cssi/SciSciGPT-SciSciNet huggingface dataset:
Syntheses, structures, magnetic properties, and X-ray absorption spectra of carnotite-type uranyl chromium(V) oxides: A[(UO2)2Cr2O8](H2O)n (A = K2, Rb2, Cs2, Mg; N = 0, 4)

Four uranyl chromium(V) oxides were synthesized by mild hydrothermal methods and their structures were solved using single-crystal X-ray diffraction data. In these structures, U occurs in edge-sharing dimers of UO 7 uranyl pentagonal bipyramids, and Cr occurs in edge-sharing dimers of CrO 5 square pyramids. The two sets of dimers share edges, forming sheets of composition [(UO 2 ) 2 Cr 2 O 8 ] 2- that are topologically identical to uranyl divanadate sheets, [(UO 2 ) 2 V 2 O 8 ] 2- , that occur in compounds of the carnotite group. K 2 [(UO 2 ) 2 Cr 2 O 8 ] (KUCr), Rb 2 [(UO 2 ) 2 Cr 2 O 8 ] (RbUCr), and Cs 2 [(UO 2 ) 2 Cr 2 O 8 ] (CsUCr) are isostructural with K 2 [(UO 2 ) 2 V 2 O 8 ], and Mg[(UO 2 ) 2 Cr 2 O 8 ](H 2 O) 4 (MgUCr) is isostructural with Ni[(UO 2 ) 2 V 2 O 8 ]-(H 2 O) 4 . The stoichiometries of these compounds imply that Cr is present as chromium(V), which is supported by the results of bond valence analyses. EPR spectra and magnetic susceptibility data for KUCr are consistent with the presence of the d 1 cation chromium(V). The KUCr moments undergo a sharp transition from paramagnetic to antiferromagnetic behavior at T N = 18.5 K. XAFS data for the U L 3 edge and XANES of the Cr K edge of KUCr yield coordinations and oxidation states consistent with the crystal structure analysis. The presence of a pre-edge peak due solely to Cr 5+ sounds a cautionary note for XANES determination of the chromate(VI) content of environmental samples. Crystallographic data for KUCr, RbUCr, CsUCr: monoclinic space group P2 1 /c (No. 14), with a = 6.5483(8) A, b = 8.3548(10) A, c = 10.4200(13) A, β = 105.040(3)°; a = 6.8677(6) A, b = 8.3599(7) A, c = 10.4625(8) A, β = 106.004(2)°; and a = 7.2643(15) A, b = 8.3803(17) A, c = 10.5100(22) A, β = 106.399(5)°, respectively. Crystal data for MgUCr: orthorhombic space group Pnma (No. 62), with a = 10.5825(19) A, b = 15.1337(27) A, c = 8.1425(14) A.
A Cut/cohesin axis alters the chromatin landscape to facilitate neuroblast death

ABSTRACT Precise control of cell death in the nervous system is essential for development. Spatial and temporal factors activate the death of Drosophila neural stem cells (neuroblasts) by controlling the transcription of multiple cell death genes through a shared enhancer. The activity of this enhancer is controlled by abdominal A and Notch, but additional inputs are needed for proper specificity. Here, we show that the Cut DNA binding protein is required for neuroblast death, regulating reaper and grim downstream of the shared enhancer and of abdominal A expression. The loss of cut accelerates the temporal progression of neuroblasts from a state of low overall levels of H3K27me3 to a higher H3K27me3 state. This is reflected in an increase in H3K27me3 modifications in the cell death gene locus in the CNS on Cut knockdown. We also show that cut regulates the expression of the cohesin subunit Stromalin. Stromalin and the cohesin regulatory subunit Nipped-B are required for neuroblast death, and knockdown of Stromalin increases H3K27me3 levels in neuroblasts. Thus, Cut and cohesin regulate apoptosis in the developing nervous system by altering the chromatin landscape.
A practical preparation of aryl β-ketophosphonates

The condensation of alkyl phosphonates with aryl esters to give β-ketophosphonates may be carried out at elevated temperatures mediated by LiHMDS under Barbier type conditions. The reaction is scalable, does not require specialized cryogenic equipment, and is general for all aryl and heteroaryl esters examined.
Addition of Olefins to Aromatic Ketones Catalyzed by Rh(I) Olefin Complexes

The rhodium bis-olefin complex [C5Me5Rh(C2H3SiMe3)2], 1, has been shown to be a catalyst for the selective addition of olefins to the ortho position of aromatic ketones. The addition of vinyltrimethylsilane to benzophenone was studied by NMR spectroscopy, which indicated that 1 was the catalyst resting state for this process. This reaction was applied to a series of olefins (allyltrimethylsilane, 1-pentene, norbornene, 2,2‘-dimethyl-3-butene, cyclopentene, and vinyl ethyl ether) and aromatic ketones (benzophenone, 4,4‘-dimethoxybenzophenone, 3,3‘-bis(trifluoromethyl)benzophenone, dibenzosuberone, acetophenone, p-chloroacetophenone, and p-(trifluoromethyl)acetophenone). The dependence of the turnover frequency on substrate concentration was investigated. In the presence of excess ketone the formation of a benzophenone complex, 5, [(C5Me5Rh)2-η4-η4-C6H5C(O)Ph] was observed after consumption of olefin. Active catalyst is regenerated upon addition of olefin to 5. On the basis of kinetic experiments and labeli...
fastBMA: scalable network inference and transitive reduction.

Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/).
Functional Genetic Screen to Identify Interneurons Governing Behaviorally Distinct Aspects of Drosophila Larval Motor Programs

Drosophila larval crawling is an attractive system to study rhythmic motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors, including head casts, turning, and feeding. It is likely that some neurons (e.g., motor neurons) are used in all these behaviors, but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We used 165 Janelia CRM-Gal4 lines—chosen for sparse neuronal expression—to ectopically express the warmth-inducible neuronal activator TrpA1, and screened for locomotor defects. The primary screen measured forward locomotion velocity, and we identified 63 lines that had locomotion velocities significantly slower than controls following TrpA1 activation (28°). A secondary screen was performed on these lines, revealing multiple discrete behavioral phenotypes, including slow forward locomotion, excessive reverse locomotion, excessive turning, excessive feeding, immobile, rigid paralysis, and delayed paralysis. While many of the Gal4 lines had motor, sensory, or muscle expression that may account for some or all of the phenotype, some lines showed specific expression in a sparse pattern of interneurons. Our results show that distinct motor programs utilize distinct subsets of interneurons, and provide an entry point for characterizing interneurons governing different elements of the larval motor program.
Schmidt reaction in ionic liquids: highly efficient and selective conversion of aromatic and heteroaromatic aldehydes to nitriles with [BMIM(SO3H)][OTf] as catalyst and [BMIM][PF6] as solvent

A mild and selective method is presented for the conversion of aromatic and heteroaromatic aldehydes to nitriles via the Schmidt reaction with TMSN3 by using [BMIM(SO3H)][OTf] as catalyst and [BMIM][PF6] as solvent. The method offers high yields and simple product isolation, and avoids the use of liquid superacids or corrosive Lewis acids commonly employed for this transformation. It also offers some potential for recycling/reuse of the IL solvent.
Health systems have a role in preventing firearm injury

Every day in the United States, health professionals confront the effects of firearm injury. In emergency rooms, trauma centers, ambulatory offices, and acute care and rehabilitation facilities, health professionals and the health systems they work within attempt to heal the wounds that firearms can inflict on individuals and their families. Thus, hospitals and health systems are in a position to grasp the scope of the issue and find opportunities to prevent firearm injury and death. In 2016 and 2017, the physicians and nurses at Kaiser Permanente treated more than 3,400 gunshot victims. These patients—from a single health system among the hundreds that operate across the United States—are a sobering reminder of the toll of firearm injury. Firearm injury—including other-directed, self-directed, and unintentional injury and death—is a broad-based public health issue. In 2016, more than 38,000 people were killed by firearms.1 In the same year, half of those who died by suicide used a firearm, and three quarters of homicides involved a firearm.1 Firearm death is the third leading cause of death for children under age 17 in the United States. A social network analysis by Kalesan and colleagues2 showed that almost everyone in the United States, at some point, will know someone injured or killed by a firearm. The nature of the injury varies—homicide is the more likely cause of a firearm death in an urban area, whereas suicide is more likely in a rural area—but every community in every geographic region and every socioeconomic stratum is … Address correspondence to: Questions or comments should be directed to Melissa French at mfrench{at}nas.edu.
Stochastic non-preemptive co-flow scheduling with time-indexed relaxation

Co-flows model a modern scheduling setting that is commonly found in a variety of applications in distributed and cloud computing. A stochastic co-flow task contains a set of parallel flows with randomly distributed sizes. Further, many applications require non-preemptive scheduling of co-flow tasks. This paper gives an approximation algorithm for stochastic non-preemptive co-flow scheduling. The proposed approach uses a time-indexed linear relaxation, and uses its solution to come up with a feasible schedule. This algorithm is shown to achieve a competitive ratio of (2 log m+1)(1 + √mΔ)(1+m√Δ)(3 + Δ)/2 for zero-release times, and (2 log m+1)(1 + √mΔ)(1+m√Δ)(2 + Δ) for general release times, where A represents the upper bound of squared coefficient of variation of processing times, and m is the number of servers.
Self-supervised Audio-visual Co-segmentation

Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data. In this paper we develop a neural network model for visual object segmentation and sound source separation that learns from natural videos through self-supervision. The model is an extension of recently proposed work that maps image pixels to sounds [1]. Here, we introduce a learning approach to disentangle concepts in the neural networks, and assign semantic categories to network feature channels to enable independent image segmentation and sound source separation after audio-visual training on videos. Our evaluations show that the disentangled model outperforms several baselines in semantic segmentation and sound source separation.
RANDOMIZED SMOOTHING FOR STOCHASTIC OPTIMIZATION

We analyze convergence rates of stochastic optimization algorithms for nonsmooth convex optimization problems. By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates of stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for nonsmooth optimization. We give several applications of our results to statistical estimation problems and provide experimental results that demonstrate the effectiveness of the proposed algorithms. We also describe how a combination of our algorithm with recent work on decentralized optimization yields a distributed stochastic optimization algorithm that is order-optimal.
Minimizing content staleness in dynamo-style replicated storage systems

Consistency in data storage systems requires any read operation to return the most recent written version of the content. In replicated storage systems, consistency comes at the price of delay due to large-scale write and read operations. Many applications with low latency requirements tolerate data staleness in order to provide high availability and low operation latency. Using age of information as the staleness metric, we examine a data updating system in which real-time content updates are replicated and stored in a Dynamo-style quorum-based distributed system. A source sends updates to all the nodes in the system and waits for acknowledgements from the earliest subset of nodes, known as a write quorum. An interested client fetches the update from another set of nodes, defined as a read quorum. We analyze the staleness-delay tradeoff in replicated storage by varying the write quorum size. With a larger write quorum, an instantaneous read is more likely to get the latest update written by the source. However, the age of the content written to the system is more likely to become stale as the write quorum size increases. For shifted exponential distributed write delay, we derive the age optimized write quorum size that balances the likelihood of reading the latest update and the freshness of the latest update written by the source.
Using optimization to obtain a width-independent, parallel, simpler, and faster positive SDP solver

We study the design of polylogarithmic depth algorithms for approximately solving packing and covering semidefinite programs (or positive SDPs for short). This is a natural SDP generalization of the well-studied positive LP problem.Although positive LPs can be solved in polylogarithmic depth while using only log2n/e3 parallelizable iterations [4], the best known positive SDP solvers due to Jain and Yao [18] require log14n/e13 parallelizable iterations. Several alternative solvers have been proposed to reduce the exponents in the number of iterations [19, 30]. However, the correctness of the convergence analyses in these works has been called into question [30], as they both rely on algebraic monotonicity properties that do not generalize to matrix algebra.In this paper, we propose a very simple algorithm based on the optimization framework proposed in [4] for LP solvers. Our algorithm only needs log2n/e3 iterations, matching that of the best LP solver. To surmount the obstacles encountered by previous approaches, our analysis requires a new matrix inequality that extends Lieb-Thirring's inequality, and a sign-consistent, randomized variant of the gradient truncation technique proposed in [3, 4].
Grid Minors in Damaged Grids

We prove upper and lower bounds on the size of the largest square grid graph that is a subgraph, minor, or shallow minor of a graph in the form of a larger square grid from which a specified number of vertices have been deleted. Our bounds are tight to within constant factors. We also provide less-tight bounds on analogous problems for higher-dimensional grids.
Comparing Codimension and Absolute Length in Complex Reflection Groups

Reflection length and codimension of fixed point spaces induce partial orders on a complex reflection group. Motivated by connections to the algebraic structure of cohomology governing deformations of skew group algebras, we show that Coxeter groups and the infinite family G(m, 1, n) are the only irreducible complex reflection groups for which reflection length and codimension coincide. We then discuss implications for the degrees of generators of Hochschild cohomology. Along the way, we describe the codimension atoms for the infinite family G(m, p, n), give algorithms using character theory, and determine two-variable Poincare polynomials recording reflection length and codimension.
Elliptic actions on Teichmuller space

Let $S$ be an oriented surface of finite type, $\mathcal{MCG}(S)$ its mapping class group, and $\mathcal{T}(S)$ its Teichmuller space with the Teichmuller metric. Let $H \leq \mathcal{MCG}(S)$ be a finite subgroup and consider the subset of $\mathcal{T}(S)$ fixed by $H$, $\mathrm{Fix}(H) \subset \mathcal{T}(S)$. For any $R>0$, we prove that the set of points whose $H$-orbits have diameter bounded by $R$, $\mathrm{Fix}_R^T(H)$, lives in a bounded neighborhood of $\mathrm{Fix}(H)$. As an application, we show that the orbit of any point $X \in \mathcal{T}(S)$ under the action of a finite order mapping class has a fixed coarse barycenter. By contrast, we show that $\mathrm{Fix}^T_R(H)$ need not be quasiconvex with an explicit family of examples.
How Many Samples are Needed to Learn a Convolutional Neural Network

A widespread folklore for explaining the success of convolutional neural network (CNN) is that CNN is a more compact representation than the fully connected neural network (FNN) and thus requires fewer samples for learning. We initiate the study of rigorously characterizing the sample complexity of learning convolutional neural networks. We show that for learning an m-dimensional convolutional filter with linear activation acting on a d-dimensional input, the sample complexity of achieving population prediction error of ϵ is ˜ O (m/ϵ2) whereas its FNN counterpart needs at least Ω(d/ϵ2) samples. Since m≪d, this result demonstrates the advantage of using CNN. We further consider the sample complexity of learning a one-hidden-layer CNN with linear activation where both the m-dimensional convolutional filter and the r-dimensional output weights are unknown. For this model, we show the sample complexity is ˜ O ((m+r)/ϵ2) when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are localized empirical process and a new lemma characterizing the convolutional structure. We believe these tools may inspire further developments in understanding CNN.
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols

We describe an effort to annotate a corpus of natural language instructions consisting of 622 wet lab protocols to facilitate automatic or semi-automatic conversion of protocols into a machine-readable format and benefit biological research. Experimental results demonstrate the utility of our corpus for developing machine learning approaches to shallow semantic parsing of instructional texts. We make our annotated Wet Lab Protocol Corpus available to the research community.
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks

Deep neural networks are vulnerable to adversarial examples. Prior defenses attempted to make deep networks more robust by either changing the network architecture or augmenting the training set with adversarial examples, but both have inherent limitations. Motivated by recent research that shows outliers in the training set have a high negative influence on the trained model, we studied the relationship between model robustness and the quality of the training set. We first show that outliers give the model better generalization ability but weaker robustness. Next, we propose an adversarial example detection framework, in which we design two methods for removing outliers from training set to obtain the sanitized model and then detect adversarial example by calculating the difference of outputs between the original and the sanitized model. We evaluated the framework on both MNIST and SVHN. Based on the difference measured by Kullback-Leibler divergence, we could detect adversarial examples with accuracy between 94.67% to 99.89%.
Dihedral group frames with the Haar property

AbstractWe consider a unitary representation of the Dihedral group obtained by inducing the trivial character from the co-normal subgroup This representation is naturally realized as acting on the vector space We prove that the orbit of almost every vector in with respect to the Lebesgue measure has the Haar property (every subset of cardinality n of the orbit is a basis for ) if n is an odd integer. Moreover, we provide explicit sufficient conditions for vectors in whose orbits have the Haar property. Finally, we derive that the orbit of almost every vector in under the action of the representation has the Haar property if and only if n is odd. This completely settles a problem which was partially solved by the first author.
Totally reflexive modules over rings that are close to Gorenstein

Abstract Let S be a deeply embedded, equicharacteristic, Artinian Gorenstein local ring. We prove that if R is a non-Gorenstein quotient of S of small colength, then every totally reflexive R-module is free. Indeed, the second syzygy of the canonical module of R has a direct summand T which is a test module for freeness over R in the sense that if Tor + R ( T , N ) = 0 , for some finitely generated R-module N, then N is free.
Latent structured ranking

Many latent (factorized) models have been proposed for recommendation tasks like collaborative filtering and for ranking tasks like document or image retrieval and annotation. Common to all those methods is that during inference the items are scored independently by their similarity to the query in the latent embedding space. The structure of the ranked list (i.e. considering the set of items returned as a whole) is not taken into account. This can be a problem because the set of top predictions can be either too diverse (contain results that contradict each other) or are not diverse enough. In this paper we introduce a method for learning latent structured rankings that improves over existing methods by providing the right blend of predictions at the top of the ranked list. Particular emphasis is put on making this method scalable. Empirical results on large scale image annotation and music recommendation tasks show improvements over existing approaches.
Categorified invariants and the braid group

We investigate two "categorified" braid conjugacy class invariants, one coming from Khovanov homology and the other from Heegaard Floer homology. We prove that each yields a solution to the word problem but not the conjugacy problem in the braid group.
Robust Comparison of Kernel Densities on Spherical Domains

While spherical data arises in many contexts, including in directional statistics, the current tools for density estimation and population comparison on spheres are quite limited. Popular approaches for comparing populations (on Euclidean domains) mostly involve a two-step procedure: (1) estimate probability density functions (pdf s) from their respective samples, most commonly using the kernel density estimator, and (2) compare pdf s using a metric such as the $\mathbb {L}^{2}$ norm. However, both the estimated pdf s and their differences depend heavily on the chosen kernels, bandwidths, and sample sizes. Here we develop a framework for comparing spherical populations that is robust to these choices. Essentially, we characterize pdf s on spherical domains by quantifying their smoothness. Our framework uses a spectral representation, with densities represented by their coefficients with respect to the eigenfunctions of the Laplacian operator on a sphere. The change in smoothness, akin to using different kernel bandwidths, is controlled by exponential decays in coefficient values. Then we derive a proper distance for comparing pdf coefficients while equalizing smoothness levels, negating influences of sample size and bandwidth. This signifies a fair and meaningful comparisons of populations, despite vastly different sample sizes, and leads to a robust and improved performance. We demonstrate this framework using examples of variables on $\mathbb {S}^{1}$ and $\mathbb {S}^{2}$ , and evaluate its performance using a number of simulations and real data experiments.
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification

In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH). As these histology images are too large to fit into GPU memory, we first propose using Inception V3 to perform patch level classification. The patch level predictions are then passed through an ensemble fusion framework involving majority voting, gradient boosting machine (GBM), and logistic regression to obtain the image level prediction. We improve the sensitivity of the Normal and Benign predicted classes by designing a Dual Path Network (DPN) to be used as a feature extractor where these extracted features are further sent to a second layer of ensemble prediction fusion using GBM, logistic regression, and support vector machine (SVM) to refine predictions. Experimental results demonstrate our framework shows a 12.5\(\%\) improvement over the state-of-the-art model.
Symplectomorphic registration with phase space regularization by entropy spectrum pathways

Purpose The ability to register image data to a common coordinate system is a critical feature of virtually all imaging studies. However, in spite of the abundance of literature on the subject and the existence of several variants of registration algorithms, their practical utility remains problematic, as commonly acknowledged even by developers of these methods. Methods A new registration method is presented that utilizes a Hamiltonian formalism and constructs registration as a sequence of symplectomorphic maps in conjunction with a novel phase space regularization. For validation of the framework a panel of deformations expressed in analytical form is developed that includes deformations based on known physical processes in MRI and reproduces various distortions and artifacts typically present in images collected using these different MRI modalities. Results The method is demonstrated on the three different magnetic resonance imaging (MRI) modalities by mapping between high resolution anatomical (HRA) volumes, medium resolution diffusion weighted MRI (DW-MRI) and HRA volumes, and low resolution functional MRI (fMRI) and HRA volumes. Conclusions The method has shown an excellent performance and the panel of deformations was instrumental to quantify its repeatability and reproducibility in comparison to several available alternative approaches.
Universal joint image clustering and registration using partition information

The problem of joint clustering and registration of images is studied in a universal setting. We define universal joint clustering and registration algorithms using multivariate information functionals. We first study the problem of registering two images using maximum mutual information and prove its asymptotic optimality. We then show the shortcomings of pairwise registration in multi-image registration, and design an asymptotically optimal algorithm based on multi-information. Finally, we define a novel multivariate information functional to perform joint clustering and registration of images, and prove consistency of the algorithm.
Symplectic embeddings from concave toric domains into convex ones

Embedded contact homology gives a sequence of obstructions to four-dimensional symplectic embeddings, called ECH capacities. In “Symplectic embeddings into four-dimensional concave toric domains”, the author, Choi, Frenkel, Hutchings and Ramos computed the ECH capacities of all “concave toric domains”, and showed that these give sharp obstructions in several interesting cases. We show that these obstructions are sharp for all symplectic embeddings of concave toric domains into “convex” ones. In an appendix with Choi, we prove a new formula for the ECH capacities of convex toric domains, which shows that they are determined by the ECH capacities of a corresponding collection of balls.
Controlled collaboration for linear coherent estimation in wireless sensor networks

We consider a wireless sensor network consisting of multiple nodes that are coordinated by a fusion center (FC) in order to estimate a common signal of interest. In addition to being coordinated, the sensors are also able to collaborate, i.e., share observations with other neighboring nodes, prior to transmission. In an earlier work, we derived the energy-optimal collaboration strategy for the single-snapshot framework, where the inference has to be made based on observations collected at one particular instant. In this paper, we make two important contributions. Firstly, for the single-snapshot framework, we gain further insights into partially connected collaboration networks (nearest-neighbor and random geometric graphs for example) through the analysis of a family of topologies with regular structure. Secondly, we explore the estimation problem by adding the dimension of time, where the goal is to estimate a time-varying signal in a power-constrained network. To model the time dynamics, we consider the stationary Gaussian process with exponential covariance (sometimes referred to as Ornstein-Uhlenbeck process) as our representative signal. For such a signal, we show that it is always beneficial to sample as frequently as possible, despite the fact that the samples get increasingly noisy due to the power-constrained nature of the problem. Simulation results are presented to corroborate our analytical results.
Creating a Linked Data-Friendly Metadata Application Profile for Archival Description

The objective of this poster is to provide an overview of efforts to apply and extend Schema.org for archives and archival description. The authors see the application of Schema.org and extensions as a low barrier means to publish easily consumable linked data about archival resources, institutions that hold them, and contextual entities such as people and organizations responsible for their creation.
Efficient algorithms for large-scale generalized eigenvector computation and canonical correlation analysis

This paper considers the problem of canonical-correlation analysis, and more broadly, the generalized eigenvector problem for a pair of symmetric matrices. We consider the setting of finding top-k canonical/eigen subspace, and solve these problems through a general framework that simply requires black box access to an approximate linear system solver. Instantiating this framework with accelerated gradient descent we obtain a running time of O(zk√κ/ρ log(1/e) log (kκ/ρ)) where z is the total number of nonzero entries, κ is the condition number and ρ is the relative eigenvalue gap of the appropriate matrices. Our algorithm is linear in the input size and the number of components k up to a log(k) factor, which is essential for handling large-scale matrices that appear in practice. To the best of our knowledge this is the first such algorithm with global linear convergence. We hope that our results prompt further research improving the practical running time for performing these important data analysis procedures on large-scale data sets.
Dynamic Co-Simulation Methods for Combined Transmission-Distribution System With Integration Time Step Impact on Convergence

Combined transmission and distribution systems (CoTDS) simulation for power systems requires development of algorithms and software that are numerically stable and at the same time accurately simulate dynamic events that can occur in practical systems. The time scales of simulation can be orders of magnitude apart making the combined simulation extremely challenging. This has led to increased research in applying co-simulation techniques for integrated simulation of the two systems. In this paper, two methods for dynamic co-simulation of CoTDS are proposed using parallel and series computation of the transmission system and distribution systems. The convergence of numerical methods in co-simulation is addressed from a fundamental mathematical perspective and the impact of the integration time-step is studied on proposed CoTDS dynamic co-simulation methods. Both these co-simulation methods are validated against total system simulation in a single time-domain simulation environment. The proposed CoTDS co-simulation method is also validated against commercial EMTP dynamic simulation. CoTDS dynamic co-simulation is further demonstrated on a New England 39-bus transmission system with ten load buses replaced by distribution systems including a 5780-node distribution system on one load bus for a detailed fault-induced delayed voltage recovery simulation study.
Optimal sample complexity for stable matrix recovery

Tremendous efforts have been made to study the theoretical and algorithmic aspects of sparse recovery and low-rank matrix recovery. This paper establishes (near) optimal sample complexities for stable matrix recovery without constants or log factors. We treat sparsity, low-rankness, and other parsimonious structures within the same framework: constraint sets that have small covering numbers or Minkowski dimensions, which include notoriously challenging cases such as simultaneously sparse and low-rank matrices. We consider three types of random measurement matrices (unstructured, rank-1, and symmetric rank-1 matrices), following probability distributions that satisfy some mild conditions. In all these cases, we prove a fundamental achievability result - the recovery of matrices with parsimonious structures, using an optimal (or near optimal) number of measurements, is stable with high probability.
Zero cycles of degree one on principal homogeneous spaces

Abstract Let k be a field of characteristic different from 2. Let G be an absolutely simple, simply connected or adjoint semisimple algebraic k -group of classical type. We show that if a principal homogeneous space under G over k admits a zero cycle of degree 1 then it has a k -rational point.
Reconstruction Algorithms for a Class of Restricted Ray Transforms Without Added Singularities

Let X and \(X^*\) denote a restricted ray transform along curves and a corresponding backprojection operator, respectively. Theoretical analysis of reconstruction from the data Xf is usually based on a study of the composition \(X^* D X\), where D is some local operator (usually a derivative). If \(X^*\) is chosen appropriately, then \(X^* D X\) is a Fourier integral operator (FIO) with singular symbol. The singularity of the symbol leads to the appearance of artifacts (added singularities) that can be as strong as the original (or, useful) singularities. By choosing D in a special way one can reduce the strength of added singularities, but it is impossible to get rid of them completely. In the paper we follow a similar approach, but make two changes. First, we replace D with a nonlocal operator \(\tilde{D}\) that integrates Xf along a curve in the data space. The result \(\tilde{D} Xf\) resembles the generalized Radon transform R of f. The function \(\tilde{D} Xf\) is defined on pairs \((x_0,\Theta )\in U\times S^2\), where \(U\subset {\mathbb R}^3\) is an open set containing the support of f, and \(S^2\) is the unit sphere in \({\mathbb R}^3\). Second, we replace \(X^*\) with a backprojection operator \(R^*\) that integrates with respect to \(\Theta \) over \(S^2\). It turns out that if \(\tilde{D}\) and \(R^*\) are appropriately selected, then the composition \(R^* \tilde{D} X\) is an elliptic pseudodifferential operator of order zero with principal symbol 1. Thus, we obtain an approximate reconstruction formula that recovers all the singularities correctly and does not produce artifacts. The advantage of our approach is that by inserting \(\tilde{D}\) we get access to the frequency variable \(\Theta \). In particular, we can incorporate suitable cut-offs in \(R^*\) to eliminate bad directions \(\Theta \), which lead to added singularities.
Optimal CSMA-based wireless communication with worst-case delay and non-uniform sizes

Carrier Sense Multiple Access (CSMA) protocols have been shown to reach the full capacity region for data communication in wireless networks, with polynomial complexity. However, current literature achieves the throughput optimality with an exponential delay scaling with the network size, even in a simplified scenario for transmission jobs with uniform sizes. Although CSMA protocols with order-optimal average delay have been proposed for specific topologies, no existing work can provide worst-case delay guarantee for each job in general network settings, not to mention the case when the jobs have non-uniform lengths while the throughput optimality is still targeted. In this paper, we tackle on this issue by proposing a two-timescale CSMA-based data communication protocol with dynamic decisions on rate control, link scheduling, job transmis- sion and dropping in polynomial complexity. Through rigorous analysis, we demonstrate that the proposed protocol can achieve a throughput utility arbitrarily close to its offline optima for jobs with non-uniform sizes and worst-case delay guarantees, with a tradeoff of longer maximum allowable delay.
Two-stage algorithms for covering array construction

Modern software systems often consist of many different components, each with a number of options. Although unit tests may reveal faulty options for individual components, functionally correct components may interact in unforeseen ways to cause a fault. Covering arrays are used to test for interactions among components systematically. A two-stage framework, providing a number of concrete algorithms, is developed for the efficient construction of covering arrays. %Our framework divides the construction in two stages. In the first stage, a time and memory efficient randomized algorithm covers most of the interactions. In the second stage, a more sophisticated search covers the remainder in relatively few tests. In this way, the storage limitations of the sophisticated search algorithms are avoided; hence the range of the number of components for which the algorithm can be applied is extended, without increasing the number of tests. Many of the framework instantiations can be tuned to optimize a memory-quality trade-off, so that fewer tests can be achieved using more memory. The algorithms developed outperform the currently best known methods when the number of components ranges from 20 to 60, the number of options for each ranges from 3 to 6, and $t$-way interactions are covered for $t\in \{5,6\}$. In some cases a reduction in the number of tests by more than $50\%$ is achieved.
Lattices Related to Extensions of Presentations of Transversal Matroids

For a presentation $\mathcal{A}$ of a transversal matroid $M$, we study the ordered set $T_{\mathcal{A}}$ of single-element transversal extensions of $M$ that have presentations that extend $\mathcal{A}$; extensions are ordered by the weak order. We show that $T_{\mathcal{A}}$ is a distributive lattice, and that each finite distributive lattice is isomorphic to $T_{\mathcal{A}}$ for some presentation $\mathcal{A}$ of some transversal matroid $M$. We show that $T_{\mathcal{A}}\cap T_{\mathcal{B}}$, for any two presentations $\mathcal{A}$ and $\mathcal{B}$ of $M$, is a sublattice of both $T_{\mathcal{A}}$ and $T_{\mathcal{B}}$. We prove sharp upper bounds on $|T_{\mathcal{A}}|$ for presentations $\mathcal{A}$ of rank less than $r(M)$ in the order on presentations; we also give a sharp upper bound on $|T_{\mathcal{A}}\cap T_{\mathcal{B}}|$. The main tool we introduce to study $T_{\mathcal{A}}$ is the lattice $L_{\mathcal{A}}$ of closed sets of a certain closure operator on the lattice of subsets of $\{1,2,\ldots,r(M)\}$.
Proton Skins, Neutron Skins, and Proton Radii of Mirror Nuclei

We present predictions for proton skins based on isospin-asymmetric equations of state derived microscopically from high-precision chiral few-nucleon interactions. Moreover, we investigate the relation between the neutron skin of a nucleus and the difference between the proton radii of the corresponding mirror nuclei.
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure

Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging. In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of “concentration of measure” in metric measure spaces. We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations.One class of concentrated metric probability spaces are the so-called Levy families that include many natural distributions. In this special case, our attacks only need to perturb the test instance by at most O(√n) to make it misclassified, where n is the data dimension. Using our general result about Levy instance spaces, we first recover as special case some of the previously proved results about the existence of adversarial examples. However, many more Levy families are known (e.g., product distribution under the Hamming distance) for which we immediately obtain new attacks that find adversarial examples of distance O(√n).Finally, we show that concentration of measure for product spaces implies the existence of forms of “poisoning” attacks in which the adversary tampers with the training data with the goal of degrading the classifier. In particular, we show that for any learning algorithm that uses m training examples, there is an adversary who can increase the probability of any “bad property” (e.g., failing on a particular test instance) that initially happens with non-negligible probability to ≈ 1 by substituting only Oe(√m) of the examples with other (still correctly labeled) examples.
The Scott-Vogelius finite elements revisited

We prove that the Scott-Vogelius finite elements are inf-sup stable on shape-regular meshes for piecewise quartic velocity fields and higher ($k \ge 4$).
Simultaneous Sensor and Actuator Selection/Placement through Output Feedback Control

In most dynamic networks, it is impractical to measure all of the system states; instead, only a subset of the states are measured through sensors. Consequently, and unlike full state feedback controllers, output feedback control utilizes only the measured states to obtain a stable closed-loop performance. This paper explores the interplay between the selection of minimal number of sensors and actuators (SaA) that yield a stable closed-loop system performance. Through the formulation of the static output feedback control problem, we show that the simultaneous selection of minimal set of SaA is a combinatorial optimization problem with mixed-integer nonlinear matrix inequality constraints. To address the computational complexity, we develop two approaches: The first approach relies on integer/disjunctive programming principles, while the second approach is a simple algorithm that is akin to binary search routines. The optimality of the two approaches is also discussed. Numerical experiments are included showing the performance of the developed approaches.
Constraints on the spin evolution of young planetary-mass companions

Surveys of young star-forming regions have discovered a growing population of planetary-mass (<13 M_(Jup)) companions around young stars. There is an ongoing debate as to whether these companions formed like planets (that is, from the circumstellar disk), or if they represent the low-mass tail of the star-formation process3. In this study, we utilize high-resolution spectroscopy to measure rotation rates of three young (2–300 Myr) planetary-mass companions and combine these measurements with published rotation rates for two additional companions to provide a picture of the spin distribution of these objects. We compare this distribution to complementary rotation-rate measurements for six brown dwarfs with masses <20 M_(Jup), and show that these distributions are indistinguishable. This suggests that either these two populations formed via the same mechanism, or that processes regulating rotation rates are independent of formation mechanism. We find that rotation rates for both populations are well below their break-up velocities and do not evolve significantly during the first few hundred million years after the end of accretion. This suggests that rotation rates are set during the late stages of accretion, possibly by interactions with a circumplanetary disk. This result has important implications for our understanding of the processes regulating the angular momentum evolution of young planetary-mass objects, and of the physics of gas accretion and disk coupling in the planetary-mass regime.
A separating surface for Sitnikov-like n+1-body problems

Abstract We consider the generalized Sitnikov problem of Newtonian mechanics. For periodic, planar configurations of n bodies which are symmetric under rotation by a fixed angle, the z-axis is invariant. We consider the effect of placing a massless particle on the z-axis. The study of the motion of this particle can then be modeled as a time-dependent Hamiltonian System. We give a geometric construction of a surface in the three-dimensional phase space separating orbits for which the massless particle escapes to infinity from those for which it does not. A procedure for removing the periodicity condition of the planar configuration is outlined. The construction of the surface is demonstrated numerically in a few examples.
Collaborative High-Accuracy Localization in Mobile Multipath Environments

We study the problem of high-accuracy localization of mobile nodes in a multipath-rich environment where submeter accuracy values are required. We employ a peer-to-peer framework where nodes can get pairwise multipath-degraded ranging estimates in local neighborhoods, with the multipath noise correlated across time. The challenge is to enable high-accuracy positioning under severe multipath conditions when the fraction of received signals corrupted by multiple paths is significant. Our contributions are twofold. We provide a practical distributed localization algorithm by invoking an analytical graphical model framework based on particle filtering, and we validate its potential for high-accuracy localization through simulations. In a practical dedicated short-range communication (DSRC) mobile simulation setup, we show that the algorithm can achieve errors of $ 1 m 90% of the time, even when the fraction of line-of-sight (LOS) signals is less than 35%. We also address design questions such as “how many anchors and what fraction of LOS measurements are needed to achieve a specified target accuracy?” by showing that the Cramer–Rao lower bound (CRLB) for localization can be expressed as a product of two factors: a scalar function that depends only on the parameters of the noise distribution and a matrix that depends only on the geometry of node locations and the underlying connectivity graph . A simplified expression is obtained that provides an insightful understanding of the bound and that helps deduce the scaling behavior of the estimation error as a function of the number of agents and anchors in the network.