cssi/SciSciGPT-SciSciNet huggingface dataset:
Synthesizing Datalog Programs using Numerical Relaxation.

The problem of learning logical rules from examples arises in diverse fields, including program synthesis, logic programming, and machine learning. Existing approaches either involve solving computationally difficult combinatorial problems, or performing parameter estimation in complex statistical models. In this paper, we present Difflog, a technique to extend the logic programming language Datalog to the continuous setting. By attaching real-valued weights to individual rules of a Datalog program, we naturally associate numerical values with individual conclusions of the program. Analogous to the strategy of numerical relaxation in optimization problems, we can now first determine the rule weights which cause the best agreement between the training labels and the induced values of output tuples, and subsequently recover the classical discrete-valued target program from the continuous optimum. We evaluate Difflog on a suite of 34 benchmark problems from recent literature in knowledge discovery, formal verification, and database query-by-example, and demonstrate significant improvements in learning complex programs with recursive rules, invented predicates, and relations of arbitrary arity.
Mixing Time of Markov Chains for the 1–2 Model

A 1–2 model configuration is a subset of edges of a hexagonal lattice satisfying the constraint that each vertex is incident to 1 or 2 edges. We introduce Markov chains to sample the 1–2 model configurations on the 2D hexagonal lattice and prove that the mixing time of these chains is polynomial in the sizes of the graphs for a large class of probability measures.
The effects of message framing and healthcare provider recommendation on adult hepatitis B vaccination: A randomized controlled trial.

Abstract Many adults in the U.S. do not receive recommended vaccines, and the research literature remains inconclusive on the best communication strategies for increasing this behavior. This study examined the association of message framing (gained-framed vs. loss-framed vs. control), and healthcare provider (HCP) recommendation (offered vs. recommended) on uptake of adult hepatitis B virus (HBV) vaccination in a high risk population using a 3 × 2 block design randomized controlled trial. Fear of shots, fear of vaccines, and perceived message framing were examined in secondary analyses. Of the 1747 participants, 47.7% (n = 833) received 0 doses of HBV vaccine, 27.8% (n = 485) received 1 dose, 10.4% received 2 doses, and 14.1% received all 3 recommended doses. There was not a significant interaction between message framing and HCP recommendation (p = .59). Mean number of doses received by the gain-framed group (m = 0.96) was not significantly different from the loss-framed group (m = 0.97, RR = 0.99, 95% CI = 0.88–1.12). However, those receiving any framing message received significantly more doses (m = 0.96) than those in the control condition (m = 0.81, RR = 1.17, 95%CI = 1.06–1.31). Participants who received a HCP recommendation received significantly more vaccine doses (m = 0.95) than those in the vaccine-offered condition (mean = 0.82, RR = 1.16, 95%CI = 1.05–1.28). These results suggest there is no difference in vaccine uptake between gain-frame and loss-frame messages, but both are better than a control message. These results also support advising HCP to provide a strong recommendation for vaccinations beyond merely offering it to patients. This study has implications for vaccine uptake beyond HBV, and can inform future research on effective vaccine communication research. Clinicaltrials.gov Identifier: NCT00739752 . Registration date: August 20, 2008.
The great human expansion

Genetic and paleoanthropological evidence is in accord that today’s human population is the result of a great demic (demographic and geographic) expansion that began approximately 45,000 to 60,000 y ago in Africa and rapidly resulted in human occupation of almost all of the Earth’s habitable regions. Genomic data from contemporary humans suggest that this expansion was accompanied by a continuous loss of genetic diversity, a result of what is called the “serial founder effect.” In addition to genomic data, the serial founder effect model is now supported by the genetics of human parasites, morphology, and linguistics. This particular population history gave rise to the two defining features of genetic variation in humans: genomes from the substructured populations of Africa retain an exceptional number of unique variants, and there is a dramatic reduction in genetic diversity within populations living outside of Africa. These two patterns are relevant for medical genetic studies mapping genotypes to phenotypes and for inferring the power of natural selection in human history. It should be appreciated that the initial expansion and subsequent serial founder effect were determined by demographic and sociocultural factors associated with hunter-gatherer populations. How do we reconcile this major demic expansion with the population stability that followed for thousands years until the inventions of agriculture? We review advances in understanding the genetic diversity within Africa and the great human expansion out of Africa and offer hypotheses that can help to establish a more synthetic view of modern human evolution.
Computing limiting average availability of a repairable system through discretization

Abstract Formulas for limiting average availability of a repairable system exist only for some special cases: (1) either the lifetime or the repair time is exponential; or (2) there is one spare unit and one repair facility. We consider a more general setting involving several spare units and several repair facilities; and we allow arbitrary life- and repair time distributions. Under periodic monitoring, which essentially discretizes the time variable, we compute the limiting average availability. The discretization approach closely approximates the existing results in the special cases; and increases the limiting average availability as we include additional spare unit or additional repair facility.
Service Discovery for the Connected Car with Semantic Accessors

Connected cars have the potential to transform a vehicle from a transportation platform to a platform for integrating humans with a city. To that end we introduce semantic accessors (actor based local proxies for remote services) as a novel, and powerful discovery mechanism for connected vehicles that bridges the domains of Internet of Things (IoT) composition frameworks and the semantic web of things. The primary components of this approach include a local semantic repository used for maintaining the vehicle's perspective of its real-world context, accessors for querying and dynamically updating the repository to match evolving vehicular context information, accessors for services (such as parking) linked to a service ontology, and a swarmlet controller responsible for managing the above in accordance with user input. We demonstrate this semantic accessor architecture with a prototype Dashboard display that downloads accessors for new services as they become available and dynamically renders their self-described user interface components.
A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data

Abstract Rhodosporidium toruloides is a red, basidiomycetes yeast that can accumulate a large amount of lipids and produce carotenoids. To better assess this non-model yeast’s metabolic capabilities, we reconstructed a genome-scale model of R. toruloides IFO0880’s metabolic network (iRhto1108) accounting for 2204 reactions, 1985 metabolites and 1108 genes. In this work, we integrated and supplemented the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism’s metabolic capabilities. Predictions of genotype-phenotype relations were improved through manual curation of gene-protein-reaction rules for 543 reactions leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloides’s utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model’s fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for triacylglycerol overproduction. Suggested interventions recapitulated many of the previous successful implementations of genetic modifications and put forth a few new ones.
Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations

Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of stand-alone sentences. We propose DiscoEval, a test suite of tasks to evaluate whether sentence representations include broader context information. We also propose a variety of training objectives that makes use of natural annotations from Wikipedia to build sentence encoders capable of modeling discourse. We benchmark sentence encoders pretrained with our proposed training objectives, as well as other popular pretrained sentence encoders on DiscoEval and other sentence evaluation tasks. Empirically, we show that these training objectives help to encode different aspects of information in document structures. Moreover, BERT and ELMo demonstrate strong performances over DiscoEval with individual hidden layers showing different characteristics.
Empathy amulet: a wearable to connect with strangers.

The Empathy Amulet is a wearable interpretation of Philip K. Dick's empathy box from his novel Do Androids Dream of Electronic Sheep? [3]. In the novel, thousands of people were anonymously connected with each other both haptically and emotionally when they grabbed the handles of their empathy boxes. The Empathy Amulet similarly networks a group of strangers together through shared experiences of physical warmth. It is not yet another technology for staying in touch with people you already know (and falling short). Rather, it encourages its wearer to make a deliberate and generous choice to invest their time and energy in connection with strangers, and it incorporates reciprocity into its design, such that helping oneself means helping other people. In today's world, people are less likely to feel empathy towards those not in their immediate network of family and friends, and, despite a proliferation of connective technologies, loneliness is on the rise [2, 5]. Surprisingly, it is the perceived sense of loneliness, and not actually being physically alone that has numerous health consequences for a significant portion of the population. Lakoff and Johnson's theory of embodied mind asserts that our physical and subjective experiences are inextricably linked, and the Empathy Amulet leverages the powerful connection between the physical experience of warmth and the subjective experience of social connectedness to combat loneliness and cultivate a stronger sense of connection with strangers [1, 4].
Genomic Decoding of Neuronal Depolarization by Stimulus-Specific NPAS4 Heterodimers

Cells regulate gene expression in response to salient external stimuli. In neurons, depolarization leads to the expression of inducible transcription factors (ITFs) that direct subsequent gene regulation. Depolarization encodes both a neuron's action potential (AP) output and synaptic inputs, via excitatory postsynaptic potentials (EPSPs). However, it is unclear if distinct types of electrical activity can be transformed by an ITF into distinct modes of genomic regulation. Here, we show that APs and EPSPs in mouse hippocampal neurons trigger two spatially segregated and molecularly distinct induction mechanisms that lead to the expression of the ITF NPAS4. These two pathways culminate in the formation of stimulus-specific NPAS4 heterodimers that exhibit distinct DNA binding patterns. Thus, NPAS4 differentially communicates increases in a neuron's spiking output and synaptic inputs to the nucleus, enabling gene regulation to be tailored to the type of depolarizing activity along the somato-dendritic axis of a neuron.
Resolution limit of label-free far-field microscopy

The Abbe diffraction limit, which relates the maximum optical resolution to the numerical aperture of the lenses involved and the optical wavelength, is generally considered a practical limit that cannot be overcome with conventional imaging systems. However, it does not represent a fundamental limit to optical resolution, as demonstrated by several new imaging techniques that prove the possibility of finding the subwavelength information from the far field of an optical image. These include super-resolution fluorescence microscopy, imaging systems that use new data processing algorithms to obtain dramatically improved resolution, and the use of super-oscillating metamaterial lenses. This raises the key question of whether there is in fact a fundamental limit to the optical resolution, as opposed to practical limitations due to noise and imperfections, and if so then what it is. We derive the fundamental limit to the resolution of optical imaging and demonstrate that while a limit to the resolution of a fundamental nature does exist, contrary to the conventional wisdom it is neither exactly equal to nor necessarily close to Abbe’s estimate. Furthermore, our approach to imaging resolution, which combines the tools from the physics of wave phenomena and the methods of information theory, is general and can be extended beyond optical microscopy, e.g., to geophysical and ultrasound imaging.
Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications

In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past three years. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.
Extended MARTINI water model for heat transfer studies

The computationally efficient classical MARTINI model is extended to simulate heat transfer simulations of water. The current MARTINI model, variations of it and other coarse grain water models foc...
Algorithmic Randomness in Continuous-Time Markov Chains

In this paper we develop the elements of the theory of algorithmic randomness in continuous-time Markov chains (CTMCs). Our main contribution is a rigorous, useful notion of what it means for an individual trajectory of a CTMC to be random. CTMCs have discrete state spaces and operate in continuous time. This, together with the fact that trajectories may or may not halt, presents challenges not encountered in more conventional developments of algorithmic randomness.Although we formulate algorithmic randomness in the general context of CTMCs, we are primarily interested in the computational power of stochastic chemical reaction networks, which are special cases of CTMCs. This leads us to embrace situations in which the long-term behavior of a network depends essentially on its initial state and hence to eschew assumptions that are frequently made in Markov chain theory to avoid such dependencies.After defining the randomness of trajectories in terms of a new kind of martingale (algorithmic betting strategy), we prove equivalent characterizations in terms of constructive measure theory and Kolmogorov complexity.
Age, Equality, and Vulnerability

This Article uses age as an entry point for examining how temporal and methodological issues in egalitarianism make substantive equality an unattractive goal for vulnerability theory. Instead, vulnerability theory should adopt a continuous doctrine of sufficiency, which is a better ft with vulnerability theory’s underlying aims and rhetoric. Instead of evaluating what individuals have in relation to others, sufficiency refocuses the inquiry on whether we have enough throughout the lifecourse. In the context of vulnerability theory, enough should be defined as the capability to be resilient as guaranteed by the responsive state.
Sparse optimal control of networks with multiplicative noise via policy gradient

Abstract We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks. Various regularization schemes are examined and incorporated into the optimization by the use of gradient, subgradient, and proximal gradient methods. Numerical experiments on a large networked system show that the algorithms converge to performant sparse mean-square stabilizing controllers.
Persistent Surveillance with Energy-Constrained UAVs and Mobile Charging Stations

Abstract We address the problem of achieving persistent surveillance over an environment by using energy-constrained unmanned aerial vehicles (UAVs), which are supported by unmanned ground vehicles (UGVs) serving as mobile charging stations. Specifically we plan the trajectories of all vehicles and the charging schedule of UAVs to minimize the long-term maximum age, where age is defined as the time between two consecutive visits to regions of interest in a partitioned environment. We introduce a scalable planning strategy based on 1) creating UAV-UGV teams, 2) decomposing the environment into optimal partitions that can be covered by any of the teams in a single fuel cycle, 3) uniformly distributing the teams over a cyclic path traversing those partitions, and 4) having the UAVs in each team cover their current partition and be transported to the next partition while being recharged by the UGV. We show some results related to the safety and performance of the proposed strategy.
Conformal constraints on defects

In this paper we study the constraints imposed by conformal invariance on extended objects a.k.a. defects in a conformal field theory. We identify a particularly nice class of defects that is closed under conformal transformations. Correlation function of the defect with a bulk local operator is fixed by conformal invariance up to an overall constant. This gives rise to the notion of defect expansion, where the defect itself is expanded in terms of local operators. This expansion generalizes the idea of the boundary state. We will show how one can fix the correlation function of two defects from the knowledge of the defect expansion. The defect correlator admits a number of conformal cross-ratios depending on their dimensionality. We find the differential equation obeyed by the conformal block and solve them in certain special cases.
Singleton Field Theory and Flato–Frønsdal Dipole Equation

We study solutions of the equations (△ -λ )φ = 0 and (△ -λ )2φ = 0 in global coordinates on the covering space CAdS d of the d-dimensional Anti de-Sitter space subject to various boundary conditions and their connection to the unitary irreducible representations of $$\overline {{\text{SO}}} _0 $$ (d-1,2). The ‘vanishing flux’ boundary conditions at spatial infinity lead to the standard quantization scheme for CAdS d in which solutions of the second- and the fourth-order equations are equivalent. To include fields realizing the singleton unitary representation in the bulk of CAdS d one has to relax the boundary conditions thus allowing for the nontrivial space of solutions of the dipole equation known as the Gupta–Bleuler triplet. We obtain explicit expressions for the modes of the Gupta–Bleuler triplet and the corresponding two-point function. To avoid negative-energy states one must also introduce an additional constraint in the space of solutions of the dipole equation.
Strain-controlled magnetic ordering in 2D carbon metamaterials

Abstract We use ab initio spin-polarized density functional theory to study the magnetic order in a Kagome-like 2D metamaterial consisting of pristine or substitutionally doped phenalenyl radicals polymerized into a nanoporous, graphene-like structure. In this and in a larger class of related structures, the constituent polyaromatic hydrocarbon molecules can be considered as quantum dots that may carry a net magnetic moment. The structure of this porous system and the coupling between the quantum dots may be changed significantly by applying moderate strain, thus allowing to control the magnetic order and the underlying electronic structure.
Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitments

The implementation of AI systems has led to new forms of harm in various sensitive social domains. We analyze these as problems How to address these harms remains at the center of controversial debate. In this paper, we discuss the inherent normative uncertainty and political debates surrounding the safety of AI systems.of vagueness to illustrate the shortcomings of current technical approaches in the AI Safety literature, crystallized in three dilemmas that remain in the design, training and deployment of AI systems. We argue that resolving normative uncertainty to render a system 'safe' requires a sociotechnical orientation that combines quantitative and qualitative methods and that assigns design and decision power across affected stakeholders to navigate these dilemmas through distinct channels for dissent. We propose a set of sociotechnical commitments and related virtues to set a bar for declaring an AI system 'human-compatible', implicating broader interdisciplinary design approaches.
Elliptic Families of Solutions of the Kadomtsev-Petviashvili Equation and the Field Elliptic Calogero-Moser System ∗

We present a Lax pair for the field elliptic Calogero-Moser system and establish a connection between this system and the Kadomtsev-Petviashvili equation.Namely, we consider elliptic families of solutions of the KP equation such that their poles satisfy a constraint of being balanced.We show that the dynamics of these poles is described by a reduction of the field elliptic CM system. We construct a wide class of solutions to the field elliptic CM system by showing that any N -fold branched cover of an elliptic curve gives rise to an elliptic family of solutions of the KP equation with balanced poles. The main goal of this paper is to establish a connection between the field analog of the elliptic Calogero-Moser system (CM) introduced in (10) and the Kadomtsev-Petviashvili equation (KP). This connection is a next step along the line that goes back to the paper (1), where the dynamics of the poles of elliptic (rational or trigonometric) solutions of the Korteweg-de Vries equation (KdV) was described in terms of commuting flows of the elliptic (rational or trigonometric) CM system. It was shown in the earlier paper (7) of one of the authors that the constrained correspondence between a theory of the elliptic CM system and a theory of the elliptic solutions of the KdV equation becomes an isomorphism for the case of the KP equation.It turns out that a function u(x, y, t) that is an elliptic function with respect to the variable x satisfies the KP equation if and only if it has the form
Handover Probability in Drone Cellular Networks

This letter analyzes the handover probability in a drone cellular network where the initial positions of drone base stations (DBSs) serving a set of user equipment (UE) on the ground are modeled by a homogeneous Poisson point process (PPP). Inspired by the mobility model considered in the third generation partnership project (3GPP) studies, we assume that all the DBSs move along straight lines in random directions. We further consider two different scenarios for the DBS speeds: (i) same speed model (SSM), and (ii) different speed model (DSM). Assuming nearest-neighbor association policy for the UEs on the ground, we characterize the handover probability of this network for both mobility scenarios. For the SSM, we compute the exact handover probability by establishing equivalence with a single-tier terrestrial cellular network, in which the base stations (BSs) are static while the UEs are mobile. We then derive a lower bound for the handover probability in the DSM by characterizing the evolution of the spatial distribution of the DBSs over time.
The Role of Heterogeneous Risk Preferences, Discount Rates, and Earnings Expectations in College Major Choice

Abstract We estimate a rich model of college major choice using a panel of experimentally-derived data. Our estimation strategy combines two types of data: data on self-reported beliefs about future earnings from potential human capital decisions and survey-based measures of risk and time preferences. We show how to use these data to identify a general life-cycle model, allowing for rich patterns of heterogeneous beliefs and preferences. Our data allow us to separate perceptions about the degree of risk or about the current versus future payoffs for a choice from the individual’s preference for risk and patience. Comparing our estimates of the general model to estimates of models which ignore heterogeneity in risk and time preferences, we find that these restricted models overstate the importance of earnings to major choice. Additionally, we show that while men are less risk averse and patient than women, gender differences in expectations about own-earnings, risk aversion, and patience cannot explain gender gaps in major choice.
Systems Biology Analysis Reveals Eight SLC22 Transporter Subgroups, Including OATs, OCTs, and OCTNs

The SLC22 family of OATs, OCTs, and OCTNs is emerging as a central hub of endogenous physiology. Despite often being referred to as "drug" transporters, they facilitate the movement of metabolites and key signaling molecules. An in-depth reanalysis supports a reassignment of these proteins into eight functional subgroups, with four new subgroups arising from the previously defined OAT subclade: OATS1 (SLC22A6, SLC22A8, and SLC22A20), OATS2 (SLC22A7), OATS3 (SLC22A11, SLC22A12, and Slc22a22), and OATS4 (SLC22A9, SLC22A10, SLC22A24, and SLC22A25). We propose merging the OCTN (SLC22A4, SLC22A5, and Slc22a21) and OCT-related (SLC22A15 and SLC22A16) subclades into the OCTN/OCTN-related subgroup. Using data from GWAS, in vivo models, and in vitro assays, we developed an SLC22 transporter-metabolite network and similar subgroup networks, which suggest how multiple SLC22 transporters with mono-, oligo-, and multi-specific substrate specificity interact to regulate metabolites. Subgroup associations include: OATS1 with signaling molecules, uremic toxins, and odorants, OATS2 with cyclic nucleotides, OATS3 with uric acid, OATS4 with conjugated sex hormones, particularly etiocholanolone glucuronide, OCT with neurotransmitters, and OCTN/OCTN-related with ergothioneine and carnitine derivatives. Our data suggest that the SLC22 family can work among itself, as well as with other ADME genes, to optimize levels of numerous metabolites and signaling molecules, involved in organ crosstalk and inter-organismal communication, as proposed by the remote sensing and signaling theory.
Gravity Without Apology: the Science of Elasticities, Distance and Trade

Gravity as both fact and theory is one of the great success stories of recent research on international trade, and has featured prominently in the policy debate over Brexit. We first review the facts, noting the overwhelming evidence that trade tends to fall with distance. We then introduce some expository tools for understanding constant-elasticity-of-substitution theories of gravity as a simple general-equilibrium system. Next, we point out some anomalies with the theory: mounting evidence against constant trade elasticities, and implausible predictions for bilateral trade balances. Finally, we sketch an approach based on subconvex gravity as a promising direction to resolving them.
Redrawing the Map of Global Capital Flows: The Role of Cross-Border Financing and Tax Havens

Global firms finance themselves through foreign subsidiaries, often shell companies in tax havens, which obscures their nationality in aggregate statistics. We associate the universe of traded securities with their issuer’s ultimate parent and restate bilateral investment positions to better reflect the true financial linkages connecting countries around the world. We find that portfolio investment from developed countries to firms in large emerging markets is dramatically larger than previously thought. The national accounts of the United States, for example, understate the U.S. position in Chinese firms by nearly 600 billion dollars, while China’s official net creditor position to the rest of the world is overstated by about 50 percent. We additionally show how taking account of offshore issuance is important for our understanding of the currency composition of external portfolio liabilities, the nature of foreign direct investment, and the growth of financial globalization.
System Level Synthesis with State and Input Constraints

This paper addresses the problem of designing distributed controllers with state and input constraints in the System Level Synthesis (SLS) framework. Using robust optimization, we show how state and actuation constraints can be incorporated into the SLS structure. Moreover, we show that the dual variable associated with the constraint has the same sparsity pattern as the SLS parametrization, and therefore the computation distributes using a simple primal-dual algorithm. We provide a stability analysis for SLS design with input saturation under the Internal Model Control (IMC) framework. We show that the closed-loop system with saturation is stable if the controller has a gain less than one. In addition, a saturation compensation scheme that incorporates the saturation information is proposed which improves the naive SLS design under saturation.
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO

We develop a two-stage deep learning pipeline architecture to estimate the uplink massive MIMO channel with one-bit ADCs. This deep learning pipeline is composed of two separate generative deep learning models. The first one is a supervised learning model and designed to compensate for the quantization loss. The second one is an unsupervised learning model and optimized for denoising. Our results show that the proposed deep learning-based channel estimator can significantly outperform other state-of-the-art channel estimators for one-bit quantized massive MIMO systems. In particular, our design provides 5–10 dB gain in channel estimation error. Furthermore, it requires a reasonable amount of pilots, on the order of 20 per coherence time interval.
Resilient Distributed Recovery of Large Fields

This paper studies the resilient distributed recovery of large fields under measurement attacks, by a team of agents, where each measures a small subset of the components of a large spatially distributed field. An adversary corrupts some of the measurements. The agents collaborate to process their measurements, and each is interested in recovering only a fraction of the field. We present a field recovery consensus+innovations type distributed algorithm that is resilient to measurement attacks, where an agent maintains and updates a local state based on its neighbors states and its own measurement. Under sufficient conditions on the attacker and the connectivity of the communication network, each agent’s state, even those with compromised measurements, converges to the true value of the field components that it is interested in recovering. Finally, we illustrate the performance of our algorithm through numerical examples.
New Techniques for Deterministic Test Pattern Generation

This paper presents new techniques for speeding up deterministic test pattern generation for VLSI circuits. These techniques improve the PODEM algorithm by reducing number of backtracks with a low computational cost. This is achieved by finding more necessary signal line assignments, by detecting conflicts earlier, and by avoiding unnecessary work during test generation. We have incorporated these techniques into an advanced ATPG system for combinational circuits, called ATOM. The performance results for the ISCAS85 and full scan version of the ISCAS89 benchmark circuits demonstrated the effectiveness of these techniques on the test generation performance. ATOM detected all the testable faults and proved all the redundant faults to be redundant with a small number of backtracks in a short amount of time.
Most Powerful Test

The Neyman–Pearson approach to testing of hypotheses is described. Neyman–Pearson fundamental lemma and its ramifications are discussed. Keywords: null and alternative hypotheses; power function; level and size of a test; Neyman–Pearson lemma; monotone likelihood ratio; most powerful tests
Dimension of time in strange attractors

Scientific phenomenon has an artistic aesthetic that transcends its ability to attempt to explain the world around us.
19 – Reactor Safety

Publisher Summary This chapter examines the precautions taken to prevent release of radioactive materials to the surroundings and discusses the philosophy of safety. The chapter also describes the dependence of numbers of neutrons and reactor power on the multiplication factor, which is in turn affected by temperature and control rod absorbers. Delayed neutrons and temperature effects provide inherent reactor safety. Control rods permit rapid shutdown, and reactor components are designed and constructed to minimize the chance of failure. Emergency core cooling equipment is installed to reduce the hazard in the event of an accident. Licensing is administered by the Nuclear Regulatory Commission, which expects plants to use probabilistic safety analysis (PRA).
Official Financial Flows, Capital Mobility, and Global Imbalances

Abstract We use a cross-country panel framework to analyze the effect of net official flows (chiefly foreign exchange intervention) on current accounts. We find that net official flows have a large but plausible effect on current account balances. The estimated effects are larger with instrumental variables (42 cents to the dollar on average compared to 24 without instruments), reflecting a possible downward bias in regressions without instruments owing to an endogenous response of net official flows to private financial flows. We consistently find larger impacts of net official flows when international capital flows are restricted and smaller impacts when capital is highly mobile. A further result is that there is an important positive effect of lagged net official flows (embodied in the lagged stock of official assets) on current accounts that we believe operates through the portfolio balance channel. 1
Simultaneous Excitation of Multiple-Input/Multiple-Output CFD-Based Unsteady Aerodynamic Systems

A significant improvement to the development of computational-fluid-dynamics-based unsteady aerodynamic reduced-order models is presented. The improvement involves the simultaneous excitation of the structural modes of the computational-fluid-dynamics-based unsteady aerodynamic system. This improvement enables the computation of the unsteady aerodynamic state-space model using a single computational fluid dynamics execution, independent of the number of structural modes. Two new types of input functions are presented that can be used for the simultaneous excitation of the unsteady aerodynamic system via the excitation of the structural modes. Results are presented for a semispan configuration using the CFL3Dv6.4 code.
The Effects of Selective and Indiscriminate Repression on the 2013 Gezi Park Nonviolent Resistance Campaign

We investigate the differential effects of selective and indiscriminate repression on the rate of protest actions during the nonviolent resistance campaign in Gezi Park, Turkey, in 2013. After deri...
Who has voice in a deliberative democracy? Evidence from transcripts of village parliaments in south India

Abstract The role of deliberation among citizens to determine and forge agreement on policy is often seen as a crucial feature of democratic government. This paper provides the first large-N empirical evidence on the credibility of voice in a deliberative democracy in an non-laboratory setting, using a unique dataset collected from transcripts of deliberation that occurred between January and September 2003 in 127 functioning village parliaments (gram sabhas) in Southern India. We exploit a natural experiment in the arrangement of India's state borders across ethnolinguistic lines that led exogenously to increased caste fragmentation and a reduced degree of consensus on public goods priorities. We then examine the patterns of deliberation. We reject the presence of pure cheap talk in both heterogeneous and homogeneous villages. Instead, we show that in caste‐fragmented South Indian villages, where there is less village-wide agreement on the relative importance of different public goods, the probability of an individual's highest priority being discussed increases as the household becomes more credible: its preferences approach the pivotal agent in a pure representative democracy, the median household. These effects are lower in ethnically homogeneous villages where there is greater consensus on the prioritization of public goods. Taken together, our results suggest that India's village parliaments, rather than being mere talking shops or being entirely captured by elites, seem instead to be both democratically representative and to be assigning roles to credible agents in their deliberative processes.
Novel Stealthy Attack and Defense Strategies for Networked Control Systems

This article studies novel attack and defense strategies, based on a class of stealthy attacks, namely the zero-dynamics attack (ZDA), for multiagent control systems. ZDA poses a formidable security challenge since its attack signal is hidden in the null space of the state-space representation of the control system and hence it can evade conventional detection methods. An intuitive defense strategy builds on changing the aforementioned representation via switching through a set of carefully crafted topologies. In this article, we propose realistic ZDA variations where the attacker is aware of this topology-switching strategy, and hence employs the following policies to avoid detection: first, pause, update, and resume ZDA according to the knowledge of switching topologies; and second, cooperate with a concurrent stealthy topology attack that alters network topology at switching times, such that the original ZDA is feasible under the corrupted topology. We first systematically study the proposed ZDA variations, and then develop defense strategies against them under the realistic assumption that the defender has no knowledge of attack starting, pausing, and resuming times and the number of misbehaving agents. Particularly, we characterize conditions for detectability of the proposed ZDA variations, in terms of the network topologies to be maintained, the set of agents to be monitored, and the measurements of the monitored agents that should be extracted, while simultaneously preserving the privacy of the states of the nonmonitored agents. We then propose an attack detection algorithm based on the Luenberger observer, using the characterized detectability conditions. We provide numerical simulation results to demonstrate our theoretical findings.
On the Round Complexity of Covert Computation.

In STOC’05, von Ahn, Hopper and Langford introduced the notion of covert computation. In covert computation, a party runs a secure computation protocol over a covert (or steganographic) channel without knowing if the other parties are participating as well or not. At the end of the protocol, if all parties participated in the protocol and if the function output is “favorable” to all parties, then the output is revealed (along with the fact that everyone participated). All covert computation protocols known so far require a large polynomial number of rounds. In this work, we first study the question of the round complexity of covert computation and obtain the following results: • There does not exist a constant round covert computation protocol with respect to black box simulation even for the case of two parties. (In comparison, such protocols are known even for the multi-party case if there is no covertness requirement.) • By relying on the two slot non-black-box simulation technique of Pass (STOC’04) and techniques from cryptography in NC (Applebaum et al, FOCS’04), we obtain a construction of a constant round covert multi-party computation protocol. Put together, the above adds one more example to the growing list of tasks for which non-black-box simulation techniques (introduced in the work of Barak in FOCS’01) are necessary. Finally, we study the problem of covert multi-party computation in the setting where the parties only have point to point (covert) communication channels. We observe that our covert computation protocol for the broadcast channel inherits, from the protocol of Pass, the property of secure composition in the bounded concurrent setting. Then, as an application of this protocol, somewhat surprisingly we show the existence of covert multi-party computation with point to point channels (assuming that the number of parties is a constant).
Comparison of AC Motors to an Ideal Machine Part II—Non-Sinusoidal AC Machines

This two-part article compares the torque performance of 12 types of ac machines based on the same surface current density, tooth flux density, and rotor dimensions. Part II examines the torque capability of machines more recently developed which utilize the wave shaping capability of a solid-state frequency inverter. Essen's equation, as utilized in Part II, is modified to accommodate machines requiring non-sinusoidal waveforms. The “tau” factor, introduced in Part I, is also modified and is again used as means for comparison of any ac machine with an “ideal” machine based now upon a modified form of Essen's equation.
Regularity of the centered fractional maximal function on radial functions

Abstract We study the regularity properties of the centered fractional maximal function M β . More precisely, we prove that the map f ↦ | ∇ M β f | is bounded and continuous from W 1 , 1 ( R d ) to L q ( R d ) in the endpoint case q = d / ( d − β ) if f is a radial function. For d = 1 , the radiality assumption can be removed. This corresponds to the counterparts of known results for the non-centered fractional maximal function. The main new idea consists in relating the centered and non-centered fractional maximal function at the derivative level.
Deep Global Registration

We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estimation, and a robust gradient-based SE(3) optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data.
Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words

Although models using contextual word embeddings have achieved state-of-the-art results on a host of NLP tasks, little is known about exactly what information these embeddings encode about the context words that they are understood to reflect. To address this question, we introduce a suite of probing tasks that enable fine-grained testing of contextual embeddings for encoding of information about surrounding words. We apply these tasks to examine the popular BERT, ELMo and GPT contextual encoders, and find that each of our tested information types is indeed encoded as contextual information across tokens, often with near-perfect recoverability---but the encoders vary in which features they distribute to which tokens, how nuanced their distributions are, and how robust the encoding of each feature is to distance. We discuss implications of these results for how different types of models break down and prioritize word-level context information when constructing token embeddings.
Logic-Guided Data Augmentation and Regularization for Consistent Question Answering.

Many natural language questions require qualitative, quantitative or logical comparisons between two entities or events. This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models. Our method leverages logical and linguistic knowledge to augment labeled training data and then uses a consistency-based regularizer to train the model. Improving the global consistency of predictions, our approach achieves large improvements over previous methods in a variety of question answering (QA) tasks, including multiple-choice qualitative reasoning, cause-effect reasoning, and extractive machine reading comprehension. In particular, our method significantly improves the performance of RoBERTa-based models by 1-5% across datasets. We advance state of the art by around 5-8% on WIQA and QuaRel and reduce consistency violations by 58% on HotpotQA. We further demonstrate that our approach can learn effectively from limited data.
A discrete choice model of dividend reinvestment plans: classification and prediction

We use a discrete choice recursive model to classify companies with and without dividend reinvestment plans (DRIPs). Our model classifies 72.0% of companies correctly. We interpret misclassified companies as being likely to switch their plan status. For example, if financial data erroneously suggest that a company should have a DRIP then we expect that it would be more likely to institute a plan than other companies in the sample. Our results support this conjecture. Companies that add DRIPs tend to have more extreme levels of variables that control for management entrenchment, higher levels of variables that control for the ability to pay dividends and higher payout ratios. Copyright © 2011 John Wiley & Sons, Ltd.