cssi/SciSciGPT-SciSciNet huggingface dataset:
Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems

Abstract For decades, uncertainty quantification techniques based on the spectral approach have been demonstrated to be computationally more efficient than the Monte Carlo method for a wide variety of problems, particularly when the dimensionality of the probability space is relatively low. The time-dependent generalized polynomial chaos (TD-gPC) is one such technique that uses an evolving orthogonal basis to better represent the stochastic part of the solution space in time. In this paper, we present a new numerical method that uses the concept of enriched stochastic flow maps to track the evolution of the stochastic part of the solution space in time. The computational cost of this proposed flow-driven stochastic chaos (FSC) method is an order of magnitude lower than TD-gPC for comparable solution accuracy. This gain in computational cost is realized because, unlike most existing methods, the number of basis vectors required to track the stochastic part of the solution space does not depend upon the dimensionality of the probability space. Four representative numerical examples are presented to demonstrate the performance of the FSC method for long-time integration of second-order stochastic dynamical systems in the context of stochastic dynamics of structures.
ALGORITHMS FOR SOLVING LINEAR CONGRUENCES AND SYSTEMS OF LINEAR CONGRUENCES

In this article we determine several theorems and methods for solving linear congruences and systems of linear congruences and we find the number of distinct solutions. Many examples of solving congruences are given.
An adaptive and explicit fourth order Runge–Kutta–Fehlberg method coupled with compact finite differencing for pricing American put options

We propose an adaptive and explicit Runge–Kutta–Fehlberg method coupled with a fourth-order compact scheme to solve the American put options problem. First, the free boundary problem is converted into a system of partial differential equations with a fixed domain by using logarithm transformation and taking additional derivatives. With the addition of an intermediate function with a fixed free boundary, a quadratic formula is derived to compute the velocity of the optimal exercise boundary analytically. Furthermore, we implement an extrapolation method to ensure that at least, a third-order accuracy in space is maintained at the boundary point when computing the optimal exercise boundary from its derivative. As such, it enables us to employ fourth-order spatial and temporal discretization with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, option Greeks, and the optimal exercise boundary. The advantage of the Runge–Kutta–Fehlberg method is based on error control and the adjustment of the time step to maintain the error at a certain threshold. By comparing with some existing methods in the numerical experiment, it shows that the present method has a better performance in terms of computational speed and provides a more accurate solution.
Robust Multi-Agent Multi-Armed Bandits

Recent works have shown that agents facing independent instances of a stochastic K-armed bandit can collaborate to decrease regret. However, these works assume that each agent always recommends their individual best-arm estimates to other agents, which is unrealistic in envisioned applications (machine faults in distributed computing or spam in social recommendation systems). Hence, we generalize the setting to include n honest and m malicious agents who recommend best-arm estimates and arbitrary arms, respectively. We first show that even with a single malicious agent, existing collaboration-based algorithms fail to improve regret guarantees over a single-agent baseline. We propose a scheme where honest agents learn who is malicious and dynamically reduce communication with (i.e., "block") them. We show that collaboration indeed decreases regret for this algorithm, assuming m is small compared to K but without assumptions on malicious agents' behavior, thus ensuring that our algorithm is robust against any malicious recommendation strategy.
Learning from Crowds by Modeling Common Confusions

Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost. However, the annotation quality of annotators varies considerably, which imposes new challenges in learning a high-quality model from the crowdsourced annotations. In this work, we provide a new perspective to decompose annotation noise into common noise and individual noise and differentiate the source of confusion based on instance difficulty and annotator expertise on a per-instance-annotator basis. We realize this new crowdsourcing model by an end-to-end learning solution with two types of noise adaptation layers: one is shared across annotators to capture their commonly shared confusions, and the other one is pertaining to each annotator to realize individual confusion. To recognize the source of noise in each annotation, we use an auxiliary network to choose from the two noise adaptation layers with respect to both instances and annotators. Extensive experiments on both synthesized and real-world benchmarks demonstrate the effectiveness of our proposed common noise adaptation solution.
On Continuous Local BDD-Based Search for Hybrid SAT Solving.

We explore the potential of continuous local search (CLS) in SAT solving by proposing a novel approach for finding a solution of a hybrid system of Boolean constraints. The algorithm is based on CLS combined with belief propagation on binary decision diagrams (BDDs). Our framework accepts all Boolean constraints that admit compact BDDs, including symmetric Boolean constraints and small-coefficient pseudo-Boolean constraints as interesting families. We propose a novel algorithm for efficiently computing the gradient needed by CLS. We study the capabilities and limitations of our versatile CLS solver, GradSAT, by applying it on many benchmark instances. The experimental results indicate that GradSAT can be a useful addition to the portfolio of existing SAT and MaxSAT solvers for solving Boolean satisfiability and optimization problems.
Efficient Distributed Algorithms for Convolutional Neural Networks

Several efficient distributed algorithms have been developed for matrix-matrix multiplication: the 3D algorithm, the 2D SUMMA algorithm, and the 2.5D algorithm. Each of these algorithms was independently conceived and they trade-off memory needed per node and the inter-node data communication volume. The convolutional neural network (CNN) computation may be viewed as a generalization of matrix-multiplication combined with neighborhood stencil computations. We develop communication-efficient distributed-memory algorithms for CNNs that are analogous to the 2D/2.5D/3D algorithms for matrix-matrix multiplication.
Axisymmetric shell analysis of the Space Shuttle solid rocket booster field joint

The Space Shuttle Challenger (STS 51-L) accident led to an intense investigation of the structural behavior of the solid rocket booster (SRB) tang and clevis field joints. Results are presented of axisymmetric shell analyses that parametrically assess the structural behavior of SRB field joints subjected to quasi-steady-state internal pressure loading for both the original joint flown on mission STS 51-L and the redesigned joint flown for the first time after the STS 51-L accident on the Space Shuttle Discovery. Discussion of axisymmetric shell modeling issues and details is presented and a generic method for simulating contact between adjacent shells of revolution is described. Results are presented that identify the performance trends of the joints for a wide range of joint parameters. An important finding is that the redesigned joint exhibits significantly smaller O-ring gap changes and much less sensitivity to joint clearances than the original joint. For a wide range of joint parameters, the result presented indicate that the redesigned joint provides a much better pressure seal than the original joint.
Using Stakeholder Value Analysis to Build Exploration Sustainability

Massachusetts Institute of Technology, Cambridge, MA 02139Abstract: The sustainabi lity of space exploration will depend in large part on its ability toconsistently and reliably deliver valued benefits to societal stakeholders over an extendedperiod. This on -going research studies the values of prospective stakeholders in the spaceex ploration enterprise — both in the near term and with a perspective extending overdecades. The immediate focus is human and robotic exploration of the Earth/Moon system,but extends to the exploration of Mars as well. Potential beneficiaries of space expl orationare identified in broad societal sectors. An analysis of these stakeholders, their values andneeds leads to the development of a comprehensive set of space exploration objectives thataddress those needs. The relative priority of exploration obj ectives is weighted usinginformation about stakeholder characteristics, values, and their role and place in theexploration value stream. The weighted exploration objectives can then be used to assess therelative value of different technical system arch itectures, and to design explorationenterprise architecture, attributes and policy frameworks to enable value delivery to societalstakeholders. Ultimately, through stakeholders’ continuing support, sustainable spaceexploration will be delivered.
Do people know how others view them? Two approaches for identifying the accuracy of metaperceptions.

Abstract Self-knowledge includes not only beliefs about one’s own traits and abilities, but beliefs about how others view the self. Are such metaperceptions accurate? This article identifies two distinct standards used to determine meta-accuracy. The correlational approach tests whether metaperceptions correlate with an accuracy criterion (i.e. social perceptions). The mean-level approach instead asks whether metaperceptions tend to err in a systematic direction. This article reviews complementary lessons gleaned from research taking one approach or the other: whether metaperceptions merely reflect self-perceptions, whose metaperceptions are more or less accurate, and what psychological processes impede meta-accuracy, among others. Ultimately, neither approach is endorsed as unconditionally superior. Instead, which approach offers the proper accuracy standard should depend on the decisions those metaperceptions will guide.
Recent improvements in aerodynamic design optimization on unstructured meshes

Recent improvements in an unstructured-grid method for large-scale aerodynamic design are presented. Previous work had shown such computations to be prohibitively long in a sequential processing environment. Also, robust adjoint solutions and mesh movement procedures were difficult to realize, particularly for viscous flows. To overcome these limiting factors, a set of design codes based on a discrete adjoint method is extended to a multiprocessor environment using a shared memory approach. A nearly linear speedup is demonstrated, and the consistency of the linearizations is shown to remain valid. The full linearization of the residual is used to precondition the adjoint system, and a significantly improved convergence rate is obtained. A new mesh movement algorithm is implemented, and several advantages over an existing technique are presented
The phase diagram of carbon dioxide from correlation functions and a many-body potential.

The phase stability and equilibria of carbon dioxide are investigated from 125–325 K and 1–10 000 atm using extensive molecular dynamics (MD) simulations and the Two-Phase Thermodynamics (2PT) method. We devise a direct approach for calculating phase diagrams, in general, by considering the separate chemical potentials of the isolated phase at specific points on the P–T diagram. The unique ability of 2PT to accurately and efficiently approximate the entropy and Gibbs energy of liquids allows for assignment of phase boundaries from relatively short (∼100 ps) MD simulations. We validate our approach by calculating the critical properties of the flexible elementary physical model 2, showing good agreement with previous results. We show, however, that the incorrect description of the short-range Pauli force and the lack of molecular charge polarization lead to deviations from experiments at high pressures. We, thus, develop a many-body, fluctuating charge model for CO2, termed CO2–Fq, from high level quantum mechanics (QM) calculations that accurately capture the condensed phase vibrational properties of the solid (including the Fermi resonance at 1378 cm−1) as well as the diffusional properties of the liquid, leading to overall excellent agreement with experiments over the entire phase diagram. This work provides an efficient computational approach for determining phase diagrams of arbitrary systems and underscores the critical role of QM charge reorganization physics in molecular phase stability.
Performance Bounds for Neural Network Estimators: Applications in Fault Detection

We exploit recent results in quantifying the robustness of neural networks to input variations to construct and tune a model-based anomaly detector, where the data-driven estimator model is provided by an autoregressive neural network. In tuning, we specifically provide upper bounds on the rate of false alarms expected under normal operation. To accomplish this, we provide a theory extension to allow for the propagation of multiple confidence ellipsoids through a neural network. The ellipsoid that bounds the output of the neural network under the input variation informs the sensitivity - and thus the threshold tuning - of the detector. We demonstrate this approach on a linear and nonlinear dynamical system.
Approximate Implication with d-Separation

The graphical structure of Probabilistic Graphical Models (PGMs) encodes the conditional independence (CI) relations that hold in the modeled distribution. Graph algorithms, such as d-separation, use this structure to infer additional conditional independencies, and to query whether a specific CI holds in the distribution. The premise of all current systems-of-inference for deriving CIs in PGMs, is that the set of CIs used for the construction of the PGM hold exactly. In practice, algorithms for extracting the structure of PGMs from data, discover approximate CIs that do not hold exactly in the distribution. In this paper, we ask how the error in this set propagates to the inferred CIs read off the graphical structure. More precisely, what guarantee can we provide on the inferred CI when the set of CIs that entailed it hold only approximately? It has recently been shown that in the general case, no such guarantee can be provided. We prove that such a guarantee exists for the set of CIs inferred in directed graphical models, making the d-separation algorithm a sound and complete system for inferring approximate CIs. We also prove an approximation guarantee for independence relations derived from marginal CIs.
Schur–Weyl duality for tensor powers of the Burau representation

Artin’s braid group $$B_n$$ is generated by $$\sigma _1, \ldots , \sigma _{n-1}$$ subject to the relations $$\begin{aligned} \sigma _i \sigma _{i+1} \sigma _i = \sigma _{i+1} \sigma _i \sigma _{i+1}, \quad \sigma _i\sigma _j = \sigma _j \sigma _i \text { if } |i-j|>1. \end{aligned}$$ For complex parameters $$q_1,q_2$$ such that $$q_1q_2 \ne 0$$ , the group $$B_n$$ acts on the vector space $$\mathbf {E}= \sum _i \mathbb {C}\mathbf {e}_i$$ with basis $$\mathbf {e}_1, \ldots , \mathbf {e}_n$$ by $$\begin{aligned} \sigma _i \cdot \mathbf {e}_i= & {} (q_1+q_2)\mathbf {e}_i + q_1\mathbf {e}_{i+1}, \quad \sigma _i \cdot \mathbf {e}_{i+1} = -q_2\mathbf {e}_i, \\ \sigma _i \cdot \mathbf {e}_j= & {} q_1 \mathbf {e}_j \quad \text { if }\; j \ne i,i+1. \end{aligned}$$ This representation is (a slight generalization of) the Burau representation. If $$q = -q_2/q_1$$ is not a root of unity, we show that the algebra of all endomorphisms of $$\mathbf {E}^{\otimes r}$$ commuting with the $$B_n$$ -action is generated by the place-permutation action of the symmetric group $$S_r$$ and the operator $$p_1$$ , given by $$\begin{aligned} p_1(\mathbf {e}_{j_1} \otimes \mathbf {e}_{j_2} \otimes \cdots \otimes \mathbf {e}_{j_r}) = q^{j_1-1} \, \sum _{i=1}^n \mathbf {e}_i \otimes \mathbf {e}_{j_2} \otimes \cdots \otimes \mathbf {e}_{j_r} . \end{aligned}$$ Equivalently, as a $$(\mathbb {C}B_n, \mathcal {P}'_r([n]_q))$$ -bimodule, $$\mathbf {E}^{\otimes r}$$ satisfies Schur–Weyl duality, where $$\mathcal {P}'_r([n]_q)$$ is a certain subalgebra of the partition algebra $$\mathcal {P}_r([n]_q)$$ on 2r nodes with parameter $$[n]_q = 1+q+\cdots + q^{n-1}$$ , isomorphic to the semigroup algebra of the “rook monoid” studied by W. D. Munn, L. Solomon, and others.
Preferential Trade Agreements and MFN Tariffs: Global Evidence

Abstract We study the effects of countries’ import composition on multilateral liberalization using a global tariff database that covers the 2000–2011 period. Employing a theoretically motivated empirical approach and instrumental variable strategy, we provide evidence that greater preferential trade agreement (PTA) import shares induce tariff cuts on non-member countries. Our baseline estimates imply that a 10 percentage point increase in the share of imports from PTA partners, or alternatively a 1 percentage point PTA-induced decline in applied tariffs, lowers most-favored nation (MFN) tariff rates by about 0.4 percentage points. This effect is driven by countries that negotiate deeper preferential trade deals. PTAs that span more policy fields are prone to lead to more inefficient trade diversion, which creates a stronger incentive to subsequently cut MFN tariffs. At the same time, our results are remarkably consistent across other subsamples emphasized in the literature, including high- and low-tariff importers, poorer and richer economies as well as large and small countries.
Generating optical supercontinuum and frequency comb in tenuous plasmas

There are several mechanisms by which the frequency spectrum of a laser broadens when it propagates at near-relativistic intensity in a tenuous plasma. Focusing on one-dimensional effects, we identify two strong optical nonlinearities, namely, four-wave mixing (FWM) and forward Raman scattering (FRS), for creating octave-wide spectra. FWM dominates the interaction when the laser pulse is short and intense, and its combination with phase modulation produces a symmetrically broadened supercontinuum. FRS dominates when the laser pulse is long and relatively weak, and it broadens the laser spectrum mainly toward lower frequencies and produces a frequency comb. The frequency chirping combined with group velocity dispersion compresses the laser pulse, causing higher peak intensity.
Thresholding Based Efficient Outlier Robust PCA

We consider the problem of outlier robust PCA (OR-PCA) where the goal is to recover principal directions despite the presence of outlier data points. That is, given a data matrix $M^*$, where $(1-\alpha)$ fraction of the points are noisy samples from a low-dimensional subspace while $\alpha$ fraction of the points can be arbitrary outliers, the goal is to recover the subspace accurately. Existing results for \OR-PCA have serious drawbacks: while some results are quite weak in the presence of noise, other results have runtime quadratic in dimension, rendering them impractical for large scale applications. In this work, we provide a novel thresholding based iterative algorithm with per-iteration complexity at most linear in the data size. Moreover, the fraction of outliers, $\alpha$, that our method can handle is tight up to constants while providing nearly optimal computational complexity for a general noise setting. For the special case where the inliers are obtained from a low-dimensional subspace with additive Gaussian noise, we show that a modification of our thresholding based method leads to significant improvement in recovery error (of the subspace) even in the presence of a large fraction of outliers.
Airfoil lift calculation using wind tunnel wall pressures

An experimental method to calculate lift using static pressure ports on the wind tunnel walls and its associated limits has been explored in this article. While the wall-pressure measurement (WPM) ...
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA.

The wide deployment of Deep Neural Networks (DNN) in high-performance cloud computing platforms has emerged field-programmable gate arrays (FPGA) as a popular choice of accelerator to boost performance due to its hardware reprogramming flexibility. To improve the efficiency of hardware resource utilization, growing efforts have been invested in FPGA virtualization, enabling the co-existence of multiple independent tenants in a shared FPGA chip. Such a multi-tenant FPGA setup for DNN acceleration potentially exposes the DNN interference task under severe threat from malicious users. This work, to the best of our knowledge, is the first to explore DNN model vulnerabilities in multi-tenant FPGAs. We propose a novel adversarial attack framework: Deep-Dup, in which the adversarial tenant can inject faults to the DNN model of victim tenant in FPGA. Specifically, she can aggressively overload the shared power distribution system of FPGA with malicious power-plundering circuits, achieving adversarial weight duplication (AWD) hardware attack that duplicates certain DNN weight packages during data transmission between off-chip memory and on-chip buffer, with the objective to hijack DNN function of the victim tenant. Further, to identify the most vulnerable DNN weight packages for a given malicious objective, we propose a generic vulnerable weight package searching algorithm, called Progressive Differential Evolution Search (P-DES), which is, for the first time, adaptive to both deep learning white-box and black-box attack models. Unlike prior works only working in a deep learning white-box setup, our adaptiveness mainly comes from the fact that the proposed P-DES does not require any gradient information of DNN model.
Optimality of Graph Scanning Statistic for Online Community Detection

Sequential change detection for graphs is a fundamental problem for streaming network data and has wide applications in social networks and power systems. Given fixed vertices and a sequence of random graphs, the objective is to detect the change-point where the underlying distribution of the random graph changes. In particular, we focus on the local change that only affects a small subgraph. We adopt the classical Erdős-Renyi model and revisit the generalized likelihood ratio (GLR) procedure. The scan statistic is computed by sequentially estimating the most-likely subgraph where the change happens. We provide theoretical analysis for the asymptotic optimality of the proposed procedure and we comment on generalizations to other random graph models. We demonstrate the efficiency of our detection algorithm using simulations.
Nasal Feeding Tubes Are Associated with Fewer Adverse Events than Feeding via Ostomy in Hospitalized Patients Receiving Enteral Nutrition.

Abstract Background Surgical feeding ostomies (eg, gastrostomy) have become required by many nursing facilities for all patients receiving enteral nutrition, whether for short- or long-term use. These policies lack supportive evidence. Comparisons of adverse event rates between surgical and natural orifice tubes are few and lacking in the inpatient setting. Additionally, we hypothesize that adverse events related to feeding tubes are underreported. We sought to quantify adverse events to test the relative safety of surgical feeding ostomies and natural orifice (eg. nasogastric or orogastric) feeding tubes in hospitalized patients. Methods This was a prospective observational cohort study of enterally fed inpatients using semiweekly focused physical examination, scripted survey, and chart review. Results All tube-fed patients admitted to a large, urban, academic hospital received semiweekly bedside evaluation and chart review over a 9-week period (n = 226 unique patients, mean 6.25 visits each, total 1118 observations). Demographics were comparable between 148 subjects with natural orifice and 113 subjects with surgical feeding tubes. A higher incidence of adverse events was observed with surgical tubes (3.34 vs 1.25 events per 100 subject days, P Conclusions Surgical feeding tubes are associated with significantly higher in-hospital adverse event rates when compared with natural orifice (nasal or oral) feeding tubes. Policies requiring surgical feeding ostomies should be reevaluated.
Computing leaky modes of optical fibers using a FEAST algorithm for polynomial eigenproblems

Abstract An efficient contour integral technique to approximate a cluster of nonlinear eigenvalues of a polynomial eigenproblem, circumventing certain large inversions from a linearization, is presented. It is applied to the nonlinear eigenproblem that arises from a frequency-dependent perfectly matched layer. This approach is shown to result in an accurate method for computing leaky modes of optical fibers. Extensive computations on an antiresonant fiber with a complex transverse microstructure are reported. This structure is found to present substantial computational difficulties: Even when employing over one million degrees of freedom, the fiber model appears to remain in a preasymptotic regime where computed confinement loss values are likely to be off by orders of magnitude. Other difficulties in computing mode losses, together with practical techniques to overcome them, are detailed.
Resonant scanning design and control for fast spatial sampling

Two-dimensional, resonant scanners have been utilized in a large variety of imaging modules due to their compact form, low power consumption, large angular range, and high speed. However, resonant scanners have problems with non-optimal and inflexible scanning patterns and inherent phase uncertainty, which limit practical applications. Here we propose methods for optimized design and control of the scanning trajectory of two-dimensional resonant scanners under various physical constraints, including high frame-rate and limited actuation amplitude. First, we propose an analytical design rule for uniform spatial sampling. We demonstrate theoretically and experimentally that by expanding the design space, the proposed designs outperform previous designs in terms of scanning range and fill factor. Second, we show that we can create flexible scanning patterns that allow focusing on user-defined Regions-of-Interest (RoI) by modulation of the scanning parameters. The scanning parameters are found by an optimization algorithm. In simulations, we demonstrate the benefits of these designs with standard metrics and higher-level computer vision tasks (LiDAR odometry and 3D object detection). Finally, we experimentally implement and verify both unmodulated and modulated scanning modes using a two-dimensional, resonant MEMS scanner. Central to the implementations is high bandwidth monitoring of the phase of the angular scans in both dimensions. This task is carried out with a position-sensitive photodetector combined with high-bandwidth electronics, enabling fast spatial sampling at $$\sim 100$$ Hz frame-rate.
Unbundling "Contactless" Work: Marketing Workforce, and Workplace Differently?

The Corporations resilience during Covid-19 relies heavily on being able to adapt swiftlyto changing circumstances. Technology has changed the way we work, and the Future of Work (FoW) demands building a new social contract. Employee-centric work practices with flexibility and choice are the new mantra of future-oriented organizations. A new world of work built on trust and flexibility provides more success for organizations and career fulfillment for workers. The organization’s senior executives (C-Suite) are marketing their products and services differently to change customers’ wow experiences. This research explored thelong-term marketing impact of the Covid-19 pandemic on the FoW. With the rise of technological, social, and cognitive skills, this study investigated: How C-Suite leaders reshuffle workplace marketing priorities to cultivate a more resilient, future-ready workforce where both employees and organizations can succeed now and in the long term? The author examined the insights from surveys and research studies to present a marketable and agile employee engagement strategy for the FoW. Findings offer guidance to the emerging trends—a flexible remote work phenomenon and where and how of the FoW. The results revealed the C-suite decisions primarily shifting towards a flexible and hybrid world influencing the FoW. Thestudy recommends organizations look for untapped talent, the workers that are, left out of traditional recruitment processes. The study concludes that in the post-pandemic market, employee engagement, protection, and empowerment are more crucial to an organization’s success in managing work to benefit both workers and businesses. Implications for academics,practitioners, and policymakers discussed. https://doi.org/10.20935/AL3649
Canonical resolutions in hereditary abelian model categories

Each object of any abelian model category has a canonical resolution as described in this article. When the model structure is hereditary we show how morphism sets in the associated homotopy category may be realized as cohomology groups computed from these resolutions. We also give an alternative description of the morphism sets in terms of Yoneda Ext groups.
Gaussian Asymptotics of Jack Measures on Partitions from Weighted Enumeration of Ribbon Paths

In this paper we determine two asymptotic results for Jack measures on partitions, a model defined by two specializations of Jack polynomials proposed by Borodin-Olshanski in [European J. Combin. 26.6 (2005): 795-834]. Assuming these two specializations are the same, we derive limit shapes and Gaussian fluctuations for the anisotropic profiles of these random partitions in three asymptotic regimes associated to diverging, fixed, and vanishing values of the Jack parameter. To do so, we introduce a generalization of Motzkin paths we call "ribbon paths", show for general Jack measures that certain joint cumulants are weighted sums of connected ribbon paths on $n$ sites with $n-1+g$ pairings, and derive our two results from the contributions of $(n,g)=(1,0)$ and $(2,0)$, respectively. Our analysis makes use of Nazarov-Sklyanin's spectral theory for Jack polynomials. As a consequence, we give new proofs of several results for Schur measures, Plancherel measures, and Jack-Plancherel measures. In addition, we relate our weighted sums of ribbon paths to the weighted sums of ribbon graphs of maps on non-oriented real surfaces recently introduced by Chapuy-Dolega.
A computational screen for alternative genetic codes in over 250,000 genomes.

All life forms rely on a ‘code’ to translate their genetic information into proteins. This code relies on limited permutations of three nucleotides – the building blocks that form DNA and other types of genetic information. Each ‘triplet’ of nucleotides – or codon – encodes a specific amino acid, the basic component of proteins. Reading the sequence of codons in the right order will let the cell know which amino acid to assemble next on a growing protein. For instance, the codon CGG – formed of the nucleotides guanine (G) and cytosine (C) – codes for the amino acid arginine. From bacteria to humans, most life forms rely on the same genetic code. Yet certain organisms have evolved to use slightly different codes, where one or several codons have an altered meaning. To better understand how alternative genetic codes have evolved, Shulgina and Eddy set out to find more organisms featuring these altered codons, creating a new software called Codetta that can analyze the genome of a microorganism and predict the genetic code it uses. Codetta was then used to sift through the genetic information of 250,000 microorganisms. This was made possible by the sequencing, in recent years, of the genomes of hundreds of thousands of bacteria and other microorganisms – including many never studied before. These analyses revealed five groups of bacteria with alternative genetic codes, all of which had changes in the codons that code for arginine. Amongst these, four had genomes with a low proportion of guanine and cytosine nucleotides. This may have made some guanine and cytosine-rich arginine codons very rare in these organisms and, therefore, easier to be reassigned to encode another amino acid. The work by Shulgina and Eddy demonstrates that Codetta is a new, useful tool that scientists can use to understand how genetic codes evolve. In addition, it can also help to ensure the accuracy of widely used protein databases, which assume which genetic code organisms use to predict protein sequences from their genomes.
Sexual dimorphism and the effect of wild introgressions on recombination in cassava (Manihot esculenta Crantz) breeding germplasm.

Recombination has essential functions in meiosis, evolution, and breeding. The frequency and distribution of crossovers dictate the generation of new allele combinations and can vary across species and between sexes. Here, we examine recombination landscapes across the 18 chromosomes of cassava (Manihot esculenta Crantz) with respect to male and female meioses and known introgressions from the wild relative Manihot glaziovii. We used SHAPEIT2 and duoHMM to infer crossovers from genotyping-by-sequencing (GBS) data and a validated multi-generational pedigree from the International Institute of Tropical Agriculture (IITA) cassava breeding germplasm consisting of 7,020 informative meioses. We then constructed new genetic maps and compared them to an existing map previously constructed by the International Cassava Genetic Map Consortium (ICGMC). We observed higher recombination rates in females compared to males, and lower recombination rates in M. glaziovii introgression segments on chromosomes 1 and 4, with suppressed recombination along the entire length of the chromosome in the case of the chromosome 4 introgression. Finally, we discuss hypothesized mechanisms underlying our observations of heterochiasmy and crossover suppression and discuss the broader implications for plant breeding.
Modeling and simulation of transitional Rayleigh–Taylor flow with partially averaged Navier–Stokes equations

The partially averaged Navier–Stokes (PANS) equations are used to predict the variable-density Rayleigh–Taylor (RT) flow at Atwood number 0.5 and maximum Reynolds number 500. This is a prototypical problem of material mixing, featuring laminar, transitional, and turbulent flow, instabilities and coherent structures, density fluctuations, and production of turbulence kinetic energy by both shear and buoyancy mechanisms. These features pose numerous challenges to modeling and simulation, making the RT flow ideal to develop the validation space of the recently proposed PANS Besnard–Harlow–Rauenzahn-linear eddy viscosity model closure. The numerical simulations are conducted at different levels of physical resolution and test three approaches to set the parameters fϕ defining the range of physically resolved scales. The computations demonstrate the efficiency (accuracy vs cost) of the PANS model predicting the spatiotemporal development of the RT flow. Results comparable to large-eddy simulations and direct numerical simulations are obtained at significantly lower physical resolution without the limitations of the Reynolds-averaged Navier–Stokes equations in these transitional flows. The data also illustrate the importance of appropriate selection of the physical resolution and the resolved fraction of each dependent quantity ϕ of the turbulent closure, fϕ. These two aspects determine the ability of the model to resolve the flow phenomena not amenable to modeling by the closure and, as such, the computations' fidelity.
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling

Multi-agent behavior modeling and trajectory forecasting are crucial for the safe navigation of autonomous agents in interactive scenarios. Variational Autoencoder (VAE) has been widely applied in multi-agent interaction modeling to generate diverse behavior and learn a low-dimensional representation for interacting systems. However, existing literature did not formally discuss if a VAE-based model can properly encode interaction into its latent space. In this work, we argue that one of the typical formulations of VAEs in multi-agent modeling suffers from an issue we refer to as social posterior collapse, i.e., the model is prone to ignoring historical social context when predicting the future trajectory of an agent. It could cause significant prediction errors and poor generalization performance. We analyze the reason behind this under-explored phenomenon and propose several measures to tackle it. Afterward, we implement the proposed framework and experiment on real-world datasets for multi-agent trajectory prediction. In particular, we propose a novel sparse graph attention message-passing (sparse-GAMP) layer, which helps us detect social posterior collapse in our experiments. In the experiments, we verify that social posterior collapse indeed occurs. Also, the proposed measures are effective in alleviating the issue. As a result, the model attains better generalization performance when historical social context is informative for prediction.
Fairness-aware Online Price Discrimination with Nonparametric Demand Models.

Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concern in fairness, which even violates the regulation and law. This paper studies the problem of dynamic discriminatory pricing under fairness constraints. In particular, we consider a finite selling horizon of length T for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. For each selling period, the seller determines the price for each group and observes their purchase behavior. While existing literature mainly focuses on maximizing revenue, ensuring fairness among different customers has not been fully explored in the dynamic pricing literature. In this work, we adopt the fairness notion from (Cohen et al. 2021a). For price fairness, we propose an optimal dynamic pricing policy in terms of regret, which enforces the strict price fairness constraint. In contrast to the standard√T-type regret in online learning, we show that the optimal regret in our case is O(T^{4/5}). We further extend our algorithm to a more general notion of fairness, which includes demand fairness as a special case. To handle this general class, we propose a soft fairness constraint and develop the dynamic pricing policy that achieves O(T^{4/5}) regret.
Collapse and Revival of an Artificial Atom Coupled to a Structured Photonic Reservoir

A structured electromagnetic reservoir can result in novel dynamics of quantum emitters. In particular, the reservoir can be tailored to have a memory of past interactions with emitters, in contrast to memory-less Markovian dynamics of typical open systems. In this Article, we investigate the non-Markovian dynamics of a superconducting qubit strongly coupled to a superconducting slow-light waveguide reservoir. Tuning the qubit into the spectral vicinity of the passband of this waveguide, we find non-exponential energy relaxation as well as substantial changes to the qubit emission rate. Further, upon addition of a reflective boundary to one end of the waveguide, we observe revivals in the qubit population on a timescale 30 times longer than the inverse of the qubit's emission rate, corresponding to the round-trip travel time of an emitted photon. By tuning of the qubit-waveguide interaction strength, we probe a crossover between Markovian and non-Markovian qubit emission dynamics. These attributes allow for future studies of multi-qubit circuits coupled to structured reservoirs, in addition to constituting the necessary resources for generation of multiphoton highly entangled states.
Developmental Coupling of Cerebral Blood Flow and fMRI Fluctuations in Youth

To support brain development during youth, the brain must balance energy delivery and consumption. Previous studies in adults have demonstrated high coupling between cerebral blood flow and brain function as measured using functional neuroimaging, but how this relationship evolves over adolescence is unknown. To address this gap, we studied a sample of 831 children and adolescents (478 females, ages 8-22) from the Philadelphia Neurodevelopmental Cohort who were scanned at 3T with both arterial spin-labeled (ASL) MRI and resting-state functional MRI (fMRI). Local coupling between cerebral blood flow (CBF, from ASL) and the amplitude of low frequency fluctuations (ALFF, from fMRI) was first quantified using locally weighted regressions on the cortical surface. We then used generalized additive models to evaluate how CBF-ALFF coupling was associated with age, sex, and executive function. Enrichment of effects within canonical functional networks was evaluated using spin-based permutation tests. Our analyses revealed tight CBF-ALFF coupling across the brain. Whole-brain CBF-ALFF coupling decreased with age, largely driven by coupling decreases in the inferior frontal cortex, precuneus, visual cortex, and temporoparietal cortex (pfdr<0.05). Furthermore, we found that females had stronger coupling in the frontoparietal network than males (pfdr<0.05) and that better executive function was associated with decreased coupling in the somatomotor network (pfdr<0.05). These results emphasize that CBF-ALFF coupling undergoes development in youth and is linked to individual differences in cognition. As such, this relationship may be a valuable marker of both normal brain development and abnormalities associated with neuropsychiatric illness in youth.
Interactions between a mechanosensitive channel and cell wall integrity signaling influence pollen germination in Arabidopsis thaliana.

Cells employ multiple systems to maintain cellular integrity, including mechanosensitive (MS) ion channels and the cell wall integrity (CWI) pathway. Here, we use pollen as a model system to ask how these different mechanisms are interconnected at the cellular level. MscS-Like (MSL)8 is an MS channel required to protect Arabidopsis thaliana pollen from osmotic challenges during in vitro rehydration, germination and tube growth. New CRISPR/Cas9 and artificial microRNA-generated msl8 alleles produced unexpected pollen phenotypes, including the ability to germinate a tube after bursting, dramatic defects in cell wall structure and disorganized callose deposition at the germination site. We document complex genetic interactions between MSL8 and two previously established components of the CWI pathway, MARIS, and ANXUR1/2. Overexpression of MARIS R240C-FP suppressed the bursting, germination, and callose deposition phenotypes of msl8 mutant pollen. Null msl8 alleles suppressed the internalized callose structures observed in MARIS R240C-FP lines. Similarly, MSL8-YFP overexpression suppressed bursting in the anxur1/2 mutant background, while anxur1/2 alleles reduced the strong rings of callose around ungerminated pollen grains in MSL8-YFPover-expressors.These data show that MS ion channels modulate callose deposition in pollen and provides evidence that cell wall and membrane surveillance systems coordinate in a complex manner to maintain cell integrity.
Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR)

The Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV-TR) (American Psychiatric Association [APA], 2000) is a compendium of mental disorders, a listing of the criteria used to diagnose them, and a detailed system for their definition, organization, and classification. Put simply, it is the primary diagnostic manual for mental health professionals in the United States and much of the Western world. Diagnosis refers to the identification and labeling of a mental disorder by examination and analysis. Mental health professionals diagnose individuals based on the symptoms that they report experiencing and the signs of illness with which they present. The DSM-IV-TR aids professionals in understanding and diagnosing mental disorders through its provision of explicit diagnostic criteria and an official classification system. Keywords: diagnosis; classification; mental illness; psychopathology
The reaction of β-propiolactone with inosine 5′-phosphate: The preparation of N-(5-amino-1-β-d-ribofuranosyl-imidazole-4-carbonyl)-β-alanine 5′-phosphate

Abstract Under carefully controlled conditions of pH, the site of alkylation of inosine 5′-phosphate by β-propiolactone can be directed to provide in good yields either 1- or 7-(2-carboxyethyl)inosinic acid (II and III, respectively). Ring opening of the pyrimidine portion of 1-(2-carboxyethyl)inosine 5′-phosphate is shown to be a convenient route to N-(5- amino -1-β- d -ribofuranosyl-imidazole -4- carbonyl )-β- alanine 5′-phosphate (V), an analogue of N-(5- amino -1-β- d -ribofuranosyl-imidazole -4- carbonyl)- l -aspartic acid 5′-phosphate (VI). The purification, spectral characterizations and paper chromatographic behavior of these nucleotides are reported.
Trypanosome metabolism of myristate, the fatty acid required for the variant surface glycoprotein membrane anchor

The trypanosome variant surface glycoprotein (VSG) is anchored to the outer leaflet of the parasite plasma membrane by a glycosyl phosphatidylinositol (GPI). The VSG anchor is unique among GPIs in containing exclusively dimyristoylglycerol as its lipid moiety. Myristate is incorporated into the anchor precursor by sequential deacylation and specific reacylation with myristate. Although myristate is required for the VSG anchor, trypanosomes cannot synthesize this fatty acid and must import their entire supply from the host bloodstream, where it exists in low abundance. Chemical analysis of these parasites reveals that most of their myristate is in VSG protein, with no major lipid storage form. Unexpectedly, when these cells are radiolabeled with [3H]myristate in culture, most of the label is incorporated into phospholipids, with little into VSG. This apparent contradiction is explained by the fact that trypanosomes in culture medium elongate much of the [3H]myristate into palmitate and stearate, probably because the medium (with only 5% serum) contains limiting amounts of these fatty acids. In contrast, trypanosomes radiolabeled in whole blood (with higher concentrations of palmitate and stearate) do not modify most of the [3H]myristate, and instead utilize the major portion of it for GPI synthesis. Our studies suggest that bloodstream trypanosomes have evolved highly efficient means of directing myristate into the GPI biosynthetic pathway.
GASTRIC ACID SUPPRESSION BY INTRAVENOUS GLUCOSE SOLUTIONS

Abstract Twenty-four and 12-hr basal acid collections were obtained from 15 healthy, young (ages 22 to 27) male volunteers. During collections, subjects were infused either with saline or a 5 or 15% glucose solution. Five per cent glucose infusion significantly suppressed basal acid secretion (mean percentage of suppression=33.8%) when compared with saline infusion. Plasma glucose levels were significantly higher during glucose infusion when compared with saline infusion. The basis for acid suppression by glucose infusion is not known but may be related to elevated plasma glucose levels or plasma osmolality. The suppressant effect on acid output of intravenous glucose solutions may have therapeutic application and must be taken into account when testing gastric acid function in subjects receiving intravenous glucose solutions.