cssi/SciSciGPT-SciSciNet huggingface dataset:
Supply Spillovers During the Pandemic: Evidence from High-Frequency Shipping Data

World trade contracted dramatically during the global economic crisis induced by the COVID-19 pandemic. Disruptions in international supply chains were widely reported as governments imposed containment measures (lockdowns) to halt the spread of the disease. At the same time, demand declined as households and firms scaled back spending. This paper attempts to disentangle the supply and demand channels in trade by quantifying the causal effect of supply spillovers from lockdowns. We utilize a novel dataset of daily bilateral seaborne trade, and design a shift-share identification strategy that leverages geography-induced cargo delivery lags to track the transmission of supply disruptions across space. We find strong but short-lived supply spillovers of lockdowns through international trade. Moreover, the evidence is suggestive of the downstream propagation of countries' lockdowns through global supply chains.
Echocardiography-guided percutaneous left ventricular intracavitary injection as a cell delivery approach in infarcted mice

In the field of cell therapy for heart disease, a new paradigm of repeated dosing of cells has recently emerged. However, the lack of a repeatable cell delivery method in preclinical studies in rodents is a major obstacle to investigating this paradigm. We have established and standardized a method of echocardiography-guided percutaneous left ventricular intracavitary injection (echo-guided LV injection) as a cell delivery approach in infarcted mice. Here, we describe the method in detail and address several important issues regarding it. First, by integrating anatomical and echocardiographic considerations, we have established strategies to determine a safe anatomical window for injection in infarcted mice. Second, we summarize our experience with this method (734 injections). The overall survival rate was 91.4%. Third, we examined the efficacy of this cell delivery approach. Compared with vehicle treatment, cardiac mesenchymal cells (CMCs) delivered via this method improved cardiac function assessed both echocardiographically and hemodynamically. Furthermore, repeated injections of CMCs via this method yielded greater cardiac function improvement than single-dose administration. Echo-guided LV injection is a feasible, reproducible, relatively less invasive and effective delivery method for cell therapy in murine models of heart disease. It is an important approach that could move the field of cell therapy forward, especially with regard to repeated cell administrations.
Adherence to Personal Health Devices: A Case Study in Diabetes Management

Personal health devices can enable continuous monitoring of health parameters. However, the benefit of these devices is often directly related to the frequency of use. Therefore, adherence to personal health devices is critical. This paper takes a data mining approach to study continuous glucose monitor use in diabetes management. We evaluate two independent datasets from a total of 44 subjects for 60 - 270 days. Our results show that: 1) missed target goals (i.e. suboptimal outcomes) is a factor that is associated with wearing behavior of personal health devices, and 2) longer duration of non-adherence, identified through missing data or data gaps, is significantly associated with poorer outcomes. More specifically, we found that up to 33% of data gaps occurred when users were in abnormal blood glucose categories. The longest data gaps occurred in the most severe (i.e. very low / very high) glucose categories. Additionally, subjects with poorly-controlled diabetes had longer average data gap duration than subjects with well-controlled diabetes. This work contributes to the literature on the design of context-aware systems that can leverage data-driven approaches to understand factors that influence non-wearing behavior. The results can also support targeted interventions to improve health outcomes.
Genome-Wide Binding Analyses of HOXB1 Revealed a Novel DNA Binding Motif Associated with Gene Repression

Knowledge of the diverse DNA binding specificities of transcription factors is important for understanding their specific regulatory functions in animal development and evolution. We have examined the genome-wide binding properties of the mouse HOXB1 protein in embryonic stem cells differentiated into neural fates. Unexpectedly, only a small number of HOXB1 bound regions (7%) correlate with binding of the known HOX cofactors PBX and MEIS. In contrast, 22% of the HOXB1 binding peaks display co-occupancy with the transcriptional repressor REST. Analyses revealed that co-binding of HOXB1 with PBX correlates with active histone marks and high levels of expression, while co-occupancy with REST correlates with repressive histone marks and repression of the target genes. Analysis of HOXB1 bound regions uncovered enrichment of a novel 15 base pair HOXB1 binding motif HB1RE (HOXB1 response element). In vitro template binding assays showed that HOXB1, PBX1, and MEIS can bind to this motif. In vivo, this motif is sufficient for direct expression of a reporter gene and over-expression of HOXB1 selectively represses this activity. Our analyses suggest that HOXB1 has evolved an association with REST in gene regulation and the novel HB1RE motif contributes to HOXB1 function in part through a repressive role in gene expression.
Enhancing Direct Exoplanet Spectroscopy with Apodizing and Beam Shaping Optics

Direct exoplanet spectroscopy aims to measure the spectrum of an exoplanet while simultaneously minimizing the light collected from its host star. Isolating the planet light from the starlight improves the signal-to-noise ratio (S/N) per spectral channel when noise due to the star dominates, which may enable new studies of the exoplanet atmosphere with unprecedented detail at high spectral resolution (>30,000). However, the optimal instrument design depends on the flux level from the planet and star compared to the noise due to other sources, such as detector noise and thermal background. Here we present the design, fabrication, and laboratory demonstration of specially-designed optics to improve the S/N in two potential regimes in direct exoplanet spectroscopy with adaptive optics instruments. The first is a pair of beam-shaping lenses that increase the planet signal by improving the coupling efficiency into a single-mode fiber at the known position of the planet. The second is a grayscale apodizer that reduces the diffracted starlight for planets at small angular separations from their host star. The former especially increases S/N when dominated by detector noise or thermal background, while the latter helps reduce stellar noise. We show good agreement between the theoretical and experimental point spread functions in each case and predict the exposure time reduction ($\sim 33\%$) that each set of optics provides in simulated observations of 51 Eridani b using the Keck Planet Imager and Characterizer instrument at W.M. Keck Observatory.
Testing Positive Semi-Definiteness via Random Submatrices

We study the problem of testing whether a matrix $\mathrm{A}\in \mathbb{R}^{n\times n}$ with bounded entries ( $\Vert \mathrm{A}\Vert_{\infty}\leq 1$ ) is positive semidefinite (PSD), or $\epsilon$ -far in Euclidean distance from the PSD cone, meaning that $\min\nolimits_{\mathrm{B}\succeq 0}\Vert \mathrm{A}-\mathrm{B}\Vert_{F}^{2} > \epsilon n^{2}$ , where $\mathrm{B}\succeq 0$ denotes that B is PSD. Our main algorithmic contribution is a non-adaptive tester which distinguishes between these cases using only $\tilde{O}(1/\epsilon^{4})$ queries to the entries of A.11Throughout the paper, $\tilde{O}(\cdot)$ hides $\log(1/\epsilon)$ factors. If instead of the Eucledian norm we considered the distance in spectral norm, we obtain the “ $\ell_{\infty}$ -gap problem”, where A is either PSD or satisfies $\min\nolimits_{\mathrm{B}\succ 0}\Vert \mathrm{A}-\mathrm{B}\Vert_{2} > \epsilon n$ . For this related problem, we give a $\tilde{O}(1/\epsilon^{2})$ query tester, which we show is optimal up to $\log(1/\epsilon)$ factors. Both our testers randomly sample a collection of principal sub-matrices and check whether these sub-matrices are PSD. Consequentially, our algorithms achieve one-sided error: whenever they output that A is not PSD, they return a certificate that A has negative eigenvalues. We complement our upper bound for PSD testing with Eucledian norm distance by giving a $\tilde{\Omega}(1/\epsilon^{2})$ lower bound for any non-adaptive algorithm. Our lower bound construction is general, and can be used to derive lower bounds for a number of spectral testing problems. As an example of the applicability of our construction, we obtain a new $\tilde{\Omega}(1/\epsilon^{4})$ sampling lower bound for testing the Schatten-1 norm with a $\epsilon n^{1.5}$ gap, extending a result of Balcan, Li, Woodruff, and Zhang [11]. In addition, our hard instance results in new sampling lower bounds for estimating the Ky-Fan Norm, and the cost of rank- $k$ approximations, i.e. $\Vert\mathrm{A}-\mathrm{A}_{k}\Vert_{F}^{2}=\sum\nolimits_{i > k}\sigma_{i}^{2}(\mathrm{A})$ .
Measuring Accounting Fraud and Irregularities Using Public and Private Enforcement

Most accounting studies use only public enforcement actions (SEC cases) to measure accounting fraud. However, private cases (securities class actions) also play an important enforcement role. We discuss the legal standards and processes for both public and private enforcement regimes, emphasize the importance of screening cases for credible fraud allegations, and show both yield credible fraud measures. Further, we demonstrate these research design choices affect inferences from prior research and a hypothetical research setting. Finally, we show common measures of accounting irregularities using Audit Analytics to proxy for fraud result in significant false positives and negatives and develop a fraud prediction model for use in future research. We recommend using both public and private enforcement with appropriate screening when examining accounting fraud to reduce Type I and II errors, or reporting the sensitivity of findings across regimes. This is particularly important given the reduction in accounting-related enforcement after 2005.
Financial Inclusion and Bank Competition in Sub-Saharan Africa

In this paper we study how competition and financial soundness affect financial inclusion in Sub-Saharan Africa (SSA). We use detailed individual-level survey data, combined with key country-level indicators of bank competition and financial soundness, to study the effect on the adoption of several financial products (bank accounts, credit and debit cards, and bank loans). We find that more competition tends to increase the probability of access to these financial products. On the contrary, we do not find strong evidence of the effect of bank-balance sheet variables (i.e. capital adequacy or liquidity) on borrowing by individuals. Our results may help policy makers design regulations that could improve financial inclusion, which could potentially impact economic growth and long-term economic development.
Usage bibliometrics

Scholarly usage data provides unique opportunities to address the known shortcomings of citation analysis. However, the collection, processing and analysis of usage data remains an area of active research. This article provides a review of the state-of-the-art in usage-based informetric, i.e. the use of usage data to study the scholarly process.
More Dynamic Data Structures for Geometric Set Cover with Sublinear Update Time

We study geometric set cover problems in dynamic settings, allowing insertions and deletions of points and objects. We present the first dynamic data structure that can maintain an O(1)-approximation in sublinear update time for set cover for axis-aligned squares in 2D . More precisely, we obtain randomized update time O(n^{2/3+δ}) for an arbitrarily small constant δ > 0. Previously, a dynamic geometric set cover data structure with sublinear update time was known only for unit squares by Agarwal, Chang, Suri, Xiao, and Xue [SoCG 2020]. If only an approximate size of the solution is needed, then we can also obtain sublinear amortized update time for disks in 2D and halfspaces in 3D . As a byproduct, our techniques for dynamic set cover also yield an optimal randomized O(nlog n)-time algorithm for static set cover for 2D disks and 3D halfspaces, improving our earlier O(nlog n(log log n)^{O(1)}) result [SoCG 2020].
The effects of default choice on student loan borrowing: Experimental evidence from a public research university

Abstract We explore the role of defaults and choice architecture on student loan decision-making, experimentally testing the impact pre-populating either decline or accept decisions compared to an active choice, no pre-population, decision. We demonstrate that the default choice presented does influence student loan borrowing decisions. Specifically, compared to active choice, students presented within a pre-populated decline decision (opt-in) were almost five percent less likely to accept all packaged loans and borrowed between 4.6% and 4.8% less in federal student loans. The reductions in borrowing appears to be concentrated within unsubsidized loans with those assigned to the opt-in condition borrowing 8.3% less in unsubsidized student loans. These changes in borrowing did not induce substitution towards private or Parent PLUS loans nor did they negatively impact enrollment, academic performance, or on-campus work outcomes in the same academic year.
The local economic impacts of the oil and gas industry: Boom, bust and resilience to shocks

Abstract In this paper, we study the impact of the oil and gas industry on county-level employment and wage earnings across not only the boom, but also the bust cycle. Our paper is among the first to estimate wage and employment impacts of the bust cycle for the U.S. oil and gas industry and directly compare these employment and wage impacts for the boom. We then evaluate spillovers into other sectors in the economy, comparing impacts on tradable and non-tradable industries for three distinct geographic regions and estimate separate models for rural and urban areas. We find variation across geographic context, but in general the oil and gas bust was associated with a significant decrease in overall employment, with the effect most notable in non-tradable industries in rural counties. Finally, we investigate the differential impact of the 2008 financial crisis on labor in producing and non-producing counties. We find that, employment and wages in oil and gas producing counties were impacted by the financial crisis less than non-oil and gas counties and recovery in oil and gas counties started earlier.
The US–China trade war and Phase One agreement

Abstract The Trump administration changed US trade policy toward China in ways that will take years for researchers to sort out. This paper makes four specific contributions to that research agenda. The first is to carefully mark the timing, definitions, and scale of the products subject to the tariff changes affecting US–China trade from January 20, 2017 through January 20, 2021. One result was each country increasing its average duty on the other to rates of roughly 20 percent, with the new tariffs and counter-tariffs covering more than 50 percent of bilateral trade. The second contribution is to highlight two additional channels through which bilateral tariffs changed during this period that received less research attention. One tariff change is through product exclusions, another is trade remedy policies of antidumping and countervailing duties. The third contribution is to provide an initial exploration into why China fell more than 40 percent short of meeting the goods purchase commitments set out under the first year of the Phase One agreement. The last contribution is to consider additional trade policy actions—involving forced labor, export controls for reasons of national security or human rights, and reclassification of trade with Hong Kong—likely to affect US–China trade beyond the Trump administration.
Study of real-valued distance prediction for protein structure prediction with deep learning

MOTIVATION Inter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. RESULTS Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models. AVAILABILITY AND IMPLEMENTATION https://github.com/j3xugit/RaptorX-3DModeling. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
CONTRA: Contrarian statistics for controlled variable selection

The holdout randomization test (HRT) discovers a set of covariates most predictive of a response. Given the covariate distribution, HRTs can explicitly control the false discovery rate (FDR). However, if this distribution is unknown and must be estimated from data, HRTs can inflate the FDR. To alleviate the inflation of FDR, we propose the contrarian randomization test (CONTRA), which is designed explicitly for scenarios where the covariate distribution must be estimated from data and may even be misspecified. Our key insight is to use an equal mixture of two "contrarian" probabilistic models in determining the importance of a covariate. One model is fit with the real data, while the other is fit using the same data, but with the covariate being tested replaced with samples from an estimate of the covariate distribution. CONTRA is flexible enough to achieve a power of 1 asymptotically, can reduce the FDR compared to state-of-the-art CVS methods when the covariate distribution is misspecified, and is computationally efficient in high dimensions and large sample sizes. We further demonstrate the effectiveness of CONTRA on numerous synthetic benchmarks, and highlight its capabilities on a genetic dataset.
True Random Number Generation using Latency Variations of Commercial MRAM Chips

The emerging magneto-resistive RAM (MRAM) has considerable potential to become a universal memory technology because of its several advantages: unlimited endurance, lower read/write latency, ultralow-power operation, high-density, and CMOS compatibility, etc. This paper will demonstrate an effective technique to generate random numbers from energy-efficient consumer-off-the-shelf (COTS) MRAM chips. In the proposed scheme, the inherent (intrinsic/extrinsic process variation) stochastic switching behavior of magnetic tunnel junctions (MTJs) is exploited by manipulating the write latency of COTS MRAM chips. This is the first system-level experimental implementation of true random number generator (TRNG) using COTS toggle MRAM technology to the best of our knowledge. The experimental results and subsequent NIST SP-800-22 suite test reveal that the proposed latency-based TRNG is acceptably fast ($\sim$ 22Mbit/s in the worst case) and robust over a wide range of operating conditions.
An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study.

Background: COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions. Objective: This study sought to redefine the Healthy People 2030’s SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data. Methods: The process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data into drivers and outcomes, incorporating data science techniques for calculating measurable indicators from the raw variables, and studying the extent to which interventions are identified or developed to mitigate drivers that lead to the undesired outcomes. Results: We generated and classified the indicators of social determinants of health, which are linked to COVID-19. To display the functionalities of the UPHO platform, we presented a prototype design to demonstrate its features. We provided a use case scenario for 4 different users. Conclusions: UPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health.
Symplectic 4-manifolds admit Weinstein trisections

We prove that every symplectic 4-manifold admits a trisection that is compatible with the symplectic structure in the sense that the symplectic form induces a Weinstein structure on each of the three sectors of the trisection. Along the way, we show that a (potentially singular) symplectic braided surface in $\mathbb{CP}^2$ can be symplectically isotoped into bridge position.
Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process Through Queues: Age of Information and Beyond

Recently, a connection between the age of information and remote estimation error was found in a sampling problem of Wiener processes: If the sampler has no knowledge of the signal being sampled, the optimal sampling strategy is to minimize the age of information; however, by exploiting causal knowledge of the signal values, it is possible to achieve a smaller estimation error. In this paper, we generalize the previous study by investigating a problem of sampling a stationary Gauss-Markov process named the Ornstein-Uhlenbeck (OU) process, where we aim to find useful insights for solving the problems of sampling more general signals. The optimal sampling problem is formulated as a constrained continuous-time Markov decision process (MDP) with an uncountable state space. We provide an exact solution to this MDP: The optimal sampling policy is a threshold policy on instantaneous estimation error and the threshold is found. Further, if the sampler has no knowledge of the OU process, the optimal sampling problem reduces to an MDP for minimizing a nonlinear age of information metric. The age-optimal sampling policy is a threshold policy on expected estimation error and the threshold is found. In both problems, the optimal sampling policies can be computed by low-complexity algorithms (e.g., bisection search and Newton’s method), and the curse of dimensionality is circumvented. These results hold for (i) general service time distributions of the queueing server and (ii) sampling problems both with and without a sampling rate constraint. Numerical results are provided to compare different sampling policies.
A cell wall invertase controls nectar volume and sugar composition

Nectar volume and sugar composition are key determinants in the strength of plant-pollinator mutualisms. The main nectar sugars are sucrose, glucose, and fructose, which can vary widely in ratio and concentration across species. Brassica spp. produce a hexose-dominant nectar (high in the monosaccharides glucose and fructose) with very low levels of the disaccharide sucrose. Cell wall invertases (CWINVs) catalyze the irreversible hydrolysis of sucrose into glucose and fructose in the apoplast. We found that BrCWINV4A is highly expressed in the nectaries of Brassica rapa. Moreover, a brcwinv4a null mutant has (1) greatly reduced cell wall invertase activity in the nectaries, and (2) produces a sucrose-rich nectar, but (3) with significantly less volume. These results definitively demonstrate that CWINV activity is not only essential for the production of a hexose-rich nectar, but also support a hypothetical model of nectar secretion in which its hydrolase activity is required for maintaining a high intracellular-to-extracellular sucrose ratio that facilitates the continuous export of sucrose into the nectary apoplast. Extracellular hydrolysis of each sucrose into two hexoses by BrCWINV4A also likely creates the osmotic potential required for nectar droplet formation. These results cumulatively indicate that modulation of CWINV activity can at least partially account for naturally occurring differences in nectar volume and sugar composition. Lastly, honey bees prefer nectars with some sucrose, but wild-type B. rapa flowers were much more heavily visited than those of brcwinv4a, suggesting that the potentially attractive sucrose-rich nectar of brcwinv4a could not compensate for its low volume.
Systems Analysis and Structural Design of an Unpressurized Cargo Delivery Vehicle

The International Space Station will require a continuous supply of replacement parts for ongoing maintenance and repair after the planned retirement of the Space Shuttle in 2010. These parts are existing line-replaceable items collectively called Orbital Replacement Units, and include heavy and oversized items such as Control Moment Gyroscopes and stowed radiator arrays originally intended for delivery aboard the Space Shuttle. Current resupply spacecraft have limited to no capability to deliver these external logistics. In support of NASA's Exploration Systems Architecture Study, a team at Langley Research Center designed an Unpressurized Cargo Delivery Vehicle to deliver bulk cargo to the Space Station. The Unpressurized Cargo Delivery Vehicle was required to deliver at least 13,200 lbs of cargo mounted on at least 18 Flight Releasable Attachment Mechanisms. The Crew Launch Vehicle design recommended in the Exploration Systems Architecture Study would be used to launch one annual resupply flight to the International Space Station. The baseline vehicle design developed here has a cargo capacity of 16,000 lbs mounted on up to 20 Flight Releasable Attachment Mechanisms. Major vehicle components are a 5.5m-diameter cargo module containing two detachable cargo pallets with the payload, a Service Module to provide propulsion and power, and an aerodynamic nose cone. To reduce cost and risk, the Service Module is identical to the one used for the Crew Exploration Vehicle design.
Estimation and inference for policy relevant treatment effects

Abstract The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy under consideration. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects. These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, this paper is the first to develop limit distribution theories for inference about the PRTE.
Revisiting Document Representations for Large-Scale Zero-Shot Learning

Zero-shot learning aims to recognize unseen objects using their semantic representations. Most existing works use visual attributes labeled by humans, not suitable for large-scale applications. In this paper, we revisit the use of documents as semantic representations. We argue that documents like Wikipedia pages contain rich visual information, which however can easily be buried by the vast amount of non-visual sentences. To address this issue, we propose a semi-automatic mechanism for visual sentence extraction that leverages the document section headers and the clustering structure of visual sentences. The extracted visual sentences, after a novel weighting scheme to distinguish similar classes, essentially form semantic representations like visual attributes but need much less human effort. On the ImageNet dataset with over 10,000 unseen classes, our representations lead to a 64% relative improvement against the commonly used ones.
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation

Robustness and counterfactual bias are usually evaluated on a test dataset. However, are these evaluations robust? If the test dataset is perturbed slightly, will the evaluation results keep the same? In this paper, we propose a “double perturbation” framework to uncover model weaknesses beyond the test dataset. The framework first perturbs the test dataset to construct abundant natural sentences similar to the test data, and then diagnoses the prediction change regarding a single-word substitution. We apply this framework to study two perturbation-based approaches that are used to analyze models’ robustness and counterfactual bias in English. (1) For robustness, we focus on synonym substitutions and identify vulnerable examples where prediction can be altered. Our proposed attack attains high success rates (96.0%-99.8%) in finding vulnerable examples on both original and robustly trained CNNs and Transformers. (2) For counterfactual bias, we focus on substituting demographic tokens (e.g., gender, race) and measure the shift of the expected prediction among constructed sentences. Our method is able to reveal the hidden model biases not directly shown in the test dataset. Our code is available at https://github.com/chong-z/nlp-second-order-attack.
Extraction of aluminum and iron from bauxite: A unique closed-loop ore refining process utilizing oxalate chemistry

The Bayer process holds an exclusive status for alumina extraction, but a massive amount of caustic “red mud” waste is generated. In this work, three oxalate reagents: potassium hydrogen oxalate (KHC2O4), potassium tetraoxalate (KHC2O4·H2C2O4), and oxalic acid (H2C2O4) were investigated for the Al and Fe extraction process from NIST SRM 600 – Australian Darling range bauxite ore. More than 90% of Al and Fe was extracted into the aqueous phase in less than 2 h with 0.50 M C2O42- for all three reagents. The Fe and Al can be selectively precipitated by hydrolyzing the aqueous phase. By acidifying the Al and Fe free filtrate, 80% of the C2O42- can be precipitated as KHC2O4·H2C2O4. Greater than 90% of the aqueous acid can also be recycled using a cation exchange resin. The proposed closed-loop process is an energy-efficient, cost-effective, environmentally-friendly route for extracting Al and Fe from bauxite ore.
Oneshot Differentially Private Top-k Selection

Being able to efficiently and accurately select the top-$k$ elements with differential privacy is an integral component of various private data analysis tasks. In this paper, we present the oneshot Laplace mechanism, which generalizes the well-known Report Noisy Max mechanism to reporting noisy top-$k$ elements. We show that the oneshot Laplace mechanism with a noise level of $\widetilde{O}(\sqrt{k}/\eps)$ is approximately differentially private. Compared to the previous peeling approach of running Report Noisy Max $k$ times, the oneshot Laplace mechanism only adds noises and computes the top $k$ elements once, hence much more efficient for large $k$. In addition, our proof of privacy relies on a novel coupling technique that bypasses the use of composition theorems. Finally, we present a novel application of efficient top-$k$ selection in the classical problem of ranking from pairwise comparisons.
Optimal guidance and propulsion control for transatmospheric vehicles

Problems associated with on-board trajectory optimization and with the synthesis of guidance laws are addressed for ascent to LEO of an air-breathing, single-stage-to-orbit vehicle. A multimode propulsion system is assumed which incorporates turbojet, ramjet, scramjet, and rocket engines. An energy-state approximation is applied to a four-state dynamic model for flight of a point mass over a spherical nonrotating earth. An algorithm for generating fuel-optimal climb profiles is derived via singular perturbation theory. This algorithm results from application of the minimum principle to a low-order dynamic model that includes general functional dependence on angle of attack and a normal component of thrust. Switching conditions are derived which, under appropriate assumptions, govern optimal transition from one propulsion mode to another. The use of bank angle to modulate the magnitude of the vertical component of lift is shown to improve the index performance. Numerical results illustrate the nature of the resulting fuel-optimal climb paths.
Sweeping Jet Optimization Studies

Progress on experimental efforts to optimize sweeping jet actuators for active flow control (AFC) applications with large adverse pressure gradients is reported. Three sweeping jet actuator configurations, with the same orifice size but di↵erent internal geometries, were installed on the flap shoulder of an unswept, NACA 0015 semi-span wing to investigate how the output produced by a sweeping jet interacts with the separated flow and the mechanisms by which the flow separation is controlled. For this experiment, the flow separation was generated by deflecting the wing's 30% chord trailing edge flap to produce an adverse pressure gradient. Steady and unsteady pressure data, Particle Image Velocimetry data, and force and moment data were acquired to assess the performance of the three actuator configurations. The actuator with the largest jet deflection angle, at the pressure ratios investigated, was the most efficient at controlling flow separation on the flap of the model. Oil flow visualization studies revealed that the flow field controlled by the sweeping jets was more three-dimensional than expected. The results presented also show that the actuator spacing was appropriate for the pressure ratios examined.
PANTHER: Pathway Augmented Nonnegative Tensor Factorization for HighER-order Feature Learning

Genetic pathways usually encode molecular mechanisms that can inform targeted interventions. It is often challenging for existing machine learning approaches to jointly model genetic pathways (higher-order features) and variants (atomic features), and present to clinicians interpretable models. In order to build more accurate and better interpretable machine learning models for genetic medicine, we introduce Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning (PANTHER). PANTHER selects informative genetic pathways that directly encode molecular mechanisms. We apply genetically motivated constrained tensor factorization to group pathways in a way that reflects molecular mechanism interactions. We then train a softmax classifier for disease types using the identified pathway groups. We evaluated PANTHER against multiple state-of-the-art constrained tensor/matrix factorization models, as well as group guided and Bayesian hierarchical models. PANTHER outperforms all state-of-the-art comparison models significantly (p
Improving Robustness to Model Inversion Attacks via Mutual Information Regularization.

This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given the access to a target machine learning model. Existing defense mechanisms rely on model-specific heuristics or noise injection. While being able to mitigate attacks, existing methods significantly hinder model performance. There remains a question of how to design a defense mechanism that is applicable to a variety of models and achieves better utility-privacy tradeoff. In this paper, we propose the Mutual Information Regularization based Defense (MID) against MI attacks. The key idea is to limit the information about the model input contained in the prediction, thereby limiting the ability of an adversary to infer the private training attributes from the model prediction. Our defense principle is model-agnostic and we present tractable approximations to the regularizer for linear regression, decision trees, and neural networks, which have been successfully attacked by prior work if not attached with any defenses. We present a formal study of MI attacks by devising a rigorous game-based definition and quantifying the associated information leakage. Our theoretical analysis sheds light on the inefficacy of DP in defending against MI attacks, which has been empirically observed in several prior works. Our experiments demonstrate that MID leads to state-of-the-art performance for a variety of MI attacks, target models and datasets.
The Framers' Intent: John Adams, his Era, and the Fourth Amendment

INTRODUCTION 979 I. THE STRUCTURE OF THE FOURTH AMENDMENT AND ITS DISPUTED HISTORICAL MEANING 982 II. JOHN ADAMS AND THE HISTORICAL CONTEXT: 1761 TO 1780 989 A. HISTORICAL CONTEXT 989 B. JOHN ADAMS AND THE WRITS OF ASSISTANCE CASE 992 C. THE ENGLISH GENERAL WARRANT CASES 979 D. JOHN ADAMS’S LIBRARY 1012 E. ADAMS AS LITIGATOR AND OBSERVER 1018 F. ADAMS AS DELEGATE TO THE CONTINENTAL CONGRESS 1026 III. 1776 TO 1791: THE EVOLUTION OF SEARCH AND SEIZURE PROVISIONS ........ 1027 A. ARTICLE 14 AND OTHER EARLY SEARCH AND SEIZURE PROVISIONS ..... 1027 B. THE CONSTITUTIONAL CONVENTION OF 1787 1029 C. THE CONFEDERATION CONGRESS 1030 D. THE RATIFICATION OF THE CONSTITUTION BY THE STATES 1031 E. THE DRAFTING OF THE FOURTH AMENDMENT 1044 IV. ADAMS’S VIEWS AND INFLUENCE 1052 CONCLUSION 1060
Apparent Thermal Conductivity Measurements by an Unguarded Technique

An unguarded longitudinal heat flow apparatus for measuring the apparent thermal conductivity (λa) of insulations was tested with mean specimen temper atures from 300 to 330°K on samples up to 0.91 m wide, 1.52 m long, and 0.15 m thick. Heat flow is provided by a horizontal electrically heated Nichrome screen that is sandwiched between test samples that are bounded by tempera ture controlled copper plates and 9 cm of mineral fiber insulation. A deter minate error analysis shows λa measurement uncertainty to be less than ±1.7% for insulating materials as thin as 3 cm. Three-dimensional thermal modeling indicates negligible error in λa due to edge loss for insulations up to 7.62 cm thick when the temperature difference across the sample is measured at the screen center. System repeatability and reproducibility were determined to be ±0.2%.Differences of λ a results from the screen tester and results from the National Bureau of Standards were 0.1% for a 10-kg/m3 Calibration Transfer Standard and 0.9% for 127-...
Oracle-Efficient Learning and Auction Design

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren, who showed that oracle-efficient algorithms do not exist in general and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders’ valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis.
Reduced pediatric urgent asthma utilization and exacerbations during the COVID-19 pandemic.

The COVID-19 pandemic has had a profound impact on healthcare access and utilization, which could have important implications for children with chronic diseases, including asthma. We sought to evaluate changes in healthcare utilization and outcomes in children with asthma during the COVID-19 pandemic. We used electronic health records data to evaluate healthcare use and asthma outcomes in 3959 children and adolescents, 5-17 years of age, with a prior diagnosis of asthma who had a history of well-child visits and encounters within the healthcare system. We assessed all-cause healthcare encounters and asthma exacerbations in the 12-months preceding the start of the COVID-19 pandemic (March 1, 2019-February 29, 2020) and the first 12 months of the pandemic (March 1, 2020-February 28, 2021). All-cause healthcare encounters decreased significantly during the pandemic compared to the preceding year, including well-child visits (48.1% during the pandemic vs. 66.6% in the prior year; p < .01), emergency department visits (9.7% vs. 21.0%; p < .01), and inpatient admissions (1.6% vs. 2.5%; p < .01), though there was over a 100-fold increase in telehealth encounters. Asthma exacerbations that required treatment with systemic steroids also decreased (127 vs. 504 exacerbations; p < .01). Race/ethnicity was not associated with changes in healthcare utilization or asthma outcomes. The COVID-19 pandemic corresponded to dramatic shifts in healthcare utilization, including increased telehealth use and improved outcomes among children with asthma. Social distancing measures may have also reduced asthma trigger exposure.
Spatial Decay of Kubo's Canonical Correlation Function

Kubo’s canonical correlation functions (canonical correlators) describe the static response of a system in equilibrium to infinitesimal local perturbations. Knowing their decay properties with respect to spatial distance is important for many theoretical and experimental applications. For a thermal state of a system with short-range interactions, we prove that any knowledge of the decay rate of ordinary correlators readily translates into that of canonical correlators. As the former have been extensively studied, our result can lead to many new results on the latter. Throughout the paper, we use the framework of infinite-volume quantum lattice model. However, the method we use is adaptable for other physical scenarios not considered in this paper.
Exact symmetries and threshold states in two-dimensional models for QCD

Two-dimensional SU(N) gauge theory coupled to a Majorana fermion in the adjoint representation is a nice toy model for higher-dimensional gauge dynamics. It possesses a multitude of “gluinoball” bound states whose spectrum has been studied using numerical diagonalizations of the light-cone Hamiltonian. We extend this model by coupling it to Nf flavors of fundamental Dirac fermions (quarks). The extended model also contains meson-like bound states, both bosonic and fermionic, which in the large-N limit decouple from the gluinoballs. We study the large-N meson spectrum using the Discretized Light-Cone Quantization (DLCQ). When all the fermions are massless, we exhibit an exact $$ \mathfrak{osp} $$ (1|4) symmetry algebra that leads to an infinite number of degeneracies in the DLCQ approach. More generally, we show that many single-trace states in the theory are threshold bound states that are degenerate with multi-trace states. These exact degeneracies can be explained using the Kac-Moody algebra of the SU(N) current. We also present strong numerical evidence that additional threshold states appear in the continuum limit. Finally, we make the quarks massive while keeping the adjoint fermion massless. In this case too, we observe some exact degeneracies that show that the spectrum of mesons becomes continuous above a certain threshold. This demonstrates quantitatively that the fundamental string tension vanishes in the massless adjoint QCD2 without explicit four-fermion operators.
Dominant Resource Fairness with Meta-Types

Inspired by the recent COVID-19 pandemic, we study a generalization of the multi-resource allocation problem with heterogeneous demands and Leontief utilities. Unlike existing settings, we allow each agent to specify a constraint to only accept allocations from a subset of the total supply for each resource. Such constraints often arise from location constraints (e.g. among all of the volunteer nurses, only those who live nearby can work at hospital A due to commute constraints. So hospital A can only receive allocations of volunteers from a subset of the total supply). This can also model a type of substitute effect where some agents need 1 unit of resource A \emph{or} B, but some other agents specifically want A, and some specifically want B. We propose a new mechanism called Group Dominant Resource Fairness which determines the allocations by solving a small number of linear programs. The proposed method satisfies Pareto optimality, envy-freeness, strategy-proofness, and a notion of sharing incentive for our setting. To the best of our knowledge this is the first mechanism to achieve all four properties in our setting. Furthermore, we show numerically that our method scales better to large problems than alternative approaches. Finally, although motivated by the problem of medical resource allocation in a pandemic, our mechanism can be applied more broadly to resource allocation under Leontief utilities with accessibility constraints.