cssi/SciSciGPT-SciSciNet huggingface dataset:
Spectral functions in V-QCD with matter: masses, susceptibilities, diffusion and conductivity

We consider a holographic model of QCD in the Veneziano limit of a large number of colors N c and flavors N f but fixed x = N f /N c (V-QCD). The model exhibits a first order deconfined but chirally broken transition, followed by a second order chirally restored transition in the μ − T plane for a range of plausible holographic parameters. We study the quasi-normal mode spectrum, and derive the pertinent vector and axial spectral functions across the transition regions. The pole masses, susceptibilities, diffusion constants and electric conductivity are also discussed. In particular, the pole masses are found to survive the deconfining transition, to quickly dissolve in the the chirally restored phase by developing substantial widths. The flavor electric conductivities arise sharply in the transition region. The flavor susceptibility is shown to be consistent with the one derived from bulk thermodynamics.
Molecule-optimized basis sets and Hamiltonians for accelerated electronic structure calculations of atoms and molecules.

Molecule-optimized basis sets, based on approximate natural orbitals, are developed for accelerating the convergence of quantum calculations with strongly correlated (multireferenced) electrons. We use a low-cost approximate solution of the anti-Hermitian contracted Schrodinger equation (ACSE) for the one- and two-electron reduced density matrices (RDMs) to generate an approximate set of natural orbitals for strongly correlated quantum systems. The natural-orbital basis set is truncated to generate a molecule-optimized basis set whose rank matches that of a standard correlation-consistent basis set optimized for the atoms. We show that basis-set truncation by approximate natural orbitals can be viewed as a one-electron unitary transformation of the Hamiltonian operator and suggest an extension of approximate natural-orbital truncations through two-electron unitary transformations of the Hamiltonian operator, such as those employed in the solution of the ACSE. The molecule-optimized basis set from the ACSE improves the accuracy of the equivalent standard atom-optimized basis set at little additional computational cost. We illustrate the method with the potential energy curves of hydrogen fluoride and diatomic nitrogen. Relative to the hydrogen fluoride potential energy curve from the ACSE in a polarized triple-ζ basis set, the ACSE curve in a molecule-optimized basis set, equivalent in size to a polarized double-ζ basis, has a nonparallelity error of 0.0154 au, which is significantly better than the nonparallelity error of 0.0252 au from the polarized double-ζ basis set.
Automorphisms and Derivations of the Insertion–Elimination Algebra and Related Graded Lie Algebras

This paper addresses several structural aspects of the insertion–elimination algebra \({\mathfrak{g}}\), a Lie algebra that can be realized in terms of tree-inserting and tree-eliminating operations on the set of rooted trees. In particular, we determine the finite-dimensional subalgebras of \({\mathfrak{g}}\), the automorphism group of \({\mathfrak{g}}\), the derivation Lie algebra of \({\mathfrak{g}}\), and a generating set. Several results are stated in terms of Lie algebras admitting a triangular decomposition and can be used to reproduce results for the generalized Virasoro algebras.
Extraction efficiency of drifting electrons in a two-phase xenon time projection chamber

We present a measurement of the extraction efficiency of quasi-free electrons from the liquid into the gas phase in a two-phase xenon time-projection chamber. The measurements span a range of electric fields from 2.4 to 7.1 kV/cm in the liquid xenon, corresponding to 4.5 to 13.1 kV/cm in the gaseous xenon. Extraction efficiency continues to increase at the highest extraction fields, implying that additional charge signal may be attained in two-phase xenon detectors through careful high-voltage engineering of the gate-anode region.
Transition state redox during dynamical processes in semiconductors and insulators

Activation barriers associated with ion diffusion and chemical reactions are vital to understand and predict a wide range of phenomena, such as material growth, ion transport, and catalysis. In the calculation of activation barriers for non-redox processes in semiconductors and insulators, it has been widely assumed that the charge state remains fixed to that of the initial electronic ground state throughout a dynamical process. In this work, we demonstrate that this assumption is generally inaccurate and that a rate-limiting transition state can have a different charge state from the initial ground state. This phenomenon can significantly lower the activation barrier of a dynamical process that depends strongly on charge state, such as carbon vacancy diffusion in 4H-SiC. With inclusion of such transition state redox, the activation barrier varies continuously with Fermi level, in contrast to the step-line feature predicted by the traditional fixed-charge assumption. In this study, a straightforward approach to include the transition state redox effect is provided, the typical situations where the effect plays a significant role are identified, and the relevant electron dynamics are discussed. Modeling of reaction dynamics can be improved by rethinking a traditional belief about the charge of a transition state. Chemists usually assume that for a non-redox reaction, the state with the highest energy along a reaction path, or the transition state, has the same charge as the initial state. But Guangfu Luo, Thomas Kuech and Dane Morgan from the University of Wisconsin-Madison in the USA have now demonstrated that a transition state with unfixed charge is more realistic for dynamical processes involving semiconductors or insulators. Through atomistic simulations of dynamics, such as vacancy diffusion in gallium arsenide, the team reports that allowing a change of charge during a dynamical process can reduce its energy barrier relative to that predicted by the traditional approach and predict a more realistic continuous variation of energy barrier with Fermi level. Ab initio calculations reveal that the rate-limiting transition state during a dynamical process in a semiconductor or an insulator can have a different charge state from that of the initial ground state, rather than the same charge state as generally assumed. Such “transition state redox” effect may significantly lower the activation barrier and can be realized through the hopping of an initial excited state and possibly an electron exchange with the bulk during the dynamical process.
Memory properties of transformations of linear processes

In this paper, we study the memory properties of transformations of linear processes. Dittmann and Granger (J Econ 110:113–133, 2002) studied the polynomial transformations of Gaussian FARIMA(0, d, 0) processes by applying the orthonormality of the Hermite polynomials under the measure for the standard normal distribution. Nevertheless, the orthogonality does not hold for transformations of non-Gaussian linear processes. Instead, we use the decomposition developed by Ho and Hsing (Ann Stat 24:992–1024, 1996; Ann Probab 25:1636–1669, 1997) to study the memory properties of nonlinear transformations of linear processes, which include the FARIMA(p, d, q) processes, and obtain consistent results as in the Gaussian case. In particular, for stationary processes, the transformations of short-memory time series still have short-memory and the transformation of long-memory time series may have different weaker memory parameters which depend on the power rank of the transformation. On the other hand, the memory properties of transformations of non-stationary time series may not depend on the power ranks of the transformations. This study has application in econometrics and financial data analysis when the time series observations have non-Gaussian heavy tails. As an example, the memory properties of call option processes at different strike prices are discussed in details.
Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data

We consider the problem of simultaneous variable selection and estimation in additive, partially linear models for longitudinal/clustered data. We propose an estimation procedure via polynomial splines to estimate the nonparametric components and apply proper penalty functions to achieve sparsity in the linear part. Under reasonable conditions, we obtain the asymptotic normality of the estimators for the linear components and the consistency of the estimators for the nonparametric components. We further demonstrate that, with proper choice of the regularization parameter, the penalized estimators of the non-zero coefficients achieve the asymptotic oracle property. The finite sample behavior of the penalized estimators is evaluated with simulation studies and illustrated by a longitudinal CD4 cell count data set.
The Small Scale Velocity Dispersion of Galaxies: A Comparison of Cosmological Simulations

The velocity dispersion of galaxies on small scales (r D 1 h~1 Mpc), can be estimated from the p 12 (r), anisotropy of the galaxy-galaxy correlation function in redshift space (Davis & Peebles). We apply this technique to "" mock catalogs II extracted from N-body simulations of several di†erent variants of cold dark matterEdominated cosmological models, including models with cold ) hot dark matter, to obtain results that may be compared consistently with similar results from observations. We -nd a large varia- tion in the value of h~1 Mpc) in di†erent regions of the same simulation. We investigate the e†ects p 12 (1 of removing clusters from the simulations, using an automated cluster-removing routine, and -nd that this reduces the sky variance but also reduces the discrimination between models. However, studying p 12 as clusters with di†erent internal velocity dispersions are removed leads to interesting information about the amount of power on cluster and subcluster scales. We compute the pairwise velocity dispersion directly, in order to check the Davis-Peebles method, and -nd agreement of better than 20% in all the models studied. We also calculate the mean streaming velocity and the pairwise peculiar velocity dis- tribution in the simulations, and compare these with the models used in the Davis-Peebles method. We -nd that the model for the mean streaming velocity may be a substantial source of error in the calcu- lation of p 12 . Subject headings: cosmology: theory E galaxies: clusters: general E galaxies: distances and redshifts E large-scale structure of universe
Material degradation due to moisture and temperature. Part 1: mathematical model, analysis, and analytical solutions

The mechanical response, serviceability, and load-bearing capacity of materials and structural components can be adversely affected due to external stimuli, which include exposure to a corrosive chemical species, high temperatures, temperature fluctuations (i.e., freezing–thawing), cyclic mechanical loading, just to name a few. It is, therefore, of paramount importance in several branches of engineering—ranging from aerospace engineering, civil engineering to biomedical engineering—to have a fundamental understanding of degradation of materials, as the materials in these applications are often subjected to adverse environments. As a result of recent advancements in material science, new materials such as fiber-reinforced polymers and multi-functional materials that exhibit high ductility have been developed and widely used, for example, as infrastructural materials or in medical devices (e.g., stents). The traditional small-strain approaches of modeling these materials will not be adequate. In this paper, we study degradation of materials due to an exposure to chemical species and temperature under large strain and large deformations. In the first part of our research work, we present a consistent mathematical model with firm thermodynamic underpinning. We then obtain semi-analytical solutions of several canonical problems to illustrate the nature of the quasi-static and unsteady behaviors of degrading hyperelastic solids.
Rationality problems and conjectures of Milnor and Bloch–Kato

We show how the techniques of Voevodsky’s proof of the Milnor conjecture and the Voevodsky–Rost proof of its generalization the Bloch–Kato conjecture can be used to study counterexamples to the classical Luroth problem. By generalizing a method due to Peyre, we produce for any prime number , the varieties that are constructed are furthermore unirational and non-rationality cannot be detected by a torsion unramified etale cohomology class of lower degree.
The Origin of Double-peaked Narrow Lines in Active Galactic Nuclei. IV. Association with Galaxy Mergers

Double-peaked narrow emission lines in active galactic nucleus (AGN) spectra can be produced by AGN outflows, rotation, or dual AGNs, which are AGN pairs in ongoing galaxy mergers. Consequently, double-peaked narrow AGN emission lines are useful tracers of the coevolution of galaxies and their supermassive black holes, as driven by AGN feedback and AGN fueling. We investigate this concept further with follow-up optical longslit observations of a sample of 95 Sloan Digital Sky Survey (SDSS) galaxies that have double-peaked narrow AGN emission lines. Based on a kinematic analysis of the longslit spectra, we confirm previous work that finds that the majority of double-peaked narrow AGN emission lines are associated with outflows. We also find that eight of the galaxies have companion galaxies with line-of-sight velocity separations < 500 km/s and physical separations <30 kpc. Since we find evidence of AGNs in both galaxies, all eight of these systems are compelling dual AGN candidates. Galaxies with double-peaked narrow AGN emission lines occur in such galaxy mergers at least twice as often as typical active galaxies. Finally, we conclude that at least 3% of SDSS galaxies with double-peaked narrow AGN emission lines are found in galaxy mergers where both galaxies are resolved in SDSS imaging.
Relative importance of nonlinear electron-phonon coupling and vertex corrections in the Holstein model

Determining the range of validity of Migdal’s approximation for electron-phonon (e-ph) coupled systems is a long-standing problem. Many attempts to answer this question employ the Holstein Hamiltonian, where the electron density couples linearly to local lattice displacements. When these displacements are large, however, nonlinear corrections to the interaction must also be included, which can significantly alter the physical picture obtained from this model. Using determinant quantum Monte Carlo and the self-consistent Migdal approximation, we compared superconducting and charge-density-wave correlations in the Holstein model with and without second-order nonlinear interactions. We find a disagreement between the two cases, even for relatively small values of the e-ph coupling strength, and, importantly, that this can occur in the same parameter regions where Migdal’s approximation holds. Our results demonstrate that questions regarding the validity of Migdal’s approximation go hand in hand with questions of the validity of a linear e-ph interaction. Phonon mediated superconductivity is understood using Migdal’s approximation and models with linear electron-phonon coupling, but there are limitations to the information these approximations can provide. Here, the authors investigate the influence of nonlinear electron-phonon interactions on the charge density wave and superconducting phases to determine their relevance on the validity of Migdal’s approximation.
Binary frames with prescribed dot products and frame operator

This paper extends three results from classical finite frame theory over real or complex numbers to binary frames for the vector space ${\mathbb Z}_2^d$. Without the notion of inner products or order, we provide an analog of the "fundamental inequality" of tight frames. In addition, we prove the binary analog of the characterization of dual frames with given inner products and of general frames with prescribed norms and frame operator.
When is Chemical Disequilibrium in Earth-like Planetary Atmospheres a Biosignature versus an Anti-biosignature? Disequilibria from Dead to Living Worlds

Chemical disequilibrium in exoplanetary atmospheres (detectable with remote spectroscopy) can indicate life. The modern Earth's atmosphere-ocean system has a much larger chemical disequilibrium than other solar system planets with atmospheres because of oxygenic photosynthesis. However, no analysis exists comparing disequilibrium on lifeless, prebiotic planets to disequilibrium on worlds with primitive chemotrophic biospheres that live off chemicals and not light. Here, we use a photochemical-microbial ecosystem model to calculate the atmosphere-ocean disequilibria of Earth with no life and with a chemotrophic biosphere. We show that the prebiotic Earth likely had a relatively large atmosphere-ocean disequilibrium due to the coexistence of water and volcanic H2, CO2, and CO. Subsequent chemotrophic life probably destroyed nearly all of the prebiotic disequilibrium through its metabolism, leaving a likely smaller disequilibrium between N2, CO2, CH4, and liquid water. So, disequilibrium fell with the rise of chemotrophic life then later rose with atmospheric oxygenation due to oxygenic photosynthesis. We conclude that big prebiotic disequilibrium between H2 and CO2 or CO and water is an anti-biosignature because these easily metabolized species can be eaten due to redox reactions with low activation energy barriers. However, large chemical disequilibrium can also be a biosignature when the disequilibrium arises from a chemical mixture with biologically insurmountable activation energy barriers, and clearly identifiable biogenic gases. Earth's modern disequilibrium between O2, N2, and liquid water along with minor CH4 is such a case. Thus, the interpretation of disequilibrium requires context. With context, disequilibrium can be used to infer dead or living worlds.
How to Defuse Earth Impact Threat Announcements

Announcements of newly discovered near-Earth asteroids with unusually high Earth-collision chances in the near future have been regularly highlighted in the press and on TV since the 1980s. Recentl...
Heterogeneous learning from demonstration

The development of human-robot systems able to leverage the strengths of both humans and their robotic counterparts has been greatly sought after because of the foreseen, broad-ranging impact across industry and research. We believe the true potential of these systems cannot be reached unless the robot is able to act with a high level of autonomy, reducing the burden of manual tasking or teleoperation. To achieve this level of autonomy, robots must be able to work fluidly with its human partners, inferring their needs without explicit commands. This inference requires the robot to be able to detect and classify the heterogeneity of its partners. We propose a framework for learning from heterogeneous demonstration based upon Bayesian inference and evaluate a suite of approaches on a real-world dataset of gameplay from StarCraft II. This evaluation provides evidence that our Bayesian approach can outperform conventional methods by up to 12.8%.
Transition-based Parsing with Stack-Transformers.

Modeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, or local state modeling of contextualized features, e.g. Bi-LSTM parsers. Given the success of Transformer architectures in recent parsing systems, this work explores modifications of the sequence-to-sequence Transformer architecture to model either global or local parser states in transition-based parsing. We show that modifications of the cross attention mechanism of the Transformer considerably strengthen performance both on dependency and Abstract Meaning Representation (AMR) parsing tasks, particularly for smaller models or limited training data.
All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement

Although analyzing user behavior within individual communities is an active and rich research domain, people usually interact with multiple communities both on- and off-line. How do users act in such multi-community environments? Although there are a host of intriguing aspects to this question, it has received much less attention in the research community in comparison to the intra-community case. In this paper, we examine three aspects of multi-community engagement: the sequence of communities that users post to, the language that users employ in those communities, and the feedback that users receive, using longitudinal posting behavior on Reddit as our main data source, and DBLP for auxiliary experiments. We also demonstrate the effectiveness of features drawn from these aspects in predicting users' future level of activity. One might expect that a user's trajectory mimics the "settling-down" process in real life: an initial exploration of sub-communities before settling down into a few niches. However, we find that the users in our data continually post in new communities; moreover, as time goes on, they post increasingly evenly among a more diverse set of smaller communities. Interestingly, it seems that users that eventually leave the community are "destined" to do so from the very beginning, in the sense of showing significantly different "wandering" patterns very early on in their trajectories; this finding has potentially important design implications for community maintainers. Our multi-community perspective also allows us to investigate the "situation vs. personality" debate from language usage across different communities.
Rationale for Proposed Revisions to Federal Standard 209B (Cleanrooms)

This paper discusses the rationale behind the proposed revisions of Federal Standard 209B on cleanroom air cleanliness classifications. The proposed revisions were developed in a series of meetings held by the RP-50 committee of the Institute of Environmental Sciences. The major recommended changes are:1. Class 1 and Class 10 be added2. The two proposed new classes may be met at particle diameters of 0.1,0.2,0.3, and 0.5 μm, (microns), and Class 100 may be met at 0.2,0.3 μm as well as 0.5 μm, with suitable adjustments in the required concentrations3. Other classes (e.g., Class 50) be defined as appropriate4. The number of sampling points be specified differently, and a distinction be drawn between unidirectional and non-unidirectional flow, rather than laminar and turbulent flow5. Minimum sampling volumes be set, based on measurement errors6. Verification be done initially and periodically7. Verification require particle counting8. Particle counting instruments require periodic calibration9. An area be de...
Early classification of motor tasks using dynamic functional connectivity graphs from EEG.

Objective Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem. Approach The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification. Main results Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data. Significance Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.
Topological defects promote layer formation in Myxococcus xanthus colonies

The soil bacterium Myxococcus xanthus lives in densely packed groups that form dynamic three-dimensional patterns in response to environmental changes, such as droplet-like fruiting bodies during starvation1. The development of these multicellular structures begins with the sequential formation of cell layers in a process that is poorly understood2. Here, using confocal three-dimensional imaging, we find that motile, rod-shaped M. xanthus cells are densely packed and aligned in each layer, forming an active nematic liquid crystal. Cell alignment is nearly perfect throughout the population except at point defects that carry half-integer topological charge. We observe that new cell layers preferentially form at the position of +1/2 defects, whereas holes preferentially open at −1/2 defects. To explain these findings, we model the bacterial colony as an extensile active nematic fluid with anisotropic friction. In agreement with our experimental measurements, this model predicts an influx of cells towards the +1/2 defects and an outflux of cells from the −1/2 defects. Our results suggest that cell motility and mechanical cell–cell interactions are sufficient to promote the formation of cell layers at topological defects, thereby seeding fruiting bodies in colonies of M. xanthus. Topological defects in active nematic systems such as epithelial tissues and neural progenitor cells can be associated with biological functions. Here, the authors show that defects can play a role in the layer formation of the soil bacterium Myxococcus xanthus.
A Meta-Analysis of Technology: Interventions in Collegiate Economics Classes.

Technological interventions have been sold as improving student understanding of economics for decades. Yet despite the panoply of ways to incorporate technology, it is not clear which types of int...
ISO Order Imbalances and Individual Stock Returns

This paper studies the relation between intermarket sweep order, ISO, order imbalances and daily returns of individual stocks. I show that ISO order imbalances positively affect contemporaneous returns. Second, I find that price pressures emanating from ISO imbalances are persistent and result in predicting positive cumulative abnormal returns up to two months. The predictive power of ISO order imbalances on contemporaneous and future abnormal returns is stronger in small-sized firms. Finally, I analyze the herding among ISO order imbalances and find strong commonality exists in ISO order imbalances, particularly in large-sized firms. In contrast to previous studies, my results indicate that ISOs contribute to both short- and long-term price and return formation.
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

Abstract Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we propose a novel neural network model to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings. We apply the model to screening mammography interpretation: predicting the presence or absence of benign and malignant lesions. On the NYU Breast Cancer Screening Dataset, our model outperforms (AUC = 0.93) ResNet-34 and Faster R-CNN in classifying breasts with malignant findings. On the CBIS-DDSM dataset, our model achieves performance (AUC = 0.858) on par with state-of-the-art approaches. Compared to ResNet-34, our model is 4.1x faster for inference while using 78.4% less GPU memory. Furthermore, we demonstrate, in a reader study, that our model surpasses radiologist-level AUC by a margin of 0.11.
Detection of Backdoors in Trained Classifiers Without Access to the Training Set.

With wide deployment of deep neural network (DNN) classifiers, there is great potential for harm from adversarial learning attacks. Recently, a special type of data poisoning (DP) attack, known as a backdoor (or Trojan), was proposed. These attacks do not seek to degrade classification accuracy, but rather to have the classifier learn to classify to a target class t* whenever the backdoor pattern is present in a test example originally from a source class s*. Launching backdoor attacks does not require knowledge of the classifier or its training process--only the ability to poison the training set with exemplars containing a backdoor pattern (labeled with the target class). Defenses against backdoors can be deployed before/during training, post-training, or at test time. Here, we address post-training detection in DNN image classifiers, seldom considered in existing works, wherein the defender does not have access to the poisoned training set, but only to the trained classifier itself, as well as to clean (unpoisoned) examples from the classification domain. This scenario is of great interest because e.g., a classifier may be the basis of a phone app that will be shared with many users. Detection may thus reveal a widespread attack. We propose a purely unsupervised anomaly detection (AD) defense against imperceptible backdoor attacks that: 1) detects whether the trained DNN has been backdoor-attacked; 2) infers the source and target classes in a detected attack; 3) estimates the backdoor pattern itself. Our AD approach involves learning (via suitable cost function minimization) the minimum size/norm perturbation (putative backdoor) required to induce the classifier to misclassify (most) examples from class s to class t, for all (s,t) pairs. Our hypothesis is that nonattacked pairs require large perturbations, while the attacked pair (s*, t*) requires much smaller ones. This is convincingly borne out experimentally. We identify a variety of plausible cost functions and devise a novel, robust hypothesis testing approach to perform detection inference. We test our approach, in comparison with the state-of-the-art methods, for several backdoor patterns, attack settings and mechanisms, and data sets and demonstrate its favorability. Our defense essentially requires setting a single hyperparameter (the detection threshold), which can e.g., be chosen to fix the system's false positive rate.\enlargethispage8pt.
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems

The increase of cyber attacks in both the numbers and varieties in recent years demands to build a more sophisticated network intrusion detection system (NIDS). These NIDS perform better when they can monitor all the traffic traversing through the network like when being deployed on a Software-Defined Network (SDN). Because of the inability to detect zeroday attacks, signature-based NIDS which were traditionally used for detecting malicious traffic are beginning to get replaced by anomaly-based NIDS built on neural networks. However, recently it has been shown that such NIDS have their own drawback namely being vulnerable to the adversarial example attack. Moreover, they were mostly evaluated on the old datasets which don't represent the variety of attacks network systems might face these days. In this paper, we present Reconstruction from Partial Observation (RePO) as a new mechanism to build an NIDS with the help of denoising autoencoders capable of detecting different types of network attacks in a low false alert setting with an enhanced robustness against adversarial example attack. Our evaluation conducted on a dataset with a variety of network attacks shows denoising autoencoders can improve detection of malicious traffic by up to 29% in a normal setting and by up to 45% in an adversarial setting compared to other recently proposed anomaly detectors.
Remarks on a Tropical Key Exchange System

© 2020 D. Rudy and C. Monico, published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License.
A framework for estimating bikeshare origin destination flows using a multiple discrete continuous system

Abstract Given the burgeoning growth in bikeshare system installations and their growing adoption for trip making, it is important to develop modeling frameworks to understand bikeshare demand flows in the system. The current study examines two choice dimensions for capturing the system level bikeshare system demand: (1) total station level demand and (2) distribution of bike flows from an origin station across the network. A linear mixed model is used to estimate the first choice and Multiple Discrete Continuous Extreme Value (MDCEV) model is used to analyze the latter. The data is drawn from the New York City bikeshare system (CitiBike) for six months (January through June 2017). For our analysis, we examine demand and distribution patterns on a weekly basis controlling for a host of independent variables (trip, socio-demographics, bicycle infrastructure, land use and built environment, temporal and weather). Model validation exercise results revealed that the proposed model performs well for low demand destinations. A policy exercise evaluating destination choice behavior demonstrated how the impact of distance is compensated by additional bicycling infrastructure in the farther locations. The results from the study help bikesharing system planners and operators to better evaluate and improve bikeshare systems.
How to quantify and avoid finite size effects in computational studies of crystal nucleation: The case of heterogeneous ice nucleation

Computational studies of crystal nucleation can be impacted by finite size effects, primarily due to unphysical interactions between crystalline nuclei and their periodic images. It is, however, not always feasible to systematically investigate the sensitivity of nucleation kinetics and mechanism to system size due to large computational costs of nucleation studies. Here, we use jumpy forward flux sampling to accurately compute the rates of heterogeneous ice nucleation in the vicinity of square-shaped model structureless ice nucleating particles (INPs) of different sizes and identify three distinct regimes for the dependence of rate on the INP dimension, L. For small INPs, the rate is a strong function of L due to the artificial spanning of critical nuclei across the periodic boundary. Intermediate-sized INPs, however, give rise to the emergence of non-spanning “proximal” nuclei that are close enough to their periodic images to fully structure the intermediary liquid. While such proximity can facilitate nucleation, its effect is offset by the higher density of the intermediary liquid, leading to artificially small nucleation rates overall. The critical nuclei formed at large INPs are neither spanning nor proximal. Yet, the rate is a weak function of L, with its logarithm scaling linearly with 1/L. The key heuristic emerging from these observations is that finite size effects will be minimal if critical nuclei are neither spanning nor proximal and if the intermediary liquid has a region that is structurally indistinguishable from the supercooled liquid under the same conditions.
Hybrid Control of Flowrate in Microextrusion-Based Direct-Write Additive Manufacturing

Flowrate control has been a continuous challenge in Direct-Write Additive Manufacturing (DW AM) due to capacitive energy storage in the system and the lack of suitable flowrate sensors at the microscale, resulting in poor dimensional control of material addition. While a pressure sensor can be used for pressure-based feedback control, the system will become marginally stable if the material loses contact with the pressure sensor, and thus feedback is lost. In this letter, we design and implement a pressure-based hybrid controller with a state-dependent switching strategy that eliminates the stability problem due to a loss of pressure feedback, and thus enables the real time, stable control of flowrate in DW AM. The stability of the hybrid controller is assessed using phase portraits and Multiple Lyapunov-like Functions. The hybrid dynamical model and the hybrid controller are experimentally implemented and validated. Stability results indicate that the hybrid controller resolved the marginal stability issue, and is stable in the sense of Lyapunov. Moreover, the hybrid control scheme implementation in the case study demonstrated that the shape fidelity of the parts is appreciably enhanced compared to the open loop control case.
Inequality, participation, and polarization

The upward co-movement of income inequality and partisan polarization in the U.S. is typically attributed to intensified class conflict or a political wealth bias. This paper formalizes a theory of polarization where changes in the income distribution do not affect citizens’ policy preferences, but instead change their patterns of political participation: aggregate voting decreases relative to aggregate giving, reducing the electoral penalty for partisan policies. By endogenizing party composition the model captures both the ideological and compositional dimensions of polarization, and addresses less-discussed polarization features, such as intra-party homogeneity and the increase in safe seats. According to the model, observed polarization patterns imply that parties have diverged more than candidates, and that the gap between party and candidate divergence has increased with income inequality.
Living next to godliness: Residential property values and churches

This article extends the analysis concerning the impact of neighborhood churches on residential property values by investigating nearly 5,000 residential property transactions in Henderson, Nevada, between January 1986 and December 1990. We find that real property values decrease, at a decreasing rate, as distance from a neighborhood church increases. This result is the opposite of that reported by Do, Wilbur, and Short in a previous edition of this journal. We bolster our findings by showing that distance from the site of a future church has little or no impact on residential property values, whereas distance from an existing church is associated with lower property values. Our evidence indicates that neighborhood churches are amenities that enhance the value of neighborhood residential property. Finally, we demonstrate that larger churches (as measured by square foot of lot size) tend to have a greaterpositive impact on residential property values.
Cosmopolitanism and Compatriot Duties

Defenders of duties of justice to compatriots sometimes criticize the aspirations of cosmopolitans to develop accounts of justice that that are global in scope. The criticisms are many and varied, including in particular concerns about whether cosmopolitanism can allow for appropriate patriotic regard for fellow-citizens, whether cosmopolitanism can allow for the political self-determination of states, and whether cosmopolitanism can account for the basis of duties of justice absent the existence of political communities.
Monetary policy implications of comovements among long-term interest rates

Abstract This paper examines the monetary policy implications from the greater integration of major capital markets using long-term interest rates. Proof that globalization has affected the behavior of interest rates and made them more synchronized across countries is provided from the way disturbances in a market transmit to other markets thereby affecting the conduct of monetary policy in all involved parties. The results also confirm greater convergence among countries in the European Union (EU) as Germany still retains its hegemonic status. The implications for monetary policy are that countries now will have to deal with more outside shocks and these shocks will be more diverse, intense and persistent. Thus, global monetary policy, at least among the major capital markets, henceforth will have to be played interactively, which may necessitate a greater financial supervision in order to ensure continued world stability and prosperity.
HEDONIC PRICES AND IMPLICIT MARKETS: ESTIMATING MARGINAL WILLINGNESS TO PAY FOR DIFFERENTIATED PRODUCTS WITHOUT INSTRUMENTAL VARIABLES

The hedonic model of Rosen (1974) has become a workhorse for valuing the characteristics of differentiated products despite a number of well-documented econometric problems. For example, Bartik (1987) and Epple (1987) each describe a source of endogeneity in the second stage of Rosen's procedure that has proven difficult to overcome. In this paper, we propose a new approach for recovering the marginal willingness-to-pay function that altogether avoids these endogeneity problems. Applying this estimator to data on large changes in violent crime rates, we find that marginal willingness-to-pay increases by ten cents with each additional violent crime per 100,000 residents.
Consumer Information and Price Discrimination: Does the Internet Affect the Pricing of New Cars to Women and Minorities?

Using a large dataset of automobile transaction prices, we find that offline African-American and Hispanic consumers pay approximately 2% more than do other offline consumers; however, we can explain 65% of this price premium with differences in observable traits such as income, education, and search costs. Our estimates of unexplained race premia are smaller than previous estimates in the literature. Online, we find that minority buyers pay nearly the same prices as do whites controlling for consumers' income, education, and neighborhood characteristics. These results are consistent with the Internet facilitating information search and removing cues to a consumer's willingness to pay. Our results imply that the Internet is particularly beneficial to those whose characteristics disadvantage them in negotiating.
The scalar and tensor glueballs in the valence approximation

Abstract We evaluate the infinite volume, continuum limit of 0++ and 2++ glueball masses in the valence approximation. We find m0++ = 1740 ± 71 MeV and m2++ = 2359 ± 128 MeV, consistent with the interpretation of f0(1710) as the lightest scalar glueball.
Cross-Selling: Offering the Right Product to the Right Customer at the Right Time

Cross-selling is an old and valuable technique used by salespeople to increase order size and to transform single-product buyers into multi-product ones. More recently, cross-selling has evolved into a strategy for customer relationship management. This article starts with a discussion of the benefits and pitfalls of cross-selling as a strategy for customer development within the context of CRM, oriented towards increasing the firm's share of the customer wallet, broadening the scope of the relationship with the customer, and increasing customer retention. This discussion is followed by a review of some of the analytical tools for identifying prospects for cross-selling, and by a discussion of technological and organizational requirements for the successful implementation of cross-selling.
Private Actions under the Magnuson-Moss Warranty Act

In this Article, Professor Schroeder examines the changes that the Magnuson-Moss Warranty Act brings to the law of consumer product warranties. Particular emphasis is given to the relationship between the Act and the Uniform Commercial Code and to the ambiguities created by the language of the Act. The federal action is parallel to and does not supplant state warranty remedies because the Act provides it does not restrict any 'right or remedy of any consumer under State law or any other Federal law.' Since the consumer may recover all litigation expenses, including attorneys' fees, in a successful Magnuson-Moss action, bringing a warranty action as a matter of federal rather than state law has advantages. There are, however, important differences between warranty recovery under Magnuson-Moss and under state law. This Article explores the new federal warranty law and compares it with the law of warranty which has developed under the Uniform Commercial Code (U.C.C.).
Market making and risk management in options markets

This article examines the personal trading strategies of member proprietary traders in the natural gas futures options market. Trading activity is found to mirror previous findings in futures markets, specifically high frequency trading, with low risk exposure. The portfolio of risk holdings by member proprietary traders are also examined to identify whether they are instantaneously hedged using the underlying futures market, as well as to investigate how they manage their inventory holding, rebalancing, and volatility risk exposures. Findings of longer-term risk management practices by option markets indicate that instantaneous hedging does not take place in this market. Exposure to price and volatility risks is actively managed, while rebalancing risk exposure has a significant impact on profit for this trading group.
The Failure of Good Intentions: The WorldCom Fraud and the Collapse of American Telecommunications After Deregulation

Seven years of attempted deregulation of telecommunications in the United States yield several lessons. First, the transactions costs of the regulatory process have grown since enactment of the Telecommunications Act of 1996. Second, if the Federal Communications Commission (“FCC”) had used a consumer-welfare standard rather than a competitor-welfare standard when interpreting the Act, the agency’s regulations on mandatory unbundling of local telecommunications networks would have been simpler and more socially beneficial. Third, despite its micromanagement of competition in local telecommunications, the FCC missed WorldCom’s fraud and bankruptcy. WorldCom’s false Internet traffic reports and accounting fraud encouraged overinvestment in long-distance capacity and Internet backbone capacity. Because Internet traffic data are proprietary and WorldCom dominated Internet backbone services, and because WorldCom was subject to regulatory oversight, it was reasonable for rival carriers to believe WorldCom’s misrepresentation of Internet traffic growth. WorldCom’s accounting fraud may have destroyed billions of dollars of shareholder value in other telecommunications firms. In addition, WorldCom’s misconduct may have been intended to harm competition by inducing exit (or forfeiture of market share) by the efficient rivals. Chapter 11 reorganization of WorldCom would further distort competition in the long-distance and Internet backbone markets. The FCC has a unique obligation to investigate the harm that WorldCom caused the telecommunications industry. If WorldCom is unqualified to hold its FCC licenses and authorizations, that legal conclusion would promptly, and properly, propel WorldCom toward liquidation.