cssi/SciSciGPT-SciSciNet huggingface dataset:
Strategic Analysis of Auctions

In many markets, transaction prices are determined in auctions. In the most common form, prospective buyers compete by submitting bids to a seller. Each bid is an offer to buy that states a quantity and a maximum price. The seller then allocates the available supply among those offering the highest prices exceeding the seller’s asking price. The actual price paid by a successful bidder depends on a pricing rule, usually selected by the seller: two common pricing rules are that each successful bidder pays the price bid; or they all pay the same price, usually the highest rejected bid or the lowest accepted bid. Auctions have been used for millennia, and remain the simplest and most familiar means of price determination for multilateral trading without intermediary ‘market makers’ such as brokers and specialists. Their trading procedures, which simply process bids and offers, are direct extensions of the usual forms of bilateral bargaining. Auctions also implement directly the demand submission procedures used in Walrasian models of markets. They therefore have prominent roles in the theory of exchange and in studies of the effects of economic institutions on the volume and terms of trade. Their allocative efficiency in many contexts ensures their continued prominence in economic theory. They are also favored in experimental designs investigating the predictive power of economic theories. Auctions are apt subjects for applications of game theory because they present explicit trading rules that largely fix the ‘rules of the game’. Moreover, they present substantive problems of strategic behavior of practical importance. They are particularly valuable as illustrations of games of incomplete information because bidders’ private information is the main factor affecting strategic behavior. The simpler forms of auctions induce normal-form games that are essentially ‘solved’ by applying directly the basic
Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback.

Many real-world problems like Social Influence Maximization face the dilemma of choosing the best $K$ out of $N$ options at a given time instant. This setup can be modeled as a combinatorial bandit which chooses $K$ out of $N$ arms at each time, with an aim to achieve an efficient trade-off between exploration and exploitation. This is the first work for combinatorial bandits where the feedback received can be a non-linear function of the chosen $K$ arms. The direct use of multi-armed bandit requires choosing among $N$-choose-$K$ options making the state space large. In this paper, we present a novel algorithm which is computationally efficient and the storage is linear in $N$. The proposed algorithm is a divide-and-conquer based strategy, that we call CMAB-SM. Further, the proposed algorithm achieves a \textit{regret bound} of $\tilde O(K^{\frac{1}{2}}N^{\frac{1}{3}}T^{\frac{2}{3}})$ for a time horizon $T$, which is \textit{sub-linear} in all parameters $T$, $N$, and $K$. %When applied to the problem of Social Influence Maximization, the performance of the proposed algorithm surpasses the UCB algorithm and some more sophisticated domain-specific methods.
Do the basis and other predictors of futures return also predict spot return with the same signs and magnitudes? Evidence from the LME

Abstract The LME is a major international commodity exchange for industrial metals. Both futures contracts and spot metals are traded there, and the textbook principle of the futures-spot convergence precisely holds there. In this paper, we formulate two claims about spot and futures return prediction. First, the predictors of futures price return should also predict spot price return with the same signs and magnitudes under certain conditions. Second, the futures-spot basis is an exception. These claims lead to testable hypotheses, and provide theory-based restrictions for the coefficients of spot and futures return regressions. We investigate six industrial metals and find empirical support for our hypotheses. The in-sample and out-of-sample evidence shows that financial variables, proxies for global economic activities, and the basis predict futures and spot price returns consistently with our hypotheses. Furthermore, our out-of-sample trading exercises document economic significance of the restrictions.
Power Laws Used to Extrapolate the Coupled Cluster Correlation Energy to the Thermodynamic Limit.

Recent calculations using coupled cluster on solids have raised the discussion of using a N-1/3 power law to fit the correlation energy when extrapolating to the thermodynamic limit, an approach which differs from the more commonly used N-1 power law, which is, for example, often used by quantum Monte Carlo methods. In this paper, we present one way to reconcile these viewpoints. Coupled cluster doubles calculations were performed on uniform electron gases reaching system sizes of 922 electrons for an extremely wide range of densities (0.1 < rs < 100.0) to study how the correlation energy approaches the thermodynamic limit. The data were corrected for the basis set incompleteness error and use a selected twist angle approach to mitigate the finite size error from shell filling effects. Analyzing these data, we initially find that a power law of N-1/3 appears to fit the data better than a N-1 power law in the large system size limit. However, we provide an analysis of the transition structure factor showing that N-1 still applies to large system sizes and that the apparent N-1/3 power law occurs only at low N.
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers

We propose a new framework for reasoning about generalization in deep learning. The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error into: (1) the Ideal World test error plus (2) the gap between the two worlds. If the gap (2) is universally small, this reduces the problem of generalization in offline learning to the problem of optimization in online learning. We then give empirical evidence that this gap between worlds can be small in realistic deep learning settings, in particular supervised image classification. For example, CNNs generalize better than MLPs on image distributions in the Real World, but this is "because" they optimize faster on the population loss in the Ideal World. This suggests our framework is a useful tool for understanding generalization in deep learning, and lays a foundation for future research in the area.
The Role of Peer Events in Corporate Governance: Evidence from Data Breaches

Economic theory suggests that negative peer events can result in market-wide spillovers which help unaffected firms take real actions to enhance corporate governance. Motivated by the SEC’s concern about cybersecurity, I study the role of peer events in corporate governance using the setting of data breaches. While controlling for firm-specific time-varying unobservable characteristics, I find that peer data breaches are associated with a reduction in future internal control material weaknesses for non-breached firms. The association is robust to a changes analysis and varies cross-sectionally with breach, firm, and board characteristics. Inferences remain consistent when studying IT-related material weaknesses only. Finally, non-breached firms are more likely to have a cybersecurity expert on the top management team after a peer breach. My findings have important implications for mandatory disclosure regulation in general and, in particular, suggest that regulators can help reduce market-wide exposure to cyber risk by facilitating disclosure of cyber incidents.
Interface Joint Strength between SS316L Wrought Substrate and Powder Bed Fusion Built Parts.

Metal powder bed fusion (PBF) additive manufacturing (AM) builds metal parts layer by layer upon a substrate material. The strength of this interface between the substrate and the printed material is important to characterize, especially in applications where the substrate is retained and included in the finished part. Ensuring that this interface between the original and the printed material has adequate material properties enables the use of this PBF AM process to repair existing structures and create new parts using both AM and conventional manufacturing. This paper studies the tensile and torsional shear strengths of wrought and PBF-built SS316L specimens and compares them to specimens that are composed of half wrought material and half PBF material. These specimens were created by building new material via PBF onto existing wrought SS316L blocks, then cutting the specimens to include both materials. The specimens are also examined using optical microscopy and electron backscatter diffraction (EBSD). The PBF specimens consistently exhibited higher strength and lower ductility than the wrought specimens. The hybrid PBF/wrought specimens performed similarly to the wrought material. In none of the specimens did any failure appear to occur at or near the interface between the wrought substrate and the PBF material. In addition, most of the deformation in the PBF/wrought specimens appeared to be limited to the wrought portion of the specimens. These results are consistent with optical microscopy and EBSD showing smaller grain size in the PBF material, which correlates to increased strength in SS316L due to the Hall–Petch relationship. With the strength at the interface meeting or exceeding the strength of the original wrought material, this process shows great promise as a method for adding additional features or repairing existing structures using metal PBF AM.
Integration of magnetic tweezers and traction force microscopy for the exploration of matrix rheology and keratinocyte mechanobiology: Model force- and displacement-controlled experiments

In this work, we present a new experimental methodology that integrates magnetic tweezers (MT) with substrate deformation tracking microscopy (DTM) and traction force microscopy (TFM). Two types of MT-DTM/TFM experiments are described: force-control mode and displacement-control mode experiments. In model bead-on-gel experiments for each mode, an MT device is used to apply a controlled force or displacement waveform to a fibronectin-coated superparamagnetic bead attached to a fibrillar type I collagen gel containing a layer of covalently attached red-fluorescent microspheres. Serial fast time-lapse differential interference contrast and epifluorescence image acquisition steps are used to capture displacements of the bead and microspheres, respectively, in response to the applied force or displacement. Due to the large number of acquired images and the dynamic nature of the experiment, new quantitative approaches are implemented to adapt TFM for the analysis of the data, including (i) a temporospatial correction algorithm for improved tracking of microsphere displacements, (ii) a method for the objective determination of L2 regularization parameters for computing incremental traction stress solutions, and (iii) an empirical means for identifying time intervals within the data that can be approximated by elastostatic conditions. We also illustrate how force and energy balances in a force-control mode bead-on-gel experiment can be used to estimate the elastic modulus of a collagen substrate. Finally, in a proof-of-concept, bead-on-cell demonstration, measurements of incremental cell–matrix traction stresses are used to observe how a force applied to a focal contact on the apical surface of a keratinocyte is transmitted to the collagen substrate below the cell.
Service-Oriented Architecture: A Review of State-of-the-Art Literature From an Organizational Perspective

Over the past two decades, the topic of service-oriented architecture (SOA) has attracted considerable attention among researchers and practitioners. Despite a lot of efforts to study it from diffe...
Sequences of Well-Distributed Vertices on Graphs and Spectral Bounds on Optimal Transport

Given a graph $$G=(V,E)$$ , suppose we are interested in selecting a sequence of vertices $$(x_j)_{j=1}^n$$ such that $$\left\{ x_1, \dots , x_k\right\} $$ is ‘well-distributed’ uniformly in k. We describe a greedy algorithm motivated by potential theory and corresponding developments in the continuous setting. The algorithm performs nicely on graphs and may be of use for sampling problems. We can interpret the algorithm as trying to greedily minimize a negative Sobolev norm; we explain why this is related to Wasserstein distance by establishing a purely spectral bound on the Wasserstein distance on graphs that mirrors R. Peyre’s estimate in the continuous setting. We illustrate this with many examples and discuss several open problems.
Genomic Considerations for FHIR®; eMERGE Implementation Lessons

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.
Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists

Despite the promises of data-driven artificial intelligence (AI), little is known about how we can bridge the gulf between traditional physician-driven diagnosis and a plausible future of medicine automated by AI. Specifically, how can we involve AI usefully in physicians' diagnosis workflow given that most AI is still nascent and error-prone (\eg in digital pathology)? To explore this question, we first propose a series of collaborative techniques to engage human pathologists with AI given AI's capabilities and limitations, based on which we prototype Impetus --- a tool where an AI takes various degrees of initiatives to provide various forms of assistance to a pathologist in detecting tumors from histological slides. We summarize observations and lessons learned from a study with eight pathologists and discuss recommendations for future work on human-centered medical AI systems.
Epitaxy, exfoliation, and strain-induced magnetism in rippled Heusler membranes.

Single-crystalline membranes of functional materials enable the tuning of properties via extreme strain states; however, conventional routes for producing membranes require the use of sacrificial layers and chemical etchants, which can both damage the membrane and limit the ability to make them ultrathin. Here we demonstrate the epitaxial growth of the cubic Heusler compound GdPtSb on graphene-terminated Al2O3 substrates. Despite the presence of the graphene interlayer, the Heusler films have epitaxial registry to the underlying sapphire, as revealed by x-ray diffraction, reflection high energy electron diffraction, and transmission electron microscopy. The weak Van der Waals interactions of graphene enable mechanical exfoliation to yield free-standing GdPtSb membranes, which form ripples when transferred to a flexible polymer handle. Whereas unstrained GdPtSb is antiferromagnetic, measurements on rippled membranes show a spontaneous magnetic moment at room temperature, with a saturation magnetization of 5.2 bohr magneton per Gd. First-principles calculations show that the coupling to homogeneous strain is too small to induce ferromagnetism, suggesting a dominant role for strain gradients. Our membranes provide a novel platform for tuning the magnetic properties of intermetallic compounds via strain (piezomagnetism and magnetostriction) and strain gradients (flexomagnetism). Single crystalline membranes enable the tuning of materials properties via strain states that are not accessible to bulk crystals or epitaxially clamped films. Here, the authors demonstrate the synthesis and strain gradient-induced magnetism in membranes of the Heusler compound GdPtSb.
Interplanetary institutionalization: Should humans become space faring?

Humans stand poised to transcend economic, social, and ecological challenges arising from earthbound limitations by becoming an interplanetary species. The ethical commercialization of the solar system regions beyond Earth’s orbit offers a potential means for sustainably continuing population expansion, economic growth, and human well-being. Certain scholars propose that the benefits outweigh the risks and costs while others posit that the existential dangers are not worth the likely yet unknowable and unintended consequences of humans settling outer space. Nonetheless there is consensus that difficult institutional reassembling and high investment change must occur if humans are to become space faring. Our research asks should humans become space faring? Through our multidisciplinary literature survey, we gain insight about five advancing logics and other constraining forces.
Bayesian Multivariate Quantile Regression Using Dependent Dirichlet Process Prior

Abstract In this article, we consider a non-parametric Bayesian approach to multivariate quantile regression. The proposed approach involves modeling of related conditional distributions of a response vector given the covariates using a Dependent Dirichlet Process (DDP) prior. The DDP is used to introduce dependence across covariates. The flexible covariate-dependent mixture of multivariate Gaussian kernels gives rise to an induced posterior for the desired multivariate quantile. For posterior computations, we use a truncated stick-breaking representation of the DDP, and use a block Gibbs sampler for estimating the model parameters. We illustrate our method with simulation studies, and a data containing blood pressures of 40 women. Finally, we provide a theoretical justification for the proposed method through posterior consistency and support properties of the prior.
Resource Uptake and the Evolution of Moderately Efficient Enzymes.

Enzymes speed up reactions that would otherwise be too slow to sustain the metabolism of self-replicators. Yet, most enzymes seem only moderately efficient, exhibiting kinetic parameters orders of magnitude lower than their expected physically achievable maxima and spanning over surprisingly large ranges of values. Here, we question how these parameters evolve using a mechanistic model where enzyme efficiency is a key component of individual competition for resources. We show that kinetic parameters are under strong directional selection only up to a point, above which enzymes appear to evolve under near-neutrality, thereby confirming the qualitative observation of other modelling approaches. While the existence of a large fitness plateau could potentially explain the extensive variation in enzyme features reported, we show using a population genetics model that such a widespread distribution is an unlikely outcome of evolution on a common landscape, as mutation-selection-drift balance occupy a narrow area even when very moderate biases towards lower efficiency are considered. Instead, differences in the evolutionary context encountered by each enzyme should be involved, such that each evolves on an individual, unique landscape. Our results point to drift and effective population size playing an important role, along with the kinetics of nutrient transporters, the tolerance to high concentrations of intermediate metabolites, and the reversibility of reactions. Enzyme concentration also shapes selection on kinetic parameters, but we show that the joint evolution of concentration and efficiency does not yield extensive variance in evolutionary outcomes when documented costs to protein expression are applied.
An emergent autonomous flow for mean-field spin glasses

We study the dynamics of symmetric and asymmetric spin-glass models of size N. The analysis is in terms of the double empirical process: this contains both the spins, and the field felt by each spin, at a particular time (without any knowledge of the correlation history). It is demonstrated that in the large N limit, the dynamics of the double empirical process becomes deterministic and autonomous over finite time intervals. This does not contradict the well-known fact that SK spin-glass dynamics is non-Markovian (in the large N limit) because the empirical process has a topology that does not discern correlations in individual spins at different times. In the large N limit, the evolution of the density of the double empirical process approaches a nonlocal autonomous PDE operator $$\Phi _t$$ . Because the emergent dynamics is autonomous, in future work one will be able to apply PDE techniques to analyze bifurcations in $$\Phi _t$$ . Preliminary numerical results for the SK Glauber dynamics suggest that the ‘glassy dynamical phase transition’ occurs when a stable fixed point of the flow operator $$\Phi _t$$ destabilizes.
Removing Carbon Dioxide Through Ocean Alkalinity Enhancement and Seaweed Cultivation: Legal Challenges and Opportunities

Scientists increasingly agree that carbon dioxide removal will be needed, alongside deep emissions cuts, to stave off the worst impacts of climate change. A wide variety of technologies and strategies have been proposed to remove carbon dioxide from the atmosphere. To date, most research has focused on terrestrial-based approaches, but they often have large land requirements, and may present other risks and challenges. As such, there is growing interest in using the oceans, which have already absorbed more than a quarter of anthropogenic carbon dioxide emissions, and could become an even larger carbon sink in the future. This paper examines the international and U.S. legal frameworks applicable to two ocean-based carbon dioxide removal strategies—ocean alkalinity enhancement and seaweed cultivation. Ocean alkalinity enhancement involves adding alkalinity to ocean waters, either by discharging alkaline rocks or through an electrochemical process, which increases ocean pH levels and thereby enables greater uptake of carbon dioxide, as well as reducing the adverse impacts of ocean acidification. Seaweed cultivation involves the growing of kelp and other macroalgae to store carbon in biomass, which can then either be used to replace more greenhouse gas-intensive products or sequestered.
Glassy carbon microelectrode arrays enable voltage-peak separated simultaneous detection of dopamine and serotonin using fast scan cyclic voltammetry

Progress in real-time, simultaneous in vivo detection of multiple neurotransmitters will help accelerate advances in neuroscience research. The need for development of probes capable of stable electrochemical detection of rapid neurotransmitter fluctuations with high sensitivity and selectivity and sub-second temporal resolution has, therefore, become compelling. Additionally, a higher spatial resolution multi-channel capability is required to capture the complex neurotransmission dynamics across different brain regions. These research needs have inspired the introduction of glassy carbon (GC) microelectrode arrays on flexible polymer substrates through carbon MEMS (C-MEMS) microfabrication process followed by a novel pattern transfer technique. These implantable GC microelectrodes provide unique advantages in electrochemical detection of electroactive neurotransmitters through the presence of active carboxyl, carbonyl, and hydroxyl functional groups. In addition, they offer fast electron transfer kinetics, capacitive electrochemical behavior, and wide electrochemical window. Here, we combine the use of these GC microelectrodes with the fast scan cyclic voltammetry (FSCV) technique to optimize the co-detection of dopamine (DA) and serotonin (5-HT) in vitro and in vivo. We demonstrate that using optimized FSCV triangular waveform at scan rates ≤700 V s-1 and holding and switching at potentials of 0.4 and 1 V respectively, it is possible to discriminate voltage reduction and oxidation peaks of DA and 5-HT, with 5-HT contributing distinct multiple oxidation peaks. Taken together, our results present a compelling case for a carbon-based MEA platform rich with active functional groups that allows for repeatable and stable detection of electroactive multiple neurotransmitters at concentrations as low as 1.1 nM.
A New Integer Programming Formulation of the Graphical Traveling Salesman Problem

In the Traveling Salesman Problem (TSP), a salesman wants to visit a set of cities and return home. There is a cost \(c_{ij}\) of traveling from city i to city j, which is the same in either direction for the Symmetric TSP. The objective is to visit each city exactly once, minimizing total travel costs. In the Graphical TSP, a city may be visited more than once, which may be necessary on a sparse graph. We present a new integer programming formulation for the Graphical TSP requiring only two classes of constraints that are either polynomial in number or polynomially separable, while addressing an open question proposed by Denis Naddef.
Michigan ZoomIN: Validating Crowd-Sourcing to Identify Mammals from Camera Surveys

Camera trap studies have become a popular medium to assess many ecological phenomena including population dynamics, patterns of biodiversity, and monitoring of endangered species. In conjunction with the benefit to scientists, camera traps present an unprecedented opportunity to involve the public in scientific research via image classifications. However, this engagement strategy comes with a myriad of complications. Volunteers vary in their familiarity with wildlife, and thus, the accuracy of user-derived classifications may be biased by the commonness or popularity of species and user-experience. From an extensive multi-site camera trap study across Michigan U.S.A, images were compiled and identified through a public science platform called Michigan ZoomIN. We aggregated responses from 15 independent users per image using multiple consensus methods to assess accuracy by comparing to species identification completed by wildlife experts. We also evaluated how different factors including consensus algorithms, study area, wildlife species, user support, and camera type influenced the accuracy of user-derived classifications. Overall accuracy of user-derived classification was 97%; although, several canid (e.g., Canis lupus, Vulpes vulpes) and mustelid (e.g., Neovison vison) species were repeatedly difficult to identify by users and had lower accuracy. When validating user-derived classification, we found that study area, consensus method, and user support best explained accuracy. To continue to overcome stigma associated with data from untrained participants, we demonstrated their value by showing the accuracy from volunteers was comparable to experts when classifying North American mammals. Our hierarchical workflow that integrated multiple consensus methods lead to more image classifications without extensive training and even when the expertise of the volunteer was unknown. Ultimately, adopting such an approach can harness broader participation, expedite future camera trap data synthesis, and improve allocation of resources by scholars to enhance performance of public participants and increase accuracy of user-derived data.
Nematic quantum criticality in an Fe-based superconductor revealed by strain-tuning

Quantum criticality may be essential to understanding a wide range of exotic electronic behavior; however, conclusive evidence of quantum critical fluctuations has been elusive in many materials of current interest. An expected characteristic feature of quantum criticality is power-law behavior of thermodynamic quantities as a function of a nonthermal tuning parameter close to the quantum critical point (QCP). Here, we observed power-law behavior of the critical temperature of the coupled nematic/structural phase transition as a function of uniaxial stress in a representative family of iron-based superconductors, providing direct evidence of quantum critical nematic fluctuations in this material. These quantum critical fluctuations are not confined within a narrow regime around the QCP but rather extend over a wide range of temperatures and compositions.
Electron-beam floating-zone refined UCoGe

The interplay between unconventional superconductivity and quantum critical ferromagnetism in the U-Ge compounds represents an open problem in strongly correlated electron systems. Sample quality can have a strong influence on both of these ordered states in the compound UCoGe, as is true for most unconventional superconductors. We report results of a different approach at UCoGe crystal growth using a floating-zone method with potential for improvements of sample quality and size as compared with traditional means such as Czochralski growth. Single crystals of the ferromagnetic superconductor UCoGe were produced using an ultra-high vacuum electron-beam floating-zone refining technique. Annealed single crystals show well-defined signatures of bulk ferromagnetism and superconductivity at ${T}_{c}\ensuremath{\sim}2.6\phantom{\rule{0.28em}{0ex}}\mathrm{K}$ and ${T}_{s}\ensuremath{\sim}0.61\phantom{\rule{0.28em}{0ex}}\mathrm{K}$, respectively, in the resistivity and heat capacity. Scanning electron microscopy of samples with different surface treatments shows evidence of an off-stoichiometric uranium-rich phase of UCoGe collected in cracks and voids that might be limiting sample quality.
Isotropic topological second-order spatial differentiator operating in transmission mode

Differentiation has widespread applications, particularly in image processing for edge detection. Significant advances have been made in using nanophotonic structures and metamaterials to perform such operations. In particular, a recent work demonstrated a topological differentiator in which the transfer function exhibited a topological charge, making the differentiation operation robust to variations in operating conditions. The demonstrated topological differentiator, however, operates in reflection mode at off-normal incidence and is difficult to integrate into compact imaging systems. In this work, we design a topological differentiator that operates isotropically in transmission mode at normal incidence. The device exhibits an optical transfer function with a symmetry-protected topological charge of ±2 and performs second-order differentiation. Our work points to the potential of harnessing topological concepts for optical computing applications.
Examining recent trends in the racial disparity gap in tap water consumption: NHANES 2011-2018.

OBJECTIVE As tap water distrust has grown in the USA with greater levels among Black and Hispanic households, we aimed to examine recent trends in not drinking tap water including the period covering the US Flint Water Crisis and racial/ethnic disparities in these trends. DESIGN Cross-sectional analysis. We used log-binomial regressions and marginal predicted probabilities to examine US nationally representative trends in tap and bottled water consumption overall and by race/ethnicity. SETTING The National Health and Nutrition Examination Survey data, 2011-2018. PARTICIPANTS Nationally representative sample of 9439 children aged 2-19 years and 17 268 adults. RESULTS Among US children and adults, respectively, in 2017-2018 there was a 63 % (adjusted prevalence ratio (PR): 1·63, 95 % CI (1·25, 2·12), P < 0·001)) and 40 % (PR: 1·40, 95 % CI (1·16, 1·69), P = 0·001)) higher prevalence of not drinking tap water compared to 2013-2014 (pre-Flint Water Crisis). For Black children and adults, the probability of not drinking tap water increased significantly from 18·1 % (95 % CI (13·4, 22·8)) and 24·6 % (95 % CI (20·7, 28·4)) in 2013-2014 to 29·3 % (95 % CI (23·5, 35·1)) and 34·5 % (95 % CI (29·4, 39·6)) in 2017-2018. Among Hispanic children and adults, not drinking tap water increased significantly from 24·5 % (95 % CI (19·4, 29·6)) and 27·1 % (95 % CI (23·0, 31·2)) in 2013-2014 to 39·7 % (95 % CI (32·7, 46·8)) and 38·1 % (95 % CI (33·0, 43·1)) in 2017-2018. No significant increases were observed among Asian or White persons between 2013-2014 and 2017-2018. Similar trends were found in bottled water consumption. CONCLUSIONS This study found persistent disparities in the tap water consumption gap from 2011 to 2018. Black and Hispanics' probability of not drinking tap water increased following the Flint Water Crisis.
Wild Legalities: Animals and Settler Colonialism in Palestine/Israel

This article examines the underlying biopolitical premises of wildlife management in Palestine/Israel that make, remake, and unmake this region's settler colonial landscape. Drawing on interviews with Israeli nature officials and observations of their work, the article tells several animal stories that illuminate the hierarchies and slippages between wild and domestic, nature and culture, native and settler, and human and nonhuman life in Palestine/Israel. Animal bodies are especially apt technologies of settler colonialism, I show here. They naturalize and normalize settler modes of existence, while criminalizing native livelihoods and relations. Utilizing the terra nullius doctrine, creating biblical landscapes by reintroducing extirpated wild animals, controlling the movement of Palestinians and their animals while letting Jewish settlers and their animals roam unhindered, criminalizing the Palestinians’ more-than-human relations, and introducing restrictions on native engagement with animals are all an inherent part of nature administration in Palestine/Israel. But while they serve as tools for advancing colonial practices, nonhuman animals are also subject to the same violence that afflicts humans. Understanding the more-than-human dimensions of the settler colonial order is instrumental for thinking about how to subvert this order and redirect its violence toward decolonized, or “wild,” legalities.
Exponential communication separations between notions of selfishness

We consider the problem of implementing a fixed social choice function between multiple players (which takes as input a type ti from each player i and outputs an outcome f(t1,…, tn)), in which each player must be incentivized to follow the protocol. In particular, we study the communication requirements of a protocol which: (a) implements f, (b) implements f and computes payments that make it ex-post incentive compatible (EPIC) to follow the protocol, and (c) implements f and computes payments in a way that makes it dominant-strategy incentive compatible (DSIC) to follow the protocol. We show exponential separations between all three of these quantities, already for just two players. That is, we first construct an f such that f can be implemented in communication c, but any EPIC implementation of f (with any choice of payments) requires communication exp(c). This answers an open question of [Fadel and Segal, 2009; Babaioff et. al., 2013]. Second, we construct an f such that an EPIC protocol implements f with communication C, but all DSIC implementations of f require communication exp(C).
Learning Human Search Behavior from Egocentric Visual Inputs

"Looking for things" is a mundane but critical task we repeatedly carry on in our daily life. We introduce a method to develop a human character capable of searching for a randomly located target object in a detailed 3D scene using its locomotion capability and egocentric vision perception represented as RGBD images. By depriving the privileged 3D information from the human character, it is forced to move and look around simultaneously to account for the restricted sensing capability, resulting in natural navigation and search behaviors. Our method consists of two components: 1) a search control policy based on an abstract character model, and 2) an online replanning control module for synthesizing detailed kinematic motion based on the trajectories planned by the search policy. We demonstrate that the combined techniques enable the character to effectively find often occluded household items in indoor environments. The same search policy can be applied to different full-body characters without the need for retraining. We evaluate our method quantitatively by testing it on randomly generated scenarios. Our work is a first step toward creating intelligent virtual agents with humanlike behaviors driven by onboard sensors, paving the road toward future robotic applications.
Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding

Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance. However, the state-of-the-art neural channel decoders cannot achieve high decoding performance and low complexity simultaneously. To overcome this challenge, in this paper we propose doubly residual neural (DRN) decoder. By integrating both the residual input and residual learning to the design of neural channel decoder, DRN enables significant decoding performance improvement while maintaining low complexity. Extensive experiment results show that on different types of channel codes, our DRN decoder consistently outperform the state-of-the-art decoders in terms of decoding performance, model sizes and computational cost.
Portable Laser Guided Robotic Metrology System

This paper introduces the new Portable Laser Guided Robotic antenna range (PLGR) at the National Aeronautics and Space Administration's (NASA) Glenn Research Center. Previous work used industrial robots in fixed facilities to characterize antennas and required fixtures that do not lend themselves to portable applications.NASA's PLGR system is designed for in-situ antenna measurements at a remote site. The system consists of a robot arm mounted on a vertical lift and a laser tracker, each on a portable base. The lift and laser tracker enable scanning a surface larger than the robot's reach. To accomplish this the robot first collects all points within its reach, then the system is moved and the laser tracker is used to relocate the robot before additional points are captured.The PLGR architecture will be discussed including how safety systems and path planning are combined to effectively characterize antennas. Software was written in high level languages for flexible integration of vector network analyzers and antenna controllers. Lastly, data will be shown to demonstrate the system functionality and accuracy.
Comparison of Advanced Engines for Parabolic Dish Solar Thermal Power Plants

A paraboloidal dish solar thermal power plant produces electrical energy by a two-step conversion process. The collector subsystem is composed of a two-axis tracking paraboloidal concentrator and a cavity receiver. The concentrator focuses intercepted sunlight (direct, normal insolation) into a cavity receiver whose aperture encircles the focal point of the concentrator. At the internal wall of the receiver the electromagnetic radiation is converted to thermal energy. A heat engine/generator assembly, which is mounted directly behind th receiver, then converts the thermal energy captured by the receiver to electricity. Developmental activity has been concentrated on relatively small power modules which employ 11- to 12-m-diam dishes to generate nominal power levels of approximately 20 kW. A comparison of advanced heat engines for use on the dish power module is presented in terms of the performance potential of each engine as weighed against its requirements for advanced technology development. Three advanced engine possibilities are considered. These are the Brayton (gas turbine), Brayton/Rankine combined cycle, and Stirling engines. All three engine candidates are attractive in terms of overall system performance potential.
Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution

Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) due to its demanding but unique nature: a theoretic requirement on representing and reasoning based on spatial-temporal knowledge in mind, and an applied requirement on a high-level cognitive system capable of navigating and acting in space and time. Recent works have focused on an abstract reasoning task of this kind—Raven’s Progressive Matrices (RPM). Despite the encouraging progress on RPM that achieves human-level performance in terms of accuracy, modern approaches have neither a treatment of human-like reasoning on generalization, nor a potential to generate answers. To fill in this gap, we propose a neuro-symbolic Probabilistic Abduction and Execution (PrAE) learner; central to the PrAE learner is the process of probabilistic abduction and execution on a probabilistic scene representation, akin to the mental manipulation of objects. Specifically, we disentangle perception and reasoning from a monolithic model. The neural visual perception frontend predicts objects’ attributes, later aggregated by a scene inference engine to produce a probabilistic scene representation. In the symbolic logical reasoning backend, the PrAE learner uses the representation to abduce the hidden rules. An answer is predicted by executing the rules on the probabilistic representation. The en-tire system is trained end-to-end in an analysis-by-synthesis manner without any visual attribute annotations. Extensive experiments demonstrate that the PrAE learner improves cross-configuration generalization and is capable of rendering an answer, in contrast to prior works that merely make a categorical choice from candidates.
Fractal Autoencoders for Feature Selection

Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE). It trains a neural network to pinpoint informative features for global exploring of representability and for local excavating of diversity. Architecturally, FAE extends autoencoders by adding a one-to-one scoring layer and a small sub-neural network for feature selection in an unsupervised fashion. With such a concise architecture, FAE achieves state-of-the-art performances; extensive experimental results on fourteen datasets, including very high-dimensional data, have demonstrated the superiority of FAE over existing contemporary methods for unsupervised feature selection. In particular, FAE exhibits substantial advantages on gene expression data exploration, reducing measurement cost by about 15% over the widely used L1000 landmark genes. Further, we show that the FAE framework is easily extensible with an application.
Oscillation Modes of Strange Quark Stars with a Strangelet Crust

We study the non-radial oscillation modes of strange quark stars with a homogeneous core and a crust made of strangelets. Using a 2-component equation of state (core+crust) for strange quark stars that can produce stars as heavy as 2 solar masses, we identify the high-frequency l=2 spheroidal (f, p) in Newtonian gravity, using the Cowling approximation. The results are compared to the case of homogeneous compact stars such as polytropic neutron stars, as well as bare strange stars. We find that the strangelet crust only increases very slightly the frequency of the spheroidal modes, and that Newtonian gravity overestimates the mode frequencies of the strange star, as is the case for neutron stars.
Detection of Racial Bias from Physiological Responses.

Despite the evolution of norms and regulations to mitigate the harm from biases, harmful discrimination linked to an individual’s unconscious biases persists. Our goal is to better understand and detect the physiological and behavioral indicators of implicit biases. This paper investigates whether we can reliably detect racial bias from physiological responses, including heart rate, conductive skin response, skin temperature, and micro-body movements. We analyzed data from 46 subjects whose physiological data was collected with Empatica E4 wristband while taking an Implicit Association Test (IAT). Our machine learning and statistical analysis show that implicit bias can be predicted from physiological signals with 76.1% accuracy. Our results also show that the EDA signal associated with skin response has the strongest correlation with racial bias and that there are significant differences between the values of EDA features for biased and unbiased participants.
Multi-Qubit Correction for Quantum Annealers

We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels — i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value — and successively converges to the ground state. Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as spin-reversal transforms, classical post-processing techniques, and increased inter-sample delay between successive measurements.
Camera-based piano sheet music identification

This paper presents a method for large-scale retrieval of piano sheet music images. Our work differs from previous studies on sheet music retrieval in two ways. First, we investigate the problem at a much larger scale than previous studies, using all solo piano sheet music images in the entire IMSLP dataset as a searchable database. Second, we use cell phone images of sheet music as our input queries, which lends itself to a practical, user-facing application. We show that a previously proposed fingerprinting method for sheet music retrieval is far too slow for a real-time application, and we diagnose its shortcomings. We propose a novel hashing scheme called dynamic n-gram fingerprinting that significantly reduces runtime while simultaneously boosting retrieval accuracy. In experiments on IMSLP data, our proposed method achieves a mean reciprocal rank of 0.85 and an average runtime of 0.98 seconds per query.
Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematic integration of zonal occupancy patterns to accurately identify short-term grid service opportunities. We evaluate the performance of the framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone office building for a range of design choices. With the help of a prototype system that integrates an interactive Data Analytics and Visualization frontend, we demonstrate a way for the building operators to monitor the flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.
Perspective on Tsallis statistics for nuclear and particle physics

This is a concise introduction to the topic of nonextensive Tsallis statistics meant especially for those interested in its relation to high-energy proton–proton, proton–nucleus and nucleus–nucleus...