cssi/SciSciGPT-SciSciNet huggingface dataset:
Director networks and informed traders

Abstract We provide evidence that sophisticated investors like short sellers, option traders, and financial institutions are more informed when trading stocks of companies with more connected board members. For firms with large director networks, the annualized return difference between the highest and lowest quintile of informed trading ranges from 4% to 7.2% compared to the same return difference in firms with less connected directors. Sophisticated investors better predict outcomes of upcoming earnings surprises and firm-specific news sentiment for companies with more connected directors. Changes in board connectedness are positively associated with changes in measures of adverse selection.
Billy-Bob Teeth Saves Porn Star: Coping with Defective Work-for-Hire Registrations

This paper begins by briefly reviewing statutory provisions that determine initial copyright ownership, govern title transfers, establish requisites to infringement litigation, and bar untimely suits.It then examines Billy-Bob Teeth and Jules Jordan Video and explains how, in the latter case, the Ninth Circuit applied rationales adopted by the Seventh Circuit in the former case to overturn a JMOL unfavorable to an "adult film" star.The third part of the paper reviews use of the copyright statute of limitations to resolve competing ownership claims.The last part of the paper, flagging important differences between § 201(b) and § 204(a), explains why one approach used by the Seventh Circuit to clear title was inappropriately applied by the Ninth Circuit. It concludes that, had the copyright statute of limitations been pled in the later case, the opportunity to misapply § 204(a) should not have arisen. Summary judgment would have been warranted on § 507(b), and that would have made it unnecessary for the Ninth Circuit to offer three alternative ways to extricate a copyright owner from the horns of a dilemma.A version of the paper, citing later copyright and patent opinions refusing acceptance of nunc pro tunc agreements, is published in 9 U.N.H. L.Rev. 409 (2011).
Evidence on the Incremental Information Contained in the Components of Restructuring Charges

Among the new disclosures required by EITF 94–3 is the requirement that firms disclose the nature and amounts of the material components of a restructuring charge. The objective of this paper is to assess whether these components provide information to financial statement users beyond that contained in the aggregate charge. The evidence is consistent with the decomposition of the charge providing incremental information that would be lost if only the aggregate number is reported. The results also appear to suggest that analysts interpret restructurings as bad news and that inventory writedowns and employee terminations are interpreted as the most negative restructuring components.
Across‐sample Incomparability of R2s and Additional Evidence on Value Relevance Changes Over Time

: Given the increasing popularity of across-sample R2 comparisons in accounting research, this paper illustrates why the regression R2s are incomparable across samples and the general nature of this problem. The regression residual dispersion with proper control for scale is proposed as the alternative measure of explanatory power for across-sample comparisons. In market-on-accounting variable regressions, this measure can be conveniently interpreted as the degree of accounting-based pricing errors and be used as a measure of value relevance of accounting information. As an application, the issue of over-time value relevance changes is re-visited. In contrast to prior mixed findings based on the R2 measure, a decline of value relevance since the early 1970s is robustly detected using the alternative measure.
THE ARGENTINA PARADOX: MICROEXPLANATIONS AND MACROPUZZLES

The economic history of Argentina presents one of the most dramatic examples of divergence in the modern era. What happened and why? This paper reviews the wide range of competing explanations in the literature and argues that, setting aside deeper social and political determinants, the various economic mechanisms in play defy the idea of a monocausal explanation.
Anthropocentrism and the Argument from Gaia Theory

Anthropocentrism holds that the only things valuable in themselves are: human beings, their desires and needs, and the satisfaction of those. In turn, Gaia theory holds that the Earth and all creatures on it constitute something akin to a vast living being. Many layfolk maintain that Gaia theory implies the falsity of anthropocentrism, and thus puts the kibosh on that doctrine. But philosophical writers deny this implication. This paper therefore argues for what we may call "the Kibosh Thesis"—that Gaia theory, when correctly understood, does indeed put the kibosh on anthropocentrism. It defends this thesis by appealing to "the Part-Whole Thesis"—that no parts of a living being which do not constitute the whole being can have as much intrinsic value as the being itself has. Since the evidence supporting Gaia theory is mounting, this thesis appears to provide a fairly strong argument against anthropocentrism. In arguing for this position, I show why anthropocentrism is a plausible doctrine, specify Gaia theory's main claims, meet the main philosophical objections to the Kibosh Thesis, and develop the argument from the Part-Whole Thesis.
Global Environmental Policy and Global Trade Policy

The global climate regime, as represented by the Kyoto Protocol, may be on a collision course with the global trade policy regime, as represented by the WTO (World Trade Organization). Environmentalists fear that international trade will undercut reductions in greenhouse gas emissions as dirty production migrates to non-participating countries, a phenomenon known as leakage. Meanwhile businesspeople fear the effects on their own competitiveness of the same phenomenon. These fears have now become prominent in the policy-making process. In early 2008, legislation to enact long-term targets for reduced emission of greenhouse gases included provisions for possible barriers against imports from countries perceived as non-participating--in both Washington, DC (where the bills have not yet passed) and in Brussels (where the EU Commission Directive has gone into effect). Such provisions could be interpreted as violations of the rules of the WTO, which poses the nightmare scenario of a WTO panel rejecting a major country's climate change legislation. In light of the hostile feelings that such a scenario would unleash, it would be a nightmare for the supporters of the WTO and free trade as much as for the supporters of the Kyoto Protocol and environmental protection. The issue is just the latest and largest instance of fears among many environmentalists that the WTO is an obstacle to their goals in general. The issue transcends institutions. For the critics, the WTO is a symbol of globalization, and their fears attach also to that larger phenomenon. The first part of this paper discusses the broader issue of whether environmental goals in general are threatened by free trade and the WTO. The second half of the paper focuses exclusively on the narrower question of trade aspects of nations' efforts to implement climate change policy and whether they are likely to come into conflict with the WTO.
A bigger piece of the pie: The relative importance of affiliative and change-oriented citizenship and task performance in predicting overall job performance

Abstract We examine the extent to which both in-role (task performance) and extra-role dimensions of performance (organizational citizenship behaviors) account for variance in ratings of overall job performance by utilizing currently available meta-analytic estimates. Relative weight analysis results show that overall performance is determined more by three OCB forms in combination (RW = 0.34; %RW = 72.9%) than by task performance (RW = 0.12; %RW = 27.1%). Among the OCB forms, the relative weight of OCB-O (RW = 0.17; %RW = 36.9%) is greater than those of OCB-I (RW = 0.11; %RW = 0.22.9%) and OCB-CH (RW = 0.06; %RW = 13.1%). Consistent with the results from a relative weight analysis, results from a series of multiple regression analyses also show that the incremental contribution (Δ R 2 ) of each performance dimension above and beyond the other performance dimensions is the highest for OCB-O (0.056), followed by those of task performance (0.041), OCB-CH (0.007), and OCB-I (0.003). We discuss the theoretical and practical implications of these findings along with study limitations and future research directions.
Integrating Managerial and Tax Objectives in Transfer Pricing

This paper examines transfer pricing in multinational firms when individual divisions face different income tax rates. Assuming that a firm decouples its internal transfer price from the arm's length price used for tax purposes, we analyze the effectiveness of alternative pricing rules under both cost‐ and market‐based transfer pricing. In a tax‐free world, Hirshleifer (1956) advocated that the internal transfer price be set equal to the marginal cost of the supplying division. Extending this solution, we argue that the optimal internal transfer price should be a weighted average of the pre‐tax marginal cost and the most favorable arm's length price. When the supplying division also sells the intermediate product in question to outside parties, the external price becomes a natural candidate for the arm's length price. We argue that for internal performance evaluation purposes firms should generally not value internal transactions at the prevailing market price if the supplying division has monopoly power ...
From Child Labor “Problem” to Human Trafficking “Crisis”: Child Advocacy and Anti-Trafficking Legislation in Ghana

This article examines the relationship between multidimensional child advocacy campaigns and the enactment of Ghana's Human Trafficking Act (2005). I argue that while child advocacy has a rich history, the diffuse labor-oriented advocacy characteristic of the 1990s failed to articulate an achievable goal. Child labor advocacy was impotent because the diverse agencies involved adopted different positions about the permissibility of child labor. By contrast, the anti-trafficking initiatives of the early 2000s focused narrowly on legislative remedy and formulated a discourse of “crisis.” An anti-trafficking coalition built on an international regulation model that emerged from the cocoa industry. As agents of social and political change, domestic, regional, and foreign nongovernmental organizations and intergovernmental organizations played important roles in shaping debates and framing new law. The Ghanaian law, however, is of limited effectiveness because it ignores autochthonous social practices with historically rich traditions, and it enjoins a narrow, economic model for the proliferation of trafficking.
Why have measures of earnings quality changed over time

The properties of earnings have changed dramatically over the past 40 years. Prior studies interpret this trend as a decline in earnings quality but disagree on whether it results from changes in the real economy or changes in accounting standards. I find that each new cohort of listed firms exhibits lower earnings quality than its predecessors, mainly because of higher intangible intensity. I conclude that the trend of decline in earnings quality is due more to changes in the sample of firms than to changes in generally accepted accounting principles (GAAP) or in the earnings quality of previously listed firms.
Coded Caching in the Presence of a Wire and a Cache Tapping Adversary of Type II

This paper introduces the notion of cache-tapping into the information theoretic models of coded caching. The wiretap channel II in the presence of multiple receivers equipped with fixed-size cache memories, and an adversary which selects symbols to tap into from cache placement and/or delivery is introduced. The legitimate terminals know neither whether placement, delivery, or both are tapped, nor the positions in which they are tapped. Only the size of the overall tapped set is known. For two receivers and two files, the strong secrecy capacity– the maximum achievable file rate while keeping the overall library strongly secure– is identified. Lower and upper bounds on the strong secrecy file rate are derived when the library has more than two files. Achievability relies on a code design which combines wiretap coding, security embedding codes, one-time pad keys, and coded caching. A genie-aided upper bound, in which the transmitter is provided with user demands before placement, establishes the converse for the two-files case. For more than two files, the upper bound is constructed by three successive channel transformations. Our results establish provable security guarantees against a powerful adversary which optimizes its tapping over both phases of communication in a cache-aided system.
REDISTRIBUTION AND TAX EXPENDITURES: THE EARNED INCOME TAX CREDIT

This paper examines the distributional and behavioral effects of the Earned Income Tax Credit (EITC). We chart the growth of the program over time, and argue that several expansions show that real responses to taxes are important. We use tax data to show the distribution of benefi ts by income and family size, and examine the impacts of hypothetical reforms to the credit. Finally, we calculate the effi ciency effects of marginal changes to EITC parameters.
Real Options in Technology Licensing

This paper examines the use of options contracts by firms acquiring rights to commercialize university technologies. By combining information about the sequence of licensing decisions with characteristics of the firms and technologies involved, I explore factors that shape decisions to purchase and exercise option contracts for these technologies. Decisions by firms that considered but did not purchase an option or a license are included in the sample. Consistent with the basic premise of real-options theory, I find that firms are more likely to purchase option contracts for more uncertain technologies. Also in line with theoretical predictions, I find that firms that are better able to evaluate an external technology are less likely to purchase options before licensing. The results also highlight more complex motives for exercising options in technology licensing. On the one hand, firms appear to benefit from their ability to learn about the technology during the option period. On the other hand, firms that are better able to “absorb” the technology during the contract period may have reduced incentives to subsequently license the invention.
Introducing phonics to learners who struggle: content and embedded cognitive elements

A brief experiment was designed to examine cognitive flexibility practice embedded in beginning phonics instruction for kindergarteners with limited early literacy learning. Previously tested phonics content included single- and high-frequency two-letter grapheme-phoneme correspondences (GPCs), introduced at a rate of 2–4 correspondences per week. Children entered with minimal alphabet knowledge and were randomly assigned within classrooms, stratified by English Learner status, to one of two conditions delivered individually over 6 weeks: plain explicit phonics (n = 35) or Flex instruction (n = 33) which covered the same Plain phonics content but with teaching tasks for practice switching letter or word dimensions. Results showed that kindergarteners in the Plain condition made significantly greater gains on tasks of writing taught letter-sound correspondences and spelling. Findings inform a rate for introducing letter-sound correspondences and learning of mixed-size GPCs. The Plain explicit phonics focus on initial accuracy had benefits for encoding taught letter correspondences. Findings support future research on effective tasks to develop reading-related cognitive flexibility in beginners, the optimal timing of this practice, and whether it benefits in particular those children most at risk for acquiring this foundational alphabetic knowledge.
The dark side of CEO social capital: Evidence from real earnings management and future operating performance

Abstract We examine the role of CEO social capital as an important driver of the widespread practice of real earnings management (REM). Using the number of social connections to outside executives and directors to measure CEO social capital, we first find that well-connected CEOs associate with higher levels and volatilities of REM. The positive relation between REM and CEO network size is stronger when the CEO connects with more informed and influential persons, and when a more severe misalignment of interests can occur. Second, we find a contagion of REM among well-connected CEOs in an industry. Third, the level of REM induced by a large CEO social network associates negatively with future operating performance. This result is consistent with social capital circulating REM-related information ex-ante and increasing the power and influence for the CEO to deviate from optimal operating policies ex-post. Social capital shields the well-connected executive in the takeover and labor markets despite possible suboptimal future operating performance. While the prior literature finds that CEO social capital reduces accrual earnings management, our findings suggest a dark side of CEO social capital: it induces excessive levels and volatilities of REM costly to the firm in the long run while imposing relatively low personal risk on the top executive.
Airborne transmission of virus-laden aerosols inside a music classroom: Effects of portable purifiers and aerosol injection rates

The ongoing COVID-19 pandemic has shifted attention to the airborne transmission of exhaled droplet nuclei within indoor environments. The spread of aerosols through singing and musical instruments in music performances has necessitated precautionary methods such as masks and portable purifiers. This study investigates the effects of placing portable air purifiers at different locations inside a classroom and the effects of different aerosol injection rates (e.g., with and without masks, different musical instruments, and different injection modes). Aerosol deposition, airborne concentration, and removal are analyzed in this study. It was found that using purifiers could help in achieving ventilation rates close to the prescribed values by the World Health Organization, while also achieving aerosol removal times within the Center of Disease Control and Prevention recommended guidelines. This could help in deciding break periods between classroom sessions, which was around 25 min through this study. Moreover, proper placement of purifiers could offer significant advantages in reducing airborne aerosol numbers (offering several orders of magnitude higher aerosol removal when compared to nearly zero removal when having no purifiers), and improper placement of the purifiers could worsen the situation. This study suggests the purifier to be placed close to the injector to yield a benefit and away from the people to be protected. The injection rate was found to have an almost linear correlation with the average airborne aerosol suspension rate and deposition rate, which could be used to predict the trends for scenarios with other injection rates.
Stabilizing Radial Basis Function Methods for Conservation Laws Using Weakly Enforced Boundary Conditions

It is well understood that boundary conditions (BCs) may cause global radial basis function (RBF) methods to become unstable for hyperbolic conservation laws (CLs). Here we investigate this phenomenon and identify the strong enforcement of BCs as the mechanism triggering such stability issues. Based on this observation we propose a technique to weakly enforce BCs in RBF methods. In the case of hyperbolic CLs, this is achieved by carefully building RBF methods from the weak form of the CL, rather than the typically enforced strong form. Furthermore, we demonstrate that global RBF methods may violate conservation, yielding physically unreasonable solutions when the approximation does not take into account these considerations. Numerical experiments validate our theoretical results.
Comparison of explicit and mean-field models of cytoskeletal filaments with crosslinking motors

In cells, cytoskeletal filament networks are responsible for cell movement, growth, and division. Filaments in the cytoskeleton are driven and organized by crosslinking molecular motors. In reconstituted cytoskeletal systems, motor activity is responsible for far-from-equilibrium phenomena such as active stress, self-organized flow, and spontaneous nematic defect generation. How microscopic interactions between motors and filaments lead to larger-scale dynamics remains incompletely understood. To build from motor–filament interactions to predict bulk behavior of cytoskeletal systems, more computationally efficient techniques for modeling motor–filament interactions are needed. Here, we derive a coarse-graining hierarchy of explicit and continuum models for crosslinking motors that bind to and walk on filament pairs. We compare the steady-state motor distribution and motor-induced filament motion for the different models and analyze their computational cost. All three models agree well in the limit of fast motor binding kinetics. Evolving a truncated moment expansion of motor density speeds the computation by $$10^3$$ – $$10^6$$ compared to the explicit or continuous-density simulations, suggesting an approach for more efficient simulation of large networks. These tools facilitate further study of motor–filament networks on micrometer to millimeter length scales.
A 2D Advancing-Front Delaunay Mesh Refinement Algorithm

I present a generalization of Chew's first algorithm for Delaunay mesh refinement. I split the line segments of an input planar straight line graph (PSLG) such that the lengths of split segments are asymptotically proportional to the local feature size at their endpoints. By employing prior algorithms, I then refine the truly or constrained Delaunay triangulation of the PSLG by inserting off-center Steiner vertices of "skinny" triangles while prioritizing triangles with shortest edges first. This technique inserts Steiner vertices in an advancing front manner such that we obtain a size-optimal, truly or constrained Delaunay mesh if the desired minimum angle is less than 30° (in the absence of small input angles). This is an improvement over prior algorithms that produce size-optimal meshes with minimum angles of about 26.4°and 28.6°for truly and constrained Delaunay meshes, respectively. Even in the presence of small input angles, the upper bound on the maximum angle is an angle strictly greater than 120° (an improvement from about 137°). The lower bound on the minimum angle in the presence of small angles is identical to prior bounds.
Taking aim at the perceptual side of motor learning: Exploring how explicit and implicit learning encode perceptual error information through depth vision

Motor learning in visuomotor adaptation tasks results from both explicit and implicit processes, each responding differently to an error signal. Although the motor output side of these processes has been extensively studied, the visual input side is relatively unknown. We investigated if and how depth perception affects the computation of error information by explicit and implicit motor learning. Two groups of participants made reaching movements to bring a virtual cursor to a target in the frontoparallel plane. The Delayed group was allowed to reaim and their feedback was delayed to emphasize explicit learning, whereas the camped group received task-irrelevant clamped cursor feedback and continued to aim straight at the target to emphasize implicit adaptation. Both groups played this game in a highly detailed virtual environment (depth condition), leveraging a cover task of playing darts in a virtual tavern, and in an empty environment (no-depth condition). The delayed group showed an increase in error sensitivity under depth relative to no-depth. In contrast, the clamped group adapted to the same degree under both conditions. The movement kinematics of the delayed participants also changed under the depth condition, consistent with the target appearing more distant, unlike the Clamped group. A comparison of the delayed behavioral data with a perceptual task from the same individuals showed that the greater reaiming in the depth condition was consistent with an increase in the scaling of the error distance and size. These findings suggest that explicit and implicit learning processes may rely on different sources of perceptual information.NEW & NOTEWORTHY We leveraged a classic sensorimotor adaptation task to perform a first systematic assessment of the role of perceptual cues in the estimation of an error signal in the 3-D space during motor learning. We crossed two conditions presenting different amounts of depth information, with two manipulations emphasizing explicit and implicit learning processes. Explicit learning responded to the visual conditions, consistent with perceptual reports, whereas implicit learning appeared to be independent of them.
Continuous N\'{e}el-VBS quantum phase transition in non-local one-dimensional systems with SO(3) symmetry

One dimensional $(1d)$ interacting systems with local Hamiltonians can be studied with various well-developed analytical methods. Recently novel $1d$ physics was found numerically in systems with either spatially nonlocal interactions, or at the $1d$ boundary of $2d$ quantum critical points, and the critical fluctuation in the bulk also yields effective nonlocal interactions at the boundary. This work studies the edge states at the $1d$ boundary of $2d$ strongly interacting symmetry protected topological (SPT) states, when the bulk is driven to a disorder-order phase transition. We will take the $2d$ Affleck-Kennedy-Lieb-Tasaki (AKLT) state as an example, which is a SPT state protected by the SO(3) spin symmetry and spatial translation. We found that the original $(1+1)d$ boundary conformal field theory of the AKLT state is unstable due to coupling to the boundary avatar of the bulk quantum critical fluctuations. When the bulk is fixed at the quantum critical point, within the accuracy of our expansion method, we find that by tuning one parameter at the boundary, there is a generic direct transition between the long range antiferromagnetic Neel order and the valence bond solid (VBS) order. This transition is very similar to the Neel-VBS transition recently found in numerical simulation of a spin-1/2 chain with nonlocal spatial interactions. Connections between our analytical studies and recent numerical results concerning the edge states of the $2d$ AKLT-like state at a bulk quantum phase transition will also be discussed.
The Power of Proxy Advisors: Myth or Reality?

Recent regulatory changes increasing shareholder voting authority have focused attention on the role of proxy advisors. In particular, greater shareholder empowerment raises the question of how much proxy advisors influence voting outcomes. This Article analyzes the significance of voting recommendations issued by four proxy advisory firms in connection with uncontested director elections. We find, consistent with press reports, that Institutional Shareholder Services (ISS) is the most powerful proxy advisor and that, of the others, only Glass, Lewis & Co. seems to have a meaningful impact on shareholder voting. This Article also attempts to measure the impact of voting recommendations on voting outcomes. Unlike prior literature, it distinguishes correlation from causality by examining both the recommendation itself and the underlying factors that may influence a shareholder’s vote. Using several different tests, we conclude that popular accounts substantially overstate the influence of ISS. Our findings reveal that the impact of an ISS recommendation is reduced greatly once companyand firm-specific factors important to investors are taken into consideration. Overall, we estimate that an ISS recommendation shifts 6%–10% of shareholder votes. We also * Murray and Kathleen Bring Professor of Law, New York University School of Law. ** Perry Golkin Professor of Law, University of Pennsylvania Law School. ∗∗∗ George T. Lowy Professor of Law, New York University School of Law. The authors thank Glass Lewis, Proxy Governance, and Egan Jones for providing access to their voting recommendations and policies. Earlier drafts of this paper were presented at Duke University School of Law, University of Virginia School of Law, UCLA School of Law, and Yale Law School, and we received many useful comments at each session. We also thank Un Kyung Park for her helpful comments. Marcel Kahan thanks the Milton and Miriam Handler Foundation for financial support. The Filomen D'Agostino and Max E. Greenberg Research Fund provided summer research support for this Article. CHOIFISCHKAHAN GALLEYSFINAL 7/8/2010 2:55 PM 870 EMORY LAW JOURNAL [Vol. 59 determine that a major component of ISS’s influence stems from its role as an information agent, aggregating factors that its subscribers consider important.
Did the ADA Evolve into Our Ramp to Full Employment? An Analysis of 18 Years of the Americans with Disabilities Act

In 1990, Congress passed the Americans with Disabilities Act(ADA) to improve the labor market opportunities of people with disabilities. Immediately following the enactment of the ADA, the employment rate of people with disabilities declined. However, the long-term employment consequences of the ADA have not been studied. Interest in the long-term post-ADA employment trends of people with disabilities derives from the weakening of the ADAâ€™s employment provisions by U.S. Courts. The weakening of these provisions has decreased the cost to employers of hiring disabled employees. This paper uses variation in state disability laws and data from twenty-three years of the Current Population Survey to determine the long-term impact of the ADA on the employment of people with disabilities.
Fixed-Time Newton-Like Extremum Seeking

Abstract In this paper, we present a novel Newton-based extremum seeking controller for the solution of multivariable model-free optimization problems in static maps. Unlike existing asymptotic and fixed-time results in the literature, we present a scheme that achieves (practical) finite time convergence to a neighborhood of the optimal point, with a convergence time that is independent of the initial conditions and the Hessian of the cost function, and therefore can be arbitrarily assigned a priori by the designer with an appropriate choice of parameters in the algorithm. The extremum seeking dynamics exploit a class of fixed time convergence properties recently established in the literature for a family of Newton flows, as well as averaging results for perturbed dynamical systems that are not necessarily Lipschitz continuous. The proposed extremum seeking algorithm is model-free and does not require any explicit knowledge of the gradient and Hessian of the cost function. Instead, real-time optimization with fixed-time convergence is achieved by using real time measurements of the cost, which is perturbed by a suitable class of periodic excitation signals generated by a dynamic oscillator. Numerical examples illustrate the performance of the algorithm.
Somatosensory versus cerebellar contributions to proprioceptive changes associated with motor skill learning: A theta burst stimulation study.

Abstract Background It is well established that proprioception (position sense) is important for motor control, yet its role in motor learning and associated plasticity is not well understood. We previously demonstrated that motor skill learning is associated with enhanced proprioception and changes in sensorimotor neurophysiology. However, the neural substrates mediating these effects are unclear. Objective To determine whether suppressing activity in the cerebellum and somatosensory cortex (S1) affects proprioceptive changes associated with motor skill learning. Methods 54 healthy young adults practiced a skill involving visually-guided 2D reaching movements through an irregular-shaped track using a robotic manipulandum with their right hand. Proprioception was measured using a passive two-alternative choice task before and after motor practice. Continuous theta burst stimulation (cTBS) was delivered over S1 or the cerebellum (CB) at the end of training for two consecutive days. We compared group differences (S1, CB, Sham) in proprioception and motor skill, quantified by a speed-accuracy function, measured on a third consecutive day (retention). Results As shown previously, the Sham group demonstrated enhanced proprioceptive sensitivity after training and at retention. The S1 group had impaired proprioceptive function at retention through online changes during practice, whereas the CB group demonstrated offline decrements in proprioceptive function. All groups demonstrated motor skill learning. However, the magnitude of learning differed between the CB and Sham groups, consistent with a role for the cerebellum in motor learning. Conclusion Overall, these findings suggest that the cerebellum and S1 are important for distinct aspects of proprioceptive changes during skill learning.
Sasakian geometry on sphere bundles

Abstract The purpose of this paper is to study the Sasakian geometry on odd dimensional sphere bundles over a smooth projective algebraic variety N with the ultimate, but probably unachievable goal of understanding the existence and non-existence of extremal and constant scalar curvature Sasaki metrics. We apply the fiber join construction of Yamazaki [48] for K-contact manifolds to the Sasaki case. This construction depends on the choice of d + 1 integral Kahler classes [ ω j ] on N that are not necessarily colinear in the Kahler cone. We show that the colinear case is equivalent to a subclass of a different join construction originally described in [7] , [11] , applied to the spherical case by the authors in [19] , [20] when d = 1 , and known as cone decomposable [13] . The non-colinear case gives rise to infinite families of new inequivalent cone indecomposable Sasaki contact CR structures on certain sphere bundles. We prove that the Sasaki cone for some of these structures contains an open set of extremal Sasaki metrics and, for certain specialized cases, the regular ray within this cone is shown to have constant scalar curvature. We also compute the cohomology groups of all such sphere bundles over a product of Riemann surfaces.
Reaping the Benefits of Bundling under High Production Costs.

It has long been known in the economics literature that selling different goods in a single bundle can significantly increase revenue, even when the valuations for the goods are independent. However, bundling is no longer profitable if the goods have high production costs. To overcome this issue, we introduce Pure Bundling with Disposal for Cost (PBDC), where after buying the bundle, we allow the customer to return any subset of items for their production cost. We demonstrate using classical examples that PBDC captures the concentration effects of bundling while allowing for the flexibility of individual sales, extracting all of the consumer welfare in situations where previous simple mechanisms could not.Furthermore, we prove a theoretical guarantee on the performance of PBDC that holds for arbitrary independent distributions, using techniques from the mechanism design literature. We transform the problem with costs to a problem with negative valuations, extend the mechanism design techniques to negative valuations, and use the Core-Tail decomposition of Babaioff et al. from [BILW14] to show that either PBDC or individual sales will obtain at least 1/5.2 of the optimal profit. This also improves the bound of 1/6 from [BILW14]. We advance the upper bound as well, constructing two IID items with zero cost where mixed bundling earns 1.19 more revenue than either pure bundling or individual sales.Our numerical experiments show that PBDC outperforms all other simple pricing schemes, including the Bundle-Size Pricing (BSP) introduced by Chu et al. in [CLS11], under the same families of distributions used in [CLS11]. We also provide the first theoretical explanation for some of the great experimental successes in [CLS11]. All in all, our work shows establishes PBDC as a robust, computationally-minimal heuristic that is easy to market to the customer.
Discrete Cosine Transform Based Causal Convolutional Neural Network for Drift Compensation in Chemical Sensors

Sensor drift is a major problem in chemical sensors that requires addressing for reliable and accurate detection of chemical analytes. In this paper, we develop a causal convolutional neural network (CNN) with a Discrete Cosine Transform (DCT) layer to estimate the drift signal. In the DCT module, we apply soft-thresholding nonlinearity in the transform domain to denoise the data and obtain a sparse representation of the drift signal. The soft-threshold values are learned during training. Our results show that DCT layer-based CNNs are able to produce a slowly varying baseline drift signal. We train the CNN on synthetic data and test it on real chemical sensor data. Our results show that we can have an accurate and smooth drift estimate even when the observed sensor signal is very noisy.
Control barrier functions for stochastic systems

Abstract Control Barrier Functions (CBFs) aim to ensure safety by constraining the control input at each time step so that the system state remains within a desired safe region. This paper presents a framework for CBFs in stochastic systems in the presence of Gaussian process and measurement noise. We first consider the case where the system state is known at each time step, and present reciprocal and zero CBF constructions that guarantee safety with probability 1. We extend our results to high relative degree systems and present both general constructions and the special case of linear dynamics and affine safety constraints. We then develop CBFs for incomplete state information environments, in which the state must be estimated using sensors that are corrupted by Gaussian noise . We prove that our proposed CBF ensures safety with probability 1 when the state estimate is within a given bound of the true state, which can be achieved using an Extended Kalman Filter when the system is linear or the process and measurement noise are sufficiently small. We propose control policies that combine these CBFs with Control Lyapunov Functions in order to jointly ensure safety and stochastic stability. Our results are validated via numerical study on a multi-agent collision avoidance scenario.
Robust Question Answering Through Sub-part Alignment

Current textual question answering (QA) models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns, so they fail to generalize to out-of-distribution settings. To make a more robust and understandable QA system, we model question answering as an alignment problem. We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and align the question to a subgraph of the context in order to find the answer. We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite using beam search for approximate inference. Our use of explicit alignments allows us to explore a set of constraints with which we can prohibit certain types of bad model behavior arising in cross-domain settings. Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input lead the model to choose the answer without relying on post-hoc explanation techniques. We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets. The results show that our model is more robust than the standard BERT QA model, and constraints derived from alignment scores allow us to effectively trade off coverage and accuracy.
RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure

Glaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test’s innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field. RetiNerveNet uses information from the more objective Spectral-Domain Optical Coherence Tomography (SDOCT). RetiNerveNet attempts to trace-back the arcuate convergence of the retinal nerve fibers, starting from the Retinal Nerve Fiber Layer (RNFL) thickness around the optic disc, to estimate individual age-corrected 24-2 SAP values. Recursive passes through the proposed network sequentially yield estimates of the visual locations progressively farther from the optic disc. While all the methods used for our experiments exhibit lower performance for the advanced disease group (possibly due to the “floor effect” for the SDOCT test), the proposed network is observed to be more accurate than all the baselines for estimating the individual visual field values. We further augment the proposed network to additionally predict the SAP Mean Deviation values and also facilitate the assignment of higher weightage to the underrepresented groups in the data. We then study the resulting performance trade-offs of the RetiNerveNet on the early, moderate and severe disease groups.
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images

Distinguishing subtle differences in attributes is valuable, yet learning to make visual comparisons remains nontrivial. Not only is the number of possible comparisons quadratic in the number of training images, but also access to images adequately spanning the space of fine-grained visual differences is limited. We propose to overcome the sparsity of supervision problem via synthetically generated images. Building on a state-of-the-art image generation engine, we sample pairs of training images exhibiting slight modifications of individual attributes. Augmenting real training image pairs with these examples, we then train attribute ranking models to predict the relative strength of an attribute in novel pairs of real images. Our results on datasets of faces and fashion images show the great promise of bootstrapping imperfect image generators to counteract sample sparsity for learning to rank.
Relative Deviation Margin Bounds

We present a series of new and more favorable margin-based learning guarantees that depend on the empirical margin loss of a predictor. We give two types of learning bounds, both data-dependent ones and bounds valid for general families, in terms of the Rademacher complexity or the empirical $\ell_\infty$ covering number of the hypothesis set used. We also briefly highlight several applications of these bounds and discuss their connection with existing results.
Unsupervised Label Refinement Improves Dataless Text Classification

Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descriptions of the label set for each downstream task. This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice. In this paper, we ask the following question: how can we improve dataless text classification using the inputs of the downstream task dataset? Our primary solution is a clustering based approach. Given a dataless classifier, our approach refines its set of predictions using k-means clustering. We demonstrate the broad applicability of our approach by improving the performance of two widely used classifier architectures, one that encodes text-category pairs with two independent encoders and one with a single joint encoder. Experiments show that our approach consistently improves dataless classification across different datasets and makes the classifier more robust to the choice of label descriptions.
Support for the adaptive decoupling hypothesis from whole-transcriptome profiles of a hypermetamorphic and sexually dimorphic insect, Neodiprion lecontei.

Though seemingly bizarre, the dramatic morphological and ecological transformation that occurs when immature life stages metamorphose into reproductive adults is one of the most successful developmental strategies on the planet. The adaptive decoupling hypothesis (ADH) proposes that metamorphosis is an adaptation for breaking developmental links between traits expressed in different life stages, thereby facilitating their independent evolution when exposed to opposing selection pressures. Here, we draw inspiration from the ADH to develop a conceptual framework for understanding changes in gene expression across ontogeny. We hypothesized that patterns of stage-biased and sex-biased gene expression are the product of both decoupling mechanisms and selection history. To test this hypothesis, we characterized transcriptome-wide patterns of gene-expression traits for three ecologically distinct larval stages (all male) and adult males and females of a hypermetamorphic insect (Neodiprion lecontei). We found that stage-biased gene expression was most pronounced between larval and adult males, which is consistent with the ADH. However, even in the absence of a metamorphic transition, considerable stage-biased expression was observed among morphologically and behaviorally distinct larval stages. Stage-biased expression was also observed across ecologically relevant GO categories and genes, highlighting the role of ecology in shaping patterns of gene expression. We also found that the magnitude and prevalence of stage-biased expression far exceeded adult sex-biased expression. Overall, our results highlight how the adaptive decoupling hypothesis can shed light on transcriptome-wide patterns of gene expression in organisms with complex life cycles. For maximal insight, detailed knowledge of organismal ecology is also essential.
On Minimizing the Number of Running Buffers for Tabletop Rearrangement

For tabletop rearrangement problems with overhand grasps, storage space outside the tabletop workspace, or buffers, can temporarily hold objects which greatly facilitates the resolution of a given rearrangement task. This brings forth the natural question of how many running buffers are required so that certain classes of tabletop rearrangement problems are feasible. In this work, we examine the problem for both the labeled (where each object has a specific goal pose) and the unlabeled (where goal poses of objects are interchangeable) settings. On the structural side, we observe that finding the minimum number of running buffers (MRB) can be carried out on a dependency graph abstracted from a problem instance, and show that computing MRB on dependency graphs is NP-hard. We then prove that under both labeled and unlabeled settings, even for uniform cylindrical objects, the number of required running buffers may grow unbounded as the number of objects to be rearranged increases; we further show that the bound for the unlabeled case is tight. On the algorithmic side, we develop highly effective algorithms for finding MRB for both labeled and unlabeled tabletop rearrangement problems, scalable to over a hundred objects under very high object density. Employing these algorithms, empirical evaluations show that random labeled and unlabeled instances, which more closely mimics real-world setups, have much smaller MRB.