cssi/SciSciGPT-SciSciNet huggingface dataset:
Replication fork slowing and stalling are distinct, checkpoint-independent consequences of replicating damaged DNA

In response to DNA damage during S phase, cells slow DNA replication. This slowing is orchestrated by the intra-S checkpoint and involves inhibition of origin firing and reduction of replication fork speed. Slowing of replication allows for tolerance of DNA damage and suppresses genomic instability. Although the mechanisms of origin inhibition by the intra-S checkpoint are understood, major questions remain about how the checkpoint regulates replication forks: Does the checkpoint regulate the rate of fork progression? Does the checkpoint affect all forks, or only those encountering damage? Does the checkpoint facilitate the replication of polymerase-blocking lesions? To address these questions, we have analyzed the checkpoint in the fission yeast Schizosaccharomyces pombe using a single-molecule DNA combing assay, which allows us to unambiguously separate the contribution of origin and fork regulation towards replication slowing, and allows us to investigate the behavior of individual forks. Moreover, we have interrogated the role of forks interacting with individual sites of damage by using three damaging agents-MMS, 4NQO and bleomycin-that cause similar levels of replication slowing with very different frequency of DNA lesions. We find that the checkpoint slows replication by inhibiting origin firing, but not by decreasing fork rates. However, the checkpoint appears to facilitate replication of damaged templates, allowing forks to more quickly pass lesions. Finally, using a novel analytic approach, we rigorously identify fork stalling events in our combing data and show that they play a previously unappreciated role in shaping replication kinetics in response to DNA damage.
A new synthetic strategy for the synthesis of bioactive stilbene dimers. A direct synthesis of amurensin H

Abstract 2,3-Diarylbenzofurans are efficiently generated by the cyclization of ortho-benzyloxybenzophenones using the hindered phosphazene base P 4 - t -Bu.
Enantioselective Rhodium(I)-Catalyzed Hydrogenation of Trifluoromethyl Ketones

The asymmetric hydrogenation of trifluoromethyl ketones to yield chiral α-trifluoromethyl alcohols with enantiomeric excesses up to 98% was achieved in the presence of chiral rhodium-(amidephosphine-phosphinite) complexes.
Stereocontrolled Synthesis of 1,3‐Diamino‐2‐ols by Aminohydroxylation of Bicyclic Methylene‐Aziridines

Nitrogen-containing stereotriads, which are defined as compounds with three adjacent stereodefined carbon atoms, are common structural motifs in several biologically relevant compounds. The 1,3-diamino-2-ol motif in particular is an important pharmacophore for which there are limited stereoselective synthetic approaches. In this communication, we describe the aminohydroxylation of a series of bicyclic methylene-aziridines obtained from the aziridination of a series of homoallenic carbamates. The unusual electronic and steric features of these useful heterocyclic scaffolds render the Os-catalyzed aminohydroxylation of the exocyclic alkene highly regio- and stereoselective. Rearrangement of the proposed N,O-aminal intermediate to a 1,3-diamino-2-one is followed by reduction with NaBH4 to deliver the desired 1,3-diamino-2-ols in good yields with high diastereomeric ratios.
Ce2AgYb5/3Se6, La2CuErTe5, and Ce2CuTmTe5: Three new quaternary interlanthanide chalcogenides

Abstract Three new ordered quaternary interlanthanide chalcogenides, Ce2AgYb5/3Se6, La2CuErTe5, and Ce2CuTmTe5, have been prepared by direct reaction of the elements in molten NaBr at 900 °C. Each compound forms a new structure-type. The Ce2AgYb5/3Se6 structure consists of ∞ 2 { [ AgYb 5 / 6 Se 6 ] 6 – } layers intercalated by Ce3+ cations. These layers are composed of ∞ 1 { [ Yb 5 / 3 Se 6 ] 7 – } quadruplet ribbons of [YbSe6]9– octahedra and infinite ∞ 1 { [ AgSe 6 ] 11 – } double chains of [AgSe5]9–. The La2CuErTe5 structure is made of one-dimensional ∞ 1 { [ CuErTe 5 ] 6 – } ribbons separated by La3+ cations. These ribbons are formed by cis-edge sharing ∞ 1 { [ CuTe 2 ] 3 – } tetrahedral chains and trans-edge sharing ∞ 1 { [ ErTe 4 ] 5 – } chains. While La2CuErTe5 crystallizes in the orthorhombic space group Pnma, Ce2CuTmTe5 crystallizes in the monoclinic space group C2/m. The latter crystal structure is assembled from ∞ 2 { [ CuTmTe 5 ] 6 – } layers intercalated by Ce3+ cations. These layers consist of single ∞ 1 { [ TmTe 4 ] 5 – } chains connected to each other through dimers or pseudo-double chains.
Diels–Alder reactions of 2- and 3-nitroindoles. A simple hydroxycarbazole synthesis

Abstract A Diels–Alder reaction of 3- and 2-nitroindoles with Danishefsky's diene gives the expected 2- and 3-hydroxycarbazoles in very good to excellent yields (73–91%) and with apparent complete regioselectivity.
Rhodium-Catalyzed Rearrangement of α-Diazo Thiol Esters to Thio-Substituted Ketenes. Application in the Synthesis of Cyclobutanones, Cyclobutenones, and β-Lactams

Exposure of α-diazo thiol esters (1) to the action of catalytic rhodium(II) acetate leads to a remarkably facile “thia-Wolff rearrangement”, producing thio-substituted ketenes which combine with a ...
High-dimensional characterization of IL-10 production and IL-10 dependent regulation during primary gammaherpesvirus infection

IL-10 is a potent immunomodulatory cytokine produced by multiple cell types to restrain immune activation. Many herpesviruses use the IL-10 pathway to facilitate infection, but how endogenous IL-10 is regulated during primary infection in vivo remains poorly characterized. In this study, we infected mice with murine gammaherpesvirus 68 (γHV68) and analyzed the production and genetic contribution of IL-10 by mass cytometry (cytometry by time-of-flight) analysis. γHV68 infection elicited a breadth of effector CD4 T cells in the lungs of acutely infected mice, including a highly activated effector subset that coexpressed IFN-γ, TNF-α, and IL-10. By using IL-10 GFP transcriptional reporter mice, we identified that IL-10 was primarily expressed within CD4 T cells during acute infection in the lungs. IL10gfp-expressing CD4 T cells were highly proliferative and characterized by the expression of multiple coinhibitory receptors, including PD-1 and LAG-3. When we analyzed acute γHV68 infection of IL-10-deficient mice, we found that IL-10 limits the frequency of both myeloid and effector CD4 T cell subsets in the infected lung, with minimal changes at a distant mucosal site. These data emphasize the unique insights that high-dimensional analysis can afford in investigating antiviral immunity and provide new insights into the breadth, phenotype, and function of IL-10-expressing effector CD4 T cells during acute virus infection.
Ventral tegmental area astrocytes orchestrate avoidance and approach behavior

The ventral tegmental area (VTA) is a heterogeneous midbrain structure, containing neurons and astrocytes, that coordinates behaviors by integrating activity from numerous afferents. Within neuron-astrocyte networks, astrocytes control signals from distinct afferents in a circuit-specific manner, but whether this capacity scales up to drive motivated behavior has been undetermined. Using genetic and optical dissection strategies we report that VTA astrocytes tune glutamatergic signaling selectively on local inhibitory neurons to drive a functional circuit for learned avoidance. In this circuit, astrocytes facilitate excitation of VTA GABA neurons to increase inhibition of dopamine neurons, eliciting real-time and learned avoidance behavior that is sufficient to impede expression of preference for reward. Loss of one glutamate transporter (GLT-1) from VTA astrocytes selectively blocks these avoidance behaviors and spares preference for reward. Thus, VTA astrocytes selectively regulate excitation of local GABA neurons to drive a distinct avoidance circuit that opposes approach behavior.
The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry.

Abstract Background The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adjust for comorbidities, using as an example the association between insurance and mortality. Materials and methods Using all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 International Classification of Disease, Ninth Revision–derived comorbidities. We estimated odds ratios (ORs) for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights for CCI, LPCA, and separate covariates. Results In 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD = 0.086, OR = 1.17), which was almost entirely eliminated by inverse probability of treatment weights on separate covariates (mean SMD = 0.012, OR = 1.36). The CCI performed similarly to the unweighted sample (mean SMD = 0.080, OR = 1.25), whereas two LPCA axes were better able to control for confounding (mean SMD = 0.04, OR = 1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas two LPCA axes accounted for 91.3%. Conclusions The use of the CCI to adjust for confounding may result in residual confounding, and alternative strategies should be considered. LPCA may be a viable alternative to adjusting for each comorbidity when samples are small or positivity assumptions are violated.
Gene-drive-mediated extinction is thwarted by population structure and evolution of sib mating

Background and objectives Genetic engineering combined with CRISPR technology has developed to the point that gene drives can, in theory, be engineered to cause extinction in countless species. Success of extinction programs now rests on the possibility of resistance evolution, which is largely unknown. Depending on the gene-drive technology, resistance may take many forms, from mutations in the nuclease target sequence (e.g. for CRISPR) to specific types of non-random population structures that limit the drive (that may block potentially any gene-drive technology). Methodology We develop mathematical models of various deviations from random mating to consider escapes from extinction-causing gene drives. A main emphasis here is sib mating in the face of recessive-lethal and Y-chromosome drives. Results Sib mating easily evolves in response to both kinds of gene drives and maintains mean fitness above 0, with equilibrium fitness depending on the level of inbreeding depression. Environmental determination of sib mating (as might stem from population density crashes) can also maintain mean fitness above 0. A version of Maynard Smith's haystack model shows that pre-existing population structure can enable drive-free subpopulations to be maintained against gene drives. Conclusions and implications Translation of mean fitness into population size depends on ecological details, so understanding mean fitness evolution and dynamics is merely the first step in predicting extinction. Nonetheless, these results point to possible escapes from gene-drive-mediated extinctions that lie beyond the control of genome engineering. Lay summary Recent gene drive technologies promise to suppress and even eradicate pests and disease vectors. Simple models of gene-drive evolution in structured populations show that extinction-causing gene drives can be thwarted both through the evolution of sib mating as well as from purely demographic processes that cluster drive-free individuals.
Enantioselective addition of diethylzinc to benzaldehyde catalyzed by chiral titanate complexes with helical ligands

Abstract Enantioselective alkylation of benzaldehyde with Et2Zn has been studied. This reaction is catalyzed by helical titanium complexes of tetradentate ligands and has been found to give good to excellent enantioselectivities.
Lewis Acid-Promoted Reactions of N1,N4-Dibenzoyl-1,4-benzoquinone Diimines with (E)-Propenylbenzenes: An Unexpected Reaction

BF3-promoted reactions of 2-alkoxy-N1,N4-dibenzoyl-1,4-benzoquinone diimines (4) with (E)-propenylbenzenes yield (1R*,2R*)-2-[[2-alkoxy-4-(benzoylamino)phenyl]amino]-1-phenylpropyl benzoates (7/8) stereoselectively.
Synthesis of (′)-isoretronecanol, (′)-curassanecine, (′)-heliotridane, (′)-tashiromine and (′)-5-epitashiromine via α-(N-carbamoyl)alkylcuprate chemistry

Abstract Vinylation of N-Boc-2-pyrrolidinylcuprate reagents with functionalized vinyl iodides followed by N-Boc deprotection and cyclization affords 1-methylidine pyrrolizidine and indolizidine carbon skeletons. Functional group manipulation of the exo-cyclic olefin provides direct synthetic entries to the pyrrolizidine alkaloids (±)-isoretronecanol, (±)-curassanecine, (±)-heliotridane or the indolizidine alkaloids (±)-tashiromine and (±)-epitashiromine. This synthetic approach to pyrrolizidine and indolizidine alkaloids requires masking of the tertiary amine during functional group interconversions involving the alkene functionality.
Rating Reliability and Bias in News Articles: Does AI Assistance Help Everyone?

With the spread of false and misleading information in current news, many algorithmic tools have been introduced with the aim of assessing bias and reliability in written content. However, there has been little work exploring how effective these tools are at changing human perceptions of content. To this end, we conduct a study with 654 participants to understand if algorithmic assistance improves the accuracy of reliability and bias perceptions, and whether there is a difference in the effectiveness of the AI assistance for different types of news consumers. We find that AI assistance with featurebased explanations improves the accuracy of news perceptions. However, some consumers are helped more than others. Specifically, we find that participants who read and share news often on social media are worse at recognizing bias and reliability issues in news articles than those who do not, while frequent news readers and those familiar with politics perform much better. We discuss these differences and their implication to offer insights for future research.
Effective Divisors in the Projectivized Hodge Bundle

We compute the class of the closure of the locus of canonical divisors in the projectivization of the Hodge bundle $\mathbb{P}\overline{\mathcal{H}}_g$ over $\overline{\mathcal{M}}_g$ which have a zero at a Weierstrass point. We also show that the strata of canonical and bicanonical divisors with a double zero span extremal rays of the respective pseudoeffective cones.
Generating probability distributions using multivalued stochastic relay circuits

The problem of random number generation dates back to von Neumann's work in 1951. Since then, many algorithms have been developed for generating unbiased bits from complex correlated sources as well as for generating arbitrary distributions from unbiased bits. An equally interesting, but less studied aspect is the structural component of random number generation as opposed to the algorithmic aspect. That is, given a network structure imposed by nature or physical devices, how can we build networks that generate arbitrary probability distributions in an optimal way? In this paper, we study the generation of arbitrary probability distributions in multivalued relay circuits, a generalization in which relays can take on any of N states and the logical ‘and’ and ‘or’ are replaced with ‘min’ and ‘max’ respectively. Previous work was done on two-state relays. We generalize these results, describing a duality property and networks that generate arbitrary rational probability distributions. We prove that these networks are robust to errors and design a universal probability generator which takes input bits and outputs arbitrary binary probability distributions.
Learning Exact Topology of a Loopy Power Grid from Ambient Dynamics

Estimation of the operational topology of the power grid is necessary for optimal market settlement and reliable dynamic operation of the grid. This paper presents a novel framework for topology estimation for general power grids (loopy or radial) using time-series measurements of nodal voltage phase angles that arise from the swing dynamics. Our learning framework utilizes multivariate Wiener filtering to unravel the interaction between fluctuations in voltage angles at different nodes and identifies operational edges by considering the phase response of the elements of the multivariate Wiener filter. The performance of our learning framework is demonstrated through simulations on standard IEEE test cases.
Multiplication is Discontinuous in the Hawaiian Earring Group (with the Quotient Topology)

The natural quotient map q from the space of based loops in the Hawaiian earring onto the fundamental group provides a new example of a quotient map such that q x q fails to be a quotient map. This also settles in the negative the question of whether the fundamental group (with the quotient topology) of a compact metric space is always a topological group with the standard operations.
Robust binary hypothesis testing under contaminated likelihoods

In hypothesis testing, the phenomenon of label noise, in which hypothesis labels are switched at random, contaminates the likelihood functions. In this paper, we develop a new method to determine the decision rule when we do not have knowledge of the uncontaminated likelihoods and contamination probabilities, but only have knowledge of the contaminated likelihoods. In particular we pose a minimax optimization problem that finds a decision rule robust against this lack of knowledge. The method simplifies by application of linear programming theory. Motivation for this investigation is provided by problems encountered in workforce analytics.
Optimal measurement selection algorithm and estimator for ultra-wideband symmetric ranging localization

A state estimator is derived for an agent with the ability to measure single ranges to fixed points in its environment, and equipped with an accelerometer and a rate gyroscope. The state estimator makes no agent-specific assumptions, and can be immediately applied to any rigid body with these sensors. Also, the state estimator doesn't use any trilateration-based method to calculate position from range measurements. As the considered system can only make a single range measurement at a time, we present a greedy optimization algorithm for selecting the best measurement. Experiments in an indoor testbed using an externally controlled multicopter demonstrate the efficacy of the algorithm, specifically showing an improvement over a naive strategy of a fixed sequence of measurements. In separate experiments, the algorithm is also used in feedback control, to control the position of the multicopter.
Coloring Graphs with Forbidden Minors

Abstract Hadwiger's conjecture from 1943 states that for every integer t ≥ 1 , every graph either can be t -colored or has a subgraph that can be contracted to the complete graph on t + 1 vertices. As pointed out by Paul Seymour in his recent survey on Hadwiger's conjecture, proving that graphs with no K 7 minor are 6-colorable is the first case of Hadwiger's conjecture that is still open. It is not known yet whether graphs with no K 7 minor are 7-colorable. Using a Kempe-chain argument along with the fact that an induced path on three vertices is dominating in a graph with independence number two, we first give a very short and computer-free proof of a recent result of Albar and Goncalves and generalize it to the next step by showing that every graph with no K t minor is ( 2 t − 6 ) -colorable, where t ∈ { 7 , 8 , 9 } . We then prove that graphs with no K 8 − minor are 9-colorable, and graphs with no K 8 = minor are 8-colorable. Finally we prove that if Mader's bound for the extremal function for K t minors is true, then every graph with no K t minor is ( 2 t − 6 ) -colorable for all t ≥ 6 . This implies our first result. We believe that the Kempe-chain method we have developed in this paper is of independent interest.
Small gaps between zeros of twisted L-functions

We use the asymptotic large sieve, developed by the authors, to prove that if the Generalized Riemann Hypothesis is true, then there exist many Dirichlet L-functions that have a pair of consecutive zeros closer together than 0.37 times their average spacing. More generally, we investigate zero spacings within the family of twists by Dirichlet characters of a fixed L-function and give precise bounds for small gaps which depend only on the degree of the L-function.
Stronger Baselines for Trustable Results in Neural Machine Translation

Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over “vanilla” NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-the-art production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.
Anti)Fragility and Convex Responses in Medicine

This paper applies risk analysis to medical problems, through the properties of nonlinear responses (convex or concave). It shows (1) necessary relations between the nonlinearity of dose-response and the statistical properties of the outcomes, particularly the effect of the variance (i.e., the expected frequency of the various results and other properties such as their average and variations); (2) The description of “antifragility” as a mathematical property for local convex response and its generalization and the designation “fragility” as its opposite, locally concave; (3) necessary relations between dosage, severity of conditions, and iatrogenics.
Finite type link invariants and the non-invertibility of links

We show that a variation of Milnor's $\bar\mu$-invariants, the so-called Campbell-Hausdorff invariants introduced recently by Stefan Papadima, are of finite type with respect to {\it marked singular links}. These link invariants are stronger than quantum invariants in the sense that they detect easily the non-invertibility of links with more than one components. It is still open whether some effectively computable knot invariants, e.g. finite type knot invariants (Vassiliev invariants), could detect the non-invertibility of knots.
On some provably correct cases of variational inference for topic models

Variational inference is an efficient, popular heuristic used in the context of latent variable models. We provide the first analysis of instances where variational inference algorithms converge to the global optimum, in the setting of topic models. Our initializations are natural, one of them being used in LDA-c, the most popular implementation of variational inference. In addition to providing intuition into why this heuristic might work in practice, the multiplicative, rather than additive nature of the variational inference updates forces us to use non-standard proof arguments, which we believe might be of general theoretical interest.
Passive scalars, moving boundaries, and Newton's law of cooling

We study the evolution of passive scalars in both rigid and moving slab-like domains, in both horizontally periodic and infinite contexts. The scalar is required to satisfy Robin-type boundary conditions corresponding to Newton's law of cooling, which lead to nontrivial equilibrium configurations. We study the equilibration rate of the passive scalar in terms of the parameters in the boundary condition and the equilibration rates of the background velocity field and moving domain.
Machine learning models of plastic flow based on representation theory

We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen input-output map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. With these developments, we enable rapid model building in real-time with experiments, and guide data collection and feature discovery.
Probabilistically safe policy transfer

Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy. We formalize the idea of safe learning in a probabilistic sense by defining an optimization problem: we desire to maximize the expected return while keeping the expected damage below a given safety limit. We study this optimization for the case of a robot manipulator with safety-based torque limits. We would like to ensure that the damage constraint is maintained at every step of the optimization and not just at convergence. To achieve this aim, we introduce a novel method which predicts how modifying the torque limit, as well as how updating the policy parameters, might affect the robot's safety. We show through a number of experiments that our approach allows the robot to improve its performance while ensuring that the expected damage constraint is not violated during the learning process.
On the Chudnovsky-Seymour-Sullivan Conjecture on Cycles in Triangle-free Digraphs

For a simple digraph G without directed triangles or digons, let \beta(G) be the size of the smallest subset X of E(G) such that G\X has no directed cycles, and let \gamma(G) be the number of unordered pairs of nonadjacent vertices in G. In 2008, Chudnovsky, Seymour, and Sullivan showed that \beta(G) \leq \gamma(G) and conjectured that \beta(G) \leq \gamma(G)/2. Recently, Dunkum, Hamburger, and Por proved that \beta(G)\leq .88\gamma(G). In this note, we prove that \beta(G) \leq .8616 \gamma(G).
Groups Not Acting on Manifolds

In this article, we collect a series of observations that constrain actions of many groups on compact manifolds. In particular, we show that “generic” finitely generated groups have no smooth volume-preserving actions on compact manifolds while also producing many finitely presented, torsion-free groups with the same property.
Predicting the behavior of interacting humans by fusing data from multiple sources

Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but high-fidelity simulations to produce an accurate model of a system of interest at minimal cost. They have proven useful in modeling physical systems and have been applied to engineering problems such as wing-design optimization. During human-in-the-loop experimentation, it has become increasingly common to use online platforms, like Mechanical Turk, to run low-fidelity experiments to gather human performance data in an efficient manner. One concern with these experiments is that the results obtained from the online environment generalize poorly to the actual domain of interest. To address this limitation, we extend traditional multi-fidelity approaches to allow us to combine fewer data points from high-fidelity human-in-the-loop experiments with plentiful but less accurate data from low-fidelity experiments to produce accurate models of how humans interact. We present both model-based and model-free methods, and summarize the predictive performance of each method under different conditions.
Topologically Protected States in One-Dimensional Systems

We study a class of periodic Schrodinger operators, which we prove have Dirac points. We then show that the introduction of an \edge" via adiabatic modulation of a periodic potential by a domain wall results in the bifurcation of spatially localized \edge states", associated with the topologically protected zero- energy mode of an asymptotic one-dimensional Dirac operator. The bound states we construct can be realized as highly robust TM- electromagnetic modes for a class of photonic waveguides with a phase-defect. Our model also captures many aspects of the phenomenon of topologically protected edge states for two-dimensional bulk structures such as the honeycomb structure of graphene.
Higher-order finite element methods for elliptic problems with interfaces

We present higher-order piecewise continuous finite element methods for solving a class of interface problems in two dimensions. The method is based on correction terms added to the right-hand side in the standard variational formulation of the problem. We prove optimal error estimates of the methods on general quasi-uniform and shape regular meshes in maximum norms. In addition, we apply the method to a Stokes interface problem, adding correction terms for the velocity and the pressure, obtaining optimal convergence results.
Integrated Key based Strict Friendliness Verification of Neighbors in MANET

Abstract —A novel Strict Friendliness Verification (SFV) scheme based on the integrated key consisting of symmetric node identity, geographic location and round trip response time between the sender and the receiver radio in MANET is proposed. This key is dynamically updated for encryption and decryption of each packet to resolve Wormhole attack and Sybil attack. Additionally, it meets the minimal key lengths required for symmetric ciphers to provide adequate commercial security. Furthermore, the foe or unfriendly node detection is found significantly increasing with the lower number of symmetric IDs. This paper presents the simulation demonstrating the performance of SFV in terms of dynamic range using directional antenna on radios (or nodes), and the performance in terms of aggregate throughput, average end to end delay and packet delivered ratio. Keywords- Integrated key; Strict Friendliness Verification; Mobile Ad hoc Networks ; Wormhole Attack; Sybil Attack; Foe Detection Rate.
Complexity of holomorphic maps from the complex unit ball to classical domains

We study the complexity of holomorphic isometries and proper maps from the complex unit ball to type IV classical domains. We investigate on degree estimates of holomorphic isometries and holomorphic maps with minimum target dimension. We also construct a real-parameter family of mutually inequivalent holomorphic isometries from the unit ball to type IV domains. We also provide examples of non-isometric proper holomorphic maps from the complex unit ball to classical domains.
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes

We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.
Normalized online learning

We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale. This has several useful effects: there is no need to prenormalize data, the test-time and test-space complexity are reduced, and the algorithms are more robust.
The classification problem for simple unital finite rank dimension groups

The Borel complexity of the isomorphism problem for finite-rank unital simple dimension groups increases with rank. This implies that the isomorphism problems for the corresponding classes of Bratteli diagrams and LDA-groups also increase with rank.
Partial Regularity for Singular Solutions to the Monge-Ampère Equation

We prove that solutions to the Monge-Ampere inequality in ℝn are strictly convex away from a singular set of Hausdorff (n-1)-dimensional measure zero. Furthermore, we show this is optimal by constructing solutions to det D2u = 1 with singular set of Hausdorff dimension as close as we like to n-1. As a consequence we obtain W2,1 regularity for the Monge-Ampere equation with bounded right-hand side and unique continuation for the Monge-Ampere equation with sufficiently regular right-hand side. © 2015 Wiley Periodicals, Inc.
Spectral Compressed Sensing via Structured Matrix Completion

The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples. Specifically, the object of interest with ambient dimension n is assumed to be a mixture of r complex multi-dimensional sinusoids, while the underlying frequencies can assume any value in the unit disk. Conventional compressed sensing paradigms suffer from the basis mismatch issue when imposing a discrete dictionary on the Fourier representation. To address this problem, we develop a novel nonparametric algorithm, called enhanced matrix completion (EMaC), based on structured matrix completion. The algorithm starts by arranging the data into a low-rank enhanced form with multi-fold Hankel structure, then attempts recovery via nuclear norm minimization. Under mild incoherence conditions, EMaC allows perfect recovery as soon as the number of samples exceeds the order of O(r log2 n). We also show that, in many instances, accurate completion of a low-rank multi-fold Hankel matrix is possible when the number of observed entries is proportional to the information theoretical limits (except for a logarithmic gap). The robustness of EMaC against bounded noise and its applicability to super resolution are further demonstrated by numerical experiments.
Fast k-nearest neighbour search via dynamic continuous indexing

Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a novel randomized algorithm that runs in time linear in dimensionality of the space and sub-linear in the intrinsic dimensionality and the size of the dataset and takes space constant in dimensionality of the space and linear in the size of the dataset. The proposed algorithm allows fine-grained control over accuracy and speed on a per-query basis, automatically adapts to variations in data density, supports dynamic updates to the dataset and is easy-to-implement. We show appealing theoretical properties and demonstrate empirically that the proposed algorithm outperforms locality-sensitivity hashing (LSH) in terms of approximation quality, speed and space efficiency.
A hybrid approach to machine learning annotation of large galaxy image databases

Abstract Modern astronomy relies on massive databases collected by robotic telescopes and digital sky surveys, acquiring data in a much faster pace than what manual analysis can support. Among other data, these sky surveys collect information about millions and sometimes billions of extra-galactic objects. Since the very large number of objects makes manual observation impractical, automatic methods that can analyze and annotate extra-galactic objects are required to fully utilize the discovery power of these databases. Machine learning methods for annotation of celestial objects can be separated broadly into methods that use the photometric information collected by digital sky surveys, and methods that analyze the image of the object. Here we describe a hybrid method that combines photometry and image data to annotate galaxies by their morphology, and a method that uses that information to identify objects that are visually similar to a query object (query-by-example). The results are compared to using just photometric information from SDSS, and to using just the morphological descriptors extracted directly from the images. The comparison shows that for automatic classification the image data provide marginal addition to the information provided by the photometry data. For query-by-example, however, the analysis of the image data provides more information that improves the automatic detection substantially. The source code and binaries of the method can be downloaded through the Astrophysics Source Code Library.
EdgeBench: Benchmarking Edge Computing Platforms

The emerging trend of edge computing has led several cloud providers to release their own platforms for performing computation at the 'edge' of the network. We compare two such platforms, Amazon AWS Greengrass and Microsoft Azure IoT Edge, using a new benchmark comprising a suite of performance metrics. We also compare the performance of the edge frameworks to cloud-only implementations available in their respective cloud ecosystems. Amazon AWS Greengrass and Azure IoT Edge use different underlying technologies, edge Lambda functions vs. containers, and so we also elaborate on platform features available to developers. Our study shows that both of these edge platforms provide comparable performance, which nevertheless differs in important ways for key types of workloads used in edge applications. Finally, we discuss several current issues and challenges we faced in deploying these platforms.
John–Nirenberg Inequalities and Weight Invariant BMO Spaces

This work explores new deep connections between John–Nirenberg type inequalities and Muckenhoupt weight invariance for a large class of BMO-type spaces. The results are formulated in a very general framework in which BMO spaces are constructed using a base of sets, used also to define weights with respect to a non-negative measure (not necessarily doubling), and an appropriate oscillation functional. This includes as particular cases many different function spaces on geometric settings of interest. As a consequence, the weight invariance of several BMO and Triebel–Lizorkin spaces considered in the literature is proved. Most of the invariance results obtained under this unifying approach are new even in the most classical settings.
Fine-Grained Head Pose Estimation Without Keypoints

Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. Traditionally head pose is computed by estimating some keypoints from the target face and solving the 2D to 3D correspondence problem with a mean human head model. We argue that this is a fragile method because it relies entirely on landmark detection performance, the extraneous head model and an ad-hoc fitting step. We present an elegant and robust way to determine pose by training a multi-loss convolutional neural network on 300W-LP, a large synthetically expanded dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from image intensities through joint binned pose classification and regression. We present empirical tests on common in-the-wild pose benchmark datasets which show state-of-the-art results. Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state-of-the-art depth pose methods. We open-source our training and testing code as well as release our pre-trained models.
RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies

This letter addresses two challenges facing samplingbased kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning. First, we use deep reinforcement learning to learn an obstacle-avoiding policy that maps a robot's sensor observations to actions, which is used as a local planner during planning and as a controller during execution. Second, we train a reachability estimator in a supervised manner, which predicts the RL policy's time to reach a state in the presence of obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local planner, and the reachability estimator as the distance function to bias tree-growth towards promising regions. We evaluate our method on three kinodynamic systems, including physical robot experiments. Results across all three robots tested indicate that RL-RRT outperforms state of the art kinodynamic planners in efficiency, and also provides a shorter path finish time than a steering function free method. The learned local planner policy and accompanying reachability estimator demonstrate transferability to the previously unseen experimental environments, making RL-RRT fast because the expensive computations are replaced with simple neural network inference.
Finite temperature phase diagram of the classical Kitaev-Heisenberg model

We investigate the finite-temperature phase diagram of the classical Kitaev-Heisenberg model on the hexagonal lattice. Due to the anisotropy introduced by the Kitaev interaction, the model is magnetically ordered at low temperatures. The ordered phase is stabilized entropically by an order-by-disorder mechanism where thermal fluctuations of classical spins select collinear magnetic states in which magnetic moments point along one of the cubic directions. We find that there is an intermediate phase between the low-temperature ordered phase and the high-temperature disordered phase. We show that the intermediate phase is a critical Kosterlitz-Thouless phase exhibiting correlations of the order parameter that decay algebraically in space. Using finite-size-scaling analysis, we determine the boundaries of the critical phase with reasonable accuracy. We show that the Kitaev interaction plays a crucial role in understanding the finite-temperature properties of $A$${}_{2}$IrO${}_{3}$ systems.
Efficient Attention using a Fixed-Size Memory Representation

The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20% for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments.
Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading

Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train deep learning architectures for spatio‐temporal modeling. Training a deep architecture is achieved by stochastic gradient descent and dropout for parameter regularization with a goal of minimizing out‐of‐sample predictive mean squared error. To illustrate our methodology, we first predict the sharp discontinuities in traffic flow data, and secondly, we develop a classification rule to predict short‐term futures market prices using order book depth. Finally, we conclude with directions for future research.
Non-CMC Solutions of the Einstein Constraint Equations on Compact Manifolds with Apparent Horizon Boundaries

In this article we continue our effort to do a systematic development of the solution theory for conformal formulations of the Einstein constraint equations on compact manifolds with boundary. By building in a natural way on our recent work in Holst and Tsogtgerel (Class Quantum Gravity 30:205011, 2013), and Holst et al. (Phys Rev Lett 100(16):161101, 2008, Commun Math Phys 288(2):547–613, 2009), and also on the work of Maxwell (J Hyperbolic Differ Eqs 2(2):521–546, 2005a, Commun Math Phys 253(3):561–583, 2005b, Math Res Lett 16(4):627–645, 2009) and Dain (Class Quantum Gravity 21(2):555–573, 2004), under reasonable assumptions on the data we prove existence of both near- and far-from-constant mean curvature (CMC) solutions for a class of Robin boundary conditions commonly used in the literature for modeling black holes, with a third existence result for CMC appearing as a special case. Dain and Maxwell addressed initial data engineering for space-times that evolve to contain black holes, determining solutions to the conformal formulation on an asymptotically Euclidean manifold in the CMC setting, with interior boundary conditions representing excised interior black hole regions. Holst and Tsogtgerel compiled the interior boundary results covered by Dain and Maxwell, and then developed general interior conditions to model the apparent horizon boundary conditions of Dainand Maxwell for compact manifolds with boundary, and subsequently proved existence of solutions to the Lichnerowicz equation on compact manifolds with such boundary conditions. This paper picks up where Holst and Tsogtgerel left off, addressing the general non-CMC case for compact manifolds with boundary. As in our previous articles, our focus here is again on low regularity data and on the interaction between different types of boundary conditions. While our work here serves primarily to extend the solution theory for the compact with boundary case, we also develop several technical tools that have potential for use for other cases.