cssi/SciSciGPT-SciSciNet huggingface dataset:
Expert system gradient descent style training: Development of a defensible artificial intelligence technique

Abstract Artificial intelligence systems, which are designed with a capability to learn from the data presented to them, are used throughout society. These systems are used to screen loan applicants, make sentencing recommendations for criminal defendants, scan social media posts for disallowed content and more. Because these systems do not assign meaning to their complex learned correlation network, they can learn associations that do not equate to causality, resulting in non-optimal and indefensible decisions being made. In addition to making decisions that are sub-optimal, these systems may create legal liability for their designers and operators by learning correlations that violate anti-discrimination and other laws regarding what factors can be used in different types of decision making. This paper presents the use of a machine learning expert system, which is developed with meaning-assigned nodes (facts) and correlations (rules). Multiple potential implementations are considered and evaluated under different conditions, including different network error and augmentation levels and different training levels. The performance of these systems is compared to random and fully connected networks.
Time domain flutter analysis of cascades using a full-potential solver

A time domain approach is used to determine the aeroelastic stability of a cascade of blades. The structural model for each blade is a typical section with two degrees of freedom. The aerodynamic model is unsteady, two-dimensional, full-potential flow through the cascade of airfoils. The unsteady equations of motion for the structure and the fluid are integrated simultaneously in time starting with a steady flowfield and a small initial disturbance applied to the airfoils. Each blade is allowed to move independently, and the motion of all blades is analyzed to determine the aeroelastic stability of the cascade
Low frost-point humidity generator

A low frost-point humidity generator has been developed at NBS to provide a capability for calibration, testing, and research at very low levels of water vapor content in such gases as atmospheric air, carbon dioxide and nitrogen. The generator produces frost points from -30 to -100 C at ambient pressures from 500 to 200,000 pascals (0.005 to 2 atm.). This is equivalent to mixing ratios of 4 micrograms to 51 grams of water vapor per kilogram of dry air and to vapor pressures of .0014 to 38 pascals. The generated test gas can be fed to a test chamber with independent temperature control between +25 and -100 C. The uncertainty of the frost point in the test chamber is estimated not to exceed 0.05 deg C.
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inference suggest symbolically representing all horizon-length paths through the Markov chain. We ask whether this perspective advances the state-of-the-art in probabilistic model checking. First, we formally describe both approaches in order to highlight their key differences. Then, using these insights we develop Rubicon, a tool that transpiles Prism models to the probabilistic inference tool Dice. Finally, we demonstrate better scalability compared to probabilistic model checkers on selected benchmarks. All together, our results suggest that probabilistic inference is a valuable addition to the probabilistic model checking portfolio, with Rubicon as a first step towards integrating both perspectives.
A stress-free sum-of-squares lower bound for coloring

We prove that with high probability over the choice of a random graph G from the Erdos-Renyi distribution G(n, 1/2), a natural nO(e2 log n)-time, degree O(e2 log n) sum-of-squares semidefinite program cannot refute the existence of a valid k-coloring of G for k = n1/2+e. Our result implies that the refutation guarantee of the basic semidefinite program (a close variant of the Lovasz theta function) cannot be appreciably improved by a natural o(log n)-degree sum-of-squares strengthening, and this is tight up to a no(1) slack in k. To the best of our knowledge, this is the first lower bound for coloring G(n, 1/2) for even a single round strengthening of the basic SDP in any SDP hierarchy. Our proof relies on a new variant of instance-preserving non-pointwise complete reduction within SoS from coloring a graph to finding large independent sets in it. Our proof is (perhaps surprisingly) short, simple and does not require complicated spectral norm bounds on random matrices with dependent entries that have been otherwise necessary in the proofs of many similar results [12, 33, 45, 28, 51]. Our result formally holds for a constraint system where vertices are allowed to belong to multiple color classes; we leave the extension to the formally stronger formulation of coloring, where vertices must belong to unique colors classes, as an outstanding open problem.
Tariff Pass-Through and Welfare in the Tablet Computer Market*

This paper estimates short‐run effects of tariffs on United States tablet prices and welfare. Market data are used to estimate a model of demand and supply and to simulate prices and sales in scenarios with tariffs. A 25 per cent tariff on firms assembling in China results in tariff elasticity of tablet prices for taxed firms of 1.108, a 29.6 per cent decline in profits, and a deadweight loss of 28.8 per cent. Firms assembling elsewhere benefit from the reduction in rivals’ competitiveness. Long‐run implications are that firms may decide to shift production from ‘uncompetitive’ facilities in China to countries that are politically favored.
Unpacking the Expressed Consequences of AI Research in Broader Impact Statements

The computer science research community and the broader public have become increasingly aware of negative consequences of algorithmic systems. In response, the top-tier Neural Information Processing Systems (NeurIPS) conference for machine learning and artificial intelligence research required that authors include a statement of broader impact to reflect on potential positive and negative consequences of their work. We present the results of a qualitative thematic analysis of a sample of statements written for the 2020 conference. The themes we identify broadly fall into categories related to how consequences are expressed (e.g., valence, specificity, uncertainty), areas of impacts expressed (e.g., bias, the environment, labor, privacy), and researchers' recommendations for mitigating negative consequences in the future. In light of our results, we offer perspectives on how the broader impact statement can be implemented in future iterations to better align with potential goals.
Studies of salt and stress sensitivity on arterial pressure in renin-b deficient mice

Excessive sodium intake is known to increase the risk for hypertension, heart disease, and stroke. Individuals who are more susceptible to the effects of high salt are at higher risk for cardiovascular diseases even independent of their blood pressure status. Local activation of the renin-angiotensin system (RAS) in the brain, among other mechanisms, has been hypothesized to play a key role in contributing to salt balance. We have previously shown that deletion of the alternative renin isoform termed renin-b disinhibits the classical renin-a encoding preprorenin in the brain resulting in elevated brain RAS activity. Thus, we hypothesized that renin-b deficiency results in higher susceptibility to salt-induced elevation in blood pressure. Telemetry implanted Ren-bNull and wildtype littermate mice were first offered a low salt diet for a week and subsequently a high salt diet for another week. A high salt diet induced a mild blood pressure elevation in both Ren-bNull and wildtype mice, but mice lacking renin-b did not exhibit an exaggerated pressor response. When renin-b deficient mice were exposed to a high salt diet for a longer duration (4 weeks), there was a trend for increased myocardial enlargement in Ren-bNull mice when compared with control mice, but this did not reach statistical significance. Multiple studies have also demonstrated the association of environmental stress with hypertension. Activation of the RAS in the rostral ventrolateral medulla and the hypothalamus is required for stress-induced hypertension. Thus, we next questioned whether the lack of renin-b would result in exacerbated response to an acute restraint-stress. Wildtype and Ren-bNull mice equally exhibited elevated blood pressure in response to restraint-stress, which was similar in mice fed either a low or high salt diet. These studies suggest that mechanisms unrelated to salt and acute stress alter the cardiovascular phenotype in mice lacking renin-b.
Towards a Unified Framework for Fair and Stable Graph Representation Learning

As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual fairness and stability and leverage it to propose a novel framework, NIFTY (uNIfying Fairness and stabiliTY), which can be used with any GNN to learn fair and stable representations. We introduce a novel objective function that simultaneously accounts for fairness and stability and develop a layer-wise weight normalization using the Lipschitz constant to enhance neural message passing in GNNs. In doing so, we enforce fairness and stability both in the objective function as well as in the GNN architecture. Further, we show theoretically that our layer-wise weight normalization promotes counterfactual fairness and stability in the resulting representations. We introduce three new graph datasets comprising of high-stakes decisions in criminal justice and financial lending domains. Extensive experimentation with the above datasets demonstrates the efficacy of our framework.
Chimera states and frequency clustering in systems of coupled inner-ear hair cells

Coupled hair cells of the auditory and vestibular systems perform the crucial task of converting the energy of sound waves and ground-borne vibrations into ionic currents. We mechanically couple groups of living, active hair cells with artificial membranes, thus mimicking in vitro the coupled dynamical system. We identify chimera states and frequency clustering in the dynamics of these coupled nonlinear, autonomous oscillators. We find that these dynamical states can be reproduced by our numerical model with heterogeneity of the parameters. Furthermore, we find that this model is most sensitive to external signals when poised at the onset of synchronization, where chimera and cluster states are likely to form. We, therefore, propose that the partial synchronization in our experimental system is a manifestation of a system poised at the verge of synchronization with optimal sensitivity.
Refined Brill–Noether theory for all trigonal curves

Trigonal curves provide an example of Brill–Noether special curves. Theorem 1.3 of Larson (Invent Math 224(3):767–790, 2021) characterizes the Brill–Noether theory of general trigonal curves and the refined stratification by Brill–Noether splitting loci, which parametrize line bundles whose push-forward to $$\mathbb {P}^1$$ has a specified splitting type. This note describes the refined stratification for all trigonal curves. Given the Maroni invariant of a trigonal curve, we determine the dimensions of all Brill–Noether splitting loci and describe their irreducible components. When the dimension is positive, these loci are connected, and if furthermore the Maroni invariant is 0 or 1, they are irreducible.
Neutralizing antibodies against coronaviruses

Neutralizing monoclonal antibodies against SARS-CoV-2 are being used as therapeutics against COVID-19. The Bjorkman laboratory is investigating the mechanisms of virus neutralization by human antibodies that bind the SARSCoV- 2 spike protein. Using structural techniques, including single-particle cryoelectron microscopy and X-ray crystallography, the laboratory has solved spike trimer-antibody structures that allow classifying antibodies with respect to spike recognition and neutralization. They are also designing and testing mosaic nanoparticles that induce broadly cross-reactive antibodies with the goal of creating a pan-coronavirus vaccine that could protect against SARS-CoV-2 and future coronaviruses with the potential to spill over into humans.
Evaluation of electrolytic capacitors for high peak current pulse duty

A new type of electrolytic capacitor has been developed specifically for high-peak-current applications. Preliminary results of an ongoing research effort to determine the operational characteristics of this new type of electrolytic capacitor are reported. The new electrolytic capacitors, designed for pulse discharge application, were tested in a low-inductance discharge circuit to evaluate the maximum current extractable, internal inductance of the capacitors (equivalent series inductance), internal losses of the capacitor (equivalent series resistance), and the potential lifetimes of the capacitors. The peak currents extracted ranged from 17 kA for the 2000- mu F unit to 27 kA for the 6800- mu F unit, with pulsewidths ranging from approximately 100 to 280 mu s for the 2000- and 6800- mu F units, respectively. >
GenFloor: Interactive generative space layout system via encoded tree graphs

Abstract Automated floorplanning or space layout planning has been a long-standing NP-hard problem in the field of computer-aided design, with applications in integrated circuits, architecture, urbanism, and operational research. In this paper, we introduce GenFloor, an interactive design system that takes geometrical, topological, and performance goals and constraints as input and provides optimized spatial design solutions as output. As part of our work, we propose three novel permutation methods for existing space layout graph representations, namely O-Tree and B∗-Tree representations. We implement our proposed floorplanning methods as a package for Dynamo, a visual programming tool, with a custom GUI and additional evaluation functionalities to facilitate designers in their generative design workflow. Furthermore, we illustrate the performance of GenFloor in two sets of case-study experiments for residential floorplanning tasks by (a) measuring the ability of the proposed system to find a known optimal solution, and (b) observing how the system can generate diverse floorplans while addressing given a constant residential design problem. Our results indicate that convergence to the global optimum is achieved while offering a diverse set of solutions of a residential floorplan corresponding to local optimums of the solution landscape.
Parametric investigation of mercury hollow- cathode neutralizers

From the results of this study, the following conclusions emerge: 1) The colloid core concept is feasible from the standpoint of reactor design. With a solid particulate fuel a specific impulse of 1200 lb-sec/lbw is obtainable. 2) The preliminary design of the GTR resulted in a weight of about 41,000 Ib (or 19,000 kg) for a thrust of 100,000 Ib (45,000 kg). No attempt was made to optimize the weight of the ground test system. 3) Several technological problem areas requiring further examination may be recommended, i.e.: a) fuel vaporization losses, b) fuel particle fission fragmentation, and c) liner material erosion.
MR Elasticity Reconstruction Using Statistical Physical Modeling and Explicit Data-Driven Denoising Regularizer

Elasticity image, visualizing the quantitative map of tissue stiffness, can be reconstructed by solving an inverse problem. Classical methods for magnetic resonance elastography (MRE) try to solve a regularized optimization problem comprising a deterministic physical model and a prior constraint as data-fidelity term and regularization term, respectively. For improving the elasticity reconstructions, appropriate prior about the underlying elasticity distribution is required which is not unique. This article proposes an infused approach for MRE reconstruction by integrating the statistical representation of the physical laws of harmonic motions and learning-based prior. For data-fidelity term, we use a statistical linear-algebraic model of equilibrium equations and for the regularizer, data-driven regularization by denoising (RED) is utilized. In the proposed optimization paradigm, the regularizer gradient is simply replaced by the residual of learned denoiser leading to time-efficient computation and convex explicit objective function. Simulation results of elasticity reconstruction verify the effectiveness of the proposed approach.
Compound Secure Groupcast: Key Assignment for Selected Broadcasting

The compound secure groupcast problem is considered, where the key variables at $K$ receivers are designed so that a transmitter can securely groupcast a message to any $N$ out of the $K$ receivers through a noiseless broadcast channel. The metric is the information theoretic tradeoff between key storage $\alpha$ , i.e., the number of bits of the key variable per message bit, and broadcast bandwidth $\beta$ , i.e., the number of bits of the broadcast information per message bit. We present two results. First, when broadcast bandwidth is minimized, i.e., when $\beta=1$ , we show that the minimum key storage is $\alpha=N$ . Second, when key storage is minimized, i.e., when $\alpha=1$ , we show that broadcast bandwidth $\beta=\min(N, K-N+1)$ is achievable.
HIV-Prevalence Mapping Using Small Area Estimation in Kenya, Tanzania, and Mozambique at the First Sub-National Level.

Background Local estimates of HIV-prevalence provide information that can be used to target interventions and consequently increase the efficiency of resources. This enhanced allocation can lead to better health outcomes, including the control of the disease spread, and for more people. Methods In this study, we used the DHS data phase V to estimate HIV prevalence at the first-subnational level in Kenya, Tanzania, and Mozambique. We fitted the data to a spatial random effect intrinsic conditional autoregressive (ICAR) model to smooth the outcome. Further, we used a sampling specification from a multistage cluster design. Results We found that Nyanza (Pi = 13.6%) and Nairobi (Pi = 7.1%) in Kenya, Iringa (Pi = 15.4%) and Mbeya (Pi = 9.3%) in Tanzania, and Gaza (Pi = 15.2%) and Maputo City (Pi = 12.9%) in Mozambique are the regions with the highest prevalence of HIV, within country. Our results are based on publicly available data that through statistically rigorous methods, allowed us to obtain an accurate visual representation of the HIV prevalence at a regional level. Conclusions These results can help in identification and targeting of high-prevalent regions to increase the supply of healthcare services to reduce the spread of the disease and increase the health quality of people living with HIV.
Strong negative electrothermal feedback in thermal kinetic inductance detectors

We demonstrate strong negative electrothermal feedback accelerating and linearizing the response of a thermal kinetic inductance detector (TKID). TKIDs are a proposed highly multiplexable replacement to transition-edge sensors and measure power through the temperature-dependent resonant frequency of a superconducting microresonator bolometer. At high readout probe power and probe frequency detuned from the TKID resonant frequency, we observe electrothermal feedback loop gain up to L≈16 through measuring the reduction of settling time. We also show that the detector response has no detectable non-linearity over a 38% range of incident power and that the noise-equivalent power is below the design photon noise.
AutoFormer: Searching Transformers for Visual Recognition

Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth, embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search. AutoFormer entangles the weights of different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify the transferability of AutoFormer by providing the performance on downstream benchmarks and distillation experiments. Code and models are available at this https URL.
The Gittins Policy in the M/G/1 Queue

The Gittins policy is a highly general scheduling policy that minimizes a wide variety of mean holding cost metrics in the M/G/1 queue. Perhaps most famously, Gittins minimizes mean response time in the M/G/1 when jobs’ service times are unknown to the scheduler. Gittins also minimizes weighted versions of mean response time. For example, the well-known "cμ rule", which minimizes class-weighted mean response time in the multiclass M/M/1, is a special case of Gittins.However, despite the extensive literature on Gittins in the M/G/1, it contains no fully general proof of Gittins’s optimality. This is because Gittins was originally developed for the multiarmed bandit problem. Translating arguments from the multiarmed bandit to the M/G/1 is technically demanding, so it has only been done rigorously in some special cases. The extent of Gittins’s optimality in the M/G/1 is thus not entirely clear.In this work we provide the first fully general proof of Gittins’s optimality in the M/G/1. The optimality result we obtain is even more general than was previously known. For example, we show that Gittins minimizes mean slowdown in the M/G/1 with unknown or partially known service times, and we show that Gittins’s optimality holds under batch arrivals. Our proof uses a novel approach that works directly with the M/G/1, avoiding the difficulties of translating from the multi-armed bandit problem.
When and why PINNs fail to train: A neural tangent kernel perspective

Abstract Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained neural networks behave during their training via gradient descent. More importantly, even less is known about why such models sometimes fail to train at all. In this work, we aim to investigate these questions through the lens of the Neural Tangent Kernel (NTK); a kernel that captures the behavior of fully-connected neural networks in the infinite width limit during training via gradient descent. Specifically, we derive the NTK of PINNs and prove that, under appropriate conditions, it converges to a deterministic kernel that stays constant during training in the infinite-width limit. This allows us to analyze the training dynamics of PINNs through the lens of their limiting NTK and find a remarkable discrepancy in the convergence rate of the different loss components contributing to the total training error. To address this fundamental pathology, we propose a novel gradient descent algorithm that utilizes the eigenvalues of the NTK to adaptively calibrate the convergence rate of the total training error. Finally, we perform a series of numerical experiments to verify the correctness of our theory and the practical effectiveness of the proposed algorithms. The data and code accompanying this manuscript are publicly available at https://github.com/PredictiveIntelligenceLab/PINNsNTK .
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network

An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at seasonal scales remains an outstanding challenge. Here, we present an approach to forecasting the quantiles of the maximum daily precipitation in each week up to six months ahead using the temporal fusion transformer (TFT) model. Through experiments in two regions, we compare TFT predictions with those of two baselines: climatology and a calibrated ECMWF SEAS5 ensemble forecast (S5). Our results show that, in terms of quantile risk at six month lead time, the TFT predictions significantly outperform those from S5 and show an overall small improvement compared to climatology. The TFT also responds positively to departures from normal that climatology cannot.
No homotopy 4–sphere invariants using ECH = SWF

In relation to the 4-dimensional smooth Poincare conjecture we construct an invariant of smooth homotopy 4-spheres using embedded contact homology (and Seiberg-Witten theory). But they turn out to vanish, for good reason.
Target space entanglement in Matrix Models

We study target space entanglement in gauged multi-matrix models as models of entanglement between groups of D-branes separated by a planar entangling surface, paying close attention to the implementation of gauge invariance. We open with a review of target space entanglement between identical particles, which shares some important features (specifically a gauged permutation symmetry) with our main problem. For our matrix models, we implement a gauge fixing well-adapted to the entangling surface. In this gauge, we map the matrix model problem to that of entanglement of a U(1) gauge theory on a complete or all-to-all lattice. Matrix elements corresponding to open strings stretching across the entangling surface in the target space lead to interesting contributions to the entanglement entropy.
Lactobacillus rhamnosus reduces the cytotoxic effects of group B streptococcus on HeLa cells

Group B Streptococcus (GBS) is an opportunistic pathogen found in the vaginal tract and is a leading cause of preterm birth and neonatal illness. Aside from GBS, the vaginal tract is predominantly colonized by commensal Lactobacillus species that are thought to protect the vaginal tract from pathogens, including GBS. Studies that examined if, and how Lactobacilli modulate GBS pathogenicity remain limited. This study sought to investigate the potential protective role of Lactobacillus rhamnosus against GBS, using an in vitro model system. Immunofluorescence microscopy and Scanning Electron Microscopy (SEM) captured images of infected HeLa cells and were analyzed using the image analysis program ImageJ. Results indicate that GBS causes HeLa cell detachment unless L. rhamnosus is present. SEM images show that GBS reduces length and number of microvilli on HeLa cell surface, as well as size of secreted vesicles. L. rhamnosus partially inhibits GBS-dependent microvilli and vesicle disruption. GBS also disrupts HeLa cell F-actin fibers unless L. rhamnosus is present. These results reveal effects of GBS infection on the host cell cytoskeleton and implies a protective role of L. rhamnosus against GBS colonization.
How to think about planned lead times

A fundamental construct of most planning systems is the planned lead time. A planned lead time is a control parameter that refers to the planned amount of time for a production, transportation or replenishment activity. The intent of this note is to provide a brief, critical examination of planned lead times. We first discuss why planned lead times are used and why they are they important. We then discuss the trade-offs to be considered in setting the planned lead times. Finally we offer a simple model to provide some insight on how the planned lead time for an activity depends on the variability of the requirements on the activity, and on the flexibility and utilization of the resource associated with the activity.
Global ill-posedness for a dense set of initial data to the isentropic system of gas dynamics

Abstract In dimension n = 2 and 3, we show that for any initial datum belonging to a dense subset of the energy space, there exist infinitely many global-in-time admissible weak solutions to the isentropic Euler system whenever 1 γ ⩽ 1 + 2 n . This result can be regarded as a compressible counterpart of the one obtained by Szekelyhidi–Wiedemann (ARMA, 2012) for incompressible flows. Similarly to the incompressible result, the admissibility condition is defined in its integral form. Our result is based on a generalization of a key step of the convex integration procedure. This generalization allows, even in the compressible case, to convex integrate any smooth positive Reynolds stress. A large family of subsolutions can then be considered. These subsolutions can be generated, for instance, via regularization of any weak inviscid limit of an associated compressible Navier–Stokes system with degenerate viscosities.
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions

We give lower bounds on the performance of two of the most popular sampling methods in practice, the Metropolis-adjusted Langevin algorithm (MALA) and multi-step Hamiltonian Monte Carlo (HMC) with a leapfrog integrator, when applied to well-conditioned distributions. Our main result is a nearly-tight lower bound of $\widetilde{\Omega}(\kappa d)$ on the mixing time of MALA from an exponentially warm start, matching a line of algorithmic results up to logarithmic factors and answering an open question of Chewi et. al. We also show that a polynomial dependence on dimension is necessary for the relaxation time of HMC under any number of leapfrog steps, and bound the gains achievable by changing the step count. Our HMC analysis draws upon a novel connection between leapfrog integration and Chebyshev polynomials, which may be of independent interest.
On sensitivity of meta-learning to support data

Meta-learning algorithms are widely used for few-shot learning. For example, image recognition systems that readily adapt to unseen classes after seeing only a few labeled examples. Despite their success, we show that modern meta-learning algorithms are extremely sensitive to the data used for adaptation, i.e. support data. In particular, we demonstrate the existence of (unaltered, in-distribution, natural) images that, when used for adaptation, yield accuracy as low as 4\% or as high as 95\% on standard few-shot image classification benchmarks. We explain our empirical findings in terms of class margins, which in turn suggests that robust and safe meta-learning requires larger margins than supervised learning.
Single-cell multiomics defines tolerogenic extrathymic Aire-expressing populations with unique homology to thymic epithelium.

The autoimmune regulator (Aire), a well-defined transcriptional regulator in the thymus, is also found in extrathymic Aire-expressing cells (eTACs) in the secondary lymphoid organs. eTACs are hemat...
Single-photon pulse induced giant response in N > 100 qubit system

The temporal dynamics of large quantum systems perturbed weakly by a single excitation can give rise to unique phenomena at the quantum phase boundaries. Here, we develop a time-dependent model to study the temporal dynamics of a single photon interacting with a defect within a large system of interacting spin qubits (N > 100). Our model predicts a quantum resource, giant susceptibility, when the system of qubits is engineered to simulate a first-order quantum phase transition (QPT). We show that the absorption of a single-photon pulse by an engineered defect in the large qubit system can nucleate a single shot quantum measurement through spin noise read-out. This concept of a single-shot detection event (“click”) is different from parameter estimation, which requires repeated measurements. The crucial step of amplifying the weak quantum signal occurs by coupling the defect to a system of interacting qubits biased close to a QPT point. The macroscopic change in long-range order during the QPT generates amplified magnetic noise, which can be read out by a classical device. Our work paves the way for studying the temporal dynamics of large quantum systems interacting with a single-photon pulse.
Linear strands of initial ideals of determinantal facet ideals

A determinantal facet ideal (DFI) is an ideal $J_\Delta$ generated by maximal minors of a generic matrix parametrized by an associated simplicial complex $\Delta$. In this paper, we construct an explicit linear strand for the initial ideal with respect to any diagonal term order $<$ of an arbitrary DFI. In particular, we show that if $\Delta$ has no \emph{1-nonfaces}, then the Betti numbers of the linear strand of $J_\Delta$ and its initial ideal coincide. We apply this result to prove a conjecture of Ene, Herzog, and Hibi on Betti numbers of closed binomial edge ideals in the case that the associated graph has at most $2$ maximal cliques. More generally, we show that the linear strand of the initial ideal (with respect to $<$) of \emph{any} DFI is supported on a polyhedral cell complex obtained as an induced subcomplex of the \emph{complex of boxes}, introduced by Nagel and Reiner.
Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks

To shift the computational burden from real-time to offline in delay-critical power systems applications, recent works entertain the idea of using a deep neural network (DNN to predict the solutions of the AC optimal power flow (AC-OPF once presented load demands. As network topologies may change, training this DNN in a sample-efficient manner becomes a necessity. To improve data efficiency, this work utilizes the fact OPF data are not simple training labels, but constitute the solutions of a parametric optimization problem. We thus advocate training a sensitivity-informed DNN (SI-DNN to match not only the OPF optimizers, but also their partial derivatives with respect to the OPF parameters (loads . It is shown that the required Jacobian matrices do exist under mild conditions, and can be readily computed from the related primal/dual solutions. The proposed SI-DNN is compatible with a broad range of OPF solvers, including a non-convex quadratically constrained quadratic program (QCQP, its semidefinite program (SDP relaxation, and MATPOWER; while SI-DNN can be seamlessly integrated in other learning-to-OPF schemes. Numerical tests on three benchmark power systems corroborate the advanced generalization and constraint satisfaction capabilities for the OPF solutions predicted by an SI-DNN over a conventionally trained DNN, especially in low-data setups.
Significance testing for canonical correlation analysis in high dimensions

We consider the problem of testing for the presence of linear relationships between large sets of random variables based on a post-selection inference approach to canonical correlation analysis. The challenge is to adjust for the selection of subsets of variables having linear combinations with maximal sample correlation. To this end, we construct a stabilized one-step estimator of the euclidean-norm of the canonical correlations maximized over subsets of variables of pre-specified cardinality. This estimator is shown to be consistent for its target parameter and asymptotically normal provided the dimensions of the variables do not grow too quickly with sample size. We also develop a greedy search algorithm to accurately compute the estimator, leading to a computationally tractable omnibus test for the global null hypothesis that there are no linear relationships between any subsets of variables having the pre-specified cardinality. Further, we develop a confidence interval for the target parameter that takes the variable selection into account.
Complexity aspects of local minima and related notions

Abstract We consider the notions of (i) critical points, (ii) second-order points, (iii) local minima, and (iv) strict local minima for multivariate polynomials. For each type of point, and as a function of the degree of the polynomial, we study the complexity of deciding (1) if a given point is of that type, and (2) if a polynomial has a point of that type. Our results characterize the complexity of these two questions for all degrees left open by prior literature. Our main contributions reveal that many of these questions turn out to be tractable for cubic polynomials. In particular, we present an efficiently-checkable necessary and sufficient condition for local minimality of a point for a cubic polynomial. We also show that a local minimum of a cubic polynomial can be efficiently found by solving semidefinite programs of size linear in the number of variables. By contrast, we show that it is strongly NP-hard to decide if a cubic polynomial has a critical point. We also prove that the set of second-order points of any cubic polynomial is a spectrahedron, and conversely that any spectrahedron is the projection of the set of second-order points of a cubic polynomial. In our final section, we briefly present a potential application of finding local minima of cubic polynomials to the design of a third-order Newton method.
A Simple Asset Pricing Model with Heterogeneous Beliefs

This chapter extends the asset pricing model in order to include heterogeneous beliefs. The model features two investors, each with logarithmic utility. In equilibrium, prices are set as if there is a representative investor whose probability density function is a wealth-weighted convex combination of the individual investors' density functions. The model can be structured to reflect the major empirical findings where one investor predicts continuation and the other investor predicts reversal. In this case, the representative investor's probability density function might well be bimodal in respect to long time horizons. In this chapter, the state price model is developed and then extended to develop the two-securities model. The simplest such model features two agents with different beliefs, trading over time in a market for two securities, a risk-free security and a risky security, which can be viewed as the market portfolio. The formal analysis focuses on underlying state prices. The equilibrium pricing equation involves the state price vector ν. The equation indicates that a state price is a ratio of a discounted probability to the cumulative consumption growth rate. Notably, the state price embodies heterogeneity through the discounted probability. The discounted probability is a relative wealth-weighted convex combination of the individual investors' probabilities.
JWST Pathfinder Telescope Risk Reduction Cryo Test Program

In 2014, the Optical Ground Support Equipment was integrated into the large cryo vacuum chamber at Johnson Space Center (JSC) and an initial Chamber Commissioning Test was completed. This insured that the support equipment was ready for the three Pathfinder telescope cryo tests. The Pathfinder telescope which consists of two primary mirror segment assemblies and the secondary mirror was delivered to JSC in February 2015 in support of this critical risk reduction test program prior to the flight hardware. This paper will detail the Chamber Commissioning and first optical test of the JWST Pathfinder telescope.
Conflict-of-Law Rules in Conflict and Competition with Substantive Law Rules: The Case of Corporate Acquisitions (La Cession De Contrôle En Droit International Privé)

Suppose that a citizen of UK and a citizen of France meet in Italy and sign a contract to be performed in Germany. If a legal issue arises regarding the interpretation of the contract, which national law should apply? Conflict-of-law rules exist to resolve this question. Among the criteria used to determine which law applies (in the case when the parties to the contract did not choose it) are the country where the contract is signed, the country where the contract is performed, and in the case of corporate law, the country in which the corporation in question is registered. Conflict-of-law rules are for the most part a product of national law and a court before which the dispute is brought will apply the conflict-of-law rule of the country of which it is part.Nevertheless, a court may find that conflict-of-law rules that it applies are themselves in conflict. For example, a legal dispute may involve issues about contract law and corporate law and a conflict-of-law rule applicable to contract law may require a different result than the conflict-of-law rules applicable to corporate law. One way to resolve this conflict of conflict-of law-rules is to apply different conflict-of-law rules to various aspects of the disputed matter. But this differential application of the conflict-of law rules to the same issue avoids consideration of the matter in question as a whole, introduces complexity, and may produce incoherent results. Another way to resolve the conflict of conflict-of-law rules is to weigh the different interests at stake in light of the underlying purposes of the substantive law. This method has been applied by courts in some instances. But with both approaches, the viability of traditional conflict-of-law methods is called into question. Moreover, the problem becomes even more complicated when substantive rules on international (rather than national) law are involved. This study describes the different conflicts that may occur and analyzes the different solutions for resolving them through the example of corporate acquisitions.
Eosinophilic leukemoid reaction. Report of a case, its relationship to eosinophilic leukemia, and review of the pediatric literature.

The very existence of eosinophilic leukemia remains a controversial issue. Although several recent reviews have attempted to establish criteria for the diagnosis, there are few, if any, obvious cases in the pediatric literature. The case reported here adds another dimension to the spectrum of disorders called eosinophilic leukemia. Alternative explanations are offered for the courses of previously reported cases in children. Additional information regarding eosinophil migration and factors mediating eosinophilia is reviewed, and more strict criteria for establishing the diagnosis of eosinophilic leukemia are suggested.
Narrowband process quantization

Summary form only given, as follows. We introduce a technique of tracking and quantizing the complex envelope of a narrowband process, which is a continuous time Gauss-Markov process of arbitrary order. The narrowband process is modeled in a state space framework and several quantization schemes using the in-phase and the quadrature components of the narrowband process are considered. These in-phase and quadrature components are baseband processes, and thus are much more slowly varying than the original narrowband process. The advantage of such a scheme lies in the fact that the sampling in such a system is less sensitive to timing errors. We compare the performance of these schemes with several previously analyzed schemes both with respect to a time averaged smoothed error, and their robustness with respect to the changes in the input spectrum. We compare the performance of the envelope quantization schemes with differential state quantization for a second order narrowband process. The envelope quantization schemes perform better than state vector quantization but worse than differential state quantization. Differential state quantization considers the quantization of a state consisting of the proces and its derivatives and was shown to be a superior quantization scheme for Gauss-Markov processes. We also define a measure of robustness for different source coding systems when the input spectrum changes and compare the performance of this envelope quantization scheme with several other previously analyzed schemes.
Idiopathic eosinophilic infiltration of the gastrointestinal tract, diffuse and circumscribed: A proposed classification and review of the literature, with two additional cases

Abstract A classification is proposed for idiopathic eosinophilic infiltrations of the gastrointestinal tract, based on pathologic, clinical and laboratory features. Two relevant cases are added to the literature. In general, patients with a long-standing history of upper gastrointestinal complaints, with or without overt allergies, a roentgenographic picture of a constricting antrum or pylorus, and/or segmental narrowing and dilatation of the small bowel, associated with eosinophilia and leukocytosis, have diffuse eosinophilic gastroenteritis of the monoenteric or polyenteric type. The evidence suggests that these patients can be treated successfully with adrenocortical steroids and that surgery may be avoided. The regional type of this disease is confined for the most part to the pyloric area, causing obstruction in the absence of peptic ulcer, with little in the peripheral blood to aid in the diagnosis. Circumscribed eosinophilic infiltrated granulomas are well demarcated lesions of the regional or polypoid type, occurring anywhere in the gastrointestinal tract, giving local signs, and easily confused with neoplasm.
Fluid Turbulence: Deterministic or Statistical

The deterministic view of turbulence suggests that the classical theory of fluid turbulence may be treating the wrong entity. We explore the physical implications of such an abstract mathematical result, and provide a constructive computational demonstration of the deterministic and the wave nature of fluid turbulence. The associated pressure disturbance for restoring solenoidal velocity is the primary agent, and its reflection from solid surface(s) the dominant mechanism of turbulence production. Statistical properties and their modeling must address to the statistics of the uncertainties of initial boundary data of the ensemble.
On the interannual variability of the Indian monsoon and the Southern Oscillation

The mechanisms of the interannual variability of the Indian monsoon are investigated from observations in the Indian Ocean sector through correlation and stratification techniques. Years with abundant Southwest monsoon rainfall in India are characterized by anomalously low pressure over South Asia and the adjacent waters, enhanced cross-equatorial flow in the western, and increased cloudiness over the northern portion of the Indian Ocean, continuing from the pre-monsoon through the post-monsoon season; positive temperature anomalies over land and in the Arabian Sea in the pre-monsoon season, changing to negative departures after the monsoon onset. The following causality chain is suggested: The anomalously warm surfaces of South Asia and the adjacent ocean in the pre-monsoon season induce a thermal low, thus enhancing the northward directed pressure gradient, and favoring a vigorous cross-equatorial flow over the Indian Ocean. After the monsoon onset the land surfaces are cooled by evaporation, and the Arabian Sea ausrface waters by various wind stress effects. However, latent heat release over South Asia can now maintain the meridional topography gradients essential to the monsoon circulation.