cssi/SciSciGPT-SciSciNet huggingface dataset:
The latency of a visual evoked potential tracks the onset of decision making

Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; O'Connell et al., 2012; Philiastides et al., 2014). In this study we directly tested the hypothesis that the latency of the N200 recorded by EEG (a negative peak occurring between 150 and 275 ms after stimulus presentation in human participants) reflects the visual encoding time (VET) required for completion of figure-ground segregation before evidence accumulation. We show that N200 latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds, reflecting a linear regression slope of 1. Simulations of cognitive decision-making theory show that variation in human response times not related to evidence accumulation (non-decision time; NDT), including VET, are tracked by the fastest response times. Evidence that VET is tracked by N200 latencies was found by fitting a linear model between trial-averaged N200 latencies and the 10th percentiles of response times, a model-independent estimate of NDT. Fitting a novel neuro-cognitive model of decision making also yielded a slope of 1 between N200 latency and model-estimated NDT in multiple visual noise conditions, indicating that N200 latencies track the completion of visual encoding and the onset of evidence accumulation. The N200 waveforms were localized to the cortical surface at distributed temporal and extrastriate locations, consistent with a distributed network engaged in figure-ground segregation of the target stimulus.
An Asymmetric Synthesis of Optically Pure α,α-Disubstituted Amino Aldehydes, α,α-Disubstituted Amino Acids, and Sterically Demanding Dipeptides

Optically pure α-methyl-α-alkyl amino aldehydes were efficiently synthesized from the cyclohexylimine of propanal bearing an α-4(R),5(S)-diphenyl-2-oxazolidinone by asymmetric alkylation of the imine anion. α-Methylphenylalanine and α-methylleucine were synthesized in two steps from the corresponding amino aldehydes. Utilizing an oxaziridine rearrangement, amino aldehydes were converted to dipeptides in which both the amino and carboxy components were α,α-disubstituted.
Heterogeneous viral strategies promote coexistence in virus-microbe systems

Viral infections of microbial cells often culminate in lysis and the release of new virus particles. However, viruses of microbes can also initiate chronic infections, in which new viruses particles are released via budding and without cell lysis. In chronic infections, viral genomes may also be passed on from mother to daughter cells during division. The consequences of chronic infections for the population dynamics of viruses and microbes remains under-explored. In this paper we present a model of chronic infections as well as a model of interactions between lytic and chronic viruses competing for the same microbial population. In the chronic only model, we identify conditions underlying complex bifurcations such as saddle-node, backward and Hopfbifurcations, leading to parameter regions with multiple attractors and/or oscillatory behavior. We then utilize invasion analysis to examine the coupled nonlinear system of microbes, lytic viruses, and chronic viruses. In so doing we find unexpected results, including a regime in which the chronic virus requires the lytic virus for survival, invasion, and persistence. In this regime, lytic viruses decrease total cell densities, so that a subpopulation of chronically infected cells experience decreased niche competition. As such, even when chronically infected cells have a growth disadvantage, lytic viruses can, paradoxically, enable the proliferation of both chronically infected cells and chronic viruses. We discuss the implications of our results for understanding the ecology and long-term evolution of heterogeneous viral strategies.
New chiral ligands for the asymmetric copper catalyzed conjugate addition of Grignard reagents to enones

Abstract The copper catalyzed conjugate addition of n-butyl Grignard to enones in the presence of chiral ferrocenyl phosphine oxazoline ligands has been studied and found to provide useful levels of asymmetric induction. A comparison of the ferrocene derived ligands 3 and 6 with the corresponding phenyl derived ligands 8 and 9 reveals that the ferrocene template plays an essential role in the reaction.
An enantioselective approach to the synthesis of manzamine A

Abstract A new approach for asymmetric construction of the pyrrolo[2,3-i]-isoquinoline core of manzamine A is described. The synthesis starts with D-(−)-quinic acid and features an intramolecular Mannich reaction as the central step.
A Novel Transformation of Esters to Alkynes with 1-Substituted Benzotriazoles

Reactions of lithio benzotriazol-1-yl derivatives 2, 11, and 25 with aromatic and aliphatic esters 3, 12, and 26 gave α-(benzotriazol-1-yl) ketones 4, 13, and 27, respectively, in high yields. Alternatively, α-(benzotriazol-1-yl) ketones 22 can be accessed by the reaction of α-(benzotriazol-1-yl) esters 20 with Grignard reagents. Condensation of 4, 13, 22, and 27 with (p-toluenesulfonyl)hydrazine provided p-tosylhydrazones 5, 14, 21, and 28. Treatment of hydrazones 5, 21, and 28 with n-butyllithium in diethyl ether resulted in the elimination of the tosyl group, dinitrogen, and benzotriazolyl group to afford the corresponding acetylenes 9, 23, and 29 in good yields. When α-(benzotriazol-1-yl) 1-α-phenoxy hydrazones 14 were treated with methyllithium, n-butyllithium, or phenyllithium, alkynes 18 were obtained, in which phenoxy groups were replaced by the lithium reagents.
Ionization Potentials for the Titanium, Zirconium, and the Mixed Metal Met-Cars

Photoionization spectra of the titanium and zirconium metallocarbohedrene clusters (Met-Cars) of the stoichiometry Ti8-xZrxC12 (x = 0−4, 8) have been investigated near threshold. Study of the photoionization efficiencies of these species at different photon energies led to a determination that the ionization potentials (IP's) for the titanium and zirconium Met-Cars, Ti8C12 and Zr8C12, are 4.40 ± 0.02 and 3.95 ± 0.02 eV, respectively. The IP's for the binary metal Met-Cars, Ti8-xZrxC12 (x = 1−4), were also determined, and it was found that the IP decreases continuously from that of the pure titanium Met-Car toward that of the pure zirconium Met-Car as the number of substituting zirconium atoms increases.
Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials

Abstract Reliable models of the thermodynamic properties of materials are critical for industrially relevant applications that require a good understanding of equilibrium phase diagrams, thermal and chemical transport, and microstructure evolution. The goal of thermodynamic models is to capture data from both experimental and computational studies and then make reliable predictions when extrapolating to new regions of parameter space. These predictions will be impacted by artifacts present in real data sets such as outliers, systematic errors, and unreliable or missing uncertainty bounds. Such issues increase the probability of the thermodynamic model producing erroneous predictions. We present a Bayesian framework for the selection, calibration, and quantification of uncertainty of thermodynamic property models. The modular framework addresses numerous concerns regarding thermodynamic models including thermodynamic consistency, robustness to outliers, and systematic errors by the use of hyperparameter weightings and robust Likelihood and Prior distribution choices. Furthermore, the framework’s inherent transparency (e.g. our choice of probability functions and associated parameters) enables insights into the complex process of thermodynamic assessment. We introduce these concepts through examples where the true property model is known. In addition, we demonstrate the utility of the framework through the creation of a property model from a large set of experimental specific heat and enthalpy measurements of Hafnium metal from 0 to 4900K.
Masked oxo sulfinimines (N-sulfinyl imines) in the asymmetric synthesis of proline and pipecolic acid derivatives.

On addition of Et2AlCN/i-PrOH, masked oxo sulfinimines give α-amino nitriles that afford oxo α-amino acids on hydrolysis. These amino acids cyclize and are reduced to cis proline and cis pipecolic acids derivatives in high ee and good yield. This new procedure avoids many of the limitations related to the preparation of oxo amino acids from proteinogenic amino acids.
Causal comparative effectiveness analysis of dynamic continuous-time treatment initiation rules with sparsely measured outcomes and death

Evidence supporting the current World Health Organization recommendations of early antiretroviral therapy (ART) initiation for adolescents is inconclusive. We leverage a large observational data and compare, in terms of mortality and CD4 cell count, the dynamic treatment initiation rules for human immunodeficiency virus-infected adolescents. Our approaches extend the marginal structural model for estimating outcome distributions under dynamic treatment regimes, developed in Robins et al. (2008), to allow the causal comparisons of both specific regimes and regimes along a continuum. Furthermore, we propose strategies to address three challenges posed by the complex data set: continuous-time measurement of the treatment initiation process; sparse measurement of longitudinal outcomes of interest, leading to incomplete data; and censoring due to dropout and death. We derive a weighting strategy for continuous-time treatment initiation, use imputation to deal with missingness caused by sparse measurements and dropout, and define a composite outcome that incorporates both death and CD4 count as a basis for comparing treatment regimes. Our analysis suggests that immediate ART initiation leads to lower mortality and higher median values of the composite outcome, relative to other initiation rules.
First Interstellar HCO+ Maser

A previously unseen maser in the J = 1-0 transition of HCO+ has been detected by the Combined Array for Millimeter-wave Astronomy (CARMA). A subarcsecond map was produced of the 2 arcmin2 region around DR21(OH), which has had previous detections of OH and methanol masers. This new object has remained undetected until now due to its extremely compact size. The object has a brightness temperature of >2500 K and an FWHM linewidth of 0.497 km s–1, both of which suggest non-thermal line emission consistent with an unsaturated maser. This object coincides in position and velocity with the methanol maser named DR21(OH)-1 by Plambeck & Menten. No compact HCO+ emission was present in the CARMA data toward the other methanol masers described in that Letter. These new results support the theory introduced in Plambeck & Menten that these masers likely arise from strong outflows interacting with low mass, high density pockets of molecular gas. This is further supported by recent observations of a CO outflow by Zapata et al. that traces the outflow edges and confirms that the maser position lies along the edge of the outflow where interaction with molecular tracers can occur.
AuCuSe4: A Mixed Polychalcogenide with Se32- and Se2- Anions

The polychalcogenide compound AuCuSe(4) crystallizes in the monoclinic space group P2(1)/m with a = 4.318(2) A, b = 7.447(1) A, c = 8.099(1) A, beta = 93.33(2)(o), and Z = 2. The structure of AuCuSe(4) is a unique three-dimensional framework assembled from formally Au(3+) and Cu(+) ions and Se(3)(2-) and Se(2-) fragments. Conceptually, the framework is composed of one-dimensional [Au(Se(3))Se](n)(n-) chains linked by Cu(+) atoms. The Au atoms adopt a square planar geometry while Cu atoms adopt a severely distorted tetrahedral geometry. The Se(3)(2-) ligand in this structure shows an unprecedented ligation mode using both its terminal and internal selenium atoms. The material is a p-type narrow gap semiconductor with a band gap of approximately 0.52 eV.
Stereoselective synthesis of 2β‐carbomethoxy‐3β‐phenyltropane derivatives. Enhanced stereoselectivity observed for the conjugate addition reaction of phenylmagnesium bromide derivatives with anhydro dichloromethane

The use of dichloromethane as a solvent for the conjugate addition reaction of preformed etheral solutions of phenylmagnesium bromide derivatives with anhydroecgonine methyl ester (2) was found to enhance the stereoselectivity of the reaction and provide the 2β-carbomethoxy-3β-phenyltropane derivatives 3a-d in high yield.
An integrated transcriptomics and metabolomics analysis of the Cucurbita pepo nectary implicates key modules of primary metabolism involved in nectar synthesis and secretion

Nectar is the main reward that flowers offer to pollinators to entice repeated visitation. Cucurbita pepo (squash) is an excellent model for studying nectar biology, as it has large nectaries that produce large volumes of nectar relative to most other species. Squash is also monoecious, having both female and male flowers on the same plant, which allows comparative analyses of nectary function in one individual. Here, we report the nectary transcriptomes from both female and male nectaries at four stages of floral maturation. Analysis of these transcriptomes and subsequent confirmatory experiments revealed a metabolic progression in nectaries leading from starch synthesis to starch degradation and to sucrose biosynthesis. These results are consistent with previously published models of nectar secretion and also suggest how a sucrose-rich nectar can be synthesized and secreted in the absence of active transport across the plasma membrane. Nontargeted metabolomic analyses of nectars also confidently identified 40 metabolites in both female and male nectars, with some displaying preferential accumulation in nectar of either male or female flowers. Cumulatively, this study identified gene targets for reverse genetics approaches to study nectary function, as well as previously unreported nectar metabolites that may function in plant-biotic interactions.
Generation and O2 Adsorption Studies of the Microporous Magnets CsNi[Cr(CN)6] (TC = 75 K) and Cr3[Cr(CN)6]2·6H2O (TN = 219 K)

Dehydration of the Prussian blue analogues CsNi[Cr(CN)6]·2H2O (1) and Cr3[Cr(CN)6]2·10H2O (2) affords two new microporous magnets: CsNi[Cr(CN)6] (1d) and Cr3[Cr(CN)6]2·6H2O (2d). Compounds 1d and 2d maintain the Prussian blue structure, and N2 adsorption measurements at 77 K show them to be microporous with BET surface areas of 360 and 400 m2/g, respectively. Both solids largely retain the magnetic properties of their parent hydrates, with 1d ordering at 75 K and 2d ordering at 219 K, by far the highest ordering temperature yet observed for a microporous magnet. The compounds further show unexpected changes in their magnetic properties upon adsorption of O2. In 2d, adsorption of O2 results in a reversible decrease in the magnetic moment of the system, as well as a reduction of the coercivity from 110 to 10 G and of the remnant magnetization from 1200 to 400 emu·G/mol, indicating a net antiferromagnetic interaction between O2 and the framework. In 1d, adsorption of O2 instead results in a reversible increa...
Mechanical feedback enables catch bonds to selectively stabilize scanning microvilli at T-cell surfaces.

T-cells use microvilli to actively scan antigen-presenting cells. Computational modeling is used to show that antigenic ligands, which form catch bonds, can selectively stabilize the microvilli and...
The direct conversion of carbamates to ureas using aluminum amides

The conversion of carbamates into ureas using aluminum amide complexes is reported. This reaction is a convenient method to prepare bi-, tri- and tetra-substituted ureas from carbamate-protected primary or secondary amines by reaction with primary or secondary amines in the presence of stoichometric quantities of trimethylaluminum. A reactivity trend of the various carbamates was observed and methyl and benzyl carbamates were reacted selectively in the presence of t-butyl carbamates.
Novel N6-(Substituted-phenylcarbamoyl)adenosine-5‘-uronamides as Potent Agonists for A3 Adenosine Receptors

A series of adenosine-5‘-uronamide derivatives bearing N6-phenylurea groups have been synthesized and tested for their affinity at A1 and A2A adenosine receptors in rat brain membranes and at cloned rat A3 receptors from stably transfected CHO cells. Some N6-arylcarbamoyl derivatives, N6-((2-chlorophenyl)carbamoyl)-, N6-((3-chlorophenyl)carbamoyl)-, and N6-((4-methoxyphenyl)carbamoyl)adenosine-5‘-ethyluronamide (4l−n), were found to have affinity at A3 receptors in the low nanomolar range (Ki values < 10 nM). In CHO cells stably transfected with the rat A3 receptor, compound 4n was found to be a full agonist in inhibiting adenylate cyclase activity. The present study represents the first example of N6-acyl-substituted adenosine analogs having high affinity at adenosine receptors and, in particular, at the A3 receptor subtype.
Cycloadditions of Singlet Oxygen for Responsive Fluorescent Polymers

This account describes progress in the author’s laboratory in the area of new fluorescent polymers that respond to the reactive oxygen species singlet oxygen (1O2). Key to the development of these materials are the [4+2] cycloaddition reactions between singlet oxygen and dienes such as acenes and furans. When covalently bound to conjugated polymer backbones, cycloadditions of these dienes with singlet oxygen can yield dramatic changes in the wavelength and intensity of luminescence: three such examples are given here. The account also summarizes our work to understand how changing the chemical structures of acenes affects reactivity with singlet oxygen as well as the cycloreversion of the resulting endoperoxides. 1 Introduction 2 Motivation 3 Organic Soluble Diene-Linked Conjugated Polymers 4 Red-Shifting Woes Lead to Reversibility 5 Building a Database 6 Integrating Other Acenes 7 Conclusion and Looking Forward
Subpicosecond solvent dynamics in charge-transfer transitions: challenges and opportunities in resonance Raman spectroscopy.

Resonance Raman spectroscopy is an established tool for the determination of structure and dynamics in electronically excited states. In condensed-phase systems, Raman excitation profiles and electronic absorption spectra depend on changes in molecular geometry and solvation structure induced by electronic excitation. Recent studies of solvent isotope effects on resonance Raman intensities in charge-transfer excitations reveal solvent dynamics taking place on a subpicosecond time scale and vibrational mode-specific solute−solvent interactions. These discoveries present challenges to the current working theories for analysis of resonance Raman and absorption spectra.
MemFlow: Memory-Driven Data Scheduling With Datapath Co-Design in Accelerators for Large-Scale Inference Applications

The increasing importance of inference algorithms, such as neural networks (NNs), principle component analysis (PCA), and singular value decomposition (SVD), etc., has led to the emergence of hardware accelerators to address power-performance tradeoffs in their implementation. Their large data sets make DRAM access the bottleneck for power and performance. Private SRAM scratch-pad memory is used to mitigate the DRAM access penalty but it is a limited resource in size and bandwidth. Thus, accelerator design is not just about computation, but also how data flow is scheduled across the memory hierarchy, including DRAM, scratch-pad SRAM, and datapath registers. Current accelerator design tools automate the generation of customized datapaths to improve performance, but have limited support for reducing DRAM/SRAM accesses during the computation. In this paper, we propose a memory-driven accelerator design methodology for large-scale inference applications, to maximize data access in the datapath and SRAM. We demonstrate its efficacy using several key kernels from large-scale inference applications.
Magnetic Alignment of Polymer Macro‐Nanodiscs Enables Residual‐Dipolar‐Coupling‐Based High‐Resolution Structural Studies by NMR Spectroscopy

Experimentally measured residual dipolar couplings (RDCs) are highly valuable for atomic-resolution structural and dynamic studies of molecular systems ranging from small molecules to large proteins by solution NMR spectroscopy. Here we demonstrate the first use of magnetic-alignment behavior of lyotropic liquid-crystalline polymer macro-nanodiscs (>20 nm in diameter) as a novel alignment medium for the measurement of RDCs using high-resolution NMR. The easy preparation of macro-nanodiscs, their high stability against pH changes and the presence of divalent metal ions, and their high homogeneity make them an efficient tool to investigate a wide range of molecular systems including natural products, proteins, and RNA.
Knowledge Synthesis for Scientific Management: Practical Integration for Complexity Versus Scientific Fragmentation for Simplicity:

Within the boundary of scientific knowledge for management, we discuss the divergence between practical demand for knowledge integration to solve complex problems and scientific fragmentation of ac...
Take a Stand or Keep Your Seat: Board Turnover after Social Movement Boycotts

While prior work has shown that a firm’s market performance affects the loyalty of its board, little is known about how corporate social responsibility affects directors’ willingness to serve. In t...
On Data-Driven Saak Transform

Abstract Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in this work. The Saak transform consists of three steps: (1) building the optimal linear subspace approximation with orthonormal bases using the second-order statistics of input vectors, (2) augmenting each transform kernel with its negative, (3) applying the rectified linear unit (ReLU) to the transform output. The Karhunen-Loeve transform (KLT) is used in the first step. The integration of Steps 2 and 3 is powerful since they resolve the sign confusion problem, remove the rectification loss and allow a straightforward implementation of the inverse Saak transform at the same time. Multiple Saak transforms are cascaded to transform images of a larger size. All Saak transform kernels are derived from the second-order statistics of input random vectors in a one-pass feedforward manner. Neither data labels nor backpropagation is used in kernel determination. Multi-stage Saak transforms offer a family of joint spatial-spectral representations between two extremes; namely, the full spatial-domain representation and the full spectral-domain representation. We select Saak coefficients of higher discriminant power to form a feature vector for pattern recognition, and use the MNIST dataset classification problem as an illustrative example.
Maximal Cohen-Macaulay modules over certain Segre products

AbstractWe prove some results on the non-existence of rank one maximal Cohen–Macaulay modules over certain Segre product rings. As an application we show that over these Segre product rings there do not exist maximal Cohen–Macaulay modules with multiplicity less than or equal to the parameter degree of the ring. This disproves a conjecture of Schoutens [14].
A complete recipe for stochastic gradient MCMC

Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic simulations. However, such stochastic gradient MCMC samplers have lagged behind their full-data counterparts in terms of the complexity of dynamics considered since proving convergence in the presence of the stochastic gradient noise is non-trivial. Even with simple dynamics, significant physical intuition is often required to modify the dynamical system to account for the stochastic gradient noise. In this paper, we provide a general recipe for constructing MCMC samplers—including stochastic gradient versions—based on continuous Markov processes specified via two matrices. We constructively prove that the framework is complete. That is, any continuous Markov process that provides samples from the target distribution can be written in our framework. We show how previous continuous-dynamic samplers can be trivially "reinvented" in our framework, avoiding the complicated sampler-specific proofs. We likewise use our recipe to straightforwardly propose a new state-adaptive sampler: stochastic gradient Riemann Hamiltonian Monte Carlo (SGRHMC). Our experiments on simulated data and a streaming Wikipedia analysis demonstrate that the proposed SGRHMC sampler inherits the benefits of Riemann HMC, with the scalability of stochastic gradient methods.
Learning Determinantal Point Processes with Moments and Cycles

Determinantal Point Processes (DPPs) are a family of probabilistic models that have a repulsive behavior, and lend themselves naturally to many tasks in machine learning where returning a diverse set of objects is important. While there are fast algorithms for sampling, marginalization and conditioning, much less is known about learning the parameters of a DPP. Our contribution is twofold: (i) we establish the optimal sample complexity achievable in this problem and show that it is governed by a natural parameter, which we call the \emph{cycle sparsity}; (ii) we propose a provably fast combinatorial algorithm that implements the method of moments efficiently and achieves optimal sample complexity. Finally, we give experimental results that confirm our theoretical findings.
Quandle coloring quivers

We consider a quiver structure on the set of quandle colorings of an oriented knot or link diagram. This structure contains a wealth of knot and link invariants and provides a categorification of t...
A Prediction Tournament Paradox

In a prediction tournament, contestants “forecast” by asserting a numerical probability for each of (say) 100 future real-world events. The scoring system is designed so that (regardless of the unk...
Weakly asymptotically hyperbolic manifolds

We introduce a class of "weakly asymptotically hyperbolic" geometries whose sectional curvatures tend to $-1$ and are $C^0$, but are not necessarily $C^1$, conformally compact. We subsequently investigate the rate at which curvature invariants decay at infinity, identifying a conformally invariant tensor which serves as an obstruction to "higher order decay" of the Riemann curvature operator. Finally, we establish Fredholm results for geometric elliptic operators, extending the work of Rafe Mazzeo and John M. Lee to this setting. As an application, we show that any weakly asymptotically hyperbolic metric is conformally related to a weakly asymptotically hyperbolic metric of constant negative curvature.
Input sparsity time low-rank approximation via ridge leverage score sampling

We present a new algorithm for finding a near optimal low-rank approximation of a matrix A in O(nnz(A)) time. Our method is based on a recursive sampling scheme for computing a representative subset of A's columns, which is then used to find a low-rank approximation.This approach differs substantially from prior O(nnz(A)) time algorithms, which are all based on fast Johnson-Lindenstrauss random projections. Our algorithm matches the guarantees of the random projection methods while offering a number of advantages.In addition to better performance on sparse and structured data, sampling algorithms can be applied in settings where random projections cannot. For example, we give new streaming algorithms for the column subset selection and projection-cost preserving sample problems. Our method has also been used in the fastest algorithms for provably accurate Nystrom approximation of kernel matrices [56].
Approximation by analytic operator functions. Factorizations and very badly approximable functions

This is a continuation of our earlier paper \cite{PT3}. We consider here operator-valued functions (or infinite matrix functions) on the unit circle $\T$ and study the problem of approximation by bounded analytic operator functions. We discuss thematic and canonical factorizations of operator functions and study badly approximable and very badly approximable operator functions. We obtain algebraic and geometric characterizations of badly approximable and very badly approximable operator functions. Note that there is an important difference between the case of finite matrix functions and the case of operator functions. Our criteria for a function to be very badly approximable in the case of finite matrix functions also guarantee that the zero function is the only superoptimal approximant. However in the case of operator functions this is not true.
Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge.

Abstract Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization by integrating information on both brain structure and function. In particular, incorporating anatomical knowledge leads to desirable outcomes such as increased accuracy in brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear advantages, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to inherent deficiencies in existing imaging technology. This calls for the development of advanced network modeling tools that appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach which models the brain functional networks via sparse precision matrices whose degree of edge specific shrinkage is a random variable that is modeled using both anatomical structure and an independent baseline component. The proposed approach adaptively shrinks functional connections and flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of misspecified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm which yields maximum a posteriori estimates. Extensive numerical studies involving multiple functional network structures reveal the clear advantages of the proposed approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is misspecified up to a certain degree. An application of the approach to data from the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.
Uniqueness of Normal Forms for Shallow Term Rewrite Systems

Uniqueness of normal forms (UN=) is an important property of term rewrite systems. UN= is decidable for ground (i.e., variable-free) systems and undecidable in general. Recently, it was shown to be decidable for linear, shallow systems. We generalize this previous result and show that this property is decidable for shallow rewrite systems, in contrast to confluence, reachability, and other related properties, which are all undecidable for flat systems. We also prove an upper bound on the complexity of our algorithm. Our decidability result is optimal in a sense, since we prove that the UN= property is undecidable for two classes of linear rewrite systems: left-flat systems in which right-hand sides are of height at most two and right-flat systems in which left-hand sides are of height at most two.
Sets resilient to erosion

The erosion of a set in Euclidean space by a radius r > 0 is the subset of X consisting of points at distance ≥ r from the complement of X. A set is resilient to erosion if it is similar to its erosion by some positive radius. We give a somewhat surprising characterization of resilient sets, consisting in one part of simple geometric constraints on convex resilient sets, and, in another, a correspondence between nonconvex resilient sets and scale-invariant (e.g., ‘exact fractal’) sets.
Success Probability and Area Spectral Efficiency of a VANET Modeled as a Cox Process

This letter analyzes the performance of a vehicular ad hoc network (VANET) modeled as a Cox process, where the spatial layout of the roads is modeled by a Poisson line process and the locations of nodes on each line are modeled as a 1-D Poisson point process (PPP). For this setup, we characterize the success probability of a typical link and the area spectral efficiency (ASE) of the network assuming slotted ALOHA as the channel access scheme. We then concretely establish that the success probability of a typical link in a VANET modeled using a Cox process converges to that of a 1-D and 2-D PPP for some extreme values of the line and node densities. We also study the trends in success probability as a function of the system parameters and show that the optimum transmission probability that maximizes the ASE for this Cox process model differs significantly from those of the relatively simpler 1-D and 2-D PPP models used commonly in the literature to model vehicular networks.
Detection of Compromised Smart Grid Devices with Machine Learning and Convolution Techniques

The smart grid concept has transformed the traditional power grid into a massive cyber- physical system that depends on advanced two-way communication infrastructure to integrate a myriad of different smart devices. While the introduction of the cyber component has made the grid much more flexible and efficient with so many smart devices, it also broadened the attack surface of the power grid. Particularly, compromised devices pose great danger to the healthy operations of the smart-grid. For instance, the attackers can control the devices to change the behaviour of the grid and can impact the measurements. In this paper, to detect such misbehaving malicious smart grid devices, we propose a machine learning and convolution-based classification framework. Our framework specifically utilizes system and library call lists at the kernel level of the operating system on both resource-limited and resource-rich smart grid devices such as RTUs, PLCs, PMUs, and IEDs. Focusing on the types and other valuable features extracted from the system calls, the framework can successfully identify malicious smart-grid devices. In order to test the efficacy of the proposed framework, we built a representative testbed conforming to the IEC-61850 protocol suite and evaluated its performance with different system calls. The proposed framework in different evaluation scenarios yields very high accuracy (avg. 91%) which reveals that the framework is effective to overcome compromised smart grid devices problem.
Learning Adversary Behavior in Security Games: A PAC Model Perspective

Recent applications of Stackelberg Security Games (SSG), from wildlife crime to urban crime, have employed machine learning tools to learn and predict adversary behavior using available data about defender-adversary interactions. Given these recent developments, this paper commits to an approach of directly learning the response function of the adversary. Using the PAC model, this paper lays a firm theoretical foundation for learning in SSGs and provides utility guarantees when the learned adversary model is used to plan the defender's strategy. The paper also aims to answer practical questions such as how much more data is needed to improve an adversary model's accuracy. Additionally, we explain a recently observed phenomenon that prediction accuracy of learned adversary behavior is not enough to discover the utility maximizing defender strategy. We provide four main contributions: (1) a PAC model of learning adversary response functions in SSGs; (2) PAC-model analysis of the learning of key, existing bounded rationality models in SSGs; (3) an entirely new approach to adversary modeling based on a non-parametric class of response functions with PAC-model analysis and (4) identification of conditions under which computing the best defender strategy against the learned adversary behavior is indeed the optimal strategy. Finally, we conduct experiments with real-world data from a national park in Uganda, showing the benefit of our new adversary modeling approach and verification of our PAC model predictions.
Mobile Edge Computing Empowers Internet of Things

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods are validated via extensive simulations.
Network Utility Maximization in Adversarial Environments

Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, these models fail to capture non-stationary or even adversarial network dynamics which are of increasing importance for modeling the behavior of networks under malicious attacks or characterizing short-term transient behavior. In this paper, we consider the network utility maximization problem in adversarial network settings. In particular, we focus on the tradeoffs between total queue length and utility regret which measures the difference in network utility between a causal policy and an “oracle” that knows the future within a finite time horizon. Two adversarial network models are developed to characterize the adversary's behavior. We provide lower bounds on the tradeoff between utility regret and queue length under these adversarial models, and analyze the performance of two control policies (i.e., the Drift-plus-Penalty algorithm and the Tracking Algorithm).
Toward an efficient algorithm for deciding the vanishing of local cohomology modules in prime characteristic

Let $$R=k[x_1,\dots ,x_n]$$ be a ring of polynomials over a field k of characteristic $$p>0$$ . There is an algorithm due to Lyubeznik for deciding the vanishing of local cohomology modules $$H^i_I(R)$$ where $$I\subset R$$ is an ideal. This algorithm has not been implemented because its complexity grows very rapidly with the growth of p which makes it impractical. In this paper we produce a modification of this algorithm that consumes a modest amount of memory.
Interesting, Important, and Impactful Operations Management

Operations management has evolved since the founding of M&SOM: new departments have been created in our journals, new tracks have been established in our conferences, and new methodologies have bee...
Location based performance model for indoor mmWave wearable communication

Simultaneous use of high-end wearable wireless devices like smart glasses is challenging in a dense indoor environment due to the high nature of interference. In this scenario, the millimeter wave (mmWave) band offers promising potential for achieving gigabits per second throughput. Here we propose a novel system model for analyzing system performance of mmWave based communication among wearables. The proposed model accounts for the non-isotropy of the indoor environment and the effects of reflections that are predominant for indoor mmWave signals. The effect of human body blockages are modeled and the system performance is shown to hugely vary depending on the user location, body orientation and the density of the network. Closed form expressions for spatially averaged signal to interference plus noise ratio distribution are also derived as a function of the location and orientation of a reference user.
Context Encoders: Feature Learning by Inpainting

We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders – a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.
Distributed convergence verification for Gaussian belief propagation

Gaussian belief propagation (BP) is a computationally efficient method to approximate the marginal distribution and has been widely used for inference with high dimensional data as well as distributed estimation in large-scale networks. However, the convergence of Gaussian BP is still an open issue. Though sufficient convergence conditions have been studied in the literature, verifying these conditions requires gathering all the information over the whole network, which defeats the main advantage of distributed computing by using Gaussian BP. In this paper, we propose a novel sufficient convergence condition for Gaussian BP that applies to both the pairwise linear Gaussian model and to Gaussian Markov random fields. We show analytically that this sufficient convergence condition can be easily verified in a distributed way that satisfies the network topology constraint.
Musings on Q(1=4): Arithmetic spin structures on elliptic curves

We introduce and study arithmetic spin structures on elliptic curves. We show that there is a unique isogeny class of elliptic curves over $\F_{p^2}$ which carries a unique arithmetic spin structure and provides a geometric object of weight 1/2 in the sense of Deligne and Grothendieck. This object is thus a candidate for $\Q(1/4)$.
The homology of $\mathrm{tmf}$

We compute the mod $2$ homology of the spectrum $\mathrm{tmf}$ of topological modular forms by proving a 2-local equivalence $\mathrm{tmf} \wedge DA(1) \simeq \mathrm{tmf}_1(3) \simeq BP\left \langle 2\right\rangle$, where $DA(1)$ is an eight cell complex whose cohomology "doubles" the subalgebra $\mathcal{A}(1)$ of the Steenrod algebra generated by $\mathrm{Sq}^1$ and $\mathrm{Sq}^2$. To do so, we give, using the language of stacks, a modular description of the elliptic homology of $DA(1)$ via level three structures. We briefly discuss analogs at odd primes and recover the stack-theoretic description of the Adams-Novikov spectral sequence for $\mathrm{tmf}$.
Bayesian Counterfactual Risk Minimization

We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback. Using PAC-Bayesian analysis, we derive a new generalization bound for the truncated inverse propensity score estimator. We apply the bound to a class of Bayesian policies, which motivates a novel, potentially data-dependent, regularization technique for CRM. Experimental results indicate that this technique outperforms standard $L_2$ regularization, and that it is competitive with variance regularization while being both simpler to implement and more computationally efficient.
Stability of depth functions of cover ideals of balanced hypergraphs

We prove that the depth functions of cover ideals of balanced hypergraph have the nonincreasing property. Furthermore, we also give a bound for the index of depth stability of these ideals.
Centers, cocenters and simple quantum groups

Abstract We define the notion of a (linearly reductive) center for a linearly reductive quantum group, and show that the quotient of a such a quantum group by its center is simple whenever its fusion semiring is free in the sense of Banica and Vergnioux. We also prove that the same is true of free products of quantum groups under very mild non-degeneracy conditions. Several natural families of compact quantum groups, some with non-commutative fusion semirings and hence very “far from classical”, are thus seen to be simple. Examples include quotients of free unitary groups by their centers, recovering previous work, as well as quotients of quantum reflection groups by their centers.
CONSTRUCTING 1-CUSPED ISOSPECTRAL NON-ISOMETRIC HYPERBOLIC 3-MANIFOLDS

We construct infinitely many examples of pairs of isospectral but non-isometric 1-cusped hyperbolic 3-manifolds. These examples have infinite discrete spectrum and the same Eisenstein series. Our constructions are based on an application of Sunada’s method in the cusped setting, and so in addition our pairs are finite covers of the same degree of a 1-cusped hyperbolic 3-orbifold (indeed manifold) and also have the same complex length spectra. Finally we prove that any finite volume hyperbolic 3-manifold isospectral to the figure-eight knot complement is homeomorphic to the figure-eight knot complement.
The Price of Anarchy for Transportation Networks with Mixed Autonomy

We study routing behavior in transportation networks with mixed autonomy, that is, networks in which a fraction of the vehicles on each road are equipped with autonomous capabilities such as adaptive cruise control that enable reduced headways and increased road capacity. Motivated by capacity models developed for such roads with mixed autonomy, we consider transportation networks in which the delay on each road or link is an affine function of two quantities: the number of vehicles with autonomous capabilities on the link and the number of regular vehicles on the link. We particularly study the price of anarchy for such networks, that is, the ratio of the total delay experienced by selfish routing to the socially optimal routing policy. Unlike the case when all vehicles are of the same type, for which the price of anarchy is known to be bounded, we first show that the price of anarchy can be arbitrarily large for such mixed autonomous networks. Next, we define a notion of asymmetry corresponding to the maximum possible travel time improvement due to the presence of autonomous vehicles. We show that when the degree of asymmetry of all links in the network is bounded by a factor less than 4, the price of anarchy is bounded. We also bound the bicriteria, which is a bound on the cost of selfishly routing traffic compared to the cost of optimally routing additional traffic. These bounds depend on the degree of asymmetry and recover classical bounds on the price of anarchy and bicriteria in the case when no asymmetry exists. Further, we show with examples that these bound are tight in particular cases.