cssi/SciSciGPT-SciSciNet huggingface dataset:
THE IONESCU–WAINGER MULTIPLIER THEOREM AND THE ADELES

The Ionescu--Wainger multiplier theorem establishes good $L^p$ bounds for Fourier multiplier operators localized to major arcs; it has become an indispensible tool in discrete harmonic analysis. We give a simplified proof of this theorem with more explicit constants (removing logarithmic losses that were present in previous versions of the theorem), and give a more general variant involving adelic Fourier multipliers. We also establish a closely related adelic sampling theorem that shows that $\ell^p(\Z^d)$ norms of functions with Fourier transform supported on major arcs are comparable to the $L^p(\A_\Z^d)$ norm of their adelic counterparts.
Scattering from Infinity of the Maxwell Klein Gordon Equations in Lorenz Gauge

We prove global existence backwards from the scattering data posed at infinity for the Maxwell Klein Gordon equations in Lorenz gauge satisfying the weak null condition. The asymptotics of the solutions to the Maxwell Klein Gordon equations in Lorenz gauge were shown to be wave like at null infinity and homogeneous towards timelike infinity in Candy et al. (Commun Math Phys 367(2):683–716, 2019) and expressed in terms of radiation fields, and thus our scattering data will be given in the form of radiation fields in the backward problem. We give a refinement of the asymptotics results in Candy et al. (2019), and then making use of this refinement, we find a global solution which attains the prescribed scattering data at infinity. Our work starts from the approach in [21] and is more delicate since it involves with nonlinearities with fewer derivatives. Our result corresponds to “existence of scattering states” in the scattering theory. The method of proof relies on a suitable construction of the approximate solution from the scattering data, a weighted conformal Morawetz energy estimate and a spacetime version of Hardy inequality.
Distantly Supervised Transformers For E-Commerce Product QA

We propose a practical instant question answering (QA) system on product pages of e-commerce services, where for each user query, relevant community question answer (CQA) pairs are retrieved. User queries and CQA pairs differ significantly in language characteristics making relevance learning difficult. Our proposed transformer-based model learns a robust relevance function by jointly learning unified syntactic and semantic representations without the need for human labeled data. This is achieved by distantly supervising our model by distilling from predictions of a syntactic matching system on user queries and simultaneously training with CQA pairs. Training with CQA pairs helps our model learning semantic QA relevance and distant supervision enables learning of syntactic features as well as the nuances of user querying language. Additionally, our model encodes queries and candidate responses independently allowing offline candidate embedding generation thereby minimizing the need for real-time transformer model execution. Consequently, our framework is able to scale to large e-commerce QA traffic. Extensive evaluation on user queries shows that our framework significantly outperforms both syntactic and semantic baselines in offline as well as large scale online A/B setups of a popular e-commerce service.
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed increasing efforts in detecting anomalies with discrete event sequences. However, it remains an extremely difficult task due to several intrinsic challenges including data imbalance issues, discrete property of the events, and sequential nature of the data. To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences. Specifically, OC4Seq integrates the anomaly detection objective with recurrent neural networks (RNNs) to embed the discrete event sequences into latent spaces, where anomalies can be easily detected. In addition, given that an anomalous sequence could be caused by either individual events, subsequences of events, or the whole sequence, we design a multi-scale RNN framework to capture different levels of sequential patterns simultaneously. We fully implement and evaluate OC4Seq on three real-world system log datasets. The results show that OC4Seq consistently outperforms various representative baselines by a large margin. Moreover, through both quantitative and qualitative analysis, the importance of capturing multi-scale sequential patterns for event anomaly detection is verified. To encourage reproducibility, we make the code and data publicly available.
Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples

Fuzzy similarity join is an important database operator widely used in practice. So far the research community has focused exclusively on optimizing fuzzy joinscalability. However, practitioners today also struggle to optimize fuzzy-joinquality, because they face a daunting space of parameters (e.g., distance-functions, distance-thresholds, tokenization-options, etc.), and often have to resort to a manual trial-and-error approach to program these parameters in order to optimize fuzzy-join quality. This key challenge of automatically generating high-quality fuzzy-join programs has received surprisingly little attention thus far. In this work, we study the problem of "auto-program'' fuzzy-joins. Leveraging a geometric interpretation of distance-functions, we develop an unsupervised Auto-FuzzyJoin framework that can infer suitable fuzzy-join programs on given input tables, without requiring explicit human input such as labelled training data. Using Auto-FuzzyJoin, users only need to provide two input tables L and R, and a desired precision target τ (say 0.9). Auto-FuzzyJoin leverages the fact that one of the input is a reference table to automatically program fuzzy-joins that meet the precision target τ in expectation, while maximizing fuzzy-join recall (defined as the number of correctly joined records). Experiments on both existing benchmarks and a new benchmark with 50 fuzzy-join tasks created from Wikipedia data suggest that the proposed Auto-FuzzyJoin significantly outperforms existing unsupervised approaches, and is surprisingly competitive even against supervised approaches (e.g., Magellan and DeepMatcher) when 50% of ground-truth labels are used as training data. We have released our code and benchmark on GitHub\footnote\urlhttps://github.com/chu-data-lab/AutomaticFuzzyJoin to facilitate future research.
Fairness for Image Generation with Uncertain Sensitive Attributes

This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification setting. Moreover, while traditional group fairness definitions are typically defined with respect to specified protected groups -- camouflaging the fact that these groupings are artificial and carry historical and political motivations -- we emphasize that there are no ground truth identities. For instance, should South and East Asians be viewed as a single group or separate groups? Should we consider one race as a whole or further split by gender? Choosing which groups are valid and who belongs in them is an impossible dilemma and being "fair" with respect to Asians may require being "unfair" with respect to South Asians. This motivates the introduction of definitions that allow algorithms to be \emph{oblivious} to the relevant groupings. We define several intuitive notions of group fairness and study their incompatibilities and trade-offs. We show that the natural extension of demographic parity is strongly dependent on the grouping, and \emph{impossible} to achieve obliviously. On the other hand, the conceptually new definition we introduce, Conditional Proportional Representation, can be achieved obliviously through Posterior Sampling. Our experiments validate our theoretical results and achieve fair image reconstruction using state-of-the-art generative models.
Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories

Many robotic applications involve interactions between multiple agents where an agent’s decisions affect the behavior of other agents. Such behaviors can be captured by the equilibria of differential games which provide an expressive framework for modeling the agents’ mutual influence. However, finding the equilibria of differential games is in general challenging as it involves solving a set of coupled optimal control problems. In this work, we propose to leverage the special structure of multi-agent interactions to generate interactive trajectories by simply solving a single optimal control problem, namely, the optimal control problem associated with minimizing the potential function of the differential game. Our key insight is that for a certain class of multi-agent interactions, the underlying differential game is indeed a potential differential game for which equilibria can be found by solving a single optimal control problem. We introduce such an optimal control problem and build on single-agent trajectory optimization methods to develop a computationally tractable and scalable algorithm for planning multi-agent interactive trajectories. We will demonstrate the performance of our algorithm in simulation and show that our algorithm outperforms the state-of-the-art game solvers. To further show the real-time capabilities of our algorithm, we will demonstrate the application of our proposed algorithm in a set of experiments involving interactive trajectories for two quadcopters.
Infinite-Dimensional Fisher Markets: Equilibrium, Duality and Optimization.

This paper considers a linear Fisher market with $n$ buyers and a continuum of items. In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual. Regarding the new convex programs, we establish existence of optimal solutions, the existence of KKT-type conditions, as well as strong duality. All these properties are established via non-standard arguments, which circumvent the limitations of duality theory in optimization over infinite-dimensional vector spaces. Furthermore, we show that there exists a pure equilibrium allocation, i.e., a division of the item space. Similar to the finite-dimensional case, a market equilibrium under the infinite-dimensional Fisher market is Pareto optimal, envy-free and proportional. We also show how to obtain the (a.e. unique) equilibrium price vector and a pure equilibrium allocation from the (unique) $n$-dimensional equilibrium bang-per-buck vector. When the item space is the unit interval $[0,1]$ and buyers have piecewise linear utilities, we show that $\epsilon$-approximate equilibrium prices can be computed in time polynomial in the market size and $\log \frac{1}{\epsilon}$. This is achieved by solving a finite-dimensional convex program using the ellipsoid method. To this end, we give nontrivial and efficient subgradient and separation oracles. For general buyer valuations, we propose computing market equilibrium using stochastic dual averaging, which finds an approximate equilibrium price vector with high probability.
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers – groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.
Convergent series of integers with missing digits

A classical theorem of Kempner states that the sum of the reciprocals of positive integers with missing decimal digits converges. This result is extended to much larger families of “missing digits” sets of positive integers with convergent harmonic series.
Review of Carbon-Based Grid Development Activities for Ion Thrusters

The primary life-limiting mechanism for state-of-the-art ion thrusters is erosion of the accelerator grid by charge-exchange ions produced in the plume of the thruster. This paper reviews carbon-based grid development activities, specifically addressing grid manufacturing, optics performance, and launch and operational survivability.
Assessment of serological assays for identifying high titer convalescent plasma.

Background The COVID-19 pandemic has been accompanied by the largest mobilization of therapeutic convalescent plasma (CCP) in over a century. Initial identification of high titer units was based on dose-response data using the Ortho VITROS IgG assay. The proliferation of severe acute respiratory syndrome coronavirus 2 serological assays and non-uniform application has led to uncertainty about their interrelationships. The purpose of this study was to establish correlations and analogous cutoffs between multiple serological assays. Methods We compared the Ortho, Abbott, Roche, an anti-spike (S) ELISA, and a virus neutralization assay. Relationships relative to FDA-approved cutoffs under the CCP emergency use authorization were identified in convalescent plasma from a cohort of 79 donors from April 2020. Results Relative to the neutralization assay, the spearman r value of the Ortho Clinical, Abbott, Roche, anti-S ELISA assays was 0.65, 0.59, 0.45, and 0.76, respectively. The best correlative index for establishing high-titer units was 3.87 signal-to-cutoff (S/C) for the Abbott, 13.82 cutoff index for the Roche, 1:1412 for the anti-S ELISA, 1:219 by the neutralization assay, and 15.9 S/C by the Ortho Clinical assay. The overall agreement using derived cutoffs compared to a neutralizing titer of 1:250 was 78.5% for Abbott, 74.7% for Roche, 83.5% for the anti-S ELISA, and 78.5% for Ortho Clinical. Discussion Assays based on antibodies against the nucleoprotein were positively associated with neutralizing titers and the Ortho assay, although their ability to distinguish FDA high-titer specimens was imperfect. The resulting relationships help reconcile results from the large body of serological data generated during the COVID-19 pandemic.
Numerical semigroups, polyhedra, and posets II: locating certain families of semigroups

Several recent papers have examined a rational polyhedron $P_m$ whose integer points are in bijection with the numerical semigroups (cofinite, additively closed substets of the non-negative integers) containing $m$. A combinatorial description of the faces of $P_m$ was recently introduced, one that can be obtained from the divisibility posets of the numerical semigroups a given face contains. In this paper, we study the faces of $P_m$ containing arithmetical numerical semigroups and those containing certain glued numerical semigroups, as an initial step towards better understanding the full face structure of $P_m$. In most cases, such faces only contain semigroups from these families, yielding a tight connection to the geometry of $P_m$.
Can changes in sentiments influence consumer behavior? Evidence from the Trump-Russia investigation

We analyze how consumer spending is affected by the news coverage of the investigation into coordination between Donald Trump's presidential campaign and Russia. Using daily consumption data, we found that Republicans and Democrats responded differently to the news. Increased media coverage led households in counties that voted for Hillary Clinton to spend less: On the days of heaviest news coverage, spending declined by $0.007 to $0.011 per household member for every point added to Clinton's vote share. There is also evidence that in Republican-voting counties the news coverage had the opposite effect.
Cone mitochondria act as microlenses to enhance light delivery and confer Stiles-Crawford-like direction sensitivity

Abstract Sensory systems that efficiently transduce physical energy into neural signaling are advantageous for survival. The vertebrate retina poses a challenge to such efficiency, featuring an inverted structure with multiple neural layers through which photons must pass, risking premature absorption or scattering. Moreover, mammalian photoceptors aggregate an unusual amount of mitochondria in the ellipsoid region immediately before the light-sensitive outer segments (OS). While these mitochondria are required to support the high metabolic demands of phototransduction, it is yet unknown their impact on light transmission. Here we demonstrate via direct live-imaging and computational modeling that such tightly packed mitochondria concentrate light to enter the OS for detection. Intriguingly, this “microlens”-like feature of cone mitochondria delivers light with an angular dependence akin to the Stiles-Crawford effect, an essential visual phenomenon that improves resolution. We thus establish an unconventional optical function for cone mitochondria, energy-producing organelles, providing insight into their role in the interpretation of noninvasive optical tools for vision research and ophthalmology clinics.
Sanguine: Visual analysis for patient blood management:

Blood transfusion is a frequently performed medical procedure in surgical and nonsurgical contexts. Although it is often necessary or even life-saving, it has been identified as one of the most ove...
Pseudorandom Functions and Permutations Provably Secure Against Related-Key Attacks.

This paper fills an important foundational gap with the first proofs, under standard assumptions and in the standard model, of the existence of pseudorandom functions (PRFs) and pseudorandom permutations (PRPs) resisting rich and relevant forms of related-key attacks (RKA). An RKA allows the adversary to query the function not only under the target key but under other keys derived from it in adversary-specified ways. Based on the Naor-Reingold PRF we obtain an RKA-PRF whose keyspace is a group and that is proven, under DDH, to resist attacks in which the key may be operated on by arbitrary adversary-specified group elements. Previous work was able only to provide schemes in idealized models (ideal cipher, random oracle), under new, non-standard assumptions, or for limited classes of attacks. The reason was technical difficulties that we resolve via a new approach and framework that, in addition to the above, yields other RKA-PRFs including a DLIN-based one derived from the Lewko-Waters PRF. Over the last 15 years cryptanalysts and blockcipher designers have routinely and consistently targeted RKA-security; it is visibly important for abuse-resistant cryptography; and it helps protect against fault-injection sidechannel attacks. Yet ours are the first significant proofs of existence of secure constructs. We warn that our constructs are proofs-of-concept in the foundational style and not practical. 1 Department of Computer Science & Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. Email: mihir@cs.ucsd.edu. URL: http://www.cs.ucsd.edu/users/mihir. Supported in part by NSF grants CNS-0627779 and CCF-0915675. 2 Department of Computer Science & Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. Email: cdc@ucsd.edu. URL: http://www.cs.ucsd.edu/users/cdcash. Supported in part by NSF grants CNS-0627779 and CCF-0915675.
Learning Koopman Eigenfunctions and Invariant Subspaces from Data: Symmetric Subspace Decomposition

This paper develops data-driven methods to identify eigenfunctions of the Koopman operator associated to a dynamical system and subspaces that are invariant under the operator. We build on Extended Dynamic Mode Decomposition (EDMD), a data-driven method that finds a finite-dimensional approximation of the Koopman operator on the span of a predefined dictionary of functions. We propose a necessary and sufficient condition to identify Koopman eigenfunctions based on the application of EDMD forward and backward in time. Moreover, we propose the Symmetric Subspace Decomposition (SSD) algorithm, an iterative method which provably identifies the maximal Koopman-invariant subspace and the Koopman eigenfunctions in the span of the dictionary. We also introduce the Streaming Symmetric Subspace Decomposition (SSSD) algorithm, an online extension of SSD that only requires a small, fixed memory and incorporates new data as is received. Finally, we propose an extension of SSD that approximates Koopman eigenfunctions and invariant subspaces when the dictionary does not contain sufficient informative eigenfunctions.
Divergence in Population Coding for Space between Dorsal and Ventral CA1

Molecular, anatomic, and behavioral studies show that the hippocampus is structurally and functionally heterogeneous, with dorsal hippocampus implicated in mnemonic processes and spatial navigation and ventral hippocampus involved in affective processes. By performing electrophysiological recordings of large neuronal populations in dorsal and ventral CA1 in head-fixed mice navigating a virtual environment, we found that this diversity resulted in different strategies for population coding of space. Populations of neurons in dorsal CA1 showed more complex patterns of activity, which resulted in a higher dimensionality of neural representations that translated to more information being encoded, as compared ensembles in vCA1. Furthermore, a pairwise maximum entropy model was better at predicting the structure of these global patterns of activity in ventral CA1 as compared with dorsal CA1. Taken together, the different coding strategies we uncovered likely emerge from anatomic and physiological differences along the longitudinal axis of hippocampus and that may, in turn, underpin the divergent ethological roles of dorsal and ventral CA1.
The mitochondrial permeability transition pore activates the mitochondrial unfolded protein response and promotes aging

Mitochondrial activity determines aging rate and the onset of chronic diseases. The mitochondrial permeability transition pore (mPTP) is a pathological pore in the inner mitochondrial membrane thought to be composed of the F-ATP synthase (complex V). OSCP, a subunit of F-ATP synthase, helps protect against mPTP formation. How the destabilization of OSCP may contribute to aging, however, is unclear. We have found that loss OSCP in the nematode Caenorhabditis elegans initiates the mPTP and shortens lifespan specifically during adulthood, in part via initiation of the mitochondrial unfolded protein response (UPRmt). Pharmacological or genetic inhibition of the mPTP inhibits the UPRmt and restores normal lifespan. Loss of the putative pore-forming component of F-ATP synthase extends adult lifespan, suggesting that the mPTP normally promotes aging. Our findings reveal how an mPTP/UPRmt nexus may contribute to aging and age-related diseases and how inhibition of the UPRmt may be protective under certain conditions.
Minimal Gap in the Spectrum of the Sierpiński Gasket

This paper studies the size of the minimal gap between any two consecutive eigenvalues in the Dirichlet and in the Neumann spectrum of the standard Laplace operator on the Sierpinski gasket. The main result shows the remarkable fact that this minimal gap is achieved and coincides with the spectral gap. The Dirichlet case is more challenging and requires some key observations in the behavior of the dynamical system that describes the spectrum.
Computing the Death Rate of COVID-19

The Infection Fatality Rate (IFR) of COVID-19 is difficult to estimate because the number of infections is unknown and there is a lag between each infection and the potentially subsequent death. We introduce a new approach for estimating the IFR by first estimating the entire sequence of daily infections. Unlike prior approaches, we incorporate existing data on the number of daily COVID-19 tests into our estimation; knowing the test rates helps us estimate the ratio between the number of cases and the number of infections. Also unlike prior approaches, rather than determining a constant lag from studying a group of patients, we treat the lag as a random variable, whose parameters we determine empirically by fitting our infections sequence to the sequence of deaths. Our approach allows us to narrow our estimation to smaller time intervals in order to observe how the IFR changes over time. We analyze a 250 day period starting on March 1, 2020. We estimate that the IFR in the U.S. decreases from a high of \(0.68\%\) down to \(0.24\%\) over the course of this time period. We also provide IFR and lag estimates for Italy, Denmark, and the Netherlands, all of which also exhibit decreasing IFRs but to different degrees.
Radiation Pattern Synthesis For A Prolate Spheroidal Antenna

An antenna is designed consisting of a prolate spheroidal metallic surface which is excited by an arbitrarily oriented and rotationally symmetric surface current. The surface current is then chosen to synthesize a far field radiation pattern which best approximates a desired far field pattern for a given superdirective ratio. This desired radiation pattern is specified in terms of gain, phase, and polarization, and must be rotationally symmetric about the major axis of the antenna. The electromagnetic fields are expressed as sums of prolate spheroidal wave functions whose expansion coefficients are obtained utilizing the asymptotic behavior of the radial prolate spheroidal functions and the dual orthoganility of the angular prolate spheroidal functions.
Ice Inheritance in Dynamical Disk Models

The compositions of planet-forming disks are set by a combination of material inherited from the interstellar medium and material reprocessed during disk formation and evolution. Indeed, comets and primitive meteorites exhibit interstellar-like isotopic ratios and/or volatile compositions, supporting that some pristine material was incorporated intact into icy planetesimals in the Solar Nebula. To date, the survival of volatile interstellar material in the disk stage has not been modeled using realistic disk physics. Here, we present a modeling framework to track the destruction of interstellar ices on dust grains undergoing transport processes within a disk, with a particular focus on explaining the incorporation of pristine material into icy planetesimals. We find it is difficult to explain inheritance through the local assembly of comets, as ice destruction is rapid for small (<10um) grains in the inner few tens of au. Instead, a plausible pathway to inheritance is to form pebbles at larger disk radii, which then drift inwards to the comet-forming zone with their ices mostly preserved. Small grains beyond ~100 au can experience ice photodissociation at the tens of percent level, however little of the ice is actually lost from the grain, likely making this a robust site for in situ ice chemistry. Our models also indicate that many complex organic species should survive passage through the disk intact. This raises the possibility that organics synthesized in the interstellar medium can be delivered to terrestrial planets by icy body impact and thus potentially participate in origins of life chemistry.
Augmented Hip Proprioception Influences Mediolateral Foot Placement During Walking

Hip abductor proprioception contributes to the control of mediolateral foot placement, which varies with step-by-step fluctuations in pelvis dynamics. Prior work has used hip abductor vibration as a sensory probe to investigate the link between vibration within a single step and subsequent foot placement. Here, we extended prior findings by applying time and location varying vibration in every step, seeking to predictably manipulate the continuous, step-by-step relationship between pelvis dynamics and foot placement. We compared participants’ (n = 32; divided into two groups of 16 with slightly different vibration control) gait behavior across four treadmill walking conditions: 1) No feedback; 2) Random feedback, with vibration unrelated to pelvis motion; 3) Augmented feedback, with vibration designed to evoke proprioceptive feedback paralleling the actual pelvis motion; 4) Disrupted feedback, with vibration designed to evoke proprioceptive feedback inversely related to pelvis motion. We hypothesized that the relationship between pelvis dynamics and foot placement would be strengthened by Augmented feedback but weakened by Disrupted feedback. For both participant groups, the strength of the relationship between pelvis dynamics at the start of a step and foot placement at the end of a step was significantly (p ≤ 0.0002) influenced by the feedback condition. The link between pelvis dynamics and foot placement was strongest with Augmented feedback, but not significantly weakened with Disrupted feedback, partially supporting our hypotheses. Our approach to augmenting proprioceptive feedback during gait may have implications for clinical populations with a weakened relationship between pelvis motion and foot placement.
A three question math quiz: Who is ready to learn finance?

We use Markov chain Monte Carlo (MCMC) analysis to construct a three-question math quiz to assess key skills needed for introductory finance. We begin with data collected from a ten-question criter...
Inverse source problems in transport equations with external forces

Abstract This paper is concerned with the inverse source problem for the transport equation with external force. We show that both direct and inverse problems are uniquely solvable for generic absorption and scattering coefficients. In particular, for inverse problems, generic injectivity and a stability estimate of the source are derived. The analysis employs the Fredholm theorem and the Santalo's formula.
Reactor scale simulations of ALD and ALE: Ideal and non-ideal self-limited processes in a cylindrical and a 300 mm wafer cross-flow reactor

We have developed a simulation tool to model self-limited processes such as atomic layer deposition (ALD) and atomic layer etching inside reactors of arbitrary geometry as well the output of in situ quartz crystal microbalance and mass spectrometry. We have applied this model to two standard types of cross-flow reactors: a cylindrical tube reactor and a model 300 mm wafer reactor, and explored both ideal and nonideal self-limited kinetics. The model results are in agreement with experimental results and analytic expressions obtained using a simple plug-flow model for the cylindrical tube reactor. We also extended the simulations to consider two nonideal self-limited processes: soft-saturating processes characterized by a slow reaction pathway and processes where surface by-products can compete with the precursor for the same pool of adsorption sites. Our results show that it is possible to have a self-limited process with saturated yet inhomogeneous growth profiles due to the competition of reactor by-products. This is in agreement with experimental observations for titanium dioxide ALD from titanium tetraisopropoxide and titanium tetrachloride precursors.
An Attention Transfer Model for Human-Assisted Failure Avoidance in Robot Manipulations

Due to real-world dynamics and hardware uncertainty, robots inevitably fail in task executions, resulting in undesired or even dangerous executions. In order to avoid failures and improve robot performance, it is critical to identify and correct abnormal robot executions at an early stage. However, due to limited reasoning capability and knowledge storage, it is challenging for robots to self-diagnose and - correct their own abnormality in both planning and executing. To improve robot self diagnosis capability, in this research a novel human-to-robot attention transfer (H2R-AT) method was developed to identify robot manipulation errors by leveraging human instructions. H2R-AT was developed by fusing attention mapping mechanism into a novel stacked neural networks model, transferring human verbal attention into robot visual attention. With the attention transfer, a robot understands what and where human concerns are to identify and correct abnormal manipulations. Two representative task scenarios: “serve water for a human in a kitchen” and “pick up a defective gear in a factory” were designed in a simulation framework CRAIhri with abnormal robot manipulations; and 252 volunteers were recruited to provide about 12000 verbal reminders to learn and test H2R-AT. The method effectiveness was validated by the high accuracy of 73.68% in transferring attention, and the high accuracy of 66.86% in avoiding grasping failures.
Uncertainty bounds for multivariate machine learning predictions on high-strain brittle fracture

Abstract Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive. The cost increases even more if multiple simulations are needed to account for the randomness in crack length, location, and orientation, which is inherently found in real-world materials. Constructing a machine learning emulator can make the process faster by orders of magnitude. There has been little work, however, on assessing the error associated with their predictions. Estimating these errors is imperative for meaningful overall uncertainty quantification. In this work, we extend the heteroscedastic uncertainty estimates to bound a multiple output machine learning emulator. We find that the response prediction is accurate within its predicted errors, but with a somewhat conservative estimate of uncertainty.
Birkhoff averages and the breakdown of invariant tori in volume-preserving maps

Abstract In this paper, we develop numerical methods based on the weighted Birkhoff average for studying two-dimensional invariant tori for volume-preserving maps. The methods do not rely on symmetries, such as time-reversal symmetry, nor on approximating tori by periodic orbits. The rate of convergence of the average gives a sharp distinction between chaotic and regular dynamics and allows accurate computation of rotation vectors for regular orbits. Resonant and rotational tori are distinguished by computing the resonance order of the rotation vector to a given precision. Critical parameter values, where tori are destroyed, are computed by a sharp decrease in convergence rate of the Birkhoff average. We apply these methods for a three-dimensional generalization of Chirikov’s standard map: an angle–action map with two angle variables. Computations on grids in frequency and perturbation amplitude allow estimates of the critical set. We also use continuation to follow tori with fixed rotation vectors. We test three conjectures for cubic fields that have been proposed to give locally robust invariant tori, but are not able to provide compelling evidence that one of these three fields is more robust than the other two.
From limited observations to the state of turbulence: Fundamental difficulties of flow reconstruction

Is it possible to reconstruct all the scales of turbulence from limited observations? If so, what is the minimum resolution of observations for a successful reconstruction? How much information about a turbulent flow field can be decoded from an isolated, instantaneous measurement? These fundamental questions are addressed using variational data assimilation, where the observations are infused in simulations and are decoded using the Navier-Stokes equations. We highlight the ``dual butterfly effect'' and how the stochasticity of turbulence obfuscates the interpretation of measurements.
The Duty of Data Security

As data breaches become larger and more frequent, the question naturally arises: what precautions does the law require of the data custodians who hold our personal information in their digital files? What is the legal duty of data security? According to some scholars and lawyers, the law is insufficiently specific, concrete, or uniform to answer that question. Attorneys representing companies that have been breached go so far as to argue that the duty of data security is “an unknown (and unknowable) standard.” Under this view, private entities warehouse vast quantities of personal data, but cannot possibly ascertain the obligations the law imposes on them to protect it. That claim is balderdash. This Article demonstrates that the law is already settling upon a well-defined, if context-dependent, duty of data security. It examines fourteen different sources of data security obligations for private companies in the United States, half of them formal legal rules and half derived from the private ordering of industry-based requirements. This analysis demonstrates how all these frameworks, selected to represent the breadth of data security obligations, are converging on a common set of standards. The numerous sources of a duty of data security sound together in harmony, not cacophony. The nascent consensus formulates a duty just as clear as countless other requirements of reasonableness that permeate the law. In addition, this Article identifies normative justifications for the content and nature of this emerging duty of data security, particularly its underpinning in principles of reasonableness and risk assessment. Indeed, the duty of data security is taking its early steps along a well-worn path in the law. It is being guided by deeply familiar legal forces, including the preference for standards over rules when governing fast-moving and complex subjects; the adoption of industry custom, which has shaped law from early contract doctrine to modern professional liability; and even a version of Judge Learned Hand’s cost-benefit calculus from the legendary Carroll Towing decision.
Deletion of Stim1 in Hypothalamic Arcuate Nucleus Kiss1 Neurons Potentiates Synchronous GCaMP Activity and Protects against Diet-Induced Obesity.

Kisspeptin (Kiss1) neurons are essential for reproduction, but their role in the control of energy balance and other homeostatic functions remains unclear. High frequency firing of hypothalamic arcuate Kiss1 (Kiss1ARH) neurons releases kisspeptin into the median eminence, and neurokinin B (NKB) and dynorphin onto neighboring Kiss1ARH neurons to generate a slow excitatory postsynaptic potential (EPSP) mediated by TRPC5 channels that entrains intermittent, synchronous firing of Kiss1ARH neurons. High frequency optogenetic stimulation of Kiss1ARH neurons also releases glutamate to excite the anorexigenic proopiomelanocortin (POMC) neurons and inhibit the orexigenic neuropeptide Y/agouti-related peptide (AgRP) neurons via metabotropic glutamate receptors. At the molecular level, the endoplasmic reticulum calcium-sensing protein stromal interaction molecule 1 (STIM1) is critically involved in the regulation of neuronal Ca2+ signaling and neuronal excitability through its interaction with plasma membrane calcium (e.g., TRPC) channels. Therefore, we hypothesized that deletion of Stim1 in Kiss1ARH neurons would increase neuronal excitability and their synchronous firing, which ultimately would affect energy homeostasis. Using optogenetics in combination with whole-cell recording and GCaMP6 imaging in slices, we discovered that deletion of Stim1 in Kiss1 neurons significantly increased the amplitude and duration of the slow EPSP and augmented synchronous [Ca2+]i oscillations in Kiss1ARH neurons. Deletion of Stim1 in Kiss1ARH neurons amplified the actions of NKB and protected ovariectomized female mice from developing obesity and glucose intolerance with high-fat dieting. Therefore, STIM1 appears to play a critical role in regulating synchronous firing of Kiss1ARH neurons, which ultimately affects the coordination between energy homeostasis and reproduction.Significance StatementHypothalamic arcuate kisspeptin (Kiss1ARH) neurons are essential for stimulating the pulsatile release of gonadotropin releasing hormone (GnRH) and maintaining fertility. However, Kiss1ARH neurons appear to be a key player in coordinating energy balance with reproduction. The regulation of calcium channels and hence calcium signaling is critically dependent on the endoplasmic reticulum calcium-sensing protein stromal interaction molecule 1 (STIM1), which interacts with the plasma membrane calcium channels. We have conditionally deleted Stim1 in Kiss1ARH neurons and found that it significantly increased the excitability of Kiss1ARH neurons and protected ovariectomized female mice from developing obesity and glucose intolerance with high-fat dieting.
Entanglement entropy bounds in the higher spin XXZ chain

We consider the Heisenberg XXZ spin-J chain (J∈N/2) with anisotropy parameter Δ. Assuming that Δ > 2J and introducing threshold energies EK≔K1−2JΔ, we show that the bipartite entanglement entropy of states belonging to any spectral subspace with energy less than EK+1 satisfies a logarithmically corrected area law with the prefactor (2⌊K/J⌋ − 2). This generalizes the previous results by Beaud and Warzel [J. Math. Phys. 59, 012109 (2018)], as well as by Abdul-Rahman, Fischbacher, and Stolz [Ann. Henri Poincare 21, 2327 (2020)], who covered the spin-1/2 case.
Impact of testicular delivery and vasal vein ligation on clinical outcomes in men undergoing microsurgical varicocelectomy.

PURPOSE To assess the impact of microsurgical varicocelectomy technique on clinical outcomes. METHODS Men diagnosed with varicocele between 2017 and 2020 were reviewed. We included men who underwent microsurgical varicocelectomy by two high-volume surgeons who differed in surgical technique: Method (1) testicular delivery with gubernacular vein ligation, and ligation of vasal veins > 2.5 mm; Method (2) no delivery and ligation of dilated vasal veins in cases of recurrence. Post-operative changes for semen parameters, DNA fragmentation, and serum testosterone were evaluated for each technique and compared. RESULTS 313 patients were included; 162 with Method 1 and 151 with Method 2. The cohorts were of similar age (median 35 years, interquartile range (IQR) 28-43; 34, IQR 28-39, respectively), and BMI (25 kg/m2, IQR 23-27; 25, IQR 23-28, respectively). For Method 1, 84 (51.9%) had bilateral surgery, and 78 (48.1%) had unilateral surgery. For Method 2, 63 (41.7%) had bilateral surgery, and 88 (58.3%) unilateral surgery. In patients with sperm concentration > 5 M/mL, both techniques resulted in an improvement (p < 0.01), but there was no difference between the methods (p = 0.18). Both methods were associated with an improvement in total motile count (p < 0.05) and the amount of DNA fragmentation (p < 0.05), although no differences were apparent between the techniques (p = 0.09, p = 0.81, respectively). Finally, testosterone levels improved with Method 1, but the post-operative difference was not different than Method 2 (p = 0.06). CONCLUSION Delivery of the testis and ligation of dilated vasal veins compared to not performing those steps do not impact semen parameters, but are associated with improvement in testosterone levels.
Finite-time singularity formation for $C^{1,\alpha }$ solutions to the incompressible Euler equations on $\mathbb{R}^3$

It has been known since work of Lichtenstein [42] and Gunther [29] in the 1920's that the $3D$ incompressible Euler equation is locally well-posed in the class of velocity fields with Holder continuous gradient and suitable decay at infinity. It is shown here that these local solutions can develop singularities in finite time, even for some of the simplest three-dimensional flows.
Binding of AP Endonuclease-1 to G-Quadruplex DNA Depends on the N-Terminal Domain, Mg2+, and Ionic Strength

The base excision repair enzyme apurinic/apyrimidinic endonuclease-1 (APE1) is also engaged in transcriptional regulation. APE1 can function in both pathways when the protein binds to a promoter G-...
Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids

Abstract Constitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensive. For example, in fluid dynamics, constitutive models are required to describe nonlocal, unresolved physics such as turbulence and laminar–turbulent transition. However, traditional constitutive models based on partial differential equations (PDEs) often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that can be learned with data. The model predicts the closure variable at a point based on the flow information in its neighborhood. Such nonlocal information is represented by a group of points, each having a feature vector attached to it, and thus the input is referred to as vector cloud. The cloud is mapped to the closure variable through a frame-independent neural network, invariant both to coordinate translation and rotation and to the ordering of points in the cloud. As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations . The merits of the proposed network are demonstrated for scalar transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains.
A Highway Toll Lane Framework that Unites Autonomous Vehicles and High-occupancy Vehicles

We consider the scenario where human-driven/autonomous vehicles with low/high occupancy are sharing a segment of highway and autonomous vehicles are capable of increasing the traffic throughput by preserving a shorter headway than human-driven vehicles. We propose a toll lane framework where a lane on the highway is reserved freely for autonomous vehicles with high occupancy, which have the greatest capability to increase the social mobility, and the other three classes of vehicles can choose to use the toll lane with a toll or use the other regular lanes freely. All vehicles are assumed to be only interested in minimizing their own travel costs. We explore the resulting lane choice equilibria under the framework and establish desirable properties of the equilibria, which implicitly compare high-occupancy vehicles with autonomous vehicles in terms of their capabilities to increase the social mobility. We further use numerical examples in the optimal toll design, the occupancy threshold design and the policy design problems to clarify the various potential applications of this toll lane framework that unites high-occupancy vehicles and autonomous vehicles. To our best knowledge, this is the first work that systematically studies a toll lane framework that unites autonomous vehicles and high-occupancy vehicles on the roads.