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Title: Collusions in Teichmüller expansions, Abstract: If $\mathfrak{p} \subseteq \mathbb{Z}[\zeta]$ is a prime ideal over $p$ in the $(p^d - 1)$th cyclotomic extension of $\mathbb{Z}$, then every element $\alpha$ of the completion $\mathbb{Z}[\zeta]_\mathfrak{p}$ has a unique expansion as a power series in $p$ with coefficients in $\mu_{p^d -1} \cup \{0\}$ called the Teichmüller expansion of $\alpha$ at $\mathfrak{p}$. We observe three peculiar and seemingly unrelated patterns that frequently appear in the computation of Teichmüller expansions, then develop a unifying theory to explain these patterns in terms of the dynamics of an affine group action on $\mathbb{Z}[\zeta]$.

Title: Topological $\mathbb{Z}_2$ Resonating-Valence-Bond Spin Liquid on the Square Lattice, Abstract: A one-parameter family of long-range resonating valence bond (RVB) state on the square lattice was previously proposed to describe a critical spin liquid (SL) phase of the spin-$1/2$ frustrated Heisenberg model. We provide evidence that this RVB state in fact also realises a topological (long-range entangled) $\mathbb{Z}_2$ SL, limited by two transitions to critical SL phases. The topological phase is naturally connected to the $\mathbb{Z}_2$ gauge symmetry of the local tensor. This work shows that, on one hand, spin-$1/2$ topological SL with $C_{4v}$ point group symmetry and $SU(2)$ spin rotation symmetry exists on the square lattice and, on the other hand, criticality and nonbipartiteness are compatible. We also point out that, strong similarities between our phase diagram and the ones of classical interacting dimer models suggest both can be described by similar Kosterlitz-Thouless transitions. This scenario is further supported by the analysis of the one-dimensional boundary state.

Title: Scalable Inference for Space-Time Gaussian Cox Processes, Abstract: The log-Gaussian Cox process is a flexible and popular class of point pattern models for capturing spatial and space-time dependence for point patterns. Model fitting requires approximation of stochastic integrals which is implemented through discretization over the domain of interest. With fine scale discretization, inference based on Markov chain Monte Carlo is computationally burdensome because of the cost of matrix decompositions and storage, such as the Cholesky, for high dimensional covariance matrices associated with latent Gaussian variables. This article addresses these computational bottlenecks by combining two recent developments: (i) a data augmentation strategy that has been proposed for space-time Gaussian Cox processes that is based on exact Bayesian inference and does not require fine grid approximations for infinite dimensional integrals, and (ii) a recently developed family of sparsity-inducing Gaussian processes, called nearest-neighbor Gaussian processes, to avoid expensive matrix computations. Our inference is delivered within the fully model-based Bayesian paradigm and does not sacrifice the richness of traditional log-Gaussian Cox processes. We apply our method to crime event data in San Francisco and investigate the recovery of the intensity surface.

Title: The MISRA C Coding Standard and its Role in the Development and Analysis of Safety- and Security-Critical Embedded Software, Abstract: The MISRA project started in 1990 with the mission of providing world-leading best practice guidelines for the safe and secure application of both embedded control systems and standalone software. MISRA C is a coding standard defining a subset of the C language, initially targeted at the automotive sector, but now adopted across all industry sectors that develop C software in safety- and/or security-critical contexts. In this paper, we introduce MISRA C, its role in the development of critical software, especially in embedded systems, its relevance to industry safety standards, as well as the challenges of working with a general-purpose programming language standard that is written in natural language with a slow evolution over the last 40+ years. We also outline the role of static analysis in the automatic checking of compliance with respect to MISRA C, and the role of the MISRA C language subset in enabling a wider application of formal methods to industrial software written in C.

Title: Tailoring spin defects in diamond, Abstract: Atomic-size spin defects in solids are unique quantum systems. Most applications require nanometer positioning accuracy, which is typically achieved by low energy ion implantation. So far, a drawback of this technique is the significant residual implantation-induced damage to the lattice, which strongly degrades the performance of spins in quantum applications. In this letter we show that the charge state of implantation-induced defects drastically influences the formation of lattice defects during thermal annealing. We demonstrate that charging of vacancies localized at e.g. individual nitrogen implantation sites suppresses the formation of vacancy complexes, resulting in a tenfold-improved spin coherence time of single nitrogen-vacancy (NV) centers in diamond. This has been achieved by confining implantation defects into the space charge layer of free carriers generated by a nanometer-thin boron-doped diamond structure. Besides, a twofold-improved yield of formation of NV centers is observed. By combining these results with numerical calculations, we arrive at a quantitative understanding of the formation and dynamics of the implanted spin defects. The presented results pave the way for improved engineering of diamond spin defect quantum devices and other solid-state quantum systems.

Title: Relaxed Oracles for Semi-Supervised Clustering, Abstract: Pairwise "same-cluster" queries are one of the most widely used forms of supervision in semi-supervised clustering. However, it is impractical to ask human oracles to answer every query correctly. In this paper, we study the influence of allowing "not-sure" answers from a weak oracle and propose an effective algorithm to handle such uncertainties in query responses. Two realistic weak oracle models are considered where ambiguity in answering depends on the distance between two points. We show that a small query complexity is adequate for effective clustering with high probability by providing better pairs to the weak oracle. Experimental results on synthetic and real data show the effectiveness of our approach in overcoming supervision uncertainties and yielding high quality clusters.

Title: Measuring Information Leakage in Website Fingerprinting Attacks, Abstract: Tor is a low-latency anonymity system intended to provide low-latency anonymous and uncensored network access against a local or network adversary. Because of the design choice to minimize traffic overhead (and increase the pool of potential users) Tor allows some information about the client's connections to leak in the form of packet timing. Attacks that use (features extracted from) this information to infer the website a user visits are referred to as Website Fingerprinting (WF) attacks. We develop a methodology and tools to directly measure the amount of information about a website leaked by a given set of features. We apply this tool to a comprehensive set of features extracted from a large set of websites and WF defense mechanisms, allowing us to make more fine-grained observations about WF attack and defense mechanisms.

Title: Improving galaxy morphology with machine learning, Abstract: This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy (H) and gradient pattern analysis parameter (GA). Except concentration, all parameters performed a image segmentation pre-processing. For supervision and to compute confusion matrices, we used as true label the galaxy classification from GalaxyZoo. With a 48145 objects dataset after preprocessing (44760 galaxies labeled as S and 3385 as E), we performed experiments with Support Vector Machine (SVM) and Decision Tree (DT). Whit a 1962 objects balanced dataset, we applied K- means and Agglomerative Hierarchical Clustering. All experiments with supervision reached an Overall Accuracy OA >= 97%.

Title: On symmetric intersecting families, Abstract: A family of sets is said to be \emph{symmetric} if its automorphism group is transitive, and \emph{intersecting} if any two sets in the family have nonempty intersection. Our purpose here is to study the following question: for $n, k\in \mathbb{N}$ with $k \le n/2$, how large can a symmetric intersecting family of $k$-element subsets of $\{1,2,\ldots,n\}$ be? As a first step towards a complete answer, we prove that such a family has size at most \[\exp\left(-\frac{c(n-2k)\log n}{k( \log n - \log k)} \right) \binom{n}{k},\] where $c > 0$ is a universal constant. We also describe various combinatorial and algebraic approaches to constructing such families.

Title: The Geometry of Limit State Function Graphs and Subset Simulation, Abstract: In the last fifteen the subset sampling method has often been used in reliability problems as a tool for calculating small probabilities. This method is extrapolating from an initial Monte Carlo estimate for the probability content of a failure domain found by a suitable higher level of the original limit state function. Then iteratively conditional probabilities are estimated for failures domains decreasing to the original failure domain. But there are assumptions not immediately obvious about the structure of the failure domains which must be fulfilled that the method works properly. Here examples are studied that show that at least in some cases if these premises are not fulfilled, inaccurate results may be obtained. For the further development of the subset sampling method it is certainly desirable to find approaches where it is possible to check that these implicit assumptions are not violated. Also it would be probably important to develop further improvements of the concept to get rid of these limitations.

Title: Covering and separation of Chebyshev points for non-integrable Riesz potentials, Abstract: For Riesz $s$-potentials $K(x,y)=|x-y|^{-s}$, $s>0$, we investigate separation and covering properties of $N$-point configurations $\omega^*_N=\{x_1, \ldots, x_N\}$ on a $d$-dimensional compact set $A\subset \mathbb{R}^\ell$ for which the minimum of $\sum_{j=1}^N K(x, x_j)$ is maximal. Such configurations are called $N$-point optimal Riesz $s$-polarization (or Chebyshev) configurations. For a large class of $d$-dimensional sets $A$ we show that for $s>d$ the configurations $\omega^*_N$ have the optimal order of covering. Furthermore, for these sets we investigate the asymptotics as $N\to \infty$ of the best covering constant. For these purposes we compare best-covering configurations with optimal Riesz $s$-polarization configurations and determine the $s$-th root asymptotic behavior (as $s\to \infty$) of the maximal $s$-polarization constants. In addition, we introduce the notion of "weak separation" for point configurations and prove this property for optimal Riesz $s$-polarization configurations on $A$ for $s>\text{dim}(A)$, and for $d-1\leqslant s < d$ on the sphere $\mathbb{S}^d$.

Title: Existence of global weak solutions to the kinetic Peterlin model, Abstract: We consider a class of kinetic models for polymeric fluids motivated by the Peterlin dumbbell theories for dilute polymer solutions with a nonlinear spring law for an infinitely extensible spring. The polymer molecules are suspended in an incompressible viscous Newtonian fluid confined to a bounded domain in two or three space dimensions. The unsteady motion of the solvent is described by the incompressible Navier-Stokes equations with the elastic extra stress tensor appearing as a forcing term in the momentum equation. The elastic stress tensor is defined by the Kramers expression through the probability density function that satisfies the corresponding Fokker-Planck equation. In this case, a coefficient depending on the average length of polymer molecules appears in the latter equation. Following the recent work of Barrett and Süli we prove the existence of global-in-time weak solutions to the kinetic Peterlin model in two space dimensions.

Title: Passivation and Cooperative Control of Equilibrium-Independent Passivity-Short Systems, Abstract: Maximal equilibrium-independent passivity (MEIP) is a recently introduced system property which has acquired special attention in the study of networked dynamical systems. MEIP requires a system to be passive with respect to any forced equilibrium configuration and the associated steady-state input-output map must be maximally monotone. In practice, however, most of the systems are not well behaved and possess shortage of passivity or non-passiveness in their operation. In this paper, we consider a class of passivity-short systems, namely equilibrium-independent passivity-short (EIPS) systems, and presents an input-output transformation based generalized passivation approach to ensure their MEIP properties. We characterize the steady-state input-output relations of the EIPS systems and establish their connection with that of the transformed MEIP systems. We further study the diffusively-coupled networked interactions of such EIPS systems and explore their connection to a pair of dual network optimization problems, under the proposed matrix transformation. A simulation example is given to illustrate the theoretical results.

Title: OpenCluster: A Flexible Distributed Computing Framework for Astronomical Data Processing, Abstract: The volume of data generated by modern astronomical telescopes is extremely large and rapidly growing. However, current high-performance data processing architectures/frameworks are not well suited for astronomers because of their limitations and programming difficulties. In this paper, we therefore present OpenCluster, an open-source distributed computing framework to support rapidly developing high-performance processing pipelines of astronomical big data. We first detail the OpenCluster design principles and implementations and present the APIs facilitated by the framework. We then demonstrate a case in which OpenCluster is used to resolve complex data processing problems for developing a pipeline for the Mingantu Ultrawide Spectral Radioheliograph. Finally, we present our OpenCluster performance evaluation. Overall, OpenCluster provides not only high fault tolerance and simple programming interfaces, but also a flexible means of scaling up the number of interacting entities. OpenCluster thereby provides an easily integrated distributed computing framework for quickly developing a high-performance data processing system of astronomical telescopes and for significantly reducing software development expenses.

Title: Magnetic order and spin dynamics across a ferromagnetic quantum critical point: $μ$SR investigations of YbNi$_4$(P$_{1-x}$As$_x$)$_2$, Abstract: In the quasi-1D heavy-fermion system YbNi$_4$(P$_{1-x}$As$_x$)$_2$ the presence of a ferromagnetic (FM) quantum critical point (QCP) at $x_c$ $\approx 0.1$ with unconventional quantum critical exponents in the thermodynamic properties has been recently reported. Here, we present muon-spin relaxation ($\mu$SR) experiments on polycrystals of this series to study the magnetic order and the low energy 4$f$-electronic spin dynamics across the FM QCP. The zero field $\mu$SR measurements on pure YbNi$_4$(P$_{2}$ proved static long range magnetic order and suggested a strongly reduced ordered Yb moment of about 0.04$\mu_B$. With increasing As substitution the ordered moment is reduced by half at $x = 0.04$ and to less than 0.005 $\mu_B$ at $x=0.08$. The dynamic behavior in the $\mu$SR response show that magnetism remains homogeneous upon As substitution, without evidence for disorder effect. In the paramagnetic state across the FM QCP the dynamic muon-spin relaxation rate follows 1/$T_{1}T\propto T^{-n}$ with $1.01 \pm 0.04 \leq n \leq 1.13 \pm 0.06$. The critical fluctuations are very slow and are even becoming slower when approaching the QCP.

Title: Deep Reinforcement Learning for Swarm Systems, Abstract: Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant. Therefore, we propose a new state representation for deep multi-agent RL based on mean embeddings of distributions. We treat the agents as samples of a distribution and use the empirical mean embedding as input for a decentralized policy. We define different feature spaces of the mean embedding using histograms, radial basis functions and a neural network learned end-to-end. We evaluate the representation on two well known problems from the swarm literature (rendezvous and pursuit evasion), in a globally and locally observable setup. For the local setup we furthermore introduce simple communication protocols. Of all approaches, the mean embedding representation using neural network features enables the richest information exchange between neighboring agents facilitating the development of more complex collective strategies.

Title: Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering, Abstract: Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibility of data analysis pipelines in large population studies. We formulate the problem as a collaborative filtering process, with constraints on the construction of the training set. We propose 6 different strategies to build the training set, which we evaluate on 2 datasets, a synthetic one modeling a population with a growing number of subject types, and a real one obtained with neuroinformatics pipelines. Results show that one sampling method, "Random File Numbers (Uniform)" is able to predict computational reproducibility with a good accuracy. We also analyze the relevance of including file and subject biases in the collaborative filtering model. We conclude that the proposed method is able to speedup reproducibility evaluations substantially, with a reduced accuracy loss.

Title: Consensus measure of rankings, Abstract: A ranking is an ordered sequence of items, in which an item with higher ranking score is more preferred than the items with lower ranking scores. In many information systems, rankings are widely used to represent the preferences over a set of items or candidates. The consensus measure of rankings is the problem of how to evaluate the degree to which the rankings agree. The consensus measure can be used to evaluate rankings in many information systems, as quite often there is not ground truth available for evaluation. This paper introduces a novel approach for consensus measure of rankings by using graph representation, in which the vertices or nodes are the items and the edges are the relationship of items in the rankings. Such representation leads to various algorithms for consensus measure in terms of different aspects of rankings, including the number of common patterns, the number of common patterns with fixed length and the length of the longest common patterns. The proposed measure can be adopted for various types of rankings, such as full rankings, partial rankings and rankings with ties. This paper demonstrates how the proposed approaches can be used to evaluate the quality of rank aggregation and the quality of top-$k$ rankings from Google and Bing search engines.

Title: Possible resonance effect of dark matter axions in SNS Josephson junctions, Abstract: Dark matter axions can generate peculiar effects in special types of Josephson junctions, so-called SNS junctions. One can show that the axion field equations in a Josephson environment allow for very small oscillating supercurrents, which manifest themselves as a tiny wiggle in the I-V curve, a so-called Shapiro step, which occurs at a frequency given by the axion mass. The effect is very small but perfectly measurable in modern nanotechnological devices. In this paper I will summarize the theory and then present evidence that candidate Shapiro steps of this type have indeed been seen in several independent condensed matter experiments. Assuming the observed tiny Shapiro steps are due to axion flow then these data point to an axion mass of $(106 \pm 6)\mu$eV, consistent with what is expected for the QCD axion. In addition to the above small Shapiro resonance effects at frequencies in the GHz region one also expects to see broad-band noise effects at much lower frequencies. Overall this approach provides a novel pathway for the future design of new types of axionic dark matter detectors. The resonant Josephson data summarized in this paper are consistent with a 'vanilla' axion with a coupling constant $f_a=\sqrt{v_{EW}m_{Pl}}=5.48 \cdot 10^{10}$GeV given by the geometric average of the electroweak symmetry breaking scale $v_{EW}$ and the Planck mass $m_{Pl}$.

Title: Soliton solutions for the elastic metric on spaces of curves, Abstract: In this article we investigate a first order reparametrization-invariant Sobolev metric on the space of immersed curves. Motivated by applications in shape analysis where discretizations of this infinite-dimensional space are needed, we extend this metric to the space of Lipschitz curves, establish the wellposedness of the geodesic equation thereon, and show that the space of piecewise linear curves is a totally geodesic submanifold. Thus, piecewise linear curves are natural finite elements for the discretization of the geodesic equation. Interestingly, geodesics in this space can be seen as soliton solutions of the geodesic equation, which were not known to exist for reparametrization-invariant Sobolev metrics on spaces of curves.

Title: Influence of Personal Preferences on Link Dynamics in Social Networks, Abstract: We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.

Title: JHelioviewer - Time-dependent 3D visualisation of solar and heliospheric data, Abstract: Context. Solar observatories are providing the world-wide community with a wealth of data, covering large time ranges, multiple viewpoints, and returning large amounts of data. In particular, the large volume of SDO data presents challenges: it is available only from a few repositories, and full-disk, full-cadence data for reasonable durations of scientific interest are difficult to download practically due to their size and download data rates available to most users. From a scientist's perspective this poses three problems: accessing, browsing and finding interesting data as efficiently as possible. Aims. To address these challenges, we have developed JHelioviewer, a visualisation tool for solar data based on the JPEG2000 compression standard and part of the open source ESA/NASA Helioviewer Project. Since the first release of JHelioviewer, the scientific functionality of the software has been extended significantly, and the objective of this paper is to highlight these improvements. Methods. The JPEG2000 standard offers useful new features that facilitate the dissemination and analysis of high-resolution image data and offers a solution to the challenge of efficiently browsing petabyte-scale image archives. The JHelioviewer software is open source, platform independent and extendable via a plug-in architecture. Results. With JHelioviewer, users can visualise the Sun for any time period between September 1991 and today. They can perform basic image processing in real time, track features on the Sun and interactively overlay magnetic field extrapolations. The software integrates solar event data and a time line display. As a first step towards supporting science planning of the upcoming Solar Orbiter mission, JHelioviewer offers a virtual camera model that enables users to set the vantage point to the location of a spacecraft or celestial body at any given time.

Title: Model-based Design Evaluation of a Compact, High-Efficiency Neutron Scatter Camera, Abstract: This paper presents the model-based design and evaluation of an instrument that estimates incident neutron direction using the kinematics of neutron scattering by hydrogen-1 nuclei in an organic scintillator. The instrument design uses a single, nearly contiguous volume of organic scintillator that is internally subdivided only as necessary to create optically isolated pillars. Scintillation light emitted in a given pillar is confined to that pillar by a combination of total internal reflection and a specular reflector applied to the four sides of the pillar transverse to its long axis. The scintillation light is collected at each end of the pillar using a photodetector. In this optically segmented design, the (x, y) position of scintillation light emission (where the x and y coordinates are transverse to the long axis of the pillars) is estimated as the pillar's (x, y) position in the scintillator "block", and the z-position (the position along the pillar's long axis) is estimated from the amplitude and relative timing of the signals produced by the photodetectors at each end of the pillar. For proton recoils greater than 1 MeV, we show that the (x, y, z)-position of neutron-proton scattering can be estimated with < 1 cm root-mean-squared [RMS] error and the proton recoil energy can be estimated with < 50 keV RMS error by fitting the photodetectors' response time history to models of optical photon transport within the scintillator pillars. Finally, we evaluate several alternative designs of this proposed single-volume scatter camera made of pillars of plastic scintillator (SVSC-PiPS), studying the effect of pillar dimensions, scintillator material, and photodetector response vs. time. Specifically, we conclude that an SVSC-PiPS constructed using EJ-204 and an MCP-PM will produce the most precise estimates of incident neutron direction and energy.

Title: Anisotropic mechanical and optical response and negative Poissons ratio in Mo2C nanomembranes revealed by first-principles simulations, Abstract: Transition metal carbides include a wide variety of materials with attractive properties that are suitable for numerous and diverse applications. Most recent experimental advance could provide a path toward successful synthesis of large-area and high-quality ultrathin Mo2C membranes with superconducting properties. In the present study, we used first-principles density functional theory calculations to explore the mechanical and optical response of single-layer and free-standing Mo2C. Uniaxial tensile simulations along the armchair and zigzag directions were conducted and we found that while the elastic properties are close along various loading directions, nonlinear regimes in stress-strain curves are considerably different. We found that Mo2C sheets present negative Poisson's ratio and thus can be categorized as an auxetic material. Our simulations also reveal that Mo2C films retain their metallic electronic characteristic upon the uniaxial loading. We found that for Mo2C nanomembranes the dielectric function becomes anisotropic along in-plane and out-of plane directions. Our findings can be useful for the practical application of Mo2C sheets in nanodevices.

Title: A critical analysis of string APIs: The case of Pharo, Abstract: Most programming languages, besides C, provide a native abstraction for character strings, but string APIs vary widely in size, expressiveness, and subjective convenience across languages. In Pharo, while at first glance the API of the String class seems rich, it often feels cumbersome in practice; to improve its usability, we faced the challenge of assessing its design. However, we found hardly any guideline about design forces and how they structure the design space, and no comprehensive analysis of the expected string operations and their different variations. In this article, we first analyse the Pharo 4 String library, then contrast it with its Haskell, Java, Python, Ruby, and Rust counterparts. We harvest criteria to describe a string API, and reflect on features and design tensions. This analysis should help language designers in understanding the design space of strings, and will serve as a basis for a future redesign of the string library in Pharo.

Title: Power Maxwell distribution: Statistical Properties, Estimation and Application, Abstract: In this article, we proposed a new probability distribution named as power Maxwell distribution (PMaD). It is another extension of Maxwell distribution (MaD) which would lead more flexibility to analyze the data with non-monotone failure rate. Different statistical properties such as reliability characteristics, moments, quantiles, mean deviation, generating function, conditional moments, stochastic ordering, residual lifetime function and various entropy measures have been derived. The estimation of the parameters for the proposed probability distribution has been addressed by maximum likelihood estimation method and Bayes estimation method. The Bayes estimates are obtained under gamma prior using squared error loss function. Lastly, real-life application for the proposed distribution has been illustrated through different lifetime data.

Title: Factorization tests and algorithms arising from counting modular forms and automorphic representations, Abstract: A theorem of Gekeler compares the number of non-isomorphic automorphic representations associated with the space of cusp forms of weight $k$ on $\Gamma_0(N)$ to a simpler function of $k$ and $N$, showing that the two are equal whenever $N$ is squarefree. We prove the converse of this theorem (with one small exception), thus providing a characterization of squarefree integers. We also establish a similar characterization of prime numbers in terms of the number of Hecke newforms of weight $k$ on $\Gamma_0(N)$. It follows that a hypothetical fast algorithm for computing the number of such automorphic representations for even a single weight $k$ would yield a fast test for whether $N$ is squarefree. We also show how to obtain bounds on the possible square divisors of a number $N$ that has been found to not be squarefree via this test, and we show how to probabilistically obtain the complete factorization of the squarefull part of $N$ from the number of such automorphic representations for two different weights. If in addition we have the number of such Hecke newforms for even a single weight $k$, then we show how to probabilistically factor $N$ entirely. All of these computations could be performed quickly in practice, given the number(s) of automorphic representations and modular forms as input.

Title: ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems, Abstract: Model-free policy learning has enabled robust performance of complex tasks with relatively simple algorithms. However, this simplicity comes at the cost of requiring an Oracle and arguably very poor sample complexity. This renders such methods unsuitable for physical systems. Variants of model-based methods address this problem through the use of simulators, however, this gives rise to the problem of policy transfer from simulated to the physical system. Model mismatch due to systematic parameter shift and unmodelled dynamics error may cause sub-optimal or unsafe behavior upon direct transfer. We introduce the Adaptive Policy Transfer for Stochastic Dynamics (ADAPT) algorithm that achieves provably safe and robust, dynamically-feasible zero-shot transfer of RL-policies to new domains with dynamics error. ADAPT combines the strengths of offline policy learning in a black-box source simulator with online tube-based MPC to attenuate bounded model mismatch between the source and target dynamics. ADAPT allows online transfer of policy, trained solely in a simulation offline, to a family of unknown targets without fine-tuning. We also formally show that (i) ADAPT guarantees state and control safety through state-action tubes under the assumption of Lipschitz continuity of the divergence in dynamics and, (ii) ADAPT results in a bounded loss of reward accumulation relative to a policy trained and evaluated in the source environment. We evaluate ADAPT on 2 continuous, non-holonomic simulated dynamical systems with 4 different disturbance models, and find that ADAPT performs between 50%-300% better on mean reward accrual than direct policy transfer.

Title: Skin Lesion Classification Using Hybrid Deep Neural Networks, Abstract: Skin cancer is one of the major types of cancers and its incidence has been increasing over the past decades. Skin lesions can arise from various dermatologic disorders and can be classified to various types according to their texture, structure, color and other morphological features. The accuracy of diagnosis of skin lesions, specifically the discrimination of benign and malignant lesions, is paramount to ensure appropriate patient treatment. Machine learning-based classification approaches are among popular automatic methods for skin lesion classification. While there are many existing methods, convolutional neural networks (CNN) have shown to be superior over other classical machine learning methods for object detection and classification tasks. In this work, a fully automatic computerized method is proposed, which employs well established pre-trained convolutional neural networks and ensembles learning to classify skin lesions. We trained the networks using 2000 skin lesion images available from the ISIC 2017 challenge, which has three main categories and includes 374 melanoma, 254 seborrheic keratosis and 1372 benign nevi images. The trained classifier was then tested on 150 unlabeled images. The results, evaluated by the challenge organizer and based on the area under the receiver operating characteristic curve (AUC), were 84.8% and 93.6% for Melanoma and seborrheic keratosis binary classification problem, respectively. The proposed method achieved competitive results to experienced dermatologist. Further improvement and optimization of the proposed method with a larger training dataset could lead to a more precise, reliable and robust method for skin lesion classification.

Title: Nice derivations over principal ideal domains, Abstract: In this paper we investigate to what extent the results of Z. Wang and D. Daigle on nice derivations of the polynomial ring in three variables over a field k of characteristic zero extend to the polynomial ring over a PID R, containing the field of rational numbers. One of our results shows that the kernel of a nice derivation on the polynomial ring in four variables over k of rank at most three is a polynomial ring over k.

Title: SETI in vivo: testing the we-are-them hypothesis, Abstract: After it was proposed that life on Earth might descend from seeding by an earlier civilization, some authors noted that this alternative offers a testable aspect: the seeds could be supplied with a signature that might be found in extant organisms. In particular, it was suggested that the optimal location for such an artifact is the genetic code, as the least evolving part of cells. However, as the mainstream view goes, this scenario is too speculative and cannot be meaningfully tested because encoding/decoding a signature within the genetic code is ill-defined, so any retrieval attempt is doomed to guesswork. Here we refresh the seeded-Earth hypothesis and discuss the motivation for inserting a signature. We then show that "biological SETI" involves even weaker assumptions than traditional SETI and admits a well-defined methodological framework. After assessing the possibility in terms of molecular and evolutionary biology, we formalize the approach and, adopting the guideline of SETI that encoding/decoding should follow from first principles and be convention-free, develop a retrieval strategy. Applied to the canonical code, it reveals a nontrivial precision structure of interlocked systematic attributes. To assess this result in view of the initial assumption, we perform statistical, comparison, interdependence, and semiotic analyses. Statistical analysis reveals no causal connection to evolutionary models of the code, interdependence analysis precludes overinterpretation, and comparison analysis shows that known code variations lack any precision-logic structures, in agreement with these variations being post-seeding deviations from the canonical code. Finally, semiotic analysis shows that not only the found attributes are consistent with the initial assumption, but that they make perfect sense from SETI perspective, as they maintain some of the most universal codes of culture.

Title: Transversality for local Morse homology with symmetries and applications, Abstract: We prove the transversality result necessary for defining local Morse chain complexes with finite cyclic group symmetry. Our arguments use special regularized distance functions constructed using classical covering lemmas, and an inductive perturbation process indexed by the strata of the isotropy set. A global existence theorem for symmetric Morse-Smale pairs is also proved. Regarding applications, we focus on Hamiltonian dynamics and rigorously establish a local contact homology package based on discrete action functionals. We prove a persistence theorem, analogous to the classical shifting lemma for geodesics, asserting that the iteration map is an isomorphism for good and admissible iterations. We also consider a Chas-Sullivan product on non-invariant local Morse homology, which plays the role of pair-of-pants product, and study its relationship to symplectically degenerate maxima. Finally, we explore how our invariants can be used to study bifurcation of critical points (and periodic points) under additional symmetries.

Title: The complex case of Schmidt's going-down Theorem, Abstract: In 1967, Schmidt wrote a seminal paper [10] on heights of subspaces of R n or C n defined over a number field K, and diophantine approximation problems. The going-down Theorem -- one of the main theorems he proved in his paper -- remains valid in two cases depending on whether the embedding of K in the complex field C is a real or a complex non-real embedding. For the latter, and more generally as soon as K is not totally real, at some point of the proof, the arguments in [10] do not exactly work as announced. In this note, Schmidt's ideas are worked out in details and his proof of the complex case is presented, solving the aforementioned problem. Some definitions of Schmidt are reformulated in terms of multilinear algebra and wedge product, following the approaches of Laurent [5], Bugeaud and Laurent [1] and Roy [7], [8]. In [5] Laurent introduces in the case K = Q a family of exponents and he gives a series of inequalities relating them. In Section 5 these exponents are defined for an arbitrary number field K. Using the going-up and the going-down Theorems Laurent's inequalities are generalized to this setting.

Title: Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints, Abstract: Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence of uncertainties. We adopt a chance constraint approach that accounts for arbitrary correlations between renewable resources at each of the buses. We show how the problem can be solved efficiently using historical samples via a stochastic quasi gradient method. We also show that this optimization problem is convex for a wide variety of probabilistic distributions. Compared to conventional per-bus chance constraints, our formulation is more robust to uncertainty and more computationally tractable. We illustrate the results using standard IEEE distribution test feeders.

Title: Achieving and Managing Availability SLAs with ITIL Driven Processes, DevOps, and Workflow Tools, Abstract: System and application availability continues to be a fundamental characteristic of IT services. In recent years the IT Operations team at Wolters Kluwer CT Corporation has placed special focus on this area. Using a combination of goals, metrics, processes, organizational models, communication methods, corrective maintenance, root cause analysis, preventative engineering, automated alerting, and workflow automation significant progress has been made in meeting availability SLAs or Service Level Agreements. This paper presents the background of this work, approach, details of its implementation, and results. A special focus is provided on the use of a classical ITIL view as operationalized in an Agile and DevOps environment. Keywords: System Availability, Software Reliability, ITIL, Workflow Automation, Process Engineering, Production Support, Customer Support, Product Support, Change Management, Release Management, Incident Management, Problem Management, Organizational Design, Scrum, Agile, DevOps, Service Level Agreements, Software Measurement, Microsoft SharePoint.

Title: Online Adaptive Methods, Universality and Acceleration, Abstract: We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard convergence rate in the stochastic optimization setting. To the best of our knowledge, this is the first method that simultaneously applies to all of the above settings. At the heart of our method is an adaptive learning rate rule that employs importance weights, in the spirit of adaptive online learning algorithms (Duchi et al., 2011; Levy, 2017), combined with an update that linearly couples two sequences, in the spirit of (Allen-Zhu and Orecchia, 2017). An empirical examination of our method demonstrates its applicability to the above mentioned scenarios and corroborates our theoretical findings.

Title: Learning Steerable Filters for Rotation Equivariant CNNs, Abstract: In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. In this work, we develop Steerable Filter CNNs (SFCNNs) which achieve joint equivariance under translations and rotations by design. The proposed architecture employs steerable filters to efficiently compute orientation dependent responses for many orientations without suffering interpolation artifacts from filter rotation. We utilize group convolutions which guarantee an equivariant mapping. In addition, we generalize He's weight initialization scheme to filters which are defined as a linear combination of a system of atomic filters. Numerical experiments show a substantial enhancement of the sample complexity with a growing number of sampled filter orientations and confirm that the network generalizes learned patterns over orientations. The proposed approach achieves state-of-the-art on the rotated MNIST benchmark and on the ISBI 2012 2D EM segmentation challenge.

Title: Stochastic Multi-armed Bandits in Constant Space, Abstract: We consider the stochastic bandit problem in the sublinear space setting, where one cannot record the win-loss record for all $K$ arms. We give an algorithm using $O(1)$ words of space with regret \[ \sum_{i=1}^{K}\frac{1}{\Delta_i}\log \frac{\Delta_i}{\Delta}\log T \] where $\Delta_i$ is the gap between the best arm and arm $i$ and $\Delta$ is the gap between the best and the second-best arms. If the rewards are bounded away from $0$ and $1$, this is within an $O(\log 1/\Delta)$ factor of the optimum regret possible without space constraints.

Title: Comparing Different Models for Investigating Cascading Failures in Power Systems, Abstract: This paper centers on the comparison of three different models that describe cascading failures of power systems. Specifically, these models are different in characterizing the physical properties of power networks and computing the branch power flow. Optimal control approach is applied on these models to identify the critical disturbances that result in the worst-case cascading failures of power networks. Then we compare these models by analyzing the critical disturbances and cascading processes. Significantly, comparison results on IEEE 9 bus system demonstrate that physical and electrical properties of power networks play a crucial role in the evolution of cascading failures, and it is necessary to take into account these properties appropriately while applying the model in the analysis of cascading blackout.

Title: Two-dimensional Fermi gases near a p-wave resonance: effect of quantum fluctuations, Abstract: We study the stability of p-wave superfluidity against quantum fluctuations in two-dimensional Fermi gases near a p-wave Feshbach resonance . An analysis is carried out in the limit when the interchannel coupling is strong. By investigating the effective potential for the pairing field via the standard loop expansion, we show that a homogeneous p-wave pairing state becomes unstable when two-loop quantum fluctuations are taken into account. This is in contrast to the previously predicted $p + ip$ supefluid in the weak-coupling limit [V. Gurarie et al., Phys. Rev. Lett. 94, 230403 (2005)]. It implies a possible onset of instability at certain intermediate interchannel coupling strength. Alternatively, the instability can also be driven by lowering the particle density. We also discuss the validity of our analysis.

Title: Optimal Strong Rates of Convergence for a Space-Time Discretization of the Stochastic Allen-Cahn Equation with multiplicative noise, Abstract: The stochastic Allen-Cahn equation with multiplicative noise involves the nonlinear drift operator ${\mathscr A}(x) = \Delta x - \bigl(\vert x\vert^2 -1\bigr)x$. We use the fact that ${\mathscr A}(x) = -{\mathcal J}^{\prime}(x)$ satisfies a weak monotonicity property to deduce uniform bounds in strong norms for solutions of the temporal, as well as of the spatio-temporal discretization of the problem. This weak monotonicity property then allows for the estimate $ \underset{1 \leq j \leq J}\sup {\mathbb E}\bigl[ \Vert X_{t_j} - Y^j\Vert_{{\mathbb L}^2}^2\bigr] \leq C_{\delta}(k^{1-\delta} + h^2)$ for all small $\delta>0$, where $X$ is the strong variational solution of the stochastic Allen-Cahn equation, while $\big\{Y^j:0\le j\le J\big\}$ solves a structure preserving finite element based space-time discretization of the problem on a temporal mesh $\{ t_j;\, 1 \leq j \leq J\}$ of size $k>0$ which covers $[0,T]$.

Title: On the Essential Spectrum of Schrödinger Operators on Trees, Abstract: It is known that the essential spectrum of a Schrödinger operator $H$ on $\ell^{2}\left(\mathbb{N}\right)$ is equal to the union of the spectra of right limits of $H$. The natural generalization of this relation to $\mathbb{Z}^{n}$ is known to hold as well. In this paper we generalize the notion of right limits to general infinite connected graphs and construct examples of graphs for which the essential spectrum of the Laplacian is strictly bigger than the union of the spectra of its right limits. As these right limits are trees, this result is complemented by the fact that the equality still holds for general bounded operators on regular trees. We prove this and characterize the essential spectrum in the spherically symmetric case.

Title: The equilibrium of over-pressurised polytropes, Abstract: We investigate the impact of an external pressure on the structure of self-gravitating polytropes for axially symmetric ellipsoids and rings. The confinement of the fluid by photons is accounted for through a boundary condition on the enthalpy $H$. Equilibrium configurations are determined numerically from a generalised "Self-Consistent-Field"-method. The new algorithm incorporates an intra-loop re-scaling operator ${\cal R}(H)$, which is essential for both convergence and getting self-normalised solutions. The main control parameter is the external-to-core enthalpy ratio. In the case of uniform rotation rate and uniform surrounding pressure, we compute the mass, the volume, the rotation rate and the maximum enthalpy. This is repeated for a few polytropic indices $n$. For a given axis ratio, over-pressurization globally increases all output quantities, and this is more pronounced for large $n$. Density profiles are flatter than in the absence of an external pressure. When the control parameter asymptotically tends to unity, the fluid converges toward the incompressible solution, whatever the index, but becomes geometrically singular. Equilibrium sequences, obtained by varying the axis ratio, are built. States of critical rotation are greatly exceeded or even disappear. The same trends are observed with differential rotation. Finally, the typical response to a photon point source is presented. Strong irradiation favours sharp edges. Applications concern star forming regions and matter orbiting young stars and black holes.

Title: From voids to filaments: environmental transformations of galaxies in the SDSS, Abstract: We investigate the impact of filament and void environments on galaxies, looking for residual effects beyond the known relations with environment density. We quantified the host environment of galaxies as the distance to the spine of the nearest filament, and compared various galaxy properties within 12 bins of this distance. We considered galaxies up to 10 $h^{-1}$Mpc from filaments, i.e. deep inside voids. The filaments were defined by a point process (the Bisous model) from the Sloan Digital Sky Survey data release 10. In order to remove the dependence of galaxy properties on the environment density and redshift, we applied weighting to normalise the corresponding distributions of galaxy populations in each bin. After the normalisation with respect to environment density and redshift, several residual dependencies of galaxy properties still remain. Most notable is the trend of morphology transformations, resulting in a higher elliptical-to-spiral ratio while moving from voids towards filament spines, bringing along a corresponding increase in the $g-i$ colour index and a decrease in star formation rate. After separating elliptical and spiral subsamples, some of the colour index and star formation rate evolution still remains. The mentioned trends are characteristic only for galaxies brighter than about $M_{r} = -20$ mag. Unlike some other recent studies, we do not witness an increase in the galaxy stellar mass while approaching filaments. The detected transformations can be explained by an increase in the galaxy-galaxy merger rate and/or the cut-off of extragalactic gas supplies (starvation) near and inside filaments. Unlike voids, large-scale galaxy filaments are not a mere density enhancement, but have their own specific impact on the constituent galaxies, reducing the star formation rate and raising the chances of elliptical morphology also at a fixed environment density level.

Title: Distributed Algorithms Made Secure: A Graph Theoretic Approach, Abstract: In the area of distributed graph algorithms a number of network's entities with local views solve some computational task by exchanging messages with their neighbors. Quite unfortunately, an inherent property of most existing distributed algorithms is that throughout the course of their execution, the nodes get to learn not only their own output but rather learn quite a lot on the inputs or outputs of many other entities. This leakage of information might be a major obstacle in settings where the output (or input) of network's individual is a private information. In this paper, we introduce a new framework for \emph{secure distributed graph algorithms} and provide the first \emph{general compiler} that takes any "natural" non-secure distributed algorithm that runs in $r$ rounds, and turns it into a secure algorithm that runs in $\widetilde{O}(r \cdot D \cdot poly(\Delta))$ rounds where $\Delta$ is the maximum degree in the graph and $D$ is its diameter. The security of the compiled algorithm is information-theoretic but holds only against a semi-honest adversary that controls a single node in the network. This compiler is made possible due to a new combinatorial structure called \emph{private neighborhood trees}: a collection of $n$ trees $T(u_1),\ldots,T(u_n)$, one for each vertex $u_i \in V(G)$, such that each tree $T(u_i)$ spans the neighbors of $u_i$ {\em without going through $u_i$}. Intuitively, each tree $T(u_i)$ allows all neighbors of $u_i$ to exchange a \emph{secret} that is hidden from $u_i$, which is the basic graph infrastructure of the compiler. In a $(d,c)$-private neighborhood trees each tree $T(u_i)$ has depth at most $d$ and each edge $e \in G$ appears in at most $c$ different trees. We show a construction of private neighborhood trees with $d=\widetilde{O}(\Delta \cdot D)$ and $c=\widetilde{O}(D)$.

Title: Thermalization in simple metals: The role of electron-phonon and phonon-phonon scatterings, Abstract: We study the electron and phonon thermalization in simple metals excited by a laser pulse. The thermalization is investigated numerically by solving the Boltzmann transport equation taking into account all the relevant scattering mechanism: the electron-electron, electron-phonon (e-ph), phonon-electron (ph-e), and phonon-phonon (ph-ph) scatterings. In the initial stage of the relaxation, most of the excitation energy is transferred from the electrons to phonons through the e-ph scattering. This creates hot high-frequency phonons due to the ph-e scatterings, followed by an energy redistribution between phonon subsystems through the ph-ph scatterings. This yields an overshoot of the total longitudinal-acoustic phonon energy at a time, across which a crossover occurs from a nonequilibrium state, where the e-ph and ph-e scatterings frequently occur, to a state, where the ph-ph scattering occurs to reach a thermal equilibrium. This picture is quite different from the scenario of the well-known two-temperature model (2TM). The behavior of the relaxation dynamics is compared with those calculated by several models, including the 2TM, the four-temperature model, and nonequilibrium electron or phonon models. The relationship between the relaxation time and the initial distribution function is also discussed.

Title: A Nonparametric Bayesian Approach to Copula Estimation, Abstract: We propose a novel Dirichlet-based Pólya tree (D-P tree) prior on the copula and based on the D-P tree prior, a nonparametric Bayesian inference procedure. Through theoretical analysis and simulations, we are able to show that the flexibility of the D-P tree prior ensures its consistency in copula estimation, thus able to detect more subtle and complex copula structures than earlier nonparametric Bayesian models, such as a Gaussian copula mixture. Further, the continuity of the imposed D-P tree prior leads to a more favorable smoothing effect in copula estimation over classic frequentist methods, especially with small sets of observations. We also apply our method to the copula prediction between the S\&P 500 index and the IBM stock prices during the 2007-08 financial crisis, finding that D-P tree-based methods enjoy strong robustness and flexibility over classic methods under such irregular market behaviors.

Title: Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone, Abstract: In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem. We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles.

Title: Time-of-Flight Electron Energy Loss Spectroscopy by Longitudinal Phase Space Manipulation with Microwave Cavities, Abstract: The possibility to perform high-resolution time-resolved electron energy loss spectroscopy has the potential to impact a broad range of research fields. Resolving small energy losses with ultrashort electron pulses, however, is an enormous challenge due to the low average brightness of a pulsed beam. In this letter, we propose to use time-of-flight measurements combined with longitudinal phase space manipulation using resonant microwave cavities. This allows for both an accurate detection of energy losses with a high current throughput, and efficient monochromation. First, a proof-of-principle experiment is presented, showing that with the incorporation of a compression cavity the flight time resolution can be improved significantly. Then, it is shown through simulations that by adding a cavity-based monochromation technique, a full-width-at-half-maximum energy resolution of 22 meV can be achieved with 3.1 ps pulses at a beam energy of 30 keV with currently available technology. By combining state-of-the-art energy resolutions with a pulsed electron beam, the technique proposed here opens up the way to detecting short-lived excitations within the regime of highly collective physics.

Title: Capacitated Covering Problems in Geometric Spaces, Abstract: In this article, we consider the following capacitated covering problem. We are given a set $P$ of $n$ points and a set $\mathcal{B}$ of balls from some metric space, and a positive integer $U$ that represents the capacity of each of the balls in $\mathcal{B}$. We would like to compute a subset $\mathcal{B}' \subseteq \mathcal{B}$ of balls and assign each point in $P$ to some ball in $\mathcal{B}$ that contains it, such that the number of points assigned to any ball is at most $U$. The objective function that we would like to minimize is the cardinality of $\mathcal{B}$. We consider this problem in arbitrary metric spaces as well as Euclidean spaces of constant dimension. In the metric setting, even the uncapacitated version of the problem is hard to approximate to within a logarithmic factor. In the Euclidean setting, the best known approximation guarantee in dimensions $3$ and higher is logarithmic in the number of points. Thus we focus on obtaining "bi-criteria" approximations. In particular, we are allowed to expand the balls in our solution by some factor, but optimal solutions do not have that flexibility. Our main result is that allowing constant factor expansion of the input balls suffices to obtain constant approximations for these problems. In fact, in the Euclidean setting, only $(1+\epsilon)$ factor expansion is sufficient for any $\epsilon > 0$, with the approximation factor being a polynomial in $1/\epsilon$. We obtain these results using a unified scheme for rounding the natural LP relaxation; this scheme may be useful for other capacitated covering problems. We also complement these bi-criteria approximations by obtaining hardness of approximation results that shed light on our understanding of these problems.

Title: Position-based coding and convex splitting for private communication over quantum channels, Abstract: The classical-input quantum-output (cq) wiretap channel is a communication model involving a classical sender $X$, a legitimate quantum receiver $B$, and a quantum eavesdropper $E$. The goal of a private communication protocol that uses such a channel is for the sender $X$ to transmit a message in such a way that the legitimate receiver $B$ can decode it reliably, while the eavesdropper $E$ learns essentially nothing about which message was transmitted. The $\varepsilon $-one-shot private capacity of a cq wiretap channel is equal to the maximum number of bits that can be transmitted over the channel, such that the privacy error is no larger than $\varepsilon\in(0,1)$. The present paper provides a lower bound on the $\varepsilon$-one-shot private classical capacity, by exploiting the recently developed techniques of Anshu, Devabathini, Jain, and Warsi, called position-based coding and convex splitting. The lower bound is equal to a difference of the hypothesis testing mutual information between $X$ and $B$ and the "alternate" smooth max-information between $X$ and $E$. The one-shot lower bound then leads to a non-trivial lower bound on the second-order coding rate for private classical communication over a memoryless cq wiretap channel.

Title: Geometric vulnerability of democratic institutions against lobbying: a sociophysics approach, Abstract: An alternative voting scheme is proposed to fill the democratic gap between a president elected democratically via universal suffrage (deterministic outcome, the actual majority decides), and a president elected by one person randomly selected from the population (probabilistic outcome depending on respective supports). Moving from one voting agent to a group of r randomly selected voting agents reduces the probabilistic character of the outcome. Building r such groups, each one electing its president, to constitute a group of the groups with the r local presidents electing a higher-level president, does reduce further the outcome probabilistic aspect. Repeating the process n times leads to a n-level bottom-up pyramidal structure. The hierarchy top president is still elected with a probability but the distance from a deterministic outcome reduces quickly with increasing n. At a critical value n_{c,r} the outcome turns deterministic recovering the same result a universal suffrage would yield. The scheme yields several social advantages like the distribution of local power to the competing minority making the structure more resilient, yet preserving the presidency allocation to the actual majority. An area is produced around fifty percent for which the president is elected with an almost equiprobability slightly biased in favor of the actual majority. However, our results reveal the existence of a severe geometric vulnerability to lobbying. A tiny lobbying group is able to kill the democratic balance by seizing the presidency democratically. It is sufficient to complete a correlated distribution of a few agents at the hierarchy bottom. Moreover, at the present stage, identifying an actual killing distribution is not feasible, which sheds a disturbing light on the devastating effect geometric lobbying can have on democratic hierarchical institutions.

Title: New estimates for the $n$th prime number, Abstract: In this paper we establish a new explicit upper and lower bound for the $n$-th prime number, which improve the currently best estimates given by Dusart in 2010. As the main tool we use some recently obtained explicit estimates for the prime counting function. A further main tool is the usage of estimates concerning the reciprocal of $\log p_n$. As an application we derive refined estimates for $\vartheta(p_n)$ in terms of $n$, where $\vartheta(x)$ is Chebyshev's $\vartheta$-function.

Title: Unbiased Markov chain Monte Carlo for intractable target distributions, Abstract: Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions. Markov chain Monte Carlo (MCMC) algorithms have been proposed for this setting, such as the pseudo-marginal method for latent variable models and the exchange algorithm for a class of undirected graphical models. As with any MCMC algorithm, the resulting estimators are justified asymptotically in the limit of the number of iterations, but exhibit a bias for any fixed number of iterations due to the Markov chains starting outside of stationarity. This "burn-in" bias is known to complicate the use of parallel processors for MCMC computations. We show how to use coupling techniques to generate unbiased estimators in finite time, building on recent advances for generic MCMC algorithms. We establish the theoretical validity of some of these procedures by extending existing results to cover the case of polynomially ergodic Markov chains. The efficiency of the proposed estimators is compared with that of standard MCMC estimators, with theoretical arguments and numerical experiments including state space models and Ising models.

Title: D4M 3.0, Abstract: The D4M tool is used by hundreds of researchers to perform complex analytics on unstructured data. Over the past few years, the D4M toolbox has evolved to support connectivity with a variety of database engines, graph analytics in the Apache Accumulo database, and an implementation using the Julia programming language. In this article, we describe some of our latest additions to the D4M toolbox and our upcoming D4M 3.0 release.

Title: What drives gravitational instability in nearby star-forming spirals? The impact of CO and HI velocity dispersions, Abstract: The velocity dispersion of cold interstellar gas, sigma, is one of the quantities that most radically affect the onset of gravitational instabilities in galaxy discs, and the quantity that is most drastically approximated in stability analyses. Here we analyse the stability of a large sample of nearby star-forming spirals treating molecular gas, atomic gas and stars as three distinct components, and using radial profiles of sigma_CO and sigma_HI derived from HERACLES and THINGS observations. We show that the radial variations of sigma_CO and sigma_HI have a weak effect on the local stability level of galaxy discs, which remains remarkably flat and well above unity, but is low enough to ensure (marginal) instability against non-axisymmetric perturbations and gas dissipation. More importantly, the radial variation of sigma_CO has a strong impact on the size of the regions over which gravitational instabilities develop, and results in a characteristic instability scale that is one order of magnitude larger than the Toomre length of molecular gas. Disc instabilities are driven, in fact, by the self-gravity of stars at kpc scales. This is true across the entire optical disc of every galaxy in the sample, with few exceptions. In the linear phase of the disc instability process, stars and molecular gas are strongly coupled, and it is such a coupling that ultimately triggers local gravitational collapse/fragmentation in the molecular gas.

Title: An improved belief propagation algorithm for detecting meso-scale structure in complex networks, Abstract: The framework of statistical inference has been successfully used to detect the meso-scale structures in complex networks, such as community structure, core-periphery (CP) structure. The main principle is that the stochastic block model (SBM) is used to fit the observed network and the learnt parameters indicate the group assignment, in which the parameters of model are often calculated via an expectation-maximization (EM) algorithm and a belief propagation (BP) algorithm is implemented to calculate the decomposition itself. In the derivation process of the BP algorithm, some approximations were made by omitting the effects of node's neighbors, the approximations do not hold if networks are dense or some nodes holding large degrees. As a result, for example, the BP algorithm cannot well detect CP structure in networks and even yields wrong detection because the nodal degrees in core group are very large. In doing so, we propose an improved BP algorithm to solve the problem in the original BP algorithm without increasing any computational complexity. By comparing the improved BP algorithm with the original BP algorithm on community detection and CP detection, we find that the two algorithms yield the same performance on the community detection when the network is sparse, for the community structure in dense networks or CP structure in networks, our improved BP algorithm is much better and more stable. The improved BP algorithm may help us correctly partition different types of meso-scale structures in networks.

Title: Combinatorics of involutive divisions, Abstract: The classical involutive division theory by Janet decomposes in the same way both the ideal and the escalier. The aim of this paper, following Janet's approach, is to discuss the combinatorial properties of involutive divisions, when defined on the set of all terms in a fixed degree D, postponing the discussion of ideal membership and related test. We adapt the theory by Gerdt and Blinkov, introducing relative involutive divisions and then, given a complete description of the combinatorial structure of a relative involutive division, we turn our attention to the problem of membership. In order to deal with this problem, we introduce two graphs as tools, one is strictly related to Seiler's L-graph, whereas the second generalizes it, to cover the case of "non-continuous" (in the sense of Gerdt-Blinkov) relative involutive divisions. Indeed, given an element in the ideal (resp. escalier), walking backwards (resp. forward) in the graph, we can identify all the other generators of the ideal (resp. elements of degree D in the escalier).

Title: A wearable general-purpose solution for Human-Swarm Interaction, Abstract: Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. Controlling the motion and behavior of these swarms presents unique challenges for human operators, who cannot yet effectively convey their high-level intentions to a group of robots in application. This work proposes a new human-swarm interface based on novel wearable gesture-control and haptic-feedback devices. This work seeks to combine a wearable gesture recognition device that can detect high-level intentions, a portable device that can detect Cartesian information and finger movements, and a wearable advanced haptic device that can provide real-time feedback. This project is the first to envisage a wearable Human-Swarm Interaction (HSI) interface that separates the input and feedback components of the classical control loop (input, output, feedback), as well as being the first of its kind suitable for both indoor and outdoor environments.

Title: Exact results for directed random networks that grow by node duplication, Abstract: We present exact analytical results for the degree distribution and for the distribution of shortest path lengths (DSPL) in a directed network model that grows by node duplication. Such models are useful in the study of the structure and growth dynamics of gene regulatory and scientific citation networks. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network and duplicates, with probability p, each one of the outgoing links of the mother node. In addition, the daughter node forms a directed link to the mother node itself. Thus, the model is referred to as the corded directed-node-duplication (DND) model. We obtain analytical results for the in-degree distribution, $P_t(K_{in})$, and for the out-degree distribution, $P_t(K_{out})$, of the network at time t. It is found that the in-degrees follow a shifted power-law distribution, so the network is asymptotically scale free. In contrast, the out-degree distribution is a narrow distribution, that converges to a Poisson distribution in the sparse limit and to a Gaussian distribution in the dense limit. Using these distributions we calculate the mean degree, $\langle K_{in} \rangle_t = \langle K_{out} \rangle_t$. To calculate the DSPL we derive a master equation for the time evolution of the probability $P_t(L=\ell)$, $\ell=1,2,\dots$, that for two nodes, i and j, selected randomly at time t, the shortest path from i to j is of length $\ell$. Solving the master equation, we obtain a closed form expression for $P_t(L=\ell)$. It is found that the DSPL at time t consists of a convolution of the initial DSPL, $P_0(L=\ell)$, with a Poisson distribution and a sum of Poisson distributions. The mean distance, $<L>_t$, is found to depend logarithmically on the network size, $N_t$, namely the corded DND network is a small-world network.

Title: Investigating the past history of EXors: the cases of V1118 Ori, V1143 Ori, and NY Ori, Abstract: EXor objects are young variables that show episodic variations of brightness commonly associated to enhanced accretion outbursts. With the aim of investigating the long-term photometric behaviour of a few EXor sources, we present here data from the archival plates of the Asiago Observatory, showing the Orion field where the three EXors V1118, V1143, and NY are located. A total of 484 plates were investigated, providing a total of more than 1000 magnitudes for the three stars, which cover a period of about 35 yrs between 1959 to 1993. We then compared our data with literature data. Apart from a newly discovered flare-up of V1118, we identify the same outbursts already known, but we provide two added values: (i) a long-term sampling of the quiescence phase; and (ii) repeated multi-colour observations (BVRI bands). The former allows us to give a reliable characterisation of the quiescence, which represents a unique reference for studies that will analyze future outbursts and the physical changes induced by these events. The latter is useful for confirming whether the intermittent increases of brightness are accretion-driven (as in the case of V1118), or extinction-driven (as in the case of V1143). Accordingly, doubts arise about the V1143 classification as a pure EXor object. Finally, although our plates do not separate NY Ori and the star very close to it, they indicate that this EXor did not undergo any major outbursts during our 40 yrs of monitoring.

Title: Bundle Optimization for Multi-aspect Embedding, Abstract: Understanding semantic similarity among images is the core of a wide range of computer vision applications. An important step towards this goal is to collect and learn human perceptions. Interestingly, the semantic context of images is often ambiguous as images can be perceived with emphasis on different aspects, which may be contradictory to each other. In this paper, we present a method for learning the semantic similarity among images, inferring their latent aspects and embedding them into multi-spaces corresponding to their semantic aspects. We consider the multi-embedding problem as an optimization function that evaluates the embedded distances with respect to the qualitative clustering queries. The key idea of our approach is to collect and embed qualitative measures that share the same aspects in bundles. To ensure similarity aspect sharing among multiple measures, image classification queries are presented to, and solved by users. The collected image clusters are then converted into bundles of tuples, which are fed into our bundle optimization algorithm that jointly infers the aspect similarity and multi-aspect embedding. Extensive experimental results show that our approach significantly outperforms state-of-the-art multi-embedding approaches on various datasets, and scales well for large multi-aspect similarity measures.

Title: A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data, Abstract: This paper proposes a clustering procedure for samples of multivariate functions in $(L^2(I))^{J}$, with $J\geq1$. This method is based on a k-means algorithm in which the distance between the curves is measured with a metrics that generalizes the Mahalanobis distance in Hilbert spaces, considering the correlation and the variability along all the components of the functional data. The proposed procedure has been studied in simulation and compared with the k-means based on other distances typically adopted for clustering multivariate functional data. In these simulations, it is shown that the k-means algorithm with the generalized Mahalanobis distance provides the best clustering performances, both in terms of mean and standard deviation of the number of misclassified curves. Finally, the proposed method has been applied to two real cases studies, concerning ECG signals and growth curves, where the results obtained in simulation are confirmed and strengthened.

Title: Tidal Dissipation in WASP-12, Abstract: WASP-12 is a hot Jupiter system with an orbital period of $P= 1.1\textrm{ day}$, making it one of the shortest-period giant planets known. Recent transit timing observations by Maciejewski et al. (2016) and Patra et al. (2017) find a decreasing period with $P/|\dot{P}| = 3.2\textrm{ Myr}$. This has been interpreted as evidence of either orbital decay due to tidal dissipation or a long term oscillation of the apparent period due to apsidal precession. Here we consider the possibility that it is orbital decay. We show that the parameters of the host star are consistent with either a $M_\ast \simeq 1.3 M_\odot$ main sequence star or a $M_\ast \simeq 1.2 M_\odot$ subgiant. We find that if the star is on the main sequence, the tidal dissipation is too inefficient to explain the observed $\dot{P}$. However, if it is a subgiant, the tidal dissipation is significantly enhanced due to nonlinear wave breaking of the dynamical tide near the star's center. The subgiant models have a tidal quality factor $Q_\ast'\simeq 2\times10^5$ and an orbital decay rate that agrees well with the observed $\dot{P}$. It would also explain why the planet survived for $\simeq 3\textrm{ Gyr}$ while the star was on the main sequence and yet is now inspiraling on a 3 Myr timescale. Although this suggests that we are witnessing the last $\sim 0.1\%$ of the planet's life, the probability of such a detection is a few percent given the observed sample of $\simeq 30$ hot Jupiters in $P<3\textrm{ day}$ orbits around $M_\ast>1.2 M_\odot$ hosts.

Title: Intervals between numbers that are sums of two squares, Abstract: In this paper, we improve the moment estimates for the gaps between numbers that can be represented as a sum of two squares of integers. We consider certain sum of Bessel functions and prove the upper bound for its weighted mean value. This bound provides estimates for the $\gamma$-th moments of gaps for all $\gamma\leq 2$.

Title: Subexponentially growing Hilbert space and nonconcentrating distributions in a constrained spin model, Abstract: Motivated by recent experiments with two-component Bose-Einstein condensates, we study fully-connected spin models subject to an additional constraint. The constraint is responsible for the Hilbert space dimension to scale only linearly with the system size. We discuss the unconventional statistical physical and thermodynamic properties of such a system, in particular the absence of concentration of the underlying probability distributions. As a consequence, expectation values are less suitable to characterize such systems, and full distribution functions are required instead. Sharp signatures of phase transitions do not occur in such a setting, but transitions from singly peaked to doubly peaked distribution functions of an "order parameter" may be present.

Title: Out-of-time-order Operators and the Butterfly Effect, Abstract: Out-of-time-order (OTO) operators have recently become popular diagnostics of quantum chaos in many-body systems. The usual way they are introduced is via a quantization of classical Lyapunov growth, which measures the divergence of classical trajectories in phase space due to the butterfly effect. However, it is not obvious how exactly they capture the sensitivity of a quantum system to its initial conditions beyond the classical limit. In this paper, we analyze sensitivity to initial conditions in the quantum regime by recasting OTO operators for many-body systems using various formulations of quantum mechanics. Notably, we utilize the Wigner phase space formulation to derive an $\hbar$-expansion of the OTO operator for spatial degrees of freedom, and a large spin $1/s$-expansion for spin degrees of freedom. We find in each case that the leading term is the Lyapunov growth for the classical limit of the system and argue that quantum corrections become dominant at around the scrambling time, which is also when we expect the OTO operator to saturate. We also express the OTO operator in terms of propagators and see from a different point of view how it is a quantum generalization of the divergence of classical trajectories.

Title: Monocular Vision-based Vehicle Localization Aided by Fine-grained Classification, Abstract: Monocular camera systems are prevailing in intelligent transportation systems, but by far they have rarely been used for dimensional purposes such as to accurately estimate the localization information of a vehicle. In this paper, we show that this capability can be realized. By integrating a series of advanced computer vision techniques including foreground extraction, edge and line detection, etc., and by utilizing deep learning networks for fine-grained vehicle model classification, we developed an algorithm which can estimate vehicles location (position, orientation and boundaries) within the environment down to 3.79 percent position accuracy and 2.5 degrees orientation accuracy. With this enhancement, current massive surveillance camera systems can potentially play the role of e-traffic police and trigger many new intelligent transportation applications, for example, to guide vehicles for parking or even for autonomous driving.

Title: On the second Feng-Rao distance of Algebraic Geometry codes related to Arf semigroups, Abstract: We describe the second (generalized) Feng-Rao distance for elements in an Arf numerical semigroup that are greater than or equal to the conductor of the semigroup. This provides a lower bound for the second Hamming weight for one point AG codes. In particular, we can obtain the second Feng-Rao distance for the codes defined by asymptotically good towers of function fields whose Weierstrass semigroups are inductive. In addition, we compute the second Feng-Rao number, and provide some examples and comparisons with previous results on this topic. These calculations rely on Apéry sets, and thus several results concerning Apéry sets of Arf semigroups are presented.

Title: Sample-Efficient Learning of Mixtures, Abstract: We consider PAC learning of probability distributions (a.k.a. density estimation), where we are given an i.i.d. sample generated from an unknown target distribution, and want to output a distribution that is close to the target in total variation distance. Let $\mathcal F$ be an arbitrary class of probability distributions, and let $\mathcal{F}^k$ denote the class of $k$-mixtures of elements of $\mathcal F$. Assuming the existence of a method for learning $\mathcal F$ with sample complexity $m_{\mathcal{F}}(\epsilon)$, we provide a method for learning $\mathcal F^k$ with sample complexity $O({k\log k \cdot m_{\mathcal F}(\epsilon) }/{\epsilon^{2}})$. Our mixture learning algorithm has the property that, if the $\mathcal F$-learner is proper/agnostic, then the $\mathcal F^k$-learner would be proper/agnostic as well. This general result enables us to improve the best known sample complexity upper bounds for a variety of important mixture classes. First, we show that the class of mixtures of $k$ axis-aligned Gaussians in $\mathbb{R}^d$ is PAC-learnable in the agnostic setting with $\widetilde{O}({kd}/{\epsilon ^ 4})$ samples, which is tight in $k$ and $d$ up to logarithmic factors. Second, we show that the class of mixtures of $k$ Gaussians in $\mathbb{R}^d$ is PAC-learnable in the agnostic setting with sample complexity $\widetilde{O}({kd^2}/{\epsilon ^ 4})$, which improves the previous known bounds of $\widetilde{O}({k^3d^2}/{\epsilon ^ 4})$ and $\widetilde{O}(k^4d^4/\epsilon ^ 2)$ in its dependence on $k$ and $d$. Finally, we show that the class of mixtures of $k$ log-concave distributions over $\mathbb{R}^d$ is PAC-learnable using $\widetilde{O}(d^{(d+5)/2}\epsilon^{-(d+9)/2}k)$ samples.

Title: P-wave superfluidity of atomic lattice fermions, Abstract: We discuss the emergence of p-wave superfluidity of identical atomic fermions in a two-dimensional optical lattice. The optical lattice potential manifests itself in an interplay between an increase in the density of states on the Fermi surface and the modification of the fermion-fermion interaction (scattering) amplitude. The density of states is enhanced due to an increase of the effective mass of atoms. In deep lattices the scattering amplitude is strongly reduced compared to free space due to a small overlap of wavefunctions of fermion sitting in the neighboring lattice sites, which suppresses the p-wave superfluidity. However, for moderate lattice depths the enhancement of the density of states can compensate the decrease of the scattering amplitude. Moreover, the lattice setup significantly reduces inelastic collisional losses, which allows one to get closer to a p-wave Feshbach resonance. This opens possibilities to obtain the topological $p_x+ip_y$ superfluid phase, especially in the recently proposed subwavelength lattices. We demonstrate this for the two-dimensional version of the Kronig-Penney model allowing a transparent physical analysis.

Title: Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach, Abstract: With the rapid growth of social media, massive misinformation is also spreading widely on social media, such as microblog, and bring negative effects to human life. Nowadays, automatic misinformation identification has drawn attention from academic and industrial communities. For an event on social media usually consists of multiple microblogs, current methods are mainly based on global statistical features. However, information on social media is full of noisy and outliers, which should be alleviated. Moreover, most of microblogs about an event have little contribution to the identification of misinformation, where useful information can be easily overwhelmed by useless information. Thus, it is important to mine significant microblogs for a reliable misinformation identification method. In this paper, we propose an Attention-based approach for Identification of Misinformation (AIM). Based on the attention mechanism, AIM can select microblogs with largest attention values for misinformation identification. The attention mechanism in AIM contains two parts: content attention and dynamic attention. Content attention is calculated based textual features of each microblog. Dynamic attention is related to the time interval between the posting time of a microblog and the beginning of the event. To evaluate AIM, we conduct a series of experiments on the Weibo dataset and the Twitter dataset, and the experimental results show that the proposed AIM model outperforms the state-of-the-art methods.

Title: Electrical Tuning of Polarizaion-state Using Graphene-Integrated Metasurfaces, Abstract: Plasmonic metasurfaces have been employed for tuning and controlling light enabling various novel applications. Their appeal is enhanced with the incorporation of an active element with the metasurfaces paving the way for dynamic control. In this letter, we realize a dynamic polarization state generator using graphene-integrated anisotropic metasurface (GIAM), where a linear incidence polarization is controllably converted into an elliptical one. The anisotropic metasurface leads to an intrinsic polarization conversion when illuminated with non-orthogonal incident polarization. Additionally, the single-layer graphene allows us to tune the phase and intensity of the reflected light on the application of a gate voltage, enabling dynamic polarization control. The stokes polarization parameters of the reflected light are measured using rotating polarizer method and it is demonstrated that a large change in the ellipticity as well as orientation angle can be induced by this device. We also provide experimental evidence that the titl angle can change independent of the ellipticity going from positive values to nearly zero to negative values while ellipticity is constant.

Title: Feature Model-to-Ontology for SPL Application Realisation, Abstract: Feature model are widely used to capture commonalities and variabilities of artefacts in Software Product Line (SPL). Several studies have discussed the formal representation of feature diagram using ontologies with different styles of mapping. However, they still focused on the ontology approach for problem space and keep the solution space aside. In this paper, we present the modelling of feature model using OWL ontology and produce an application based on the ontology. Firstly, we map the features in a running example feature diagram to OWL classes and properties. Secondly, we verify the consistency of the OWL ontology by using reasoning engines. Finally, we use the ontology as an input of Zotonic framework for application realisation.

Title: A Spectroscopic Orbit for the late-type Be star $β$ CMi, Abstract: The late-type Be star $\beta$ CMi is remarkably stable compared to other Be stars that have been studied. This has led to a realistic model of the outflowing Be disk by Klement et al. These results showed that the disk is likely truncated at a finite radius from the star, which Klement et al.~suggest is evidence for an unseen binary companion in orbit. Here we report on an analysis of the Ritter Observatory spectroscopic archive of $\beta$ CMi to search for evidence of the elusive companion. We detect periodic Doppler shifts in the wings of the H$\alpha$ line with a period of 170 d and an amplitude of 2.25 km s$^{-1}$, consistent with a low-mass binary companion ($M\approx 0.42 M_\odot$). We then compared the small changes in the violet-to-red peak height changes ($V/R$) with the orbital motion. We find weak evidence that it does follow the orbital motion, as suggested by recent Be binary models by Panoglou et al. Our results, which are similar to those for several other Be stars, suggest that $\beta$ CMi may be a product of binary evolution where Roche lobe overflow has spun up the current Be star, likely leaving a hot subdwarf or white dwarf in orbit around the star. Unfortunately, no direct sign of this companion star is found in the very limited archive of {\it International Ultraviolet Explorer} spectra.

Title: Approximate String Matching: Theory and Applications (La Recherche Approchée de Motifs : Théorie et Applications), Abstract: The approximate string matching is a fundamental and recurrent problem that arises in most computer science fields. This problem can be defined as follows: Let $D=\{x_1,x_2,\ldots x_d\}$ be a set of $d$ words defined on an alphabet $\Sigma$, let $q$ be a query defined also on $\Sigma$, and let $k$ be a positive integer. We want to build a data structure on $D$ capable of answering the following query: find all words in $D$ that are at most different from the query word $q$ with $k$ errors. In this thesis, we study the approximate string matching methods in dictionaries, texts, and indexes, to propose practical methods that solve this problem efficiently. We explore this problem in three complementary directions: 1) The approximate string matching in the dictionary. We propose two solutions to this problem, the first one uses hash tables for $k \geq 2$, the second uses the Trie and reverse Trie, and it is restricted to (k = 1). The two solutions are adaptable, without loss of performance, to the approximate string matching in a text. 2) The approximate string matching for \textit{autocompletion}, which is, find all suffixes of a given prefix that may contain errors. We give a new solution better in practice than all the previous proposed solutions. 3) The problem of the alignment of biological sequences can be interpreted as an approximate string matching problem. We propose a solution for peers and multiple sequences alignment. \medskip All the results obtained showed that our algorithms, give the best performance on sets of practical data (benchmark from the real world). All our methods are proposed as libraries, and they are published online.

Title: Imaging anomalous nematic order and strain in optimally doped BaFe$_2$(As,P)$_2$, Abstract: We present the strain and temperature dependence of an anomalous nematic phase in optimally doped BaFe$_2$(As,P)$_2$. Polarized ultrafast optical measurements reveal broken 4-fold rotational symmetry in a temperature range above $T_c$ in which bulk probes do not detect a phase transition. Using ultrafast microscopy, we find that the magnitude and sign of this nematicity vary on a ${50{-}100}~\mu$m length scale, and the temperature at which it onsets ranges from 40 K near a domain boundary to 60 K deep within a domain. Scanning Laue microdiffraction maps of local strain at room temperature indicate that the nematic order appears most strongly in regions of weak, isotropic strain. These results indicate that nematic order arises in a genuine phase transition rather than by enhancement of local anisotropy by a strong nematic susceptibility. We interpret our results in the context of a proposed surface nematic phase.

Title: On time and consistency in multi-level agent-based simulations, Abstract: The integration of multiple viewpoints became an increasingly popular approach to deal with agent-based simulations. Despite their disparities, recent approaches successfully manage to run such multi-level simulations. Yet, are they doing it appropriately? This paper tries to answer that question, with an analysis based on a generic model of the temporal dynamics of multi-level simulations. This generic model is then used to build an orthogonal approach to multi-level simulation called SIMILAR. In this approach, most time-related issues are explicitly modeled, owing to an implementation-oriented approach based on the influence/reaction principle.

Title: A multi-layered energy consumption model for smart wireless acoustic sensor networks, Abstract: Smart sensing is expected to become a pervasive technology in smart cities and environments of the near future. These services are improving their capabilities due to integrated devices shrinking in size while maintaining their computational power, which can run diverse Machine Learning algorithms and achieve high performance in various data-processing tasks. One attractive sensor modality to be used for smart sensing are acoustic sensors, which can convey highly informative data while keeping a moderate energy consumption. Unfortunately, the energy budget of current wireless sensor networks is usually not enough to support the requirements of standard microphones. Therefore, energy efficiency needs to be increased at all layers --- sensing, signal processing and communication --- in order to bring wireless smart acoustic sensors into the market. To help to attain this goal, this paper introduces WASN-EM: an energy consumption model for wireless acoustic sensors networks (WASN), whose aim is to aid in the development of novel techniques to increase the energy-efficient of smart wireless acoustic sensors. This model provides a first step of exploration prior to custom design of a smart wireless acoustic sensor, and also can be used to compare the energy consumption of different protocols.

Title: Internal DLA on Sierpinski gasket graphs, Abstract: Internal diffusion-limited aggregation (IDLA) is a stochastic growth model on a graph $G$ which describes the formation of a random set of vertices growing from the origin (some fixed vertex) of $G$. Particles start at the origin and perform simple random walks; each particle moves until it lands on a site which was not previously visited by other particles. This random set of occupied sites in $G$ is called the IDLA cluster. In this paper we consider IDLA on Sierpinski gasket graphs, and show that the IDLA cluster fills balls (in the graph metric) with probability 1.

Title: Synthesis of Spatial Charging/Discharging Patterns of In-Vehicle Batteries for Provision of Ancillary Service and Mitigation of Voltage Impact, Abstract: We develop an algorithm for synthesizing a spatial pattern of charging/discharging operations of in-vehicle batteries for provision of Ancillary Service (AS) in power distribution grids. The algorithm is based on the ODE (Ordinary Differential Equation) model of distribution voltage that has been recently introduced. In this paper, firstly, we derive analytical solutions of the ODE model for a single straight-line feeder through a partial linearization, thereby providing a physical insight to the impact of spatial EV charging/discharging to the distribution voltage. Second, based on the analytical solutions, we propose an algorithm for determining the values of charging/discharging power (active and reactive) by in-vehicle batteries in the single feeder grid, so that the power demanded as AS (e.g. a regulation signal to distribution system operator for primary frequency control reserve) is provided by EVs, and the deviation of distribution voltage from a nominal value is reduced in the grid. Effectiveness of the algorithm is established with numerical simulations on the single feeder grid and on a realistic feeder grid with multiple bifurcations.

Title: Matter fields interacting with photons, Abstract: We have extended the biquaternionic Dirac's equation to include interactions with photons. The electric field is found to be perpendicular to the matter magnetic field, and the magnetic field is perpendicular to the matter inertial field. Inertial and magnetic masses are found to be conserved separately. The magnetic mass density is a consequence of the coupling between the vector potential and the matter inertial field. The presence of the vector and scalar potentials, and the matter inertial and magnetic fields are found to modify the standard form of the derived Maxwell's equations. The resulting interacting electrodynamics equations are found to generalize those of axion-like fields of Frank Wilczek or Chern-Simons equations. The axion field satisfies massive Klein-Gordon equation if Lorenz gauge condition is violated. Therefore, axion could be our massive photon. The electromagnetic field vector, $\vec{F}=\vec{E}+ic\vec{B}$, is found to satisfy massive Dirac's equation in addition to the fact that $\vec{\nabla}\cdot\vec{F}=0$, where $\vec{E}$ and $\vec{B}$ are the electric and magnetic fields, respectively.

Title: Dark Matter Annihilation in the Circumgalactic Medium at High Redshifts, Abstract: Annihilating dark matter (DM) models offer promising avenues for future DM detection, in particular via modification of astrophysical signals. However when modelling such potential signals at high redshift the emergence of both dark matter and baryonic structure, as well as the complexities of the energy transfer process, need to be taken into account. In the following paper we present a detailed energy deposition code and use this to examine the energy transfer efficiency of annihilating dark matter at high redshift, including the effects on baryonic structure. We employ the PYTHIA code to model neutralino-like DM candidates and their subsequent annihilation products for a range of masses and annihilation channels. We also compare different density profiles and mass-concentration relations for 10^5-10^7 M_sun haloes at redshifts 20 and 40. For these DM halo and particle models, we show radially dependent ionisation and heating curves and compare the deposited energy to the haloes' gravitational binding energy. We use the "filtered" annihilation spectra escaping the halo to calculate the heating of the circumgalactic medium and show that the mass of the minimal star forming object is increased by a factor of 2-3 at redshift 20 and 4-5 at redshift 40 for some DM models.

Title: Learning Dynamics and the Co-Evolution of Competing Sexual Species, Abstract: We analyze a stylized model of co-evolution between any two purely competing species (e.g., host and parasite), both sexually reproducing. Similarly to a recent model of Livnat \etal~\cite{evolfocs14} the fitness of an individual depends on whether the truth assignments on $n$ variables that reproduce through recombination satisfy a particular Boolean function. Whereas in the original model a satisfying assignment always confers a small evolutionary advantage, in our model the two species are in an evolutionary race with the parasite enjoying the advantage if the value of its Boolean function matches its host, and the host wishing to mismatch its parasite. Surprisingly, this model makes a simple and robust behavioral prediction. The typical system behavior is \textit{periodic}. These cycles stay bounded away from the boundary and thus, \textit{learning-dynamics competition between sexual species can provide an explanation for genetic diversity.} This explanation is due solely to the natural selection process. No mutations, environmental changes, etc., need be invoked. The game played at the gene level may have many Nash equilibria with widely diverse fitness levels. Nevertheless, sexual evolution leads to gene coordination that implements an optimal strategy, i.e., an optimal population mixture, at the species level. Namely, the play of the many "selfish genes" implements a time-averaged correlated equilibrium where the average fitness of each species is exactly equal to its value in the two species zero-sum competition. Our analysis combines tools from game theory, dynamical systems and Boolean functions to establish a novel class of conservative dynamical systems.

Title: Temperature inside tumor as time function in RF hyperthermia, Abstract: A simplified 2-D model which is an example of regional RF hyperthermia is presented. Human body is inside the wire with exciting current and the electromagnetic energy is concentrated within the tumor. The analyzed model is therefore a coupling of the electromagnetic field and the temperature field. Exciting current density in human body has been calculated using the finite element method, and then bioheat equation in timedepended nonstationary case has been resolved. At the and obtained results are presented.

Title: Detection principle of gravitational wave detectors, Abstract: With the first two detections in late 2015, astrophysics has officially entered into the new era of gravitational wave observations. Since then, much has been going on in the field with a lot of work focussing on the observations and implications for astrophysics and tests of general relativity in the strong regime. However much less is understood about how gravitational detectors really work at their fundamental level. For decades, the response to incoming signals has been customarily calculated using the very same physical principle, which has proved so successful in the first detections. In this paper we review the physical principle that is behind such a detection at the very fundamental level, and we try to highlight the peculiar subtleties that make it so hard in practice. We will then mention how detectors are built starting from this fundamental measurement element.

Title: auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks, Abstract: auDeep is a Python toolkit for deep unsupervised representation learning from acoustic data. It is based on a recurrent sequence to sequence autoencoder approach which can learn representations of time series data by taking into account their temporal dynamics. We provide an extensive command line interface in addition to a Python API for users and developers, both of which are comprehensively documented and publicly available at this https URL. Experimental results indicate that auDeep features are competitive with state-of-the art audio classification.

Title: Achievable Rate Region of Non-Orthogonal Multiple Access Systems with Wireless Powered Decoder, Abstract: Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme in 5G systems for the simultaneous access of tremendous number of wireless nodes. On the other hand, RF-enabled wireless energy harvesting is a promising technology for self-sustainable wireless nodes. In this paper, we consider a NOMA system where the near user harvests energy from the strong radio signal to power-on the information decoder. A generalized energy harvesting scheme is proposed by combining the conventional time switching and power splitting scheme. The achievable rate region of the proposed scheme is characterized under both constant and dynamic decoding power consumption models. If the decoding power is constant, the achievable rate region can be found by solving two convex optimization subproblems, and the regions for two special cases: time switching and power splitting, are characterized in closed-form. If the decoding power is proportional to data rate, the achievable rate region can be found by exhaustive search algorithm. Numerical results show that the achievable rate region of the proposed generalized scheme is larger than those of time switching scheme and power splitting scheme, and rate-dependent decoder design helps to enlarge the achievable rate region.

Title: Super Rogers-Szegö polynomials associated with $BC_N$ type of Polychronakos spin chains, Abstract: As is well known, multivariate Rogers-Szegö polynomials are closely connected with the partition functions of the $A_{N-1}$ type of Polychronakos spin chains having long-range interactions. Applying the `freezing trick', here we derive the partition functions for a class of $BC_N$ type of Polychronakos spin chains containing supersymmetric analogues of polarized spin reversal operators and subsequently use those partition functions to obtain novel multivariate super Rogers-Szegö (SRS) polynomials depending on four types of variables. We construct the generating functions for such SRS polynomials and show that these polynomials can be written as some bilinear combinations of the $A_{N-1}$ type of SRS polynomials. We also use the above mentioned generating functions to derive a set of recursion relations for the partition functions of the $BC_N$ type of Polychronakos spin chains involving different numbers of lattice sites and internal degrees of freedom.

Title: Junk News on Military Affairs and National Security: Social Media Disinformation Campaigns Against US Military Personnel and Veterans, Abstract: Social media provides political news and information for both active duty military personnel and veterans. We analyze the subgroups of Twitter and Facebook users who spend time consuming junk news from websites that target US military personnel and veterans with conspiracy theories, misinformation, and other forms of junk news about military affairs and national security issues. (1) Over Twitter we find that there are significant and persistent interactions between current and former military personnel and a broad network of extremist, Russia-focused, and international conspiracy subgroups. (2) Over Facebook, we find significant and persistent interactions between public pages for military and veterans and subgroups dedicated to political conspiracy, and both sides of the political spectrum. (3) Over Facebook, the users who are most interested in conspiracy theories and the political right seem to be distributing the most junk news, whereas users who are either in the military or are veterans are among the most sophisticated news consumers, and share very little junk news through the network.

Title: Unsteady Propulsion by an Intermittent Swimming Gait, Abstract: Inviscid computational results are presented on a self-propelled swimmer modeled as a virtual body combined with a two-dimensional hydrofoil pitching intermittently about its leading edge. Lighthill (1971) originally proposed that this burst-and-coast behavior can save fish energy during swimming by taking advantage of the viscous Bone-Lighthill boundary layer thinning mechanism. Here, an additional inviscid Garrick mechanism is discovered that allows swimmers to control the ratio of their added mass thrust-producing forces to their circulatory drag-inducing forces by decreasing their duty cycle, DC, of locomotion. This mechanism can save intermittent swimmers as much as 60% of the energy it takes to swim continuously at the same speed. The inviscid energy savings are shown to increase with increasing amplitude of motion, increase with decreasing Lighthill number, Li, and switch to an energetic cost above continuous swimming for sufficiently low DC. Intermittent swimmers are observed to shed four vortices per cycle that form into groups that are self-similar with the DC. In addition, previous thrust and power scaling laws of continuous self-propelled swimming are further generalized to include intermittent swimming. The key is that by averaging the thrust and power coefficients over only the bursting period then the intermittent problem can be transformed into a continuous one. Furthermore, the intermittent thrust and power scaling relations are extended to predict the mean speed and cost of transport of swimmers. By tuning a few coefficients with a handful of simulations these self-propelled relations can become predictive. In the current study, the mean speed and cost of transport are predicted to within 3% and 18% of their full-scale values by using these relations.

Title: Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue, Abstract: More and more of the information on the web is dialogic, from Facebook newsfeeds, to forum conversations, to comment threads on news articles. In contrast to traditional, monologic Natural Language Processing resources such as news, highly social dialogue is frequent in social media, making it a challenging context for NLP. This paper tests a bootstrapping method, originally proposed in a monologic domain, to train classifiers to identify two different types of subjective language in dialogue: sarcasm and nastiness. We explore two methods of developing linguistic indicators to be used in a first level classifier aimed at maximizing precision at the expense of recall. The best performing classifier for the first phase achieves 54% precision and 38% recall for sarcastic utterances. We then use general syntactic patterns from previous work to create more general sarcasm indicators, improving precision to 62% and recall to 52%. To further test the generality of the method, we then apply it to bootstrapping a classifier for nastiness dialogic acts. Our first phase, using crowdsourced nasty indicators, achieves 58% precision and 49% recall, which increases to 75% precision and 62% recall when we bootstrap over the first level with generalized syntactic patterns.

Title: New ALMA constraints on the star-forming ISM at low metallicity: A 50 pc view of the blue compact dwarf galaxy SBS0335-052, Abstract: Properties of the cold interstellar medium of low-metallicity galaxies are not well-known due to the faintness and extremely small scale on which emission is expected. We present deep ALMA band 6 (230GHz) observations of the nearby, low-metallicity (12 + log(O/H) = 7.25) blue compact dwarf galaxy SBS0335-052 at an unprecedented resolution of 0.2 arcsec (52 pc). The 12CO J=2-1 line is not detected and we report a 3-sigma upper limit of LCO(2-1) = 3.6x10^4 K km/s pc^2. Assuming that molecular gas is converted into stars with a given depletion time, ranging from 0.02 to 2 Gyr, we find lower limits on the CO-to-H2 conversion factor alpha_CO in the range 10^2-10^4 Msun pc^-2 (K km/s)^-1. The continuum emission is detected and resolved over the two main super star clusters. Re-analysis of the IR-radio spectral energy distribution suggests that the mm-fluxes are not only free-free emission but are most likely also associated with a cold dust component coincident with the position of the brightest cluster. With standard dust properties, we estimate its mass to be as large as 10^5 Msun. Both line and continuum results suggest the presence of a large cold gas reservoir unseen in CO even with ALMA.

Title: Design of Capacity Approaching Ensembles of LDPC Codes for Correlated Sources using EXIT Charts, Abstract: This paper is concerned with the design of capacity approaching ensembles of Low-Densiy Parity-Check (LDPC) codes for correlated sources. We consider correlated binary sources where the data is encoded independently at each source through a systematic LDPC encoder and sent over two independent channels. At the receiver, a iterative joint decoder consisting of two component LDPC decoders is considered where the encoded bits at the output of each component decoder are used at the other decoder as the a priori information. We first provide asymptotic performance analysis using the concept of extrinsic information transfer (EXIT) charts. Compared to the conventional EXIT charts devised to analyze LDPC codes for point to point communication, the proposed EXIT charts have been completely modified to able to accommodate the systematic nature of the codes as well as the iterative behavior between the two component decoders. Then the developed modified EXIT charts are deployed to design ensembles for different levels of correlation. Our results show that as the average degree of the designed ensembles grow, the thresholds corresponding to the designed ensembles approach the capacity. In particular, for ensembles with average degree of around 9, the gap to capacity is reduced to about 0.2dB. Finite block length performance evaluation is also provided for the designed ensembles to verify the asymptotic results.

Title: Evolution of Morphological and Physical Properties of Laboratory Interstellar Organic Residues with Ultraviolet Irradiation, Abstract: Refractory organic compounds formed in molecular clouds are among the building blocks of the solar system objects and could be the precursors of organic matter found in primitive meteorites and cometary materials. However, little is known about the evolutionary pathways of molecular cloud organics from dense molecular clouds to planetary systems. In this study, we focus on the evolution of the morphological and viscoelastic properties of molecular cloud refractory organic matter. We found that the organic residue, experimentally synthesized at about 10 K from UV-irradiated H2O-CH3OH-NH3 ice, changed significantly in terms of its nanometer- to micrometer-scale morphology and viscoelastic properties after UV irradiation at room temperature. The dose of this irradiation was equivalent to that experienced after short residence in diffuse clouds (equal or less than 10,000 years) or irradiation in outer protoplanetary disks. The irradiated organic residues became highly porous and more rigid and formed amorphous nanospherules. These nanospherules are morphologically similar to organic nanoglobules observed in the least-altered chondrites, chondritic porous interplanetary dust particles, and cometary samples, suggesting that irradiation of refractory organics could be a possible formation pathway for such nanoglobules. The storage modulus (elasticity) of photo-irradiated organic residues is about 100 MPa irrespective of vibrational frequency, a value that is lower than the storage moduli of minerals and ice. Dust grains coated with such irradiated organics would therefore stick together efficiently, but growth to larger grains might be suppressed due to an increase in aggregate brittleness caused by the strong connections between grains.

Title: A semianalytical approach for determining the nonclassical mechanical properties of materials, Abstract: In this article, a semianalytical approach for demonstrating elastic waves propagation in nanostructures has been presented based on the modified couple-stress theory including acceleration gradients. Using the experimental results and atomic simulations, the static and dynamic length scales were calculated for several materials, zinc oxide (ZnO), silicon (Si), silicon carbide (SiC), indium antimonide (InSb), and diamond. To evaluate the predicted static and dynamic length scales as well as the presented model, the natural frequencies of a beam in addition to the phase velocity and group velocity of Si were studied and compared with the available static length scales, estimated using strain-gradient theory without considering acceleration gradients. These three criteria, natural frequency, phase velocity, and group velocity, show that the presented model is dynamically stable even for larger wavevector values. Furthermore, it is explained why the previous works, which all are based on the strain-gradient theory without acceleration gradients, predicted very small values for the static length scale in the longitudinal direction rather than the static length scale in the transverse directions.

Title: Artificial intelligence in peer review: How can evolutionary computation support journal editors?, Abstract: With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

Title: Invariant Bianchi type I models in $f\left(R,T\right)$ Gravity, Abstract: In this paper, we search the existence of invariant solutions of Bianchi type I space-time in the context of $f\left(R,T\right)$ gravity. The exact solution of the Einstein's field equations are derived by using Lie point symmetry analysis method that yield two models of invariant universe for symmetries $X^{(1)}$ and $X^{(3)}$. The model with symmetries $X^{(1)}$ begins with big bang singularity while the model with symmetries $X^{(3)}$ does not favour the big bang singularity. Under this specification, we find out at set of singular and non singular solution of Bianchi type I model which present several other physically valid features within the framework of $f\left(R,T\right)$.

Title: Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics, Abstract: We present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator. Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input. We demonstrate the efficacy of our approach with a user study. We also analyze the individual nature of the learned models by comparing the effectiveness of our approach when the demonstration data comes from a user's own interactions, from the interactions of a group of users and from a domain expert. Positive results include statistically significant improvements on task metrics when comparing a user-only control paradigm with our shared control paradigm. Surprising results include findings that suggest that individualizing the model based on a user's own data does not effect the ability to learn a useful dynamic system. We explore this tension as it relates to developing human-in-the-loop systems further in the discussion.

Title: Look-Ahead in the Two-Sided Reduction to Compact Band Forms for Symmetric Eigenvalue Problems and the SVD, Abstract: We address the reduction to compact band forms, via unitary similarity transformations, for the solution of symmetric eigenvalue problems and the computation of the singular value decomposition (SVD). Concretely, in the first case we revisit the reduction to symmetric band form while, for the second case, we propose a similar alternative, which transforms the original matrix to (unsymmetric) band form, replacing the conventional reduction method that produces a triangular--band output. In both cases, we describe algorithmic variants of the standard Level-3 BLAS-based procedures, enhanced with look-ahead, to overcome the performance bottleneck imposed by the panel factorization. Furthermore, our solutions employ an algorithmic block size that differs from the target bandwidth, illustrating the important performance benefits of this decision. Finally, we show that our alternative compact band form for the SVD is key to introduce an effective look-ahead strategy into the corresponding reduction procedure.