cssi/SciSciGPT-SciSciNet huggingface dataset:
Meaning and Belief in Constitutional Interpretation

The distinction between a concept and its different conceptions plays a prominent role in debates about constitutional interpretation. Proponents of a dynamic reading of the Constitution—espousing interpretation of constitutional concepts according to their contemporary understandings— typically rely on the idea that the Constitution entrenches only the general concepts it deploys, without authoritatively favoring any particular conception of them—specifically, without favoring the particular conception of the relevant concept that the framers of the Constitution may have had in mind. Originalists argue, to the contrary, that fidelity to the Constitution requires an understanding of its provisions according to the particular conception of the abstract concepts prevalent at the time of enactment, and not those we may now favor. My main purpose in this essay is to put some pressure on the linguistic considerations that are presented in this debate, arguing that they are much more problematic than the proponents of both positions assume. I will try to show that the debate here is actually a moral-political one, mostly about the main rationale of a constitutional regime and the conditions of its legitimacy. It is, primarily, a debate about what constitutions are for, and what makes them legitimate. But I will only get to this moral issue at the end. The main part of the essay will strive to show that the semantic considerations employed in this debate are inconclusive; the way concepts are used in a given context depends on various pragmatic determinants, and those, in turn, depend on the nature of the conversation in question. The moral disagreement is, ultimately, about the kind of conversation that constitutional regimes are taken to establish.
Observations from the U.S. Federal Income Tax to Distinguish Between Measures of Progressivity and Redistributive Capacity

This study provides insights on the attributes of a tax that are measured by two different classes of progressivity indices – those defined by Kakwani (1977), Suits (1977), Stroup (2005), and Mathews (2016) and those defined by Musgrave & Thin (1948) and Reynolds & Smolensky (1977). Index values are determined for the U.S. Federal Income Tax from 1929 through 2010. These values illustrate that the indices of Kakwani, Suits, Stroup, and Mathews gauge the progressivity of the tax, while those of Musgrave & Thin and Reynolds & Smolensky measure the redistributive capacity of the tax. In the early 1940s the progressivity of this tax significantly decreased at the same time when the redistributive capacity of the tax significantly increased. Since the mid-1970s this tax has (i) been more progressive than it was from the early 1950s through the mid-1970s and (ii) redistributed income to a greater degree than it did from the early 1950s through the mid-1970s.
Harm, Ambiguity, and the Regulation of Illegal Contracts

Courts have long preferred to remedy illegal contracts by denying state enforcement of the parties’ interests in those contracts. The primary justification for this non-enforcement—which leaves the parties in the position that courts find them—is to deter these bargains from forming in the first place. According to the prevailing logic, a party who has to act first under the terms of an illegal contract would be hesitant to make an upfront payment or render a service on credit if an attempt to enforce the promise would result in a large, legally-sanctioned forfeiture. But the reasons that often justify the state’s imposition of liability for private behavior—the prevention of harm and the maximization of social welfare—play little or no role in the determination that the nonenforcement remedy should apply. This structure puts the illegal contract
Do Women Shy Away From Competition? Do Men Compete Too Much?

We examine whether men and women of the same ability differ in their selection into a competitive environment. Participants in a laboratory experiment solve a real task, first under a noncompetitive piece rate and then a competitive tournament incentive scheme. Although there are no gender differences in performance, men select the tournament twice as much as women when choosing their compensation scheme for the next performance. While 73 percent of the men select the tournament, only 35 percent of the women make this choice. This gender gap in tournament entry is not explained by performance, and factors such as risk and feedback aversion only playa negligible role. Instead, the tournament-entry gap is driven by men being more overconfident and by gender differences in preferences for performing in a competition. The result is that women shy away from competition and men embrace it.
Outsourcing the Information System: Determinants, Risks, and Implications for Management Control Systems

ABSTRACT: Organizations are outsourcing traditional functions that have financial reporting implications, such as transaction processing and valuation assessments, with increasing frequency. This practice of outsourcing aspects of the information system transforms the control structure of the outsourcing company and creates new challenges for management and accountants that are not explicitly addressed by current research. In this paper we synthesize academic literature and regulatory guidance on using third parties to perform functions with financial reporting implications (i.e., service organizations) and identify specific opportunities for future accounting research. To provide structure to our review and highlight knowledge gaps, we develop an organizing framework that links existing theories of outsourcing determinants to outsourcing risks and identifies the advantages and limitations of certain control mechanisms to mitigate such risks. Very little academic research exists on the use of service orga...
Campaign finance regulation with competing interest groups

Abstract Regulatory caps on contributions to political campaigns are the cornerstones of campaign finance legislation in many established democracies, and their introduction is considered by most emerging ones. Are these regulations desirable? This paper studies contribution caps in a menu auction lobbying model with limited budgets and costly entry. In the absence of entry, contribution caps improve welfare by “leveling the political playing field”. With entry, however, a competition effect and a bargaining effect may arise, resulting in inefficient entry and exit decisions. In particular, a cap may lead to worse policies than the status quo; and even if better policies are chosen, the resulting gain in welfare may be more than offset by the entry costs. Regulation can also lead to the simultaneous entry of competing groups, creating costly rent-seeking on issues previously unaffected by lobbying.
Distance, Trade, and Income - The 1967 to 1975 Closing of the Suez Canal as a Natural Experiment

Abstract This paper exploits a temporary shock to distance, the closing of the Suez canal in 1967 and its reopening in 1975, to examine the effect of distance on trade and the effect of trade on income. Time series variation in sea distance allows for the inclusion of pair effects which account for static differences in tastes and culture between countries. Distance is found to have a significant impact on trade with an elasticity that is about half as large as estimates from typical cross sectional estimates. Since the shock to trade is exogenous for most countries, predicted trade volume from the shock can be used to identify the effect of trade on income. Trade is found to have a significant impact on income. The time series dimension allows for country fixed effects which control for all long run income differences. Because identification is through changes in sea distance, the effect is coming entirely through trade in goods and not through alternative channels such as technology transfer, tourism, or foreign direct investment.
Wages and Foreign Ownership: A Comparative Study of Mexico, Venezuela, and the United States

Abstract This paper explores the relationship between wages and foreign investment in Mexico, Venezuela, and the United States. Despite very different economic conditions and levels of development, we find one fact that is robust across all three countries: higher levels of foreign investment are associated with higher wages. However, in Mexico and Venezuela, foreign investment is associated with higher wages only for foreign-owned firms — there is no evidence of wage spillovers leading to higher wages for domestic firms. The lack of spillovers in Mexico and Venezuela is consistent with significant wage differentials between foreign and domestic enterprises. In the United States, where the evidence suggests some wage spillovers from foreign to domestic enterprises, wage differentials are smaller.
Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis

A computationally scalable, statistically efficient aggregate-disaggregate data fusion method that corrects for selection bias is applied to model customer relationship dynamics at a subscription-b...
Genetic or toxicant-induced disruption of vesicular monoamine storage and global metabolic profiling in Caenorhabditis elegans.

The proper storage and release of monoamines contributes to a wide range of neuronal activity. Here, we examine the effects of altered vesicular monoamine transport in the nematode Caenorhabditis elegans. The gene cat-1 is responsible for the encoding of the vesicular monoamine transporter (VMAT) in C. elegans and is analogous to the mammalian vesicular monoamine transporter 2 (VMAT2). Our laboratory has previously shown that reduced VMAT2 activity confers vulnerability on catecholamine neurons in mice. The purpose of this article was to determine whether this function is conserved and to determine the impact of reduced VMAT activity in C. elegans. Here we show that deletion of cat-1/VMAT increases sensitivity to the neurotoxicant 1-methyl-4-phenylpyridinium (MPP+) as measured by enhanced degeneration of dopamine neurons. Reduced cat-1/VMAT also induces changes in dopamine-mediated behaviors. High-resolution mass spectrometry-based metabolomics in the whole organism reveals changes in amino acid metabolism, including tyrosine metabolism in the cat-1/VMAT mutants. Treatment with MPP+ disrupted tryptophan metabolism. Both conditions altered glycerophospholipid metabolism, suggesting a convergent pathway of neuronal dysfunction. Our results demonstrate the evolutionarily conserved nature of monoamine function in C. elegans and further suggest that high-resolution mass spectrometry-based metabolomics can be used in this model to study environmental and genetic contributors to complex human disease.
Good times for wireless research

Abstract Wireless communications has been one of the major success stories of engineering, and as a field of research, it’s future maybe even brighter than its past. The field will need many breakthroughs to achieve the grand vision of next-generation networks, and hence, it is important to empower thousands of researchers. One of the challenges is how do we empower experiment-based wireless research at a scale and speed not possible today. In this paper, we discuss the challenges faced by the experiment-based wireless research and ongoing efforts to address those challenges. We discuss the RENEW platform as part of the POWDER-RENEW city-scale testbed in Salt Lake City, which is dedicated to the deployment of massive MIMO technology and the tools that are developed for experimentation.
Towards Heim and Neuhauser’s unimodality conjecture on the Nekrasov–Okounkov polynomials

Let $$Q_n(z)$$ be the polynomials associated with the Nekrasov–Okounkov formula $$\begin{aligned} \sum _{n\ge 1} Q_n(z) q^n := \prod _{m = 1}^\infty (1 - q^m)^{-z - 1}. \end{aligned}$$ In this paper we partially answer a conjecture of Heim and Neuhauser, which asks if $$Q_n(z)$$ is unimodal, or stronger, log-concave for all $$n \ge 1$$ . Through a new recursive formula, we show that if $$A_{n,k}$$ is the coefficient of $$z^k$$ in $$Q_n(z)$$ , then $$A_{n,k}$$ is log-concave in k for $$k \ll n^{1/6}/\log n$$ and monotonically decreasing for $$k \gg \sqrt{n}\log n$$ . We also propose a conjecture that can potentially close the gap.
Multiview and multiclass image segmentation using deep learning in fetal echocardiography

Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States. Despite training efforts and substantial advancement in ultrasound technology over the past years, CHD remains an abnormality that is frequently missed during prenatal ultrasonography. Therefore, computeraided detection of CHD can play a critical role in prenatal care by improving screening and diagnosis. Since many CHDs involve structural abnormalities, automatic segmentation of anatomical structures is an important step in the analysis of fetal echocardiograms. While existing methods mainly focus on the four-chamber view with a small number of structures, here we present a more comprehensive deep learning segmentation framework covering 14 anatomical structures in both three-vessel trachea and four-chamber views. Specifically, our framework enhances the V-Net with spatial dropout, group normalization, and deep supervision to train a segmentation model that can be applied on both views regardless of abnormalities. By identifying the pitfall of using the Dice loss when some labels are unavailable in some images, this framework integrates information from multiple views and is robust to missing structures due to anatomical anomalies, achieving an average Dice score of 79%.
Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion

We consider robotic pick-and-place of partially visible, novel objects, where goal placements are non-trivial, e.g., tightly packed into a bin. One approach is (a) use object instance segmentation and shape completion to model the objects and (b) use a regrasp planner to decide grasps and places displacing the models to their goals. However, it is critical for the planner to account for uncertainty in the perceived models, as object geometries in unobserved areas are just guesses. We account for perceptual uncertainty by incorporating it into the regrasp planner's cost function. We compare seven different costs. One of these, which uses neural networks to estimate probability of grasp and place stability, consistently outperforms uncertainty-unaware costs and evaluates faster than Monte Carlo sampling. On a real robot, the proposed cost results in successfully packing objects tightly into a bin 7.8% more often versus the commonly used minimum-number-of-grasps cost.
Predicting the probability of violence in actor–target relational dyads: Self-control and interpersonal provocations as mutual properties

If disputes are ever-present in human interaction, all relational dyads contain potential offenders and targets. We theorize that each dyad partner’s self-control independently influences the likelihood of violence, and that low self-control will express itself in provocative behavior. Using two waves from the Interpersonal and Conflict Resolution (iCOR) survey, with measures collected from each member of 443 couples, we create dyads and analyze the independent contributions of the specified variables for both would-be offenders and the potential target. We found that a potential target with low self-control was more likely to be attacked by the actor, irrespective of the actor’s self-control. This effect was explained by a tendency of both partners to engage in verbally provocative behavior. These results are supportive of self-control theory’s predictions concerning the importance of target decision-making, and indicate that other criminological theories can profit from considering the target’s role in violent crime causation.
Back to the future: A refined single-user photostation for massively scaling herbarium digitization

Summary The digitization and online mobilization of herbarium specimens has greatly facilitated their access and helped ignite a revolution in the biodiversity sciences (Drew et al., 2017; Hedrick et al., 2020; Nelson et al., 2015; Soltis, 2017; Sweeney et al., 2018; Thiers et al., 2016). These efforts have mobilized millions of specimens with significant economies of scale and accelerated advances in scientific investigations, including phenological studies of climate change, species range assessments, and biotic interactions (Hedrick et al., 2020; Meineke et al., 2019; Meineke et al., 2018; Pearson et al., 2020; Willis et al., 2017). In addition, the use of natural history collections to answer scientific questions using only their digitized representation, rather than the physical specimen itself–i.e., Digitization 2.0 sensuHedrick et al. (2020)–has sparked the integration and development of new scholarly disciplines and lines of inquiry not previously possible. Despite these exciting new directions, however, Digitization 1.0 sensuHedrick et al. (2020)–i.e., the generation of digitized products from the physical specimen–remains an active area of innovation and development. This relates to both hardware and workflow innovations as well as their integration with advancements in software. Along these lines, innovations in these areas have greatly increased the cost-effectiveness of digitizing herbarium specimens and enabled the successful mobilization of entire collections and whole floristic regions (Heerlien et al., 2015; Pignal and Michiels, 2012; Schorn et al., 2016; Slijkhuis, 2014; Sweeney et al., 2018; van Oever and Gofferje, 2012). Here, we present a novel photostation and workstation design for imaging herbarium specimen that represents a dramatic improvement upon existing approaches and is scalable for large and small institutions alike.
On a Batalin-Vilkovisky operator generating higher Koszul brackets on differential forms

We introduce a formal $$\hbar $$ -differential operator $$\Delta $$ that generates higher Koszul brackets on the algebra of (pseudo)differential forms on a $$P_{\infty }$$ -manifold. Such an operator was first mentioned by Khudaverdian and Voronov in arXiv:1808.10049. (This operator is an analogue of the Koszul–Brylinski boundary operator $$\partial _P$$ which defines Poisson homology for an ordinary Poisson structure.) Here, we introduce $$\Delta =\Delta _P$$ by a different method and establish its properties. We show that this BV type operator generating higher Koszul brackets can be included in a one-parameter family of BV type formal $$\hbar $$ -differential operators, which can be understood as a quantization of the cotangent $$L_{\infty }$$ -bialgebroid. We obtain symmetric description on both $$\Pi TM$$ and $$\Pi T^*M$$ . For the purpose of the above, we develop in detail a theory of formal $$\hbar $$ -differential operators and also of operators acting on densities on dual vector bundles. In particular, we have a statement about operators that can be seen as a quantization of the Mackenzie–Xu canonical diffeomorphism. Another interesting feature is that we are able to introduce a grading, not a filtration, on our algebras of operators. When operators act on objects on vector bundles, we obtain a bi-grading.
Some Large Deviations Results for Latin Hypercube Sampling

Large deviations theory is a well-studied area which has shown to have numerous applications. Broadly speaking, the theory deals with analytical approximations of probabilities of certain types of rare events. Moreover, the theory has recently proven instrumental in the study of complexity of methods that solve stochastic optimization problems by replacing expectations with sample averages (such an approach is called sample average approximation in the literature). The typical results, however, assume that the underlying random variables are either i.i.d. or exhibit some form of Markovian dependence. Our interest in this paper is to study the application of large deviations results in the context of estimators built with Latin Hypercube sampling, a well-known sampling technique for variance reduction. We show that a large deviation principle holds for Latin Hypercube sampling for functions in one dimension and for separable multi-dimensional functions. Moreover, the upper bound of the probability of a large deviation in these cases is no higher under Latin Hypercube sampling than it is under Monte Carlo sampling. We extend the latter property to functions that are monotone in each argument. Numerical experiments illustrate the theoretical results presented in the paper.
Invariant Gibbs measure and global strong solutions for the Hartree NLS equation in dimension three

In this paper, we consider the defocusing Hartree nonlinear Schrodinger equations on T3 with real-valued and even potential V and Fourier multiplier decaying such as |k|−β. By relying on the method of random averaging operators [Deng et al., arXiv:1910.08492 (2019)], we show that there exists β0, which is less than but close to 1, such that for β > β0, we have invariance of the associated Gibbs measure and global existence of strong solutions in its statistical ensemble. In this way, we extend Bourgain’s seminal result [J. Bourgain, J. Math. Pures Appl. 76, 649–702 (1997)], which requires β > 2 in this case.
GW170817 event rules out general relativity in favor of vector gravity

The observation of gravitational waves by the three LIGO-Virgo interferometers allows the examination of the polarization of gravitational waves. Here, we analyze the binary neutron star event GW170817, whose source location and distance are determined precisely by concurrent electromagnetic observations. We apply a signal accumulation procedure to the LIGO-Virgo strain data and find that the measured LIGO-Livingston signal is substantially reduced in certain frequency intervals due to real-time noise subtraction. We obtain ratios of the signals detected by the three interferometers excluding these “depleted” regions from the data analysis. We find that the signal ratios are inconsistent with the tensor polarization predictions of general relativity and Einstein’s theory is ruled out at 99% confidence level. Moreover, we find that the signal ratios and distance to the source are consistent with the vector theory of gravity (Phys Scr 92:125001, 2017) and that vector GW polarization is favored over tensor polarization and scalar polarization with exponentially large Bayes factors. If, however, we erroneously include in the data analysis the frequency regions in which the Livingston signal is depleted by noise filtering, we reproduce the result of the LIGO-Virgo collaboration favoring tensor GW polarization over vector polarization with an exponentially large Bayes factor.
A Generalization in Space of Jung's Theorem

Proof: Let P1,...,Pn . be the points. Let S1(O1,r1) be a sphere of center O1 and radius r1 , which contains all these points. We note r2 = max 1≤i≤n PiO1 = P1O1 and construct the sphere S2 (O1,r2 ) , r2 ≤ r1 , with P1 ∈Fr(S2 ) , where 2 ( ) Fr S = frontier (surface) of 2 S . We apply a homothety H in space, of center P1 , such that the new sphere H (S2 ) = S3(O3,r3) has the property: Fr(S3) contains another point, for example P2 , and of course S3 contains all points Pi . 1) If P1,P2 are diametrically opposite in S3 then rmin = a 2 .
DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction

This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for other potential users and predict the corresponding user rankings. Inferring user interests from diffusion data lies the foundation of diffusion prediction, because users often forward the information in which they are interested or the information from those who share similar interests. Their interests also evolve over time as the result of the dynamic social influence from neighbors and the time-sensitive information gained inside/outside the social media. Existing works fail to model users' intrinsic interests from the diffusion data and assume user interests remain static along the time. DyDiff-VAE advances the state of the art in two directions: (i) We propose a dynamic encoder to infer the evolution of user interests from observed diffusion data. (ii) We propose a dual attentive decoder to estimate the propagation likelihood by integrating information from both the initial cascade content and the forwarding user sequence. Extensive experiments on four real-world datasets from Twitter and Youtube demonstrate the advantages of the proposed model; we show that it achieves 43.3%relative gains over the best baseline on average. Moreover, it has the lowest run-time compared with recurrent neural network based models.
An overview on optimal flocking

Abstract The decentralized aggregate motion of many individual robots is known as robotic flocking. The study of robotic flocking has received considerable attention in the past twenty years. As we begin to deploy flocking control algorithms on physical multi-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance. In this paper, we present an overview the literature focusing on optimization approaches to achieve flocking behavior that provide strong safety guarantees. We separate the literature into cluster and line flocking, and categorize cluster flocking with respect to the system-level objective, which may be realized by a reactive or planning control algorithm. We also categorize the line flocking literature by the energy-saving mechanism that is exploited by the agents. We present several approaches aimed at minimizing the communication and computational requirements in real systems via neighbor filtering and event-driven planning, and conclude with our perspective on the outlook and future research direction of optimal flocking as a field.
A technical analog of the cosmological constant problem and a solution thereof

The near vanishing of the cosmological constant is one of the most puzzling open problems in theoretical physics. We consider a system, the so-called framid, that features a technically similar problem. Its stress-energy tensor has a Lorentz-invariant expectation value on the ground state, yet there are no standard, symmetry-based selection rules enforcing this, since the ground state spontaneously breaks boosts. We verify the Lorentz invariance of the expectation value in question with explicit one-loop computations. These, however, yield the expected result only thanks to highly nontrivial cancellations, which are quite mysterious from the low-energy effective theory viewpoint.
Optimal control and numerical methods for hybrid stochastic SIS models

Abstract This work focuses on optimal controls of a class of stochastic SIS epidemic models under regime switching. By assuming that a decision maker can influence the infectivity period, our aim is to minimize the expected discounted cost due to illness, medical treatment, and the adverse effect on the society. In addition, a model with the incorporation of vaccination is proposed. Numerical schemes are developed by approximating the continuous-time dynamics using Markov chain approximation methods. It is demonstrated that the approximation schemes converge to the optimal strategy as the mesh size goes to zero. Numerical examples are provided to illustrate our results.
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers.

We study the problem of robustly estimating the mean of a $d$-dimensional distribution given $N$ examples, where most coordinates of every example may be missing and $\varepsilon N$ examples may be arbitrarily corrupted. Assuming each coordinate appears in a constant factor more than $\varepsilon N$ examples, we show algorithms that estimate the mean of the distribution with information-theoretically optimal dimension-independent error guarantees in nearly-linear time $\widetilde O(Nd)$. Our results extend recent work on computationally-efficient robust estimation to a more widely applicable incomplete-data setting.
Constant speed penetration into granular materials: drag forces from the quasistatic to inertial regime

Predicting the force exerted on an object as it penetrates a granular medium is of interest in engineering, locomotive, and geotechnical applications. Current models of granular drag, however, vary widely in applicability and parameterization, and the physical origin of the granular resistive force itself is a subject of debate. Here we perform constant speed penetration experiments, combined with calibrated, large-scale molecular dynamics simulations, at velocities up to 2 m/s to test the effect of impact velocity on the depth dependent ‘hydrostatic’ drag force. We discover that the evolution of the granular flow field around an intruder regulates the presence of depth dependent drag forces. In addition, we find that the observed linear depth dependence is commensurate with the mass of flowing grains. These results suggest that, as the impact speed increases beyond the quasistatic regime, the depth dependent drag term becomes intertwined with inertial effects.
Evolving understanding of the classification of fibromyalgia following surgery

Fibromyalgia (FM) is a condition characterized by chronic widespread pain, psychological disturbances, and physical impairments with controversy surrounding the underlying pathophysiology and no diagnostic confirmatory test. There are many shared features between FM and CRPS, and we theorize that some sub-set of clinically diagnosed FM is the result of a diffuse form of CRPS. Our objective in this case series is to outline two post-surgical patients with eventual diagnosis of FM. Case Report Case 1: African-American male veteran with medical history significant for repeat incision and drainage of sebaceous cysts in his bilateral axilla. Patient noted throbbing bilateral axillary pain for up to 90 days after each incision and drainage. After his surgeries were completed in the late 1980s he continued to demonstrate increasingly chronic and more widespread pain, throbbing in nature, no alleviating factors, and exacerbated by movement and cold weather. Diagnosed with fibromyalgia by the American College of Rheumatology criteria in 2001. Case 2: Female veteran with a medical history significant for uterine cancer and cervical cancer. Initial cervical cancer surgery in 1991. Hysterectomy in 1999. Post hysterectomy, she noted diffuse body pain with failed pharmacologic regimens including opiates, herbal therapies, trigger point injections, and steroid injections. She was formally diagnosed by the American College of Rheumatology criteria with fibromyalgia in 1999. This case series presents two patients with no prior history of chronic pain or no known psychiatric history who developed post surgically fibromyalgia. There is a very small body of evidence correlating fibromyalgia with a post surgical patient with most literature focusing on CRPS in the post surgical patient or FM/CRPS in the post-traumatic patient. Further cohort studies and meta-analysis are needed to demonstrate variables that may predispose surgical patients to FM.
Persistent plasma waves in interstellar space detected by Voyager 1

In 2012, Voyager 1 became the first in situ probe of the very local interstellar medium1. The Voyager 1 Plasma Wave System has given point estimates of the plasma density spanning about 30 au of interstellar space, revealing a large-scale density gradient2,3 and turbulence4 outside of the heliopause. Previous studies of the plasma density relied on the detection of discrete plasma oscillation events triggered ahead of shocks propagating outwards from the Sun, which were used to infer the plasma frequency and, hence, density5,6. We present the detection of a class of very weak, narrowband plasma wave emission in the Voyager 1 data that persists from 2017 onwards and enables a steadily sampled measurement of the interstellar plasma density over about 10 au with an average sampling distance of 0.03 au. We find au-scale density fluctuations that trace interstellar turbulence between episodes of previously detected plasma oscillations. Possible mechanisms for the narrowband emission include thermally excited plasma oscillations and quasi-thermal noise, and they could be clarified by new findings from Voyager or a future interstellar mission. The emission’s persistence suggests that Voyager 1 may be able to continue tracking the interstellar plasma density in the absence of shock-generated plasma oscillation events. The Voyager 1 spacecraft is now probing interstellar space beyond the heliopause. Here, measurements from the Plasma Wave System reveal au-scale density fluctuations that trace interstellar turbulence without the need for solar shock-generated plasma oscillation events.
Product selection in online marketplaces

A marketplace such as Amazon hosts many products by third party sellers and acts as a first party or private label retailer. Assuming an advantage of Amazon in logistics and of sellers in marketing, we investigate whether entry by Amazon is excessive from the point of view of consumers. With competitive sellers, entry may be either overprovided or underprovided, but the incentives of Amazon and consumers are correctly aligned for a family of power surplus functions (generating for instance linear, isoelastic, and loglinear demands). Competition for customers with other retailers reduces commissions and prices preserving the efficiency result. Market power by sellers increases (reduces) the incentives to retail private label (first party) products, and generates a bias toward underprovision of entry. We extend the analysis to delivery fulfillment by the marketplace, product differentiation with price competition on the platform, and a dynamic analysis of the incentives of sellers to enter and the marketplace to launch copycat products.
Full-path localization of directed polymers

Certain polymer models are known to exhibit path localization in the sense that at low temperatures, the average fractional overlap of two independent samples from the Gibbs measure is bounded away from $0$. Nevertheless, the question of where along the path this overlap takes place has remained unaddressed. In this article, we prove that on linear scales, overlap occurs along the entire length of the polymer. Namely, we consider time intervals of length $\varepsilon N$, where $\varepsilon>0$ is fixed but arbitrarily small. We then identify a constant number of distinguished trajectories such that the Gibbs measure is concentrated on paths having, with one of these distinguished paths, a fixed positive overlap simultaneously in every such interval. This result is obtained in all dimensions for a Gaussian random environment by using a recent non-local result as a key input.
Experimental and computational studies of the control of convection of non-conducting liquids during solidification by means of a magnetic field gradient

The elimination of convection is essential in experimental investigations of diffusive transport (of heat and matter) during solidification. One classical approach to damping convection in a conducting liquid is the application of a magnetic field. The damping phenomenon is the induction, by the motion of a conductor in a magnetic field, of currents which interact with the field to produce Lorentz forces that oppose the flow. However, there are many liquids which are not sufficiently conducting to exploit this phenomenon; examples include the transparent liquids (such as succinonitrile-acetone) that are used as "model alloys" in fundamental solidification studies. There have been several investigations of the solidification of these liquids that have been carried out in orbiting laboratories to eliminate natural convection. The paper describes an investigation of an alternative approach whereby a magnetic field gradient is applied to the liquid. A magnetic body force then arises which is dependent on the susceptibility of the liquid and thereby on the temperature and or concentration. With the field gradient aligned vertically and of correct magnitude, the variation of gravitational body force due to temperature/concentration dependent density can be counterbalanced by a variation in magnetic body force. Experiments have been carried out in a super-conducting magnet at Marshall Space Flight Center to measure velocities in an aqueous manganese chloride solution. The solution was contained in a chamber with temperature controlled end walls and glass side walls. Velocities were measured by particle image velocimetry. Starting from zero current in the magnet (zero field gradient) flow driven by the temperature difference between the end walls was measured. At a critical current the flow was halted. At higher currents the normal convection was reversed. The experiments included ones where the solution was solidified and were accompanied by solution of the flow/transport equations using the software package FLUENT.
Few-Shot Conformal Prediction with Auxiliary Tasks

We develop a novel approach to conformal prediction when the target task has limited data available for training. Conformal prediction identifies a small set of promising output candidates in place of a single prediction, with guarantees that the set contains the correct answer with high probability. When training data is limited, however, the predicted set can easily become unusably large. In this work, we obtain substantially tighter prediction sets while maintaining desirable marginal guarantees by casting conformal prediction as a meta-learning paradigm over exchangeable collections of auxiliary tasks. Our conformalization algorithm is simple, fast, and agnostic to the choice of underlying model, learning algorithm, or dataset. We demonstrate the effectiveness of this approach across a number of few-shot classification and regression tasks in natural language processing, computer vision, and computational chemistry for drug discovery.
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However, it is often difficult to specify such features a priori, such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation, rather than regression-oriented meta-learning of features to fit input-output data. Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With a nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.
Living It Up at the Hotel California: Employee Mobility Barriers and Collaborativeness in Firms’ Innovation

Research has long recognized the importance of collaboration for innovation, but relatively little is known about the strategic drivers of collaborative innovation in firms. We posit that robust co...
Educación y aprendizaje desde la biopedagogía. Perspectivas de cambio para los procesos educativos

El articulo reflexiona el concepto de aprendizaje desde la propuesta de la biopedagogia, que situa a la educacion como punto de referencia para la transformacion de las personas. Por tanto, se comprenden los procesos de aprendizaje como la capacidad cognitiva y adaptiva, que le permiten al ser humano el encuentro y la construccion de una identidad, no solo desde el lugar de la razon, sino desde el afecto, el gozo, el reconocimiento del otro y la conciencia de si mismo. Es asi que, desde la teoria de la complejidad, se comprende la configuracion de entornos de aprendizaje como mediaciones vitales que permiten a los ninos y las ninas entender sus realidades, participar en ellas y desarrollarse emocionalmente.
How to extract energy from turbulence in flight by fast tracking

We analyse a way to make flight vehicles harvest energy from homogeneous turbulence by fast tracking in the way that falling inertial particles do. Mean air speed increases relative to flight through quiescent fluid when turbulent eddies sweep particles and vehicles along in a productive way. Once swept, inertia tends to carry a vehicle into tailwinds more often than headwinds. We introduce a forcing that rescales the effective inertia of rotorcraft in computer simulations. Given a certain thrust and effective inertia, we find that flight energy consumption can be calculated from measurements of mean particle settling velocities and acceleration variances alone, without the need for other information. In calculations using a turbulence model, we optimize the balance between the work performed to generate the forcing and the advantages induced by fast tracking. The results show net energy reductions of up to approximately 10 % relative to flight through quiescent fluid and mean velocities up to 40 % higher. The forcing expands the range of conditions under which fast tracking operates by a factor of approximately ten. We discuss how the mechanism can operate for any vehicle, how it may be even more effective in real turbulence and for fixed-wing aircraft and how modifications might yield yet greater performance.
Graph Neural Network to Dilute Outliers for Refactoring Monolith Application.

Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can happen independently. It also provides natural benefits when deployed on the cloud since resources can be allocated dynamically to necessary components based on demand. Therefore, enterprises as part of their journey to cloud, are increasingly looking to refactor their monolith application into one or more candidate microservices; wherein each service contains a group of software entities (e.g., classes) that are responsible for a common functionality. Graphs are a natural choice to represent a software system. Each software entity can be represented as nodes and its dependencies with other entities as links. Therefore, this problem of refactoring can be viewed as a graph based clustering task. In this work, we propose a novel method to adapt the recent advancements in graph neural networks in the context of code to better understand the software and apply them in the clustering task. In that process, we also identify the outliers in the graph which can be directly mapped to top refactor candidates in the software. Our solution is able to improve state-of-the-art performance compared to works from both software engineering and existing graph representation based techniques.
High-purity solid parahydrogen.

Alkali atoms trapped in solid hydrogen matrices have demonstrated ultralong electron spin coherence times and are promising as quantum sensors. Their spin coherence is limited by magnetic noise from naturally occurring orthohydrogen molecules in the parahydrogen matrix. In the gas phase, the orthohydrogen component of hydrogen can be converted to parahydrogen by flowing it over a catalyst held at cryogenic temperatures, with lower temperatures giving a lower orthohydrogen fraction. In this work, we use a single cryostat to reduce the orthohydrogen fraction of hydrogen gas and grow a solid matrix from the resulting high-purity parahydrogen. We demonstrate the operation of the catalyst down to a temperature of 8 K, and we spectroscopically verify that orthohydrogen impurities in the resulting solid are at a level <10−6. We also find that, at sufficiently low temperatures, the cryogenic catalyst provides isotopic purification, reducing the HD fraction.
Active Dynamical Prospection: Modeling Mental Simulation as Particle Filtering for Sensorimotor Control during Pathfinding

What do humans do when confronted with a common challenge: we know where we want to go but we are not yet sure the best way to get there, or even if we can. This is the problem posed to agents during spatial navigation and pathfinding, and its solution may give us clues about the more abstract domain of planning in general. In this work, we model pathfinding behavior in a continuous, explicitly exploratory paradigm. In our task, participants (and agents) must coordinate both visual exploration and navigation within a partially observable environment. Our contribution has three primary components: 1) an analysis of behavioral data from 81 human participants in a novel pathfinding paradigm conducted as an online experiment, 2) a proposal to model prospective mental simulation during navigation as particle filtering, and 3) an instantiation of this proposal in a computational agent. We show that our model, Active Dynamical Prospection, demonstrates similar patterns of map solution rate, path selection, and trial duration, as well as attentional behavior (at both aggregate and individual levels) when compared with data from human participants. We also find that both distal attention and delay prior to first move (both potential correlates of prospective simulation) are predictive of task performance.
On semigroup algebras with rational exponents

In this paper, a semigroup algebra consisting of polynomial expressions with coefficients in a field $F$ and exponents in an additive submonoid $M$ of $\mathbb{Q}_{\ge 0}$ is called a Puiseux algebra and denoted by $F[M]$. Here we study the atomic structure of Puiseux algebras. To begin with, we answer the Isomorphism Problem for the class of Puiseux algebras, that is, we show that for a field $F$ if two Puiseux algebras $F[M_1]$ and $F[M_2]$ are isomorphic, then the monoids $M_1$ and $M_2$ must be isomorphic. Then we construct three classes of Puiseux algebras satisfying the following well-known atomic properties: the ACCP property, the bounded factorization property, and the finite factorization property. We show that there are bounded factorization Puiseux algebras with extremal systems of sets of lengths, which allows us to prove that Puiseux algebras cannot be determined up to isomorphism by their arithmetic of lengths. Finally, we give a full description of the seminormal closure, root closure, and complete integral closure of a Puiseux algebra, and use such description to provide a class of antimatter Puiseux algebras (i.e., Puiseux algebras containing no irreducibles).
Robust Vehicle Lane Keeping Control with Networked Proactive Adaptation

Road condition is an important environmental factor for autonomous vehicle control. A dramatic change in the road condition from the nominal status is a source of uncertainty that can lead to a system failure. Once the vehicle encounters an uncertain environment, such as hitting an ice patch, it is too late to reduce the speed, and the vehicle can lose control. To cope with unforeseen uncertainties in advance, we study a proactive robust adaptive control architecture for autonomous vehicles' lane-keeping control problems. The data center generates a prior environmental uncertainty estimate by combining weather forecasts and measurements from anonymous vehicles through a spatio-temporal filter. The prior estimate contributes to designing a robust heading controller and nominal longitudinal velocity for proactive adaptation to each new condition. The control parameters are updated based on posterior information fusion with on-board measurements.