cssi/SciSciGPT-SciSciNet huggingface dataset:
Identifying Spurious Correlations for Robust Text Classification

The predictions of text classifiers are often driven by spurious correlations – e.g., the term “Spielberg” correlates with positively reviewed movies, even though the term itself does not semantically convey a positive sentiment. In this paper, we propose a method to distinguish spurious and genuine correlations in text classification. We treat this as a supervised classification problem, using features derived from treatment effect estimators to distinguish spurious correlations from “genuine” ones. Due to the generic nature of these features and their small dimensionality, we find that the approach works well even with limited training examples, and that it is possible to transport the word classifier to new domains. Experiments on four datasets (sentiment classification and toxicity detection) suggest that using this approach to inform feature selection also leads to more robust classification, as measured by improved worst-case accuracy on the samples affected by spurious correlations.
CrypTFlow2: Practical 2-Party Secure Inference

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct -- i.e., their outputs are bitwise equivalent to the cleartext execution -- and efficient -- they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. These DNNs are at least an order of magnitude larger than those considered in the prior work of 2-party DNN inference. Even on the benchmarks considered by prior work, CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.
On the Balance of Intercalation and Conversion Reactions in Battery Cathodes

Author(s): Hannah, DC; Sai Gautam, G; Canepa, P; Ceder, G | Abstract: A thermodynamic analysis of the driving forces is presented for intercalation and conversion reactions in battery cathodes across a range of possible working ion, transition metal, and anion chemistries. Using this body of results, the importance of polymorph selection as well as chemical composition on the ability of a host cathode to support intercalation reactions is analyzed. It is found that the accessibility of high energy charged polymorphs in oxides generally leads to larger intercalation voltages favoring intercalation reactions, whereas sulfides and selenides tend to favor conversion reactions. Furthermore, it is observed that Cr-containing cathodes favor intercalation more strongly than those with other transition metals. Finally, it is concluded that two-electron reduction of transition metals (as is possible with the intercalation of a 2 + ion) will favor conversion reactions in the compositions studied.
The Aesthetics of Astrophysics: How to Make Appealing Color-composite Images that Convey the Science

Astronomy has a rich tradition of using color photography and imaging, for visualization in research as well as for sharing scientific discoveries in formal and informal education settings (i.e., for "public outreach.") In the modern era, astronomical research has benefitted tremendously from electronic cameras that allow data and images to be generated and analyzed in a purely digital form with a level of precision not previously possible. Advances in image-processing software have also enabled color-composite images to be made in ways much more complex than with darkroom techniques, not only at optical wavelengths but across the electromagnetic spectrum. And the internet has made it possible to rapidly disseminate these images to eager audiences. Alongside these technological advances, there have been gains in understanding how to make images that are scientifically illustrative as well as aesthetically pleasing. Studies have also given insights on how the public interprets astronomical images, and how that can be different than professional astronomers. An understanding of these differences will help in the creation of images that are meaningful to both groups. In this invited review we discuss the techniques behind making color-composite images as well as examine the factors one should consider when doing so, whether for data visualization or public consumption. We also provide a brief history of astronomical imaging with a focus on the origins of the "modern era" during which distribution of high-quality astronomical images to the public is a part of nearly every professional observatory's public outreach. We review relevant research into the expectations and misconceptions that often affect the public's interpretation of these images.
Multi-soliton solutions of affine Toda models

Abstract Hirota's method is used to construct multi-soliton and plane-wave solitons for affine Toda field theories with imaginary coupling.
A Bayesian Assessment of p-Values for Significance Estimation of Power Spectra and an Alternative Procedure, with Application to Solar Neutrino Data

The usual procedure for estimating the significance of a peak in a power spectrum is to calculate the probability of obtaining that value or a larger value by chance (known as the 'p-value'), on the assumption that the time series contains only noise-typically that the measurements are derived from random samplings of a Gaussian distribution. However, since the use of p-values in other contexts is known to be misleading, it seems prudent to examine the implications of using p-values for significance estimation of power spectra. We really need to know the probability that the time series is-or is not-compatible with the 'null hypothesis' that the measurements are derived from noise. This probability can be calculated by Bayesian analysis, but this requires one to specify and evaluate a second hypothesis that the time series does contain a contribution other than noise. We show that the requirement that the p-value should be identical to the probability that the null hypothesis is true leads to an unacceptable form for the likelihood function associated with this hypothesis. We claim that, for this reason, the p-value is not an acceptable method for significance estimation of a power spectrum. In order to obtain an acceptable significancemore » estimate, it is necessary to explicitly consider a second hypothesis, and the key challenge is to identify an appropriate likelihood function for this hypothesis. We first propose four simple conditions that it seems reasonable to impose on this function. We then examine a general functional form for the function, and find the simplest form (which has one free parameter) that meets these conditions. We then define two different ways of combining information derived from two independent power estimates. One procedure is to calculate the post-probabilities of the null hypothesis, convert these to odds-values, and sum the log-odds. The second procedure is to combine the p-values using a procedure due to R.A. Fisher, and to calculate the corresponding post-probability and hence the corresponding log-odds. It seems sensible-even if not logically essential-to seek a likelihood function for which the two procedures lead to the same answer. We find that this consistency condition may be satisfied, to good approximation, by a special case of the previously proposed likelihood function. We find that the resulting significance estimates are considerably more conservative than those usually associated with the p-values. As two examples, we apply the new procedure to two recent analyses of solar neutrino data: (1) power spectrum analysis of Super-Kamiokande data and (2) the combined analysis of radiochemical neutrino data and irradiance data.« less
GRASP: A green energy aware SDN platform

The transition to renewable energy sources for data centers has become a popular trend in the IT industry. However, the volatility of renewable energy, such as solar and wind power, impedes the operation of green data centers. In this work, we leverage Software Defined Networking (SDN) to build GRASP, a platform that schedules job requests to distributed data centers according to the amount of green energy available at each site. GRASP can be re-configured with different scheduling algorithms to address diverse factors such as amounts of instantly available solar power, wind power and CPU load of data centers. We utilize realistic green energy datasets from National Solar Radiation Database and evaluate GRASP in the GENI testbed; in addition, we create necessary GENI artifacts to repeat our experiment. GRASP can serve as a practical platform to test various job scheduling mechanisms for distributed green data centers.
Spatial Birth–Death Wireless Networks

We propose and study a novel continuous space-time model for wireless networks, which considers the stochastic interactions in both space through interference and in time due to randomness in traffic. Our model consists of an interacting particle birth–death dynamics incorporating information-theoretic spectrum-sharing. Roughly speaking, particles (or more generally wireless links) arrive according to a Poisson point process on space-time, and stay for a duration governed by the local configuration of points present, and then exit the network after completion of a file transfer. We analyze this particle dynamics to derive an explicit condition for time ergodicity (i.e., stability), which is tight. We also prove that when the dynamics is ergodic, the steady-state point process of links (or particles) exhibits a form statistical clustering . Based on the clustering, we propose a conjecture, which we leverage to derive approximations, bounds, and asymptotics on performance characteristics, such as delay and mean number of links per unit-space in the stationary regime. The mathematical analysis is combined with discrete event simulation to study the performance of this type of networks.
Effective field theories

These lectures introduce some of the basic ideas of effective field theories. The topics discussed include: relevant and irrelevant operators and scaling, renormalization in effective field theories, decoupling of heavy particles, power counting, and naive dimensional analysis. Effective Lagrangians are used to study the ΔS=2 weak interactions and chiral perturbation theory.
From the microscopic to the macroscopic world: from nucleons to neutron stars

Recent observations of neutron-star properties, in particular the recent detection of gravitational waves emitted from binary neutron stars, GW170817, open the way to put strong constraints on nuclear interactions. In this paper, we review the state of the art in calculating the equation of state of strongly interacting matter from first principle calculations starting from microscopic interactions among nucleons. We then review selected properties of neutron stars that can be directly compared with present and future observations.
On the degrees of polynomial divisors over finite fields

We show that the proportion of polynomials of degree $n$ over the finite field with $q$ elements, which have a divisor of every degree below $n$, is given by $c_q n^{-1} + O(n^{-2})$. More generally, we give an asymptotic formula for the proportion of polynomials, whose set of degrees of divisors has no gaps of size greater than $m$. To that end, we first derive an improved estimate for the proportion of polynomials of degree $n$, all of whose non-constant divisors have degree greater than $m$. In the limit as $q \to \infty$, these results coincide with corresponding estimates related to the cycle structure of permutations.
A Perspective on Regularization and Curvature

A global connection on the Connes Marcolli renormalization bundle relates β-functions of a class of regularization schemes by gauge transformations, as well as local solutions to β-functions over curved space–time.
Dispersion engineering of high-Q silicon microresonators via thermal oxidation

We propose and demonstrate a convenient and sensitive technique for precise engineering of group-velocity dispersion in high-Q silicon microresonators. By accurately controlling the surface-oxidation thickness of silicon microdisk resonators, we are able to precisely manage the zero-dispersion wavelength, while simultaneously further improving the high optical quality of our devices, with the optical Q close to a million. The demonstrated dispersion management allows us to achieve parametric generation with precisely engineerable emission wavelengths, which shows great potential for application in integrated silicon nonlinear and quantum photonics.
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping

The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual information. However, such a model could still make mistakes if its features favor a wrong entity type. In this paper, we utilize Wikipedia as an open knowledge base to improve multilingual NER systems. Central to our approach is the construction of high-accuracy, high-coverage multilingual Wikipedia entity type mappings. These mappings are built from weakly annotated data and can be extended to new languages with no human annotation or language-dependent knowledge involved. Based on these mappings, we develop several approaches to improve an NER system. We evaluate the performance of the approaches via experiments on NER systems trained for 6 languages. Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18.3 F1 score improvement).
Whittaker unitary dual of affine graded Hecke algebras of type E

This paper gives the classification of the Whittaker unitary dual for affine graded Hecke algebras of type E. By the Iwahori–Matsumoto involution, this is also equivalent to the classification of the spherical unitary dual for type E. Together with some results of Barbasch and Moy (D. Barbasch and A. Moy, Unitary spherical spectrum for p-adic classical groups, Acta Appl. Math. 44 (1996), 3–37; D. Barbasch, The spherical unitary spectrum of split classical real and p-adic groups, Preprint (2006), math/0609828) and Ciubotaru (D. Ciubotaru, The Iwahori spherical unitary dual of the split group of type F4, Represent. Theory 9 (2005), 94–137), this work completes the classification of the Whittaker Iwahori-spherical unitary dual or, equivalently, the spherical unitary dual of any split p-adic group.
Anticorrelated Hard/Soft X-Ray Emission from the X-Ray Burster 4U 0614+091

We have detected transient X-ray activity from the X-ray burster 4U 0614+091 simultaneously with BATSE on board the Compton Gamma Ray Observatory (20-100 keV) and the all-sky monitor (ASM) on board the Rossi X-Ray Timing Explorer (1-12 keV). The peak fluxes reach approximately 40 mcrab in both instruments over a period of about 20 days. The variable emission shows a clear anticorrelation of the hard X-ray flux with the soft X-ray count rate. The observed anticorrelation is another clear counterexample to the notion that only black hole binaries exhibit such correlations. The individual spectra during this period can be fitted by power laws with photon indices 2.2 ± 0.3 (ASM) and 2.7 ± 0.4 (BATSE), while the combined spectra can be described by a single power law with index 2.09 ± 0.08. BATSE and the Rossi X-Ray Timing Explorer ASM are a good combination for monitoring X-ray sources over a wide energy band.
Numerical multi-loop integrals and applications

Higher-order radiative corrections play an important role in precision studies of the electroweak and Higgs sector, as well as for the detailed understanding of large backgrounds to new physics searches. For corrections beyond the one-loop level and involving many independent mass and momentum scales, it is in general not possible to find analytic results, so that one needs to resort to numerical methods instead. This article presents an overview of a variety of numerical loop integration techniques, highlighting their range of applicability, suitability for automatization, and numerical precision and stability.
Temporal quantum noise reduction acquired by an electron-multiplying charge-coupled-device camera

Electron-multiplying charge-coupled-device cameras (EMCCDs) have been used to observe quantum noise reductions in beams of light in the transverse spatial degree of freedom. For the quantum noise reduction in the temporal domain, ‘bucket detectors,’ usually composed of photodiodes with operational amplifiers, are used to register the intensity fluctuations in beams of light within the detectors’ bandwidth. Here, we report on measurements of the temporal quantum noise reduction in bright twin beams using an EMCCD camera. The four-wave mixing process in an atomic rubidium vapor cell is used to generate the bright twin beams of light. We observe ∼ 25% of temporal quantum noise reduction with respect to the shot-noise limit in images captured by the EMCCD camera. The temporal images captured by our technique are potentially important in obtaining dynamical information on evolving systems.
Collective Coordinate Action for Charged Sigma-Model Vortices in Finite Geometries

We apply the method of Lund to formulate a variational principle for the motion of charged vortices in an effective nonlinear Schrodinger field theory describing finite size two-dimensional quantum Hall samples under the influence of an arbitrary perpendicular magnetic field. Freezing out variations in the modulus of the effective field yields a U(1) sigma-model. A duality transformation on the sigma-model reduces the problem to finding the Green function for a related electrostatics problem. This duality connects the plasma analogy to the Laughlin wave function directly to a vortex gas description of the fractional quantum Hall effect.
Multiple Tensor-on-Tensor Regression: An Approach for Modeling Processes With Heterogeneous Sources of Data

In recent years, measurement or collection of heterogeneous sets of data such as those containing scalars, waveform signals, images, and even structured point clouds, has become more common. Statis...
Adjoint-optimized nanoscale light extractor for nitrogen-vacancy centers in diamond

We designed a nanoscale light extractor (NLE) for efficient outcoupling and beaming of broadband light emitted by shallow, negatively charged nitrogen-vacancy (NV) centers in bulk diamond. The NLE consists of a patterned silicon layer on diamond and requires no etching of the diamond surface. Our design process is based on adjoint optimization using broadband time-domain simulations and yields structures that are inherently robust to positioning and fabrication errors. Our NLE functions like a transmission antenna for the NV center, enhancing the optical power extracted from an NV center positioned 10 nm below the diamond surface by a factor of more than 35, and beaming the light into a +/-30° cone in the far field. This approach to light extraction can be readily adapted to other solid-state color centers.
Squeeze Free Space Using Nonlocal Flat Optics

We show that free space can be substituted with nonlocal flat o ptics with a wavevector-dependent transfer function. We provide a concrete photonic crystal slab device, which can substitute free space of 144 times greater thickness.
PIP2 promotes conformation-specific dimerization of the EphA2 membrane region

The impact of the EphA2 receptor on cancer malignancy hinges on the two different ways it can be activated. EphA2 induces antioncogenic signaling after ligand binding, but ligand-independent activation of EphA2 is pro-oncogenic. It is believed that the transmembrane (TM) domain of EphA2 adopts two alternate conformations in the ligand-dependent and the ligand-independent states. However, it is poorly understood how the difference in TM helical crossing angles found in the two conformations impacts the activity and regulation of EphA2. We devised a method that uses hydrophobic matching to stabilize two conformations of a peptide comprising the EphA2 TM domain and a portion of the intracellular juxtamembrane (JM) segment. The two conformations exhibit different TM crossing angles, resembling the ligand-dependent and ligand-independent states. We developed a single-molecule technique using styrene maleic acid lipid particles to measure dimerization in membranes. We observed that the signaling lipid PIP2 promotes TM dimerization, but only in the small crossing angle state, which we propose corresponds to the ligand-independent conformation. In this state the two TMs are almost parallel, and the positively charged JM segments are expected to be close to each other, causing electrostatic repulsion. The mechanism PIP2 uses to promote dimerization might involve alleviating this repulsion due to its high density of negative charges. Our data reveal a conformational coupling between the TM and JM regions and suggest that PIP2 might directly exert a regulatory effect on EphA2 activation in cells that is specific to the ligand-independent conformation of the receptor.
Finite sample change point inference and identification for high‐dimensional mean vectors

Cumulative sum (CUSUM) statistics are widely used in the change point inference and identification. For the problem of testing for existence of a change point in an independent sample generated from the mean‐shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data dependent and it has strong theoretical guarantees under arbitrary dependence structures and mild moment conditions. Specifically, we show that with a boundary removal parameter the bootstrap CUSUM test enjoys the uniform validity in size under the null and it achieves the minimax separation rate under the sparse alternatives when the dimension p can be larger than the sample size n. Once a change point is detected, we estimate the change point location by maximising the l∞‐norm of the generalised CUSUM statistics at two different weighting scales corresponding to covariance stationary and non‐stationary CUSUM statistics. For both estimators, we derive their rates of convergence and show that dimension impacts the rates only through logarithmic factors, which implies that consistency of the CUSUM estimators is possible when p is much larger than n. In the presence of multiple change points, we propose a principled bootstrap‐assisted binary segmentation (BABS) algorithm to dynamically adjust the change point detection rule and recursively estimate their locations. We derive its rate of convergence under suitable signal separation and strength conditions. The results derived in this paper are non‐asymptotic and we provide extensive simulation studies to assess the finite sample performance. The empirical evidence shows an encouraging agreement with our theoretical results.
Machine Learning-Based Self-Compensating Approximate Computing

Dedicated hardware accelerators are suitable for parallel computational tasks. Moreover, they have the tendency to accept inexact results. These hardware accelerators are extensively used in image processing and computer vision applications, e.g., to process the dense 3-D maps required for self-driving cars. Such error-tolerant hardware accelerators can be designed approximately for reduced power consumption and/or processing time. However, since for some inputs the output errors may reach unacceptable levels, the main challenge is to enhance the accuracy of the results of approximate accelerators and keep the error magnitude within an allowed range. Towards this goal, in this paper, we propose a novel machine learning-based self-compensating approximate accelerators for energy efficient systems. The proposed error compensation module, which is integrated within the architecture of approximate hardware accelerators, efficiently reduces the accumulated error at its output. It utilizes lightweight supervised machine learning techniques, i.e., decision tree, to capture input dependency of the error. We consider image blending application in multiplication mode to demonstrate a practical application of self-compensating approximate computing. Simulation results show that the proposed design of self-compensating approximate accelerator can achieve about 9% accuracy enhancement, with negligible overhead in other performance measures, i.e., power, area, delay and energy.
Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

Alzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI). A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.
Single branch of chiral Majorana modes from doubly degenerate Fermi surfaces

The authors use a boundary on boundary method to show that a single branch of chiral Majorana mode appears as an intrinsic property of superconductors in the absence of spin-orbit coupling.
Intelligent Design and the First Amendment: A Response

In September 2005, a federal district judge in Pennsylvania began presiding over the nation's first trial regarding the constitutionality of introducing the concept of "intelligent design" (ID), a purportedly scientific alternative to the theory of evolution, into the public schools. My previous work has argued that teaching ID in the public schools would raise serious constitutional problems. In a series of writings, including a full length book and several articles, Baylor University professor Francis Beckwith has argued that public schools may constitutionally teach ID. In doing so, Beckwith has critiqued a number of arguments I have previously advanced in my own writing, calling them, for example, "wide of the mark" and "patently unreasonable." In this essay, I respond to Beckwith's arguments regarding ID, both those that specifically critique my own arguments, as well as those that stand on their own. Specifically I disagree with Beckwith in three substantive areas: whether courts should find that ID constitutes a religious belief, whether the Supreme Court's decision in Edwards v. Aguillard casts doubt on the constitutionality of teaching ID, and whether teachers have any first amendment academic freedom right to teach ID in direct contravention of clear school policy.
Survival and the art of profit maximization

We study the relationship between rationality and economic survival in a simple dynamic model, where agents from different populations interact repeatedly through random matching. An explicit criterion (“bankruptcy”) determines whether agents survive each interaction; all agents are presumed to be aware of this criterion. Survival in each interaction depends on two factors: the strategies agents adopt when they interact, and the wealth levels they bring to the game. The model is completely symmetric with the only difference between the agents of different populations being their objectives.
Individual option prices for climate change mitigation

Abstract Willingness-to-pay for climate change mitigation depends on people's perceptions about just how bad things will get if nothing is done. Individual subjective distributions for future climate conditions are combined with stated choices over alternative climate policies to estimate individual option prices (the appropriate ex ante welfare measure in the face of uncertainty) for climate change mitigation. We find statistically significant sensitivity of estimated option prices to both expected future conditions and uncertainty about future conditions.
Does Disability Insurance Receipt Discourage Work? Using Examiner Assignment to Estimate Causal Effects of SSDI Receipt

Abstract We present the first causal estimates of the effect of Social Security Disability Insurance benefit receipt on labor supply using all program applicants. We use administrative data to matc...
Debarring Faithless Corporate and Religious Fiduciaries in Bankruptcy

Fiduciary duties for the top governance officials of both business and religious organizations demand faithfulness to the institution’s mission, a seemingly strict demand. Meaningful sanctions for breach, however, are difficult to obtain and may not deter future misconduct, including that kind of conduct leading to organizational bankruptcy. This article advocates that, to attain both special and general deterrence, bankruptcy law should look to other regulatory regimes and permit a bankruptcy court to debar faithless secular and ecclesiastical fiduciaries from holding certain leadership positions. Although written shortly before the 2012 Supreme Court Hosanna-Tabor decision, that opinion – addressing the “ministerial exception” for employees − does not alter the constitutional assessment of the position argued for in this article with respect to harm-causing, non-ministerial governing officials.
NEURAL CIRCUITS FOR GENERATING RHYTHMIC MOVEMENTS

Publisher Summary This chapter presents an overview on the neural circuits for generating rhythmic movements. The rhythmic movements of vertebrate and invertebrate animals are usually generated by neural elements wholly within the central nervous system. Models that explain how neurons of the central nervous system can produce rhythmic activity fall into two general classes: (1) endogenous polarization rhythms that depend on special oscillatory properties of individual neurons, and (2) network oscillations that depend on oscillatory properties devolving from the connections linking a set of neurons with endogenously stable polarization levels. The pattern of motor neuron activity in rhythmic movements of vertebrates is not necessarily more complex than the corresponding pattern in analogous movements of invertebrates. Therefore, the greater number of neurons in the central nervous system of vertebrates does not necessarily imply a greater complexity of the central oscillators generating their rhythmic movements. The current list of fundamentally different and theoretically plausible types of neuronal oscillators is not only quite short but also of long standing. Thus, on these grounds it is reasonable to expect that the circuits discussed in the chapter are of general applicability to the generation of rhythmic movements in the whole animal kingdom.
Utilizing the Theory of Planned Behavior to Understand Convenience Use of Credit Cards

48 Introduction Consumer credit plays an important role in how U.S. households handle their personal finances. According to a report released by the Federal Reserve Board (2009), although the amount of outstanding consumer credit declined by 1.5% during 2009, the estimated amount of debt was at $2,519.5 billion in May 2009 as compared to $2,387.7 billion in 2006. Such a high amount of outstanding consumer credit is not surprising, considering the recent recession of the economy which Industry Week believes started in late 2007 (Duesterberg, 2008). Research shows that when economic conditions worsen, many households experience a shortage of available cash and cash equivalent assets from income, savings, and/or investments (Roe, 2003). As a result, more households are forced to investigate their options of borrowing money (Grant, 2007).
Automatic bankruptcy auctions and fire-sales

Abstract We test for fire-sale tendencies in automatic bankruptcy auctions. We find evidence consistent with fire-sale discounts when the auction leads to piecemeal liquidation, but not when the bankrupt firm is acquired as a going concern. Neither industry-wide distress nor the industry affiliation of the buyer affect prices in going-concern sales. Bids are often structured as leveraged buyouts, which relaxes liquidity constraints and reduces bidder underinvestment incentives in the presence of debt overhang. Prices in “prepack” auctions (sales agreements negotiated prior to bankruptcy filing) are on average lower than for in-auction going-concern sales, suggesting that prepacks may help preempt excessive liquidation when the auction is expected to be illiquid. Prepack targets have a greater industry-adjusted probability of refiling for bankruptcy, indicating that liquidation preemption is a risky strategy.
Islamic Banks and Financial Stability: An Empirical Analysis

The relative financial strength of Islamic banks is assessed empirically based on evidence covering individual Islamic and commercial banks in 19 banking systems with a substantial presence of Islamic banking. We find that (a) small Islamic banks tend to be financially stronger than small commercial banks; (b) large commercial banks tend to be financially stronger than large Islamic banks; and (c) small Islamic banks tend to be financially stronger than large Islamic banks, which may reflect challenges of credit risk management in large Islamic banks. We also find that the market share of Islamic banks does not have a significant impact on the financial strength of other banks.
Implications of Non-GAAP earnings for real activities and accounting choices

Abstract Managers almost always define non-GAAP earnings to exclude the effects of acquisition and restructuring expenses, the amortization of intangibles, and impairments. I find that managers with a history of reporting non-GAAP earnings act as if they place lower weight on these excluded expenses when making real activities and accounting choices. They pursue more and larger acquisitions, have higher total capital investment, are more likely to restructure, and are more likely to recognize discretionary impairments. In a difference-in-differences setting, I find that non-GAAP reporting firms are less likely to alter their restructuring activities following a significant change in accounting rules for restructuring expense recognition. Finally, in supplementary analyses, I find that non-GAAP-reporting firms tend to repeat these real activities and accounting choices year-after-year, resulting in more persistent special-item expenses.
What Effects Do Legal Rules Have on Service Innovation

Intellectual property , contract, and tort laws likely have some effects on levels of innovation in service sector s of the economy. Legal rules that are too strong or too strict may discourage investment in service innovation ; yet, rules that are too weak or too loose may result in suboptimal investments in sound innovation. Intellectual property protections have traditionally been quite strong in protecting innovation in manufacturing sectors, but much less so in service sectors. Services have, for example, traditionally been unpatentable because they were perceived to be non-technological. Whether digital information services, such as web services , should be patentable is currently unsettled and highly controversial. Contract and tort rules are currently quite strict as to manufactured goods, but less so as to services.
Financial amplification of foreign exchange risk premia

Abstract Theories of financial frictions in international capital markets suggest that financial intermediaries' balance sheet constraints amplify fundamental shocks. We present empirical evidence for such theories by decomposing the U.S. dollar risk premium into components associated with macroeconomic fundamentals, and a component associated with financial intermediary balance sheets. Relative to the benchmark model with only macroeconomic state variables, balance sheets amplify the U.S. dollar risk premium. We discuss applications to financial stability monitoring.
The Supreme Court, the Law of Nations, and Citations of Foreign Law: The Lessons of History

The Supreme Court's reliance on "foreign" law has become the subject of heated controversy, particularly with regard to the relevance of foreign authority in constitutional cases. Critics view the Court's citation of foreign legal authority as a threat to the autonomy and integrity of American law, while defenders view the practice as refreshingly cosmopolitan.' This controversy has extended beyond the legal academy and even the legal profession into the public arena, sometimes with startling vehemence. One flashpoint for the current debate is Justice Kennedy's opinion in Lawrence v. Texas.2 In striking down a Texas law criminalizing homosexual conduct, Justice Kennedy found support in European human rights law.3 He emphasized that five years before Bowers (which Lawrence overruled), the European Court of Human Rights (ECHR) considered the
Financial Disclosure Levels and Foreign Stock Exchange Listing Decisions

Firms are increasingly listing their shares on foreign stock exchanges. However, not all exchanges have had equal appeal. Anecdotal evidence suggests that when firms are making foreign listing decisions, they are influenced by financial disclosure requirements. As a result, regulatory authorities around the globe are weighing increasing demands for foreign capital and investment opportunities against the desire to protect domestic investors from possibly misleading foreign financial disclosures. The competitiveness of domestic stock exchanges often hangs in the balance. This study examines a key question in this debate: whether firms' choices regarding alternative foreign stock exchange listings are influenced by financial disclosure levels. Examined are the listings of 302 internationally traded firms with at least one foreign listing, on one of nine major exchanges, as of year‐end 1987. Also examined are changes in listings between 1981 and 1987, an important design feature since these changes are more likely to have been influenced by differences across countries in financial disclosure levels during this period. Financial disclosure levels are obtained from a survey of 142 experts actively involved in the foreign listing process. Test results based on the cross‐section of listings at year‐end 1987 are consistent with the hypothesis that exchange choices are influenced by financial disclosure levels. However, they do not lend support to a second hypothesis suggesting that this effect should operate only for firms whose domestic disclosure levels are lower than those of a given foreign exchange. Tests based on changes in listings between 1981 and 1987 support both hypotheses. Overall, the results lend credence to concerns expressed by regulatory authorities and exchange officials that stringent disclosure levels could reduce access to foreign capital and foreign investment opportunities. Copyright © 1992, Wiley Blackwell. All rights reserved
Analyst Coverage and the Cost of Raising Equity Capital: Evidence from Underpricing of Seasoned Equity Offerings*

There is limited direct evidence on the impact of analyst coverage on the cost of capital. In this paper, we hypothesize that the amount and nature of analyst coverage can reduce information asymmetry among investors and thus lower the cost of raising equity capital. We investigate the effect of analyst coverage on the underpricing of seasoned equity offerings (SEOs), which is a substantial cost of issuing new shares. Based on 4,766 SEOs in the period 1984-2000, our results suggest that more analyst coverage is associated with lower SEO underpricing. Compared with firms without analyst coverage, firms with the median level of analyst coverage – three analysts – have a 1.19% lower SEO underpricing, a relative decrease of 38%. This effect is robust to controlling for other factors affecting SEO underpricing. We also examine additional attributes of analyst coverage and find that firms followed by analysts working for the lead underwriter, with a reputation for superior ability, or with lower forecast dispersion have incrementally lower SEO underpricing.
Social Protection for the Poor and Poorest in Developing Countries: Reflections on a Quiet Revolution

The theory and practice of social protection in developing countries has advanced at a rapid pace over the last decade or so. There is a growing consensus around the view that social protection constitutes an effective response to poverty and vulnerability in developing countries, and an essential component of economic and social development strategies. This paper argues that the rise of social protection constitutes a response to global trends, but with considerable regional diversity. It examines the factors determining the future course of social protection and identifies urgent research needs.
Forecasting the Leading Indicator of a Recession: The 10-Year Minus 3-Month Treasury Yield Spread

In this research paper, I have applied various econometric time series and two machine learning models to forecast the daily data on the yield spread − the difference between the 10-year Treasury yields and the 3-month Treasury bills. First, I decomposed the yield curve into its principal components, then simulated various paths of the yield spread using the Vasicek model. After constructing univariate ARIMA models, and multivariate models such as ARIMAX, VAR, and Long Short Term Memory (LSTM), I calibrated the root mean squared error (RMSE) to measure how far the models’ results deviate from the current values. Through impulse response functions, I measured the impact of various shocks on the difference yield spread. The results indicate that the parsimonious univariate ARIMA model (RMSE = 0.05185) outperforms the richly parameterized VAR method (RMSE = 0.4648), and the complex LSTM with multivariate data performs equally well as the simple ARIMA model.
The moral consequences of economic growth: An empirical investigation

Abstract In The Moral Consequences of Economic Growth, Benjamin Friedman argues that growth reduces the strength of interpersonal income comparisons, and thereby tends to increases the desire for pro-social legislation, a position he supports by drawing on the historical records of the US and several Western European countries. We test this hypothesis using a variable from the World Values Survey that measures an individual's taste for government responsibility, which we interpret as a measure of the demand for egalitarian social policy. Our results provide support for a modified version of Friedman's hypothesis. In particular, we find that the taste for government responsibility is positively related to the recent change in the growth rate and negatively related to the change in income inequality. We conclude by discussing the implications of these findings for attempts to further the egalitarian social goals.
Financial De-Dollarization: A Global Perspective and the Peruvian Experience

We re-appraise the cross-country evidence on the dollarization of financial systems in emerging market economies. Amidst striking heterogeneity of patterns across regions, we identify a broad global trend towards financial sector de-dollarization from the early 2000s to the eve of the global financial crisis of 2008-09. Since then, de-dollarization has broadly stalled or even reversed in many economies. Yet a few of them have continued to de-dollarize. This suggests that domestic factors are also important and interact with global factors. To gain insight into such an interaction, we examine the experience of Peru since the early 1990s and find that low global interest rates, low global risk-aversion, and high commodity prices have fostered de-dollarization. Domestic macro-prudential measures that raise the relative cost of domestic dollar loans and the introduction and adherence to inflation targeting have also been key.
Accounting quality and the transmission of monetary policy

Abstract We examine how firms' accounting quality affects their reaction to monetary policy. The balance sheet channel of monetary policy predicts that the quality of firms' accounting reports plays a role in transmitting monetary policy by affecting information asymmetries between firms and capital providers. Consistent with this prediction, we find that accounting quality moderates firms' equity market response and future investment sensitivity to unexpected changes in monetary policy. Moreover, the former relation is amplified for firms with more growth opportunities and more financial constraints, further consistent with accounting quality moderating the transmission of monetary policy.
Tumbling Political Theory

Blogging in the classroom is not a new idea, but my students have tended to view the blogging assignment negatively, as a homework assignment that simply needed to be done each week and then forgotten until the next time they were required to visit the blog. With that in mind, I decided to employ a very different method of educational blogging – using the Tumblr platform – in which each student would take ownership of his or her own blog and which would make the best possible use of social networking technology to boost interaction among the students and engagement with the course material.
The forward discount anomaly and the risk premium: A survey of recent evidence

Forward exchange rate unbiasedness is rejected in tests from the current floating exchange rate era. This paper surveys advances in this area since the publication of Hodrick's (1987) survey. It documents that the change in the future exchange rate is generally negatively related to the forward discount. Properties of the expected forward forecast error are reviewed. Issues such as the relation of uncovered interest parity to real interest parity, and the implications of uncovered interest parity for cointegration of various quantities are discussed. The modeling and testing for risk premiums is surveyed. Included in this area are tests of the consumption CAPM, tests of the latent variable model, and portfolio-balance models of risk premiums. General equilibrium models of the risk premium are examined and their empirical implications explored. The survey does not cover the important areas of learning and peso problems, tests of rational expectations based on survey data, or the models of irrational expectations and speculative bubbles.
Pricing liquidity risk and cost in the stock market: How different was the financial crisis?

Past literature has proven that liquidity plays a role in stock pricing, but our study shows that this role is remarkably bigger during an unstable period – the subprime crisis. The market became more sensitive to illiquidity costs and investors were paying a higher premium in crisis than in boom. This conclusion holds even when the overall illiquidity cost is divided into an expected part and an unexpected part. In contrast, while the market remained largely as sensitive to liquidity risk as before, liquidity risk itself, represented by the liquidity betas, changed remarkably in the crisis period compared to the prior boom period.