cssi/SciSciGPT-SciSciNet huggingface dataset:
Crimes Involving Intangible Property

A well-known cliche came to life in 2012 when “[t]he pope’s butler was convicted… of stealing the pontiff’s private documents and leaking them to a journalist….” This paper was prompted by his lawyer’s unsuccessfully arguing that taking “only photocopies, not original documents” should not be criminal.The paper therefore considers bases for prosecuting the theft of purely intangible interests under U.S. federal and state law.Because the text in question had been fixed, states would have no authority to prosecute for unauthorized copying in the nature of copyright infringement. Moreover, the seminal Dowling case finds that penal jurisdiction under the Copyright Act displaces federal liability for theft of tangible property — at least in the absence of other subject-specific statutes. Yet, successful prosecution would require an examination of the retail value of works not produced for sale.Because the text in question had not been published, unauthorized taking might also qualify as theft of trade secrets. The paper therefore considers the 1996 Economic Espionage as a basis for federal jurisdiction and turns to the recent Aleynikov opinion. Barring simultaneous theft of tangible property, the Second Circuit likewise finds only subject-specific prosecution possible for theft of compute code. Although the code qualified as a trade secret, and the federal EEA facially applied, a key requirement could not be satisfied. Roughly half of the paper considers state penal statutes. Because of preemption, unauthorized duplication of published documents cannot be prosecuted, but theft of the contents of unpublished documents would be possible. Some state statutes name trade secrets as subject property; others name intangibles generally; and others name neither. Were a case such as the one involving the Vatican papers to be tried in a state court, a variety of problems would be encountered under each type of statute. The paper uses three cases to illustrate some of those problems.Looking well beyond the papal papers, the paper ultimately suggests that something akin to a penal version of the Uniform Trade Secret Act might bring more resources to bear on increasingly disruptive criminal activities.
Dynamic Models and Structural Estimation in Corporate Finance

Introduction. 2. Dynamic Contingent and Claims Models. 3. Discrete-Time Investment Models. 4. Structural Estimation. 5. Conclusion. References
Psst! Wanna Buy a Bridge? IP Transfers of Non-Existent Property

It is common practice when hiring a researcher at a university or a laboratory to require the new employee to sign a patent transfer agreement—essentially to agree to give to the employer any inventions that the employee may conceive of during his employment. However, the nature of that pre-invention agreement— which until 1991 was universally thought of as imposing an equitable duty but not as an actual transfer of legal title to an imaginary asset— has been changed by the Federal Circuit and the U.S. Supreme Court. This Article reviews more than 170 years of legal history dealing with transfers of non-existent assets, and argues that the concept of an “automatic” assignment in patent law rests on shaky ground. Instead, our system of IP law is much better served by a return to common law principles—both “first in time, first in right” and “you may not give what you do not own.” Q: Are you sure it’s for sale? A: Why else would it have a for sale sticker on it?1 * Visiting Professor of Law, Texas Tech University School of Law; LL.M. Taxation, University of Washington School of Law (2000); J.D., J. Reuben Clark School of Law, Brigham Young University (1994); B.S., Brigham Young University (1988). My thanks for the superlative research assistance provided by Devin Arnold and Brittany Weaver. I gratefully acknowledge the financial support of the Texas Tech University School of Law in the preparation of this article. 1. See Gabriel Cohen, For You, Half Price, N.Y. TIMES, Nov. 27, 2005, at Q4. 1 Black: Psst! Wanna Buy a Bridge? IP Transfers of Non-Existent Property Published by Reading Room, 2014 524 GEORGIA STATE UNIVERSITY LAW REVIEW [Vol. 31:3
Market-based measures of viewpoint diversity☆

Abstract Existing market-based measures of viewpoint diversity in the media have been criticized for lacking a theoretical foundation. This paper proposes a theoretical framework to develop market-based measures of viewpoint diversity. It then uses this framework to develop four viewpoint diversity measures using a panel dataset of local television ratings. Finally, an econometric model relates these viewpoint diversity measures to media ownership variables. It does not appear that market-based measures of viewpoint diversity are strongly related to media ownership structures.
A FLUX ROPE NETWORK AND PARTICLE ACCELERATION IN THREE-DIMENSIONAL RELATIVISTIC MAGNETIC RECONNECTION

We investigate magnetic reconnection and particle acceleration in relativistic pair plasmas with three-dimensional particle-in-cell simulations of a kinetic-scale current sheet in a periodic geometry. We include a guide field that introduces an inclination between the reconnecting field lines and explore outside-of-the-current sheet magnetizations that are significantly below those considered by other authors carrying out similar calculations. Thus, our simulations probe the transitional regime in which the magnetic and plasma pressures are of the same order of magnitude. The tearing instability is the dominant mode in the current sheet for all guide field strengths, while the linear kink mode is less important even without the guide field, except in the lower magnetization case. Oblique modes seem to be suppressed entirely. In its nonlinear evolution, the reconnection layer develops a network of interconnected and interacting magnetic flux ropes. As smaller flux ropes merge into larger ones, the reconnection layer evolves toward a three-dimensional, disordered state in which the resulting flux rope segments contain magnetic substructure on plasma skin depth scales. Embedded in the flux ropes, we detect spatially and temporally intermittent sites of dissipation reflected in peaks in the parallel electric field. Magnetic dissipation and particle acceleration persist until the end of the simulations, with simulations with higher magnetization and lower guide field strength exhibiting greater and faster energy conversion and particle energization. At the end of our largest simulation, the particle energy spectrum attains a tail extending to high Lorentz factors that is best modeled with a combination of two additional thermal components. We confirm that the primary energization mechanism is acceleration by the electric field in the X-line region. The highest-energy positrons (electrons) are moderately beamed with median angles ~30°-40° relative to (the opposite of) the direction of the initial current density, but we speculate that reconnection in more highly magnetized plasmas would give rise to stronger beaming. Finally, we discuss the implications of our results for macroscopic reconnection sites, and which of our results may be expected to hold in systems with higher magnetizations.
Accounting for Lease Renewal Options: The Informational Effects of Unit of Account Choices

ABSTRACT: This study examines the informational effects of unit of account choices in the context of a proposed standard on lease accounting. Standard-setters have tentatively decided that leases i...
Spirituality and Academic Performance at a Catholic Law School: An Empirical Study

This empirical study explores whether a student's spirituality affects academic performance during the first year of study at the University of St. Thomas School of Law (Minnesota). A spirituality index measured 1) frequency of attendance at religious worship, 2) frequency of discussion of religion with others from different faith traditions, 3) the presence and strength of the connection between God and morality, and 4) the presence and strength of the view that entry into the legal profession is a divine calling. The spirituality index was correlated with academic performance measured by comparing actual performance and predicted performance (using the LSAT and the student's undergraduate grade point average). Strong spirituality had a negative correlation with academic performance. Medium and low spirituality had no correlation. And among the third of the students who performed substantially under expectations, the negative correlation was more significant than the broader positive correlation of either the LSAT or the undergraduate grade point average. This study is especially interesting and powerful because the University of St. Thomas School of Law has a strong Catholic identity and affirmatively promotes its faith-based mission at all levels of operation. The author discusses possible explanations for his findings that high spirituality correlates negatively with expected academic performance.
Public information and heuristic trade

Abstract We characterize the steady-state equilibrium in which informed traders who exhibit heuristic (i.e., representativeness, as opposed to Bayesian) and Bayesian behaviors achieve the same expected utility. Then, we show how the endogenous, steady-state proportion of heuristic traders is affected by the quality of public information and other exogenous features of our model. Finally, we discuss how the presence of heuristic traders potentially alters the link between improved public disclosure and market liquidity, the variance in the change in price, and market efficiency.
Managing Customer Value in Business-to- Business Markets

In the European Union, SMEs are defined as enterprises which have fewer than 250 employees, with an annual turnover not exceeding 50 million euro, and/or an annual balance sheet total not exceeding 43 million euro. SMEs in the EU total 23 million (99% of all firms), provide 67% of total employment, and 58% of gross value add (European Commission 2005; Ecorys 2012). In the United States, SMEs are typically defined as independent businesses with fewer than 500 employees. They represent more than 99% of U.S. employer firms, provide nearly half of all private-sector employment, and produce 46% of private nonfarm GDP (SBA Office of Advocacy 2012). Open image in new window
Legal Accountability in an Era of Privatized Welfare

When the federal welfare system was reformed in 1996, Congress devolved much of the authority over welfare delivery to the states and gave them the option of contracting out administration of their programs to private entities. Moreover, after welfare reform, enacted as the Personal Responsibility and Work Opportunity Reconciliation Act (PRA), welfare recipients are expected to work to receive benefits. This means that front-line workers must engage in intensive interpersonal counseling rather than simply confirm objective eligibility criteria and dispense checks. As a result, front-line workers have vastly increased discretion. When privatization is layered over this discretionary scheme, issues of accountability to program beneficiaries deepen. For over thirty years, it has been a tenet of public benefits law that due process protections attach to the government's delivery of benefits. Yet when private entities deliver the same benefits, constitutional protections may fall by the wayside. This article explores the implications of welfare privatization on welfare beneficiaries' procedural rights. It explains how the Supreme Court's current state action doctrine may well insulate private welfare providers and their state contracting partners from constitutional claims. Accordingly, the Article also explores other potential federal and state bases for enforcing accountability in welfare programs in privatized jurisdictions, ranging from statutory to contractual to equitable claims. The Article concludes that the procedural rights of welfare recipients under PRA are greatly diminished after welfare reform.
The Importance of Role-Specific Performance and Sociopolitical Factors for Chief Financial Officer Employment Outcomes

SYNOPSIS This paper investigates the importance of role-specific performance measures and sociopolitical factors in the career paths of CFOs. We find that forced CFO turnover is associated with poo...
Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics.

Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using spectral information collected non-destructively from the radiation of relativistic electron beam. We demonstrate the tool’s accuracy for three different case studies with experimental or simulated data. For each case, we introduce a method to increase the confidence in the VD tool. We anticipate that spectral VD would improve the setup and understanding of experimental configurations at DOE’s user facilities as well as data sorting and analysis. The spectral VD can provide confident knowledge of the longitudinal bunch properties at the next generation of high-repetition rate linear accelerators while reducing the load on data storage, readout and streaming requirements.
A residual smoothing strategy for accelerating Newton method continuation

Abstract A technique for accelerating global convergence of pseudo-transient continuation Newton methods is proposed based on residual smoothing. The technique is motivated by the effectiveness of local nonlinear smoothers at overcoming strong nonlinear transients. In the limit of a small pseudo-time step, the method reduces to a local nonlinear smoothing technique, while in the limit of large pseudo-time steps, an exact Newton method is recovered along with its quadratic convergence properties. The formulation relies on the addition of a smoothing source term while leaving the Newton Jacobian matrix unchanged, thus simplifying implementations for existing Newton solvers. The proposed technique is demonstrated on a steady-state and an implicit time-dependent computational fluid dynamics problem, showing significant gains in overall solution efficiency.
Incomplete removal of extracellular glutamate controls synaptic transmission and integration at a cerebellar synapse

Synapses of glutamatergic mossy fibers (MFs) onto cerebellar unipolar brush cells (UBCs) generate slow excitatory (ON) or inhibitory (OFF) postsynaptic responses dependent on the complement of glutamate receptors expressed on the UBC's large dendritic brush. Using mouse brain slice recording and computational modeling of synaptic transmission, we found that substantial glutamate is maintained in the UBC synaptic cleft, sufficient to modify spontaneous firing in OFF UBCs and tonically desensitize AMPARs of ON UBCs. The source of this ambient glutamate was spontaneous, spike-independent exocytosis from the MF terminal, and its level was dependent on activity of glutamate transporters EAAT1-2. Increasing levels of ambient glutamate shifted the polarity of evoked synaptic responses in ON UBCs and altered the phase of responses to in vivo-like synaptic activity. Unlike classical fast synapses, receptors at the UBC synapse are virtually always exposed to a significant level of glutamate, which varies in a graded manner during transmission.
A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies

American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before–after comparisons typically used to evaluate the success of these countermeasures often suffer from selection bias. This paper motivates the tendency for selection bias to overestimate the benefits of traffic safety policy, using New York City’s Vision Zero strategy as an example. The NASS General Estimates System, Fatality Analysis Reporting System and other databases are combined into a Bayesian hierarchical model to calculate a more realistic before–after comparison. The results confirm the before–after analysis of New York City’s Vision Zero policy did in fact overestimate the effect of the policy, and a more realistic estimate is roughly two-thirds the size.
Local regression distribution estimators

Abstract This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using this result, we study the asymptotic efficiency of the estimators, and show that a carefully crafted minimum distance implementation based on “redundant” regressors can lead to efficiency gains. Second, we establish uniform linearizations and strong approximations for the estimators, and employ these results to construct valid confidence bands. Third, we develop extensions to weighted distributions with estimated weights and to local L 2 estimation. Finally, we illustrate our methods with two applications in program evaluation: counterfactual density testing, and IV specification and heterogeneity density analysis. Companion software packages in Stata and R are available.
A multi-dimensional examination of adolescent writing: considering the writer, genre and task demands

We examined the contributions of English proficiency, genre, and the use of textual sources to adolescent writing. The sample included 1819 native English speakers and language minority students from 127 seventh- and eighth-grade classes in an urban school district. Students were randomly assigned one of three source-based essay prompts (narrative, explanatory or argumentative) as part of the annual state assessment, and all students wrote a common, on-demand argumentative essay that did not require the use of textual sources. Overall, language minority students who were fluent English proficient wrote higher quality nonsource-based essays that contained more difficult vocabulary, were better structured, and used examples and details more effectively than native English speakers. They also outperformed native English speakers in source-based writing. Students with limited English proficiency showed weaker performance on both source-based and nonsource-based writing tasks. Differences in English proficiency held across genres. Students who wrote argumentative essays obtained higher ratings than those who wrote narrative or explanatory essays. Source-based and nonsource-based writing were moderately correlated. Regression analyses revealed that in addition to English proficiency and genre, reading comprehension's contribution to source-based writing was almost double that of nonsource-based writing. Implications of the findings for theory and practice are discussed.
Learn to Earn: Enabling Coordination Within a Ride-Hailing Fleet

The problem of optimizing social welfare objectives on multi-sided ride-hailing platforms such as Uber, Lyft, etc., is challenging, due to misalignment of objectives between drivers, passengers, and the platform itself. An ideal solution aims to minimize the response time for each hyperlocal passenger ride request, while simultaneously maintaining high demand satisfaction and supply utilization across the entire city. Economists tend to rely on dynamic pricing mechanisms that stifle price-sensitive excess demand and resolve supply-demand imbalances that emerge in specific neighborhoods. In contrast, computer scientists primarily view it as a demand prediction problem with the goal of preemptively repositioning supply to such neighborhoods using black-box coordinated multi-agent deep reinforcement learning-based approaches. Here, we introduce explainability in the existing supply-repositioning approaches by establishing the need for coordination between the drivers at specific locations and times. Explicit need-based coordination allows our framework to use a simpler non-deep reinforcement learning-based approach, thereby enabling it to explain its recommendations ex-post. Moreover, it provides envy-free recommendations i.e., drivers at the same location and time do not envy one another’s expected future earnings. Our experimental evaluation demonstrates the effectiveness, robustness, and generalizability of our framework. Finally, in contrast to previous works, we make available a reinforcement learning environment for end-to-end reproducibility of our work and to encourage future comparative studies.
No preference for prosocial helping behavior in rats with concurrent social interaction opportunities.

Helping behavior tasks are proposed to assess prosocial or “empathic” behavior in rodents. This paradigm characterizes the behavior of subject animals presented with the opportunity to release a conspecific from a distressing situation. Previous studies found a preference in rats for releasing restrained or distressed conspecifics over other controls (e.g., empty restrainers or inanimate objects). An empathy account was offered to explain the observed behaviors, claiming subjects were motivated to reduce the distress of others based on a rodent homologue of empathy. An opposing account attributes all previous results to subjects seeking social contact. To dissociate these two accounts for helping behavior, we presented subject rats with three simultaneous choice alternatives: releasing a restrained conspecific, engaging a nonrestrained conspecific, or not socializing. Subjects showed an initial preference for socializing with the nonrestrained conspecific, and no preference for helping. This result contradicts the empathy account, but is consistent with the social-contact account of helping behavior.
A Simple New Way To Account for Free Volume in Glassy Dynamics: Model-Free Estimation of the Close-Packed Volume from PVT Data.

In this article we focus on the important role of well-defined free volume (Vfree) in dictating the structural relaxation times, τ, of glass-forming liquids and polymer melts. Our definition of Vfr...
Quantifying the temperature of heated microdevices using scanning thermal probes

Quantifying the temperature of microdevices is critical for probing nanoscale energy transport. Such quantification is often accomplished by integrating resistance thermometers into microdevices. However, such thermometers frequently become structurally unstable and fail due to thermal stresses at elevated temperatures. Here, we show that custom-fabricated scanning thermal probes (STPs) with a sharp tip and an integrated heater/thermometer can accurately measure the temperature of microdevices held at elevated temperatures. This measurement is accomplished by introducing a modulated heat input to the STP after contacting the microdevice with the STP's tip and characterizing the DC and AC components of the STP's temperature. From these measured temperature components, the tip-to-sample thermal resistance and the microdevice surface temperature are deduced simultaneously via a simple lumped-capacitance model. The advances presented here can greatly facilitate temperature measurements of a variety of heated microdevices.
Belief propagation for networks with loops

Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of networks that contain short loops. Here, we provide a solution to this long-standing problem, deriving a belief propagation method that allows for fast calculation of probability distributions in systems with short loops, potentially with high density, as well as giving expressions for the entropy and partition function, which are notoriously difficult quantities to compute. Using the Ising model as an example, we show that our approach gives excellent results on both real and synthetic networks, improving substantially on standard message passing methods. We also discuss potential applications of our method to a variety of other problems.
The Effects of High Estimate Uncertainty in Auditor Negligence Litigation

We examine how jurors’ negligence judgments and attorneys’ out-of-court settlements are differently impacted by two features of a materially misstated accounting estimate—the amount of estimate uncertainty and whether the misstated account is disaggregated into its own line-item or aggregated with other accounts into a single financial statement line-item. We predict and find that jurors and attorneys react to estimate uncertainty in opposite directions under common conditions. This finding is important because, when jurors’ judgments and attorneys’ settlements differ, research into juror judgments alone may not capture a complete picture of auditor liability, since the vast majority of audit litigation is resolved by attorneys in out-of-court settlement without ever going to trial. Consistent with attribution theory, results from our first experiment show that jurors hold auditors more responsible for misstatements of lower estimate uncertainty when the misstated account is disaggregated, as opposed to misstatements that are of higher uncertainty and/or aggregated with other, accurate accounts. However, in a second experiment we find that attorneys negotiate auditor settlements under the incorrect assumption that jurors will hold auditors more responsible for failing to prevent misstatements of higher uncertainty. Our results illustrate that accounting research should not focus solely on juror judgments in the study of how specific factors impact auditor liability, and that attorneys would benefit from a better understanding of juror decision making.
Stochastic Bandits with Linear Constraints

We study a constrained contextual linear bandit setting, where the goal of the agent is to produce a sequence of policies, whose expected cumulative reward over the course of $T$ rounds is maximum, and each has an expected cost below a certain threshold $\tau$. We propose an upper-confidence bound algorithm for this problem, called optimistic pessimistic linear bandit (OPLB), and prove an $\widetilde{\mathcal{O}}(\frac{d\sqrt{T}}{\tau-c_0})$ bound on its $T$-round regret, where the denominator is the difference between the constraint threshold and the cost of a known feasible action. We further specialize our results to multi-armed bandits and propose a computationally efficient algorithm for this setting. We prove a regret bound of $\widetilde{\mathcal{O}}(\frac{\sqrt{KT}}{\tau - c_0})$ for this algorithm in $K$-armed bandits, which is a $\sqrt{K}$ improvement over the regret bound we obtain by simply casting multi-armed bandits as an instance of contextual linear bandits and using the regret bound of OPLB. We also prove a lower-bound for the problem studied in the paper and provide simulations to validate our theoretical results.
Evaluating Stochastic Seeding Strategies in Networks

When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element o...
Using Markov Decision Process to Model Deception for Robotic and Interactive Game Applications

This paper investigates deception in the context of motion using a simulated mobile robot. We analyze some previously designed deceptive strategies on a mobile robot simulator. We then present a novel approach to adaptively choose target-oriented deceptive trajectories to deceive humans for multiple interactions. Additionally, we propose a new metric to evaluate deception on data collected from the users when interacting with the mobile robot simulator. We performed a user study to test our proposed adaptive deceptive algorithm, which shows that our algorithm deceives humans even for multiple interactions and it is more effective than random choice of deceptive strategies.
Fast implementation of partial least squares for function-on-function regression

Abstract People employ the function-on-function regression to model the relationship between two stochastic processes. Fitting this model, widely used strategies include functional partial least squares algorithms which typically require iterative eigen-decomposition. Here we introduce a route of functional partial least squares based upon Krylov subspace. Our route can be expressed in two forms equivalent to each other in exact arithmetic: One is non-iterative with explicit expressions of the estimator and prediction, facilitating the theoretical derivation and potential extensions; the other one stabilizes numerical outputs. The consistency of estimation and prediction is established under regularity conditions. It is highlighted that our proposal is competitive in terms of both estimation and prediction accuracy but consumes much less execution time.
GODZILA: A Low-resource Algorithm for Path Planning in Unknown Environments

We propose a novel sensor-based path-planning and obstacle avoidance algorithm GODZILA for navigation in unknown environments. No prior knowledge of the environment is required. The path-planning algorithm follows a purely local approach using only the current range sensor measurements at each sampling instant and requiring only a small number of stored variables in memory. No map of the environment is built during navigation. This minimizes the memory and computational requirements for implementation of the algorithm, a feature that is especially attractive for small autonomous vehicles. The algorithm utilizes three components: an optimization algorithm, a local straight-line path planner to visible targets, and random navigation. It is proved, for navigation in any finite-dimensional space, that the path-planning algorithm converges in finite time with probability 1. The performance of the algorithm is demonstrated through simulations for path-planning in two-dimensional (2D) and three-dimensional (3D) spaces. It is seen that a relatively small number of range sensor measurements is sufficient even in complex unknown environments.
Whitham modulation theory for generalized Whitham equations and a general criterion for modulational instability

The Whitham equation was proposed as a model for surface water waves that combines the quadratic flux nonlinearity $f(u) = \tfrac{1}{2}u^2$ of the Korteweg-de Vries equation and the full linear dispersion relation $\Omega(k) = \sqrt{k\tanh k}$ of uni-directional gravity water waves in suitably scaled variables. This paper proposes and analyzes a generalization of Whitham's model to unidirectional nonlinear wave equations consisting of a general nonlinear flux function $f(u)$ and a general linear dispersion relation $\Omega(k)$. Assuming the existence of periodic traveling wave solutions to this generalized Whitham equation, their slow modulations are studied in the context of Whitham modulation theory. A multiple scales calculation yields the modulation equations, a system of three conservation laws that describe the slow evolution of the periodic traveling wave's wavenumber, amplitude, and mean. In the weakly nonlinear limit, explicit, simple criteria in terms of general $f(u)$ and $\Omega(k)$ establishing the strict hyperbolicity and genuine nonlinearity of the modulation equations are determined. This result is interpreted as a generalized Lighthill-Whitham criterion for modulational instability.
Three-phase alternating current liquid metal vortex magnetohydrodynamic generator

Summary Magnetohydrodynamic (MHD) generators directly convert mechanical energy to electrical energy. However, due to production of low amplitude voltages at low fluid velocities, they are not useful for electronic devices requiring power at watt scale. This work introduces vortex MHD, capable of producing voltages on scale of volts and generating power on a scale of watts. This is achieved by using Galinstan, a highly conductive metallic fluid, which remains liquid at room temperature. The proposed device comprises an impeller and set of copper coils positioned in a ferromagnetic housing. Three-phase AC current is passed in the coils producing a rotating magnetic field. The interaction of a moving conductive fluid and rotating magnetic field governed by Faraday's law of induction serves as a mechanism of electrical current generation. The study investigates the system performance and, in particular, variation of power with respect to system parameters like fluid inlet velocity and stator current.
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity.

It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as '`resource-constrained'' when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and that achieves Real-time execution and high accuracy, leveraging fine-grained structured sparse model Inference and compiler optimizations for Mobiles. We start by proposing a new fine-grained structured sparsity scheme through the Block-based Column-Row (BCR) pruning. Based on this new fine-grained structured sparsity, our GRIM framework consists of two parts: (a) the compiler optimization and code generation for real-time mobile inference; and (b) the BCR pruning optimizations for determining pruning hyperparameters and performing weight pruning. We compare GRIM with Alibaba MNN, TVM, TensorFlow-Lite, a sparse implementation based on CSR, PatDNN, and ESE (a representative FPGA inference acceleration framework for RNNs), and achieve up to 14.08x speedup.
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling

The canonical approach to video-and-language learning (e.g., video question answering) dictates a neural model to learn from offline-extracted dense video features from vision models and text features from language models. These feature extractors are trained independently and usually on tasks different from the target domains, rendering these fixed features sub-optimal for downstream tasks. Moreover, due to the high computational overload of dense video features, it is often difficult (or infeasible) to plug feature extractors directly into existing approaches for easy finetuning. To provide a remedy to this dilemma, we propose a generic framework CLIPBERT that enables affordable endto-end learning for video-and-language tasks, by employing sparse sampling, where only a single or a few sparsely sampled short clips from a video are used at each training step. Experiments on text-to-video retrieval and video question answering on six datasets demonstrate that CLIPBERT outperforms (or is on par with) existing methods that exploit full-length videos, suggesting that end-to-end learning with just a few sparsely sampled clips is often more accurate than using densely extracted offline features from full-length videos, proving the proverbial less-is-more principle. Videos in the datasets are from considerably different domains and lengths, ranging from 3-second genericdomain GIF videos to 180-second YouTube human activity videos, showing the generalization ability of our approach. Comprehensive ablation studies and thorough analyses are provided to dissect what factors lead to this success. Our code is publicly available.1
Ideal of hypercyclic operators that factor through $\ell ^p$

We study the injective and surjective hull of operator ideals generated by hypercyclic backward weighted shifts that factor through $\ell^p$.
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy. Our experiments on multiple classification and regression datasets show that DP-EBM models suffer surprisingly little accuracy loss even with strong differential privacy guarantees. In addition to high accuracy, two other benefits of applying DP to EBMs are: a) trained models provide exact global and local interpretability, which is often important in settings where differential privacy is needed; and b) the models can be edited after training without loss of privacy to correct errors which DP noise may have introduced.
Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process.

Motivation The location, timing, and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns. Results We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications. Availability The related R/C ++ source code is available at https://github.com/Minzhe/BOOST-GP. Supplementary information Supplementary data are available at Bioinformatics online.
Gravitational Effects on Process-Induced Defects in Single Crystal Silicon

Description and discussion of a series of experiments which vary each of several important processing parameters including wafer orientation, temperature, gravity greater than 1 g, and ultimately zero gravity. These experiments are in progress and results to date are included from experiments which control temperature, duration of thermal cycle, and wafer orientation during 1 g processing. Preliminary evidence is presented which indicates that gravitational stresses play a role in defect generation during processing at 1 g.
Sampling-Based Motion Planning on Sequenced Manifolds

We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion. The problem is formulated as a fixed sequence of intersecting manifolds, which the robot needs to traverse in order to solve the task. We specify a class of sequential motion planning problems that fulfill a particular property of the change in the free configuration space when transitioning between manifolds. For this problem class, the algorithm Planning on Sequenced Manifolds (PSM) is developed which searches for optimal intersection points between manifolds by using RRT in an inner loop with a novel steering strategy. We provide a theoretical analysis regarding PSM*s probabilistic completeness and asymptotic optimality. Further, we evaluate its planning performance on multi-robot object transportation tasks.
Optimal Linear Coding Schemes for the Secure Decentralized Pliable Index Coding Problem

This paper studies the secure decentralized Pliable Index CODing (PICOD) problem, where the security constraint forbids users to decode more than one message while the decentralized setting imposes that there is no central transmitter in the system, and thus transmissions occur only among users. A converse bound from the Authors’ previous work showed a factor of three difference in optimal code-length between the centralized and the decentralized versions of the problem, under the constraint of linear encoding. This paper first lists all linearly infeasible cases, that is, problems where no linear code can simultaneously achieve both correctness/decodability and security. Then, it proposes linear coding schemes for the remaining cases and shows that their code-length is to within an additive constant gap from the converse bound.
A statistical pipeline for identifying physical features that differentiate classes of 3D shapes

The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global patterns in morphological variation. Studies, which focus on identifying differences between shapes, have been limited to simple pairwise comparisons and rely on prespecified landmarks (that are often known). We present SINATRA, the first statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our novel algorithm takes in two classes of shapes and highlights the physical features that best describe the variation between them. We use a rigorous simulation framework to assess our approach. Lastly, as a case study we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.
Deep Learning for Flight Demand and Delays Forecasting

The last few years have seen an increased interest in deep learning (DL) due to its success in applications such as computer vision, natural language processing (NLP), and self-driving cars. Inspired by this success, this paper applied DL to predict flight demand and delays, which have been a concern for airlines and the other stakeholders in the National Airspace System (NAS). Demand and delay prediction can be formulated as a supervised learning problem, where, given an understanding of past historical demand and delays, a deep learning network can examine sequences of historic data to predict current and future sequences. With that in mind, we applied a well-known DL method, sequence to sequence (seq2seq), to solve the problem. Our results show that the seq2seq method can reduce demand prediction mean squared error (MSE) by 50%, compared to two classical baseline algorithms.
Morphometric relationships and their contribution to biomass and cannabinoid yield in hybrids of hemp (Cannabis sativa).

The breeding of hybrid cultivars of hemp (Cannabis sativa L.) is not well described, especially the segregation and inheritance of traits that are important for yield. A total of 23 families were produced from genetically diverse parents to investigate the inheritance of morphological traits and their association with biomass accumulation and cannabinoid yield. In addition, a novel classification method for canopy architecture was developed. The strong linear relationship between wet and dry biomass provided an accurate estimate of final dry stripped floral biomass. Of all field and aerial measurements, basal stem diameter was determined to be the single best selection criterion for final dry stripped floral biomass yield. Along with stem diameter, canopy architecture and stem growth predictors described the majority of the explainable variation of biomass yield. Within-family variance for morphological and cannabinoid measurements reflected the heterozygosity of the parents. While selfed populations suffered from inbreeding depression, hybrid development in hemp will require at least one inbred parent to achieve uniform growth and biomass yield. Nevertheless, floral phenology remains a confounding factor in selection because of its underlying influence on biomass production highlighting the need to understand the genetic basis for flowering time in the breeding of uniform cultivars.
g -representations and differential equations

This paper extends earlier results concerningg- andh-representations for nonlinear input-output maps so that vector-valued inputs and outputs can be treated. It establishes that the range of applicability of the extended results is very wide, and it illustrates how they can be used, within the context of systems governed by differential equations. In particular, it is shown that the nonlinear variation of constants formula is a special case of our main representation theorem.
Pkd1 Mutation Has No Apparent Effects on Peroxisome Structure or Lipid Metabolism

Background: Multiple studies of tissue and cell samples from patients and pre-clinical models of autosomal dominant polycystic kidney disease report abnormal mitochondrial function and morphology and suggest metabolic reprogramming is an intrinsic feature of this disease. Peroxisomes interact with mitochondria physically and functionally, and congenital peroxisome biogenesis disorders can cause various phenotypes, including mitochondrial defects, metabolic abnormalities and renal cysts. We hypothesized that a peroxisomal defect might contribute to the metabolic and mitochondrial impairments observed in autosomal dominant polycystic kidney disease. Methods: Using control and Pkd1-/-kidney epithelial cells, we investigated peroxisome abundance, biogenesis and morphology by immunoblotting, immunofluorescent and live cell imaging of peroxisome-related proteins and assayed peroxisomal specific β-oxidation. We further analyzed fatty acid composition by mass spectrometry in kidneys of Pkd1fl/fl; Ksp-Cre mice. We also evaluated peroxisome lipid metabolism in published metabolomics datasets of Pkd1 mutant cells and kidneys. Lastly, we investigated if the C-terminus or full-length polycystin-1 co-localize with peroxisome markers by imaging studies. Results: Peroxisome abundance, morphology and peroxisome-related protein expression in Pkd1 -/- cells were normal, suggesting preserved peroxisome biogenesis. Peroxisomal β-oxidation was not impaired in Pkd1-/-cells, and there was no obvious accumulation of very long chain fatty acids in kidneys of mutant mice. Re-analysis of published datasets provide little evidence of peroxisomal abnormalities in independent sets of Pkd1 mutant cells and cystic kidneys, while providing further evidence of mitochondrial fatty acid oxidation defects. Imaging studies with either full length polycystin-1 or its C-terminus, a fragment previously shown to go to the mitochondria, showed minimal co-localization with peroxisome markers restricted to putative mitochondrion-peroxisome contact sites. Conclusions: Our studies showed that loss of Pkd1 does not disrupt peroxisome biogenesis nor peroxisome-dependent fatty acid (FA) metabolism.
Collision Prediction and Prevention in Contact Sports Using RFID Tags and Haptic Feedback

American football is a leading sport for contact-related injuries such as cervical spine injuries, some of which result from an unforeseen hit. The use of a feedback mechanism to alert an athlete of a potential hit may mitigate the risk for sport-related concussion and catastrophic injury by allowing athletes to effectively brace for an otherwise unforeseen impact. In this project, we created a proximity sensor system using radio frequency identification (RFID) technology to send haptic feedback to the wearer before a potential impact. Our pilot test consisted of three participants who wore our novel sensor system, who ran simulated routes to represent player-to-player contact. Additionally, we used simulated data from a Network Live Football (NFL) league game to further validate our contact prediction algorithm. Results show that our method can predict players’ collisions with less than 14% false-alarms.