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Title: Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization, Abstract: Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation. We treat this task as a factor decomposition problem: we aim to separate what appears to be healthy and where disease is (as a map). The two factors are then recombined (by a network) to reconstruct the input disease image. We train our models in an adversarial way using either paired or unpaired settings, where we pair disease images and maps (as segmentation masks) when available. We quantitatively evaluate the quality of pseudo healthy images. We show in a series of experiments, performed in ISLES and BraTS datasets, that our method is better than conditional GAN and CycleGAN, highlighting challenges in using adversarial methods in the image translation task of pseudo healthy image generation.

Title: Integrable structure of products of finite complex Ginibre random matrices, Abstract: We consider the squared singular values of the product of $M$ standard complex Gaussian matrices. Since the squared singular values form a determinantal point process with a particular Meijer G-function kernel, the gap probabilities are given by a Fredholm determinant based on this kernel. It was shown by Strahov \cite{St14} that a hard edge scaling limit of the gap probabilities is described by Hamiltonian differential equations which can be formulated as an isomonodromic deformation system similar to the theory of the Kyoto school. We generalize this result to the case of finite matrices by first finding a representation of the finite kernel in integrable form. As a result we obtain the Hamiltonian structure for a finite size matrices and formulate it in terms of a $(M+1) \times (M+1)$ matrix Schlesinger system. The case $M=1$ reproduces the Tracy and Widom theory which results in the Painlevé V equation for the $(0,s)$ gap probability. Some integrals of motion for $M = 2$ are identified, and a coupled system of differential equations in two unknowns is presented which uniquely determines the corresponding $(0,s)$ gap probability.

Title: Superintegrable systems on 3-dimensional curved spaces: Eisenhart formalism and separability, Abstract: The Eisenhart geometric formalism, which transforms an Euclidean natural Hamiltonian $H=T+V$ into a geodesic Hamiltonian ${\cal T}$ with one additional degree of freedom, is applied to the four families of quadratically superintegrable systems with multiple separability in the Euclidean plane. Firstly, the separability and superintegrability of such four geodesic Hamiltonians ${\cal T}_r$ ($r=a,b,c,d$) in a three-dimensional curved space are studied and then these four systems are modified with the addition of a potential ${\cal U}_r$ leading to ${\cal H}_r={\cal T}_r +{\cal U}_r$. Secondly, we study the superintegrability of the four Hamiltonians $\widetilde{\cal H}_r= {\cal H}_r/ \mu_r$, where $\mu_r$ is a certain position-dependent mass, that enjoys the same separability as the original system ${\cal H}_r$. All the Hamiltonians here studied describe superintegrable systems on non-Euclidean three-dimensional manifolds with a broken spherically symmetry.

Title: Optimal Gossip Algorithms for Exact and Approximate Quantile Computations, Abstract: This paper gives drastically faster gossip algorithms to compute exact and approximate quantiles. Gossip algorithms, which allow each node to contact a uniformly random other node in each round, have been intensely studied and been adopted in many applications due to their fast convergence and their robustness to failures. Kempe et al. [FOCS'03] gave gossip algorithms to compute important aggregate statistics if every node is given a value. In particular, they gave a beautiful $O(\log n + \log \frac{1}{\epsilon})$ round algorithm to $\epsilon$-approximate the sum of all values and an $O(\log^2 n)$ round algorithm to compute the exact $\phi$-quantile, i.e., the the $\lceil \phi n \rceil$ smallest value. We give an quadratically faster and in fact optimal gossip algorithm for the exact $\phi$-quantile problem which runs in $O(\log n)$ rounds. We furthermore show that one can achieve an exponential speedup if one allows for an $\epsilon$-approximation. We give an $O(\log \log n + \log \frac{1}{\epsilon})$ round gossip algorithm which computes a value of rank between $\phi n$ and $(\phi+\epsilon)n$ at every node.% for any $0 \leq \phi \leq 1$ and $0 < \epsilon < 1$. Our algorithms are extremely simple and very robust - they can be operated with the same running times even if every transmission fails with a, potentially different, constant probability. We also give a matching $\Omega(\log \log n + \log \frac{1}{\epsilon})$ lower bound which shows that our algorithm is optimal for all values of $\epsilon$.

Title: A parallel approach to bi-objective integer programming, Abstract: To obtain a better understanding of the trade-offs between various objectives, Bi-Objective Integer Programming (BOIP) algorithms calculate the set of all non-dominated vectors and present these as the solution to a BOIP problem. Historically, these algorithms have been compared in terms of the number of single-objective IPs solved and total CPU time taken to produce the solution to a problem. This is equitable, as researchers can often have access to widely differing amounts of computing power. However, the real world has recently seen a large uptake of multi-core processors in computers, laptops, tablets and even mobile phones. With this in mind, we look at how to best utilise parallel processing to improve the elapsed time of optimisation algorithms. We present two methods of parallelising the recursive algorithm presented by Ozlen, Burton and MacRae. Both new methods utilise two threads and improve running times. One of the new methods, the Meeting algorithm, halves running time to achieve near-perfect parallelisation. The results are compared with the efficiency of parallelisation within the commercial IP solver IBM ILOG CPLEX, and the new methods are both shown to perform better.

Title: Vehicle Localization and Control on Roads with Prior Grade Map, Abstract: We propose a map-aided vehicle localization method for GPS-denied environments. This approach exploits prior knowledge of the road grade map and vehicle on-board sensor measurements to accurately estimate the longitudinal position of the vehicle. Real-time localization is crucial to systems that utilize position-dependent information for planning and control. We validate the effectiveness of the localization method on a hierarchical control system. The higher level planner optimizes the vehicle velocity to minimize the energy consumption for a given route by employing traffic condition and road grade data. The lower level is a cruise control system that tracks the position-dependent optimal reference velocity. Performance of the proposed localization algorithm is evaluated using both simulations and experiments.

Title: Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints, Abstract: Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterations controls its model capacity and generalization error. The impacts of stochastic gradient methods on generalization error for non-convex learning problems not only have important theoretical consequences, but are also critical to generalization errors of deep learning. In this paper, we study the generalization errors of Stochastic Gradient Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian results respectively. The stability-based theory obtains a bound of $O\left(\frac{1}{n}L\sqrt{\beta T_k}\right)$, where $L$ is uniform Lipschitz parameter, $\beta$ is inverse temperature, and $T_k$ is aggregated step sizes. For PAC-Bayesian theory, though the bound has a slower $O(1/\sqrt{n})$ rate, the contribution of each step is shown with an exponentially decaying factor by imposing $\ell^2$ regularization, and the uniform Lipschitz constant is also replaced by actual norms of gradients along trajectory. Our bounds have no implicit dependence on dimensions, norms or other capacity measures of parameter, which elegantly characterizes the phenomenon of "Fast Training Guarantees Generalization" in non-convex settings. This is the first algorithm-dependent result with reasonable dependence on aggregated step sizes for non-convex learning, and has important implications to statistical learning aspects of stochastic gradient methods in complicated models such as deep learning.

Title: Effective gravity and effective quantum equations in a system inspired by walking droplets experiments, Abstract: In this paper we suggest a macroscopic toy system in which a potential-like energy is generated by a non-uniform pulsation of the medium (i.e. pulsation of transverse standing oscillations that the elastic medium of the system tends to support at each point). This system is inspired by walking droplets experiments with submerged barriers. We first show that a Poincaré-Lorentz covariant formalization of the system causes inconsistency and contradiction. The contradiction is solved by using a general covariant formulation and by assuming a relation between the metric associated with the elastic medium and the pulsation of the medium. (Calculations are performed in a Newtonian-like metric, constant in time). We find ($i$) an effective Schrödinger equation with external potential, ($ii$) an effective de Broglie-Bohm guidance formula and ($iii$) an energy of the `particle' which has a direct counterpart in general relativity as well as in quantum mechanics. We analyze the wave and the `particle' in an effective free fall and with a harmonic potential. This potential-like energy is an effective gravitational potential, rooted in the pulsation of the medium at each point. The latter, also conceivable as a natural clock, makes easy to understand why proper time varies from place to place.

Title: Injective stabilization of additive functors. I. Preliminaries, Abstract: This paper is the first one in a series of three dealing with the concept of injective stabilization of the tensor product and its applications. Its primary goal is to collect known facts and establish a basic operational calculus that will be used in the subsequent parts. This is done in greater generality than is necessary for the stated goal. Several results of independent interest are also established. They include, among other things, connections with satellites, an explicit construction of the stabilization of a finitely presented functor, various exactness properties of the injectively stable functors, a construction, from a functor and a short exact sequence, of a doubly-infinite exact sequence by splicing the injective stabilization of the functor and its derived functors. When specialized to the tensor product with a finitely presented module, the injective stabilization with coefficients in the ring is isomorphic to the 1-torsion functor. The Auslander-Reiten formula is extended to a more general formula, which holds for arbitrary (i.e., not necessarily finite) modules over arbitrary associative rings with identity. Weakening of the assumptions in the theorems of Eilenberg and Watts leads to characterizations of the requisite zeroth derived functors. The subsequent papers, provide applications of the developed techniques. Part~II deals with new notions of torsion module and cotorsion module of a module. This is done for arbitrary modules over arbitrary rings. Part~III introduces a new concept, called the asymptotic stabilization of the tensor product. The result is closely related to different variants of stable homology (these are generalizations of Tate homology to arbitrary rings). A comparison transformation from Vogel homology to the asymptotic stabilization of the tensor product is constructed and shown to be epic.

Title: The heavy path approach to Galton-Watson trees with an application to Apollonian networks, Abstract: We study the heavy path decomposition of conditional Galton-Watson trees. In a standard Galton-Watson tree conditional on its size $n$, we order all children by their subtree sizes, from large (heavy) to small. A node is marked if it is among the $k$ heaviest nodes among its siblings. Unmarked nodes and their subtrees are removed, leaving only a tree of marked nodes, which we call the $k$-heavy tree. We study various properties of these trees, including their size and the maximal distance from any original node to the $k$-heavy tree. In particular, under some moment condition, the $2$-heavy tree is with high probability larger than $cn$ for some constant $c > 0$, and the maximal distance from the $k$-heavy tree is $O(n^{1/(k+1)})$ in probability. As a consequence, for uniformly random Apollonian networks of size $n$, the expected size of the longest simple path is $\Omega(n)$.

Title: A fast algorithm for maximal propensity score matching, Abstract: We present a new algorithm which detects the maximal possible number of matched disjoint pairs satisfying a given caliper when a bipartite matching is done with respect to a scalar index (e.g., propensity score), and constructs a corresponding matching. Variable width calipers are compatible with the technique, provided that the width of the caliper is a Lipschitz function of the index. If the observations are ordered with respect to the index then the matching needs $O(N)$ operations, where $N$ is the total number of subjects to be matched. The case of 1-to-$n$ matching is also considered. We offer also a new fast algorithm for optimal complete one-to-one matching on a scalar index when the treatment and control groups are of the same size. This allows us to improve greedy nearest neighbor matching on a scalar index. Keywords: propensity score matching, nearest neighbor matching, matching with caliper, variable width caliper.

Title: Challenges testing the no-hair theorem with gravitational waves, Abstract: General relativity's no-hair theorem states that isolated astrophysical black holes are described by only two numbers: mass and spin. As a consequence, there are strict relationships between the frequency and damping time of the different modes of a perturbed Kerr black hole. Testing the no-hair theorem has been a longstanding goal of gravitational-wave astronomy. The recent detection of gravitational waves from black hole mergers would seem to make such tests imminent. We investigate how constraints on black hole ringdown parameters scale with the loudness of the ringdown signal---subject to the constraint that the post-merger remnant must be allowed to settle into a perturbative, Kerr-like state. In particular, we require that---for a given detector---the gravitational waveform predicted by numerical relativity is indistinguishable from an exponentially damped sine after time $t^\text{cut}$. By requiring the post-merger remnant to settle into such a perturbative state, we find that confidence intervals for ringdown parameters do not necessarily shrink with louder signals. In at least some cases, more sensitive measurements probe later times without necessarily providing tighter constraints on ringdown frequencies and damping times. Preliminary investigations are unable to explain this result in terms of a numerical relativity artifact.

Title: The Australian PCEHR system: Ensuring Privacy and Security through an Improved Access Control Mechanism, Abstract: An Electronic Health Record (EHR) is designed to store diverse data accurately from a range of health care providers and to capture the status of a patient by a range of health care providers across time. Realising the numerous benefits of the system, EHR adoption is growing globally and many countries invest heavily in electronic health systems. In Australia, the Government invested $467 million to build key components of the Personally Controlled Electronic Health Record (PCEHR) system in July 2012. However, in the last three years, the uptake from individuals and health care providers has not been satisfactory. Unauthorised access of the PCEHR was one of the major barriers. We propose an improved access control model for the PCEHR system to resolve the unauthorised access issue. We discuss the unauthorised access issue with real examples and present a potential solution to overcome the issue to make the PCEHR system a success in Australia.

Title: Optimal Transport: Fast Probabilistic Approximation with Exact Solvers, Abstract: We propose a simple subsampling scheme for fast randomized approximate computation of optimal transport distances. This scheme operates on a random subset of the full data and can use any exact algorithm as a black-box back-end, including state-of-the-art solvers and entropically penalized versions. It is based on averaging the exact distances between empirical measures generated from independent samples from the original measures and can easily be tuned towards higher accuracy or shorter computation times. To this end, we give non-asymptotic deviation bounds for its accuracy in the case of discrete optimal transport problems. In particular, we show that in many important instances, including images (2D-histograms), the approximation error is independent of the size of the full problem. We present numerical experiments that demonstrate that a very good approximation in typical applications can be obtained in a computation time that is several orders of magnitude smaller than what is required for exact computation of the full problem.

Title: Separatrix crossing in rotation of a body with changing geometry of masses, Abstract: We consider free rotation of a body whose parts move slowly with respect to each other under the action of internal forces. This problem can be considered as a perturbation of the Euler-Poinsot problem. The dynamics has an approximate conservation law - an adiabatic invariant. This allows to describe the evolution of rotation in the adiabatic approximation. The evolution leads to an overturn in the rotation of the body: the vector of angular velocity crosses the separatrix of the Euler-Poinsot problem. This crossing leads to a quasi-random scattering in body's dynamics. We obtain formulas for probabilities of capture into different domains in the phase space at separatrix crossings.

Title: Path-by-path regularization by noise for scalar conservation laws, Abstract: We prove a path-by-path regularization by noise result for scalar conservation laws. In particular, this proves regularizing properties for scalar conservation laws driven by fractional Brownian motion and generalizes the respective results obtained in [Gess, Souganidis; Comm. Pure Appl. Math. (2017)]. In addition, we introduce a new path-by-path scaling property which is shown to be sufficient to imply regularizing effects.

Title: An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog, Abstract: We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and incorporate structured KB query results into system responses to successfully complete task-oriented dialogs. The proposed model produces well-structured system responses by jointly learning belief tracking and KB result processing conditioning on the dialog history. We evaluate the model in a restaurant search domain using a dataset that is converted from the second Dialog State Tracking Challenge (DSTC2) corpus. Experiment results show that the proposed model can robustly track dialog state given the dialog history. Moreover, our model demonstrates promising results in producing appropriate system responses, outperforming prior end-to-end trainable neural network models using per-response accuracy evaluation metrics.

Title: Convexification of Queueing Formulas by Mixed-Integer Second-Order Cone Programming: An Application to a Discrete Location Problem with Congestion, Abstract: Mixed-Integer Second-Order Cone Programs (MISOCPs) form a nice class of mixed-inter convex programs, which can be solved very efficiently due to the recent advances in optimization solvers. Our paper bridges the gap between modeling a class of optimization problems and using MISOCP solvers. It is shown how various performance metrics of M/G/1 queues can be molded by different MISOCPs. To motivate our method practically, it is first applied to a challenging stochastic location problem with congestion, which is broadly used to design socially optimal service networks. Four different MISOCPs are developed and compared on sets of benchmark test problems. The new formulations efficiently solve large-size test problems, which cannot be solved by the best existing method. Then, the general applicability of our method is shown for similar optimization problems that use queue-theoretic performance measures to address customer satisfaction and service quality.

Title: Discriminative Metric Learning with Deep Forest, Abstract: A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. The case of the fully supervised learning is studied when the class labels of individual training examples are known. The main idea underlying the algorithm is to assign weights to decision trees in random forest in order to reduce distances between objects from the same class and to increase them between objects from different classes. The weights are training parameters. A specific objective function which combines Euclidean and Manhattan distances and simplifies the optimization problem for training the DisDF is proposed. The numerical experiments illustrate the proposed distance metric algorithm.

Title: Controllability of Conjunctive Boolean Networks with Application to Gene Regulation, Abstract: A Boolean network is a finite state discrete time dynamical system. At each step, each variable takes a value from a binary set. The value update rule for each variable is a local function which depends only on a selected subset of variables. Boolean networks have been used in modeling gene regulatory networks. We focus in this paper on a special class of Boolean networks, namely the conjunctive Boolean networks (CBNs), whose value update rule is comprised of only logic AND operations. It is known that any trajectory of a Boolean network will enter a periodic orbit. Periodic orbits of a CBN have been completely understood. In this paper, we investigate the orbit-controllability and state-controllability of a CBN: We ask the question of how one can steer a CBN to enter any periodic orbit or to reach any final state, from any initial state. We establish necessary and sufficient conditions for a CBN to be orbit-controllable and state-controllable. Furthermore, explicit control laws are presented along the analysis.

Title: Challenges to Keeping the Computer Industry Centered in the US, Abstract: It is undeniable that the worldwide computer industry's center is the US, specifically in Silicon Valley. Much of the reason for the success of Silicon Valley had to do with Moore's Law: the observation by Intel co-founder Gordon Moore that the number of transistors on a microchip doubled at a rate of approximately every two years. According to the International Technology Roadmap for Semiconductors, Moore's Law will end in 2021. How can we rethink computing technology to restart the historic explosive performance growth? Since 2012, the IEEE Rebooting Computing Initiative (IEEE RCI) has been working with industry and the US government to find new computing approaches to answer this question. In parallel, the CCC has held a number of workshops addressing similar questions. This whitepaper summarizes some of the IEEE RCI and CCC findings. The challenge for the US is to lead this new era of computing. Our international competitors are not sitting still: China has invested significantly in a variety of approaches such as neuromorphic computing, chip fabrication facilities, computer architecture, and high-performance simulation and data analytics computing, for example. We must act now, otherwise, the center of the computer industry will move from Silicon Valley and likely move off shore entirely.

Title: Contiguous Relations, Laplace's Methods and Continued Fractions for 3F2(1), Abstract: Using contiguous relations we construct an infinite number of continued fraction expansions for ratios of generalized hypergeometric series 3F2(1). We establish exact error term estimates for their approximants and prove their rapid convergences. To do so we develop a discrete version of Laplace's method for hypergeometric series in addition to the use of ordinary (continuous) Laplace's method for Euler's hypergeometric integrals.

Title: A note on the violation of Bell's inequality, Abstract: With Bell's inequalities one has a formal expression to show how essentially all local theories of natural phenomena that are formulated within the framework of realism may be tested using a simple experimental arrangement. For the case of entangled pairs of spin-1/2 particles we propose an alternative measurement setup which is consistent to the necessary assumptions corresponding to the derivation of the Bell inequalities. We find that the Bell inequalities are never violated with respect to our suggested measurement process.

Title: Scalable Realistic Recommendation Datasets through Fractal Expansions, Abstract: Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap we propose to generate more massive user/item interaction data sets by expanding pre-existing public data sets. User/item incidence matrices record interactions between users and items on a given platform as a large sparse matrix whose rows correspond to users and whose columns correspond to items. Our technique expands such matrices to larger numbers of rows (users), columns (items) and non zero values (interactions) while preserving key higher order statistical properties. We adapt the Kronecker Graph Theory to user/item incidence matrices and show that the corresponding fractal expansions preserve the fat-tailed distributions of user engagements, item popularity and singular value spectra of user/item interaction matrices. Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark Recommender Systems and the systems employed to train them. We provide algorithms to produce such expansions and apply them to the MovieLens 20 million data set comprising 20 million ratings of 27K movies by 138K users. The resulting expanded data set has 10 billion ratings, 2 million items and 864K users in its smaller version and can be scaled up or down. A larger version features 655 billion ratings, 7 million items and 17 million users.

Title: Accelerating Discrete Wavelet Transforms on GPUs, Abstract: The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques. Until now, several papers compared the performance of such transform on graphics processing units (GPUs). However, all of them only dealt with lifting and convolution computation schemes. In this paper, we show that corresponding horizontal and vertical lifting parts of the lifting scheme can be merged into non-separable lifting units, which halves the number of steps. We also discuss an optimization strategy leading to a reduction in the number of arithmetic operations. The schemes were assessed using the OpenCL and pixel shaders. The proposed non-separable lifting scheme outperforms the existing schemes in many cases, irrespective of its higher complexity.

Title: Quantifying the distribution of editorial power and manuscript decision bias at the mega-journal PLOS ONE, Abstract: We analyzed the longitudinal activity of nearly 7,000 editors at the mega-journal PLOS ONE over the 10-year period 2006-2015. Using the article-editor associations, we develop editor-specific measures of power, activity, article acceptance time, citation impact, and editorial renumeration (an analogue to self-citation). We observe remarkably high levels of power inequality among the PLOS ONE editors, with the top-10 editors responsible for 3,366 articles -- corresponding to 2.4% of the 141,986 articles we analyzed. Such high inequality levels suggest the presence of unintended incentives, which may reinforce unethical behavior in the form of decision-level biases at the editorial level. Our results indicate that editors may become apathetic in judging the quality of articles and susceptible to modes of power-driven misconduct. We used the longitudinal dimension of editor activity to develop two panel regression models which test and verify the presence of editor-level bias. In the first model we analyzed the citation impact of articles, and in the second model we modeled the decision time between an article being submitted and ultimately accepted by the editor. We focused on two variables that represent social factors that capture potential conflicts-of-interest: (i) we accounted for the social ties between editors and authors by developing a measure of repeat authorship among an editor's article set, and (ii) we accounted for the rate of citations directed towards the editor's own publications in the reference list of each article he/she oversaw. Our results indicate that these two factors play a significant role in the editorial decision process. Moreover, these two effects appear to increase with editor age, which is consistent with behavioral studies concerning the evolution of misbehavior and response to temptation in power-driven environments.

Title: Threshold Constraints with Guarantees for Parity Objectives in Markov Decision Processes, Abstract: The beyond worst-case synthesis problem was introduced recently by Bruyère et al. [BFRR14]: it aims at building system controllers that provide strict worst-case performance guarantees against an antagonistic environment while ensuring higher expected performance against a stochastic model of the environment. Our work extends the framework of [BFRR14] and follow-up papers, which focused on quantitative objectives, by addressing the case of $\omega$-regular conditions encoded as parity objectives, a natural way to represent functional requirements of systems. We build strategies that satisfy a main parity objective on all plays, while ensuring a secondary one with sufficient probability. This setting raises new challenges in comparison to quantitative objectives, as one cannot easily mix different strategies without endangering the functional properties of the system. We establish that, for all variants of this problem, deciding the existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity class as classical parity games. Hence, our framework provides additional modeling power while staying in the same complexity class. [BFRR14] Véronique Bruyère, Emmanuel Filiot, Mickael Randour, and Jean-François Raskin. Meet your expectations with guarantees: Beyond worst-case synthesis in quantitative games. In Ernst W. Mayr and Natacha Portier, editors, 31st International Symposium on Theoretical Aspects of Computer Science, STACS 2014, March 5-8, 2014, Lyon, France, volume 25 of LIPIcs, pages 199-213. Schloss Dagstuhl - Leibniz - Zentrum fuer Informatik, 2014.

Title: GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures, Abstract: VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, the data we model possesses continuous attributes that we would like to take into account at generation time. We propose in this paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational AutoEncoder architecture and its generalizations which allows a fine control on the embedding of the data into the latent space. When augmenting the VAE loss with this regularization, changes in the learned latent space reflects changes of the attributes of the data. This deeper understanding of the VAE latent space structure offers the possibility to modulate the attributes of the generated data in a continuous way. We demonstrate its efficiency on a monophonic music generation task where we manage to generate variations of discrete sequences in an intended and playful way.

Title: A New Torsion Pendulum for Gravitational Reference Sensor Technology Development, Abstract: We report on the design and sensitivity of a new torsion pendulum for measuring the performance of ultra-precise inertial sensors and for the development of associated technologies for space-based gravitational wave observatories and geodesy missions. The apparatus comprises a 1 m-long, 50 um-diameter, tungsten fiber that supports an inertial member inside a vacuum system. The inertial member is an aluminum crossbar with four hollow cubic test masses at each end. This structure converts the rotation of the torsion pendulum into translation of the test masses. Two test masses are enclosed in capacitive sensors which provide readout and actuation. These test masses are electrically insulated from the rest of the cross-bar and their electrical charge is controlled by photoemission using fiber-coupled ultraviolet light emitting diodes. The capacitive readout measures the test mass displacement with a broadband sensitivity of 30 nm / sqrt(Hz), and is complemented by a laser interferometer with a sensitivity of about 0.5 nm / sqrt(Hz). The performance of the pendulum, as determined by the measured residual torque noise and expressed in terms of equivalent force acting on a single test mass, is roughly 200 fN / sqrt(Hz) around 2 mHz, which is about a factor of 20 above the thermal noise limit of the fiber.

Title: Autonomous drone race: A computationally efficient vision-based navigation and control strategy, Abstract: Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping and visual inertial odometry, which are computationally expensive for micro aerial vehicles (MAVs), we developed the highly efficient snake gate detection algorithm for visual navigation, which can detect the gate at 20HZ on a Parrot Bebop drone. Then, with the gate detection result, we developed a robust pose estimation algorithm which has better tolerance to detection noise than a state-of-the-art perspective-n-point method. During the race, sometimes the gates are not in the drone's field of view. For this case, a state prediction-based feed-forward control strategy is developed to steer the drone to fly to the next gate. Experiments show that the drone can fly a half-circle with 1.5m radius within 2 seconds with only 30cm error at the end of the circle without any position feedback. Finally, the whole system is tested in a complex environment (a showroom in the faculty of Aerospace Engineering, TU Delft). The result shows that the drone can complete the track of 15 gates with a speed of 1.5m/s which is faster than the speeds exhibited at the 2016 and 2017 IROS autonomous drone races.

Title: Controlling motile disclinations in a thick nematogenic material with an electric field, Abstract: Manipulating topological disclination networks that arise in a symmetry-breaking phase transfor- mation in widely varied systems including anisotropic materials can potentially lead to the design of novel materials like conductive microwires, self-assembled resonators, and active anisotropic matter. However, progress in this direction is hindered by a lack of control of the kinetics and microstructure due to inherent complexity arising from competing energy and topology. We have studied thermal and electrokinetic effects on disclinations in a three-dimensional nonabsorbing nematic material with a positive and negative sign of the dielectric anisotropy. The electric flux lines are highly non-uniform in uniaxial media after an electric field below the Fréedericksz threshold is switched on, and the kinetics of the disclination lines is slowed down. In biaxial media, depending on the sign of the dielectric anisotropy, apart from the slowing down of the disclination kinetics, a non-uniform electric field filters out disclinations of different topology by inducing a kinetic asymmetry. These results enhance the current understanding of forced disclination networks and establish the pre- sented method, which we call fluctuating electronematics, as a potentially useful tool for designing materials with novel properties in silico.

Title: Exploiting network topology for large-scale inference of nonlinear reaction models, Abstract: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters, but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved "between-model" proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.

Title: Dynamics of higher-order rational solitons for the nonlocal nonlinear Schrodinger equation with the self-induced parity-time-symmetric potential, Abstract: The integrable nonlocal nonlinear Schrodinger (NNLS) equation with the self-induced parity-time-symmetric potential [Phys. Rev. Lett. 110 (2013) 064105] is investigated, which is an integrable extension of the standard NLS equation. Its novel higher-order rational solitons are found using the nonlocal version of the generalized perturbation (1, N-1)-fold Darboux transformation. These rational solitons illustrate abundant wave structures for the distinct choices of parameters (e.g., the strong and weak interactions of bright and dark rational solitons). Moreover, we also explore the dynamical behaviors of these higher-order rational solitons with some small noises on the basis of numerical simulations.

Title: Some preliminary results on the set of principal congruences of a finite lattice, Abstract: In the second edition of the congruence lattice book, Problem 22.1 asks for a characterization of subsets $Q$ of a finite distributive lattice $D$ such that there is a finite lattice $L$ whose congruence lattice is isomorphic to $D$ and under this isomorphism $Q$ corresponds the the principal congruences of $L$. In this note, we prove some preliminary results.

Title: Neural Probabilistic Model for Non-projective MST Parsing, Abstract: In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTM-CNNs which benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM and CNN. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees. We evaluate our model on 17 different datasets, across 14 different languages. By exploiting Kirchhoff's Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straight-forward end-to-end model training procedure via back-propagation. Our parser achieves state-of-the-art parsing performance on nine datasets.

Title: Fair mixing: the case of dichotomous preferences, Abstract: Agents vote to choose a fair mixture of public outcomes; each agent likes or dislikes each outcome. We discuss three outstanding voting rules. The Conditional Utilitarian rule, a variant of the random dictator, is Strategyproof and guarantees to any group of like-minded agents an influence proportional to its size. It is easier to compute and more efficient than the familiar Random Priority rule. Its worst case (resp. average) inefficiency is provably (resp. in numerical experiments) low if the number of agents is low. The efficient Egalitarian rule protects similarly individual agents but not coalitions. It is Excludable Strategyproof: I do not want to lie if I cannot consume outcomes I claim to dislike. The efficient Nash Max Product rule offers the strongest welfare guarantees to coalitions, who can force any outcome with a probability proportional to their size. But it fails even the excludable form of Strategyproofness.

Title: Absorption and Emission Probabilities of Electrons in Electric and Magnetic Fields for FEL, Abstract: We consider induced emission of ultrarelativistic electrons in strong electric (magnetic) fields that are uniform along the direction of the electron motion and are not uniform in the transverse direction. The stimulated absorption and emission probabilities are found in such system.

Title: Conditional Time Series Forecasting with Convolutional Neural Networks, Abstract: We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. We test and analyze the performance of the convolutional network both unconditionally as well as conditionally for financial time series forecasting using the S&P500, the volatility index, the CBOE interest rate and several exchange rates and extensively compare it to the performance of the well-known autoregressive model and a long-short term memory network. We show that a convolutional network is well-suited for regression-type problems and is able to effectively learn dependencies in and between the series without the need for long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

Title: Community structure detection and evaluation during the pre- and post-ictal hippocampal depth recordings, Abstract: Detecting and evaluating regions of brain under various circumstances is one of the most interesting topics in computational neuroscience. However, the majority of the studies on detecting communities of a functional connectivity network of the brain is done on networks obtained from coherency attributes, and not from correlation. This lack of studies, in part, is due to the fact that many common methods for clustering graphs require the nodes of the network to be `positively' linked together, a property that is guaranteed by a coherency matrix, by definition. However, correlation matrices reveal more information regarding how each pair of nodes are linked together. In this study, for the first time we simultaneously examine four inherently different network clustering methods (spectral, heuristic, and optimization methods) applied to the functional connectivity networks of the CA1 region of the hippocampus of an anaesthetized rat during pre-ictal and post-ictal states. The networks are obtained from correlation matrices, and its results are compared with the ones obtained by applying the same methods to coherency matrices. The correlation matrices show a much finer community structure compared to the coherency matrices. Furthermore, we examine the potential smoothing effect of choosing various window sizes for computing the correlation/coherency matrices.

Title: Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications, Abstract: The global sensitivity analysis of a numerical model aims to quantify, by means of sensitivity indices estimate, the contributions of each uncertain input variable to the model output uncertainty. The so-called Sobol' indices, which are based on the functional variance analysis, present a difficult interpretation in the presence of statistical dependence between inputs. The Shapley effect was recently introduced to overcome this problem as they allocate the mutual contribution (due to correlation and interaction) of a group of inputs to each individual input within the group.In this paper, using several new analytical results, we study the effects of linear correlation between some Gaussian input variables on Shapley effects, and compare these effects to classical first-order and total Sobol' indices.This illustrates the interest, in terms of sensitivity analysis setting and interpretation, of the Shapley effects in the case of dependent inputs. We also investigate the numerical convergence of the estimated Shapley effects. For the practical issue of computationally demanding computer models, we show that the substitution of the original model by a metamodel (here, kriging) makes it possible to estimate these indices with precision at a reasonable computational cost.

Title: Ridesourcing Car Detection by Transfer Learning, Abstract: Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesourcing cars from a pool of cars based on their trajectories. Since licensed ridesourcing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i.e, taxis and buses, to ridesourcing detection among ordinary vehicles. We propose a two-stage transfer learning framework. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesourcing detection, and iteratively refine RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus traces show that our transfer learning framework, with no need of a pre-labeled ridesourcing dataset, can achieve similar accuracy as the supervised learning methods.

Title: Collapsing hyperkähler manifolds, Abstract: Given a projective hyperkahler manifold with a holomorphic Lagrangian fibration, we prove that hyperkahler metrics with volume of the torus fibers shrinking to zero collapse in the Gromov-Hausdorff sense (and smoothly away from the singular fibers) to a compact metric space which is a half-dimensional special Kahler manifold outside a singular set of real Hausdorff codimension 2 and is homeomorphic to the base projective space.

Title: Hidden multiparticle excitation in weakly interacting Bose-Einstein Condensate, Abstract: We investigate multiparticle excitation effect on a collective density excitation as well as a single-particle excitation in a weakly interacting Bose--Einstein condensate (BEC). We find that although the weakly interacting BEC offers weak multiparticle excitation spectrum at low temperatures, this multiparticle excitation effect may not remain hidden, but emerges as bimodality in the density response function through the single-particle excitation. Identification of spectra in the BEC between the single-particle excitation and the density excitation is also assessed at nonzero temperatures, which has been known to be unique nature in the BEC at absolute zero temperature.

Title: Learning to Acquire Information, Abstract: We consider the problem of diagnosis where a set of simple observations are used to infer a potentially complex hidden hypothesis. Finding the optimal subset of observations is intractable in general, thus we focus on the problem of active diagnosis, where the agent selects the next most-informative observation based on the results of previous observations. We show that under the assumption of uniform observation entropy, one can build an implication model which directly predicts the outcome of the potential next observation conditioned on the results of past observations, and selects the observation with the maximum entropy. This approach enjoys reduced computation complexity by bypassing the complicated hypothesis space, and can be trained on observation data alone, learning how to query without knowledge of the hidden hypothesis.

Title: How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV, Abstract: This work explores the feasibility of steering a drone with a (recurrent) neural network, based on input from a forward looking camera, in the context of a high-level navigation task. We set up a generic framework for training a network to perform navigation tasks based on imitation learning. It can be applied to both aerial and land vehicles. As a proof of concept we apply it to a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a room containing a number of obstacles. So far only feedforward neural networks (FNNs) have been used to train UAV control. To cope with more complex tasks, we propose the use of recurrent neural networks (RNN) instead and successfully train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision based control is a sequential prediction problem, known for its highly correlated input data. The correlation makes training a network hard, especially an RNN. To overcome this issue, we investigate an alternative sampling method during training, namely window-wise truncated backpropagation through time (WW-TBPTT). Further, end-to-end training requires a lot of data which often is not available. Therefore, we compare the performance of retraining only the Fully Connected (FC) and LSTM control layers with networks which are trained end-to-end. Performing the relatively simple task of crossing a room already reveals important guidelines and good practices for training neural control networks. Different visualizations help to explain the behavior learned.

Title: Generalization for Adaptively-chosen Estimators via Stable Median, Abstract: Datasets are often reused to perform multiple statistical analyses in an adaptive way, in which each analysis may depend on the outcomes of previous analyses on the same dataset. Standard statistical guarantees do not account for these dependencies and little is known about how to provably avoid overfitting and false discovery in the adaptive setting. We consider a natural formalization of this problem in which the goal is to design an algorithm that, given a limited number of i.i.d.~samples from an unknown distribution, can answer adaptively-chosen queries about that distribution. We present an algorithm that estimates the expectations of $k$ arbitrary adaptively-chosen real-valued estimators using a number of samples that scales as $\sqrt{k}$. The answers given by our algorithm are essentially as accurate as if fresh samples were used to evaluate each estimator. In contrast, prior work yields error guarantees that scale with the worst-case sensitivity of each estimator. We also give a version of our algorithm that can be used to verify answers to such queries where the sample complexity depends logarithmically on the number of queries $k$ (as in the reusable holdout technique). Our algorithm is based on a simple approximate median algorithm that satisfies the strong stability guarantees of differential privacy. Our techniques provide a new approach for analyzing the generalization guarantees of differentially private algorithms.

Title: Attribution of extreme rainfall in Southeast China during May 2015, Abstract: Anthropogenic climate change increased the probability that a short-duration, intense rainfall event would occur in parts of southeast China. This type of event occurred in May 2015, causing serious flooding.

Title: Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks, Abstract: A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator's experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.

Title: Estimation of Relationship between Stimulation Current and Force Exerted during Isometric Contraction, Abstract: In this study, we developed a method to estimate the relationship between stimulation current and volatility during isometric contraction. In functional electrical stimulation (FES), joints are driven by applying voltage to muscles. This technology has been used for a long time in the field of rehabilitation, and recently application oriented research has been reported. However, estimation of the relationship between stimulus value and exercise capacity has not been discussed to a great extent. Therefore, in this study, a human muscle model was estimated using the transfer function estimation method with fast Fourier transform. It was found that the relationship between stimulation current and force exerted could be expressed by a first-order lag system. In verification of the force estimate, the ability of the proposed model to estimate the exerted force under steady state response was found to be good.

Title: One-step Estimation of Networked Population Size with Anonymity Using Respondent-Driven Capture-Recapture and Hashing, Abstract: Estimates of population size for hidden and hard-to-reach individuals are of particular interest to health officials when health problems are concentrated in such populations. Efforts to derive these estimates are often frustrated by a range of factors including social stigma or an association with illegal activities that ordinarily preclude conventional survey strategies. This paper builds on and extends prior work that proposed a method to meet these challenges. Here we describe a rigorous formalization of a one-step, network-based population estimation procedure that can be employed under conditions of anonymity. The estimation procedure is designed to be implemented alongside currently accepted strategies for research with hidden populations. Simulation experiments are described that test the efficacy of the method across a range of implementation conditions and hidden population sizes. The results of these experiments show that reliable population estimates can be derived for hidden, networked population as large as 12,500 and perhaps larger for one family of random graphs. As such, the method shows potential for cost-effective implementation health and disease surveillance officials concerned with hidden populations. Limitations and future work are discussed in the concluding section.

Title: Real single ion solvation free energies with quantum mechanical simulation, Abstract: Single ion solvation free energies are one of the most important properties of electrolyte solutions and yet there is ongoing debate about what these values are. Only the values for neutral ion pairs are known. Here, we use DFT interaction potentials with molecular dynamics simulation (DFT-MD) combined with a modified version of the quasi-chemical theory (QCT) to calculate these energies for the lithium and fluoride ions. A method to correct for the error in the DFT functional is developed and very good agreement with the experimental value for the lithium fluoride pair is obtained. Moreover, this method partitions the energies into physically intuitive terms such as surface potential, cavity and charging energies which are amenable to descriptions with reduced models. Our research suggests that lithium's solvation free energy is dominated by the free energetics of a charged hard sphere, whereas fluoride exhibits significant quantum mechanical behavior that cannot be simply described with a reduced model.

Title: Crowdsourcing with Sparsely Interacting Workers, Abstract: We consider estimation of worker skills from worker-task interaction data (with unknown labels) for the single-coin crowd-sourcing binary classification model in symmetric noise. We define the (worker) interaction graph whose nodes are workers and an edge between two nodes indicates whether or not the two workers participated in a common task. We show that skills are asymptotically identifiable if and only if an appropriate limiting version of the interaction graph is irreducible and has odd-cycles. We then formulate a weighted rank-one optimization problem to estimate skills based on observations on an irreducible, aperiodic interaction graph. We propose a gradient descent scheme and show that for such interaction graphs estimates converge asymptotically to the global minimum. We characterize noise robustness of the gradient scheme in terms of spectral properties of signless Laplacians of the interaction graph. We then demonstrate that a plug-in estimator based on the estimated skills achieves state-of-art performance on a number of real-world datasets. Our results have implications for rank-one matrix completion problem in that gradient descent can provably recover $W \times W$ rank-one matrices based on $W+1$ off-diagonal observations of a connected graph with a single odd-cycle.

Title: Training deep learning based denoisers without ground truth data, Abstract: Recent deep learning based denoisers often outperform state-of-the-art conventional denoisers such as BM3D. They are typically trained to minimize the mean squared error (MSE) between the output of a deep neural network and the ground truth image. In deep learning based denoisers, it is important to use high quality noiseless ground truth for high performance, but it is often challenging or even infeasible to obtain such a clean image in application areas such as hyperspectral remote sensing and medical imaging. We propose a Stein's Unbiased Risk Estimator (SURE) based method for training deep neural network denoisers only with noisy images. We demonstrated that our SURE based method without ground truth was able to train deep neural network denoisers to yield performance close to deep learning denoisers trained with ground truth and to outperform state-of-the-art BM3D. Further improvements were achieved by including noisy test images for training denoiser networks using our proposed SURE based method.

Title: Language Design and Renormalization, Abstract: Here we consider some well-known facts in syntax from a physics perspective, which allows us to establish some remarkable equivalences. Specifically, we observe that the operation MERGE put forward by N. Chomsky in 1995 can be interpreted as a physical information coarse-graining. Thus, MERGE in linguistics entails information renormalization in physics, according to different time scales. We make this point mathematically formal in terms of language models, i.e., probability distributions over word sequences, widely used in natural language processing as well as other ambits. In this setting, MERGE corresponds to a 3-index probability tensor implementing a coarse-graining, akin to a probabilistic context-free grammar. The probability vectors of meaningful sentences are naturally given by stochastic tensor networks (TN) that are mostly loop-free, such as Tree Tensor Networks and Matrix Product States. These structures have short-ranged correlations in the syntactic distance by construction and, because of the peculiarities of human language, they are extremely efficient to manipulate computationally. We also propose how to obtain such language models from probability distributions of certain TN quantum states, which we show to be efficiently preparable by a quantum computer. Moreover, using tools from entanglement theory, we use these quantum states to prove classical lower bounds on the perplexity of the probability distribution for a set of words in a sentence. Implications of these results are discussed in the ambits of theoretical and computational linguistics, artificial intelligence, programming languages, RNA and protein sequencing, quantum many-body systems, and beyond. Our work shows how many of the key linguistic ideas from the last century, including developments in computational linguistics, fit perfectly with known physical concepts linked to renormalization.

Title: On the geometry of the moduli space of sheaves supported on curves of genus two in a quadric surface, Abstract: We study the moduli space of stable sheaves of Euler characteristic 2, supported on curves of arithmetic genus 2 contained in a smooth quadric surface. We show that this moduli space is rational. We compute its Betti numbers and we give a classification of the stable sheaves involving locally free resolutions.

Title: Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions, Abstract: Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging problem because of the ambiguity and complexity of two-dimensional handwriting. Moreover, the lack of large training data is a serious issue, especially for academic recognition systems. In this paper, we propose pattern generation strategies that generate shape and structural variations to improve the performance of recognition systems based on a small training set. For data generation, we employ the public databases: CROHME 2014 and 2016 of online HMEs. The first strategy employs local and global distortions to generate shape variations. The second strategy decomposes an online HME into sub-online HMEs to get more structural variations. The hybrid strategy combines both these strategies to maximize shape and structural variations. The generated online HMEs are converted to images for offline HME recognition. We tested our strategies in an end-to-end recognition system constructed from a recent deep learning model: Convolutional Neural Network and attention-based encoder-decoder. The results of experiments on the CROHME 2014 and 2016 databases demonstrate the superiority and effectiveness of our strategies: our hybrid strategy achieved classification rates of 48.78% and 45.60%, respectively, on these databases. These results are competitive compared to others reported in recent literature. Our generated datasets are openly available for research community and constitute a useful resource for the HME recognition research in future.

Title: Collective spin excitations of helices and magnetic skyrmions: review and perspectives of magnonics in non-centrosymmetric magnets, Abstract: Magnetic materials hosting correlated electrons play an important role for information technology and signal processing. The currently used ferro-, ferri- and antiferromagnetic materials provide microscopic moments (spins) that are mainly collinear. Recently more complex spin structures such as spin helices and cycloids have regained a lot of interest. The interest has been initiated by the discovery of the skyrmion lattice phase in non-centrosymmetric helical magnets. In this review we address how spin helices and skyrmion lattices enrich the microwave characteristics of magnetic materials. When discussing perspectives for microwave electronics and magnonics we focus particularly on insulating materials as they avoid eddy current losses, offer low spin-wave damping, and might allow for electric field control of collective spin excitations. Thereby, they further fuel the vision of magnonics operated at low energy consumption.

Title: Regret Bounds for Reinforcement Learning via Markov Chain Concentration, Abstract: We give a simple optimistic algorithm for which it is easy to derive regret bounds of $\tilde{O}(\sqrt{t_{\rm mix} SAT})$ after $T$ steps in uniformly ergodic Markov decision processes with $S$ states, $A$ actions, and mixing time parameter $t_{\rm mix}$. These bounds are the first regret bounds in the general, non-episodic setting with an optimal dependence on all given parameters. They could only be improved by using an alternative mixing time parameter.

Title: The affine approach to homogeneous geodesics in homogeneous Finsler spaces, Abstract: In a recent paper, it was claimed that any homogeneous Finsler space of odd dimension admits a homogeneous geodesic through any point. For the proof, the algebraic method dealing with the reductive decomposition of the Lie algebra of the isometry group was used. However, the proof contains a serious gap. In the present paper, homogeneous geodesics in Finsler homogeneous spaces are studied using the affine method, which was developed in earlier papers by the author. The mentioned statement is proved correctly and it is further proved that any homogeneous Berwald space or homogeneous reversible Finsler space admits a homogeneous geodesic through any point.

Title: Fate of the spin-\frac{1}{2} Kondo effect in the presence of temperature gradients, Abstract: We consider a strongly interacting quantum dot connected to two leads held at quite different temperatures. Our aim is to study the behavior of the Kondo effect in the presence of large thermal biases. We use three different approaches, namely, a perturbation formalism based on the Kondo Hamiltonian, a slave-boson mean-field theory for the Anderson model at large charging energies and a truncated equation-of-motion approach beyond the Hartree-Fock approximation. The two former formalisms yield a suppression of the Kondo peak for thermal gradients above the Kondo temperature, showing a remarkably good agreement despite their different ranges of validity. The third technique allows us to analyze the full density of states within a wide range of energies. Additionally, we have investigated the quantum transport properties (electric current and thermocurrent) beyond linear response. In the voltage-driven case, we reproduce the split differential conductance due to the presence of different electrochemical potentials. In the temperature-driven case, we observe a strongly nonlinear thermocurrent as a function of the applied thermal gradient. Depending on the parameters, we can find nontrivial zeros in the electric current for finite values of the temperature bias. Importantly, these thermocurrent zeros yield direct access to the system's characteristic energy scales (Kondo temperature and charging energy).

Title: Geometrical dependence of domain wall propagation and nucleation fields in magnetic domain wall sensor devices, Abstract: We study the key domain wall properties in segmented nanowires loop-based structures used in domain wall based sensors. The two reasons for device failure, namely the distribution of domain wall propagation field (depinning) and the nucleation field are determined with Magneto-Optical Kerr Effect (MOKE) and Giant Magnetoresistance (GMR) measurements for thousands of elements to obtain significant statistics. Single layers of Ni$_{81}$Fe$_{19}$, a complete GMR stack with Co$_{90}$Fe$_{10}$/Ni$_{81}$Fe$_{19}$ as a free layer and a single layer of Co$_{90}$Fe$_{10}$ are deposited and industrially patterned to determine the influence of the shape anisotropy, the magnetocrystalline anisotropy and the fabrication processes. We show that the propagation field is little influenced by the geometry but significantly by material parameters. The domain wall nucleation fields can be described by a typical Stoner-Wohlfarth model related to the measured geometrical parameters of the wires and fitted by considering the process parameters. The GMR effect is subsequently measured in a substantial number of devices (3000), in order to accurately gauge the variation between devices. This reveals a corrected upper limit to the nucleation fields of the sensors that can be exploited for fast characterization of working elements.

Title: Faster Rates for Policy Learning, Abstract: This article improves the existing proven rates of regret decay in optimal policy estimation. We give a margin-free result showing that the regret decay for estimating a within-class optimal policy is second-order for empirical risk minimizers over Donsker classes, with regret decaying at a faster rate than the standard error of an efficient estimator of the value of an optimal policy. We also give a result from the classification literature that shows that faster regret decay is possible via plug-in estimation provided a margin condition holds. Four examples are considered. In these examples, the regret is expressed in terms of either the mean value or the median value; the number of possible actions is either two or finitely many; and the sampling scheme is either independent and identically distributed or sequential, where the latter represents a contextual bandit sampling scheme.

Title: Learning Deep Networks from Noisy Labels with Dropout Regularization, Abstract: Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is overdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.

Title: On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs, Abstract: Modern networks are of huge sizes as well as high dynamics, which challenges the efficiency of community detection algorithms. In this paper, we study the problem of overlapping community detection on distributed and dynamic graphs. Given a distributed, undirected and unweighted graph, the goal is to detect overlapping communities incrementally as the graph is dynamically changing. We propose an efficient algorithm, called \textit{randomized Speaker-Listener Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of label propagation. Besides detecting high-quality communities, rSLPA can incrementally update the detected communities after a batch of edge insertion and deletion operations. To the best of our knowledge, rSLPA is the first algorithm that can incrementally capture the same communities as those obtained by applying the detection algorithm from the scratch on the updated graph. Extensive experiments are conducted on both synthetic and real-world datasets, and the results show that our algorithm can achieve high accuracy and efficiency at the same time.

Title: Spatially distributed multipartite entanglement enables Einstein-Podolsky-Rosen steering of atomic clouds, Abstract: A key resource for distributed quantum-enhanced protocols is entanglement between spatially separated modes. Yet, the robust generation and detection of nonlocal entanglement between spatially separated regions of an ultracold atomic system remains a challenge. Here, we use spin mixing in a tightly confined Bose-Einstein condensate to generate an entangled state of indistinguishable particles in a single spatial mode. We show experimentally that this local entanglement can be spatially distributed by self-similar expansion of the atomic cloud. Spatially resolved spin read-out is used to reveal a particularly strong form of quantum correlations known as Einstein-Podolsky-Rosen steering between distinct parts of the expanded cloud. Based on the strength of Einstein-Podolsky-Rosen steering we construct a witness, which testifies up to genuine five-partite entanglement.

Title: A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer, Abstract: Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accurate survival model from electronic health records is challenging. With this motivation, we address this issue and provide a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we introduce a simple effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African-Americans and white patients. The results indicate that our approach outperforms significantly than baseline models.

Title: Free quantitative fourth moment theorems on Wigner space, Abstract: We prove a quantitative Fourth Moment Theorem for Wigner integrals of any order with symmetric kernels, generalizing an earlier result from Kemp et al. (2012). The proof relies on free stochastic analysis and uses a new biproduct formula for bi-integrals. A consequence of our main result is a Nualart-Ortiz-Latorre type characterization of convergence in law to the semicircular distribution for Wigner integrals. As an application, we provide Berry-Esseen type bounds in the context of the free Breuer-Major theorem for the free fractional Brownian motion.

Title: Towards understanding startup product development as effectual entrepreneurial behaviors, Abstract: Software startups face with multiple technical and business challenges, which could make the startup journey longer, or even become a failure. Little is known about entrepreneurial decision making as a direct force to startup development outcome. In this study, we attempted to apply a behaviour theory of entrepreneurial firms to understand the root-cause of some software startup s challenges. Six common challenges related to prototyping and product development in twenty software startups were identified. We found the behaviour theory as a useful theoretical lens to explain the technical challenges. Software startups search for local optimal solutions, emphasise on short-run feedback rather than long-run strategies, which results in vague prototype planning, paradox of demonstration and evolving throw-away prototypes. The finding implies that effectual entrepreneurial processes might require a more suitable product development approach than the current state-of-practice.

Title: Generalized Dirac structure beyond the linear regime in graphene, Abstract: We show that a generalized Dirac structure survives beyond the linear regime of the low-energy dispersion relations of graphene. A generalized uncertainty principle of the kind compatible with specific quantum gravity scenarios with a fundamental minimal length (here graphene lattice spacing) and Lorentz violation (here the particle/hole asymmetry, the trigonal warping, etc.) is naturally obtained. We then show that the corresponding emergent field theory is a table-top realization of such scenarios, by explicitly computing the third order Hamiltonian, and giving the general recipe for any order. Remarkably, our results imply that going beyond the low-energy approximation does not spoil the well-known correspondence with analogue massless quantum electrodynamics phenomena (as usually believed), but rather it is a way to obtain experimental signatures of quantum-gravity-like corrections to such phenomena.

Title: Generative Mixture of Networks, Abstract: A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks together and update the clusters of the data. We call this model Mixture of Networks. The provided model is a platform that can be used for any deep structure and be trained by any conventional objective function for distribution modeling. As the components of the model are neural networks, it has high capability in characterizing complicated data distributions as well as clustering data. We apply the algorithm on MNIST hand-written digits and Yale face datasets. We also demonstrate the clustering ability of the model using some real-world and toy examples.

Title: On the quantum differentiation of smooth real-valued functions, Abstract: Calculating the value of $C^{k\in\{1,\infty\}}$ class of smoothness real-valued function's derivative in point of $\mathbb{R}^+$ in radius of convergence of its Taylor polynomial (or series), applying an analog of Newton's binomial theorem and $q$-difference operator. $(P,q)$-power difference introduced in section 5. Additionally, by means of Newton's interpolation formula, the discrete analog of Taylor series, interpolation using $q$-difference and $p,q$-power difference is shown.

Title: The Peridynamic Stress Tensors and the Non-local to Local Passage, Abstract: We re-examine the notion of stress in peridynamics. Based on the idea of traction we define two new peridynamic stress tensors $\mathbf{P}^{\mathbf{y}}$ and $\mathbf{P}$ which stand, respectively, for analogues of the Cauchy and 1st Piola-Kirchhoff stress tensors from classical elasticity. We show that the tensor $\mathbf{P}$ differs from the earlier defined peridynamic stress tensor $\nu$; though their divergence is equal. We address the question of symmetry of the tensor $\mathbf{P}^{\mathbf{y}}$ which proves to be symmetric in case of bond-based peridynamics; as opposed to the inverse Piola transform of $\nu$ (corresponding to the analogue of Cauchy stress tensor) which fails to be symmetric in general. We also derive a general formula of the force-flux in peridynamics and compute the limit of $\mathbf{P}$ for vanishing non-locality, denoted by $\mathbf{P}_0$. We show that this tensor $\mathbf{P}_0$ surprisingly coincides with the collapsed tensor $\nu_0$, a limit of the original tensor $\nu$. At the end, using this flux-formula, we suggest an explanation why the collapsed tensor $\mathbf{P}_0$ (and hence $\nu_0$) can be indeed identified with the 1st Piola-Kirchhoff stress tensor.

Title: Learning Efficient Image Representation for Person Re-Identification, Abstract: Color names based image representation is successfully used in person re-identification, due to the advantages of being compact, intuitively understandable as well as being robust to photometric variance. However, there exists the diversity between underlying distribution of color names' RGB values and that of image pixels' RGB values, which may lead to inaccuracy when directly comparing them in Euclidean space. In this paper, we propose a new method named soft Gaussian mapping (SGM) to address this problem. We model the discrepancies between color names and pixels using a Gaussian and utilize the inverse of covariance matrix to bridge the gap between them. Based on SGM, an image could be converted to several soft Gaussian maps. In each soft Gaussian map, we further seek to establish stable and robust descriptors within a local region through a max pooling operation. Then, a robust image representation based on color names is obtained by concatenating the statistical descriptors in each stripe. When labeled data are available, one discriminative subspace projection matrix is learned to build efficient representations of an image via cross-view coupling learning. Experiments on the public datasets - VIPeR, PRID450S and CUHK03, demonstrate the effectiveness of our method.

Title: Helium-like and Lithium-like ions: Ground state energy, Abstract: It is shown that the non-relativistic ground state energy of helium-like and lithium-like ions with static nuclei can be interpolated in full physics range of nuclear charges $Z$ with accuracy of not less than 6 decimal digits (d.d.) or 7-8 significant digits (s.d.) using a meromorphic function in appropriate variable with a few free parameters. It is demonstrated that finite nuclear mass effects do not change 4-5 s.d. for $Z \in [1,50]$ for 2-,3-electron systems and the leading relativistic and QED corrections leave unchanged 3-4 s.d. for $Z \in [1,12]$ in the ground state energy for 2-electron system, thus, the interpolation reproduces definitely those figures. A meaning of proposed interpolation is in a construction of unified, {\it two-point} Pade approximant (for both small and large $Z$ expansions) with fitting some parameters at intermediate $Z$.

Title: Koszul A-infinity algebras and free loop space homology, Abstract: We introduce a notion of Koszul A-infinity algebra that generalizes Priddy's notion of a Koszul algebra and we use it to construct small A-infinity algebra models for Hochschild cochains. As an application, this yields new techniques for computing free loop space homology algebras of manifolds that are either formal or coformal (over a field or over the integers). We illustrate these techniques in two examples.

Title: Learning RBM with a DC programming Approach, Abstract: By exploiting the property that the RBM log-likelihood function is the difference of convex functions, we formulate a stochastic variant of the difference of convex functions (DC) programming to minimize the negative log-likelihood. Interestingly, the traditional contrastive divergence algorithm is a special case of the above formulation and the hyperparameters of the two algorithms can be chosen such that the amount of computation per mini-batch is identical. We show that for a given computational budget the proposed algorithm almost always reaches a higher log-likelihood more rapidly, compared to the standard contrastive divergence algorithm. Further, we modify this algorithm to use the centered gradients and show that it is more efficient and effective compared to the standard centered gradient algorithm on benchmark datasets.

Title: Sparse Inverse Covariance Estimation for Chordal Structures, Abstract: In this paper, we consider the Graphical Lasso (GL), a popular optimization problem for learning the sparse representations of high-dimensional datasets, which is well-known to be computationally expensive for large-scale problems. Recently, we have shown that the sparsity pattern of the optimal solution of GL is equivalent to the one obtained from simply thresholding the sample covariance matrix, for sparse graphs under different conditions. We have also derived a closed-form solution that is optimal when the thresholded sample covariance matrix has an acyclic structure. As a major generalization of the previous result, in this paper we derive a closed-form solution for the GL for graphs with chordal structures. We show that the GL and thresholding equivalence conditions can significantly be simplified and are expected to hold for high-dimensional problems if the thresholded sample covariance matrix has a chordal structure. We then show that the GL and thresholding equivalence is enough to reduce the GL to a maximum determinant matrix completion problem and drive a recursive closed-form solution for the GL when the thresholded sample covariance matrix has a chordal structure. For large-scale problems with up to 450 million variables, the proposed method can solve the GL problem in less than 2 minutes, while the state-of-the-art methods converge in more than 2 hours.

Title: Orthogonal free quantum group factors are strongly 1-bounded, Abstract: We prove that the orthogonal free quantum group factors $\mathcal{L}(\mathbb{F}O_N)$ are strongly $1$-bounded in the sense of Jung. In particular, they are not isomorphic to free group factors. This result is obtained by establishing a spectral regularity result for the edge reversing operator on the quantum Cayley tree associated to $\mathbb{F}O_N$, and combining this result with a recent free entropy dimension rank theorem of Jung and Shlyakhtenko.

Title: Enhanced spin ordering temperature in ultrathin FeTe films grown on a topological insulator, Abstract: We studied the temperature dependence of the diagonal double-stripe spin order in one and two unit cell thick layers of FeTe grown on the topological insulator Bi_2Te_3 via spin-polarized scanning tunneling microscopy. The spin order persists up to temperatures which are higher than the transition temperature reported for bulk Fe_1+yTe with lowest possible excess Fe content y. The enhanced spin order stability is assigned to a strongly decreased y with respect to the lowest values achievable in bulk crystal growth, and effects due to the interface between the FeTe and the topological insulator. The result is relevant for understanding the recent observation of a coexistence of superconducting correlations and spin order in this system.

Title: A SAT+CAS Approach to Finding Good Matrices: New Examples and Counterexamples, Abstract: We enumerate all circulant good matrices with odd orders divisible by 3 up to order 70. As a consequence of this we find a previously overlooked set of good matrices of order 27 and a new set of good matrices of order 57. We also find that circulant good matrices do not exist in the orders 51, 63, and 69, thereby finding three new counterexamples to the conjecture that such matrices exist in all odd orders. Additionally, we prove a new relationship between the entries of good matrices and exploit this relationship in our enumeration algorithm. Our method applies the SAT+CAS paradigm of combining computer algebra functionality with modern SAT solvers to efficiently search large spaces which are specified by both algebraic and logical constraints.

Title: Involutive bordered Floer homology, Abstract: We give a bordered extension of involutive HF-hat and use it to give an algorithm to compute involutive HF-hat for general 3-manifolds. We also explain how the mapping class group action on HF-hat can be computed using bordered Floer homology. As applications, we prove that involutive HF-hat satisfies a surgery exact triangle and compute HFI-hat of the branched double covers of all 10-crossing knots.

Title: HTEM data improve 3D modelling of aquifers in Paris Basin, France, Abstract: In Paris Basin, we evaluate how HTEM data complement the usual borehole, geological and deep seismic data used for modelling aquifer geometries. With these traditional data, depths between ca. 50 to 300m are often relatively ill-constrained, as most boreholes lie within the first tens of meters of the underground and petroleum seismic is blind shallower than ca. 300m. We have fully reprocessed and re-inverted 540km of flight lines of a SkyTEM survey of 2009, acquired on a 40x12km zone with 400m line spacing. The resistivity model is first "calibrated" with respect to ca. 50 boreholes available on the study area. Overall, the correlation between EM resistivity models and the hydrogeological horizons clearly shows that the geological units in which the aquifers are developed almost systematically correspond to relative increase of resistivity, whatever the "background" resistivity environment and the lithology of the aquifer. In 3D Geomodeller software, this allows interpreting 11 aquifer/aquitar layers along the flight lines and then jointly interpolating them in 3D along with the borehole data. The resulting model displays 3D aquifer geometries consistent with the SIGES "reference" regional hydrogeological model and improves it in between the boreholes and on the 50-300m depth range.

Title: Social Network based Short-Term Stock Trading System, Abstract: This paper proposes a novel adaptive algorithm for the automated short-term trading of financial instrument. The algorithm adopts a semantic sentiment analysis technique to inspect the Twitter posts and to use them to predict the behaviour of the stock market. Indeed, the algorithm is specifically developed to take advantage of both the sentiment and the past values of a certain financial instrument in order to choose the best investment decision. This allows the algorithm to ensure the maximization of the obtainable profits by trading on the stock market. We have conducted an investment simulation and compared the performance of our proposed with a well-known benchmark (DJTATO index) and the optimal results, in which an investor knows in advance the future price of a product. The result shows that our approach outperforms the benchmark and achieves the performance score close to the optimal result.

Title: Towards an Understanding of the Effects of Augmented Reality Games on Disaster Management, Abstract: Location-based augmented reality games have entered the mainstream with the nearly overnight success of Niantic's Pokémon Go. Unlike traditional video games, the fact that players of such games carry out actions in the external, physical world to accomplish in-game objectives means that the large-scale adoption of such games motivate people, en masse, to do things and go places they would not have otherwise done in unprecedented ways. The social implications of such mass-mobilisation of individual players are, in general, difficult to anticipate or characterise, even for the short-term. In this work, we focus on disaster relief, and the short- and long-term implications that a proliferation of AR games like Pokémon Go, may have in disaster-prone regions of the world. We take a distributed cognition approach and focus on one natural disaster-prone region of New Zealand, the city of Wellington.

Title: Identification of Conduit Countries and Community Structures in the Withholding Tax Networks, Abstract: Due to economic globalization, each country's economic law, including tax laws and tax treaties, has been forced to work as a single network. However, each jurisdiction (country or region) has not made its economic law under the assumption that its law functions as an element of one network, so it has brought unexpected results. We thought that the results are exactly international tax avoidance. To contribute to the solution of international tax avoidance, we tried to investigate which part of the network is vulnerable. Specifically, focusing on treaty shopping, which is one of international tax avoidance methods, we attempt to identified which jurisdiction are likely to be used for treaty shopping from tax liabilities and the relationship between jurisdictions which are likely to be used for treaty shopping and others. For that purpose, based on withholding tax rates imposed on dividends, interest, and royalties by jurisdictions, we produced weighted multiple directed graphs, computed the centralities and detected the communities. As a result, we clarified the jurisdictions that are likely to be used for treaty shopping and pointed out that there are community structures. The results of this study suggested that fewer jurisdictions need to introduce more regulations for prevention of treaty abuse worldwide.

Title: Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks, Abstract: We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure. In our model, a bipartite graph captures the relationship between actions and a common set of unknowns such that choosing an action reveals observations for the unknowns that it is connected to. This models a common scenario in online social networks where users respond to their friends' activity, thus providing side information about each other's preferences. Our contributions are as follows: 1) We derive an asymptotic lower bound (with respect to time) as a function of the bi-partite network structure on the regret of any uniformly good policy that achieves the maximum long-term average reward. 2) We propose two policies - a randomized policy; and a policy based on the well-known upper confidence bound (UCB) policies - both of which explore each action at a rate that is a function of its network position. We show, under mild assumptions, that these policies achieve the asymptotic lower bound on the regret up to a multiplicative factor, independent of the network structure. Finally, we use numerical examples on a real-world social network and a routing example network to demonstrate the benefits obtained by our policies over other existing policies.

Title: Verifying Safety of Functional Programs with Rosette/Unbound, Abstract: The goal of unbounded program verification is to discover an inductive invariant that safely over-approximates all possible program behaviors. Functional languages featuring higher order and recursive functions become more popular due to the domain-specific needs of big data analytics, web, and security. We present Rosette/Unbound, the first program verifier for Racket exploiting the automated constrained Horn solver on its backend. One of the key features of Rosette/Unbound is the ability to synchronize recursive computations over the same inputs allowing to verify programs that iterate over unbounded data streams multiple times. Rosette/Unbound is successfully evaluated on a set of non-trivial recursive and higher order functional programs.

Title: Extended Formulations for Polytopes of Regular Matroids, Abstract: We present a simple proof of the fact that the base (and independence) polytope of a rank $n$ regular matroid over $m$ elements has an extension complexity $O(mn)$.

Title: Nesterov's Smoothing Technique and Minimizing Differences of Convex Functions for Hierarchical Clustering, Abstract: A bilevel hierarchical clustering model is commonly used in designing optimal multicast networks. In this paper, we consider two different formulations of the bilevel hierarchical clustering problem, a discrete optimization problem which can be shown to be NP-hard. Our approach is to reformulate the problem as a continuous optimization problem by making some relaxations on the discreteness conditions. Then Nesterov's smoothing technique and a numerical algorithm for minimizing differences of convex functions called the DCA are applied to cope with the nonsmoothness and nonconvexity of the problem. Numerical examples are provided to illustrate our method.

Title: Heisenberg equation for a nonrelativistic particle on a hypersurface: from the centripetal force to a curvature induced force, Abstract: In classical mechanics, a nonrelativistic particle constrained on an $N-1$ curved hypersurface embedded in $N$ flat space experiences the centripetal force only. In quantum mechanics, the situation is totally different for the presence of the geometric potential. We demonstrate that the motion of the quantum particle is "driven" by not only the the centripetal force, but also a curvature induced force proportional to the Laplacian of the mean curvature, which is fundamental in the interface physics, causing curvature driven interface evolution.

Title: Higher order molecular organisation as a source of biological function, Abstract: Molecular interactions have widely been modelled as networks. The local wiring patterns around molecules in molecular networks are linked with their biological functions. However, networks model only pairwise interactions between molecules and cannot explicitly and directly capture the higher order molecular organisation, such as protein complexes and pathways. Hence, we ask if hypergraphs (hypernetworks), that directly capture entire complexes and pathways along with protein-protein interactions (PPIs), carry additional functional information beyond what can be uncovered from networks of pairwise molecular interactions. The mathematical formalism of a hypergraph has long been known, but not often used in studying molecular networks due to the lack of sophisticated algorithms for mining the underlying biological information hidden in the wiring patterns of molecular systems modelled as hypernetworks. We propose a new, multi-scale, protein interaction hypernetwork model that utilizes hypergraphs to capture different scales of protein organization, including PPIs, protein complexes and pathways. In analogy to graphlets, we introduce hypergraphlets, small, connected, non-isomorphic, induced sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these multi-scale molecular hypergraphs and to mine them for new biological information. We apply them to model the multi-scale protein networks of baker yeast and human and show that the higher order molecular organisation captured by these hypergraphs is strongly related to the underlying biology. Importantly, we demonstrate that our new models and data mining tools reveal different, but complementary biological information compared to classical PPI networks. We apply our hypergraphlets to successfully predict biological functions of uncharacterised proteins.

Title: Semi-equivelar maps on the torus are Archimedean, Abstract: If the face-cycles at all the vertices in a map on a surface are of same type then the map is called semi-equivelar. There are eleven types of Archimedean tilings on the plane. All the Archimedean tilings are semi-equivelar maps. If a map $X$ on the torus is a quotient of an Archimedean tiling on the plane then the map $X$ is semi-equivelar. We show that each semi-equivelar map on the torus is a quotient of an Archimedean tiling on the plane. Vertex-transitive maps are semi-equivelar maps. We know that four types of semi-equivelar maps on the torus are always vertex-transitive and there are examples of other seven types of semi-equivelar maps which are not vertex-transitive. We show that the number of ${\rm Aut}(Y)$-orbits of vertices for any semi-equivelar map $Y$ on the torus is at most six. In fact, the number of orbits is at most three except one type of semi-equivelar maps. Our bounds on the number of orbits are sharp.

Title: Global teleconnectivity structures of the El Niño-Southern Oscillation and large volcanic eruptions -- An evolving network perspective, Abstract: Recent work has provided ample evidence that global climate dynamics at time-scales between multiple weeks and several years can be severely affected by the episodic occurrence of both, internal (climatic) and external (non-climatic) perturbations. Here, we aim to improve our understanding on how regional to local disruptions of the "normal" state of the global surface air temperature field affect the corresponding global teleconnectivity structure. Specifically, we present an approach to quantify teleconnectivity based on different characteristics of functional climate network analysis. Subsequently, we apply this framework to study the impacts of different phases of the El Niño-Southern Oscillation (ENSO) as well as the three largest volcanic eruptions since the mid 20th century on the dominating spatiotemporal co-variability patterns of daily surface air temperatures. Our results confirm the existence of global effects of ENSO which result in episodic breakdowns of the hierarchical organization of the global temperature field. This is associated with the emergence of strong teleconnections. At more regional scales, similar effects are found after major volcanic eruptions. Taken together, the resulting time-dependent patterns of network connectivity allow a tracing of the spatial extents of the dominating effects of both types of climate disruptions. We discuss possible links between these observations and general aspects of atmospheric circulation.

Title: Unbiased Shrinkage Estimation, Abstract: Shrinkage estimation usually reduces variance at the cost of bias. But when we care only about some parameters of a model, I show that we can reduce variance without incurring bias if we have additional information about the distribution of covariates. In a linear regression model with homoscedastic Normal noise, I consider shrinkage estimation of the nuisance parameters associated with control variables. For at least three control variables and exogenous treatment, I establish that the standard least-squares estimator is dominated with respect to squared-error loss in the treatment effect even among unbiased estimators and even when the target parameter is low-dimensional. I construct the dominating estimator by a variant of James-Stein shrinkage in a high-dimensional Normal-means problem. It can be interpreted as an invariant generalized Bayes estimator with an uninformative (improper) Jeffreys prior in the target parameter.

Title: Characterizing The Influence of Continuous Integration. Empirical Results from 250+ Open Source and Proprietary Projects, Abstract: Continuous integration (CI) tools integrate code changes by automatically compiling, building, and executing test cases upon submission of code changes. Use of CI tools is getting increasingly popular, yet how proprietary projects reap the benefits of CI remains unknown. To investigate the influence of CI on software development, we analyze 150 open source software (OSS) projects, and 123 proprietary projects. For OSS projects, we observe the expected benefits after CI adoption, e.g., improvements in bug and issue resolution. However, for the proprietary projects, we cannot make similar observations. Our findings indicate that only adoption of CI might not be enough to the improve software development process. CI can be effective for software development if practitioners use CI's feedback mechanism efficiently, by applying the practice of making frequent commits. For our set of proprietary projects we observe practitioners commit less frequently, and hence not use CI effectively for obtaining feedback on the submitted code changes. Based on our findings we recommend industry practitioners to adopt the best practices of CI to reap the benefits of CI tools for example, making frequent commits.

Title: On Controllable Abundance Of Saturated-input Linear Discrete Systems, Abstract: Several theorems on the volume computing of the polyhedron spanned by a n-dimensional vector set with the finite-interval parameters are presented and proved firstly, and then are used in the analysis of the controllable regions of the linear discrete time-invariant systems with saturated inputs. A new concept and continuous measure on the control ability, control efficiency of the input variables, and the diversity of the control laws, named as the controllable abundance, is proposed based on the volume computing of the regions and is applied to the actuator placing and configuring problems, the optimizing problems of dynamics and kinematics of the controlled plants, etc.. The numerical experiments show the effectiveness of the new concept and methods for investigating and optimizing the control ability and efficiency.

Title: To the Acceleration of Charged Particles with Travelling Laser Focus, Abstract: We describe here the latest results of calculations with FlexPDE code of wake-fields induced by the bunch in micro-structures. These structures, illuminated by swept laser bust, serve for acceleration of charged particles. The basis of the scheme is a fast sweeping device for the laser bunch. After sweeping, the laser bunch has a slope ~45o with respect to the direction of propagation. So the every cell of the microstructure becomes excited locally only for the moment when the particles are there. Self-consistent parameters of collider based on this idea allow consideration this type of collider as a candidate for the near-future accelerator era.

Title: An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning, Abstract: Agent-based Internet of Things (IoT) applications have recently emerged as applications that can involve sensors, wireless devices, machines and software that can exchange data and be accessed remotely. Such applications have been proposed in several domains including health care, smart cities and agriculture. However, despite their increased adoption, deploying these applications in specific settings has been very challenging because of the complex static and dynamic variability of the physical devices such as sensors and actuators, the software application behavior and the environment in which the application is embedded. In this paper, we propose a modeling approach for IoT analytics based on learning embodied agents (i.e. situated agents). The approach involves: (i) a variability model of IoT embodied agents; (ii) feedback evaluative machine learning; and (iii) reconfiguration of a group of agents in accordance with environmental context. The proposed approach advances the state of the art in that it facilitates the development of Agent-based IoT applications by explicitly capturing their complex and dynamic variabilities and supporting their self-configuration based on an context-aware and machine learning-based approach.

Title: Neural Sequence Model Training via $α$-divergence Minimization, Abstract: We propose a new neural sequence model training method in which the objective function is defined by $\alpha$-divergence. We demonstrate that the objective function generalizes the maximum-likelihood (ML)-based and reinforcement learning (RL)-based objective functions as special cases (i.e., ML corresponds to $\alpha \to 0$ and RL to $\alpha \to1$). We also show that the gradient of the objective function can be considered a mixture of ML- and RL-based objective gradients. The experimental results of a machine translation task show that minimizing the objective function with $\alpha > 0$ outperforms $\alpha \to 0$, which corresponds to ML-based methods.

Title: Persuasive Technology For Human Development: Review and Case Study, Abstract: Technology is an extremely potent tool that can be leveraged for human development and social good. Owing to the great importance of environment and human psychology in driving human behavior, and the ubiquity of technology in modern life, there is a need to leverage the insights and capabilities of both fields together for nudging people towards a behavior that is optimal in some sense (personal or social). In this regard, the field of persuasive technology, which proposes to infuse technology with appropriate design and incentives using insights from psychology, behavioral economics, and human-computer interaction holds a lot of promise. Whilst persuasive technology is already being developed and is at play in many commercial applications, it can have the great social impact in the field of Information and Communication Technology for Development (ICTD) which uses Information and Communication Technology (ICT) for human developmental ends such as education and health. In this paper we will explore what persuasive technology is and how it can be used for the ends of human development. To develop the ideas in a concrete setting, we present a case study outlining how persuasive technology can be used for human development in Pakistan, a developing South Asian country, that suffers from many of the problems that plague typical developing country.