cssi/SciSciGPT-SciSciNet huggingface dataset:
Synthesis and structure of bishomohexaprismanedione

Abstract Synthesis of bishomohexaprimanedione, the D 2h dimer of norbornadienone and a potential precursor to hexaprismane, is reported. The synthesis includes an unusual ring contraction of a cycloalkene using a cis -hydroxylation/periodate cleavage/aldol sequence. The structure of bishomohexaprimanedione determined from single crystal x-ray analysis is well reproduced by ab initio calculations.
Structural Rearrangements in Water Viewed Through Two-Dimensional Infrared Spectroscopy

Compared with other molecular liquids, water is highly structured because of its ability to form up to four hydrogen bonds, resulting in a tetrahedral network of molecules. However, this underlying...
Highly Diastereoselective α-Mannopyranosylation in the Absence of Participating Protecting Groups

S-Phenyl 2,6-di-O-benzyl-3,4-O-(2‘,3‘-dimethoxybutane-2‘,3‘-diyl)-1-thia-α-d-mannopyranoside and its sulfoxide, following activation at −78 °C with benzenesulfenyl triflate or triflic anhydride, re...
Simple, stereocontrolled syntheses of 24(S),25-epoxy-22(R)-hydroxycholesterol, 22(R),24(S)-dihydroxycholesterol and diastereomers, new ligands for binding and activation of LXRs

Abstract Described herein are stereocontrolled syntheses of a series of sidechain oxygenated cholesterols for the study of binding and activation of nuclear receptors of the LXR family.
Reductive Cyclization of δ-Hydroxy Nitriles: A New Synthesis of Glycosylamines†

The stereoselective cyclization of δ-hydroxy nitriles to afford N,O-acetals and the application of this reaction toward the synthesis of glycosylamines is described.
Synthesis and antiproliferative and antiviral activity of carbohydrate-modified pyrrolo[2,3-d]pyridazin-7-one nucleosides.

Sugar-modified analogs of 4-amino-1-(β-d-ribofuranosyl)pyrrolo[2,3-d]pyridazin-7-one (1) and 4-amino-3-bromo-1-(β-d-ribofuranosyl)pyrrolo[2,3-d]pyridazin-7-one (3) were prepared in an effort to obt...
Efficient Detection of Communities in Biological Bipartite Networks

Methods to efficiently uncover and extract community structures are required in a number of biological applications where networked data and their interactions can be modeled as graphs, and observing tightly-knit groups of vertices (“communities”) can offer insights into the structural and functional building blocks of the underlying network. Classical applications of community detection have largely focused on unipartite networks — i.e., graphs built out of a single type of objects. However, due to increased availability of biological data from various sources, there is now an increasing need for handling heterogeneous networks which are built out of multiple types of objects. In this paper, we address the problem of identifying communities from biological bipartite networks — i.e., networks where interactions are observed between two different types of objects (e.g., genes and diseases, drugs and protein complexes, plants and pollinators, and hosts and pathogens). Toward detecting communities in such bipartite networks, we make the following contributions: i) ( metric ) we propose a variant of bipartite modularity; ii) ( algorithms ) we present an efficient algorithm called biLouvain that implements a set of heuristics toward fast and precise community detection in bipartite networks ( https://github.com/paolapesantez/biLouvain ); and iii) ( experiments ) we present a thorough experimental evaluation of our algorithm including comparison to other state-of-the-art methods to identify communities in bipartite networks. Experimental results show that our biLouvain algorithm identifies communities that have a comparable or better quality (as measured by bipartite modularity) than existing methods, while significantly reducing the time-to-solution between one and four orders of magnitude.
Cross-coupling reactions of alkenylsilanols with fluoroalkylsulfonates.

The design and development of an effective protocol for the palladium-catalyzed cross-coupling of (E)- and (Z)-heptenyldimethylsilanols with organo-triflates and nonaflates is described. Optimization of this coupling focused on the issues of both reactivity and stability of the psuedohalides in the presence of the nucleophilic fluoride promoter for the coupling. The crucial role of varying amounts of water to modulate the reactivity of the fluoride ion is highlighted.
The strategic generation and interception of palladium-hydrides for use in alkene functionalization reactions

We review methods that our lab has developed for the generation of Pd-hydrides and the manipula- tion of these useful intermediates via β-hydride elimination and migratory insertion steps. For a given alkene functionalization reaction, careful understanding of the dynamics of β-hydride elimination, migratory inser- tion, and transmetallation have allowed for the selective functionalization of Pd-alkyl intermediates. This has afforded us a means by which to transpose palladium to a desired position on a substrate for subsequent functionalization, empowering a number of useful C-H, C-O, and C-C bond-forming reactions.
Data-Driven Analysis of Age, Sex, and Tissue Effects on Gene Expression Variability in Alzheimer's Disease

Alzheimer's disease (AD) has been categorized by the Centers for Disease Control and Prevention (CDC) as the 6th leading cause of death in the United States. AD is a significant health-care burden because of its increased occurrence (specifically in the elderly population), and the lack of effective treatments and preventive methods. With an increase in life expectancy, the CDC expects AD cases to rise to 15 million by 2060. Aging has been previously associated with susceptibility to AD, and there are ongoing efforts to effectively differentiate between normal and AD age-related brain degeneration and memory loss. AD targets neuronal function and can cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary tangles. Our study aims to identify temporal changes within gene expression profiles of healthy controls and AD subjects. We conducted a meta-analysis using publicly available microarray expression data from AD and healthy cohorts. For our meta-analysis, we selected datasets that reported donor age and gender, and used Affymetrix and Illumina microarray platforms (8 datasets, 2,088 samples). Raw microarray expression data were re-analyzed, and normalized across arrays. We then performed an analysis of variance, using a linear model that incorporated age, tissue type, sex, and disease state as effects, as well as study to account for batch effects, and included binary interactions between factors. Our results identified 3,735 statistically significant (Bonferroni adjusted p < 0.05) gene expression differences between AD and healthy controls, which we filtered for biological effect (10% two-tailed quantiles of mean differences between groups) to obtain 352 genes. Interesting pathways identified as enriched comprised of neurodegenerative diseases pathways (including AD), and also mitochondrial translation and dysfunction, synaptic vesicle cycle and GABAergic synapse, and gene ontology terms enrichment in neuronal system, transmission across chemical synapses and mitochondrial translation. Overall our approach allowed us to effectively combine multiple available microarray datasets and identify gene expression differences between AD and healthy individuals including full age and tissue type considerations. Our findings provide potential gene and pathway associations that can be targeted to improve AD diagnostics and potentially treatment or prevention.
Scalable and Privacy-Preserving Design of On/Off-Chain Smart Contracts

The rise of smart contract systems such as Ethereum has resulted in a proliferation of blockchain-based decentralized applications including applications that store and manage a wide range of data. Current smart contracts are designed to be executed solely by miners and are revealed entirely on-chain, resulting in reduced scalability and privacy. In this paper, we discuss that scalability and privacy of smart contracts can be enhanced by splitting a given contract into an off-chain contract and an on-chain contract. Specifically, functions of the contract that involve high-cost computation or sensitive information can be split and included as the off-chain contract, that is signed and executed by only the interested participants. The proposed approach allows the participants to reach unanimous agreement off-chain when all of them are honest, allowing computing resources of miners to be saved and content of the off-chain contract to be hidden from the public. In case of a dispute caused by any dishonest participants, a signed copy of the off-chain contract can be revealed so that a verified instance can be created to make miners enforce the true execution result. Thus, honest participants have the ability to redress and penalize any fraudulent or dishonest behavior, which incentivizes all participants to honestly follow the agreed off-chain contract. We discuss techniques for splitting a contract into a pair of on/off-chain contracts and propose a mechanism to address the challenges of handling dishonest participants in the system. Our implementation and evaluation of the proposed approach using an example smart contract demonstrate the effectiveness of the proposed approach in Ethereum.
Derivation of the Sum-of-Squares Relationship

The sum-of-squares relationship connects the square of elementary particle masses to the square of the Fermi scale. It constrains the spectrum of free parameters in the Standard Model (SM) and it suggests a straightforward resolution to the hierarchy problem. Here we show that this relationship follows from the minimal fractal structure of spacetime near the Fermi scale and supports the view that Dark Matter behaves as long-range multifractal replica of the SM.
XScan: An Integrated Tool for Understanding Open Source Community-Based Scientific Code

Many scientific communities have adopted community-based models that integrate multiple components to simulate whole system dynamics. The community software projects’ complexity, stems from the integration of multiple individual software components that were developed under different application requirements and various machine architectures, has become a challenge for effective software system understanding and continuous software development. The paper presents an integrated software toolkit called X-ray Software Scanner (in abbreviation, XScan) for a better understanding of large-scale community-based scientific codes. Our software tool provides support to quickly summarize the overall information of scientific codes, including the number of lines of code, programming languages, external library dependencies, as well as architecture-dependent parallel software features. The XScan toolkit also realizes a static software analysis component to collect detailed structural information and provides an interactive visualization and analysis of the functions. We use a large-scale community-based Earth System Model to demonstrate the workflow, functions and visualization of the toolkit. We also discuss the application of advanced graph analytics techniques to assist software modular design and component refactoring.
Discrimination and Clustering for Multivariate Time Series

This entry gives a summary of the time and frequency domain approaches to classification and clustering for biostatistical and medical data that have been collected as multivariate correlated time series. Keywords: spectral analysis; state-space models; feature extraction; classification; clustering; loglikelihood functions
Assessing the performance of residential energy management control Algorithms: Multi-criteria decision making using the analytical hierarchy process

Abstract For homes to become active participants in a smart electric grid, intelligent control algorithms are needed to facilitate autonomous interactions that take homeowner preferences into consideration. Many control algorithms for demand response have been proposed in the literature. Comparing the performance of these algorithms has been difficult because each algorithm makes different assumptions or considers different scenarios, i.e., peak load reduction or minimizing cost in response to the variable price of electricity. This work proposes a novel, flexible assessment framework using the Analytical Hierarchy Process to compare and rank residential energy management control algorithms. The framework is a hybrid mechanism that derives a ranking from a combination of subjective user input representing preferences, and objective data from the performance of the control algorithms related to energy consumption, cost, and comfort. A new algorithm was developed to map objective performance data to the Analytical Hierarchy Process's fundamental scale and form a matrix of pairwise comparisons. The assessment framework results in a single overall score for each control algorithm that can be used to rank the alternatives. The approach is illustrated by applying the assessment process to six residential energy management control algorithms.
Class Proportion Estimation with Application to Multiclass Anomaly Rejection

This work addresses two classication problems that fall under the heading of domain adaptation, wherein the distributions of training and testing examples dier. The rst problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set given labeled examples of each class. Compared to previous work on this problem, our approach has the novel feature that it does not require labeled training data from one of the classes. This property allows us to address the second domain adaptation problem, namely, multiclass anomaly rejection. Here, the goal is to design a classier that has the option of assigning a \reject" label, indicating that the instance did not arise from a class present in the training data. We establish consistent learning strategies for both of these domain adaptation problems, which to our knowledge are the rst of their kind. We also implement the class proportion estimation technique and demonstrate its performance on several benchmark data sets.
A Framework for Probabilistic Generic Traffic Scene Prediction

In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approaches aimed to predict multiple vehicles, they either predicted each vehicle independently with no considerations on possible interaction with surrounding entities or generated discretized joint motions which cannot be directly used in decision making and motion planning for autonomous vehicle. In this paper, we present a probabilistic framework that is able to jointly predict continuous motions for multiple interacting road participants under any driving scenarios and is capable of forecasting the duration of each interaction, which can enhance the prediction performance and efficiency. The proposed traffic scene prediction framework contains two hierarchical modules: the upper module and the lower module. The upper module forecasts the intention of the predicted vehicle, while the lower module predicts motions for interacting scene entities. An exemplar real-world scenario is used to implement and examine the proposed framework.
Logarithm laws for one parameter unipotent flows

We prove logarithm laws and shrinking target properties for unipotent flows on the homogenous space Γ\ G with \({G= {\rm SL}_2(\mathbb{R})^{r_1}\times{\rm SL}_2(\mathbb{C})^{r_2}}\) and \({\Gamma \subseteq G}\) an irreducible non-uniform lattice. Our method relies on certain estimates for the norms of (incomplete) theta series in this setting.
Seven Conjectures in Geometry and Number Theory

In this short paper we propose four conjectures in synthetic geometry that generalize Erdos-Mordell Theorem, and three conjectures in number theory that generalize Fermat Numbers.
Differential Myocardial Mechanics in Volume and Pressure Loaded Right Ventricles Demonstrated by Cardiac Magnetic Resonance

In patients with D-looped transposition of the great arteries (D-TGA) status post atrial switch operation, the systemic right ventricle (RV) shifts to predominantly circumferential (CS) rather than longitudinal strain (LS), which may represent adaptation or dysfunction. We aimed to evaluate myocardial mechanics in pressure loaded, volume-loaded, and normal RVs by cardiac magnetic resonance (CMR). Patients with D-TGA post atrial switch operation with CMR from 2008 to 2015 were matched 1:1 for age and RV ejection fraction (EF) with repaired tetralogy of Fallot (TOF) patients (volume-loaded RVs), and 1:1 for age with control patients. RV free wall LS and CS were measured using feature tracking software (TomTec, Unterscleissheim, Germany). A total of 32 D-TGA (median age 32 years, 56% male), 32 TOF, and 32 control patients were included. D-TGA patients had less dilatation than TOF patients (125 ± 35 ml/m2 vs. 149 ± 44 ml/m2, p = 0.02) and lower RVEF than controls (42.9 ± 7.7% vs. 56.3 ± 5.6%, p < 0.0001). RV LS was similar in D-TGA and TOF ( − 13.2 ± 4.5% vs. − 14.5 ± 5.9%, p = 0.32), both decreased compared to controls. However, CS in D-TGA was higher than controls ( − 14.1 ± 4.1% vs. − 11.4 ± 4.4%, p = 0.01), with a higher CS:LS ratio (1.2 ± 0.7 vs. 0.6 ± 0.3, p < 0.0001), while CS in TOF and controls did not differ. RVEF in D-TGA correlated closely with CS (r = − 0.85, p < 0.0001) but not LS (r = 0.10, p = 0.58). I n conclusion, CMR can differentiate strain patterns in pressure- and volume-loaded RVs, with decreased LS in both conditions, while systemic RVs compensate with supra-normal CS. CS may be a more clinically relevant measure of RV function in this population.
Generalization and exploration via randomized value functions

We propose randomized least-squares value iteration (RLSVI) - a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized value functions. We explain why versions of least-squares value iteration that use Boltzmann or e-greedy exploration can be highly inefficient, and we present computational results that demonstrate dramatic efficiency gains enjoyed by RLSVI. Further, we establish an upper bound on the expected regret of RLSVI that demonstrates nearoptimality in a tabula rasa learning context. More broadly, our results suggest that randomized value functions offer a promising approach to tackling a critical challenge in reinforcement learning: synthesizing efficient exploration and effective generalization.
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

There is growing interest in combining model-free and model-based approaches in reinforcement learning with the goal of achieving the high performance of model-free algorithms with low sample complexity. This is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency.
Ramanujan bigraphs associated with $SU(3)$ over a $p$-adic field

We use the representation theory of the quasisplit form G of SU(3) over a p-adic field to investigate whether certain quotients of the Bruhat-Tits tree associated to this form are Ramanujan bigraphs. We show that a quotient of the tree associated with G (which is a biregular bigraph) is Ramanujan if and only if G satisfies a Ramanujan type conjecture. This result is analogous to the seminal case of PGL 2 (Q p ) considered by Lubotzky, Phillips, and Sarnak. As a consequence, the classification of the automorphic spectrum of the unitary group in three variables by Rogawski implies the existence of certain infinite families of Ramanujan bigraphs.
RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning.

Motivated by what is required for real-time path planning, the paper starts out by presenting sRMPD, a new recursive "local" planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of sRMPD by using it as a "connect" subroutine call in a higher-level sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence, mRMPD spawns a larger number of space exploring trees in regions of the configuration space that are characterized by a higher density of obstacles. The overall effect is a hybrid tree growing strategy with a trade-off between random exploration as made possible by multi-RRT based logic and immediate exploitation of opportunities to connect two states as made possible by sRMPD. The mRMPD planner can be biased with regard to this trade-off for solving different kinds of planning problems efficiently. Based on the test cases we have run, our experiments show that mRMPD can reduce planning time by up to 80% compared to basic RRT.
Indicators of bismash products from exact symmetric group factorizations

ABSTRACTWe prove that all irreducible representations of the bismash product H=&#x1d55c;G#&#x1d55c;Sn−k have Frobenius–Schur indicators +1 or 0 where &#x1d55c; is an algebraically closed field and Sn=Sn−k⋅G is an exact factorization. Moreover, we have a description of those simple modules which are not self-dual. We prove some new results for bismash products in general and make conjectures about bismash products that arise from other exact factorizations of Sn.
On a non-commutative analogue of a classical result of Namioka and Phelps

Abstract A classical result of Namioka and Phelps states that the square is a test object to verify semi-simplexity in the tensor theory of convex compact sets. By using the quantization of generalized Namioka–Phelps test spaces we formulate a nuclearity criterion for C ⁎ -algebras, which establishes a non-commutative version of their result. The proof we suggest covers the nuclearity characterization via non-commutative polyhedron outlined by Effros [8] . Several matrix systems studied by Farenick and Paulsen [13] are shown to be test systems for nuclearity. We also prove that the standard Namioka–Phelps test space is C ⁎ -nuclear. We propose a partition of unity property for C ⁎ -algebras which distinguishes nuclear C ⁎ -algebras among the others.
Tool Demonstration: FSolidM for Designing Secure Ethereum Smart Contracts

Blockchain-based distributed computing platforms enable the trusted execution of computation—defined in the form of smart contracts—without trusted agents. Smart contracts are envisioned to have a variety of applications, ranging from financial to IoT asset tracking. Unfortunately, the development of smart contracts has proven to be extremely error prone. In practice, contracts are riddled with security vulnerabilities comprising a critical issue since bugs are by design non-fixable and contracts may handle financial assets of significant value. To facilitate the development of secure smart contracts, we have created the FSolidM framework, which allows developers to define contracts as finite state machines (FSMs) with rigorous and clear semantics. FSolidM provides an easy-to-use graphical editor for specifying FSMs, a code generator for creating Ethereum smart contracts, and a set of plugins that developers may add to their FSMs to enhance security and functionality.
On lattices and their ideal lattices, and posets and their ideal posets

For $P$ a poset or lattice, let $\mathrm{Id}(P)$ denote the poset, respectively, lattice, of upward directed downsets in $P,$ including the empty set, and let $\mathrm{id}(P)=\mathrm{Id}(P)-\{\emptyset\}.$ This note obtains various results to the effect that $\mathrm{Id}(P)$ is always, and $\mathrm{id}(P)$ often, “essentially larger” than $P.$ In the first vein, we find that a poset $P$ admits no $\!\lt\!$-respecting map (and so in particular, no one-to-one isotone map) from $\mathrm{Id}(P)$ into $P,$ and, going the other way, that an upper semilattice $P$ admits no semilattice homomorphism from any subsemilattice of itself onto $\mathrm{Id}(P).$ The slightly smaller object $\mathrm{id}(P)$ is known to be isomorphic to $P$ if and only if $P$ has ascending chain condition. This result is strengthened to say that the only posets $P_0$ such that for every natural number $n$ there exists a poset $P_n$ with $\mathrm{id}^n(P_n)\cong P_0$ are those having ascending chain condition. On the other hand, a wide class of cases is noted where $\mathrm{id}(P)$ is embeddable in $P.$ Counterexamples are given to many variants of the statements proved.
Inexact Proximal Gradient Methods for Non-Convex and Non-Smooth Optimization

In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization. Inexact proximal gradient methods are extremely important when exactly solving the proximal operator is time-consuming, or the proximal operator does not have an analytic solution. However, existing inexact proximal gradient methods only consider convex problems. The knowledge of inexact proximal gradient methods in the non-convex setting is very limited. To address this challenge, in this paper, we first propose three inexact proximal gradient algorithms, including the basic version and Nesterov’s accelerated version. After that, we provide the theoretical analysis to the basic and Nesterov’s accelerated versions. The theoretical results show that our inexact proximal gradient algorithms can have the same convergence rates as the ones of exact proximal gradient algorithms in the non-convex setting. Finally, we show the applications of our inexact proximal gradient algorithms on three representative non-convex learning problems. Empirical results confirm the superiority of our new inexact proximal gradient algorithms.
Torsion of abelian varieties, Weil classes and cyclotomic extensions

Let $K$ be a field finitely generated over the field of rational numbers, $K(c)$ the extension of $K$ obtained by adjoining all roots of unity, $L$ an infinite Galois extension of $K$, $X$ an abelian variety defined over $K$. We prove that under certain conditions on $X$ and $K$ the existence of infinitely many L-rational points of finite order on $X$ implies that the intersection of $L$ and $K(c)$ has infinite degree over $K$.
Bayesian graphical models for multivariate functional data

Graphical models express conditional independence relationships among variables. Although methods for vector-valued data are well established, functional data graphical models remain underdeveloped. By functional data, we refer to data that are realizations of random functions varying over a continuum (e.g., images, signals). We introduce a notion of conditional independence between random functions, and construct a framework for Bayesian inference of undirected, decomposable graphs in the multivariate functional data context. This framework is based on extending Markov distributions and hyper Markov laws from random variables to random processes, providing a principled alternative to naive application of multivariate methods to discretized functional data. Markov properties facilitate the composition of likelihoods and priors according to the decomposition of a graph. Our focus is on Gaussian process graphical models using orthogonal basis expansions. We propose a hyper-inverse-Wishart-process prior for the covariance kernels of the infinite coefficient sequences of the basis expansion, and establish its existence and uniqueness. We also prove the strong hyper Markov property and the conjugacy of this prior under a finite rank condition of the prior kernel parameter. Stochastic search Markov chain Monte Carlo algorithms are developed for posterior inference, assessed through simulations, and applied to a study of brain activity and alcoholism.
Looking for a Black Cat in a Dark Room: Security Visualization for Cyber-Physical System Design and Analysis

Today, there is a plethora of software security tools employing visualizations that enable the creation of useful and effective interactive security analyst dashboards. Such dashboards can assist the analyst to understand the data at hand and, consequently, to conceive more targeted preemption and mitigation security strategies. Despite the recent advances, model-based security analysis is lacking tools that employ effective dashboards—to manage potential attack vectors, system components, and requirements. This problem is further exacerbated because model-based security analysis produces significantly larger result spaces than security analysis applied to realized systems—where platform specific information, software versions, and system element dependencies are known. Therefore, there is a need to manage the analysis complexity in model-based security through better visualization techniques. Towards that goal, we propose an interactive security analysis dashboard that provides different views largely centered around the system, its requirements, and its associated attack vector space. This tool makes it possible to start analysis earlier in the system lifecycle. We apply this tool in a significant area of engineering design—the design of cyber-physical systems—where security violations can lead to safety hazards.
Ulrich Bundles on Enriques Surfaces

We study Ulrich bundles and their moduli on unnodal Enriques surfaces. In particular, we prove that unnodal Enriques surfaces are of wild representation type by constructing moduli spaces of stable Ulrich bundles of arbitrary rank and arbitrarily large dimension.
Rapid Fading Due to Human Blockage in Pedestrian Crowds at 5G Millimeter-Wave Frequencies

Rapidly fading channels caused by pedestrians in dense urban environments will have a significant impact on millimeter-wave (mmWave) communications systems that employ electrically-steerable and narrow beamwidth antenna arrays. A peer- to-peer (P2P) measurement campaign was conducted with 7o, 15o, and 60o half- power beamwidth (HPBW) antenna pairs at 73.5 GHz and with 1 GHz of RF null-to-null bandwidth in a heavily populated open square scenario in Brooklyn, New York, to study blockage events caused by typical pedestrian traffic. Antenna beamwidths that range approximately an order of magnitude were selected to gain knowledge of fading events for antennas with different beamwidths since antenna patterns for mmWave systems will be electronically-adjustable. Two simple modeling approaches in the literature are introduced to characterize the blockage events by either a two-state Markov model or a four-state piecewise linear modeling approach. Transition probability rates are determined from the measurements and it is shown that average fade durations with a -5 dB threshold are 299.0 ms for 7o HPBW antennas and 260.2 ms for 60o HPBW antennas. The four-state piecewise linear modeling approach shows that signal strength decay and rise times are asymmetric for blockage events and that mean signal attenuations (average fade depths) are inversely proportional to antenna HPBW, where 7o and 60o HPBW antennas resulted in mean signal fades of 15.8 dB and 11.5 dB, respectively. The models presented herein are valuable for extending statistical channel models at mmWave to accurately simulate real- world pedestrian blockage events when designing fifth-generation (5G) wireless systems.
A Fast and Accurate Failure Frequency Approximation for $k$ -Terminal Reliability Systems

This paper considers the problem of approximating the failure frequency of large-scale composite $\boldsymbol{k}$ -terminal reliability systems. In such systems, the nodes ( $\boldsymbol{k}$ of which are terminals) are connected through components, which are subject to random failure and repair processes. At any time, a system failure occurs if the surviving system fails to connect all the $\boldsymbol{k}$ terminals together. We assume that each component's up times and down times follow statistically independent stationary random processes, and these processes are statistically independent across the components. In this setting, the exact computation of failure frequency is known to be computationally intractable (NP-hard). In this paper, we present an algorithm to approximate the failure frequency for any given multiplicative error factor that runs in polynomial time in the number of (minimal) cutsets. Moreover, for the special case of all-terminal reliability systems, i.e., where all the nodes are terminals, we propose an algorithm for approximating the failure frequency within an arbitrary multiplicative error that runs in polynomial time in the number of nodes (which can be much smaller than the number of cutsets). Our simulation results confirm that the proposed method is much faster and more accurate than the standard Monte Carlo simulation technique for approximating the failure frequency.
The classification of quasi-alternating Montesinos links

In this note, we complete the classification of quasi-alternating Montesinos links. We show that the quasi-alternating Montesinos links are precisely those identified independently by Qazaqzeh-Chbili-Qublan and Champanerkar-Ording. A consequence of our proof is that a Montesinos link $L$ is quasi-alternating if and only if its double branched cover is an L-space, and bounds both a positive definite and a negative definite 4-manifold with vanishing first homology.
Nonlinear dimensionality reduction on graphs

In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while preserving task-related characteristics, going beyond pairwise data correlations. The present paper puts forth a nonlinear dimensionality reduction framework that accounts for data lying on known graphs. The novel framework turns out to encompass most of the existing dimensionality reduction methods as special cases, and it is capable of capturing and preserving possibly nonlinear correlations that are ignored by linear methods, as well as taking into account information from multiple graphs. An efficient algorithm admitting closed-form solution is developed and tested on synthetic datasets to corroborate its effectiveness.
On the Lusternik–Schnirelmann category of a simplicial map

Abstract In this paper, we study the Lusternik–Schnirelmann category of a simplicial map between simplicial complexes, generalizing the simplicial category of a complex to that of a map. Several properties of this new invariant are shown, including its relevance to simplicial products and fibrations. We relate this category of a map to the classical Lusternik–Schnirelmann category of a map between finite topological spaces. Finally, we show how the simplicial category of a map may be used to define and study a simplicial version of the category weight of Y. Rudyak and J. Strom.
NanoMap: Fast, Uncertainty-Aware Proximity Queries with Lazy Search Over Local 3D Data

We would like robots to be able to safely navigate at high speed, efficiently use local 3D information, and robustly plan motions that consider pose uncertainty of measurements in a local map structure. This is hard to do with previously existing mapping approaches, like occupancy grids, that are focused on incrementally fusing 3D data into a common world frame. In particular, both their fragile sensitivity to state estimation errors and computational cost can be limiting. We develop an alternative framework, NanoMap, which alleviates the need for global map fusion and enables a motion planner to efficiently query pose-uncertainty-aware local 3D geometric information. The key idea of NanoMap is to store a history of noisy relative pose transforms and search over a corresponding set of depth sensor measurements for the minimum-uncertainty view of a queried point in space. This approach affords a variety of capabilities not offered by traditional mapping techniques: (a) the pose uncertainty associated with 3D data can be incorporated in motion planning, (b) poses can be updated (i.e., from loop closures) with minimal computational effort, and (c) 3D data can be fused lazily for the purpose of planning. We provide an open-source implementation of NanoMap, and analyze its capabilities and computational efficiency in simulation experiments. Finally, we demonstrate in hardware its effectiveness for fast 3D obstacle avoidance onboard a quadrotor flying up to 10 m/s.
Synchronization of coupled chaotic maps

Abstract We prove a sufficient condition for synchronization for coupled one-dimensional maps and estimate the size of the window of parameters where synchronization takes place. It is shown that coupled systems on graphs with positive eigenvalues of the normalized graph Laplacian concentrated around 1 are more amenable for synchronization. In the light of this condition, we review spectral properties of Cayley, quasirandom, power-law graphs, and expanders and relate them to synchronization of the corresponding networks. The analysis of synchronization on these graphs is illustrated with numerical experiments. The results of this paper highlight the advantages of random connectivity for synchronization of coupled chaotic dynamical systems.
Is Witsenhausen's counterexample a relevant toy?

This paper answers a question raised by Doyle on the relevance of the Witsenhausen counterexample as a toy decentralized control problem. The question has two sides, the first of which focuses on the lack of an external channel in the counterexample. Using existing results, we argue that the core difficulty in the counterexample is retained even in the presence of such a channel. The second side questions the LQG formulation of the counterexample. We consider alternative formulations and show that the understanding developed for the LQG case guides the investigation for these other cases as well. Specifically, we consider 1) a variation on the original counterexample with general, but bounded, noise distributions, and 2) an adversarial extension with bounded disturbance and quadratic costs. For each of these formulations, we show that quantization-based nonlinear strategies outperform linear strategies by an arbitrarily large factor. Further, these nonlinear strategies also perform within a constant factor of the optimal, uniformly over all possible parameter choices (for fixed noise distributions in the Bayesian case). Fortuitously, the assumption of bounded noise results in a significant simplification of proofs as compared to those for the LQG formulation. Therefore, the results in this paper are also of pedagogical interest.
Neural Argument Generation Augmented with Externally Retrieved Evidence

High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments.
Relative exchangeability with equivalence relations

We describe an Aldous–Hoover-type characterization of random relational structures that are exchangeable relative to a fixed structure which may have various equivalence relations. Our main theorem gives the common generalization of the results on relative exchangeability due to Ackerman (Representations of $$\text {Aut}(\mathcal {M})$$ -invariant measures: part I, 2015. arXiv:1509.06170 ) and Crane and Towsner (Relatively exchangeable structures, 2015) and hierarchical exchangeability results due to Austin and Panchenko (Probab Theory Relat Fields 159(3–4):809–823, 2014).
Balanced Allocation Through Random Walk

Abstract We consider the allocation problem in which m ≤ ( 1 − e ) d n items are to be allocated to n bins with capacity d. The items x 1 , x 2 , … , x m arrive sequentially and when item x i arrives it is given two possible bin locations p i = h 1 ( x i ) , q i = h 2 ( x i ) via hash functions h 1 , h 2 . We consider a random walk procedure for inserting items and show that the expected time insertion time is constant provided e = Ω ( log ⁡ d d ) .
Non-elementary amenable subgroups of automata groups

We consider groups of automorphisms of rooted locally finite trees, and give conditions on its subgroups that imply that they are not elementary amenable. We give a unified proof for all known examples of non-elementary amenable groups that act on the trees: groups of intermediate growths and Basilica group. Moreover, we show that all finitely generated branch groups are not elementary amenable, which was conjectured by Grigorchuk.
Towards High-Fidelity Nonlinear 3D Face Morphable Model

Embedding 3D morphable basis functions into deep neural networks opens great potential for models with better representation power. However, to faithfully learn those models from an image collection, it requires strong regularization to overcome ambiguities involved in the learning process. This critically prevents us from learning high fidelity face models which are needed to represent face images in high level of details. To address this problem, this paper presents a novel approach to learn additional proxies as means to side-step strong regularizations, as well as, leverages to promote detailed shape/albedo. To ease the learning, we also propose to use a dual-pathway network, a carefully-designed architecture that brings a balance between global and local-based models. By improving the nonlinear 3D morphable model in both learning objective and network architecture, we present a model which is superior in capturing higher level of details than the linear or its precedent nonlinear counterparts. As a result, our model achieves state-of-the-art performance on 3D face reconstruction by solely optimizing latent representations.
REAPPROACHING THE SPIN ESTIMATE OF GX 339-4

We systematically reanalyze two previous observations of the black hole (BH) GX 339-4 in the very high and intermediate state taken with XMM-Newton and Suzaku. We utilize up-to-date data reduction procedures and implement the recently developed, self-consistent model for X-ray reflection and relativistic ray tracing, relxill. In the very high and intermediate state, the rate of accretion is high and thus the disk remains close to the innermost stable circular orbit. We require a common spin parameter and inclination when fitting the two observations since these parameters should remain constant across all states. This allows for the most accurate determination of the spin parameter of this galactic BH binary from fitting the Fe Kα emission line and provides a chance to test previous estimates. We find GX 339-4 to be consistent with a near maximally spinning BH with a spin parameter with an inclination of 36° ± 4°. This spin value is consistent with previous high estimates for this object. Further, if the inner disk is aligned with the binary inclination, this modest inclination returns a high BH mass, but they need not be aligned. Additionally, we explore how the spin is correlated with the power of the jet emitted but find no correlation between the two.
Guaranteed model-based fault detection in cyber–physical systems: A model invalidation approach

Abstract This paper presents a sound and complete fault detection approach for cyber–physical systems represented by hidden-mode switched affine models with time varying parametric uncertainty. The fault detection approach builds upon techniques from model invalidation. In particular, a set-membership approach is taken where the noisy input–output data is compared to the set of behaviors of a nominal model. As we show, this set-membership check can be reduced to the feasibility of a mixed-integer linear programming (MILP) problem, which can be solved efficiently by leveraging the state-of-the-art MILP solvers. In the second part of the paper, given a system model and a fault model, the concept of T -detectability is introduced. If a pair of system and fault models satisfies T -detectability property for a finite T , this allows the model invalidation algorithm to be implemented in a receding horizon manner, without compromising detection guarantees. In addition, the concept of weak-detectability is introduced which extends the proposed approach to a more expressive class of fault models that capture language constraints on the mode sequences. Finally, the efficiency of the approach is illustrated with numerical examples motivated by smart building radiant systems.
Double branched covers of theta-curves

We prove a folklore theorem of Thurston, which provides necessary and sufficient conditions for primality of a certain class of theta-curves. Namely, a theta-curve in the 3-sphere with an unknotted constituent knot κ is prime, if and only if lifting the third arc of the theta-curve to the double branched cover over κ produces a prime knot. We apply this result to Kinoshita’s theta-curve.
Attacking quantum key distribution with single-photon two-qubit quantum logic

The Fuchs-Peres-Brandt (FPB) probe realizes the most powerful individual attack on Bennett-Brassard 1984 quantum key distribution (BB84 QKD) by means of a single controlled-NOT (CNOT) gate. This paper describes a complete physical simulation of the FPB-probe attack on polarization-based BB84 QKD using a deterministic CNOT constructed from single-photon two-qubit quantum logic. Adding polarization-preserving quantum nondemolition measurements of photon number to this configuration converts the physical simulation into a true deterministic realization of the FPB attack.
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many samples to produce desirable results. In this paper, we show how to create procedural models which learn how to satisfy constraints. We augment procedural models with neural networks which control how the model makes random choices based on the output it has generated thus far. We call such models neurally-guided procedural models. As a pre-computation, we train these models to maximize the likelihood of example outputs generated via SMC. They are then used as efficient SMC importance samplers, generating high-quality results with very few samples. We evaluate our method on L-system-like models with image-based constraints. Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models.