cssi/SciSciGPT-SciSciNet huggingface dataset:
The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks

We find that on average, an announcement of rising unemployment is good news for stocks during economic expansions and bad news during economic contractions. Unemployment news bundles three types of primitive information relevant for valuing stocks: information about future interest rates, the equity risk premium, and corporate earnings and dividends. The nature of the information bundle, and hence the relative importance of the three effects, changes over time depending on the state of the economy. For stocks as a group, information about interest rates dominates during expansions and information about future corporate dividends dominates during contractions. THIS STUDY INVESTIGATES THE SHORT-RUN response of stock prices to the arrival of macroeconomic news. The particular news event we consider is the Bureau of Labor Statistic’s (BLS) monthly announcement of the unemployment rate. We establish that the stock market’s response to unemployment news arrival depends on whether the economy is expanding or contracting. On average, the stock market responds positively to news of rising unemployment in expansions, and negatively in contractions. Since the economy is usually in an expansion phase, it follows that the stock market usually rises on the announcement of
Some reflections on the 1968–93 SNA revision

The 1968 System of National Accounts (SNA 68) represented an important milestone in national accounting. In providing a more detailed structuring of the economy and integrating a correspondingly more relevant system of basic, producer and purchasers’ prices by commodities and industry sectors, it helped lay to rest the early schism that developed between Keynes and Tinbergen over the question of the legitimacy of empirical economic modeling. The system was readily embraced by the advanced countries of Western Europe because it responded directly to the contemporary political imperatives of development planning and the need for economic forecasting models. But a large part of the non-Western “free world” encountered almost insurmountable difficulties in the full implementation of the system and became quickly bogged down in the quagmire of inter-industry statistics and valuation problems. Nancy and Richard Ruggles pressed for a revision providing workable solutions that would make the system more adaptable to the policy needs and statistical capacities of the majority of UN member countries. What actually happened took very much longer to reach fruition than was ever intended. The SNA 93 now represents the “gold standard” for national accounts, covering every aspect of economic activity. It is a masterpiece of conceptual coherence. Its encyclopedic character allows analysts and practitioners alike to dip into its voluminous pages for reasoned answers to why certain valuation questions and estimation procedures should be dealt with in a particular way. But SNA 93 remains a formidable document and it is not the operational data friendly framework that the Ruggles initially had envisaged.
Bruno de Finetti and the Case of the Critical Line's Last Segment

Abstract The anticipatory views of Bruno de Finetti on portfolio theory, set out by the author in a 1940 article, have recently been discovered by Mark Rubinstein and reviewed by Harry Markowitz. This paper analyzes the crucial parts of de Finetti’s paper and discusses the controversial issue of the critical line’s last segment, i.e. the segment that leads to the minimum-variance efficient portfolio. Markowitz [Markowitz, Harry M., 2006. De Finetti scoops Markowitz. In: A Literature postscript. J. Invest. Manag. 4 (3), 3–18 (special issue)] derives the criterion for the last segment to lie on one of the boundaries of the set of legitimate portfolios in two (and n ) dimensions and Pressacco [Pressacco, Flavio, 2005. De Finetti, Markowitz e la congettura dell’ultimo segmento. Rendiconti per gli Studi Economici Quantitativi, Universita Ca Foscari di Venezia] shows a necessary and sufficient condition for the last segment to lie inside the legitimate set in three dimensions when the correlations are uniformly positive. This paper revises the terms of the problem and completes the analysis.
The view from Vienna: An analysis of the renewed interest in the Mises-Hayek theory of the business cycle

We review the post-crisis literature that engages Austrian business cycle theory and we discuss what is being said that is correct, what is being said that is incorrect, and what is not being said that ought to be said. This last category is important due to the fact that the post-crisis literature engaging Austrian business cycle theory has not addressed advances in the theory made since the days of Mises and Hayek. We also highlight three key areas of contemporary economics where Austrian business cycle theory has the potential to do significant work.
Acquisition of Host Cytosolic Protein by Toxoplasma gondii Bradyzoites

ABSTRACT Toxoplasma gondii is a protozoan parasite that persists in the central nervous system as intracellular chronic-stage bradyzoites that are encapsulated by a thick cyst wall. While the cyst wall separates bradyzoites from the host cytosol, it has been posited that small solutes can traverse the cyst wall to sustain bradyzoites. Recently, it was found that host cytosolic macromolecules can cross the parasitophorous vacuole and are ingested and digested by actively replicating acute-stage tachyzoites. However, the extent to which bradyzoites have an active ingestion pathway remained unknown. To interrogate this, we modified previously published protocols that look at tachyzoite acquisition and digestion of host proteins by measuring parasite accumulation of a host-expressed reporter protein after impairment of an endolysosomal protease (cathepsin protease L [CPL]). Using two cystogenic parasite strains (ME49 and Pru), we demonstrate that T. gondii bradyzoites can ingest host-derived cytosolic mCherry. Bradyzoites acquire host mCherry within 4 h of invasion and after cyst wall formation. This study provides direct evidence that host macromolecules can be internalized by T. gondii bradyzoites across the cyst wall in infected cells. IMPORTANCE Chronic infection of humans with Toxoplasma gondii is common, but little is known about how this intracellular parasite obtains the resources that it needs to persist indefinitely inside neurons and muscle cells. Here, we provide evidence that the chronic-stage form of T. gondii can internalize proteins from the cytosol of infected cells despite residing within an intracellular cyst that is surrounded by a cyst wall. We also show that accumulation of host-derived protein within the chronic-stage parasites is enhanced by disruption of a parasite protease, suggesting that such protein is normally degraded to generate peptides and amino acids. Taken together, our findings imply that chronic-stage T. gondii can ingest and digest host proteins, potentially to support its persistence.
Dicta and Article III

ion norm. Courts and commentators tend to treat Article III as addressing the question of what cases federal courts may hear.6 Yet Article III is also relevant to the question of how those cases that can be heard ought to be decided. In other words, Article III raises jurispruden-
A MODIFIED INFORMATION CRITERION FOR COINTEGRATION TESTS BASED ON A VAR APPROXIMATION

We consider the cointegration tests of Johansen (1988, Journal of Economic Dynamics and Control 12, 231–254; 1991, Econometrica 59, 1551–1580) when a vector autoregressive (VAR) process of order k is used to approximate a more general linear process with a possibly infinite VAR representation. Traditional methods to select the lag order, such as Akaike's information criterion (AIC) or the Bayesian information criterion, often lead to too parsimonious a model with the implication that the cointegration tests suffer from substantial size distortions in finite samples. We extend the analysis of Ng and Perron (2001, Econometrica 69, 1519–1554) to derive a modified Akaike's information criterion (MAIC) in this multivariate setting. The idea is to use the information specified by the null hypothesis as it relates to restrictions on the parameters of the model to keep an extra term in the penalty function of the AIC. This MAIC takes a very simple form for which this extra term is simply the likelihood ratio test for testing the null hypothesis of r against more than r cointegrating vectors. We provide theoretical analyses of its validity and of the fact that cointegration tests constructed from a VAR whose lag order is selected using the MAIC have the same limit distribution as when the order is finite and known. We also provide theoretical and simulation analyses to show how the MAIC leads to VAR approximations that yield tests with drastically improved size properties with little loss of power.We are grateful to two referees for especially useful and constructive comments.
The causes and consequences of land use regulation: Evidence from Greater Boston☆

Abstract Over the past 30 years, eastern Massachusetts has seen a remarkable combination of rising home prices and declining supply of new homes, which doesn't appear to reflect any lack of land. In this paper, we examine the increasing number of land-use regulations in Greater Boston. These regulations vary widely over space, and are hard to predict with any variables other than historical density levels. Minimum lot size and other land use controls are associated with reductions in new construction activity. These regulations are associated with higher prices when we do not control for contemporary density and demographics, but not when we add these contemporaneous controls. These results are compatible with economic theory, which predicts that production restraints on a good won't increase the price of that good relative to sufficiently close substitutes. Current density levels appear to be too low to maximize local land values.
On Virtual Economies.

Several million people currently have accounts in massively multi-player online games, places in cyberspace that are effectively large-scale shared virtual reality environments. The population of these virtual worlds has grown rapidly since their inception in 1996; significantly, each world also seems to grow its own economy, with production, assets, and trade with Earth economies. This paper explores two questions about these developments. First, will these economies grow in importance? Second, if they do grow, how will that affect real-world economies and governments? To shed light on the first question, the paper presents a brief history of these games along with a simple choice model of the demand for game time. The history suggests that the desire to live in a game world is deep-rooted and driven by game technology. The model reveals a certain puzzle about puzzles and games: in the demand for these kinds of interactive entertainment goods, people reveal that they are willing to pay money to be constrained. Still, the nature of games as a produced good suggests that technological advances, and heavy competition, will drive the future development of virtual worlds. If virtual worlds do become a large part of the daily life of humans, their development may have an impact on the macroeconomies of Earth. It will also raise certain constitutional issues, since it is not clear, today, exactly who has jurisdiction over these new economies. JEL Classification: L86.
The Heterogeneous Impact of Corporate Social Responsibility Activities That Target Different Stakeholders

We aggregate different dimensions of corporate social responsibility (CSR) activities following the stakeholder framework proposed in Clarkson (Acad Manag Rev 20(1), 92–117, 1995) and present consistent evidence that CSR strengths targeting different stakeholders have their unique impact on firm risk and financial performance. Institutional CSR activities that target secondary stakeholders are negatively associated with firm risk, measured by total risk and systematic risk. Technical CSR that target primary stakeholders are positively associated with firm financial performance, measured by Tobin’s Q, ROA, and cash flow returns. Our results, based on a sample of S&P 500 component firms over the period of 1995–2009, are consistent with the risk management view of “altruistic” CSR activities and with the stakeholder salience theory. We also show that the impact of CSR activities on risk varies with the ethical climate, as proved in our subsample analyses on pre- and post-Sarbanes–Oxley periods. Our empirical analyses mitigate possible omitted variables and endogeneity concerns that are often overlooked in previous research. Our findings are robust to alternative CSR measures, to alternative risk and performance measures, and to alternative estimation methods.
The path integral of 3D gravity near extremality; or, JT gravity with defects as a matrix integral

We propose that a class of new topologies, for which there is no classical solution, should be included in the path integral of three-dimensional pure gravity, and that their inclusion solves pathological negativities in the spectrum, replacing them with a nonperturbative shift of the BTZ extremality bound. We argue that a two dimensional calculation using a dimensionally reduced theory captures the leading effects in the near extremal limit. To make this argument, we study a closely related two-dimensional theory of Jackiw-Teitelboim gravity with dynamical defects. We show that this theory is equivalent to a matrix integral.
Captivate: Building blocks for implementing active learning

In this study, the authors propose a set of key elements that impact the success of an active learning implementation: content delivery, active learning methods, physical environment, technology en...
Facing Up to the Complexities of the ILO’s Core Labour Standards Agenda

This article responds to two detailed critiques by Brian Langille and Francis Maupain of an article dealing with the 'transformation of the international labour rights regime' which followed the adoption by the ILO of the 1998 Declaration on Fundamental Principles and Rights at Work. The author argues that the ILO does, in fact, play a central role in the process of defining core labour standards and that its approach is invoked by a wide range of actors seeking legitimacy for their own approaches to such standards, whether in the context of bilateral free trade agreements or of private voluntary initiatives. For these reasons it is important to understand clearly the role played by the ILO, to acknowledge the extent to which the Declaration has become detached from the existing jurisprudence of labour rights, and to seek to ensure that the ILO adopts a more balanced approach which does not unduly privilege a limited range of procedural rights at the expense of equally important substantive social rights. The article concludes by outlining the steps which the ILO ought to take in order to ensure that it remains a relevant and influential actor in efforts to protect the basic rights of workers in the 21st century.
Determinants of the spread in a two-tier foreign exchange market

Abstract The determinants of the spread between the commercial and financial exchange rates are analyzed in a two-tier exchange market model derived from utility maximization. Contrary to popular belief, it is found that domestic policy and non-policy variables have no effect on the spread; only foreign factors matter.
Consistent GMM Residuals-Based Tests of Functional Form

This paper presents a consistent Generalized Method of Moments (GMM) residuals-based test of functional form for time series models. By relating two moments we deliver a vector moment condition in which at least one element must be nonzero if the model is misspecified. The test will never fail to detect misspecification of any form for large samples, and is asymptotically chi-squared under the null, allowing for fast and simple inference. A simulation study reveals randomly selecting the nuisance parameter leads to more power than supremum-tests, and can obtain empirical power nearly equivalent to the most powerful test for even relatively small n.
Rights of the Dead

Many legal rules suggest that the dead do not have rights. Often, the dead cannot marry, divorce, or vote. The executor of an estate cannot sue for the libel or slander of a deceased person. And the right to medical privacy substantially erodes at death, giving family members the ability to obtain sensitive information about a decedent’s medical conditions. On the other hand, various legal institutions have spent considerable time trying to protect the rights of the dead. As a result, most testamentary distributions, burial requests, and organ donation designations are held to be valid even if they contradict the preferences of the living. Certain destructions of property requested in wills are honored even though they may have a negative impact on the living. Some states even statutorily recognize a posthumous right of publicity, and recent case law suggests there may be a posthumous right to reproductive autonomy. This Article asks why the law gives decedents certain legal rights but not others. While many legal rules favoring the dead may be explained simply as an attempt to control the behaviors of living persons, such an explanation is incomplete because it ignores cultural norms, including an innate desire among the living to honor the wishes of the dead even when those wishes negatively impact their own interests. The fact that courts and legislatures often use “rights” language when creating legal rules that benefit decedents’ interests suggests that the desire to honor the wishes of the dead does not spring solely out of a self-interested desire in having one’s own wishes honored at death. The use of this language would be unnecessary if the true goal of a proposed
What Federalism & Why? Science Versus Doctrine

The Constitution does not use the words federal or federalism. It gives Congress a set of powers and prohibits the national government, the states or both from doing some things. The Court has inferred principles of federalism from those provisions. The political science community has treated the advantages of federalism as contingent on whether federalism deepens or diffuses conflict or opens competition for power. The United States Supreme Court's approach does neither; it has been trying to clarify and police a very different boundary. Even on its own terms, however, the Court's justifications do not work - a problem made clearer by reference to the empirically based work of political science. The result is that the Court's focus misses entirely the kinds of questions which might affect the security of the union or the quality of American democracy. The first section of the essay describes the Court's efforts to create a categorical federalism and its explanations for its approach. The second section makes clear that there are alternative approaches to federalism, in the absence of which further analysis would be pointless. The third section of the essay outlines major lines of inquiry by political scientists and their conclusions. Finally, the essay compares the Court's definitional federalism and its explanations for its approach with the concerns of political scientists. This essay concludes that the Supreme Court's categorical federalism is at best irrelevant and at worst a barrier to contemporary conflict resolution.
Zeroing in on leadership effectiveness: The methodological challenges of benchmarking leadership practices

Business success in the 21st century will be based on the widespread mastery of leadership roles, rather than on the directives of a professional or leadership elite. The first step for systematically enacting a leadership development agenda for our nation is to identify the factors, values or qualities most important to leadership effectiveness. Next, we must improve our understanding of how these guiding ideas and insights translate into what successful leaders do. As we perfect this understanding, we will need to operationally define the “best practices” of successful leaders. These best practices must then become “benchmarks’ for intensive leadership development efforts.
To Pool or Not to Pool: Revisited

This paper provides a new comparative analysis of pooled least squares and fixed effects (FE) estimators of the slope coefficients in the case of panel data models when the time dimension (T) is fixed while the cross section dimension (N) is allowed to increase without bounds. The individual effects are allowed to be correlated with the regressors, and the comparison is carried out in terms of an exponent coefficient, δ, which measures the degree of pervasiveness of the FE in the panel. The use of δ allows us to distinguish between poolability of small N dimensional panels with large T from large N dimensional panels with small T. It is shown that the pooled estimator remains consistent so long as δ<1, and is asymptotically normally distributed if δ<1/2, for a fixed T and as N→∞. It is further shown that when δ<1/2, the pooled estimator is more efficient than the FE estimator. We also propose a Hausman type diagnostic test of δ<1/2 as a simple test of poolability, and propose a pretest estimator that could be used in practice. Monte Carlo evidence supports the main theoretical findings and gives some indications of gains to be made from pooling when δ<1/2.
Suspicious Patterns in Hedge Fund Returns and the Risk of Fraud

Recent cases of hedge fund fraud have caused large losses for investors and have fueled the debate regarding the ability of regulators to oversee the industry. This article proposes a set of performance flags, based on suspicious patterns in returns, as indicators of a heightened risk of fraud. We collect a sample of hedge funds charged with legal or regulatory violations and find that funds charged with misappropriation, overvaluation, misrepresentation, or Ponzi schemes trigger the performance flags at a higher frequency than other funds. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.
Short Interest and Aggregate Stock Returns

We show that short interest is arguably the strongest known predictor of aggregate stock returns. It outperforms a host of popular return predictors both in and out of sample, with annual R2 statistics of 12.89% and 13.24%, respectively. In addition, short interest can generate utility gains of over 300 basis points per annum for a mean-variance investor. A vector autoregression decomposition shows that the economic source of short interest’s predictive power stems predominantly from a cash flow channel. Overall, our evidence indicates that short sellers are informed traders who are able to anticipate future aggregate cash flows and associated market returns.
Decomposing Underwriting Spreads for GSEs and Frequent Issuer Financial Firms

This paper investigates the determinants of underwriting fees charged to active government-sponsored enterprises (GSEs) and financial industry borrowers on debt issuances, how such fees change over...
Estimating informativeness of samples with Smooth Unique Information

We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final weights and how much it informs the function computed by the weights. Though related, we show that these quantities have a qualitatively different behavior. We give efficient approximations of these quantities using a linearized network and demonstrate empirically that the approximation is accurate for real-world architectures, such as pre-trained ResNets. We apply these measures to several problems, such as dataset summarization, analysis of under-sampled classes, comparison of informativeness of different data sources, and detection of adversarial and corrupted examples. Our work generalizes existing frameworks, but enjoys better computational properties for heavily over-parametrized models, which makes it possible to apply it to real-world networks.
Third-degree price discrimination in oligopoly with endogenous input costs

Abstract This paper examines the output effect of third-degree price discrimination in symmetrically differentiated oligopoly. We find that when the sellers’ input costs are chosen endogenously by an upstream supplier with market power, as opposed to being fixed exogenously, long-standing qualitative conclusions about the effect of price discrimination on aggregate output can be reversed. In contrast to previous findings (e.g., by Holmes, 1989), more intense competition in the strong market than in the weak market can make it less likely that price discrimination raises aggregate output. For linear demand functions, we establish necessary and sufficient conditions under which the output effect changes sign when input costs are endogenized.
Grain-resolved temperature-dependent anisotropy in hexagonal Ti-7Al revealed by synchrotron X-ray diffraction

Abstract Hexagonal metals have anisotropic coefficients of thermal expansion causing grain-level internal stresses during heating. High energy x-ray diffraction microscopy, a non-destructive, in situ, micromechanical and microstructural characterization technique, has been used to determine the anisotropic coefficients of thermal expansion (CTEs) for Ti-7Al. Two samples of polycrystalline α-phase Ti-7Al were continuously heated from room temperature to 850 °C while far-field HEDM scans were collected. The results showed a change in the ratio of the CTEs in the ‘a’ and ‘c’ directions which explains discrepancies found in the literature. The CTE additionally appears to be affected by the dissolution of α2 precipitates. Analysis of the grain-resolved micromechanical data also shows reconfiguration of the grain scale stresses likely due to anisotropic expansion driving crystallographic slip.
Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) from cognitive normal (CN). This can make it challenging for clinicians to make efficient and objective clinical diagnoses. It is possible to reduce the number of variables needed to make a reasonably accurate classification using machine learning. OBJECTIVE The goal of this study was to develop a deep learning algorithm to identify a few significant neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for diagnosis. METHODS Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer's Disease Neuroimaging Initiative database. A neural network algorithm was trained on data split 90% for training and 10% testing using 10-fold cross-validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. We also evaluated five different feature selection methods. RESULTS The five feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes, Delayed total recall, Modified Preclinical Alzheimer Cognitive Composite with Trails test, Modified Preclinical Alzheimer Cognitive Composite with Digit test, and Mini-Mental State Examination. The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset. CONCLUSION The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.
Towards an engineering framework for ultrafast quantum nonlinear optics

The advent of dispersion-engineered and highly nonlinear nanophotonics is expected to open up an all-optical path towards the strong-interaction regime of quantum optics by combining high transverse field confinement with ultra-short-pulse operation. Obtaining a full understanding of photon dynamics in such broadband devices, however, poses major challenges in the modeling and simulation of multimode non-Gaussian quantum physics, highlighting the need for sophisticated reduced models that facilitate efficient numerical study while providing useful physical insight. In this manuscript, we review our recent efforts in modeling broadband optical systems at varying levels of abstraction and generality, ranging from multimode extensions of quantum input-output theory for sync-pumped oscillators to the development of numerical methods based on a field-theoretic description of nonlinear waveguides. We expect our work not only to guide ongoing theoretical and experimental efforts towards next-generation quantum devices but also to uncover essential physics of broadband quantum photonics.
Cost function dependent barren plateaus in shallow parametrized quantum circuits.

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is $${\mathcal{O}}(\mathrm{log}\,n)$$ . Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation. Parametrised quantum circuits are a promising hybrid classical-quantum approach, but rigorous results on their effective capabilities are rare. Here, the authors explore the feasibility of training depending on the type of cost functions, showing that local ones are less prone to the barren plateau problem.
Native Ubiquitin Structural Changes Resulting from Complexation with β-Methylamino-l-alanine (BMAA).

The objective of this research was to investigate potential changes to unfolding energy barriers for ubiquitin in the presence of the noncanonical amino acid β-methylamino-l-alanine (BMAA). Although BMAA has been implicated in neurodegenerative disease, its specific role remains unclear. We hypothesized that formation of a ubiquitin + BMAA noncovalent complex would alter the protein's unfolding dynamics in comparison with native ubiquitin alone or in noncovalent complexes with other amino acids. Ion mobility-mass spectrometry (IM-MS) revealed that at sufficiently high concentrations BMAA did in fact form a noncovalent complex with ubiquitin, and similar complexes were identified for a range of additional amino acids. Collision-induced unfolding (CIU) was used to interrogate the unfolding of native ubiquitin and these Ubq-amino acid complexes, showing a major transition from its compact native state (∼1200 A2) to an unfolded state (∼1400 A2) at activation energies in the range from 8.0 to 9.0 V (entrance grid delta). The Ubq-BMAA complex, on the other hand, was observed to have a significantly higher energy barrier to unfolding, requiring more than 10.5 V. This indicates that the complex remains more stable under native conditions and this may indicate that BMAA has attached to a critical binding location worthy of further study for its potential role in the onset of neurodegenerative disease.
What is the Largest Sparsity Pattern That Can Be Recovered by 1-Norm Minimization?

Much of the existing literature in sparse recovery is concerned with the following question: given a sparsity pattern and a corresponding regularizer, derive conditions on the dictionary under which exact recovery is possible. In this paper, we study the opposite question: given a dictionary and the $\ell _{1}$ -norm regularizer, find the largest sparsity pattern that can be recovered. We show that such a pattern is described by a mathematical object called a “maximum abstract simplicial complex,” and provide two different characterizations of this object: one based on extreme points and the other based on vectors of minimal support. In addition, we show how this new framework is useful in the study of sparse recovery problems when the dictionary takes the form of a graph incidence matrix or a partial discrete Fourier transform. In case of incidence matrices, we show that the largest sparsity pattern that can be recovered is determined by the set of simple cycles of the graph. As a byproduct, we show that standard sparse recovery can be certified in polynomial time, although this is known to be NP-hard for general matrices. In the case of the partial discrete Fourier transform, our characterization of the largest sparsity pattern that can be recovered requires the unknown signal to be real and its dimension to be a prime number.
Length-factoriality in commutative monoids and integral domains

Abstract An atomic monoid M is called a length-factorial monoid (or an other-half-factorial monoid) if for each non-invertible element x ∈ M no two distinct factorizations of x have the same length. The notion of length-factoriality was introduced by Coykendall and Smith in 2011 as a dual of the well-studied notion of half-factoriality. They proved that in the setting of integral domains, length-factoriality can be taken as an alternative definition of a unique factorization domain. However, being a length-factorial monoid is, in general, weaker than being a factorial monoid (i.e., a unique factorization monoid). Here we further investigate length-factoriality. First, we offer two characterizations of a length-factorial monoid M, and we use such characterizations to describe the set of Betti elements and obtain a formula for the catenary degree of M. Then we study the connection between length-factoriality and purely long (resp., purely short) irreducibles, which are irreducible elements that appear in the longer (resp., shorter) part of any unbalanced factorization relation. Finally, we prove that an integral domain cannot contain purely long and purely short irreducibles simultaneously, and we construct a Dedekind domain containing purely long (resp., purely short) irreducibles but not purely short (resp., purely long) irreducibles.
Exploring End-to-End Multi-Channel ASR with Bias Information for Meeting Transcription

Joint optimization of multi-channel front-end and automatic speech recognition (ASR) has attracted much interest. While promising results have been reported for various tasks, past studies on its meeting transcription application were limited to small scale experiments. It is still unclear whether such a joint framework can be beneficial for a more practical setup where a massive amount of single channel training data can be leveraged for building a strong ASR back-end. In this work, we present our investigation on the joint modeling of a mask-based beamformer and Attention-Encoder-Decoder-based ASR in the setting where we have 75k hours of single-channel data and a relatively small amount of real multi-channel data for model training. We explore effective training procedures, including a comparison of simulated and real multi-channel training data. To guide the recognition towards a target speaker and deal with overlapped speech, we also explore various combinations of bias information, such as direction of arrivals and speaker profiles. We propose an effective location bias integration method called deep concatenation for the beamformer network. In our evaluation on various meeting recordings, we show that the proposed framework achieves a substantial word error rate reduction.
Divergence of Perturbation Theory in Quantum Electrodynamics

An argument is presented which leads tentatively to the conclusion that all the power-series expansions currently in use in quantum electrodynamics are divergent after the renormalization of mass and charge. The divergence in no way restricts the accuracy of practical calculations that can be made with the theory, but raises important questions of principle concerning the nature of the physical concepts upon which the theory is built.
Understanding Local Robustness of Deep Neural Networks under Natural Variations

Deep Neural Networks (DNNs) are being deployed in a wide range of settings today, from safety-critical applications like autonomous driving to commercial applications involving image classifications. However, recent research has shown that DNNs can be brittle to even slight variations of the input data. Therefore, rigorous testing of DNNs has gained widespread attention.
Group presentations for links in thickened surfaces

Using a combinatorial argument, we prove the well-known result that the Wirtinger and Dehn presentations of a link in 3-space describe isomorphic groups. The result is not true for links l in a thi...
Entropic dynamics on Gibbs statistical manifolds

Entropic dynamics is a framework in which the laws of dynamics are derived as an application of entropic methods of inference. Its successes include the derivation of quantum mechanics and quantum field theory from probabilistic principles. Here, we develop the entropic dynamics of a system, the state of which is described by a probability distribution. Thus, the dynamics unfolds on a statistical manifold that is automatically endowed by a metric structure provided by information geometry. The curvature of the manifold has a significant influence. We focus our dynamics on the statistical manifold of Gibbs distributions (also known as canonical distributions or the exponential family). The model includes an “entropic” notion of time that is tailored to the system under study; the system is its own clock. As one might expect that entropic time is intrinsically directional; there is a natural arrow of time that is led by entropic considerations. As illustrative examples, we discuss dynamics on a space of Gaussians and the discrete three-state system.
Fast covariance parameter estimation of spatial Gaussian process models using neural networks

Gaussian processes (GPs) are a popular model for spatially referenced data and allow descriptive statements, predictions at new locations, and simulation of new fields. Often a few parameters are sufficient to parameterize the covariance function, and maximum likelihood (ML) methods can be used to estimate these parameters from data. ML methods, however, are computationally demanding. For example, in the case of local likelihood estimation, even fitting covariance models on modest size windows can overwhelm typical computational resources for data analysis. This limitation motivates the idea of using neural network (NN) methods to approximate ML estimates. We train NNs to take moderate size spatial fields or variograms as input and return the range and noise-to-signal covariance parameters. Once trained, the NNs provide estimates with a similar accuracy compared to ML estimation and at a speedup by a factor of 100 or more. Although we focus on a specific covariance estimation problem motivated by a climate science application, this work can be easily extended to other, more complex, spatial problems and provides a proof-of-concept for this use of machine learning in computational statistics.
Force transduction creates long-ranged coupling in ribosomes stalled by arrest peptides.

Abstract Force-sensitive arrest peptides regulate protein biosynthesis by stalling the ribosome as they are translated. Synthesis can be resumed when the nascent arrest peptide experiences a pulling force of sufficient magnitude to break the stall. Efficient stalling is dependent on the specific identity of a large number of amino acids, including amino acids that are tens of angstroms away from the peptidyl transferase center (PTC). The mechanism of force-induced restart and the role of these essential amino acids far from the PTC is currently unknown. We use hundreds of independent molecular dynamics trajectories spanning over 120 μ s in combination with kinetic analysis to characterize multiple barriers along the force-induced restart pathway for the arrest peptide SecM. We find that the essential amino acids far from the PTC play a major role in controlling the transduction of applied force. In successive states along the stall-breaking pathway, the applied force propagates up the nascent chain until it reaches the C-terminus of SecM and the PTC, inducing conformational changes that allow for restart of translation. A similar mechanism of force propagation through multiple states is observed in the VemP stall-breaking pathway, but secondary structure in VemP allows for heterogeneity in the order of transitions through intermediate states. Results from both arrest peptides explain how residues that are tens of angstroms away from the catalytic center of the ribosome impact stalling efficiency by mediating the response to an applied force and shielding the amino acids responsible for maintaining the stalled state of the PTC.
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.
Heisenberg uncertainty of spatially gated electromagnetic fields

A Heisenberg uncertainty relation is derived for spatially-gated electric ΔE and magnetic ΔH field fluctuations. The uncertainty increases for small gating sizes, which implies that in confined spaces, the quantum nature of the electromagnetic field must be taken into account. Optimizing the state of light to minimize ΔE at the expense of ΔH and vice versa should be possible. Spatial confinements and quantum fields may alternatively be realized without gating by interaction of the field with a nanostructure. Possible applications include nonlinear spectroscopy of nanostructures and optical cavities and chiral signals.
ACTOR: a latent Dirichlet model to compare expressed isoform proportions to a reference panel

The relative proportion of RNA isoforms expressed for a given gene has been associated with disease states in cancer, retinal diseases, and neurological disorders. Examination of relative isoform proportions can help determine biological mechanisms, but such analyses often require a per-gene investigation of splicing patterns. Leveraging large public data sets produced by genomic consortia as a reference, one can compare splicing patterns in a data set of interest with those of a reference panel in which samples are divided into distinct groups, such as tissue of origin, or disease status. We propose A latent Dirichlet model to Compare expressed isoform proportions TO a Reference panel (ACTOR), a latent Dirichlet model with Dirichlet Multinomial observations to compare expressed isoform proportions in a data set to an independent reference panel. We use a variational Bayes procedure to estimate posterior distributions for the group membership of one or more samples. Using the Genotype-Tissue Expression project as a reference data set, we evaluate ACTOR on simulated and real RNA-seq data sets to determine tissue-type classifications of genes. ACTOR is publicly available as an R package at https://github.com/mccabes292/actor.
{SENSEI}: Aligning Video Streaming Quality with Dynamic User Sensitivity

This paper aims to improve video streaming by leveraging a simple observation: users are more sensitive to low quality in certain parts of a video than in others. For instance, rebuffering during key moments of a sports video (e.g., before a goal is scored) is more annoying than rebuffering during normal gameplay. Such dynamic quality sensitivity, however, is rarely captured by current approaches, which predict QoE (quality-of-experience) using one-size-fits-all heuristics that are too simplistic to understand the nuances of video content. Instead of proposing yet another heuristic, we take a different approach: we run a separate crowdsourcing experiment for each video to derive users' quality sensitivity at different parts of the video. Of course, the cost of doing this at scale can be prohibitive, but we show that careful experiment design combined with a suite of pruning techniques can make the cost negligible compared to how much content providers invest in content generation and distribution. Our ability to accurately profile time-varying user sensitivity inspires a new approach: dynamically aligning higher (lower) quality with higher (lower) sensitivity periods. We present a new video streaming system called SENSEI that incorporates dynamic quality sensitivity into existing quality adaptation algorithms. We apply SENSEI to two state-of-the-art adaptation algorithms. SENSEI can take seemingly unusual actions: e.g., lowering bitrate (or initiating a rebuffering event) even when bandwidth is sufficient so that it can maintain a higher bitrate without rebuffering when quality sensitivity becomes higher in the near future. Compared to state-of-the-art approaches, SENSEI improves QoE by 15.1% or achieves the same QoE with 26.8% less bandwidth on average.