cssi/SciSciGPT-SciSciNet huggingface dataset:
Crystal chemistry, band engineering, and photocatalytic activity of the LiNb3O8-CuNb3O8 solid solution.

A new solid solution has been prepared in the system LiNb3O8–CuNb3O8, and the impacts of chemical composition and crystal structure have been investigated for the resulting band gap sizes and photocatalytic activities for water reduction to hydrogen under visible light. All members of the solid solution were synthesized by solid-state methods within evacuated fused-silica vessels, and their phase purities were confirmed via powder X-ray diffraction techniques (space group P21/a, a = 15.264(5)–15.367(1) A, b = 5.031(3)–5.070(1) A, c = 7.456(1)–7.536(8) A, and β = 107.35(1)–107.14(8)°, for 0 ≤ x ≤ 1). Rietveld refinements were carried out for the x = 0.09, 0.50, and 0.70 members of the solid solution, which reveal the prevailing isostructurality of the continuous solid solution. The structure consists of chains of (Li/Cu)O6 and NbO6 octahedra. The optical band gap size across the solid solution exhibits a significant red-shift from ∼3.89 eV (direct) to ∼1.45 eV and ∼1.27 eV (direct and indirect) with increa...
On the validity and errors of the pseudo-first-order kinetics in ligand–receptor binding

Abstract The simple bimolecular ligand–receptor binding interaction is often linearized by assuming pseudo-first-order kinetics when one species is present in excess. Here, a phase-plane analysis allows the derivation of a new condition for the validity of pseudo-first-order kinetics that is independent of the initial receptor concentration. The validity of the derived condition is analyzed from two viewpoints. In the first, time courses of the exact and approximate solutions to the ligand–receptor rate equations are compared when all rate constants are known. The second viewpoint assesses the validity through the error induced when the approximate equation is used to estimate kinetic constants from data. Although these two interpretations of validity are often assumed to coincide, we show that they are distinct, and that large errors are possible in estimated kinetic constants, even when the linearized and exact rate equations provide nearly identical solutions.
Detecting High-Order Epistasis in Nonlinear Genotype-Phenotype Maps.

High-order epistasis has been observed in many genotype-phenotype maps. These multi-way interactions between mutations may be useful for dissecting complex traits and could have profound implications for evolution. Alternatively, they could be a statistical artifact. High-order epistasis models assume the effects of mutations should add, when they could in fact multiply or combine in some other nonlinear way. A mismatch in the “scale” of the epistasis model and the scale of the underlying map would lead to spurious epistasis. In this article, we develop an approach to estimate the nonlinear scales of arbitrary genotype-phenotype maps. We can then linearize these maps and extract high-order epistasis. We investigated seven experimental genotype-phenotype maps for which high-order epistasis had been reported previously. We find that five of the seven maps exhibited nonlinear scales. Interestingly, even after accounting for nonlinearity, we found statistically significant high-order epistasis in all seven maps. The contributions of high-order epistasis to the total variation ranged from 2.2 to 31.0%, with an average across maps of 12.7%. Our results provide strong evidence for extensive high-order epistasis, even after nonlinear scale is taken into account. Further, we describe a simple method to estimate and account for nonlinearity in genotype-phenotype maps.
Synthesis of a new inhibitor of α-fucosidase

Abstract A new fucose-type iminosugar in which the nitrogen is placed in the anomeric position was synthesized from d -ribose and was shown to be a potent inhibitor of α-fucosidase (Ki = 8.4 μM).
Supercoiling DNA Locates Mismatches

Single base-pair mismatches in DNA can be pinpointed by twisting the molecule until it buckles.
Russian Doll Genes and Complex Chromosome Rearrangements in Oxytricha trifallax

Ciliates have two different types of nuclei per cell, with one acting as a somatic, transcriptionally active nucleus (macronucleus; abbr. MAC) and another serving as a germline nucleus (micronucleus; abbr. MIC). Furthermore, Oxytricha trifallax undergoes extensive genome rearrangements during sexual conjugation and post-zygotic development of daughter cells. These rearrangements are necessary because the precursor MIC loci are often both fragmented and scrambled, with respect to the corresponding MAC loci. Such genome architectures are remarkably tolerant of encrypted MIC loci, because RNA-guided processes during MAC development reorganize the gene fragments in the correct order to resemble the parental MAC sequence. Here, we describe the germline organization of several nested and highly scrambled genes in Oxytricha trifallax These include cases with multiple layers of nesting, plus highly interleaved or tangled precursor loci that appear to deviate from previously described patterns. We present mathematical methods to measure the degree of nesting between precursor MIC loci, and revisit a method for a mathematical description of scrambling. After applying these methods to the chromosome rearrangement maps of O. trifallax we describe cases of nested arrangements with up to five layers of embedded genes, as well as the most scrambled loci in O. trifallax.
Recent Developments in the Synthesis of Annonaceous Acetogenins

Synthetic routes to various bis-THF annonaceous acetogenins and prototypes thereof are reviewed. These C35–C37 long-chain diethers exhibit a remarkable range of biological activities. The core unit, with six oxygenated stereogenic centers, provides a challenging target for asymmetric synthesis.
New reaction and new catalyst—a personal perspective

Abstract A number of new synthetic methods are reviewed. Most of the methods are based on aluminum, boron, tin, silver Lewis acids and/or Bronsted acid catalysts. Concepts of combined acid catalysis and super Bronsted acid catalysis are also summarized. These methods are useful for selective organic transformations including simple natural product synthesis.
Alkyl, Unsaturated, (Hetero)aryl, and N-Protected α-Amino Ketones by Acylation of Organometallic Reagents

Stable and easily accessible N-acylbenzotriazoles, derived from a variety of aliphatic, unsaturated, (hetero)aromatic, and N-protected α-amino carboxylic acids, were reacted with Grignard and heteroaryllithium reagents to afford corresponding ketones in good to excellent yields.
Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images.

Abstract Numerous factors have been reported to underlie the representation of complex images in high-level human visual cortex, including categories (e.g. faces, objects, scenes), animacy, and real-world size, but the extent to which this organization reflects behavioral judgments of real-world stimuli is unclear. Here, we compared representations derived from explicit behavioral similarity judgments and ultra-high field (7T) fMRI of human visual cortex for multiple exemplars of a diverse set of naturalistic images from 48 object and scene categories. While there was a significant correlation between similarity judgments and fMRI responses, there were striking differences between the two representational spaces. Behavioral judgements primarily revealed a coarse division between man-made (including humans) and natural (including animals) images, with clear groupings of conceptually-related categories (e.g. transportation, animals), while these conceptual groupings were largely absent in the fMRI representations. Instead, fMRI responses primarily seemed to reflect a separation of both human and non-human faces/bodies from all other categories. Further, comparison of the behavioral and fMRI representational spaces with those derived from the layers of a deep neural network (DNN) showed a strong correspondence with behavior in the top-most layer and with fMRI in the mid-level layers. These results suggest a complex relationship between localized responses in high-level visual cortex and behavioral similarity judgments – each domain reflects different properties of the images, and responses in high-level visual cortex may correspond to intermediate stages of processing between basic visual features and the conceptual categories that dominate the behavioral response.
A CONVENIENT SYNTHESIS OF ALKYL-1-CYCLOHEXENYL SULFIDES

Abstract A convenient procedure for synthesis of alkyl-1-cyclohexenyl sulfides is presented. The procedure uses 1-cyclohexenyl thioacetate for in situ generation of 1-cyclohexenyl thiolate which is subsequently alkylated.
Creep resistance of bulk copper–niobium composites: An inverse effect of multilayer length scale

Abstract Metallic multilayer systems show promising performance in extreme environments, with high stability of bi-metal interfaces down to nanometer length scales. The creep behavior of bulk, accumulative roll bonded (ARB) Copper–Niobium (Cu–Nb) composites has been studied at 400 °C as a function of layer thickness, ranging from 2 μ m to 65 n m . Similar to single phase metallic systems, three regimes are observed during creep: transient, steady-state and tertiary. The mechanism controlling minimum creep rate for all conditions tested has a strong dependence on stress, consistent with dislocation-dominated creep. Unlike the conventional effect of grain size on creep resistance, this study reveals that decreasing length scale increases creep resistance.
A simple and robust procedure for preparing graphene-oxide cryo-EM grids

Graphene oxide (GO) sheets have been used successfully as a supporting substrate film in several recent cryogenic electron-microscopy (cryo-EM) studies of challenging biological macromolecules. However, difficulties in preparing GO-covered holey carbon EM grids have limited their widespread use. Here, we report a simple and robust method for covering holey carbon EM grids with GO sheets and demonstrate that these grids can be used for high-resolution single particle cryo-EM. GO substrates adhere macromolecules, allowing cryo-EM grid preparation with lower specimen concentrations and provide partial protection from the air-water interface. Additionally, the signal of the GO lattice beneath the frozen-hydrated specimen can be discerned in many motion-corrected micrographs, providing a high-resolution fiducial for evaluating beam-induced motion correction.
One-pot Pd-catalyzed hydrostannation/Stille reaction with acid chlorides as the electrophiles

A one-pot hydrostannation/Stille coupling sequence amenable to the employment of acid chloride electrophiles has been developed. In this protocol, palladium mediated alkyne hydrostannations using Me3SnF/PMHS as an in situ trimethyltin hydride source are followed by the addition of the acid chloride to afford a variety of a,b-unsaturated ketones in a single pot. 2005 Elsevier B.V. All rights reserved.
Cyclodehydration of 4-[(Carboxymethyl)amino]pyridin-2-ones. A New, Efficient Synthesis of Pyrrolo[3,2-c]pyridin-4-ones and Pyrido[3,4-b]pyrrolizidin-1-ones

Abstract This paper describes an efficient new route for the synthesis of pyrrolo[3,2- c ]pyridin-2-ones and pyrido[3,4- b ]pyrrolizidin-1-ones starting from 4-chloro- N -benzyl-2(1 H )-pyridinone and amino acid salts. Intermediate 4-[(carboxymethyl)amino]pyridin-2-ones undergo an acylative cyclodehydration reaction to afford 3-acetoxy pyrroles. These intermediates are then converted into 3-oxytriflate derivatives in high yield using a new one-step method. These compounds undergo palladium catalyzed methoxycarbonylation.
A convenient, practical synthesis of substituted resorcinols: synthesis of DB-2073 and olivetol

Abstract A wide variety of easily accesible 1,3-cyclohexanediones are readily transformed to substituted dimethyl resorcinols with iodine and methanol.
4D X-Ray CT Reconstruction using Multi-Slice Fusion

There is an increasing need to reconstruct objects in four or more dimensions corresponding to space, time and other independent parameters. The best 4D reconstruction algorithms use regularized iterative reconstruction approaches such as model based iterative reconstruction (MBIR), which depends critically on the quality of the prior modeling. Recently, Plug-and-Play methods have been shown to be an effective way to incorporate advanced prior models using state-of-the-art denoising algorithms designed to remove additive white Gaussian noise (AWGN). However, state-of-the-art denoising algorithms such as BM4D and deep convolutional neural networks (CNNs) are primarily available for 2D and sometimes 3D images. In particular, CNNs are difficult and computationally expensive to implement in four or more dimensions, and training may be impossible if there is no associated high-dimensional training data.In this paper, we present Multi-Slice Fusion, a novel algorithm for 4D and higher-dimensional reconstruction, based on the fusion of multiple low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium (MACE), an extension of Plug-and-Play, as a framework for integrating the multiple lower-dimensional prior models. We apply our method to the problem of 4D cone-beam X-ray CT reconstruction for Non Destructive Evaluation (NDE) of moving parts. This is done by solving the MACE equations using lower-dimensional CNN denoisers implemented in parallel on a heterogeneous cluster. Results on experimental CT data demonstrate that Multi-Slice Fusion can substantially improve the quality of reconstructions relative to traditional 4D priors, while also being practical to implement and train.
Mental accounts and the marginal propensity to give

Neoclassical theory holds that different sources of income are fungible at the margin. In contrast, mental accounting holds that appropriate uses for income vary by source, making them infungible. This study investigates which theory better describes giving at the margin when income may have multiple sources. Dictators accrue differing amounts of (1) earned income from a real-effort task, (2) windfall income, or (3) both. I find that dictators treat marginal earned and windfall income as partially infungible, supporting mental accounting. Dictators who had a single income source gave 14% of a marginal windfall token and 5% of a marginal earned token. Strikingly, dictators who had income from both sources were sharply less generous with each, giving only 2% and 1%, respectively. Multiple accounts enabled greater selfishness at the margin. A follow-up experiment shows that two accounts must be qualitatively different, not just multiple in number, to produce this effect.
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We first propose a distributed state estimator assuming regular system operation that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to 1) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, 2) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and 3) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies.
Normal bundles of rational curves on complete intersections

Let X ⊂ ℙn be a general Fano complete intersection of type (d1,…,dk). If at least one di is greater than 2, we show that X contains rational curves of degree e ≤ n with balanced normal bundle. If a...
Exact Moderate and Large Deviations for Linear Processes

Large and moderate deviation probabilities play an important role in many applied areas, such as insurance and risk analysis. This paper studies the exact moderate and large deviation asymptotics in non-logarithmic form for linear processes with independent innovations. The linear processes we analyze are general and therefore they include the long memory case. We give an asymptotic representation for probability of the tail of the normalized sums and specify the zones in which it can be approximated either by a standard normal distribution or by the marginal distribution of the innovation process. The results are then applied to regression estimates, moving averages, fractionally integrated processes, linear processes with regularly varying exponents and functions of linear processes. We also consider the computation of value at risk and expected shortfall, fundamental quantities in risk theory and nance.
Dpca: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets

Principal component analysis (PCA) has well-documented merits for data extraction and dimensionality reduction. PCA deals with a single dataset at a time, and it is challenged when it comes to analyzing multiple datasets. Yet in certain setups, one wishes to extract the most significant information of one dataset relative to other datasets. Specifically, the interest may be on identifying or extracting features that are specific to a single target dataset but not the others. This paper presents a novel approach for such so-termed discriminative data analysis, and establishes its optimality in the least-squares sense under suitable assumptions. The criterion reveals linear combinations of variables by maximizing the ratio of the variance of the target data to that of the remainders. The novel approach solves a generalized eigenvalue problem by performing SVD just once. Numerical tests using synthetic and real datasets showcase the merits of the proposed approach relative to its competing alternatives.
Multiscale Analysis of 1-rectifiable Measures II: Characterizations

A measure is 1-rectifiable if there is a countable union of finite length curves whose complement has zero measure. We characterize 1-rectifiable Radon measures μ in n-dimensional Euclidean space for all n ≥ 2 in terms of positivity of the lower density and finiteness of a geometric square function, which loosely speaking, records in an L gauge the extent to which μ admits approximate tangent lines, or has rapidly growing density ratios, along its support. In contrast with the classical theorems of Besicovitch, Morse and Randolph, and Moore, we do not assume an a priori relationship between μ and 1-dimensional Hausdorff measure H. We also characterize purely 1-unrectifiable Radon measures, i.e. locally finite measures that give measure zero to every finite length curve. Characterizations of this form were originally conjectured to exist by P. Jones. Along the way, we develop an L variant of P. Jones’ traveling salesman construction, which is of independent interest.
Recognizing Abnormal Heart Sounds Using Deep Learning.

The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.
THE SECOND QUANDLE HOMOLOGY OF THE TAKASAKI QUANDLE OF AN ODD ABELIAN GROUP IS AN EXTERIOR SQUARE OF THE GROUP

We prove that if G is an abelian group of odd order then there is an isomorphism from the second quandle homology to G ∧ G, where ∧ is the exterior product. In particular, for , k odd, we have . Nontrivial allows us to use 2-cocycles to construct new quandles from T(G), and to construct link invariants. Computation of is also the first, fundamental step in the direction of computing homology of Takasaki quandles in general.
Green Energy Aware Avatar Migration Strategy in Green Cloudlet Networks

We propose a Green Cloudlet Network (GCN) architecture to provide seamless Mobile Cloud Computing (MCC) services to User Equipments (UEs) with low latency in which each cloudlet is powered by both green and brown energy. Fully utilizing green energy can significantly reduce the operational cost of cloudlet providers. However, owing to the spatial dynamics of energy demand and green energy generation, the energy gap among different cloudlets in the network is unbalanced, i.e., some cloudlets' energy demands can be fully provided by green energy but others need to utilize on-grid energy (i.e., brown energy) to satisfy their energy demands. We propose a Green-energy awarE Avatar migRation (GEAR) strategy to minimize the on-grid energy consumption in GCN by redistributing the energy demands via Avatar migration among cloudlets according to cloudlets' green energy generation. Furthermore, GEAR ensures the Service Level Agreement (SLA) in terms of the maximum Avatar propagation delay by avoiding Avatars hosted in the remote cloudlets. We formulate the GEAR strategy as a mixed integer linear programming problem, which is NP-hard, and thus apply the Branch and Bound search to find its sub-optimal solution. Simulation results demonstrate that GEAR can save on-grid energy consumption significantly as compared to the Follow me AvataR (FAR) migration strategy, which aims to minimize the propagation delay between an UE and its Avatar.
ON COHOMOLOGY OF THE HIGSON COMPACTIFICATION OF HYPERBOLIC SPACES

We show that in dimensions > 1 the cohomology groups of the Higson compactification of the hyperbolic space ℍn with respect to the C0 coarse structure are trivial. Also we prove that the cohomology groups of the Higson compactification of ℍn for the bounded coarse structure are trivial in all even dimensions.
A Distributed Stochastic Gradient Tracking Method

In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex. The global objective is to find a common solution that minimizes the average of all cost functions. Assuming agents only have access to unbiased estimates of the gradients of their local cost functions, we consider a distributed stochastic gradient tracking method. We show that, in expectation, the iterates generated by each agent are attracted to a neighborhood of the optimal solution, where they accumulate exponentially fast (under a constant step size choice). More importantly, the limiting (expected) error bounds on the distance of the iterates from the optimal solution decrease with the network size, which is a comparable performance to a centralized stochastic gradient algorithm. Numerical examples further demonstrate the effectiveness of the method.
Separation of signals consisting of amplitude and instantaneous frequency RRC pulses using SNR uniform training

This work presents sample mean and sample variance based features that distinguish continuous phase FSK from QAM and PSK modulations. Root raised cosine pulses are used for signal generation. Support vector machines are employed for signals separation. They are trained for only one value of SNR and used to classify the signals from a wide range of SNR. A priori information about carrier amplitude, carrier phase, carrier offset, roll-off factor and initial symbol phase is relaxed. Effectiveness of the method is tested by observing the joint effects of AWGN, carrier offset, lack of symbol and sampling synchronization, and fast fading.
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.
On the Impossibility of Learning the Missing Mass

This paper shows that one cannot learn the probability of rare events without imposing further structural assumptions. The event of interest is that of obtaining an outcome outside the coverage of an i.i.d. sample from a discrete distribution. The probability of this event is referred to as the “missing mass”. The impossibility result can then be stated as: the missing mass is not distribution-free learnable in relative error. The proof is semi-constructive and relies on a coupling argument using a dithered geometric distribution. Via a reduction, this impossibility also extends to both discrete and continuous tail estimation. These results formalize the folklore that in order to predict rare events without restrictive modeling, one necessarily needs distributions with “heavy tails”.
Vertical sheaves and Fourier-Mukai transform on elliptic Calabi-Yau threefolds

This paper studies the action of the Fourier-Mukai transform on moduli spaces of vertical torsion sheaves on elliptic Calabi-Yau threefolds in Weierstrass form. Moduli stacks of semistable one dimensional sheaves on such threefolds are identified with open and closed substacksof moduli stacks of vertical semistable two dimensional sheaves on their Fourier-Mukai duals. In particular, this yields explicit conjectural results for Donaldson-Thomas invariants of vertical two dimensional sheaves on K3-fibered elliptic Calabi-Yau threefolds.
Harmonic spinors on the Davis hyperbolic 4-manifold

In this paper, we use the G-spin theorem to show that the Davis hyperbolic 4-manifold admits harmonic spinors. This is the first example of a closed hyperbolic 4-manifold that admits harmonic spino...
A short proof of backward uniqueness for some geometric evolution equations

We present a simple, direct proof of the backward uniqueness of solutions to a class of second-order geometric evolution equations which includes the Ricci and cross-curvature flows. The proof, based on a classical argument of Agmon–Nirenberg, uses the logarithmic convexity of a certain energy quantity in the place of Carleman inequalities. We further demonstrate the applicability of the technique to the L2-curvature flow and other higher-order equations.
An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses

Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner. Automatic text summarization systems can be leveraged to summarize the student feedback, submitted immediately after each lecture, but it is left to be discovered what makes a good summary for student responses. In this work we explore a new methodology that effectively extracts summary phrases from the student responses. Each phrase is tagged with the number of students who raise the issue. The phrases are evaluated along two dimensions: with respect to text content, they should be informative and well-formed, measured by the ROUGE metric; additionally, they shall attend to the most pressing student needs, measured by a newly proposed metric. This work is enabled by a phrase-based annotation and highlighting scheme, which is new to the summarization task. The phrase-based framework allows us to summarize the student responses into a set of bullet points and present to the instructor promptly.
Higher‐Order Accurate Spectral Density Estimation of Functional Time Series

Under the frequency domain framework for weakly dependent functional time series, a key element is the spectral density kernel which encapsulates the second‐order dynamics of the process. We propose a class of spectral density kernel estimators based on the notion of a flat‐top kernel. The new class of estimators employs the inverse Fourier transform of a flat‐top function as the weight function employed to smooth the periodogram. It is shown that using a flat‐top kernel yields a bias reduction and results in a higher‐order accuracy in terms of optimizing the integrated mean square error (IMSE). Notably, the higher‐order accuracy of flat‐top estimation comes at the sacrifice of the positive semi‐definite property. Nevertheless, we show how a flat‐top estimator can be modified to become positive semi‐definite (even strictly positive definite) in finite samples while retaining its favorable asymptotic properties. In addition, we introduce a data‐driven bandwidth selection procedure realized by an automatic inspection of the estimated correlation structure. Our asymptotic results are complemented by a finite‐sample simulation where the higher‐order accuracy of flat‐top estimators is manifested in practice.
Anyonic Topological Quantum Computation and the Virtual Braid Group

We introduce a recoupling theory for virtual braided trees. This recoupling theory can be utilized to incorporate swap gates into anyonic models of quantum computation.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior

Bayesian optimization usually assumes that a Bayesian prior is given. However, the strong theoretical guarantees in Bayesian optimization are often regrettably compromised in practice because of unknown parameters in the prior. In this paper, we adopt a variant of empirical Bayes and show that, by estimating the Gaussian process prior from offline data sampled from the same prior and constructing unbiased estimators of the posterior, variants of both GP-UCB and \emph{probability of improvement} achieve a near-zero regret bound, which decreases to a constant proportional to the observational noise as the number of offline data and the number of online evaluations increase. Empirically, we have verified our approach on challenging simulated robotic problems featuring task and motion planning.
The Delaunay tessellation in hyperbolic space

The Delaunay tessellation of a locally finite subset of the hyperbolic space ℍ n is constructed via convex hulls in ℝ n+1. For finite and lattice-invariant sets it is proven to be a polyhedral decomposition, and versions (necessarily modified from the Euclidean setting) of the empty circumspheres condition and geometric duality with the Voronoi tessellation are proved. Some pathological examples of infinite, non lattice-invariant sets are exhibited.
Quark masses in two-flavor QCD

∗Speaker. †I am grateful to the award I received from Alexander von Humboldt Foundation to visit the University of Mainz and which partially supported my attendence to this meeting. This manuscript has been authored under contract number DE-AC02-98CH10886 with the U.S. Department of Energy. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes.
Why is the Main Sequence of NGC 2482 So Fat

We present the results of high resolution spectra of seven stars in the field of NGC 2482, an open star cluster of age 447 Myr. We confirm the previously published values of the radial velocity and metallicity of one giant star. This gives us confidence that another giant star is a bona fide cluster member, and that three stars significantly above the main sequence in a color-magnitude diagram are not members, on the basis of discordant radial velocities. Another star ~1.7 mag above the main sequence may or may not be a member. Its [Fe/H] value is ~0.1 dex more positive than two giant stars studied, and its radial velocity is 3-4 km/s less than that of the two giant stars, which is a significant difference if the velocity dispersion of the cluster is less than +/-1 km/s. To a large extent the width of the main sequence seems to be due to the presence of foreground and background stars in the same general direction, stars that masquerade as main sequence stars in the cluster.
Central limit theorem for the variable bandwidth kernel density estimators

Abstract In this paper we study the ideal variable bandwidth kernel density estimator introduced by McKay (1993a, b) and Jones et al. (1994) and the plug-in practical version of the variable bandwidth kernel estimator with two sequences of bandwidths as in Gine and Sang (2013). Based on the bias and variance analysis of the ideal and plug-in variable bandwidth kernel density estimators, we study the central limit theorems for each of them. The simulation study confirms the central limit theorem and demonstrates the advantage of the plug-in variable bandwidth kernel method over the classical kernel method.
Queues Driven by Hawkes Processes

Many stochastic systems have arrival processes that exhibit clustering behavior. In these systems, arriving entities influence additional arrivals to occur through self-excitation of the arrival pr...
Deep Visual MPC-Policy Learning for Navigation

Humans can routinely follow a trajectory defined by a list of images/landmarks. However, traditional robot navigation methods require accurate mapping of the environment, localization, and planning. Moreover, these methods are sensitive to subtle changes in the environment. In this letter, we propose PoliNet, a deep visual model predictive control-policy learning method that can perform visual navigation while avoiding collisions with unseen objects on the navigation path. PoliNet takes in as input a visual trajectory and 360 $^{\circ }$ images from robot's current view and outputs velocity commands for a planning horizon of $N$ steps that optimally balance between trajectory following and obstacle avoidance. PoliNet is trained using a differentiable neural image predictive model and a traversability estimation model in an model predictive control setup, with minimal human supervision. PoliNet can be applied to visual trajectory in new scenes without retraining. We show experimentally that the robot can follow a visual trajectory even if it does not start from the exact same position and in the presence of previously unseen obstacles. We validated our algorithm with tests both in a realistic simulation environment and in the real world outperforming state-of-the-art baselines under similar conditions in success rate, coverage rate of the trajectory, and with lower computational load. We also show that we can generate visual trajectory in simulation and execute the corresponding path in the real environment.
Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion.

We consider the problem of minimizing the delay of jobs moving through a directed graph of service nodes. In this problem, each node may have several links and is constrained to serve one link at a time. As jobs move through the network, they can pass through a node only after they have been serviced by that node. The objective is to minimize the delay jobs incur sitting in queues waiting to be serviced. Two distinct approaches to this problem have emerged from respective work in queuing theory and dynamic scheduling: the backpressure algorithm and schedule-driven control. In this paper, we present a hybrid approach of those two methods that incorporates the stability of queuing theory into a schedule-driven control framework. We then demonstrate how this hybrid method outperforms the other two in a real-time traffic signal control problem, where the nodes are traffic lights, the links are roads, and the jobs are vehicles. We show through simulations that, in scenarios with heavy congestion, the hybrid method results in 50% and 15% reductions in delay over schedule-driven control and backpressure respectively. A theoretical analysis also justifies our results.
A Constructor-Based Reachability Logic for Rewrite Theories

Reachability logic has been applied to \(\mathbb {K}\) rewrite-rule-based language definitions as a language-generic logic of programs. To be able to verify not just code but also distributed system designs, a new rewrite-theory-generic reachability logic is presented and proved sound for a wide class of rewrite theories. Constructor-based semantic unification, matching, and satisfiability procedures greatly increase the range of decidable background theories that can be used in reachability logic proofs. New methods for proving invariants of possibly never terminating distributed systems are developed, and experiments with a prototype implementation illustrating the new proof methods are presented.
Urban scaling in Europe

Over the last few decades, in disciplines as diverse as economics, geography and complex systems, a perspective has arisen proposing that many properties of cities are quantitatively predictable due to agglomeration or scaling effects. Using new harmonized definitions for functional urban areas, we examine to what extent these ideas apply to European cities. We show that while most large urban systems in Western Europe (France, Germany, Italy, Spain, UK) approximately agree with theoretical expectations, the small number of cities in each nation and their natural variability preclude drawing strong conclusions. We demonstrate how this problem can be overcome so that cities from different urban systems can be pooled together to construct larger datasets. This leads to a simple statistical procedure to identify urban scaling relations, which then clearly emerge as a property of European cities. We compare the predictions of urban scaling to Zipf's law for the size distribution of cities and show that while the former holds well the latter is a poor descriptor of European cities. We conclude with scenarios for the size and properties of future pan-European megacities and their implications for the economic productivity, technological sophistication and regional inequalities of an integrated European urban system.
Incentive decision processes

We consider Incentive Decision Processes, where a principal seeks to reduce its costs due to another agent's behavior, by offering incentives to the agent for alternate behavior. We focus on the case where a principal interacts with a greedy agent whose preferences are hidden and static. Though IDPs can be directly modeled as partially observable Markov decision processes (POMDP), we show that it is possible to directly reduce or approximate the IDP as a polynomiallysized MDP: when this representation is approximate, we prove the resulting policy is boundedly-optimal for the original IDP. Our empirical simulations demonstrate the performance benefit of our algorithms over simpler approaches, and also demonstrate that our approximate representation results in a significantly faster algorithm whose performance is extremely close to the optimal policy for the original IDP.
The limit point of the pentagram map

The pentagram map is a discrete dynamical system defined on the space of polygons in the plane. In the first paper on the subject, R. Schwartz proved that the pentagram map produces from each convex polygon a sequence of successively smaller polygons that converges exponentially to a point. We investigate the limit point itself, giving an explicit description of its Cartesian coordinates as roots of certain degree three polynomials.
Physical interpretation of the spectral approach to delocalization in infinite disordered systems

In this paper we introduce the spectral approach to delocalization in infinite disordered systems and provide a physical interpretation in context of the classical model of Edwards and Thouless. We argue that spectral analysis is an important contribution to localization problems since it avoids issues related to the use of boundary conditions. Applying the method to 2D and 3D numerical simulations with various amount of disorder W shows that delocalization occurs for W ≤ 0.6 in 2D and for W ≤ 5 for 3D.
Modeling Single-Peakedness for Votes with Ties.

Single-peakedness is one of the most important and well-known domain restrictions on preferences. The computational study of single-peaked electorates has largely been restricted to elections with tie-free votes, and recent work that studies the computational complexity of manipulative attacks for single-peaked elections for votes with ties has been restricted to nonstandard models of single-peaked preferences for top orders. We study the computational complexity of manipulation for votes with ties for the standard model of single-peaked preferences and for single-plateaued preferences. We show that these models avoid the anomalous complexity behavior exhibited by the other models. We also state a surprising result on the relation between the societal axis and the complexity of manipulation for single-peaked preferences.
Density of Monochromatic Infinite Subgraphs

For any countably infinite graph G, Ramsey’s theorem guarantees an infinite monochromatic copy of G in any r-coloring of the edges of the countably infinite complete graph Kℕ. Taking this a step further, it is natural to wonder how “large” of a monochromatic copy of G we can find with respect to some measure - for instance, the density (or upper density) of the vertex set of G in the positive integers. Unlike finite Ramsey theory, where this question has been studied extensively, the analogous problem for infinite graphs has been mostly overlooked. In one of the few results in the area, Erdős and Galvin proved that in every 2-coloring of Kℕ, there exists a monochromatic path whose vertex set has upper density at least 2/3, but it is not possible to do better than 8/9. They also showed that for some sequence en → 0, there exists a monochromatic path P such that for infinitely many n, the set (1,2,...,n contains the first $$\left(\frac{1}{3+\sqrt{3}}-\epsilon_n\right)n$$ vertices of P, but it is not possible to do better than 2n/3. We improve both results, in the former case achieving an upper density at least 3/4 and in the latter case obtaining a tight bound of 2/3. We also consider related problems for directed paths, trees (connected subgraphs), and a more general result which includes locally finite graphs for instance.
A note on dispersing particles on a line

We consider a synchronous dispersion process introduced in \cite{CRRS} and we show that on the infinite line the final set of occupied sites takes up $O(n)$ space, where $n$ is the number of particles involved.
Kirigami-based Elastic Metamaterials with Anisotropic Mass Density for Subwavelength Flexural Wave Control

A novel design of an elastic metamaterial with anisotropic mass density is proposed to manipulate flexural waves at a subwavelength scale. The three-dimensional metamaterial is inspired by kirigami, which can be easily manufactured by cutting and folding a thin metallic plate. By attaching the resonant kirigami structures periodically on the top of a host plate, a metamaterial plate can be constructed without any perforation that degrades the strength of the pristine plate. An analytical model is developed to understand the working mechanism of the proposed elastic metamaterial and the dispersion curves are calculated by using an extended plane wave expansion method. As a result, we verify an anisotropic effective mass density stemming from the coupling between the local resonance of the kirigami cells and the global flexural wave propagations in the host plate. Finally, numerical simulations on the directional flexural wave propagation in a two-dimensional array of kirigami metamaterial as well as super-resolution imaging through an elastic hyperlens are conducted to demonstrate the subwavelength-scale flexural wave control abilities. The proposed kirigami-based metamaterial has the advantages of no-perforation design and subwavelength flexural wave manipulation capability, which can be highly useful for engineering applications including non-destructive evaluations and structural health monitoring.
Unsupervised learning via total correlation explanation

Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous realm of unsupervised learning where goals and labels are not provided. Barlow (1961) suggested that the signal that brains leverage for unsupervised learning is dependence, or redundancy, in the sensory environment. Dependence can be characterized using the information-theoretic multivariate mutual information measure called total correlation. The principle of Total Correlation Ex-planation (CorEx) is to learn representations of data that "explain" as much dependence in the data as possible. We review some manifestations of this principle along with successes in unsupervised learning problems across diverse domains including human behavior, biology, and language.
Attention for fine-grained categorization

This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the attention RNN. Most work in attention models to date focuses on tasks with toy or more constrained visual environments, whereas we present results for fine-grained categorization better than the state-of-the-art GoogLeNet classification model. We show that our model learns to direct high resolution attention to the most discriminative regions without any spatial supervision such as bounding boxes, and it is able to discriminate fine-grained dog breeds moderately well even when given only an initial low-resolution context image and narrow, inexpensive glimpses at faces and fur patterns. This and similar attention models have the major advantage of being trained end-to-end, as opposed to other current detection and recognition pipelines with hand-engineered components where information is lost. While our model is state-of-the-art, further work is needed to fully leverage the sequential input.
A criterion for parameter identification in structural equation models

This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper establishes a sufficient criterion for identifying individual causal effects and provides a procedure computing identified causal effects in terms of observed covariance matrix.