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Title: Spherical polyharmonics and Poisson kernels for polyharmonic functions, Abstract: We introduce and develop the notion of spherical polyharmonics, which are a natural generalisation of spherical harmonics. In particular we study the theory of zonal polyharmonics, which allows us, analogously to zonal harmonics, to construct Poisson kernels for polyharmonic functions on the union of rotated balls. We find the representation of Poisson kernels and zonal polyharmonics in terms of the Gegenbauer polynomials. We show the connection between the classical Poisson kernel for harmonic functions on the ball, Poisson kernels for polyharmonic functions on the union of rotated balls, and the Cauchy-Hua kernel for holomorphic functions on the Lie ball.

Title: SPH calculations of Mars-scale collisions: the role of the Equation of State, material rheologies, and numerical effects, Abstract: We model large-scale ($\approx$2000km) impacts on a Mars-like planet using a Smoothed Particle Hydrodynamics code. The effects of material strength and of using different Equations of State on the post-impact material and temperature distributions are investigated. The properties of the ejected material in terms of escaping and disc mass are analysed as well. We also study potential numerical effects in the context of density discontinuities and rigid body rotation. We find that in the large-scale collision regime considered here (with impact velocities of 4km/s), the effect of material strength is substantial for the post-impact distribution of the temperature and the impactor material, while the influence of the Equation of State is more subtle and present only at very high temperatures.

Title: A global sensitivity analysis and reduced order models for hydraulically-fractured horizontal wells, Abstract: We present a systematic global sensitivity analysis using the Sobol method which can be utilized to rank the variables that affect two quantity of interests -- pore pressure depletion and stress change -- around a hydraulically-fractured horizontal well based on their degree of importance. These variables include rock properties and stimulation design variables. A fully-coupled poroelastic hydraulic fracture model is used to account for pore pressure and stress changes due to production. To ease the computational cost of a simulator, we also provide reduced order models (ROMs), which can be used to replace the complex numerical model with a rather simple analytical model, for calculating the pore pressure and stresses at different locations around hydraulic fractures. The main findings of this research are: (i) mobility, production pressure, and fracture half-length are the main contributors to the changes in the quantities of interest. The percentage of the contribution of each parameter depends on the location with respect to pre-existing hydraulic fractures and the quantity of interest. (ii) As the time progresses, the effect of mobility decreases and the effect of production pressure increases. (iii) These two variables are also dominant for horizontal stresses at large distances from hydraulic fractures. (iv) At zones close to hydraulic fracture tips or inside the spacing area, other parameters such as fracture spacing and half-length are the dominant factors that affect the minimum horizontal stress. The results of this study will provide useful guidelines for the stimulation design of legacy wells and secondary operations such as refracturing and infill drilling.

Title: The PdBI Arcsecond Whirlpool Survey (PAWS). The Role of Spiral Arms in Cloud and Star Formation, Abstract: The process that leads to the formation of the bright star forming sites observed along prominent spiral arms remains elusive. We present results of a multi-wavelength study of a spiral arm segment in the nearby grand-design spiral galaxy M51 that belongs to a spiral density wave and exhibits nine gas spurs. The combined observations of the(ionized, atomic, molecular, dusty) interstellar medium (ISM) with star formation tracers (HII regions, young <10Myr stellar clusters) suggest (1) no variation in giant molecular cloud (GMC) properties between arm and gas spurs, (2) gas spurs and extinction feathers arising from the same structure with a close spatial relation between gas spurs and ongoing/recent star formation (despite higher gas surface densities in the spiral arm), (3) no trend in star formation age either along the arm or along a spur, (4) evidence for strong star formation feedback in gas spurs: (5) tentative evidence for star formation triggered by stellar feedback for one spur, and (6) GMC associations (GMAs) being no special entities but the result of blending of gas arm/spur cross-sections in lower resolution observations. We conclude that there is no evidence for a coherent star formation onset mechanism that can be solely associated to the presence of the spiral density wave. This suggests that other (more localized) mechanisms are important to delay star formation such that it occurs in spurs. The evidence of star formation proceeding over several million years within individual spurs implies that the mechanism that leads to star formation acts or is sustained over a longer time-scale.

Title: Higher structure in the unstable Adams spectral sequence, Abstract: We describe a variant construction of the unstable Adams spectral the sequence for a space $Y$, associated to any free simplicial resolution of $H^*(Y;R)$ for $R=\mathbb{F}_p$ or $\mathbb{Q}$. We use this construction to describe the differentials and filtration in the spectral sequence in terms of appropriate systems of higher cohomology operations.

Title: Deciphering noise amplification and reduction in open chemical reaction networks, Abstract: The impact of random fluctuations on the dynamical behavior a complex biological systems is a longstanding issue, whose understanding would shed light on the evolutionary pressure that nature imposes on the intrinsic noise levels and would allow rationally designing synthetic networks with controlled noise. Using the Itō stochastic differential equation formalism, we performed both analytic and numerical analyses of several model systems containing different molecular species in contact with the environment and interacting with each other through mass-action kinetics. These systems represent for example biomolecular oligomerization processes, complex-breakage reactions, signaling cascades or metabolic networks. For chemical reaction networks with zero deficiency values, which admit a detailed- or complex-balanced steady state, all molecular species are uncorrelated. The number of molecules of each species follow a Poisson distribution and their Fano factors, which measure the intrinsic noise, are equal to one. Systems with deficiency one have an unbalanced non-equilibrium steady state and a non-zero S-flux, defined as the flux flowing between the complexes multiplied by an adequate stoichiometric coefficient. In this case, the noise on each species is reduced if the flux flows from the species of lowest to highest complexity, and is amplified is the flux goes in the opposite direction. These results are generalized to systems of deficiency two, which possess two independent non-vanishing S-fluxes, and we conjecture that a similar relation holds for higher deficiency systems.

Title: Diffraction-Aware Sound Localization for a Non-Line-of-Sight Source, Abstract: We present a novel sound localization algorithm for a non-line-of-sight (NLOS) sound source in indoor environments. Our approach exploits the diffraction properties of sound waves as they bend around a barrier or an obstacle in the scene. We combine a ray tracing based sound propagation algorithm with a Uniform Theory of Diffraction (UTD) model, which simulate bending effects by placing a virtual sound source on a wedge in the environment. We precompute the wedges of a reconstructed mesh of an indoor scene and use them to generate diffraction acoustic rays to localize the 3D position of the source. Our method identifies the convergence region of those generated acoustic rays as the estimated source position based on a particle filter. We have evaluated our algorithm in multiple scenarios consisting of a static and dynamic NLOS sound source. In our tested cases, our approach can localize a source position with an average accuracy error, 0.7m, measured by the L2 distance between estimated and actual source locations in a 7m*7m*3m room. Furthermore, we observe 37% to 130% improvement in accuracy over a state-of-the-art localization method that does not model diffraction effects, especially when a sound source is not visible to the robot.

Title: Density large deviations for multidimensional stochastic hyperbolic conservation laws, Abstract: We investigate the density large deviation function for a multidimensional conservation law in the vanishing viscosity limit, when the probability concentrates on weak solutions of a hyperbolic conservation law conservation law. When the conductivity and dif-fusivity matrices are proportional, i.e. an Einstein-like relation is satisfied, the problem has been solved in [4]. When this proportionality does not hold, we compute explicitly the large deviation function for a step-like density profile, and we show that the associated optimal current has a non trivial structure. We also derive a lower bound for the large deviation function, valid for a general weak solution, and leave the general large deviation function upper bound as a conjecture.

Title: mixup: Beyond Empirical Risk Minimization, Abstract: Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.

Title: Suzaku Analysis of the Supernova Remnant G306.3-0.9 and the Gamma-ray View of Its Neighborhood, Abstract: We present an investigation of the supernova remnant (SNR) G306.3$-$0.9 using archival multi-wavelength data. The Suzaku spectra are well described by two-component thermal plasma models: The soft component is in ionization equilibrium and has a temperature $\sim$0.59 keV, while the hard component has temperature $\sim$3.2 keV and ionization time-scale $\sim$$2.6\times10^{10}$ cm$^{-3}$ s. We clearly detected Fe K-shell line at energy of $\sim$6.5 keV from this remnant. The overabundances of Si, S, Ar, Ca, and Fe confirm that the X-ray emission has an ejecta origin. The centroid energy of the Fe-K line supports that G306.3$-$0.9 is a remnant of a Type Ia supernova (SN) rather than a core-collapse SN. The GeV gamma-ray emission from G306.3$-$0.9 and its surrounding were analyzed using about 6 years of Fermi data. We report about the non-detection of G306.3$-$0.9 and the detection of a new extended gamma-ray source in the south-west of G306.3$-$0.9 with a significance of $\sim$13$\sigma$. We discuss several scenarios for these results with the help of data from other wavebands to understand the SNR and its neighborhood.

Title: Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention, Abstract: Previous approaches to training syntax-based sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English. Thus, we propose the use of tree-structured Long Short-Term Memory with an attention mechanism that pays attention to each subtree of the parse tree. Experimental results indicate that our model achieves the state-of-the-art performance in a Japanese sentiment classification task.

Title: Covariances, Robustness, and Variational Bayes, Abstract: Mean-field Variational Bayes (MFVB) is an approximate Bayesian posterior inference technique that is increasingly popular due to its fast runtimes on large-scale datasets. However, even when MFVB provides accurate posterior means for certain parameters, it often mis-estimates variances and covariances. Furthermore, prior robustness measures have remained undeveloped for MFVB. By deriving a simple formula for the effect of infinitesimal model perturbations on MFVB posterior means, we provide both improved covariance estimates and local robustness measures for MFVB, thus greatly expanding the practical usefulness of MFVB posterior approximations. The estimates for MFVB posterior covariances rely on a result from the classical Bayesian robustness literature relating derivatives of posterior expectations to posterior covariances and include the Laplace approximation as a special case. Our key condition is that the MFVB approximation provides good estimates of a select subset of posterior means---an assumption that has been shown to hold in many practical settings. In our experiments, we demonstrate that our methods are simple, general, and fast, providing accurate posterior uncertainty estimates and robustness measures with runtimes that can be an order of magnitude faster than MCMC.

Title: Generalized Approximate Message-Passing Decoder for Universal Sparse Superposition Codes, Abstract: Sparse superposition (SS) codes were originally proposed as a capacity-achieving communication scheme over the additive white Gaussian noise channel (AWGNC) [1]. Very recently, it was discovered that these codes are universal, in the sense that they achieve capacity over any memoryless channel under generalized approximate message-passing (GAMP) decoding [2], although this decoder has never been stated for SS codes. In this contribution we introduce the GAMP decoder for SS codes, we confirm empirically the universality of this communication scheme through its study on various channels and we provide the main analysis tools: state evolution and potential. We also compare the performance of GAMP with the Bayes-optimal MMSE decoder. We empirically illustrate that despite the presence of a phase transition preventing GAMP to reach the optimal performance, spatial coupling allows to boost the performance that eventually tends to capacity in a proper limit. We also prove that, in contrast with the AWGNC case, SS codes for binary input channels have a vanishing error floor in the limit of large codewords. Moreover, the performance of Hadamard-based encoders is assessed for practical implementations.

Title: Simultaneous non-vanishing for Dirichlet L-functions, Abstract: We extend the work of Fouvry, Kowalski and Michel on correlation between Hecke eigenvalues of modular forms and algebraic trace functions in order to establish an asymptotic formula for a generalized cubic moment of modular L-functions at the central point s = 1/2 and for prime moduli q. As an application, we exploit our recent result on the mollification of the fourth moment of Dirichlet L-functions to derive that for any pair $(\omega_1,\omega_2)$ of multiplicative characters modulo q, there is a positive proportion of $\chi$ (mod q) such that $L(\chi, 1/2 ), L(\chi\omega_1, 1/2 )$ and $L(\chi\omega_2, 1/2)$ are simultaneously not too small.

Title: Parallelism, Concurrency and Distribution in Constraint Handling Rules: A Survey, Abstract: Constraint Handling Rules is an effective concurrent declarative programming language and a versatile computational logic formalism. CHR programs consist of guarded reactive rules that transform multisets of constraints. One of the main features of CHR is its inherent concurrency. Intuitively, rules can be applied to parts of a multiset in parallel. In this comprehensive survey, we give an overview of concurrent and parallel as well as distributed CHR semantics, standard and more exotic, that have been proposed over the years at various levels of refinement. These semantics range from the abstract to the concrete. They are related by formal soundness results. Their correctness is established as correspondence between parallel and sequential computations. We present common concise sample CHR programs that have been widely used in experiments and benchmarks. We review parallel CHR implementations in software and hardware. The experimental results obtained show a consistent parallel speedup. Most implementations are available online. The CHR formalism can also be used to implement and reason with models for concurrency. To this end, the Software Transaction Model, the Actor Model, Colored Petri Nets and the Join-Calculus have been faithfully encoded in CHR. Under consideration in Theory and Practice of Logic Programming (TPLP).

Title: The Query Complexity of Cake Cutting, Abstract: We study the query complexity of cake cutting and give lower and upper bounds for computing approximately envy-free, perfect, and equitable allocations with the minimum number of cuts. The lower bounds are tight for computing connected envy-free allocations among n=3 players and for computing perfect and equitable allocations with minimum number of cuts between n=2 players. We also formalize moving knife procedures and show that a large subclass of this family, which captures all the known moving knife procedures, can be simulated efficiently with arbitrarily small error in the Robertson-Webb query model.

Title: Superconducting properties of Cu intercalated Bi$_2$Se$_3$ studied by Muon Spin Spectroscopy, Abstract: We present muon spin rotation measurements on superconducting Cu intercalated Bi$_2$Se$_3$, which was suggested as a realization of a topological superconductor. We observe a clear evidence of the superconducting transition below 4 K, where the width of magnetic field distribution increases as the temperature is decreased. The measured broadening at mK temperatures suggests a large London penetration depth in the $ab$ plane ($\lambda_{\mathrm{eff}}\sim 1.6$ $\mathrm{\mu}$m). We show that the temperature dependence of this broadening follows the BCS prediction, but could be consistent with several gap symmetries.

Title: Efficient and consistent inference of ancestral sequences in an evolutionary model with insertions and deletions under dense taxon sampling, Abstract: In evolutionary biology, the speciation history of living organisms is represented graphically by a phylogeny, that is, a rooted tree whose leaves correspond to current species and branchings indicate past speciation events. Phylogenies are commonly estimated from molecular sequences, such as DNA sequences, collected from the species of interest. At a high level, the idea behind this inference is simple: the further apart in the Tree of Life are two species, the greater is the number of mutations to have accumulated in their genomes since their most recent common ancestor. In order to obtain accurate estimates in phylogenetic analyses, it is standard practice to employ statistical approaches based on stochastic models of sequence evolution on a tree. For tractability, such models necessarily make simplifying assumptions about the evolutionary mechanisms involved. In particular, commonly omitted are insertions and deletions of nucleotides -- also known as indels. Properly accounting for indels in statistical phylogenetic analyses remains a major challenge in computational evolutionary biology. Here we consider the problem of reconstructing ancestral sequences on a known phylogeny in a model of sequence evolution incorporating nucleotide substitutions, insertions and deletions, specifically the classical TKF91 process. We focus on the case of dense phylogenies of bounded height, which we refer to as the taxon-rich setting, where statistical consistency is achievable. We give the first polynomial-time ancestral reconstruction algorithm with provable guarantees under constant rates of mutation. Our algorithm succeeds when the phylogeny satisfies the "big bang" condition, a necessary and sufficient condition for statistical consistency in this context.

Title: Pattern-forming fronts in a Swift-Hohenberg equation with directional quenching - parallel and oblique stripes, Abstract: We study the effect of domain growth on the orientation of striped phases in a Swift-Hohenberg equation. Domain growth is encoded in a step-like parameter dependence that allows stripe formation in a half plane, and suppresses patterns in the complement, while the boundary of the pattern-forming region is propagating with fixed normal velocity. We construct front solutions that leave behind stripes in the pattern-forming region that are parallel to or at a small oblique angle to the boundary. Technically, the construction of stripe formation parallel to the boundary relies on ill-posed, infinite-dimensional spatial dynamics. Stripes forming at a small oblique angle are constructed using a functional-analytic, perturbative approach. Here, the main difficulties are the presence of continuous spectrum and the fact that small oblique angles appear as a singular perturbation in a traveling-wave problem. We resolve the former difficulty using a farfield-core decomposition and Fredholm theory in weighted spaces. The singular perturbation problem is resolved using preconditioners and boot-strapping.

Title: Exploring RNN-Transducer for Chinese Speech Recognition, Abstract: End-to-end approaches have drawn much attention recently for significantly simplifying the construction of an automatic speech recognition (ASR) system. RNN transducer (RNN-T) is one of the popular end-to-end methods. Previous studies have shown that RNN-T is difficult to train and a very complex training process is needed for a reasonable performance. In this paper, we explore RNN-T for a Chinese large vocabulary continuous speech recognition (LVCSR) task and aim to simplify the training process while maintaining performance. First, a new strategy of learning rate decay is proposed to accelerate the model convergence. Second, we find that adding convolutional layers at the beginning of the network and using ordered data can discard the pre-training process of the encoder without loss of performance. Besides, we design experiments to find a balance among the usage of GPU memory, training circle and model performance. Finally, we achieve 16.9% character error rate (CER) on our test set which is 2% absolute improvement from a strong BLSTM CE system with language model trained on the same text corpus.

Title: Stationary crack propagation in a two-dimensional visco-elastic network model, Abstract: We investigate crack propagation in a simple two-dimensional visco-elastic model and find a scaling regime in the relation between the propagation velocity and energy release rate or fracture energy, together with lower and upper bounds of the scaling regime. On the basis of our result, the existence of the lower and upper bounds is expected to be universal or model-independent: the present simple simulation model provides generic insight into the physics of crack propagation, and the model will be a first step towards the development of a more refined coarse-grained model. Relatively abrupt changes of velocity are predicted near the lower and upper bounds for the scaling regime and the positions of the bounds could be good markers for the development of tough polymers, for which we provide simple views that could be useful as guiding principles for toughening polymer-based materials.

Title: A note on the fundamental group of Kodaira fibrations, Abstract: The fundamental group $\pi$ of a Kodaira fibration is, by definition, the extension of a surface group $\Pi_b$ by another surface group $\Pi_g$, i.e. \[ 1 \rightarrow \Pi_g \rightarrow \pi \rightarrow \Pi_b \rightarrow 1. \] Conversely, we can inquire about what conditions need to be satisfied by a group of that sort in order to be the fundamental group of a Kodaira fibration. In this short note we collect some restriction on the image of the classifying map $m \colon \Pi_b \to \Gamma_g$ in terms of the coinvariant homology of $\Pi_g$. In particular, we observe that if $\pi$ is the fundamental group of a Kodaira fibration with relative irregularity $g-s$, then $g \leq 1+ 6s$, and we show that this effectively constrains the possible choices for $\pi$, namely that there are group extensions as above that fail to satisfy this bound, hence cannot be the fundamental group of a Kodaira fibration. In particular this provides examples of symplectic $4$--manifolds that fail to admit a Kähler structure for reasons that eschew the usual obstructions.

Title: Split-and-augmented Gibbs sampler - Application to large-scale inference problems, Abstract: This paper derives two new optimization-driven Monte Carlo algorithms inspired from variable splitting and data augmentation. In particular, the formulation of one of the proposed approaches is closely related to the alternating direction method of multipliers (ADMM) main steps. The proposed framework enables to derive faster and more efficient sampling schemes than the current state-of-the-art methods and can embed the latter. By sampling efficiently the parameter to infer as well as the hyperparameters of the problem, the generated samples can be used to approximate Bayesian estimators of the parameters to infer. Additionally, the proposed approach brings confidence intervals at a low cost contrary to optimization methods. Simulations on two often-studied signal processing problems illustrate the performance of the two proposed samplers. All results are compared to those obtained by recent state-of-the-art optimization and MCMC algorithms used to solve these problems.

Title: Primordial perturbations from inflation with a hyperbolic field-space, Abstract: We study primordial perturbations from hyperinflation, proposed recently and based on a hyperbolic field-space. In the previous work, it was shown that the field-space angular momentum supported by the negative curvature modifies the background dynamics and enhances fluctuations of the scalar fields qualitatively, assuming that the inflationary background is almost de Sitter. In this work, we confirm and extend the analysis based on the standard approach of cosmological perturbation in multi-field inflation. At the background level, to quantify the deviation from de Sitter, we introduce the slow-varying parameters and show that steep potentials, which usually can not drive inflation, can drive inflation. At the linear perturbation level, we obtain the power spectrum of primordial curvature perturbation and express the spectral tilt and running in terms of the slow-varying parameters. We show that hyperinflation with power-law type potentials has already been excluded by the recent Planck observations, while exponential-type potential with the exponent of order unity can be made consistent with observations as far as the power spectrum is concerned. We also argue that, in the context of a simple $D$-brane inflation, the hyperinflation requires exponentially large hyperbolic extra dimensions but that masses of Kaluza-Klein gravitons can be kept relatively heavy.

Title: Learning Sparse Representations in Reinforcement Learning with Sparse Coding, Abstract: A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations. In this work, we develop a supervised sparse coding objective for policy evaluation. Despite the non-convexity of this objective, we prove that all local minima are global minima, making the approach amenable to simple optimization strategies. We empirically show that it is key to use a supervised objective, rather than the more straightforward unsupervised sparse coding approach. We compare the learned representations to a canonical fixed sparse representation, called tile-coding, demonstrating that the sparse coding representation outperforms a wide variety of tilecoding representations.

Title: A Variational Characterization of Rényi Divergences, Abstract: Atar, Chowdhary and Dupuis have recently exhibited a variational formula for exponential integrals of bounded measurable functions in terms of Rényi divergences. We develop a variational characterization of the Rényi divergences between two probability distributions on a measurable sace in terms of relative entropies. When combined with the elementary variational formula for exponential integrals of bounded measurable functions in terms of relative entropy, this yields the variational formula of Atar, Chowdhary and Dupuis as a corollary. We also develop an analogous variational characterization of the Rényi divergence rates between two stationary finite state Markov chains in terms of relative entropy rates. When combined with Varadhan's variational characterization of the spectral radius of square matrices with nonnegative entries in terms of relative entropy, this yields an analog of the variational formula of Atar, Chowdary and Dupuis in the framework of finite state Markov chains.

Title: Interlayer coupling and gate-tunable excitons in transition metal dichalcogenide heterostructures, Abstract: Bilayer van der Waals (vdW) heterostructures such as MoS2/WS2 and MoSe2/WSe2 have attracted much attention recently, particularly because of their type II band alignments and the formation of interlayer exciton as the lowest-energy excitonic state. In this work, we calculate the electronic and optical properties of such heterostructures with the first-principles GW+Bethe-Salpeter Equation (BSE) method and reveal the important role of interlayer coupling in deciding the excited-state properties, including the band alignment and excitonic properties. Our calculation shows that due to the interlayer coupling, the low energy excitons can be widely tunable by a vertical gate field. In particular, the dipole oscillator strength and radiative lifetime of the lowest energy exciton in these bilayer heterostructures is varied by over an order of magnitude within a practical external gate field. We also build a simple model that captures the essential physics behind this tunability and allows the extension of the ab initio results to a large range of electric fields. Our work clarifies the physical picture of interlayer excitons in bilayer vdW heterostructures and predicts a wide range of gate-tunable excited-state properties of 2D optoelectronic devices.

Title: Enumeration of singular varieties with tangency conditions, Abstract: We construct the algebraic cobordism theory of bundles and divisors on varieties. It has a simple basis (over Q) from projective spaces and its rank is equal to the number of Chern numbers. An application of this algebraic cobordism theory is the enumeration of singular subvarieties with give tangent conditions with a fixed smooth divisor, where the subvariety is the zero locus of a section of a vector bundle. We prove that the generating series of numbers of such subvarieties gives a homomorphism from the algebraic cobordism group to the power series ring. This implies that the enumeration of singular subvarieties with tangency conditions is governed by universal polynomials of Chern numbers, when the vector bundle is sufficiently ample. This result combines and generalizes the Caporaso-Harris recursive formula, Gottsche's conjecture, classical De Jonquiere's Formula and node polynomials from tropical geometry.

Title: ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information, Abstract: Object detection in wide area motion imagery (WAMI) has drawn the attention of the computer vision research community for a number of years. WAMI proposes a number of unique challenges including extremely small object sizes, both sparse and densely-packed objects, and extremely large search spaces (large video frames). Nearly all state-of-the-art methods in WAMI object detection report that appearance-based classifiers fail in this challenging data and instead rely almost entirely on motion information in the form of background subtraction or frame-differencing. In this work, we experimentally verify the failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a heatmap-based fully convolutional neural network (CNN), and propose a novel two-stage spatio-temporal CNN which effectively and efficiently combines both appearance and motion information to significantly surpass the state-of-the-art in WAMI object detection. To reduce the large search space, the first stage (ClusterNet) takes in a set of extremely large video frames, combines the motion and appearance information within the convolutional architecture, and proposes regions of objects of interest (ROOBI). These ROOBI can contain from one to clusters of several hundred objects due to the large video frame size and varying object density in WAMI. The second stage (FoveaNet) then estimates the centroid location of all objects in that given ROOBI simultaneously via heatmap estimation. The proposed method exceeds state-of-the-art results on the WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped objects, as well as being the first proposed method in wide area motion imagery to detect completely stationary objects.

Title: Viscous dynamics of drops and bubbles in Hele-Shaw cells: drainage, drag friction, coalescence, and bursting, Abstract: In this review article, we discuss recent studies on drops and bubbles in Hele-Shaw cells, focusing on how scaling laws exhibit crossovers from the three-dimensional counterparts and focusing on topics in which viscosity plays an important role. By virtue of progresses in analytical theory and high-speed imaging, dynamics of drops and bubbles have actively been studied with the aid of scaling arguments. However, compared with three dimensional problems, studies on the corresponding problems in Hele-Shaw cells are still limited. This review demonstrates that the effect of confinement in the Hele-Shaw cell introduces new physics allowing different scaling regimes to appear. For this purpose, we discuss various examples that are potentially important for industrial applications handling drops and bubbles in confined spaces by showing agreement between experiments and scaling theories. As a result, this review provides a collection of problems in hydrodynamics that may be analytically solved or that may be worth studying numerically in the near future.

Title: Detection of Nonlinearly Distorted OFDM Signals via Generalized Approximate Message Passing, Abstract: In this paper, we propose a practical receiver for multicarrier signals subjected to a strong memoryless nonlinearity. The receiver design is based on a generalized approximate message passing (GAMP) framework, and this allows real-time algorithm implementation in software or hardware with moderate complexity. We demonstrate that the proposed receiver can provide more than a 2dB gain compared with an ideal uncoded linear OFDM transmission at a BER range $10^{-4}\div10^{-6}$ in the AWGN channel, when the OFDM signal is subjected to clipping nonlinearity and the crest-factor of the clipped waveform is only 1.9dB. Simulation results also demonstrate that the proposed receiver provides significant performance gain in frequency-selective multipath channels

Title: Poynting's theorem in magnetic turbulence, Abstract: Poynting's theorem is used to obtain an expression for the turbulent power-spectral density as function of frequency and wavenumber in low-frequency magnetic turbulence. No reference is made to Elsasser variables as is usually done in magnetohydrodynamic turbulence mixing mechanical and electromagnetic turbulence. We rather stay with an implicit form of the mechanical part of turbulence as suggested by electromagnetic theory in arbitrary media. All of mechanics and flows is included into a turbulent response function which by appropriate observations can be determined from knowledge of the turbulent fluctuation spectra. This approach is not guided by the wish of developing a complete theory of turbulence. It aims on the identification of the response function from observations as input into a theory which afterwards attempts its interpretation. Combination of both the magnetic and electric power spectral densities leads to a representation of the turbulent response function, i.e. the turbulent conductivity spectrum $\sigma_{\omega k}$ as function of frequency $\omega$ and wavenumber $k$. {It is given as the ratio of magnetic to electric power spectral densities in frequency space. This knowledge allows for formally writing down a turbulent dispersion relation. Power law inertial range spectra result in a power law turbulent conductivity spectrum. These can be compared with observations in the solar wind. Keywords: MHD turbulence, turbulent dispersion relation, turbulent response function, solar wind turbulence

Title: Exploration-exploitation tradeoffs dictate the optimal distributions of phenotypes for populations subject to fitness fluctuations, Abstract: We study a minimal model for the growth of a phenotypically heterogeneous population of cells subject to a fluctuating environment in which they can replicate (by exploiting available resources) and modify their phenotype within a given landscape (thereby exploring novel configurations). The model displays an exploration-exploitation trade-off whose specifics depend on the statistics of the environment. Most notably, the phenotypic distribution corresponding to maximum population fitness (i.e. growth rate) requires a non-zero exploration rate when the magnitude of environmental fluctuations changes randomly over time, while a purely exploitative strategy turns out to be optimal in two-state environments, independently of the statistics of switching times. We obtain analytical insight into the limiting cases of very fast and very slow exploration rates by directly linking population growth to the features of the environment.

Title: Optimizing Mission Critical Data Dissemination in Massive IoT Networks, Abstract: Mission critical data dissemination in massive Internet of things (IoT) networks imposes constraints on the message transfer delay between devices. Due to low power and communication range of IoT devices, data is foreseen to be relayed over multiple device-to-device (D2D) links before reaching the destination. The coexistence of a massive number of IoT devices poses a challenge in maximizing the successful transmission capacity of the overall network alongside reducing the multi-hop transmission delay in order to support mission critical applications. There is a delicate interplay between the carrier sensing threshold of the contention based medium access protocol and the choice of packet forwarding strategy selected at each hop by the devices. The fundamental problem in optimizing the performance of such networks is to balance the tradeoff between conflicting performance objectives such as the spatial frequency reuse, transmission quality, and packet progress towards the destination. In this paper, we use a stochastic geometry approach to quantify the performance of multi-hop massive IoT networks in terms of the spatial frequency reuse and the transmission quality under different packet forwarding schemes. We also develop a comprehensive performance metric that can be used to optimize the system to achieve the best performance. The results can be used to select the best forwarding scheme and tune the carrier sensing threshold to optimize the performance of the network according to the delay constraints and transmission quality requirements.

Title: Gaussian fluctuations of Jack-deformed random Young diagrams, Abstract: We introduce a large class of random Young diagrams which can be regarded as a natural one-parameter deformation of some classical Young diagram ensembles; a deformation which is related to Jack polynomials and Jack characters. We show that each such a random Young diagram converges asymptotically to some limit shape and that the fluctuations around the limit are asymptotically Gaussian.

Title: Revisiting (logarithmic) scaling relations using renormalization group, Abstract: We explicitly compute the critical exponents associated with logarithmic corrections (the so-called hatted exponents) starting from the renormalization group equations and the mean field behavior for a wide class of models at the upper critical behavior (for short and long range $\phi^n$-theories) and below it. This allows us to check the scaling relations among these critical exponents obtained by analysing the complex singularities (Lee-Yang and Fisher zeroes) of these models. Moreover, we have obtained an explicit method to compute the $\hat{\coppa}$ exponent [defined by $\xi\sim L (\log L)^{\hat{\coppa}}$] and, finally, we have found a new derivation of the scaling law associated with it.

Title: Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods, Abstract: We obtain a Bernstein-type inequality for sums of Banach-valued random variables satisfying a weak dependence assumption of general type and under certain smoothness assumptions of the underlying Banach norm. We use this inequality in order to investigate in the asymptotical regime the error upper bounds for the broad family of spectral regularization methods for reproducing kernel decision rules, when trained on a sample coming from a $\tau-$mixing process.

Title: Inverse monoids and immersions of cell complexes, Abstract: An immersion $f : {\mathcal D} \rightarrow \mathcal C$ between cell complexes is a local homeomorphism onto its image that commutes with the characteristic maps of the cell complexes. We study immersions between finite-dimensional connected $\Delta$-complexes by replacing the fundamental group of the base space by an appropriate inverse monoid. We show how conjugacy classes of the closed inverse submonoids of this inverse monoid may be used to classify connected immersions into the complex. This extends earlier results of Margolis and Meakin for immersions between graphs and of Meakin and Szakács on immersions into $2$-dimensional $CW$-complexes.

Title: Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments, Abstract: We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most $M$ experiments in a non-adaptive manner. We consider the optimal learning strategy in terms of minimizing the portions of the structure that remains unknown given the limited number of experiments in both Bayesian and minimax setting. We characterize the theoretical optimal solution and propose an algorithm, which designs the experiments efficiently in terms of time complexity. We show that for bounded degree graphs, in the minimax case and in the Bayesian case with uniform priors, our proposed algorithm is a $\rho$-approximation algorithm, where $\rho$ is independent of the order of the underlying graph. Simulations on both synthetic and real data show that the performance of our algorithm is very close to the optimal solution.

Title: Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study, Abstract: We present a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent control or plant parameters. The proposed framework differs from the majority of co-design literature where there exists a closed-form model of the system dynamics. Furthermore, we utilize the idea of Batch Bayesian Optimization at the plant optimization level to generate a set of plant designs at each iteration of the overall optimization process, recognizing that there will exist economies of scale in running multiple experiments in each iteration of the plant design process. We validate the proposed framework for a Buoyant Airborne Turbine (BAT). We choose the horizontal stabilizer area, longitudinal center of mass relative to center of buoyancy (plant parameters), and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that these plant and control parameters converge to their respective optimal values within only a few iterations.

Title: Lagrangian fibers of Gelfand-Cetlin systems, Abstract: Motivated by the study of Nishinou-Nohara-Ueda on the Floer thoery of Gelfand-Cetlin systems over complex partial flag manifolds, we provide a complete description of the topology of Gelfand-Cetlin fibers. We prove that all fibers are \emph{smooth} isotropic submanifolds and give a complete description of the fiber to be Lagrangian in terms of combinatorics of Gelfand-Cetlin polytope. Then we study (non-)displaceability of Lagrangian fibers. After a few combinatorial and numercal tests for the displaceability, using the bulk-deformation of Floer cohomology by Schubert cycles, we prove that every full flag manifold $\mathcal{F}(n)$ ($n \geq 3$) with a monotone Kirillov-Kostant-Souriau symplectic form carries a continuum of non-displaceable Lagrangian tori which degenerates to a non-torus fiber in the Hausdorff limit. In particular, the Lagrangian $S^3$-fiber in $\mathcal{F}(3)$ is non-displaceable the question of which was raised by Nohara-Ueda who computed its Floer cohomology to be vanishing.

Title: A local ensemble transform Kalman particle filter for convective scale data assimilation, Abstract: Ensemble data assimilation methods such as the Ensemble Kalman Filter (EnKF) are a key component of probabilistic weather forecasting. They represent the uncertainty in the initial conditions by an ensemble which incorporates information coming from the physical model with the latest observations. High-resolution numerical weather prediction models ran at operational centers are able to resolve non-linear and non-Gaussian physical phenomena such as convection. There is therefore a growing need to develop ensemble assimilation algorithms able to deal with non-Gaussianity while staying computationally feasible. In the present paper we address some of these needs by proposing a new hybrid algorithm based on the Ensemble Kalman Particle Filter. It is fully formulated in ensemble space and uses a deterministic scheme such that it has the ensemble transform Kalman filter (ETKF) instead of the stochastic EnKF as a limiting case. A new criterion for choosing the proportion of particle filter and ETKF update is also proposed. The new algorithm is implemented in the COSMO framework and numerical experiments in a quasi-operational convective-scale setup are conducted. The results show the feasibility of the new algorithm in practice and indicate a strong potential for such local hybrid methods, in particular for forecasting non-Gaussian variables such as wind and hourly precipitation.

Title: Resolving the age bimodality of galaxy stellar populations on kpc scales, Abstract: Galaxies in the local Universe are known to follow bimodal distributions in the global stellar populations properties. We analyze the distribution of the local average stellar-population ages of 654,053 sub-galactic regions resolved on ~1-kpc scales in a volume-corrected sample of 394 galaxies, drawn from the CALIFA-DR3 integral-field-spectroscopy survey and complemented by SDSS imaging. We find a bimodal local-age distribution, with an old and a young peak primarily due to regions in early-type galaxies and star-forming regions of spirals, respectively. Within spiral galaxies, the older ages of bulges and inter-arm regions relative to spiral arms support an internal age bimodality. Although regions of higher stellar-mass surface-density, mu*, are typically older, mu* alone does not determine the stellar population age and a bimodal distribution is found at any fixed mu*. We identify an "old ridge" of regions of age ~9 Gyr, independent of mu*, and a "young sequence" of regions with age increasing with mu* from 1-1.5 Gyr to 4-5 Gyr. We interpret the former as regions containing only old stars, and the latter as regions where the relative contamination of old stellar populations by young stars decreases as mu* increases. The reason why this bimodal age distribution is not inconsistent with the unimodal shape of the cosmic-averaged star-formation history is that i) the dominating contribution by young stars biases the age low with respect to the average epoch of star formation, and ii) the use of a single average age per region is unable to represent the full time-extent of the star-formation history of "young-sequence" regions.

Title: From 4G to 5G: Self-organized Network Management meets Machine Learning, Abstract: In this paper, we provide an analysis of self-organized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

Title: A simulation technique for slurries interacting with moving parts and deformable solids with applications, Abstract: A numerical method for particle-laden fluids interacting with a deformable solid domain and mobile rigid parts is proposed and implemented in a full engineering system. The fluid domain is modeled with a lattice Boltzmann representation, the particles and rigid parts are modeled with a discrete element representation, and the deformable solid domain is modeled using a Lagrangian mesh. The main issue of this work, since separately each of these methods is a mature tool, is to develop coupling and model-reduction approaches in order to efficiently simulate coupled problems of this nature, as occur in various geological and engineering applications. The lattice Boltzmann method incorporates a large-eddy simulation technique using the Smagorinsky turbulence model. The discrete element method incorporates spherical and polyhedral particles for stiff contact interactions. A neo-Hookean hyperelastic model is used for the deformable solid. We provide a detailed description of how to couple the three solvers within a unified algorithm. The technique we propose for rubber modeling/coupling exploits a simplification that prevents having to solve a finite-element problem each time step. We also develop a technique to reduce the domain size of the full system by replacing certain zones with quasi-analytic solutions, which act as effective boundary conditions for the lattice Boltzmann method. The major ingredients of the routine are are separately validated. To demonstrate the coupled method in full, we simulate slurry flows in two kinds of piston-valve geometries. The dynamics of the valve and slurry are studied and reported over a large range of input parameters.

Title: On the Spectrum of Random Features Maps of High Dimensional Data, Abstract: Random feature maps are ubiquitous in modern statistical machine learning, where they generalize random projections by means of powerful, yet often difficult to analyze nonlinear operators. In this paper, we leverage the "concentration" phenomenon induced by random matrix theory to perform a spectral analysis on the Gram matrix of these random feature maps, here for Gaussian mixture models of simultaneously large dimension and size. Our results are instrumental to a deeper understanding on the interplay of the nonlinearity and the statistics of the data, thereby allowing for a better tuning of random feature-based techniques.

Title: Solving the multi-site and multi-orbital Dynamical Mean Field Theory using Density Matrix Renormalization, Abstract: We implement an efficient numerical method to calculate response functions of complex impurities based on the Density Matrix Renormalization Group (DMRG) and use it as the impurity-solver of the Dynamical Mean Field Theory (DMFT). This method uses the correction vector to obtain precise Green's functions on the real frequency axis at zero temperature. By using a self-consistent bath configuration with very low entanglement, we take full advantage of the DMRG to calculate dynamical response functions paving the way to treat large effective impurities such as those corresponding to multi-orbital interacting models and multi-site or multi-momenta clusters. This method leads to reliable calculations of non-local self energies at arbitrary dopings and interactions and at any energy scale.

Title: Learning from Between-class Examples for Deep Sound Recognition, Abstract: Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds. We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio. We then input the mixed sound to the model and train the model to output the mixing ratio. The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher's criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes. The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial. Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning. As a result, we achieved a performance surpasses the human level.

Title: On nonlinear profile decompositions and scattering for a NLS-ODE model, Abstract: In this paper, we consider a Hamiltonian system combining a nonlinear Schr\" odinger equation (NLS) and an ordinary differential equation (ODE). This system is a simplified model of the NLS around soliton solutions. Following Nakanishi \cite{NakanishiJMSJ}, we show scattering of $L^2$ small $H^1$ radial solutions. The proof is based on Nakanishi's framework and Fermi Golden Rule estimates on $L^4$ in time norms.

Title: Chain effects of clean water: The Mills-Reincke phenomenon in early twentieth-century Japan, Abstract: This study explores the validity of chain effects of clean water, which are known as the "Mills-Reincke phenomenon," in early twentieth-century Japan. Recent studies have reported that water purifications systems are responsible for huge contributions to human capital. Although a few studies have investigated the short-term effects of water-supply systems in pre-war Japan, little is known about the benefits associated with these systems. By analyzing city-level cause-specific mortality data from the years 1922-1940, we found that eliminating typhoid fever infections decreased the risk of deaths due to non-waterborne diseases. Our estimates show that for one additional typhoid death, there were approximately one to three deaths due to other causes, such as tuberculosis and pneumonia. This suggests that the observed Mills-Reincke phenomenon could have resulted from the prevention typhoid fever in a previously-developing Asian country.

Title: Learning Transferable Architectures for Scalable Image Recognition, Abstract: Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (the "NASNet search space") which enables transferability. In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, NASNet achieves 2.4% error rate, which is state-of-the-art. On ImageNet, NASNet achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset.

Title: Clamped seismic metamaterials: Ultra-low broad frequency stop-bands, Abstract: The regularity of earthquakes, their destructive power, and the nuisance of ground vibration in urban environments, all motivate designs of defence structures to lessen the impact of seismic and ground vibration waves on buildings. Low frequency waves, in the range $1$ to $10$ Hz for earthquakes and up to a few tens of Hz for vibrations generated by human activities, cause a large amount of damage, or inconvenience, depending on the geological conditions they can travel considerable distances and may match the resonant fundamental frequency of buildings. The ultimate aim of any seismic metamaterial, or any other seismic shield, is to protect over this entire range of frequencies, the long wavelengths involved, and low frequency, have meant this has been unachievable to date. Elastic flexural waves, applicable in the mechanical vibrations of thin elastic plates, can be designed to have a broad zero-frequency stop-band using a periodic array of very small clamped circles. Inspired by this experimental and theoretical observation, all be it in a situation far removed from seismic waves, we demonstrate that it is possible to achieve elastic surface (Rayleigh) and body (pressure P and shear S) wave reflectors at very large wavelengths in structured soils modelled as a fully elastic layer periodically clamped to bedrock. We identify zero frequency stop-bands that only exist in the limit of columns of concrete clamped at their base to the bedrock. In a realistic configuration of a sedimentary basin 15 meters deep we observe a zero frequency stop-band covering a broad frequency range of $0$ to $30$ Hz.

Title: Difference analogue of second main theorems for meromorphic mapping into algebraic variety, Abstract: In this paper, we prove some difference analogue of second main theorems of meromorphic mapping from Cm into an algebraic variety V intersecting a finite set of fixed hypersurfaces in subgeneral position. As an application, we prove a result on algebraically degenerate of holomorphic curves intersecting hypersurfaces and difference analogue of Picard's theorem on holomorphic curves. Furthermore, we obtain a second main theorem of meromorphic mappings intersecting hypersurfaces in N-subgeneral position for Veronese embedding in Pn(C) and a uniqueness theorem sharing hypersurfaces.

Title: Intersections of $ω$ classes in $\overline{\mathcal{M}}_{g,n}$, Abstract: We provide a graph formula which describes an arbitrary monomial in {\omega} classes (also referred to as stable {\psi} classes) in terms of a simple family of dual graphs (pinwheel graphs) with edges decorated by rational functions in {\psi} classes. We deduce some numerical consequences and in particular a combinatorial formula expressing top intersections of \k{appa} classes on Mg in terms of top intersections of {\psi} classes.

Title: SPIRou Input Catalog: Activity, Rotation and Magnetic Field of Cool Dwarfs, Abstract: Based on optical high-resolution spectra obtained with CFHT/ESPaDOnS, we present new measurements of activity and magnetic field proxies of 442 low-mass K5-M7 dwarfs. The objects were analysed as potential targets to search for planetary-mass companions with the new spectropolarimeter and high-precision velocimeter, SPIRou. We have analysed their high-resolution spectra in an homogeneous way: circular polarisation, chromospheric features, and Zeeman broadening of the FeH infrared line. The complex relationship between these activity indicators is analysed: while no strong connection is found between the large-scale and small-scale magnetic fields, the latter relates with the non-thermal flux originating in the chromosphere. We then examine the relationship between various activity diagnostics and the optical radial-velocity jitter available in the literature, especially for planet host stars. We use this to derive for all stars an activity merit function (higher for quieter stars) with the goal of identifying the most favorable stars where the radial-velocity jitter is low enough for planet searches. We find that the main contributors to the RV jitter are the large-scale magnetic field and the chromospheric non-thermal emission. In addition, three stars (GJ 1289, GJ 793, and GJ 251) have been followed along their rotation using the spectropolarimetric mode, and we derive their magnetic topology. These very slow rotators are good representatives of future SPIRou targets. They are compared to other stars where the magnetic topology is also known. The poloidal component of the magnetic field is predominent in all three stars.

Title: Objective Procedure for Reconstructing Couplings in Complex Systems, Abstract: Inferring directional connectivity from point process data of multiple elements is desired in various scientific fields such as neuroscience, geography, economics, etc. Here, we propose an inference procedure for this goal based on the kinetic Ising model. The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks. For these, we develop simple methods based on information theory and computational statistics. Applications to data from artificial and \textit{in vitro} neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, with low computational cost. These results highlight the potential utility of the kinetic Ising model to analyze real interacting systems with event occurrences.

Title: Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach, Abstract: Support vector machines (SVMs) are an important tool in modern data analysis. Traditionally, support vector machines have been fitted via quadratic programming, either using purpose-built or off-the-shelf algorithms. We present an alternative approach to SVM fitting via the majorization--minimization (MM) paradigm. Algorithms that are derived via MM algorithm constructions can be shown to monotonically decrease their objectives at each iteration, as well as be globally convergent to stationary points. We demonstrate the construction of iteratively-reweighted least-squares (IRLS) algorithms, via the MM paradigm, for SVM risk minimization problems involving the hinge, least-square, squared-hinge, and logistic losses, and 1-norm, 2-norm, and elastic net penalizations. Successful implementations of our algorithms are presented via some numerical examples.

Title: Scholars on Twitter: who and how many are they?, Abstract: In this paper we present a novel methodology for identifying scholars with a Twitter account. By combining bibliometric data from Web of Science and Twitter users identified by Altmetric.com we have obtained the largest set of individual scholars matched with Twitter users made so far. Our methodology consists of a combination of matching algorithms, considering different linguistic elements of both author names and Twitter names; followed by a rule-based scoring system that weights the common occurrence of several elements related with the names, individual elements and activities of both Twitter users and scholars matched. Our results indicate that about 2% of the overall population of scholars in the Web of Science is active on Twitter. By domain we find a strong presence of researchers from the Social Sciences and the Humanities. Natural Sciences is the domain with the lowest level of scholars on Twitter. Researchers on Twitter also tend to be younger than those that are not on Twitter. As this is a bibliometric-based approach, it is important to highlight the reliance of the method on the number of publications produced and tweeted by the scholars, thus the share of scholars on Twitter ranges between 1% and 5% depending on their level of productivity. Further research is suggested in order to improve and expand the methodology.

Title: General notions of regression depth function, Abstract: As a measure for the centrality of a point in a set of multivariate data, statistical depth functions play important roles in multivariate analysis, because one may conveniently construct descriptive as well as inferential procedures relying on them. Many depth notions have been proposed in the literature to fit to different applications. However, most of them are mainly developed for the location setting. In this paper, we discuss the possibility of extending some of them into the regression setting. A general concept of regression depth function is also provided.

Title: The g-Good-Neighbor Conditional Diagnosability of Locally Twisted Cubes, Abstract: In the work of Peng et al. in 2012, a new measure was proposed for fault diagnosis of systems: namely, g-good-neighbor conditional diagnosability, which requires that any fault-free vertex has at least g fault-free neighbors in the system. In this paper, we establish the g-good-neighbor conditional diagnosability of locally twisted cubes under the PMC model and the MM^* model.

Title: Preliminary corrosion studies of IN-RAFM steel with stagnant Lead Lithium at 550 C, Abstract: Corrosion of Indian RAFMS (reduced activation ferritic martensitic steel) material with liquid metal, Lead Lithium ( Pb-Li) has been studied under static condition, maintaining Pb-Li at 550 C for different time durations, 2500, 5000 and 9000 hours. Corrosion rate was calculated from weight loss measurements. Microstructure analysis was carried out using SEM and chemical composition by SEM-EDX measurements. Micro Vickers hardness and tensile testing were also carried out. Chromium was found leaching from the near surface regions and surface hardness was found to decrease in all the three cases. Grain boundaries were affected. Some grains got detached from the surface giving rise to pebble like structures in the surface micrographs. There was no significant reduction in the tensile strength, after exposure to liquid metal. This paper discusses the experimental details and the results obtained.

Title: Robust Estimation of Change-Point Location, Abstract: We introduce a robust estimator of the location parameter for the change-point in the mean based on the Wilcoxon statistic and establish its consistency for $L_1$ near epoch dependent processes. It is shown that the consistency rate depends on the magnitude of change. A simulation study is performed to evaluate finite sample properties of the Wilcoxon-type estimator in standard cases, as well as under heavy-tailed distributions and disturbances by outliers, and to compare it with a CUSUM-type estimator. It shows that the Wilcoxon-type estimator is equivalent to the CUSUM-type estimator in standard cases, but outperforms the CUSUM-type estimator in presence of heavy tails or outliers in the data.

Title: Linear time-periodic dynamical systems: An H2 analysis and a model reduction framework, Abstract: Linear time-periodic (LTP) dynamical systems frequently appear in the modeling of phenomena related to fluid dynamics, electronic circuits, and structural mechanics via linearization centered around known periodic orbits of nonlinear models. Such LTP systems can reach orders that make repeated simulation or other necessary analysis prohibitive, motivating the need for model reduction. We develop here an algorithmic framework for constructing reduced models that retains the linear time-periodic structure of the original LTP system. Our approach generalizes optimal approaches that have been established previously for linear time-invariant (LTI) model reduction problems. We employ an extension of the usual H2 Hardy space defined for the LTI setting to time-periodic systems and within this broader framework develop an a posteriori error bound expressible in terms of related LTI systems. Optimization of this bound motivates our algorithm. We illustrate the success of our method on two numerical examples.

Title: Utilizing artificial neural networks to predict demand for weather-sensitive products at retail stores, Abstract: One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand prediction methods for weather sensitive products at retail stores. We employ historical datasets from Walmart, whose customers and markets are often exposed to extreme weather events which can have a huge impact on sales regarding the affected stores and products. We want to accurately predict the sales of 111 potentially weather-sensitive products around the time of major weather events at 45 of Walmart retails locations in the U.S. Intuitively, we may expect an uptick in the sales of umbrellas before a big thunderstorm, but it is difficult for replenishment managers to predict the level of inventory needed to avoid being out-of-stock or overstock during and after that storm. While they rely on a variety of vendor tools to predict sales around extreme weather events, they mostly employ a time-consuming process that lacks a systematic measure of effectiveness. We employ all the methods critical to any analytics project and start with data exploration. Critical features are extracted from the raw historical dataset for demand forecasting accuracy and robustness. In particular, we employ Artificial Neural Network for forecasting demand for each product sold around the time of major weather events. Finally, we evaluate our model to evaluate their accuracy and robustness.

Title: Continuously tempered Hamiltonian Monte Carlo, Abstract: Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) method for performing approximate inference in complex probabilistic models of continuous variables. In common with many MCMC methods, however, the standard HMC approach performs poorly in distributions with multiple isolated modes. We present a method for augmenting the Hamiltonian system with an extra continuous temperature control variable which allows the dynamic to bridge between sampling a complex target distribution and a simpler unimodal base distribution. This augmentation both helps improve mixing in multimodal targets and allows the normalisation constant of the target distribution to be estimated. The method is simple to implement within existing HMC code, requiring only a standard leapfrog integrator. We demonstrate experimentally that the method is competitive with annealed importance sampling and simulating tempering methods at sampling from challenging multimodal distributions and estimating their normalising constants.

Title: Scaling Law for Three-body Collisions in Identical Fermions with $p$-wave Interactions, Abstract: We experimentally confirmed the threshold behavior and scattering length scaling law of the three-body loss coefficients in an ultracold spin-polarized gas of $^6$Li atoms near a $p$-wave Feshbach resonance. We measured the three-body loss coefficients as functions of temperature and scattering volume, and found that the threshold law and the scattering length scaling law hold in limited temperature and magnetic field regions. We also found that the breakdown of the scaling laws is due to the emergence of the effective-range term. This work is an important first step toward full understanding of the loss of identical fermions with $p$-wave interactions.

Title: An attentive neural architecture for joint segmentation and parsing and its application to real estate ads, Abstract: In processing human produced text using natural language processing (NLP) techniques, two fundamental subtasks that arise are (i) segmentation of the plain text into meaningful subunits (e.g., entities), and (ii) dependency parsing, to establish relations between subunits. In this paper, we develop a relatively simple and effective neural joint model that performs both segmentation and dependency parsing together, instead of one after the other as in most state-of-the-art works. We will focus in particular on the real estate ad setting, aiming to convert an ad to a structured description, which we name property tree, comprising the tasks of (1) identifying important entities of a property (e.g., rooms) from classifieds and (2) structuring them into a tree format. In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks. For this purpose, we perform an extensive comparative study of the pipeline methods and the new proposed joint model, reporting an improvement of over three percentage points in the overall edge F1 score of the property tree. Also, we propose attention methods, to encourage our model to focus on salient tokens during the construction of the property tree. Thus we experimentally demonstrate the usefulness of attentive neural architectures for the proposed joint model, showcasing a further improvement of two percentage points in edge F1 score for our application.

Title: On algebraically integrable domains in Euclidean spaces, Abstract: Let $D$ be a bounded domain $D$ in $\mathbb R^n $ with infinitely smooth boundary and $n$ is odd. We prove that if the volume cut off from the domain by a hyperplane is an algebraic function of the hyperplane, free of real singular points, then the domain is an ellipsoid. This partially answers a question of V.I. Arnold: whether odd-dimensional ellipsoids are the only algebraically integrable domains?

Title: A Survey of Model Compression and Acceleration for Deep Neural Networks, Abstract: Deep convolutional neural networks (CNNs) have recently achieved great success in many visual recognition tasks. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance. During the past few years, tremendous progress has been made in this area. In this paper, we survey the recent advanced techniques for compacting and accelerating CNNs model developed. These techniques are roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation. Methods of parameter pruning and sharing will be described at the beginning, after that the other techniques will be introduced. For each scheme, we provide insightful analysis regarding the performance, related applications, advantages, and drawbacks etc. Then we will go through a few very recent additional successful methods, for example, dynamic capacity networks and stochastic depths networks. After that, we survey the evaluation matrix, the main datasets used for evaluating the model performance and recent benchmarking efforts. Finally, we conclude this paper, discuss remaining challenges and possible directions on this topic.

Title: Stochastic Gradient Monomial Gamma Sampler, Abstract: Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when the target posterior is multimodal, mixing performance is often poor. This results in inadequate exploration of the posterior distribution. A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. A generalized kinetic function is leveraged, delivering superior stationary mixing, especially for multimodal distributions. Techniques are also discussed to overcome the practical issues introduced by this generalization. It is shown that the proposed approach is better at exploring complex multimodal posterior distributions, as demonstrated on multiple applications and in comparison with other stochastic gradient MCMC methods.

Title: Training Neural Networks Using Features Replay, Abstract: Training a neural network using backpropagation algorithm requires passing error gradients sequentially through the network. The backward locking prevents us from updating network layers in parallel and fully leveraging the computing resources. Recently, there are several works trying to decouple and parallelize the backpropagation algorithm. However, all of them suffer from severe accuracy loss or memory explosion when the neural network is deep. To address these challenging issues, we propose a novel parallel-objective formulation for the objective function of the neural network. After that, we introduce features replay algorithm and prove that it is guaranteed to converge to critical points for the non-convex problem under certain conditions. Finally, we apply our method to training deep convolutional neural networks, and the experimental results show that the proposed method achieves {faster} convergence, {lower} memory consumption, and {better} generalization error than compared methods.

Title: The anti-spherical category, Abstract: We study a diagrammatic categorification (the "anti-spherical category") of the anti-spherical module for any Coxeter group. We deduce that Deodhar's (sign) parabolic Kazhdan-Lusztig polynomials have non-negative coefficients, and that a monotonicity conjecture of Brenti's holds. The main technical observation is a localisation procedure for the anti-spherical category, from which we construct a "light leaves" basis of morphisms. Our techniques may be used to calculate many new elements of the $p$-canonical basis in the anti-spherical module. The results use generators and relations for Soergel bimodules ("Soergel calculus") in a crucial way.

Title: Unified Treatment of Spin Torques using a Coupled Magnetisation Dynamics and Three-Dimensional Spin Current Solver, Abstract: A three-dimensional spin current solver based on a generalised spin drift-diffusion description, including the spin Hall effect, is integrated with a magnetisation dynamics solver. The resulting model is shown to simultaneously reproduce the spin-orbit torques generated using the spin Hall effect, spin pumping torques generated by magnetisation dynamics in multilayers, as well as the spin transfer torques acting on magnetisation regions with spatial gradients, whilst field-like and spin-like torques are reproduced in a spin valve geometry. Two approaches to modelling interfaces are analysed, one based on the spin mixing conductance and the other based on continuity of spin currents where the spin dephasing length governs the absorption of transverse spin components. In both cases analytical formulas are derived for the spin-orbit torques in a heavy metal / ferromagnet bilayer geometry, showing in general both field-like and damping-like torques are generated. The limitations of the analytical approach are discussed, showing that even in a simple bilayer geometry, due to the non-uniformity of the spin currents, a full three-dimensional treatment is required. Finally the model is applied to the quantitative analysis of the spin Hall angle in Pt by reproducing published experimental data on the ferromagnetic resonance linewidth in the bilayer geometry.

Title: Quantum Speed Limit is Not Quantum, Abstract: The quantum speed limit (QSL), or the energy-time uncertainty relation, describes the fundamental maximum rate for quantum time evolution and has been regarded as being unique in quantum mechanics. In this study, we obtain a classical speed limit corresponding to the QSL using the Hilbert space for the classical Liouville equation. Thus, classical mechanics has a fundamental speed limit, and QSL is not a purely quantum phenomenon but a universal dynamical property of the Hilbert space. Furthermore, we obtain similar speed limits for the imaginary-time Schroedinger equations such as the master equation.

Title: A dual framework for low-rank tensor completion, Abstract: One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization. However, most existing works in this direction learn a sparse combination of tensors. In this work, we fill this gap by proposing a variant of the latent trace norm that helps in learning a non-sparse combination of tensors. We develop a dual framework for solving the low-rank tensor completion problem. We first show a novel characterization of the dual solution space with an interesting factorization of the optimal solution. Overall, the optimal solution is shown to lie on a Cartesian product of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian optimization framework for proposing computationally efficient trust region algorithm. The experiments illustrate the efficacy of the proposed algorithm on several real-world datasets across applications.

Title: Human perception in computer vision, Abstract: Computer vision has made remarkable progress in recent years. Deep neural network (DNN) models optimized to identify objects in images exhibit unprecedented task-trained accuracy and, remarkably, some generalization ability: new visual problems can now be solved more easily based on previous learning. Biological vision (learned in life and through evolution) is also accurate and general-purpose. Is it possible that these different learning regimes converge to similar problem-dependent optimal computations? We therefore asked whether the human system-level computation of visual perception has DNN correlates and considered several anecdotal test cases. We found that perceptual sensitivity to image changes has DNN mid-computation correlates, while sensitivity to segmentation, crowding and shape has DNN end-computation correlates. Our results quantify the applicability of using DNN computation to estimate perceptual loss, and are consistent with the fascinating theoretical view that properties of human perception are a consequence of architecture-independent visual learning.

Title: Analyses and estimation of certain design parameters of micro-grooved heat pipes, Abstract: A numerical analysis of heat conduction through the cover plate of a heat pipe is carried out to determine the temperature of the working substance, average temperature of heating and cooling surfaces, heat spread in the transmitter, and the heat bypass through the cover plate. Analysis has been extended for the estimation of heat transfer requirements at the outer surface of the con- denser under different heat load conditions using Genetic Algorithm. This paper also presents the estimation of an average heat transfer coefficient for the boiling and condensation of the working substance inside the microgrooves corresponding to a known temperature of the heat source. The equation of motion of the working fluid in the meniscus of an equilateral triangular groove has been presented from which a new term called the minimum surface tension required for avoiding the dry out condition is defined. Quantitative results showing the effect of thickness of cover plate, heat load, angle of inclination and viscosity of the working fluid on the different aspects of the heat transfer, minimum surface tension required to avoid dry out, velocity distribution of the liquid, and radius of liquid meniscus inside the micro-grooves have been presented and discussed.

Title: Merge decompositions, two-sided Krohn-Rhodes, and aperiodic pointlikes, Abstract: This paper provides short proofs of two fundamental theorems of finite semigroup theory whose previous proofs were significantly longer, namely the two-sided Krohn-Rhodes decomposition theorem and Henckell's aperiodic pointlike theorem, using a new algebraic technique that we call the merge decomposition. A prototypical application of this technique decomposes a semigroup $T$ into a two-sided semidirect product whose components are built from two subsemigroups $T_1,T_2$, which together generate $T$, and the subsemigroup generated by their setwise product $T_1T_2$. In this sense we decompose $T$ by merging the subsemigroups $T_1$ and $T_2$. More generally, our technique merges semigroup homomorphisms from free semigroups.

Title: Retrospective Higher-Order Markov Processes for User Trails, Abstract: Users form information trails as they browse the web, checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. First-order and higher-order Markov chains have been widely used methods to study such sequences of data. First-order Markov chains are easy to estimate, but lack accuracy when history matters. Higher-order Markov chains, in contrast, have too many parameters and suffer from overfitting the training data. Fitting these parameters with regularization and smoothing only offers mild improvements. In this paper we propose the retrospective higher-order Markov process (RHOMP) as a low-parameter model for such sequences. This model is a special case of a higher-order Markov chain where the transitions depend retrospectively on a single history state instead of an arbitrary combination of history states. There are two immediate computational advantages: the number of parameters is linear in the order of the Markov chain and the model can be fit to large state spaces. Furthermore, by providing a specific structure to the higher-order chain, RHOMPs improve the model accuracy by efficiently utilizing history states without risks of overfitting the data. We demonstrate how to estimate a RHOMP from data and we demonstrate the effectiveness of our method on various real application datasets spanning geolocation data, review sequences, and business locations. The RHOMP model uniformly outperforms higher-order Markov chains, Kneser-Ney regularization, and tensor factorizations in terms of prediction accuracy.

Title: Geometrically stopped Markovian random growth processes and Pareto tails, Abstract: Many empirical studies document power law behavior in size distributions of economic interest such as cities, firms, income, and wealth. One mechanism for generating such behavior combines independent and identically distributed Gaussian additive shocks to log-size with a geometric age distribution. We generalize this mechanism by allowing the shocks to be non-Gaussian (but light-tailed) and dependent upon a Markov state variable. Our main results provide sharp bounds on tail probabilities and simple formulas for Pareto exponents. We present two applications: (i) we show that the tails of the wealth distribution in a heterogeneous-agent dynamic general equilibrium model with idiosyncratic endowment risk decay exponentially, unlike models with investment risk where the tails may be Paretian, and (ii) we show that a random growth model for the population dynamics of Japanese prefectures is consistent with the observed Pareto exponent but only after allowing for Markovian dynamics.

Title: Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?, Abstract: Self-supervised learning (SSL) is a reliable learning mechanism in which a robot enhances its perceptual capabilities. Typically, in SSL a trusted, primary sensor cue provides supervised training data to a secondary sensor cue. In this article, a theoretical analysis is performed on the fusion of the primary and secondary cue in a minimal model of SSL. A proof is provided that determines the specific conditions under which it is favorable to perform fusion. In short, it is favorable when (i) the prior on the target value is strong or (ii) the secondary cue is sufficiently accurate. The theoretical findings are validated with computational experiments. Subsequently, a real-world case study is performed to investigate if fusion in SSL is also beneficial when assumptions of the minimal model are not met. In particular, a flying robot learns to map pressure measurements to sonar height measurements and then fuses the two, resulting in better height estimation. Fusion is also beneficial in the opposite case, when pressure is the primary cue. The analysis and results are encouraging to study SSL fusion also for other robots and sensors.

Title: Playing Atari with Six Neurons, Abstract: Deep reinforcement learning on Atari games maps pixel directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it. Aiming at devoting entire deep networks to decision making alone, we propose a new method for learning policies and compact state representations separately but simultaneously for policy approximation in reinforcement learning. State representations are generated by a novel algorithm based on Vector Quantization and Sparse Coding, trained online along with the network, and capable of growing its dictionary size over time. We also introduce new techniques allowing both the neural network and the evolution strategy to cope with varying dimensions. This enables networks of only 6 to 18 neurons to learn to play a selection of Atari games with performance comparable---and occasionally superior---to state-of-the-art techniques using evolution strategies on deep networks two orders of magnitude larger.

Title: Contraction and uniform convergence of isotonic regression, Abstract: We consider the problem of isotonic regression, where the underlying signal $x$ is assumed to satisfy a monotonicity constraint, that is, $x$ lies in the cone $\{ x\in\mathbb{R}^n : x_1 \leq \dots \leq x_n\}$. We study the isotonic projection operator (projection to this cone), and find a necessary and sufficient condition characterizing all norms with respect to which this projection is contractive. This enables a simple and non-asymptotic analysis of the convergence properties of isotonic regression, yielding uniform confidence bands that adapt to the local Lipschitz properties of the signal.

Title: Radially distributed values and normal families, Abstract: Let $L_0$ and $L_1$ be two distinct rays emanating from the origin and let ${\mathcal F}$ be the family of all functions holomorphic in the unit disk ${\mathbb D}$ for which all zeros lie on $L_0$ while all $1$-points lie on $L_1$. It is shown that ${\mathcal F}$ is normal in ${\mathbb D}\backslash\{0\}$. The case where $L_0$ is the positive real axis and $L_1$ is the negative real axis is studied in more detail.

Title: Characterizing Exoplanet Habitability, Abstract: A habitable exoplanet is a world that can maintain stable liquid water on its surface. Techniques and approaches to characterizing such worlds are essential, as performing a census of Earth-like planets that may or may not have life will inform our understanding of how frequently life originates and is sustained on worlds other than our own. Observational techniques like high contrast imaging and transit spectroscopy can reveal key indicators of habitability for exoplanets. Both polarization measurements and specular reflectance from oceans (also known as "glint") can provide direct evidence for surface liquid water, while constraining surface pressure and temperature (from moderate resolution spectra) can indicate liquid water stability. Indirect evidence for habitability can come from a variety of sources, including observations of variability due to weather, surface mapping studies, and/or measurements of water vapor or cloud profiles that indicate condensation near a surface. Approaches to making the types of measurements that indicate habitability are diverse, and have different considerations for the required wavelength range, spectral resolution, maximum noise levels, stellar host temperature, and observing geometry.

Title: Combining learned and analytical models for predicting action effects, Abstract: One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, dynamics are approximated by physics-based analytical models. These models rely on specific state representations that may be hard to obtain from raw sensory data, especially if no knowledge of the object shape is assumed. More recently, we have seen learning approaches that can predict the effect of complex physical interactions directly from sensory input. It is however an open question how far these models generalize beyond their training data. In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a convolutional neural network to convert raw depth images or organized point clouds into a suitable representation for the analytical model and compare this approach to using neural networks for both, perception and prediction. A systematic evaluation of the proposed approach on a very large real-world dataset shows two main advantages of the hybrid architecture. Compared to a pure neural network, it significantly (i) reduces required training data and (ii) improves generalization to novel physical interaction.

Title: Qualification Conditions in Semi-algebraic Programming, Abstract: For an arbitrary finite family of semi-algebraic/definable functions, we consider the corresponding inequality constraint set and we study qualification conditions for perturbations of this set. In particular we prove that all positive diagonal perturbations, save perhaps a finite number of them, ensure that any point within the feasible set satisfies Mangasarian-Fromovitz constraint qualification. Using the Milnor-Thom theorem, we provide a bound for the number of singular perturbations when the constraints are polynomial functions. Examples show that the order of magnitude of our exponential bound is relevant. Our perturbation approach provides a simple protocol to build sequences of "regular" problems approximating an arbitrary semi-algebraic/definable problem. Applications to sequential quadratic programming methods and sum of squares relaxation are provided.

Title: A one-dimensional model for water desalination by flow-through electrode capacitive deionization, Abstract: Capacitive deionization (CDI) is a fast-emerging water desalination technology in which a small cell voltage of ~1 V across porous carbon electrodes removes salt from feedwaters via electrosorption. In flow-through electrode (FTE) CDI cell architecture, feedwater is pumped through macropores or laser perforated channels in porous electrodes, enabling highly compact cells with parallel flow and electric field, as well as rapid salt removal. We here present a one-dimensional model describing water desalination by FTE CDI, and a comparison to data from a custom-built experimental cell. The model employs simple cell boundary conditions derived via scaling arguments. We show good model-to-data fits with reasonable values for fitting parameters such as the Stern layer capacitance, micropore volume, and attraction energy. Thus, we demonstrate that from an engineering modeling perspective, an FTE CDI cell may be described with simpler one-dimensional models, unlike more typical flow-between electrodes architecture where 2D models are required.

Title: Preventing Hospital Acquired Infections Through a Workflow-Based Cyber-Physical System, Abstract: Hospital acquired infections (HAI) are infections acquired within the hospital from healthcare workers, patients or from the environment, but which have no connection to the initial reason for the patient's hospital admission. HAI are a serious world-wide problem, leading to an increase in mortality rates, duration of hospitalisation as well as significant economic burden on hospitals. Although clear preventive guidelines exist, studies show that compliance to them is frequently poor. This paper details the software perspective for an innovative, business process software based cyber-physical system that will be implemented as part of a European Union-funded research project. The system is composed of a network of sensors mounted in different sites around the hospital, a series of wearables used by the healthcare workers and a server side workflow engine. For better understanding, we describe the system through the lens of a single, simple clinical workflow that is responsible for a significant portion of all hospital infections. The goal is that when completed, the system will be configurable in the sense of facilitating the creation and automated monitoring of those clinical workflows that when combined, account for over 90\% of hospital infections.

Title: OpenML Benchmarking Suites and the OpenML100, Abstract: We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. Major distinguishing features of OpenML benchmark suites are (a) ease of use through standardized data formats, APIs, and existing client libraries; (b) machine-readable meta-information regarding the contents of the suite; and (c) online sharing of results, enabling large scale comparisons. As a first such suite, we propose the OpenML100, a machine learning benchmark suite of 100~classification datasets carefully curated from the thousands of datasets available on OpenML.org.

Title: From mindless mathematics to thinking meat?, Abstract: Deconstruction of the theme of the 2017 FQXi essay contest is already an interesting exercise in its own right: Teleology is rarely useful in physics --- the only known mainstream physics example (black hole event horizons) has a very mixed score-card --- so the "goals" and "aims and intentions" alluded to in the theme of the 2017 FQXi essay contest are already somewhat pushing the limits. Furthermore, "aims and intentions" certainly carries the implication of consciousness, and opens up a whole can of worms related to the mind-body problem. As for "mindless mathematical laws", that allusion is certainly in tension with at least some versions of the "mathematical universe hypothesis". Finally "wandering towards a goal" again carries the implication of consciousness, with all its attendant problems. In this essay I will argue, simply because we do not yet have any really good mathematical or physical theory of consciousness, that the theme of this essay contest is premature, and unlikely to lead to any resolution that would be widely accepted in the mathematics or physics communities.

Title: When Streams of Optofluidics Meet the Sea of Life, Abstract: Luke P. Lee is a Tan Chin Tuan Centennial Professor at the National University of Singapore. In this contribution he describes the power of optofluidics as a research tool and reviews new insights within the areas of single cell analysis, microphysiological analysis, and integrated systems.

Title: Willis Theory via Graphs, Abstract: We study the scale and tidy subgroups of an endomorphism of a totally disconnected locally compact group using a geometric framework. This leads to new interpretations of tidy subgroups and the scale function. Foremost, we obtain a geometric tidying procedure which applies to endomorphisms as well as a geometric proof of the fact that tidiness is equivalent to being minimizing for a given endomorphism. Our framework also yields an endomorphism version of the Baumgartner-Willis tree representation theorem. We conclude with a construction of new endomorphisms of totally disconnected locally compact groups from old via HNN-extensions.

Title: The Effect of Site-Specific Spectral Densities on the High-Dimensional Exciton-Vibrational Dynamics in the FMO Complex, Abstract: The coupled exciton-vibrational dynamics of a three-site model of the FMO complex is investigated using the Multi-layer Multi-configuration Time-dependent Hartree (ML-MCTDH) approach. Emphasis is put on the effect of the spectral density on the exciton state populations as well as on the vibrational and vibronic non-equilibrium excitations. Models which use either a single or site-specific spectral densities are contrasted to a spectral density adapted from experiment. For the transfer efficiency, the total integrated Huang-Rhys factor is found to be more important than details of the spectral distributions. However, the latter are relevant for the obtained non-equilibrium vibrational and vibronic distributions and thus influence the actual pattern of population relaxation.

Title: On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising, Abstract: We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in $d$-dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accuracy of every NPMLE. Our results imply, in particular, that every NPMLE achieves near parametric risk (up to logarithmic multiplicative factors) when the true density is a discrete Gaussian mixture without any prior information on the number of mixture components. NPMLEs can naturally be used to yield empirical Bayes estimates of the Oracle Bayes estimator in the Gaussian denoising problem. We prove bounds for the accuracy of the empirical Bayes estimate as an approximation to the Oracle Bayes estimator. Here our results imply that the empirical Bayes estimator performs at nearly the optimal level (up to logarithmic multiplicative factors) for denoising in clustering situations without any prior knowledge of the number of clusters.

Title: PRE-render Content Using Tiles (PRECUT). 1. Large-Scale Compound-Target Relationship Analyses, Abstract: Visualizing a complex network is computationally intensive process and depends heavily on the number of components in the network. One way to solve this problem is not to render the network in real time. PRE-render Content Using Tiles (PRECUT) is a process to convert any complex network into a pre-rendered network. Tiles are generated from pre-rendered images at different zoom levels, and navigating the network simply becomes delivering relevant tiles. PRECUT is exemplified by performing large-scale compound-target relationship analyses. Matched molecular pair (MMP) networks were created using compounds and the target class description found in the ChEMBL database. To visualize MMP networks, the MMP network viewer has been implemented in COMBINE and as a web application, hosted at this http URL.

Title: Convolution Semigroups of Probability Measures on Gelfand Pairs, Revisited, Abstract: Our goal is to find classes of convolution semigroups on Lie groups $G$ that give rise to interesting processes in symmetric spaces $G/K$. The $K$-bi-invariant convolution semigroups are a well-studied example. An appealing direction for the next step is to generalise to right $K$-invariant convolution semigroups, but recent work of Liao has shown that these are in one-to-one correspondence with $K$-bi-invariant convolution semigroups. We investigate a weaker notion of right $K$-invariance, but show that this is, in fact, the same as the usual notion. Another possible approach is to use generalised notions of negative definite functions, but this also leads to nothing new. We finally find an interesting class of convolution semigroups that are obtained by making use of the Cartan decomposition of a semisimple Lie group, and the solution of certain stochastic differential equations. Examples suggest that these are well-suited for generating random motion along geodesics in symmetric spaces.

Title: Temporal correlation detection using computational phase-change memory, Abstract: For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes increasingly data-centric and as the scalability limits in terms of performance and power are being reached, alternative computing paradigms are searched for in which computation and storage are collocated. A fascinating new approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. Here we present a large-scale experimental demonstration using one million phase-change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Also presented is an application of such a computational memory to process real-world data-sets. The results show that this co-existence of computation and storage at the nanometer scale could be the enabler for new, ultra-dense, low power, and massively parallel computing systems.

Title: Quantum Interference of Glory Rescattering in Strong-Field Atomic Ionization, Abstract: During the ionization of atoms irradiated by linearly polarized intense laser fields, we find for the first time that the transverse momentum distribution of photoelectrons can be well fitted by a squared zeroth-order Bessel function because of the quantum interference effect of Glory rescattering. The characteristic of the Bessel function is determined by the common angular momentum of a bunch of semiclassical paths termed as Glory trajectories, which are launched with different nonzero initial transverse momenta distributed on a specific circle in the momentum plane and finally deflected to the same asymptotic momentum, which is along the polarization direction, through post-tunneling rescattering. Glory rescattering theory (GRT) based on the semiclassical path-integral formalism is developed to address this effect quantitatively. Our theory can resolve the long-standing discrepancies between existing theories and experiments on the fringe location, predict the sudden transition of the fringe structure in holographic patterns, and shed light on the quantum interference aspects of low-energy structures in strong-field atomic ionization.

Title: On vector measures and extensions of transfunctions, Abstract: We are interested in extending operators defined on positive measures, called here transfunctions, to signed measures and vector measures. Our methods use a somewhat nonstandard approach to measures and vector measures. The necessary background, including proofs of some auxiliary results, is included.