cssi/SciSciGPT-SciSciNet huggingface dataset:
Epigenetic clock measuring age acceleration via DNA methylation levels in blood is associated with decreased oocyte yield

To investigate how biologic age (phenotypic age at which your body functions) greater than chronologic age, (age acceleration (AgeAccel)), correlates with oocyte yield. Thirty-nine women undergoing ovarian stimulation, inclusive of all infertility diagnoses, were included in this pilot study. Methylome analysis of peripheral blood was utilized to determine biologic age. AgeAccel was defined as biologic age > 2 years older than chronologic age. A negative binomial model was used to obtain the crude association of AgeAccel with number of oocytes. A parsimonious adjusted model for the number of oocytes was obtained using backwards selection (p < 0.05). Measures of age were negatively correlated with number of oocytes (chronological age Pearson ρ = − 0.45, biologic age Pearson ρ = − 0.46) and AMH was positively correlated with number of oocytes (Pearson ρ = 0.91). Patients with AgeAccel were noted to have lower AMH values (1.29 ng/mL vs. 2.29, respectively (p = 0.049)) and lower oocyte yield (5.50 oocytes vs. 14.50 oocytes, respectively (p = 0.0030)). A crude association of a 7-oocyte reduction in the age-accelerated group was found (− 6.9 oocytes (CI − 11.6, − 2.4)). In a model with AMH and antral follicle count, AgeAccel was associated with a statistically significant 3.3 reduction in the number of oocytes (− 3.1; 95% CI − 6.5, − 0.1; p = 0.036). In this small pilot study, AgeAccel is associated with a lower AMH and lower oocyte yield providing preliminary evidence that biologic age, specifically AgeAccel, may serve as an epigenetic biomarker to improve the ability of predictive models to assess ovarian reserve.
Cadence optimisation and exoplanetary parameter sensitivity

To achieve maximum planet yield for a given radial velocity survey, the observing strategy must be carefully considered. In particular, the adopted cadence can greatly affect the sensitivity to exoplanetary parameters such as period and eccentricity. Here we describe simulations which aim to maximise detections based upon the target parameter space of the survey.
Flight Demonstration of Unmanned Aircraft System (UAS) Traffic Management (UTM) at Technical Capability Level 3

The goal of the Unmanned Aircraft System (UAS) Traffic Management (UTM) effort at NASA is to enable access to low-altitude airspace for small UAS. This goal is being achieved partly through partnerships that NASA has developed with the FAA, other government agencies, the UAS stakeholder community, and the designated FAA UAS Test Sites. This paper reports the technical and operational capabilities demonstrated during the UTM flight demonstration, March 6 through May 30, 2018. The demonstration featured geographically diverse operations, involving FAA UAS Test Sites in Alaska, Nevada, New York, North Dakota, Texas and Virginia. The demonstration leveraged the contributions of 30 partner organizations serving as UAS service suppliers, UAS operators, and/or providers of sensors, surveillance, connectivity, and management roles. Utilizing the UTM architecture developed at NASA, the demonstration explored 11 use cases for small UAS operations to highlight UTM capabilities at what NASA calls “Technical Capability Level (TCL) 3.” TCL 3 is characterized by multiple small UAS safely operating in moderately populated areas and beyond the visual line of sight of their operators. The TCL 3 flights demonstrated the basic feasibility of such operations in the UTM environment, including USS exchanges; communication, navigation and surveillance functions; sense and avoid capabilities; and technologies and procedures to enable them.
Geographic Disease Risk

The examination of disease risk summaries across space has a long history in epidemiology and may be performed for disease mapping, cluster detection, examination of clustering, or spatial regression. Often these exercises are carried out to gain clues to etiology with respect to putative environmental exposures. The spatial nature of the data has positive and negative aspects. For disease mapping, the spatial aspect can be exploited, since one expects near-by areas to be more similar in terms of their risk. For spatial regression, the analysis is complicated by the spatial aspect because the usual independence assumption of conventional analysis methods is violated, and there may also be “confounding by location”. As in all epidemiological investigations, interpretation depends crucially on issues of data quality, in particular, errors in population and disease counts and exposure misclassification. Ecological bias, which arises when aggregate associations are assumed to apply at the level of the individual, is also an issue that requires serious consideration. Keywords: disease mapping; ecological bias; ecological correlation studies; environmental exposures; epidemiology
When Discounts Raise Costs: The Effect of Copay Coupons on Generic Utilization

Branded pharmaceutical manufacturers frequently offer "copay coupons" that insulate consumers from cost-sharing, thereby undermining insurers' ability to influence drug utilization. We study the impact of copay coupons on branded drugs first facing generic entry between 2007 and 2010. To overcome endogeneity concerns, we exploit cross-state and cross-consumer variation in coupon legality. We find that coupons increase branded sales by 60+ percent, entirely by reducing the sales of bioequivalent generics. During the five years following generic entry, we estimate that coupons increase total spending by $30 to $120 million per drug, or $700 million to $2.7 billion for our sample alone.
Sitara: Spectrum Measurement Goes Mobile Through Crowd-Sourcing

Software-defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowd-sourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address the needs of such experiments, we design and develop a compact, portable, untethered, and inexpensive SDR we call Sitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. The Sitara offers true portability: it operates up to one week on battery power, requires no external wired connections and occupies a footprint smaller than a credit card. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. Multiple Sitaras form a distributed system capable of conducting experiments in wireless networking and communication in addition to RF monitoring and sensing activities. In this paper, we describe our design, evaluate our solution, present experimental results from multi-sensor deployments and discuss the value of this system in future experimentation.
New Technologies/New Literacies: Reconstructing Education for the New Millennium

In a period of dramatic technological and social change, educators need to foster a variety of new types of literacies to make education relevant to the demands of a new millennium. My assumptions are that new technologies are altering every aspect of our society and culture, and that we need to comprehend and make use of them both to understand and transform our worlds. Introducing new literacies to empower individuals and groups traditionally excluded would require a reconstruction of education to make it more responsive to the challenges of a democratic and multicultural society. This project would involve a strong emphasis on print literacy, combined with development of media literacy, computer and information literacies, and an expanded concept of multiple literacies to help reconstruct and democratize education and society.
Identifiability in phylogenetics using algebraic matroids

Abstract Identifiability is a crucial property for a statistical model since distributions in the model uniquely determine the parameters that produce them. In phylogenetics, the identifiability of the tree parameter is of particular interest since it means that phylogenetic models can be used to infer evolutionary histories from data. In this paper we introduce a new computational strategy for proving the identifiability of discrete parameters in algebraic statistical models that uses algebraic matroids naturally associated to the models. We then use this algorithm to prove that the tree parameters are generically identifiable for 2-tree CFN and K3P mixtures. We also show that the k-cycle phylogenetic network parameter is identifiable under the K2P and K3P models.
The Berenstein–Zelevinsky quantum cluster algebra conjecture

We prove the Berenstein-Zelevinsky conjecture that the quantized coordinate rings of the double Bruhat cells of all finite dimensional simple algebraic groups admit quantum cluster algebra structures with initial seeds as specified by [4]. We furthermore prove that the corresponding upper quantum cluster algebras coincide with the constructed quantum cluster algebras and exhibit a large number of explicit quantum seeds. Along the way a detailed study of the properties of quantum double Bruhat cells from the viewpoint of noncommutative UFDs is carried out and a quantum analog of the Fomin-Zelevinsky twist map is constructed and investigated for all double Bruhat cells. The results are valid over base fields of arbitrary characteristic and the deformation parameter is only assumed to be a non-root of unity.
Good News and Bad News About Incentives to Violate the Health Insurance Portability and Accountability Act (HIPAA): Scenario-Based Questionnaire Study.

Background: The health care industry has more insider breaches than any other industry. Soon-to-be graduates are the trusted insiders of tomorrow, and their knowledge can be used to compromise organizational security systems. Objective: The objective of this paper was to identify the role that monetary incentives play in violating the Health Insurance Portability and Accountability Act’s (HIPAA) regulations and privacy laws by the next generation of employees. The research model was developed using the economics of crime literature and rational choice theory. The primary research question was whether higher perceptions of being apprehended for violating HIPAA regulations were related to higher requirements for monetary incentives. Methods: Five scenarios were developed to determine if monetary incentives could be used to influence subjects to illegally obtain health care information and to release that information to individuals and media outlets. The subjects were also asked about the probability of getting caught for violating HIPAA laws. Correlation analysis was used to determine whether higher perceptions of being apprehended for violating HIPAA regulations were related to higher requirements for monetary incentives. Results: Many of the subjects believed there was a high probability of being caught. Nevertheless, many of them could be incentivized to violate HIPAA laws. In the nursing scenario, 45.9% (240/523) of the participants indicated that there is a price, ranging from US $1000 to over US $10 million, that is acceptable for violating HIPAA laws. In the doctors’ scenario, 35.4% (185/523) of the participants indicated that there is a price, ranging from US $1000 to over US $10 million, for violating HIPAA laws. In the insurance agent scenario, 45.1% (236/523) of the participants indicated that there is a price, ranging from US $1000 to over US $10 million, for violating HIPAA laws. When a personal context is involved, the percentages substantially increase. In the scenario where an experimental treatment for the subject’s mother is needed, which is not covered by insurance, 78.4% (410/523) of the participants would accept US $100,000 from a media outlet for the medical records of a politician. In the scenario where US $50,000 is needed to obtain medical records about a famous reality star to help a friend in need of emergency medical transportation, 64.6% (338/523) of the participants would accept the money. Conclusions: A key finding of this study is that individuals perceiving a high probability of being caught are less likely to release private information. However, when the personal context involves a friend or family member, such as a mother, they will probably succumb to the incentive, regardless of the probability of being caught. The key to reducing noncompliance will be to implement organizational procedures and constantly monitor and develop educational and training programs to encourage HIPAA compliance.
An experimental investigation of silicon wafer surface roughness and its effect on the full strength of plated metals

Plated silicon wafers with surface roughness ranging from 0.4 to 130 microinches were subjected to tensile pull strength tests. Electroless Ni/electroless Cu/electroplated Cu and electroless Ni/electroplated Cu were the two types of plate contacts tested. It was found that smoother surfaces had higher pull strength than rougher, chemically etched surfaces. The presence of the electroless Cu layer was found to be important to adhesion. The mode of fracture of the contact as it left the silicon was studied, and it was found that in almost all cases separation was due to fracture of the bulk silicon phase. The correlation between surface roughness and mode of contact failure is presented and interpreted.
Acoustically Driving the Single-Quantum Spin Transition of Diamond Nitrogen-Vacancy Centers

Using a high quality factor 3 GHz bulk acoustic wave resonator device, we demonstrate the acoustically driven single quantum spin transition ($|{m}_{s}=0⟩\ensuremath{\leftrightarrow}|\ifmmode\pm\else\textpm\fi{}1⟩$) for diamond nitrogen-vacancy (N-$V$) centers and characterize the corresponding stress susceptibility. A key challenge is to disentangle the unintentional magnetic driving field generated by the device current from the intentional stress driving within the device. We quantify these driving fields independently using Rabi spectroscopy before studying the more complicated case in which both are resonant with the single quantum spin transition. By building an equivalent circuit model to describe the device's current and mechanical dynamics, we quantitatively model the experiment to establish their relative contributions and compare with our results. We find that the stress susceptibility of the N-$V$ center spin single quantum transition is around $\sqrt{2}(0.5\ifmmode\pm\else\textpm\fi{}0.2)$ times that for double quantum transition ($|+1⟩\ensuremath{\leftrightarrow}|\ensuremath{-}1⟩$). Although acoustic driving in the double quantum basis is valuable for quantum-enhanced sensing applications, double quantum driving lacks the ability to manipulate N-$V$ center spins out of the $|{m}_{s}=0⟩$ initialization state. Our results demonstrate that efficient all-acoustic quantum control over N-$V$ centers is possible, and is especially promising for sensing applications that benefit from the compact footprint and location selectivity of acoustic devices.
A New Dataset for Natural Language Inference from Code-mixed Conversations

Natural Language Inference (NLI) is the task of inferring the logical relationship, typically entailment or contradiction, between a premise and hypothesis. Code-mixing is the use of more than one language in the same conversation or utterance, and is prevalent in multilingual communities all over the world. In this paper, we present the first dataset for code-mixed NLI, in which both the premises and hypotheses are in code-mixed Hindi-English. We use data from Hindi movies (Bollywood) as premises, and crowd-source hypotheses from Hindi-English bilinguals. We conduct a pilot annotation study and describe the final annotation protocol based on observations from the pilot. Currently, the data collected consists of 400 premises in the form of code-mixed conversation snippets and 2240 code-mixed hypotheses. We conduct an extensive analysis to infer the linguistic phenomena commonly observed in the dataset obtained. We evaluate the dataset using a standard mBERT-based pipeline for NLI and report results.
Predict and Match: Prophet Inequalities with Uncertain Supply

We consider the problem of selling perishable items to a stream of buyers in order to maximize social welfare. A seller starts with a set of identical items, and each arriving buyer wants any one item, and has a valuation drawn i.i.d. from a known distribution. Each item, however, disappears after an a priori unknown amount of time that we term the horizon for that item. The seller knows the (possibly different) distribution of the horizon for each item, but not its realization till the item actually disappears. As with the classic prophet inequalities, the goal is to design an online pricing scheme that competes with the prophet that knows the horizon and extracts full social surplus (or welfare). Our main results are for the setting where items have independent horizon distributions satisfying the monotone-hazard-rate (MHR) condition. Here, for any number of items, we achieve a constant-competitive bound via a conceptually simple policy that balances the rate at which buyers are accepted with the rate at which items are removed from the system. We implement this policy via a novel technique of matching via probabilistically simulating departures of the items at future times. Moreover, for a single item and MHR horizon distribution with mean, we show a tight result: There is a fixed pricing scheme that has competitive ratio at most 2 - 1/μ, and this is the best achievable in this class. We further show that our results are best possible. First, we show that the competitive ratio is unbounded without the MHR assumption even for one item. Further, even when the horizon distributions are i.i.d. MHR and the number of items becomes large, the competitive ratio of any policy is lower bounded by a constant greater than 1, which is in sharp contrast to the setting with identical deterministic horizons.
Hypernetwork science via high-order hypergraph walks

We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in hypergraphs is quantitative, yielding hypergraph walks with both length and width. Graph methods which then generalize to hypergraphs include connected component analyses, graph distance-based metrics such as closeness centrality, and motif-based measures such as clustering coefficients. We apply high-order analogs of these methods to real world hypernetworks, and show they reveal nuanced and interpretable structure that cannot be detected by graph-based methods. Lastly, we apply three generative models to the data and find that basic hypergraph properties, such as density and degree distributions, do not necessarily control these new structural measurements. Our work demonstrates how analyses of hypergraph-structured data are richer when utilizing tools tailored to capture hypergraph-native phenomena, and suggests one possible avenue towards that end.
Impact of beef consumption on saturated fat intake in the United States adult population: Insights from modeling the influences of bovine genetics and nutrition.

Abstract We sought to determine the impact of breed and finishing ration that reduces the saturated fat (SFA) content of beef on SFA intake (%E) in adults. Using National Health and Nutrition Examination Surveys (2001–2016), we replaced the current fatty acid profile of beef with that from two breeds (Angus, Wagyu) and three finishing rations (pasture, 15% flaxseed supplementation, 35% wet distiller's grain (WDG) supplementation). Dietary replacement levels in the model were 10%, 25%, 50%, 100%. Overall, men consumed more beef fat than women (12.0 g (11.6–12.4 g, 95%CI) and 6.6 g (6.4–6.9 g, 95%CI), respectively). The contribution of beef fat to SFA intake was 2.1%E (2.1–2.2%E, 95%CI) in men and 1.6%E (1.6–1.7%E, 95%CI) in women. SFA intake decreased with each increased replacement level for all beef types. At 100% replacement, SFA intake decreased 0.5% (Angus), 2.8% (Wagyu), 1.9% (pasture), 4.1% (flaxseed), 2.6% (WDG). Our findings demonstrate that breed and finishing ration that reduces the SFA content of beef can decrease population-level SFA intake.
Combining No-regret and Q-learning

Counterfactual Regret Minimization (CFR) has found success in settings like poker which have both terminal states and perfect recall. We seek to understand how to relax these requirements. As a first step, we introduce a simple algorithm, local no-regret learning (LONR), which uses a Q-learning-like update rule to allow learning without terminal states or perfect recall. We prove its convergence for the basic case of MDPs (where Q-learning already suffices), as well as limited extensions of them. With a straightforward modification, we extend the basic premise of LONR to work in multi-agent settings and present empirical results showing that it achieves last iterate convergence in a number of settings. Most notably, we show this for NoSDE games, a class of Markov games specifically designed to be impossible for Q-value-based methods to learn and where no prior algorithm is known to achieve convergence to a stationary equilibrium even on average. Furthermore, by leveraging last iterate converging no-regret algorithms (one of which we introduce), we show empirical last iterate convergence in all domains tested with LONR.
Minimizing Interference and Selection Bias in Network Experiment Design

Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can "spill over" from treatment nodes to control nodes and lead to biased causal effect estimation. Prominent methods for network experiment design rely on two-stage randomization, in which sparsely-connected clusters are identified and cluster randomization dictates the node assignment to treatment and control. Here, we show that cluster randomization does not ensure sufficient node randomization and it can lead to selection bias in which treatment and control nodes represent different populations of users. To address this problem, we propose a principled framework for network experiment design which jointly minimizes interference and selection bias. We introduce the concepts of edge spillover probability and cluster matching and demonstrate their importance for designing network A/B testing. Our experiments on a number of real-world datasets show that our proposed framework leads to significantly lower error in causal effect estimation than existing solutions.
Dropout as an Implicit Gating Mechanism For Continual Learning

In recent years, neural networks have demonstrated an outstanding ability to achieve complex learning tasks across various domains. However, they suffer from the "catastrophic forgetting" problem when they face a sequence of learning tasks, where they forget the old ones as they learn new tasks. This problem is also highly related to the "stability-plasticity dilemma". The more plastic the network, the easier it can learn new tasks, but the faster it also forgets previous ones. Conversely, a stable network cannot learn new tasks as fast as a very plastic network. However, it is more reliable to preserve the knowledge it has learned from the previous tasks. Several solutions have been proposed to overcome the forgetting problem by making the neural network parameters more stable, and some of them have mentioned the significance of dropout in continual learning. However, their relationship has not been sufficiently studied yet. In this paper, we investigate this relationship and show that a stable network with dropout learns a gating mechanism such that for different tasks, different paths of the network are active. Our experiments show that the stability achieved by this implicit gating plays a very critical role in leading to performance comparable to or better than other involved continual learning algorithms to overcome catastrophic forgetting. 1
Ramification in The Inverse Galois Problem

Abstract This paper focuses on a refinement of the inverse Galois problem. We explore what finite groups appear as the Galois group of an extension of the rational numbers in which only a predetermined set of primes may ramify. After presenting new results regarding extensions in which only a single finite prime ramifies, we move on to studying the more complex situation in which multiple primes from a finite set of arbitrary size may ramify. We then continue by examining a conjecture of Harbater that the minimal number of generators of the Galois group of a tame, Galois extension of the rational numbers is bounded above by the sum of a constant and the logarithm of the product of the ramified primes. We prove the validity of Harbater's conjecture in a number of cases, including the situation where we restrict our attention to finite groups containing a nilpotent subgroup of index 1 , 2 , or 3. We also derive some consequences that are implied by the truth of this conjecture.
Ramsey, Paper, Scissors

We introduce a graph Ramsey game called Ramsey, Paper, Scissors. This game has two players, Proposer and Decider. Starting from an empty graph on $n$ vertices, on each turn Proposer proposes a potential edge and Decider simultaneously decides (without knowing Proposer's choice) whether to add it to the graph. Proposer cannot propose an edge which would create a triangle in the graph. The game ends when Proposer has no legal moves remaining, and Proposer wins if the final graph has independence number at least $s$. We prove a threshold phenomenon exists for this game by exhibiting randomized strategies for both players that are optimal up to constants. Namely, there exist constants $0 B\sqrt{n}\log{n}$. This is a factor of $\Theta(\sqrt{\log{n}})$ larger than the lower bound coming from the off-diagonal Ramsey number $r(3,s)$.
On the Credibility of Information Flows in Real-Time Wireless Networks

This paper considers a wireless network where multiple flows are delivering status updates about their respective information sources. An end user aims to make accurate real-time estimations about the status of each information source using its received packets. As the accuracy of estimation is most impacted by events in the recent past, we propose to measure the credibility of an information flow by the number of timely deliveries in a window of the recent past, and say that a flow suffers from a loss-of-credibility (LoC) if this number is insufficient for the end user to make an accurate estimation. We then study the problem of minimizing the system-wide LoC in wireless networks where each flow has different requirement and link quality. We show that the problem of minimizing the system-wide LoC requires the control of temporal variance of timely deliveries for each flow. This feature makes our problem significantly different from other optimization problems that only involves the average of control variables. Surprisingly, we show that there exists a simple online scheduling algorithm that is near-optimal. Simulation results show that our proposed algorithm is significantly better than other state-of-the-art policies.
The Hodge Laplacian on axisymmetric domains and its discretization

We study the mixed formulation of the abstract Hodge Laplacian on axisymmetric domains with general data through Fourer-finite-element-methods in weighted functions spaces. Closed Hilbert complexes and commuting projectors are used through a family of finite element spaces recently introduced for general axisymmetric problems. In order to get stability results and error estimates for the discrete mixed formulation, we construct commuting projectors that can be applied to functions with low regularity.
Differentially Private Controller Synthesis With Metric Temporal Logic Specifications

Privacy is an important concern in various multi-agent systems in which data collected from the agents are sensitive. We propose a differentially private controller synthesis approach for multi-agent systems subject to high-level specifications expressed in metric temporal logic (MTL). We consider a setting where each agent sends data to a cloud (computing station) through a set of local hubs and the cloud is responsible for computing the control inputs of the agents. Specifically, each agent adds privacy noise (e.g., Gaussian noise) point-wise in time to its own outputs before sharing them with a local hub. Each local hub runs a Kalman filter to estimate the state of the corresponding agent and periodically sends such state estimates to the cloud. The cloud computes the optimal inputs for each agent subject to an MTL specification. While guaranteeing differential privacy of each agent, the controller is also synthesized to ensure a probabilistic guarantee for satisfying the MTL specification. We provide an implementation of the proposed method on a simulation case study with two Baxter-On-Wheels robots as the agents.
Pediatric pulmonology year in review 2019: Physiology

Pulmonary physiologic assessments are critical for the care and study of pediatric respiratory disease. In 2019, there were numerous contributions to this topic in Pediatric Pulmonology.
13C Electron Nuclear Double Resonance Spectroscopy Shows Acetyl-CoA Synthase Binds Two Substrate CO in Multiple Binding Modes and Reveals the Importance of a CO-Binding "alcove"

EPR and Electron Nuclear Double Resonance spectroscopies here characterize CO binding to the active-site A cluster of wild-type (WT) Acetyl-CoA Synthase (ACS) and two variants, F229W and F229A. The...
Survey of millimeter wave antennas

An overview over the area of mm-wave antennas is presented. For the purpose of the review, mm-wave radiating structures are grouped into two classes, i.e., antennas of conventional configuration and antennas based on new design concepts.
Understanding Tax Policy: How Do People Reason?

I study how people understand, reason, and learn about tax policy. The goal is to uncover the mental models that people use to think about income and estate taxes. To that end, I run large-scale online surveys and experiments on representative U.S. samples to elicit not only respondents' factual knowledge about tax policy and the income or wealth distributions, but also their understanding of the mechanisms of tax policy and their reasoning about it. The detailed survey questions are designed to address the three main factors emphasized in our core tax model that can shape support for or opposition to taxes: efficiency effects, distributional implications, and fairness considerations. But they also elicit broader concerns that could influence policy views, such as misperceptions, views of government, perceived spillovers from taxes, and views on how tax revenues are or should be spent. I decompose policy views into the various underlying factors and find that support for tax policy is most strongly correlated with views on the benefits of redistribution and fairness, as well as with views of the government. Efficiency concerns play a more minor role. These correlational patterns are confirmed by the experimental approach, which shows people instructional videos that explain the workings and consequences of one of the aspects of tax policy (the "Redistribution" and the "Efficiency" treatments) or that bring the two together and focus on the trade-off (the "Economist" treatment). The Redistribution treatment and Economist treatments significantly increase support for more progressive taxes. I also find that there are partisan divergences not just in the final policy views, but also at every step of the reasoning about the underlying mechanisms of taxes, and most starkly on the fairness considerations.
Authente-Kente: Enabling Authentication for Artisanal Economies with Deep Learning

The economy for artisanal products, such as Navajo rugs or Pashmina shawls are often threatened by mass-produced fakes. We propose the use of AI-based authentication as one part of a larger system that would replace extractive economies with generative circulation. In this case study we examine initial experiments towards the development of a cell phone-based authentication app for kente cloth in West Africa. We describe the context of weavers and cloth sales; an initial test of a machine learning algorithm for distinguishing between real and fake kente, and an outline of the next stages of development.
The Creation and Validation of Load Time Series for Synthetic Electric Power Systems

Synthetic power systems that imitate functional and statistical characteristics of the actual grid have been developed to promote researchers’ access to public system models. Developing time series to represent different operating conditions of these synthetic systems will expand the potential of synthetic power systems applications. This paper proposes a methodology to create synthetic time series of bus-level load using publicly available data. Comprehensive validation metrics are provided to assure that the quality of synthetic time series data is sufficiently realistic. This paper also includes an example application in which the methodology is used to construct load scenarios for a 10,000-bus synthetic case.
Free Speech on Campus: A New Forum-Based Approach:

Students and faculty at public colleges and universities frequently find themselves in conflict with administrators over questions of free speech. Unfortunately, the courts have provided precious l...
Demonstrating NISQ era challenges in algorithm design on IBM's 20 qubit quantum computer

As superconducting qubits continue to advance technologically, the realization of quantum algorithms from theoretical abstraction to physical implementation requires knowledge of both quantum circuit construction as well as hardware limitations. In this study, we present results from experiments run on IBM’s 20-qubit “Poughkeepsie” architecture, with the goal of demonstrating various qubit qualities and challenges that arise in designing quantum algorithms. These include experimentally measuring T1 and T2 coherence times, gate fidelities, sequential CNOT gates, techniques for handling ancilla qubits, and finally CCNOT and QFT† circuits implemented on several different qubit geometries. Our results demonstrate various techniques for improving quantum circuits, which must compensate for limited connectivity, either through the use of SWAP gates or additional ancilla qubits.
Neural Approaches to Conversational AI.

This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.
Human Interface for Teleoperated Object Manipulation with a Soft Growing Robot

Soft growing robots are proposed for use in applications such as complex manipulation tasks or navigation in disaster scenarios. Safe interaction and ease of production promote the usage of this technology, but soft robots can be challenging to teleoperate due to their unique degrees of freedom. In this paper, we propose a human-centered interface that allows users to teleoperate a soft growing robot for manipulation tasks using arm movements. A study was conducted to assess the intuitiveness of the interface and the performance of our soft robot, involving a pick-and-place manipulation task. The results show that users were able to complete the task 97% of the time and achieve placement errors below 2 cm on average. These results demonstrate that our body-movement-based interface is an effective method for control of a soft growing robot manipulator.
The KW equations and the Nahm pole boundary condition with knots

It is conjectured that the coefficients of the Jones polynomial can be computed by counting solutions of the KW equations on a four-dimensional half-space, with certain boundary conditions that depend on a knot. The boundary conditions are defined by a "Nahm pole" away from the knot with a further singularity along the knot. In a previous paper, we gave a precise formulation of the Nahm pole boundary condition in the absence of knots, in the present paper, we do this in the more general case with knots included. We show that the KW equations with generalized Nahm pole boundary conditions are elliptic, and that the solutions are polyhomogeneous near the boundary and near the knot, with exponents determined by solutions of appropriate indicial equations. This involves the analysis of a "depth two incomplete iterated edge operator." As in our previous paper, a key ingredient in the analysis is a convenient new Weitzenb\"ock formula that is well-adapted to the specific problem.}
Estimation Rates for Sparse Linear Cyclic Causal Models.

Causal models are important tools to understand complex phenomena and predict the outcome of controlled experiments, also known as interventions. In this work, we present statistical rates of estimation for linear cyclic causal models under the assumption of homoscedastic Gaussian noise by analyzing both the LLC estimator introduced by Hyttinen, Eberhardt and Hoyer and a novel two-step penalized maximum likelihood estimator. We establish asymptotic near minimax optimality for the maximum likelihood estimator over a class of sparse causal graphs in the case of near-optimally chosen interventions. Moreover, we find evidence for practical advantages of this estimator compared to LLC in synthetic numerical experiments.
A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters.

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and me...
Entry of Platforms into Complementary Hardware Access Product Markets

This paper analyzes the entry of digital platforms such as Google into complementary hardware markets (e.g., internet service provision) using a game-theoretic model.
A Note on Branch Flow Models With Line Shunts

When the shunt elements in the $\Pi$ circuit line model are assumed zero, it has been proved that branch flow models are equivalent to bus injection models and that the second-order cone relaxation of optimal power flow problems on a radial network is exact under certain conditions. In this note we propose a branch flow model that includes nonzero line shunts and prove that the equivalence and the exactness of relaxation continue to hold under essentially the same conditions as for zero shunt elements.
Comparison of HLA ligand elution data and binding predictions reveals varying prediction performance for the multiple motifs recognized by HLA‐DQ2.5

Binding prediction tools are commonly used to identify peptides presented on MHC class II molecules. Recently, a wealth of data in the form of naturally eluted ligands has become available and discrepancies between ligand elution data and binding predictions have been reported. Quantitative metrics for such comparisons are currently lacking. In this study, we assessed how efficiently MHC class II binding predictions can identify naturally eluted peptides, and investigated instances with discrepancies between the two methods in detail. We found that, in general, MHC class II eluted ligands are predicted to bind to their reported restriction element with high affinity. But, for several studies reporting an increased number of ligands that were not predicted to bind, we found that the reported MHC restriction was ambiguous. Additional analyses determined that most of the ligands predicted to not bind, are predicted to bind other co-expressed MHC class II molecules. For selected alleles, we addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQA1*05:01/DQB1*02:01 (DQ2.5) however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively. These findings have important implications for the interpretation of ligand elution data and for the improvement of MHC class II binding predictions.
Tuberculosis, human rights, and law reform: Addressing the lack of progress in the global tuberculosis response.

In 2018, the United Nations General Assembly convened the first-ever high-level meeting (HLM) on tuberculosis (TB). Since that time news on the world’s most lethal infectious disease is not good—the 2019 WHO TB report shows 1.2 million people died from TB, a number that has fallen just 11% since 2015, less than one-third of the way towards the End TB Strategy milestone of a 35% reduction (to about 850 million deaths) by 2020. The same number of people, 10.0 million, are estimated to have fallen ill with TB in 2018 as in 2017. The stubborn persistence of TB is attributable to glaring gaps in case detection and treatment. While case detection has increased in recent years, there is still a significant gap between the 7 million new cases reported and the 10 million incident cases estimated in the most recent WHO data—and treatment success continues to hover at only 85%. There has long been a call for a shift toward a “human rights-based approach” in TB—building policies and programs explicitly on the norms and values set out in international human rights law, treating people with TB as rights holders in their interaction with the state and the health system, and working to overcome stigma and discrimination. Yet laws in high TB-burden countries (HBCs) have not been reformed to include basic rights protections. This helps explain the lack of progress, as individuals who fear discrimination and coercion may avoid diagnosis and treatment. Thus, TB stands in stark contrast to HIV/AIDS, for which the international community has moved far more decisively to recognize and protect key rights, even as more progress is needed. At the UN, heads of state committed to removing discriminatory laws and policies against people with TB, protecting and promoting their human rights and dignity, as a key part of a strategy to improve the TB response and end the epidemic. Here, we analyze key areas of law with particular importance for TB, focusing on five aspects of compulsory public health powers in law and on laws related to migration. We identify significant gaps between core human rights norms and existing legal environments.
Gaussian Multiple and Random Access in the Finite Blocklength Regime

This paper presents finite-blocklength achievability bounds for the Gaussian multiple access channel (MAC) and random access channel (RAC) under average-error and maximal-power constraints. Using random codewords uniformly distributed on a sphere and a maximum likelihood decoder, the derived MAC bound on each transmitter's rate matches the MolavianJazi-Laneman bound (2015) in its first- and second- order terms, improving the remaining terms to $\frac{1}{2}\frac{{\log n}}{n} + O\left( {\frac{1}{n}} \right)$ bits per channel use. The result then extends to a RAC model in which neither the encoders nor the decoder knows which of K possible transmitters are active. In the proposed rateless coding strategy, decoding occurs at a time n t that depends on the decoder's estimate t of the number of active transmitters k. Single-bit feedback from the decoder to all encoders at each potential decoding time n i , i ≤ t, informs the encoders when to stop transmitting. For this RAC model, the proposed code achieves the same first-, second-, and third-order performance as the best known result for the Gaussian MAC in operation.
Optimization of the design of superconducting inhomogeneous nanowires

We study the optimization of the superconducting properties of inhomogeneous nanowires. The main goal of this research is to find an optimized geometry that allows one to maximize the desired property of superconductors, such as the maximum value of the local superconducting gap or total condensation energy. We consider the axially symmetric design of multilayered nanowires with the possibility of adjusting and changing the layer thickness. We use numerical solution of the Bogoliubov–de Gennes equations to obtain the local superconducting gap for different arrangements of inhomogeneous structures. The values of the optimized properties can be up to 300% greater than for a non-optimized geometry. The optimized configuration of multilayers strongly depends on the desired property one wants to optimize and on the number of layers in the nanowire.
Prediction, Retrodiction, and the Amount of Information Stored in the Present

We introduce an ambidextrous view of stochastic dynamical systems, comparing their forward-time and reverse-time representations and then integrating them into a single time-symmetric representation. The perspective is useful theoretically, computationally, and conceptually. Mathematically, we prove that the excess entropy—a familiar measure of organization in complex systems—is the mutual information not only between the past and future, but also between the predictive and retrodictive causal states. Practically, we exploit the connection between prediction and retrodiction to directly calculate the excess entropy. Conceptually, these lead one to discover new system measures for stochastic dynamical systems: crypticity (information accessibility) and causal irreversibility. Ultimately, we introduce a time-symmetric representation that unifies all of these quantities, compressing the two directional representations into one. The resulting compression offers a new conception of the amount of information stored in the present.
Higher topological charge and the QCD vacuum

The authors show that gauge field configurations with higher topological charge modify the structure of the QCD vacuum, which is reflected in its dependence on the CP-violating topological phase $\ensuremath{\theta}$.
Momentum of charge-magnetic coil systems

Solenoids and toroidal solenoids exposed to an electric field have been thought to contain hidden momentum. Here I show that there is no hidden momentum in these systems.
Closed Geodesics in Alexandrov Spaces of Curvature Bounded from Above

In this paper, we show a local energy convexity of W 1,2 maps into CAT(K) spaces. This energy convexity allows us to extend Colding and Minicozzi’s width-sweepout construction to produce closed geodesics in any closed Alexandrov space of curvature bounded from above, which also provides a generalized version of the Birkhoff-Lyusternik theorem on the existence of non-trivial closed geodesics in the Alexandrov setting.