cssi/SciSciGPT-SciSciNet huggingface dataset:
Beyond Schumpeter vs. Arrow: How Antitrust Fosters Innovation

Antitrust enforcement surely keeps prices low and output high, but is it good for innovation? The question arises because the relationship be tween competition and innovation is the subject of a familiar contro versy in economics. One view, often associated with Joseph Schumpeter, argues that monopolies favor innovation. An opposite view, often associ ated with Kenneth Arrow, argues that competition favors innovation. Taking their cue from this debate, some commentators qualify their sup port for antitrust policy, reserving judgment as to whether antitrust en forcement is good for innovation.1
The role of international courts as interpreters and developers of the law: working out the jurisgenerative practice of interpretation

The contribution illustrates the still influential view on the role of international courts that belittles their qualities as actors in the making of international law. It summarizes why the outward show of judicial argument itself eclipses this dimension of judicial practice and then speaks on the metaphor of sources and how it has (mis)shaped accounts of international lawmaking (II). The second section then takes a step back and picks up the proposition that it is impossible to find meaning anywhere else other than in the concrete use of legal provisions. It highlights how law comes to life in the practice of interpretation and argues that this practice itself has to bear the burden of distinguishing correct from incorrect applications (III.). The third step first elucidates international courts’ semantic authority by focussing on the mighty spell of judicial precedents in international legal discourse and then turns to sketching the principal normative implications (IV). The concluding prognosis recommends paying closer regard to the qualities of international courts as actors in the jurisgenerative practice of interpretation. It ends with the suggestion to develop a better understanding of the role of international courts in a normative pluriverse in which they interact with actors on other levels of governance and negotiate spheres of authority (V).
Accounting for growth in post-Soviet Russia

Abstract In pursuit of its transition from a command to a market economy, post-Soviet Russia has witnessed enormous regional differences in economic growth rates. We find that these regional differences exhibit remarkable correspondence with the formation of new legal enterprises. In turn, regional variations in the adoption of economic reforms appear to be an important factor in accounting for regional variations in new-enterprise formation.
Development and Outcomes of Investment Treaty Arbitration

The legitimacy of investment treaty arbitration is a matter of heated debate. Asserting that arbitration is unfairly tilted toward the developed world, some countries have withdrawn from World Bank dispute resolution bodies or are taking steps to eliminate arbitration. In order to assess whether investment arbitration is the equivalent of tossing a two-headed coin to resolve investment disputes, this Article explores the role of development status in arbitration outcomes. It first presents descriptive, quantitative research about the developmental background of the presiding arbitrators who exert particular control over the arbitration process. The Article then assesses how (1) the development status of the respondent state, (2) the development status of the presiding arbitrator, and (3) the interaction of these variables affect the outcome of investment arbitration. The results demonstrate that, at the macro level, development status does not have a statistically significant relationship with outcome. This suggests that the investment treaty arbitration system, as a whole, functions fairly and that the eradication or radical overhaul of the arbitration process is unnecessary. The existence of two statistically significant simple effects�namely that tribunals with presiding arbitrators from the developing world made smaller awards against developed states in particular circumstances�suggests that particularized reform could enhance the procedural integrity of arbitration. Irrespective of whether future research replicates the results, reforms targeted to redress possible imbalance in the system have the potential to enhance procedural justice and the perceived legitimacy of arbitration in an area with profound political and economic implications.
Skilled Labor Mobility and Firm Value: Evidence from Green Card Allocations

This paper studies how the labor market frictions of skilled workers affect corporate valuation. The analysis features immigrant workers’ mobility constraints imposed by the U.S. green card application process and exploits exogenous variations caused by imperfections in the current immigration system. The study finds that relaxing mobility constraints negatively influences firm value. This effect is stronger for firms with higher labor adjustment costs. Reductions in investments and increases in labor costs are channels through which labor mobility adversely affects firm value. The findings suggest that monopoly rent over skilled workers is an important economic determinant of corporate valuation.
HIV-1 Envelope Glycosylation and the Signal Peptide

The RV144 trial represents the only vaccine trial to demonstrate any protective effect against HIV-1 infection. While the reason(s) for this protection are still being evaluated, it serves as justification for widespread efforts aimed at developing new, more effective HIV-1 vaccines. Advances in our knowledge of HIV-1 immunogens and host antibody responses to these immunogens are crucial to informing vaccine design. While the envelope (Env) protein is the only viral protein present on the surface of virions, it exists in a complex trimeric conformation and is decorated with an array of variable N-linked glycans, making it an important but difficult target for vaccine design. Thus far, efforts to elicit a protective humoral immune response using structural mimics of native Env trimers have been unsuccessful. Notably, the aforementioned N-linked glycans serve as a component of many of the epitopes crucial for the induction of potentially protective broadly neutralizing antibodies (bnAbs). Thus, a greater understanding of Env structural determinants, most critically Env glycosylation, will no doubt be of importance in generating effective immunogens. Recent studies have identified the HIV-1 Env signal peptide (SP) as an important contributor to Env glycosylation. Further investigation into the mechanisms by which the SP directs glycosylation will be important, both in the context of understanding HIV-1 biology and in order to inform HIV-1 vaccine design.
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots

Reliable real-time planning for robots is essential in today’s rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models’ generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car.
Topology-Aware Segmentation Using Discrete Morse Theory

In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern. Topological correctness, such as vessel connectivity and membrane closure, is crucial for downstream analysis tasks. In this paper, we propose a new approach to train deep image segmentation networks for better topological accuracy. In particular, leveraging the power of discrete Morse theory (DMT), we identify global structures, including 1D skeletons and 2D patches, which are important for topological accuracy. Trained with a novel loss based on these global structures, the network performance is significantly improved especially near topologically challenging locations (such as weak spots of connections and membranes). On diverse datasets, our method achieves superior performance on both the DICE score and topological metrics.
An embedded deep learning system for augmented reality in firefighting applications

Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a firefighter’s situational awareness and scene navigation through improved interpretation of that scene. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter’s personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery and then deploy already developed deep learning models to analyze the input data in real time. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform that visualizes the results of the analyzed inputs and draws the firefighter’s attention to objects of interest, such as doors and windows otherwise invisible through smoke and flames.
Certain Hyperbolic Regular Polygonal Tiles are Isoperimetric

The hexagon is the least-perimeter tile in the Euclidean plane. On hyperbolic surfaces, the isoperimetric problem differs for every given area. Cox conjectured that a regular k-gonal tile with 120-degree angles is isoperimetric for its area. We prove his conjecture and more.
Learning Orientation Distributions for Object Pose Estimation

For robots to operate robustly in the real world, they should be aware of their uncertainty. However, most methods for object pose estimation return a single point estimate of the object’s pose. In this work, we propose two learned methods for estimating a distribution over an object’s orientation. Our methods take into account both the inaccuracies in the pose estimation as well as the object symmetries. Our first method, which regresses from deep learned features to an isotropic Bing-ham distribution, gives the best performance for orientation distribution estimation for non-symmetric objects. Our second method learns to compare deep features and generates a non-parameteric histogram distribution. This method gives the best performance on objects with unknown symmetries, accurately modeling both symmetric and non-symmetric objects, without any requirement of symmetry annotation. We show that both of these methods can be used to augment an existing pose estimator. Our evaluation compares our methods to a large number of baseline approaches for uncertainty estimation across a variety of different types of objects. Code available at https://bokorn.github.io/orientation-distributions/
Suppression of power losses during laser pulse propagation in underdense plasma slab

For current state-of-the-art terawatt lasers, the primary laser scattering mechanisms in plasma include forward Raman scattering (FRS), excitation of plasma waves, and the filamentation instability. Using 2D particle-in-cell (PIC) simulations, we demonstrate that FRS dominates in the regime with medium-to-low density plasma and non-relativistic laser fields. We numerically show that FRS can be suppressed using a two-color laser with frequency detuning exceeding the plasma frequency, Δ ω > ω pe, leading to a more efficient laser energy transmission. An optimal laser pulse energy redistribution ratio is predicted analytically and verified by PIC simulations.
Plasma Proteomic Profiling in Hypertrophic Cardiomyopathy Patients Before and After Surgical Myectomy Reveals Post-Procedural Reduction in Systemic Inflammation

Left Ventricular Outflow Tract (LVOT) obstruction occurs in approximately 70% of Hypertrophic Cardiomyopathy (HCM) patients and currently requires imaging or invasive testing for diagnosis, sometimes in conjunction with provocative physiological or pharmaceutical stimuli. To identify potential biomarkers of LVOT obstruction, we performed proteomics profiling of 1305 plasma proteins in 12 HCM patients with documented LVOT obstruction, referred for surgical myectomy. Plasma was collected at the surgical preoperative visit, approximately one month prior to surgery and then at the post-surgical visit, approximately 3 months later. Proteomic profiles were generated using the aptamer-based SOMAscan assay. Principal Component Analysis using the highest statistically significant proteins separated all preoperative samples from all postoperative samples. Further analysis revealed a set of 25 proteins that distinguished the preoperative and postoperative states with a paired t-test p-value of <0.01. Ingenuity Pathway analysis facilitated the generation of protein interaction networks and the elucidation of key upstream regulators of differentially expressed proteins, such as interferon-γ, TGF-β1, and TNF. Biological pathways affected by surgery included organ inflammation, migration, and motility of leukocytes, fibrosis, vasculogenesis, angiogenesis, acute coronary events, endothelial proliferation, eicosanoid metabolism, calcium flux, apoptosis, and morphology of the cardiovascular system. Our results indicate that surgical relief of dynamic outflow tract obstruction in HCM patients is associated with unique alterations in plasma proteomic profiles that likely reflect improvement in organ inflammation and physiological function.
A virucidal face mask based on the reverse-flow reactor concept for thermal inactivation of SARS-CoV-2.

While facial coverings reduce the spread of SARS-CoV-2 by viral filtration, masks capable of viral inactivation by heating can provide a complementary method to limit transmission. Inspired by reverse-flow chemical reactors, we introduce a new virucidal face mask concept driven by the oscillatory flow of human breath. The governing heat and mass transport equations are solved to evaluate virus and CO2 transport. Given limits imposed by the kinetics of SARS-CoV-2 thermal inactivation, human breath, safety, and comfort, heated masks may inactivate SARS-CoV-2 to medical-grade sterility. We detail one design, with a volume of 300 ml at 90°C that achieves a 3-log reduction in viral load with minimal impedance within the mask mesh, with partition coefficient around 2. This is the first quantitative analysis of virucidal thermal inactivation within a protective face mask, and addresses a pressing need for new approaches for personal protective equipment during a global pandemic.
Performance Analysis of Cell-Free Massive MIMO Systems with Massive Connectivity

In this paper, we investigate the performance of cell-free massive MIMO systems with massive connectivity. With the generalized approximate message passing (GAMP) algorithm, we obtain the minimum mean-squared error (MMSE) estimate of the effective channel coefficients from all users to all access points (APs) in order to perform joint user activity detection and channel estimation. Subsequently, using the decoupling properties of MMSE estimation for large linear systems and state evolution equations of the GAMP algorithm, we obtain the variances of both the estimated channel coefficients and the corresponding channel estimation error. Finally, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO system. With numerical results, we analyze the impact of the number of pilots used for joint activity detection and channel estimation, the number of APs, and signal-to-noise ratio (SNR) on the achievable rates.
How the United Kingdom Controls Pharmaceutical Prices and Spending: Learning From Its Experience:

To control costs and improve access, nations can adopt strategies employed in the United Kingdom to control pharmaceutical prices and spending. Current policy evolved from a system created in 1957 that allowed manufacturers to set launch prices, capped manufacturers' rates of return, and later cut list prices. These policies did not effectively control spending and had limited effects on purchase prices. The United Kingdom currently controls pharmaceutical spending in 4 ways. (a) Since 1999, it has typically paid no more than is cost-effective. (b) Since 2017, for medicines that will have a significant budget impact, National Health Service England seeks discounts from cost-effective prices or seeks to limit access for 2 years to patients with the greatest need. (c) Since 2014, statutes and a voluntary scheme have required branded manufacturers to pay the government rebates to recoup the difference between the global pharmaceutical budget and actual spending. (d) For hospitals, generics and some patented drugs are procured through competitive bidding; community pharmacies are reimbursed through a system that provides an incentive to beat average generic market prices. These policies controlled the growth of spending, with the largest effects following budget controls in 2014. Changes since 2008 have reduced savings, first by paying more than is cost-effective for cancer drugs and then by applying higher cost-effectiveness thresholds for some drugs used to treat cancer and certain other drugs.
Games and Other Uncopyrightable Systems

Games are deceptively simple objects of human culture. They are familiar, commonplace, and often easy to learn: young children play them at an early age. For most people, games are a pastime, a form of recreation that involves relatively little preparation or time commitment. They are thus the very opposite of work, and hardly comparable to such serious pursuits as scholarship or art. For all their seeming ingenuousness, however, games are also deeply puzzling. Defining games is a notoriously difficult enterprise. Scholars from several different disciplines have struggled to determine what the nature, or essence, of games really is. And the elusiveness of games poses problems for intellectual property law as well. Games seem to straddle the boundaries between copyright and patent, between author, performer, and reader, and between protected and unprotected material. Games are an entertainment medium on par with such copyrightable material as music, films, or novels. But games are also comprised of rules and instructions, like uncopyrightable recipes and patentable procedures. Games convey an experience of play to users, just as music and drama convey an aesthetic or narrative experience to viewers and listeners. But the experience of game
Obstructions to matricial stability of discrete groups and almost flat K-theory

Abstract A discrete countable group G is matricially stable if the finite dimensional approximate unitary representations of G are perturbable to genuine representations in the point-norm topology. For large classes of groups G, we show that matricial stability implies the vanishing of the rational cohomology of G in all nonzero even dimensions. We revisit a method of constructing almost flat K-theory classes of BG which involves the dual assembly map and quasidiagonality properties of G. The existence of almost flat K-theory classes of BG which are not flat represents an obstruction to matricial stability of G due to continuity properties of the approximate monodromy correspondence.
Falloff of radiated energy in black hole spacetimes

The goal of much research in relativity is to understand gravitational waves generated by a strong-field dynamical spacetime. Quantities of particular interest for many calculations are the Weyl scalar ${\ensuremath{\psi}}_{4}$, which is simply related to the flux of gravitational waves far from the source, and the flux of energy carried to distant observers, $\stackrel{\ifmmode \dot{}\else \textperiodcentered \fi{}}{E}$. Conservation laws guarantee that, in asympotically flat spacetimes, ${\ensuremath{\psi}}_{4}\ensuremath{\propto}1/r$ and $\stackrel{\ifmmode \dot{}\else \textperiodcentered \fi{}}{E}\ensuremath{\propto}1/{r}^{2}$ as $r\ensuremath{\rightarrow}\ensuremath{\infty}$. Most calculations extract these quantities at some finite extraction radius. An understanding of finite radius corrections to ${\ensuremath{\psi}}_{4}$ and $\stackrel{\ifmmode \dot{}\else \textperiodcentered \fi{}}{E}$ allows us to more accurately infer their asymptotic values from a computation. In this paper, we show that, if the final state of the system is a black hole, then the leading correction to ${\ensuremath{\psi}}_{4}$ is $\mathcal{O}(1/{r}^{3})$, and that to the energy flux is $\mathcal{O}(1/{r}^{4})$---not $\mathcal{O}(1/{r}^{2})$ and $\mathcal{O}(1/{r}^{3})$, as one might naively guess. Our argument only relies on the behavior of the curvature scalars for black hole spacetimes. Using black hole perturbation theory, we calculate the corrections to the leading falloff, showing that it is quite easy to correct for finite extraction radius effects.
Cwl0971, a novel peptidoglycan hydrolase, plays pleiotropic roles in Clostridioides difficile R20291

Clostridioides difficile is a Gram-positive, spore-forming, toxin-producing anaerobe that can cause nosocomial antibiotic-associated intestinal disease. Although the production of toxin A (TcdA) and toxin B (TcdB) contribute to the main pathogenesis of C. difficile, the mechanism of TcdA and TcdB release from cell remains unclear. In this study, we identified and characterized a new cell wall hydrolase Cwl0971 (CDR20291_0971) from C. difficile R20291, which is involved in bacterial autolysis. The gene 0971 deletion mutant (R20291Δ0971) generated with CRISPR-AsCpfI exhibited significantly delayed cell autolysis and increased cell viability compared to R20291, and the purified Cwl0971 exhibited hydrolase activity for Bacillus subtilis cell wall. Meanwhile, 0971 gene deletion impaired TcdA and TcdB release due to the decreased cell autolysis in the stationary/late phase of cell growth. Moreover, sporulation of the mutant strain decreased significantly compared to the wild type strain. In vivo, the defect of Cwl0971 decreased fitness over the parent strain in a mouse infection model. Collectively, Cwl0971 is involved in cell wall lysis and cell viability, which affects toxin release, sporulation, germination, and pathogenicity of R20291, indicating that Cwl0971 could be an attractive target for C. difficile infection therapeutics and prophylactics.
Multi-Objective DNN-Based Precoder for MIMO Communications

This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. First, a rotation-based precoder is developed to solve the above problems independently. Rotation-based precoding is a new precoding and power allocation scheme that beats existing solutions for PHY security and multicasting and is reliable in different antenna settings. Next, a DNN-based precoder is designed to unify the solution for all objectives. The proposed DNN concurrently learns the solutions given by conventional methods, i.e., analytical or rotation-based solutions. A binary vector is designed as an input feature to distinguish the objectives. Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99.45% of the averaged optimal solutions). The new precoder is also more robust to the variations of the numbers of antennas at the receivers.
Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference

Non-linear hierarchical models are commonly used in many disciplines. However, inference in the presence of non-nested effects and on large datasets is challenging and computationally burdensome. This paper provides two contributions to scalable and accurate inference. First, I derive a new mean-field variational algorithm for estimating binomial logistic hierarchical models with an arbitrary number of non-nested random effects. Second, I propose “marginally augmented variational Bayes” (MAVB) that further improves the initial approximation through a step of Bayesian post-processing. I prove that MAVB provides a guaranteed improvement in the approximation quality at low computational cost and induces dependencies that were assumed away by the initial factorization assumptions. I apply these techniques to a study of voter behavior using a high-dimensional application of the popular approach of multilevel regression and post-stratification (MRP). Existing estimation took hours whereas the algorithms proposed run in minutes. The posterior means are well-recovered even under strong factorization assumptions. Applying MAVB further improves the approximation by partially correcting the under-estimated variance. The proposed methodology is implemented in an open source software package.
RVAgene: generative modeling of gene expression time series data

Motivation Methods to model dynamic changes in gene expression at a genome-wide level are not currently sufficient for large (temporally rich or single-cell) datasets. Variational autoencoders offer means to characterize large datasets and have been used effectively to characterize features of single-cell datasets. Here we extend these methods for use with gene expression time series data. Results We present RVAgene: a recurrent variational autoencoder to model gene expression dynamics. RVAgene learns to accurately and efficiently reconstruct temporal gene profiles. It also learns a low dimensional representation of the data via a recurrent encoder network that can be used for biological feature discovery, and from which we can generate new gene expression data by sampling the latent space. We test RVAgene on simulated and real biological datasets, including embryonic stem cell differentiation and kidney injury response dynamics. In all cases, RVAgene accurately reconstructed complex gene expression temporal profiles. Via cross validation, we show that a low-error latent space representation can be learnt using only a fraction of the data. Through clustering and gene ontology term enrichment analysis on the latent space, we demonstrate the potential of RVAgene for unsupervised discovery. In particular, RVAgene identifies new programs of shared gene regulation of Lox family genes in response to kidney injury. Availability RVAgene is available in Python, at gihub: https://github.com/maclean-lab/RVAgene; Zenodo archive: http://doi.org/10.5281/zenodo.4271097. Supplementary information Supplementary data are available at Bioinformatics online.
The Birman exact sequence does not virtually split

This paper answers a basic question about the Birman exact sequence in the theory of mapping class groups. We prove that the Birman exact sequence does not admit a section over any subgroup $\Gamma$ contained in the Torelli group with finite index. A fortiori this proves that there is no section of the Birman exact sequence for any finite-index subgroup of the full mapping class group. This theorem was announced in a 1990 preprint of G. Mess, but an error was uncovered and described in a recent paper of the first author.
Ultrasound Elasticity Imaging Using Physics-Based Models and Learning-Based Plug-and-Play Priors

Existing physical model-based imaging methods for ultrasound elasticity reconstruction utilize fixed variational regularizers that may not be appropriate for the application of interest or may not capture complex spatial prior information about the underlying tissues. On the other hand, end-to-end learning-based methods count solely on the training data, not taking advantage of the governing physical laws of the imaging system. Integrating learning-based priors with physical forward models for ultrasound elasticity imaging, we present a joint reconstruction framework which guarantees that learning driven reconstructions are consistent with the underlying physics. For solving the elasticity inverse problem as a regularized optimization problem, we propose a plug-and-play (PnP) reconstruction approach in which each iteration of the elasticity image estimation process involves separate updates incorporating data fidelity and learning-based regularization. In this methodology, the data fidelity term is developed using a statistical linear algebraic model of quasi-static equilibrium equation revealing the relationship of the observed displacement fields to the unobserved elastic modulus. The regularizer comprises a convolutional neural network (CNN) based denoiser that captures the learned prior structure of the underlying tissues. Preliminary simulation results demonstrate the robustness and effectiveness of the proposed approach with limited training datasets and noisy displacement measurements.
Developing a growth-mindset pedagogy for higher education and testing its efficacy

Abstract Choosing the right teaching pedagogy is the first step a teacher can take to ensure that students retain course content. This research developed and tested a growth-mindset teaching pedagogy that helps undergraduate students develop a growth-mindset belief system about their own abilities. Students might believe that their personal attributes and qualities are carved in stone, a fixed mindset, or they might believe they can cultivate, grow, and improve them through their efforts, a growth mindset. To validate a psychology-based growth mindset pedagogy, a pretest-posttest control group design was used. We implemented this growth-mindset teaching pedagogy in 17 sections in two different courses, and we used 14 additional sections that utilized lecture style teaching as control groups. Students exposed to the growth-mindset teaching pedagogy improved their growth-mindset beliefs by 3.44% and decreased their fixed-mindset by 3.48% compared to lecture style teaching.
Compactness and Sharp Lower Bound for a 2D Smectics Model

We consider a 2D smectics model $$\begin{aligned} E_{\epsilon }\left( u\right) =\frac{1}{2}\int _\varOmega \frac{1}{\varepsilon }\left( u_{z}-\frac{1 }{2}u_{x}^{2}\right) ^{2}+\varepsilon \left( u_{xx}\right) ^{2}\mathrm{d}x\,\mathrm{d}z. \end{aligned}$$ For $$\varepsilon _{n}\rightarrow 0$$ and a sequence $$\left\{ u_{n}\right\} $$ with bounded energies $$E_{\varepsilon _{n}}\left( u_{n}\right) $$ , we prove compactness of $$\{\partial _zu_{n}\}$$ in $$L^{2}$$ and $$\{\partial _xu_n\}$$ in $$L^q$$ for any $$1\le q<p$$ under the additional assumption $$\Vert \partial _xu_{n}\Vert _{L^{p }}\le C$$ for some $$p>6$$ . We also prove a sharp lower bound on $$E_{\varepsilon }$$ when $$\varepsilon \rightarrow 0.$$ The sharp bound corresponds to the energy of a 1D ansatz in the transition region.
FT-BLAS: a high performance BLAS implementation with online fault tolerance

Basic Linear Algebra Subprograms (BLAS) is a core library in scientific computing and machine learning. This paper presents FT-BLAS, a new implementation of BLAS routines that not only tolerates soft errors on the fly, but also provides comparable performance to modern state-of-the-art BLAS libraries on widely-used processors such as Intel Skylake and Cascade Lake. To accommodate the features of BLAS, which contains both memory-bound and computing-bound routines, we propose a hybrid strategy to incorporate fault tolerance into our brand-new BLAS implementation: duplicating computing instructions for memory-bound Level-1 and Level-2 BLAS routines and incorporating an Algorithm-Based Fault Tolerance mechanism for computing-bound Level-3 BLAS routines. Our high performance and low overhead are obtained from delicate assembly-level optimization and a kernel-fusion approach to the computing kernels. Experimental results demonstrate that FT-BLAS offers high reliability and high performance -- faster than Intel MKL, OpenBLAS, and BLIS by up to 3.50%, 22.14% and 21.70%, respectively, for routines spanning all three levels of BLAS we benchmarked, even under hundreds of errors injected per minute.
Nonlocal dispersal equations with almost periodic dependence. I. Principal spectral theory

Abstract This series of two papers is devoted to the study of the principal spectral theory of nonlocal dispersal operators with almost periodic dependence and the study of the asymptotic dynamics of nonlinear nonlocal dispersal equations with almost periodic dependence. In this first part of the series, we investigate the principal spectral theory of nonlocal dispersal operators from two aspects: top Lyapunov exponents and generalized principal eigenvalues. Among others, we provide various characterizations of the top Lyapunov exponents and generalized principal eigenvalues, establish the relations between them, and study the effect of time and space variations on them. In the second part of the series, we will study the asymptotic dynamics of nonlinear nonlocal dispersal equations with almost periodic dependence applying the principal spectral theory to be developed in this part.
Getting to India's electric vehicle targets cost-effectively: To subsidize or not, and how?

Abstract In the context of India's ambitious electric vehicle targets, we examine two related questions. First, based on lifetime costs, which electric vehicles need to be subsidized? Second, based on lifetime subsidies, which subsidy option would be the most cost-effective? We find that electric two-wheelers, three-wheelers, four-wheeler-taxis, and buses do not need to be subsidized given that they are already cost competitive with comparable vehicles using internal combustion engines. We also find that personal cars and long-haul trucks need to be subsidized significantly, by one-third to one-half of the upfront costs. Finally, we find that an upfront subsidy is the most cost-effective subsidy option, followed by a per kilometer subsidy, which is effectively 19–31% costlier, whereas financing subsidies are found to be the least cost effective. Our results suggest that a cost-effective policy in India would be to subsidize only personal cars and long-haul trucks, while using the upfront subsidies.
What Will it Take to Fix Benchmarking in Natural Language Understanding

Evaluation for many natural language understanding (NLU) tasks is broken: Unreliable and biased systems score so highly on standard benchmarks that there is little room for researchers who develop better systems to demonstrate their improvements. The recent trend to abandon IID benchmarks in favor of adversarially-constructed, out-of-distribution test sets ensures that current models will perform poorly, but ultimately only obscures the abilities that we want our benchmarks to measure. In this position paper, we lay out four criteria that we argue NLU benchmarks should meet. We argue most current benchmarks fail at these criteria, and that adversarial data collection does not meaningfully address the causes of these failures. Instead, restoring a healthy evaluation ecosystem will require significant progress in the design of benchmark datasets, the reliability with which they are annotated, their size, and the ways they handle social bias.
On the Transformer Growth for Progressive BERT Training.

As the excessive pre-training cost arouses the need to improve efficiency, considerable efforts have been made to train BERT progressively–start from an inferior but low-cost model and gradually increase the computational complexity. Our objective is to help advance the understanding of such Transformer growth and discover principles that guide progressive training. First, we find that similar to network architecture selection, Transformer growth also favors compound scaling. Specifically, while existing methods only conduct network growth in a single dimension, we observe that it is beneficial to use compound growth operators and balance multiple dimensions (e.g., depth, width, and input length of the model). Moreover, we explore alternative growth operators in each dimension via controlled comparison to give practical guidance for operator selection. In light of our analyses, the proposed method CompoundGrow speeds up BERT pre-training by 73.6% and 82.2% for the base and large models respectively while achieving comparable performances.
Metamagnetism of few-layer topological antiferromagnets

${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$ (MBT) materials are a promising class of antiferromagnetic topological insulators whose films provide access to novel and technologically important topological phases, including quantum anomalous Hall states and axion insulators. MBT device behavior is expected to be sensitive to the various collinear and noncollinear magnetic phases that are accessible in applied magnetic fields. Here, we use classical Monte Carlo simulations and electronic structure models to calculate the ground state magnetic phase diagram as well as topological and optical properties for few-layer films with up to six septuple layers. Using magnetic interaction parameters appropriate for MBT, we find that it is possible to prepare a variety of different magnetic stacking sequences, some of which have sufficient symmetry to disallow nonreciprocal optical response and Hall transport coefficients. Other stacking arrangements do yield large Faraday and Kerr signals, even when the ground state Chern number vanishes.
Optimal Patrol of a Perimeter

A defender dispatches patrollers to circumambulate a perimeter to guard against potential attacks. The defender decides on the time points to dispatch patrollers and each patroller’s direction and ...
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model.

Recently, the vulnerability of deep neural network (DNN)-based audio systems to adversarial attacks has obtained increasing attention. However, the existing audio adversarial attacks allow the adversary to possess the entire user's audio input as well as granting sufficient time budget to generate the adversarial perturbations. These idealized assumptions, however, make the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e.g., playing unnoticeable adversarial perturbations along with user's streaming input). To overcome these limitations, in this paper we propose fast audio adversarial perturbation generator (FAPG), which uses generative model to generate adversarial perturbations for the audio input in a single forward pass, thereby drastically improving the perturbation generation speed. Built on the top of FAPG, we further propose universal audio adversarial perturbation generator (UAPG), a scheme to craft universal adversarial perturbation that can be imposed on arbitrary benign audio input to cause misclassification. Extensive experiments on DNN-based audio systems show that our proposed FAPG can achieve high success rate with up to 214X speedup over the existing audio adversarial attack methods. Also our proposed UAPG generates universal adversarial perturbations that can achieve much better attack performance than the state-of-the-art solutions.
Inversion of Gravity Anomalies Using Primal-Dual Interior Point Methods

Structural inversion of gravity datasets based on the use of density anomalies to derive robust images of the subsurface (delineating lithologies and their boundaries) constitutes a fundamental non-invasive tool for geological exploration. The use of experimental techniques in geophysics to estimate and interpret di erences in the substructure based on its density properties have proven e cient; however, the inherent non-uniqueness associated with most geophysical datasets make this the ideal scenario for the use of recently developed robust constrained optimization techniques. We present a constrained optimization approach for a least squares inversion problem aimed to characterize 2-Dimensional Earth density structure models based on Bouguer gravity anomalies. The proposed formulation is solved with a Primal-Dual Interior-Point method including equality and inequality physical and structural constraints. We validate our results using synthetic density crustal structure models with varying complexity and illustrate the behavior of the algorithm using di erent initial density structure models and increasing noise levels in the observations. Based on these implementations, we conclude that the algorithm using Primal-Dual Interior-Point methods is robust, and its results always honor the geophysical constraints. Some of the advantages of using this approach for structural inversion of gravity data are the incorporation of a priori information related to the model parameters (coming from actual physical properties of the subsurface) and the reduction of the solution space contingent on these boundary conditions.
Aerodynamic Features of Designed Strake-Wing Configurations

Sixteen analytically and empirically designed strakes have been aerodynamically tested on a common wing-body to determine the longitudinal characteristics of the configurations. These strakes were selected, in general, due to their superior performance in a water-tunnel test. There they exhibited a good correlation between the hypothesized high value of angle of attack for strake vortex breakdown and the high value of leading-edge suction at the strake tip. The lift and pitch data were reasonably well predicted by an extended suction analogy method, and the lift agreement improved with increasing strake area. The strake-vortex-breakdown angle and configuration maximum lift generally increase with increasing strake area and, to some extent, strake tip leading-edge suction value. The best strake configurations all developed about the same maximum area efficiency value despite their differences in size.
N‐terminal trimer extension of nominal CD8 T cell epitopes is sufficient to promote cross‐presentation to cognate CD8 T cells in vivo

Cross-priming is the process in which Ag-presenting dendritic cells (DCs) acquire, process, and present Ags scavenged from other cells, and use these cells to activate naive CD8 T cells. Cross-priming of cognate CD8 cells can result in either tolerance or immunity, depending upon the activation status of the Ag-presenting DC. Previous studies have shown that nominal peptide is inefficiently cross-presented and that proteins and large polypeptides that require proteasomal processing are the main source of naturally cross-presented Ags. In this study we show that N-terminal extension of nominal peptide by as few as three residues is sufficient to produce a substrate for TAP-dependent cross-presentation that is highly efficient in cross-priming murine CD8 T cells in vivo. On a molar basis, cross-priming with 3-mer-extended peptide is 20-fold more efficient than priming with intact protein. This method of peptide extension should prove of great value in facilitating in vivo studies of CD8 immunity and tolerance that rely on cross-presentation.
Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry. To address this problem, we provide a risk bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon. Risk is defined as the probability of collision with uncertain obstacles. Existing approaches to address risk bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization. To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem. Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk. The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization. We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk bounded trajectories without time discretization. The provided approach deals with arbitrary probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems.
Protistan grazing impacts microbial communities and carbon cycling at deep-sea hydrothermal vents

Microbial eukaryotes (or protists) in marine ecosystems are a link between primary producers and all higher trophic levels, and the rate at which heterotrophic protistan grazers consume microbial prey is a key mechanism for carbon transport and recycling in microbial food webs. At deep-sea hydrothermal vents, chemosynthetic bacteria and archaea form the base of a food web that functions in the absence of sunlight, but the role of protistan grazers in these highly productive ecosystems is largely unexplored. Here, we pair grazing experiments with a molecular survey to quantify protistan grazing and to characterize the composition of vent-associated protists in low-temperature diffuse venting fluids from Gorda Ridge in the northeast Pacific Ocean. Results reveal protists exert higher predation pressure at vents compared to the surrounding deep seawater environment and may account for consuming 28 to 62% of the daily stock of prokaryotic biomass within discharging hydrothermal vent fluids. The vent-associated protistan community was more species rich relative to the background deep sea, and patterns in the distribution and co-occurrence of vent microbes provide additional insights into potential predator-prey interactions. Ciliates, followed by dinoflagellates, Syndiniales, rhizaria, and stramenopiles, dominated the vent protistan community and included bacterivorous species, species known to host symbionts, and parasites. Our findings provide an estimate of protistan grazing pressure within hydrothermal vent food webs, highlighting the important role that diverse protistan communities play in deep-sea carbon cycling.
Using Suppression Hearing Testimony to Prove Good Faith Under United States v. Leon

Imagine a narcotics prosecution based largely on evidence obtained under the authority of a search warrant. At the suppression hearing, the defendant argues that the warrant is invalid under Illinois v. Gates because the affidavit supporting the warrant application failed to provide the magistrate with even a “‘substantial basis for concluding’ that probable cause existed.” The defendant further argues that the fruits of the search must be suppressed despite the United States v. Leon good faith exception because the affidavit was “‘so lacking in indicia of probable cause as to render official belief in its existence entirely unreasonable.’” When the trial court seems inclined to accept these arguments, the prosecution offers to introduce testimony from the affiant officer about information supporting probable cause that she knew at the time of the warrant application but neglected to present to the magistrate. It is clear that if this additional information had been included in the warrant application, the warrant would have been supported by probable cause. Does United States v. Leon allow the prosecution to use the affiant’s suppression hearing testimony to cure the probable-cause deficiencies in the affidavit and establish the officer’s good faith? Most courts that have explicitly addressed this question have adopted bright-line rules, holding either that information known to the officer but not communicated to the magistrate can always be used to establish good