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null | inputs
dict | prediction
null | prediction_agent
null | annotation
list | annotation_agent
null | multi_label
bool 1
class | explanation
null | id
stringlengths 1
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{
"abstract": " We provide a compositional coalgebraic semantics for strategic games. In our\nframework, like in the semantics of functional programming languages,\ncoalgebras represent the observable behaviour of systems derived from the\nbehaviour of the parts over an unobservable state space. We use coalgebras to\ndescribe and program stage games, finitely and potentially infinitely repeated\nhierarchical or parallel games with imperfect and incomplete information based\non deterministic, non-deterministic or probabilistic decisions of learning\nagents in possibly endogenous networks. Our framework is compositional in that\narbitrarily complex network of games can be composed. The coalgebraic approach\nallows to represent self-referential or reflexive structures like institutional\ndynamics, strategic network formation from within the network, belief\nformation, learning agents or other self-referential phenomena that\ncharacterise complex social systems of cognitive agents. And finally our games\nrepresent directly runnable code in functional programming languages that can\nalso be analysed by sophisticated verification and logical tools of software\nengineering.\n",
"title": "A Compositional Coalgebraic Semantics of Strategic Games"
}
| null | null | null | null | true | null |
10401
| null |
Default
| null | null |
null |
{
"abstract": " Anytime almost-surely asymptotically optimal planners, such as RRT*,\nincrementally find paths to every state in the search domain. This is\ninefficient once an initial solution is found as then only states that can\nprovide a better solution need to be considered. Exact knowledge of these\nstates requires solving the problem but can be approximated with heuristics.\nThis paper formally defines these sets of states and demonstrates how they\ncan be used to analyze arbitrary planning problems. It uses the well-known\n$L^2$ norm (i.e., Euclidean distance) to analyze minimum-path-length problems\nand shows that existing approaches decrease in effectiveness factorially (i.e.,\nfaster than exponentially) with state dimension. It presents a method to\naddress this curse of dimensionality by directly sampling the prolate\nhyperspheroids (i.e., symmetric $n$-dimensional ellipses) that define the $L^2$\ninformed set.\nThe importance of this direct informed sampling technique is demonstrated\nwith Informed RRT*. This extension of RRT* has less theoretical dependence on\nstate dimension and problem size than existing techniques and allows for linear\nconvergence on some problems. It is shown experimentally to find better\nsolutions faster than existing techniques on both abstract planning problems\nand HERB, a two-arm manipulation robot.\n",
"title": "Informed Sampling for Asymptotically Optimal Path Planning (Consolidated Version)"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10402
| null |
Validated
| null | null |
null |
{
"abstract": " We propose a novel approach to 3D human pose estimation from a single depth\nmap. Recently, convolutional neural network (CNN) has become a powerful\nparadigm in computer vision. Many of computer vision tasks have benefited from\nCNNs, however, the conventional approach to directly regress 3D body joint\nlocations from an image does not yield a noticeably improved performance. In\ncontrast, we formulate the problem as estimating per-voxel likelihood of key\nbody joints from a 3D occupancy grid. We argue that learning a mapping from\nvolumetric input to volumetric output with 3D convolution consistently improves\nthe accuracy when compared to learning a regression from depth map to 3D joint\ncoordinates. We propose a two-stage approach to reduce the computational\noverhead caused by volumetric representation and 3D convolution: Holistic 2D\nprediction and Local 3D prediction. In the first stage, Planimetric Network\n(P-Net) estimates per-pixel likelihood for each body joint in the holistic 2D\nspace. In the second stage, Volumetric Network (V-Net) estimates the per-voxel\nlikelihood of each body joints in the local 3D space around the 2D estimations\nof the first stage, effectively reducing the computational cost. Our model\noutperforms existing methods by a large margin in publicly available datasets.\n",
"title": "Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation"
}
| null | null | null | null | true | null |
10403
| null |
Default
| null | null |
null |
{
"abstract": " Selection of appropriate collective variables for enhancing sampling of\nmolecular simulations remains an unsolved problem in computational biophysics.\nIn particular, picking initial collective variables (CVs) is particularly\nchallenging in higher dimensions. Which atomic coordinates or transforms there\nof from a list of thousands should one pick for enhanced sampling runs? How\ndoes a modeler even begin to pick starting coordinates for investigation? This\nremains true even in the case of simple two state systems and only increases in\ndifficulty for multi-state systems. In this work, we solve the initial CV\nproblem using a data-driven approach inspired by the filed of supervised\nmachine learning. In particular, we show how the decision functions in\nsupervised machine learning (SML) algorithms can be used as initial CVs\n(SML_cv) for accelerated sampling. Using solvated alanine dipeptide and\nChignolin mini-protein as our test cases, we illustrate how the distance to the\nSupport Vector Machines' decision hyperplane, the output probability estimates\nfrom Logistic Regression, the outputs from deep neural network classifiers, and\nother classifiers may be used to reversibly sample slow structural transitions.\nWe discuss the utility of other SML algorithms that might be useful for\nidentifying CVs for accelerating molecular simulations.\n",
"title": "Automated design of collective variables using supervised machine learning"
}
| null | null | null | null | true | null |
10404
| null |
Default
| null | null |
null |
{
"abstract": " We study the random conductance model on the lattice $\\mathbb{Z}^d$, i.e. we\nconsider a linear, finite-difference, divergence-form operator with random\ncoefficients and the associated random walk under random conductances. We allow\nthe conductances to be unbounded and degenerate elliptic, but they need to\nsatisfy a strong moment condition and a quantified ergodicity assumption in\nform of a spectral gap estimate. As a main result we obtain in dimension $d\\geq\n3$ quantitative central limit theorems for the random walk in form of a\nBerry-Esseen estimate with speed $t^{-\\frac 1 5+\\varepsilon}$ for $d\\geq 4$ and\n$t^{-\\frac{1}{10}+\\varepsilon}$ for $d=3$. Additionally, in the uniformly\nelliptic case in low dimensions $d=2,3$ we improve the rate in a quantitative\nBerry-Esseen theorem recently obtained by Mourrat. As a central analytic\ningredient, for $d\\geq 3$ we establish near-optimal decay estimates on the\nsemigroup associated with the environment process. These estimates also play a\ncentral role in quantitative stochastic homogenization and extend some recent\nresults by Gloria, Otto and the second author to the degenerate elliptic case.\n",
"title": "Berry-Esseen Theorem and Quantitative homogenization for the Random Conductance Model with degenerate Conductances"
}
| null | null | null | null | true | null |
10405
| null |
Default
| null | null |
null |
{
"abstract": " A very important problem in combinatorial optimization is partitioning a\nnetwork into communities of densely connected nodes; where the connectivity\nbetween nodes inside a particular community is large compared to the\nconnectivity between nodes belonging to different ones. This problem is known\nas community detection, and has become very important in various fields of\nscience including chemistry, biology and social sciences. The problem of\ncommunity detection is a twofold problem that consists of determining the\nnumber of communities and, at the same time, finding those communities. This\ndrastically increases the solution space for heuristics to work on, compared to\ntraditional graph partitioning problems. In many of the scientific domains in\nwhich graphs are used, there is the need to have the ability to partition a\ngraph into communities with the ``highest quality'' possible since the presence\nof even small isolated communities can become crucial to explain a particular\nphenomenon. We have explored community detection using the power of quantum\nannealers, and in particular the D-Wave 2X and 2000Q machines. It turns out\nthat the problem of detecting at most two communities naturally fits into the\narchitecture of a quantum annealer with almost no need of reformulation. This\npaper addresses a systematic study of detecting two or more communities in a\nnetwork using a quantum annealer.\n",
"title": "Detecting Multiple Communities Using Quantum Annealing on the D-Wave System"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10406
| null |
Validated
| null | null |
null |
{
"abstract": " Estimating the influence of a given feature to a model prediction is\nchallenging. We introduce ROAR, RemOve And Retrain, a benchmark to evaluate the\naccuracy of interpretability methods that estimate input feature importance in\ndeep neural networks. We remove a fraction of input features deemed to be most\nimportant according to each estimator and measure the change to the model\naccuracy upon retraining. The most accurate estimator will identify inputs as\nimportant whose removal causes the most damage to model performance relative to\nall other estimators. This evaluation produces thought-provoking results -- we\nfind that several estimators are less accurate than a random assignment of\nfeature importance. However, averaging a set of squared noisy estimators (a\nvariant of a technique proposed by Smilkov et al. (2017)), leads to significant\ngains in accuracy for each method considered and far outperforms such a random\nguess.\n",
"title": "Evaluating Feature Importance Estimates"
}
| null | null | null | null | true | null |
10407
| null |
Default
| null | null |
null |
{
"abstract": " For large-scale industrial processes under closed-loop control, process\ndynamics directly resulting from control action are typical characteristics and\nmay show different behaviors between real faults and normal changes of\noperating conditions. However, conventional distributed monitoring approaches\ndo not consider the closed-loop control mechanism and only explore static\ncharacteristics, which thus are incapable of distinguishing between real\nprocess faults and nominal changes of operating conditions, leading to\nunnecessary alarms. In this regard, this paper proposes a distributed\nmonitoring method for closed-loop industrial processes by concurrently\nexploring static and dynamic characteristics. First, the large-scale\nclosed-loop process is decomposed into several subsystems by developing a\nsparse slow feature analysis (SSFA) algorithm which capture changes of both\nstatic and dynamic information. Second, distributed models are developed to\nseparately capture static and dynamic characteristics from the local and global\naspects. Based on the distributed monitoring system, a two-level monitoring\nstrategy is proposed to check different influences on process characteristics\nresulting from changes of the operating conditions and control action, and thus\nthe two changes can be well distinguished from each other. Case studies are\nconducted based on both benchmark data and real industrial process data to\nillustrate the effectiveness of the proposed method.\n",
"title": "Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
10408
| null |
Validated
| null | null |
null |
{
"abstract": " The problem of high-dimensional and large-scale representation of visual data\nis addressed from an unsupervised learning perspective. The emphasis is put on\ndiscrete representations, where the description length can be measured in bits\nand hence the model capacity can be controlled. The algorithmic infrastructure\nis developed based on the synthesis and analysis prior models whose\nrate-distortion properties, as well as capacity vs. sample complexity\ntrade-offs are carefully optimized. These models are then extended to\nmulti-layers, namely the RRQ and the ML-STC frameworks, where the latter is\nfurther evolved as a powerful deep neural network architecture with fast and\nsample-efficient training and discrete representations. For the developed\nalgorithms, three important applications are developed. First, the problem of\nlarge-scale similarity search in retrieval systems is addressed, where a\ndouble-stage solution is proposed leading to faster query times and shorter\ndatabase storage. Second, the problem of learned image compression is targeted,\nwhere the proposed models can capture more redundancies from the training\nimages than the conventional compression codecs. Finally, the proposed\nalgorithms are used to solve ill-posed inverse problems. In particular, the\nproblems of image denoising and compressive sensing are addressed with\npromising results.\n",
"title": "Learning to compress and search visual data in large-scale systems"
}
| null | null | null | null | true | null |
10409
| null |
Default
| null | null |
null |
{
"abstract": " Recently, Czumaj et.al. (arXiv 2017) presented a parallel (almost)\n$2$-approximation algorithm for the maximum matching problem in only\n$O({(\\log\\log{n})^2})$ rounds of the massive parallel computation (MPC)\nframework, when the memory per machine is $O(n)$. The main approach in their\nwork is a way of compressing $O(\\log{n})$ rounds of a distributed algorithm for\nmaximum matching into only $O({(\\log\\log{n})^2})$ MPC rounds.\nIn this note, we present a similar algorithm for the closely related problem\nof approximating the minimum vertex cover in the MPC framework. We show that\none can achieve an $O(\\log{n})$ approximation to minimum vertex cover in only\n$O(\\log\\log{n})$ MPC rounds when the memory per machine is $O(n)$. Our\nalgorithm for vertex cover is similar to the maximum matching algorithm of\nCzumaj et.al. but avoids many of the intricacies in their approach and as a\nresult admits a considerably simpler analysis (at a cost of a worse\napproximation guarantee). We obtain this result by modifying a previous\nparallel algorithm by Khanna and the author (SPAA 2017) for vertex cover that\nallowed for compressing $O(\\log{n})$ rounds of a distributed algorithm into\nconstant MPC rounds when the memory allowed per machine is $O(n\\sqrt{n})$.\n",
"title": "Simple Round Compression for Parallel Vertex Cover"
}
| null | null | null | null | true | null |
10410
| null |
Default
| null | null |
null |
{
"abstract": " We present ELDAR, a new method that exploits the potential of medium- and\nnarrow-band filter surveys to securely identify active galactic nuclei (AGN)\nand determine their redshifts. Our methodology improves on traditional\napproaches by looking for AGN emission lines expected to be identified against\nthe continuum, thanks to the width of the filters. To assess its performance,\nwe apply ELDAR to the data of the ALHAMBRA survey, which covered an effective\narea of $2.38\\,{\\rm deg}^2$ with 20 contiguous medium-band optical filters down\nto F814W$\\simeq 24.5$. Using two different configurations of ELDAR in which we\nrequire the detection of at least 2 and 3 emission lines, respectively, we\nextract two catalogues of type-I AGN. The first is composed of 585 sources\n($79\\,\\%$ of them spectroscopically-unknown) down to F814W$=22.5$ at $z_{\\rm\nphot}>1$, which corresponds to a surface density of $209\\,{\\rm deg}^{-2}$. In\nthe second, the 494 selected sources ($83\\,\\%$ of them\nspectroscopically-unknown) reach F814W$=23$ at $z_{\\rm phot}>1.5$, for a\ncorresponding number density of $176\\,{\\rm deg}^{-2}$. Then, using samples of\nspectroscopically-known AGN in the ALHAMBRA fields, for the two catalogues we\nestimate a completeness of $73\\,\\%$ and $67\\,\\%$, and a redshift precision of\n$1.01\\,\\%$ and $0.86\\,\\%$ (with outliers fractions of $8.1\\,\\%$ and $5.8\\,\\%$).\nAt $z>2$, where our selection performs best, we reach $85\\,\\%$ and $77\\,\\%$\ncompleteness and we find no contamination from galaxies.\n",
"title": "ELDAR, a new method to identify AGN in multi-filter surveys: the ALHAMBRA test-case"
}
| null | null |
[
"Physics"
] | null | true | null |
10411
| null |
Validated
| null | null |
null |
{
"abstract": " The hard X-ray emission in a solar flare is typically characterized by a\nnumber of discrete sources, each with its own spectral, temporal, and spatial\nvariability. Establishing the relationship amongst these sources is critical to\ndetermine the role of each in the energy release and transport processes that\noccur within the flare. In this paper we present a novel method to identify and\ncharacterize each source of hard X-ray emission. The method permits a\nquantitative determination of the most likely number of subsources present, and\nof the relative probabilities that the hard X-ray emission in a given subregion\nof the flare is represented by a complicated multiple source structure or by a\nsimpler single source. We apply the method to a well-studied flare on\n2002~February~20 in order to assess competing claims as to the number of\nchromospheric footpoint sources present, and hence to the complexity of the\nunderlying magnetic geometry/toplogy. Contrary to previous claims of the need\nfor multiple sources to account for the chromospheric hard X-ray emission at\ndifferent locations and times, we find that a simple\ntwo-footpoint-plus-coronal-source model is the most probable explanation for\nthe data. We also find that one of the footpoint sources moves quite rapidly\nthroughout the event, a factor that presumably complicated previous analyses.\nThe inferred velocity of the footpoint corresponds to a very high induced\nelectric field, compatible with those in thin reconnecting current sheets.\n",
"title": "Identification of multiple hard X-ray sources in solar flares: A Bayesian analysis of the February 20 2002 event"
}
| null | null | null | null | true | null |
10412
| null |
Default
| null | null |
null |
{
"abstract": " We propose a general framework for interactively learning models, such as\n(binary or non-binary) classifiers, orderings/rankings of items, or clusterings\nof data points. Our framework is based on a generalization of Angluin's\nequivalence query model and Littlestone's online learning model: in each\niteration, the algorithm proposes a model, and the user either accepts it or\nreveals a specific mistake in the proposal. The feedback is correct only with\nprobability $p > 1/2$ (and adversarially incorrect with probability $1 - p$),\ni.e., the algorithm must be able to learn in the presence of arbitrary noise.\nThe algorithm's goal is to learn the ground truth model using few iterations.\nOur general framework is based on a graph representation of the models and\nuser feedback. To be able to learn efficiently, it is sufficient that there be\na graph $G$ whose nodes are the models and (weighted) edges capture the user\nfeedback, with the property that if $s, s^*$ are the proposed and target\nmodels, respectively, then any (correct) user feedback $s'$ must lie on a\nshortest $s$-$s^*$ path in $G$. Under this one assumption, there is a natural\nalgorithm reminiscent of the Multiplicative Weights Update algorithm, which\nwill efficiently learn $s^*$ even in the presence of noise in the user's\nfeedback.\nFrom this general result, we rederive with barely any extra effort classic\nresults on learning of classifiers and a recent result on interactive\nclustering; in addition, we easily obtain new interactive learning algorithms\nfor ordering/ranking.\n",
"title": "A General Framework for Robust Interactive Learning"
}
| null | null | null | null | true | null |
10413
| null |
Default
| null | null |
null |
{
"abstract": " Gravitational lensing provides a means to measure mass that does not rely on\ndetecting and analysing light from the lens itself. Compact objects are ideal\ngravitational lenses, because they have relatively large masses and are dim. In\nthis paper we describe the prospects for predicting lensing events generated by\nthe local population of compact objects, consisting of 250 neutron stars, 5\nblack holes, and approximately 35,000 white dwarfs. By focusing on a population\nof nearby compact objects with measured proper motions and known distances from\nus, we can measure their masses by studying the characteristics of any lensing\nevent they generate. Here we concentrate on shifts in the position of a\nbackground source due to lensing by a foreground compact object. With HST,\nJWST, and Gaia, measurable centroid shifts caused by lensing are relatively\nfrequent occurrences. We find that 30-50 detectable events per decade are\nexpected for white dwarfs. Because relatively few neutron stars and black holes\nhave measured distances and proper motions, it is more difficult to compute\nrealistic rates for them. However, we show that at least one isolated neutron\nstar has likely produced detectable events during the past several decades.\nThis work is particularly relevant to the upcoming data releases by the Gaia\nmission and also to data that will be collected by JWST. Monitoring predicted\nmicrolensing events will not only help to determine the masses of compact\nobjects, but will also potentially discover dim companions to these stellar\nremnants, including orbiting exoplanets.\n",
"title": "Predicting Gravitational Lensing by Stellar Remnants"
}
| null | null | null | null | true | null |
10414
| null |
Default
| null | null |
null |
{
"abstract": " Three separation properties for a closed subgroup $H$ of a locally compact\ngroup $G$ are studied: (1) the existence of a bounded approximate indicator for\n$H$, (2) the existence of a completely bounded invariant projection of\n$VN\\left(G\\right)$ onto $VN_{H}\\left(G\\right)$, and (3) the approximability of\nthe characteristic function $\\chi_{H}$ by functions in $M_{cb}A\\left(G\\right)$\nwith respect to the weak$^{*}$ topology of $M_{cb}A\\left(G_{d}\\right)$. We show\nthat the $H$-separation property of Kaniuth and Lau is characterized by the\nexistence of certain bounded approximate indicators for $H$ and that a\ndiscretized analogue of the $H$-separation property is equivalent to (3).\nMoreover, we give a related characterization of amenability of $H$ in terms of\nany group $G$ containing $H$ as a closed subgroup. The weak amenability of $G$\nor that $G_{d}$ satisfies the approximation property, in combination with the\nexistence of a natural projection (in the sense of Lau and Ülger), are shown\nto suffice to conclude (3). Several consequences of (2) involving the\ncb-multiplier completion of $A\\left(G\\right)$ are given. Finally, a convolution\ntechnique for averaging over the closed subgroup $H$ is developed and used to\nweaken a condition for the existence of a bounded approximate indicator for\n$H$.\n",
"title": "Weak separation properties for closed subgroups of locally compact groups"
}
| null | null | null | null | true | null |
10415
| null |
Default
| null | null |
null |
{
"abstract": " Machine learning (ML) is increasingly deployed in real world contexts,\nsupplying actionable insights and forming the basis of automated\ndecision-making systems. While issues resulting from biases pre-existing in\ntraining data have been at the center of the fairness debate, these systems are\nalso affected by technical and emergent biases, which often arise as\ncontext-specific artifacts of implementation. This position paper interprets\ntechnical bias as an epistemological problem and emergent bias as a dynamical\nfeedback phenomenon. In order to stimulate debate on how to change machine\nlearning practice to effectively address these issues, we explore this broader\nview on bias, stress the need to reflect on epistemology, and point to\nvalue-sensitive design methodologies to revisit the design and implementation\nprocess of automated decision-making systems.\n",
"title": "A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics"
}
| null | null | null | null | true | null |
10416
| null |
Default
| null | null |
null |
{
"abstract": " The paper presents a solution to the Boltzmann kinetic equation based on the\nconstruction of its discrete conservative model. Discrete analogue of the\ncollision integral is presented as a contraction of a tensor, which is\nindependent from the initial distribution function, colliding with a tensor\ncomposed of medium densities in the cells. Numerical implementation of the\ndiscrete model is demonstrated on the example of the isotropic gas relaxation\nproblem applied to the hard spheres model. The key feature of the method is\nindependence of the collision tensor components from the distribution function.\nConsequently the components of the collision tensor are calculated once for\nvarious initial distribution functions, which substantially increases\nperformance of the suggested method.\n",
"title": "Solution to the relaxation problem for a gas with a distribution function dependent on the velocity modulus"
}
| null | null | null | null | true | null |
10417
| null |
Default
| null | null |
null |
{
"abstract": " The first step to realize automatic experimental data analysis for fusion\nplasma experiments is fitting noisy data of temperature and density spatial\nprofiles, which are obtained routinely. However, it has been difficult to\nconstruct algorithms that fit all the data without over- and under-fitting. In\nthis paper, we show that this difficulty originates from the lack of knowledge\nof the probability distribution that the measurement data follow. We\ndemonstrate the use of a machine learning technique to estimate the data\ndistribution and to construct an optimal generative model. We show that the\nfitting algorithm based on the generative modeling outperforms classical\nheuristic methods in terms of the stability as well as the accuracy.\n",
"title": "Robust Regression for Automatic Fusion Plasma Analysis based on Generative Modeling"
}
| null | null | null | null | true | null |
10418
| null |
Default
| null | null |
null |
{
"abstract": " This article expands on research that has been done to develop a recurrent\nneural network (RNN) capable of predicting aircraft engine vibrations using\nlong short-term memory (LSTM) neurons. LSTM RNNs can provide a more\ngeneralizable and robust method for prediction over analytical calculations of\nengine vibration, as analytical calculations must be solved iteratively based\non specific empirical engine parameters, making this approach ungeneralizable\nacross multiple engines. In initial work, multiple LSTM RNN architectures were\nproposed, evaluated and compared. This research improves the performance of the\nmost effective LSTM network design proposed in the previous work by using a\npromising neuroevolution method based on ant colony optimization (ACO) to\ndevelop and enhance the LSTM cell structure of the network. A parallelized\nversion of the ACO neuroevolution algorithm has been developed and the evolved\nLSTM RNNs were compared to the previously used fixed topology. The evolved\nnetworks were trained on a large database of flight data records obtained from\nan airline containing flights that suffered from excessive vibration. Results\nwere obtained using MPI (Message Passing Interface) on a high performance\ncomputing (HPC) cluster, evolving 1000 different LSTM cell structures using 168\ncores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%,\nreducing prediction error from 5.51% to 4.17% when predicting excessive engine\nvibrations 10 seconds in the future, while at the same time dramatically\nreducing the number of weights from 21,170 to 11,810.\n",
"title": "Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration"
}
| null | null | null | null | true | null |
10419
| null |
Default
| null | null |
null |
{
"abstract": " We present PhyShare, a new haptic user interface based on actuated robots.\nVirtual reality has recently been gaining wide adoption, and an effective\nhaptic feedback in these scenarios can strongly support user's sensory in\nbridging virtual and physical world. Since participants do not directly observe\nthese robotic proxies, we investigate the multiple mappings between physical\nrobots and virtual proxies that can utilize the resources needed to provide a\nwell rounded VR experience. PhyShare bots can act either as directly touchable\nobjects or invisible carriers of physical objects, depending on different\nscenarios. They also support distributed collaboration, allowing remotely\nlocated VR collaborators to share the same physical feedback.\n",
"title": "PhyShare: Sharing Physical Interaction in Virtual Reality"
}
| null | null | null | null | true | null |
10420
| null |
Default
| null | null |
null |
{
"abstract": " Every graph $G=(V,E)$ is an induced subgraph of some Kneser graph of rank\n$k$, i.e., there is an assignment of (distinct) $k$-sets $v \\mapsto A_v$ to the\nvertices $v\\in V$ such that $A_u$ and $A_v$ are disjoint if and only if $uv\\in\nE$. The smallest such $k$ is called the Kneser rank of $G$ and denoted by\n$f_{\\rm Kneser}(G)$. As an application of a result of Frieze and Reed\nconcerning the clique cover number of random graphs we show that for constant\n$0< p< 1$ there exist constants $c_i=c_i(p)>0$, $i=1,2$ such that with high\nprobability \\[ c_1 n/(\\log n)< f_{\\rm Kneser}(G) < c_2 n/(\\log n). \\] We apply\nthis for other graph representations defined by Boros, Gurvich and Meshulam. A\n{\\em $k$-min-difference representation} of a graph $G$ is an assignment of a\nset $A_i$ to each vertex $i\\in V(G)$ such that \\[ ij\\in E(G) \\,\\,\n\\Leftrightarrow \\, \\, \\min \\{|A_i\\setminus A_j|,|A_j\\setminus A_i| \\}\\geq k. \\]\nThe smallest $k$ such that there exists a $k$-min-difference representation of\n$G$ is denoted by $f_{\\min}(G)$. Balogh and Prince proved in 2009 that for\nevery $k$ there is a graph $G$ with $f_{\\min}(G)\\geq k$. We prove that there\nare constants $c''_1, c''_2>0$ such that $c''_1 n/(\\log n)< f_{\\min}(G) <\nc''_2n/(\\log n)$ holds for almost all bipartite graphs $G$ on $n+n$ vertices.\n",
"title": "Kneser ranks of random graphs and minimum difference representations"
}
| null | null | null | null | true | null |
10421
| null |
Default
| null | null |
null |
{
"abstract": " Graph drawings are useful tools for exploring the structure and dynamics of\ndata that can be represented by pair-wise relationships among a set of objects.\nTypical real-world social, biological or technological networks exhibit high\ncomplexity resulting from a large number and broad heterogeneity of objects and\nrelationships. Thus, mapping these networks into a low-dimensional space to\nvisualize the dynamics of network-driven processes is a challenging task. Often\nwe want to analyze how a single node is influenced by or is influencing its\nlocal network as the source of a spreading process. Here I present a network\nlayout algorithm for graphs with millions of nodes that visualizes spreading\nphenomena from the perspective of a single node. The algorithm consists of\nthree stages to allow for an interactive graph exploration: First, a global\nsolution for the network layout is found in spherical space that minimizes\ndistance errors between all nodes. Second, a focal node is interactively\nselected, and distances to this node are further optimized. Third, node\ncoordinates are mapped to a circular representation and drawn with additional\nfeatures to represent the network-driven phenomenon. The effectiveness and\nscalability of this method are shown for a large collaboration network of\nscientists, where we are interested in the citation dynamics around a focal\nauthor.\n",
"title": "Visualizing spreading phenomena on complex networks"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10422
| null |
Validated
| null | null |
null |
{
"abstract": " One of the ultimate goals in biology is to understand the design principles\nof biological systems. Such principles, if they exist, can help us better\nunderstand complex, natural biological systems and guide the engineering of de\nnovo ones. Towards deciphering design principles, in silico evolution of\nbiological systems with proper abstraction is a promising approach. Here, we\ndemonstrate the application of in silico evolution combined with rule-based\nmodelling for exploring design principles of cellular signaling networks. This\napplication is based on a computational platform, called BioJazz, which allows\nin silico evolution of signaling networks with unbounded complexity. We provide\na detailed introduction to BioJazz architecture and implementation and describe\nhow it can be used to evolve and/or design signaling networks with defined\ndynamics. For the latter, we evolve signaling networks with switch-like\nresponse dynamics and demonstrate how BioJazz can result in new biological\ninsights on network structures that can endow bistable response dynamics. This\nexample also demonstrated both the power of BioJazz in evolving and designing\nsignaling networks and its limitations at the current stage of development.\n",
"title": "In silico evolution of signaling networks using rule-based models: bistable response dynamics"
}
| null | null |
[
"Quantitative Biology"
] | null | true | null |
10423
| null |
Validated
| null | null |
null |
{
"abstract": " Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world\npopulation. Epileptic patients suffer from chronic unprovoked seizures, which\ncan result in broad spectrum of debilitating medical and social consequences.\nSince seizures, in general, occur infrequently and are unpredictable, automated\nseizure detection systems are recommended to screen for seizures during\nlong-term electroencephalogram (EEG) recordings. In addition, systems for early\nseizure detection can lead to the development of new types of intervention\nsystems that are designed to control or shorten the duration of seizure events.\nIn this article, we investigate the utility of recurrent neural networks (RNNs)\nin designing seizure detection and early seizure detection systems. We propose\na deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for\nseizure detection. We use publicly available data in order to evaluate our\nmethod and demonstrate very promising evaluation results with overall accuracy\nclose to 100 %. We also systematically investigate the application of our\nmethod for early seizure warning systems. Our method can detect about 98% of\nseizure events within the first 5 seconds of the overall epileptic seizure\nduration.\n",
"title": "Deep Recurrent Neural Networks for seizure detection and early seizure detection systems"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10424
| null |
Validated
| null | null |
null |
{
"abstract": " During maintenance, software developers deal with numerous change requests\nmade by the users of a software system. Studies show that the developers find\nit challenging to select appropriate search terms from a change request during\nconcept location. In this paper, we propose a novel technique--QUICKAR--that\nautomatically suggests helpful reformulations for a given query by leveraging\nthe crowdsourced knowledge from Stack Overflow. It determines semantic\nsimilarity or relevance between any two terms by analyzing their adjacent word\nlists from the programming questions of Stack Overflow, and then suggests\nsemantically relevant queries for concept location. Experiments using 510\nqueries from two software systems suggest that our technique can improve or\npreserve the quality of 76% of the initial queries on average which is\npromising. Comparison with one baseline technique validates our preliminary\nfindings, and also demonstrates the potential of our technique.\n",
"title": "QUICKAR: Automatic Query Reformulation for Concept Location using Crowdsourced Knowledge"
}
| null | null | null | null | true | null |
10425
| null |
Default
| null | null |
null |
{
"abstract": " We consider the weighted belief-propagation (WBP) decoder recently proposed\nby Nachmani et al. where different weights are introduced for each Tanner graph\nedge and optimized using machine learning techniques. Our focus is on\nsimple-scaling models that use the same weights across certain edges to reduce\nthe storage and computational burden. The main contribution is to show that\nsimple scaling with few parameters often achieves the same gain as the full\nparameterization. Moreover, several training improvements for WBP are proposed.\nFor example, it is shown that minimizing average binary cross-entropy is\nsuboptimal in general in terms of bit error rate (BER) and a new \"soft-BER\"\nloss is proposed which can lead to better performance. We also investigate\nparameter adapter networks (PANs) that learn the relation between the\nsignal-to-noise ratio and the WBP parameters. As an example, for the (32,16)\nReed-Muller code with a highly redundant parity-check matrix, training a PAN\nwith soft-BER loss gives near-maximum-likelihood performance assuming simple\nscaling with only three parameters.\n",
"title": "Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation"
}
| null | null | null | null | true | null |
10426
| null |
Default
| null | null |
null |
{
"abstract": " We study a pattern forming instability in a laser driven optically thick\ncloud of cold two-level atoms with a planar feedback mirror. A theoretical\nmodel is developed, enabling a full analysis of transverse patterns in a medium\nwith saturable nonlinearity, taking into account diffraction within the medium,\nand both the transmission and reflection gratings. Focus of the analysis is on\ncombined treatment of nonlinear propagation in a diffractively- and\noptically-thick medium and the boundary condition given by feedback. We\ndemonstrate explicitly how diffraction within the medium breaks the degeneracy\nof Talbot modes inherent in thin slice models. Existence of envelope curves\nbounding all possible pattern formation thresholds is predicted. The importance\nof envelope curves and their interaction with threshold curves is illustrated\nby experimental observation of a sudden transition between length scales as\nmirror displacement is varied.\n",
"title": "Thick-medium model of transverse pattern formation in optically excited cold two-level atoms with a feedback mirror"
}
| null | null | null | null | true | null |
10427
| null |
Default
| null | null |
null |
{
"abstract": " We study the electronic and spin structures of the giant Rashba-split surface\nstates of the Bi/Si(111)-($\\sqrt{3} \\times \\sqrt{3}$)R30 trimer phase by means\nof spin- and angle-resolved photoelectron spectroscopy (spin-ARPES). Supported\nby tight-binding calculations of the surface state dispersion and spin\norientation, our findings show that the spin experiences a vortex-like\nstructure around the $\\bar{\\Gamma}$-point of the surface Brillouin zone - in\naccordance with the standard Rashba model. Moreover, we find no evidence of a\nspin vortex around the $\\bar{\\mathrm{K}}$-point in the hexagonal Brillouin\nzone, and thus no peculiar Rashba split around this point, something that has\nbeen suggested by previous works. Rather the opposite, our results show that\nthe spin structure around $\\bar{\\mathrm{K}}$ can be fully understood by taking\ninto account the symmetry of the Brillouin zone and the intersection of spin\nvortices centered around the $\\bar{\\Gamma}$-points in neighboring Brillouin\nzones. As a result, the spin structure is consistently explained within the\nstandard framework of the Rashba model although the spin-polarized surface\nstates experience a more complex dispersion compared to free-electron like\nparabolic states.\n",
"title": "A reinvestigation of the giant Rashba-split states on Bi-covered Si(111)"
}
| null | null |
[
"Physics"
] | null | true | null |
10428
| null |
Validated
| null | null |
null |
{
"abstract": " Technological developments alongside VLSI achievements enable mobile devices\nto be equipped with multiple radio interfaces which is known as multihoming. On\nthe other hand, the combination of various wireless access technologies, known\nas Next Generation Wireless Networks (NGWNs) has been introduced to provide\ncontinuous connection to mobile devices in any time and location. Cognitive\nradio networks as a part of NGWNs aroused to overcome spectrum inefficiency and\nspectrum scarcity issues. In order to provide seamless and ubiquitous\nconnection across heterogeneous wireless access networks in the context of\ncognitive radio networks, utilizing Mobile IPv6 is beneficial. In this paper, a\nmobile device equipped with two radio interfaces is considered in order to\nevaluate performance of spectrum handover in terms of handover latency. The\nanalytical results show that the proposed model can achieve better performance\ncompared to other related mobility management protocols mainly in terms of\nhandover latency.\n",
"title": "Performance Evaluation of Spectrum Mobility in Multi-homed Mobile IPv6 Cognitive Radio Cellular Networks"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10429
| null |
Validated
| null | null |
null |
{
"abstract": " Quantum phase transitions are ubiquitous in many exotic behaviors of\nstrongly-correlated materials. However the microscopic complexity impedes their\nquantitative understanding. Here, we observe thoroughly and comprehend the rich\nstrongly-correlated physics in two profoundly dissimilar regimes of quantum\ncriticality. With a circuit implementing a quantum simulator for the\nthree-channel Kondo model, we reveal the universal scalings toward different\nlow-temperature fixed points and along the multiple crossovers from quantum\ncriticality. Notably, an unanticipated violation of the maximum conductance for\nballistic free electrons is uncovered. The present charge pseudospin\nimplementation of a Kondo impurity opens access to a broad variety of\nstrongly-correlated phenomena.\n",
"title": "Tunable Quantum Criticality and Super-ballistic Transport in a `Charge' Kondo Circuit"
}
| null | null | null | null | true | null |
10430
| null |
Default
| null | null |
null |
{
"abstract": " Future observations of cosmic microwave background (CMB) polarisation have\nthe potential to answer some of the most fundamental questions of modern\nphysics and cosmology. In this paper, we list the requirements for a future CMB\npolarisation survey addressing these scientific objectives, and discuss the\ndesign drivers of the CORE space mission proposed to ESA in answer to the \"M5\"\ncall for a medium-sized mission. The rationale and options, and the\nmethodologies used to assess the mission's performance, are of interest to\nother future CMB mission design studies. CORE is designed as a near-ultimate\nCMB polarisation mission which, for optimal complementarity with ground-based\nobservations, will perform the observations that are known to be essential to\nCMB polarisation scienceand cannot be obtained by any other means than a\ndedicated space mission.\n",
"title": "Exploring Cosmic Origins with CORE: Survey requirements and mission design"
}
| null | null |
[
"Physics"
] | null | true | null |
10431
| null |
Validated
| null | null |
null |
{
"abstract": " A detailed thermal analysis of a Niobium (Nb) based superconducting radio\nfrequency (SRF) cavity in a liquid helium bath is presented, taking into\naccount the temperature and magnetic field dependence of the surface resistance\nand thermal conductivity in the superconducting state of the starting Nb\nmaterial (for SRF cavity fabrication) with different impurity levels. The drop\nin SRF cavity quality factor (Q_0) in the high acceleration gradient regime\n(before ultimate breakdown of the SRF cavity) is studied in details. It is\nargued that the high field Q_0-drop in SRF cavity is considerably influenced by\nthe intrinsic material parameters such as electrical conductivity, and thermal\ndiffusivity. The detail analysis also shows that the current specification on\nthe purity of niobium material for SRF cavity fabrication is somewhat over\nspecified. Niobium material with a relatively low purity can very well serve\nthe purpose for the accelerators dedicated for spallation neutron source (SNS)\nor accelerator driven sub-critical system (ADSS) applications, where the\nrequired accelerating gradient is typically up to 20 MV/m,. This information\nwill have important implication towards the cost reduction of superconducting\ntechnology based particle accelerators for various applications.\n",
"title": "Influence of material parameters on the performance of niobium based superconducting RF cavities"
}
| null | null | null | null | true | null |
10432
| null |
Default
| null | null |
null |
{
"abstract": " The shear viscosity plays an important role in studies of transport phenomena\nin ultracold Fermi gases and serves as a diagnostic of various microscopic\ntheories. Due to the complicated phase structures of population-imbalanced\nFermi gases, past works mainly focus on unpolarized Fermi gases. Here we\ninvestigate the shear viscosity of homogeneous, population-imbalanced Fermi\ngases with tunable attractive interactions at finite temperatures by using a\npairing fluctuation theory for thermodynamical quantities and a gauge-invariant\nlinear response theory for transport coefficients. In the unitary and BEC\nregimes, the shear viscosity increases with the polarization because the excess\nmajority fermions cause gapless excitations acting like a normal fluid. In the\nweak BEC regime the excess fermions also suppress the noncondensed pairs at low\npolarization, and we found a minimum in the ratio of shear viscosity and\nrelaxation time. To help constrain the relaxation time from linear response\ntheory, we derive an exact relation connecting some thermodynamic quantities\nand transport coefficients at the mean-field level for unitary Fermi\nsuperfluids with population imbalance. An approximate relation beyond\nmean-field theory is proposed and only exhibits mild deviations from numerical\nresults.\n",
"title": "Shear Viscosity of Uniform Fermi Gases with Population Imbalance"
}
| null | null | null | null | true | null |
10433
| null |
Default
| null | null |
null |
{
"abstract": " A cyclic proof system gives us another way of representing inductive\ndefinitions and efficient proof search. In 2011 Brotherston and Simpson\nconjectured the equivalence between the provability of the classical cyclic\nproof system and that of the classical system of Martin-Lof's inductive\ndefinitions.\nThis paper studies the conjecture for intuitionistic logic.\nThis paper first points out that the countermodel of FOSSACS 2017 paper by\nthe same authors shows the conjecture for intuitionistic logic is false in\ngeneral. Then this paper shows the conjecture for intuitionistic logic is true\nunder arithmetic, namely, the provability of the intuitionistic cyclic proof\nsystem is the same as that of the intuitionistic system of Martin-Lof's\ninductive definitions when both systems contain Heyting arithmetic HA.\nFor this purpose, this paper also shows that HA proves Podelski-Rybalchenko\ntheorem for induction and Kleene-Brouwer theorem for induction. These results\nimmediately give another proof to the conjecture under arithmetic for classical\nlogic shown in LICS 2017 paper by the same authors.\n",
"title": "Equivalence of Intuitionistic Inductive Definitions and Intuitionistic Cyclic Proofs under Arithmetic"
}
| null | null |
[
"Computer Science",
"Mathematics"
] | null | true | null |
10434
| null |
Validated
| null | null |
null |
{
"abstract": " In this paper, we revisit the recently established theoretical guarantees for\nthe convergence of the Langevin Monte Carlo algorithm of sampling from a smooth\nand (strongly) log-concave density. We improve the existing results when the\nconvergence is measured in the Wasserstein distance and provide further\ninsights on the very tight relations between, on the one hand, the Langevin\nMonte Carlo for sampling and, on the other hand, the gradient descent for\noptimization. Finally, we also establish guarantees for the convergence of a\nversion of the Langevin Monte Carlo algorithm that is based on noisy\nevaluations of the gradient.\n",
"title": "Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent"
}
| null | null | null | null | true | null |
10435
| null |
Default
| null | null |
null |
{
"abstract": " We employ the Grand Canonical Adaptive Resolution Molecular Dynamics\nTechnique (GC-AdResS) to test the spatial locality of the 1-ethyl 3-methyl\nimidazolium chloride liquid. In GC-AdResS atomistic details are kept only in an\nopen sub-region of the system while the environment is treated at\ncoarse-grained level, thus if spatial quantities calculated in such a\nsub-region agree with the equivalent quantities calculated in a full atomistic\nsimulation then the atomistic degrees of freedom outside the sub-region play a\nnegligible role. The size of the sub-region fixes the degree of spatial\nlocality of a certain quantity. We show that even for sub-regions whose radius\ncorresponds to the size of a few molecules, spatial properties are reasonably\n{reproduced} thus suggesting a higher degree of spatial locality, a hypothesis\nput forward also by other {researchers} and that seems to play an important\nrole for the characterization of fundamental properties of a large class of\nionic liquids.\n",
"title": "Probing Spatial Locality in Ionic Liquids with the Grand Canonical Adaptive Resolution Molecular Dynamics Technique"
}
| null | null |
[
"Physics"
] | null | true | null |
10436
| null |
Validated
| null | null |
null |
{
"abstract": " Daily operation of a large-scale experiment is a challenging task,\nparticularly from perspectives of routine monitoring of quality for data being\ntaken. We describe an approach that uses Machine Learning for the automated\nsystem to monitor data quality, which is based on partial use of data qualified\nmanually by detector experts. The system automatically classifies marginal\ncases: both of good an bad data, and use human expert decision to classify\nremaining \"grey area\" cases.\nThis study uses collision data collected by the CMS experiment at LHC in\n2010. We demonstrate that proposed workflow is able to automatically process at\nleast 20\\% of samples without noticeable degradation of the result.\n",
"title": "Towards automation of data quality system for CERN CMS experiment"
}
| null | null | null | null | true | null |
10437
| null |
Default
| null | null |
null |
{
"abstract": " The aim of this paper is to find the approximate solution of HIV infection\nmodel of CD4+T cells. For this reason, the homotopy analysis transform method\n(HATM) is applied. The presented method is combination of traditional homotopy\nanalysis method (HAM) and the Laplace transformation. The convergence of\npresented method is discussed by preparing a theorem which shows the\ncapabilities of method. The numerical results are shown for different values of\niterations. Also, the regions of convergence are demonstrated by plotting\nseveral h-curves. Furthermore in order to show the efficiency and accuracy of\nmethod, the residual error for different iterations are presented.\n",
"title": "Solving a non-linear model of HIV infection for CD4+T cells by combining Laplace transformation and Homotopy analysis"
}
| null | null | null | null | true | null |
10438
| null |
Default
| null | null |
null |
{
"abstract": " It is well known that the \"store language\" of every pushdown automaton -- the\nset of store configurations (state and stack contents) that can appear as an\nintermediate step in accepting computations -- is a regular language. Here many\nmodels of language acceptors with various data structures are examined, along\nwith a study of their store languages. For each model, an attempt is made to\nfind the simplest model that accepts their store languages. Some connections\nbetween store languages of one-way and two-way machines generally are\ndemonstrated, as with connections between nondeterministic and deterministic\nmachines. A nice application of these store language results is also presented,\nshowing a general technique for proving families accepted by many deterministic\nmodels are closed under right quotient with regular languages, resolving some\nopen questions (and significantly simplifying proofs for others that are known)\nin the literature. Lower bounds on the space complexity for recognizing store\nlanguages for the languages to be non-regular are obtained.\n",
"title": "On Store Languages of Language Acceptors"
}
| null | null | null | null | true | null |
10439
| null |
Default
| null | null |
null |
{
"abstract": " Cu(pyz)(NO3)2 is a quasi one-dimensional molecular antiferromagnet that\nexhibits three dimensional long-range magnetic order below TN=110 mK due to the\npresence of weak inter-chain exchange couplings. Here we compare calculations\nof the three largest exchange coupling constants in this system using two\ntechniques based on plane-wave basis-set density functional theory: (i) a dimer\nfragment approach and (ii) an approach using periodic boundary conditions. The\ncalculated values of the large intrachain coupling constant are found to be\nconsistent with experiment, showing the expected level of variation between\ndifferent techniques and implementations. However, the interchain coupling\nconstants are found to be smaller than the current limits on the resolution of\nthe calculations. This is due to the computational limitations on convergence\nof absolute energy differences with respect to basis set, which are larger than\nthe inter-chain couplings themselves. Our results imply that errors resulting\nfrom such limitations are inherent in the evaluation of small exchange\nconstants in systems of this sort, and that many previously reported results\nshould therefore be treated with caution.\n",
"title": "Exchange constants in molecule-based magnets derived from density functional methods"
}
| null | null | null | null | true | null |
10440
| null |
Default
| null | null |
null |
{
"abstract": " The session search task aims at best serving the user's information need\ngiven her previous search behavior during the session. We propose an extended\nrelevance model that captures the user's dynamic information need in the\nsession. Our relevance modelling approach is directly driven by the user's\nquery reformulation (change) decisions and the estimate of how much the user's\nsearch behavior affects such decisions. Overall, we demonstrate that, the\nproposed approach significantly boosts session search performance.\n",
"title": "An Extended Relevance Model for Session Search"
}
| null | null | null | null | true | null |
10441
| null |
Default
| null | null |
null |
{
"abstract": " Recently, graph neural networks (GNNs) have revolutionized the field of graph\nrepresentation learning through effectively learned node embeddings, and\nachieved state-of-the-art results in tasks such as node classification and link\nprediction. However, current GNN methods are inherently flat and do not learn\nhierarchical representations of graphs---a limitation that is especially\nproblematic for the task of graph classification, where the goal is to predict\nthe label associated with an entire graph. Here we propose DiffPool, a\ndifferentiable graph pooling module that can generate hierarchical\nrepresentations of graphs and can be combined with various graph neural network\narchitectures in an end-to-end fashion. DiffPool learns a differentiable soft\ncluster assignment for nodes at each layer of a deep GNN, mapping nodes to a\nset of clusters, which then form the coarsened input for the next GNN layer.\nOur experimental results show that combining existing GNN methods with DiffPool\nyields an average improvement of 5-10% accuracy on graph classification\nbenchmarks, compared to all existing pooling approaches, achieving a new\nstate-of-the-art on four out of five benchmark data sets.\n",
"title": "Hierarchical Graph Representation Learning with Differentiable Pooling"
}
| null | null | null | null | true | null |
10442
| null |
Default
| null | null |
null |
{
"abstract": " People participate and activate in online social networks and thus tremendous\namount of network data is generated; data regarding their interactions,\ninterests and activities. Some people search for specific questions through\nonline social platforms such as forums and they may receive a suitable response\nvia experts. To categorize people as experts and to evaluate their willingness\nto cooperate, one can use ranking and cooperation problems from complex\nnetworks. In this paper, we investigate classical ranking algorithms besides\nthe prisoner dilemma game to simulate cooperation and defection of agents. We\ncompute the correlation among the node rank and node cooperativity via three\nstrategies. The first strategy is involved in node level; however, other\nstrategies are calculated regarding neighborhood of nodes. We find out\ncorrelations among specific ranking algorithms and cooperativtiy of nodes. Our\nobservations may be applied to estimate the propensity of people (experts) to\ncooperate in future based on their ranking values.\n",
"title": "Ranking and Cooperation in Real-World Complex Networks"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10443
| null |
Validated
| null | null |
null |
{
"abstract": " This note continues our previous work on special secant defective\n(specifically, conic connected and local quadratic entry locus) and dual\ndefective manifolds. These are now well understood, except for the prime Fano\nones. Here we add a few remarks on this case, completing the results in our\npapers \\cite{LQEL I}, \\cite{LQEL II}, \\cite{CC}, \\cite{HC} and \\cite{DD}; see\nalso the recent book \\cite{Russo}\n",
"title": "Remarks on defective Fano manifolds"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10444
| null |
Validated
| null | null |
null |
{
"abstract": " The Met Office's weather and climate simulation code the Unified Model is\nused for both operational Numerical Weather Prediction and Climate modelling.\nThe computational performance of the model running on parallel supercomputers\nis a key consideration. A Krylov sub-space solver is employed to solve the\nequations of the dynamical core of the model, known as ENDGame. These describe\nthe evolution of the Earth's atmosphere. Typically, 64-bit precision is used\nthroughout weather and climate applications. This work presents a\nmixed-precision implementation of the solver, the beneficial effect on run-time\nand the impact on solver convergence. The complex interplay of errors arising\nfrom accumulated round-off in floating-point arithmetic and other numerical\neffects is discussed. A careful analysis is required, however, the\nmixed-precision solver is now employed in the operational forecast to satisfy\nrun-time constraints without compromising the accuracy of the solution.\n",
"title": "Precision of the ENDGame: Mixed-precision arithmetic in the iterative solver of the Unified Model"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10445
| null |
Validated
| null | null |
null |
{
"abstract": " Self Organizing Networks (SONs) are considered as vital deployments towards\nupcoming dense cellular networks. From a mobile carrier point of view,\ncontinuous coverage optimization is critical for better user perceptions. The\nmajority of SON contributions introduce novel algorithms that optimize specific\nperformance metrics. However, they require extensive processing delays and\nadvanced knowledge of network statistics that may not be available. In this\nwork, a progressive Autonomous Coverage Optimization (ACO) method combined with\nadaptive cell dimensioning is proposed. The proposed method emphasizes the fact\nthat the effective cell coverage is a variant on actual user distributions. ACO\nalgorithm builds a generic Space-Time virtual coverage map per cell to detect\ncoverage holes in addition to limited or extended coverage conditions.\nProgressive levels of optimization are followed to timely resolve coverage\nissues with maintaining optimization stability. Proposed ACO is verified under\nboth simulations and practical deployment in a pilot cluster for a worldwide\nmobile carrier. Key Performance Indicators show that proposed ACO method\nsignificantly enhances system coverage and performance.\n",
"title": "Intra-Cluster Autonomous Coverage Optimization For Dense LTE-A Networks"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10446
| null |
Validated
| null | null |
null |
{
"abstract": " In topological semimetals the Dirac points can form zero-dimensional and\none-dimensional manifolds, as predicted for Dirac/Weyl semimetals and\ntopological nodal line semimetals, respectively. Here, based on\nfirst-principles calculations, we predict a topological nodal line semimetal\nphase in the two-dimensional compounds $X_2Y$ ($X$=Ca, Sr, and Ba; $Y$=As, Sb,\nand Bi) in the absence of spin-orbit coupling (SOC) with a band inversion at\nthe M point. The mirror symmetry as well as the electrostatic interaction, that\ncan be engineered via strain, are responsible for the nontrivial phase. In\naddition, we demonstrate that the exotic edge states can be also obtained\nwithout and with SOC although a tiny gap appears at the nodal line for the bulk\nstates when SOC is included.\n",
"title": "Two-dimensional topological nodal line semimetal in layered $X_2Y$ ($X$ = Ca, Sr, and Ba; $Y$ = As, Sb, and Bi)"
}
| null | null | null | null | true | null |
10447
| null |
Default
| null | null |
null |
{
"abstract": " A many-valued modal logic is introduced that combines the usual Kripke frame\nsemantics of the modal logic K with connectives interpreted locally at worlds\nby lattice and group operations over the real numbers. A labelled tableau\nsystem is provided and a coNEXPTIME upper bound obtained for checking validity\nin the logic. Focussing on the modal-multiplicative fragment, the labelled\ntableau system is then used to establish completeness for a sequent calculus\nthat admits cut-elimination and an axiom system that extends the multiplicative\nfragment of Abelian logic.\n",
"title": "A Real-Valued Modal Logic"
}
| null | null | null | null | true | null |
10448
| null |
Default
| null | null |
null |
{
"abstract": " We construct a sequence of compact, oriented, embedded, two-dimensional\nsurfaces of genus one into Euclidean 3-space with prescribed, almost constant,\nmean curvature of the form $H(X)=1+{A}{|X|^{-\\gamma}}$ for $|X|$ large, when\n$A<0$ and $\\gamma\\in(0,2)$. Such surfaces are close to sections of unduloids\nwith small necksize, folded along circumferences centered at the origin and\nwith larger and larger radii. The construction involves a deep study of the\ncorresponding Jacobi operators, an application of the Lyapunov-Schmidt\nreduction method and some variational argument.\n",
"title": "Embedded tori with prescribed mean curvature"
}
| null | null | null | null | true | null |
10449
| null |
Default
| null | null |
null |
{
"abstract": " Let $\\pi $ be an irreducible smooth complex representation of a general\nlinear $p$-adic group and let $\\sigma $ be an irreducible complex supercuspidal\nrepresentation of a classical $p$-adic group of a given type, so that\n$\\pi\\otimes\\sigma $ is a representation of a standard Levi subgroup of a\n$p$-adic classical group of higher rank. We show that the reducibility of the\nrepresentation of the appropriate $p$-adic classical group obtained by\n(normalized) parabolic induction from $\\pi\\otimes\\sigma $ does not depend on\n$\\sigma $, if $\\sigma $ is \"separated\" from the supercuspidal support of $\\pi\n$. (Here, \"separated\" means that, for each factor $\\rho $ of a representation\nin the supercuspidal support of $\\pi $, the representation parabolically\ninduced from $\\rho\\otimes\\sigma $ is irreducible.) This was conjectured by E.\nLapid and M. Tadić. (In addition, they proved, using results of C. Jantzen,\nthat this induced representation is always reducible if the supercuspidal\nsupport is not separated.)\nMore generally, we study, for a given set $I$ of inertial orbits of\nsupercuspidal representations of $p$-adic general linear groups, the category\n$\\CC _{I,\\sigma}$ of smooth complex finitely generated representations of\nclassical $p$-adic groups of fixed type, but arbitrary rank, and supercuspidal\nsupport given by $\\sigma $ and $I$, show that this category is equivalent to a\ncategory of finitely generated right modules over a direct sum of tensor\nproducts of extended affine Hecke algebras of type $A$, $B$ and $D$ and\nestablish functoriality properties, relating categories with disjoint $I$'s. In\nthis way, we extend results of C. Jantzen who proved a bijection between\nirreducible representations corresponding to these categories. The proof of the\nabove reducibility result is then based on Hecke algebra arguments, using\nKato's exotic geometry.\n",
"title": "On the reducibility of induced representations for classical p-adic groups and related affine Hecke algebras"
}
| null | null | null | null | true | null |
10450
| null |
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| null | null |
null |
{
"abstract": " The purpose of this paper is to show that functions that derivate the\ntwo-variable product function and one of the exponential, trigonometric or\nhyperbolic functions are also standard derivations. The more general problem\nconsidered is to describe finite sets of differentiable functions such that\nderivations with respect to this set are automatically standard derivations.\n",
"title": "On derivations with respect to finite sets of smooth functions"
}
| null | null | null | null | true | null |
10451
| null |
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| null | null |
null |
{
"abstract": " The RNiO$_3$ perovskites are known to order antiferromagnetically below a\nmaterial-dependent Néel temperature $T_\\text{N}$. We report experimental\nevidence indicating the existence of a second magnetically-ordered phase in\nTlNiO$_3$ above $T_\\text{N} = 104$ K, obtained using nuclear magnetic resonance\nand muon spin rotation spectroscopy. The new phase, which persists up to a\ntemperature $T_\\text{N}^* = 202$ K, is suppressed by the application of an\nexternal magnetic field of approximately 1 T. It is not yet known if such a\nphase also exists in other perovskite nickelates.\n",
"title": "A new magnetic phase in the nickelate perovskite TlNiO$_3$"
}
| null | null | null | null | true | null |
10452
| null |
Default
| null | null |
null |
{
"abstract": " During routine state space circuit analysis of an arbitrarily connected set\nof nodes representing a lossless LC network, a matrix was formed that was\nobserved to implicitly capture connectivity of the nodes in a graph similar to\nthe conventional incidence matrix, but in a slightly different manner. This\nmatrix has only 0, 1 or -1 as its elements. A sense of direction (of the graph\nformed by the nodes) is inherently encoded in the matrix because of the\npresence of -1. It differs from the incidence matrix because of leaving out the\ndatum node from the matrix. Calling this matrix as forward adjacency matrix, it\nwas found that its inverse also displays useful and interesting physical\nproperties when a specific style of node-indexing is adopted for the nodes in\nthe graph. The graph considered is connected but does not have any closed\nloop/cycle (corresponding to closed loop of inductors in a circuit) as with its\npresence the matrix is not invertible. Incidentally, by definition the graph\nbeing considered is a tree. The properties of the forward adjacency matrix and\nits inverse, along with rigorous proof, are presented.\n",
"title": "On the Inverse of Forward Adjacency Matrix"
}
| null | null | null | null | true | null |
10453
| null |
Default
| null | null |
null |
{
"abstract": " We discuss the practical problems arising when constructing any (new or old)\nscales on slide rules, i.e. realizing the theory in the practice. This might\nhelp anyone in planning and realizing (mainly the magnitude and labeling of)\nnew scales on slide rules in the future. In Sections 1-7 we deal with technical\nproblems, Section 8 is devoted to the relationship among different scales. In\nthe last Section we provide an interesting fact as a surprise to those readers\nwho wish to skip this long article.\n",
"title": "Constructing and Understanding New and Old Scales on Slide Rules"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10454
| null |
Validated
| null | null |
null |
{
"abstract": " Let $V$ be a minimal valuation overring of an integral domain $D$ and let\n$\\mathrm{Zar}(D)$ be the Zariski space of the valuation overrings of $D$.\nStarting from a result in the theory of semistar operations, we prove a\ncriterion under which the set $\\mathrm{Zar}(D)\\setminus\\{V\\}$ is not compact.\nWe then use it to prove that, in many cases, $\\mathrm{Zar}(D)$ is not a\nNoetherian space, and apply it to the study of the spaces of Kronecker function\nrings and of Noetherian overrings.\n",
"title": "Non-compact subsets of the Zariski space of an integral domain"
}
| null | null | null | null | true | null |
10455
| null |
Default
| null | null |
null |
{
"abstract": " We present a finite-temperature extension of the retarded cumulant Green's\nfunction for calculations of exited-state and thermodynamic properties of\nelectronic systems. The method incorporates a cumulant to leading order in the\nscreened Coulomb interaction $W$ and improves excited state properties compared\nto the $GW$ approximation of many-body perturbation theory. Results for the\nhomogeneous electron gas are presented for a wide range of densities and\ntemperatures, from cool to warm dense matter regime, which reveal several\nhitherto unexpected properties. For example, correlation effects remain strong\nat high $T$ while the exchange-correlation energy becomes small. In addition,\nthe spectral function broadens and damping increases with temperature, blurring\nthe usual quasi-particle picture. Similarly Compton scattering exhibits\nsubstantial many-body corrections that persist at normal densities and\nintermediate $T$. Results for exchange-correlation energies and potentials are\nin good agreement with existing theories and finite-temperature DFT\nfunctionals.\n",
"title": "Finite temperature Green's function approach for excited state and thermodynamic properties of cool to warm dense matter"
}
| null | null | null | null | true | null |
10456
| null |
Default
| null | null |
null |
{
"abstract": " Given a graph $ G $ with $ n $ vertices and a set $ S $ of $ n $ points in\nthe plane, a point-set embedding of $ G $ on $ S $ is a planar drawing such\nthat each vertex of $ G $ is mapped to a distinct point of $ S $. A\nstraight-line point-set embedding is a point-set embedding with no edge bends\nor curves. The point-set embeddability problem is NP-complete, even when $ G $\nis $ 2 $-connected and $ 2 $-outerplanar. It has been solved polynomially only\nfor a few classes of planar graphs. Suppose that $ S $ is the set of vertices\nof a simple polygon. A straight-line polygon embedding of a graph is a\nstraight-line point-set embedding of the graph onto the vertices of the polygon\nwith no crossing between edges of graph and the edges of polygon. In this\npaper, we present $ O(n) $-time algorithms for polygon embedding of path and\ncycle graphs in simple convex polygon and same time algorithms for polygon\nembedding of path and cycle graphs in a large type of simple polygons where $n$\nis the number of vertices of the polygon.\n",
"title": "Geometric Embedding of Path and Cycle Graphs in Pseudo-convex Polygons"
}
| null | null | null | null | true | null |
10457
| null |
Default
| null | null |
null |
{
"abstract": " Despite significant recent progress in the area of Brain-Computer Interface,\nthere are numerous shortcomings associated with collecting\nElectroencephalography (EEG) signals in real-world environments. These include,\nbut are not limited to, subject and session data variance, long and arduous\ncalibration processes and performance generalisation issues across\ndifferentsubjects or sessions. This implies that many downstream applications,\nincluding Steady State Visual Evoked Potential (SSVEP) based classification\nsystems, can suffer from a shortage of reliable data. Generating meaningful and\nrealistic synthetic data can therefore be of significant value in circumventing\nthis problem. We explore the use of modern neural-based generative models\ntrained on a limited quantity of EEG data collected from different subjects to\ngenerate supplementary synthetic EEG signal vectors subsequently utilised to\ntrain an SSVEP classifier. Extensive experimental analyses demonstrate the\nefficacy of our generated data, leading to significant improvements across a\nvariety of evaluations, with the crucial task of cross-subject generalisation\nimproving by over 35% with the use of synthetic data.\n",
"title": "Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification"
}
| null | null | null | null | true | null |
10458
| null |
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| null | null |
null |
{
"abstract": " In this paper, we investigate the parametric knapsack problem, in which the\nitem profits are affine functions depending on a real-valued parameter. The aim\nis to provide a solution for all values of the parameter. It is well-known that\nany exact algorithm for the problem may need to output an exponential number of\nknapsack solutions. We present a fully polynomial-time approximation scheme\n(FPTAS) for the problem that, for any desired precision $\\varepsilon \\in\n(0,1)$, computes $(1-\\varepsilon)$-approximate solutions for all values of the\nparameter. This is the first FPTAS for the parametric knapsack problem that\ndoes not require the slopes and intercepts of the affine functions to be\nnon-negative but works for arbitrary integral values. Our FPTAS outputs\n$\\mathcal{O}(\\frac{n^2}{\\varepsilon})$ knapsack solutions and runs in strongly\npolynomial-time $\\mathcal{O}(\\frac{n^4}{\\varepsilon^2})$. Even for the special\ncase of positive input data, this is the first FPTAS with a strongly polynomial\nrunning time. We also show that this time bound can be further improved to\n$\\mathcal{O}(\\frac{n^2}{\\varepsilon} \\cdot A(n,\\varepsilon))$, where\n$A(n,\\varepsilon)$ denotes the running time of any FPTAS for the traditional\n(non-parametric) knapsack problem.\n",
"title": "An FPTAS for the parametric knapsack problem"
}
| null | null |
[
"Computer Science",
"Mathematics"
] | null | true | null |
10459
| null |
Validated
| null | null |
null |
{
"abstract": " Dozens of new models on fixation prediction are published every year and\ncompared on open benchmarks such as MIT300 and LSUN. However, progress in the\nfield can be difficult to judge because models are compared using a variety of\ninconsistent metrics. Here we show that no single saliency map can perform well\nunder all metrics. Instead, we propose a principled approach to solve the\nbenchmarking problem by separating the notions of saliency models, maps and\nmetrics. Inspired by Bayesian decision theory, we define a saliency model to be\na probabilistic model of fixation density prediction and a saliency map to be a\nmetric-specific prediction derived from the model density which maximizes the\nexpected performance on that metric given the model density. We derive these\noptimal saliency maps for the most commonly used saliency metrics (AUC, sAUC,\nNSS, CC, SIM, KL-Div) and show that they can be computed analytically or\napproximated with high precision. We show that this leads to consistent\nrankings in all metrics and avoids the penalties of using one saliency map for\nall metrics. Our method allows researchers to have their model compete on many\ndifferent metrics with state-of-the-art in those metrics: \"good\" models will\nperform well in all metrics.\n",
"title": "Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics"
}
| null | null | null | null | true | null |
10460
| null |
Default
| null | null |
null |
{
"abstract": " This study focusses on self-balancing microgrids to smartly utilize and\nprevent overdrawing of available power capacity of the grid. A distributed\nframework for automated distribution of optimal power demand is proposed, where\nall building in a microgrid dynamically and simultaneously adjusts their own\npower consumption to reach their individual optimal power demands while\ncooperatively striving to maintain the overall grid stable. Emphasis has been\ngiven to aspects of algorithm that yields lower time of convergence and is\ndemonstrated through quantitative and qualitative analysis of simulation\nresults.\n",
"title": "Distributed Framework for Optimal Demand Distribution in Self-Balancing Microgrid"
}
| null | null | null | null | true | null |
10461
| null |
Default
| null | null |
null |
{
"abstract": " Optical frequency combs (OFC) provide a convenient reference for the\nfrequency stabilization of continuous-wave lasers. We demonstrate a frequency\ncontrol method relying on tracking over a wide range and stabilizing the beat\nnote between the laser and the OFC. The approach combines fast frequency ramps\non a millisecond timescale in the entire mode-hop free tuning range of the\nlaser and precise stabilization to single frequencies. We apply it to a\ncommercially available optical parametric oscillator (OPO) and demonstrate\ntuning over more than 60 GHz with a ramping speed up to 3 GHz/ms. Frequency\nramps spanning 15 GHz are performed in less than 10 ms, with the OPO instantly\nrelocked to the OFC after the ramp at any desired frequency. The developed\ncontrol hardware and software is able to stabilize the OPO to sub-MHz precision\nand to perform sequences of fast frequency ramps automatically.\n",
"title": "Fast, precise, and widely tunable frequency control of an optical parametric oscillator referenced to a frequency comb"
}
| null | null |
[
"Physics"
] | null | true | null |
10462
| null |
Validated
| null | null |
null |
{
"abstract": " A linear operator $T$ between two lattice-normed spaces is said to be\n$p$-compact if, for any $p$-bounded net $x_\\alpha$, the net $Tx_\\alpha$ has a\n$p$-convergent subnet. $p$-Compact operators generalize several known classes\nof operators such as compact, weakly compact, order weakly compact,\n$AM$-compact operators, etc. Similar to $M$-weakly and $L$-weakly compact\noperators, we define $p$-$M$-weakly and $p$-$L$-weakly compact operators and\nstudy some of their properties. We also study $up$-continuous and $up$-compact\noperators between lattice-normed vector lattices.\n",
"title": "Compact-Like Operators in Lattice-Normed Spaces"
}
| null | null | null | null | true | null |
10463
| null |
Default
| null | null |
null |
{
"abstract": " We present the results of a pilot near-infrared (NIR) spectroscopic campaign\nof five very massive galaxies ($\\log(\\text{M}_\\star/\\text{M}_\\odot)>11.45$) in\nthe range of $1.7<z<2.7$. We measure an absorption feature redshift for one\ngalaxy at $z_\\text{spec}=2.000\\pm0.006$. For the remaining galaxies, we combine\nthe photometry with the continuum from the spectra to estimate continuum\nredshifts and stellar population properties. We define a continuum redshift\n($z_{\\rm cont}$ ) as one in which the redshift is estimated probabilistically\nusing EAZY from the combination of catalog photometry and the observed\nspectrum. We derive the uncertainties on the stellar population synthesis\nproperties using a Monte Carlo simulation and examine the correlations between\nthe parameters with and without the use of the spectrum in the modeling of the\nspectral energy distributions (SEDs). The spectroscopic constraints confirm the\nextreme stellar masses of the galaxies in our sample. We find that three out of\nfive galaxies are quiescent (star formation rate of $\\lesssim 1\nM_\\odot~yr^{-1}$) with low levels of dust obscuration ($A_{\\rm V} < 1$) , that\none galaxy displays both high levels of star formation and dust obscuration\n(${\\rm SFR} \\approx 300 M_\\odot~{\\rm yr}^{-1}$, $A_{\\rm V} \\approx 1.7$~mag),\nand that the remaining galaxy has properties that are intermediate between the\nquiescent and star-forming populations.\n",
"title": "Near-infrared spectroscopy of 5 ultra-massive galaxies at 1.7 < z < 2.7"
}
| null | null | null | null | true | null |
10464
| null |
Default
| null | null |
null |
{
"abstract": " Mining relationships between treatment(s) and medical problem(s) is vital in\nthe biomedical domain. This helps in various applications, such as decision\nsupport system, safety surveillance, and new treatment discovery. We propose a\ndeep learning approach that utilizes both word level and sentence-level\nrepresentations to extract the relationships between treatment and problem.\nWhile deep learning techniques demand a large amount of data for training, we\nmake use of a rule-based system particularly for relationship classes with\nfewer samples. Our final relations are derived by jointly combining the results\nfrom deep learning and rule-based models. Our system achieved a promising\nperformance on the relationship classes of I2b2 2010 relation extraction task.\n",
"title": "A hybrid deep learning approach for medical relation extraction"
}
| null | null | null | null | true | null |
10465
| null |
Default
| null | null |
null |
{
"abstract": " Internet or things (IoT) is changing our daily life rapidly. Although new\ntechnologies are emerging everyday and expanding their influence in this\nrapidly growing area, many classic theories can still find their places. In\nthis paper, we study the important applications of the classic network coding\ntheory in two important components of Internet of things, including the IoT\ncore network, where data is sensed and transmitted, and the distributed cloud\nstorage, where the data generated by the IoT core network is stored. First we\npropose an adaptive network coding (ANC) scheme in the IoT core network to\nimprove the transmission efficiency. We demonstrate the efficacy of the scheme\nand the performance advantage over existing schemes through simulations. %Next\nwe study the application of network coding in the distributed cloud storage.\nNext we introduce the optimal storage allocation problem in the network coding\nbased distributed cloud storage, which aims at searching for the most reliable\nallocation that distributes the $n$ data components into $N$ data centers,\ngiven the failure probability $p$ of each data center. Then we propose a\npolynomial-time optimal storage allocation (OSA) scheme to solve the problem.\nBoth the theoretical analysis and the simulation results show that the storage\nreliability could be greatly improved by the OSA scheme.\n",
"title": "Performance Optimization of Network Coding Based Communication and Reliable Storage in Internet of Things"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10466
| null |
Validated
| null | null |
null |
{
"abstract": " A probabilistic description is essential for understanding growth processes\nfar from equilibrium. In this paper, we compute time-dependent Probability\nDensity Functions (PDFs) in order to investigate stochastic logistic and\nGompertz models, which are two of the most popular growth models. We consider\ndifferent types of short-correlated internal (multiplicative) and external\n(additive) stochastic noises and compare the time-dependent PDFs in the two\nmodels, elucidating the effects of the additive and multiplicative noises on\nthe form of PDFs. We demonstrate an interesting transition from a unimodal to a\nbimodal PDF as the multiplicative noise increases for a fixed value of the\nadditive noise. A much weaker (leaky) attractor in the Gompertz model leads to\na significant (singular) growth of the population of a very small size. We\npoint out the limitation of using stationary PDFs, mean value and variance in\nunderstanding statistical properties of the growth far from equilibrium,\nhighlighting the importance of time-dependent PDFs. We further compare these\ntwo models from the perspective of information change that occurs during the\ngrowth process. Specifically, we define an infinitesimal distance at any time\nby comparing two PDFs at times infinitesimally apart and sum these distances in\ntime. The total distance along the trajectory quantifies the total number of\ndifferent states that the system undergoes in time, and is called the\ninformation length. We show that the time-evolution of the two models become\nmore similar when measured in units of the information length and point out the\nmerit of using the information length in unifying and understanding the dynamic\nevolution of different growth processes.\n",
"title": "Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models"
}
| null | null | null | null | true | null |
10467
| null |
Default
| null | null |
null |
{
"abstract": " In the Steiner Forest problem, we are given a graph and a collection of\nsource-sink pairs, and the goal is to find a subgraph of minimum total length\nsuch that all pairs are connected. The problem is APX-Hard and can be\n2-approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein,\nand Ravi from 1995.\nWe give a local-search-based constant-factor approximation for the problem.\nLocal search brings in new techniques to an area that has for long not seen any\nimprovements and might be a step towards a combinatorial algorithm for the more\ngeneral survivable network design problem. Moreover, local search was an\nessential tool to tackle the dynamic MST/Steiner Tree problem, whereas dynamic\nSteiner Forest is still wide open.\nIt is easy to see that any constant factor local search algorithm requires\nsteps that add/drop many edges together. We propose natural local moves which,\nat each step, either (a) add a shortest path in the current graph and then drop\na bunch of inessential edges, or (b) add a set of edges to the current\nsolution. This second type of moves is motivated by the potential function we\nuse to measure progress, combining the cost of the solution with a penalty for\neach connected component. Our carefully-chosen local moves and potential\nfunction work in tandem to eliminate bad local minima that arise when using\nmore traditional local moves.\n",
"title": "A Local-Search Algorithm for Steiner Forest"
}
| null | null | null | null | true | null |
10468
| null |
Default
| null | null |
null |
{
"abstract": " Antenna current optimization is often used to analyze the optimal performance\nof antennas. Antenna performance can be quantified in e.g., minimum Q-factor\nand efficiency. The performance of MIMO antennas is more involved and, in\ngeneral, a single parameter is not sufficient to quantify it. Here, the\ncapacity of an idealized channel is used as the main performance quantity. An\noptimization problem in the current distribution for optimal capacity, measured\nin spectral efficiency, given a fixed Q-factor and efficiency is formulated as\na semi-definite optimization problem. A model order reduction based on\ncharacteristic and energy modes is employed to improve the computational\nefficiency. The performance bound is illustrated by solving the optimization\nproblem numerically for rectangular plates and spherical shells.\n",
"title": "Fundamental bounds on MIMO antennas"
}
| null | null | null | null | true | null |
10469
| null |
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| null | null |
null |
{
"abstract": " The knowledge regarding the function of proteins is necessary as it gives a\nclear picture of biological processes. Nevertheless, there are many protein\nsequences found and added to the databases but lacks functional annotation. The\nlaboratory experiments take a considerable amount of time for annotation of the\nsequences. This arises the need to use computational techniques to classify\nproteins based on their functions. In our work, we have collected the data from\nSwiss-Prot containing 40433 proteins which is grouped into 30 families. We pass\nit to recurrent neural network(RNN), long short term memory(LSTM) and gated\nrecurrent unit(GRU) model and compare it by applying trigram with deep neural\nnetwork and shallow neural network on the same dataset. Through this approach,\nwe could achieve maximum of around 78% accuracy for the classification of\nprotein families.\n",
"title": "DeepProteomics: Protein family classification using Shallow and Deep Networks"
}
| null | null |
[
"Statistics"
] | null | true | null |
10470
| null |
Validated
| null | null |
null |
{
"abstract": " In recent years, several powerful techniques have been developed to design\n{\\em randomized} polynomial-space parameterized algorithms. In this paper, we\nintroduce an enhancement of color coding to design deterministic\npolynomial-space parameterized algorithms. Our approach aims at reducing the\nnumber of random choices by exploiting the special structure of a solution.\nUsing our approach, we derive the following deterministic algorithms (see\nIntroduction for problem definitions).\n1. Polynomial-space $O^*(3.86^k)$-time (exponential-space $O^*(3.41^k)$-time)\nalgorithm for {\\sc $k$-Internal Out-Branching}, improving upon the previously\nfastest {\\em exponential-space} $O^*(5.14^k)$-time algorithm for this problem.\n2. Polynomial-space $O^*((2e)^{k+o(k)})$-time (exponential-space\n$O^*(4.32^k)$-time) algorithm for {\\sc $k$-Colorful Out-Branching} on\narc-colored digraphs and {\\sc $k$-Colorful Perfect Matching} on planar\nedge-colored graphs.\nTo obtain our polynomial space algorithms, we show that $(n,k,\\alpha\nk)$-splitters ($\\alpha\\ge 1$) and in particular $(n,k)$-perfect hash families\ncan be enumerated one by one with polynomial delay.\n",
"title": "Designing Deterministic Polynomial-Space Algorithms by Color-Coding Multivariate Polynomials"
}
| null | null | null | null | true | null |
10471
| null |
Default
| null | null |
null |
{
"abstract": " Let $S$ be a string of length $n$. In this paper we introduce the notion of\n\\emph{string attractor}: a subset of the string's positions $[1,n]$ such that\nevery distinct substring of $S$ has an occurrence crossing one of the\nattractor's elements. We first show that the minimum attractor's size yields\nupper-bounds to the string's repetitiveness as measured by its linguistic\ncomplexity and by the length of its longest repeated substring. We then prove\nthat all known compressors for repetitive strings induce a string attractor\nwhose size is bounded by their associated repetitiveness measure, and can\ntherefore be considered as approximations of the smallest one. Using further\nreductions, we derive the approximation ratios of these compressors with\nrespect to the smallest attractor and solve several open problems related to\nthe asymptotic relations between repetitiveness measures (in particular,\nbetween the the sizes of the Lempel-Ziv factorization, the run-length\nBurrows-Wheeler transform, the smallest grammar, and the smallest macro\nscheme). These reductions directly provide approximation algorithms for the\nsmallest string attractor. We then apply string attractors to solve efficiently\na fundamental problem in the field of compressed computation: we present a\nuniversal compressed data structure for text extraction that improves existing\nstrategies simultaneously for \\emph{all} known dictionary compressors and that,\nby recent lower bounds, almost matches the optimal running time within the\nresulting space. To conclude, we consider generalizations of string attractors\nto labeled graphs, show that the attractor problem is NP-complete on trees, and\nprovide a logarithmic approximation computable in polynomial time.\n",
"title": "String Attractors"
}
| null | null | null | null | true | null |
10472
| null |
Default
| null | null |
null |
{
"abstract": " Purpose of review: This paper presents a review of the current state of the\nart in remote sensing based monitoring of forest disturbances and forest\ndegradation from optical Earth Observation data. Part one comprises an overview\nof currently available optical remote sensing sensors, which can be used for\nforest disturbance and degradation mapping. Part two reviews the two main\ncategories of existing approaches: classical image-to-image change detection\nand time series analysis. Recent findings: With the launch of the Sentinel-2a\nsatellite and available Landsat imagery, time series analysis has become the\nmost promising but also most demanding category of degradation mapping\napproaches. Four time series classification methods are distinguished. The\nmethods are explained and their benefits and drawbacks are discussed. A\nseparate chapter presents a number of recent forest degradation mapping studies\nfor two different ecosystems: temperate forests with a geographical focus on\nEurope and tropical forests with a geographical focus on Africa. Summary: The\nreview revealed that a wide variety of methods for the detection of forest\ndegradation is already available. Today, the main challenge is to transfer\nthese approaches to high resolution time series data from multiple sensors.\nFuture research should also focus on the classification of disturbance types\nand the development of robust up-scalable methods to enable near real time\ndisturbance mapping in support of operational reactive measures.\n",
"title": "Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review"
}
| null | null | null | null | true | null |
10473
| null |
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| null | null |
null |
{
"abstract": " We study the problem of testing for community structure in networks using\nrelations between the observed frequencies of small subgraphs. We propose a\nsimple test for the existence of communities based only on the frequencies of\nthree-node subgraphs. The test statistic is shown to be asymptotically normal\nunder a null assumption of no community structure, and to have power\napproaching one under a composite alternative hypothesis of a degree-corrected\nstochastic block model. We also derive a version of the test that applies to\nmultivariate Gaussian data. Our approach achieves near-optimal detection rates\nfor the presence of community structure, in regimes where the signal-to-noise\nis too weak to explicitly estimate the communities themselves, using existing\ncomputationally efficient algorithms. We demonstrate how the method can be\neffective for detecting structure in social networks, citation networks for\nscientific articles, and correlations of stock returns between companies on the\nS\\&P 500.\n",
"title": "Testing for Global Network Structure Using Small Subgraph Statistics"
}
| null | null | null | null | true | null |
10474
| null |
Default
| null | null |
null |
{
"abstract": " Magnetic activity strongly impacts stellar RVs and the search for small\nplanets. We showed previously that in the solar case it induces RV variations\nwith an amplitude over the cycle on the order of 8 m/s, with signals on short\nand long timescales. The major component is the inhibition of the convective\nblueshift due to plages. We explore a new approach to correct for this major\ncomponent of stellar radial velocities in the case of solar-type stars. The\nconvective blueshift depends on line depths; we use this property to develop a\nmethod that will characterize the amplitude of this effect and to correct for\nthis RV component. We build realistic RV time series corresponding to RVs\ncomputed using different sets of lines, including lines in different depth\nranges. We characterize the performance of the method used to reconstruct the\nsignal without the convective component and the detection limits derived from\nthe residuals. We identified a set of lines which, combined with a global set\nof lines, allows us to reconstruct the convective component with a good\nprecision and to correct for it. For the full temporal sampling, the power in\nthe range 100-500~d significantly decreased, by a factor of 100 for a RV noise\nbelow 30 cm/s. We also studied the impact of noise contributions other than the\nphoton noise, which lead to uncertainties on the RV computation, as well as the\nimpact of the temporal sampling. We found that these other sources of noise do\nnot greatly alter the quality of the correction, although they need a better\nnoise level to reach a similar performance level. A very good correction of the\nconvective component can be achieved providing very good RV noise levels\ncombined with a very good instrumental stability and realistic granulation\nnoise. Under the conditions considered in this paper, detection limits at 480~d\nlower than 1 MEarth could be achieved for RV noise below 15 cm/s.\n",
"title": "A new method of correcting radial velocity time series for inhomogeneous convection"
}
| null | null | null | null | true | null |
10475
| null |
Default
| null | null |
null |
{
"abstract": " Differential testing to solve the oracle problem has been applied in many\nscenarios where multiple supposedly equivalent implementations exist, such as\nmultiple implementations of a C compiler. If the multiple systems disagree on\nthe output for a given test input, we have likely discovered a bug without\nevery having to specify what the expected output is. Research on variational\nanalyses (or variability-aware or family-based analyses) can benefit from\nsimilar ideas. The goal of most variational analyses is to perform an analysis,\nsuch as type checking or model checking, over a large number of configurations\nmuch faster than an existing traditional analysis could by analyzing each\nconfiguration separately. Variational analyses are very suitable for\ndifferential testing, since the existence nonvariational analysis can provide\nthe oracle for test cases that would otherwise be tedious or difficult to\nwrite. In this experience paper, I report how differential testing has helped\nin developing KConfigReader, a tool for translating the Linux kernel's kconfig\nmodel into a propositional formula. Differential testing allows us to quickly\nbuild a large test base and incorporate external tests that avoided many\nregressions during development and made KConfigReader likely the most precise\nkconfig extraction tool available.\n",
"title": "Differential Testing for Variational Analyses: Experience from Developing KConfigReader"
}
| null | null | null | null | true | null |
10476
| null |
Default
| null | null |
null |
{
"abstract": " Explicitly or implicitly, most of dimensionality reduction methods need to\ndetermine which samples are neighbors and the similarity between the neighbors\nin the original highdimensional space. The projection matrix is then learned on\nthe assumption that the neighborhood information (e.g., the similarity) is\nknown and fixed prior to learning. However, it is difficult to precisely\nmeasure the intrinsic similarity of samples in high-dimensional space because\nof the curse of dimensionality. Consequently, the neighbors selected according\nto such similarity might and the projection matrix obtained according to such\nsimilarity and neighbors are not optimal in the sense of classification and\ngeneralization. To overcome the drawbacks, in this paper we propose to let the\nsimilarity and neighbors be variables and model them in low-dimensional space.\nBoth the optimal similarity and projection matrix are obtained by minimizing a\nunified objective function. Nonnegative and sum-to-one constraints on the\nsimilarity are adopted. Instead of empirically setting the regularization\nparameter, we treat it as a variable to be optimized. It is interesting that\nthe optimal regularization parameter is adaptive to the neighbors in\nlow-dimensional space and has intuitive meaning. Experimental results on the\nYALE B, COIL-100, and MNIST datasets demonstrate the effectiveness of the\nproposed method.\n",
"title": "Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction"
}
| null | null | null | null | true | null |
10477
| null |
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| null | null |
null |
{
"abstract": " Blind Source Separation (BSS) is a challenging matrix factorization problem\nthat plays a central role in multichannel imaging science. In a large number of\napplications, such as astrophysics, current unmixing methods are limited since\nreal-world mixtures are generally affected by extra instrumental effects like\nblurring. Therefore, BSS has to be solved jointly with a deconvolution problem,\nwhich requires tackling a new inverse problem: deconvolution BSS (DBSS). In\nthis article, we introduce an innovative DBSS approach, called DecGMCA, based\non sparse signal modeling and an efficient alternative projected least square\nalgorithm. Numerical results demonstrate that the DecGMCA algorithm performs\nvery well on simulations. It further highlights the importance of jointly\nsolving BSS and deconvolution instead of considering these two problems\nindependently. Furthermore, the performance of the proposed DecGMCA algorithm\nis demonstrated on simulated radio-interferometric data.\n",
"title": "Joint Multichannel Deconvolution and Blind Source Separation"
}
| null | null | null | null | true | null |
10478
| null |
Default
| null | null |
null |
{
"abstract": " The monomorphism category $\\mathscr{S}(A, M, B)$ induced by a bimodule\n$_AM_B$ is the subcategory of $\\Lambda$-mod consisting of\n$\\left[\\begin{smallmatrix} X\\\\ Y\\end{smallmatrix}\\right]_{\\phi}$ such that\n$\\phi: M\\otimes_B Y\\rightarrow X$ is a monic $A$-map, where\n$\\Lambda=\\left[\\begin{smallmatrix} A&M\\\\0&B \\end{smallmatrix}\\right]$. In\ngeneral, it is not the monomorphism categories induced by quivers. It could\ndescribe the Gorenstein-projective $\\m$-modules. This monomorphism category is\na resolving subcategory of $\\modcat{\\Lambda}$ if and only if $M_B$ is\nprojective. In this case, it has enough injective objects and Auslander-Reiten\nsequences, and can be also described as the left perpendicular category of a\nunique basic cotilting $\\Lambda$-module. If $M$ satisfies the condition ${\\rm\n(IP)}$, then the stable category of $\\mathscr{S}(A, M, B)$ admits a recollement\nof additive categories, which is in fact a recollement of singularity\ncategories if $\\mathscr{S}(A, M, B)$ is a {\\rm Frobenius} category.\nRingel-Schmidmeier-Simson equivalence between $\\mathscr{S}(A, M, B)$ and its\ndual is introduced. If $M$ is an exchangeable bimodule, then an {\\rm RSS}\nequivalence is given by a $\\Lambda$-$\\Lambda$ bimodule which is a two-sided\ncotilting $\\Lambda$-module with a special property; and the Nakayama functor\n$\\mathcal N_\\m$ gives an {\\rm RSS} equivalence if and only if both $A$ and $B$\nare Frobenius algebras.\n",
"title": "Bimodule monomorphism categories and RSS equivalences via cotilting modules"
}
| null | null | null | null | true | null |
10479
| null |
Default
| null | null |
null |
{
"abstract": " The focus of this paper is on the analysis of the boundary layer and the\nassociated vanishing viscosity limit for two classes of flows with symmetry,\nnamely, Plane-Parallel Channel Flows and Parallel Pipe Flows. We construct\nexplicit boundary layer correctors, which approximate the difference between\nthe Navier-Stokes and the Euler solutions. Using properties of these\ncorrectors, we establish convergence of the Navier-Stokes solution to the Euler\nsolution as viscosity vanishes with optimal rates of convergence. In addition,\nwe investigate vorticity production on the boundary in the limit of vanishing\nviscosity. Our work significantly extends prior work in the literature.\n",
"title": "The Vanishing viscosity limit for some symmetric flows"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10480
| null |
Validated
| null | null |
null |
{
"abstract": " The recent experimental discovery of three-dimensional (3D) materials hosting\na strong Rashba spin-orbit coupling calls for the theoretical investigation of\ntheir transport properties. Here we study the zero temperature dc conductivity\nof a 3D Rashba metal in the presence of static diluted impurities. We show\nthat, at variance with the two-dimensional case, in 3D systems spin-orbit\ncoupling affects dc charge transport in all density regimes. We find in\nparticular that the effect of spin-orbit interaction strongly depends on the\ndirection of the current, and we show that this yields strongly anisotropic\ntransport characteristics. In the dominant spin-orbit coupling regime where\nonly the lowest band is occupied, the SO-induced conductivity anisotropy is\ngoverned entirely by the anomalous component of the renormalized current. We\npropose that measurements of the conductivity anisotropy in bulk Rashba metals\nmay give a direct experimental assessment of the spin-orbit strength.\n",
"title": "Anisotropy of transport in bulk Rashba metals"
}
| null | null | null | null | true | null |
10481
| null |
Default
| null | null |
null |
{
"abstract": " High quality gene models are necessary to expand the molecular and genetic\ntools available for a target organism, but these are available for only a\nhandful of model organisms that have undergone extensive curation and\nexperimental validation over the course of many years. The majority of gene\nmodels present in biological databases today have been identified in draft\ngenome assemblies using automated annotation pipelines that are frequently\nbased on orthologs from distantly related model organisms. Manual curation is\ntime consuming and often requires substantial expertise, but is instrumental in\nimproving gene model structure and identification. Manual annotation may seem\nto be a daunting and cost-prohibitive task for small research communities but\ninvolving undergraduates in community genome annotation consortiums can be\nmutually beneficial for both education and improved genomic resources. We\noutline a workflow for efficient manual annotation driven by a team of\nprimarily undergraduate annotators. This model can be scaled to large teams and\nincludes quality control processes through incremental evaluation. Moreover, it\ngives students an opportunity to increase their understanding of genome biology\nand to participate in scientific research in collaboration with peers and\nsenior researchers at multiple institutions.\n",
"title": "A quick guide for student-driven community genome annotation"
}
| null | null | null | null | true | null |
10482
| null |
Default
| null | null |
null |
{
"abstract": " In the past few years, Convolutional Neural Networks (CNNs) have been\nachieving state-of-the-art performance on a variety of problems. Many companies\nemploy resources and money to generate these models and provide them as an API,\ntherefore it is in their best interest to protect them, i.e., to avoid that\nsomeone else copies them. Recent studies revealed that state-of-the-art CNNs\nare vulnerable to adversarial examples attacks, and this weakness indicates\nthat CNNs do not need to operate in the problem domain (PD). Therefore, we\nhypothesize that they also do not need to be trained with examples of the PD in\norder to operate in it.\nGiven these facts, in this paper, we investigate if a target black-box CNN\ncan be copied by persuading it to confess its knowledge through random\nnon-labeled data. The copy is two-fold: i) the target network is queried with\nrandom data and its predictions are used to create a fake dataset with the\nknowledge of the network; and ii) a copycat network is trained with the fake\ndataset and should be able to achieve similar performance as the target\nnetwork.\nThis hypothesis was evaluated locally in three problems (facial expression,\nobject, and crosswalk classification) and against a cloud-based API. In the\ncopy attacks, images from both non-problem domain and PD were used. All copycat\nnetworks achieved at least 93.7% of the performance of the original models with\nnon-problem domain data, and at least 98.6% using additional data from the PD.\nAdditionally, the copycat CNN successfully copied at least 97.3% of the\nperformance of the Microsoft Azure Emotion API. Our results show that it is\npossible to create a copycat CNN by simply querying a target network as\nblack-box with random non-labeled data.\n",
"title": "Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data"
}
| null | null | null | null | true | null |
10483
| null |
Default
| null | null |
null |
{
"abstract": " Computing the medoid of a large number of points in high-dimensional space is\nan increasingly common operation in many data science problems. We present an\nalgorithm Med-dit which uses O(n log n) distance evaluations to compute the\nmedoid with high probability. Med-dit is based on a connection with the\nmulti-armed bandit problem. We evaluate the performance of Med-dit empirically\non the Netflix-prize and the single-cell RNA-Seq datasets, containing hundreds\nof thousands of points living in tens of thousands of dimensions, and observe a\n5-10x improvement in performance over the current state of the art. Med-dit is\navailable at this https URL\n",
"title": "Medoids in almost linear time via multi-armed bandits"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
10484
| null |
Validated
| null | null |
null |
{
"abstract": " We consider truncated SVD (or spectral cut-off, projection) estimators for a\nprototypical statistical inverse problem in dimension $D$. Since calculating\nthe singular value decomposition (SVD) only for the largest singular values is\nmuch less costly than the full SVD, our aim is to select a data-driven\ntruncation level $\\widehat m\\in\\{1,\\ldots,D\\}$ only based on the knowledge of\nthe first $\\widehat m$ singular values and vectors. We analyse in detail\nwhether sequential {\\it early stopping} rules of this type can preserve\nstatistical optimality. Information-constrained lower bounds and matching upper\nbounds for a residual based stopping rule are provided, which give a clear\npicture in which situation optimal sequential adaptation is feasible. Finally,\na hybrid two-step approach is proposed which allows for classical oracle\ninequalities while considerably reducing numerical complexity.\n",
"title": "Early stopping for statistical inverse problems via truncated SVD estimation"
}
| null | null | null | null | true | null |
10485
| null |
Default
| null | null |
null |
{
"abstract": " Sampling logconcave functions arising in statistics and machine learning has\nbeen a subject of intensive study. Recent developments include analyses for\nLangevin dynamics and Hamiltonian Monte Carlo (HMC). While both approaches have\ndimension-independent bounds for the underlying $\\mathit{continuous}$ processes\nunder sufficiently strong smoothness conditions, the resulting discrete\nalgorithms have complexity and number of function evaluations growing with the\ndimension. Motivated by this problem, in this paper, we give a general\nalgorithm for solving multivariate ordinary differential equations whose\nsolution is close to the span of a known basis of functions (e.g., polynomials\nor piecewise polynomials). The resulting algorithm has polylogarithmic depth\nand essentially tight runtime - it is nearly linear in the size of the\nrepresentation of the solution.\nWe apply this to the sampling problem to obtain a nearly linear\nimplementation of HMC for a broad class of smooth, strongly logconcave\ndensities, with the number of iterations (parallel depth) and gradient\nevaluations being $\\mathit{polylogarithmic}$ in the dimension (rather than\npolynomial as in previous work). This class includes the widely-used loss\nfunction for logistic regression with incoherent weight matrices and has been\nsubject of much study recently. We also give a faster algorithm with $\n\\mathit{polylogarithmic~depth}$ for the more general and standard class of\nstrongly convex functions with Lipschitz gradient. These results are based on\n(1) an improved contraction bound for the exact HMC process and (2) logarithmic\nbounds on the degree of polynomials that approximate solutions of the\ndifferential equations arising in implementing HMC.\n",
"title": "Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities"
}
| null | null | null | null | true | null |
10486
| null |
Default
| null | null |
null |
{
"abstract": " The celebrated integer relation finding algorithm PSLQ has been successfully\nused in many applications. PSLQ was only analyzed theoretically for exact input\ndata, however, when the input data are irrational numbers, they must be\napproximate ones due to the finite precision of the computer. When the\nalgorithm takes empirical data (inexact data with error bounded) instead of\nexact real numbers as its input, how do we theoretically ensure the output of\nthe algorithm to be an exact integer relation?\nIn this paper, we investigate the PSLQ algorithm for empirical data as its\ninput. Firstly, we give a termination condition for this case. Secondly, we\nanalyze a perturbation on the hyperplane matrix constructed from the input data\nand hence disclose a relationship between the accuracy of the input data and\nthe output quality (an upper bound on the absolute value of the inner product\nof the exact data and the computed integer relation), which naturally leads to\nan error control strategy for PSLQ. Further, we analyze the complexity bound of\nthe PSLQ algorithm for empirical data. Examples on transcendental numbers and\nalgebraic numbers show the meaningfulness of our error control strategy.\n",
"title": "The PSLQ Algorithm for Empirical Data"
}
| null | null | null | null | true | null |
10487
| null |
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| null | null |
null |
{
"abstract": " Tensor completion is a problem of filling the missing or unobserved entries\nof partially observed tensors. Due to the multidimensional character of tensors\nin describing complex datasets, tensor completion algorithms and their\napplications have received wide attention and achievement in areas like data\nmining, computer vision, signal processing, and neuroscience. In this survey,\nwe provide a modern overview of recent advances in tensor completion algorithms\nfrom the perspective of big data analytics characterized by diverse variety,\nlarge volume, and high velocity. We characterize these advances from four\nperspectives: general tensor completion algorithms, tensor completion with\nauxiliary information (variety), scalable tensor completion algorithms\n(volume), and dynamic tensor completion algorithms (velocity). Further, we\nidentify several tensor completion applications on real-world data-driven\nproblems and present some common experimental frameworks popularized in the\nliterature. Our goal is to summarize these popular methods and introduce them\nto researchers and practitioners for promoting future research and\napplications. We conclude with a discussion of key challenges and promising\nresearch directions in this community for future exploration.\n",
"title": "Tensor Completion Algorithms in Big Data Analytics"
}
| null | null | null | null | true | null |
10488
| null |
Default
| null | null |
null |
{
"abstract": " A path (resp. cycle) decomposition of a graph $G$ is a set of edge-disjoint\npaths (resp. cycles) of $G$ that covers the edge set of $G$. Gallai (1966)\nconjectured that every graph on $n$ vertices admits a path decomposition of\nsize at most $\\lfloor (n+1)/2\\rfloor$, and Hajós (1968) conjectured that\nevery Eulerian graph on $n$ vertices admits a cycle decomposition of size at\nmost $\\lfloor (n-1)/2\\rfloor$. Gallai's Conjecture was verified for many\nclasses of graphs. In particular, Lovász (1968) verified this conjecture for\ngraphs with at most one vertex of even degree, and Pyber (1996) verified it for\ngraphs in which every cycle contains a vertex of odd degree. Hajós'\nConjecture, on the other hand, was verified only for graphs with maximum degree\n$4$ and for planar graphs. In this paper, we verify Gallai's and Hajós'\nConjectures for graphs with treewidth at most $3$. Moreover, we show that the\nonly graphs with treewidth at most $3$ that do not admit a path decomposition\nof size at most $\\lfloor n/2\\rfloor$ are isomorphic to $K_3$ or $K_5-e$.\nFinally, we use the technique developed in this paper to present new proofs for\nGallai's and Hajós' Conjectures for graphs with maximum degree at most $4$,\nand for planar graphs with girth at least $6$.\n",
"title": "On Gallai's and Hajós' Conjectures for graphs with treewidth at most 3"
}
| null | null | null | null | true | null |
10489
| null |
Default
| null | null |
null |
{
"abstract": " We investigate the transport properties of neutral, fermionic atoms passing\nthrough a one-dimensional quantum wire containing a mesoscopic lattice. The\nlattice is realized by projecting individually controlled, thin optical\nbarriers on top of a ballistic conductor. Building an increasingly longer\nlattice, one site after another, we observe and characterize the emergence of a\nband insulating phase, demonstrating control over quantum-coherent transport.\nWe explore the influence of atom-atom interactions and show that the insulating\nstate persists as contact interactions are tuned from moderately to strongly\nattractive. Using bosonization and classical Monte-Carlo simulations we analyze\nsuch a model of interacting fermions and find good qualitative agreement with\nthe data. The robustness of the insulating state supports the existence of a\nLuther-Emery liquid in the one-dimensional wire. Our work realizes a tunable,\nsite-controlled lattice Fermi gas strongly coupled to reservoirs, which is an\nideal test bed for non-equilibrium many-body physics.\n",
"title": "Band and correlated insulators of cold fermions in a mesoscopic lattice"
}
| null | null | null | null | true | null |
10490
| null |
Default
| null | null |
null |
{
"abstract": " An increasing body of evidence suggests that the trial-to-trial variability\nof spiking activity in the brain is not mere noise, but rather the reflection\nof a sampling-based encoding scheme for probabilistic computing. Since the\nprecise statistical properties of neural activity are important in this\ncontext, many models assume an ad-hoc source of well-behaved, explicit noise,\neither on the input or on the output side of single neuron dynamics, most often\nassuming an independent Poisson process in either case. However, these\nassumptions are somewhat problematic: neighboring neurons tend to share\nreceptive fields, rendering both their input and their output correlated; at\nthe same time, neurons are known to behave largely deterministically, as a\nfunction of their membrane potential and conductance. We suggest that spiking\nneural networks may, in fact, have no need for noise to perform sampling-based\nBayesian inference. We study analytically the effect of auto- and\ncross-correlations in functionally Bayesian spiking networks and demonstrate\nhow their effect translates to synaptic interaction strengths, rendering them\ncontrollable through synaptic plasticity. This allows even small ensembles of\ninterconnected deterministic spiking networks to simultaneously and\nco-dependently shape their output activity through learning, enabling them to\nperform complex Bayesian computation without any need for noise, which we\ndemonstrate in silico, both in classical simulation and in neuromorphic\nemulation. These results close a gap between the abstract models and the\nbiology of functionally Bayesian spiking networks, effectively reducing the\narchitectural constraints imposed on physical neural substrates required to\nperform probabilistic computing, be they biological or artificial.\n",
"title": "Stochasticity from function - why the Bayesian brain may need no noise"
}
| null | null | null | null | true | null |
10491
| null |
Default
| null | null |
null |
{
"abstract": " Current searches for a dark photon in the mass range below 1 GeV require an\nelectron-positron collider with a luminosity at the level of at least $10^{34}$\ncm$^{-2}$s$^{-1}$. The challenge is that, at such low energies, the collider\nluminosity rapidly drops off due to increase in the beam sizes, strong mutual\nfocusing of the colliding beams, and enhancement of collective effects. Using\nrecent advances in accelerator technology such as the nano-beam scheme of\nSuperKEK-B, high-current Energy Recovery Linacs (ERL), and magnetized beams, we\npropose a new configuration of an electron-positron collider based on a\npositron storage ring and an electron ERL. It allows one to achieve a\nluminosity of $>10^{34}$ cm$^{-2}$s$^{-1}$ at the center of momentum energy of\n<1 GeV. We present general considerations and a specific example of such a\nfacility using the parameters of the SuperKEK-B positron storage ring and\nCornell ERL project.\n",
"title": "Very Asymmetric Collider for Dark Matter Search below 1 GeV"
}
| null | null | null | null | true | null |
10492
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, we gave some properties of binomial coefficient.\n",
"title": "Some divisibility properties of binomial coefficients"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10493
| null |
Validated
| null | null |
null |
{
"abstract": " In this paper, we present a method to determine the global horizontal\nirradiance (GHI) from the power measurements of one or more PV systems, located\nin the same neighborhood. The method is completely unsupervised and is based on\na physical model of a PV plant. The precise assessment of solar irradiance is\npivotal for the forecast of the electric power generated by photovoltaic (PV)\nplants. However, on-ground measurements are expensive and are generally not\nperformed for small and medium-sized PV plants. Satellite-based services\nrepresent a valid alternative to on site measurements, but their space-time\nresolution is limited. Results from two case studies located in Switzerland are\npresented. The performance of the proposed method at assessing GHI is compared\nwith that of free and commercial satellite services. Our results show that the\npresented method is generally better than satellite-based services, especially\nat high temporal resolutions.\n",
"title": "An Unsupervised Method for Estimating the Global Horizontal Irradiance from Photovoltaic Power Measurements"
}
| null | null |
[
"Statistics"
] | null | true | null |
10494
| null |
Validated
| null | null |
null |
{
"abstract": " The anisotropy of the Fe-based superconductors is much smaller than that of\nthe cuprates and the theoretical calculations. A credible understanding for\nthis experimental fact is still lacking up to now. Here we experimentally study\nthe magnetic-field-angle dependence of electronic resistivity in the\nsuperconducting phase of iron-based superconductor\nCaFe$_{0.882}$Co$_{0.118}$AsF, and find the strongest anisotropy effect of the\nupper critical field among the iron-based superconductors based on the\nframework of Ginzburg-Landau theory. The evidences of energy band structure and\ncharge density distribution from electronic structure calculations demonstrate\nthat the observed strong anisotropic effect mainly comes from the strong ionic\nbonding in between the ions of Ca$^{2+}$ and F$^-$, which weakens the\ninterlayer coupling between the layers of FeAs and CaF. This finding provides a\nsignificant insight into the nature of experimentally observed strong\nanisotropic effect of electronic resistivity, and also paves an avenue to\ndesign exotic two dimensional artificial unconventional superconductors in\nfuture.\n",
"title": "Strong anisotropy effect in iron-based superconductor CaFe$_{0.882}$Co$_{0.118}$AsF"
}
| null | null | null | null | true | null |
10495
| null |
Default
| null | null |
null |
{
"abstract": " A simple polytope $P$ is said to be \\emph{B-rigid} if its combinatorial\nstructure is characterized by its Tor-algebra, and is said to be \\emph{C-rigid}\nif its combinatorial structure is characterized by the cohomology ring of a\nquasitoric manifold over $P$. It is known that a B-rigid simple polytope is\nC-rigid. In this paper, we, further, show that the B-rigidity is not equivalent\nto the C-rigidity.\n",
"title": "Example of C-rigid polytopes which are not B-rigid"
}
| null | null | null | null | true | null |
10496
| null |
Default
| null | null |
null |
{
"abstract": " The concept of emergence is a powerful concept to explain very complex\nbehaviour by simple underling rules. Existing approaches of producing emergent\ncollective behaviour have many limitations making them unable to account for\nthe complexity we see in the real world. In this paper we propose a new\ndynamic, non-local, and time independent approach that uses a network like\nstructure to implement the laws or the rules, where the mathematical equations\nrepresenting the rules are converted to a series of switching decisions carried\nout by the network on the particles moving in the network. The proposed\napproach is used to generate patterns with different types of symmetry.\n",
"title": "Dynamic Switching Networks: A Dynamic, Non-local, and Time-independent Approach to Emergence"
}
| null | null | null | null | true | null |
10497
| null |
Default
| null | null |
null |
{
"abstract": " The Trouvé group $\\mathcal G_{\\mathcal A}$ from image analysis consists of\nthe flows at a fixed time of all time-dependent vectors fields of a given\nregularity $\\mathcal A(\\mathbb R^d,\\mathbb R^d)$. For a multitude of regularity\nclasses $\\mathcal A$, we prove that the Trouvé group $\\mathcal G_{\\mathcal\nA}$ coincides with the connected component of the identity of the group of\norientation preserving diffeomorphims of $\\mathbb R^d$ which differ from the\nidentity by a mapping of class $\\mathcal A$. We thus conclude that $\\mathcal\nG_{\\mathcal A}$ has a natural regular Lie group structure. In many cases we\nshow that the mapping which takes a time-dependent vector field to its flow is\ncontinuous. As a consequence we obtain that the scale of Bergman spaces on the\npolystrip with variable width is stable under solving ordinary differential\nequations.\n",
"title": "The Trouvé group for spaces of test functions"
}
| null | null | null | null | true | null |
10498
| null |
Default
| null | null |
null |
{
"abstract": " Motion planning classically concerns the problem of accomplishing a goal\nconfiguration while avoiding obstacles. However, the need for more\nsophisticated motion planning methodologies, taking temporal aspects into\naccount, has emerged. To address this issue, temporal logics have recently been\nused to formulate such advanced specifications. This paper will consider Signal\nTemporal Logic in combination with Model Predictive Control. A robustness\nmetric, called Discrete Average Space Robustness, is introduced and used to\nmaximize the satisfaction of specifications which results in a natural\nrobustness against noise. The comprised optimization problem is convex and\nformulated as a Linear Program.\n",
"title": "Robust Motion Planning employing Signal Temporal Logic"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10499
| null |
Validated
| null | null |
null |
{
"abstract": " In this paper, we present a new R package COREclust dedicated to the\ndetection of representative variables in high dimensional spaces with a\npotentially limited number of observations. Variable sets detection is based on\nan original graph clustering strategy denoted CORE-clustering algorithm that\ndetects CORE-clusters, i.e. variable sets having a user defined size range and\nin which each variable is very similar to at least another variable.\nRepresentative variables are then robustely estimate as the CORE-cluster\ncenters. This strategy is entirely coded in C++ and wrapped by R using the Rcpp\npackage. A particular effort has been dedicated to keep its algorithmic cost\nreasonable so that it can be used on large datasets. After motivating our work,\nwe will explain the CORE-clustering algorithm as well as a greedy extension of\nthis algorithm. We will then present how to use it and results obtained on\nsynthetic and real data.\n",
"title": "COREclust: a new package for a robust and scalable analysis of complex data"
}
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10500
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