cssi/SciSciGPT-SciSciNet huggingface dataset:
A pathology of asymptotic multiplicity in the relative setting

We point out an example of a projective family $\pi : X \to S$, a $\pi$-pseudoeffective divisor $D$ on $X$, and a subvariety $V \subset X$ for which the asymptotic multiplicity $\sigma_V(D;X/S)$ is infinite. This shows that the divisorial Zariski decomposition is not always defined for pseudoeffective divisors in the relative setting.
Dimension reduction for polynomials over gaussian space and applications

We introduce a new technique for reducing the dimension of the ambient space of low-degree polynomials in the Gaussian space while preserving their relative correlation structure. As an application, we obtain an explicit upper bound on the dimension of an e-optimal noise-stable Gaussian partition. In fact, we address the more general problem of upper bounding the number of samples needed to e-approximate any joint distribution that can be non-interactively simulated from a correlated Gaussian source. Our results significantly improve (from Ackermann-like to "merely" exponential) the upper bounds recently proved on the above problems by De, Mossel & Neeman [CCC 2017, SODA 2018 resp.] and imply decidability of the larger alphabet case of the gap non-interactive simulation problem posed by Ghazi, Kamath & Sudan [FOCS 2016].Our technique of dimension reduction for low-degree polynomials is simple and can be seen as a generalization of the Johnson-Lindenstrauss lemma and could be of independent interest.
The Coherent-Constructible Correspondence for Toric Deligne-Mumford Stacks

We extend our previous work [19] on coherent-constructible correspondence for toric varieties to include toric Deligne-Mumford (DM) stacks. Following Borisov-Chen-Smith [9], a toric DM stack XΣ is described by a “stacky fan” Σ = (N,Σ, β), where N is a finitely generated abelian group and Σ is a simplicial fan in NR = N ⊗Z R. From Σ we define a conical Lagrangian ΛΣ inside the cotangent T ∗MR of the dual vector space MR of NR, such that torus-equivariant, coherent sheaves on XΣ are equivalent to constructible sheaves on MR with singular support in ΛΣ. The microlocalization theorem of Nadler and the last author [40, 38] relates constructible sheaves on MR to a Fukaya category on the cotangent T ∗MR, giving a version of homological mirror symmetry for toric DM stacks.
Techniques for effective vocabulary selection.

The vocabulary of a continuous speech recognition (CSR) system is a significant factor in determining its performance. In this paper, we present three principled approaches to select the target vocabulary for a particular domain by trading off between the target out-of-vocabulary (OOV) rate and vocabulary size. We evaluate these approaches against an ad-hoc baseline strategy. Results are presented in the form of OOV rate graphs plotted against increasing vocabulary size for each technique.
Non-conventional ergodic averages for several commuting actions of an amenable group

Let (X, μ) be a probability space, G a countable amenable group, and (Fn)n a left Folner sequence in G. This paper analyzes the non-conventional ergodic averages $$\frac{1}{{\left| {{F_n}} \right|}}\sum\limits_{g \in {F_n}} {\mathop \Pi \limits_{i = 1}^d } \left( {{f_i} \circ T_1^g \cdots T_i^g} \right)$$ associated to a commuting tuple of μ-preserving actions \({T_1}, \ldots {T_d}:G \curvearrowright X\) and f1,..., fd ∈ L∞(μ). We prove that these averages always converge in \({\left\| \cdot \right\|_2}\), and that they witness a multiple recurrence phenomenon when f1 =... = fd = 1A for a non-negligible set A ⊆ X. This proves a conjecture of Bergelson, McCutcheon and Zhang. The proof relies on an adaptation from earlier works of the machinery of sated extensions.
Online Combinatorial Optimization for Interconnected Refrigeration Systems: Linear Approximation and Submodularity

Abstract This paper proposes a new control method that can save the energy consumption of multi-case supermarket refrigerators by explicitly taking into account their interconnected and switched system dynamics. Its novelty is a bilevel combinatorial optimization formulation to generate ON/OFF control actions for expansion valves and compressors. The inner optimization module keeps display case temperatures in a desirable range and the outer optimization module minimizes energy consumption. In addition to its energy-saving capability, the proposed controller significantly reduces the frequency of compressor switchings by employing a conservative compressor control strategy. However, solving this bilevel optimization problem associated with interconnected and switched systems is a computationally challenging task. To solve the problem in near real time, we propose two approximation algorithms that can solve both the inner and outer optimization problems at once. The first algorithm uses a linear approximation, and the second is based on the submodular structure of the optimization problem. Both are (polynomial-time) scalable algorithms and generate near-optimal solutions with performance guarantees. Our work complements existing optimization-based control methods (e.g., MPC) for commercial refrigerators, as our algorithms can be adopted as a tool for solving combinatorial optimization problems arising in these methods.
Super-isolated abelian varieties

Abstract We call an abelian variety over a finite field F q super-isolated if its ( F q -rational) isogeny class contains a single isomorphism class. In this paper, we use the Honda-Tate theorem to characterize super-isolated ordinary simple abelian varieties by certain algebraic integers. Our main result is that for a fixed dimension g ≥ 3 , there are finitely many such varieties over all finite fields.
A classification of small operators using graph theory

Given a real $$n \times m$$ matrix B, its operator norm can be defined as $$\begin{aligned} |B|=\max _{|v|=1}|Bv|. \end{aligned}$$We consider a matrix “small” if it has non-negative integer entries and its operator norm is less than 2. These matrices correspond to bipartite graphs with spectral radius less than 2, which can be classified as disjoint unions of Coxeter graphs. This gives a direct route to an ADE-classification result in terms of very basic mathematical objects. Our goal here is to see these results as part of a general program of classification of small objects, relating quadratic forms, reflection groups, root systems, and Lie algebras.
Simulation optimization of risk measures with adaptive risk levels

Optimizing risk measures such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) of a general loss distribution is usually difficult, because (1) the loss function might lack structural properties such as convexity or differentiability since it is often generated via black-box simulation of a stochastic system; (2) evaluation of risk measures often requires rare-event simulation, which is computationally expensive. In this paper, we study the extension of the recently proposed gradient-based adaptive stochastic search to the optimization of risk measures VaR and CVaR. Instead of optimizing VaR or CVaR at the target risk level directly, we incorporate an adaptive updating scheme on the risk level, by initializing the algorithm at a small risk level and adaptively increasing it until the target risk level is achieved while the algorithm converges at the same time. This enables us to adaptively reduce the number of samples required to estimate the risk measure at each iteration, and thus improving the overall efficiency of the algorithm.
Towards Predicting Efficient and Anonymous Tor Circuits

The Tor anonymity system provides online privacy for millions of users, but it is slower than typical web browsing. To improve Tor performance, we propose PredicTor, a path selection technique that uses a Random Forest classifier trained on recent measurements of Tor to predict the performance of a proposed path. If the path is predicted to be fast, then the client builds a circuit using those relays. We implemented PredicTor in the Tor source code and show through live Tor experiments and Shadow simulations that PredicTor improves Tor network performance by 11% to 23% compared to Vanilla Tor and by 7% to 13% compared to the previous state-of-the-art scheme. Our experiments show that PredicTor is the first path selection algorithm to dynamically avoid highly congested nodes during times of high congestion and avoid long-distance paths during times of low congestion. We evaluate the anonymity of PredicTor using standard entropy-based and time-to-first-compromise metrics, but these cannot capture the possibility of leakage due to the use of location in path selection. To better address this, we propose a new anonymity metric called CLASI: Client Autonomous System Inference. CLASI is the first anonymity metric in Tor that measures an adversary's ability to infer client Autonomous Systems (ASes) by fingerprinting circuits at the network, country, and relay level. We find that CLASI shows anonymity loss for location-aware path selection algorithms, where entropy-based metrics show little to no loss of anonymity. Additionally, CLASI indicates that PredicTor has similar sender AS leakage compared to the current Tor path selection algorithm due to PredicTor building circuits that are independent of client location.
Extinction debt repayment via timely habitat restoration

Habitat destruction threatens the viability of many populations, but its full consequences can take considerable time to unfold. Much of the discourse surrounding extinction debts—the number of species that persist transiently following habitat loss, despite being headed for extinction—frames ultimate population crashes as the means of settling the debt. However, slow population decline also opens an opportunity to repay the debt by restoring habitat. The timing necessary for such habitat restoration to rescue a population from extinction has not been well studied. Here, we determine habitat restoration deadlines for a spatially implicit Levins/Tilman population model modified by an Allee effect. We find that restoration deadlines depend nonlinearly on initial, temporary, and final habitat destruction values. Conditions that hinder detection of an extinction debt also provide forgiving restoration timeframes. Our results highlight the importance of transient dynamics in restoration and suggest the beginnings of an analytic theory to understand outcomes of temporary press perturbations in a broad class of ecological systems.
Machine learning with operational costs

This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us to explore the range of operational costs associated with the set of reasonable statistical models, so as to provide a useful way for practitioners to understand uncertainty. To do this, the operational cost is cast as a regularization term in a learning algorithm's objective function, allowing either an optimistic or pessimistic view of possible costs, depending on the regularization parameter. From another perspective, if we have prior knowledge about the operational cost, for instance that it should be low, this knowledge can help to restrict the hypothesis space, and can help with generalization. We provide a theoretical generalization bound for this scenario. We also show that learning with operational costs is related to robust optimization.
Quickest change detection approach to optimal control in Markov decision processes with model changes

Optimal control in non-stationary Markov decision processes (MDP) is a challenging problem. The aim in such a control problem is to maximize the long-term discounted reward when the transition dynamics or the reward function can change over time. When a prior knowledge of change statistics is available, the standard Bayesian approach to this problem is to reformulate it as a partially observable MDP (POMDP) and solve it using approximate POMDP solvers, which are typically computationally demanding. In this paper, the problem is analyzed through the viewpoint of quickest change detection (QCD), a set of tools for detecting a change in the distribution of a sequence of random variables. Current methods applying QCD to such problems only passively detect changes by following prescribed policies, without optimizing the choice of actions for long term performance. We demonstrate that ignoring the reward-detection trade-off can cause a significant loss in long term rewards, and propose a two threshold switching strategy to solve the issue. A non-Bayesian problem formulation is also proposed for scenarios where a Bayesian formulation cannot be defined. The performance of the proposed two threshold strategy is examined through numerical analysis on a non-stationary MDP task, and the strategy outperforms the state-of-the-art QCD methods in both Bayesian and non-Bayesian settings.
Long-time existence for Yang–Mills flow

We establish that finite-time singularities do not occur in four-dimensional Yang–Mills flow, confirming the conjecture of Schlatter et al. (Am J Math 120:117–128, 1998). The proof relies on a weighted energy identity and sharp decay estimates in the neck region.
Attacks meet interpretability: attribute-steered detection of adversarial samples

Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors. Recent research has demonstrated the widespread presence and the devastating consequences of such attacks. Existing defense techniques either assume prior knowledge of specific attacks or may not work well on complex models due to their underlying assumptions. We argue that adversarial sample attacks are deeply entangled with interpretability of DNN models: while classification results on benign inputs can be reasoned based on the human perceptible features/attributes, results on adversarial samples can hardly be explained. Therefore, we propose a novel adversarial sample detection technique for face recognition models, based on interpretability. It features a novel bi-directional correspondence inference between attributes and internal neurons to identify neurons critical for individual attributes. The activation values of critical neurons are enhanced to amplify the reasoning part of the computation and the values of other neurons are weakened to suppress the uninterpretable part. The classification results after such transformation are compared with those of the original model to detect adversaries. Results show that our technique can achieve 94% detection accuracy for 7 different kinds of attacks with 9.91% false positives on benign inputs. In contrast, a state-of-the-art feature squeezing technique can only achieve 55% accuracy with 23.3% false positives.
Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations

Non-negative matrix factorization is a basic tool for decomposing data into the feature and weight matrices under non-negativity constraints, and in practice is often solved in the alternating minimization framework. However, it is unclear whether such algorithms can recover the ground-truth feature matrix when the weights for different features are highly correlated, which is common in applications. This paper proposes a simple and natural alternating gradient descent based algorithm, and shows that with a mild initialization it provably recovers the ground-truth in the presence of strong correlations. In most interesting cases, the correlation can be in the same order as the highest possible. Our analysis also reveals its several favorable features including robustness to noise. We complement our theoretical results with empirical studies on semi-synthetic datasets, demonstrating its advantage over several popular methods in recovering the ground-truth.
Non-Hamiltonian actions with isolated fixed points

We construct a non-Hamiltonian symplectic circle action on a closed, connected, six-dimensional symplectic manifold with exactly 32 fixed points.
A Pliable Lasso

We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of some prespecified modifying variables. These modifiers might be variables such ...
On r-uniform hypergraphs with circumference less than r

Abstract We show that for each k ≥ 4 and n > r ≥ k + 1 , every n -vertex hypergraph with edge sizes at least r and no Berge cycle of length at least k has at most ( k − 1 ) ( n − 1 ) r edges. The bound is exact, and we describe the extremal hypergraphs. This implies and slightly refines the theorem of Győri, Katona and Lemons that for n > r ≥ k ≥ 3 , every n -vertex r -uniform hypergraph with no Berge path of length k has at most ( k − 1 ) n r + 1 edges. To obtain the bounds, we study bipartite graphs with no cycles of length at least 2 k , and then translate the results into the language of multi-hypergraphs.
Tris(pyrazolyl)borate Copper Hydroxide Complexes Featuring Tunable Intramolecular H-Bonding.

A modular synthesis provides access to a series of new tris(pyrazolyl)borate ligands XpyMeTpK that possess a single functionalized pendant pyridyl (py) or pyrimidyl (pyd) arm designed to engage in ...
Orbit spaces of linear circle actions

In this paper, it is shown that non-isomorphic effective linear circle actions yield non-diffeomorphic differential structures on the corresponding orbit spaces.
Real-time Person Re-identification at the Edge: A Mixed Precision Approach

A critical part of multi-person multi-camera tracking is person re-identification (re-ID) algorithm, which recognizes and retains identities of all detected unknown people throughout the video stream. Many re-ID algorithms today exemplify state of the art results, but not much work has been done to explore the deployment of such algorithms for computation and power constrained real-time scenarios. In this paper, we study the effect of using a light-weight model, MobileNet-v2 for re-ID and investigate the impact of single (FP32) precision versus half (FP16) precision for training on the server and inference on the edge nodes. We further compare the results with the baseline model which uses ResNet-50 on state of the art benchmarks including CUHK03, Market-1501, and Duke-MTMC. The MobileNet-V2 mixed precision training method can improve both inference throughput on the edge node, and training time on server 3.25\(\times \) reaching to 27.77 fps and 1.75\(\times \), respectively and decreases power consumption on the edge node by 1.45\(\times \), while it deteriorates accuracy only 5.6% in respect to ResNet-50 single precision on the average for three different datasets. The code and pre-trained networks are publicly available. (https://github.com/TeCSAR-UNCC/person-reid)
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems

The recent drive towards achieving greater autonomy and intelligence in robotics has led to high levels of complexity. Autonomous robots increasingly depend on third-party off-the-shelf components and complex machine-learning techniques. This trend makes it challenging to provide strong design-time certification of correct operation. To address these challenges, we present SOTER, a robotics programming framework with two key components: (1) a programming language for implementing and testing high-level reactive robotics software, and (2) an integrated runtime assurance (RTA) system that helps enable the use of uncertified components, while still providing safety guarantees. SOTER provides language primitives to declaratively construct a RTA module consisting of an advanced, high-performance controller (uncertified), a safe, lower-performance controller (certified), and the desired safety specification. The framework provides a formal guarantee that a well-formed RTA module always satisfies the safety specification, without completely sacrificing performance by using higher performance uncertified components whenever safe. SOTER allows the complex robotics software stack to be constructed as a composition of RTA modules, where each uncertified component is protected using a RTA module. To demonstrate the efficacy of our framework, we consider a real-world case-study of building a safe drone surveillance system. Our experiments both in simulation and on actual drones show that the SOTER-enabled RTA ensures the safety of the system, including when untrusted third-party components have bugs or deviate from the desired behavior.
Corporate Liquidity and NBER Recession Announcements

Using announcement memos released by the National Bureau of Economic Research (NBER), we show that corporations increase liquidity during the quarter the NBER announces a peak in the business cycle. This reaction is primarily restricted to memos about peaks in the business cycle, whether it is a preliminary announcement or an official confirmation. Federal Open Market Committee and other monetary policy news, real-time data releases, and tightening credit conditions do not drive the increase. This finding adds to the precautionary cash holdings literature by suggesting that corporations adjust liquidity not necessarily before recessions, but upon confirmation of a recession.
On the Lateral Instability Analysis of MEMS Comb-Drive Electrostatic Transducers

This paper investigates the lateral pull-in effect of an in-plane overlap-varying transducer. The instability is induced by the translational and rotational displacements. Based on the principle of virtual work, the equilibrium conditions of force and moment in lateral directions are derived. The analytical solutions of the critical voltage, at which the pull-in phenomenon occurs, are developed when considering only the translational stiffness or only the rotational stiffness of the mechanical spring. The critical voltage in a general case is numerically determined by using nonlinear optimization techniques, taking into account the combined effect of translation and rotation. The influences of possible translational offsets and angular deviations to the critical voltage are modeled and numerically analyzed. The investigation is then expanded for the first time to anti-phase operation mode and Bennet’s doubler configuration of the two transducers.
Moving beyond histogram bin frequencies as prima facie evidence of earnings management (or a discussion of “Understanding the ‘numbers game’”)

Bird, Karolyi, and Ruchti (2019) estimate a structural model of earnings management in the setting of meeting-or-beating the analyst consensus forecast. I provide an overview of their methods and findings, and then discuss the assumptions, benefits, and limitations of their approach.
The effect of soil temperature seasonality on climate reconstructions from paleosols

Paleosols comprise an important archive of continental paleoclimate information throughout geologic history, but resolving temperature seasonality poses a challenge to the application of paleosol-derived temperature proxies. Not only does seasonality exert a strong control on the biosphere, but it can also obfuscate accurate interpretation of proxy records of paleotemperature. In order to examine the effect temperature seasonality has on paleosol temperature proxies, soil temperature data were compiled from over 200 stations that comprise the NRCS Soil Climate Analysis Network. Observed and modeled seasonal soil temperature variations were then compared to quantify system behavior. Greater than predicted soil temperature seasonality is observed at nearly half of the sites, driven primarily by direct heating of the soil surface by solar radiation. The ground-heating effect becomes more pronounced at sites receiving less than 600 mm of annual precipitation, with an average effect > 4 °C when mean annual precipitation falls below 300 mm. Large ground heating effects cannot be presumed for all carbonate-bearing paleosols, but the effect can be approximated when combined with paleo-precipitation estimates. Approximately two-fifths of sites record less temperature variation than predicted. This reduction in soil temperature seasonality is a result of warmer cold-season soil temperatures, driven by processes such as snow cover insulation and explains why pedotransfer functions break down below mean annual air temperatures (MAAT) of 6 to 8 °C. Clumped isotope measurements of pedogenic carbonate from a stacked series of late Eocene paleosols from Northeastern Spain are also examined to demonstrate how the documented seasonal trends in modern soils can inform paleo-applications, by considering carbonate formation depth and predicted ground heating effect. These paleosol results are best explained by a MAAT of ∼27 °C with annual temperature seasonality of 25 °C.
Slow Mixing of Glauber Dynamics for the Six-Vertex Model in the Ordered Phases

The six-vertex model in statistical physics is a weighted generalization of the ice model on $\mathbb{Z}^2$ (i.e., Eulerian orientations) and the zero-temperature three-state Potts model (i.e., proper three-colorings). The phase diagram of the model depicts its physical properties and suggests where local Markov chains will be efficient. In this paper, we analyze the mixing time of Glauber dynamics for the six-vertex model in the ordered phases. Specifically, we show that for all Boltzmann weights in the ferroelectric phase, there exist boundary conditions such that local Markov chains require exponential time to converge to equilibrium. This is the first rigorous result bounding the mixing time of Glauber dynamics in the ferroelectric phase. Our analysis demonstrates a fundamental connection between correlated random walks and the dynamics of intersecting lattice path models (or routings). We analyze the Glauber dynamics for the six-vertex model with free boundary conditions in the antiferroelectric phase and significantly extend the region for which local Markov chains are known to be slow mixing. This result relies on a Peierls argument and novel properties of weighted non-backtracking walks.
Genome of the Parasitoid Wasp Diachasma alloeum, an Emerging Model for Ecological Speciation and Transitions to Asexual Reproduction.

Parasitoid wasps are among the most speciose animals, yet have relatively few available genomic resources. We report a draft genome assembly of the wasp Diachasma alloeum (Hymenoptera: Braconidae), a host-specific parasitoid of the apple maggot fly Rhagoletis pomonella (Diptera: Tephritidae), and a developing model for understanding how ecological speciation can "cascade" across trophic levels. Identification of gene content confirmed the overall quality of the draft genome, and we manually annotated ∼400 genes as part of this study, including those involved in oxidative phosphorylation, chemosensation, and reproduction. Through comparisons to model hymenopterans such as the European honeybee Apis mellifera and parasitoid wasp Nasonia vitripennis, as well as a more closely related braconid parasitoid Microplitis demolitor, we identified a proliferation of transposable elements in the genome, an expansion of chemosensory genes in parasitoid wasps, and the maintenance of several key genes with known roles in sexual reproduction and sex determination. The D. alloeum genome will provide a valuable resource for comparative genomics studies in Hymenoptera as well as specific investigations into the genomic changes associated with ecological speciation and transitions to asexuality.
DNA Methyltransferase 1 Is Essential for and Uniquely Regulates Hematopoietic Stem and Progenitor Cells.

DNA methylation is essential for development and in diverse biological processes. The DNA methyltransferase Dnmt1 maintains parental cell methylation patterns on daughter DNA strands in mitotic cells; however, the precise role of Dnmt1 in regulation of quiescent adult stem cells is not known. To examine the role of Dnmt1 in adult hematopoietic stem cells (HSCs), we conditionally disrupted Dnmt1 in the hematopoietic system. Defects were observed in Dnmt1-deficient HSC self-renewal, niche retention, and in the ability of Dnmt1-deficient HSCs to give rise to multilineage hematopoiesis. Loss of Dnmt1 also had specific impact on myeloid progenitor cells, causing enhanced cell cycling and inappropriate expression of mature lineage genes. Dnmt1 regulates distinct patterns of methylation and expression of discrete gene families in long-term HSCs and multipotent and lineage-restricted progenitors, suggesting that Dnmt1 differentially controls these populations. These findings establish a unique and critical role for Dnmt1 in the primitive hematopoietic compartment.
Supply Chain Financing: Funding The Supply Chain And The Organization

This book is a comprehensive introduction to supply chain financing as a business model that enables companies to reduce costs, improve their working capital and manage risks more tightly. Supply chain financing is using the supply chain to fund the organization and using the organization to fund the supply chain. Supply chain financing is of growing importance, the book explains what supply chain funding is and its different components as well as its impact and potential not only on companies using it, but more globally. The content moves from the basics of supply chain management to how to structure a global supply chain finance program in today's marketplace, the emergence of fintech providers, and alternative methods of payment, while also offering a view of the future that incorporates new platforms and analytical tools to optimize efficiencies in an organization and increase working capital flows.
In Defense of the Lecture: Revisiting and Reassessing Its Place within Management Pedagogy

Long the staple or go-to technique in management education, instruction via the lecture has fallen on hard times. Dismissed as professorially heavy-handed and lacking creativity, the lecture has yi...
Geomorphic controls on shrub canopy volume and spacing of creosote bush in northern Mojave Desert, USA

Studies on the role of geomorphology on vegetation structure at the basin scale are rarely available and less likely in the future due to access, funding, and potential health risks. Our goal is to determine the primary abiotic controlling factor(s) on shrub canopy structure using a dataset of approximately 23 million individual shrubs, generated using remote sensing and ground-truthing by Young et al. (in Remote Sens 9(5):16, https://doi.org/10.3390/rs9050458 , 2017). We posit that landscape position and local-scale geomorphic features in a desert alluvial fan environment will influence canopy volume and shrub spacing of creosote bush Larrea tridentata. We relate selected characteristics (canopy volume and spatial distribution) of identified L. tridentata to aspect and surface geomorphology at each shrub location. Statistical analyses included K-S testing, distribution fitting, and several generalized linear models (GLMs). The study was located in Eldorado Valley, Nevada, USA. Aspect and surface age have demonstrable influences on both shrub canopy volume and shrub spacing for all 5 geomorphic surfaces studied, with the highest median canopy volumes on east-facing surface (0.758 m3) almost 5 × larger than the lowest median volumes (0.152 m3) on the WNW-facing surfaces; variability in shrub volume was also higher on east-facing than west-facing surfaces. Shrub spacing on alluvial flat and fan skirt surfaces (2.418 and 2.333 m, respectively) were larger than older alluvial fan, fan piedmont and fan remnant surfaces (1.776, 1.837 and 1.892 m, respectively). Results show a significant relationship between shrub spacing and canopy volume by aspect and geomorphic surface, indicating a threshold at which biotic effects on canopy structure from intra-species competition transition to abiotic effects governed by geomorphic and climatological factors.
Exploring Memory Persistency Models for GPUs

Given its high integration density, high speed, byte addressability, and low standby power, non-volatile or persistent memory is expected to supplement/replace DRAM as main memory. Through persistency programming model (which defines durability ordering of stores) and durable transaction constructs, the programmer can provide recoverable data structure (RDS) which allows programs to recover to a consistent state after a failure. While persistency models have been well studied for CPUs, they have been neglected for graphics processing units (GPUs). Considering the importance of GPUs as a dominant accelerator for high performance computing, we investigate persistency models for GPUs. GPU applications exhibit substantial differences with CPUs applications, hence in this paper we adapt, re-architect, and optimize CPU persistency models for GPUs. We design a pragma-based compiler scheme for expressing persistency model for GPUs. We identify that the thread hierarchy in GPUs offers intuitive scopes to form epochs and durable transactions. We find that undo logging produces significant performance overheads. We propose to use idempotency analysis to reduce both logging frequency and the size of logs. Through both real-system and simulation evaluations, we show low overheads of our proposed architecture support.
A new instability strip for hot degenerates

A new kind of variable star, designated as PG1159-035 is distinguished not only by the complete lack of hydrogen in its spectrum but also by an effective temperature that exceeds 8 x 10/sup 4/ K. The star does not fall near any of the known regions of instability in the HR diagram which suggests that the instability mechanism will not be helium and hydrogen ionization as in the Cepheid variables. The more unusual compositions are examined in order to discover the cause of the instability in PG1159-035. (GHT)
Mutual Information in Community Detection with Covariate Information and Correlated Networks

We study the problem of community detection when there is covariate information about the node labels and one observes multiple correlated networks. We provide an asymptotic upper bound on the per-node mutual information as well as a heuristic analysis of a multivariate performance measure called the MMSE matrix. These results show that the combined effects of seemingly very different types of information can be characterized explicitly in terms of formulas involving low-dimensional estimation problems in additive Gaussian noise. Our analysis is supported by numerical simulations.
Are those who believe in God really more prosocial

Whether those who believe in God are more prosocial has been a long debated topic. Here we shed new light on this question by examining giving in incentivized Dictator Games where no mention of rel...
Generalization error bounds of gradient descent for learning over-parameterized deep relu networks

Empirical studies show that gradient-based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. Very recently, a line of work explains in theory that with over-parameterization and proper random initialization, gradient-based methods can find the global minima of the training loss for DNNs. However, existing generalization error bounds are unable to explain the good generalization performance of over-parameterized DNNs. The major limitation of most existing generalization bounds is that they are based on uniform convergence and are independent of the training algorithm. In this work, we derive an algorithm-dependent generalization error bound for deep ReLU networks, and show that under certain assumptions on the data distribution, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small generalization error. Our work sheds light on explaining the good generalization performance of over-parameterized deep neural networks.
Teacher Anxiety Program for Elementary Students (TAPES): intervention development and proposed randomized controlled trial.

Excessive student anxiety is a common problem that severely impairs short- and long-term academic functioning and increases teacher burden. Reducing student anxiety has been associated with improvement in educational functioning. Because anxiety manifests daily in the classroom, teachers are in an ideal position to identify and help students manage their anxiety. Unfortunately, teachers lack the knowledge and skills to support the learning of students with excessive anxiety. The Teacher Anxiety Program for Elementary Students (TAPES), a novel teacher-administered school-home collaborative intervention, was designed to address this gap. This manuscript describes the protocol for developing and evaluating TAPES. Specifically, we present a description of: (1) the intervention and theoretical model; and (2) methods for the proposed randomized controlled trial comparing TAPES to a standard professional development seminar focused on reducing student anxiety. Primary aims examine the impact of the TAPES training on teacher knowledge and skill. Secondary aims examine the impact of TAPES on student outcomes. Exploratory aims will examine mediators based on our proposed theory of change. If effective, TAPES has the potential to directly benefit teachers (improving skills) and students (reducing anxiety and improving functioning). ClinicalTrials.gov, NCT03899948. Registered on 28 March 2019.
Efficient implementation of partitioned stiff exponential Runge-Kutta methods

Abstract Multiphysics systems are driven by multiple processes acting simultaneously, and their simulation leads to partitioned systems of differential equations. This paper studies the solution of partitioned systems of differential equations using exponential Runge-Kutta methods. We propose specific multiphysics implementations of exponential Runge-Kutta methods satisfying stiff order conditions that were developed in Hochbruck et al. (1998) [22] and Luan and Osterman (2014) [25] . We reformulate stiffly–accurate exponential Runge–Kutta methods in a way that naturally allows of the structure of multiphysics systems, and discuss their application to both component and additively partitioned systems. The resulting partitioned exponential methods only compute matrix functions of the Jacobians of individual components, rather than the Jacobian of the full, coupled system. We derive modified formulations of particular methods of order two, three and four, and apply them to solve a partitioned reaction-diffusion problem. The proposed methods retain full order for several partitionings of the discretized problem, including by components and by physical processes.
Robots that Take Advantage of Human Trust

Humans often assume that robots are rational. We believe robots take optimal actions given their objective; hence, when we are uncertain about what the robot’s objective is, we interpret the robot’s actions as optimal with respect to our estimate of its objective. This approach makes sense when robots straightforwardly optimize their objective, and enables humans to learn what the robot is trying to achieve. However, our insight is that–when robots are aware that humans learn by trusting that the robot actions are rational–intelligent robots do not act as the human expects; instead, they take advantage of the human’s trust, and exploit this trust to more efficiently optimize their own objective. In this paper, we formally model instances of human-robot interaction (HRI) where the human does not know the robot’s objective using a two-player game. We formulate different ways in which the robot can model the uncertain human, and compare solutions of this game when the robot has conservative, optimistic, rational, and trusting human models. In an offline linear-quadratic case study and a real-time user study, we show that trusting human models can naturally lead to communicative robot behavior, which influences end-users and increases their involvement.
Regulation of CSF and Brain Tissue Sodium Levels by the Blood-CSF and Blood-Brain Barriers During Migraine.

Cerebrospinal fluid (CSF) and brain tissue sodium levels increase during migraine. However, little is known regarding the underlying mechanisms of sodium homeostasis disturbance in the brain during the onset and propagation of migraine. Exploring the cause of sodium dysregulation in the brain is important, since correction of the altered sodium homeostasis could potentially treat migraine. Under the hypothesis that disturbances in sodium transport mechanisms at the blood-CSF barrier (BCSFB) and/or the blood-brain barrier (BBB) are the underlying cause of the elevated CSF and brain tissue sodium levels during migraines, we developed a mechanistic, differential equation model of a rat's brain to compare the significance of the BCSFB and the BBB in controlling CSF and brain tissue sodium levels. The model includes the ventricular system, subarachnoid space, brain tissue and blood. Sodium transport from blood to CSF across the BCSFB, and from blood to brain tissue across the BBB were modeled by influx permeability coefficients P BCSFB and P BBB , respectively, while sodium movement from CSF into blood across the BCSFB, and from brain tissue to blood across the BBB were modeled by efflux permeability coefficients P B C S F B ' and P B B B ' , respectively. We then performed a global sensitivity analysis to investigate the sensitivity of the ventricular CSF, subarachnoid CSF and brain tissue sodium concentrations to pathophysiological variations in P BCSFB , P BBB , P B C S F B ' and P B B B ' . Our results show that the ventricular CSF sodium concentration is highly influenced by perturbations of P BCSFB , and to a much lesser extent by perturbations of P B C S F B ' . Brain tissue and subarachnoid CSF sodium concentrations are more sensitive to pathophysiological variations of P BBB and P B B B ' than variations of P BCSFB and P B C S F B ' within 30 min of the onset of the perturbations. However, P BCSFB is the most sensitive model parameter, followed by P BBB and P B B B ' , in controlling brain tissue and subarachnoid CSF sodium levels within 3 h of the perturbation onset.
qBSA: Logic Design of a 32-bit Block-Skewed RSFQ Arithmetic Logic Unit

Single flux quantum (SFQ) circuits are an attractive beyond-CMOS technology because they promise two orders of magnitude lower power at clock frequencies exceeding 25 GHz. However, every SFQ gate is clocked creating very deep gate-level pipelines that are difficult to keep full, particularly for sequences that include data-dependent operations. This paper proposes to increase the throughput of SFQ pipelines by redesigning the datapath to accept and operate on least-significant bits (LSBs) clock cycles earlier than more significant bits. This skewed datapath approach reduces the latency of the LSB side which can be feedback earlier for use in subsequent data-dependent operations increasing their throughput. In particular, we propose to group the bits into 4-bit blocks that are operated on concurrently and create block-skewed datapath units for 32-bit operation. This skewed approach allows a subsequent data-dependent operation to start evaluating as soon as the first 4-bit block completes. Using this general approach, we develop a block-skewed MIPS-compatible 32-bit ALU. Our gate-level Verilog design improves the throughput of 32-bit data dependent operations by 2x and 1.5x compared to previously proposed 4-bit bit-slice and 32-bit Ladner-Fischer ALUs respectively. We have quantified the benefit of this design on instructions per cycle (IPC) for various RISC-V benchmarks assuming a range of non-ALU operation latencies from one to ten cycles. Averaging across benchmarks, our experimental results show that compared to the 32-bit Ladner-Fischer our proposed architecture provides a range of IPC improvements between 1.37x assuming one-cycle non-ALU latency to 1.2x assuming ten-cycle non-ALU latency. Moreover, our average IPC improvements compared to a 32-bit ALU based on the 4-bit bit-slice range from 2.93x to 4x.
On the dynamics characterization of complex projective spaces

We show that a closed weakly-monotone symplectic manifold of dimension 2n which has minimal Chern number greater than or equal to $$n+1$$ and admits a Hamiltonian toric pseudo-rotation is necessarily monotone and its quantum homology is isomorphic to that of the complex projective space. As a consequence when $$n=2$$, the manifold is symplectomorphic to $${\mathbb {C}}P^2$$.
Performance of dual‐channel codeless and semicodeless processing

While dual-channel codeless and semicodeless processing are mature techniques used on the Global Positioning System P(Y) signals, the literature lacks assessment of their performance, with derivations from first principles. This paper fills that gap, providing mathematical models of three dualchannel codeless or semicodeless processing approaches. Each exploits a situation where the same signal is transmitted on two carrier frequencies, and the receiver cannot generate a signal replica for conventional processing. The mathematical formulation leads to derivation of analytical models for performance of the three techniques, all having the same parametric form but different parameter values. The models apply to more general cases than previously addressed, allowing any relationship between input signal-to-noise ratios, and including different signal design variants. Computer simulations crosscheck the analytical performance, and comprehensive numerical results are provided. The results provide the foundation for a companion paper assessing how GPS III signal generation affects these types of receivers.
Field emission microscopy of carbon nanotube fibers: evaluating and interpreting spatial emission

In this work, the authors quantify field emission properties of cathodes made from carbon nanotube (CNT) fibers. The cathodes were arranged in different configurations to determine the effect of cathode geometry on the emission properties. Various geometries were investigated including (1) flat cut fiber tip, (2) folded fiber, (3) looped fiber, and (4) fibers wound around a cylinder. The authors employ a custom field emission microscope to quantify I-V characteristics in combination with laterally resolved field-dependent electron emission area. Additionally, they look at the very early emission stages, first when a CNT fiber is turned on for the first time, which is then followed by multiple ramp-up/down runs. Upon the first turn on, all fibers demonstrated limited and discrete emission area. During ramping runs, all CNT fibers underwent multiple (minor and/or major) breakdowns, which improved emission properties in that turn-on field decreased and field enhancement factor and emission area both increased. It is proposed that breakdowns are responsible for removing initially undesirable emission sites caused by stray fibers higher than average. This initial breakdown process gives way to a larger emission area that is created when the CNT fiber subcomponents unfold and align with the electric field. The authors' results form the basis for careful evaluation of CNT fiber cathodes for dc or low frequency pulsed power systems in which large uniform area emission is required or for narrow beam high frequency applications in which high brightness is a must.
Determinants of the combined use of external beam radiation therapy and brachytherapy for low-risk localized prostate cancer.

e16039 Background: Prostate cancer treatment patterns have been shown to vary by physician and patient characteristics. For low-risk localized prostate cancer patients, we examined the association ...