cssi/SciSciGPT-SciSciNet huggingface dataset:
Familia: Unifying Interfaces, Type Classes, and Family Polymorphism

Parametric polymorphism and inheritance are both important, extensively explored language mechanisms for providing code reuse and extensibility. But harmoniously integrating these apparently distinct mechanisms—and powerful recent forms of them, including type classes and family polymorphism—in a single language remains an elusive goal. In this paper, we show that a deep unification can be achieved by generalizing the semantics of interfaces and classes. The payoff is a significant increase in expressive power with little increase in programmer-visible complexity. Salient features of the new programming language include retroactive constraint modeling, underpinning both object-oriented programming and generic programming, and module-level inheritance with further-binding, allowing family polymorphism to be deployed at large scale. The resulting mechanism is syntactically light, and the more advanced features are transparent to the novice programmer. We describe the design of a programming language that incorporates this mechanism; using a core calculus, we show that the type system is sound. We demonstrate that this language is highly expressive by illustrating how to use it to implement highly extensible software and by showing that it can not only concisely model state-of-the-art features for code reuse, but also go beyond them.
Emergent Symbolic Language Based Deep Medical Image Classification

Modern deep learning systems for medical image classification have demonstrated exceptional capabilities for distinguishing between image based medical categories. However, they are severely hindered by their inability to explain the reasoning behind their decision making. This is partly due to the uninterpretable continuous latent representations of neural networks. Emergent languages (EL) have recently been shown to enhance the capabilities of neural networks by equipping them with symbolic representations in the framework of referential games. Symbolic representations are one of the cornerstones of highly explainable good old fashioned AI (GOFAI) systems. In this work, we demonstrate for the first time, the emergence of deep symbolic representations of emergent language in the framework of image classification. We show that EL based classification models can perform as well as, if not better than state of the art deep learning models. We demonstrate the EL classification framework on immune cell marker based cell classification and chest X-ray classification using the CheXpert dataset. Code is available at https://github.com/AriChow/EL.
Convective Enhancement of Icing Roughness Elements in Stagnation Region Flows

To improve existing ice accretion simulation codes, more data regarding ice roughness and its effects on convective heat transfer are required. To build on existing research on this topic, this study used the Vertical Icing Studies Tunnel (VIST) at NASA Glenn Research to model realistic ice roughness in the stagnation region of a NACA 0012 airfoil. Using the VIST, a test plate representing the leading 2% chord of the airfoil was subjected to flows of 7.62 m/s (25 ft/s), 12.19 m/s (40 ft/s), and 16.76 m/s (55 ft/s). The test plate was fitted with 3 surfaces, each with a different representation of ice roughness: 1) a control surface with no ice roughness, 2) a surface with ice roughness with element height scaled by 10x and streamwise rough zone width from the stagnation point scaled by 10x, and 3) a surface with ice roughness with element height scaled by 10x and streamwise rough zone width from the stagnation point scaled by 25x. Temperature data from the tests were recorded using an infrared camera and thermocouples imbedded in the test plate. From the temperature data, a convective heat transfer coefficient map was created for each case. Additional testing was also performed to validate the VIST's flow quality. These tests included five-hole probe and hot-wire probe velocity traces to provide flow visualization and to study boundary layer formation on the various test surfaces. The knowledge gained during the experiments will help improve ice accretion codes by providing heat transfer coefficient validation data and by providing flow visualization data helping understand current and future experiments performed in the VIST.
Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles.

Summary In humans and other mammals, the stillness of sleep is punctuated by bursts of rapid eye movements (REMs) and myoclonic twitches of the limbs. 1 Like the spontaneous activity that arises from the sensory periphery in other modalities (e.g., retinal waves), 2 sensory feedback arising from twitches is well suited to drive activity-dependent development of the sensorimotor system. 3 It is partly because of the behavioral activation of REM sleep that this state is also called active sleep (AS), in contrast with the behavioral quiescence that gives quiet sleep (QS)—the second major stage of sleep—its name. In human infants, for which AS occupies 8 h of each day, 4 twitching helps to identify the state; 5 , 6 , 7 , 8 nonetheless, we know little about the structure and functions of twitching across development. Recently, in sleeping infants, 9 we documented a shift in the temporal expression of twitching beginning around 3 months of age that suggested a qualitative change in how twitches are produced. Here, we combine behavioral analysis with high-density electroencephalography (EEG) to demonstrate that this shift reflects the emergence of limb twitches during QS. Twitches during QS are not only unaccompanied by REMs, but they also occur synchronously with sleep spindles, a hallmark of QS. As QS-related twitching increases with age, sleep spindle rate also increases along the sensorimotor strip. The emerging synchrony between subcortically generated twitches and cortical oscillations suggests the development of functional connectivity among distant sensorimotor structures, with potential implications for detecting and explaining atypical developmental trajectories.
Does regulatory cooperation help integrate equity markets

Abstrac This study tests whether cooperation between securities regulators influences global market integration. I measure cooperation using arrangements between securities regulators that enable enhanced cross-border enforcement, better regulatory decisions, and reduced compliance obligations for cross-border activities. These arrangements-formed at different times for different country pairs-are associated with an 11% increase in cross-border investment. I find similar increases using other proxies for market integration. Cross-border investment and market integration thus depend, in part, on regulators working together to extend legal and institutional capacities across borders. This reframes our understanding of the role of institutions in global capital markets.
The Capacity of Quiver Representations and Brascamp–Lieb Constants

Let $Q$ be a bipartite quiver, $V$ a real representation of $Q$, and $\sigma$ an integral weight of $Q$ orthogonal to the dimension vector of $V$. Guided by quiver invariant theoretic considerations, we introduce the Brascamp-Lieb operator $T_{V,\sigma}$ associated to $(V,\sigma)$ and study its capacity, denoted by $\mathbf{D}_Q(V, \sigma)$. When $Q$ is the $m$-subspace quiver, the capacity of quiver data is intimately related to the Brascamp-Lieb constants that occur in the $m$-multilinear Brascamp-Lieb inequality in analysis. We show that the positivity of $\mathbf{D}_Q(V, \sigma)$ is equivalent to the $\sigma$-semi-stability of $V$. We also find a character formula for $\mathbf{D}_Q(V, \sigma)$ whenever it is positive. Our main tool is a quiver version of a celebrated result of Kempf-Ness on closed orbits in invariant theory. This result leads us to consider certain real algebraic varieties that carry information relevant to our main objects of study. It allows us to express the capacity of quiver data in terms of the character induced by $\sigma$ and sample points of the varieties involved. Furthermore, we use this character formula to prove a factorization of the capacity of quiver data. We also show that the existence of gaussian extremals for $(V, \sigma)$ is equivalent to $V$ being $\sigma$-polystable, and that the uniqueness of gaussian extremals implies that $V$ is $\sigma$-stable. Finally, we explain how to find the gaussian extremals of a gaussian-extremisable datum $(V, \sigma)$ using the algebraic variety associated to $(V,\sigma)$.
gSoFa : Scalable Sparse Symbolic LU Factorization on GPUs

Decomposing a matrix $\mathbf {A}$ A into a lower matrix $\mathbf {L}$ L and an upper matrix $\mathbf {U}$ U , which is also known as LU decomposition, is an essential operation in numerical linear algebra. For a sparse matrix, LU decomposition often introduces more nonzero entries in the $\mathbf {L}$ L and $\mathbf {U}$ U factors than in the original matrix. A symbolic factorization step is needed to identify the nonzero structures of $\mathbf {L}$ L and $\mathbf {U}$ U matrices. Attracted by the enormous potentials of the Graphics Processing Units (GPUs), an array of efforts have surged to deploy various LU factorization steps except for the symbolic factorization, to the best of our knowledge, on GPUs. This article introduces gSoFa , the first G PU-based s ymb o lic fa ctorization design with the following three optimizations to enable scalable LU symbolic factorization for nonsymmetric pattern sparse matrices on GPUs. First, we introduce a novel fine-grained parallel symbolic factorization algorithm that is well suited for the Single Instruction Multiple Thread (SIMT) architecture of GPUs. Second, we tailor supernode detection into a SIMT friendly process and strive to balance the workload, minimize the communication and saturate the GPU computing resources during supernode detection. Third, we introduce a three-pronged optimization to reduce the excessive space consumption problem faced by multi-source concurrent symbolic factorization. Taken together, gSoFa achieves up to 31× speedup from 1 to 44 Summit nodes (6 to 264 GPUs) and outperforms the state-of-the-art CPU project, on average, by 5×. Notably, gSoFa also achieves up to 47 percent of the peak memory throughput of a V100 GPU in the Summit Supercomputer.
PROGRESSION‐FREE SURVIVAL AT 24 MONTHS AS A LANDMARK AFTER AUTOLOGOUS STEM CELL TRANSPLANT IN RELAPSED OR REFRACTORY DIFFUSE LARGE B‐CELL LYMPHOMA

7522Background: Patients with newly diagnoseddiffuse large B-cell lymphoma (DLBCL) who achieve event-free survival at 24 months (EFS24) following immunochemotherapy (IC) have excellent overall surv...
Exactness of OPF Relaxation on Three-Phase Radial Networks With Delta Connections

Simulations have shown that while semi-definite relaxations of AC optimal power flow (AC-OPF) on three-phase radial networks with only wye connections tend to be exact, the presence of delta connections seem to render them inexact. This article shows that such inexactness originates from the non-uniqueness of relaxation solutions and numerical errors amplified by the non-uniqueness. This finding motivates two algorithms to recover the exact solution of AC-OPF in unbalanced distribution networks featuring both wye and delta connections. In simulations using IEEE 13, 37 and 123-bus systems, the proposed algorithms provide exact optimal solutions up to numerical precision.
Relating the Strength of Density Dependence and the Spatial Distribution of Individuals

Spatial patterns in ecology contain useful information about underlying mechanisms and processes. Although there are many summary statistics used to quantify these spatial patterns, there are far fewer models that directly link explicit ecological mechanisms to observed patterns easily derived from available data. We present a model of intraspecific spatial aggregation that quantitatively relates static spatial patterning to negative density dependence. Individuals are placed according to the colonization rule consistent with the Maximum Entropy Theory of Ecology (METE), and die with probability proportional to their abundance raised to a power α, a parameter indicating the degree of density dependence. This model can therefore be interpreted as a hybridization of MaxEnt and mechanism. Our model shows quantitatively and generally that increasing density dependence randomizes spatial patterning. α = 1 recovers the strongly aggregated METE distribution that is consistent with many ecosystems empirically, and as α → 2 our prediction approaches the binomial distribution consistent with random placement. For 1 < α < 2, our model predicts more aggregation than random placement but less than METE. We additionally relate our mechanistic parameter α to the statistical aggregation parameter k in the negative binomial distribution, giving it an ecological interpretation in the context of density dependence. We use our model to analyze two contrasting datasets, a 50 ha tropical forest and a 64 m2 serpentine grassland plot. For each dataset, we infer α for individual species as well as a community α parameter. We find that α is generally larger in the tightly packed forest than the sparse grassland, and the degree of density dependence increases at smaller scales. These results are consistent with current understanding in both ecosystems, and we infer this underlying density dependence using only empirical spatial patterns. Our model can easily be applied to other datasets where spatially explicit data are available.
Uniqueness results for bodies of constant width in the hyperbolic plane

Following Santal\'{o}'s approach, we prove several characterizations of a disc among bodies of constant width, constant projections lengths, or constant section lengths on given families of geodesics.
Dynamical obstructions to classification by (co)homology and other TSI-group invariants

In the spirit of Hjorth's turbulence theory, we introduce "unbalancedness": a new dynamical obstruction to classifying orbit equivalence relations by actions of Polish groups which admit a two side invariant metric (TSI). Since abelian groups are TSI, unbalancedness can be used for identifying which classification problems cannot be solved by classical homology and cohomology theories. In terms of applications, we show that Morita equivalence of continuous-trace $C^*$-algebras, as well as isomorphism of Hermitian line bundles, are not classifiable by actions of TSI groups. In the process, we show that the Wreath product of any two non-compact subgroups of $S_{\infty}$ admits an action whose orbit equivalence relation is generically ergodic against any action of a TSI group and we deduce that there is an orbit equivalence relation of a CLI group which is not classifiable by actions of TSI groups.
Throughput and delay optimality of power-of-d choices in inhomogeneous load balancing systems

Abstract It is well-known that the power-of-d choices routing algorithm maximizes throughput and is heavy-traffic optimal in load balancing systems with homogeneous servers. However, if the servers are heterogeneous, throughput optimality does not hold in general. We find necessary and sufficient conditions for throughput optimality of power-of-d choices when the servers are heterogeneous, and we prove that almost the same conditions are sufficient to show heavy-traffic optimality. Additionally, we generalize the sufficient condition for throughput optimality to a larger class of routing policies.
The Double-Edged Sword of Global Integration: Robustness, Fragility & Contagion in the International Firm Network†

We use daily equity returns to estimate global inter-firm networks across all major industries from 1981-2016 and test whether the network is robust or fragile, relating multinational firms' overall health with global integration. More connected firms are less likely to be in distress and have higher profit growth and equity returns, but are also more exposed to direct contagion from distressed neighboring firms and network level crises. Our machine learning analysis reveals the centrality of finance in the international firm network and increased globalization over time, with greater potential for crises to spread globally when they do occur.
The Biological Mechanism of Aging: Is It Still an Enigma?

It is proposed that in broad outline the biological basis of aging is understood. The current concepts of evolutionary biology of aging provide the framework upon which this understanding is based. Although some species or some individuals within a species may have processes that are unique to and a major component of their aging, there is a general proximate aging mechanism common to all organisms which plays the major role in most. This mechanism is the imbalance between the actions of long-term, low intensity stressors and the counteractions of protective and repair processes, the result of which is the progressive organismic accumulation of damage characteristic of aging or senescence. Research aimed at validating or invalidating this broad outline of the biological basis of aging should be of the highest priority.
Decoding Methods for Neural Narrative Generation

Narrative generation is an open-ended NLP task in which a model generates a story given a prompt. The task is similar to neural response generation for chatbots; however, innovations in response generation are often not applied to narrative generation, despite the similarity between these tasks. We aim to bridge this gap by applying and evaluating advances in decoding methods for neural response generation to neural narrative generation. In particular, we employ GPT-2 and perform ablations across nucleus sampling thresholds and diverse decoding hyperparameters—specifically, maximum mutual information—analyzing results over multiple criteria with automatic and human evaluation. We find that (1) nucleus sampling is generally best with thresholds between 0.7 and 0.9; (2) a maximum mutual information objective can improve the quality of generated stories; and (3) established automatic metrics do not correlate well with human judgments of narrative quality on any qualitative metric.
Stiefel-Whitney classes and immersions of orientable and Spin manifolds

Abstract We determine a nice simple formula for the largest Euclidean space for which there is an orientable n-manifold with a nonimmersion detected by Stiefel-Whitney classes. For Spin manifolds, we prove the analogue of the upper bound and establish the complete answer for n ≤ 23 and 32 ≤ n ≤ 33 . Results similar to many of these were obtained some 50 years ago, but in a much less tractable form. The sharp results for Spin manifolds require detailed calculations of ko-homology groups of mod-2 Eilenberg MacLane spaces.
PONT: A Protocol for Online Neuropsychological Testing.

A major challenge for neuropsychological research arises from the fact that we are dealing with a limited resource: the patients. Not only is it difficult to identify and recruit these individuals, but their ability to participate in research projects can be limited by their medical condition. As such, sample sizes are small, and considerable time (e.g., 2 years) is required to complete a study. To address limitations inherent to laboratory-based neuropsychological research, we developed a protocol for online neuropsychological testing (PONT). We describe the implementation of PONT and provide the required information and materials for recruiting participants, conducting remote neurological evaluations, and testing patients in an automated, self-administered manner. The protocol can be easily tailored to target a broad range of patient groups, especially those who can be contacted via support groups or multisite collaborations. To highlight the operation of PONT and describe some of the unique challenges that arise in online neuropsychological research, we summarize our experience using PONT in a research program involving individuals with Parkinson's disease and spinocerebellar ataxia. In a 10-month period, by contacting 646 support group coordinators, we were able to assemble a participant pool with over 100 patients in each group from across the United States. Moreover, we completed six experiments (n > 300) exploring their performance on a range of tasks examining motor and cognitive abilities. The efficiency of PONT in terms of data collection, combined with the convenience it offers the participants, promises a new approach that can increase the impact of neuropsychological research.
Null Models and Community Detection in Multi-Layer Networks

Multi-layer networks of multiplex type represent relational data on a set of entities (nodes) with multiple types of relations (edges) among them where each type of relation is represented as a network layer. A large group of popular community detection methods in networks are based on optimizing a quality function known as the modularity score, which is a measure of the extent of presence of module or community structure in networks compared to a suitable null model. Here we introduce several multi-layer network modularity and model likelihood quality function measures using different null models of the multi-layer network, motivated by empirical observations in networks from a diverse field of applications. In particular, we define multi-layer variants of the Chung-Lu expected degree model as null models that differ in their modeling of the multi-layer degrees. We propose simple estimators for the models and prove their consistency properties. A hypothesis testing procedure is also proposed for selecting an appropriate null model for data. These null models are used to define modularity measures as well as model likelihood based quality functions. The proposed measures are then optimized to detect the optimal community assignment of nodes (Code available at: https://u.osu.edu/subhadeep/codes/ ). We compare the effectiveness of the measures in community detection in simulated networks and then apply them to four real multi-layer networks.
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement learning (RL) systems. However, the existing attacks require the ability to arbitrarily modify an agent's observation, constraining the application scope to simple RL systems such as Atari games. In this paper, we migrate backdoor attacks to more complex RL systems involving multiple agents and explore the possibility of triggering the backdoor without directly manipulating the agent's observation. As a proof of concept, we demonstrate that an adversary agent can trigger the backdoor of the victim agent with its own action in two-player competitive RL systems. We prototype and evaluate BACKDOORL in four competitive environments. The results show that when the backdoor is activated, the winning rate of the victim drops by 17% to 37% compared to when not activated.
Hidden Cosets and Applications to Unclonable Cryptography

In 2012, Aaronson and Christiano introduced the idea of hidden subspace states to build public-key quantum money [STOC ’12]. Since then, this idea has been applied to realize several other cryptographic primitives which enjoy some form of unclonability.
Intergenerational Transmission of Disadvantage: Negative Parental Selection, Economic Upheaval, and Smoking

Recessions negatively impact the health of individuals experiencing hardship. In this paper, we investigate whether there are also long-term effects for those born during difficult economic times through the effects on their health behavior. Based on a theoretical model of parental socialization against smoking and using data from the German Socioeconomic Panel, we assess smoking behavior of children born in the years immediately after the fall of the Berlin Wall in the East. Using a difference-in-differences specification with West Germans as a control group, we find that men born during this time were 40% more likely to smoke in young adulthood than men born during the years before or afterwards. The fall of the Berlin Wall led to enormous social and economic upheaval and resulted in a stark drop in incomes and fertility in East Germany. Fertility, however, dropped least among the lower educated and younger mothers. The resulting negative parental selection, as measured by parental education and childhood family environment, explains about one fifth of the higher incidence of smoking of those born during this time. We posit that the effect of negatively selected parents was amplified by the compounding effect of disadvantage in childhood caused by the economic upheaval, which likely reduced both the amount and quality of parental socialization against smoking, leading to the increased smoking rates that we observe.
Limits and singularities of normal functions

We find a new method to detect the linearly independence of $${\mathbb {R}}$$ -regulator indecomposable $$K_1$$ -cycles which is based on the singularities and limits of admissible normal functions. We also construct a collection of higher Chow cycles on certain surfaces in $${\mathbb {P}}^3$$ of degree $$d \geqslant 4$$ which degenerate to an arrangement of d planes in general position. By applying our method, we show that these higher Chow cycles are enough to show the surjectivity of the real regulator map when $$d = 4$$ . Hence our construction gives a new explicit proof of the Hodge- $${\mathcal {D}}$$ -Conjecture for a certain type of K3 surfaces. As an application, we also construct a general semistable degeneration family of degree $$d + 1$$ threefolds in $${\mathbb {P}}^3 \,{\times }\, {\mathbb {P}}^1 \,{\times }\, {\mathbb {P}}^1$$ such that a codimension 1 stratum is a surface of the above type. The real regulator indecomposability of our higher Chow cycles implies that the Griffiths group of the general fiber of these threefolds is non-trivial.
Cavity control of nonlinear phononics

The authors develop a technique to control the coherent flow of energy between nonlinearly coupled phonon modes by tuning resonances in an optical cavity.
Operator Inference of Non-Markovian Terms for Learning Reduced Models from Partially Observed State Trajectories

This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The proposed approach compensates for the loss of information due to the partially observed states by constructing non-Markovian reduced models that make future-state predictions based on a history of reduced states, in contrast to traditional Markovian reduced models that rely on the current reduced state alone to predict the next state. The core contributions of this work are a data sampling scheme to sample partially observed states from high-dimensional dynamical systems and a formulation of a regression problem to fit the non-Markovian reduced terms to the sampled states. Under certain conditions, the proposed approach recovers from data the very same non-Markovian terms that one obtains with intrusive methods that require the governing equations and discrete operators of the high-dimensional dynamical system. Numerical results demonstrate that the proposed approach leads to non-Markovian reduced models that are predictive far beyond the training regime. Additionally, in the numerical experiments, the proposed approach learns non-Markovian reduced models from trajectories with only 20% observed state components that are about as accurate as traditional Markovian reduced models fitted to trajectories with 99% observed components.
An updated assessment of the acute impacts of ocean carbon sequestration by direct injection

Abstract This paper evaluates the expected environmental impact of several promising schemes for ocean carbon sequestration by direct injection of CO 2 , and serves as a major update to the assessment by Auerbach et al. (1997) and Caulfield et al. (1997) of water quality impacts and the induced mortality to zooplankton. Three discharge approaches are considered, each designed to maximize dilution over the water column: a point release of negatively buoyant CO 2 hydrate particles from a moving ship; a stationary point release of CO 2 hydrate particles forming a sinking plume; and a long, bottom-mounted diffuser discharging buoyant liquid CO 2 droplets. Two of these scenarios take advantage of the enhanced dilution offered by CO 2 hydrate particles, and are based on recent laboratory and field studies on the formation and behavior of such particles. Overall, results suggest that it is possible with present or near present technology to engineer discharge configurations that achieve sufficient dilution to largely avoid acute impacts. In particular, the moving ship hydrate discharge is identified as the most promising due to its operational flexibility. In addition to lethal effects, sub-lethal and ecosystem effects are discussed qualitatively, though not analyzed quantitatively. Our main conclusion is that ocean carbon sequestration by direct injection should not be dismissed as a climate change mitigation strategy on the basis of environmental impact alone. Rather, it can be considered as a viable option for further study, especially in regions where geologic sequestration proves impractical.
Opportunistic Spectrum Access: Does Maximizing Throughput Minimize File Transfer Time?

The Opportunistic Spectrum Access (OSA) model has been developed for the secondary users (SUs) to exploit the stochastic dynamics of licensed channels for file transfer in an opportunistic manner. Common approaches to design channel sensing strategies for throughput-oriented applications tend to maximize the long-term throughput, with the hope that it provides reduced file transfer time as well. In this paper, we show that this is not correct in general, especially for small files. Unlike prior delay-related works that seldom consider the heterogeneous channel rate and bursty incoming packets, our work explicitly considers minimizing the file transfer time of a single file consisting of multiple packets in a set of heterogeneous channels. We formulate a mathematical framework for the static policy, and extend to dynamic policy by mapping our file transfer problem to the stochastic shortest path problem. We analyze the performance of our proposed static optimal and dynamic optimal policies over the policy that maximizes long-term throughput. We then propose a heuristic policy that takes into account the performance-complexity tradeoff and an extension to online implementation with unknown channel parameters, and also present the regret bound for our online algorithm. We also present numerical simulations that reflect our analytical results.
A Perspective on Collective Properties of Atoms on 2D Materials

Atoms deposited on two-dimensional (2D) electronic materials, such as graphene, can exhibit unconventional many-body correlations, not accessible in other settings. All of these are driven by van der Waals forces: between the atoms themselves and atom-material interactions. For example $^4$He atoms on 2D materials can potentially form a variety of exotic quantum states of matter, such as two-dimensional supersolids and superfluids, in addition to solid phases. For the "most quantum" case of a single helium layer we discuss, from a theoretical perspective, how the effective low-energy (Bose-Hubbard) description can take advantage of the extreme sensitivity of this unique system to the interplay between the atomic (helium) and solid-state (graphene) components. Due to the extraordinary variety and tunability of 2D electronic materials, we envisage that a wide range of correlated atomic phases can be realized under favorable conditions. We also outline exciting possibilities in the opposite extreme of many atomic layers forming a liquid on top of graphene -- in this case a so-called "spinodal de-wetting" pattern can form at the liquid-vapor interface which reflects the presence and electronic properties of graphene underneath. Such patterns could be manipulated by choosing different atoms and materials, with potential technological applications.
Global Position Control on Underactuated Bipedal Robots: Step-to-step Dynamics Approximation for Step Planning

Global position control for underactuated bipedal walking is a challenging problem due to the lack of actuation on the feet of the robots. In this paper, we apply the Hybrid-Linear Inverted Pendulum (H-LIP) based stepping on 3D underactuated bipedal robots for global position control. The step-to-step (S2S) dynamics of the H-LIP walking approximates the actual S2S dynamics of the walking of the robot, where the step size is considered as the input. Thus the feedback controller based on the H-LIP approximately controls the robot to behave like the H-LIP, the differences between which stay in an error invariant set. Model Predictive Control (MPC) is applied to the H-LIP for global position control in 3D. The H-LIP stepping then generates desired step sizes for the robot to track. Moreover, turning behavior is integrated with the step planning. The proposed framework is verified on the 3D underactuated bipedal robot Cassie in simulation together with a proof-of-concept experiment.
Directional Mean Curvature for Textured Image Demixing

Abstract Approximation theory plays an important role in image processing, especially image deconvolution and decomposition. For piecewise smooth images, there are many methods that have been developed over the past thirty years. The goal of this study is to devise similar and practical methodology for handling textured images. This problem is motivated by forensic imaging, since fingerprints, shoeprints and bullet ballistic evidence are textured images. In particular, it is known that texture information is almost destroyed by a blur operator, such as a blurred ballistic image captured from a low-cost microscope. The contribution of this work is twofold: first, we propose a mathematical model for textured image deconvolution and decomposition into four meaningful components, using a high-order partial differential equation approach based on the directional mean curvature. Second, we uncover a link between functional analysis and multiscale sampling theory, e.g., harmonic analysis and filter banks. Both theoretical results and examples with natural images are provided to illustrate the performance of the proposed model.
The Association between Audit Office Team Diversity and Audit Quality

Surveys and field studies find that high-performing teams are diverse teams. Diverse teams value different perspectives and encourage the participation of team members through psychological safety, leading to higher team performance. This paper argues that team diversity is an office-level characteristic that is distinguishable from other characteristics studied in the prior auditing literature and that has an incremental effect on audit quality. I find a positive association between team diversity and audit quality that is robust to controlling for other audit office and client characteristics. Further, this positive association is stronger for more complex and non-routine audit engagements. These findings should be of interest to regulators who regulate how the auditing industry attracts and retains talent worldwide. In addition, these findings should be informative to audit committees who make auditor selection decisions and to investors and accounting researchers interested in the relation between audit team personnel and audit quality.
Accounting for Macrofinancial Fluctuations and Turbulence

This paper investigates the sources of macrofinancial fluctuations and turbulence within the framework of an approximate linear dynamic stochastic general equilibrium model of the world economy, augmented with structural shocks exhibiting potentially asymmetric generalized autoregressive conditional heteroskedasticity. Very strong evidence of asymmetric autoregressive conditional heteroskedasticity is found, providing a basis for jointly decomposing the levels and volatilities of key macrofinancial variables into time varying contributions from sets of shocks. Risk premia shocks are estimated to contribute disproportionately to cyclical output fluctuations and turbulence during swings in financial conditions, across the fifteen largest national economies in the world.
Wireless Charging Power Side-Channel Attacks

This paper demonstrates that today's wireless charging interface is vulnerable to power side-channel attacks; a smartphone that charges wirelessly leaks information about its activity to the wireless charger transmitter. We present a website fingerprinting attack and other preliminary attacks through the wireless charging side channel on iOS and Android devices. The website fingerprinting attack monitors the current draw of a wireless charger while the smartphone it charges loads a website from the Alexa top sites list. Our classifier identifies the website loaded on an iPhone 11 or a Google Pixel 4 with over 90% accuracy using wireless charging current traces. This attack represents a considerable security threat because wireless charging will always initiate when a compatible device is within the range of a charging transmitter. We find that the performance of the attack deteriorates as the contents of websites change over time. Additionally, this study finds that the wireless charging side channel is comparable to the wired USB charging side channel. Information leakage in both interfaces heavily depends on the battery level; minimal information leaks at low battery levels.
Variable Selection Consistency of Gaussian Process Regression

Bayesian nonparametric regression under a rescaled Gaussian process prior offers smoothness-adaptive function estimation with near minimax-optimal error rates. Hierarchical extensions of this approach, equipped with stochastic variable selection, are known to also adapt to the unknown intrinsic dimension of a sparse true regression function. But it remains unclear if such extensions offer variable selection consistency, that is, if the true subset of important variables could be consistently learned from the data. It is shown here that variable consistency may indeed be achieved with such models at least when the true regression function has finite smoothness to induce a polynomially larger penalty on inclusion of false positive predictors. Our result covers the high-dimensional asymptotic setting where the predictor dimension is allowed to grow with the sample size. The proof utilizes Schwartz theory to establish that the posterior probability of wrong selection vanishes asymptotically. A necessary and challenging technical development involves providing sharp upper and lower bounds to small ball probabilities at all rescaling levels of the Gaussian process prior, a result that could be of independent interest.
Dynamic multi-robot task allocation under uncertainty and temporal constraints

We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of unsuccessful tasks at the end of the operation horizon. We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination, and addresses them in a hierarchical manner. The lower layer computes policies for individual agents using dynamic programming with tree search, and the upper layer resolves conflicts in individual plans to obtain a valid multi-agent allocation. Our algorithm, Stochastic Conflict-Based Allocation (SCoBA), is optimal in expectation and complete under some reasonable assumptions. In practice, SCoBA is computationally efficient enough to interleave planning and execution online. On the metric of successful task completion, SCoBA consistently outperforms a number of baseline methods and shows strong competitive performance against an oracle with complete lookahead. It also scales well with the number of tasks and agents. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
Atkins v. Virginia: A Psychiatric Can of Worms

This article provides a psychiatric perspective on the problems Atkins raises for courts that handle death penalty cases. In contrast to the overarching aim of the majority's opinion in Atkins - making the administration of capital punishment more equitable - the Supreme Court's latest prescription of psychiatric help may only add a new layer of complexity and confusion to the already capricious process through which the U.S. criminal justice system imposes death sentences. The article briefly review's the Supreme Court's 1989 Penry decision, focusing on the role that evidence of mental retardation played in death penalty cases before Atkins was decided. The article then looks at how the Supreme Court majority in Atkins characterized the appellant's mental condition and the diagnostic process. Subsequent sections discuss: the process of diagnosing mental retardation, the ambiguities in that process, and the way that courts and legislatures may distort clinical diagnosis for use in legal proceedings; the contradiction between professional organizations' treatment of, and response to, Atkins and these organizations' customary stance on the use of diagnoses for non-clinical purposes; potential implications for capital defendants with psychiatric problems as incapacitating as, or more disabling than, mental retardation; the potential effect of Atkins on testimony by mental health experts; the effect of such testimony in future death-sentencing determinations.
Geometric Optimal Controls for Flapping Wing UAV on a Lie Group

Abstract Inspired by flight characteristics captured from live Monarch butterflies, an optimal control problem is presented while accounting the effects of low-frequency flapping and abdomen undulation. A flapping-wing aerial vehicle is modeled as an articulated rigid body, and its dynamics are developed according to Lagrangian mechanics on an abstract Lie group. This provides an elegant, global formulation of the dynamics for flapping-wing aerial vehicles, avoiding complexities and singularities associated with local coordinates. This is utilized to identify an optimal periodic motion that minimizes energy variations, and an optimal control is formulated to stabilize the periodic motion. Furthermore, the outcome of this paper can be applied to optimal control for any Lagrangian system on a Lie group with a configuration-dependent inertia.
A characterization of GVZ groups in terms of fully ramified characters

In this paper‎, ‎we obtain a characterization of GVZ-groups in terms of commutators and monolithic quotients‎. ‎This characterization is based on counting formulas due to Gallagher‎.
Comparison of Indexes to Measure Comorbidity Burden and Predict All-Cause Mortality in Rheumatoid Arthritis.

Objectives: To examine the comorbidity burden in patients with rheumatoid arthritis (RA) patients using a nationwide population-based cohort by assessing the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Multimorbidity Index (MMI), and Rheumatic Disease Comorbidity Index (RDCI) scores and to investigate their predictive ability for all-cause mortality. Methods: We identified 24,767 RA patients diagnosed from 1998 to 2008 in Taiwan and followed up until 31 December 2013. The incidence of comorbidities was estimated in three periods (before, during, and after the diagnostic period). The incidence rate ratios were calculated by comparing during vs. before and after vs. before the diagnostic period. One- and 5-year mortality rates were calculated and discriminated by low and high-score groups and modified models for each index. Results: The mean score at diagnosis was 0.8 in CCI, 2.8 in ECI, 0.7 in MMI, and 1.3 in RDCI, and annual percentage changes are 11.0%, 11.3%, 9.7%, and 6.8%, respectively. The incidence of any increase in the comorbidity index was significantly higher in the periods of “during” and “after” the RA diagnosis (incidence rate ratios for different indexes: 1.33–2.77). The mortality rate significantly differed between the high and low-score groups measured by each index (adjusted hazard ratios: 2.5–4.3 for different indexes). CCI was slightly better in the prediction of 1- and 5-year mortality rates. Conclusions: Comorbidities are common before and after RA diagnosis, and the rate of accumulation accelerates after RA diagnosis. All four comorbidity indexes are useful to measure the temporal changes and to predict mortality.
Intelligence and Learning in O-RAN for Data-Driven NextG Cellular Networks

Next generation (NextG) cellular networks will be natively cloud-based and built on programmable, virtualized, and disaggregated architectures. The separation of control functions from the hardware fabric and the introduction of standardized control interfaces will enable the definition of custom closed-control loops, which will ultimately enable embedded intelligence and real-time analytics, thus effectively realizing the vision of autonomous and self-optimizing networks. This article explores the disaggregated network architecture proposed by the O-RAN Alliance as a key enabler of NextG networks. Within this architectural context, we discuss the potential, the challenges, and the limitations of data-driven optimization approaches to network control over different timescales. We also present the first large-scale integration of O-RAN-compliant software components with an open source full-stack softwarized cellular network. Experiments conducted on Colosseum, the world's largest wireless network emulator, demonstrate closed-loop integration of real-time analytics and control through deep reinforcement learning agents. We also show the feasibility of radio access network (RAN) control through xApps running on the near-real-time RAN intelligent controller to optimize the scheduling policies of coexisting network slices, leveraging the O-RAN open interfaces to collect data at the edge of the network.
Bayesian multivariate augmented Beta rectangular regression models for patient-reported outcomes and survival data.

Many longitudinal studies (e.g. observational studies and randomized clinical trials) have collected multiple rating scales at each visit in the form of patient-reported outcomes (PROs) in the close unit interval [0,1]. We propose a joint modeling framework to address the issues from the following data features: (1) multiple correlated PROs; (2) the presence of the boundary values of zeros and ones; (3) extreme outliers and heavy tails; (4) the PRO-dependent terminal events such as death and dropout. Our modeling framework consists of a multivariate augmented mixed-effects sub-model based on Beta rectangular distributions for the multiple longitudinal outcomes and a Cox model for the terminal events. The simulation studies suggest that in the presence of outliers, heavy tails, and dependent terminal event, our proposed models provide more accurate parameter estimates than the joint model based on Beta distributions. The proposed models are applied to the motivating Long-term Study-1 (LS-1 study, n = 1741)...
Optical heterodyne detection based on dual-mode injection locking of modelocked semiconductor lasers

Optical heterodyne detection based on a novel injection locking technique is demonstrated using synchronized optical frequency comb sets from two independent modelocked semiconductor lasers. The measurement is performed with RZ coded 2/sup 23/-1 long 500 Mb/s PRBS.