cssi/SciSciGPT-SciSciNet huggingface dataset:
LIABILITY RULES FOR CONSTITUTIONAL RIGHTS: THE CASE OF MASS DETENTIONS

Constitutional law assumes that rights should always be protected by property rules--that is, the government can only take them with the individual's consent. This Article extends to constitutional law the insights of Calabresi and Melamed's famous article on property and liability rules. Whether rights should be protected by property rules or liability rules depends on the transaction costs of negotiating a transfer of rights. As transaction costs rise, liability rules become more attractive. This Article shows that liability rules can have an important role in constitutional law. Using mass detentions in national security emergencies as a case study, it shows that property rule protection of individual rights sometimes leads to perverse and inefficient results. While the government has repeatedly resorted to mass detentions in emergencies, the Court has never blocked such measures. This is a perverse result of constitutional law's insistence on property rule protection even when transaction costs of transferring liberty rights become extraordinarily high. Holding that a policy violates rights would require, under a property rule, enjoining potentially vital security measures. The Court tends to be unwilling to impose such costs on society. Thus it simply avoids finding that mass detentions violate rights. This creates groups of uncompensated victims, who are often members of vulnerable ethnic minorities. It also stunts and distorts the development of constitutional law. In some unusual but extraordinarily important situations liability rules may be preferable to property rules. The former will be the best way to protect constitutional rights under the same circumstances that make them preferable in private law--where high transaction costs prevent efficient rights-transfers under a property rule. Switching to liability rules in mass detention situations can, counterintuitively, result in greater redress for detainees, as well as deterring detentions and preserving the integrity and predictability of substantive law. The transaction cost analysis developed in this Article has implications that extend beyond mass detentions to a variety of other constitutional issues, such as coercive public health measures and military conscription.
Quantile regression on inactivity time.

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary meas...
Network Coding Gaps for Completion Times of Multiple Unicasts

We study network coding gaps for the problem of makespan minimization of multiple unicasts. In this problem distinct packets at different nodes in a network need to be delivered to a destination specific to each packet, as fast as possible. The network coding gap specifies how much coding packets together in a network can help compared to the more natural approach of routing. While makespan minimization using routing has been intensely studied for the multiple unicasts problem, no bounds on network coding gaps for this problem are known. We develop new techniques which allow us to upper bound the network coding gap for the makespan of $k$ unicasts, proving this gap is at most polylogarithmic in $k$ . Complementing this result, we show there exist instances of $k$ unicasts for which this coding gap is polylogarithmic in $k$ . Our results also hold for average completion time, and more generally any $\ell_{p}$ norm of completion times.
Visually Analyzing Contextualized Embeddings

In this paper we introduce a method for visually analyzing contextualized embeddings produced by deep neural network-based language models. Our approach is inspired by linguistic probes for natural language processing, where tasks are designed to probe language models for linguistic structure, such as parts-of-speech and named entities. These approaches are largely confirmatory, however, only enabling a user to test for information known a priori. In this work, we eschew supervised probing tasks, and advocate for unsupervised probes, coupled with visual exploration techniques, to assess what is learned by language models. Specifically, we cluster contextualized embeddings produced from a large text corpus, and introduce a visualization design based on this clustering and textual structure – cluster co-occurrences, cluster spans, and cluster-word membership– to help elicit the functionality of, and relationship between, individual clusters. User feedback highlights the benefits of our design in discovering different types of linguistic structures.
Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States

The objective of this study is to examine the transmission risk of COVID-19 based on cross-county population co-location data from Facebook. The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing, travel reduction and stay-at-home orders are recognized as essential non-pharmacologic approaches to control the infection and spatial spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020 for conducting spatial network analysis where nodes represent counties and edge weights are associated with the co-location probability of populations of the counties. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases across counties. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new confirmed cases with one week of delay. The analysis categorizes counties based on the number of confirmed COVID-19 cases and examines co-location patterns within and across groups. Significant segregation is found among different county groups. The results suggest that within-group co-location probabilities (e.g., co-location probabilities among counties with high numbers of cases) remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.
Assistant Principal Mobility and Its Relationship With Principal Turnover

Assistant principals (APs) are important education personnel, but empirical evidence about their career outcomes remains scarce. Using administrative data from Tennessee and Missouri, we provide th...
Joint Placement and Allocation of VNF Nodes With Budget and Capacity Constraints

With the advent of Network Function Virtualization (NFV), network services that traditionally run on proprietary dedicated hardware can now be realized using Virtual Network Functions (VNFs) that are hosted on general-purpose commodity hardware. This new network paradigm offers a great flexibility to Internet service providers (ISPs) for efficiently operating their networks (collecting network statistics, enforcing management policies, etc.). However, introducing NFV requires an investment to deploy VNFs at certain network nodes (called VNF-nodes), which has to account for practical constraints such as the deployment budget and the VNF-node capacity. To that end, it is important to design a joint VNF-nodes placement and capacity allocation algorithm that can maximize the total amount of network flows that are fully processed by the VNF-nodes while respecting such practical constraints. In contrast to most prior work that often neglects either the budget constraint or the capacity constraint, we explicitly consider both of them. We prove that accounting for these constraints introduces several new challenges. Specifically, we prove that the studied problem is not only NP-hard but also non-submodular. To address these challenges, we introduce a novel relaxation method such that the objective function of the relaxed placement subproblem becomes submodular. Leveraging this useful submodular property, we propose two algorithms that achieve an approximation ratio of $\frac {1}{2}(1-1/e)$ and $\frac {1}{3}(1-1/e)$ for the original non-relaxed problem, respectively. Finally, we corroborate the effectiveness of the proposed algorithms through extensive evaluations using trace-driven simulations.
Gaussian Primes in Narrow Sectors

We generalize a Theorem of Ricci and count Gaussian primes $\mathfrak{p}$ with short interval restrictions on both the norm and the argument of $\mathfrak{p}$.
Parabolic and elliptic equations with singular or degenerate coefficients: The Dirichlet problem

We consider the Dirichlet problem for a class of elliptic and parabolic equations in the upper-half space $\mathbb{R}^d_+$, where the coefficients are the product of $x_d^\alpha, \alpha \in (-\infty, 1),$ and a bounded uniformly elliptic matrix of coefficients. Thus, the coefficients are singular or degenerate near the boundary $\{x_d =0\}$ and they may not locally integrable. The novelty of the work is that we find proper weights under which the existence, uniqueness, and regularity of solutions in Sobolev spaces are established. These results appear to be the first of their kind and are new even if the coefficients are constant. They are also readily extended to systems of equations.
Nested Array-Based Spatially Coupled LDPC Codes

Linear nested codes, where two or more sub-codes are nested in a global code, have been proposed as candidates for reliable multi-terminal communication. In this article, we consider nested array-based spatially coupled low-density parity-check (SC-LDPC) codes and propose a line-counting based optimization scheme for minimizing the number of dominant absorbing sets in order to improve its performance in the high signal-to-noise ratio regime. Since the parity-check matrices of different nested sub-codes partially overlap, the optimization of one nested sub-code imposes constraints on the optimization of the other sub-codes. To tackle these constraints, a multi-step optimization process is applied first to one of the nested codes, then sequential optimization of the remaining nested codes is carried out based on the constraints imposed by the previously optimized sub-codes. Results show that the order of optimization has a significant impact on the number of dominant absorbing sets in the Tanner graph of the code, resulting in a trade-off between the performance of a nested code structure and its optimization sequence: the code which is optimized without constraints has fewer harmful structures than the code which is optimized with constraints. We also show that for certain code parameters, dominant absorbing sets in the Tanner graphs of all nested codes are completely removed using our proposed optimization strategy.
Pre-Launch Calibration of the Nasa Tropics Constellation Mission

The NASA TROPICS mission [1] hosts a high performance radiometer to estimate temperature and water vapor profiles, precipitation, and Tropical Cyclone intensity. Noise Diodes (ND) are an attractive option for calibration of small satellite sounders, especially in the face of volume constraints that preclude extensive radiative shielding of internal calibration targets that is utilized in operational missions [2]. Recent operational missions [3], [4] demonstrated on-orbit ND calibration stability better than 0.1 K over more than four years when prelaunch screening of the ND is used. An essential step for TROPICS is the precise prelaunch characterization of the ND performance over a variety of thermal conditions, which will be augmented by post-launch cross-comparisons similar to the techniques used by the GPM Intercalibration (X-CAL) Working Group. This paper introduces the first radiometric calibration and verification with the radiometer integrated on the satellite platform under flight-like conditions.
A FAUSTIAN BARGAIN THAT UNDERMINES RESEARCH PARTICIPANTS' PRIVACY RIGHTS AND RETURN OF RESULTS.

A 2018 committee report published by the highly respected National Academies of Science, Engineering, and Medicine (the Report) recommends stripping research participants of crucial data privacy rights and discarding decades of carefully deliberated consensus guidelines for the ethical return of results and data from research. This Article traces these disturbing recommendations to three root causes: (1) a statement of task that blocked careful and impartial analysis of a disputed legal matter central to the Report; (2) a piecemeal legal analysis that omitted relevant strands of law; and (3) the inappropriate conflation of two distinct concepts—the return of individual research results (the stated subject of the Report) and privacy-enabling individual access rights, which have a nearly fifty-year legal history long predating the modern debate about return of results. The Report’s recommendations would erect new barriers to the return of results and, simultaneously, dial back a core data privacy right that Americans—including many research participants—currently enjoy. We urge extreme caution in implementing this Report’s flawed recommendations. Congress has elevated the right to see one’s personal information to the status of a civil right in many different data environments. Diminishing individual access in the research context erodes its status as a right more broadly.
Synergy in Mergers and Acquisitions: Typology, Lifecycles, and Value

Value in mergers and acquisitions (M&A) derives from the synergistic combination of an acquirer and a target. We advance the existing conceptualization of synergies in three ways. First, we develop...
KMT coupling for random walk bridges

In this paper we prove an analogue of the Komlos–Major–Tusnady (KMT) embedding theorem for random walk bridges. The random bridges we consider are constructed through random walks with i.i.d jumps that are conditioned on the locations of their endpoints. We prove that such bridges can be strongly coupled to Brownian bridges of appropriate variance when the jumps are either continuous or integer valued under some mild technical assumptions on the jump distributions. Our arguments follow a similar dyadic scheme to KMT’s original proof, but they require more refined estimates and stronger assumptions necessitated by the endpoint conditioning. In particular, our result does not follow from the KMT embedding theorem, which we illustrate via a counterexample.
Combining Probability Forecasts: 60% and 60% Is 60%, but Likely and Likely Is Very Likely

How do we combine others’ probability forecasts? Prior research has shown that when advisors provide numeric probability forecasts, people typically average them (i.e., they move closer to the aver...
Lower memory oblivious (tensor) subspace embeddings with fewer random bits: Modewise methods for least squares

In this paper new general modewise Johnson--Lindenstrauss (JL) subspace embeddings are proposed that can be both generated much faster and stored more easily than traditional JL embeddings when wor...
Graph Neural Network Architecture Search for Molecular Property Prediction

Predicting the properties of a molecule from its structure is a challenging task. Recently, deep learning methods have improved the state of the art for this task because of their ability to learn useful features from the given data. By treating molecule structure as graphs, where atoms and bonds are modeled as nodes and edges, graph neural networks (GNNs) have been widely used to predict molecular properties. However, the design and development of GNNs for a given dataset rely on labor-intensive design and tuning of the network architectures. Neural architecture search (NAS) is a promising approach to discover high-performing neural network architectures automatically. To that end, we develop an NAS approach to automate the design and development of GNNs for molecular property prediction. Specifically, w e f ocus o n a utomated d evelopment of message-passing neural networks (MPNNs) to predict the molecular properties of small molecules in quantum mechanics and physical chemistry datasets from the MoleculeNet benchmark. We demonstrate the superiority of the automatically discovered MPNNs by comparing them with manually designed GNNs from the MoleculeNet benchmark. We demonstrate that customizing the architecture is critical to enhancing performance in molecular property prediction and that the proposed approach can perform customization automatically with minimal manual effort.
US Fiscal cycle and the dollar

Abstract A stronger US fiscal condition predicts a higher excess return on the dollar against foreign currencies in the following year, and more so against foreign currencies with higher dollar betas. A stronger foreign fiscal condition does not have such forecasting power. These findings can be explained by the unique role the US government debt plays as reserve assets. When the US fiscal condition deteriorates, financial intermediaries’ reserve constraint tightens and triggers a flight to the dollar, creating imbalances in capital flows and driving global risk premia that affect both the dollar and foreign currencies.
Heegaard Floer invariants of contact structures on links of surface singularities

Let a contact 3-manifold $(Y, \xi_0)$ be the link of a normal surface singularity equipped with its canonical contact structure $\xi_0$. We prove a special property of such contact 3-manifolds of "algebraic" origin: the Heegaard Floer invariant $c^+(\xi_0)\in HF^+(-Y)$ cannot lie in the image of the $U$-action on $HF^+(-Y)$. It follows that Karakurt's "height of $U$-tower" invariants are always 0 for canonical contact structures on singularity links, which contrasts the fact that the height of $U$-tower can be arbitrary for general fillable contact structures. Our proof uses the interplay between the Heegaard Floer homology and Nemethi's lattice cohomology.
Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling

We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, we use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, we adapt the ``tail-up'' model from the context of spatial statistics to the traffic network setting. We demonstrate our approach's superior performance compared to the state-of-the-art methods for both synthetic and real data.
Security Theorems via Model Theory

A model-theoretic approach can establish security theorems, which are formulas expressing authentication and non-disclosure properties of protocols. Security theorems have a special form, namely quantified implications ∀~x .(φ ⊃ ∃~y .ψ). Models (interpretations) for these formulas are skeletons, partially ordered structures consisting of a number of local protocol behaviors. Realized skeletons contain enough local sessions to explain all the behavior, when combined with some possible adversary behaviors. We show two results. (1) If φ is the antecedent of a security goal, then there is a skeleton Aφ such that, for every skeleton B, φ is satisfied in B iff there is a homomorphism from Aφ to B. (2) A protocol enforces ∀~x .(φ ⊃ ∃~y .ψ) iff every realized homomorphic image of Aφ satisfies ψ . Since the program CPSA finds the minimal realized skeletons, or “shapes,” that are homomorphic images of Aφ , if ψ holds in each of these shapes, then the goal holds.
Network Cluster-Robust Inference

Network data commonly consists of observations on a single large network. Accordingly, researchers often partition the network into clusters in order to apply cluster-robust inference methods. All existing such methods require clusters to be asymptotically independent. We show that for this requirement to hold, under certain conditions, it is necessary and sufficient for clusters to have small "conductance," which is the ratio of edge boundary size to volume. This yields a quantitative measure of cluster quality. Unfortunately, there are important classes of networks for which small-conductance clusters appear not to exist. Our simulation results show that for such networks, cluster-robust methods can exhibit substantial size distortion. Based on well-known results in spectral graph theory, we suggest using the eigenvalues of the graph Laplacian to determine the existence and number of small-conductance clusters. We also discuss the use of spectral clustering for constructing clusters in practice.
Friction-induced instability and vibration in a precision motion stage with a friction isolator:

Motion stages are widely used for precision positioning in manufacturing and metrology applications. However, they suffer from nonlinear premotion (i.e. “static”) friction, which adversely affects ...
Density of numerical sets associated to a numerical semigroup

A numerical set with Frobenius number f is a subset T of N that contains zero and max(N∖T)=f. Each numerical set has an associated semigroup A(T)={t|t+T⊆T}, which has the same Frobenius number as T...
Anti-tRNA synthetase syndrome interstitial lung disease: A single center experience.

Abstract Background Recognition of Anti-tRNA synthetase (ARS) related interstitial lung disease (ILD) is key to ensuring patients have prompt access to immunosuppressive therapies. The purpose of this retrospective cohort study was to identify factors that may delay recognition of ARS-ILD. Methods Patients seen at Vanderbilt University Medical Center between 9/17/2017-10/31/2018 were included in this observational cohort. Clinical and laboratory features were obtained via chart abstraction. Kruskal-Wallis ANOVA, Mann-Whitney U, and Fisher's exact t tests were utilized to determine statistical significance. Results Patients with ARS were found to have ILD in 51.9% of cases, which was comparable to the frequency of ILD in systemic sclerosis (59.5%). The severity of FVC reduction in ARS (53.2%) was comparable to diffuse cutaneous systemic sclerosis (56.8%, p = 0.48) and greater than dermatomyositis (66.9%, p = 0.005) or limited cutaneous systemic sclerosis (71.8%, p = 0.005). Frank honeycombing was seen with ARS antibodies but not other myositis autoantibodies. ARS patients were more likely to first present to a pulmonary provider in a tertiary care setting (53.6%), likely due to fewer extrapulmonary manifestations. Only 33% of ARS-ILD were anti-nuclear antibody, rheumatoid factor, or anti-cyclic citrullinated peptide positive. Patients with ARS-ILD had a two-fold longer median time to diagnosis compared to other myositis-ILD patients (11.0 months, IQR 8.5–43 months vs. 5.0 months, IQR 3.0–9.0 months, p = 0.003). Conclusions ARS patients without prominent extra-pulmonary manifestations are at high risk for not being recognized as having a connective tissue disease related ILD and miscategorized as usual interstitial pneumonia/idiopathic pulmonary fibrosis without comprehensive serologies.
Effective Ultrasonic Stimulation in Human Peripheral Nervous System.

Objective Low-intensity ultrasound can stimulate excitable cells in a noninvasive and targeted manner, but which parameters are effective has remained elusive. This question has been difficult to answer because differences in transducers and parametersfrequency in particularlead to profound differences in the stimulated tissue volumes. The objective of this study is to control for these differences and evaluate which ultrasound parameters are effective in stimulating excitable cells. Methods Here, we stimulated the human peripheral nervous system using a single transducer operating in a range of frequencies, and matched the stimulated volumes with an acoustic aperture. Results We found that low frequencies (300 kHz) are substantially more effective in generating tactile and nociceptive responses in humans compared to high frequencies (900 kHz). The strong effect of ultrasound frequency was observed for all pressures tested, for continuous and pulsed stimuli, and for tactile and nociceptive responses. Conclusion This prominent effect may be explained by a mechanical force associated with ultrasound. The effect is not due to heating, which would be weaker at the low frequency. Significance This controlled study reveals that ultrasonic stimulation of excitable cells is stronger at lower frequencies, which guides the choice of transducer hardware for effective ultrasonic stimulation of the peripheral nervous system in humans.
On No-Sensing Adversarial Multi-Player Multi-Armed Bandits With Collision Communications

We study the notoriously difficult no-sensing adversarial multi-player multi-armed bandits (MP-MAB) problem from a new perspective. Instead of focusing on the hardness of multiple players, we introduce a new dimension of hardness, called attackability . All adversaries can be categorized based on the attackability and we introduce Adversary-Adaptive Collision-Communication (A2C2), a family of algorithms with forced-collision communication among players. Both attackability-aware and unaware settings are studied, and information-theoretic tools of the Z-channel model and error-correction coding are utilized to address the challenge of implicit communication without collision information in an adversarial environment. For the more challenging attackability-unaware problem, we propose a simple method to estimate the attackability enabled by a novel error-detection repetition code and randomized communication for synchronization. Theoretical analysis proves that asymptotic attackability-dependent sublinear regret can be achieved, with or without knowing the attackability. In particular, the asymptotic regret does not have an exponential dependence on the number of players, revealing a fundamental tradeoff between the two dimensions of hardness in this problem.
Unsupervised learning of atomic environments from simple features

Abstract I present a strategy for unsupervised manifold learning on local atomic environments in molecular simulations based on simple rotation- and permutation-invariant three-body features. These features are highly descriptive, generalize to multiple chemical species, and are human-interpretable. The low-dimensional embeddings of each atomic environment can be used to understand and quantify messy crystal structures such as those near interfaces and defects or well-ordered crystal lattices such as in bulk materials without modification. The same method can also yield collective variables describing collections of particles such as for an entire simulation domain. I demonstrate the method on colloidal crystallization, ice crystals, and binary mesophases to illustrate its broad applicability. In each case, the learned latent space yields insights into the details of the observed microstructures. For ices and mesophases, supervised classifiers are trained based on the learned manifolds and directly compared against a recent neural-network-based approach. Notably, while this method provides comparable classification performance, it can also be deployed on even a handful of observed environments without labels or a priori knowledge. Thus, the current approach provides an incredibly versatile strategy to characterize and classify local atomic environments, and may unlock insights in a wide variety of molecular simulation contexts.
Beyond the Command: Feminist STS Research and Critical Issues for the Design of Social Machines

Machines, from artificially intelligent digital assistants to embodied robots, are becoming more pervasive in everyday life. Drawing on feminist science and technology studies (STS) perspectives, we demonstrate how machine designers are not just crafting neutral objects, but relationships between machines and humans that are entangled in human social issues such as gender and power dynamics. Thus, in order to create a more ethical and just future, the dominant assumptions currently underpinning the design of these human-machine relations must be challenged and reoriented toward relations of justice and inclusivity. This paper contributes the "social machine" as a model for technology designers who seek to recognize the importance, diversity and complexity of the social in their work, and to engage with the agential power of machines. In our model, the social machine is imagined as a potentially equitable relationship partner that has agency and as an "other" that is distinct from, yet related to, humans, objects, and animals. We critically examine and contrast our model with tendencies in robotics that consider robots as tools, human companions, animals or creatures, and/or slaves. In doing so, we demonstrate ingrained dominant assumptions about human-machine relations and reveal the challenges of radical thinking in the social machine design space. Finally, we present two design challenges based on non-anthropomorphic figuration and mutuality, and call for experimentation, unlearning dominant tendencies, and reimagining of sociotechnical futures.
Dataset on the questionnaire-based survey of the Perceived risk of COVID-19 infection and Contact lens (CL) wearers.

The data set presents data collected by an online questionnaire, applying closed-ended question types (multiple-choice, Likert scale) and questions Skip Logic. Instruments from previous studies in the field of optometry such as Contact Lens User Experience (CLUE Scales), Quality of Life Questionnaire (CLIQ), Quality of Vision (QoV) Questionnaire, did not meet the needs of this study. Therefore, in order to provide a statistically valid and consistent instrument to examine the psychopathological reactions in Contact lens wearers due to Covid-19, a new questionnaire was designed. The steps followed to collect and manage data were, definition of the objectives of the study, questionnaire design, questionnaire pilot testing (validity, reliability, repeatability), data administration and results interpretation. This descriptive survey employed online data collection using an anonymized questionnaire on "SurveyMonkey" software. The questionnaire was made available via an online link on social networks from 28/09/2020 to 11/10/2020.The questionnaire, in order to record the psychopathological reactions in Contact lens wearers due to Covid-19, was structured according to three psychopathological reactions (COVID-RS scales), a) disorganized behaviors, b) avoidant behaviors, c) maladaptive information consumption. Therefore includes, a) socio-demographic variables such as, (age, gender, educational level, profession status, geographical area of habitation), b) Health and Consumer behavior in Contact lens wearers, c) Perceived risk of infection due to Covid-19 in Contact lens users, with a total of 22 items and maximum completion time 4 minutes. The survey included 1676 participants, 1037 were Contact Lens users and completed the online questionnaire. Of them, 76.7% (795) were female and 23.3% (242) were male. The educational level of the participants were, 7.5 % (8) primary education, 18.9 % (197) Secondary education, 57.9 % (601) Undergraduate education, 22.3 % (231) Postgraduate education. According to the age groups, 68.2 % (707) participants were between 18-34 years old, 23.4 % (278) participants were between 35-54 years old, 4.8 % (50) participants were between 55-74 years old, 0.2 % (2) participants were 75 and over. All data were exported to Excel spreadsheets. Only the completed questionnaires were used in the analyses. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software version 24. Cronbach's alpha showed for the group, Health and Consumer behavior in Contact lens wearers (6 items), to reach acceptable reliability α= 0,881. Cronbach's alpha showed for the group, Perceived risk of infection due to Covid-19 in contact lens wearers (6 items), to reach acceptable reliability α= 0,886. The data set can be used to understand the relationship between the perceived risk of Covid-19 infection and contact lens (CL) use, the potential effect of the pandemic in Health and consumer behavior in Contact lens users, to draft a survey strategy and design, to verify results, etc.
Robust Budget Allocation Via Continuous Submodular Functions

The optimal allocation of resources for maximizing influence, spread of information or coverage, has gained attention in the past years, in particular in machine learning and data mining. But in applications, the parameters of the problem are rarely known exactly, and using wrong parameters can lead to undesirable outcomes. We hence revisit a continuous version of the Budget Allocation or Bipartite Influence Maximization problem introduced by Alon et al. (in: WWW’12 - Proceedings of the 21st Annual Conference on World Wide, ACM, New York, 2012) from a robust optimization perspective, where an adversary may choose the least favorable parameters within a confidence set. The resulting problem is a nonconvex–concave saddle point problem (or game). We show that this nonconvex problem can be solved exactly by leveraging connections to continuous submodular functions, and by solving a constrained submodular minimization problem. Although constrained submodular minimization is hard in general, here, we establish conditions under which such a problem can be solved to arbitrary precision $$\varepsilon $$ .
Strategic Policymaking for Implementing Renewable Portfolio Standards: A Tri-Level Optimization Approach

Appropriately designed renewable support policies can play a leading role in promoting renewable expansions and contribute to low emission goals. Meanwhile, ill-designed policies may distort electricity markets, put power utilities and generation companies on an unlevel playing field and, in turn, cause inefficiencies. This paper proposes a framework to optimize policymaking for renewable energy sources, while incorporating conflicting interests and objectives of different stakeholders. We formulate a tri-level optimization problem where each level represents a different entity: a state regulator, a power utility and a wholesale electricity market. To solve this tri-level problem, we exploit optimality conditions and develop a modification of the Column-and-Cut Generation (C&CG) algorithm that generates cuts for bilinear terms. The case study based on the ISO New England 8-zone test system reveals different policy trade-offs that policymakers face under different decarbonization goals and implementation scenarios.
A sustained type I IFN-neutrophil-IL-18 axis drives pathology during mucosal viral infection

Herpes simplex virus (HSV) is a human pathogen that causes genital herpes, an incurable disease that results in recurrent sores and inflammation. Infection with HSV induces a strong antiviral immune response, which results in large numbers of immune cells arriving at these lesions. But while some of these cells help to control viral replication, others might contribute to the inflammation that drives the disease. One of the first immune cells to respond to infection are neutrophils. Although neutrophils are generally protective, especially against bacteria and fungi, they have also been implicated in tissue damage and severe inflammation during viral infections. But what determines whether a neutrophil will help to fight off an infection or increase disease severity is still an open question. To investigate this, Lebratti, Lim et al. studied mice that had been infected with the genital herpes virus HSV-2, which is known to cause significant amounts of inflammation in mice. The experiments revealed that a signaling molecule called type I interferon, which is thought to be antiviral, causes neutrophils at the site of the infection to produce proteins, such as IL-18, which trigger an inflammatory reaction. Lebratti, Lim et al. found that type I interferon and IL-18 had shifting roles during the course of infection. In the early stages, both molecules had a protective effect, confirming results from previous studies. However, as the infection progressed, sustained levels of type I interferon signaling in neutrophils led to excess amounts of IL-18. Lebratti, Lim et al. discovered that blocking interferon signaling or decreasing the levels of IL-18 later during infection unexpectedly reduced the severity of the disease and resulted in less genital tissue damage. Further experiments also showed that mice infected with another genital herpes virus called HSV-1 did not experience sustained levels of type I interferon. This may explain why this virus causes less severe disease in mice. Understanding how the immune system reacts to viruses could reveal new targets for treatments of genital herpes. At the moment, there is little information about IL-18 production during genital herpes in humans. So, the next step is to see whether neutrophils behave in the same way and whether IL-18 can be detected during human disease. It is possible that the same immune components could promote disease in other infections too. If so, this work may help uncover new drug targets for other viral diseases.
Economic, Environmental, and Health Impacts of the Fracking Boom

The shale gas boom revolutionized the energy sector through hydraulic fracturing (fracking). High levels of energy production force communities, states, and nations to consider the externalities an...
A Linear-Time Algorithm for Discrete Radius Optimally Augmenting Paths in a Metric Space

Let P be a path graph of n vertices embedded in a metric space. We consider the problem of adding a new edge to P so that the radius of the resulting graph is minimized, where any center is constra...
Metal shell technology

Metallic shells are fabricated for a size range from several millimeters down to less than 500 microns in diameter. The technology, developed at JPL, is based on the hollow-jet instability for the large-shell formation, and in addition, on the Bernoulli's force for the microbubble generation. Various materials were processed into hollow spheres, such as AL1100, tin, lead, and a metallic glass-forming alloy, AuPbSb. They exhibited a range of contrasting solidification behaviors and surface characteristics. Some of their potential applications will be explored in this paper.
Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020.

Availability of SARS-CoV-2 testing in the United States (U.S.) has fluctuated through the course of the COVID-19 pandemic, including in the U.S. state of Illinois. Despite substantial ramp-up in test volume, access to SARS-CoV-2 testing remains limited, heterogeneous, and insufficient to control spread. We compared SARS-CoV-2 testing rates across geographic regions, over time, and by demographic characteristics (i.e., age and racial/ethnic groups) in Illinois during March through December 2020. We compared age-matched case fatality ratios and infection fatality ratios through time to estimate the fraction of SARS-CoV-2 infections that have been detected through diagnostic testing. By the end of 2020, initial geographic differences in testing rates had closed substantially. Case fatality ratios were higher in non-Hispanic Black and Hispanic/Latino populations in Illinois relative to non-Hispanic White populations, suggesting that tests were insufficient to accurately capture the true burden of COVID-19 disease in the minority populations during the initial epidemic wave. While testing disparities decreased during 2020, Hispanic/Latino populations consistently remained the least tested at 1.87 tests per 1000 population per day compared with 2.58 and 2.87 for non-Hispanic Black and non-Hispanic White populations, respectively, at the end of 2020. Despite a large expansion in testing since the beginning of the first wave of the epidemic, we estimated that over half (50–80%) of all SARS-CoV-2 infections were not detected by diagnostic testing and continued to evade surveillance. Systematic methods for identifying relatively under-tested geographic regions and demographic groups may enable policymakers to regularly monitor and evaluate the shifting landscape of diagnostic testing, allowing officials to prioritize allocation of testing resources to reduce disparities in COVID-19 burden and eventually reduce SARS-CoV-2 transmission.
Extracellular matrix protein composition dynamically changes during murine forelimb development

The extracellular matrix (ECM) is an integral part of multicellular organisms, connecting different cell layers and tissue types. During morphogenesis and growth, tissues undergo substantial reorganization involving cellular proliferation, migration, and differentiation. While it is intuitive that the ECM remodels in concert, little is known regarding how matrix composition and organization change during development. We utilized tissue fractionation and mass spectrometry to define ECM protein (matrisome) dynamics during murine forelimb development and resolved significant differences in ECM composition as a function of development, disease and tissue type. Additionally, we used bioorthogonal non-canonical amino acid tagging (BONCAT) to label newly synthesized ECM within the developing forelimb. We demonstrate the feasibility of using BONCAT to enrich for newly synthesized matrisome components and identified differences in ECM synthesis between morphogenesis and growth. This resource will guide future research investigating the role of the matrisome during complex tissue development.
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding

Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model’s capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.
Object representations in the human brain reflect the co-occurrence statistics of vision and language.

A central regularity of visual perception is the co-occurrence of objects in the natural environment. Here we use machine learning and fMRI to test the hypothesis that object co-occurrence statistics are encoded in the human visual system and elicited by the perception of individual objects. We identified low-dimensional representations that capture the latent statistical structure of object co-occurrence in real-world scenes, and we mapped these statistical representations onto voxel-wise fMRI responses during object viewing. We found that cortical responses to single objects were predicted by the statistical ensembles in which they typically occur, and that this link between objects and their visual contexts was made most strongly in parahippocampal cortex, overlapping with the anterior portion of scene-selective parahippocampal place area. In contrast, a language-based statistical model of the co-occurrence of object names in written text predicted responses in neighboring regions of object-selective visual cortex. Together, these findings show that the sensory coding of objects in the human brain reflects the latent statistics of object context in visual and linguistic experience. When people view an object, they can often guess the setting from which it was drawn and the other objects that might be found in that setting. Here the authors identify regions of the human visual system that represent this information about which objects tend to appear together in the world.
Learning Generalizable Robotic Reward Functions from “In-The-Wild” Human Videos

We are motivated by the goal of generalist robots that can complete a wide range of tasks across many environments. Critical to this is the robot’s ability to acquire some metric of task success or reward, which is necessary for reinforcement learning, planning, or knowing when to ask for help. For a general-purpose robot operating in the real world, this reward function must also be able to generalize broadly across environments, tasks, and objects, while depending only on on-board sensor observations (e.g. RGB images). While deep learning on large and diverse datasets has shown promise as a path towards such generalization in computer vision and natural language, collecting high quality datasets of robotic interaction at scale remains an open challenge. In contrast, “in-the-wild” videos of humans (e.g. YouTube) contain an extensive collection of people doing interesting tasks across a diverse range of settings. In this work, we propose a simple approach, Domain-agnostic Video Discriminator (DVD), that learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task, and can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos. We find that by leveraging diverse human datasets, this reward function (a) can generalize zero shot to unseen environments, (b) generalize zero shot to unseen tasks, and (c) can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.
What Happens If Central Banks Misdiagnose a Slowdown in Potential Output

In the last few decades, real GDP growth and investment in advanced countries have declined in tandem. This slowdown was not the result of weak demand (there has been no shift along the Okun curve), but of a decline in potential output growth (which has shifted the Okun curve to the left). We analyze what happens if central banks mistakenly diagnose the problem as insufficient demand, when it is actually a supply problem. We do this in a real model, in which inflation is not an issue. We show that aggressive central bank action may revive gross investment, but it will not revive net investment or growth. Moreover, low interest rates will lead to an increase in the capital output ratio, a low return on capital and high leverage. We show that these forecasts are in line with what has happened in major advanced countries.