cssi/SciSciGPT-SciSciNet huggingface dataset:
One-time nitrogen fertilization shifts switchgrass soil microbiomes within a context of larger spatial and temporal variation

Soil microbiome responses to short-term nitrogen (N) inputs remain uncertain when compared with previous research that has focused on long-term fertilization responses. Here, we examined soil bacterial/archaeal and fungal communities pre- and post-N fertilization in an 8 year-old switchgrass field, in which twenty-four plots received N fertilization at three levels (0, 100, and 200 kg N ha-1 as NH4NO3) for the first time since planting. Soils were collected at two depths, 0-5 and 5-15 cm, for DNA extraction and amplicon sequencing of 16S rRNA genes and ITS regions for assessment of microbial community composition. Baseline assessments prior to fertilization revealed no significant pre-existing divergence in either bacterial/archaeal or fungal communities across plots. The one-time N fertilizations increased switchgrass yields and tissue N content, and the added N was nearly completely removed from the soil of fertilized plots by the end of the growing season. Both bacterial/archaeal and fungal communities showed large spatial (by depth) and temporal variation (by season) within each plot, accounting for 17 and 12-22% of the variation as calculated from the Sq. root of PERMANOVA tests for bacterial/archaeal and fungal community composition, respectively. While N fertilization effects accounted for only ~4% of overall variation, some specific microbial groups, including the bacterial genus Pseudonocardia and the fungal genus Archaeorhizomyces, were notably repressed by fertilization at 200 kg N ha-1. Bacterial groups varied with both depth in the soil profile and time of sampling, while temporal variability shaped the fungal community more significantly than vertical heterogeneity in the soil. These results suggest that short-term effects of N fertilization are significant but subtle, and other sources of variation will need to be carefully accounted for study designs including multiple intra-annual sampling dates, rather than one-time "snapshot" analyses that are common in the literature. Continued analyses of these trends over time with fertilization and management are needed to understand how these effects may persist or change over time.
Limits of Chromosome Compaction by Loop-Extruding Motors

A complex of proteins responsible for packing chromosomes into tight loops during cell division must have additional compacting capabilities not yet seen in experiments, a new model predicts.
Dimensionality in recurrent spiking networks: global trends in activity and local origins in connectivity

The dimensionality of a network’s collective activity is of increasing interest in neuroscience. This is because dimensionality provides a compact measure of how coordinated network-wide activity is, in terms of the number of modes (or degrees of freedom) that it can independently explore. A low number of modes suggests a compressed low dimensional neural code and reveals interpretable dynamics [1], while findings of high dimension may suggest flexible computations [2, 3]. Here, we address the fundamental question of how dimensionality is related to connectivity, in both autonomous and stimulus-driven networks. Working with a simple spiking network model, we derive three main findings. First, the dimensionality of global activity patterns can be strongly, and systematically, regulated by local connectivity structures. Second, the dimensionality is a better indicator than average correlations in determining how constrained neural activity is. Third, stimulus evoked neural activity interacts systematically with neural connectivity patterns, leading to network responses of either greater or lesser dimensionality than the stimulus.
Remarks on the energy of regular graphs

Abstract The energy of a graph is the sum of the absolute values of the eigenvalues of its adjacency matrix. This note is about the energy of regular graphs. It is shown that graphs that are close to regular can be made regular with a negligible change of the energy. Also a k -regular graph can be extended to a k -regular graph of a slightly larger order with almost the same energy. As an application, it is shown that for every sufficiently large n , there exists a regular graph G of order n whose energy ‖ G ‖ ⁎ satisfies ‖ G ‖ ⁎ > 1 2 n 3 / 2 − n 13 / 10 . Several infinite families of graphs with maximal or submaximal energy are given, and the energy of almost all regular graphs is determined.
Efficient optimization of perturbative gadgets

Perturbative gadgets are general techniques for reducing many-body spin interactions to two-body ones using perturbation theory. This allows for potential realization of effective many-body interactions using more physically viable two-body ones. In parallel with prior work (arXiv:1311.2555 [quant-ph]), here we consider minimizing the physical resource required for implementing the gadgets initially proposed by Kempe, Kitaev and Regev (arXiv:quant-ph/0406180) and later generalized by Jordan and Farhi (arXiv:0802.1874v4). The main innovation of our result is a set of methods that efficiently compute tight upper bounds to errors in the perturbation theory. We show that in cases where the terms in the target Hamiltonian commute, the bounds produced by our algorithm are sharp for arbitrary order perturbation theory. We provide numerics which show orders of magnitudes improvement over gadget constructions based on trivial upper bounds for the error term in the perturbation series. We also discuss further improvement of our result by adopting the Schrieffer-Wolff formalism of perturbation theory and supplement our observation with numerical results.
Universal Style Transfer via Feature Transforms

Universal style transfer aims to transfer arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality. In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles. The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network. The whitening and coloring transforms reflect direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer. We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods. We also analyze our method by visualizing the whitened features and synthesizing textures by simple feature coloring.
An Experimental Mathematics Approach to the Area Statistic of Parking Functions

We illustrate the experimental, empirical, approach to mathematics (that contrary to popular belief, is often rigorous), by using parking functions and their "area" statistic, as a case study. Our methods are purely finitistic and elementary, taking full advantage, of course, of our beloved silicon servants.
Reachability under uncertainty

In this paper we introduce a new network reachability problem where the goal is to find the most reliable path between two nodes in a network, represented as a directed acyclic graph. Individual edges within this network may fail according to certain probabilities, and these failure probabilities may depend on the values of one or more hidden variables. This problem may be viewed as a generalization of shortest-path problems for finding minimum cost paths or Viterbi-type problems for finding highest-probability sequences of states, where the addition of the hidden variables introduces correlations that are not handled by previous algorithms. We give theoretical results characterizing this problem including an NP-hardness proof. We also give an exact algorithm and a more efficient approximation algorithm for this problem.
Asymptotic invariants, complexity of groups and related problems

We survey results about computational complexity of the word problem in groups, Dehn functions of groups and related problems.
Yamabe Classification and Prescribed Scalar Curvature in the Asymptotically Euclidean Setting

We prove a necessary and sufficient condition for an asymptotically Euclidean manifold to be conformally related to one with specified nonpositive scalar curvature: the zero set of the desired scalar curvature must have a positive Yamabe invariant, as defined in the article. We show additionally how the sign of the Yamabe invariant of a measurable set can be computed from the sign of certain generalized "weighted" eigenvalues of the conformal Laplacian. Using the prescribed scalar curvature result we give a characterization of the Yamabe classes of asymptotically Euclidean manifolds. We also show that the Yamabe class of an asymptotically Euclidean manifold is the same as the Yamabe class of its conformal compactification.
Discriminants and automorphism groups of Veronese subrings of skew polynomial rings

We study important invariants and properties of the Veronese subalgebras of q-skew polynomial rings, including their discriminant, center and automorphism group, as well as cancellation property and the Tits alternative.
TASI Lectures on Non-supersymmetric BSM Models

These lectures provide a self-contained introduction to the essential aspects of non-supersymmetric beyond the Standard Model (BSM) physics for beginning graduate students who are already familiar with quantum field theory. After a detailed review of the physical meaning of the hierarchy problem, we introduce the key ingredients of the physics of Goldstone bosons necessary for many non-supersymmetric new physics models. Next we discuss the concept of collective symmetry breaking and present the main elements leading to little Higgs/composite Higgs models. We then turn to extra dimensional theories. After covering some of the basics of extra dimensional physics, we describe warped extra dimensions and explain how the AdS/CFT correspondence leads to realistic RS models and the holographic minimal composite Higgs model.
Adaptive MTD Security using Markov Game Modeling

Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker’s optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.
Limited-memory BFGS systems with diagonal updates

Abstract In this paper, we investigate a formula to solve systems of the form ( B + σ I ) x = y , where B is a limited-memory BFGS quasi-Newton matrix and σ is a positive constant. These types of systems arise naturally in large-scale optimization such as trust-region methods as well as doubly-augmented Lagrangian methods. We show that provided a simple condition holds on B 0 and σ , the system ( B + σ I ) x = y can be solved via a recursion formula that requires only vector inner products. This formula has complexity M 2 n , where M is the number of L-BFGS updates and n ≫ M is the dimension of x .
Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

The Internet of Things (IoT) will encompass a massive number of machine type devices that must wirelessly transmit, in near real-time, a diverse set of messages sensed from their environment. Designing resource allocation schemes to support such coexistent, heterogeneous communication is hence a key IoT challenge. In particular, there is a need for self-organizing resource allocation solutions that can account for unique IoT features, such as massive scale and stringent resource constraints. In this paper, a novel finite memory multistate sequential learning framework is proposed to enable diverse IoT devices to share limited communication resources, while transmitting both delay-tolerant, periodic messages and urgent, critical messages. The proposed learning framework enables the IoT devices to learn the number of critical messages and to reallocate the communication resources for the periodic messages to be used for the critical messages. Furthermore, the proposed learning framework explicitly accounts for IoT device limitations in terms of memory and computational capabilities. The convergence of the proposed learning framework is proved, and the lowest expected delay that the IoT devices can achieve using this learning framework is derived. Furthermore, the effectiveness of the proposed learning algorithm in IoT networks with different delay targets, network densities, probabilities of detection, and memory sizes is analyzed in terms of the probability of a successful random access (RA) request and percentage of devices that learned correctly. Simulation results show that, for a delay threshold of 1.25 ms, the average achieved delay is 0.71 ms and the delay threshold is satisfied with probability 0.87. Moreover, for a massive network, a delay threshold of 2.5 ms is satisfied with probability 0.92. The results also show that the proposed learning algorithm is very effective in reducing the delay of urgent, critical messages by intelligently reallocating the communication resources allocated to the delay-tolerant, periodic messages.
Analysis and Optimization of Aperture Design in Computational Imaging

There is growing interest in the use of coded aperture imaging systems for a variety of applications. Using an analysis framework based on mutual information, we examine the fundamental limits of such systems-and the associated optimum aperture coding-under simple but meaningful propagation and sensor models. Among other results, we show that when SNR is high and thermal noise dominates shot noise, spectrally-flat masks, which have 50% transmissivity, are optimal, but that when shot noise dominates thermal noise, randomly generated masks with lower transmissivity offer greater performance. We also provide comparisons to classical pinhole and lens-based cameras.
Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models

Abstract The spatio-temporal variation in the demand for transportation, particularly taxis, in the highly dynamic urban space of a metropolis such as New York City is impacted by various factors such as commuting, weather, road work and closures, disruptions in transit services, etc. This study endeavors to explain the user demand for taxis through space and time by proposing a generalized spatio-temporal autoregressive (STAR) model. It deals with the high dimensionality of the model by proposing the use of LASSO-type penalized methods for tackling parameter estimation. The forecasting performance of the proposed models is measured using the out-of-sample mean squared prediction error (MSPE), and the proposed models are found to outperform other alternative models such as vector autoregressive (VAR) models. The proposed modeling framework has an easily interpretable parameter structure and is suitable for practical application by taxi operators. The efficiency of the proposed model also helps with model estimation in real-time applications.
Stability of multiclass queueing networks under longest-queue and longest-dominating-queue scheduling

We consider the stability of robust scheduling policies for multiclass queueing networks. These are open networks with arbitrary routeing matrix and several disjoint groups of queues in which at most one queue can be served at a time. The arrival and potential service processes and routeing decisions at the queues are independent, stationary, and ergodic. A scheduling policy is called robust if it does not depend on the arrival and service rates nor on the routeing probabilities. A policy is called throughput-optimal if it makes the system stable whenever the parameters are such that the system can be stable. We propose two robust policies: longest-queue scheduling and a new policy called longest-dominating-queue scheduling. We show that longest-queue scheduling is throughput-optimal for two groups of two queues. We also prove the throughput-optimality of longest-dominating-queue scheduling when the network topology is acyclic, for an arbitrary number of groups and queues.
Fluctuation-dissipation relations for stochastic gradient descent

The notion of the stationary equilibrium ensemble has played a central role in statistical mechanics. In machine learning as well, training serves as generalized equilibration that drives the probability distribution of model parameters toward stationarity. Here, we derive stationary fluctuation-dissipation relations that link measurable quantities and hyperparameters in the stochastic gradient descent algorithm. These relations hold exactly for any stationary state and can in particular be used to adaptively set training schedule. We can further use the relations to efficiently extract information pertaining to a loss-function landscape such as the magnitudes of its Hessian and anharmonicity. Our claims are empirically verified.
Streaming Kernel Principal Component Analysis

Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time for large values of $n$. Techniques such as the Nystrom method and random feature maps can help towards this goal, but they do not explicitly maintain the basis vectors in a stream and take more space than desired. We propose a new approach for streaming KPCA which maintains a small set of basis elements in a stream, requiring space only logarithmic in $n$, and also improves the dependence on the error parameter. Our technique combines together random feature maps with recent advances in matrix sketching, it has guaranteed spectral norm error bounds with respect to the original kernel matrix, and it compares favorably in practice to state-of-the-art approaches.
A natural lower bound for the size of nodal sets

We prove that, for an n-dimensional compact Riemannian manifold (M,g), the (n−1)-dimensional Hausdorff measure |Zλ| of the zero-set Zλ of an eigenfunction eλ of the Laplacian having eigenvalue −λ, where λ≥1, and normalized by ∫ M|eλ|2dVg=1 satisfies C | Z λ | ≥ λ 1 2 ∫ M | e λ | d V g 2 for some uniform constant C. As a consequence, we recover the lower bound |Zλ|≳λ(3−n)∕4.
Engineering safety in machine learning

Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must consider the safety of systems involving machine learning. In this paper, we first discuss the definition of safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. Then we examine dimensions, such as the choice of cost function and the appropriateness of minimizing the empirical average training cost, along which certain real-world applications may not be completely amenable to the foundational principle of modern statistical machine learning: empirical risk minimization. In particular, we note an emerging dichotomy of applications: ones in which safety is important and risk minimization is not the complete story (we name these Type A applications), and ones in which safety is not so critical and risk minimization is sufficient (we name these Type B applications). Finally, we discuss how four different strategies for achieving safety in engineering (inherently safe design, safety reserves, safe fail, and procedural safeguards) can be mapped to the machine learning context through inter-pretability and causality of predictive models, objectives beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software.
Symmetric Stochastic Integrals with Respect to a Class of Self-similar Gaussian Processes

We study the asymptotic behavior of the $$\nu $$ -symmetric Riemann sums for functionals of a self-similar centered Gaussian process X with increment exponent $$0<\alpha <1$$ . We prove that, under mild assumptions on the covariance of X, the law of the weak $$\nu $$ -symmetric Riemann sums converge in the Skorohod topology when $$\alpha =(2\ell +1)^{-1}$$ , where $$\ell $$ denotes the largest positive integer satisfying $$\int _{0}^{1}x^{2j}\nu (\mathrm{d}x)=(2j+1)^{-1}$$ for all $$j=0,\dots , \ell -1$$ . In the case $$\alpha >(2\ell +1)^{-1}$$ , we prove that the convergence holds in probability.
Norm estimates for k-plane transforms and geometric inequalities

Abstract The article is devoted to remarkable interrelation between the norm estimates for k-plane transforms in weighted and unweighted L p spaces and geometric integral inequalities for cross-sections of measurable sets in R n . A similar interrelation is studied for more general j-plane to k-plane transforms on affine Grassmannians and their compact modifications. The article contains a series of new integral-geometric inequalities with sharp constants, explicit equalities, conjectures, and open problems.
Discrete Stochastic Submodular Maximization: Adaptive vs. Non-adaptive vs. Offline

We consider the problem of stochastic monotone submodular function maximization, subject to constraints. We give results on adaptivity gaps, and on the gap between the optimal offline and online solutions. We present a procedure that transforms a decision tree adaptive algorithm into a non-adaptive chain. We prove that this chain achieves at least $$\tau $$ times the utility of the decision tree, over a product distribution and binary state space, where $$\tau =\min _{i,j} \Pr [x_i=j]$$. This proves an adaptivity gap of $$\frac{1}{\tau }$$ which is $$2$$ in the case of a uniform distribution for the problem of stochastic monotone submodular maximization subject to state-independent constraints. For a cardinality constraint, we prove that a simple adaptive greedy algorithm achieves an approximation factor of $$1-\frac{1}{e^\tau }$$ with respect to the optimal offline solution; previously, it has been proven that the algorithm achieves an approximation factor of $$1-\frac{1}{e}$$ with respect to the optimal adaptive online solution. Finally, we show that there exists a non-adaptive solution for the stochastic max coverage problem that is within a factor $$1-\frac{1}{e}$$ of the optimal adaptive solution and within a factor of $$\tau 1-\frac{1}{e}$$ of the optimal offline solution.
Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes

We propose a sparse coefficient estimation and automated model selection procedure for autoregressive processes with heavy-tailed innovations based on penalized conditional maximum likelihood. Under mild moment conditions on the innovation processes, the penalized conditional maximum likelihood estimator satisfies a strong consistency, OP(N−1/2) consistency, and the oracle properties, where N is the sample size. We have the freedom in choosing penalty functions based on the weak conditions on them. Two penalty functions, least absolute shrinkage and selection operator and smoothly clipped average deviation, are compared. The proposed method provides a distribution-based penalized inference to AR models, which is especially useful when the other estimation methods fail or under perform for AR processes with heavy-tailed innovations [Feigin, Resnick. Pitfalls of fitting autoregressive models for heavy-tailed time series. Extremes. 1999;1:391–422]. A simulation study confirms our theoretical results. At the ...
A note on the quantum collision and set equality problems

A collision for a function f is two distinct inputs x1 ≠ x2 such that f outputs the same value on both inputs: f(x1) = f(x2). The quantum query complexity of finding collisions has been shown [9, 2, 4, 11] in some settings to be θ(N1/3); however, these results do not apply to random functions. The issues are two-fold. First, the Ω(N1/3) lower bound only applies when the domain is no larger than the co-domain, which precludes many of the cryptographically interesting applications. Second, most of the results in the literature only apply to r-to-1 functions, which are quite different from random functions. Understanding the collision problem for random functions is of great importance to cryptography, and we seek to fill the gaps of knowledge for this problem. To that end, we prove that, as expected, a quantum query complexity of θ(N1/3) holds for all interesting domain and co-domain sizes. Our proofs are simple, and combine existing techniques with several novel tricks to obtain the desired results. Using our techniques, we also give an optimal Ω(M1/3) lower bound for the set equality problem. This lower bound can be used to improve the relationship between classical randomized query complexity and quantum query complexity for so-called permutation-symmetric functions.
On almost everywhere divergence of Bochner–Riesz means on compact Lie groups

Let G be a connected, simply connected, compact semisimple Lie group of dimension n. It has been shown by Clerc (Ann Inst Fourier Grenoble 24(1):149–172, 1974) that, for any $$f\in L^1(G)$$ , the Bochner–Riesz mean $$S_R^\delta (f)$$ converges almost everywhere to f, provided $$\delta >(n-1)/2$$ . In this paper, we show that, at the critical index $$\delta =(n-1)/2$$ , there exists an $$f\in L^1(G)$$ such that $$\begin{aligned} \limsup _{R\rightarrow \infty } \big |S_{R}^{(n-1)/2}(f)(x)\big |=\infty ,\quad \ a.e.\ x\in G. \end{aligned}$$ This is an analogue of a well-known result of Kolmogoroff (Fund Math 4(1):324–328, 1923) for Fourier series on the circle, and a result of Stein (Ann Math 2(74):140–170, 1961) for Bochner–Riesz means on the tori $$\mathbb {T}^{n}, n\ge 2$$ . We also study localization properties of the Bochner–Riesz mean $$S_{R}^{(n-1)/2}(f)$$ for $$f\in L^1(G)$$ .
Multiscale homogenization in Kirchhoff's nonlinear plate theory

The interplay between multiscale homogenization and dimension reduction for nonlinear elastic thin plates is analyzed in the case in which the scaling of the energy corresponds to Kirchhoff's nonlinear bending theory for plates. Different limit models are deduced depending on the relative ratio between the thickness parameter h and the two homogenization scales e and e2.
On the connectivity of unions of random graphs

Graph-theoretic tools and techniques have seen wide use in the multi-agent systems literature, and the unpredictable nature of some multi-agent communications has been successfully modeled using random communication graphs. Across both network control and network optimization, a common assumption is that the union of agents' communication graphs is connected across any finite interval of some prescribed length, and some convergence results explicitly depend upon this length. Despite the prevalence of this assumption and the prevalence of random graphs in studying multi-agent systems, to the best of our knowledge, there has not been a study dedicated to determining how many random graphs must be in a union before it is connected. To address this point, this paper solves two related problems. The first bounds the number of random graphs required in a union before its expected algebraic connectivity exceeds the minimum needed for connectedness. The second bounds the probability that a union of random graphs is connected. The random graph model used is the Erdos-Renyi model, and, in solving these problems, we also bound the expectation and variance of the algebraic connectivity of unions of such graphs. Numerical results for several use cases are given to supplement the theoretical developments made.
Twisted Yangians and Finite W-Algebras

We construct an explicit set of generators for the finite W-algebras associated to nilpotent matrices in the symplectic or orthogonal Lie algebras whose Jordan blocks are all of the same size. We use these generators to show that such finite W-algebras are quotients of twisted Yangians.
Horizon Lines in the Wild

The horizon line is an important contextual attribute for a wide variety of image understanding tasks. As such, many methods have been proposed to estimate its location from a single image. These methods typically require the image to contain specific cues, such as vanishing points, coplanar circles, and regular textures, thus limiting their real-world applicability. We introduce a large, realistic evaluation dataset, Horizon Lines in the Wild (HLW), containing natural images with labeled horizon lines. Using this dataset, we investigate the application of convolutional neural networks for directly estimating the horizon line, without requiring any explicit geometric constraints or other special cues. An extensive evaluation shows that using our CNNs, either in isolation or in conjunction with a previous geometric approach, we achieve state-of-the-art results on the challenging HLW dataset and two existing benchmark datasets.
A Tight Lower Bound for Streett Complementation

Finite automata on infinite words (omega-automata) proved to be a powerful weapon for modeling and reasoning infinite behaviors of reactive systems. Complementation of omega-automata is crucial in many of these applications. But the problem is non-trivial; even after extensive study during the past two decades, we still have an important type of omega-automata, namely Streett automata, for which the gap between the current best lower bound 2^(Omega(n lg nk)) and upper bound 2^(O (nk lg nk)) is substantial, for the Streett index size k can be exponential in the number of states n. In a previous work we showed a construction for complementing Streett automata with the upper bound 2^(O(n lg n+nk lg k)) for k = O(n) and 2^(O(n^2 lg n)) for k = omega(n). In this paper we establish a matching lower bound 2^(Omega (n lg n+nk lg k)) for k = O(n) and 2^(Omega (n^2 lg n)) for k = omega(n), and therefore showing that the construction is asymptotically optimal with respect to the ^(Theta(.)) notation.
Network cloning using DNA barcodes

The connections between neurons determine the computations performed by both artificial and biological neural networks. Recently, we have proposed SYNSeq, a method for converting the connectivity of a biological network into a form that can exploit the tremendous efficiencies of high-throughput DNA sequencing. In SYNSeq, each neuron is tagged with a random sequence of DNA-a "barcode"-and synapses are represented as barcode pairs. SYNSeq addresses the analysis problem, reducing a network into a suspension of barcode pairs. Here, we formulate a complementary synthesis problem: How can the suspension of barcode pairs be used to "clone" or copy the network back into an uninitialized tabula rasa network? Although this synthesis problem might be expected to be computationally intractable, we find that, surprisingly, this problem can be solved efficiently, using only neuron-local information. We present the "one-barcode-one-cell" (OBOC) algorithm, which forces all barcodes of a given sequence to coalesce into the same neuron, and show that it converges in a number of steps that is a power law of the network size. Rapid and reliable network cloning with single-synapse precision is thus theoretically possible.
Capacity of molecular channels with imperfect particle-intensity modulation and detection

This work introduces the particle-intensity channel (PIC) as a model for molecular communication systems and characterizes the properties of the optimal input distribution and the capacity limits for this system. In the PIC, the transmitter encodes information, in symbols of a given duration, based on the number of particles released, and the receiver detects and decodes the message based on the number of particles detected during the symbol interval. In this channel, the transmitter may be unable to control precisely the number of particles released, and the receiver may not detect all the particles that arrive. We demonstrate that the optimal input distribution for this channel always has mass points at zero and the maximum number of particles that can be released. We then consider diffusive particle transport, derive the capacity expression when the input distribution is binary, and show conditions under which the binary input is capacity-achieving. In particular, we demonstrate that when the transmitter cannot generate particles at a high rate, the optimal input distribution is binary.
Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles

The energy efficiency of Connected and Automated Vehicles (CAVs) is significantly influenced by surrounding road users. This paper presents the evaluation of energy efficiency of CAVs in a mixed traffic interacted with human controlled vehicles. To simulate the interaction between the CAVs and the cut-in vehicles controlled by human drivers near the intersection, a lane changing model is proposed to emulate the politeness and patience characteristics of the human driver. The proposed lane changing model is then calibrated based on over 100,000 naturalistic lane changing events collected by the University of Michigan Safety Pilot Model Deployment Program. A case study on simulation of the cut-in scenario is carried out to demonstrate the human driver's lane changing sensitivity under different driving trajectories of a frontal CAV and the influence on the energy consumption of the CAV due to the cut-in vehicle is evaluated. The simulation results indicate that the fuel economy of the CAV can be substantially improved if its surrounding cut-in vehicles can be well handled.
Solute effects on interfacial dislocation emission in nanomaterials: Nucleation site competition and neutralization

Abstract Solutes added to stabilize nano-crystalline metals against grain growth, may segregate to grain boundaries and triple junctions where they can affect the process of the dislocation emission. We demonstrate that this effect can be very complex due to different rates of segregation at different interfaces. Moreover, at large concentrations, when the solutes form clusters, the interfaces between these clusters and the matrix can introduce new dislocation emission sources, which can be activated under lower applied stress. Thus, the strength maximum can occur at a certain solute concentration: adding solutes beyond this optimal concentration can reduce the strength of the material.
Optimal topology design for disturbance minimization in power grids

The transient response of power grids to external disturbances influences their stable operation. This paper studies the effect of topology in linear time-invariant dynamics of different power grids. For a variety of objective functions, a unified framework based on H 2 norm is presented to analyze the robustness to ambient fluctuations. Such objectives include loss reduction, weighted consensus of phase angle deviations, oscillations in nodal frequency, and other graphical metrics. The framework is then used to study the problem of optimal topology design for robust control goals of different grids. For radial grids, the problem is shown as equivalent to the hard “optimum communication spanning tree” problem in graph theory and a combinatorial topology construction is presented with bounded approximation gap. Extended to loopy (meshed) grids, a greedy topology design algorithm is discussed. The performance of the topology design algorithms under multiple control objectives are presented on both loopy and radial test grids. Overall, this paper analyzes topology design algorithms on a broad class of control problems in power grid by exploring their combinatorial and graphical properties.
The shape of random pattern-avoiding permutations

We initiate the study of limit shapes for random permutations avoiding a given pattern. Specifically, for patterns of length 3, we obtain delicate results on the asymptotics of distributions of positions of numbers in the permutations. We view the permutations as 0-1 matrices to describe the resulting asymptotics geometrically. We then apply our results to obtain a number of results on distributions of permutation statistics.
New Approaches to Final Cooling

A high-energy muon collider scenario require a “final cooling” system that reduces transverse emittance by a factor of about ~10 while allowing longitudinal emittance increase. The baseline approach has low-energy transverse cooling within high-field solenoids, with strong longitudinal heating. This approach and its recent simulation are discussed. Alternative approaches which more explicitly include emittance exchange are also presented. Round-to-flat beam transform, transverse slicing, and longitudinal bunch coalescence are possible components of the alternative approach. A more explicit understanding of solenoidal cooling beam dynamics is introduced.
The moduli space of maps with crosscaps: Fredholm theory and orientability

Just as a symmetric surface with separating fixed locus halves into two oriented bordered surfaces, an arbitrary symmetric surface halves into two oriented symmetric halfsurfaces, i.e. surfaces with crosscaps. Motivated in part by the string theory view of real Gromov-Witten invariants, we introduce moduli spaces of maps from surfaces with crosscaps, develop the relevant Fredholm theory, and resolve the orientability problem in this setting. In particular, we give an explicit formula for the holonomy of the orientation bundle of a family of real Cauchy-Riemann operators over Riemann surfaces with crosscaps. Special cases of our formulas are closely related to the orientability question for the space of real maps from symmetric Riemann surfaces to an almost complex manifold with an anti-complex involution and in fact resolve this question in genus 0. In particular, we show that the moduli space of real J-holomorphic maps from the sphere with a fixed-point free involution to a simply connected almost complex manifold with an even canonical class is orientable. In a sequel, we use the results of this paper to obtain a similar orientability statement for genus 1 real maps.
The Marr conjecture and uniqueness of wavelet transforms

The inverse question of identifying a function from the nodes (zeroes) of its wavelet transform arises in a number of fields. These include whether the nodes of a heat or hypoelliptic equation solution determine its initial conditions, and in mathematical vision theory the Marr conjecture, on whether an image is mathematically determined by its edge information. We prove a general version of this conjecture by reducing it to the moment problem, using a basis dual to the Taylor monomial basis $x^\alpha$ on $\mathbb {R}^n$.
Game-theoretic modeling of players’ ambiguities on external factors

Abstract We propose a game-theoretic framework that incorporates both incomplete information and general ambiguity attitudes on factors external to all players. Our starting point is players’ preferences on return-distribution vectors, essentially mappings from states of the world to distributions of returns to be received by players. There are two ways in which equilibria for this preference game can be defined. Also, when the preferences possess ever more features, we can gradually add more structures to the game. These include real-valued functions over return-distribution vectors, sets of probabilistic priors over states of the world, and eventually the traditional expected-utility framework involving one single prior. We establish equilibrium existence results, show the upper hemi-continuity of equilibrium sets over changing ambiguity attitudes, and uncover relations between the two versions of equilibria. Some attention is paid to the enterprising game, in which players exhibit ambiguity-seeking attitudes while betting optimistically on the favorable resolution of ambiguities. The two solution concepts are unified at this game’s pure equilibria, whose existence is guaranteed when strategic complementarities are present. The current framework can be applied to settings like auctions involving ambiguity on competitors’ assessments of item worths.
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models

Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retraining the model. By post-hoc learning latent constraints, value functions that identify regions in latent space that generate outputs with desired attributes, we can conditionally sample from these regions with gradient-based optimization or amortized actor functions. Combining attribute constraints with a universal "realism" constraint, which enforces similarity to the data distribution, we generate realistic conditional images from an unconditional variational autoencoder. Further, using gradient-based optimization, we demonstrate identity-preserving transformations that make the minimal adjustment in latent space to modify the attributes of an image. Finally, with discrete sequences of musical notes, we demonstrate zero-shot conditional generation, learning latent constraints in the absence of labeled data or a differentiable reward function. Code with dedicated cloud instance has been made publicly available (this https URL).
Metalorganic chemical vapor deposition of hexagonal boron nitride on (001) sapphire substrates for thermal neutron detector applications

Abstract This paper reports on the growth and characterization of hexagonal boron nitride (hBN) and its use for solid-state thermal neutron detection. The hBN epilayers were grown by metalorganic chemical vapor deposition on sapphire substrates at a temperature of 1350 °C. X-ray diffraction peak from the (002) hBN plane at a 2θ angle of 26.7° exhibited the c-lattice constant of 6.66 A for these films. A strong peak corresponding to the high frequency Raman active mode of hBN was found for the films at 1370.5 cm -1 hBN-based solid-state neutron detectors were fabricated and tested with a metal-semiconductor-metal configuration with an electrode spacing of 1 mm and hBN thickness of 2.5 μm. Fabricated detectors showed strong response to deep UV light as well. An intrinsic thermal neutron detection efficiency of 0.86% was measured, which is close to the theoretically expected efficiency of 0.87%. These results demonstrate that epitaxial hBN films are promising for thermal neutron detection applications.
Cone Schedules for Processing Systems in Fluctuating Environments

Consider a generalized processing system with several queues, where the available service rate combinations are fluctuating over time due to reliability and availability variations, and the scheduler must respond dynamically to the workload and service availability. We establish that cone schedules , which leverage the geometry of the queueing dynamics, maximize the system throughput for a broad class of processing systems, even under adversarial arrival processes.
Stochastic Block Transition Models for Dynamic Networks

There has been great interest in recent years on statistical models for dynamic networks. In this paper, I propose a stochastic block transition model (SBTM) for dynamic networks that is inspired by the well-known stochastic block model (SBM) for static networks and previous dynamic extensions of the SBM. Unlike most existing dynamic network models, it does not make a hidden Markov assumption on the edge-level dynamics, allowing the presence or absence of edges to directly inuence future edge probabilities while retaining the interpretability of the SBM. I derive an approximate inference procedure for the SBTM and demonstrate that it is signicantly better at reproducing durations of edges in real social network data.
Learning Compact Convolutional Neural Networks with Nested Dropout.

Recently, nested dropout was proposed as a method for ordering representation units in autoencoders by their information content, without diminishing reconstruction cost. However, it has only been applied to training fully-connected autoencoders in an unsupervised setting. We explore the impact of nested dropout on the convolutional layers in a CNN trained by backpropagation, investigating whether nested dropout can provide a simple and systematic way to determine the optimal representation size with respect to the desired accuracy and desired task and data complexity.
High Performance Zero-Memory Overhead Direct Convolutions

The computation of convolution layers in deep neural networks typically rely on high performance routines that trade space for time by using additional memory (either for packing purposes or required as part of the algorithm) to improve performance. The problems with such an approach are two-fold. First, these routines incur additional memory overhead which reduces the overall size of the network that can fit on embedded devices with limited memory capacity. Second, these high performance routines were not optimized for performing convolution, which means that the performance obtained is usually less than conventionally expected. In this paper, we demonstrate that direct convolution, when implemented correctly, eliminates all memory overhead, and yields performance that is between 10% to 400% times better than existing high performance implementations of convolution layers on conventional and embedded CPU architectures. We also show that a high performance direct convolution exhibits better scaling performance, i.e. suffers less performance drop, when increasing the number of threads.
Quiver generalization of a conjecture of King, Tollu, and Toumazet

Abstract Stretching the parameters of a Littlewood–Richardson coefficient of value 2 by a factor of n results in a coefficient of value n + 1 [12] , [9] , [19] . We give a geometric proof of a generalization for representations of quivers.
Sensing-Constrained LQG Control

Linear-Quadratic-Gaussian (LQG) control is concerned with the design of an optimal controller and estimator for linear Gaussian systems with imperfect state information. Standard LQG assumes the set of sensor measurements, to be fed to the estimator, to be given. However, in many problems, arising in networked systems and robotics, one may not be able to use all the available sensors, due to power or payload constraints, or may be interested in using the smallest subset of sensors that guarantees the attainment of a desired control goal. In this paper, we introduce the sensing-constrained LQG control problem, in which one has to jointly design sensing, estimation, and control, under given constraints on the resources spent for sensing. We focus on the realistic case in which the sensing strategy has to be selected among a finite set of possible sensing modalities. While the computation of the optimal sensing strategy is intractable, we present the first scalable algorithm that computes a near-optimal sensing strategy with provable sub-optimality guarantees. To this end, we show that a separation principle holds, which allows the design of sensing, estimation, and control policies in isolation. We conclude the paper by discussing two applications of sensing-constrained LQG control, namely, sensing-constrained formation control and resource-constrained robot navigation.