model) and three datasets.
Bert: Scalable Source Routed Multicast for Cloud Data Centers

Traditional IP multicast routing is not suitable for cloud data center (DC) networks due to the need for supporting large numbers of groups with large group sizes. State-of-the-art DC multicast routing approaches aim to overcome the scalability issues by, for instance, taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets, thereby reducing the overhead resulting from the need to store the states of flows at the network switches. However, although these scale well with the number of multicast groups, they do not do so with group sizes, and as a result, they yield substantial traffic control overhead and network congestion. In this paper, we present Bert, a scalable, source-initiated DC multicast routing approach that scales well with both the number and the size of multicast groups, and does so through clustering, by dividing the members of the multicast group into a set of clusters with each cluster employing its own forwarding rules. Compared to the state-of-the-art approach, Bert yields much lesser traffic control overhead by significantly reducing the packet header sizes and the number of extra packet transmissions, resulting from the need for compacting forwarding rules across the switches.
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation.

We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood. Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using in-domain data with an adapted teacher.
Loose Legendrian and Pseudo-Legendrian Knots in 3-Manifolds

We prove a complete classification theorem for loose Legendrian knots in an oriented 3-manifold, generalizing results of Dymara and Ding-Geiges. Our approach is to classify knots in a $3$-manifold $M$ that are transverse to a nowhere-zero vector field $V$ up to the corresponding isotopy relation. Such knots are called $V$-transverse. A framed isotopy class is simple if any two $V$-transverse knots in that class which are homotopic through $V$-transverse immersions are $V$-transverse isotopic. We show that all knot types in $M$ are simple if any one of the following three conditions hold: $1.$ $M$ is closed, irreducible and atoroidal; or $2.$ the Euler class of the $2$-bundle $V^{\perp}$ orthogonal to $V$ is a torsion class, or $3.$ if $V$ is a coorienting vector field of a tight contact structure. Finally, we construct examples of pairs of homotopic knot types such that one is simple and one is not. As a consequence of the $h$-principle for Legendrian immersions, we also construct knot types which are not Legendrian simple.
Asthma Among Hospitalized Patients with COVID-19 and Related Outcomes

Background Several underlying conditions have been associated with severe acute respiratory syndrome coronavirus 2 illness, but it remains unclear whether underlying asthma is associated with worse coronavirus disease 2019 (COVID-19) outcomes. Objective Given the high prevalence of asthma in the New York City area, our objective was to determine whether underlying asthma was associated with poor outcomes among hospitalized patients with severe COVID-19 compared with patients without asthma. Methods Electronic heath records were reviewed for 1298 sequential patients 65 years or younger without chronic obstructive pulmonary disease who were admitted to our hospital system with a confirmed positive severe acute respiratory syndrome coronavirus 2 test result. Results The overall prevalence of asthma among all hospitalized patients with COVID-19 was 12.6%, yet a higher prevalence (23.6%) was observed in the subset of 55 patients younger than 21 years. There was no significant difference in hospital length of stay, need for intubation, length of intubation, tracheostomy tube placement, hospital readmission, or mortality between patients with and without asthma. Observations between patients with and without asthma were similar when stratified by obesity, other comorbid conditions (ie, hypertension, hyperlipidemia, and diabetes), use of controller asthma medication, and absolute eosinophil count. Conclusions Among hospitalized patients 65 years or younger with severe COVID-19, asthma diagnosis was not associated with worse outcomes, regardless of age, obesity, or other high-risk comorbidities. Future population-based studies are needed to investigate the risk of developing COVID-19 among patients with asthma once universal testing becomes readily available.
Sources of inflation and the effects of balanced budgets and inflation targeting in developing economies

This paper presents a model of inflation in developing economies and uses it to evaluate macroeconomic policy in those countries. We see cross-sectoral interactions between demand and supply side forces as central and show that the standard macroeconomic policy recommendations of inflation targeting and balanced budgets (i) increase volatility by amplifying external shocks and (ii) can lead to premature deindustrialization. The analysis applies to economies with marked underemployment, a central feature of developing and emerging countries. The recent Brazilian experience is used to illustrate the argument.
Comprehensive biomedical applications of low temperature plasmas

The main component of plasma medicine is the use of low-temperature plasma (LTP) as a powerful tool for biomedical applications. LTP generates high reactivity at low temperatures and can be activated with noble gases with molecular mixtures or compressed air. LTP reactive species are quickly produced, and are a remarkably good source of reactive oxygen and nitrogen species including singlet oxygen (O2), ozone (O3), hydroxyl radicals (OH), nitrous oxide (NO), and nitrogen dioxide (NO2). Its low gas temperature and highly reactive non-equilibrium chemistry make it appropriate for the alteration of inorganic surfaces and delicate biological systems. Treatment of oral biofilm-related infections, treatment of wounds and skin diseases, assistance in cancer treatment, treatment of viruses' infections (e.g. herpes simplex), and optimization of implants surfaces are included among the extensive plasma medicine applications. Each of these applications will be discussed in this review article.
Polarization and Fluctuations in Signed Social Networks

Much recent research on social networks has focused on the modeling and analysis of how opinions evolve as a function of interpersonal relationships. It is also of great interest to model and understand the implications of friendly and antagonistic relationships. In this article, we propose a new, simple and intuitive model that incorporates the socio-psychological phenomenon of the boomerang effect in opinion dynamics. We establish that, under certain conditions on the structure of the signed network that corresponds to the so-called structural balance property, the opinions in the network polarize. Compared to other models in the literature, our model displays a richer and perhaps more intuitive behavior of the opinions when the social network does not satisfy structural balance. In particular, we analyze signed networks in which the opinions show persistent fluctuations (including the case of the so-called clustering balance ).
Safety-Critical Control of Compartmental Epidemiological Models With Measurement Delays

We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and human interventions (such as quarantining or social distancing) as control input. We consider a generalized compartmental model that represents the form of the most popular epidemiological models and we design safety-critical controllers that formally guarantee safe evolution with respect to keeping certain populations of interest under prescribed safe limits. Furthermore, we discuss how measurement delays originated from incubation period and testing delays affect safety and how delays can be compensated via predictor feedback. We demonstrate our results by synthesizing active intervention policies that bound the number of infections, hospitalizations and deaths for epidemiological models capturing the spread of COVID-19 in the USA.
Is Reserve‐ratio Arithmetic More Pleasant?

Does it matter in a revenue-neutral setting if the government changes the inflation tax base or the inflation tax rate? We answer this question within the context of an overlapping-generations model in which government bonds, capital and cash reserves coexist. We consider experiments that parallel those studied in Sargent and Wallace's ‘unpleasant monetarist arithmetic’; the government uses seigniorage to service its debt, choosing between changing either the money growth rate (the inflation tax rate) or the reserve-requirement ratio (the inflation tax base). In the former case we obtain standard unpleasant arithmetic; in the long run a permanent open market sale results in higher money growth, and higher long-run inflation. Somewhat surprisingly, it turns out that, for a given money growth rate, lower reserve requirements fund the government's interest expense. Associated with the lower reserve requirements is lower long-run inflation and higher welfare, compared with the money-growth case. The broad message is that reserve-ratio arithmetic can be pleasant even when money-growth arithmetic is not.
Quantitative Evaluation of Asphalt Binder Extraction from Hot Mix Asphalt Pavement Using Ashing and Centrifuge Methods

Abstract Asphalt binder requires more investigation to be accurately and precisely extracted since it is an effective procedure for quality assurance quality control (QA/QC) and subsequent binder characterization. In this research, the authors presented a hands-on experience with binder extraction to deliver recommendations concerning the sensitive steps that may affect the outcomes (extracted binder content, Pbe%). Two mineral matter determination methods (ashing and centrifuge) were addressed based on the centrifuge extraction method. Field cores were investigated by comparing the Pbe% to the actual binder content, Pba%. Analysis of variance (ANOVA) and Tukey Post-Hoc statistical analyses, in addition to linear least square regression analysis, were used to show the significance of difference according to 38 variant cores randomly obtained from the field segments (in-service roads) via the first two weeks from their construction dates. Such cores involved reclaimed asphalt pavement (RAP), reclaimed asphalt shingles (RAS), and a wide range of additives. The two extraction methods were compared, concluding that the centrifuge method was highly recommended based on a quantitative evaluation, which delivered the same average Pba% based on the 38 cores. Furthermore, the centrifuge method provided much saving in the experimental time (almost half the time required for the ashing method). It was found that the ashing outcomes were equal to the centrifuge outcomes when disregarding the ammonium carbonate addition. Thus, it could be recommended to reassess the ammonium carbonate addition as it might excessively compensate for minerals that have not been lost by the ignition oven.
Structural Contributions to Autocatalysis and Asymmetric Amplification in the Soai Reaction.

Diisopropylzinc alkylation of pyrimidine aldehydes—the Soai reaction, with its astonishing attribute of amplifying asymmetric autocatalysis—occupies a unique position in organic chemistry and stand...
Core excitations with excited state mean field and perturbation theory

We test the efficacy of excited state mean field theory and its excited-state-specific perturbation theory on the prediction of K-edge positions and x-ray peak separations. We find that the mean field theory is surprisingly accurate, even though it contains no accounting of differential electron correlation effects. In the perturbation theory, we test multiple core-valence separation schemes and find that, with the mean field theory already so accurate, electron-counting biases in one popular separation scheme become a dominant error when predicting K-edges. Happily, these appear to be relatively easy to correct for, leading to a perturbation theory for K-edge positions that is lower scaling and more accurate than coupled cluster theory and competitive in accuracy with recent high-accuracy results from restricted open-shell Kohn-Sham theory. For peak separations, our preliminary data show excited state mean field theory to be exceptionally accurate, but more extensive testing will be needed to see how it and its perturbation theory compare to coupled cluster peak separations more broadly.
Polyphorm: Structural Analysis of Cosmological Datasets via Interactive Physarum Polycephalum Visualization

This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycephalum, an unicellular slime mold organism that efficiently forages for nutrients, astrophysicists are able to extrapolate from sparse datasets, such as galaxy maps archived in the Sloan Digital Sky Survey, and then use these extrapolations to inform analyses of a wide range of other data, such as spectroscopic observations captured by the Hubble Space Telescope. Researchers can interactively update the simulation by adjusting model parameters, and then investigate the resulting visual output to form hypotheses about the data. We describe details of Polyphorm's simulation model and its interaction and visualization modalities, and we evaluate Polyphorm through three scientific use cases that demonstrate the effectiveness of our approach.
A Smartphone-Based System for Real-Time Early Childhood Caries Diagnosis

Early childhood caries (ECC) is the most common, yet preventable chronic disease in children under the age of 6. Treatments on severe ECC are extremely expensive and unaffordable for socioeconomically disadvantaged families. The identification of ECC in an early stage usually requires expertise in the field, and hence is often ignored by parents. Therefore, early prevention strategies and easy-to-adopt diagnosis techniques are desired. In this study, we propose a multistage deep learning-based system for cavity detection. We create a dataset containing RGB oral images labeled manually by dental practitioners. We then investigate the effectiveness of different deep learning models on the dataset. Furthermore, we integrate the deep learning system into an easy-to-use mobile application that can diagnose ECC from an early stage and provide real-time results to untrained users.
Structure theory for ensemble controllability, observability, and duality

Ensemble control deals with the problem of using a finite number of control inputs to simultaneously steer a large population (in the limit, a continuum) of control systems. Dual to the ensemble control problem, ensemble estimation deals with the problem of using a finite number of measurement outputs to estimate the initial state of every individual system in the ensemble. We introduce in the paper a novel class of ensemble systems, termed distinguished ensemble systems, and establish sufficient conditions for controllability and observability of such systems. Every distinguished ensemble system has two key components, namely a set of distinguished control vector fields and a set of codistinguished observation functions. Roughly speaking, a set of vector fields is distinguished if it is closed (up to scaling) under Lie bracket, and moreover, every vector field in the set can be obtained by a Lie bracket of two vector fields in the same set. Similarly, a set of functions is codistinguished to a set of vector fields if the Lie derivatives of the functions along the given vector fields yield (up to scaling) the same set of functions. We demonstrate in the paper that the structure of a distinguished ensemble system can significantly simplify the analysis of ensemble controllability and observability. Moreover, such a structure can be used as a guiding principle for ensemble system design. We further address in the paper the problem about existence of a distinguished ensemble system for a given manifold. We provide an affirmative answer for the case where the manifold is a connected semi-simple Lie group. Specifically, we show that every such Lie group admits a set of distinguished vector fields, together with a set of codistinguished functions. The proof is constructive, leveraging the structure theory of semi-simple real Lie algebras and representation theory. Examples will be provided along the presentation of the paper illustrating key definitions and main results.
RASA1-driven cellular export of collagen IV is required for the development of lymphovenous and venous valves in mice

RASA1, a negative regulator of Ras-MAPK signaling, is essential for the development and maintenance of lymphatic vessel valves. However, whether RASA1 is required for the development and maintenance of lymphovenous valves (LVV) and venous valves (VV) is unknown. In this study we show that induced disruption of Rasa1 in mouse embryos did not affect initial specification of LVV or central VV but did affect their continued development. Similarly, switch to expression of a catalytically inactive form of RASA1 resulted in impaired LVV and VV development. Blocked development of LVV was associated with accumulation of the basement membrane protein, collagen IV, in LVV-forming EC and could be partially or completely rescued by MAPK inhibitors and drugs that promote collagen IV folding. Disruption of Rasa1 in adult mice resulted in venous hypertension and impaired VV function that was associated with loss of EC from VV leaflets. In conclusion, RASA1 functions as a negative regulator of Ras signaling in EC that is necessary for EC export of collagen IV, thus permitting the development of LVV and the development and maintenance of VV.
All two dimensional links are null homotopic

We show that any number of disjointly embedded 2{spheres in 4{space can be pulled apart by a link homotopy, ie, by a motion in which the 2{spheres stay disjoint but are allowed to self-intersect.
A stable range description of the space of link maps

We study the space Link(P,Q;N) of link maps: maps from P⊔Q to N such that the images of P and Q are disjoint. We identify the homotopy fiber of the inclusion Link(P,Q;N)→ Map(P,N)× Map(Q,N) in a stable range, showing that it has a (2(n−p−q)−3)–connected map to the infinite loopspace of a certain Thom spectrum.
A theory of quark vs. gluon discrimination

Understanding jets initiated by quarks and gluons is of fundamental importance in collider physics. Efficient and robust techniques for quark versus gluon jet discrimination have consequences for new physics searches, precision αs studies, parton distribution function extractions, and many other applications. Numerous machine learning analyses have attacked the problem, demonstrating that good performance can be obtained but generally not providing an understanding for what properties of the jets are responsible for that separation power. In this paper, we provide an extensive and detailed analysis of quark versus gluon discrimination from first-principles theoretical calculations. Working in the strongly-ordered soft and collinear limits, we calculate probability distributions for fixed N -body kinematics within jets with up through three resolved emissions $$ \left(\mathcal{O}\left({\alpha}_s^3\right)\right) $$ . This enables explicit calculation of quantities central to machine learning such as the likelihood ratio, the area under the receiver operating characteristic curve, and reducibility factors within a well-defined approximation scheme. Further, we relate the existence of a consistent power counting procedure for discrimination to ideas for operational flavor definitions, and we use this relationship to construct a power counting for quark versus gluon discrimination as an expansion in $$ {e}^{C_F-{C}_A}\ll 1 $$ , the exponential of the fundamental and adjoint Casimirs. Our calculations provide insight into the discrimination performance of particle multiplicity and show how observables sensitive to all emissions in a jet are optimal. We compare our predictions to the performance of individual observables and neural networks with parton shower event generators, validating that our predictions describe the features identified by machine learning.
Optical tweezers as a micromechanical tool for studying defects in 2D colloidal crystals

This paper reports on some new results from the analyses of the video microscopy data obtained in a prior experiment on two-dimensional (2D) colloidal crystals. It was reported previously that optical tweezers can be used to create mono- and di-vacancies in a 2D colloidal crystal. Here we report the results on the creation of a vacancyinterstitial pair, as well as tri-vacancies. It is found the vacancy-interstitial pair can be long-lived, but they do annihilate each other. The behavior of tri-vacancies is most intriguing, as it fluctuates between a configuration of bound pairs of dislocations and that of a locally amorphous state. The relevance of this observation to the issue of the nature of 2D melting is discussed.
Continuous Regularized Wasserstein Barycenters

Wasserstein barycenters provide a geometrically meaningful way to aggregate probability distributions, built on the theory of optimal transport. They are difficult to compute in practice, however, leading previous work to restrict their supports to finite sets of points. Leveraging a new dual formulation for the regularized Wasserstein barycenter problem, we introduce a stochastic algorithm that constructs a continuous approximation of the barycenter. We establish strong duality and use the corresponding primal-dual relationship to parametrize the barycenter implicitly using the dual potentials of regularized transport problems. The resulting problem can be solved with stochastic gradient descent, which yields an efficient online algorithm to approximate the barycenter of continuous distributions given sample access. We demonstrate the effectiveness of our approach and compare against previous work on synthetic examples and real-world applications.