cssi/SciSciGPT-SciSciNet huggingface dataset:
Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media

A rapidly evolving situation such as the COVID-19 pandemic is a significant challenge for AI/ML models because of its unpredictability. The most reliable indicator of the pandemic spreading has been the number of test positive cases. However, the tests are both incomplete (due to untested asymptomatic cases) and late (due the lag from the initial contact event, worsening symptoms, and test results). Social media can complement physical test data due to faster and higher coverage, but they present a different challenge: significant amounts of noise, misinformation and disinformation. We believe that social media can become good indicators of pandemic, provided two conditions are met. The first (True Novelty) is the capture of new, previously unknown, information from unpredictably evolving situations. The second (Fact vs. Fiction) is the distinction of verifiable facts from misinformation and disinformation. Social media information that satisfy those two conditions are called live knowledge. We apply evidence-based knowledge acquisition (EBKA) approach to collect, filter, and update live knowledge through the integration of social media sources with authoritative sources. Although limited in quantity, the reliable training data from authoritative sources enable the filtering of misinformation as well as capturing truly new information. We describe the EDNA/LITMUS tools that implement EBKA, integrating social media such as Twitter and Facebook with authoritative sources such as WHO and CDC, creating and updating live knowledge on the COVID-19 pandemic.
But That Is Absurd!: Why Specific Absurdity Undermines Textualism

Absurdity is currently undefined in either the jurisprudence or scholarship; yet, it is one method by which textualists avoid the ordinary meaning of clear statutory text. Currently, the absurdity doctrine covers two very different types of statutes. It covers those statutes that are patently absurd on their face and in virtually all situations, such as a statute that allows a judge to weigh the probative value of a witness’s prior conviction when the evidence is offered by a civil defendant but not when offered by a civil plaintiff. It also covers those statutes that are absurd only when applied to specific situations, such as a statute that penalizes individuals from owning wild animals as applied to a person who rescues an injured squirrel. To date, the distinction between these types of absurdity has neither been noticed nor explored. This article fills that gap by defining the difference between specific and general absurdity and by examining why this difference matters. Given that the absurdity doctrine allows textualist judges to ignore the ordinary meaning of clear statutes, this article concludes that textualists should be especially loath to apply the doctrine in cases of specific, as opposed to general, absurdity. Yet, it is precisely in cases of specific absurdity that judicial intervention is needed most.
The Slide to Protectionism in the Great Depression: Who Succumbed and Why?

The Great Depression was marked by a severe outbreak of protectionist trade policies. But contrary to the presumption that all countries scrambled to raise trade barriers, there was substantial cross-country variation in the movement to protectionism. Specifically, countries that remained on the gold standard resorted to tariffs, import quotas, and exchange controls to a greater extent than countries that went off gold. Just as the gold standard constraint on monetary policy is critical to understanding macroeconomic developments in this period, exchange rate policies help explain changes in trade policy.
Institutional Corruption and the Pharmaceutical Policy

Today, the goals of pharmaceutical policy and medical practice are often undermined due to institutional corruption - that is, widespread or systemic practices, usually legal, that undermine an institution's objectives or integrity. In this symposium, 16 articles investigate the corruption of pharmaceutical policy, each taking a different look at the sources of corruption, how it occurs, and what is corrupted. We will see that the pharmaceutical industry's own purposes are often undermined. Furthermore, pharmaceutical industry funding of election campaigns and lobbying skews the legislative process that sets pharmaceutical policy. Moreover, certain practices have corrupted medical research, the production of medical knowledge, the practice of medicine, drug safety, the Food and Drug Administration's oversight of the pharmaceutical market, and the trustworthiness of patient advocacy organizations.
The Supreme Court, Justinian, and Antonin Scalia: Twenty Years in Retrospect

Judge Durante, Justice Calogero, Judge Marullo, Your Honors, Ladies and Gentlemen: Years ago my quest for treasure among used books turned up Piero Calamandrei’s Eulogy of Judges. You may know Calamandrei was an Italian lawyer and a teacher of civil procedure. His Eulogy of Judges is a psalm book of uncommon legal prayer, from which I should like to read two verses. Take these words as my heartfelt greeting to the judges of the American Justinian Society of Jurists. You honor New Orleans and Louisiana by your presence. Calamandrei’s words, of course, were directed to his native Italian judges; I borrow them in welcoming this country’s ItalianAmerican jurists to Louisiana:
VAT Revenues in the Russian Federation: The Role of Tax Administration in Their Decline

Value added taxes (VATs) are important major revenue sources worldwide, yet keeping these taxes as easy to administer as possible is often overlooked. A poorly administered VAT raises less revenue than possible and can change the very nature of the tax, resulting in unintended economic distortions. This article analyzes the experience with the VAT in the Russian Federation and provides evidence that poor tax administration both contributed to the decline of VAT revenues beginning in 1993 and continues to hamper VAT revenue collection today. Administrative issues must not be ignored when implementing a consumption-based tax like the VAT.
BUBBLES AND CRASHES

We present a model in which an asset bubble can persist despite the presence of rational arbitrageurs. The resilience of the bubble stems from the inability of arbitrageurs to temporarily coordinate their selling strategies. This synchronization problem together with the individual incentive to time the market results in the persistence of bubbles over a substantial period. Since the derived trading equilibrium is unique, our model rationalizes the existence of bubbles in a strong sense. The model also provides a natural setting in which news events, by enabling synchronization, can have a disproportionate impact relative to their intrinsic informational content.
Privatization and Equity in Brazil and Russia

Brazil and Russia underwent a profound process of privatization in the 1990s. The institutional starting point of this process seemed to be quite different. Brazil was a market economy, with full respect of property rights, but one in which the state had a prominent role as a producer of various types of goods, as owner of most of the country’s public utilities, and a large proportion of the country’s financial institutions 1 . Russia was coming out of seven decades of communism, where all large industries were the property of the state, where private property rights were limited, and where market institutions did not exist. What were the motives for the privatization process in each country? In the case of Brazil, it was due to the fiscal crisis, which necessitated the state to decrease its debt through the sale of its enterprises, and also due to the lack of resources to finance investments in its heavy industries and public utilities. In the case of Russia, the failure of the reforms of the 1980s within the Soviet framework and the resulting economic collapse and political coup allowed for the withdrawal of the state from the economy. Our main concern in this comparative analysis is the impact of privatization on equity in each country ‐ i.e., the distribution of assets and income. In the case of Brazil, with a long tradition of asset and income concentration, concern with privatization’s equity impact was not taken into account. In the case of Russia, which emerged from 70 years of communism, concern about asset and income distribution played a more significant role in the design of its privatization program. It is the major purpose of this article to analyze the actual distributional results of privatization in each country.
Social Security Issues in Developing Countries: The Latin American Experience

The major economic, financial, and administrative issues that confront social security systems in Latin America are surveyed. The larger systems have contributed substantially to public sector financial disequilibria. Expenditures of the systems with more limited coverage could increase dramatically as the pension plan matures, life expectancy increases, and coverage is broadened, but the narrow revenue base will force a trade-off between broader coverage and the generosity of benefits. Most plans are pay-as-you-go, and the case for full or partial funding is not found to be compelling. The inflationary environment can have a substantial effect on the financial balance, even under full indexation.
Measures of Implicit Trading Costs and Buy-Sell Asymmetry*

Abstract This paper shows that the widely documented buy–sell asymmetry in implicit institutional trading cost is mainly driven by mechanical characteristics of a specific class of measures: pre-trade measures. If a post-trade measure is used, the asymmetry is reversed in both rising and falling markets. Both pre-trade and post-trade measures are highly influenced by market movement, while during-trade measures are relatively neutral to market movement. I further show that a pre-trade measure can be decomposed into a market movement component and a during-trade measure, and empirically the market movement component is the dominant component. This paper demonstrates that simple mechanical characteristics of trading cost measures can have important implications for how empirical results are interpreted.
The real effects of financial constraints: Evidence from a financial crisis

We survey 1,050 Chief Financial Officers (CFOs) in the U.S., Europe, and Asia to directly assess whether their firms are credit constrained during the global financial crisis of 2008. We study whether corporate spending plans differ conditional on this surveybased measure of financial constraint. Our evidence indicates that constrained firms planned deeper cuts in tech spending, employment, and capital spending. Constrained firms also burned through more cash, drew more heavily on lines of credit for fear banks would restrict access in the future, and sold more assets to fund their operations. We also find that the inability to borrow externally caused many firms to bypass attractive investment opportunities, with 86% of constrained U.S. CFOs saying their investment in attractive projects was restricted during the credit crisis of 2008. More than half of the respondents said they canceled or postponed their planned investments. Our results also hold in Europe and Asia, and in many cases are stronger in those economies. Our analysis adds to the portfolio of approaches and knowledge about the impact of credit constraints on real firm behavior.
State Law Litigation of International Norms: Horizontal and Vertical Dimensions

For decades, scholars of international litigation focused their attention on the federal courts. The combination of diversity, alienage, federal question and Alien Tort Statute (ATS) jurisdiction largely justified this focus, opening multiple avenues for litigants to prosecute claims in federal courts. In recent years, however, the federal courts have closed some doors to international litigation. In response, international litigators have turned their gaze to state courts. This panel is but one example of this new direction. For an excellent earlier treatment of this topic, the U.C. Irvine Law Review published a symposium issue in 2013 dedicated to human rights litigation in state courts and under state law.Within this new domain of U.S. states and international law, the focus justifiably has been on causes of action derived from common-law sources (whether in state or federal courts): (i) state law; (ii) foreign law, through state choice of law rules, and (iii) international law, also through state choice of law. What unites these categories is that courts are responsible for the relevant lawmaking choices. But state political branches also can engage with international norms. Examining the current and potential roles for state political branches permits an examination of doctrinal and theoretical questions in state litigation. First, I will offer some examples (real and hypothetical) of state political branch involvement. Then I will discuss two sets of inquiries in these cases: vertical debates about federal versus state actors, and horizontal debates about courts versus political branches. Third, I will discuss federal court doctrines that could limit state-level litigation, but I will do so in light of these horizontal and vertical dimensions. Finally, I will comment briefly on how state political branch involvement could play out with respect to the litigation of international norms.
Velocity of Information in Efficient Markets:A Theory of Market Value Change

This article argues that market prices in contemporary financial markets are driven by the value of information multiplied by a factor of the information’s velocity. These factors combined have the potential to explain several observed market phenomenon, including market volatility during economic crisis or recession, market bubbles, flash crashes, and short-run volatility combined with overreactions and over-corrections. The theory considers both micro and macroeconomic information, and questions the assumption that market prices always reflect intrinsic value alone. While microeconomic information is helpful in determining a security’s intrinsic value relative to other securities, macroeconomic information impacts the aggregate market. The result of our contemporary high-velocity information environment suggests our financial markets are becoming informationally hyperefficient and quasi-rational. This hyperefficiency has the potential to create short-run volatility on a scale not previously experienced.
Hedge Fund Alpha: Cycle or Sunset?

The hedge fund industry has grown from $200 billion in assets under management around the turn of the millennium to now over $3 trillion. Many reports have criticized hedge funds for poor performance, particularly since the 2008 Global Financial Crisis (GFC). In this article, the author seeks to demystify hedge fund strategies by evaluating fund performance that can be attributed to the markets as well as other well-known systematic factors with an emphasis on outcomes prior to and following the 2008 GFC. When adjusted for risk to stock/bond markets, the evidence shows that, after fees and costs, hedge fund managers as a group have shown a marked decline in risk-adjusted alpha in the 10 years following the GFC. To aid in a better understanding of the alpha deterioration, he further investigates equity hedge fund returns against a suite of well-known systematic risk/return factors documented in the literature beyond traditional market factors. In all, the model explains nearly 90% of the variation in returns. Equity hedge funds as a group show meaningful and consistent exposures to many of these factors over time, but whether intended or otherwise, significant changes also occurred (including a significant decline in active risk) following the GFC, in turn influencing their performance. Armed with this information, investors are hopefully better positioned to make more informed decisions in deciding hedge fund strategy allocations. TOPICS:Mutual fund performance, factor-based models, equity portfolio management, financial crises and financial market history, performance measurement Key Findings ▪ In the 10 years following the 2008 Global Financial Crisis (GFC), hedge funds overall (and a subset of equity-focused hedge fund managers) have witnessed a deterioration in alpha: declining 4.4% annually to −1.0% a year, on average, net of fees and costs. ▪ Results of a multi-factor model explaining up to 90% of excess returns variation show that hedge funds have meaningfully altered how they drive performance, partially explaining the observed change in results post-GFC. ▪ Equity hedge fund managers significantly reduced their active risk in the post-GFC period versus the earlier period, which is likely another important contributor to the decline in alpha over the past decade.
Contracting theory and accounting

Abstract This paper reviews agency theory and its application to accounting issues. I discuss the formulation of models of incentive problems caused by moral hazard and adverse selection problems. I review theoretical research on the role of performance measures in compensation contracts, and I compare how information is aggregated for compensation purposes versus valuation purposes. I also review the literature on communication, including models where the revelation principle does not apply so that nontruthful reporting and earnings management can take place. The paper also discusses capital allocation within firms, including transfer pricing and cost allocation problems.
The accuracy of individual and group forecasts from business outlook surveys

This paper reports on the accuracy of quarterly multiperiod predictions of inflation, real growth, unemployment and percentage changes in nominal GNP and two of its more volatile components. The survey data are highly differentiated; they cover 79 professional forecasters (mostly economists, analysts and corporate executives). Combining corresponding predictions from different sources can result in significant gains; thus the group mean forecasts are on the average over time more accurate than most of the corresponding sets of individual forecasts. But there is also a moderate degree of consistency in the relative performance of a sufficient number of the survey members, as evidenced in positive rank correlations among ratios of the individual to group root mean square errors.
Public Sector Unions and the Costs of Government

Public sector unions are major interest groups in American politics, but they are rarely studied. New research would not only shed much-needed light on how these unions shape government and politics, but also broaden the way scholars think about interest groups generally: by highlighting interests that arise inside governments, drawing attention to long-ignored types of policies and decision arenas, and underlining the importance of groups in subnational politics. Here we explore the effects of public sector unions on the costs of government. We present two separate studies, using different datasets from different historical periods, and we examine several outcomes: salaries, health benefits, and employment. We find that unions and collective bargaining increase the costs of government and that the effects are especially large for benefits. We view this analysis as an opening wedge that we hope will encourage a more extensive line of new research—and new thinking about American interest groups.
On Hart's Ways: Law as Reason and as Fact

This address at the Hart Centenary Conference in Cambridge in July 2007 reflects on foundational elements in Hart's method in legal philosophy. It argues that his understanding of what it is to adopt an internal point of view was flawed by (a) inattention to the difference between descriptive history (or biography or detection) and descriptive general theory of human affairs, (b) inattention to practical reason as argument from premises, some factual but others normative (evaluative) in their content, and (c) relative inattention to the deliberations of law-makers as distinct from subjects of the law. These flaws contributed to a concept or theory of law that so truncated its account of the juridical, and of the sources of legal reasoning, that it could provide little or no guidance in situations of legal difficulty. The paper suggests that these flaws result, to some significant extent, from the skeptical doubts about morality evident particularly in his later work. All this has implications for the kind of approach to law and legal theory often self-described as positivist.
The Phoenix and the Perils of the Second Best: Why Heightened Appellate Deference to Tax Court Decisions is Undesirable

In our judicial structure, both courts of general jurisdiction and specialized courts are empowered to adjudicate federal income tax controversies. A proper relationship among those courts has proved difficult to forge and maintain. Absent an enduring intellectual and political consensus, institutional arrangements have been subject to recurring question and challenge.
INCENTIVE PAY AND PERFORMANCE: INSIDER ECONOMETRICS IN A MULTI-UNIT FIRM *†

We exploit organizational reforms in a foreign-owned bank in Central-East Europe to study the implementation of modern HRM policies in an emerging market context. We have branch-level data and use our knowledge of the process that led to the adoption of the reforms to implement two estimators that address endogeneity bias in a complementary fashion: an IV approach and Generalized Propensity Score estimation. Our results show that some of the reforms had a positive impact on productivity, but they also underscore the risks of quantity-based incentives where quality is important.
Does Early Life Racial Discrimination Explain a Mental Health Paradox among Black Adults

Objectives: To evaluate the impact of early life racial discrimination (ELRD) on mental health among Black adults. Methods: Data were from the Nashville Stress and Health Study (n = 618). Ordinary least squares regression models examined the relationship between ELRD and adult psychological distress; logistic regression estimated the probability of past-year major depressive disorder (MDD). We also assessed whether ELRD moderated the relationship between adult discrimination and mental health. Results: Childhood (b = 1.07, SE = .51, p = .04) and adolescent ELRD (b = 1.32, SE = .42, p = .002) were associated with adult distress. Individuals who experienced childhood ERLD had 88% lower odds of adult MDD than individuals with no ELRD. Significant interactions showed that childhood and adolescent ELRD was protective against adult discrimination. Discussion: While ELRD importantly shapes distress and MDD among Black adults, patterns vary by outcome. Results indicate that adult distress and MDD develop through cumulative adversity processes that are further influenced by sensitive periods in the life course.
3D bioprinting of high cell-density heterogeneous tissue models through spheroid fusion within self-healing hydrogels

Cellular models are needed to study human development and disease in vitro, and to screen drugs for toxicity and efficacy. Current approaches are limited in the engineering of functional tissue models with requisite cell densities and heterogeneity to appropriately model cell and tissue behaviors. Here, we develop a bioprinting approach to transfer spheroids into self-healing support hydrogels at high resolution, which enables their patterning and fusion into high-cell density microtissues of prescribed spatial organization. As an example application, we bioprint induced pluripotent stem cell-derived cardiac microtissue models with spatially controlled cardiomyocyte and fibroblast cell ratios to replicate the structural and functional features of scarred cardiac tissue that arise following myocardial infarction, including reduced contractility and irregular electrical activity. The bioprinted in vitro model is combined with functional readouts to probe how various pro-regenerative microRNA treatment regimes influence tissue regeneration and recovery of function as a result of cardiomyocyte proliferation. This method is useful for a range of biomedical applications, including the development of precision models to mimic diseases and the screening of drugs, particularly where high cell densities and heterogeneity are important.
Near-linear Size Hypergraph Cut Sparsifiers

Cuts in graphs are a fundamental object of study, and play a central role in the study of graph algorithms. The problem of sparsifying a graph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Benczur and Karger (1996) showed that given any $n$ -vertex undirected weighted graph $G$ and a parameter $\varepsilon\in(0,1)$ , there is a near-linear time algorithm that outputs a weighted subgraph $G^{\prime}$ of $G$ of size $\tilde{O}(n/\varepsilon^{2})$ such that the weight of every cut in $G$ is preserved to within a ( $1\pm\varepsilon$ )-factor in $G^{\prime}$ . The graph $G^{\prime}$ is referred to as a ( $1\pm\varepsilon$ )-approximate cut sparsifier of $G$ . A natural question is if such cut-preserving sparsifiers also exist for hypergraphs. Kogan and Krauthgamer (2015) initiated a study of this question and showed that given any weighted hypergraph $H$ where the cardinality of each hyperedge is bounded by $r$ , there is a polynomial-time algorithm to find a ( $1\pm\varepsilon$ )-approximate cut sparsifier of $H$ of size $\tilde{O}(\frac{nr}{\varepsilon^{2}})$ . Since $r$ can be as large as $n$ , in general, this gives a hypergraph cut sparsifier of size $\tilde{O}(n^{2}/\varepsilon^{2})$ , which is a factor $n$ larger than the Benczur-Karger bound for graphs. It has been an open question whether or not Benczur-Karger bound is achievable on hypergraphs. In this work, we resolve this question in the affirmative by giving a new polynomial-time algorithm for creating hypergraph sparsifiers of size $\tilde{O}(n/\varepsilon^{2})$ .
Fully-Dynamic Submodular Cover with Bounded Recourse

In submodular covering problems, we are given a monotone, nonnegative submodular function $f:2^{\mathcal{N}}\rightarrow \mathbb{R}_{+}$ and wish to find the min-cost set $S\subseteq \mathcal{N}$ such that $f(S)=f(\mathcal{N})$ . When $f$ is a coverage function, this captures Setcover as a special case. We introduce a general framework for solving such problems in a fully-dynamic setting where the function $f$ changes over time, and only a bounded number of updates to the solution (a.k.a. recourse) is allowed. For concreteness, suppose a nonnegative monotone submodular integer-valued function $g_{t}$ is added or removed from an active set $G^{(t)}$ at each time $t$ . If $f^{(t)}=\sum\nolimits_{g\in G^{(t)}}g$ is the sum of all active functions, we wish to maintain a competitive solution to Submodularcover for $f^{(t)}$ as this active set changes, and with low recourse. For example, if each $g_{t}$ is the (weighted) rank function of a matroid, we would be dynamically maintaining a low-cost common spanning set for a changing collection of matroids. We give an algorithm that maintains an $O(\log(f_{\max}/f_{\min}))$ - competitive solution, where $f_{\max}, f_{\min}$ are the largest/smallest marginals of $f^{(t)}$ . The algorithm guarantees a total recourse of $O(\log(c_{\max}/c_{\min})\cdot\sum\nolimits_{t , where $c_{\min}, c_{\min}$ are the largest/smallest costs of elements in $\mathcal{N}$ . This competitive ratio is best possible even in the offline setting, and the recourse bound is optimal up to the logarithmic factor. For monotone sub-modular functions that also have positive mixed third derivatives, we show an optimal recourse bound of $O(\sum\nolimits_{t . This structured class includes set-coverage functions, so our algorithm matches the known $O(\log n)$ -competitiveness and $O(1)$ recourse guarantees for fully-dynamic Setcover. Our work simultaneously simplifies and unifies previous results, as well as generalizes to a significantly larger class of covering problems. Our key technique is a new potential function inspired by Tsallis entropy. We also extensively use the idea of Mutual Coverage, which generalizes the classic notion of mutual information.
Optimal Size of Rebellions: Trade-off Between Large Group and Maintaining Secrecy

This paper studies a regime-change model in which a rebel leader mobilizing supporters faces a trade-off between increasing the size of the rebel group and risking information leaks. I find collective punishment to repress the rebellion, in which rebel participants as well as those who knew about but did not report the rebellion are punished, may result in a smaller rebel-group size compared to targeted punishment, in which only the rebel participants are punished. Meanwhile, choosing collective punishment comes at a price, by forcing people to side with the insurgency, which may decrease the authority's survival chances. My findings also indicate targeted punishment helps prevent a revolution by ordinary citizens, whereas collective punishment helps prevent a coup staged by politicians. Furthermore, when authorities compete with rebel leaders for support by threatening retribution against the non-supporters, both parties prefer using harsh methods to force civilians to choose sides.
Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats

The proliferation of modern data processing tools has given rise to open-source columnar data formats. The advantage of these formats is that they help organizations avoid repeatedly converting data to a new format for each application. These formats, however, are read-only, and organizations must use a heavy-weight transformation process to load data from on-line transactional processing (OLTP) systems. We aim to reduce or even eliminate this process by developing a storage architecture for in-memory database management systems (DBMSs) that is aware of the eventual usage of its data and emits columnar storage blocks in a universal open-source format. We introduce relaxations to common analytical data formats to efficiently update records and rely on a lightweight transformation process to convert blocks to a read-optimized layout when they are cold. We also describe how to access data from third-party analytical tools with minimal serialization overhead. To evaluate our work, we implemented our storage engine based on the Apache Arrow format and integrated it into the DB-X DBMS. Our experiments show that our approach achieves comparable performance with dedicated OLTP DBMSs while enabling orders-of-magnitude faster data exports to external data science and machine learning tools than existing methods.
Leaky Nets: Recovering Embedded Neural Network Models and Inputs Through Simple Power and Timing Side-Channels—Attacks and Defenses

With the recent advancements in machine learning theory, many commercial embedded microprocessors use neural network (NN) models for a variety of signal processing applications. However, their associated side-channel security vulnerabilities pose a major concern. There have been several proof-of-concept attacks demonstrating the extraction of their model parameters and input data. But, many of these attacks involve specific assumptions, have limited applicability, or pose huge overheads to the attacker. In this work, we study the side-channel vulnerabilities of embedded NN implementations by recovering their parameters using timing-based information leakage and simple power analysis side-channel attacks. We demonstrate our attacks on popular microcontroller platforms over networks of different precisions, such as floating point, fixed point, and binary networks. We are able to successfully recover not only the model parameters but also the inputs for the above networks. Countermeasures against timing-based attacks are implemented and their overheads are analyzed.
Meta-Learning of Structured Task Distributions in Humans and Machines

In recent years, meta-learning, in which a model is trained on a family of tasks (i.e. a task distribution), has emerged as an approach to training neural networks to perform tasks that were previously assumed to require structured representations, making strides toward closing the gap between humans and machines. However, we argue that evaluating meta-learning remains a challenge, and can miss whether meta-learning actually uses the structure embedded within the tasks. These meta-learners might therefore still be significantly different from humans learners. To demonstrate this difference, we first define a new meta-reinforcement learning task in which a structured task distribution is generated using a compositional grammar. We then introduce a novel approach to constructing a "null task distribution" with the same statistical complexity as this structured task distribution but without the explicit rule-based structure used to generate the structured task. We train a standard meta-learning agent, a recurrent network trained with model-free reinforcement learning, and compare it with human performance across the two task distributions. We find a double dissociation in which humans do better in the structured task distribution whereas agents do better in the null task distribution -- despite comparable statistical complexity. This work highlights that multiple strategies can achieve reasonable meta-test performance, and that careful construction of control task distributions is a valuable way to understand which strategies meta-learners acquire, and how they might differ from humans.
Direct measurements of neutrino mass

Abstract The turn of the 21st century witnessed a sudden shift in our fundamental understanding of particle physics. While the minimal Standard Model predicts that neutrino masses are exactly zero, the discovery of neutrino oscillations proved the Standard Model wrong. Neutrino oscillation measurements, however, shed light neither on the scale of neutrino masses, nor on the mechanism by which those are generated. The neutrino mass scale is most directly accessed by studying the energy spectrum generated by beta decay or electron capture — a technique dating back to Enrico Fermi’s formulation of radioactive decay. In this Article, we review the methods and techniques – both past and present – aimed at measuring neutrino masses kinematically. We focus on recent experimental developments that have emerged in the past decade, overview the spectral refinements that are essential in the treatment of the most sensitive experiments, and give a simple yet effective protocol for estimating the sensitivity. Finally, we provide an outlook of what future experiments might be able to achieve.
Hessian metric via transport information geometry

We propose to study the Hessian metric of a functional on the space of probability measures endowed with the Wasserstein-2 metric. We name it transport Hessian metric, which contains and extends the classical Wasserstein-2 metric. We formulate several dynamical systems associated with transport Hessian metrics. Several connections between transport Hessian metrics and mathematical physics equations are discovered. For example, the transport Hessian gradient flow, including Newton’s flow, formulates a mean-field kernel Stein variational gradient flow; the transport Hessian Hamiltonian flow of Boltzmann–Shannon entropy forms the shallow water equation; and the transport Hessian gradient flow of Fisher information leads to the heat equation. Several examples and closed-form solutions for transport Hessian distances are presented.
Controllability of Hypergraphs

In this paper, we develop a notion of controllability for hypergraphs via tensor algebra and polynomial control theory. Inspired by uniform hypergraphs, we propose a new tensor-based multilinear dynamical system representation, and derive a Kalman-rank-like condition to determine the minimum number of control nodes (MCN) needed to achieve controllability of even uniform hypergraphs. We present an efficient heuristic to obtain the MCN. MCN can be used as a measure of robustness, and we show that it is related to the hypergraph degree distribution in simulated examples. Finally, we use MCN to examine robustness in real biological networks.
Economic Reality at the Core of Apple

In Apple, Inc. v. Pepper, the Supreme Court failed to recognize the economic reality at play which sparked considerable confusion and debate about the continued vitality of Illinois Brick. Apple us...
Upper tail for homomorphism counts in constrained sparse random graphs

Consider the upper tail probability that the homomorphism count of a fixed graph $H$ within a large sparse random graph $G_n$ exceeds its expected value by a fixed factor $1+\delta$. Going beyond the Erdős-Renyi model, we establish here explicit, sharp upper tail decay rates for sparse random $d_n$-regular graphs (provided $H$ has a regular $2$-core), and for sparse uniform random graphs. We further deal with joint upper tail probabilities for homomorphism counts of multiple graphs $H_1,\ldots, H_k$ (extending the known results for $k=1$), and for inhomogeneous graph ensembles (such as the stochastic block model), we bound the upper tail probability by a variational problem analogous to the one that determines its decay rate in the case of sparse Erdős-Renyi graphs.
Do You Do Yoga? Understanding Twitter Users' Types and Motivations using Social and Textual Information

Leveraging social media data to understand people's lifestyle choices is an exciting domain to explore but requires a multiview formulation of the data. In this paper, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by incorporating social and textual information of users to understand their activities and motivations. We use well-being related tweets from Twitter, focusing on ‘Yoga’. We demonstrate our model on two downstream tasks: (i) finding user type such as either practitioner or promotional (promoting yoga studio/gym), other; (ii) finding user motivation i.e. health benefit, spirituality, love to tweet/retweet about yoga but do not practice yoga.
Gravitational Quantum Dynamics: A Geometrical Perspective

We present a gravitational quantum dynamics theory that combines quantum field theory for particle dynamics in space-time with classical Einstein’s general relativity in a non-Riemannian Finsler space. This approach is based on the geometrization of quantum mechanics proposed in Tavernelli (Ann. Phys. 371:239, 2016) and combines quantum and gravitational effects into a global curvature of the Finsler space induced by the quantum potential associated to the matter quantum fields. In order to make this theory compatible with general relativity, the quantum effects are described in the framework of quantum field theory, where a covariant definition of ‘simultaneity’ for many-body systems is introduced through the definition of a suited foliation of space-time. As in Einstein’s gravitation theory, the particle dynamics is finally described by means of a geodesic equation in a curved space-time manifold.
A Passive STAR Microwave Circuit for 1-3 GHz Self-Interference Cancellation

Simultaneous transmit and receive (STAR) allows full-duplex operation of a radio, which leads to doubled capacity for a given bandwidth. A circulator with high-isolation between transmit and receive ports, and low-loss from the antenna to receive port is typically required for achieving STAR. Conventional circulators do not offer wideband performance. Although wide-band circulators have been proposed using parametric, switched delay-line/capacitor, and N-path filter techniques using custom integrated circuits, these magnet-free devices have non-linearity, noise, aliasing, and switching noise injection issues. In this paper, a STAR front-end based on passive linear microwave circuit is proposed. Here, a dummy antenna located inside a miniature RF-silent absorption chamber allows circulator-free STAR using simple COTS components. The proposed approach is highly-linear, free from noise, does not require switching or parametric modulation circuits, and has virtually unlimited bandwidth only set by the performance of COTS passive microwave components. The trade-off is relatively large size of the miniature RF-shielded chamber, making this suitable for base-station side applications. Preliminary results show the measured performance of Tx/Rx isolation between 25-60 dB in the 1.0-3.0 GHz range, and 50-60 dB for the 2.4-2.7 GHz range.
Asthma phenotypes, associated comorbidities, and long-term symptoms in COVID-19.

Background It is unclear whether asthma and its allergic phenotype are risk factors for hospitalization or severe disease from SARS-CoV-2. Methods All patients over 28 days old testing positive for SARS-CoV-2 between March 1 and September 30, 2020, were retrospectively identified and characterized through electronic analysis at Stanford. A sub-cohort was followed prospectively to evaluate long-term COVID-19 symptoms. Results 168,190 patients underwent SARS-CoV-2 testing, and 6,976 (4.15%) tested positive. In a multivariate analysis, asthma was not an independent risk factor for hospitalization (OR 1.12 [95% CI 0.86, 1.45], p = .40). Among SARS-CoV-2-positive asthmatics, allergic asthma lowered the risk of hospitalization and had a protective effect compared with non-allergic asthma (OR 0.52 [0.28, 0.91], p = .026); there was no association between baseline medication use as characterized by GINA and hospitalization risk. Patients with severe COVID-19 disease had lower eosinophil levels during hospitalization compared with patients with mild or asymptomatic disease, independent of asthma status (p = .0014). In a patient sub-cohort followed longitudinally, asthmatics and non-asthmatics had similar time to resolution of COVID-19 symptoms, particularly lower respiratory symptoms. Conclusions Asthma is not a risk factor for more severe COVID-19 disease. Allergic asthmatics were half as likely to be hospitalized with COVID-19 compared with non-allergic asthmatics. Lower levels of eosinophil counts (allergic biomarkers) were associated with a more severe COVID-19 disease trajectory. Recovery was similar among asthmatics and non-asthmatics with over 50% of patients reporting ongoing lower respiratory symptoms 3 months post-infection.
Associations between childhood family emotional health, fronto-limbic grey matter volume, and saliva 5mC in young adulthood

Poor family emotional health (FEH) during childhood is prevalent and impactful, and likely confers similar neurodevelopmental risks as other adverse social environments. Pointed FEH study efforts are underdeveloped, and the mechanisms by which poor FEH are biologically embedded are unclear. The current exploratory study examined whether variability in 5-methyl-cytosine (5mC) and fronto-limbic grey matter volume may represent pathways through which FEH may become biologically embedded. In 98 university students aged 18–22 years, retrospective self-reported childhood FEH was associated with right hemisphere hippocampus (b = 10.4, p = 0.005), left hemisphere amygdala (b = 5.3, p = 0.009), and right hemisphere amygdala (b = 5.8, p = 0.016) volumes. After pre-processing and filtering to 5mC probes correlated between saliva and brain, analyses showed that childhood FEH was associated with 49 5mC principal components (module eigengenes; MEs) (prange = 3 × 10–6 to 0.047). Saliva-derived 5mC MEs partially mediated the association between FEH and right hippocampal volume (Burlywood ME indirect effect b = − 111, p = 0.014), and fully mediated the FEH and right amygdala volume relationship (Pink4 ME indirect effect b = − 48, p = 0.026). Modules were enriched with probes falling in genes with immune, central nervous system (CNS), cellular development/differentiation, and metabolic functions. Findings extend work highlighting neurodevelopmental variability associated with adverse social environment exposure during childhood by specifically implicating poor FEH, while informing a mechanism of biological embedding. FEH-associated epigenetic signatures could function as proxies of altered fronto-limbic grey matter volume associated with poor childhood FEH and inform further investigation into primarily affected tissues such as endocrine, immune, and CNS cell types.
Information geometric aspects of probability paths with minimum entropy production for quantum state evolution

We present an information geometric analysis of both entropic speeds and entropy production rates arising from geodesic evolution on manifolds parametrized by pure quantum states. In particular, we...
Fundamental physical and resource requirements for a Martian magnetic shield

Mars lacks a substantial magnetic field; as a result, the solar wind ablates the Martian atmosphere, and cosmic rays from solar flares make the surface uninhabitable. Therefore, any terraforming attempt will require an artificial Martian magnetic shield. The fundamental challenge of building an artificial magnetosphere is to condense planetary-scale currents and magnetic fields down to the smallest mass possible. Superconducting electromagnets offer a way to do this. However, the underlying physics of superconductors and electromagnets limits this concentration. Based upon these fundamental limitations, we show that the amount of superconducting material is proportional to , where Bc is the critical magnetic field for the superconductor and a is the loop radius of a solenoid. Since Bc is set by fundamental physics, the only truly adjustable parameter for the design is the loop radius; a larger loop radius minimizes the amount of superconducting material required. This non-intuitive result means that the ‘intuitive’ strategy of building a compact electromagnet and placing it between Mars and the Sun at the first Lagrange point is unfeasible. Considering reasonable limits on Bc, the smallest possible loop radius is ~10 km, and the magnetic shield would have a mass of ~ 1019 g. Most high-temperature superconductors are constructed of rare elements; given solar system abundances, building a superconductor with ~ 1019 g would require mining a solar system body with several times 1025 g; this is approximately 10% of Mars. We find that the most feasible design is to encircle Mars with a superconducting wire with a loop radius of ~3400 km. The resulting wire diameter can be as small as ~5 cm. With this design, the magnetic shield would have a mass of ~ 1012 g and would require mining ~ 1018 g, or only 0.1% of Olympus Mons.
Self-Training with Weak Supervision

State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning with weak rules is challenging due to their inherent heuristic and noisy nature. An additional challenge is rule coverage and overlap, where prior work on weak supervision only considers instances that are covered by weak rules, thus leaving valuable unlabeled data behind. In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. To this end, we leverage task-specific unlabeled data through self-training with a model (student) that considers contextualized representations and predicts pseudo-labels for instances that may not be covered by weak rules. We further develop a rule attention network (teacher) that learns how to aggregate student pseudo-labels with weak rule labels, conditioned on their fidelity and the underlying context of an instance. Finally, we construct a semi-supervised learning objective for end-to-end training with unlabeled data, domain-specific rules, and a small amount of labeled data. Extensive experiments on six benchmark datasets for text classification demonstrate the effectiveness of our approach with significant improvements over state-of-the-art baselines.
The design of Lil'Flo, a socially assistive robot for upper extremity motor assessment and rehabilitation in the community via telepresence.

IntroductionWe present Lil’Flo, a socially assistive robotic telerehabilitation system for deployment in the community. As shortages in rehabilitation professionals increase, especially in rural ar...